Comprehensive analyses of telomerase component DKC1 and its association with clinical, molecular and immune landscapes in uterine corpus endometrial carcinoma by Chenxi Sun, Xu Liu, Tiantian Liu, Chenliu Fan, Yang Jiang, Binggen Li, Huiyang Yuan, Chengyun Zheng, Dawei Xu

“…Higher DKC1 expression predicted shorter patient overall and progression-free survival. …”
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Enrollment and Retention Outcomes from the Veterans Health Administration for a Remote Digital Health Study: Multisite Observational Study by Jaclyn A Pagliaro, Lauren K Wash, Ka Ly, Jenny Mathew, Alison Leibowitz, Ryan Cabrera, Jolie B Wormwood, Varsha G Vimalananda

“…Device and survey data were combined with VA clinical and utilization data to develop a predictive algorithm for clinical decompensation. The geographic distribution of enrolled patients was mapped by county. …”
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Effect of Faricimab versus Aflibercept on Hyperreflective Foci in Patients with Diabetic Macular Edema from the YOSEMITE/RHINE Trials by Usha Chakravarthy, FRCOphth, PhD, Varun Chaudhary, MD, FRCS(C), Srinivas R. Sadda, MD, Colin S. Tan, FRCOphth, FRCSEd (Ophth), Stela Vujosevic, MD, PhD, Sascha Fauser, MD, Kara Gibson, PhD, Carl Glittenberg, MD, Nancy Holekamp, MD, FASRS, Ben Lanza, PhD, Andreas Maunz, PhD, Jeffrey R. Willis, MD, PhD, Rishi P. Singh, MD

“…Methods: A deep learning–based algorithm was used to automatically quantify HRF in spectral-domain OCT volume scans from YOSEMITE/RHINE. …”
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The azimuthal correlation between the leading jet and the scattered lepton in deep inelastic scattering at HERA by ZEUS Collaboration, I. Abt, R. Aggarwal, V. Aushev, O. Behnke, A. Bertolin, I. Bloch, I. Brock, N. H. Brook, R. Brugnera, A. Bruni, P. J. Bussey, A. Caldwell, C. D. Catterall, J. Chwastowski, J. Ciborowski, R. Ciesielski, A. M. Cooper-Sarkar, M. Corradi, R. K. Dementiev, S. Dusini, J. Ferrando, B. Foster, E. Gallo, D. Gangadharan, A. Garfagnini, A. Geiser, G. Grzelak, C. Gwenlan, D. Hochman, N. Z. Jomhari, I. Kadenko, U. Karshon, P. Kaur, R. Klanner, U. Klein, I. A. Korzhavina, N. Kovalchuk, M. Kuze, B. B. Levchenko, A. Levy, B. Löhr, E. Lohrmann, A. Longhin, F. Lorkowski, E. Lunghi, I. Makarenko, J. Malka, S. Masciocchi, K. Nagano, J. D. Nam, Yu. Onishchuk, E. Paul, I. Pidhurskyi, A. Polini, M. Przybycień, A. Quintero, M. Ruspa, U. Schneekloth, T. Schörner-Sadenius, I. Selyuzhenkov, M. Shchedrolosiev, L. M. Shcheglova, N. Sherrill, I. O. Skillicorn, W. Słomiński, A. Solano, L. Stanco, N. Stefaniuk, B. Surrow, K. Tokushuku, O. Turkot, T. Tymieniecka, A. Verbytskyi, W. A. T. Wan Abdullah, K. Wichmann, M. Wing, S. Yamada, Y. Yamazaki, A. F. Żarnecki, O. Zenaiev

“…Jets were reconstructed using the $$k_\textrm{T}$$ k T algorithm with the radius parameter $$R = 1$$ R = 1 . …”
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Groundwater–CO₂ emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data by L. M. van der Poel, L. V. Bataille, B. Kruijt, W. Franssen, W. Jans, J. Biermann, A. Rietman, A. J. V. Buzacott, Y. van der Velde, R. Boelens, R. W. A. Hutjes

“…Using spatiotemporal data, we train and optimize a boosted regression tree (BRT) machine learning algorithm to predict immediate CO<span class="inline-formula">₂</span> fluxes and use Shapley values and various simulations to interpret the model's outputs. …”
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Control Directo de Potencia Predictivo Robusto con Sincronismo Intrínseco by J.R. Fischer, S.A. González, I. Carugati, M.G. Judewicz, D.O. Carrica

“…In this work a novel positive sequence detector is also implemented which ensures the elimination of perturbation at the output current due to harmonics content. The control algorithm has an intrinsic synchronization scheme which avoids an additional three-phase PLL. …”
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Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis by Sebastian Sanduleanu, Koray Ersahin, Johannes Bremm, Narmin Talibova, Tim Damer, Merve Erdogan, Jonathan Kottlors, Lukas Goertz, Christiane Bruns, David Maintz, Nuran Abdullayev

“…Overall agreement between the GPT-3.5 output and the reference standard was assessed by means of inter-observer kappa values as well as accuracy, sensitivity, specificity, and positive and negative predictive values with the “Caret” and “irr” packages. …”
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Conv1D-LSTM: Autonomous Breast Cancer Detection Using a One-Dimensional Convolutional Neural Network With Long Short-Term Memory by Mitanshi Rastogi, Meenu Vijarania, Neha Goel, Akshat Agrawal, Cresantus N. Biamba, Celestine Iwendi

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Spatial and Temporal Variability of Rainfall Erosivity in the Niyang River Basin by Qingqin Bai, Lei Wang, Yangzong Cidan

“…This study enhances the understanding of rainfall erosive forces in the plateau region and provides a scientific basis for predicting soil loss, developing effective erosion control measures, and ensuring sustainable land use.…”
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Exploring the relationship between sepsis and Golgi apparatus dysfunction: bioinformatics insights and diagnostic marker discovery by Wanli Ma, Xinyi Liu, Ran Yu, Jiannan Song, Lina Hou, Ying Guo, Hongwei Wu, Dandan Feng, Qi Zhou, Haibo Li

“…A diagnostic model constructed using five pivotal genes (B3GNT5, FUT11, MAN1C1, ST6GAL1, and C1GALT1C1) exhibited predictive accuracy, with AUC values exceeding 0.96 for all genes. …”
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Clinical and Epidemiological Manifestations of Ixodic Tick-Borne Borreliosis Foci in the Tomsk region by O. V. Voronkova, E. N. Ilyinskikh, A. A. Rudikov, T. N. Poltoratskaya, I. E. Esimova, L. V. Lukashova, M. R. Karpova

“…The study of the genotypic diversity of pathogenic borrelias in relation to the species diversity of vectors, the analysis of the clinical manifestations of different etiological variants of tick-borne borreliosis (mono- and mixed infections), as well as the development of an algorithm for differential diagnostic search and a model for predicting the outcomes of the infectious process in tick-borne borreliosis and mixed infections are priority directions of problem-oriented scientific research in Tomsk region.…”
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Evaluating the Pattern of Forest Cover Changes Using Fuzzy Object-Oriented Techniques (Case Study: Kaleybar County) by Majid Pishnamaz Ahmadi, keyvan Mohammadzadeh, seyed Asadolah Hejazi

“…The results were then calculated and finalized in ArcGIS software after accurate evaluation. 3-Results and Discussion In this research, images in 200 scales sorted consecutively from 1 to 200 were segmented using low to high multi-functional hierarchical segmentation approach with shape coefficient of 0.4 and compression coefficient of 0.5 in order to construct LV graphs and the appropriate scales for image segmentation were determined using the plotted graphs. By predicting the appropriate scale for creating image units using the algorithm (ESP), the scale of 15 with coefficients of shape and compression 0.3 and 0.5 respectively was scaled as the appropriate scale for extraction of Landsat 5 and 7 satellite images, and the scale of 130 with shape coefficient of 0.4 and compression coefficient of 0.5 was chosen as the appropriate scale for Landsat 8 satellite OLI images. …”
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miR-146a deficiency in hematopoietic cells is not involved in the development of atherosclerosis. by Alberto Del Monte, Ana B Arroyo, María J Andrés-Manzano, Nuria García-Barberá, María S Caleprico, Vicente Vicente, Vanessa Roldán, Rocío González-Conejero, Constantino Martínez, Vicente Andrés

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Crosstalk between the serine/threonine kinase StkP and the response regulator ComE controls the stress response and intracellular survival of Streptococcus pneumoniae. by Germán E Piñas, Nicolás M Reinoso-Vizcaino, Nubia Y Yandar Barahona, Paulo R Cortes, Rosario Duran, Chandan Badapanda, Ankita Rathore, Dario R Bichara, Melina B Cian, Nadia B Olivero, Daniel R Perez, José Echenique

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Adverse events in the neonatal intensive care unit identified by triggers by Fabiana Bragança Albanese, Deise de Souza Ventura, Deise de Souza Ventura, Maurício Wesley Perroud, Maurício Wesley Perroud, Rafael Nogueira de Souza, Mariana Vieira Morau, Marília Berlofa Visacri, Marília Berlofa Visacri, Patricia Moriel, Patricia Moriel

“…AE severity was assessed using the Neonatal Adverse Event Severity Scale (NAESS) and the World Health Organization (WHO) classification, while ADR causality was evaluated using the WHO criteria and the algorithm proposed by Du et at. Risk factors such as gestational age, birth weight, and length of hospital stay were also analyzed.ResultsA total of 249 triggers identified 168 confirmed AEs, resulting in a Positive Predictive Value (PPV) of 67.5%. …”
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YOLOv8-Based Estimation of Estrus in Sows Through Reproductive Organ Swelling Analysis Using a Single Camera by Iyad Almadani, Mohammed Abuhussein, Aaron L. Robinson

“…In the first stage, we calculated the Mean Squared Error (MSE) between the ground truth keypoints of the labia distance and the distance between the predicted keypoints, and we performed the same calculation for the distance between the clitoris and perineum. …”
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Mapping Soil Available Nitrogen Using Crop-Specific Growth Information and Remote Sensing by Xinle Zhang, Yihan Ma, Shinai Ma, Chuan Qin, Yiang Wang, Huanjun Liu, Lu Chen, Xiaomeng Zhu

“…These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. …”
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Assessment of prostate cancer aggressiveness through the combined analysis of prostate MRI and 2.5D deep learning models by Yalei Wang, Yuqing Xin, Baoqi Zhang, Fuqiang Pan, Xu Li, Manman Zhang, Yushan Yuan, Lei Zhang, Peiqi Ma, Bo Guan, Yang Zhang

“…Models were constructed using the LightGBM algorithm: a radiomic feature model, a deep learning feature model, and a combined model integrating radiomic and deep learning features. …”
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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

“…We hypothesize that this better conspicuity leads to high-quality annotation (HAQ), enhancing deep learning (DL) algorithm detection of BMs on MPRAGE images. Methods Retrospective contrast-enhanced (gadobutrol 0.1 mmol/kg) SPACE and MPRAGE data of 157 patients with BM were used, either annotated on MPRAGE resulting in normal annotation quality (NAQ) or on coregistered SPACE resulting in HAQ. …”
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Novel bacterial cluster “Prevotella, Bacteroides and Suterella” associated with mortality in Mexican patients with acute-on-chronic liver failure (ACLF) and clinical utility of sys... by Paula A. Castaño Jiménez, Tonatiuh A. Baltazar-Díaz, Rodrigo Hernández-Basulto, Mayra P. Padilla-Sánchez, Ksenia K. Kravtchenko, Roxana García-Salcido, María T. Tapia-De la paz, Kevin J. Arellano-Arteaga, Luz A. González-Hernández, Miriam R. Bueno-Topete

“…Quality filtering, which includes removal of chimeras and non-biological sequences, was performed using the DADA2 algorithm. Resulting ASVs were taxonomically assigned through a self-trained naïve Bayesian classifier, against the SILVA database. …”
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