Control Directo de Potencia Predictivo Robusto con Sincronismo Intrínseco by J.R. Fischer, S.A. González, I. Carugati, M.G. Judewicz, D.O. Carrica

“…Palabras clave: Control Directo de Potencia, Control de Corriente Predictivo Robusto, Inversor de Tensión Controlado en Corriente, Distorsión Armónica., Keywords: Deadbeat Predictive Current Control, Grid-Tied Three-Phase Voltage Source Inverter, Direct Power Control.…”
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Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change by Robson Mateus Freitas Silveira, Angela Maria de Vasconcelos, Concepta McManus, Luiz Paulo Fávero, Iran José Oliveira da Silva

“…Applying the random forest method to classify predictions of adaptive responses based on climatic variables showed that all thermoregulatory, hormonal, biochemical, and hematological responses are important, except for urea and T₃ concentrations, which had negative importance values. …”
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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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Recommendations for All-Round Newborns and Infants Hearing Screening in Russian Federation by S. S. Chibisova, G. S. Tufatulin, L. S. Namazova-Baranova, I. V. Koroleva, E. R. Tsygankova, T. G. Markova, N. N. Volodin, G. A. Tavartkiladze

“…The prevalence rate of these factors in Russian population according to the literature and official statistics was studied. Evaluation of predictive significance of risk factors was based on retrospective data in the cohort of 393 children born in 2012 (280 with hearing loss, 113 healthy) examined in National Research Centre for Audiology and Hearing Rehabilitation at the age of 0–4 years. …”
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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

“…Model classification performance in the test set was evaluated using the area under the receiver operating characteristic curve (AUC).ResultsIn the test set, the nomogram demonstrated the highest predictive ability for prostate cancer aggressiveness (AUC = 0.919, 95% CI: 0.8107–1.0000), with a sensitivity of 0.966 and specificity of 0.833. …”
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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

“…Multiple DL methods were developed with NAQ or HAQ using either SPACE or MRPAGE images and evaluated on their detection performance using positive predictive value (PPV), sensitivity, and F1 score and on their delineation performance using volumetric Dice similarity coefficient, PPV, and sensitivity on one internal and four additional test datasets (660 patients). …”
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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

“…Furthermore, α and β diversity analyses, relative abundances, and ANCOM-BC compositional analysis were performed in the QIIME2 package. Predictive values and associations were performed using ROC curves and Spearman correlations, respectively. …”
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REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised tria... by Lily G Bessette, Elad Yom-Tov, Niteesh K Choudhry, Julie C Lauffenburger, Nancy Haff, Marie E McDonnell, Constance P Fontanet, Ellen S Sears, Erin Kim, Kaitlin Hanken, Renee A Barlev, Punam A Keller, J Joseph Buckley

“…The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. …”
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Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment by Rahul Kumar, Kiran Marla, Kyle Sporn, Phani Paladugu, Akshay Khanna, Chirag Gowda, Alex Ngo, Ethan Waisberg, Ram Jagadeesan, Alireza Tavakkoli

“…Artificial intelligence and machine learning algorithms now automate image interpretation, predict clinical outcomes, and enhance clinical decision support, complementing conventional clinical evaluations. …”
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Biological and prognostic insights into the prostaglandin D2 signaling axis in lung adenocarcinoma by Qiang Liu, Huiguo Chen, Dongfang Tang, Huibiao Zhang, Shaogeng Chen, Yiran Meng, Boying Zheng, Fei Liu, Jing Zhou, Wen Zhang

“…Furthermore, we found that a risk model developed based on this signaling axis could predict responses to immune therapy in hot and cold tumors, suggesting potential drugs that may benefit low-risk patients. …”
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Identifying bellwether sewershed sites for sustainable disease surveillance in Bengaluru, India: a longitudinal studyResearch in context by Rebecca Fern Daniel, Subash K. Kannan, Namrta Daroch, Sutharsan Ganesan, Farhina Mozaffer, Vishwanath Srikantaiah, Lingadahalli Subrahmanya Shashidhara, Rakesh Mishra, Farah Ishtiaq

“…This was achieved by integrating established metrics for wastewater analysis, calculating sample-to-sample percentage rate of change, and applying algorithms to differentiate signal from noise, thereby validating factors contributing to the precision and reliability of outbreak predictions. …”
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MicroRNA-125a-5p plays a role as a tumor suppressor in lung carcinoma cells by directly targeting STAT3 by Lou Zhong, Siyuan Sun, Jiahai Shi, Fei Cao, Xiao Han, Zhong Chen

“…The downregulation of miR-125a-5p provided very contrasting results. Computational algorithms predicted that the STAT3 is a target of miR-125a-5p. …”
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Role of ARRB1 in prognosis and immunotherapy: A Pan-Cancer analysis by Yingquan Ye, Yingquan Ye, Haili Jiang, Haili Jiang, Yue Wu, Yue Wu, Gaoxiang Wang, Gaoxiang Wang, Yi Huang, Yi Huang, Weijie Sun, Mei Zhang, Mei Zhang

“…ESTIMATE and CIBERSORT algorithms were performed to assess immune infiltration. …”
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Construction and validation of renal cell carcinoma tumor cell differentiation-related prognostic classification (RCC-TCDC): an integrated bioinformatic analysis and clinical study by Yifan Liu, Keqin Dong, Yuntao Yao, Bingnan Lu, Lei Wang, Guo Ji, Haoyu Zhang, Zihui Zhao, Xinyue Yang, Runzhi Huang, Wang Zhou, Xiuwu Pan, Xingang Cui

“…In addition, we performed correlation analysis and Cmap algorithm for regulatory network establishment and candidate inhibitor prediction. …”
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Identification and validation of key biomarkers associated with immune and oxidative stress for preeclampsia by WGCNA and machine learning by Tiantian Yu, Tiantian Yu, Tiantian Yu, Guiying Wang, Guiying Wang, Guiying Wang, Xia Xu, Xia Xu, Xia Xu, Jianying Yan, Jianying Yan, Jianying Yan

“…Additionally, we constructed a miRNA - gene regulatory network for the identified diagnostic genes and predicted potential candidate drugs. In the final step, we validated the significant hub gene using independent external datasets, the hypoxia model of the HTR-8/SVneo cell line, and human placental tissue samples.ResultsAt last, leptin (LEP) was identified as a core gene through screening and was found to be upregulated. …”
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Improving early epidemiological assessment of emerging Aedes-transmitted epidemics using historical data. by Julien Riou, Chiara Poletto, Pierre-Yves Boëlle

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Primary health care data-based early warning system for dengue outbreaks: a nationwide case study in BrazilResearch in context by Rejane Santos-Silva, Pilar Tavares Veras Florentino, Thiago Cerqueira-Silva, Vinicius de Araújo Oliveira, Juracy Bertoldo Junior, George C.G. Barbosa, Gerson O. Penna, Viviane S. Boaventura, Pablo I. Pereira Ramos, Manoel Barral-Netto, Izabel Marcilio

“…We assessed EWS timeliness, sensitivity, and positive predictive value (PPV) against fixed-incidence dengue outbreak thresholds. …”
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A prognostic model of 8-T/B cell receptor-related signatures for hepatocellular carcinoma by Xuan Zuo, Hui Li, Shi Xie, Mengfen Shi, Yujuan Guan, Huiyuan Liu, Rong Yan, Anqi Zheng, Xueying Li, Jiabang Liu, Yifan Gan, Haiyan Shi, Keng Chen, Shijie Jia, Guanmei Chen, Min Liao, Zhanhui Wang, Yanyan Han, Baolin Liao

“…Conclusions Together, our study screened a TCR/BCR-related signature prognostic model, which might turn into a beneficial and practical tool to solve the perplexities of the treatment, prognosis prediction and management for HCC patients.…”
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Circulating biomarkers associated with pediatric sickle cell disease by Cecilia Elorm Lekpor, Cecilia Elorm Lekpor, Cecilia Elorm Lekpor, Felix Abekah Botchway, Adel Driss, Alaijah Bashi, Afua D. Abrahams, Kwadwo Asamoah Kusi, Godfred Futagbi, Ernest Alema-Mensah, William Agbozo, Wesley Solomon, Adriana Harbuzariu, Andrew A. Adjei, Jonathan K. Stiles

“…We conclude that a predictive algorithm based on these biomarkers could be developed and validated through longitudinal assessment of within-genotype differences and correlation of the data with disease severity or outcomes. …”
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