Plant Disease Classifier: Detection of Dual-Crop Diseases Using Lightweight 2D CNN Architecture by Hasibul Islam Peyal, Md. Nahiduzzaman, Md. Abu Hanif Pramanik, Md. Khalid Syfullah, Saleh Mohammed Shahriar, Abida Sultana, Mominul Ahsan, Julfikar Haider, Amith Khandakar, Muhammad E. H. Chowdhury

“…The purpose of this work is to categorize 14 classes for both cotton and tomato crops, with 12 diseased classes and two healthy classes using a deep learning-based lightweight 2D CNN architecture and to implement the model in an android application named “Plant Disease Classifier” for smartphone-assisted plant disease diagnosis system, the results of the experiments reveal that the proposed model outperforms the pre-trained models VGG16, VGG19 and InceptionV3 despite having fewer parameters. …”
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Feasibility and Preliminary Effects of Acupuncture for Cognitive Dysfunction in Diverse Cancer Survivors: A Pilot, Randomized, Placebo-Controlled Trial by Xiaotong Li, Kaitlin Lampson, Tim A. Ahles, James C. Root, Q. Susan Li, Yuelin Li, Anam Ahsan, Jun J. Mao, Kevin T. Liou

“…Subjective CRCD was assessed by the Functional Assessment of Cancer Therapy-Cognitive Function—Perceived Cognitive Impairment subscale (FACT-Cog PCI) and objective CRCD was assessed by the Hopkins Verbal Learning Test—Revised (HVLT-R). (3) Results: 32 participants (57.1% of eligible patients) were enrolled. …”
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Establishment and validation of predictive model of ARDS in critically ill patients by Senhao Wei, Hua Zhang, Hao Li, Chao Li, Ziyuan Shen, Yiyuan Yin, Zhukai Cong, Zhaojin Zeng, Qinggang Ge, Dongfeng Li, Xi Zhu

“…This study aimed to observe the incidence of ARDS among high-risk patients and develop and validate an ARDS prediction model using machine learning (ML) techniques based on clinical parameters. …”
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Educating the digital generation: the role of virtual communities by E. M. Kharlanova, N. V. Sivrikova, S. V. Roslyakova, E. G. Chernikova

“…It generally implies being involved in several types of virtual communities, spending moderate amount of time in virtual communities, participating in virtual learning societies. Virtual communities have undeniable educational potential. …”
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Professional Action Competence in Education for Sustainability Among Pre-service Physical Education Teachers: A Mixed Method Approach by Valérian Cece, Thomas Royet, Olivier Vors, Vanessa Lentillon-Kaestner

“…This approach emphases the bodily dimension, learning through action, and affective and sensory aspects. …”
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Musician’s dystonia: a perspective on the strongest evidence towards new prevention and mitigation treatments by Joy Grifoni, Joy Grifoni, Valeria Crispiatico, Anna Castagna, Rosa Maria Converti, Marina Ramella, Angelo Quartarone, Teresa L’Abbate, Teresa L’Abbate, Karolina Armonaite, Karolina Armonaite, Luca Paulon, Luca Paulon, Francescaroberta Panuccio, Franca Tecchio

“…To address these challenges, we propose a novel application of the FeeSyCy principle (feedback-synchrony-plasticity), which emphasizes the pivotal role of feedback in guiding inter-neuronal synchronization and plasticity—the foundation of learning and memory. This model integrates with established literature to form a comprehensive framework for understanding MD as an impaired FeeSyCy-mediated relationship between the individual and their environment, ultimately leading to trauma. …”
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An explainable Bi-LSTM model for winter wheat yield prediction by Abhasha Joshi, Biswajeet Pradhan, Subrata Chakraborty, Subrata Chakraborty, Renuganth Varatharajoo, Abdullah Alamri, Shilpa Gite, Chang-Wook Lee

“…Accurate, reliable and transparent crop yield prediction is crucial for informed decision-making by governments, farmers, and businesses regarding food security as well as agricultural business and management. Deep learning (DL) methods, particularly Long Short-Term Memory networks, have emerged as one of the most widely used architectures in yield prediction studies, providing promising results. …”
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Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling by Jing Zhang, Tingyi Tan, Yuhao Jiang, Congming Tan, Liangliang Hu, Daowen Xiong, Yikang Ding, Guowei Huang, Junjie Qin, Yin Tian

“…Therefore, identifying working memory load is an essential area of research. Deep learning models have demonstrated remarkable potential in identifying the intensity of working memory load. …”
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Differences in Characteristics of Peripartum Patients Who Did and Did Not Require an Upgrade to the Intensive Care Unit: A Propensity Score Matching Study by Jennifer A. Walker, Jerry M. Yang, Saad Pirzada, Manahel Zahid, Samantha Asuncion, Amanda Tuchler, Gillian Cooper, Allison Lankford, Emad Elsamadicy, Quincy K. Tran

“…The Classification And Regression Tree, a machine learning algorithm, was used to identify significant predictors of ICU upgrade. …”
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Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net by Yukang Jiang, Jianying Pan, Ming Yuan, Yanhe Shen, Jin Zhu, Yishen Wang, Yewei Li, Ke Zhang, Qingyun Yu, Huirui Xie, Huiting Li, Xueqin Wang, Yan Luo

“…In this study, we develop a deep learning algorithm based on the lightweight U-Net to segment laser marks from the color fundus photos, which could help indicate a stage or providing valuable auxiliary information for the care of DR patients. …”
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The impact of implementing and sustaining the international guidelines for skin-to-skin contact in the first hour after birth in Uganda by Kajsa Brimdyr, Scovia Nalugo Mbalinda, Anna Blair, Kristin Svensson, Mike Kagawa, Melissa Reyes, Louise Racine Bastarache, Livingstone Kamoga, Edwin Kigozi, Karin Cadwell

“…The PRECESS (Practice Reflection Education and Training Combined with Ethnography for Sustainable Success) methodology provides evidence-based education, process-oriented practice, experiential learning and Interactive Analysis Labs to achieve sustainable best practice. …”
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Early Transcriptional Changes Induced by Wnt/β-Catenin Signaling in Hippocampal Neurons by Eduardo Pérez-Palma, Víctor Andrade, Mario O. Caracci, Bernabé I. Bustos, Camilo Villaman, Matías A. Medina, Miguel E. Ávila, Giorgia D. Ugarte, Giancarlo V. De Ferrari

“…We also studied molecular networks enriched upon Wnt3a activation and detected three highly significant expression modules involved in glycerolipid metabolic process (GO:0046486, p-adjusted = 4.5 × 10−19), learning or memory (GO:0007611, p-adjusted = 4.0 × 10−5), and neurotransmitter secretion (GO:0007269, p-adjusted = 5.3 × 10−12). …”
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Experiencing the risk of overutilising opioids among patients with chronic non-cancer pain in ambulatory care (ERONA): the protocol of an exploratory, randomised controlled trial by Odette Wegwarth, Joerg J Meerpohl, Norbert Donner-Banzhoff, Claudia Spies, Christine Schmucker, Erika Schulte, Edris Nury, Dirk Brockmann, Stefan Wind, Eva Goebel, Wolf-Dieter Ludwig, Ralph Hertwig

“…Experiencing the risk of overutilising opioids among patients with chronic non-cancer pain in ambulatory care (ERONA) is the first-ever conducted trial that aims at investigating the effects of these two modes of learning on individuals’ risk perception and behaviour in the long-term administration of WHO-III opioids in chronic non-cancer pain.Methods and analysis ERONA—an exploratory, randomised controlled online survey intervention trial with two parallel arms—will examine the opioid-associated risk perception and behaviour of four groups involved in the long-term administration of WHO-III opioids: (1) family physicians, (2) physicians specialised in pain therapy, (3) patients with chronic (≥3 months) non-cancer pain and (4) pharmacists who regularly dispense narcotic substances. …”
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Design of INSPIRE: Evaluation of the effectiveness of practice facilitation on implementation of screening, brief interventions, referral to treatment and medication for unhealthy... by Jennifer Bannon, Justin D. Smith, Mark J. Van Ryzin, Megan McHugh, Jennifer Heinrich, Theresa L. Walunas, Abel N. Kho

“…Additionally, we will present practice demographic data, recruitment data and results related to on-line learning module completion. Conclusion: The INSPIRE study will evaluate the ability of primary care clinicians in small practices to participate in practice education programs and implement standard screening and treatment protocols, adapted for documentation in the electronic health record (EHR). …”
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Relationship between stress hyperglycemia ratio and progression of non target coronary lesions: a retrospective cohort study by Shiqi Liu, Ziyang Wu, Gaoliang Yan, Yong Qiao, Yuhan Qin, Dong Wang, Chengchun Tang

“…Logistic regression models, restricted cubic spline analysis, and machine learning algorithms (LightGBM, decision tree, and XGBoost) were utilized to analyse the relationship of stress hyperglycemia ratio and non target lesion progression. …”
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Continuous Speech-Based Fatigue Detection and Transition State Prediction for Air Traffic Controllers by Susmitha Vekkot, Surya Teja Chavali, Charan Tej Kandavalli, Rama Sai Abhishek Podila, Deepa Gupta, Mohammed Zakariah, Yousef Ajami Alotaibi

“…The evaluation was carried out using various learning algorithms such as XGBoost, Adaboost, Random Forest, HistogramGB, and 1D-CNN. …”
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Corticosterone-induced postpartum depression induces depression-like behavior and impairs hippocampal neurogenesis in adolescent offspring via HPA axis and BDNF-mTOR pathway by Hongxiao Xie, Yanning Jiang, Xiumeng Zhang, Xinran Min, Jiuseng Zeng, Li Chen, Nan Zeng, Rong Liu

“…Furthermore, adolescent offspring of PPD mice exhibited depression-like behavior, and learning and memory deficits. These offspring also showed diminished levels of DCX+, decreased levels of synaptic proteins, and reduced dendritic spine density and length in hippocampus. …”
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Harmonizing population health data into OMOP common data model: a demonstration using COVID-19 sero-surveillance data from Nairobi Urban Health and Demographic Surveillance System by Michael Ochola, Sylvia Kiwuwa-Muyingo, Tathagata Bhattacharjee, David Amadi, Maureen Ng’etich, Damazo Kadengye, Henry Owoko, Boniface Igumba, Jay Greenfield, Jim Todd, Agnes Kiragga, Agnes Kiragga

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Is the flipped classroom more effective than the traditional classroom in clinical medical education: a systematic review and meta-analysis by Xu-Ying Shi, Qing Yin, Qiu-Wen Wang, Bing-Rui Lu, Guo-Xiang Li, Shu-Hong Huang, Zhi-Gang Sun

“…This meta-analysis aims to rigorously compare the effects of flipped classroom (FC) and traditional classroom (TC) teaching models on students’ learning in clinical medicine.MethodsWe conducted a systematic search for all randomized and quasi-randomized controlled trials (RCTs/Q-RCTs) comparing traditional classrooms and flipped classrooms in the context of clinical medical education. …”
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Unfolding insights about resilience and its coping strategies by medical academics and healthcare professionals at their workplaces: a thematic qualitative analysis by Salman Yousuf Guraya, Jacqueline Maria Dias, Mohamed Ahmed Eladl, Ayah Mohammad Rajai Rustom, Fatima Audi Saleem Alalawi, Majid Hassan Saleh Alhammadi, Yusra Ahmed Mohammed Ahmed, Amna Ali Omran Taryam Al Shamsi, Sarah Juman Bilalaga, Alfred Nicholson, Hani Malik, Shaista Salman Guraya

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