Monitoring the Morphology of M87* in 2009–2017 with the Event Horizon Telescope by Maciek Wielgus, Kazunori Akiyama, Lindy Blackburn, Chi-kwan Chan, Jason Dexter, Sheperd S. Doeleman, Vincent L. Fish, Sara Issaoun, Michael D. Johnson, Thomas P. Krichbaum, Ru-Sen Lu, Dominic W. Pesce, George N. Wong, Geoffrey C. Bower, Avery E. Broderick, Andrew Chael, Koushik Chatterjee, Charles F. Gammie, Boris Georgiev, Kazuhiro Hada, Laurent Loinard, Sera Markoff, Daniel P. Marrone, Richard Plambeck, Jonathan Weintroub, Matthew Dexter, David H. E. MacMahon, Melvyn Wright, Antxon Alberdi, Walter Alef, Keiichi Asada, Rebecca Azulay, Anne-Kathrin Baczko, David Ball, Mislav Baloković, Enrico Barausse, John Barrett, Dan Bintley, Wilfred Boland, Katherine L. Bouman, Michael Bremer, Christiaan D. Brinkerink, Roger Brissenden, Silke Britzen, Dominique Broguiere, Thomas Bronzwaer, Do-Young Byun, John E. Carlstrom, Shami Chatterjee, Ming-Tang Chen, Yongjun Chen, Ilje Cho, Pierre Christian, John E. Conway, James M. Cordes, Geoffrey B. Crew, Yuzhu Cui, Jordy Davelaar, Mariafelicia De Laurentis, Roger Deane, Jessica Dempsey, Gregory Desvignes, Sergio A. Dzib, Ralph P. Eatough, Heino Falcke, Ed Fomalont, Raquel Fraga-Encinas, Per Friberg, Christian M. Fromm, Peter Galison, Roberto García, Olivier Gentaz, Ciriaco Goddi, Roman Gold, José L. Gómez, Arturo I. Gómez-Ruiz, Minfeng Gu, Mark Gurwell, Michael H. Hecht, Ronald Hesper, Luis C. Ho, Paul Ho, Mareki Honma, Chih-Wei L. Huang, Lei Huang, David H. Hughes, Makoto Inoue, David J. James, Buell T. Jannuzi, Michael Janssen, Britton Jeter, Wu Jiang, Alejandra Jimenez-Rosales, Svetlana Jorstad, Taehyun Jung, Mansour Karami, Ramesh Karuppusamy, Tomohisa Kawashima, Garrett K. Keating, Mark Kettenis, Jae-Young Kim, Junhan Kim, Jongsoo Kim, Motoki Kino, Jun Yi Koay, Patrick M. Koch, Shoko Koyama, Michael Kramer, Carsten Kramer, Cheng-Yu Kuo, Tod R. Lauer, Sang-Sung Lee, Yan-Rong Li, Zhiyuan Li, Michael Lindqvist, Rocco Lico, Kuo Liu, Elisabetta Liuzzo, Wen-Ping Lo, Andrei P. Lobanov, Colin Lonsdale, Nicholas R. MacDonald, Jirong Mao, Nicola Marchili, Alan P. Marscher, Iván Martí-Vidal, Satoki Matsushita, Lynn D. Matthews, Lia Medeiros, Karl M. Menten, Yosuke Mizuno, Izumi Mizuno, James M. Moran, Kotaro Moriyama, Monika Moscibrodzka, Cornelia Müller, Gibwa Musoke, Hiroshi Nagai, Neil M. Nagar, Masanori Nakamura, Ramesh Narayan, Gopal Narayanan, Iniyan Natarajan, Antonios Nathanail, Roberto Neri, Chunchong Ni, Aristeidis Noutsos, Hiroki Okino, Héctor Olivares, Gisela N. Ortiz-León, Tomoaki Oyama, Feryal Özel, Daniel C. M. Palumbo, Jongho Park, Nimesh Patel, Ue-Li Pen, Vincent Piétu, Aleksandar PopStefanija, Oliver Porth, Ben Prather, Jorge A. Preciado-López, Dimitrios Psaltis, Hung-Yi Pu, Venkatessh Ramakrishnan, Ramprasad Rao, Mark G. Rawlings, Alexander W. Raymond, Luciano Rezzolla, Bart Ripperda, Freek Roelofs, Alan Rogers, Eduardo Ros, Mel Rose, Arash Roshanineshat, Helge Rottmann, Alan L. Roy, Chet Ruszczyk, Benjamin R. Ryan, Kazi L. J. Rygl, Salvador Sánchez, David Sánchez-Arguelles, Mahito Sasada, Tuomas Savolainen, F. Peter Schloerb, Karl-Friedrich Schuster, Lijing Shao, Zhiqiang Shen, Des Small, Bong Won Sohn, Jason SooHoo, Fumie Tazaki, Paul Tiede, Remo P. J. Tilanus, Michael Titus, Kenji Toma, Pablo Torne, Tyler Trent, Efthalia Traianou, Sascha Trippe, Shuichiro Tsuda, Ilse van Bemmel, Huib Jan van Langevelde, Daniel R. van Rossum, Jan Wagner, John Wardle, Derek Ward-Thompson, Norbert Wex, Robert Wharton, Qingwen Wu, Doosoo Yoon, André Young, Ken Young, Ziri Younsi, Feng Yuan, Ye-Fei Yuan, J. Anton Zensus, Guangyao Zhao, Shan-Shan Zhao, Ziyan Zhu

“…These images were produced using 230 GHz observations performed in 2017 April. Additional observations are required to investigate the persistence of the primary image feature—a ring with azimuthal brightness asymmetry—and to quantify the image variability on event horizon scales. …”
Get full text

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study. by Ilaria Amodeo, Giorgio De Nunzio, Genny Raffaeli, Irene Borzani, Alice Griggio, Luana Conte, Francesco Macchini, Valentina Condò, Nicola Persico, Isabella Fabietti, Stefano Ghirardello, Maria Pierro, Benedetta Tafuri, Giuseppe Como, Donato Cascio, Mariarosa Colnaghi, Fabio Mosca, Giacomo Cavallaro

“…Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. …”
Get full text

SMILES all around: structure to SMILES conversion for transition metal complexes by Maria H. Rasmussen, Magnus Strandgaard, Julius Seumer, Laura K. Hemmingsen, Angelo Frei, David Balcells, Jan H. Jensen

“…Comparing with the graphs made by Kneiding et al. where nodes and edges are featurized with DFT properties, we find that depending on the target property (polarizability, HOMO-LUMO gap or dipole moment) the SMILES based representations can perform equally well. …”
Get full text

An MCMC Approach to Bayesian Image Analysis in Fourier Space by Konstantinos Bakas, John Kornak, Hernando Ombao

“…Bayesian image analysis methods are commonly applied to solve image analysis problems such as noise reduction, feature enhancement, and object detection. A primary limitation of these methods is their computational cost due to the complex joint interdependencies between pixels, which limits the efficiency of performing posterior sampling through Markov chain Monte Carlo (MCMC). …”
Get full text

TRANSCATHETER AORTIC VALVE IMPLANTATION. STATE OF THE PROBLEM AND PROSPECTS IN RUSSIA by T. E. Imaev, A. E. Komlev, R. S. Akchurin

“…More than 300 TAVI procedures have been performed in Russia recently which definitely does not cover the actual needs.…”
Get full text

Through-Wall Detection with LS-SVM under Unknown Wall Characteristics by Fangfang Wang, Yerong Zhang, Huamei Zhang

“…It does not require knowledge of the background scene or rely on accurate modeling and estimation of wall parameters. Then, TWI problem is cast as a regression one and solved by means of least-squares support vector machine (LS-SVM). …”
Get full text

AI in Endoscopic Gastrointestinal Diagnosis: A Systematic Review of Deep Learning and Machine Learning Techniques by Jovita Relasha Lewis, Sameena Pathan, Preetham Kumar, Cifha Crecil Dias

“…Endoscopy is widely regarded as the gold standard for diagnosing and managing digestive disorders, affecting both the upper and lower GI tracts. Endoscopy is performed to uncover biopsy tissues used to check the presence of cancerous or benign cells, Helicobacter pylori (H. pylori) infection, or perform colonoscopy in case of the removal of polyps. …”
Get full text

Bar-driven Streaming Motions Mimic a Massive Bulge in the Inner Milky Way by Junichi Baba

“…To investigate this, we perform three-dimensional hydrodynamic simulations including cooling, heating, star formation, and feedback, under a realistic gravitational potential derived from stellar dynamical models calibrated to observational data. …”
Get full text

Mechanism of Secondary Breakage in the Overlying Strata during Repetitious Mining of an Ultrathick Coal Seam in Design Stage by Hui Li, Dongsheng Zhang, Shuyin Jiang, Gangwei Fan, Mengtang Xu

“…Physical simulations are performed on the movements of the overlying strata during slicing mining of the ultrathick coal seam, revealing the new feature of “break-joint stability-instability-secondary breakage” in the overlying strata. …”
Get full text

Research on the construction and application of pathology knowledge graph by Hong Wei, Xue Liu, Huiling Cao, Weiqi Qin, Qun Ma, Lingling Kong

“…Methods Following Design Science Research Methodology, we built a pathology-specific MKG featuring: (1) Semantic modeling of disease mechanisms (etiology-pathogenesis-morphology-clinical), (2) Cross-modal alignment of digital slides/animations/clinical cases, (3) Embedded metrics (KII/MDA/CCAE) for competency quantification. …”
Get full text

Identification of novel biomarkers related to pathogenesis and treatment of psoriasis based on integrated analysis of weighted gene co-expression network analysis and LASSO. by Chenguang Wang, Zhiyong Liu, Yan He, Yashu Zhang, Shiqi Chen, Yuhao Zhou, Wenqing Yang, Lijun Fan

“…Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression were performed to identify characteristic genes and construct the diagnostic models. …”
Get full text

Identification of Glycolysis-Related Genes in MAFLD and Their Immune Infiltration Implications: A Multi-Omics Analysis with Experimental Validation by Jiawei Chen, Siqi Yang, Diwen Shou, Bo Liu, Shaohan Li, Tongtong Luo, Huiting Chen, Chen Huang, Yongjian Zhou

“…Using four machine learning models, four feature genes were identified, along with their common transcription factors <i>YY1</i> and <i>FOXC1</i>, and the miRNA “<i>hsa-miR-590-3p</i>”. …”
Get full text

Contextual Vulnerability Should Guide Fair Subject Selection in Xenotransplantation Clinical Trials by Gianna Strand

“…Contextual vulnerability is a specific feature of a research environment that increases a subject’s risk of harm. …”
Get full text

Planning and management of logistic cycle by V. N. Kudashkin

“…Application of these methods allows forecasting material flows, creating the integrated management systems and controlling their movements, developing systems of logistic service, to optimize supply stock and solve a number of other tasks.A logistic approach to form a modern system of logistics will save time, reduce costs for the purchase of material resources, their delivery and storage.In modern conditions of the market economy, the considered time parameters of the logistic chain are essential for manufacturing enterprises because their records significantly increase the efficiency of the logistical system.Logistics is equipped with a special complex of economic and mathematical models, the main feature of which is the adaptability, i.e. ability to solve complex optimization problems in the operational mode and in the process of the management of material flows. …”
Get full text

A Unified Deep Learning Framework for Short-Duration Speaker Verification in Adverse Environments by Youngmoon Jung, Yeunju Choi, Hyungjun Lim, Hoirin Kim

“…We combine SV, VAD, and SE models in a unified deep learning framework and jointly train the entire network in an end-to-end manner. …”
Get full text

A Machine Learning Platform for Isoform-Specific Identification and Profiling of Human Carbonic Anhydrase Inhibitors by Lisa Piazza, Miriana Di Stefano, Clarissa Poles, Giulia Bononi, Giulio Poli, Gioele Renzi, Salvatore Galati, Antonio Giordano, Marco Macchia, Fabrizio Carta, Claudiu T. Supuran, Tiziano Tuccinardi

“…The best-performing models for each isoform were applied in a virtual screening campaign for ~2 million compounds. …”
Get full text

Overcoming the resistance of hepatocellular carcinoma to PD-1/PD-L1 inhibitor and the resultant immunosuppression by CD38 siRNA-loaded extracellular vesicles by Jun Deng, Hui Ke

“…Loss-of-function assays were conducted to investigate the biological functions of EVs/siCD38 in HCC cells. Xenograft mouse models were performed for further validation. High CD38 expression was found in HCC. …”
Get full text

Noise Reduction with Recursive Filtering for More Accurate Parameter Identification of Electrochemical Sources and Interfaces by Mitar Simić, Milan Medić, Milan Radovanović, Vladimir Risojević, Patricio Bulić

“…Filtering is embedded in the estimation procedure, while the optimal value of the recursive filter weighting factor is self-tuned based on the proposed search method. The distinguished feature is that the proposed method can process EIS data and perform estimation with filtering without any input from the user. …”
Get full text

Transcriptome signature for multiple biotic and abiotic stress in barley (Hordeum vulgare L.) identifies using machine learning approach by Bahman Panahi

“…Through meticulous data preprocessing, including quality assessment and batch effect correction, we have identified 4311 genes for further analysis. Feature selection was performed using five weighting algorithms, resulting in the prioritization of 400 core genes. …”
Get full text

Machine Learning Enhanced Multimodal Bioelectronics: Advancement Toward Intelligent Healthcare Systems by Myoungjae Oh, Enji Kim, Jakyoung Lee, Inhea Jeong, Eunmin Kim, Joonho Paek, Taekyeong Lee, Dayeon Kim, Seung Hyun An, Sumin Kim, Jung Ah Lim, Jang‐Ung Park

“…Machine learning has emerged as an essential tool for data interpretation and real‐time decision‐making through addressing challenges in broad data integration, feature extraction, and predictive modeling. Implementation of machine learning to multimodal devices extend their capabilities beyond conventional biosensors, performing crossmodal correlation analysis, real‐time anomaly detection, and situation‐dependent feedback. …”
Get full text