SOME FEATURES OF CHANGING TELOMERE LENGTH IN LYMPHOCYTES FROM THE PATIENTS WITH BRONCHIAL ASTHMA by V. I. Borisov, S. A. Demakov, V. M. Nepomnyashchikh, M. I. Leonova, D. V. Demina, N. A. Barovskaya, V. S. Kozhevnikov

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PFVnet, a feature enhancement network for low recognition coal and rock images by Cai Han, Zhenwen Liu, Shenglei Zhao, Yubo Li, Yanwei Duan, Xinzhou Yang, Chuanbo Hao

“…We characterized the grayscale and texture feature patterns of coal-rock media under varying degrees of interference and established a comprehensive multi-element image training sample library. …”
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Study on Photovoltaic Plant Site Selection Models Based on Geographic and Environmental Features by RAO Zhi, YANG Zaimin, YANG Xiongping, LI Jiaming, YANG Ping, WEI Zhichu

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Machine Learning-Driven Acoustic Feature Classification and Pronunciation Assessment for Mandarin Learners by Gulnur Arkin, Tangnur Abdukelim, Hankiz Yilahun, Askar Hamdulla

“…Based on acoustic feature analysis, this study systematically examines the differences in vowel pronunciation characteristics among Mandarin learners at various proficiency levels. …”
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Surface Electromyography and Gait Features in Patients after Anterior Cruciate Ligament Reconstruction by Xipeng Wu, Hao Zhang, Hongxing Cui, Wenbin Pei, Yixuan Zhao, Shanshan Wang, Zhijie Cao, Wei Li

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Back pain: from pathophysiological mechanisms and clinical features to modern approaches to therapy by N. V. Titova, Yu. N. Bezdolny, A. A. Slipko

“…When inflammatory pain patterns are present in the sacroiliac joints region, it is mandatory to rule out sacroiliitis within the spectrum of seronegative spondyloarthritis (including ankylosing spondylitis and others) using a specialized diagnostic algorithm. …”
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The Human Pathology Atlas for deciphering the prognostic features of human cancersResearch in context by Meng Yuan, Cheng Zhang, Kalle Von Feilitzen, Martin Zwahlen, Mengnan Shi, Xiangyu Li, Hong Yang, Xiya Song, Hasan Turkez, Mathias Uhlén, Adil Mardinoglu

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HLA system features in patients on waiting list for kidney transplantation in the Republic of Bashkortostan by A. A. Korzhenevsky, N. P. Korzhenevskaya

“…The aim of our study was to specify the features of the HLA system in patients registered on the waiting list for kidney transplantation in the Republic of Bashkortostan. …”
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Features of mismatch negativity in an at-risk mental state with the traits associated with the autistic spectrum by Naohito Kaneko, Naohito Kaneko, Yuko Higuchi, Yuko Higuchi, Noa Tsujii, Shimako Nishiyama, Shimako Nishiyama, Yukiko Akasaki, Kazuya Nagasawa, Kazuya Nagasawa, Daiki Sasabayashi, Daiki Sasabayashi, Michio Suzuki, Michio Suzuki, Tsutomu Takahashi, Tsutomu Takahashi

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Reconstructing coastal ponds functional classification: Integration of multi-feature remote sensing by Yijun Tong, Chen Lin, Ke Song, Tingchen Jiang, Ronghua Ma, Wenzhuo Cui, Danhua Ma, Jianchun Chen, Zhenxing Wang, Xiaofen Bai

“…Above all, the PWS classification system and data products developed in this study reveal the diverse relationships among “functional types-remote sensing features-geographical patterns” regarding PWS, implicating a crucial foundation for clarifying the ecological functional value of PWS and appropriate planning in the coastal zone.…”
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Patch-Based Texture Feature Extraction Towards Improved Clinical Task Performance by Tao Lian, Chunyan Deng, Qianjin Feng

“…Texture features can capture microstructural patterns and tissue heterogeneity, playing a pivotal role in medical image analysis. …”
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Textual, Genre and Social Features of Spoken Grammar: A Corpus-Based Approach by Carmen Pérez-Llantada

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Malicious software identification based on deep learning algorithms and API feature extraction by Wei Sun

“…This study contributes to the in-depth exploration of semantic information and behavioral patterns of application programming interface call sequences. …”
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Deep Learning with Dual-Channel Feature Fusion for Epileptic EEG Signal Classification by Bingbing Yu, Mingliang Zuo, Li Sui

“…However, the manual classification of these patterns is labor-intensive, time-consuming, and depends heavily on specialized expertise. …”
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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

“…This performance profile indicates adept navigation of the delicate balance between missed diagnoses and unnecessary interventions. Feature importance analysis revealed a multifaceted risk landscape, where screening test results contributed substantial predictive power (approximately 60%), complemented by demographic and behavioral factors, including age, reproductive history, and contraceptive usage patterns. …”
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Facial features based on the design of protective masks for children aged seven to ten by ZHOU Jie, WAN Qing, WU Xiayu, WANG Sijia

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Unsupervised feature selection based on generalized regression model with linear discriminant constraints by Xiangguang Dai, Mingyu Guan, Facheng Dai, Wei Zhang, Tingji Zhang, Hangjun Che, Xiangqin Dai

“…Benefited from this, the relationships and patterns within the high-dimensional data are retained in the reduced-dimensional feature space. …”
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Distributed Photovoltaic Communication Anomaly Detection Based on Spatiotemporal Feature Collaborative Modeling by Li Di, Zhuo Lv, Hao Chang, Junfei Cai

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N6-methyladenine identification using deep learning and discriminative feature integration by Salman Khan, Islam Uddin, Sumaiya Noor, Salman A. AlQahtani, Nijad Ahmad

“…In this study, we present Deep-N6mA, a novel Deep Neural Network (DNN) model incorporating optimal hybrid features for precise 6 mA site identification. The proposed framework captures complex patterns from DNA sequences through a comprehensive feature extraction process, leveraging k-mer, Dinucleotide-based Cross Covariance (DCC), Trinucleotide-based Auto Covariance (TAC), Pseudo Single Nucleotide Composition (PseSNC), Pseudo Dinucleotide Composition (PseDNC), and Pseudo Trinucleotide Composition (PseTNC). …”
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Plastid genomic features and phylogenetic placement in Rosa (Rosaceae) through comparative analysis by Hui Jiang, Shuilian He, Jun He, Yunjuan Zuo, Wenling Guan, Yan Zhao, Xuejiao Li, Jing Meng

“…Eight IGS regions were identified as highly variable regions, which provides potential information for developing molecular markers. Nineteen genes were discovered to have undergone significant positive selection. …”
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