Journal articles on the topic 'Quantum-enhanced filtering'

This article presents a Quantum-Enhanced Median Filtering (QEMF) method for spatial domain pre-processing in iris biometrics, designed to improve image denoising and recognition accuracy. Traditional median filtering often struggles with high noise density, leading to inconsistencies in the denoised image. Our approach enhances the median filtering process by integrating quantum-inspired principles with statistical measures, combining median and average values of neighboring pixels. This hybrid strategy preserves the structural integrity of the original image while effectively reducing noise. Additionally, a quantum-based thresholding step is introduced in the final stage to minimize ambiguities and further enhance image quality. The proposed method is evaluated using approximately one hundred standard iris images from the Chinese University of Hong Kong (CUHK) dataset, considering four types of noise: Impulse, Poisson, Gaussian, and Speckle. Comparative analysis with conventional filters, including Median and Wiener filters, demonstrates that the QEMF method achieves 99.36% similarity to the original images, surpassing Median and Wiener filters by 1.32% and 0.34%, respectively. These results highlight the potential of quantum-enhanced filtering for improved denoising performance and increased efficiency in iris recognition systems.

Through magnetic field-assisted hydrothermal synthesis, high thermoelectric performance of SnSe is obtained due to Se quantum dots and smaller nano grains, leading to enhanced density of states and energy filtering effect.

A photonic quantum well (PQW) structure based on the zero-averaged refractive index gap is constructed and the transmission properties are investigated. Quantized confined photonic states resulting from the photonic confinement effect are observed. We find that the resonant transmissions are not only weakly dependent on the incident angle but also insensitive to polarization of light. More importantly, the number of the confined photonic states can be controlled by simply adjusting the cell number of the well photonic crystal. It is shown that strong field localization is found when the well photonic crystal consists of positive-index material or single negative material. Particularly for the PQW structure containing single negative material, the field in the well region can be highly enhanced by modulating the size of the well photonic crystal. These peculiar features make this PQW structure superior to the conventional PQW structure in potential applications such as omnidirectional filtering and optical bistable switching.

An innovative method for image demosaicking is introduced, leveraging perceptual-based tone mapping, secondorder polynomial interpolation filters, and Quantum-Inspired Optimization to achieve superior image reconstruction quality. The proposed approach integrates an Autoregressive Wavelet Water Optimization (WWO) algorithm to determine coefficients for second-order polynomial filters within the LPA-ICI framework. Simultaneously, a Deep Convolutional Neural Network (Deep CNN) is employed to generate residual images, capturing intricate features. The outputs of the interpolation-based method and the Deep CNN are fused using an entropy-based metric, resulting in enhanced visual quality and reduced artifacts in the demosaicked images. Perceptual-based tone mapping is applied to address brightness discrepancies, ensuring luminance accuracy and improved image realism. Additionally, Quantum-Inspired Optimization enhances the efficiency and robustness of the filtering process. Experimental results demonstrate significant improvements in reconstruction accuracy, making the proposed method a promising alternative for applications requiring precise and visually appealing demosaicking. Future work will explore the extension of this method to multispectral images and address the challenges of real-time processing.

Abstract Continuously monitored atomic spin-ensembles allow, in principle, for real-time sensing of external magnetic fields beyond classical limits. Within the linear-Gaussian regime, thanks to the phenomenon of measurement-induced spin-squeezing, they attain a quantum-enhanced scaling of sensitivity both as a function of time, t, and the number of atoms involved, N. In our work, we rigorously study how such conclusions based on Kalman filtering methods change when inevitable imperfections are taken into account: in the form of collective noise, as well as stochastic fluctuations of the field in time. We prove that even an infinitesimal amount of noise disallows the error to be arbitrarily diminished by simply increasing N, and forces it to eventually follow a classical-like behaviour in t. However, we also demonstrate that, ‘thanks’ to the presence of noise, in most regimes the model based on a homodyne-like continuous measurement actually achieves the ultimate sensitivity allowed by the decoherence, yielding then the optimal quantum-enhancement. We are able to do so by constructing a noise-induced lower bound on the error that stems from a general method of classically simulating a noisy quantum evolution, during which the stochastic parameter to be estimated—here, the magnetic field—is encoded. The method naturally extends to schemes beyond the linear-Gaussian regime, in particular, also to ones involving feedback or active control.

Nonlinear SU(1,1) interferometers are fruitful and promising tools for spectral engineering and precise measurements with phase sensitivity below the classical bound. Such interferometers have been successfully realized in bulk and fiber-based configurations. However, rapidly developing integrated technologies provide higher efficiencies, smaller footprints, and pave the way to quantum-enhanced on-chip interferometry. In this work, we theoretically realised an integrated architecture of the multimode SU(1,1) interferometer which can be applied to various integrated platforms. The presented interferometer includes a polarization converter between two photon sources and utilizes a continuous-wave (CW) pump. Based on the potassium titanyl phosphate (KTP) platform, we show that this configuration results in almost perfect destructive interference at the output and supersensitivity regions below the classical limit. In addition, we discuss the fundamental difference between single-mode and highly multimode SU(1,1) interferometers in the properties of phase sensitivity and its limits. Finally, we explore how to improve the phase sensitivity by filtering the output radiation and using different seeding states in different modes with various detection strategies.

This work performs a numerical study of electron transport through the fundamental logic gate in valleytronics—a valley valve consisting of two or increasing number of valley filters. Various typical effects on the transport are investigated, such as those due to interface scattering, long- and short-range impurity scattering, edge roughness, strain, inter-filter spacing, or increasing number of valley filters. For illustration, we consider the class of specific valves built from graphene quantum wire valley filters in single layer or bilayer graphene, with the filters subject to separate control of in-plane, transverse electric fields. The nearest-neighbor tight-binding model of graphene is used to formulate the corresponding transport problem, and the algorithm of the recursive Green's function method is applied to solve for the corresponding transmission coefficient. In the case of two-filter valves, the result explicitly demonstrates the existence of a pronounced on-off contrast in electron transmission between the two configurations of valves, namely, one with identical and the other with opposite valley polarities in the two constituent filters. The contrast is shown to be enhanced when increasing the number of filters in valves. Signatures of Fano–Fabry–Pérot type resonances in association with interface scattering and inter-filter spacing are illustrated. Electron backscattering due to impurities is found to be sizably suppressed, with the valve performance showing considerable robustness against edge roughness scattering. On the other hand, the presence of a uniaxial strain modifies the electron transmission and results in an interesting quasi-periodic modulation of transmission as we vary the strain strength.

Abstract Modern colour image sensors face challenges in further improving sensitivity and image quality because of inherent limitations in light utilization efficiency1. A major factor contributing to these limitations is the use of passive optical filters, which absorb and dissipate a substantial amount of light, thereby reducing the efficiency of light capture2. On the contrary, active optical filtering in Foveon-type vertically stacked architectures still struggles to deliver optimal performance owing to their lack of colour selectivity, making them inefficient for precise colour imaging3. Here we introduce an innovative architecture for colour sensor arrays that uses multilayer monolithically stacked lead halide perovskite thin-film photodetectors. Perovskite bandgap tunability4 is utilized to selectively absorb the visible light spectrum’s red, green and blue regions, eliminating the need for colour filters. External quantum efficiencies of 50%, 47% and 53% are demonstrated for the red, green and blue channels, respectively, as well as a colour accuracy of 3.8% in ΔE Lab outperforming the state-of-the-art colour-filter array and Foveon-type photosensors. The image sensor design improves light utilization in colour sensors and paves the way for the next generation of highly sensitive, artefact-free images with enhanced colour fidelity.

In the present study, we propose a physical model to study spin transport through DNA system and provide apparent physical mechanism for spin dependent phenomenon. The system considered in our work is DNA bases guanine-cytosine coupled to two ferromagnetic leads (FM-(G/C)10-FM) in parallel and anti-parallel configuration case, throughout magnetic quantum contacts. Our treatment is based on the tight binding model to derive obvious formula for the transmission spectrum which is employed to investigate the spin dependent current-bias voltage characteristics and the temperature - Conductance dependence. Our calculations of for strong, weak and without backbone regimes. Various factors are involved in our -study. These are the electrical contacts between DNA molecules and electrodes, the structure of DNA molecule and the environment around DNA molecule. The system spin dependent factors, that are investigated extensively in our study include the spin dependent coupling between subsystems, the quantum contacts between active region and electrodes, majority and minority electrons spin in the ferromagnetic leads as well as externally applied bias voltage. Variation of these factors can enhanced or suppressed spin transport through (G/C)10 molecule. The transmission spectrum calculations conform that the spin transport throughout (G/C)10 originates by a coherent tunneling process between neighboring bases through the overlapping of the LUMO orbitals of the bases. Our results showed that the spin-polarized transport that can be effectively regulated by the type of regime as well as the spin configuration in the leads which can exhibit efficient spin filtering and spin switching by employing the spin blockade phenomenon

Islamic finance, guided by Shariah principles, has witnessed significant global expansion, surpassing $3 trillion in assets. Unlike conventional finance, Islamic finance prohibits interest (riba), excessive uncertainty (gharar), and unethical investments. With the rise of financial technology (FinTech), machine learning (ML) is increasingly being utilized to enhance efficiency, risk management, and decision-making in Islamic financial institutions. ML techniques such as supervised learning, unsupervised learning, reinforcement learning, and natural language processing (NLP) are revolutionizing areas like credit risk assessment, fraud detection, regulatory compliance, and investment optimization. This paper explores various ML applications in Islamic finance. ML-powered credit scoring models assess borrowers' financial behavior while ensuring compliance with risk-sharing principles. Fraud detection algorithms analyze transaction patterns to identify anomalies, minimizing financial crime. Regulatory compliance is enhanced through NLP-based systems that process legal texts and identify non-compliant financial contracts. ML also aids in investment portfolio optimization by filtering Shariah-compliant stocks and assessing market trends through sentiment analysis. AI-driven chatbots improve customer service in Islamic banks. Despite its advantages, ML adoption in Islamic finance faces challenges such as limited data availability, complex Shariah compliance requirements, and regulatory differences across jurisdictions. Ethical concerns, including algorithmic bias and transparency, must also be addressed. The future of ML in Islamic finance is promising, with advancements in explainable AI (XAI), blockchain integration, and quantum computing. By fostering collaboration between regulators, Islamic scholars, and data scientists, ML can play a transformative role in making Islamic finance more efficient, transparent, and accessible.

Thermoelectric conversion devices or modules with high performance have been expected for the electrical power generation from wasted heat. Then, thermoelectric materials with high dimensionless figure of merit zT at temperature T , described as S 2 σT/κ have been intensively sought, where S is Seebeck coefficient, σ is electrical conductivity, and κ is thermal conductivity. Therein, intercorrelation among three thermoelectric properties has been a big barrier against high zT. In conventional approach, the heavy elements such as rare metals are used because they inherently bring small κ in the materials. Now, ecofriendly ubiquitous element materials without toxic, rare, or high cost elements, showing high zT, are expected in the application view. Introduction of nanostructures into materials is one of the promising ways to increase zT because of phonon scattering [1], quantum confinement effect in low dimensional materials and by energy filtering [2], and so on. Therein, structure design determines the properties, and then, this method can be expected to bring high zT in the ecofriendly and low-cost ubiquitous element materials. Furthermore, it is recently found that low dimensional materials and quasi-low dimensional materials such as layered materials with 2D structures in the unit cell show high zT values. Therefore, low and quasi-low dimensional materials for extraordinarily high zT values have been expected to be developed. We have been developing a lot of kinds of well-controlled nanostructure films by SiO2 film technique. One of the examples: Si-based films including ultrasmall semiconductor nanodots with controlled interfaces, strains, crystal orientations, and compositions [3]. Furthermore, we have developed the epitaxial growth method of quasi-low dimensional material films composed of ecofriendly group IV elements on Si substrates: FeGeγ including 1D Ge helical structure and Ca-intercalated multi-layered silicene films. Here, we will introduce the epitaxial growth results of quasi-low dimensional material films. Epitaxial growth of FeGeγ films on Si were achieved by using Ge epitaxial nanodots as seed crystals[4]. In the case of Ca-intercalated multi-layered silicene, silicene bucked structure was found to be deformed. The deformation enhanced Seebeck coefficient. As a result, this sub-angstrom atomic displacement brought power factor enhancement while keeping high electrical conductivity [5]. In this talk, we will present the epitaxial growth process of quasi-low-dimensional material films on Si substrates for thermoelectric film devices composed of ecofriendly and low-cost thermoelectric materials . [1] Y. Nakamura, et al., Nano Energy 12, 845 (2015). [2] T. Ishibe, et al., ACS Appl. Mater. Inter. 10, 37709 (2018). [3] Y. Nakamura, et al., Sci. Technol. Adv. Mater. 19, 31 (2018). [4] T. Terada, Acta Materialia 236, 118130 (2022). [4] T. Terada, et al., Adv. Mater. Inter. 9, 2101752 (2022).

Underwater simultaneous localization and mapping (SLAM) has significant challenges due to the complexities of underwater environments, marked by limited visibility, variable conditions, and restricted global positioning system (GPS) availability. This study provides a comprehensive analysis of sensor fusion techniques in underwater SLAM, highlighting the amalgamation of proprioceptive and exteroceptive sensors to improve UUV navigational accuracy and system resilience. Essential sensor applications, including inertial measurement units (IMUs), Doppler velocity logs (DVLs), cameras, sonar, and LiDAR (light detection and ranging), are examined for their contributions to navigation and perception. Fusion methodologies, such as Kalman filters, particle filters, and graph-based SLAM, are evaluated for their benefits, limitations, and computational demands. Additionally, innovative technologies like quantum sensors and AI-driven filtering techniques are examined for their potential to enhance SLAM precision and adaptability. Case studies demonstrate practical applications, analyzing the compromises between accuracy, computational requirements, and adaptability to environmental changes. This paper proceeds to emphasize future directions, stressing the need for advanced filtering and machine learning to address sensor drift, noise, and environmental unpredictability, hence improving autonomous underwater navigation through reliable sensor fusion.

In this work, silicon-rich oxide (SRO) films with designed thickness of ~100 nm were deposited by a bipolar pulse and radio frequency magnetron co-sputtering. For comparison, the samples were then treated in a nitrogen atmosphere by conventional rapid thermal annealing (CRTA) or light-filtering rapid thermal annealing (LRTA) at 900–1100°C for 2 min. Raman spectra, grazing incident X-ray diffraction (XRD), transmission electron microscopy (TEM), Hall measurements, and current density–voltage measurements were carried out to analyze the microstructural and electrical properties of samples. Compared with the control sample using CRTA method, the crystalline volume fraction and number density of Si nanocrystals ( Si NCs ) in silicon oxide prepared by LRTA were greatly increased. The quantum effects of the short wave-length light (less than 800 nm) of these tungsten halogen lamps during the rapid thermal annealing process have negative effects on the formation of Si NCs in SiO 2 films. Si NCs with crystal volume fraction of 73%, average size of 2.53 nm, and number density of ~1.1 × 1012 cm-2 embedded in the amorphous SiO 2 matrix can be formed by LRTA at 1100°C. Enhancement of more than one order of magnitude in conductivity and higher current density were obtained from the LRTA annealed sample compared to the CRTA annealed sample. The improvements in conductivity and current density were attributed to the high density Si NCs . Our results show that the LRTA method is a suitable annealing tool for the formation of Si NC in thin SiO x films.

Pharmaceutical residuals are increasingly detected in natural waters, which made great threat to the health of the public. This study evaluated the utility of the photo-Fenton ceramic membrane filtration toward the removal and degradation of sulfamethoxazole (SMX) as a model recalcitrant micropollutant. The photo-Fenton catalyst Goethite (α-FeOOH) was coated on planar ceramic membranes as we reported previously. The removal of SMX in both simulated and real toilet wastewater were assessed by filtering the feed solutions with/without H2O2 and UV irradiation. The SMX degradation rate reached 87% and 92% respectively in the presence of UV/H2O2 for the original toilet wastewater (0.8 ± 0.05 ppb) and toilet wastewater with a spiked SMX concentration of 100 ppb. The mineralization and degradation by-products were both assessed under different degradation conditions to achieve deeper insight into the degradation mechanisms during this photo-Fenton reactive membrane filtration. Results showed that a negligible removal rate (e.g., 3%) of SMX was obtained when only filtering the feed solution through uncoated or catalyst-coated membranes. However, the removal rates of SMX were significantly increased to 67% (no H2O2) and 90% (with H2O2) under UV irradiation, respectively, confirming that photo-Fenton reactions played the key role in the degradation/mineralization process. The highest apparent quantum yield (AQY) reached up to approximately 27% when the H2O2 was 10 mmol·L−1 and UV254 intensity was 100 μW·cm−2. This study lays the groundwork for reactive membrane filtration to tackle the issues from micropollution.

Our laboratory at MIT has been interested over the past few years in new techniques to facilitate the transfer of chemical information from living organisms, specifically plants, animals and humans, for applications ranging from precision agriculture to precision medicine. This presentation will discuss recent advances on this topic. As tool towards this end, fluorescent nanosensors hold the potential to revolutionize life sciences and medicine. However, their adaptation and translation into the in vivo environment is fundamentally hampered by unfavourable tissue scattering and intrinsic autofluorescence. Here we develop wavelength-induced frequency filtering (WIFF) whereby the fluorescence excitation wavelength is modulated across the absorption peak of a nanosensor, allowing the emission signal to be separated from the autofluorescence background, increasing the desired signal relative to noise, and internally referencing it to protect against artefacts. Using highly scattering phantom tissues, an SKH1-E mouse model and other complex tissue types, we show that WIFF improves the nanosensor signal-to-noise ratio across the visible and near-infrared spectra up to 52-fold. This improvement enables the ability to track fluorescent carbon nanotube sensor responses to riboflavin, ascorbic acid, hydrogen peroxide and a chemotherapeutic drug metabolite for depths up to 5.5 ± 0.1 cm when excited at 730 nm and emitting between 1,100 and 1,300 nm, even allowing the monitoring of riboflavin diffusion in thick tissue. As an application, nanosensors aided by WIFF detect the chemotherapeutic activity of temozolomide transcranially at 2.4 ± 0.1 cm through the porcine brain without the use of fibre optic or cranial window insertion. The ability of nanosensors to monitor previously inaccessible in vivo environments will be important for life-sciences research, therapeutics and medical diagnostics. Also towards this overall objective, our laboratory at MIT has been interested in exploring the relatively new interface between living plants and non-biological nanostructures to impart the former with new and enhanced functions, which we call Plant Nanobionics. We have developed a theory of subcellular uptake and kinetic trapping of a wide range of nanoparticles, validated in-vivo in living plants. Confocal visible and near infrared fluorescent microscopy and single particle tracking of Gold-Cystein-AF405 (GNP-Cys-AF405), Streptavidin-Quantum Dot (SA-QD), Dextran and Poly(acrylic acid) nanoceria, and various polymer-wrapped SWCNT, including lipid-PEG-SWCNT, chitosan-SWCNT and (AT)15-SWCNT, were used to demonstrate that particle size and the magnitude, but not the sign, of the zeta potential are key in determining whether a particle is spontaneously and kinetically trapped within chloroplasts or the cytosol. We develop a mathematical model of this Lipid Exchange Envelope Penetration (LEEP) mechanism, which agrees well with observations of this size and zeta potential dependence. As an application, we rationally designed a chitosan-complexed single-walled carbon nanotube (SWNT) as nanocarriers to selectively deliver plasmid DNA (pDNA) to chloroplasts of different plant species without external biolistic or chemical aid. We demonstrate chloroplast-targeted transgene delivery and expression in living mature arugula (Eruca sativa) and watercress (Nasturitium officinale) plants in planta and in isolated Arabidopsis thaliana mesophyll protoplasts. Another application of nanoparticles and nanotechnology to plant sciences is in the form of biochemical sensors that operate in planta and across diverse species. Using non-destructive optical nanosensors, we find that the spatial and temporal H2O2 concentration immediately post-wounding follows a simple logistic waveform for six dicot plant species: lettuce (Lactuca sativa), arugula (Eruca sativa), spinach (Spinacia oleracea), strawberry blite (Blitum capitatum), sorrel (Rumex acetosa), and Arabidopsis thaliana, ranked in order of wave speed from 0.44 to 3.10 cm/min. The H2O2 wave tracks the concomitant surface potential wave measured electrochemically for the series of plants. We show that the plant NADPH oxidase RbohD, glutamate receptor-like channels (GLR3.3 and GLR3.6) are all critical to the propagation of the H2O2 waveform upon wounding. Our findings highlight the utility of a new type of nanosensor probe that is species-independent and capable of real-time, spatial and temporal biochemical measurements in planta.

Dear Readers, I would like to wish you all the best for the new year! It is with great pleasure that I welcome you to our first regular issue in 2024, which is already the 30th year that J.UCS has been available to authors and readers without any interruptions. I would like to gratefully acknowledge the visionary ideas of Prof. Hermann Maurer, who founded the journal and ran it successfully for many years, preparing the ground for it to become one of the longest-running open content journals in computer science. Looking back on the past year, we have further increased our visibility and taken steps to fully comply with the Diamond Open Content Standard and prepare to join the KOALA initiative. Thanks to the combined efforts of the Pensoft team and the J.UCS publishing team, we are listed and indexed in more than 40 indexing services worldwide, including DOAJ, Web of Science, and Scopus. The increased visibility and social media presence have also led to a further increase in page views and article downloads. With around 100,000 unique views, interest has doubled compared to the previous year. We can also look back on an increasing number of submitted articles and special issue proposals. We are also very pleased to report that the journal's Impact Factor has stabilised at a high level with a Web of Science Impact Factor of 1.0 and a Scopus Science Score of 2.7. We proudly look back on a total of 12 issues - 11 regular and 1 special issue - with 63 articles by 231 authors from 40 countries on new aspects of various computer science topics. The acceptance rate has fallen to below 15 per cent. These great achievements were only possible thanks to the commitment and interest of the community and the valuable support of the Editorial Board and the J.UCS Consortium members. In 2023, we welcomed 10 new members to the Editorial Board, bringing our total number of Editorial Board members to 196. We would also like to gratefully acknowledge the support of 67 guest reviewers over the past year. In particular, I would like to thank Dr. Ulrike Krießmann from the Library of the Graz University of Technology, Prof. Klaus Tochtermann from the ZBW, Prof. Christian Eckhardt from California Polytechnic State University, Prof. Krzysztof Pietroszek from the American University in Washington DC, and Prof. Muhammad Tanvir Afzal from Shifa Tameer-e-Millat University in Islamabad in Pakistan for their generous support in offering an open content journal without charging the authors for their articles. Unfortunately, some partners are withdrawing their support for 2024 due to financial restrictions, but we are very happy to welcome the Leibniz Information Centre for Science and Technology and are very grateful for their support. I would also like to thank the J.UCS team, Johanna Zeisberg for taking care of the publication process, Aleksandar Bobic and David Kerschbaumer for their social media support, and Alexander Nussbaumer for his technical support, as well as Pensoft Publishers Ltd. for hosting our journal. I look forward to continuing to work with our editors, editorial team and technical support to maintain the success of J.UCS. I would be very grateful for suggestions and feedback on how we can improve and develop J.UCS in the future. We also greatly appreciate the generous support of the J.UCS community, especially in promoting the journal and citing relevant articles in their research papers. In this regular issue, I am very pleased to present 6 accepted articles by 21 authors from 6 different countries, namely Brazil, Cuba, France, Malaysia, Spain, and the United Kingdom. In a collaborative effort between researchers from Spain and the UK, Bashar Alshouha, Jesus Serrano-Guerrero, David Elizondo, Francisco P. Romero and Jose A. Olivas look into consumer attitudes towards healthcare services by applying a transfer learning approach to detect emotions from consumer feedback. In the second article, Ana Díaz Muñoz, Moisés Rodríguez Monje, and Mario Gerardo Piattini Velthuis from Spain address the design of an environment to measure quality metrics for hybrid, classic-quantum software, propose a set of new measurements for hybrid maintainability, and develop a first prototype as a SonarQube plugin that is capable of measuring these metrics. In a research collaboration between the UK, Malaysia and France, Ngo Le Huy Hien, Ah-Lian Kor, Mei Choo Ang, Eric Rondeau, and Jean-Philippe Georges cover findings on image filtering techniques for object recognition in autonomous vehicles based on the evaluation of 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, and Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques. Francisco Iniesto and Covadonga Rodrigo from Spain look into the evaluation of MOOC accessibility as students’ experience by applying web content accessibility guidelines and an automatic tool, and investigate students’ perceptions and comparison of the two approaches. Yilena Pérez-Almaguer, Edianny Carballo-Cruz, Yailé Caballero-Mota, and Raciel Year from Cuba explore content-based group recommendations for suggesting restaurants in Havana City enhanced by extended restaurant features, virtual group profiles, and the selection of the most appropriate aggregation approach for composing group recommendations. Last but not least, Raimundo Osvaldo Vieira and Helyane Bronoski Borges from Brazil cover a systematic mapping study on dimensionality reduction for hierarchical multi-label classification. Enjoy Reading! Cordially, Christian Gütl, Managing Editor-in-ChiefGraz University of Technology, Graz, Austria

Dear Readers,  I would like to wish you all the best for the new year! It is with great pleasure that I welcome you to our first regular issue in 2024, which is already the 30th year that J.UCS has been available to authors and readers without any interruptions. I would like to gratefully acknowledge the visionary ideas of Prof. Hermann Maurer, who founded the journal and ran it successfully for many years, preparing the ground for it to become one of the longest-running open content journals in computer science.  Looking back on the past year, we have further increased our visibility and taken steps to fully comply with the Diamond Open Content Standard and prepare to join the KOALA initiative. Thanks to the combined efforts of the Pensoft team and the J.UCS publishing team, we are listed and indexed in more than 40 indexing services worldwide, including DOAJ, Web of Science, and Scopus. The increased visibility and social media presence have also led to a further increase in page views and article downloads. With around 100,000 unique views, interest has doubled compared to the previous year. We can also look back on an increasing number of submitted articles and special issue proposals. We are also very pleased to report that the journal's Impact Factor has stabilised at a high level with a Web of Science Impact Factor of 1.0 and a Scopus Science Score of 2.7. We proudly look back on a total of 12 issues - 11 regular and 1 special issue - with 63 articles by 231 authors from 40 countries on new aspects of various computer science topics. The acceptance rate has fallen to below 15 per cent.  These great achievements were only possible thanks to the commitment and interest of the community and the valuable support of the Editorial Board and the J.UCS Consortium members. In 2023, we welcomed 10 new members to the Editorial Board, bringing our total number of Editorial Board members to 196. We would also like to gratefully acknowledge the support of 67 guest reviewers over the past year.   In particular, I would like to thank Dr. Ulrike Krießmann from the Library of the Graz University of Technology, Prof. Klaus Tochtermann from the ZBW, Prof. Christian Eckhardt from California Polytechnic State University, Prof. Krzysztof Pietroszek from the American University in Washington DC, and Prof. Muhammad Tanvir Afzal from Shifa Tameer-e-Millat University in Islamabad in Pakistan for their generous support in offering an open content journal without charging the authors for their articles. Unfortunately, some partners are withdrawing their support for 2024 due to financial restrictions, but we are very happy to welcome the Leibniz Information Centre for Science and Technology and are very grateful for their support.  I would also like to thank the J.UCS team, Johanna Zeisberg for taking care of the publication process, Aleksandar Bobic and David Kerschbaumer for their social media support, and Alexander Nussbaumer for his technical support, as well as Pensoft Publishers Ltd. for hosting our journal.  I look forward to continuing to work with our editors, editorial team and technical support to maintain the success of J.UCS. I would be very grateful for suggestions and feedback on how we can improve and develop J.UCS in the future. We also greatly appreciate the generous support of the J.UCS community, especially in promoting the journal and citing relevant articles in their research papers.  In this regular issue, I am very pleased to present 6 accepted articles by 21 authors from 6 different countries, namely Brazil, Cuba, France, Malaysia, Spain, and the United Kingdom.  In a collaborative effort between researchers from Spain and the UK, Bashar Alshouha, Jesus Serrano-Guerrero, David Elizondo, Francisco P. Romero and Jose A. Olivas look into consumer attitudes towards healthcare services by applying a transfer learning approach to detect emotions from consumer feedback. In the second article, Ana Díaz Muñoz, Moisés Rodríguez Monje, and Mario Gerardo Piattini Velthuis from Spain address the design of an environment to measure quality metrics for hybrid, classic-quantum software, propose a set of new measurements for hybrid maintainability, and develop a first prototype as a SonarQube plugin that is capable of measuring these metrics. In a research collaboration between the UK, Malaysia and France, Ngo Le Huy Hien, Ah-Lian Kor, Mei Choo Ang, Eric Rondeau, and Jean-Philippe Georges cover findings on image filtering techniques for object recognition in autonomous vehicles based on the evaluation of 5 different deep learning models, YOLOv5s, EfficientNet-B7, Xception, MobilenetV3, and InceptionV4, and Hessian, Laplacian, and Hessian-based Ridge Detection filtering techniques. Francisco Iniesto and Covadonga Rodrigo from Spain look into the evaluation of MOOC accessibility as students’ experience by applying web content accessibility guidelines and an automatic tool, and investigate students’ perceptions and comparison of the two approaches. Yilena Pérez-Almaguer, Edianny Carballo-Cruz, Yailé Caballero-Mota, and Raciel Year from Cuba explore content-based group recommendations for suggesting restaurants in Havana City enhanced by extended restaurant features, virtual group profiles, and the selection of the most appropriate aggregation approach for composing group recommendations. Last but not least, Raimundo Osvaldo Vieira and Helyane Bronoski Borges from Brazil cover a systematic mapping study on dimensionality reduction for hierarchical multi-label classification.  Enjoy Reading!  Cordially,  Christian Gütl, Managing Editor-in-Chief Graz University of Technology, Graz, Austria

This article presents a Quantum-Enhanced Median Filtering (QEMF) method for spatial domain pre-processing in iris biometrics, designed to improve image denoising and recognition accuracy. Traditional median filtering often struggles with high noise density, leading to inconsistencies in the denoised image. Our approach enhances the median filtering process by integrating quantum-inspired principles with statistical measures, combining median and average values of neighboring pixels. This hybrid strategy preserves the structural integrity of the original image while effectively reducing noise. Additionally, a quantum-based thresholding step is introduced in the final stage to minimize ambiguities and further enhance image quality. The proposed method is evaluated using approximately one hundred standard iris images from the Chinese University of Hong Kong (CUHK) dataset, considering four types of noise: Impulse, Poisson, Gaussian, and Speckle. Comparative analysis with conventional filters, including Median and Wiener filters, demonstrates that the QEMF method achieves 99.36% similarity to the original images, surpassing Median and Wiener filters by 1.32% and 0.34%, respectively. These results highlight the potential of quantum-enhanced filtering for improved denoising performance and increased efficiency in iris recognition systems.

Abstract As a part of quantum image processing, quantum image filtering is a crucial technology in the development of quantum computing. Low-pass filtering can effectively achieve anti-aliasing effects on images. Currently, most quantum image filtering is based on classical domains and grayscale images, with relatively fewer studies on anti-aliasing in the quantum domain. This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain. It achieves the effect of antialiasing filtering on quantum images during the scaling process. First, we use the novel Enhanced Quantum Representation (NEQR) and the Improved Quantum Representation of Color Images (INCQI) to represent classical images. Since aliasing phenomena are more pronounced when images are scaled down, this paper focuses only on the anti-aliasing effects in the case of reduction. Subsequently, we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image, achieving the anti-aliasing effect. The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size. Finally, the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling, applying anti-aliasing filtering to the images results in smoother and clearer outputs. Additionally, it allows for manual intervention to select the desired level of image smoothness.

For some image processing algorithms such as edge detection and segmentation, filtering in the spatial domain is an important pre-processing to improve the quality of the image by removing the noise in the images. The time and memory requirements for processing also increase as the size of the images increases. Besides, excessive blurring of the image during noise removal operation can lead to excessive deterioration of image quality. Therefore, the balance between noise reduction and blur needs to be well adjusted. In this paper, a new quantum method for noise reduction on quantum images using QFT-based arithmetic operators is proposed and quantum circuits are designed. Mean filtering operators of different sizes are used to remove noise. First, the classical image is represented by the gray scale quantum image model, the novel enhanced quantum representation (NEQR) model. A quantum method is proposed and circuits are presented for the realization of Mean filters of different sizes on the basis of addition and division operations. Finally, the performance of the method is evaluated by presenting the circuit complexity of the method and the experimental results. In the proposed method, it is aimed to use less resources and reduce the circuit complexity. The optimal filtering operator size is investigated for the balance between noise reduction and image blur.

This study explores the integration of quantum algorithms, specifically Grover's algorithm, with quantum metrology to enhance the efficiency and sensitivity of gravitational-wave detection. By combining quantum matched filtering with precise parameter estimation techniques, the research aims to optimize sensor networks for the identification of gravitational waves. This integrated approach leverages the strengths of quantum superposition and entanglement to improve signal detection, reduce noise, and strategically place sensors. The findings demonstrate significant improvements in the sensitivity and accuracy of gravitational wave measurements, highlighting the potential of quantum technologies to revolutionize observational astronomy and enhance our understanding of the universe.

AbstractThe frequency degree of freedom of optical photons has been recently explored for efficient quantum information processing. Significant reduction in hardware resources and enhancement of quantum functions can be expected by leveraging the large number of frequency modes. Here, we develope an integrated photonic platform for the generation and parallel processing of quantum frequency combs (QFCs). Cavity-enhanced parametric down-conversion with Sagnac configuration is implemented to generate QFCs with identical spectral distributions. On-chip quantum interference of different frequency modes is simultaneously realized with the same photonic circuit. High interference visibility is maintained across all frequency modes with the identical circuit setting. This enables the on-chip reconfiguration of QFCs. By deterministically separating QFCs without spectral filtering, we further demonstrate high-dimensional Hong-Ou-Mandel effect. Our work provides the critical step for the efficient implementation of quantum information processing with integrated photonics using the frequency degree of freedom.

We present a driving scheme for solid-state quantum emitters, referred to as Notch-filtered Adiabatic Rapid Passage (NARP), that utilizes frequency-swept pulses containing a spectral hole resonant with the optical transition in the emitter. NARP enables high-fidelity state inversion and exhibits robustness to variations in the laser pulse parameters, benefits that derive from the insensitivity of the condition for adiabatic evolution. NARP also offers the advantage of immunity to phonon-mediated excitation-induced dephasing when positively-chirped control pulses are used. Our resonant driving approach could be combined with spectral filtering of the scattered pump light and photonic devices for enhanced collection efficiency to realize simultaneous high indistinguishability and brightness in single photon source applications.

: Filtering is an important step in the field of image processing to suppress the required parts or to remove any artifacts present in it. There are different types of filters like low pass, high pass, Band pass, IIR, FIR and adaptive filtering etc.., in these filters adaptive filters is an important filter because it is used to remove the noisy signal and images. Least Mean Square filter is a type of an adaptive filtering which is used to remove the noises present in the medical images. The working of LMS is based on the minimization of the difference between the error images using a closed loop feedback. Therefore presented technique called as Q-CSKA. Here the CSKA performs its operation in stages which is based on the nucleus stage. In the traditional CSKA the nucleus stage is depend on the parallel prefix adder in this work it is replaced by the QCA adder. The QCA adder utilizes the less area compared to PPA and it can be realized in Nanometer range also. For multiplexers, And OR Invert, OR and Invert logic is used to reduce the area and delay. Due to these advantages of the QCA, AOI-OAI logic the proposed method outperformed the LMS implementation in area, power, and accuracy and delay, this based five type image noise of medical pictures related to the best technique is out comes. It helps to medicinal practitioner to resolve the symptoms of patient with ease.

Manipulating the π-electron magnetism of single-molecule junctions is an effective means to improve the electronic and spin-polarized thermoelectric transport properties. Here, using the density functional theory combined with the nonequilibrium Green's function method, we demonstrate that the electronic conductance (σ) of molecular junctions (MJs) can be significantly enhanced by organic radicals due to the shifting of resonant states. Moreover, we find that the spin-dependent quantum interference (SDQI) effects can be largely influenced by organic radicals. The SDQI effects result in nearly 100% spin filtering efficiency in open-shell molecules and greatly enhance the Seebeck coefficients. As a result, the thermoelectric performances of open-shell MJs at room temperature are greatly improved through the combined effects of radicals and SDQI. In particular, the maximum ZTsp in the four radical junctions reaches up to 36.5. Our results show great potential for improving thermoelectric performance through the utilization of quantum interference and organic radical.

Higher manganese silicide (HMS) is a candidate thermoelectric (TE) material at medium temperature due to its non-toxicity, abundance, and competitive price. The focus on improving the TE performance of HMS is to decrease the thermal conductivity. Low-dimensionalization techniques, such as nanocrystallization, embedding quantum dots (QDs) and thin film formation are effective strategies to decrease the lattice thermal conductivity by enhancing the phonon scattering on interfaces. Additionally, the Seebeck coefficients also can be improved due to the energy filtering effect via the interface barrier, and correspondingly increasing the power factor of HMS. The TE performance of HMS can be enhanced due to synergistically optimized electrical and thermal properties.

Efficient, nanoscale precision alignment of defect center creation in photonics structures in challenges the realization of high-performance photonic devices and quantum technology applications. Here, we propose a facile self-aligned patterning technique based on conventional engineering technology, with doping precision that can reach ~15 nm. We demonstrate this technique by fabricating diamond nanopillar sensor arrays with high consistency and near-optimal photon counts. The sensor array achieves high yield approaching the theoretical limit, and high efficiency for filtering sensors with different numbers of nitrogen vacancy centers. Combined with appropriate crystal orientation, the system achieves a saturated fluorescence rate of 4.34 Mcps and effective fluorescence-dependent detection sensitivity of 1800 cps −1/2 . These sensors also show enhanced spin properties in the isotope-enriched diamond. Our technique is applicable to all similar solid-state systems and could facilitate the development of parallel quantum sensing and scalable information processing.

AbstractQuantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC‐PCSANN‐CHIO‐FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual‐Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC‐PCSANN‐CHIO‐FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC‐ANN‐FD), electrical power system fault diagnostics using hybrid quantum‐classical deep learning (QC‐CRBM‐FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC‐RF‐FD).

The demand for integrated photonic chips combining the generation and manipulation of quantum states of light is steadily increasing, driven by the need for compact and scalable platforms for quantum information technologies. While photonic circuits with diverse functionalities are being developed in different single material platforms, it has become crucial to realize hybrid photonic circuits that harness the advantages of multiple materials while mitigating their respective weaknesses, resulting in enhanced capabilities. Here, we demonstrate a hybrid III-V/silicon quantum photonic device combining the strong second-order nonlinearity and direct band gap of the III-V semiconductor platform with the high maturity and CMOS compatibility of the silicon photonic platform. Our device embeds the spontaneous parametric down-conversion (SPDC) of photon pairs into an AlGaAs source and their vertical routing to an adhesively bonded silicon-on-insulator circuitry, within an evanescent coupling scheme managing both polarization states. This enables the on-chip generation of broadband (>40 nm) telecom photons by type-0 and type-2 SPDC from the hybrid device, at room temperature and with internal pair generation rates exceeding 105s−1 for both types, while the pump beam is strongly rejected. Two-photon interference with 92% visibility (and up to 99% upon 5-nm spectral filtering) proves the high energy-time entanglement quality of the produced quantum state, thereby enabling a wide range of quantum information applications on chip, within a hybrid architecture compliant with electrical pumping and merging the assets of two mature and highly complementary platforms in view of out-of-the-lab deployment of quantum technologies. Published by the American Physical Society 2024

Abstract With an extended Su–Schrieffer–Heeger model and Green’s function method, the spin-orbit coupling (SOC) effects on spin admixture of electronic states and quantum transport in organic devices are investigated. The role of lattice distortion induced by the strong electron-lattice interaction in organics is clarified in contrast with a uniform chain. The results demonstrate an enhanced SOC effect on the spin admixture of frontier eigenstates by the lattice distortion at a larger SOC, which is explained by the perturbation theory. The quantum transport under the SOC is calculated for both nonmagnetic and ferromagnetic electrodes. A more notable SOC effect on total transmission and current is observed for ferromagnetic electrodes, where spin filtering induced by spin-flipped transmission and suppression of magnetoresistance is obtained. Unlike the spin admixture, a stronger SOC effect on transmission exists for the uniform chain rather than the organic lattices with distortion. The reason is attributed to the modified spin-polarized conducting states in the electrodes by lattice configuration and hence the spin-flip transmission, instead of the spin admixture of eigenstates. This work is helpful to understand the SOC effect in organic spin valves in the presence of lattice distortion.

AbstractA novel additive method to boost the Seebeck coefficient of doped conjugated polymers without a significant loss in electrical conductivity is demonstrated. Perovskite (CsPbBr3) quantum dots (QDs) passivated by ligands with long alkyl chains are mixed with a conjugated polymer in a solution phase to form polymer‐QD blend films. Solution sequential doping of the blend film with AuCl3 solution not only doped the conjugated polymer but also decomposed the QDs, resulting in a doped conjugated polymer film embedded with separated ions dissociated from the QDs. For the doped polymer‐molten QD blend films with the optimal QD content, it is found that a greatly enhanced Seebeck coefficient is achieved compared to that of the doped polymer film without QDs, while the doping level and electrical conductivity are not significantly reduced by the QD incorporation. Consequently, the power factor is enhanced, reaching a remarkably high value of up to 401.9 µW m−1 K−2 (≈155% increase with the QDs). The applicability of this method to a variety of conjugated polymers is also demonstrated. The enhancement in the Seebeck coefficient is attributed to ion‐induced local variations in the polymer work function, which generates an internal energy barrier for charge transport and causes an energy filtering effect.

AbstractProgrammable photonic circuits are dense assemblies of waveguide meshes in which the flow of light can be reconfigured by software to implement a wide variety of functions, ranging from radiofrequency filtering to optical computing. However, most programmable architectures to date rely on rather bulky Mach‐Zehnder interferometers (MZIs), which are not suitable for large‐scale and high‐density integration. Here, an alternative approach to MZI‐based programmable photonic circuits by using slow‐light‐enhanced periodic bimodal waveguides (PBWs) as programmable units is presented. This study experimentally demonstrates low‐loss short tuning elements of 30× 1.7 µm2 in area, achieving a two‐orders of magnitude integration density improvement compared to conventional MZIs. A rectangular arrangement of these tunable units is proposed for 3× 3 and 4× 4 matrix multiplication operations to design a feedforward circuit with a footprint of only 100× 250 µm2. Finally, the performance trade‐off and benchmark with alternative programmable unit cells are analyzed in order to address the ever‐growing demand for large computing requirements in next‐generation applications such as artificial intelligence and quantum information processing.

Detective quantum efficiency (DQE) is a prominent figure of merit for imaging detectors, and its optimization is of fundamental importance for the efficient use of the experimental apparatus. In this work, I study the potential improvement offered by data processing on a single-event basis in a counting hybrid pixel electron detector (HPD). In particular, I introduce a simple and robust method of single-event processing based on the substitution of the original cluster of pixels with an isotropic Gaussian function. Key features are a better filtering of the noise power spectrum (NPS) and readily allowing for sub-pixel resolution. The performance of the proposed method is compared to other standard techniques such as centroiding and event normalization, in the simulated realistic scenario of 100 keV electrons impinging on a 450 μm-thick silicon sensor with a pixel size of 75 μm, yielding the best results. The DQE can potentially be enhanced over the entire spatial frequency range, increasing from 0.86 to nearly 1 at zero frequency and extending up to 1.40 times the physical Nyquist frequency of the system thanks to the sub-pixel resolution capability.

Abstract Due to their inherent physical properties, thin-film Si/SiGe heterostructures have specific thermal management applications in advanced integrated circuits and this in turn is essential not only to prevent a high local temperature and overheat inside the circuit, but also generate electricity through the Seebeck effect. Here, we were able to enhance the Seebeck effect in the germanium composite quantum dots (CQDs) embedded in silicon by increasing the number of thin silicon layers inside the dot (multi-fold CQD material). The Seebeck effect in the CQD structures and multi-layer boron atomic layer-doped SiGe epitaxial films was studied experimentally at temperatures in the range from 50 to 300 K and detailed calculations for the Seebeck coefficient employing different scattering mechanisms were made. Our results show that the Seebeck coefficient is enhanced up to ≈40% in a 3-fold CQD material with respect to 2-fold Ge/Si CQDs. This enhancement was precisely modeled by taking into account the scattering of phonons by inner boundaries and the carrier filtering by the CQD inclusions. Our model is also able to reproduce the observed temperature dependence of the Seebeck coefficient in the B atomic layer-doped SiGe fairly well. We expect that the phonon scattering techniques developed here could significantly improve the thermoelectric performance of Ge/Si materials through further optimization of the layer stacks inside the quantum dot and of the dopant concentrations.

AbstractOsteoarthritis (OA) treatment mainly relies on developing new drugs or nanocarriers, while little attention is paid to building novel remedial mode and improving drug loading efficiency. This work reports an integrated nanosystem that not only realizes visual drug loading and release, but also achieves enhanced lubrication and effective joint inflammation therapy based on fluorinated graphene quantum dots (FGQDs). Oxygen introduction promotes FGQDs outstanding water-stability for months, and layered nano-sized structure further guarantees excellent lubricating properties in biomimetic synovial fluid. The special design of chemistry and structure endows FGQDs robust fluorescence in a wide range of pH conditions. Also, the excitation spectrum of FGQDs well overlaps the absorption spectrum of drugs, which further constructs a new concept of internal filtering system to visually monitor drug loading by naked eyes. More importantly, extraordinary long-term lubrication performance is reported, which is the first experimental demonstration of concentration-dependent mutations of coefficient of friction (COF). Cell incubation experiments indicate that drug-loaded FGQDs have good biocompatibility, tracking property of cellular uptake and drug release, which show efficient anti-inflammation potential for H2O2-induced chondrocyte degradation by up-regulated cartilage anabolic genes. This study establishes a promising OA treatment strategy that enables to monitor drug loading and release, to enhance long-time lubricating property, and to show effective anti-inflammatory potential for cartilage protection.

Chiral quantum dots (QDs) are expected to play a significant role in chiral molecular recognition, spin-selective filtering for charge transport, and various other fields. Photo-irradiation can enhance the photoluminescence (PL) of chiral CdSe QD films, which serves as a convenient method to modulate the optical properties. However, the effects of photo-irradiation on the absorption and spin properties of chiral QDs remain unclear. In this study, we investigate the influence of photo-irradiation on the PL, absorption, and spin properties of chiral L/D-cysteine-capped CdS QD films. Under the irradiation of the 458 nm continuous wave laser, the PL intensity of chiral CdS QD films can be significantly enhanced by over 50 times and the absorption peak exhibits a blue shift of approximately 3 nm. The transient transmission dynamics before and after irradiation indicate that the bleach recovery lifetime became longer after irradiation, which can be attributed to the increase in radiative recombination. Furthermore, the strength of the electron spin is diminished due to a decrease in the number of charge-separated electrons. The significant enhancement in PL intensity, coupled with the increased radiative recombination and the reduction in spin signals, strongly suggests that surface defects are effectively passivated. The influence of photo-irradiation on the optical properties of chiral QDs provides valuable insights for the design of chiral nanomaterials and their applications.

Abstract Zig-zag graphene nanoribbons (ZGNRs) are known to possess spin moments at the hydrogen-terminated edge carbon atoms, thus the spin-polarized electron transmission is expected while the current is longitudinally passed through the ZGNRs. However, in pristine ZGNRs, the spin-polarized transmission is not observed due to symmetric anti-parallel distributions of the spin densities between the edges. Here, the hypothesis is, any physical or chemical process that breaks such anti-parallel spin-symmetry can induce spin-polarized transmission in the ZGNRs. In this work, we have established this proof-of-concept by depositing the trimethylenemethane (TMM) radical on 6ZGNRH and investigating the quantum transport properties by employing the density functional theory in conjunction with nonequilibrium Green’s function (DFT-NEGF) method. Although TMM has a high magnetic moment (2 µB ), it does not induce magnetization in 6ZGNRH when TMM is physisorbed. But, during the chemisorption of TMM, it forms the π − π bond with the 6ZGNRH in a particular geometric configuration where the pz orbitals of carbon atoms of TMM have maximum overlap with the pz orbitals of carbon atoms of 6ZGNRH. The chemisorption of TMM transfers the spin moment to 6ZGNRH, which breaks the edge spin-symmetry of pristine 6ZGNRH. The adsorption of TMM radical results in transmission dips in the transmission spectra due to interference between localized states of TMM and 6ZGNRH states. This induces spin-polarized transmission with 60% spin-filtering efficiency (SFE) at zero bias, which can further be enhanced up to 92% by applying the bias voltage of 1.0 V.

Polymer microbubbles have garnered broad interest as potential theranostic agents. However, the capabilities of polymer MBs can be greatly enhanced, particularly regarding the imaging performance and functional versatility of the platform. This study investigates integrating fluorescent carbon nanodots within polylactic acid (PLA) microbubbles. First, the formulations are characterized by their size, microbubble counts, zeta potential, and resonance frequency. Then, the fluorescence capabilities, nanoparticle loading, and acoustic capabilities are examined. Unmodified (U-), carboxylated (C-), and aminated graphene quantum dots (A-GQDs) were separately suspended and synthesized at a 2% w/w ratio with PLA in the organic phase of the water/oil/water double emulsion process. The new microbubbles were characterized using an AccuSizer, Zetasizer, scanning electron microscopy, fluorescence microscopy and fluorimetry, a custom-built acoustic setup, and clinical ultrasound. The GQD microbubbles were sized between 1.4 and 1.9 µm (U = 1.90, C = 1.44, A = 1.72, Unloaded = 2.02 µm). The U-GQD microbubble exhibited a higher bubble concentration/mg PLA ( p < .05) and the A-GQD microbubbles exhibited the greatest shift in zeta potential. Electron microscopy revealed smooth surfaces and a spherical shape, showing that the nanoparticle addition was not deleterious. The A-GQD microbubbles were specifically detectable using DAPI-filtering with fluorescence microscopy and had the highest TRITC-filtered fluorescence. The C-GQD microbubbles had the highest loading efficiency at 59.4% ( p < .05), and the lowest max acoustic enhancement at 5 MHz (U = 19.8, C = 17.6, A = 18.9, Unloaded = 18.5 dB; p < .05). Additionally, all microbubbles were visible and susceptible to inertial cavitation utilizing clinical ultrasound. The A-GQDs showed promise toward improving the theranostic capabilities of the microbubble platform. They have imbued the most advantageous fluorescence capability and slightly improved backscatter enhancement while retaining all the necessary capabilities of an ultrasound contrast agent. Future studies will investigate the coloading potential of A-GQDs and drug within microbubbles.

AbstractIndistinguishable single photons in the telecom-bandwidth of optical fibers are indispensable for long-distance quantum communication. Solid-state single photon emitters have achieved excellent performance in key benchmarks, however, the demonstration of indistinguishability at room-temperature remains a major challenge. Here, we report room-temperature photon indistinguishability at telecom wavelengths from individual nanotube defects in a fiber-based microcavity operated in the regime of incoherent good cavity-coupling. The efficiency of the coupled system outperforms spectral or temporal filtering, and the photon indistinguishability is increased by more than two orders of magnitude compared to the free-space limit. Our results highlight a promising strategy to attain optimized non-classical light sources.

We demonstrate the generation of correlated photon pairs by using a hybrid integrated quantum photonic platform, where the dual-layer platform consists of a high-index doped silica glass (HDSG) layer to accommodate low-loss linear components and an SiN-based layer to accommodate the photon source. Leveraging the low-loss fiber coupling to the HDSG waveguide and the high nonlinearity of the SiN waveguide, we experimentally realize integrated source of photon pairs with high heralding efficiency. The directly measured photon pair rate is up to 87 KHz (corresponding to 1.74 × 10−3 pairs per pulse) when the coincidence-to-accidental ratio is greater than 10. The raw heralding efficiency can reach 18%. If the filtering loss is excluded, the heralding efficiency can further reach 29%.

Abstract Plasmonic nanocavities with highly localized fields in their nanogaps significantly enhance light–matter interactions at the nanoscale, surpassing the diffraction limit. Strong coupling between a plasmonic nanocavity and a molecule forms hybrid upper and lower branch states, resulting in Rabi splitting (RS) in optical spectra. However, scattering and absorption spectra often fail to unambiguously distinguish whether the double peaks arise from energy transparency or RS. In contrast, photoluminescence (PL) clearly reveals the quantum state of a molecule coupled with a plasmon by filtering out background fields. This paper presents a theoretical framework based on nonlocal response theory to calculate the PL of a single molecule coupled with arbitrary metallic nanostructures. Our theory provides an analytical approach to design the spatial arrangement of metallic nanostructures and molecular orbitals and to calculate the PL in strongly coupled systems, addressing limitations in previous studies. Using this framework, we investigated a coupled system comprising a gold nanoplate dimer and a planar porphyrin tape. By modifying porphyrin units to modulate coupling strength, we explored the molecular quantum state coupled with the nanocavity through PL analysis. We elucidated the spectral features of absorption, excitation, and PL in weak and strong coupling regimes and evaluated the dependence of coupling strength on the molecular position and orientation within the nanogap. Our results demonstrate that the quantum state of a molecule in an optically forbidden transition can be excited by the highly localized field in the nanogap. This work advances the fundamental understanding of light–matter interactions at the nanoscale and provides a foundation for the development of future nanophotonic devices.

In order to accomplish spin-based photoelectric information processing, it is necessary to modulate electron spin polarization in III-V semiconductor quantum dots (QDs) using an electric field. However, there is a...

Remote measurement of vital sign parameters like heartbeat and respiration rate represents a compelling challenge in monitoring an individual’s health in a noninvasive way. This could be achieved by large field-of-view, easy-to-integrate unobtrusive sensors, such as large-area thin-film photodiodes. At long distances, however, discriminating weak light signals from background disturbance demands superior near-infrared (NIR) sensitivity and optical noise tolerance. Here, we report an inherently narrowband solution–processed, thin-film photodiode with ultrahigh and controllable NIR responsivity based on a tandem-like perovskite-organic architecture. The device has low dark currents (<10 −6 mA cm −2 ), linear dynamic range >150 dB, and operational stability over time (>8 hours). With a narrowband quantum efficiency that can exceed 200% at 850 nm and intrinsic filtering of other wavelengths to limit optical noise, the device exhibits higher tolerance to background light than optically filtered silicon-based sensors. We demonstrate its potential in remote monitoring by measuring the heart rate and respiration rate from distances up to 130 cm in reflection.

International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 DOI: 10.5121/ijbb.2013.3303 25 STRATEGY FOR ELECTROMYOGRAPHY BASED DIAGNOSIS OF NEUROMUSCULAR DISEASES FOR ASSISTIVE REHABILITATION Sailesh Conjeti1 and Bijay Kumar Rout2 1Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India. 2Department of Mechanical Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India. ABSTRACT Assistive Rehabilitation aims at developing procedures and therapies which reinstate lost body functions for individuals with disabilities. Researchers have monitored electrophysiological activity of muscles using biofeedback obtained from Electromyogram signals collected at appropriate innervation points. In this paper, we present a comprehensive technique for detection of neuromuscular disease in a subject and a strategy for continuous therapeutic assessment using the Rehabilitation Assessment Matrix. The decision making tool has been trained using a wide spectrum of synthetic physiological data incorporating varying degrees of myopathy and neuropathy from beginning stages to acute. The statistical, spectral and cepstral features extracted from EMG have been used to train a Cascade Correlation Neural Network Classifier for disease assessment. The diagnostic yield of the classifier is 91.2% accuracy, 85.3% specificity and 91.35% sensitivity. The strategy has also been extended to include isotonic contractions in addition to static isometric contractions. This comprehensive strategy is proposed to aid physicians plan and schedule treatment procedures to maximize the therapeutic value of the rehabilitation process. KEYWORDS Electromyography, Rehabilitation, Myopathy, Neuropathy and Cascade Correlation Neural Network. 1.INTRODUCTION Assistive Rehabilitation of affected individuals aims at restoring original body functionality by compensating for the lost functions and thus providing opportunities to lead an independent life. Such procedures for neuromuscular rehabilitation often require design of manipulative physiotherapy procedures, following the detection of neuromuscular disease (NMD) in the subject. The biofeedback acquired from the patient is crucial to the design and execution of an effective medical rehabilitation scheme [1]. A complete and comprehensive assessment is often labour-intensive and expensive because the design and configuration of individualistic procedures require training of highly-skilled physiotherapists with appropriate expert knowledge. In this situation, Computer Aided Diagnostics of NMDs helps minimize observer bias, facilitates inter-subject comparison and aids the physicians to arrive at a more accurate diagnosis [2]-[3]. Neuromuscular facilitation mechanisms for individuals affected with NMDs must be designed considering individual&rsquo;s neurophysiology, motor-learning and motor development functions [4]. This work primarily focuses on two classes of NMD viz. Myopathy and Neuropathy. Myopathy refers to a medical condition where muscle weakness is observed due to reduced functionality and activation of muscle fibres for a particular nervous stimulation. Muscle cramps, stiffness International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 26 and spasm are the usual reported symptoms associated with myopathic disorders [5]. Neuropathy, on the other hand, is a neurogenic condition resulting in loss of movement and haptic sensation owing to nervous damage. The reported symptoms include nerve pain, partial or complete paralysis, abnormal sensations and muscle weakness [6]. The treatment of neuromuscular diseases varies from medications, physical therapy, splinting and in acute cases even surgery is suggested. The treatment is often administered on the basis of the cause and origin of NMDs and the degree of its severity. The presented work aims to develop an Electromyography based assessment approach for NMDs which is fast and reliable. The proposed methodologies would aid the physiotherapists in preparing an appropriate medical treatment scheme with proper scheduling of physiological exercise routines, thus maximizing the therapeutic value of the rehabilitation procedure instituted. Figure 1. Intramuscular EMG Recordings (a) Normal Subject (b) Neuropathic Subject and (c) Myopathic Subject 2. ELECTROMYOGRAPHY AND APPLICATIONS IN NEUROMUSCULAR DISEASE DIAGNOSIS Researchers in the field of health monitoring have used the Electromyogram (EMG) signal for detection and monitoring of NMDs as they are accessible as bioelectric signals under direct volitional control [7]. EMG is a cumulative effect of the motor unit action potentials (MUAPs), generated by the motor neurons, which are responsible for actuating the skeletal muscles for support and motion of the human skeleton [8]. Electromyography signals acquired from appropriate muscular regions reflect on the muscle&rsquo;s tone, strength, abnormalities in reflexes, ideomotor and voluntary movements and postural equilibrium reactions [9]. Figure 1 illustrates a typical intramuscular EMG recorded from three subjects (a) Healthy, (b) Neuropathy and (c) Myopathy (Figure adapted from [10]). Berzuini et al. investigated into the applicability of EMG signals collected from the right brachii muscles to detect neurogenic disorders. Variations observed in both time-domain and frequency domain parameters helped them to find topographical clusters in the multivariate space of EMG parameters corresponding to neuropathic subjects [11]. Pattiachis et al. investigated into applying Neural Network models of EMG diagnosis for detecting NMDs. They trained the networks using extracted morphological features of MUAP waveform after signal decomposition procedures on EMG [3]. These research works establish the suitability of EMG signals for detection of International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 27 neuromuscular disease and development of a reliable rehabilitation strategy for a particular subject. 3. REHABILITATION ASSESSMENT MATRIX The current styles of rehabilitation assessment forms used by medical practitioners in Physical Medicine and Rehabilitation (PMR) documents the patient&rsquo;s strengths, abilities, preferences, needs, findings, and recommendations for treatment. Inferences drawn from these ensure that appropriate rehabilitation procedures are administered on timely basis [12]. The Rehabilitation Assessment Matrix (RAM) has been developed to meet the need for comprehensive assessment of patient with NMDs which requires a quantitative progress chart to monitor the therapeutic value of the treatment being administered. The proposed design of such a matrix is shown in the Figure 2. The approach to obtain this RAM is discussed in the subsequent section. The integration of the proposed RAM to existing PMR assessment reports will provide the therapist measurable objectives for monitoring the patient&rsquo;s progress during the course of assistive rehabilitation. A motor unit refers to an &alpha;-motor neuron of the Central Nervous System and the set of muscle fibres it innervates. When a motor unit (MU) is recruited, it contributes a quantum of force to muscular contraction [13]. The changes in the active MU density are attributable to age, buildup, disease and injury. It is observed that though MU density reduces with age, it is not as severe as the effects due to NMDs. It is observed to be a very useful parameter to monitor neurogenic disease progression, motor-neuron death rate and motor development improvements during rehabilitation [14]. The active MU density in Biceps Brachii muscle has been reported as average of 109 per mm2 (Std. Dev.: 53 per mm2 ) for a stroke related myopathy patient and about 153 per mm2 (Std. Dev.: 38 per mm2 ) for a normal subject [15]. The number of active motor units per unit area, inferred from the EMG has been recorded in the X-axis of the RAM. The axis has been sub-divided into 8 classes with a class width of 15 active MUs per mm2 (shown in Fig. 2). However, it must be noted that this work does not delve into Motor Unit Number Estimation (MUNE) methods. Researchers have developed enhanced statistical approaches for MUNE like Bayesian Estimators [16], Poisson Techniques [17], Higher Order Statistics [18] and SpatioTemporal Summation approaches [19]. Figure 2. Rehabilitation Assessment Matrix The Y+ -axis of the RAM (shown in Figure 2) refers to the myopathic affected fibre fraction representing the degree of myopathy whereas the Y-- -axis refers to the neuropathic motor unit Myopathy Progress Curve Neuropathy Progress Curve International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 28 loss fraction corresponding to the degree of neuropathy. As treatment progresses, the subject&rsquo;s improvement line, both Myopathy Progress Curve and Neuropathy Progress Curve (Sample highlighted in Figure 2) moves towards the Normal body functioning line. The pattern followed in the RAM reflects the subject&rsquo;s motor learning ability and can provide useful insights into the nature of exercise the physiotherapist can administer to the subject. If the subject&rsquo;s curve is not progressive as desired, the physiotherapist&rsquo;s may be alerted to a need for change in the exercise routine and treatment being administered. 4. PHYSIOLOGICAL DATA ACQUISITION, PRE-PROCESSING AND FEATURE EXTRACTION The Electromyogram signals were synthetically generated using a physiological model developed by Wright and Stashuk. This model generates EMG signals consistent with those acquired through intramuscular needle electrodes from the limb muscles (including biceps brachii) of human subjects [20]. Though the Kinesiological EMG acquired through intramuscular electrodes is invasive over surface electrodes, it is preferred for neuromuscular disease prognosis due to its increased signal specificity and more selective recording to characterize the muscle of interest [21]. The EMG signal was generated at 31,250 Hz and band pass filtered from 10 to 10,000 Hz to remove enhance signal characteristics. The selected muscle and motorneuron pool settings of the Wright-Stashuk model correspond to the biceps brachii muscle of a human subject, simulating a constant force isometric contraction without fatigue or spasm. Isometric contractions are static muscle contractions which happen without any appreciable decrease in fibre length and change in the distance between point of electrode insertion and origin of EMG signals. The subject-age and gender were not fixed during each simulation to minimize any fixed-variable bias and enhance data set universality. The simulated intra-muscular needle electrode configuration, contraction type and the muscle model parameters are described in Table 1. Table 1. Description of parameters for EMG Generation S.N. Type of Parameter Description 1 Electrode Configuration Differential Electrode Configuration Detection Surfaces Dimension: Length: 1.0cm; Width: 1.2mm; Separation: 1.0 cm Bandwidth: 20-500Hz with a 40 dB/decade roll-off. Common Mode Rejection Ratio: &gt; 80dB Noise: &lt; 2&micro;V rms (20-400 Hz)Input Impedance: &gt; 100 M&Omega; 2 Electrode Location Middle line of Muscle Belly between the myotendonous junction and nearest innervation zone. 3 Muscle Model Parameters Contraction Level ( % MVC): 5-50% No. of Active MU Density: 75-180 per mm2 4 Disease Parameters Neuropathic Motor Unit Loss Fraction: 0.0(Healthy)-1.0 (Extreme) Step Size: 0.25 Myopathic Fiber Affected Fraction:0.0(Healthy)-1.0 (Extreme) Step Size: 0.25 4.1. Synthetic EMG Generation from Wright-Stashuk Model A total of 2000 sample-waveforms (10 Contraction-levels &times; 8 Classes of Active MU Density &times; 5 Classes of Myopathy &times; 5 Classes of Neuropathy) were synthetically generated for 5 seconds at a sample rate of 31,250 Hz. The objective behind introducing high degree of inter-simulation variability is to develop a universal approach that can be readily extended to medical domain. International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 29 The algorithms and codes for analysis have been written in MATLAB R2010b&reg; and use the inbuilt Statistics, System Identification and Neural Network Toolboxes. 4.2. Physiological Feature Extraction In the present case, the generated signals were filtered for suppressing signal aliasing and motion artifacts using a 5th order Savitsky Golay Filter. The filter fits a 5th order polynomial in a window of 41 data points. It reduces noise while preserving the waveform&rsquo;s shape, structure, the relative maxima, minima and width [22]. The smoothened signal representing muscle force for the three real-life test signals has been shown in the Figure 3.The filtered muscle data is partitioned into static windows and the features extracted from each window will constitute the characteristic feature vector. The optimal window size for this particular application is expected to vary between 100ms and 1s. Therefore, classifier&rsquo;s decision making performance would be evaluated for good trade-off between accuracy and specificity, over 10 windows from 100ms to 1s in steps of 100ms. The extracted features are explained in the following subsections. Figure 3. Savitsky-Golay Filtering 4.2.1. Statistical Features These features help in assessment of uncertainty associated with physiological signal. De Luca et al. demonstrated that time-domain statistical features of EMG are influenced by MU firing rate, number of detected active MU, the MU activation potential, duration, waveform morphology and the recruitment stability [9]-[23]. The statistical features extracted include: Global Maxima, Global Minima, Mean, Standard Deviation, Energy, Time Duration, Bandwidth, Time Bandwidth Product, 3rd order moment, 4th order moment, 5th order moment, Root Mean Square Value, Kurtosis and Skewness as tabulated in Table 2. Table 2. Statistical Features of EMG Signal S.N. Feature Name Formula S.N. Feature Name Formula 1 Global Maxima Max(x[n]) 8 Time Bandwidth Product TD  BW International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 30 2 Global Minima Min(x[n]) 9 3 rd Order Moment   N i x i N 1 3 [ ] 1 3 Mean    N i x i N x 1 [ ] 1  10 4 th Order Moment   N i x i N 1 4 [ ] 1 4 Std. Dev.     N i x i x N s 1 2 ( [ ] ) 1  11 5 th Order Moment   N i x i N 1 5 [ ] 1 5 Energy   N i x i 1 2 [ ] 12 Root Mean Square   N i x i N 1 2 [ ] 1 6 Time Duration 1/2 1 2 [ ] 1 2 [ ] 2 ( )                 N i x i N i i i x i TD  13 Kurtosis 4 ( 1) 1 4 ( [ ] ) N s N i x i x      7 Bandwidth 1/2 1 2 [] 2 2 ( [ ] [ 1]) 2 1                  N i x i N i x i x i BW  14 Skewness 3 ( 1) 1 3 ( [ ] ) N s N i x i x      4.2.2. Spectral Features In studies on rehabilitation, it is desirable to predict fatigue before it commences so that appropriate remedies may be adopted. Researchers have used the contractile force approach for evaluating fatigue points when the subject undergoes sustained contraction. Changes in the muscle force and the monitored torque indicate progress of fatigue with time. But, this method is considered inefficient as force and torque provide only a general overview of the entire muscle and not the individual motor units. In this context, the spectral modification during compression, along with the alteration of the skewness of the EMG waveform is acceptable as a Fatigue Index. EMG gives a more holistic view on muscle fatigue as individual skeletal muscle can be monitored continuously from the point of onset of the contraction. The Figure 4 below illustrates the factors of EMG which influence the spectral modification property and its interrelationships. Figure 4. Factors influencing spectral characteristics of EMG waveform Researchers have observed spectral modifications in the power spectrum of EMG acquired during tetanic muscular contractions (Tetanic refers to sustained muscle contraction without rest intervals). The skewness of the MUAP waveform is observed to alter with increasing fatigue and changes in muscle biochemistry due to continuous accumulation of lactic acid in the muscle cell (Anaerobic Respiration) [24]. The power spectrum of EMG signals was estimated using the Lomb periodogram approach because of its robustness to motion artifacts and missed data points International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 31 and lesser computational complexity for real-life biomedical applications [25]. Let S (f) represent the Lomb periodogram of the EMG signal over an input range of f: 10Hz to 10000Hz (Frequency Resolution &Delta;f: 1 Hz). The spectral features extracted are the mean, median and the maximum frequency which are given by formulae (1)-(3) respectively. (1) ( ) ( ) 10000 10 10000 10           f f mean s f f f s f f f ( ) (2) 2 1 10000 10     f median f s f f arg max( ( )) (3) max f s f imum  f 4.2.3. Cepstral Features Cepstral Coefficients are calculated from the inverse Fourier Transform of the logarithmic power spectrum of EMG. Yoshikawa et al. established that the lower order Cepstral coefficients extracted from EMG can be used in robust classification of hand motions [26].These cepstral coefficients are derived from the 10th order autoregressive model of the filtered EMG signal. Let x(k) represent the filtered EMG signal, ai is the i th coefficient of the M-order autoregressive model and e(k) is the white noise in the system (refer Formula (4)). The cepstral coefficients ci (i=1:5) are derived from ai using recursive formulae (5)-(6). ( ) ( ) ( ) (4) 10 1       M i i x k a x k i e k ( 5 ) 1 1 c   a 1 1 1 1               n i i n i i a c i n c a where 1<em> 0.452 the subject is classified as Unhealthy. For such a cutoff, the sensitivity of classifier performance was observed as 91.35% and the overall specificity was 85.3%. International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 36 Further, the designed classifier for the optimal window size of 400 ms is tested against the human Kinesiological EMG data acquired from Physionet signal database and the observed outputs from the network are tabulated in Table 5. The data includes intracellular EMG signals acquired from the biceps brachii muscle from three human subjects: a 44-year old man without any medical history of neuromuscular disease, a 62-year old man with chronic lower back pain and neuropathy and a 57-year old man with myopathy [10]. As observed from ROC analysis, the network&rsquo;s output cut-off of 0.452 is used to demarcate the healthy subject from an unhealthy subject. It is hence observed that the developed classifier accurately classifies the presented human subject acquired signals into their respective classes thus provides a proof-of-concept for the presented approach. Table 5. Results of Testing Real-Life EMG Data Against the Trained CCNN S.N. Class of Patient Target Data Avg. Output Network Output 1. H 0.0 0.1186 H 2. UH: M 1.0 0.7874 UH 3. UN: N 1.0 0.8755 UH Legend: H: Healthy UH: Unhealthy M: Myopathic N: Neuropathic For investigating the extendibility of CCNN Classifier technique to perform NMD diagnosis using dynamic isotonic contractions, the 22-attribute feature vector was extracted for a time window of 400ms from the data acquired using protocols described in Section 6. Since the subject is a healthy subject, the performance analysis metrics for testing the classifier here were decided as the output mean square error (MSE) as against accuracy and specificity. The observations are tabulated in Table 6. The data from Trail 01 and Trial 02 were diagnosed correctly and the misclassification of Healthy into Unhealthy for Trial 03 must be noted. Since the performance of Trial 02 data is better that Trail 01 and Trial 03, it is proposed that for extending the CCNN Classifier to dynamic isotonic contractions, the EMG data must be acquired through slow and steady flexion and extension motions. Table 6. Results of Testing CCNN Against Isotonic Contractions S.N. Trial Speed Target Data Avg. Output Diagnosis MSE Observed 1. T 01 Normal 1.0 0.7421 Healthy 3.211E-01 2. T 02 Slow 1.0 0.8745 Healthy 1.602E-01 3. T 03 Fast 1.0 0.4352 Unhealthy 6.458E-01 In Table 7, similar works for NMD detection available in literature are presented. Although direct comparison is not feasible, the proposed strategy compares well since it is trained using EMG data incorporating varied levels of disease severity both in neuropathy and myopathy and the learning technique for neural network training (Cascade Correlation) ensures design of an optimal classifier for the application. Table 7. Performance Evaluation of Present Technique vs. Existing Literature S.N. Author/Research Group Technique Classification Accuracy 1. Chistoudoulou et al. [34] Modular Neural Network 79.6% 2. Pattichis et al. [3] Feed-forward Network+ Self Organizing Maps 80% 3. H.B. Xie et al.[35] Support Vector Machine 82.4% 4. H.B. Xie et al.[36] Hybrid Neuro-Fuzzy Systems 88.58% 5. This Work Cascade Correlation Neural Network 91.2%(Training) 89.7%(Testing) International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 37 8. CONCLUSIONS AND FUTURE WORK The proposed diagnostic system for NMD detection utilizes the Cascade Correlation Neural Network learning methodology. The optimal window size for diagnosis was inferred as 400ms and the classification using CCN Networks resulted in 91.2% accuracy, 85.3% specificity and 91.35% sensitivity for training data. For testing data, the diagnostic yield was 89.7% accuracy and an acceptable specificity of 78.5%. The proof-of-concept for extending the CCN classifier to real-life isometric contraction is established by testing on real-life kinesiological data. Investigations on data acquired using isotonic elbow flexion and extension contractions established that this method can be extended to dynamic studies. Further, integration of the proposed Rehabilitation Assessment Matrix with existing Physical Medicine and Rehabilitation practices will provide measurable objectives for therapists to monitor the patient&rsquo;s progress and help in preparing an appropriate medical treatment scheme to maximize the therapeutic value of the rehabilitation process. In the future, decision support systems to for NMD diagnosis will be developed which incorporate a multimodal diagnosis approach fusing EMG data with inferences from biochemical analysis, neuropathology and clinical observations. This work incorporates the need for using data from subjects with different stages of NMDs and is envisaged as a step forward towards realizing a holistic and reliable EMG based NMD diagnostic system which can aid the physician in his decision making process. REFERENCES [1] S. Komada, Y.Hashimoto, N. Okuyama, T. Hisada, and J. Hirai, &ldquo;Development of a Biofeedback Therapeutic-Exercise-Supporting Manipulator,&rdquo; IEEE Trans. on Industrial Electronics, vol. 56, no. 10, pp. 3914-3920, Oct. 2009. [2] S.B. O&#39;Sullivan, and T.J. Schmitz, &ldquo;Physical Rehabilitation: Assessment and Treatment&rdquo; , 2nd ed., F.A. Davis Company, Philadelphia, PA, 1988. [3] C.S. Pattichis, C.N. Schizas, and L.T. Middleton, &ldquo;Neural Network Models in EMG diagnosis,&rdquo; IEEE Trans. in Biomedical Engg., vol. 42, no. 5, pp.486-496, May 1995. [4] T. Hisada,N. Okuyama, S. Komada, and J. Hirai, &ldquo;Preliminary study on robotic exercise therapy,&rdquo; Proc. 30th Annual Conf. IEEE Industrial Electronics Society, vol. 3, pp. 2780&ndash;2785, Nov. 2004. [5] J. Chawla, &ldquo;Stepwise Approach to Myopathy in Systemic Disease,&rdquo; Frontiers in Neurology, vol. 2(49), August 2011. [6] M.E. Shy, &ldquo;Peripheral neuropathies,&rdquo; Cecil Medicine, Chapter 446, 23rd ed. Philadelphia, Saunders Elsevier, 2007. [7] Stanford V. &ldquo;Biosignals offer potential for direct interfaces and health monitoring,&rdquo; Pervasive Computing , vol. 04, pp. 99-103, 2004. [8] J.V. Basmajian and C. J. de Luca, &ldquo;Muscles Alive &ndash; The Functions Revealed by EMG,&rdquo; The Williams &amp; Wilkins Comp., Baltimore, 1985. [9] C.J. de Luca, &ldquo;The use of surface electromyography in biomechanics,&rdquo; Journal of Applied Biomechanics, vol. 13(2), pp. 135-163, 1997. [10] http://www.physionet.org/physiobank/database/emgdb/ [11] C. Berzuini, M.M. Figini, and L. Bernardinelli, &ldquo;Evaluation of the Effectiveness of EMG Parameters in the Study of Neurogenic Diseases- Statistical Approach Using Clinical and Simulated Data,&rdquo; IEEE Trans. on Biomedical Engineering, vol. 32(1), pp. 15-27, Jan. 1985. [12] Montana State Hospital Policy and Procedure, &ldquo;Rehabilation Assessment,&rdquo; RTS-03, pp. 1-3, Dec. 2010. [13] M. Nikolic, &ldquo;Detailed Analysis of Clinical Electromyography Signals,&rdquo; Doctoral Dissertaion, University of Cophenhagen, August 2001. [14] M.B. Bromberg, &ldquo;Updating motor unit number estimation (MUNE),&rdquo; Clin. Neurophysiology, vol. 118(1), pp.1-8, Jan. 2007. [15] X. Li, Y-C Wang, N.L. Suresh, W.Z. Rymer, and P. Zhou, &ldquo;Motor Unit Number Reductions in Paretic Muscles of Stroke Survivors,&rdquo; IEEE Trans. on Inf. Tech. in Biomedicine, vol. 15(4), pp. 505-512, July 2011. International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 38 [16] P.G. Ridall ,A.N. Pettitt, R.D. Henderson, and P.A. McCombe, &ldquo;Motor unit number estimation--a Bayesian approach,&rdquo; Biometrics vol.62(4) pp.1235-50, Dec. 2006. [17] L.M. Oporto,L.C. Men&eacute;ndez-de, P.E. Bauzano, M.J. N&uacute;&ntilde;ez-Casta&iacute;n, &ldquo;Statistical (Poisson) motor unit number estimation,&rdquo; Reviews on Neurology vol. 36(7), pp.601-604, Apr. 2003. [18] S. Shahid,J. Walker, G.M. Lyons, C.A. Byrne, and A.V.Nene, &ldquo;Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential,&rdquo; IEEE Trans. on Biomedical Engg., vol.52(7), pp.1195-209, July 2005. [19] J. Fang, B.T. Shahani, D. Graupe, &ldquo;Motor unit number estimation by spatial-temporal summation of single motor unit potentials,&rdquo; Muscle Nerve,vol. 20(4), pp.461-8, Apr. 1997. [20] A.H.Wright, and D.W.Stashuk, &ldquo;Physiologically Based Simulation of EMG Signals,&rdquo; IEEE Transactions on Biomedical Engineering, vol. 52(2), pp. 171-183, Feb. 2005. [21] K.S. T&uuml;rker, &ldquo;Electromyography: some methodological problems and issues,&rdquo; Phys Ther. vol.73(10) pp.698-710, Oct. 1993. [22] S. Hargittai, &ldquo;Savitsky-Golay Least Square Polynomial Filters in ECG Signal Processing,&rdquo; Proc. of Computers in Cardiology Conference, pp. 763-766, Sept. 2005. [23] Cram J.R., Kasman G.S. and Holtz J., &ldquo;Introduction to Surface Electromyography,&rdquo; Aspen Publishers Inc., Gaithersburg, Maryland, 1998. [24] P.K. Artemiadis, K.J. Kyriakopoulos, &ldquo;Assessment of muscle fatigue using a probabilistic framework for an EMG-based robot control scenario,&rdquo; Proc. of Int. Conf. on BioInf. and BioEng. pp. 1-6, Oct. 2008. [25] P. Laguna, G. B. Moody, and R. G. Mark, &quot; Power spectral density of unevenly sampled data by leastsquare analysis: performance and application to heart signals,&quot;, &quot; IEEE Trans. Biomedical Engineering, vol. 45, no. 6, pp. 698-715, June 1998. [26] M. Yoshikawa, M. Mikawa, K. Tanaka, &ldquo;Real-Time Hand Motion Estimation Using EMG Signals with Support Vector Machines,&rdquo; SICE-ICASE, 2006. International Joint Conference, pp. 593-598, Oct. 2006. [27] L. H. Visser, &ldquo;Critical illness polyneuropathy and myopathy: clinical features, risk factors and prognosis,&rdquo; European Journal of Neurology, vol. 13, pp. 1203-1212, 2006. [28] J.N. Hwang, S.S. You, S.R. Lay, and I.C. Jou, &ldquo;The cascade-correlation learning: a projection pursuit learning perspective,&rdquo; IEEE Transactions on Neural Networks, vol. 7(2), pp. 278-289, Mar. 1996. [29] J.N.G. Ribeiro, G.C. Vasconcelos, and C.R.O. Queiroz, &ldquo;A comparative study of the cascadecorrelation architecture in pattern recognition applications,&rdquo; Proc. of IVth Brazilian Symposium on Neural Networks, pp. 31-40, December 1997. [30] Fahlman, S.E. and C. Lebiere (1990) &quot;The Cascade-Correlation Learning Architecture,&quot; Advances in Neural Information Processing Systems, Morgan-Kaufmann, Los Altos CA, 1990. [31] http://www.noraxon.com/products/instruments/myotrace400.php3 [32] G. Derringer and R. Suich, &quot;Simultaneous Optimization of Several Response Variables,&quot; Jour. of Qlty. Tech., vol. 12(4), pp. 214-219, 1980. [33] T. Fawcett, &ldquo;An Introduction to ROC Analysis,&rdquo; Pattern Recognition Letters, vol. 27, no. 8, pp. 861- 874, June 2006. [34] Christodoulou C.I., Pattichis C.S., 1995, &quot;A New Technique for the Classification and Decomposition of EMG signals&quot;, in Proc. IEEE Int. Conf. on Neural Networks, vol. 5, pp. 2303-2308, Nov. 1995. [35] H.B. Xie, Z. Wang , H. Huang, and C. Qing, &ldquo; SVM in Computer Aided Clinical EMG,&rdquo; 2nd Int. Conf. on M.L. and Cyb., pp. 1106-1108, 2003. [36] H.B. Xie, H. Huang, and Z. Wang, &ldquo; A Hybrid Neurofuzzy System for Neuromuscular Disorders Diagnosis,&rdquo; IEEE Workshop on Biomedical Circuits and Systems, Sec. 2.5, pp. 5-8, 2004. International Journal on Bioinformatics &amp; Biosciences (IJBB) Vol.3, No.3, September 2013 39 Authors Sailesh Conjeti holds a Bachelor of Engineering (Hons.) Degree in Electrical and Electronics Engineering from Birla Institute of Technology and Science, Pilani. He is currently with the School of Medical Science and Technology at Indian Institute of Technology, Kharagpur pursuing his Masters in Medic al Imaging and Informatics. His research interests include Medical Image Computing, Biomedical Signal Processing, Wearable Computing and Rehabilitation Engineering. He has participated in 5 research projects and has 8 publications to his credit. B. K. Rout completed his B. E. in Mechanical Engineering from University College of Engineering, Burla, Sambalpur (Deemed University) in the year 1990 and completed M. Tech, in Quality, Reliability and Operations Research from Indian Statistical Institute Calcutta, 1992. After graduation he worked with Escort Ancillaries and MESCO Steel Projects for 5 years. He joined BITS Pilani, in December 1998. For the last 12 years he is working as a Faculty member of Mechanical Engineering Group. While serving as a faculty member in the department of Mechanical Engineering, he completed his doctoral research in area of manipulator design under the guidance of Prof. R K Mittal in 2006. So far he has published many research papers in National and International Conferences and in International Journals. His areas of interests are Simulation and Optimization of Dynamic Systems, Design Optimization and Quality Engineering. </em>

Authors: Yalin Tang, Qian Shang, Junfeng Xiang, Qianfan Yang, Qiuju Zhou, Lin Li, Hong Zhang, Qian Li, Hongxia Sun, Aijiao Guan, Wei Jiang &amp; Wei Gai ### Abstract This protocol presents the screening of ligand(s) from medicinal plant extracts based on target recognition by using NMR spectroscopy. A detailed description of sample preparation and analysis process is provided. NMR spectroscopies described here are 1H NMR, diffusion-ordered spectroscopy (DOSY), relaxation-edited NMR, 1H–13C HSQC and HMBC. This method includes three steps: First, investigate the NMR spectroscopy properties of target and choose the suitable spectral editing NMR method; second, judge the existence of ligand(s) and determine the proton signals of ligand(s) in the extracts; third, verify the structure(s) of the ligand(s). This method allows the direct structural identification of ligand(s) from medicinal plant extracts without separation and purification. Thus it provides a very promising strategy for the fast screening of lead components based on target recognition. ### Introduction Plant-derived agents, owing to their diversity in structure and bioactivity1-6, play critical roles in pharmaceutical research. They provide lead compounds for biopharmaceutical technology and supply an economical ways for the discovery of new drugs. The drugs, which are used widely in modern medicine, mostly derived from natural compounds and their derivatives. For example, aspirin was originated from salicylic acid in the bark of the willow tree (Salix species), which is used traditionally to treat fever and inflammation in many cultures worldwide7. The successes of the early “blockbuster” drugs set the stage for ongoing drug discovery efforts from medicinal plants. The screening of bioactive ingredients based on targets recognition attracts researchers’ attention8-10. Nevertheless, these methods can hardly be applied in direct screening of bioactive ingredients from plants extracts which compose of various compounds with different contents. Currently screening from plant extracts against specific targets always follows the strategy of “Isolation  Structure identificationActivity confirmation”. Yagura et al.11 isolated isosalipurposide for anticarcinogenic compounds in the Uzbek medicinal plant by bioactivity-directed fractionation. Bai et al.12 screen the G-quadruplex ligands from Kalopanax septemlobus (Thunb) Koidz extract by high performance liquid chromatography (HPLC). Compared with these methods, our method will accelerate the screening process of lead components greatly by providing the structural information of bioactive ingredients without isolating the ligand(s) molecules experimentally. Thus it provides chemists and pharmacologists with useful info to optimize lead compound for the final drug in a more rapid way. The implementation of our goal benefits from the powerful NMR techniques. NMR has now become an important tool in drug discovery13-19, owing to its advantage in structure identification. But the complexity and overlapping of signals in complex systems bring difficulties in the recognition of spectra. With the development of spectral editing NMR techniques, selected useful information also can be obtained. Among these techniques, diffusion-ordered spectroscopy (DOSY) and relaxation-edited methods are two successful examples20-23, which could provide means for signal filtering and selecting, and consequently achieve “virtual separation”. In this protocol, DOSY and relaxation-edited NMR techniques are used to screen the bioactive ingredients interacting with target in medicinal plant extracts. Our method has drawn a profound attention on screening bioactive ingredients from medicinal plants and there are many reviews commenting on it24, 25. The reviewer of Angewandte Chemie International Edition pointed out that the predominance of this method was integration screening and structure identification, which carried out the fast screening and structure identification of bioactive ingredients in natural plant extracts. Nature China highlighted this method and commented on it with Screening Methods: Looking for Ligands as title. In order to introduce this method to more researchers who devote to search the bioactive ingredients in natural plant extracts and provide convenience for their works, it is necessary to describe this method in detail and define standard protocol in order to ensure the correct usage of this method. Here we take the screening and identifying ligands from the extract of Phellodendron chinense Schneid cortexes (PE)26 based on G-quadruplex and the extract of Flos Lonicera Japonica (FLJ) based on Avian Influenza Polymerase Protein PAC by using NMR as examples to define well-tested procedures. Experimental design The biomolecules which are reported to have important physiological significance can be used as targets. To be specific, our protocol takes G-quadruplex and Avian Influenza Polymerase Protein PAC (carboxyterminal domain of PA) as examples. G-quadruplexes27,28 can inhibit the activity of telomerase29 and play an important role in suppression of carcinogenesis30-33. The ligands of G-quadruplex have the potential for the arrest of cancer-cell growth and may be potentially valuable as antitumor drugs34-36. The interaction of G-quadruplex and its ligand affects the chemical shifts of imino protons in G-quadruplex, which locate low-field region (10-12 ppm) of NMR spectra. Additionally, the ligand of protein also can be obtained by using spectrally-filtered editing NMR techniques. For PAC, the drugs discovered by it may be effective against most influenza strains less susceptible to drug resistance due to the high conservation of the active sites in PAC37. The medicinal plant extracts which are reported to treat some kind of disease can be used as candidates for the corresponding targets. PE is chosen as a candidate because it is reported to be benefit for the treatment of cancer and contain G-quadruplex ligands38. FLJ is chosen as a candidate because it is reported to be benefit for the treatment of viral infection such as influenza A virus39. The concentration of medicinal plant extracts depends on the concentration of bioactive ingredients. The concentration of medicinal plant extracts should be benefit for observing the obvious changes from the characteristic signals of the targets. There are three steps in this process (Figure 1): First, it is necessary to choose the suitable NMR sequence according to the target. To be specific, if there are not clear and characteristic signals of target in NMR spectroscopy, relaxation-edited NMR is preferred. Otherwise, DOSY is suitable. For example, G-quadruplex has the characteristic peaks around δ 10-12 ppm which are clear and not easy to be interfered by other components. In this case, DOSY can be chose. But for proteins they usually have high molecular weights, thus their peak profiles are broadened and there are uneasy to be identified. In this case, relaxation-edited NMR can be chose. Second, judge the existence of ligand(s) and determine the proton signals of ligand(s) by spectral editing NMR techniques in the extracts. For example, G-quadruplex is taken as target in DOSY. As long as the ligand(s) binds to G-quadruplex, the chemical shifts and intensities of imino protons of G-quadruplex which are clear and characteristic signals of G-quadruplex change and the change is easy to be detected. Then extract the signal of ligand(s) by DOSY. In the relaxation-edited NMR spectra, through the comparison with the spectrum of extract without PAC, the peak of ligand can be picked out. Third, further structural illumination is used to identify the ligand(s) by 1H-13C HSQC and HMBC from the signal obtained in step 2. Limits of applicability and practical considerations If there were bioactive ingredients in medicinal plant extracts and their structures were unknown, this protocol could be employed to find them out. As long as the signals of the ingredients which could interact with the target could be distinguished in NMR spectroscopy, this protocol could be used to identify their structures without isolation. Although it provided a fast approach to structurally determine bioactive ingredients, this method had some limitations. Firstly, the content of bioactive ingredients in the extracts is a key factor that influences the result. For example, the G-quadruplex ligand detected in PE is about 0.06% (mass concentration). If there is more than one ligand in test extracts, the content of each ligand dominates the priority of ligands identified in test extracts in this protocol. For a multiligand system such as extracts of Coptis chinensis Franch rhizomes (CE)26 which contains at least two ligands (berberine and palmatine) with similar G-quadruplex binding ability, the ligand with the higher content tend to be identified and that with lower content may be omitted. The second factor is the requirement of the target. Because our protocol bases on NMR techniques, generally the target must meet the demands in NMR experiments, such as the concentration and molecular weight40. To be specific, it requires the relatively high concentration (mM level) of target when the molecular weight of target is about ten thousand owing to the sensitivity of DOSY. ### Reagents REAGENTS 1. DNA d(TTGGGTT) (Tsingke Biotechnology Co., Ltd) - TSP (3-(trimethylsilyl) propionic acid-d4 sodium salt), berberine (Sigma Co.) - Lentinan extracts (Shanghai kangzhou Fungi extract Co., Ltd) EQUIPMENT 1. Avance 600 NMR spectrometer (Bruker-Biospin). 600 MHz is not the only frequency that can be used in this method, but it is a good compromise for sensitivity and dispersion versus capital cost. - A 5 mm BBI probe capable of delivering z-field gradients. - Eppendorf pipette and pipette tips or similar REAGENT SETUP G-quadruplex In practice, the sample preparation for this experiment is simple. The preparation of G-quadruplex d(TTGGGTT)4 is the same as the procedure described in the literature41. G-quadruplex d(TTGGGTT)4 was formed by dissolving primer d(TTGGGTT) in phosphate buffer (10 mM K2HPO4/KH2PO4, 90% H2O/10% D2O, pH 7.0). The solution was equilibrated at room temperature for 24 h before experiments. The absorbance of DNA d(TTGGGTT) at 260nm is obtained by absorption spectroscopy. It is hypothesized that all DNA d(TTGGGTT) can form d(TTGGGTT)4 and the concentration of d(TTGGGTT)4 is . The minimum concentration of G-quadruplex d(TTGGGTT)4 is 0.25 mM. Under the concentration the characteristic signals of d(TTGGGTT)4 are easily distinguished in NMR spectroscopy. PAC Methods for the preparation of PAC protein were previously described5. Briefly, residues 257–716 of the PA subunit of avian H5N1 influenza A virus (A/goose/Guangdong/1/96) were cloned into a pGEX-6p vector (GE Healthcare) and transformed into Escherichia coli strain BL21. Cells were cultured in LB medium at 37℃ with 100 mg/L of Ampicillin. When the OD600 reached 0.6–0.8, the culture was induced with 0.5 mM isopropyl-thio-Dglactosidase (IPTG) at 16℃. After 20 hours of incubation, the cells expressing PAC were harvested and combined by centrifugation at 5000 rpm for 10 min. Recombinant protein was purified with a glutathione affinity column (GE Healthcare). Glutathione S-transferase (GST) was cleaved with PreScission protease (GE Healthcare), and the protein complex was further purified by Q sepharose FF ion exchange chromatography and Superdex-200 gel filtration chromatography (GE Healthcare). Extracts of Phellodendron chinense Schneid cortexes (PE) A 500 mL three-neck flask is equipped with a magnetic stirrer, thermometer, heating mantle, and a reflux condenser, 60 g powder of Phellodendron chinense Schneid cortexes and 300 ml ethanol-water solution (the volume ratio of water and ethanol was 1:3) are added to the flask. Then the mixture is heated up to 80 degrees Celsius with stirring for 5 h. The crude product was collected by filtration, and dried in a vacuum desiccator at 60 degrees Celsius for 48 h. 31.5 g product is obtained. Extracts of Flos Lonicera Japonica (FLJ) 10 g powder of FLJ was reflux extracted with 300 mL 15% ethanol-water solution for 5h. The residue was then removed by filtration, and the filtrate was desiccated in a vacuum desiccator at 60 ºC for 48 h. 1 g of FLJ extracts was finally obtained, respectively. ### Equipment EQUIPMENT SETUP NMR setup The high-resolution NMR spectroscopy could be used for screening and identifying new bioactive ingredients42. In the following, experimental methods are described using a Bruker NMR spectrometer as an example. Some common pulse techniques can be employed to screen bioactive ingredients with futher structure identification, such as standard Bruker pulse program p3919gp, stebpgp1s19, cpmgpr1d, hsqcetgp and hmbcgplpndqf. Setup on NMR spectrometers of other vendors will require adjustment accordingly. In order to obtain maximum benefit from acquiring NMR spectra, the parameters need to be set accurately. Generally, all NMR experiments should be acquired at 298.2K. Homogenization of the magnetic field (‘shimming’) for samples is important for conventional NMR probes. This can be done with TopShim on Bruker spectrometers (TopSpin 2.1). The homogenization of the magnetic field can be confirmed by the peak profile of TSP which is a single peak at 0ppm. The number of scans per experiment for different medicinal plant extracts can be adjusted according to the signal-to-noise ratio of the components of interest, but this will have an impact on the overall experiment time per sample. Water suppression When observing protons, the dynamic range of the detection can be strongly limited by the size of the water peak and the results in the signal loss for low concentration substances. Hence, the water peak has to be suppressed by using the pulse program p3919gp that applies 3–9–19 pulses with gradients for water suppression43 to improve the signal-to-noise ratio for the detection of ligands. Other spectrometer manufacturers provide the same pulse sequences, but they have different names and syntax; please discuss with the supplier. The parameters are optimized to give optimum suppression of the water resonance without reducing the signal intensity of the compound signals next to it. It is recommended to automatically adjust the receiver gain before acquiring the spectra. All NMR experiments require correct setting of 90° pulse length. It is a key to determine the offset of the water signal for the water suppression. DOSY To get the actual gradient strength in absolute values, it is necessary to obtain a gradient calibration constant, which is used by the AU program dosy to calculate and store the list (difflist) containing absolute gradient strength values. This list is used by the processing tools to calculate the correct diffusion constants. The gradient strength (g), diffusion time big delta (Δ) and diffusion gradient length little delta (δ) are important factors in DOSY experiment. It is necessary to optimize these three parameters to detect the whole decay function properly. Selecting the right values for Δ or δ is important to get good diffusion constants with less error. Using 1D versions of the diffusion pulse program for optimizing the Δ (d20) and δ (p30), experiments are carried out by comparing two spectra with different amplitude (gpz6) 2% and 95%. The parameters are set when the signal decay goes down to 5% residual signal. The comparison is convenient in dual display. Cpmgpr: The relaxation-edited NMR experiments utilized a [D/pre-saturation-90x-(Δ-180y-Δ)n-acquire] pulse sequence, in which the CPMG sequence was used for the spin-lock. Structure identification using 2D NMR spectroscopy Two-dimensional heteronuclear NMR spectroscopy is employed for identifying the connectivity between signals. 1H–13C HSQC and HMBC NMR spectra are used for structural identification44, as they highlight connectivities of protons directly attached to the carbon atoms in a molecule. ### Procedure - **Sample preparation ● TIMING 10–12 min** 1. Dissolving target in appropriate buffer solution and adjust the concentration. For G-quadruplex d(TTGGGTT)4, the concentration is 0.25 mM. For PAC, the concentration is 8.7×10-3mM. - ▲ CRITICAL STEP In the work with G-quadruplex, the formation of G-quadruplex is of paramount importance. Usually triethylamine used in the synthesis of d(TTGGGTT) usually affects the binding between G-quadruplex and ligands. Thus triethylamine needs to be removed by dialysis before the usage of d(TTGGGTT) in this protocol. - For DOSY, make the mixture of d(TTGGGTT)4 and PE and ensure the concentration is 0.25 mM and 3.5 mg mL-1, respectively. For relaxation-edited NMR, prepare two samples. One is the mixture of PAC and FLJ and the concentration is 8.7×10e-3mM and 3.5 mg mL-1, respectively. The other is FLJ and the concentration is 3.5 mg mL-1. - Put the above samples into 5mm NMR tubes and the total volume is 5×10-4L for each sample, respectively. - NMR setup ● TIMING 15–17 min - Insert the NMR tube into the spinner and measure the correct height with the gauge. - Enter the and wait for 5–10 s before entering the button. The sample is now positioned in the probe. - Wait for 5–10 minutes for the temperature equilibrium. - Optimization of NMR spectrometer: (1) tune and match the probe; (2) lock onto the lock solvent; and (3) shim the sample using the lock level. - Determine the sample-specific settings for NMR pulse sequences: (1) Set the offset value to the HDO resonance. (2) Determine the 90° pulse length at a given power level. (3) Re-adjust the frequency offset for water signal suppression, if necessary. - ？TROUBLESHOOTING - ▲ CRITICAL STEP The suppression of water properly is beneficial for the observation of tiny constituent. - **NMR acquisition ● TIMING 24 h** - First, 1D p3919gp sequence (option A) is used to test whether the extracts contain G-quadruplex ligand(s). (TIMING 12-15 min) Second, DOSY (option B) is used to pick out the peak of ligand. (TIMING 2 h) Third, based on the peak of ligand, HSQC and HMBC (option C) are used to identify the structure of ligand. (TIMING 24 h) Adjust the receiver gain per sequence and sample by using automatic receiver gain adjustment. Processing parameters: if not mentioned otherwise, the one-dimensional spectra are generally processed by applying a line broadening of 0.3–1 Hz. - ？TROUBLESHOOTING - (A) 1D p3919gp sequence (Bruker spectrometer: p3919gp) - (i) The parameters are set as follows: spectral width = 20 ppm; number of time domain data points = 32 k; relaxation delay (RD) = 2.0 s; number of scans = 128; and the receiver gain is set to fill the digitizer as closely as possible. This results in a total acquisition time of about 11 min per sample. - (ii) For processing, a target spectral resolution is difficult to define, but typically with a line-broadening of 1 Hz. (TIMING 1 min) - (B) DOSY (Bruker terminology: stebpgp1s19)45 - (i) Acquire diffusion-edited spectra using a pulse sequence with stebpgp1s19. The settings are relaxation delay = 2 s; number of scans = 128 (or higher, depending on requirements); data points in the F2 dimension = 32 k; data points (gradient strengths) in the F1 dimension = 32; spectral width = 20 ppm; diffusion delay Δ = 0.1 s; the gradient length δ = 5.6 ms; line broadening factor in F2 dimension = 0.3 Hz. The data analyses were applied to the raw experimental data using the standard 2D DOSY processing protocol in TOPSPIN (Bruker, Version 2.1) software with logarithmic scaling in the F1 (diffusion coefficient) dimension. - (C) Heteronuclear correlation spectroscopy (Bruker terminology: hsqcetgp and hmbcgplpndqf)46 - (i) Acquire 1H–13C HSQC spectrum using sensitivity improvement with echo-anti echo-TPPI. GARP decoupling of 13C is carried out during the acquisition time. 0.1 μs trim pulses are employed in the INEPT transfer and gradients in back-INEPT. The parameters are as follows: resolution in F2/F1 = 2 k/512 (depending on available experiment time); number of scans = 128 (depending on experiment time); number of dummy scans = 16; sweep width in F2/F1 = 20 ppm/170 ppm in hsqcetgp, and relaxation delay = 2 s. C-H coupling constant is 145 Hz. 1024×1024 points are used in Fourier transformation. - (ii) Collect 1H-13C phase-sensitive (TPPI) HMBC spectra. GARP decoupling of 13C is carried out during the acquisition time. The parameters are as follows: resolution in F2/F1 = 2 k/512 (depending on available experiment time); number of scans = 128 (depending on experiment time); number of dummy scans = 16; sweep width in F2/F1 = 20 ppm/250 ppm in hmbcgplpndqf, and relaxation delay = 2 s. The long range C-H coupling constant for HMBC is 6.25 Hz. 1024×1024 points are used in Fourier transformation. Optional: 9| First, 1D cpmgpr1d sequence (option A) is used in FLJ in the absence (a) and presence (b) of PAC with different spin-lock time.(generally 100-1500ms) (TIMING 24-30 min) Second, the difference spectrum is made with (a) and (b) and find out the changed peaks which belong to ligand. (5min) Third, based on the peaks of ligand, HSQC and HMBC (option C) are used to identify the structure of ligand. (TIMING 24 h) Adjust the receiver gain per sequence and sample by using automatic receiver gain adjustment. Processing parameters: if not mentioned otherwise, the one-dimensional spectra are generally processed by applying a line broadening of 0.3–1 Hz. (A) 1D cpmgpr1d sequence (Bruker spectrometer: cpmgpr1d) (i) The parameters are set as follows: pre-saturation water suppression was applied in pre-acquisition delay (D = 3 s); Δ = 1.5 ms, and 2×n×Δ = total spin-lock time; spectral width = 20 ppm; number of time domain data points = 64 k; relaxation delay (RD) = 2.0 s; number of scans = 32; and the receiver gain is set to fill the digitizer as closely as possible. This results in a total acquisition time of about 1-30 min per sample depending on the spin-lock time. **TIMING** - Steps 1–3 Sample preparation: 10–12 min - Steps 4-8 NMR setup: 15–17 min - Step 9 NMR acquisition: 24 h **？TROUBLESHOOTING** - Step 8 - The main problems can be avoided by ensuring that the water offset and 90° pulse length are adjusted on samples (90° pulse length needs to be adjusted on a sample-by-sample basis). - Step 9 - The receiver gain requires automatic receiver gain adjustment. This will avoid problems with baseline rolling artifacts. ### Anticipated Results Accurate application of the steps in this protocol will lead to consistent NMR spectra. Once these spectra have been obtained, they will require phasing and baseline correction before being subjected to further analysis. The 1H spectra of G-quadruplex d(TTGGGTT)4 with different plant extracts [with or without G-quadruplex ligand(s)], are shown in Figure 2. The evident differences in the imino region (10–12 ppm) in the 1H NMR spectra of free and bound G-quadruplexes47 can be utilized as a spectroscopic means indicating the existence/nonexistence of G-quadruplex ligand(s) in the test extracts. As shown in Figure 2, when lentinan (without G-quadruplex ligand) is added into the d(TTGGGTT)4, the chemical shift and intensity of G3-G5 are unchanged. However, when PE (with G-quadruplex ligand) is added into the d(TTGGGTT)4, a clear upfield shift of the guanine imino proton resonance signals was observed and signal intensity, especially that of G5 dropped greatly. Figure 3 shows that DOSY analysis of a mixture of PE and d-(TTGGGTT)4. Based on the principle of DOSY, if the component binds to d-(TTGGGTT)4, the component has the same diffusion coefficient with d-(TTGGGTT)4. The characteristic peaks of d-(TTGGGTT)4 are around δ 10-12. In Figure 3, the peak around δ 9 ppm has the same ordinate with the peaks around δ 10-12 which means the components from δ 9 ppm have the same diffusion coefficient with d-(TTGGGTT)4. Additionally no such peak appears in the spectrum of the G-quadruplex alone. This result, together with a consideration of the sharp drop in the diffusion coefficient of the proton that resonates around δ 9 ppm in the two samples (4.8×10-10 m2 s-1 in PE and 1.4×10-10 m2 s-1 in a mixture of PE and d-(TTGGGTT)4 respectively), led us to reasonably conclude that this peak resulted from the compound in PE bound to G-quadruplex. According to the peak at δ 9 ppm, the structure of the berberine could be recognized by analyzing the HSQC and HMBC spectra of PE26. (Supplement) The relaxation-edited NMR spectra of FLJ in the absence and presence of PAC and the difference spectrum are shown in Figure 4. In order to identify the PAC ligand from the mixtures, relaxation-edited NMR with Carr-Purcell-Meiboom-Gill (CPMG) spin-lock was applied to FLJ extract in the absence and presence of PAC. As shown in Figure 4b, the application of CPMG spin-lock will reduce the ligand peaks due to its relatively fast R2 upon binding. However, these signals were not influenced in the absence of target (Figure 4a). This difference was highlighted in the difference spectrum (Figure 4c). These attenuated peaks can be ascribed to the PAC ligand in the extract. Thus, the ligand peaks can be “picked out” from the mixture without previous isolation. The next step is to identify which molecule in the mixture is bound to PAC. Based on the characteristic signals picked out by relaxation-edited NMR, structure elucidation was carried out using the subsequent 2DNMR experiments, such as HSQC, HMBC, and total correlation spectroscopy (TOCSY). Followed by proton-carbon connections, proton-carbon long-range correlations, and proton-proton correlations provided by these 2DNMR experiments, the molecular frame of the ligand can be built. The PAC ligand can be then identified to be chlorogenic acid. ### References 1. Schmidt, B. M., Ribnicky, D. M., Lipsky, P. E. &amp; Raskin, I. 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Recognition and Stabilization of Quadruplex DNA by a Potent New Telomerase Inhibitor: NMR Studies of the 2:1 Complex of a Pentacyclic Methylacridinium Cation with d(TTAGGGT)(4) *Angew Chem Int Edit* 40, 4749-4751 (2001). ### Acknowledgements This work is supported by the Major Research Program of the National Natural Science Foundation of China (Grant No. 91027033), the General Program of the National Natural Science Foundation of China (Grant No. 81072576) and the Key Program of the Chinese Academy of Sciences (Grant No. KJCX2-EW-N06-01). ### Figures **Figure 1: The steps in this protocol** [Download Figure 1](http://www.nature.com/protocolexchange/system/uploads/2366/original/Figure_1.doc?1354781342) **Figure 2: The 1H NMR spectra of a) d(TTGGGTT)4, b) a mixture of d-(TTGGGTT)4 and lentinan extract (without G-quadruplex ligand), and c) a mixture of d(TTGGGTT)4 and PE (with G-quadruplex ligand, berberine).** [Download Figure 2](http://www.nature.com/protocolexchange/system/uploads/2367/original/Figure_2.doc?1354781426) *The concentrations of d(TTGGGTT)4, lentinan extract, and PE are 0.25 mM, 3.50 mg mL-1, and 1.0 mg mL-1, respectively. The region of 10.5–12.0 ppm is broadened and the imino proton resonance signals are labeled as G3–G5. (Reproduced with permission from ref. 25)* **Figure 3: DOSY analysis of a mixture of PE (1.0 mg mL-1) and d- (TTGGGTT)4 (0.25 mM). The peak of the d(TTGGGTT)4 ligand(s) is designated by “↓” in the F2 projection. (Reproduced with permission from ref. 25)** [Download Figure 3](http://www.nature.com/protocolexchange/system/uploads/2369/original/Figure_3.doc?1354796378) **Figure 4: The relaxation-edited NMR spectra of FLJ in the absence (a) and presence (b) of PAC and the difference spectrum (c) of a and b**. [Download Figure 4](http://www.nature.com/protocolexchange/system/uploads/2370/original/Figure_4.doc?1354796475) *The spin-lock time in a and b were both 1500 ms. Contents of FLJ extract were 3.3mg mL-1, and concentration of PAC was 8.7 × 10-3mM. The water peak located at δ 4.8, and 1mM of TSP was added to the sample as a reference (δ 0). The region of δ 6.2-7.8 is broadened, and the ligand peaks attenuated upon the addition of the target were marked with “*” in plot c*. ### Associated Publications 1. **Screening Potential Antitumor Agents from Natural Plant Extracts by G‐Quadruplex Recognition and NMR Methods**. Qiuju Zhou, Lin Li, Junfeng Xiang, Yalin Tang, Hong Zhang, Shu Yang, Qian Li, Qianfan Yang, and Guangzhi Xu. *Angewandte Chemie International Edition* 47 (30) 5590 - 5592 14/07/2008 doi:10.1002/anie.200800913 - **Screening Anti-influenza Agents that Target Avian Influenza Polymerase Protein PAC from Plant Extracts Based on NMR Methods**. Lin Li, Sheng-Hai Chang, Jun-Feng Xiang, Qian Li, Huan-Huan Liang, Jian Li, Hong-Juan Bao, Ya-Lin Tang, and Ying-Fang Liu.* Chemistry Letters* 40 (8) 801 - 803 doi:10.1246/cl.2011.801 - **Screening α-glucosidase inhibitors from mulberry extracts via DOSY and relaxation-edited NNR**. Qian Shang, Jun-Feng Xiang, and Ya-Lin Tang. *Talanta* 97 () 362 - 367 doi:10.1016/j.talanta.2012.04.046 - **Fast screening and structural elucidation of G-quadruplex ligands from a mixture via G-quadruplex recognition and NMR methods**. Qiuju Zhou, Lin Li, Junfeng Xiang, Hongxia Sun, and Yalin Tang. *Biochimie* 91 (2) 304 - 308 doi:10.1016/j.biochi.2008.10.011 - **Fishing potential antitumor agents from natural plant extracts pool by dialysis and G-quadruplex recognition**. Qian Shang, Jun-Feng Xiang, Xiu-Feng Zhang, Hong-Xia Sun, Lin Li, and Ya-Lin Tang. *Talanta* 85 (1) 820 - 823 doi:10.1016/j.talanta.2011.04.011 - **Direct screening of G-quadruplex ligands from Kalopanax septemlobus (Thunb.) Koidz extract by high performance liquid chromatography**. Ge Bai, Xueli Cao, Hong Zhang, Junfeng Xiang, Hong Ren, Li Tan, and Yalin Tang. *Journal of Chromatography A* 1218 (37) 6433 - 6438 doi:10.1016/j.chroma.2011.07.028 - **Screen Anti-influenza Lead Compounds That Target the PAC Subunit of H5N1 Viral RNA Polymerase**. Lin Li, Shenghai Chang, Junfeng Xiang, Qian Li, Huanhuan Liang, Yalin Tang, Yingfang Liu, and Jun Liu. *PLoS ONE* 7 (8) 24/08/2012 doi:10.1371/journal.pone.0035234 - **NMR identification of anti-influenza lead compound targeting at PAC subunit of H5N1 polymerase**. Lin Li, Sheng Hai Chang, Jun Feng Xiang, Qian Li, Huan Huan Liang, Ya Lin Tang, and Ying Fang Liu. *Chinese Chemical Letters* 23 (1) 89 - 92 doi:10.1016/j.cclet.2011.09.006 ### Author information **Yalin Tang**, Tang yalin **Qian Shang, Junfeng Xiang, Qianfan Yang, Qiuju Zhou, Lin Li, Hong Zhang, Qian Li, Hongxia Sun, Aijiao Guan, Wei Jiang &amp; Wei Gai**, Unaffiliated Correspondence to: Yalin Tang (tangyl@iccas.ac.cn) *Source: [Protocol Exchange](http://www.nature.com/protocolexchange/protocols/2534) (2012) doi:10.1038/protex.2012.060. Originally published online 7 December 2012*.