Comparative study of the diversity and structure of plant-pollinator interactions in forest and agricultural landscapes in Northwestern Morocco by Amine SAMIH, Sergi TRÓCOLI, Latifa ROHI, Hassan FOUGRACH, Mohammed HSAINE, Noureddin MAATOUF

“…This study examines the influence of forest and agricultural landscapes in Northwestern Morocco on plant-pollinator interactions by analysing the structure and dynamics of their networks. Data were collected between mid-March and mid-July 2023 at eleven sites in different landscapes. …”
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Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential by Audrey Sederberg, Audrey Sederberg, Audrey Sederberg, Audrey Sederberg, Aurélie Pala, Aurélie Pala, Garrett B. Stanley

“…Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. …”
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Structuring climate service user groups for capacity building: A European Delphi-based approach by Maria del Pozo Garcia, Judith Gulikers, Bregje van der Bolt, Wouter Smolenaars, Estella Oncins, Perry den Brok, Fulco Ludwig

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Hydrological dynamics, wetland morphology and vegetation structure determine riparian arthropod communities in constructed wetlands by David Åhlén, Sofia Hedman, Jerker Jarsjö, Björn K. Klatt, Lea D. Schneider, John Strand, Ayco Tack, Imenne Åhlén, Peter A. Hambäck

“…Given the variable responses to wetland hydrological and structural drivers, it seems that wetland arthropod communities would benefit from a high local wetland habitat variability, or wetlandscapes where individual wetlands have differing hydrological dynamics, morphology and vegetation.…”
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Genetic Diversity and Population Structure of Tufted Deer (<i>Elaphodus cephalophus</i>) in Chongqing, China by Fuli Wang, Chengzhong Yang, Yalin Xiong, Qian Xiang, Xiaojuan Cui, Jianjun Peng

“…This study aimed to comprehensively evaluate the genetic diversity, population structure, gene flow, and demographic history of tufted deer across this critical region. …”
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Deep learning-based prediction of velocity and temperature distributions in metal foam with hierarchical pore structure by Yixiong Lin, Zhengqi Wu, Shiqi You, Chen Yang, Qinglian Wang, Wang Yin, Ting Qiu

“…Moreover, in CNN prediction, a broader range of structure parameters and boundary conditions beyond those in the training set was used to evaluate the practicability of the trained CNN model. …”
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Hierarchy in Structuring of Resource Selection: Understanding Elk Selection Across Space, Time, and Movement Strategies by Storm Crews, Nathaniel D. Rayl, Mathew W. Alldredge, Eric J. Bergman, Chuck R. Anderson Jr., Eric H. VanNatta, Joseph D. Holbrook, Guillaume Bastille‐Rousseau

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The role of gut microbiota in modulating brain structure and psychiatric disorders: A Mendelian randomization study by Zheng Ye, Yingying Gao, Jiaqi Yuan, Feng Chen, Peng Xu, Wenbin Liu

“…In this study, we employed a mediation Mendelian randomization approach to delve into the potential causal relationships between gut microbiota and psychiatric disorders, with a focus on the mediating role of brain structural changes. We harnessed genetic data from large - scale genome - wide association studies to analyze how 196 gut microbiota taxa affect ten psychiatric disorders via alterations in 3143 brain structures.Our key findings revealed significant bidirectional causal relationships. …”
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From Digital to Real: Optimised and Functionally Integrated Shotcrete 3D Printing Elements for Multi-Storey Structures by Robin Dörrie, Stefan Gantner, Fatemeh Salehi Amiri, Lukas Lachmayer, Martin David, Tom Rothe, Niklas Freund, Ahmad Nouman, Karam Mawas, Oguz Oztoprak, Philipp Rennen, Virama Ekanayaka, André Hürkamp, Stefan Kollmannsberger, Christian Hühne, Annika Raatz, Klaus Dröder, Dirk Lowke, Norman Hack, Harald Kloft

“…By incorporating reinforcement techniques such as short rebar insertion and robotic fibre winding, this study demonstrates the structural capabilities achievable with SC3DP. In summary, the implementation of comprehensive digital workflows utilising computational design, automated data acquisition and data flow, as well as robotic fabrication is presented to demonstrate the potential of AM methods in construction. …”
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COMPARATIVE STUDY OF THE EFFECT OF REGENERATION STIMULATION DRUG ON THE STRUCTURE AND RATE OF HAIR GROWTH IN WHITE RATS by Olga B. Zhdanova, Iraida I. Okulova, Elena B. Dunaeva, Olga V. Chasovskikh

“…The study of the activation process of hair papilla stem cells when using regeneration stimulators to improve the structure of the hair and its growth rate is relevant both for the development of drugs for fur farming and alopecia treatment regimens. …”
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Auto-resolving the atomic structure at van der Waals interfaces using a generative model by Wenqiang Huang, Yucheng Jin, Zhemin Li, Lin Yao, Yun Chen, Zheng Luo, Shen Zhou, Jinguo Lin, Feng Liu, Zhifeng Gao, Jun Cheng, Linfeng Zhang, Fangping Ouyang, Jin Zhang, Shanshan Wang

“…Abstract The high-resolution visualization of atomic structures is significant for understanding the relationship between the microscopic configurations and macroscopic properties of materials. …”
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SIGNS OF POST-TRAUMATIC STRESS DISORDER IN COMBATANTS WITH DIFFERENT COMBAT EXPERIENCE AND THE STRUCTURE OF THEIR PSYCHOLOGICAL REHABILITATION by Ivan М. Okhrimenko, Nataliia O. Bilevych, Maryna Yu. Halych, Yaroslav V. Halych, Natalia E. Penkova, Petro V. Kozyra, Oksana G. Strelchenko

“…To study the signs of post-traumatic stress disorder in combatants depending on the duration of their participation in combat operations and to substantiate the structure of their psychological rehabilitation. …”
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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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DEEP GROUND PENETRATING RADAR IMAGING FOR THE PURPOSE OF EXPLORING ORE AND GEOLOGICAL STRUCTURES IN THE CALAMINE MINE, CENTRAL IRAN by Nasrin Sadrmohammadi, Selma Kadioğlu, Khalil Rezaei, Mahmoud Honarvar

“…The processed radargrams from selected profiles were compared amongst themselves and with borehole data collected along the same profiles. The geological structure was successfully visualized using two different software programs. …”
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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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