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Auroux, M., M. Millet, B. Merle, E. Fontanges, F. Duvert, E. Gineyts, J. C. Rousseau, et al. "Évaluation de la signature micro-ARN dans l’arthrose digitale." Revue du Rhumatisme 89 (December 2022): A99—A100. http://dx.doi.org/10.1016/j.rhum.2022.10.140.
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Ishiuchi, Shun-ichi, Makoto Sakai, Yuji Tsuchida, Akihiro Takeda, Yasutake Kawashima, Otto Dopfer, Klaus Müller-Dethlefs, and Masaaki Fujii. "IR signature of the photoionization-induced hydrophobic→hydrophilic site switching in phenol-Arn clusters." Journal of Chemical Physics 127, no. 11 (September 21, 2007): 114307. http://dx.doi.org/10.1063/1.2775935.
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Marie-Claire, C. "Existe-t’il une signature moléculaire de la réponse au lithium dans le trouble bipolaire ?" European Psychiatry 29, S3 (November 2014): 558. http://dx.doi.org/10.1016/j.eurpsy.2014.09.367.
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Bien que le lithium soit le traitement de première ligne pour la prévention des rechutes dans le trouble bipolaire seuls 30 % des patients sont de bons répondeurs. Ainsi, un sous-groupe important des patients, traités avec du lithium, présente des taux élevés de rechute. En l’absence de marqueurs cliniques prédictifs de réponse il reste difficile de prédire avec précision quels patients répondront en évitant de les soumettre à de longues périodes de traitements au lithium. L’identification de biomarqueurs prédictifs est donc un enjeu majeur pour améliorer la prise en charge des patients bipolaires. Le mécanisme d’action du lithium est complexe car il exerce une action inhibitrice ou activatrice sur plusieurs voies de signalisation cellulaires. L’ensemble de ces cibles et effecteurs contribuent aux effets neuroprotecteurs dans le cerveau et de stabilisation de l’humeur et complexifient la compréhension de la réponse prophylactique au lithium. Les études visant à identifier une corrélation entre la réponse au lithium et des mutations de gènes ont produit des résultats controversés. Afin de mieux comprendre la variabilité de la réponse au lithium, une approche complémentaire aux études génétiques, consiste à étudier les effets moléculaires induits par un traitement aigu ou chronique au lithium. Il a ainsi été montré que le lithium module les niveaux d’expression de gènes au niveau des ARN messagers, des protéines aussi des miRNA dans plusieurs modèles in vivo et in vitro. Ces études ouvrent de nouvelles voies de recherche pour identifier des biomarqueurs de la réponse prophylactique au lithium pouvant être transférés en clinique afin d’éviter de longues périodes de traitement inefficace au lithium et élaborer des stratégies de traitement plus personnalisées.
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Patel, Palak. "Signature Verification Using Artificial Neural Network." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (January 3, 2018): 40. http://dx.doi.org/10.23956/ijarcsse.v7i12.494.
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The human signature is most important for access. Signature of the person is important biometric attribute of a human being which is used to authenticate human identity. There are many biometric characteristics by which one can have own identity like face recognition, fingerprint detection, iris inspection and retina scanning. In non-vision based techniques voice recognition and signature verification are most widely used. Verification can be performed either Online or Offline. Online system of signature verification uses dynamic information of a signature captured at the time the signature is made. Offline system uses scanned image of signature. In this paper, I present a method for Offline Verification of signatures using a set of simple shape based geometric features. As signatures play an important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. This paper presents the off-line signature recognition & verification using neural network in which the human signature is captured and presented in the image format. Various image processing techniques are used to recognize and verify the signature. Preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. Initially system use database of signatures obtained from those individuals whose signatures have to be authenticated by the system. Then artificial neural network (ANN) is used to verify and classify the signatures. The implementation details and results are discussed in the paper.
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S. Kadhm, Mustafa, Mamoun Jassim Mohammed, and Hayder Ayad. "An accurate signature verification system based on proposed HSC approach and ANN architecture." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (January 1, 2021): 215. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp215-223.
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<span>With the rapid development of technology in all life fields, and due to the huge daily needs for banking systems process, documents processing and other similar systems. The authentication became more required key for these systems. One of the successful system to verify the any person is the signature verification system. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. However, a reliable and accurate system is still needed. For this reason, the security challenge is take place via authentic signatures. Therefore, this paper present a reliable signature verification system using proposed histogram of sparse codes (HSC) feature extraction approach and artificial neural networks (ANN) architecture for classification. The system achieved fast computing 0.09 ms and accurate verification results that is 99.7% using three different signature images datasets CEDAR, UTSig, and ICDAR.</span>
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Raimondo, Diego, Antonio Raffone, Agnese Virgilio, Stefano Ferla, Manuela Maletta, Daniele Neola, Antonio Travaglino, et al. "Molecular Signature of Endometrial Cancer with Coexistent Adenomyosis: A Multicentric Exploratory Analysis." Cancers 15, no. 21 (October 30, 2023): 5208. http://dx.doi.org/10.3390/cancers15215208.
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Adenomyosis has been associated with better survival outcomes in women with endometrial cancer. However, although the endometrial cancer patients’ risk stratification has been revolutionized by molecular findings, the impact of the molecular signature on the favorable prognosis of endometrial cancer patients with coexistent adenomyosis is unknown. The aim of our study was to compare the prevalence of molecular groups at poor and intermediate prognosis between endometrial cancer patients with and without coexistent adenomyosis. A multicentric, observational, retrospective, cohort study was performed to assess the differences in the prevalence of p53-abnormal expression (p53-abn) and mismatch repair protein-deficient expression (MMR-d) signatures between endometrial cancer patients with and without coexistent adenomyosis. A total of 147 endometrial cancer patients were included in the study: 38 in the adenomyosis group and 109 in the no adenomyosis group. A total of 37 patients showed the MMR-d signature (12 in the adenomyosis group and 25 in the no adenomyosis group), while 12 showed the p53-abn signature (3 in the adenomyosis group and 9 in the no adenomyosis group). No significant difference was found in the prevalence of p53-abn (p = 1.000) and MMR-d (p = 0.2880) signatures between endometrial cancer patients with and without coexistent adenomyosis. In conclusion, the molecular signature does not appear to explain the better prognosis associated with coexistent adenomyosis in endometrial cancer patients. Further investigation of these findings is necessary through future larger studies.
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Jain, Arpit, Jaspreet Singh, Sandeep Kumar, Țurcanu Florin-Emilian, Mihaltan Traian Candin, and Premkumar Chithaluru. "Improved Recurrent Neural Network Schema for Validating Digital Signatures in VANET." Mathematics 10, no. 20 (October 20, 2022): 3895. http://dx.doi.org/10.3390/math10203895.
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Vehicular ad hoc networks (VANETs) allow communication between stationary or moving vehicles with the assistance of wireless technology. Among various existing issues in smart VANETs, secure communication is the key challenge in VANETs with a 5G network. Smart vehicles must communicate with a broad range of advanced road systems including traffic control and smart payment systems. Many security mechanisms are used in VANETs to ensure safe transmission; one such mechanism is cryptographic digital signatures based on public key infrastructure (PKI). In this mechanism, secret private keys are used for digital signatures to validate the identity of the message along with the sender. However, the validation of the digital signatures in fast-moving vehicles is extremely difficult. Based on an improved perceptron model of an artificial neural network (ANN), this paper proposes an efficient technique for digital signature verification. Still, manual signatures are extensively used for authentication across the world. However, manual signatures are still not employed for security in automotive and mobile networks. The process of converting manual signatures to pseudo-digital-signatures was simulated using the improved Elman backpropagation (I-EBP) model. A digital signature was employed during network connection to authenticate the legitimacy of the sender’s communications. Because it contained information about the vehicle on the road, there was scope for improvement in protecting the data from attackers. Compared to existing schemes, the proposed technique achieved significant gains in computational overhead, aggregate verification delay, and aggregate signature size.
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Kumalasanti, R. A. "Comparison of Static Signature Identification using Artificial Neural Networks Based on Haar, Daubechies and Symlets Wavelet Transformations." International Journal of Applied Sciences and Smart Technologies 4, no. 1 (June 27, 2022): 97–108. http://dx.doi.org/10.24071/ijasst.v4i1.4786.
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Signature is a biometric attribute that is quite important for each individual that can be used as self-identity. Until now, the signature is still used as a sign of legal approval and is agreed upon by everyone. This makes the signature worthy of attention from a security aspect. Various approaches have been proposed in the development of signature identification to minimize signature forgery. This study will discuss the identification of signatures by using the image of the signature on paper. This identification consists of two processes, namely training and testing by utilizing Artificial Neural Networks Backpropagation and Wavelet Transform. Optimal results are obtained by using ANN which has learning rate 0,09, two hidden layers, each 20 and 10 nodes with the most superior Wavelet Haar reaching 94.44%.
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Priyadarshini, K., Nisanth Sai A, P. Sai Krishna, and Dr Shruti Bhargava Choubey. "Signature Verification Using Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1609–22. http://dx.doi.org/10.22214/ijraset.2022.40948.
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Abstract: In this paper we are about to present a latest improved off-line signature verification system using global and texture features of the signatures. This version is based on the technique that applies pre-processing on the signature to get a binary image and then calculate the global and features points from it and than maintain a updated vector. All calculations are done on the basis of these feature points. The feature vector obtained from the global and texture features is used to compare with the feature vector of incoming testing signature. Based on the values obtained, the network will decide the appropriateness of the signature. The suggested scheme discriminates between original and the forged signatures using artificial neural network (ANN) for training and verification of signatures. The method takes care of simple and the random forgeries and the skilled forgeries are also eliminated in greater extent. The objective of the work is to reduce two vital parameters, False Acceptance Rate (FAR) and the False Rejection Rate (FRR). So the results are expressed in terms of FAR and FRR and subsequently comparative analysis has been made with standard existing techniques. Results obtained by our proposed algorithm are more efficient than most of the existing techniques.
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Rahmi, Asyrofa, Vivi Nur Wijayaningrum, Wayan Firdaus Mahmudy, and Andi Maulidinnawati A. K. Parewe. "Offline Signature Recognition using Back Propagation Neural Network." Indonesian Journal of Electrical Engineering and Computer Science 4, no. 3 (December 1, 2016): 678. http://dx.doi.org/10.11591/ijeecs.v4.i3.pp678-683.
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The signature recognition is a difficult process as it requires several phases. A failure in a phase will significantly reduce the recognition accuracy. Artificial Neural Network (ANN) believed to be used to assist in the recognition or classification of the signature. In this study, the ANN algorithm used is Back Propagation. A mechanism to adaptively adjust the learning rate is developed to improve the system accuracy. The purpose of this study is to conduct the recognition of a number of signatures so that can be known whether the recognition which is done by using the Back Propagation is appropriate or not. The testing results performed by using learning rate of 0.64, the number of iterations is 100, and produces an accuracy value of 63%.
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Wylezalek, Dominika, Anthony M. Flores, Nadia L. Zakamska, Jenny E. Greene, and Rogemar A. Riffel. "Ionized gas outflow signatures in SDSS-IV MaNGA active galactic nuclei." Monthly Notices of the Royal Astronomical Society 492, no. 4 (January 20, 2020): 4680–96. http://dx.doi.org/10.1093/mnras/staa062.
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ABSTRACT The prevalence of outflow and feedback signatures in active galactic nuclei (AGNs is a major unresolved question which large integral field unit (IFU) surveys now allow to address. In this paper, we present a kinematic analysis of the ionized gas in 2778 galaxies at z ∼ 0.05 observed by Sloan Digital Sky Survey-IV (SDSS-IV) Mapping Nearby Galaxies at Apache Point Observatory (MaNGA). Specifically, we measure the kinematics of the [O iii] λ5007 Å emission line in each spatial element and fit multiple Gaussian components to account for possible non-gravitational motions of gas. Comparing the kinematics of the ionized gas between 308 MaNGA-selected AGNs that have been previously identified through emission-line diagnostics and sources not classified as AGN, we find that while 25 per cent of MaNGA-selected AGN show [O iii] components with emission-line widths of >500 km s−1 in more than 10 per cent of their spaxels, only 7 per cent of MaNGA non-AGNs show a similar signature. Even the AGNs that do not show nuclear AGN photoionization signatures and that were only identified as AGN based on their larger scale photoionization signatures show similar kinematic characteristics. In addition to obscuration, another possibility is that outflow and mechanical feedback signatures are longer lived than the AGN itself. Our measurements demonstrate that high velocity gas is more prevalent in AGN compared to non-AGN and that outflow and feedback signatures in low-luminosity, low-redshift AGN may so far have been underestimated. We show that higher luminosity MaNGA-selected AGNs are able to drive larger scale outflows than lower luminosity AGN. But estimates of the kinetic coupling efficiencies are ≪1 per cent and suggest that the feedback signatures probed in this paper are unlikely to have a significant impact on the AGN host galaxies. However, continuous energy injection may still heat a fraction of the cool gas and delay or suppress star formation in individual galaxies even when the AGN is weak.
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Lin, Jamie, Amanda Tchakarov, Noha Abdel-Wahab, Houssein Safa, Salah-Eddine Bentebibel, Maen Abdelrahim, Cassian Yee, Adi Diab, and Ala Abudayyeh. "808 Tertiary lymphoid structure gene signature detected in immune checkpoint inhibitor-associated renal immune related adverse event." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A845. http://dx.doi.org/10.1136/jitc-2021-sitc2021.808.
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BackgroundTertiary lymphoid structures (TLSs) have been previously associated with ICI induced response in patients with cancer, but a commensurate observation has not been made in ICI associated immune related adverse events (irAEs). Acute interstitial nephritis (AIN) is the predominant lesion reported in patients with renal irAEs, but various etiologies can also trigger the development of AIN including non-ICI drugs (e.g. non-steroidal anti-inflammatory drugs, antibiotics, proton pump inhibitors, etc.), and it is unknown whether these mechanisms are similar. With increasing indications for ICIs in cancer therapy, there is a critical need to define immune pathways driving the emergence of irAEs. To address this critical knowledge gap, we performed gene expression profiling on ICI-AIN, drug-AIN, and control (non-AIN) kidney biopsy specimens.MethodsTotal RNA extracted from ICI-AIN (n = 6), drug-AIN (n = 4), and control (n = 4) fixed formalin paraffin embedded archival kidney biopsy samples was analyzed by Nanostring nCounter PanCancer Immune Profiling Panel using NanoString nCounter FLEX Analysis System.ResultsThree comparisons were conducted: ICI-AIN vs control, drug-AIN vs control, and ICI-AIN vs drug-AIN. A total of 147 genes were differentially expressed in ICI-AIN vs control and the most differentially expressed genes were CXCL 9, 10, and 11. Similarly, cell marker gene expression signatures (GES) revealed significant upregulation of T and B cell markers in ICI-AIN vs control (P < 0.01) and ICI-AIN vs drug-AIN (T cell P < 0.05; B cell P < 0.01). Differences in T and B cell score were not detected in drug-AIN vs control. Since irAEs have been associated with anti-tumor efficacy, we investigated whether a TLS signature could be detected in ICI-AIN using a four GES (CD79A, MS4A1, LAMP3 and POU2AF1). The ICI-AIN group had significantly higher TLS score compared to both control and drug-AIN groups. Since several TLS signatures have been reported, we also calculated a 12-chemokine TLS GES which was also found to be statistically significant (P < 0.05). Th1 and Th17 cells have been associated with the formation of TLS, differential upregulation of Th1 associated genes but not Th17 associated genes were detected. Furthermore, differential expression IFN-y and TNF signature was also observed in ICI-AIN group.ConclusionsThis study is the first to demonstrate the presence of TLS immune signature in irAEs. Further investigations into the prognostic significance and strategies to uncouple ICI-associated anti-tumor benefits from ICI-induced irAEs should be explored.Ethics ApprovalThe study was approved by The University of Texas MD Anderson Cancer Center intuition's Ethics Board, approval number PA16-1016
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Moysidou, G. S., P. Garantziotis, M. Grigoriou, E. Stergioti, N. Malissovas, D. Nikoleri, A. Filia, et al. "AB0524 IN SLE PATIENTS TREATED WITH BELIMUMAB TYPE I INTERFERON AND BAFF GENE SIGNATURES PREDICT CLINICAL RESPONSE: EVIDENCE FOR REVERSAL OF MOLECULAR PATHWAYS LINKED TO ACTIVITY AND SEVERITY UPON TREATMENT." Annals of the Rheumatic Diseases 82, Suppl 1 (May 30, 2023): 1458.2–1459. http://dx.doi.org/10.1136/annrheumdis-2023-eular.6220.
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BackgroundBelimumab, a monoclonal antibody against B-cell activating factor (BAFF), is the first biological agent approved in patients with systemic lupus erythematosus (SLE) with persistently active or flaring disease [1].ObjectivesTo determine molecular signatures that may predict response to therapy based on RNA sequencing analysis and the impact of belimumab on previously defined molecular signatures of SLE activity and severity [2,3].MethodsWhole blood transcriptome samples were obtained from 38 patients with active moderate to severe SLE at baseline and 6 months after initiation of treatment with belimumab. Disease activity was determined using the SLEDAI-2K while response to treatment was defined as the achievement of lupus low disease activity state (LLDAS) at 6 months (M6). DESeq2 was used to call differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA) was applied to uncover gene module-traits associations. The impact of the belimumab treatment on the SLE severity signature was evaluated [2].ResultsFrom 38 patients treated with belimumab, 35 (97,2%) were women, mean age (SD) was 48,2 (±13,5) years and mean SLEDAI-2K (SD) at baseline was 7,2 (±2,1). A total of 44,4% achieved LLDAS at M6. Both, the presence of a molecular signature of type I interferon signaling and the upregulation of BAFF expression correlated with response to treatment at M6 (p = 0.05 and p = 0.02 respectively). Treatment with belimumab reversed the innate and adaptive immune system signatures in responders at 6-months. Within the non-responders, baseline transcriptome, disturbances related to metabolic processes and rRNA biogenesis were predominantly enriched. A tendency towards downregulation of the severity disease transcriptional signature upon treatment with belimumab was also observed in 6-month responders (p = 0.3203).ConclusionBoth the presence of type I interferon and BAFF gene expression signatures at baseline correlates with response to treatment with belimumab while metabolic processes and rRNA biogenesis correlate with no response. Treatment with belimumab might ameliorate the molecular signatures indicative of disease severity in the 6 month-responders. These data provide a mechanistic basis for the response to treatment.References[1]Fanouriakis A, Kostopoulou M, Alunno A, et al. 2019 update of the EULAR recommendations for the management of systemic lupus erythematosus. Ann Rheum Dis 2019;78:736-45.[2]Panousis NI, Bertsias GK, Ongen H, et al. Combined genetic and transcriptome analysis of patients with SLE: distinct, targetable signatures for susceptibility and severity. Ann Rheum Dis 2019.[3]Filia A, Bertsias G, Panousis N, et al. RNA sequencing and machine learning techniques predict major organ involvement in patients with systemic lupus erythematosus. Ann Rheum Dis 2019;78:A220.Figure 1.Correlation between the groups of differentially expressed genes from the WGCNA analysis and response to belimumab and SLEDAI at M6.AcknowledgementsThis work was supported by an investigator-initiated grant from GSK.*Georgia-Savina Moysidou and Panagiotis Garantziotis contributed equally to the abstract.Disclosure of InterestsNone Declared.
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Lyons, Paul A., Eoin F. McKinney, Tim F. Rayner, Alexander Hatton, Hayley B. Woffendin, Maria Koukoulaki, Thomas C. Freeman, David R. W. Jayne, Afzal N. Chaudhry, and Kenneth G. C. Smith. "Novel expression signatures identified by transcriptional analysis of separated leucocyte subsets in systemic lupus erythematosus and vasculitis." Annals of the Rheumatic Diseases 69, no. 6 (October 7, 2009): 1208–13. http://dx.doi.org/10.1136/ard.2009.108043.
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ObjectiveTo optimise a strategy for identifying gene expression signatures differentiating systemic lupus erythematosus (SLE) and antineutrophil cytoplasmic antibody-associated vasculitis that provide insight into disease pathogenesis and identify biomarkers.Methods44 vasculitis patients, 13 SLE patients and 25 age and sex-matched controls were enrolled. CD4 and CD8 T cells, B cells, monocytes and neutrophils were isolated from each patient and, together with unseparated peripheral blood mononuclear cells (PBMC), were hybridised to spotted oligonucleotide microarrays.ResultsUsing expression data obtained from purified cells a substantial number of differentially expressed genes were identified that were not detectable in the analysis of PBMC. Analysis of purified T cells identified a SLE-associated, CD4 T-cell signature consistent with type 1 interferon signalling driving the generation and survival of tissue homing T cells and thereby contributing to disease pathogenesis. Moreover, hierarchical clustering using expression data from purified monocytes provided significantly improved discrimination between the patient groups than that obtained using PBMC data, presumably because the differentially expressed genes reflect genuine differences in processes underlying disease pathogenesis.ConclusionAnalysis of leucocyte subsets enabled the identification of gene signatures of both pathogenic relevance and with better disease discrimination than those identified in PBMC. This approach thus provides substantial advantages in the search for diagnostic and prognostic biomarkers in autoimmune disease.
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Losev, Mikhail. "Synthesis of information control devices which are transferred to diagnostic network with package composition." Development Management 16, no. 4 (February 4, 2019): 52–63. http://dx.doi.org/10.21511/dm.4(4).2018.05.
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In the context of growing requirements for the reliability of information and a reduction in the time of data delivery, the urgent task is the development of simple and effective means of control as a process of transmission of information and equipment in distributed systems. The problem of diagnosing the efficiency of distributed systems in data exchange networks with packet switching is considered in the paper. The proposed approach to the synthesis of data control devices is most effective in verifying the transmission of a multitude of packet messages over a datagram channel in time division mode and can be used in digital test device diagnostic systems as an initialization analyzer. The practical implementation of the proposed approach allows you to create devices that have achieved a significant reduction in hardware costs and simplify the technical implementation of signature analyzers. In this case, it is not necessary to store the input information, which provides the possibility of using different characteristic of polynomials, by automatically generating this information in the device. Parallel processing of message packets or diagnostic information allows to increase the speed of analyzers, with reception of signatures that equal the signature of a single-channel analyzer. Examples of synthesis of multichannel signature analyzers that are capable of high-speed data reliably process information, localize errors in the information input sequence and determine the number of the false packet in the message or the device from the group of verifiable devices are given.
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Rocheteau, Pierre, and Shahragim Tajbakhsh. "ADN immortel ou signature épigénétique ?" médecine/sciences 24, no. 10 (October 2008): 847–52. http://dx.doi.org/10.1051/medsci/20082410847.
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Volwerk, M., M. Delva, Y. Futaana, A. Retinò, Z. Vörös, T. L. Zhang, W. Baumjohann, and S. Barabash. "Corrigendum to "Substorm activity in Venus's magnetotail" published in Ann. Geophys., 27, 2321–2330, doi:10.5194/angeo-27-2321-2009, 2009." Annales Geophysicae 28, no. 10 (October 7, 2010): 1877–78. http://dx.doi.org/10.5194/angeo-28-1877-2010.
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Abstract. A re-evaluation of the reconnection event reported by Volwerk et al. (2009) shows that the original interpretation of the magnetic field data as quadrupolar Hall-current signatures around a reconnection site was mistaken. It could be interpreted as the signature of reconnection in the presence of a guide field. The path of VEX through the active region in Venus's magnetotail is re-evaluated and the strongly energized ions associated to this event are now in agreement with the magnetic field data.
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Harrison, Chris M., David M. Alexander, Dalton J. Rosario, Jan Scholtz, and Flora Stanley. "Do AGN really suppress star formation?" Proceedings of the International Astronomical Union 15, S356 (October 2019): 199–203. http://dx.doi.org/10.1017/s1743921320002902.
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AbstractActive galactic nuclei (AGN) are believed to regulate star formation inside their host galaxies through “AGN feedback”. We summarise our on-going study of luminous AGN (z ∼ 0.2−3; LAGN,bol 1043 erg s−1), which is designed to search for observational signatures of feedback by combining observed star-formation rate (SFR) measurements from statistical samples with cosmological model predictions. Using the EAGLE hydrodynamical cosmological simulations, in combination with our Herschel + ALMA surveys, we show that – even in the presence of AGN feedback – we do not necessarily expect to see any relationships between average galaxy-wide SFRs and instantaneous AGN luminosities. We caution that the correlation with stellar mass for both SFR and AGN luminosity can contribute to apparent observed positive trends between these two quantities. On the other hand, the EAGLE simulations, which reproduce our observations, predict that a signature of AGN feedback can be seen in the wide specific SFR distributions of all massive galaxies (not just AGN hosts). Overall, whilst we can not rule out that AGN have an immediate small-scale impact on in-situ star-formation, all of our results are consistent with a feedback model where galaxy-wide in-situ star formation is not rapidly suppressed by AGN, but where the feedback likely acts over a longer timescale than a single AGN episode.
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Shah, Nirav, Anik Sinha, Aleda Thompson, Kevin Tremper, Arjun Meka, and Sachin Kheterpal. "An Automated Software Application Reduces Controlled Substance Discrepancies in Perioperative Areas." Anesthesiology 131, no. 6 (December 1, 2019): 1264–75. http://dx.doi.org/10.1097/aln.0000000000002957.
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Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Perioperative controlled substance diversion and tracking have received increased regulatory focus throughout the United States. The authors’ institution developed and implemented an automated web-based software application for perioperative controlled substance management. The authors hypothesized that implementation of such a system reduces errors as measured by missing controlled substance medications, missing controlled substance kits (a package of multiple controlled substance medications), and missing witness signatures during kit return. Methods From December 1, 2014 to March 31, 2017, the authors obtained missing controlled substance medication, controlled substance kit, and witness return signature data during the preimplementation, implementation, and study period of the controlled substance management application at a single university hospital. This before and after study was based on a QI project at the authors’ institution. The authors included all cases requiring anesthesia services. The primary outcome of this study was the rate of missing controlled substance medications. Secondary outcomes included rates for kits not returned to pharmacy and missing kit return witness signatures. Results There were 54,302 cases during the preimplementation period, 57,670 cases during the implementation period, and 65,911 cases during the study period. The number of missing controlled substance medication (difference 0.7 per 1,000 cases; 95% CI, 0.38–1.02; P < 0.001) and kit return errors (difference 0.45 per 1,000 cases; 95% CI, 0.24–0.66, P < 0.001) declined after implementation of the application. There was no difference in the number of missing witness return signatures (difference 0.09 per 1,000 cases; 95% CI, −0.08 to 0.26, P = 0.350). A user survey with 206 of 485 (42%) response rate demonstrated that providers believed the new application managed controlled substances better than the previous system. Conclusions A software application that tracks perioperative controlled substance kits with deep integration into the electronic health record and pharmacy systems is associated with a decrease in management errors.
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Zhang, Y., J. Wang, Y. Zhang, Z. Ke, X. Fang, J. Xu, and Y. Liu. "P1192 Deciphering the colonic immune landscape of UC from cellular senescence by integrated bioinformatics analysis and machine learning." Journal of Crohn's and Colitis 18, Supplement_1 (January 1, 2024): i2124—i2125. http://dx.doi.org/10.1093/ecco-jcc/jjad212.1322.
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Abstract Background Nowadays, there has been extensive interest in cellular senescence in gastrointestinal diseases. However, the pathological mechanism of cellular senescence in UC and its involvement in colonic immune characteristics remain enigmatic. Therefore, we aimed to investigate the contribution of cellular senescence-related signatures to the colonic immune landscape in UC based on bioinformatics combined machine learning strategy. Methods Three gene sets of UC were selected from the Gene Expression Omnibus database, including two training sets and one testing set. We identified cellular senescence-associated differentially expressed genes (DEGs) in UC and performed functional enrichment analysis. Afterwards, signature genes were determined by machine learning algorithms including least absolute shrinkage and selection operator (LASSO) regression, support vector machine recursive feature elimination (SVM-RFE), and artificial neural network (ANN). Furthermore, distinct immune microenvironment abnormalities in UC were comprehensively explored, and the correlation between the signature genes and the colonic immune landscape of UC was also evaluated. Based on the signature genes, we clustered UC samples into distinct subtypes by consensus clustering analysis. Finally, we performed validation of the signature genes in dextran sulfate sodium salt (DSS)-induced colitis. Results 41 cellular senescence-associated DEGs in UC were identified by intersecting DEGs with the cellular senescence-associated genes from the CellAge database, which were associated with cellular senescence and immune pathways. Following that, seven signature genes were determined by the machine learning method of LASSO regression, SVM-RFE, and ANN, comprising CXCL1, MMP9, CYR61, BAG3, PEX19, RPS6KA6, and ETS2, which were also validated in the testing set. Furthermore, we obtained the most correlated pairs of UC signature genes-immunocytes and found these signature genes were highly corelated with neutrophils and activated CD4+ memory T cells. Additionally, the expression profiling of human leukocyte antigen genes and immune checkpoints were also analyzed. Subsequently, two molecular subtypes related to senescence that exhibit significantly different immune characteristics were identified. Finally, the mRNA levels of the signature genes were confirmed in DSS-induced colitis, which were congruent with results gained from the datasets. Conclusion In summary, the result has emphasized the essential function of cellular senescence-associated genes in controlling the immune microenvironment for UC. The exploration of the etiopathogenesis of UC through cellular senescence is anticipated to lead to novel advancements in disease diagnosis and potential treatments.
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Emura, Keita, Atsuko Miyaji, and Kazumasa Omote. "Anr-Hiding Revocable Group Signature Scheme: Group Signatures with the Property of Hiding the Number of Revoked Users." Journal of Applied Mathematics 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/983040.
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If there are many displaced workers in a company, then a person who goes for job hunting might not select this company. That is, the number of members who quit is quite negative information. Similarly, in revocable group signature schemes, if one knows (or guesses) the number of revoked users (sayr), then one may guess the reason behind such circumstances, and it may lead to harmful rumors. However, no previous revocation procedure can achieve hidingr. In this paper, we propose the first revocable group signature scheme, whereris kept hidden, which we callr-hiding revocable group signature. To handle this property, we newly define the security notion called anonymity with respect to the revocation which guarantees the unlinkability of revoked users.
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Becker Tjus, Julia, Mario Hörbe, Ilja Jaroschewski, Patrick Reichherzer, Wolfgang Rhode, Marcel Schroller, and Fabian Schüssler. "Propagation of Cosmic Rays in Plasmoids of AGN Jets-Implications for Multimessenger Predictions." Physics 4, no. 2 (April 28, 2022): 473–90. http://dx.doi.org/10.3390/physics4020032.
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After the successful detection of cosmic high-energy neutrinos, the field of multiwavelength photon studies of active galactic nuclei (AGN) is entering an exciting new phase. The first hint of a possible neutrino signal from the blazar TXS 0506+056 leads to the anticipation that AGN could soon be identified as point sources of high-energy neutrino radiation, representing another messenger signature besides the established photon signature. To understand the complex flaring behavior at multiwavelengths, a genuine theoretical understanding needs to be developed. These observations of the electromagnetic spectrum and neutrinos can only be interpreted fully when the charged, relativistic particles responsible for the different emissions are modeled properly. The description of the propagation of cosmic rays in a magnetized plasma is a complex question that can only be answered when analyzing the transport regimes of cosmic rays in a quantitative way. In this paper, therefore, a quantitative analysis of the propagation regimes of cosmic rays is presented in the approach that is most commonly used to model non-thermal emission signatures from blazars, i.e., the existence of a high-energy cosmic-ray population in a relativistic plasmoid traveling along the jet axis. It is shown that in the considered energy range of high-energy photon and neutrino emission, the transition between diffusive and ballistic propagation takes place, significantly influencing not only the spectral energy distribution, but also the lightcurve of blazar flares.
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Chabanier, Solène, Frédéric Bournaud, Yohan Dubois, Nathalie Palanque-Delabrouille, Christophe Yèche, Eric Armengaud, Sébastien Peirani, and Ricarda Beckmann. "The impact of AGN feedback on the 1D power spectra from the Ly α forest using the Horizon-AGN suite of simulations." Monthly Notices of the Royal Astronomical Society 495, no. 2 (May 7, 2020): 1825–40. http://dx.doi.org/10.1093/mnras/staa1242.
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ABSTRACT The Lyman-α forest is a powerful probe for cosmology, but it is also strongly impacted by galaxy evolution and baryonic processes such as active galactic nucleus (AGN) feedback, which can redistribute mass and energy on large scales. We constrain the signatures of AGN feedback on the 1D power spectrum of the Lyman-α forest using a series of eight hydro-cosmological simulations performed with the adaptive mesh refinement code ramses. This series starts from the Horizon-AGN simulation and varies the subgrid parameters for AGN feeding, feedback, and stochasticity. These simulations cover the whole plausible range of feedback and feeding parameters according to the resulting galaxy properties. AGNs globally suppress the Lyman-α power at all scales. On large scales, the energy injection and ionization dominate over the supply of gas mass from AGN-driven galactic winds, thus suppressing power. On small scales, faster cooling of denser gas mitigates the suppression. This effect increases with decreasing redshift. We provide lower and upper limits of this signature at nine redshifts between z = 4.25 and 2.0, making it possible to account for it at post-processing stage in future work given that running simulations without AGN feedback can save considerable amounts of computing resources. Ignoring AGN feedback in cosmological inference analyses leads to strong biases with 2 per cent shift on σ8 and 1 per cent shift on ns, which represents twice the standards deviation of the current constraints on ns.
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Romero, Salome, and Sibel Pamukcu. "Characterization of Granular Systems by Digital Signal Processing of Low Strain Wave Response." Transportation Research Record: Journal of the Transportation Research Board 1548, no. 1 (January 1996): 38–45. http://dx.doi.org/10.1177/0361198196154800106.
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The characterization and quality assessment of composite materials, particularly those constructed of residual materials such as slags, ashes, and crumb rubber, are difficult because of chemical and physical inhomogeneity and inconsistency at the time of their production. The characterization of granular systems, constructed of a mixture of geological and residual materials, may provide inconclusive information when tested by existing methods. The proposed nondestructive evaluation uses low-strain, low-frequency dynamic excitation as a means to better evaluate such materials. The applied excitation results in a sample response characterized by wave patterns. The wave pattern response is analyzed by digital signal processing and an artificial neural networks (ANN) system to facilitate characterization of the material. Dynamic excitation of representative samples was accomplished using a longitudinal-torsional resonant column. Nondestructive testing was conducted at low strain levels applying a torsional oscillatory motion. The resulting sample response wave forms were recorded. The shear modulus values obtained at the resonant frequency of each sample were used to train an ANN system to characterize sample wave responses measured at random frequencies. The recorded sample response wave forms were analyzed to identify the embedded dominant frequencies, which were unique signatures of the tested materials. These signatures were then submitted to the previously trained ANN to predict the material shear modulus. The samples tested were composed of dry Ottawa sand (0.85 to 0.6 mm), Ottawa sand and crumb rubber modifier, and soda-lime spheres compacted at various densities and tested under different confining pressures. Distinctive patterns, unique to the granular sample composition, were obtained. These are termed the signature patterns. A fast Fourier transform algorithm was used to convert collected data to the frequency domain. ANN analysis was applied to enhance pattern recognition and characterize the samples according to their shear moduli
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Keel, William C., Chris J. Lintott, W. Peter Maksym, Vardha N. Bennert, S. Drew Chojnowski, Alexei Moiseev, Aleksandrina Smirnova, et al. "Fading AGN Candidates: AGN Histories and Outflow Signatures." Astrophysical Journal 835, no. 2 (February 1, 2017): 256. http://dx.doi.org/10.3847/1538-4357/835/2/256.
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Gomes, Paulo R. B., André L. F. de Almeida, João Paulo C. L. da Costa, João C. M. Mota, Daniel Valle de Lima, and Giovanni Del Galdo. "Tensor-Based Methods for Blind Spatial Signature Estimation in Multidimensional Sensor Arrays." International Journal of Antennas and Propagation 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/1615962.
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The estimation of spatial signatures and spatial frequencies is crucial for several practical applications such as radar, sonar, and wireless communications. In this paper, we propose two generalized iterative estimation algorithms to the case in which a multidimensional (R-D) sensor array is used at the receiver. The first tensor-based algorithm is anR-D blind spatial signature estimator that operates in scenarios where the source’s covariance matrix is nondiagonal and unknown. The second tensor-based algorithm is formulated for the case in which the sources are uncorrelated and exploits the dual-symmetry of the covariance tensor. Additionally, a new tensor-based formulation is proposed for anL-shaped array configuration. Simulation results show that our proposed schemes outperform the state-of-the-art matrix-based and tensor-based techniques.
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Rubinur, K. "AGN Feedback Signatures in UV Emission." Galaxies 12, no. 2 (April 4, 2024): 15. http://dx.doi.org/10.3390/galaxies12020015.
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Supermassive black holes (SMBH) are believed to influence galaxy evolution through AGN (active galactic nuclei) feedback. Galaxy mergers are key processes of galaxy formation that lead to AGN activity and star formation. The relative contribution of AGN feedback and mergers to star formation is not yet well understood. In radio-loud objects, AGN outflows are dominated by large jets. However, in radio-quiet objects, outflows are more complex and involve jet, wind, and radiation. In this review, we discuss the signatures of AGN feedback through the alignment of radio and UV emissions. Current research on AGN feedback is discussed, along with a few examples of studies such as the galaxy merger system MRK 212, the radio-quiet AGN NGC 2639, and the radio-loud system Centaurus A. Multi-frequency observations of MRK 212 indicate the presence of dual AGN, as well as feedback-induced star-forming UV clumps. The fourth episode of AGN activity was detected in radio observations of the Seyfert galaxy NGC 2639, which also showed a central cavity of 6 kpc radius in CO and UV maps. This indicates that multi-epoch jets of radio-quiet AGN can blow out cold molecular gas, which can further reduce star formation in the center of the galaxies. Recent UV observations of Cen A have revealed two sets of stellar population in the northern star-forming region, which may have two different origins. Recent studies have shown that there is evidence that both positive and negative feedback can be present in galaxies at different scales and times. High-resolution, multi-band observations of large samples of different types of AGN and their host galaxies are important for understanding the two types of AGN feedback and their effect on the host galaxies. Future instruments like INSIST and UVEX will be able to help achieve some of these goals.
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Long, James P., Shailbala Singh, Yanlan Dong, Cassian Yee, and Jamie S. Lin. "Urine proteomics defines an immune checkpoint-associated nephritis signature." Journal for ImmunoTherapy of Cancer 13, no. 1 (January 2025): e010680. https://doi.org/10.1136/jitc-2024-010680.
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Immune checkpoint inhibitor (ICI) therapy is a cornerstone treatment for many cancers, but it can induce severe immunotoxicity, including acute interstitial nephritis (AIN). Currently, kidney biopsy is required to differentiate ICI-AIN from other causes of acute kidney injury (AKI). However, this invasive approach can lead to morbidity, delayed glucocorticoid treatment for patients with AIN, and unnecessarily prolonged suspension of ICI therapy in non-AIN patients. Delayed or incorrect diagnosis of ICI-AIN is particularly detrimental, as over 50% of patients are at risk of permanent renal damage. Thus, there is an urgent need for non-invasive biomarkers that can rapidly and accurately distinguish ICI-AIN from other causes of AKI.The urine and plasma proteome contain actively secreted proteins that provide real-time insights into dynamic physiological processes. However, identification of effective biomarkers of disease using established technologies such as proximity ligation assays (PLA) and bead-based immunoassays is challenging due to their limited sensitivity and loss of precision in multiplex analysis.To address this, we employed cutting-edge NUcleic acid Linked Immuno-Sandwich Assay (NULISA) technology to measure protein expression in urine and plasma samples from AKI patients undergoing ICI therapy. NULISA offers 10,000-fold greater precision than PLA, enabling quantification of over 200 inflammatory proteins with unprecedented precision. Our analysis revealed that urine was more sensitive and specific than plasma in distinguishing ICI-AIN from non-AIN cases. Pathway analyses highlighted the involvement of JAK-STAT and tumor necrosis factor (TNF) signaling in ICI-AIN pathogenesis. We identified several novel urine biomarkers, including IL-5, Fas, TNFSF4, CD274, IL-20, TNFSF15, TSLP, TREM1 and CCL1 while confirming previously reported markers such as CXCL9 and TNF-α. Using statistical and machine learning methods, we constructed a novel urine biomarker signature—IL-5+Fas—that achieved an area under the curve of 0.94 for diagnosing ICI-AIN.By leveraging high-sensitivity proteomics, we developed a non-invasive strategy for diagnosing ICI-AIN. This approach will enable earlier intervention to mitigate immunotoxicity, preservation of antitumor efficacy of ICI therapy in non-AIN patients, and safe rechallenge of ICI therapy in patients previously treated for ICI-AIN.
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Frangou, E., P. Garantziotis, M. Grigoriou, A. Banos, N. Panousis, E. Dermitzakis, G. Bertsias, D. Boumpas, and A. Filia. "THU0014 COMPARATIVE TRANSCRIPTOME ANALYSES ACROSS TISSUES AND SPECIES IDENTIFY TARGETABLE GENES FOR HUMAN SYSTEMIC LUPUS ERYTHEMATOSUS (SLE) AND LUPUS NEPHRITIS (LN)." Annals of the Rheumatic Diseases 79, Suppl 1 (June 2020): 220.1–220. http://dx.doi.org/10.1136/annrheumdis-2020-eular.4102.
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Background:Systemic Lupus Erythematosus (SLE) is a complex disease associated with the dysfunction of multiple tissues and cells. The causal tissue for each disease phenotype is not known a priori. Despite improvements in diagnosis and treatment, major organ involvement (such as the kidneys) contributes significantly to morbidity and mortality that still remain increased. There is an unmet need for timely targeted therapy.Objectives:RNA-sequencing was performed to investigate the patterns of transcription variation across tissues between healthy and lupus-prone mice at different stages of lupus, and how these patterns associate with human Systemic Lupus Erythematosus (SLE).Methods:NZB/W-F1 lupus prone mice were sacrificed at the pre-puberty, pre-autoimmunity and nephritic stage. Age-matched C57BL/6 were used as controls. An “effector” tissue (spleen) and “end-organs” (kidneys, brain) were collected. Total RNA was isolated, and mRNA-sequencing was performed. A time-series analysis was developed and differentially expressed genes (DEGs) were analyzed with DESeq. Hierarchical clustering and functional enrichment analysis were performed with gProfiler. Human orthologs of mouse tissue DEGs were identified in the whole-blood RNA-sequencing dataset comprised of 55 lupus-nephritis (LN), 65 non-LN SLE patients and 58 healthy individuals (HI). Human orthologs were compared to human DEGs. Using machine learning, human orthologs identified in the mouse dataset were used to predict kidney involvement in the human dataset, which was split in training and validation sets.Results:Lupus susceptibility and progression signatures at different tissues and different stages of the disease were identified. Tissue-specific signatures and a common cross-tissue signature were also described. Previously described and novel biological processes and pathways were revealed. The comparative murine-human transcriptome analysis identified human orthologs from the mouse spleen-signature (including CCL5, IFIT and HLA genes) that are involved in systemic autoimmunity. It also identified human orthologs from the kidney- and brain-signature (including FCGR2A, C1Q, JAK1 and APOA2) that are involved in major “end-organ” damage and response mechanisms. Using a neural network model, 193 human orthologs accurately predicted LN patients vs HI (accuracy=0.86, sensitivity=0.82, specificity=0.91 in the validation set). Using a support vector machine model, 30 human orthologs and age and gender were the best predictors of LN vs non-LN SLE patients (accuracy=0.71, sensitivity=0.73, specificity=0.69 in the validation set).Conclusion:Murine tissue gene signatures identified by RNA-sequencing analysis revealed biological processes and pathways that could be potentially used as biomarkers or therapeutic targets in human SLE. Comparison of the murine tissue-transcriptome with the whole-blood human-transcriptome revealed common gene signatures, demonstrating similar biological processes and pathways. Machine learning identified a murine kidney lupus signature that can accurately predict kidney involvement in human SLE. Validation in other datasets is ongoing.References:[1]Panousis NI, et al. Ann Rheum Dis 2019;78:1079Acknowledgments:This work was supported by FOREUM, SYSCID and ERC -Advanced GrantDisclosure of Interests:Eleni Frangou: None declared, Panayiotis Garantziotis: None declared, Maria Grigoriou: None declared, Aggelos Banos: None declared, Nikolaos Panousis: None declared, Emmanouil Dermitzakis: None declared, George Bertsias Grant/research support from: GSK, Consultant of: Novartis, Dimitrios Boumpas: None declared, Anastasia Filia: None declared
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Dusetti, Nelson J., Nicolas Fraunhoffer, Pascal Hammel, Thierry Conroy, Remy Nicolle, Jean-Baptiste Bachet, Alexandre Harle, et al. "Development and validation of AI-assisted transcriptomic signatures to personalize adjuvant chemotherapy in patients with resectable pancreatic ductal adenocarcinoma." Journal of Clinical Oncology 42, no. 16_suppl (June 1, 2024): 4015. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.4015.
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4015 Background: Adjuvant chemotherapies for PDAC include modified FOLFIRINOX (mFFX) or gemcitabine-based regimen in fit patients and gemcitabine or 5FU single agents in other patients. While more effective, mFFX is associated with a greater toxicity than other options. Moreover, therapeutic decisions still rely mainly on the patient's performance status rather than tailored to tumor-based criteria. Our study aims to personalize treatments by developing transcriptomic signatures specific to commonly used drugs for pancreatic cancer. Methods: We analyzed the response to drugs (5-fluorouracil, oxaliplatin, and irinotecan) in three types of preclinical models (primary cell cultures, tumoroids, and patient-derived xenografts in immunodeficient mice). We then associated the detected sensitivities to drugs with transcriptomic data from each model. We also incorporated the previously developed gemcitabine signature. Finally, we used a machine learning method, the "Least Absolute Shrinkage and Selection Operator-random forest," to improve the signatures, integrating the tumor microenvironment master regulators. The learning cohort were GemPred for gemcitabine (1) and COMPASS (2) for mFFX. The resulting transcriptomic predictive tool was called Pancreas-View. We validated these signatures in the PRODIGE-24/CCTG PA6 trial cohort comprising 343 patients (3). Results: The signatures may allow to identify responsive patients to specific drugs and showed a significant improvement in their cancer-specific survival (CSS) and disease-free survival (DFS) when they received a matched therapy (mFFX or gemcitabine). Additionally, a positive association was observed between the number of drugs for which tumors predict to be sensitive and patient’s survival when appropriately treated. Patients who received “appropriate” drugs (n = 164; 47.8%) displayed a longer DFS : 50.1 months (stratified HR: 0.31; 95% CI, 0.21-0.44; p < 0.001) in the mFFX arm, and 33.7 months (stratified HR: 0.40; 95% CI, 0.17-0.59; p < 0.001) in the gemcitabine arm, respectively. Conversely, patients that received a treatment not matched with the signature prediction (n = 86; 25.1%) and those predicted to be resistant to all drugs (n = 93; 27.1%) had the poorest DFS results (10.6 and 10.8 months, respectively). Conclusions: By integrating preclinical models and machine learning, we developed a comprehensive predictive tool based on the transcriptome that may help to identify tumors sensitivity to mFFX components and gemcitabine. Crucially, these transcriptomic signatures can also lead to reduce toxicity by avoiding the unnecessary administration of drugs predicted as ineffective for a given tumor. Nicolle R, et al. Ann Oncol 2021;32:250-260. Aung K, et al Clin Cancer Res 2018;24:1344–1354. Conroy T, et al. N Engl J Med 2018; 379:2395-2406.
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Bourne, Martin A., and Sergei Nayakshin. "Inverse Compton X-ray signature of AGN feedback." Monthly Notices of the Royal Astronomical Society 436, no. 3 (October 4, 2013): 2346–51. http://dx.doi.org/10.1093/mnras/stt1739.
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Taibi, Amel, Zhen Lin, Rong Tsao, Lilian U. Thompson, and Elena M. Comelli. "Effects of Flaxseed and Its Components on Mammary Gland MiRNome: Identification of Potential Biomarkers to Prevent Breast Cancer Development." Nutrients 11, no. 11 (November 4, 2019): 2656. http://dx.doi.org/10.3390/nu11112656.
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Breast cancer is the most common cancer among women worldwide. We previously showed that early-life exposure to flaxseed (FS) or its components, FS oil (FSO) and secoisolariciresinol diglucoside (SDG), affects the mammary gland (MG) and is associated with the reduction of breast cancer risk during adulthood. However, the underlying mechanisms are not understood. This study aimed to investigate the effect of FS, FSO, and SDG on the MG miRNA signature at a late stage of development. Female C57BL/6 mice, 4–5 weeks of age, were randomized into four groups to receive: (i) basal AIN-93G, (ii) 10% FS, (iii) 3.67% FSO, or (iv) 0.15% SDG. After 21 days, the mice were sacrificed and MG miRNAs were profiled. Diet-specific MG miRNA signatures were identified. Deregulated miRNAs were associated with breast cancer and targeted genes involved in MG development, growth, and cancer. The study allowed for the identification of potential biomarkers or novel therapeutic targets to prevent and/or reduce the risk of breast cancer.
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Kharb, Preeti, and Sasikumar Silpa. "Looking for Signatures of AGN Feedback in Radio-Quiet AGN." Galaxies 11, no. 1 (February 8, 2023): 27. http://dx.doi.org/10.3390/galaxies11010027.
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In this article, we discuss the state of “AGN feedback” in radio-quiet (RQ) AGN. This study involves heterogeneous samples of nearby Seyfert and LINER galaxies as well as quasi-stellar objects (QSOs) that have been observed at low radio frequencies (few ∼100 MHz) with the upgraded Giant Meterwave Radio Telescope (GMRT) and ∼GHz frequencies with the Karl G. Jansky Very Large Array (VLA) and Very Long Baseline Array (VLBA). These multi-frequency, multi-resolution observations detect a range of arcsecond-scale radio spectral indices that are consistent with the presence of multiple contributors including starburst winds and AGN jets or winds; steep spectrum “relic” emission is observed as well. Polarization-sensitive data from the VLA and GMRT suggest that the radio outflows are stratified (e.g., in IIIZw2, Mrk231); distinct polarization signatures suggest that there could either be a “spine + sheath” structure in the radio outflow, or there could be a “jet + wind” structure. Similar nested biconical outflows can also explain the VLBA and SDSS emission-line data in the KISSR sample of double-peaked emission-line Seyfert and LINER galaxies. Furthermore, the modeling of the emission-lines with plasma modeling codes such as MAPPINGS indicates that parsec-scale jets and winds in these sources can disturb or move the narrow-line region (NLR) gas clouds via the “shock + precursor” mechanism. Apart from the presence of “relic” emission, several Seyfert and LINER galaxies show clear morphological signatures of episodic jet activity. In one such source, NGC2639, at least four distinct episodes of jets are observed, the largest one of which was only detectable at 735 MHz with the GMRT. Additionally, a ∼6 kpc hole in the CO molecular gas along with a dearth of young stars in the center of its host galaxy is observed. Multiple jet episodes on the 10–100 parsec scales and a ∼10 parsec hole in the molecular gas is also observed in the Seyfert galaxy NGC4051. This suggests a link between episodic jet activity in RQ AGN and “AGN feedback” influencing the evolution of their host galaxies. However, a similar simple relationship between radio outflows and molecular gas mass is not observed in the Palomar–Green (PG) QSO sample, indicating that “AGN feedback” is a complex phenomenon in RQ AGN. “AGN feedback” must occur through the local impact of recurring multi-component outflows in RQ AGN. However, global feedback signatures on their host galaxy properties are not always readily evident.
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Chen, Xun, Arif Mohamed, and Janet Folkes. "Grinding Monitoring through Thermal Acoustic Emission Signatures." Advanced Materials Research 325 (August 2011): 287–93. http://dx.doi.org/10.4028/www.scientific.net/amr.325.287.
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This paper presents recent development in acoustic emission (AE) technique for grinding process monitoring. It demonstrated the similarity of thermal acoustic emission feature existing in grinding processes and laser irradiation tests. An innovative concept that grinding process can be monitored by using thermal AE signatures from laser irradiation tests has been proposed. Based on such idea, an artificial neural network (ANN) was built and the results showed that grinding performance variation due to wheel wear can be identified by using the ANN. This development could bring great benefits by reducing experimental works in the preparation of an ANN for grinding monitoring.
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Tan, Hongna, Yaping Wu, Fengchang Bao, Jing Zhou, Jianzhong Wan, Jie Tian, Yusong Lin, and Meiyun Wang. "Mammography-based radiomics nomogram: a potential biomarker to predict axillary lymph node metastasis in breast cancer." British Journal of Radiology 93, no. 1111 (July 2020): 20191019. http://dx.doi.org/10.1259/bjr.20191019.
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Objective: To establish a radiomics nomogram by integrating clinical risk factors and radiomics features extracted from digital mammography (MG) images for pre-operative prediction of axillary lymph node (ALN) metastasis in breast cancer. Methods: 216 patients with breast cancer lesions confirmed by surgical excision pathology were divided into the primary cohort (n = 144) and validation cohort (n = 72). Radiomics features were extracted from craniocaudal (CC) view of mammograms, and radiomics features selection were performed using the methods of ANOVA F-value and least absolute shrinkage and selection operator; then a radiomics signature was constructed with the method of support vector machine. Multivariate logistic regression analysis was used to establish a radiomics nomogram based on the combination of radiomics signature and clinical factors. The C-index and calibration curves were derived based on the regression analysis both in the primary and validation cohorts. Results: 95 of 216 patients were confirmed with ALN metastasis by pathology, and 52 cases were diagnosed as ALN metastasis based on MG-reported criteria. The sensitivity, specificity, accuracy and AUC (area under the receiver operating characteristic curve of MG-reported criteria were 42.7%, 90.8%, 24.1% and 0.666 (95% confidence interval: 0.591–0.741]. The radiomics nomogram, comprising progesterone receptor status, molecular subtype and radiomics signature, showed good calibration and better favorite performance for the metastatic ALN detection (AUC 0.883 and 0.863 in the primary and validation cohorts) than each independent clinical features (AUC 0.707 and 0.657 in the primary and validation cohorts) and radiomics signature (AUC 0.876 and 0.862 in the primary and validation cohorts). Conclusion: The MG-based radiomics nomogram could be used as a non-invasive and reliable tool in predicting ALN metastasis and may facilitate to assist clinicians for pre-operative decision-making. Advances in knowledge: ALN status remains among the most important breast cancer prognostic factors and is essential for making treatment decisions. However, the value of detecting metastatic ALN by MG is very limited. The studies on pre-operative ALN metastasis prediction using the method of MG-based radiomics in breast cancer are very few. Therefore, we studied whether MG-based radiomics nomogram could be used as a predictive biomarker for the detection of metastatic ALN.
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Akeju, Oluwaseun, M. Brandon Westover, Kara J. Pavone, Aaron L. Sampson, Katharine E. Hartnack, Emery N. Brown, and Patrick L. Purdon. "Effects of Sevoflurane and Propofol on Frontal Electroencephalogram Power and Coherence." Anesthesiology 121, no. 5 (November 1, 2014): 990–98. http://dx.doi.org/10.1097/aln.0000000000000436.
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Abstract Background: The neural mechanisms of anesthetic vapors have not been studied in depth. However, modeling and experimental studies on the intravenous anesthetic propofol indicate that potentiation of γ-aminobutyric acid receptors leads to a state of thalamocortical synchrony, observed as coherent frontal alpha oscillations, associated with unconsciousness. Sevoflurane, an ether derivative, also potentiates γ-aminobutyric acid receptors. However, in humans, sevoflurane-induced coherent frontal alpha oscillations have not been well detailed. Methods: To study the electroencephalogram dynamics induced by sevoflurane, the authors identified age- and sex-matched patients in which sevoflurane (n = 30) or propofol (n = 30) was used as the sole agent for maintenance of general anesthesia during routine surgery. The authors compared the electroencephalogram signatures of sevoflurane with that of propofol using time-varying spectral and coherence methods. Results: Sevoflurane general anesthesia is characterized by alpha oscillations with maximum power and coherence at approximately 10 Hz, (mean ± SD; peak power, 4.3 ± 3.5 dB; peak coherence, 0.73 ± 0.1). These alpha oscillations are similar to those observed during propofol general anesthesia, which also has maximum power and coherence at approximately 10 Hz (peak power, 2.1 ± 4.3 dB; peak coherence, 0.71 ± 0.1). However, sevoflurane also exhibited a distinct theta coherence signature (peak frequency, 4.9 ± 0.6 Hz; peak coherence, 0.58 ± 0.1). Slow oscillations were observed in both cases, with no significant difference in power or coherence. Conclusions: The study results indicate that sevoflurane, like propofol, induces coherent frontal alpha oscillations and slow oscillations in humans to sustain the anesthesia-induced unconscious state. These results suggest a shared molecular and systems-level mechanism for the unconscious state induced by these drugs.
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Grishin, Evgeni, Alexey Bobrick, Ryosuke Hirai, Ilya Mandel, and Hagai B. Perets. "Supernova explosions in active galactic nuclear discs." Monthly Notices of the Royal Astronomical Society 507, no. 1 (July 12, 2021): 156–74. http://dx.doi.org/10.1093/mnras/stab1957.
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ABSTRACT Active galactic nuclei (AGNs) are prominent environments for stellar capture, growth, and formation. These environments may catalyse stellar mergers and explosive transients, such as thermonuclear and core-collapse supernovae (SNe). SN explosions in AGN discs generate strong shocks, leading to unique observable signatures. We develop an analytical model that follows the evolution of the shock propagating in the disc until it eventually breaks out. We derive the peak luminosity, bolometric light curve, and breakout time. The peak luminosities may exceed 1045 erg s−1 and last from hours to days. The brightest explosions occur in regions of reduced density: either off-plane, or in discs around low-mass central black holes (${\sim} 10^6\ \rm {M}_\odot$), or in starved subluminous AGNs. Explosions in the latter two sites are easier to observe due to a reduced AGN background luminosity. We perform suites of 1D Lagrangian radiative hydrodynamics snec code simulations to validate our results and obtain the luminosity in different bands, and 2D axisymmetric Eulerian hydrodynamics code hormone simulations to study the morphology of the ejecta and its deviation from spherical symmetry. The observed signature is expected to be a bright blue, UV or X-ray flare on top of the AGN luminosity from the initial shock breakout, while the subsequent red part of the light curve will largely be unobservable. We estimate the upper limit for the total event rate to be $\mathcal {R}\lesssim 100\ \rm yr^{-1}\ Gpc^{-3}$ for optimal conditions and discuss the large uncertainties in this estimate. Future high-cadence transient searches may reveal these events. Some existing tidal disruption event candidates may originate from AGN SNe.
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Chisedzi, Lloyd Prosper, and Mbika Muteba. "Detection of Broken Rotor Bars in Cage Induction Motors Using Machine Learning Methods." Sensors 23, no. 22 (November 9, 2023): 9079. http://dx.doi.org/10.3390/s23229079.
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In this paper, the performance of machine learning methods for squirrel cage induction motor broken rotor bar (BRB) fault detection is evaluated. Decision tree classification (DTC), artificial neural network (ANN), and deep learning (DL) methods are developed, applied, and studied to compare their performance in detecting broken rotor bar faults in squirrel cage induction motors. The training data were collected through experimental measurements. The BRB fault features were extracted from measured line-current signatures through a transformation from the time domain to the frequency domain using discrete Fourier Transform (DFT) of the frequency spectrum of the current signal. Eighty percent of the data were used for training the models, and twenty percent were used for testing. A confusion matrix was used to validate the models’ performance using accuracy, precision, recall, and f1-scores. The results evidence that the DTC is less load-dependent, and it has better accuracy and precision for both unloaded and loaded squirrel cage induction motors when compared with the DL and ANN methods. The DTC method achieved higher accuracy in the detection of the magnitudes of the twice-frequency sideband components induced in stator currents by BRB faults when compared with the DL and ANN methods. Although the detection accuracy and precision are higher for the loaded motor than the unloaded motor, the DTC method managed to also exhibit a high accuracy for the unloaded current when compared with the DL and ANN methods. The DTC is, therefore, a suitable candidate to detect broken rotor bar faults on trained data for lightly or thoroughly loaded squirrel cage induction motors using the characteristics of the measured line-current signature.
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Roy, Namrata, Kevin Bundy, Edmond Cheung, Wiphu Rujopakarn, Michele Cappellari, Francesco Belfiore, Renbin Yan, et al. "Detecting Radio AGN Signatures in Red Geysers." Astrophysical Journal 869, no. 2 (December 18, 2018): 117. http://dx.doi.org/10.3847/1538-4357/aaee72.
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Ekejiuba, Ifeanyi E. "Radiative Signatures of Neutron Beams in AGN." Symposium - International Astronomical Union 159 (1994): 345. http://dx.doi.org/10.1017/s0074180900175461.
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The escape of relativistic neutrons from their production region can have various consequencies for the morphology of active galaxies. The phenomena of luminosity gaps and radio jet lighting in extragalactic radio sources (EGRSs) fit into the model that employs relativistic neutrons as the vector for particle and energy transport out of the central engines of AGNs. The central radio gaps reveal themselves as regions of relativistic neutron beam transport. The relativistic neutrons, which decay in flight after traveling for ∼ 103 γn s, produce secondaries which are responsible for the radio jet lighting and the associated phenomena in EGRSs.
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Meurs, E. J. A., and L. Norci. "Searching for AGN signatures in IRAS galaxies." Nuclear Physics B - Proceedings Supplements 69, no. 1-3 (January 1999): 558–59. http://dx.doi.org/10.1016/s0920-5632(98)00289-8.
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Kaye, Steve, James Abbott, Steve Gentleman, Wolfgang Oertel, Myra McClure, and Christopher H. Hawkes. "121 Viral & bacterial RNA transcripts of substantia nigra and olfactory bulb in parkinson disease." Journal of Neurology, Neurosurgery & Psychiatry 90, no. 12 (November 14, 2019): e34.3-e34. http://dx.doi.org/10.1136/jnnp-2019-abn-2.115.
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IntroductionIt is known that several viral species can induce parkinsonism in animals and humans, but no specific virus has been discovered in the classical human form of PD.MethodsTo investigate this further, we used next generation sequencing (NGS) to analyse post-mortem transcriptomes of substantia nigra (SN) and olfactory bulb (OB) in four PD cases and four multiple sclerosis patients who acted as positive controls. Bioinformatic analysis of the data set removed the majority of human transcripts and the remaining sequence data were compared to existing viral sequence data sets to search for signature viral sequences.ResultsDespite high read numbers and good quality NGS data, no viral or bacterial transcripts could be identified from either of the tissues examined. The few matches to existing viral databases were to viruses which rarely, if ever, infect humans and no multiple hits (matches to more than one gene of the same virus) were observed. Similarly, no bacterial sequences were found although it should be noted the analysis was carried out at the mRNA level.ConclusionThis small but detailed analysis provided no evidence of RNA viral signatures in the OB or SN in the four samples taken from Parkinson’s disease brains. It is still possible that other brain areas known to display Lewy body pathology may contain viral sequences.
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Zhang, Shu-Rui, Yan Luo, Xiao-Jun Wu, Jian-Min Wang, Luis C. Ho, and Ye-Fei Yuan. "Electromagnetic signatures of white dwarf collisions in AGN discs." Monthly Notices of the Royal Astronomical Society 524, no. 1 (July 4, 2023): 940–51. http://dx.doi.org/10.1093/mnras/stad1855.
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ABSTRACT In the inner region of the disc of an active galactic nucleus (AGN), the collision of two white dwarfs (WDs) through Jacobi capture might be inevitable, leading to a Type Ia supernova (SN Ia) explosion. This transient event, influenced by the disc gas and the gravity of the supermassive black hole (SMBH), exhibits distinct characteristics compared with normal SNe Ia. The energy of the explosion is mainly stored in the ejecta in the form of kinetic energy. Typically, the ejecta is not decelerated effectively by the AGN disc and rushes rapidly out of the AGN disc. However, under the influence of the SMBH, most of the ejecta falls back toward the AGN disc. As the fallback ejecta becomes more dispersed, it interacts with the disc gas, converting its kinetic energy into thermal energy. This results in a high-energy transient characterized by a rapid initial rise followed by a decay with L∝t−2.8. The time-scale of the transient ranges from hours to weeks, depending on the mass of the SMBH. This process generates high-energy radiation spanning from hard X-rays to the soft γ range. Additionally, the subsequent damage to the disc may result in changing-look AGNs. Moreover, the falling back of SNe Ia ejecta on to the AGN disc significantly increases the metallicity of the AGN and can even generate heavy elements within AGN discs.
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Haupl, T., R. Biesen, B. Smiljanovic, J. R. Grun, J. Weix, B. Stuhlmuller, P. M. Villiger, G. R. Burmester, A. Radbruch, and A. Grutzkau. "The type 1 interferon signature: facts, fads and fallacies." Annals of the Rheumatic Diseases 70, Suppl 2 (February 22, 2011): A24. http://dx.doi.org/10.1136/ard.2010.148965.26.
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Maltby, David T., Omar Almaini, Ross J. McLure, Vivienne Wild, James Dunlop, Kate Rowlands, William G. Hartley, et al. "High-velocity outflows in massive post-starburst galaxies at z > 1." Monthly Notices of the Royal Astronomical Society 489, no. 1 (August 12, 2019): 1139–51. http://dx.doi.org/10.1093/mnras/stz2211.
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ABSTRACT We investigate the prevalence of galactic-scale outflows in post-starburst (PSB) galaxies at high redshift (1 < z < 1.4), using the deep optical spectra available in the UKIDSS Ultra Deep Survey (UDS). We use a sample of ∼40 spectroscopically confirmed PSBs, recently identified in the UDS field, and perform a stacking analysis in order to analyse the structure of strong interstellar absorption features such as Mg ii (λ2800 Å). We find that for massive ($M_* \gt 10^{10}\rm \, M_{\odot }$) PSBs at z > 1, there is clear evidence for a strong blue-shifted component to the Mg ii absorption feature, indicative of high-velocity outflows ($v_{\rm out}\sim 1150\pm 160\rm \, km\, s^{-1}$) in the interstellar medium. We conclude that such outflows are typical in massive PSBs at this epoch, and potentially represent the residual signature of a feedback process that quenched these galaxies. Using full spectral fitting, we also obtain a typical stellar velocity dispersion σ* for these PSBs of $\sim 200\rm \, km\, s^{-1}$, which confirms they are intrinsically massive in nature (dynamical mass $M_{\rm d}\sim 10^{11}\rm \, M_{\odot }$). Given that these high-z PSBs are also exceptionally compact (re ∼ 1–$2\rm \, kpc$) and spheroidal (Sérsic index n ∼ 3), we propose that the outflowing winds may have been launched during a recent compaction event (e.g. major merger or disc collapse) that triggered either a centralized starburst or active galactic nuclei (AGN) activity. Finally, we find no evidence for AGN signatures in the optical spectra of these PSBs, suggesting they were either quenched by stellar feedback from the starburst itself, or that if AGN feedback is responsible, the AGN episode that triggered quenching does not linger into the post-starburst phase.
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Vyan Martha, Aldo, Mukhtar Hanafi, and Auliya Burhanuddin. "Implementasi Jaringan Syaraf Tiruan (JST) untuk Mengenali Pola Tanda Tangan dengan Metode Backpropagation." Jurnal Komtika (Komputasi dan Informatika) 3, no. 2 (April 26, 2020): 51–57. http://dx.doi.org/10.31603/komtika.v3i2.3472.
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Artificial Neural Networks (ANN) is a computer technology in the field of artificial intelligence that is able to understand complex data patterns. One of ANN's technological capabilities is being able to predict solutions based on training patterns provided during the system learning process. This study aims to apply the signature pattern by applying ANN using the Backpropagation method. Backpropagation method is one of the learning algorithms related to the preparation of weights based on the value of errors in learning. The image will be processed using the Backpropagation method which will be obtained by the introduction. The results introduce 50 signature data samples and 50 signature sample data. The test is carried out using 50 samples, where each sample will be requested once. From the results of the research that has been done it can be concluded that the results obtained from the parameters with a learning rate of 0.5, epoch 100, objectives 1e-5 and momentum 0.9 with the results of 68% system testing.
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Pai, Aashay, Michael R. Blanton, and John Moustakas. "Mid-infrared Variability in Nearby Galaxies from the MaNGA Sample." Astrophysical Journal 977, no. 1 (December 1, 2024): 102. https://doi.org/10.3847/1538-4357/ad89b8.
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Abstract We use mid-infrared variability in galaxies to search for active galactic nuclei (AGN) in the local universe. We use a sample of 10,220 galaxies from the Mapping Nearby Galaxies at Apache Point Observatory survey, part of the Sloan Digital Sky Survey. For each galaxy, we examine its mid-infrared variability in the W2 [4.6 μm] band over 13 years using data from the Wide Infrared Survey Explorer (WISE) All-Sky and Near Earth Objects WISE missions. We demonstrate that we can detect variability signatures as small as about 7% in the rms variation of W2 flux for the majority of cases. Using other AGN signatures of the variable galaxies, such as optical narrow lines, optical broad lines, and WISE W1 − W2 colors, we show that ∼75% of the variables show these additional AGN signatures, indicating that the bulk of these cases are likely to be AGN. We also identify seven galaxies that have light curves characteristic of tidal disruption events. We present here a publicly available catalog of the light-curve variability in W2 of these galaxies.
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Tang, Yanqiu, Lin Chen, Yating Qiao, Weifeng Li, Rong Deng, and Mengdi Liang. "Radiomic Signature Based on Dynamic Contrast-Enhanced MRI for Evaluation of Axillary Lymph Node Metastasis in Breast Cancer." Computational and Mathematical Methods in Medicine 2022 (August 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/1507125.
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Background. To construct and validate a radiomic-based model for estimating axillary lymph node (ALN) metastasis in patients with breast cancer by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Methods. In this retrospective study, a radiomic-based model was established in a training cohort of 236 patients with breast cancer. Radiomic features were extracted from breast DCE-MRI scans. A method named the least absolute shrinkage and selection operator (LASSO) was applied to select radiomic features based on highly reproducible features. A radiomic signature was built by a support vector machine (SVM). Multivariate logistic regression analysis was adopted to establish a clinical characteristic-based model. The performance of models was analysed through discrimination ability and clinical benefits. Results. The radiomic signature comprised 6 features related to ALN metastasis and showed significant differences between the patients with ALN metastasis and without ALN metastasis ( P < 0.001 ). The area under the curve (AUC) of the radiomic model was 0.990 and 0.858, respectively, in the training and validation sets. The clinical feature-based model, including MRI-reported status and palpability, performed slightly worse, with an AUC of 0.784 in the training cohort and 0.789 in the validation cohort. The radiomic signature was confirmed to provide more clinical benefits by decision curve analysis. Conclusions. The radiomic-based model developed in this study can successfully diagnose the status of lymph nodes in patients with breast cancer, which may reduce unnecessary invasive clinical operations.
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Sutcliffe, P. R. "Substorm onset identification using neural networks and Pi2 pulsations." Annales Geophysicae 15, no. 10 (October 31, 1997): 1257–64. http://dx.doi.org/10.1007/s00585-997-1257-x.
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Abstract. The pattern recognition capabilities of artificial neural networks (ANNs) have for the first time been used to identify Pi2 pulsations in magnetometer data, which in turn serve as indicators of substorm onsets and intensifications. The pulsation spectrum was used as input to the ANN and the network was trained to give an output of +1 for Pi2 signatures and -1 for non-Pi2 signatures. In order to evaluate the degree of success of the neural-network procedure for identifying Pi2 pulsations, the ANN was used to scan a number of data sets and the results compared with visual identification of Pi2 signatures. The ANN performed extremely well with a success rate of approximately 90% for Pi2 identification and a timing accuracy generally within 1 min compared to visual identification. A number of potential applications of the neural-network Pi2 scanning procedure are discussed.
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Dugan, Zachary, Sarah Bryan, Volker Gaibler, Joseph Silk, and Marcel Haas. "STELLAR SIGNATURES OF AGN-JET-TRIGGERED STAR FORMATION." Astrophysical Journal 796, no. 2 (November 12, 2014): 113. http://dx.doi.org/10.1088/0004-637x/796/2/113.
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