Atypical cristae in bundle sheath mitochondria in leaves of Atriplex tatarica (Chenopodiaceae s. str. / Amaranthaceae s. l.) by Fediuk O., Bilyavska N., Akimov Yu., Zolotareva O.

“…Electron microscopic analysis of leaf ultrastructure patterns of Atriplex tatarica (Chenopodiaceae s. str. / Amaranthaceae s. l.) revealed a prominent feature in mitochondria in bundle sheath cells (BSCs). …”
Get full text

Liver diseases (<i>Review of the Seventeenth Russian Gastroenterological Week proceedings, October, 10–12, 2011, Moscow</i>) by I. V. Mayev, A. N. Kazyulin, Yu. A. Kucheryavy, Ye. A. Mayevskaya

Get full text

Artificial intelligence analysis in cyber domain: A review by Liguo Zhao, Derong Zhu, Wasswa Shafik, S Mojtaba Matinkhah, Zubair Ahmad, Lule Sharif, Alisa Craig

“…All the data sets consisted of a minute trait total derived from a sparkling file. The attack patterns that were not anonymized or were indicative of the vulnerability of cyber-attack were denoted with yellow. …”
Get full text

Characteristics and atmospheric drivers of large‐scale agrometeorologically relevant dry spells in sub‐seasonal to seasonal timescales over Zimbabwe by Gibbon I. T. Masukwedza, Victoria L. Boult, Melissa Lazenby, Martin C. Todd

“…Furthermore, LARDS exhibit overarching anticyclonic conditions impeding vertical cloud development with notable changes in the key local large‐scale mean climatic features influencing Southern Africa's weather. …”
Get full text

Advancing sedimentation modeling in large reservoir systems: Insights from multi-scale process coupling and machine learning by Yuning Tan, Huaixiang Liu, Yongjun Lu, Zhili Wang, Wenjie Li

“…Random Forest outperformed XGBoost, Support Vector Regression, and Artificial Neural Network, demonstrating the most robust performance and the clearest feature attribution. Our climate scenario simulations revealed uneven sedimentation patterns and projected declining sedimentation rates over the next 30 years. …”
Get full text

Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction by Raja Ayu Mahessya, Dian Eka Putra, Rostam Ahmad Efendi, Rayendra, Rozi Meri, Riyan Ikhbal Salam, Dedi Mardianto, Ikhsan, Ismael, Arif Rizki Marsa

“…This study demonstrates the capacity of decision tree models to extract clinically meaningful patterns from small, imbalanced surgical datasets that elude conventional statistical approaches.…”
Get full text

Predicting female football outcomes by machine learning: behavioural analysis of goals as high stress events by Aratz Olaizola, Ibai Errekagorri, Elsa Fernández, Julen Castellano, John Suckling, Karmele Lopez-de-Ipina

“…We applied a comprehensive approach that integrates spatiotemporal and behavioural data during the transitional period following goals focusing on team dynamics, including chaotic and collective behavioural analysis with entropy and fractality, spatial area, movement trajectories, and locomotor patterns. Several well-established ML models and feature extraction techniques were deployed with overall good performance of greater than 70% accuracy, with some specific methodology combinations have superior performance. …”
Get full text

Improving ICESat-2 photon classification and tree height estimation using Moran's I and machine learning by Mei-Kuei Lu, Sorin Popescu, Lonesome Malambo

“…Random Forest models were developed and compared, with one model incorporating Moran's I to capture spatial patterns. …”
Get full text

Cognitive constraints and lexicogrammatical variability in ASD: from diagnostic discriminators to intervention strategies by Sumi Kato, Sumi Kato, Kazuaki Hanawa

“…To clarify which linguistic variables contribute to this differentiation, the analysis focused on identifying key syntactic features associated with ASD-specific patterns of lexicogrammatical choices.MethodsThis study used the Tag Linear Model, developed in prior work, which enables identification of specific lexicogrammatical discriminators. …”
Get full text

Generation of diverse biological forms through combinatorial interactions between tissue polarity and growth. by Richard Kennaway, Enrico Coen, Amelia Green, Andrew Bangham

“…The resulting growth depends on how specified growth patterns interact within the constraints of mechanically connected tissue. …”
Get full text

Research on Machine Vision–Based Intelligent Tracking System for Maintenance Personnel by Yinglin Ma, Hongmei Shi, Yao Wang, Baofeng Li

“…Our intelligent monitoring approach involves three key steps: train maintenance personnel identification, tracking of maintenance activities to generate movement trajectories, and analysis of movement patterns to detect anomalous behavior. This study primarily addresses the challenges of personnel identification and process tracking. …”
Get full text

Mentality in the Context of Interaction Between Social and Virtual Reality by Yulia V. Gavrilova

“…This actualizes the study of changes in mentality, which forms stable behavior patterns that allow a person to respond in a timely manner to the challenges of the environment. …”
Get full text

Graph-informed convolutional autoencoder to classify brain responses during sleep by Sahar Zakeri, Somayeh Makouei, Sebelan Danishvar

“…The accuracy of the SlS-GICA classifier is 99.92% on the significant feature set. This achievement could be considered in real-time and automatic applications to develop new therapeutic strategies for sleep-related disorders.…”
Get full text

Advances in Bract Coloration: Diversity, Pigment Synthesis, and Regulatory Mechanisms in Ornamental Plants by Xiaoyang Li, Yang Liu, Zhiquan Cai, Yiwei Zhou

“…Despite these advances, significant knowledge gaps remain in genetic inheritance patterns, epigenetic regulation, cross-pigment pathway crosstalk, and environmental modulation. …”
Get full text

An Explainable Hybrid CNN–Transformer Architecture for Visual Malware Classification by Mohammed Alshomrani, Aiiad Albeshri, Abdulaziz A. Alsulami, Badraddin Alturki

“…Visual malware classification, which transforms malware binaries into grayscale images, has emerged as a promising alternative for recognizing patterns in malicious code. This study presents a hybrid deep learning architecture that combines the local feature extraction capabilities of ConvNeXt-Tiny (a CNN-based model) with the global context modeling of the Swin Transformer. …”
Get full text

Automated multi-class MRI brain tumor classification and segmentation using deformable attention and saliency mapping by Erfan Zarenia, Amirhossein Akhlaghi Far, Khosro Rezaee

“…This model effectively captures irregular and complex tumor patterns, enhancing classification performance. Following classification, a comprehensive segmentation process was conducted across a large dataset, reinforcing the model’s role as a decision support system. …”
Get full text

Graph convolutional neural networks improved target-specific scoring functions for cGAS and kRAS in virtual screening by Bo Wang, Muhammad Junaid, Wenjin Li

“…This indicates that the graph convolutional network can be generalized to the prediction of heterogeneous data based on the complex patterns of molecular protein binding that have been learned. …”
Get full text

Enhancing Education with Machine Learning: Predicting Student Readability Scores by Claire Bell

“…This improvement is attributed to RFST's ability to effectively identify and utilize key textual features. In comparison, the baseline RFC and GRO-optimized Random Forest (RFGR) models achieve accuracies of 0.932 and 0.935, respectively, highlighting their limitations in capturing the intricate linguistic patterns associated with readability. …”
Get full text

A quantum machine learning framework for predicting drug sensitivity in multiple myeloma using proteomic data by M. Priyadharshini, B. Deevena Raju, A. Faritha Banu, P. Jagdish Kumar, V. Murugesh, Oleg Rybin

“…QSVM employs quantum kernels to map data into a higher-dimensional Hilbert space, so that the model can detect complex patterns in MM drug resistance. qPCA reduces dimensionality without loss of important variance, and thus improves computation efficiency. …”
Get full text

Validation of a hypomorphic variant in CDK13 as the cause of CHDFIDD with autosomal recessive inheritance through determination of an episignature by Jan Fischer, Mariëlle Alders, Marcel M. A. M. Mannens, David Genevieve, Karl Hackmann, Evelin Schröck, Bekim Sadikovic, Joseph Porrmann

“…Using the determination of a methylation pattern and comparison with an established episignature, we reveal the first hypomorphic variant in the kinase domain of CDK13, leading to a never before described autosomal recessive form of CHDFIDD in a boy with characteristic features. …”
Get full text