Identifying Therapeutic Targets and Potential Drugs for Diabetic Retinopathy: Focus on Oxidative Stress and Immune Infiltration by Peng H, Hu Q, Zhang X, Huang J, Luo S, Zhang Y, Jiang B, Sun D

“…Analysis of protein-protein interaction (PPI) networks and machine learning algorithms were used to identify hub genes. Single-gene Gene Set Enrichment Analysis (GSEA) identified biological functions, while nomograms and ROC curves assessed diagnostic potential. …”
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Constructing a Glioblastoma Prognostic Model Related to Fatty Acid Metabolism Using Machine Learning and Identifying F13A1 as a Potential Target by Yushu Liu, Hui Deng, Ping Song, Mengxian Zhang

“…On the basis of 10 kinds of machine learning methods, we used 101 combinations of algorithms to construct prognostic models and obtain the best model. …”
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Teens and opioids postsurgery (TOPS): protocol for a prospective observational study describing associations between sleep deficiency and opioid use following outpatient surgery in... by Tonya Palermo, Jennifer A Rabbitts, Cornelius B Groenewald, Rebecca L Flack, Sophia L Kreider

“…We will apply modern machine learning algorithms to develop and validate models predicting adolescent prescription opioid misuse at 24 months from surgery.Ethics and dissemination This study was approved by Advarra’s Center for Institutional Review Board Intelligence (CIRBI) (Protocol 00072049), which serves as the single IRB of record for this multisite study.…”
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Fc-Binding Cyclopeptide Induces Allostery from Fc to Fab: Revealed Through in Silico Structural Analysis to Anti-Phenobarbital Antibody by Tao Zhou, Huiling Zhang, Xiaoting Yu, Kangliang Pan, Xiaojun Yao, Xing Shen, Hongtao Lei

“…The combination of molecular docking and multiple allosteric site prediction algorithms in these methods identified that the cyclopeptide binds to the interface of heavy chain region-1 (CH₁) in antibody Fab and heavy chain region-2 (CH₂) in antibody Fc. …”
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Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer by Rui Wang, Rui Wang, Guan-Hua Qin, Guan-Hua Qin, Yifei Jiang, Fu-Xiang Chen, Fu-Xiang Chen, Zi-Han Wang, Zi-Han Wang, Lin-Ling Ju, Lin Chen, Da Fu, En-Yu Liu, Su-Qing Zhang, Wei-Hua Cai

“…A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.ResultsOur multiomics analysis revealed that the novel 7-gene model (comprising USP36, KLF5, MT2A, KDM6B, PHLDA1, REL, and DDIT4) accurately predicts patient survival, immunotherapy response, and TME status. …”
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The effect of canine lingual attachments during maxillary arch distalization with clear aligner: a 4D finite element analysis and in vitro simulator study by Bochun Mao, Yajing Tian, Hanzhang Zhou, Yan Gu

“…Method A dual-methodological approach was employed: 1) A four-dimensional finite element model (4D FEM) incorporating automated staging simulation was developed, utilizing iterative computations for long-term tooth movement prediction and thermal expansion algorithms for CA morphology adaptation; 2) An electromechanical orthodontic simulator (OSIM) was implemented for in vitro validation. …”
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The Association of Aortic Stenosis Severity and Symptom Status With Morbidity and Mortality by Matthew D. Solomon, MD, PhD, Alan S. Go, MD, Thomas Leong, MPH, Elisha Garcia, BS, Kathy Le, MPH, Femi Philip, MD, Edward McNulty, MD, Jacob Mishell, MD, Andrew N. Rassi, MD, David C. Lange, MD, Catherine Lee, PhD, Anthony DeMaria, MD, Rick Nishimura, MD, Andrew P. Ambrosy, MD

“…Methods: In this retrospective cohort study from a large, integrated health care system serving >4.5 M individuals, we applied validated natural language processing algorithms to echocardiogram reports to identify physician-assessed AS severity and potential AS-related symptoms (eg, chest pain, syncope, dyspnea, worsening heart failure) via diagnosis codes and natural language processing-applied physician notes. …”
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Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers by Mariah Batool, Oluwafemi Sanumi, Jasna Jankovic

“…This review begins by explaining the fundamental concepts of artificial intelligence and introducing commonly used artificial intelligence-based algorithms in a simplified and clearly comprehensible way, establishing a foundational knowledge base for further discussion. …”
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Kun–Qi birth cohort (KQBC) study for the incidence, aetiology and risk factors of chronic kidney disease in Chinese children: a cohort profile by Yan Ma, Xingdong Chen, Jie Wei, Li Guo, Jian Huang, Li Xie, Lei Chen, Ping Lu, Wei Sun, Wei Shi, Feng Ding, Liping Zhang, Yue Zhang, Linlin Zhang, Rui Song, Yi Huang, Jun Shen, Hong Xu, Zhigang Chen, Qian Shen, Ying Ma, Yiwen Zhang, Hongxia Li, Yihui Zhai, Biao Xu, Xian Zhou, Jun Sun, Bing Wu, Rui Su, Xiaoyan Yu, Jianhong Zhu, Yihong Huang, Ling Gong, Nana Wu, Jingjing Sun, Tianyi Liu, He Yuan, Yiwen Shen, Jiaojiao Liu, Xia Han, Juanjuan Wu, Yinghua Sun, Suxiang Xu, Qinyi Zhang, Na Xie, Jianfeng Ji, Weizhi Mo, Yuanxin Liu, Xinghua Shi, Wanshan Qiu, Jiapeng Mi, Juhua Yan, Minghui Zhu, Pei Feng, Wenqian Sheng, Mingqin Tai, Chunqin Zhou, Qian Jin, Chengyu Gu, Xiujuan Huang, Qinfang Qian, Yixin Cao, Haochen Shen, Ziwen Xue, Yucui Ma, Juan Tian, Yingdan Zhang, Weiyuan Zhuang, Kaiqun Yang, Xiaohui Shen, Yanwei Gu, Beibei Shi, Chenhong Pan, Hongmei Zhou, Xiuchen Zhang, Meihua Tang, Kaixuan Gu, Hongping Liu, Yuanxian Li

“…We aim to develop multimodal risk prediction models by integrating genomics and artificial intelligence algorithms. …”
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Artificial Intelligence in Pediatric Orthopedics: A Comprehensive Review by Andrea Vescio, Gianluca Testa, Marco Sapienza, Filippo Familiari, Michele Mercurio, Giorgio Gasparini, Sergio de Salvatore, Fabrizio Donati, Federico Canavese, Vito Pavone

“…In spinal deformities, models such as support vector machines and convolutional neural networks achieved over 90% accuracy in classification and curve prediction. For developmental dysplasia of the hip, deep learning algorithms demonstrated high diagnostic performance in radiographic interpretation. …”
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Neuromorphic, physics-informed spiking neural network for molecular dynamics by Vuong Van Pham, Temoor Muther, Amirmasoud Kalantari Dahaghi

“…The results indicate that NP-SNN provides a robust Sci-ML framework that can make accurate predictions across diverse scientific molecular applications. …”
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In-Silico discovery of novel cephalosporin antibiotic conformers via ligand-based pharmacophore modelling and de novo molecular design by Rayhan Chowdhury, Samia Akter Saima, Md. Al Amin, Md. Kawsar Habib, Ramisa Binti Mohiuddin, Ali Mohamod Wasaf Hasan, Roksana Khanam, Shahin Mahmud

“…This research aims to predict new compounds using ligand-based pharmacophore models while optimizing existing drugs. …”
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Nondestructive estimation of leaf chlorophyll content in banana based on unmanned aerial vehicle hyperspectral images using image feature combination methods by Weiping Kong, Weiping Kong, Lingling Ma, Huichun Ye, Huichun Ye, Jingjing Wang, Chaojia Nie, Binbin Chen, Xianfeng Zhou, Wenjiang Huang, Zikun Fan

“…We concluded that the nonlinear Gaussian process regression model with the VIs and TFs-PC1 combination selected by maximal information coefficient as input achieved the highest accuracy in LCC prediction for banana, with the highest R2 of 0.776 and lowest RMSE of 2.04. …”
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Application and prospect of artificial intelligence in empowering the operation and managment of oil and gas pipelines by Qi LIAO, Chunying LIU, Jian DU, Hao LAN, Yongtu LIANG, Haoran ZHANG

“…AI methodologies have found extensive application across various research domains, largely focusing on leak detection, corrosion analysis, risk evaluation, identification, prediction, and optimization. The evolution of AI-driven research has transitioned from conventional approaches like neural networks, expert systems, fuzzy logic, and wavelet analysis towards new-generation algorithms, including deep learning, transfer learning, and reinforcement learning. …”
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A comprehensive investigation of the relationship between dietary fatty acid intake and preserved ratio impaired spirometry: multimethodology based on NHANES by Chenyuan Deng, Yu Jiang, Yuechun Lin, Hengrui Liang, Wei Wang, Jianxing He, Ying Huang

“…Subsequently, innovative implementation of the principal component analysis (PCA), Weighted Quantile Sum (WQS) regression, and Bayesian Kernel Machine Regression (BKMR) approaches were employed to assess the joint impact of the various intake of FAs, as well as total saturated, monounsaturated, and polyunsaturated FAs on PRISm. To facilitate the prediction of PRISm, six distinct machine learning algorithms were constructed, followed by the application of SHAP analysis to elucidate the contribution of individual predictors. …”
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Multiplicity dependence of charged-particle intra-jet properties in pp collisions at $$\sqrt{{{\varvec{s}}}}$$ s = 13 TeV by ALICE Collaboration, S. Acharya, D. Adamová, G. Aglieri Rinella, L. Aglietta, M. Agnello, N. Agrawal, Z. Ahammed, S. Ahmad, S. U. Ahn, I. Ahuja, A. Akindinov, M. Al-Turany, D. Aleksandrov, B. Alessandro, H. M. Alfanda, R. Alfaro Molina, B. Ali, A. Alici, N. Alizadehvandchali, A. Alkin, J. Alme, G. Alocco, T. Alt, A. R. Altamura, I. Altsybeev, J. R. Alvarado, M. N. Anaam, C. Andrei, N. Andreou, A. Andronic, E. Andronov, V. Anguelov, F. Antinori, P. Antonioli, N. Apadula, L. Aphecetche, H. Appelshäuser, C. Arata, S. Arcelli, M. Aresti, R. Arnaldi, J. G. M. C. A. Arneiro, I. C. Arsene, M. Arslandok, A. Augustinus, R. Averbeck, M. D. Azmi, H. Baba, A. Badalà, J. Bae, Y. W. Baek, X. Bai, R. Bailhache, Y. Bailung, R. Bala, A. Balbino, A. Baldisseri, B. Balis, D. Banerjee, Z. Banoo, F. Barile, L. Barioglio, M. Barlou, B. Barman, G. G. Barnaföldi, L. S. Barnby, E. Barreau, V. Barret, L. Barreto, C. Bartels, K. Barth, E. Bartsch, N. Bastid, S. Basu, G. Batigne, D. Battistini, B. Batyunya, D. Bauri, J. L. Bazo Alba, I. G. Bearden, C. Beattie, P. Becht, D. Behera, I. Belikov, A. D. C. Bell Hechavarria, F. Bellini, R. Bellwied, S. Belokurova, L. G. E. Beltran, Y. A. V. Beltran, G. Bencedi, S. Beole, Y. Berdnikov, A. Berdnikova, L. Bergmann, M. G. Besoiu, L. Betev, P. P. Bhaduri, A. Bhasin, M. A. Bhat, B. Bhattacharjee, L. Bianchi, N. Bianchi, J. Bielčík, J. Bielčíková, A. P. Bigot, A. Bilandzic, G. Biro, S. Biswas, N. Bize, J. T. Blair, D. Blau, M. B. Blidaru, N. Bluhme, C. Blume, G. Boca, F. Bock, T. Bodova, S. Boi, J. Bok, L. Boldizsár, M. Bombara, P. M. Bond, G. Bonomi, H. Borel, A. Borissov, A. G. Borquez Carcamo, H. Bossi, E. Botta, Y. E. M. Bouziani, L. Bratrud, P. Braun-Munzinger, M. Bregant, M. Broz, G. E. Bruno, M. D. Buckland, D. Budnikov, H. Buesching, S. Bufalino, P. Buhler, N. Burmasov, Z. Buthelezi, A. Bylinkin, S. A. Bysiak, J. C. Cabanillas Noris, M. Cai, H. Caines, A. Caliva, E. Calvo Villar, J. M. M. Camacho, P. Camerini, F. D. M. Canedo, S. L. Cantway, M. Carabas, A. A. Carballo, F. Carnesecchi, R. Caron, L. A. D. Carvalho, J. Castillo Castellanos, F. Catalano, S. Cattaruzzi, C. Ceballos Sanchez, R. Cerri, I. Chakaberia, P. Chakraborty, S. Chandra, S. Chapeland, M. Chartier, S. Chattopadhyay, S. Chattopadhyay, T. Cheng, C. Cheshkov, V. Chibante Barroso, D. D. Chinellato, E. S. Chizzali, J. Cho, S. Cho, P. Chochula, D. Choudhury, P. Christakoglou, C. H. Christensen, P. Christiansen, T. Chujo, M. Ciacco, C. Cicalo, M. R. Ciupek, G. Clai, F. Colamaria, J. S. Colburn, D. Colella, M. Colocci, M. Concas, G. Conesa Balbastre, Z. Conesa del Valle, G. Contin, J. G. Contreras, M. L. Coquet, P. Cortese, M. R. Cosentino, F. Costa, S. Costanza, C. Cot, J. Crkovská, P. Crochet, R. Cruz-Torres, P. Cui, A. Dainese, M. C. Danisch, A. Danu, P. Das, P. Das, S. Das, A. R. Dash, S. Dash, A. De Caro, G. de Cataldo, J. de Cuveland, A. De Falco, D. De Gruttola, N. De Marco, C. De Martin, S. De Pasquale, R. Deb, R. Del Grande, L. Dello Stritto, W. Deng, P. Dhankher, D. Di Bari, A. Di Mauro, B. Diab, R. A. Diaz, T. Dietel, Y. Ding, J. Ditzel, R. Divià, D. U. Dixit, Ø. Djuvsland, U. Dmitrieva, A. Dobrin, B. Dönigus, J. M. Dubinski, A. Dubla, S. Dudi, P. Dupieux, M. Durkac, N. Dzalaiova, T. M. Eder, R. J. Ehlers, F. Eisenhut, R. Ejima, D. Elia, B. Erazmus, F. Ercolessi, B. Espagnon, G. Eulisse, D. Evans, S. Evdokimov, L. Fabbietti, M. Faggin, J. Faivre, F. Fan, W. Fan, A. Fantoni, M. Fasel, A. Feliciello, G. Feofilov, A. Fernández Téllez, L. Ferrandi, M. B. Ferrer, A. Ferrero, C. Ferrero, A. Ferretti, V. J. G. Feuillard, V. Filova, D. Finogeev, F. M. Fionda, E. Flatland, F. Flor, A. N. Flores, S. Foertsch, I. Fokin, S. Fokin, E. Fragiacomo, E. Frajna, U. Fuchs, N. Funicello, C. Furget, A. Furs, T. Fusayasu, J. J. Gaardhøje, M. Gagliardi, A. M. Gago, T. Gahlaut, C. D. Galvan, D. R. Gangadharan, P. Ganoti, C. Garabatos, T. García Chávez, E. Garcia-Solis, C. Gargiulo, P. Gasik, A. Gautam, M. B. Gay Ducati, M. Germain, A. Ghimouz, C. Ghosh, M. Giacalone, G. Gioachin, P. Giubellino, P. Giubilato, A. M. C. Glaenzer, P. Glässel, E. Glimos, D. J. Q. Goh, V. Gonzalez, P. Gordeev, M. Gorgon, K. Goswami, S. Gotovac, V. Grabski, L. K. Graczykowski, E. Grecka, A. Grelli, C. Grigoras, V. Grigoriev, S. Grigoryan, F. Grosa, J. F. Grosse-Oetringhaus, R. Grosso, D. Grund, N. A. Grunwald, G. G. Guardiano, R. Guernane, M. Guilbaud, K. Gulbrandsen, T. Gündem, T. Gunji, W. Guo, A. Gupta, R. Gupta, R. Gupta, K. Gwizdziel, L. Gyulai, C. Hadjidakis, F. U. Haider, S. Haidlova, M. Haldar, H. Hamagaki, A. Hamdi, Y. Han, B. G. Hanley, R. Hannigan, J. Hansen, J. W. Harris, A. Harton, M. V. Hartung, H. Hassan, D. Hatzifotiadou, P. Hauer, L. B. Havener, E. Hellbär, H. Helstrup, M. Hemmer, T. Herman, G. Herrera Corral, F. Herrmann, S. Herrmann, K. F. Hetland, B. Heybeck, H. Hillemanns, B. Hippolyte, F. W. Hoffmann, B. Hofman, G. H. Hong, M. Horst, A. Horzyk, Y. Hou, P. Hristov, P. Huhn, L. M. Huhta, T. J. Humanic, A. Hutson, D. Hutter, M. C. Hwang, R. Ilkaev, H. Ilyas, M. Inaba, G. M. Innocenti, M. Ippolitov, A. Isakov, T. Isidori, M. S. Islam, M. Ivanov, M. Ivanov, V. Ivanov, K. E. Iversen, M. Jablonski, B. Jacak, N. Jacazio, P. M. Jacobs, S. Jadlovska, J. Jadlovsky, S. Jaelani, C. Jahnke, M. J. Jakubowska, M. A. Janik, T. Janson, S. Ji, S. Jia, A. A. P. Jimenez, F. Jonas, D. M. Jones, J. M. Jowett, J. Jung, M. Jung, A. Junique, A. Jusko, J. Kaewjai, P. Kalinak, A. S. Kalteyer, A. Kalweit, A. Karasu Uysal, D. Karatovic, O. Karavichev, T. Karavicheva, P. Karczmarczyk, E. Karpechev, M. J. Karwowska, U. Kebschull, R. Keidel, D. L. D. Keijdener, M. Keil, B. Ketzer, S. S. Khade, A. M. Khan, S. Khan, A. Khanzadeev, Y. Kharlov, A. Khatun, A. Khuntia, Z. Khuranova, B. Kileng, B. Kim, C. Kim, D. J. Kim, E. J. Kim, J. Kim, J. Kim, J. Kim, M. Kim, S. Kim, T. Kim, K. Kimura, S. Kirsch, I. Kisel, S. Kiselev, A. Kisiel, J. P. Kitowski, J. L. Klay, J. Klein, S. Klein, C. Klein-Bösing, M. Kleiner, T. Klemenz, A. Kluge, C. Kobdaj, T. Kollegger, A. Kondratyev, N. Kondratyeva, J. Konig, S. A. Konigstorfer, P. J. Konopka, G. Kornakov, M. Korwieser, S. D. Koryciak, A. Kotliarov, N. Kovacic, V. Kovalenko, M. Kowalski, V. Kozhuharov, I. Králik, A. Kravčáková, L. Krcal, M. Krivda, F. Krizek, K. Krizkova Gajdosova, M. Kroesen, M. Krüger, D. M. Krupova, E. Kryshen, V. Kučera, C. Kuhn, P. G. Kuijer, T. Kumaoka, D. Kumar, L. Kumar, N. Kumar, S. Kumar, S. Kundu, P. Kurashvili, A. Kurepin, A. B. Kurepin, A. Kuryakin, S. Kushpil, V. Kuskov, M. Kutyla, M. J. Kweon, Y. Kwon, S. L. La Pointe, P. La Rocca, A. Lakrathok, M. Lamanna, A. R. Landou, R. Langoy, P. Larionov, E. Laudi, L. Lautner, R. Lavicka, R. Lea, H. Lee, I. Legrand, G. Legras, J. Lehrbach, T. M. Lelek, R. C. Lemmon, I. León Monzón, M. M. Lesch, E. D. Lesser, P. Lévai, X. Li, B. E. Liang-gilman, J. Lien, R. Lietava, I. Likmeta, B. Lim, S. H. Lim, V. Lindenstruth, A. Lindner, C. Lippmann, D. H. Liu, J. Liu, G. S. S. Liveraro, I. M. Lofnes, C. Loizides, S. Lokos, J. Lömker, P. Loncar, X. Lopez, E. López Torres, P. Lu, F. V. Lugo, J. R. Luhder, M. Lunardon, G. Luparello, Y. G. Ma, M. Mager, A. Maire, E. M. Majerz, M. V. Makariev, M. Malaev, G. Malfattore, N. M. Malik, Q. W. Malik, S. K. Malik, L. Malinina, D. Mallick, N. Mallick, G. Mandaglio, S. K. Mandal, V. Manko, F. Manso, V. Manzari, Y. Mao, R. W. Marcjan, G. V. Margagliotti, A. Margotti, A. Marín, C. Markert, P. Martinengo, M. I. Martínez, G. Martínez García, M. P. P. Martins, S. Masciocchi, M. Masera, A. Masoni, L. Massacrier, O. Massen, A. Mastroserio, O. Matonoha, S. Mattiazzo, A. Matyja, C. Mayer, A. L. Mazuecos, F. Mazzaschi, M. Mazzilli, J. E. Mdhluli, Y. Melikyan, A. Menchaca-Rocha, J. E. M. Mendez, E. Meninno, A. S. Menon, M. Meres, Y. Miake, L. Micheletti, D. L. Mihaylov, K. Mikhaylov, D. Miśkowiec, A. Modak, B. Mohanty, M. Mohisin Khan, M. A. Molander, S. Monira, C. Mordasini, D. A. Moreira De Godoy, I. Morozov, A. Morsch, T. Mrnjavac, V. Muccifora, S. Muhuri, J. D. Mulligan, A. Mulliri, M. G. Munhoz, R. H. Munzer, H. Murakami, S. Murray, L. Musa, J. Musinsky, J. W. Myrcha, B. Naik, A. I. Nambrath, B. K. Nandi, R. Nania, E. Nappi, A. F. Nassirpour, A. Nath, C. Nattrass, M. N. Naydenov, A. Neagu, A. Negru, E. Nekrasova, L. Nellen, R. Nepeivoda, S. Nese, G. Neskovic, N. Nicassio, B. S. Nielsen, E. G. Nielsen, S. Nikolaev, S. Nikulin, V. Nikulin, F. Noferini, S. Noh, P. Nomokonov, J. Norman, N. Novitzky, P. Nowakowski, A. Nyanin, J. Nystrand, S. Oh, A. Ohlson, V. A. Okorokov, J. Oleniacz, A. Onnerstad, C. Oppedisano, A. Ortiz Velasquez, J. Otwinowski, M. Oya, K. Oyama, Y. Pachmayer, S. Padhan, D. Pagano, G. Paić, S. Paisano-Guzmán, A. Palasciano, S. Panebianco, H. Park, H. Park, J. Park, J. E. Parkkila, Y. Patley, B. Paul, M. M. D. M. Paulino, H. Pei, T. Peitzmann, X. Peng, M. Pennisi, S. Perciballi, D. Peresunko, G. M. Perez, Y. Pestov, V. Petrov, M. Petrovici, R. P. Pezzi, S. Piano, M. Pikna, P. Pillot, O. Pinazza, L. Pinsky, C. Pinto, S. Pisano, M. Płoskoń, M. Planinic, F. Pliquett, M. G. Poghosyan, B. Polichtchouk, S. Politano, N. Poljak, A. Pop, S. Porteboeuf-Houssais, V. Pozdniakov, I. Y. Pozos, K. K. Pradhan, S. K. Prasad, S. Prasad, R. Preghenella, F. Prino, C. A. Pruneau, I. Pshenichnov, M. Puccio, S. Pucillo, Z. Pugelova, S. Qiu, L. Quaglia, S. Ragoni, A. Rai, A. Rakotozafindrabe, L. Ramello, F. Rami, T. A. Rancien, M. Rasa, S. S. Räsänen, R. Rath, M. P. Rauch, I. Ravasenga, K. F. Read, C. Reckziegel, A. R. Redelbach, K. Redlich, C. A. Reetz, H. D. Regules-Medel, A. Rehman, F. Reidt, H. A. Reme-Ness, Z. Rescakova, K. Reygers, A. Riabov, V. Riabov, R. Ricci, M. Richter, A. A. Riedel, W. Riegler, A. G. Riffero, C. Ristea, M. V. Rodriguez, M. Rodríguez Cahuantzi, S. A. Rodríguez Ramírez, K. Røed, R. Rogalev, E. Rogochaya, T. S. Rogoschinski, D. Rohr, D. Röhrich, P. F. Rojas, S. Rojas Torres, P. S. Rokita, G. Romanenko, F. Ronchetti, A. Rosano, E. D. Rosas, K. Roslon, A. Rossi, A. Roy, S. Roy, N. Rubini, D. Ruggiano, R. Rui, P. G. Russek, R. Russo, A. Rustamov, E. Ryabinkin, Y. Ryabov, A. Rybicki, H. Rytkonen, J. Ryu, W. Rzesa, O. A. M. Saarimaki, S. Sadhu, S. Sadovsky, J. Saetre, K. Šafařík, P. Saha, S. K. Saha, S. Saha, B. Sahoo, R. Sahoo, S. Sahoo, D. Sahu, P. K. Sahu, J. Saini, K. Sajdakova, S. Sakai, M. P. Salvan, S. Sambyal, D. Samitz, I. Sanna, T. B. Saramela, D. Sarkar, P. Sarma, V. Sarritzu, V. M. Sarti, M. H. P. Sas, S. Sawan, E. Scapparone, J. Schambach, H. S. Scheid, C. Schiaua, R. Schicker, F. Schlepper, A. Schmah, C. Schmidt, H. R. Schmidt, M. O. Schmidt, M. Schmidt, N. V. Schmidt, A. R. Schmier, R. Schotter, A. Schröter, J. Schukraft, K. Schweda, G. Scioli, E. Scomparin, J. E. Seger, Y. Sekiguchi, D. Sekihata, M. Selina, I. Selyuzhenkov, S. Senyukov, J. J. Seo, D. Serebryakov, L. Serkin, L. Šerkšnytė, A. Sevcenco, T. J. Shaba, A. Shabetai, R. Shahoyan, A. Shangaraev, B. Sharma, D. Sharma, H. Sharma, M. Sharma, S. Sharma, S. Sharma, U. Sharma, A. Shatat, O. Sheibani, K. Shigaki, M. Shimomura, J. Shin, S. Shirinkin, Q. Shou, Y. Sibiriak, S. Siddhanta, T. Siemiarczuk, T. F. Silva, D. Silvermyr, T. Simantathammakul, R. Simeonov, B. Singh, B. Singh, K. Singh, R. Singh, R. Singh, R. Singh, S. Singh, V. K. Singh, V. Singhal, T. Sinha, B. Sitar, M. Sitta, T. B. Skaali, G. Skorodumovs, M. Slupecki, N. Smirnov, R. J. M. Snellings, E. H. Solheim, J. Song, C. Sonnabend, J. M. Sonneveld, F. Soramel, A. B. Soto-hernandez, R. Spijkers, I. Sputowska, J. Staa, J. Stachel, I. Stan, P. J. Steffanic, S. F. Stiefelmaier, D. Stocco, I. Storehaug, P. Stratmann, S. Strazzi, A. Sturniolo, C. P. Stylianidis, A. A. P. Suaide, C. Suire, M. Sukhanov, M. Suljic, R. Sultanov, V. Sumberia, S. Sumowidagdo, I. Szarka, M. Szymkowski, S. F. Taghavi, G. Taillepied, J. Takahashi, G. J. Tambave, S. Tang, Z. Tang, J. D. Tapia Takaki, N. Tapus, L. A. 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“…The results are compared with predictions of QCD-inspired event generators, PYTHIA 8 with Monash 2013 tune and EPOS LHC. …”
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Estimation of the water content of needles under stress by Erannis jacobsoni Djak. via Sentinel-2 satellite remote sensing by Jiaze Guo, Xiaojun Huang, Xiaojun Huang, Xiaojun Huang, Debao Zhou, Junsheng Zhang, Gang Bao, Gang Bao, Siqin Tong, Siqin Tong, Yuhai Bao, Yuhai Bao, Dashzebeg Ganbat, Dorjsuren Altanchimeg, Davaadorj Enkhnasan, Mungunkhuyag Ariunaa

“…Needle leaf water content exhibits a clear response to these changes and is highly sensitive in reflecting the degree of tree damage.MethodsIn this work, we combine vegetation indices with machine learning algorithms to estimate the water content of needles at a large scale. …”
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Distribution of age at natural menopause, age at menarche, menstrual cycle length, height and BMI in BRCA1 and BRCA2 pathogenic variant carriers and non-carriers: results from EMBR... by Nasim Mavaddat, Debra Frost, Emily Zhao, Daniel R. Barnes, Munaza Ahmed, Julian Barwell, Angela F. Brady, Paul Brennan, Hector Conti, Jackie Cook, Harriet Copeland, Rosemarie Davidson, Alan Donaldson, Emma Douglas, David Gallagher, Rachel Hart, Louise Izatt, Zoe Kemp, Fiona Lalloo, Zosia Miedzybrodzka, Patrick J. Morrison, Jennie E. Murray, Alex Murray, Hannah Musgrave, Claire Searle, Lucy Side, Katie Snape, Vishakha Tripathi, Lisa Walker, Stephanie Archer, D. Gareth Evans, Marc Tischkowitz, Antonis C. Antoniou, Douglas F. Easton

“…Conclusion Information on the distribution of cancer risk factors in PV carriers is needed for incorporating these factors into multifactorial cancer risk prediction algorithms. Contrary to previous reports, we found no evidence that BRCA1 or BRCA2 PV are associated with hormonal or anthropometric factors, except for a weak association with height. …”
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The research on enhancing LA estimation accuracy across domains for small sample data based on data augmentation and data transfer integration optimization system by Ai-Dong Wang, Rui-Jie Li, Xiang-Qian Feng, Zi-Qiu Li, Wei-Yuan Hong, Hua-Xing Wu, Dan-Ying Wang, Song Chen

“…Objective: In this research, our goal is to develop a novel framework to mitigate prediction biases in LA caused by sample limitations and data heterogeneity. …”
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DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.ResultsTotally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. Through GSEA analysis, it was found that the four genes were closely related to ribosome, cell cycle, ubiquitin-mediated proteolysis. …”
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