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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Reproductive toxicity in male rats induced by chronic arsenic exposure involves hormonal and structural changes by Syed Zahid Mahboob, Sarwat Jahan, Hussain Badshah, Razia Virk, Mehwish David, Riffat Bano, Jawaher Alzahrani, Muhammad Ayub, Azizullah Azizullah, Moona Nazish, Asif Kamal, Kamran Ullah, Muhammad Tahir Naseem

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Structural analysis of TRIM family PRYSPRY domains and its implications for E3-ligand design by Rezart Zhubi, Apirat Chaikuad, Christian J. Muñoz Sosa, Andreas C. Joerger, Stefan Knapp

“…In addition, we mapped the locations of missense mutations in MID1 associated with X-linked Opitz syndrome, suggesting that some of these mutations impair the conformational stability of the protein. Taken together, our data provide intriguing insights into the structural and functional divergence of TRIM family PRYSPRY domains, their potential druggability and substrate recognition, and the challenges of ligand design.…”
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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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Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer’s disease progression by Sepehr Aghajanian, Fateme Mohammadifard, Ida Mohammadi, Shahryar Rajai Firouzabadi, Ali Baradaran Bagheri, Elham Moases Ghaffary, Omid Mirmosayyeb

“…Conclusions These findings emphasize the value of structural MRI within the advanced DL architectures for predicting MCI-to-AD conversion. …”
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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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Structural and Functional Characterization of the Most Frequent Pathogenic PRKN Substitution p.R275W by Bernardo A. Bustillos, Liam T. Cocker, Mathew A. Coban, Caleb A. Weber, Jenny M. Bredenberg, Paige K. Boneski, Joanna Siuda, Jaroslaw Slawek, Andreas Puschmann, Derek P. Narendra, Neill R. Graff-Radford, Zbigniew K. Wszolek, Dennis W. Dickson, Owen A. Ross, Thomas R. Caulfield, Wolfdieter Springer, Fabienne C. Fiesel

“…The functional analysis was accompanied by structural investigation of PRKN p.R275W. We observed lower PRKN protein in fibroblasts with compound heterozygous p.R275W mutations. …”
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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

“…The model age for sample 3219/189.5 from Serhiivka is 3013 ± 17 Ma, which is in good agreement with previously obtained data 3.0–3.1 Ga for the Serhiivka mineralization using Re–Os method. …”
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Relationships between structural stigma, societal stigma, and minority stress among gender minority people by Kristen D. Clark, Mitchell R. Lunn, Jae M. Sevelius, Carol Dawson-Rose, Sandra J. Weiss, Torsten B. Neilands, Micah E. Lubensky, Juno Obedin-Maliver, Annesa Flentje

“…We tested variables identified in the literature as measures of structural stigma inclusive of societal stigma (LGBT + Business Climate Index, state voting behaviors, and Google Trends search data), the most commonly used structural stigma variable (State Policy Environment Tally), and proxy variables (region, population density) for comparison. …”
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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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Tracing the aggregation pathway of the scaffold protein DISC1: Structural implications for chronic mental illnesses by Abhishek Cukkemane, Nina Becker, Tatsiana Kupreichyk, Henrike Heise, Dieter Willbold, Oliver H. Weiergräber

“…Disrupted in schizophrenia 1 (DISC1) is a pleiotropic scaffold protein that is postulated to comprise large disordered regions and four distinct structured segments with a high proportion of helical or coiled-coil fold. …”
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Development and distribution characteristics of geohazards based on sedimentary-structural evolution in Xide County, southwest Sichuan by Xiaohui XIONG, Yongjian BAI, Yongbo TIE, Yanchao GAO, Hua GE, Wei XU, Lingfeng GONG, Jiazhu WANG, Kai TIAN, Menglong HE, Zhihua ZHU

“…The wide and gentle fold structures in red beds are the disaster-prone environment for landslide. …”
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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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