Evolution law of microporous structure in cement asphalt (CA) mortar under the action of polyurethane polycarboxylates by Shuncheng Xiang, Ming Yuan, Xin Yang, Lulu Li, Yingli Gao, Haotian Yu, Huan Zhou, Tingxiang Zheng, Jin Zhang, Zhen Jiang

“…According to the analysis of fractal dimension and nuclear magnetic resonance test data, the Cement Asphalt (CA) mortar ‘s pore volume fractal dimension is greater than 3, indicating that the pore structure has fractal characteristics. …”
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Features of formation of grain sorghum yield structure elements in dependence on inter-row spacing and fertilizing by В. М. Найденко

“…Laboratory, field, statistical. Results. Data on features of structural elements formation and productivity of grain sorghum in dependence on varietal characteristics, inter-row spacing and level of mineral nutrition are presen­ted. …”
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Automatic Segmentation of the Infraorbital Canal in CBCT Images: Anatomical Structure Recognition Using Artificial Intelligence by Ismail Gumussoy, Emre Haylaz, Suayip Burak Duman, Fahrettin Kalabalık, Muhammet Can Eren, Seyda Say, Ozer Celik, Ibrahim Sevki Bayrakdar

“…<b>Background/Objectives:</b> The infraorbital canal (IOC) is a critical anatomical structure that passes through the anterior surface of the maxilla and opens at the infraorbital foramen, containing the infraorbital nerve, artery, and vein. …”
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Analyzing canopy structure effects based on LiDAR for GPP-SIF relationship and GPP estimation by Shuo Shi, Shuo Shi, Shuo Shi, Shuo Shi, Zixi Shi, Fangfang Qu, Wei Gong, Wei Gong, Wei Gong, Wei Gong, Lu Xu, Chenxi Liu

“…LiDAR offers unparalleled advantages in capturing complex vertical canopy structures. In this study, we used multi-source data, particularly LiDAR-derived canopy structure products, to analyze the annual variations in canopy structural parameters and GPP/SIF across different forest types, investigate the response of canopy structure to the GPP-SIF relationship, and employ machine learning models to estimate GPP and assess the contribution of canopy structural factors. …”
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Genetically predicted brain cortical structure mediates the causality between insulin resistance and cognitive impairment by Chaojuan Huang, Yuyang Zhang, Mingxu Li, Qiuju Gong, Siqi Yu, Zhiwei Li, Mengmeng Ren, Xia Zhou, Xiaoqun Zhu, Zhongwu Sun

“…Our investigation aims to establish the causal relationship and direction between insulin resistance and cognition, while also quantifying the mediating role of brain cortical structure in this association.MethodsThe publicly available data sources for insulin resistance (fasting insulin, homeostasis model assessment beta-cell function and homeostasis model assessment insulin resistance, proinsulin), brain cortical structure, and cognitive phenotypes (visual memory, reaction time) were obtained from the MAGIC, ENIGMA, and UK Biobank datasets, respectively. …”
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Observed upper-ocean structure and seasonal production in the southern Kerguelen Plateau region, 1994-2021 by Man Liang, Annie Foppert, Annie Foppert, Karen J. Westwood, Karen J. Westwood, Sophie Bestley, Sophie Bestley

“…We collate ship-based hydrographic data on the vertical structure of the upper water column near 80°E from eight voyages spanning 1994 to 2021, from 58°S towards the Antarctic continent. …”
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Sequence-structure based prediction of pathogenicity for amino acid substitutions in proteins associated with primary immunodeficiencies by Ekaterina S. Porfireva, Anton D. Zadorozhny, Anastasia V. Rudik, Dmitry A. Filimonov, Alexey A. Lagunin, Alexey A. Lagunin

“…In this study, we have developed classification sequence-structure-property relationships (SSPR) models for predicting the pathogenicity of amino acid substitutions (AAS) in 25 proteins associated with the most important and genetically studied PIDs and encoded genes: IL2RG, JAK3, RAG1, RAG2, ADA, DCLRE1C, CD40LG, WAS, ATM, STAT3, KMT2D, BTK, FOXP3, AIRE, FAS, ELANE, ITGB2, CYBB, G6PD, GATA2, STAT1, IFIH1, NLRP3, MEFV, and SERPING1.MethodsThe data on 4825 pathogenic and benign AASs in the selected proteins were extracted from ClinVar and gnomAD. …”
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Evolution of the Structure of Double-Layer Technology Cooperation-Transfer Networks and Its Impact on Innovation Capabilities by Chen Yuling, Xing Hexiang, Du Debin

“…Therefore, this study constructed a double-layer innovation network for the Yangtze River Delta for the period 2010 to 2019 based on patent collaboration and transfer data. It characterizes the basic evolutionary features of this double-layer network, analyzes the structural development differences between the collaboration and transfer subnetworks, investigates the dynamics of their coupling evolution, and explores the spatial spillover effects of the dual-layer network structure on urban innovation capacity. …”
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Cardiometabolic state links neurovascular burden with brain structure and function: Evidence from EEG and MRI by Daniel Franco-O’Byrne, Ana M. Castro-Laguardia, Carolina Delgado, James M. Shine, David Huepe, Raul González-Gomez, Enzo Tagliazucchi, Cecilia Gonzalez-Campo, Agustín Ibañez, Vicente Medel

“…Aging affects brain structure and function alongside metabolic and vascular processes leading to energetic impairments. …”
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Role of air pollution exposure in the alteration of brain cortical structure: A Mendelian randomization study by Chaojuan Huang, Zimei Cheng, Xu Wu, Zhiwei Li, Mingxu Li, Xingliang Feng, Yuyang Zhang, Qian Zhao

“…Conclusions: Our exploration established causal relationships between air pollution exposure and brain cortical structure, underscoring the significance of mitigating air pollution to preserve brain cortical morphology.…”
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Phylogenetic Structure Shifts Across Life-History Stages in Response to Microtopography and Competition in Subtropical Forests by Weiqi Meng, Haonan Zhang, Lianhao Sun, Jianing Xu, Yajun Qiao, Haidong Li

“…This study focuses on a subtropical evergreen broad-leaved forest in China, utilizing a large permanent plot established in the Yaoluoping National Nature Reserve. By integrating data from a full-stem census and total station surveying, we analyzed the phylogenetic structure of the plant community as a whole and across different life-history stages (saplings, juveniles, and adults) while quantitatively assessing microtopographic variables and an interspecific competition index. …”
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Re-Os age of molybdenite from the Hannivka deposit, Kryvyi Rih structure, Ukrainian Shield by Yu. Ye. Khomych, V. V. Sukach, L. V. Shumlyanskyy, S. M. Bondarenko, V. O. Syomka, S. Tessalina, M. M. Reshetnyk

“…These events were active in the Inhul megablock and expressed themselves in the Kryvyi Rih-Kremenchuk suture zone and Kryvyi Rih structure through the intrusion of veins and lenses of Kropyvnytskyi granitoid complex (dated at 1959 ± 36 Ma) and aplite-pegmatoid granites (dated at 1.8 Ga).…”
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Considerations for Establishing a Behavioral Policy-Making and Nudge Institution within the Country's Executive Structure by Seyed Kamal Vaezi, Maryam Darbandi

“…Through this rigorous interview process, a wealth of qualitative data was gathered to assess both the potential and the challenges associated with forming a dedicated behavioral insight institution.ResultsThe literature review summarizes the experiences of countries with behavioral insight teams, presenting them in three areas: operational goals and tasks, institutional and organizational structure, and legal frameworks. …”
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The structure of an amyloid precursor protein/talin complex indicates a mechanical basis of Alzheimer’s disease by Charles Ellis, Natasha L. Ward, Matthew Rice, Neil J. Ball, Pauline Walle, Chloé Najdek, Devrim Kilinc, Jean-Charles Lambert, Julien Chapuis, Benjamin T. Goult

“…Here, we report the crystal structure of an APP/talin1 complex identifying a new way to couple the cytoskeletal machinery to synaptic sites through APP. …”
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Comparative Whole-Genome Analysis of Production Traits and Genetic Structure in Baiyu and Chuanzhong Black Goats by Jing Luo, Qi Min, Xueliang Sun, Xinyu Guo, Meijun Song, Xuehui Zeng, Jiazhong Guo, Hongping Zhang, Yanguo Han, Li Li

“…Furthermore, we conducted a comprehensive analysis of genetic diversity, population structure, and selection signatures using whole-genome resequencing data. …”
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Impact of the fishing ban on fish diversity and population structure in the middle reaches of the Yangtze River, China by Juan Du, Juan Du, Huiwu Tian, Zhiyuan Xiang, Kangshun Zhao, Kangshun Zhao, Lixiong Yu, Xinbin Duan, Daqing Chen, Jun Xu, Jun Xu, Mingdian Liu

“…Fish monitoring data collected from 2017–2019 (pre-ban) and 2021–2023 (post-ban) were analyzed to evaluate changes in fish body size, species diversity, and community structure. …”
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Characteristics of demersal fish community structure during summer hypoxia in the Pearl River Estuary, China by Han Lai, Sheng Bi, Huadong Yi, Haiyang Li, Xuchong Wei, Gongpei Wang, Dingli Guo, Xuange Liu, Jiahui Chen, Qiuxian Chen, Zhilun Zhang, Shuang Liu, Chenlei Huang, Li Lin, Guifeng Li

“…Further analysis demonstrated that dissolved oxygen had a significantly stronger effect on fish community structure at hypoxic sites than in the whole PRE. Moreover, other environmental variables also had significant effects on the fish community structure and interacted with dissolved oxygen in the hypoxia area. …”
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Seismic Mitigation Performance and Damage of Prefabricated Base Isolation Structure Based on the Shaking Table Test by WU Zhongtie, FAN pingping, LIU Yanhui, LI Wanrun

“…Secondly, Shaking table tests were conducted on the prefabricated base isolation structural model. And acceleration sensors and laser displacement transducers were installed on the test specimen to collect dynamic response data. …”
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Investigating heterogeneity across autism, ADHD, and typical development using measures of cortical thickness, surface area, cortical/subcortical volume, and structural covariance by Younes Sadat-Nejad, Younes Sadat-Nejad, Marlee M. Vandewouw, Marlee M. Vandewouw, R. Cardy, J. Lerch, J. Lerch, J. Lerch, M. J. Taylor, M. J. Taylor, A. Iaboni, C. Hammill, B. Syed, J. A. Brian, J. A. Brian, E. Kelley, E. Kelley, E. Kelley, M. Ayub, J. Crosbie, J. Crosbie, R. Schachar, R. Schachar, S. Georgiades, R. Nicolson, E. Anagnostou, E. Anagnostou, A. Kushki, A. Kushki

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Faktor-Faktor Yang Mempengaruhi Struktur Modal Pada Perusahaan Pertambangan Yang Terdaftar Di Bursa Efek Indonesia Periode 2013-2017 by Zul Hendri, Deni Agusrianti

“…The tool for analyzing research data is using SMART PLS. The results of the study stated that company size (X1) had no effect on capital structure (Y), profitability (X2) had no effect on capital structure (Y), company growth (X3) had no effect on capital structure (Y), and asset structure (X4 ) affect the capital structure (Y).…”
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