Prediction of recurrence after surgery for pituitary adenoma using machine learning- based models: systematic review and meta-analysis by Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Behnaz Niroomand, Nasira Faizi, Pooya Eini, Mohammad Amin Habibi, Alireza Mohseni, Mohammadmahdi Sabahi, Abdulrahman Albakr, Michael Karsy, Hamid Borghei-Razavi

“…Conclusion AI-based models show strong predictive power for recurrence in both functional and non-functional pituitary adenomas, with an average accuracy above 80%. …”
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Integration of Immunometabolic Composite Indices and Machine Learning for Diabetic Retinopathy Risk Stratification: Insights from NHANES 2011 – 2020 by Cui Xuehao, MD, PhD, Wen Dejia, MD, Li Xiaorong, PhD

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In-situ grown carbon nanotubes on waste glass powder: Resource-efficient preparation and machine-learning based dispersion evaluation by Tingquan Shao, Hongrui Zhang, Zhenjun Wang, Tonghuan Zhang, Xu Xu, Haibao Zhang, Xiaofeng Wang, Haoyan Guo

“…Carbon nanotubes (CNTs) are the strongest candidates as reinforcing and functionalizing materials in civil engineering. However, CNTs have severely limited the application of carbon nanomaterials in civil engineering materials because of high-cost preparation and inhomogeneous dispersion. …”
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A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification. by Loïc Omnes, Eric Angel, Pierre Bartet, François Radvanyi, Fariza Tahi

“…The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. …”
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Driver Attribute Filling for Genes in Interaction Network via Modularity Subspace-Based Concept Learning from Small Samples by Fei Xie, Jianing Xi, Qun Duan

“…The aberrations of a gene can influence it and the functions of its neighbour genes in gene interaction network, leading to the development of carcinogenesis of normal cells. …”
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Case Report: Pituitary hyperplasia secondary to long-neglected severe primary hypothyroidism: a case of misdiagnosis and lessons learned by Sina Du, Jiawei Sun, Chunnuo Wang, Yanghuan Zhou, Haiying Zhao, Wei Li, Jianwei Chen

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MixtureMetrics: A comprehensive package to develop additive numerical features to describe complex materials for machine learning modeling by Rahil Ashtari Mahini, Gerardo Casanola-Martin, Simone A. Ludwig, Bakhtiyor Rasulev

“…We developed and improved mixture descriptors based on 12 different mixture functions grouped into three main categories: property-based descriptors, concentration-weighted descriptors, and deviation-combination descriptors. …”
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Deep learning-aided segmentation combined with finite element analysis reveals a more natural biomechanic of dinosaur fossil by Lijia Zhang, Zixiong Cao, Qi Zhao

“…Our approach provides a practical strategy for studying the biomechanics, functional morphology, and taxonomy of extinct species.…”
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Bio particle swarm optimization and reinforcement learning algorithm for path planning of automated guided vehicles in dynamic industrial environments by Shiwei Lin, Jianguo Wang, Bomin Huang, Xiaoying Kong, Hongwu Yang

“…The Q-learning method is implemented for local path planning to avoid moving obstacles and combines with the proposed BPSO for the safe operations of automated guided vehicles. …”
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The Effect of a Working Memory Intervention Package on the Working Memory Performance of Primary School Students with Specific Learning Disabilities by Mehmet Okur, Veysel Aksoy

“…In conclusion, the WM intervention package was significant in improving the WM performance of students with SLDs, indicating that such interventions have significant potential for enhancing cognitive functions and memory. These findings highlight the critical role of WM interventions in contributing to the cognitive development of students with learning difficulties.…”
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Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients by Zhe Zhang, Yao Xiao, Jun Liu, Feng Xiao, Jie Zeng, Hong Zhu, Wei Tu, Hua Guo

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Discordance between a deep learning model and clinical-grade variant pathogenicity classification in a rare disease cohort by Sek Won Kong, In-Hee Lee, Lauren V. Collen, Michael Field, Arjun K. Manrai, Scott B. Snapper, Kenneth D. Mandl

“…Our investigation of AlphaMissense (AM)5, a deep learning tool for predicting the potential functional impact of missense variants and assessing gene essentiality, reveals limitations in identifying pathogenic missense variants over 45 rare diseases, including very early onset inflammatory bowel disease. …”
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Identification of nanomolar adenosine A2A receptor ligands using reinforcement learning and structure-based drug design by Morgan Thomas, Pierre G. Matricon, Robert J. Gillespie, Maja Napiórkowska, Hannah Neale, Jonathan S. Mason, Jason Brown, Kaan Harwood, Charlotte Fieldhouse, Nigel A. Swain, Tian Geng, Noel M. O’Boyle, Francesca Deflorian, Andreas Bender, Chris de Graaf

“…Abstract Generative chemical language models (CLMs) have demonstrated success in learning language-based molecular representations for de novo drug design. …”
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ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning by Wouter Saelens, Olga Pushkarev, Bart Deplancke

“…Abstract Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. …”
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Photonic–Electronic Modulated a-IGZO Synaptic Transistor with High Linearity Conductance Modulation and Energy-Efficient Multimodal Learning by Zhidong Hou, Jinrong Shen, Yiming Zhong, Dongping Wu

“…The device demonstrates biological synaptic functionalities, including excitatory/inhibitory post-synaptic currents (EPSCs/IPSCs) and spike-timing-dependent plasticity, while achieving excellent conductance modulation characteristics (nonlinearity of 0.0095/−0.0115 and asymmetric ratio of 0.247) and successfully implementing Pavlovian associative learning paradigms. …”
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The Effect Of Emotional Intelligence And Woring Memory And Selective Attention On Reading Comprehension Of Students With Learning Difficulties In Lebanese Inclusive Schools by Shaza Ismail, Mounah Geha

“… Emotional intelligence (EI) and executive functions (EFs) are constructs deemed vital for academic achievement. …”
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Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning by Ching-Chien Chen, Robert J. Appleton, Saswat Mishra, Kat Nykiel, Alejandro Strachan

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Floating Point Multiple-Precision Fused Multiply Add Architecture for Deep Learning Computation on Artix 7 FPGA Board by VINOTHENI, M. S., JAWAHAR SENTHIL KUMAR, V.

“…The propounded architecture is implemented using Xilinx Vivado 2022.2 on Artix-7 FPGA Basys 3 board that demonstrates the functionality and attainment. From the observed results, it is inferred that the proposed framework offers 50% area curtail with the conventional FMA architecture, while still meeting the precision requirements for deep learning tasks. …”
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Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks by Ayan Sar, Tanupriya Choudhury, Tanmay Sarkar, Ketan Kotecha

“…This article introduces PapayaFreshNet, an innovative deep learning network designed for the automatic categorization of papaya freshness through picture analysis. …”
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DK-Port: construction and validation of port autonomous driving simulation environment based on large language models and reinforcement learning by LOU Yunjie, AI Mingfei, ZHUANG Shujie, YU Hai, WANG Xin, TENG Chu, WANG Jiangcheng, SHEN Tianyu, HAO Kunkun, CUI Wen

“…This paper incorporates survey-based expert input, employing zero-shot prompting and chain-of-thought reasoning techniques to enable large language models to assist in designing reward functions efficiently. Then, human-machine mixed-traffic scenarios are constructed using complex road data, and adversarial driver models are trained with the PPO reinforcement learning algorithm to identify safety vulnerabilities in autonomous driving systems. …”
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