The Fate of the Interstellar Medium in Early-type Galaxies. V. Active Galactic Nucleus Feedback from Optical Spectral Classification by Oleh Ryzhov, Michał J. Michałowski, J. Nadolny, J. Hjorth, A. Leśniewska, M. Solar, P. Nowaczyk, C. Gall, T. T. Takeuchi

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HPGCN: A graph convolutional network-based prediction model for herbal heat/cold properties by Qikai Niu, Jing’ai Wang, Hongtao Li, Lin Tong, Haiyu Xu, Weina Zhang, Ziling Zeng, Sihong Liu, Wenjing Zong, Siqi Zhang, Siwei Tian, Huamin Zhang, Bing Li

“…Compared to previous machine learning algorithms, the HPGCN obtained optimal classification prediction results for ACC, Recall, Precision, F1, and AUC indicators by 5-fold cross-validation on the training and test sets. …”
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Decoding the m6A epitranscriptomic landscape for biotechnological applications using a direct RNA sequencing approach by Chuwei Liu, Heng Liang, Arabella H. Wan, Min Xiao, Lei Sun, Yiling Yu, Shijia Yan, Yuan Deng, Ruonian Liu, Juan Fang, Zhi Wang, Weiling He, Guohui Wan

“…Here, we introduce pum6a, an innovative attention-based framework that integrates positive and unlabeled multi-instance learning (MIL) to address the challenges of incomplete labeling and missing read-level annotations. …”
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Lipids as key biomarkers in unravelling the pathophysiology of obesity-related metabolic dysregulation by Anis Adibah Osman, Siok-Fong Chin, Lay-Kek Teh, Noraidatulakma Abdullah, Nor Azian Abdul Murad, Rahman Jamal

“…The predictive model underwent evaluation across four machine learning algorithms consistently demonstrated the highest predictive accuracy of 0.821, aligning with the findings from the classical logistic regression statistical model. …”
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Effect of Electroacupuncture on Hippocampal Functional Activity and Molecular Expression Profile in Rats with Vascular Cognitive Impairment by WANG Zeyu, DING Yanyi, DAI Yaling, YANG Minguang, HUANG Jia, ZHANG Shenghang

“…Barnes maze test was used to evaluate the spatial learning and memory ability of rats; Y maze test was used to evaluate the spatial working memory ability of rats; small animal 7.0 T magnetic resonance resting brain functional imaging was used to analyze the changes of regional homogeneity (ReHo) of hippocampal functional activity; Agilent mRNA expression microarray was used to analyze the differential gene expression of the whole hippocampus genome.Results① Behavioral analysis results: compared with the sham operation group, the Barnes maze escape latency of the model group increased significantly (<italic>P</italic>&lt;0.05), the target quadrant duration percentage decreased significantly (<italic>P</italic>&lt;0.05), and the Y maze alternation rate decreased significantly (<italic>P</italic>&lt;0.05); compared with the model group, the Barnes maze escape latency of the electroacupuncture group decreased significantly (<italic>P</italic>&lt;0.05), the target quadrant duration percentage and the Y maze alternation rate increased significantly (<italic>P</italic>&lt;0.05). ② ReHo changes results: compared with the sham operation group, the ReHo of functional activitives of the bilateral prefrontal lobes, hippocampus and other brain regions in the model group decreased significantly (<italic>P</italic>&lt;0.005); compared with the model group, the ReHo of functional activities of the bilateral hippocampus, prefrontal lobes and piriform cortex in the model group increased significantly (<italic>P</italic>&lt;0.005). ③ Genomics results: a total of 705 genes with 2-fold differential expression of hippocampal mRNA between the model group and the sham operation group (<italic>P</italic>&lt;0.05), of which 195 genes (such as Sytl1, Trpv6, Klhl14, Npsr1, Myh3, Galnt3, Nr4a1, Bmp3, Egr2, etc.) were down-regulated, of which 510 genes (such as Insl6, Efcab1, Akr1b1, Tagln, Glrb, Akr1c13, Kcne1L, Ubap1, etc.) were up-regulated. …”
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Effect of Repetitive Transcranial Magnetic Stimulation on Patients with Post-Stroke Cognitive Impairment by Zheyi YU, Weiming ZHANG

“…Before and after treatment, the cognitive function scores of the two groups were compared, the changes of mini-mental state examination (MMSE) and Montreal cognitive function (MoCA) scores were compared, the activities of daily living (ADL) and auditory verbal learning test (AVLT) were observed. ELISA method was used to observe the changes of brain-derived neurotrophic factor (BDNF), vascular endothelial growth factor (VEGF), interleukin-6(IL-6) and high-sensitivity C-reactive protein (hs-CRP) in the two groups after treatment.Results:There were no significant differences in the scores of MoCA, including orientation (ORT), visual space and executive function (EF), naming (NAM), memory (MEM), attention (ATT), language (LANG) and abstract ability (ABS) before treatment between the two groups (<italic>P</italic>&gt; 0.05). …”
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Risk factors and prediction model of breast cancer-related lymphoedema in a Chinese cancer centre: a prospective cohort study protocol by Yue Wang, Xin Li, Ying Wang, Hongmei Zhao, Qian Lu, Yujie Zhou, Liyuan Zhang, Aomei Shen, Jingru Bian, Wanmin Qiang, Jingming Ye, Hongmeng Zhao, Yubei Huang, Zhongning Zhang, Peipei Wu

“…Traditional COX regression analysis and seven common survival analysis machine learning algorithms (COX, CARST, RSF, GBSM, XGBS, SSVM and SANN) will be employed for model construction and validation.Ethics and dissemination The study protocol was approved by the Biomedical Ethics Committee of Peking University (IRB00001052-21124) and the Research Ethics Committee of Tianjin Medical University Cancer Institute and Hospital (bc2023013). …”
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improving Pain mAnagement for childreN and young people attendeD by Ambulance (PANDA): protocol for a realist review. [version 3; peer review: 2 approved, 1 approved with reservati... by Paul Leighton, Georgette Eaton, Georgie Nicholls, Kacper Sumera, Marishona Ortega, Jessica Hodgson, Michael Baliousis, Aloysius Niroshan Siriwardena, Despina Laparidou, Bill Lord, Sarah Redsell, Gregory Adam Whitley, Tatiana Bujor

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Predicting hepatocellular carcinoma outcomes and immune therapy response with ATP-dependent chromatin remodeling-related genes, highlighting MORF4L1 as a promising target by Chao Xu, Litao Liang, Guoqing Liu, Yanzhi Feng, Bin Xu, Deming Zhu, Wenbo Jia, Jinyi Wang, Wenhu Zhao, Xiangyu Ling, Yongping Zhou, Wenzhou Ding, Lianbao Kong

“…We utilized data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), applying machine learning algorithms to develop a prognostic model based on ACRRGs’ expression. …”
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Automated Quantification of Retinopathy of Prematurity Stage via Ultrawidefield OCT by Spencer S. Burt, BA, Aaron S. Coyner, PhD, Elizabeth V. Roti, BS, Yakub Bayhaqi, PhD, John Jackson, MD, Mani K. Woodward, MS, Shuibin Ni, PhD, Susan R. Ostmo, MS, Guangru Liang, BS, Yali Jia, PhD, David Huang, MD, Michael F. Chiang, MD, Benjamin K. Young, MD, Yifan Jian, PhD, John Peter Campbell, MD

“…This study evaluates whether the volume of anomalous NVT (ANVTV), defined as abnormal tissue protruding from the regular contour of the retina, can be measured automatically using deep learning to develop quantitative OCT-based biomarkers in ROP. …”
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Sequence of episodic memory-related behavioral and brain-imaging abnormalities in type 2 diabetes by Bo Hu, Ying Yu, Xin-Wen Yu, Min-Hua Ni, Yan-Yan Cui, Xin-Yu Cao, Ai-Li Yang, Yu-Xin Jin, Sheng-Ru Liang, Si-Ning Li, Pan Dai, Ke Wu, Lin-Feng Yan, Bin Gao, Guang-Bin Cui

“…The California Verbal Learning Test, Montreal cognitive assessment, and Stroop color word test was used to assess the episodic memory, general cognitive function, and executive function. …”
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Sexual and reproductive health awareness and practices among adolescents and adults in a rural farming community in Baja California, Mexico: a quantitative and qualitative cross-se... by Cristina Espinosa da Silva, Margarita Santibanez, Adrienne RS Lee, Lorena S Pacheco, Stephanie Brodine, Miguel A Fraga, Taylor B Desmarais, Noe C Crespo, Javier Martínez Hernandez, Marianne McKennett, Richard S Garfein

“…Most believed that children should learn about SRH by age 10–15 years, and 94% felt that parents should deliver such education. …”
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Association between estimated glucose disposal rate and cardiovascular diseases in patients with diabetes or prediabetes: a cross-sectional study by Jinhao Liao, Linjie Wang, Lian Duan, Fengying Gong, Huijuan Zhu, Hui Pan, Hongbo Yang

“…Methods 10,690 respondents with diabetes and prediabetes from the NHANES 1999–2016 were enrolled in the study. Three machine learning methods (SVM-RFE, XGBoost, and Boruta algorithms) were employed to select the most critical variables. …”
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Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.) by Zhu Yang, Zhu Yang, Wenjie Kan, Wenjie Kan, Ziqi Wang, Caiguo Tang, Yuan Cheng, Yuan Cheng, Dacheng Wang, Dacheng Wang, Yameng Gao, Lifang Wu, Lifang Wu

“…Additionally, the least absolute shrinkage and selection operator (LASSO) regression algorithm in machine learning was used to screen transcription factors such as bHLH, WRKY, and MYB that influenced the expression of TaAkPHY genes. …”
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Anticholinergic burden and behavioral and psychological symptoms in older patients with cognitive impairment by Sabrina Pistorio, Gianluca Scotto di Tella, Vittoria Canzanella, Raffaella Merenda, Gianluigi Cuomo, Paola Iacotucci, Vincenzo Carnovale, Leonardo Bencivenga, Maria Vargas, Annalaura Manes, Mauro Cataldi, Giuseppe Rengo, Giuseppe Rengo, Michela Zanetti, Grazia Daniela Femminella, Grazia Daniela Femminella

“…The cholinergic system plays an important role in learning processes, memory, and emotions regulation. …”
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ChromaFold predicts the 3D contact map from single-cell chromatin accessibility by Vianne R. Gao, Rui Yang, Arnav Das, Renhe Luo, Hanzhi Luo, Dylan R. McNally, Ioannis Karagiannidis, Martin A. Rivas, Zhong-Min Wang, Darko Barisic, Alireza Karbalayghareh, Wilfred Wong, Yingqian A. Zhan, Christopher R. Chin, William S. Noble, Jeff A. Bilmes, Effie Apostolou, Michael G. Kharas, Wendy Béguelin, Aaron D. Viny, Danwei Huangfu, Alexander Y. Rudensky, Ari M. Melnick, Christina S. Leslie

“…We therefore present ChromaFold, a deep learning model that predicts 3D contact maps, including regulatory interactions, from single-cell ATAC sequencing (scATAC-seq) data alone. …”
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Sequencing Silicates in the Spitzer Infrared Spectrograph Debris Disk Catalog. I. Methodology for Unsupervised Clustering by Cicero X. Lu, Tushar Mittal, Christine H. Chen, Alexis Y. Li, Kadin Worthen, B. A. Sargent, Carey M. Lisse, G. C. Sloan, Dean C. Hines, Dan M. Watson, Isabel Rebollido, Bin B. Ren, Joel D. Green

“…This study introduces CLustering UnsupErvised with Sequencer (CLUES), a novel, nonparametric, fully interpretable machine learning spectral analysis tool designed to analyze and classify the spectral data of debris disks. …”
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Pengukuran Kapabilitas Tata Kelola TI Sistem Informasi Tiras dan Transaksi Bahan Ajar Universitas Terbuka Menggunakan Cobit 5 by Denisha Trihapningsari, Dewi Agushinta R., Lintang Yuniar Banowosari

“…Abstract The Open University (UT) teaching material service implements the Information System for Learning Materials and Transactions (SITTA) which in the process encountered problems related to the operation and optimization of Information Technology. …”
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Timber and carbon sequestration potential of Chinese forests under different forest management scenarios by Hui-Ling Tian, Jian-Hua Zhu, Xiang-Dong Lei, Xin-Yun Chen, Li-Xiong Zeng, Zun-Ji Jian, Fu-Hua Li, Wen-Fa Xiao

“…This study utilised the national forest inventory (NFI) data to construct a model of forest growth and consumption using a machine learning algorithm (i.e. random forest), identified suitable areas for future forest expansion by integrating multi-source data, and set up three future forest management scenarios: business as usual (BAU), enhanced policy scenario (EPS) and maximum potential scenario (MPS). …”
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Regional-scale precision mapping of cotton suitability using UAV and satellite data in arid environments by Jianqiang He, Yonglin Jia, Yi Li, Asim Biswas, Hao Feng, Qiang Yu, Shufang Wu, Guang Yang, Kadambot.H.M. Siddique

“…Six advanced machine learning methods, including Random Forest (RF), were used alongside the ratio mean method to effectively upscale soil water and salt content models from the field to the regional level. …”
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