Molecular characterization and expression profiles of cytochrome P450 reductase gene in Sogatella furcifera (Hemiptera: Delphacidae) by YU Hang, LIU Su, ZHU Qingzi, ZHOU Wenwu, LIANG Qingmei, SHI Xiaoxiao, ZHU Zijie, ZHU Zengrong

“…The theoretical isoelectric point (pI) and calculated molecular mass of SfCPR protein are 5.48 and 76.762 ku, respectively. The secondary structure of SfCPR protein showed the marked features of typical CPRs, such as N-terminal transmembrane region, FMN-, FAD- and NADPH-binding domains and conserved catalytic residues. …”
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Real time counting method for coal mine drill pipes based on deep learning by Fukai ZHANG, Yiran SUN, Xufeng WU, Aijun LI, Peiyang LI, Dengke WANG, Guan YUAN, Shan ZHAO, Haiyan ZHANG

“…The Slim-Neck network structure is improved to balance the accuracy and complexity of the model, and the DDA Head module is designed to replace the detection head structure to enhance the attention of the model head to the multi-dimensional information of drilling rig targets; Finally, a pipe count inference method called Pipe Count is designed based on the principle of calculating the effective number of movements at the tail of the drilling rig using a two-level polygon judgment area. …”
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Metagenomic analysis reveals rumen microbiome enrichment and functional genes adjustment in carbohydrate metabolism induced by different sorting behavior in mid-lactation dairy cow... by Abdallah Alaa Mousa, Han Zhang, Hongwei Duan, Jiyou Zhang, Shengyong Mao

“…Abstract Background This study aimed to investigate differences in the structure and function of the rumen microbiome and its associated changes in rumen fermentation patterns and apparent nutrient digestibility in dairy cattle with different sorting behavior. …”
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Scaling climate information services and climate smart agriculture through bundled business models by Desire M. Kagabo, Livingstone Byandaga, Patrick Gatsinzi, Patrick Mvuyibwami, Yvonne U. Munyangeri, Nasson Ntwari, Mathieu Ouedraogo

“…Eleven CIS-CSA bundled business models with publicly available quantitative scaling readiness data (financial capacity, the number of customers, customer categories, and the number of beneficiaries) were used to quantitatively model how different CIS-CSA products and services bundling strategies, the revenue stream structure of a CIS-CSA business, and the structure of a CIS-CSA business’s stakeholder network (the diversity of its stakeholders) affect its scaling readiness. …”
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Aborto provocado: representações sociais de mulheres [Induced abortion: social representations of women] by Bárbara Angélica Gómez Pérez, Nadirlene Pereira Gomes, Maria de Fátima de Souza Santos, Normélia Maria Freire Diniz

“…A free word association test was used in 2007. Data was analyzed on the EVOC-2000 software. The structure of the representations is supported by elements of the central nucleus and by those of the peripheral nucleus. …”
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ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks by T. P. Janoski, T. P. Janoski, T. P. Janoski, T. P. Janoski, T. P. Janoski, I. Mitevski, R. J. Kramer, M. Previdi, L. M. Polvani, L. M. Polvani, L. M. Polvani

“…We selected 11 sets of radiative kernels and gave them a common nomenclature and data structure. The ClimKern Python package provides easy access to the kernel repository and functions to compute feedbacks, sometimes with a single line of code. …”
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Psychological Risk Factors and Clinical Outcome Variables in Vaginismus: A Systematic Review by Safya E Esmaeel, Manal Mazyad Alanazi, Shahad Aqeel Alenezi, Fai Salamah Alanazi, Dhay Ayed Alruwaili, Rahaf Khalid Alobaidan, Eslam K Fahmy, Manal S Fawzy

“…Sexual intimacy, self-image, fear of sex, sexual quality of life, education level, family structure, childhood trauma, cultural ideas about first sexual experiences, and misunderstandings about sexuality were among the several elements linked to vaginismus. …”
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Ice‐kNN‐South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction by Yongcheng Lin, Qinghua Yang, Xuewei Li, Xiaoran Dong, Hao Luo, Yafei Nie, Jiuke Wang, Yiguo Wang, Chao Min

“…Besides, the improvements of Ice‐kNN‐South are more pronounced in regions with high SICA variability by implementing a multi‐scale structure that combines field‐to‐field and grid‐to‐grid predictions. …”
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A bibliometric analysis of research hotspots and trends in transcranial magnetic stimulation and Alzheimer’s disease by Dingwen Xu, Yang Feng, Zhihua Lu, Ruijia Ma, Weicai Zhang, Zhen Mou, Zhen Mou, Lingling Zhang, Xiufeng Tang, Zhenxiong Zhao, Zhencang Zheng

“…We provide objective data that serves as a reference for scientific research and clinical work concerning AD and TMS.…”
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Revolutionizing Clear-Sky Humidity Profile Retrieval with Multi-Angle-Aware Networks for Ground-Based Microwave Radiometers by Yinshan Yang, Zhanqing Li, Jianping Guo, Yuying Wang, Hao Wu, Yi Shang, Ye Wang, Langfeng Zhu, Xing Yan

“…The innovative “multi-angle-aware” module in AngleNet effectively exploits previously underutilized oblique scanning angle data by accurately capturing these nonlinear relationships between BT and RH profiles, and precisely characterizes its vertical fine structure. …”
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Gender Variation, subject dispersion and citation impact in dental research: A bibliometric analysis of publications from 2009–2021 by Lubna Alkadi, Ikram Ul Haq, Wafa Allhaidan, Jumanah Almusa, Abdulsalam Alshammari

“…The analysis encompassed the progressive growth of papers, authors, and citations, the gender distribution of authors, the co-authorship structure, subject dispersion, and collaboration patterns based on affiliation, both national and international. …”
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OpenPheno: an open-access, user-friendly, and smartphone-based software platform for instant plant phenotyping by Tianqi Hu, Peng Shen, Yongshuai Zhang, Jiafei Zhang, Xin Li, Chuanzhen Xia, Ping Liu, Hao Lu, Tingting Wu, Zhiguo Han

“…It can function as a role in AI-driven breeding by providing the necessary data for genotype-phenotype analysis, thereby accelerating breeding programs. …”
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Response of streamflow components and evapotranspiration to changes in tree species composition in a subboreal permafrost watershed in the Greater Khingan Mountains of Northeastern... by Peng Hu, Zhipeng Xu, Xiuling Man, Liangliang Duan, Tijiu Cai

“…Changes in watershed water resources are often linked to land use changes, but the influence of forest structure, especially the composition of tree species, plays a crucial role in hydrological processes. …”
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Spatial Distribution Characteristics and Relationships of Salt-Based Ions and Nutrients in Old Protected Vegetable Fields by Nanbiao Zhan, Haotian Yang, Jiayang Li, Xiaodi Shi, Binhao Yang, Yuhang Sun, Gengzi Guo, Xiumin Cui

“…We hypothesize that this seemingly contradictory finding may be attributable to several factors, including the abundance of divalent cations (Ca²⁺ and Mg²⁺), the soil fertility and water retention capacity of unsaturated salt-based suitable soil, as well as good soil aggregate structure. These factors had the potential to reduce the stresses on the soil. …”
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Green and blue infrastructure but not anthropogenic drivers affect invertebrate communities in urban ecosystems by Joeri Morpurgo, Roy P. Remme, Quinten D. Mudde, Emilie A. Didaskalou, Kornelia J. Serwatowska, Krijn B. Trimbos, Mingming Hu, Peter M. van Bodegom

“…They highlighted that vegetation density, structure, and proximity to water are the primary drivers of invertebrate distributions, while direct anthropogenic pressures play a minimal role. …”
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Is there hidden genetic variability in the species of Steindachneridion Garavello, 2005 (Siluriformes: Pimelodidae)? by Daniele Aparecida Matoso, Hallana Cristina Menezes da Silva, Augusto Luiz Ferreira Júnior, Fábio Porto-Foresti, Ricardo Utsunomia, Fernanda Dotti do Prado, Roberto Ferreira Artoni

“…Nevertheless, genetic structure indexes were notably high. These findings suggest that the genetic diversity within these species may be underestimated, which has implications for both taxonomic classification and biological conservation strategies. …”
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Analysis of Genetic Diversity and Core Germplasm Construction of <i>Castanea crenata</i> Siebold and Zucc. Using Simple Sequence Repeat Markers and Morphological Traits by Yanhong Cui, Xinghua Nie, Juanjuan Liu, Shihui Chu, Hanqi Liu, Kaiyuan Xu, Yi Shao, Zhannan Wang, Ruijie Zheng, Yu Xing

“…Additionally, it evaluates core germplasm resources through cluster and population structure analyses using simple sequence repeat (SSR) marker data from 13 <i>Castanea henryi</i>, <i>18 Castanea seguinii</i>, and 27 <i>Castanea mollissima</i>, and 142 Japanese chestnut resources. …”
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Triptolide Causes Spermatogenic Disorders by Inducing Apoptosis in the Mitochondrial Pathway of Mouse Testicular Spermatocytes by Jiantao Zhao, Maosheng Cao, Haisheng Yi, Guitian He, Tong Chen, Lingyun Liu, Kaimin Guo, Yin Cao, Chunjin Li, Xu Zhou, Boqi Zhang, Hongliang Wang

“…Furthermore, TUNEL assays and Western blot analysis showed that in the TP mice’s testicular tissues, the spermatocytes had mitochondrial pathway apoptosis as well as abnormal mitochondrial morphology and structure. Triptolide induces oxidative stress in testicular tissue by enhancing pro-oxidative systems and inhibiting antioxidant systems. …”
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Comprehensive analysis of the NAC transcription factor gene family in Sophora tonkinensis Gagnep by Ximei Liang, Shuangshuang Qin, Guili Wei, Xiaoyun Guo, Shugen Wei, Fan Wei, Ying Liang

“…Phylogenetic analysis revealed that StNAC proteins were categorized into 15 subgroups based on their homology with Arabidopsis thaliana NAC proteins. Gene structure analysis demonstrated a variation in intron numbers ranging from 1 to 7, with a majority of StNAC genes containing 2–3 introns. …”
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Terpene Synthase (TPS) Family Member Identification and Expression Pattern Analysis in Flowers of <i>Dendrobium chrysotoxum</i> by Yanni Yang, Jianying Gong, Rongrong Nong, Qiao Liu, Ke Xia, Shuo Qiu, Zaihua Wang

“…It helped identify the <i>DcTPS</i> genes, gene characteristics, the phylogeny relationship, conserved motif location, gene exon/intron structure, <i>cis-elements</i> in the promoter regions, protein–protein interaction (PPI) network, tissue specific expression and verification of the expression across different flowering stages and floral organs. …”
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