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Journal articles on the topic 'Synthetic Database Generation'

Author: Grafiati

Published: 6 September 2023

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1

Priyadarshini, Pallavi, Fengqiong Qin, Ee-Peng Lim, and Wee-Keong Ng. "Parameter driven synthetic web database generation." Journal of Systems and Software 69, no. 1-2 (2004): 29–42. http://dx.doi.org/10.1016/s0164-1212(03)00002-5.

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Sanghi, Anupam, Shadab Ahmed, and Jayant R. Haritsa. "Projection-compliant database generation." Proceedings of the VLDB Endowment 15, no. 5 (2022): 998–1010. http://dx.doi.org/10.14778/3510397.3510398.

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Synthesizing data using declarative formalisms has been persuasively advocated in contemporary data generation frameworks. In particular, they specify operator output volumes through row-cardinality constraints. However, thus far, adherence to these volumetric constraints has been limited to the Filter and Join operators. A critical deficiency is the lack of support for the Projection operator, which is at the core of basic SQL constructs such as Distinct, Union and Group By. The technical challenge here is that cardinality unions in multi-dimensional space, and not mere summations, need to be

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Pujol, David, Amir Gilad, and Ashwin Machanavajjhala. "PreFair: Privately Generating Justifiably Fair Synthetic Data." Proceedings of the VLDB Endowment 16, no. 6 (2023): 1573–86. http://dx.doi.org/10.14778/3583140.3583168.

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When a database is protected by Differential Privacy (DP), its usability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the properties of the private data allows users to perform any operation on the synthetic data, while maintaining the privacy of the original data. Therefore, multiple works have been devoted to devising systems for DP synthetic data generation. However, such systems may preserve or even magnify properties of the data that make it unfair, rendering the synthetic data unfit for use. In this work, we present PreFair, a system that

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Pavez, Vicente, Gabriel Hermosilla, Francisco Pizarro, Sebastián Fingerhuth, and Daniel Yunge. "Thermal Image Generation for Robust Face Recognition." Applied Sciences 12, no. 1 (2022): 497. http://dx.doi.org/10.3390/app12010497.

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This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate therma

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Dinges, Laslo, Ayoub Al-Hamadi, Moftah Elzobi, Sherif El-etriby, and Ahmed Ghoneim. "ASM Based Synthesis of Handwritten Arabic Text Pages." Scientific World Journal 2015 (2015): 1–18. http://dx.doi.org/10.1155/2015/323575.

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Document analysis tasks, as text recognition, word spotting, or segmentation, are highly dependent on comprehensive and suitable databases for training and validation. However their generation is expensive in sense of labor and time. As a matter of fact, there is a lack of such databases, which complicates research and development. This is especially true for the case of Arabic handwriting recognition, that involves different preprocessing, segmentation, and recognition methods, which have individual demands on samples and ground truth. To bypass this problem, we present an efficient system th

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Sazonova, Kateryna, Olena Nosovets, Vitalii Babenko, and Olga Averianova. "GENERATION OF SYNTHETICAL MEDICAL DATA BY MDR-ANALYSIS." Proceedings of the National Aviation University 87, no. 2 (2021): 31–36. http://dx.doi.org/10.18372/2306-1472.87.15719.

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Purpose: The purpose of this article is to outline an algorithm for generating synthetic medical data in order to augment small samples of data. Methods: To achieve the research goal, methods such as: correlation analysis (to identify significant variables and the relationships between them), MDR analysis (to build logical chains of relationships between medical data), and regression analysis (to model medical data variables to use this to generate synthetic data) were used. Results: A database of heart failure patients that is publicly available was used to test the developed algorithm for ge

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Burman, Nitin, Claudia Manetti, Paulo Tostes, Joost Lumens, and Jan D'hooge. "A pipeline to enable large-scale generation of diverse 2D cardiac synthetic ultrasound recordings corresponding to healthy and heart failure virtual patients." Journal of the Acoustical Society of America 152, no. 4 (2022): A279. http://dx.doi.org/10.1121/10.0016267.

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Simulated ultrasound (US) data are widely used in echocardiography to develop and validate rapidly growing convolutional neural networks (CNNs) based learning algorithms for image processing and analysis. In this context, a large and diverse database of synthetic US scans is considered vital for CNN training purposes, as clinical US data are scarce and difficult to access. Major hurdles in creating an extensive database are the long US simulation time and unstable heart models for extreme parameter settings. Here, we developed and implemented a cardiac US simulation pipeline that kinematically

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Kuriki, Mikaele Silva, Francisco Lledo Santos, and Cristiano Poleto. "Small-Scale Wetland Model for Synthetic Sewage Treatment." Ciência e Natura 44 (April 21, 2022): e25. http://dx.doi.org/10.5902/2179460x68834.

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With the demand for electricity growing, the migration to renewable sources is a reality. In distributed generation, photovoltaic systems are a renewable and sustainable alternative to the main energy sources to generate electricity. Monitoring a photovoltaic system over its operating time guarantees its good performance. This requires solar radiation and temperature data measured at the installation site or the use of solarimetric stations databases. However, the differences between the results simulated with databases and with data measured at the installation site are not widely known, whic

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Baowaly, Mrinal Kanti, Chia-Ching Lin, Chao-Lin Liu, and Kuan-Ta Chen. "Synthesizing electronic health records using improved generative adversarial networks." Journal of the American Medical Informatics Association 26, no. 3 (2018): 228–41. http://dx.doi.org/10.1093/jamia/ocy142.

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AbstractObjectiveThe aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method.Materials and MethodsWe modified medGAN to obtain two synthetic data generation models—designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)—and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First,

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Loisel, Hubert, Daniel Schaffer Ferreira Jorge, Rick A. Reynolds, and Dariusz Stramski. "A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean." Earth System Science Data 15, no. 8 (2023): 3711–31. http://dx.doi.org/10.5194/essd-15-3711-2023.

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Abstract. Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrar

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Hoffmann, Martin A., Louis-Félix Nothias, Marcus Ludwig, et al. "High-confidence structural annotation of metabolites absent from spectral libraries." Nature Biotechnology 40, no. 3 (2021): 411–21. http://dx.doi.org/10.1038/s41587-021-01045-9.

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AbstractUntargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hit

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Alessandrini, M., M. De Craene, O. Bernard, et al. "A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-Access Database." IEEE Transactions on Medical Imaging 34, no. 7 (2015): 1436–51. http://dx.doi.org/10.1109/tmi.2015.2396632.

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Sauthier, Philippe, Magali Breguet, Alexandre Rozenholc, and Michaël Sauthier. "Quebec Trophoblastic Disease Registry: How to Make an Easy-To-Use Dynamic Database." International Journal of Gynecologic Cancer 25, no. 4 (2015): 729–33. http://dx.doi.org/10.1097/igc.0000000000000401.

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ObjectiveTo create an easy-to-use dynamic database designed specifically for the Quebec Trophoblastic Disease Registry (RMTQ).IntroductionIt is now well established that much of the success in managing trophoblastic diseases comes from the development of national and regional reference centers. Computerized databases allow the optimal use of data stored in these centers.MethodsWe have created an electronic data registration system by producing a database using FileMaker Pro 12. It uses 11 external tables associated with a unique identification number for each patient. Each table allows specifi

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Wang, Xiaona, Fengcheng Li, Wenqi Qiu, et al. "SYNBIP: synthetic binding proteins for research, diagnosis and therapy." Nucleic Acids Research 50, no. D1 (2021): D560—D570. http://dx.doi.org/10.1093/nar/gkab926.

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Abstract The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins fo

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Lykoshin, D. D., V. V. Zaitsev, M. A. Kostromina, and R. S. Esipov. "New-generation osteoplastic materials based on biological and synthetic matrices." Fine Chemical Technologies 16, no. 1 (2021): 36–54. http://dx.doi.org/10.32362/2410-6593-2021-16-1-36-54.

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Objectives. The purpose of this analytical review is to evaluate the market for osteoplastic materials and surgical implants, as well as study the features of new-generation materials and the results of clinical applications.Methods. This review summarizes the volumes of research articles presented in the electronic database PubMed and eLIBRARY. A total of 129 scientific articles related to biological systems, calcium phosphate, polymer, and biocomposite matrices as carriers of pharmaceutical substances, primary recombinant protein osteoinductors, antibiotics, and biologically active chemical

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Isaza, Cesar, Ivan Alonso Lujan-Cabrera, Ely Karina Anaya Rivera, Jose Amilcar Rizzo Sierra, Jonny Paul Zavala De Paz, and Cristian Felipe Ramirez-Gutierrez. "Generation of a Synthetic Database for the Optical Response of One-Dimensional Photonic Crystals Using Genetic Algorithms." Mathematics 10, no. 23 (2022): 4484. http://dx.doi.org/10.3390/math10234484.

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This work proposes an optimization tool based on genetic algorithms for the inverse design of photonic crystals. Based on target reflectance, the algorithm generates a population of chromosomes where the genes represent the thickness of a layer of a photonic crystal. Each layer is independent of another. Therefore, the sequence obtained is a disordered configuration. In the genetic algorithm, two dielectric materials are first selected to generate the population. Throughout the simulation, the chromosomes are evaluated, crossed over, and mutated to find the best-fitted one based on an error fu

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Mohammadi Far, Somayeh, Matin Beiramvand, Mohammad Shahbakhti, and Piotr Augustyniak. "Prediction of Preterm Delivery from Unbalanced EHG Database." Sensors 22, no. 4 (2022): 1507. http://dx.doi.org/10.3390/s22041507.

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Objective: The early prediction of preterm labor can significantly minimize premature delivery complications for both the mother and infant. The aim of this research is to propose an automatic algorithm for the prediction of preterm labor using a single electrohysterogram (EHG) signal. Method: The proposed method firstly employs empirical mode decomposition (EMD) to split the EHG signal into two intrinsic mode functions (IMFs), then extracts sample entropy (SampEn), the root mean square (RMS), and the mean Teager–Kaiser energy (MTKE) from each IMF to form the feature vector. Finally, the extra

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Chávez-Hernández, Ana L., Norberto Sánchez-Cruz, and José L. Medina-Franco. "Fragment Library of Natural Products and Compound Databases for Drug Discovery." Biomolecules 10, no. 11 (2020): 1518. http://dx.doi.org/10.3390/biom10111518.

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Natural products and semi-synthetic compounds continue to be a significant source of drug candidates for a broad range of diseases, including coronavirus disease 2019 (COVID-19), which is causing the current pandemic. Besides being attractive sources of bioactive compounds for further development or optimization, natural products are excellent substrates of unique substructures for fragment-based drug discovery. To this end, fragment libraries should be incorporated into automated drug design pipelines. However, public fragment libraries based on extensive collections of natural products are s

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Fu, Bo, Xiangyi Zhang, Liyan Wang, Yonggong Ren, and Dang N. H. Thanh. "A blind medical image denoising method with noise generation network." Journal of X-Ray Science and Technology 30, no. 3 (2022): 531–47. http://dx.doi.org/10.3233/xst-211098.

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BACKGROUND: In the process of medical images acquisition, the unknown mixed noise will affect image quality. However, the existing denoising methods usually focus on the known noise distribution. OBJECTIVE: In order to remove the unknown real noise in low-dose CT images (LDCT), a two-step deep learning framework is proposed in this study, which is called Noisy Generation-Removal Network (NGRNet). METHODS: Firstly, the output results of L0 Gradient Minimization are used as the labels of a dental CT image dataset to form a pseudo-image pair with the real dental CT images, which are used to train

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Wang, Yunpeng, Scarlet Ferrinho, Helen Connaris, and Rebecca J. M. Goss. "The Impact of Viral Infection on the Chemistries of the Earth’s Most Abundant Photosynthesizes: Metabolically Talented Aquatic Cyanobacteria." Biomolecules 13, no. 8 (2023): 1218. http://dx.doi.org/10.3390/biom13081218.

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Cyanobacteria are the most abundant photosynthesizers on earth, and as such, they play a central role in marine metabolite generation, ocean nutrient cycling, and the control of planetary oxygen generation. Cyanobacteriophage infection exerts control on all of these critical processes of the planet, with the phage-ported homologs of genes linked to photosynthesis, catabolism, and secondary metabolism (marine metabolite generation). Here, we analyze the 153 fully sequenced cyanophages from the National Center for Biotechnology Information (NCBI) database and the 45 auxiliary metabolic genes (AM

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Pacheco, Fernando, Gabriel Hermosilla, Osvaldo Piña, et al. "Generation of Synthetic Data for the Analysis of the Physical Stability of Tailing Dams through Artificial Intelligence." Mathematics 10, no. 23 (2022): 4396. http://dx.doi.org/10.3390/math10234396.

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In this research, we address the problem of evaluating physical stability (PS) to close tailings dams (TD) from medium-sized Chilean mining using artificial intelligence (AI) algorithms. The PS can be analyzed through the study of critical variables of the TD that allow estimating different potential failure mechanisms (PFM): seismic liquefaction, slope instability, static liquefaction, overtopping, and piping, which may occur in this type of tailings storage facilities in a seismically active country such as Chile. Thus, this article proposes the use of four machine learning algorithms, namel

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Mousses, Spyro, David Schneider, Jeff Kiefer, et al. "Application of artificial intelligence to predict a new class of novel synthetic lethal targets." Journal of Clinical Oncology 37, no. 15_suppl (2019): 2598. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.2598.

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2598 Background: Synthetic lethal targets are proteins that are contextually vulnerable. Inhibitors of PARP1, for example, selectively produce a lethal phenotype in the context of cancer cells which have lost BRCA1 or BRCA2 function. As a high mutation rate is a hallmark of many cancers, targeting synthetic lethal interactions to selectively inhibit cancer cells with altered genetic backgrounds may increase the specificity and efficacy of therapeutics. Recently, clinical trials have targeted synthetic lethal pairs such as EGFR and BRAF, TP53 and BCL2, and PTEN and CHD1. Previous attempts to id

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Wu, Xiaoying, and Dimitri Theodoratos. "Template-Based Bitmap View Selection for Optimizing Queries Over Tree Data." International Journal of Cooperative Information Systems 25, no. 03 (2016): 1650005. http://dx.doi.org/10.1142/s0218843016500052.

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Developing and exploiting flexible techniques for optimizing the evaluation of queries over loosely structured data (e.g. tree or graph databases) is of crucial importance for modern database applications. In this context, we consider a new type of views which can be materialized as compressed bitmaps over tree data. We introduce the concept of view structural template to define classes of views. We then define and address a novel view selection problem (called view class selection (VCS) problem) where the goal is to select classes of bitmap views in order to optimize the overall evaluation co

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Erland, Lauren A. E., Ryland T. Giebelhaus, Jerrin M. R. Victor, Susan J. Murch, and Praveen K. Saxena. "The Morphoregulatory Role of Thidiazuron: Metabolomics-Guided Hypothesis Generation for Mechanisms of Activity." Biomolecules 10, no. 9 (2020): 1253. http://dx.doi.org/10.3390/biom10091253.

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Thidiazuron (TDZ) is a diphenylurea synthetic herbicide and plant growth regulator used to defoliate cotton crops and to induce regeneration of recalcitrant species in plant tissue culture. In vitro cultures of African violet thin petiole sections are an ideal model system for studies of TDZ-induced morphogenesis. TDZ induces de novo shoot organogenesis at low concentrations and somatic embryogenesis at higher concentrations of exposure. We used an untargeted metabolomics approach to identify metabolites in control and TDZ-treated tissues. Statistical analysis including metabolite clustering,

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Parate, Shraddha, Vikas Kumar, Danishuddin, Jong Hong, and Keun Lee. "Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics." International Journal of Molecular Sciences 22, no. 10 (2021): 5311. http://dx.doi.org/10.3390/ijms22105311.

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Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compound

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Mas, Erick, Daniel Felsenstein, Luis Moya, A. Yair Grinberger, Rubel Das, and Shunichi Koshimura. "Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA)." Journal of Disaster Research 13, no. 7 (2018): 1257–71. http://dx.doi.org/10.20965/jdr.2018.p1257.

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The DIM2SEA research project aims to increase urban resilience to large-scale disasters. We are engaged in developing a prototype Dynamic Integrated Model for Disaster Management and Socioeconomic Analysis (DIM2SEA) that will give disaster officials, stakeholders, urban engineers and planners an analytic tool for mitigating some of the worst excesses of catastrophic events. This is achieved by harnessing state-of-the-art developments in damage assessment, spatial simulation modeling, and Geographic Information System (GIS). At the heart of DIM2SEA is an agent-based model combined with post-dis

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Rauf, Abdur, Muhammad Akram, Prabhakar Semwal, et al. "Antispasmodic Potential of Medicinal Plants: A Comprehensive Review." Oxidative Medicine and Cellular Longevity 2021 (November 11, 2021): 1–12. http://dx.doi.org/10.1155/2021/4889719.

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Numerous medicinal plants have been utilized for the treatment of different types of diseases and disorders including gastrointestinal (GI) diseases. GI diseases are the most common complaints that normally affects the largest proportion of children and adolescents with overlapping clinical manifestation in diagnosis and medical needs. Drugs with antispasmodic effects are normally applied for the symptomatic treatment of contraction and cramping of smooth muscles in gastrointestinal diseases as well as in other critical clinical situations. In alternative system of medicines, the antispasmodic

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Kuschert, Sarah, Martin Stroet, Yanni Ka-Yan Chin, et al. "Facilitating the structural characterisation of non-canonical amino acids in biomolecular NMR." Magnetic Resonance 4, no. 1 (2023): 57–72. http://dx.doi.org/10.5194/mr-4-57-2023.

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Abstract. Peptides and proteins containing non-canonical amino acids (ncAAs) are a large and important class of biopolymers. They include non-ribosomally synthesised peptides, post-translationally modified proteins, expressed or synthesised proteins containing unnatural amino acids, and peptides and proteins that are chemically modified. Here, we describe a general procedure for generating atomic descriptions required to incorporate ncAAs within popular NMR structure determination software such as CYANA, CNS, Xplor-NIH and ARIA. This procedure is made publicly available via the existing Automa

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Heesch, Mateusz, Michał Dziendzikowski, Krzysztof Mendrok, and Ziemowit Dworakowski. "Diagnostic-Quality Guided Wave Signals Synthesized Using Generative Adversarial Neural Networks." Sensors 22, no. 10 (2022): 3848. http://dx.doi.org/10.3390/s22103848.

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Guided waves are a potent tool in structural health monitoring, with promising machine learning algorithm applications due to the complexity of their signals. However, these algorithms usually require copious amounts of data to be trained. Collecting the correct amount and distribution of data is costly and time-consuming, and sometimes even borderline impossible due to the necessity of introducing damage to vital machinery to collect signals for various damaged scenarios. This data scarcity problem is not unique to guided waves or structural health monitoring, and has been partly addressed in

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Li, Haiyan, Yan Ma, Lei Guo, Haijiang Li, Jianhua Chen, and Hongsong Li. "Image restoration for irregular holes based on dual discrimination generation countermeasure network." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 2 (2021): 423–29. http://dx.doi.org/10.1051/jnwpu/20213920423.

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In order to solve the problem that the global and local generated countermeasure network cannot inpaint the random irregular large holes, and to improve the standard convolution generator, which demonstrates the defects of color difference and blur, a network architecture of inpainting irregular large holes in an image based on double discrimination generation countermeasure network is proposed. Firstly, the image generator is a U-net architecture defined by partial convolution. The normalized partial convolution only completes the end-to-end mask update for the effective pixels. The skip link

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Kelkar, Bhagyashri A., Sunil F. Rodd, and Umakant P. Kulkarni. "A Novel Parameter-Light Subspace Clustering Technique Based on Single Linkage Method." Journal of Information & Knowledge Management 18, no. 01 (2019): 1950007. http://dx.doi.org/10.1142/s0219649219500072.

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Subspace clustering is a challenging high-dimensional data mining task. There have been several approaches proposed in the literature to identify clusters in subspaces, however their performance and quality is highly affected by input parameters. A little research is done so far on identifying proper parameter values automatically. Other observed drawbacks are requirement of multiple database scans resulting into increased demand for computing resources and generation of many redundant clusters. Here, we propose a parameter light subspace clustering method for numerical data hereafter referred

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de Oliveira, Mariana Campello, Mariana Capelo Vides, Dângela Layne Silva Lassi, et al. "Toxicity of Synthetic Cannabinoids in K2/Spice: A Systematic Review." Brain Sciences 13, no. 7 (2023): 990. http://dx.doi.org/10.3390/brainsci13070990.

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(1) Background: Synthetic cannabinoids (SCs) are emerging drugs of abuse sold as ‘K2’, ‘K9’ or ‘Spice’. Evidence shows that using SCs products leads to greater health risks than cannabis. They have been associated with greater toxicity and higher addiction potential unrelated to the primary psychoactive component of marijuana, Δ9-tetrahydrocannabinol (Δ9-THC). Moreover, early cases of intoxication and death related to SCs highlight the inherent danger that may accompany the use of these substances. However, there is limited knowledge of the toxicology of Spice ingredients. This systematic revi

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Zhu, Yanfei, and Yousheng Li. "Liver X Receptors as potential therapeutic targets in atherosclerosis." Clinical & Investigative Medicine 32, no. 5 (2009): 383. http://dx.doi.org/10.25011/cim.v32i5.6927.

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Purpose: Atherosclerosis is the primary independent risk factor of cardiovascular disease, and Liver X Receptors (LXR? and LXR?) activation may play an anti-atherosclerosis effect. In this article, we summarize the current state of knowledge of roles of LXRs in physiology and homeostasis as well as the links between LXR action and atherosclerosis, and discuss the potential therapeutic effects of LXR agonists. Source: A MEDLINE database search was performed to identify relevant articles using the keywords “liver X receptors”, “LXRs”, and “atherosclerosis”. Additional papers were identified by a

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Kamanna, Kantharaju, S. Y. Khatavi, and P. B. Hiremath. "Microwave-assisted One-pot Synthesis of Amide Bond using WEB." Current Microwave Chemistry 7, no. 1 (2020): 50–59. http://dx.doi.org/10.2174/2213335606666190828114344.

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Background: Amide bond plays a key role in medicinal chemistry, and the analysis of bioactive molecular database revealed that the carboxamide group appears in more than 25% of the existing database drugs. Typically amide bonds are formed from the union of carboxylic acid and amine; however, the product formation does not occur spontaneously. Several synthetic methods have been reported for amide bond formation in literature. Present work demonstrated simple and eco-friendly amide bond formation using carboxylic acid and primary amines through in situ generation of O-acylurea. The reaction was

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Goldschmidt, Jens, Leonard Nitzsche, Sebastian Wolf, Armin Lambrecht, and Jürgen Wöllenstein. "Rapid Quantitative Analysis of IR Absorption Spectra for Trace Gas Detection by Artificial Neural Networks Trained with Synthetic Data." Sensors 22, no. 3 (2022): 857. http://dx.doi.org/10.3390/s22030857.

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Infrared absorption spectroscopy is a widely used tool to quantify and monitor compositions of gases. The concentration information is often retrieved by fitting absorption profiles to the acquired spectra, utilizing spectroscopic databases. In complex gas matrices an expanded parameter space leads to long computation times of the fitting routines due to the increased number of spectral features that need to be computed for each iteration during the fit. This hinders the capability of real-time analysis of the gas matrix. Here, an artificial neural network (ANN) is employed for rapid predictio

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Chambi, Diego, Jenny Lundqvist, Erik Nygren, et al. "Production of Exopolysaccharides by Cultivation of Halotolerant Bacillus atrophaeus BU4 in Glucose- and Xylose-Based Synthetic Media and in Hydrolysates of Quinoa Stalks." Fermentation 8, no. 2 (2022): 79. http://dx.doi.org/10.3390/fermentation8020079.

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A halotolerant, exopolysaccharide-producing bacterium isolated from the Salar de Uyuni salt flat in Bolivia was identified as Bacillus atrophaeus using next-generation sequencing. Comparisons indicate that the genome most likely (p-value: 0.0024) belongs to a subspecies previously not represented in the database. The growth of the bacterial strain and its ability to produce exopolysaccharides (EPS) in synthetic media with glucose or xylose as carbon sources, and in hydrolysates of quinoa stalks, was investigated. The strain grew well in all synthetic media, but the growth in glucose was better

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Carper, Dana L., Travis J. Lawrence, Alyssa A. Carrell, Dale A. Pelletier, and David J. Weston. "DISCo-microbe: design of an identifiable synthetic community of microbes." PeerJ 8 (February 27, 2020): e8534. http://dx.doi.org/10.7717/peerj.8534.

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Background Microbiomes are extremely important for their host organisms, providing many vital functions and extending their hosts’ phenotypes. Natural studies of host-associated microbiomes can be difficult to interpret due to the high complexity of microbial communities, which hinders our ability to track and identify individual members along with the many factors that structure or perturb those communities. For this reason, researchers have turned to synthetic or constructed communities in which the identities of all members are known. However, due to the lack of tracking methods and the dif

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Aniel-Quiroga, Íñigo, Omar Quetzalcóatl, Mauricio González, and Louise Guillou. "Tsunami run-up estimation based on a hybrid numerical flume and a parameterization of real topobathymetric profiles." Natural Hazards and Earth System Sciences 18, no. 5 (2018): 1469–91. http://dx.doi.org/10.5194/nhess-18-1469-2018.

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Abstract. Tsunami run-up is a key value to determine when calculating and assessing the tsunami hazard in a tsunami-prone area. Run-up can be accurately calculated by means of numerical models, but these models require high-resolution topobathymetric data, which are not always available, and long computational times. These drawbacks restrict the application of these models to the assessment of small areas. As an alternative method, to address large areas empirical formulae are commonly applied to estimate run-up. These formulae are based on numerical or physical experiments on idealized geomet

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Khimdas, S., K. L. Visscher, and C. M. L. Hutnik. "Besifloxacin Ophthalmic Suspension: Emerging Evidence of its Therapeutic Value in Bacterial Conjunctivitis." Ophthalmology and Eye Diseases 3 (January 2011): OED.S4102. http://dx.doi.org/10.4137/oed.s4102.

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Objective To outline the pharmacodynamics, efficacy and safety of besifloxacin ophthalmic suspension 0.6% in the treatment of bacterial conjunctivitis. Quality of Evidence MEDLINE database was searched to review recent pharmacodynamic and clinical studies evaluating besifloxacin and comparing besifloxacin to other topical antibiotics for ophthalmic use. Findings were limited to full-text articles from clinical journals in the English language. Main Message Bacterial resistance is a common source for treatment failure in bacterial conjunctivis. Besifloxacin, a novel fourth generation synthetic

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MccCane, Brendan, Terry Caelli, and Olivier de Vel. "Learning to Recognize 3D Objects using Sparse Depth and Intensity Information." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 06 (1997): 909–31. http://dx.doi.org/10.1142/s021800149700041x.

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In this paper we further explore the use of machine learning (ML) for the recognition of 3D objects in isolation or embedded in scenes. Of particular interest is the use of a recent ML technique (specifically CRG — Conditional Rule Generation) which generates descriptions of objects in terms of object parts and part-relational attribute bounds. We show how this technique can be combined with intensity-based model and scene–views to locate objects and their pose. The major contributions of this paper are: the extension of the CRG classifier to incorporate fuzzy decisions (FCRG), the application

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Edith Belise, Kenmogne, Nkambou Roger, Tadmon Calvin, and Engelbert Mephu Nguifo. "A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach." Journal of Advanced Computer Science & Technology 6, no. 2 (2017): 20. http://dx.doi.org/10.14419/jacst.v6i2.7011.

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Sequential pattern mining is an efficient technique for discovering recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan, PrefixSpan and their furth

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Wang, Nan, Kim B. Olsen, and Steven M. Day. "A frequency-dependent ground-motion spatial correlation model of within-event residuals for Fourier amplitude spectra." Earthquake Spectra 37, no. 3 (2021): 2041–65. http://dx.doi.org/10.1177/8755293020981995.

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Ground motion time series recorded at stations separated by up to about 50 km show a frequency-dependent spatial coherency structure, and the corresponding ground motion intensity measures are found to be correlated. As omitting this correlation can result in underestimation of seismic losses in risk analysis, it is critical to quantify the spatial correlation structure for ground motion Fourier spectra estimated at different sites during a single event within a region. Toward this goal, we have developed an empirical frequency-dependent spatial correlation model for the within-event residuals

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Elvira-Ortiz, David A., Juan J. Saucedo-Dorantes, Roque A. Osornio-Rios, Daniel Morinigo-Sotelo, and Jose A. Antonino-Daviu. "Power Quality Monitoring Strategy Based on an Optimized Multi-Domain Feature Selection for the Detection and Classification of Disturbances in Wind Generators." Electronics 11, no. 2 (2022): 287. http://dx.doi.org/10.3390/electronics11020287.

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Wind generation has recently become an essential renewable power supply option. Wind generators are integrated with electrical machines that require correct functionality. However, the increasing use of non-linear loads introduces undesired disturbances that may compromise the integrity of the electrical machines inside the wind generator. Therefore, this work proposes a five-step methodology for power quality disturbance detection in grids with injection of wind farm energy. First, a database with synthetic signals is generated, to be used in the training process. Then, a multi-domain feature

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Bargiacchi, Eleonora, Nils Thonemann, Jutta Geldermann, Marco Antonelli, and Umberto Desideri. "Life Cycle Assessment of Synthetic Natural Gas Production from Different CO2 Sources: A Cradle-to-Gate Study." Energies 13, no. 17 (2020): 4579. http://dx.doi.org/10.3390/en13174579.

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Fuel production from hydrogen and carbon dioxide is considered an attractive solution as long-term storage of electric energy and as temporary storage of carbon dioxide. A large variety of CO2 sources are suitable for Carbon Capture Utilization (CCU), and the process energy intensity depends on the separation technology and, ultimately, on the CO2 concentration in the flue gas. Since the carbon capture process emits more CO2 than the expected demand for CO2 utilization, the most sustainable CO2 sources must be selected. This work aimed at modeling a Power-to-Gas (PtG) plant and assessing the m

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Giebelhaus, Ryland T., Lauren A. E. Erland, and Susan J. Murch. "HormonomicsDB: a novel workflow for the untargeted analysis of plant growth regulators and hormones." F1000Research 11 (October 18, 2022): 1191. http://dx.doi.org/10.12688/f1000research.124194.1.

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Background: Metabolomics is the simultaneous determination of all metabolites in a system. Despite significant advances in the field, compound identification remains a challenge. Prior knowledge of the compound classes of interest can improve metabolite identification. Hormones are a small signaling molecules, which function in coordination to direct all aspects of development, function and reproduction in living systems and which also pose challenges as environmental contaminants. Hormones are inherently present at low levels in tissues, stored in many forms and mobilized rapidly in response

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Costa, Fabrizzio Rodrigues, Cleyton de Carvalho Carneiro, and Carina Ulsen. "Imputation of Gold Recovery Data from Low Grade Gold Ore Using Artificial Neural Network." Minerals 13, no. 3 (2023): 340. http://dx.doi.org/10.3390/min13030340.

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In a multivariate database, the missing data can be obtained through several imputation techniques, which are particularly useful for data that are difficult to obtain, for any reason, or have high uncertainties or scarce variables. A Self-Organizing Maps (SOM) neural network is an effective tool for the analysis of multidimensional data applied for the imputation of data. In this paper, data from drilling were used for training, testing, and validation using the variables: total Au recovery (%), which means gold recovery from a gravity concentration plus hydrometallurgical process, Au (g/t),

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Wang, Cong, Zian Zhang, Yongqiang Zhang, Rui Tian, and Mingli Ding. "GMSRI: A Texture-Based Martian Surface Rock Image Dataset." Sensors 21, no. 16 (2021): 5410. http://dx.doi.org/10.3390/s21165410.

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CNN-based Martian rock image processing has attracted much attention in Mars missions lately, since it can help planetary rover autonomously recognize and collect high value science targets. However, due to the difficulty of Martian rock image acquisition, the accuracy of the processing model is affected. In this paper, we introduce a new dataset called “GMSRI” that is a mixture of real Mars images and synthetic counterparts which are generated by GAN. GMSRI aims to provide a set of Martian rock images sorted by the texture and spatial structure of rocks. This paper offers a detailed analysis

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Hudu, Shuaibu Abdullahi, Saadatu Haruna Shinkafi, and Shuaibu Umar. "AN OVERVIEW OF RECOMBINANT VACCINE TECHNOLOGY, ADJUVANTS AND VACCINE DELIVERY METHODS." International Journal of Pharmacy and Pharmaceutical Sciences 8, no. 11 (2016): 19. http://dx.doi.org/10.22159/ijpps.2016v8i11.14311.

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Development of an effective vaccine is of paramount important in disease prevention and control. As such, recombinant technology can serve as a gateway for the development of safe and effective vaccines that can be delivered effectively with an appropriate adjuvant. Therefore, this paper aimed to review the role of recombinant vaccine technology, new adjuvants and the challenge of vaccine delivery. Related peer-reviewed journal article searches were conducted using a subscribed database at the Universiti Putra Malaysia library, involving areas of Health Sciences and Medicine via Medline, SCOPU

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Lei, Wenjie, Youyi Ruan, Ebru Bozdağ, et al. "Global adjoint tomography—model GLAD-M25." Geophysical Journal International 223, no. 1 (2020): 1–21. http://dx.doi.org/10.1093/gji/ggaa253.

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SUMMARY Building on global adjoint tomography model GLAD-M15, we present transversely isotropic global model GLAD-M25, which is the result of 10 quasi-Newton tomographic iterations with an earthquake database consisting of 1480 events in the magnitude range 5.5 ≤ Mw ≤ 7.2, an almost sixfold increase over the first-generation model. We calculated fully 3-D synthetic seismograms with a shortest period of 17 s based on a GPU-accelerated spectral-element wave propagation solver which accommodates effects due to 3-D anelastic crust and mantle structure, topography and bathymetry, the ocean load, el

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Vera-Olmos, Javier, Angel Torrado-Carvajal, Carmen Prieto-de-la-Lastra, et al. "How To Pseudo-CT: A Comparative Review of Deep Convolutional Neural Network Architectures for CT Synthesis." Applied Sciences 12, no. 22 (2022): 11600. http://dx.doi.org/10.3390/app122211600.

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This paper provides an overview of the different deep convolutional neural network (DCNNs) architectures that have been investigated in the past years for the generation of synthetic computed tomography (CT) or pseudo-CT from magnetic resonance (MR). The U-net, the Atrous-net and the Residual-net architectures were analyzed, implemented and compared. Each network was implemented using 2D filters and 3D filters with 2D slices and 3D patches respectively as inputs. Two datasets were used for training and evaluation. The first one is composed by pairs of 3D T1-weighted MR and Low-dose CT images f

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