A time series algorithm to predict surgery in neonatal necrotizing enterocolitis by Cheng Cui, Ling Qiu, Ling Li, Fei-Long Chen, Xiao Liu, Huan Sun, Xiao-Chen Liu, Lei Bao, Lu-Quan Li

“…Methods Data from 791 neonates diagnosed with NEC are gathered from the Neonatal Intensive Care Unit (NICU), encompassing 35 selected features. Infants are categorized into those requiring surgical intervention (n = 257) and those managed medically (n = 534) based on the Mod-Bell criteria. …”
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MCR-PFNet: A novel InSAR interferometric phase filtering method for complex noise and large gradient deformations by Shuai Wang, Yu Chen, Kaiwen Ding, Yandong Gao, Kun Tan, Peijun Du

“…To further enhance the generalization capability of MCR-PFNet, additive Gaussian noise and local phase jumps were introduced into the training dataset, and the MCR-PFNet model was trained with a custom-designed periodic phase loss function. …”
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Predicting cellular adaptation proteins dependent on eIF2α regulation under stress conditions: Physiological and pathophysiological implications in neuronal function by Víctor Herrera-Fernández, Hugo Fanlo-Ucar, Patrick Gohl, Melisa Ece Zeylan, Simge Senyuz, Ozlem Keskin, Attila Gursoy, Rubén Vicente, Baldo Oliva, Francisco J. Muñoz

“…We applied translation efficiency-based filters to existing databases of p-eIF2α-dependent translation to obtain reliable training and testing datasets. …”
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Intratumoral and peritumoral CT radiomics in predicting anaplastic lymphoma kinase mutations and survival in patients with lung adenocarcinoma: a multicenter study by Weiyue Chen, Guihan Lin, Ye Feng, Yongjun Chen, Yanjun Li, Jianbin Li, Weibo Mao, Yang Jing, Chunli Kong, Yumin Hu, Minjiang Chen, Shuiwei Xia, Chenying Lu, Jianfei Tu, Jiansong Ji

“…Methods We retrospectively collected data from 505 eligible patients with lung adenocarcinoma from four hospitals (training and external validation sets 1–3). The CT-based radiomics features were extracted separately from the gross tumor volume (GTV) and GTV incorporating peritumoral 3-, 6-, 9-, 12-, and 15-mm regions (GPTV3, GPTV6, GPTV9, GPTV12, and GPTV15), and screened the most relevant features to construct radiomics models to predict ALK (+). …”
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The study of Effective and Efficient Organizational Change Processes with an Emphasis on Islamic Management by Fazel Hajizadeh Ebrahimi, Hamid Moakedi

“…Discussion and ResultsOrganizations emphasize that management based on goals and especially management based on instructions are concepts of the past. …”
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Machine Learning Prediction Model of Waitlist Outcomes in Patients with Primary Sclerosing Cholangitis by Xun Zhao, MD, Maryam Naghibzadeh, MD, Yingji Sun, MSc, Arya Rahmani, BSc, Leslie Lilly, MD, Nazia Selzner, MD, PhD, Cynthia Tsien, MD, MPH, Elmar Jaeckel, MD, Mary Pressley Vyas, Rahul Krishnan, PhD, Gideon Hirschfield, MD, PhD, MB, Mamatha Bhat, MD, PhD

“…It achieved a C-index of 0.868 (SD 0.020) and 0.771 (SD 0.085) on the SRTR and UHN test data, respectively. Training a separate RSF model using the UHN data with PSC-specific achieved a C-index of 0.91. …”
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Polyamine metabolism related gene index prediction of prognosis and immunotherapy response in breast cancer by Ruoya Wang, Shouliang Cai, Qing Gao, Yidong Chen, Xue Han, Fangjian Shang, Chunyan Liang, Guolian Zhu, Bo Chen

“…A nomogram combining risk scores and clinicopathological features was constructed. Decision Curve Analysis (DCA) demonstrated that the model could guide clinical treatment strategies. …”
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Development and validation of a multimodal automatic interictal epileptiform discharge detection model: a prospective multi-center study by Nan Lin, Lian Li, Weifang Gao, Peng Hu, Gonglin Yuan, Heyang Sun, Fang Qi, Lin Wang, Shengsong Wang, Zi Liang, Haibo He, Yisu Dong, Zaifen Gao, Xiaoqiu Shao, Liying Cui, Qiang Lu

“…The automated IED detection model was constructed using deep learning based on video and electroencephalogram (EEG) features. …”
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Ecolodge Success Model: Antecedents and Consequences by Zohreh Dehdashti Shahrokh, Mohammadreza Karimi Alavijeh, Molood Esfahani

“…The information sources of this step included articles published in international scientific databases which have been selected based on the criteria of meta-synthesis process. …”
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Radiomic Profiling of Tumor Thrombus for Predicting Recurrence in Renal Cell Carcinoma by Zine-Eddine Khene, Isamu Tachibana, Raj Bhanvadia, Ivan Trevino, Prajwal Sharma, William Graber, Nicholas Bingham, Theophile Bertail, Raphael Fleury, Kris Gaston, Solomon L. Woldu, Karim Bensalah, Yair Lotan, Vitaly Margulis

“…Preoperative contrast-enhanced computed tomography images were used to extract radiomic features from the primary tumor and TT. Features were selected using least absolute shrinkage and selection operator (LASSO) Cox regression and incorporated into predictive models. …”
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SELF-CORRECTION OF TEACHERS’ PROFESSIONAL BURNOUT by T. F. Orekhova, T. G. Neretina, Т. V. Kruzhilina, P Ledeur

“…The methodology of the research is based on system, personal-oriented and activity approaches. …”
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Immunophenotype-guided interpretable radiomics model for predicting neoadjuvant anti-PD-1 response in stage III–IV d-MMR/MSI-H colorectal cancer by Xuan Zhang, Zhenhui Li, Yiwen Zhang, Yanli Li, Xi Zhong, Wenjing Jiang, Xiaobo Chen, Zaiyi Liu, Liebin Huang, Caixia Zhang, Lizhu Liu, Ruimin You, Xiaoping Yi

“…Furthermore, patients from center II (n=19) and center III (n=22) receiving neoadjuvant immunotherapy were assigned to training and validation cohorts. Through a two-stage selection process, immunophenotype-associated radiomic features were initially identified, followed by immunotherapy response-related radiomics features that were further identified. …”
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Multiple automated machine-learning prediction models for postoperative reintubation in patients with acute aortic dissection: a multicenter cohort study by Shuyu Wen, Chao Zhang, Junwei Zhang, Ying Zhou, Yin Xu, Minghui Xie, Jinchi Zhang, Zhu Zeng, Long Wu, Weihua Qiao, Xingjian Hu, Xingjian Hu, Nianguo Dong, Nianguo Dong

“…SHAP values demonstrated the effects of individual features on the overall model. Finally, a web calculator was developed based on XGBoost model for the clinical use.ConclusionWe have developed and validated a high-performing risk prediction model for postoperative reintubation in patients with AAD. …”
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GPS-pPLM: A Language Model for Prediction of Prokaryotic Phosphorylation Sites by Chi Zhang, Dachao Tang, Cheng Han, Yujie Gou, Miaomiao Chen, Xinhe Huang, Dan Liu, Miaoying Zhao, Leming Xiao, Qiang Xiao, Di Peng, Yu Xue

“…For model training, two deep learning methods, a transformer and a deep neural network, were employed, and a total of 10 sequence features and contextual features were integrated. …”
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A Global–Local Attention Model for 3D Point Cloud Segmentation in Intraoral Scanning: A Novel Approach by Haiwen Chen, Yuan Qin, Baoning Liu, Houzhuo Luo, Ruyue Qiang, Yanni Meng, Zhi Liu, Yanning Ma, Zuolin Jin

“…Training and evaluation were conducted using internal and external orthodontic datasets. …”
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Libraries architecture and design as a subject of teaching at the library-information faculty by M. N. Kolesnikova, Ye. V. Bakhtina, V. P. Timonin

“…Students are familiarized with basic terminology and theoretical approaches of architectural design in the training process. Architectural features of libraries’ buildings, main architectural styles, professional biographies of eminent architects are examined in the course. …”
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Leveraging a foundation model zoo for cell similarity search in oncological microscopy across devices by Gabriel Kalweit, Gabriel Kalweit, Anusha Klett, Paula Silvestrini, Paula Silvestrini, Jens Rahnfeld, Jens Rahnfeld, Mehdi Naouar, Mehdi Naouar, Yannick Vogt, Yannick Vogt, Diana Infante, Diana Infante, Rebecca Berger, Jesús Duque-Afonso, Tanja Nicole Hartmann, Marie Follo, Marie Follo, Elitsa Bodurova-Spassova, Elitsa Bodurova-Spassova, Michael Lübbert, Michael Lübbert, Roland Mertelsmann, Roland Mertelsmann, Roland Mertelsmann, Joschka Boedecker, Joschka Boedecker, Joschka Boedecker, Evelyn Ullrich, Evelyn Ullrich, Evelyn Ullrich, Maria Kalweit, Maria Kalweit

“…This especially holds true for recognizing specific cell types and states in response to treatments.ObjectiveWe aim to develop an unsupervised approach using general vision foundation models trained on diverse and extensive imaging datasets to extract rich visual features for cell-analysis across devices, including both stained and unstained live cells. …”
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Virtual reality solution to promote adapted physical activity in older adults: outcomes from VR2Care project exploratory study by Vincenzo De Luca, Malak Qbilat, Alessandra Cuomo, Antonio Bianco, Francesca Cesaroni, Chiara Lanari, Ad van Berlo, Telma Mota, Lucia Pannese, Michael Brandstötter, Matthieu Arendse, Vania Mota, Willeke van Staalduinen, Hugo Paredes, Guido Iaccarino, Guido Iaccarino, Maddalena Illario

“…The data collection is a mix of investigator site data entry and users’ self-reported data through the solutions or through online and paper-based means. Data were collected at baseline and after a follow-up of 6 weeks. …”
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Linking Electrocardiogram and Echocardiogram: Comparing Classical Machine Learning and Deep Learning Neural Networks for the Detection of Regional Wall Motion Abnormalities by Shantanu M. Joshi, Hana R. Shaik, Shivam Rai Sharma, Philip Strong, Uma Srivatsa, Imo Ebong, Hyoyoung Jeong, Chen-Nee Chuah, Lihong Mo

“…Historically, electrocardiogram (ECG) datasets have been created based on physicians’ interpretation of the ECG, which may introduce human biases and errors. …”
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A Knowledge-Enhanced Object Detection for Sustainable Agriculture by Youcef Djenouri, Ahmed Nabil Belbachir, Tomasz Michalak, Asma Belhadi, Gautam Srivastava

“…Our framework uses a knowledge base of visual features and loss values from multiple deep-learning models during the training phase to choose the most effective model for the testing phase. …”
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