bluuebunny/crossref_metadata_2025_split huggingface dataset:
Associations Between Cardiovascular Health (Life's Essential 8) and Mental Disorders
Authors: Xu Yudi , Ning Wenjing , Zhang Yuyuan , Ba Yuhao , Liu Huimin , Liu Long , Wang Libo , Guo Chunguang , Xu Hui , Weng Siyuan , Zhou Zhaokai , Cai Zongao , Ma Hongxuan , Zhang Ge , Jia Yanjie , Han Xinwei 
Published: September 2024

ABSTRACTBackgroundMental health was closely associated with cardiovascular disease (CVD). We aimed to investigate the association between cardiovascular health (CVH), as defined by Life's Essential 8 (LE8), and the presence of depression and anxiety.HypothesisWe hypothesized that CVH, as defined by LE8, was negatively associated with the prevalence of depression and anxiety.MethodsA cross‐sectional study was conducted on participants (≥ 20 years old) from the National Health and Nutrition Examination Survey (NHANES). The LE8 score (ranging from 0 to 100) was composed of the health behavior score and the health factor score, which were further categorized into three levels as follows: low (0–49), moderate (50–79), and high (80–100). Weighted multivariable logistic regressions and restricted cubic splines were utilized to assess the association between LE8 and mental disorders.ResultsAmong the 13 028 participants included in this research, 1206 were determined to have depression symptoms and 2947 were determined to have anxiety symptoms. In the weighted and adjusted model, LE8 was negatively associated with the prevalence of depression (odds ratio [OR], 95% confidence interval [CI]: 0.61, 0.58–0.65) and anxiety (OR, 95% CI: 0.78, 0.75–0.81). Furthermore, a nonlinear dose–response relationship was observed between LE8 and anxiety.ConclusionsCVH defined by the LE8 was independently and negatively associated with the prevalence of depression and anxiety. Interventions targeting LE8 components may improve both CVH and mental health.
Human Alzheimer's Disease ATN/ABC Staging Applied to Aging Rhesus Macaque Brains: Association With Cognition and MRI‐Based Regional Gray Matter Volume
Authors: Barnes Carol A. , Permenter Michele R. , Vogt Julie A. , Chen Kewei , Beach Thomas G. 
Published: September 2024

ABSTRACTThe brain changes of Alzheimer's disease (AD) include Abeta (Aβ) amyloid plaques (“A”), abnormally phosphorylated tau tangles (“T”), and neurodegeneration (“N”). These have been used to construct in vivo and postmortem diagnostic and staging classifications for evaluating the spectrum of AD in the “ATN” and “ABC” (“B” for Braak tau stage, “C” for Consortium to Establish a Registry for Alzheimer's Disease [CERAD] neuritic plaque density) systems. Another common AD feature involves cerebral amyloid angiopathy (CAA). We report the first experiment to examine relationships among cognition, brain distribution of amyloid plaques, CAA, tau/tangles, and magnetic resonance imaging (MRI)‐determined volume changes (as a measure of “N”) in the same group of behaviorally characterized nonhuman primates. Both ATN and ABC systems were applied to a group of 32 rhesus macaques aged between 7 and 33 years. When an immunohistochemical method for “T” and “B” was used, some monkeys were “triple positive” on ATN, with a maximum ABC status of A1B2C3. With silver or thioflavin S methods, however, all monkeys were classified as T‐negative and B0, indicating the absence of mature neurofibrillary tangles (NFTs) and hence neuropathologically defined AD. Although monkeys at extremes of the ATN and ABC classifications, or with frequent CAA, had significantly lower scores on some cognitive tests, the lack of fully mature NFTs or dementia‐consistent cognitive impairment indicates that fully developed AD may not occur in rhesus macaques. There were sex differences noted in the types of histopathology present, and only CAA was significantly related to gray matter volume.
The influence of sample size and sampling design on estimating population‐level intra specific trait variation (ITV) along environmental gradients
Authors: Fluck Isadora E. , Record Sydne , Strecker Angela , Zarnetske Phoebe L. , Baiser Benjamin 
Published: September 2024

AbstractUnderstanding the relationship between intraspecific trait variability (ITV) and its biotic and abiotic drivers is crucial for advancing population and community ecology. Despite its importance, there is a lack of guidance on how to effectively sample ITV and reduce bias in the resulting inferences. In this study, we explored how sample size affects the estimation of population‐level ITV, and how the distribution of sample sizes along an environmental gradient (i.e., sampling design) impacts the probabilities of committing Type I and II errors. We investigated Type I and II error probabilities using four simulated scenarios which varied sampling design and the strength of the ITV‐environment relationships. We also applied simulation scenarios to empirical data on populations of the small mammal, Peromyscus maniculatus across gradients of latitude and temperature at sites in the National Ecological Observatory Network (NEON) in the continental United States. We found that larger sample sizes reduce error rates in the estimation of population‐level ITV for both in silico and Peromyscus maniculatus populations. Furthermore, the influence of sample size on detecting ITV‐environment relationships depends on how sample sizes and population‐level ITV are distributed along environmental gradients. High correlations between sample size and the environment result in greater Type I error, while weak ITV–environmental gradient relationships showed high Type II error probabilities. Therefore, having large sample sizes that are even across populations is the most robust sampling design for studying ITV‐environment relationships. These findings shed light on the complex interplay among sample size, sampling design, ITV, and environmental gradients.
The importance of temporal scale in distribution modeling of migratory Caspian Kutum, Rutilus frisii
Authors: Moëzzi Fateh , Poorbagher Hadi , Eagderi Soheil , Feghhi Jahangir , Dormann Carsten F. , Nergi Sabah Khorshidi , Amiri Kaveh 
Published: September 2024

AbstractThe choice of temporal resolution has high importance in ecological modeling, which can greatly affect the identification of the main drivers of an organism's distribution, considering the spatiotemporal dynamism of environmental predictors as well as organisms’ abundance. The present study aimed to identify the spatiotemporal distribution patterns of Caspian Kutum, Rutilus frisii, along the southern coast of the Caspian Sea, north of Iran, evaluating multiple temporal resolutions of data. The boosted regression trees (BRT) method was used to model fish catch distribution using a set of environmental predictors. Three temporal scales of data, including seasonal, sub‐seasonal, and monthly time frames over the catch season (October–April), were considered in our modeling analyses. The monthly models, utilizing more detailed data scales, exhibited the highest potential in identifying the overall distribution patterns of the fish, compared to temporally‐coarse BRT models. The best models were the BRTs fitted using data from March and April, which represented the final months of the catch season with the highest catch levels. In the monthly models, the main determinants of the Kutum's aggregation points were found to be dynamic variables including sea surface temperature, particulate organic and inorganic carbon, as opposed to static topographic parameters such as distance to river inlets. Seasonal and sub‐seasonal models identified particulate inorganic matter and distance to river inlets as the predictors with the highest influence on fish distribution. The geographical distributions of fish biomass hotspots revealed the presence of a stable number of fish aggregation hotspot points along the eastern coast, while some cold‐spot points were identified along the central and western coasts of the Caspian Sea. Our findings indicate that utilizing fine time scales in modeling analyses can result in a more reliable explanation and prediction of fish distribution dynamics. The investigated approach allows for the identification of intra‐seasonal fluctuations in environmental conditions, particularly dynamic parameters, and their relationship with fish aggregation.
Development of a multisensor biologging collar and analytical techniques to describe high‐resolution spatial behavior in free‐ranging terrestrial mammals
Authors: Painter Michael S. , Silovský Václav , Blanco Justin , Holton Mark , Faltusová Monika , Wilson Rory , Börger Luca , Psotta Liza , Ramos‐Almodovar Fabian , Estrada Luis , Landler Lukas , Malkemper Pascal , Hart Vlastimil , Ježek Miloš 
Published: September 2024

AbstractBiologging has proven to be a powerful approach to investigate diverse questions related to movement ecology across a range of spatiotemporal scales and increasingly relies on multidisciplinary expertise. However, the variety of animal‐borne equipment, coupled with little consensus regarding analytical approaches to interpret large, complex data sets presents challenges and makes comparison between studies and study species difficult. Here, we present a combined hardware and analytical approach for standardizing the collection, analysis, and interpretation of multisensor biologging data. Here, we present (i) a custom‐designed integrated multisensor collar (IMSC), which was field tested on 71 free‐ranging wild boar (Sus scrofa) over 2 years; (ii) a machine learning behavioral classifier capable of identifying six behaviors in free‐roaming boar, validated across individuals equipped with differing collar designs; and (iii) laboratory and field‐based calibration and accuracy assessments of animal magnetic heading measurements derived from raw magnetometer data. The IMSC capacity and durability exceeded expectations, with a 94% collar recovery rate and a 75% cumulative data recording success rate, with a maximum logging duration of 421 days. The behavioral classifier had an overall accuracy of 85% in identifying the six behavioral classes when tested on multiple collar designs and improved to 90% when tested on data exclusively from the IMSC. Both laboratory and field tests of magnetic compass headings were in precise agreement with expectations, with overall median magnetic headings deviating from ground truth observations by 1.7° and 0°, respectively. Although multisensor equipment and sophisticated analyses are now commonplace in biologging studies, the IMSC hardware and analytical framework presented here provide a valuable tool for biologging researchers and will facilitate standardization of biologging data across studies. In addition, we highlight the potential of additional analyses available using this framework that can be adapted for use in future studies on terrestrial mammals.
Spatial Distribution Patterns and Beta Diversity of Fish Communities in a Tropical River Basin of Deccan Plateau
Authors: Dwivedi Arvind Kumar , De Kritish 
Published: September 2024

ABSTRACTRiverine ecosystems exhibit significant spatial variations in their communities, and understanding of these variances is vital to comprehend the mechanisms that maintain biodiversity. This study was conducted to understand pattern of distribution and beta diversity of fish communities in the Bhima River basin in the Deccan Plateau region of India. Fish distribution data from 59 sites across 11 rivers of the Bhima River basin were collected, and pair‐wise βDissimilarity and site‐wise local contribution to beta diversity (LCβDSorensen) and its components (LCβDTurnover and LCβDNestedness) were calculated. Power function regression was analysed to understand the decay in community similarity with geographical and environmental distances. Redundancy analysis was performed to investigate the relationship between fish distribution and environmental variables. Results indicated that species richness declined with increasing longitude from west to east. A negative relationship was observed between species richness and LCβDSorensen. LCβDTurnover formed Ո‐shaped curve at both ends of the species richness gradient, while LCβDNestedness values exhibited Ս‐shaped curve. No significant difference between mean LCβDTurnover and mean LCβDNestedness across the sites was observed. The distance decay relationship between community similarity and both geographical and environmental distances follows the power law shape, and the slope of this relationship flattened with increasing distance. Precipitation, topography, temperature variation and dissolved oxygen played a pivotal role in shaping fish assemblages. This study provides insight into the spatial distribution of fish communities in the Bhima River basin, which will be helpful to comprehend the ordination of biodiversity in the riverine ecosystem of the Deccan Plateau region.
Mind the information gap: How sampling and clustering impact the predictability of reach‐scale channel types in California (USA)
Authors: Guillon Hervé , Lane Belize , Byrne Colin F. , Sandoval‐Solis Samuel , Pasternack Gregory B. 
Published: September 2024

AbstractClustering and machine learning‐based predictions are increasingly used for environmental data analysis and management. In fluvial geomorphology, examples include predicting channel types throughout a river network and segmenting river networks into a series of channel types, or groups of channel forms. However, when relevant information is unevenly distributed throughout a river network, the discrepancy between data‐rich and data‐poor locations creates an information gap. Combining clustering and predictions addresses this information gap, but challenges and limitations remain poorly documented. This is especially true when considering that predictions are often achieved with two approaches that are meaningfully different in terms of information processing: decision trees (e.g., RF: random forest) and deep learning (e.g., DNNs: deep neural networks). This presents challenges for downstream management decisions and when comparing clusters and predictions within or across study areas. To address this, we investigate the performance of RF and DNN with respect to the information gap between clustering data and prediction data. We use nine regional examples of clustering and predicting river channel types, stemming from a single clustering methodology applied in California, USA. Our results show that prediction performance decreases when the information gap between field‐measured data and geospatial predictors increases. Furthermore, RF outperforms DNN, and their difference in performance decreases when the information gap between field‐measured and geospatial data decreases. This suggests that mismatched scales between field‐derived channel types and geospatial predictors hinder sequential information processing in DNN. Finally, our results highlight a sampling trade‐off between uniformly capturing geomorphic variability and ensuring robust generalisation.
Seasonality of zooplankton active flux in subtropical waters
Authors: Hernández‐León Santiago , Sarmiento‐Lezcano Airam , Couret María , Armengol Laia , Medina‐Suárez Ione , Fatira Effrosyni , Tuset Víctor , Limam Abdallahi , Sánchez Díez Antonio , Díaz‐Pérez Javier , Landeira José María 
Published: September 2024

AbstractThe biological carbon pump (BCP) is the mechanism by which the ocean transports organic matter below the mixed layer, exporting or sequestering it for years to millennia. Physical transport of dissolved and particulate organic carbon, the sinking of particles, and the carbon transported by diel and seasonal vertical migrants are the three main mechanisms of the BCP. In the study of active flux, seasonality is almost unknown and changes in ocean productivity during the annual cycle could promote differences in this transport. Here, we show the results of a cruise performed during spring in the Canary Current System, where we studied zooplankton active flux in two transects from the coastal zone off Northwest Africa toward the ocean. We measured biomass and the enzymatic activity of the electron transfer system (ETS) as a proxy for respiration in the water column down to a depth of 900 m. Compared with a previous survey during fall, we found higher values of specific ETS activity in the mesopelagic zone, promoting a higher active flux. Our results showed that the seasonality of active flux is driven not only by differences in biomass but also by differences in respiration rates in the mesopelagic zone, mainly due to differences in zooplankton body size. A review of the zooplankton active flux values around the Canary Islands showed a fourfold increase during spring compared with other seasons. This small window of higher flux should be considered in models of active carbon export in the ocean.
Research on optimization and self‐repair of fiber‐optic sensor network based on a novel elliptical topology structure
Authors: Hu Xingliu , Si Haifei , Zhou Zhihui 
Published: September 2024

AbstractWe designed a novel elliptical topology fiber‐optic sensing network to improve the coverage and reliability of fiber‐optic sensing networks. A sensitive area model detection experiment of fiber Bragg‐grating sensors was conducted, and a fiber‐optic sensing network based on an elliptical topology model was established based on the optimal coverage. To further optimize the network performance, the graph theory's adjacency matrix, coupled with the shortest‐path algorithm, efficiently uncovers and establishes the demodulation pathway for failed sensors in the fiber‐optic sensing network at a higher rate, thereby improving the response speed of self‐repair. Floyd's algorithm demonstrated a significant improvement in repair time compared with the standard practice, specifically reducing the repair time by approximately 88.25%.
Large‐language‐model empowered 3D dose prediction for intensity‐modulated radiotherapy
Authors: Dong Zehao , Chen Yixin , Gay Hiram , Hao Yao , Hugo Geoffrey D. , Samson Pamela , Zhao Tianyu 
Published: September 2024

AbstractBackgroundTreatment planning is currently a patient specific, time‐consuming, and resource demanding task in radiotherapy. Dose‐volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radiotherapy plans and organs‐at‐risk (OAR) and planning target volume (PTV) has been well established. This study explores the potential of deep learning models for predicting DVHs using images and subsequent human intervention facilitated by a large‐language model (LLM) to enhance the planning quality.MethodWe propose a pipeline to convert unstructured images to a structured graph consisting of image‐patch nodes and dose nodes. A novel Dose Graph Neural Network (DoseGNN) model is developed for predicting DVHs from the structured graph. The proposed DoseGNN is enhanced with the LLM to encode massive knowledge from prescriptions and interactive instructions from clinicians. In this study, we introduced an online human‐AI collaboration (OHAC) system as a practical implementation of the concept proposed for the automation of intensity‐modulated radiotherapy (IMRT) planning.ResultsThe proposed DoseGNN model was compared to widely employed DL models used in radiotherapy, including Swin Transformer, 3D U‐Net CNN, and vanilla MLP. For PTV, DoseGNN achieved the mean absolute error (MAE) of , , , and between true plans and predicted plans that were 64%, 53%, 64%, 61% of the best baseline model. For the worst case among OARs (left lung, right lung, chest wall, heart, spinal cord), DoseGNN achieved the mean absolute error of , , that were 85%, 91%, 80% of the best baseline model. Moreover, the LLM‐empowered DoseGNN model facilitates seamless adjustment to treatment plans through interaction with clinicians using natural language.ConclusionWe developed DoseGNN, a novel deep learning model for predicting delivered radiation doses from medical images, enhanced by LLM to allow adjustment through seamless interaction with clinicians. The preliminary results confirm DoseGNN's superior accuracy in DVH prediction relative to typical DL methods, highlighting its potential to facilitate an online clinician‐AI collaboration system for streamlined treatment planning automation.
In vivo cardiac diffusion tensor imaging on an MR system featuring ultrahigh performance gradients with 200 mT/m maximum gradient strength
Authors: Kara Danielle , Liu Yuchi , Chen Shi , Garrett Thomas , Younis Arwa , Sugawara Masafumi , Bolen Michael A. , Bi Xiaoming , Wazni Oussama , Nakagawa Hiroshi , Kwon Deborah , Nguyen Christopher 
Published: September 2024

AbstractPurposeOur aim is to assess the potential of an MR system with ultrahigh performance gradients (200 mT/m maximum gradient strength) to address two interrelated challenges in cardiac DTI: low SNR and sensitivity to bulk motion.MethodsImaging was performed in 20 healthy volunteers, two patients, and one swine post‐myocardial infarction. The impact of maximum gradient strength was assessed with spin echo cardiac DTI featuring second‐order motion compensation and varying maximum system gradient strengths (40, 80, 200 mT/m). Motion compensation requirements at 200 mT/m were assessed with sequences featuring zeroth‐, first‐, and second‐order motion compensation. SNR, mean diffusivity, fractional anisotropy, helix angle transmurality, and secondary eigenvector angle in the left ventricle were compared.ResultsIncreasing maximum system gradient strength from 40 and 80 mT/m to 200 mT/m increased SNR of b = 500 s/mm2 images by 150% and 40% due to reductions in TE. Observed improvements in DTI metrics included reduction in variance in mean diffusivity and helix angle transmurality across healthy volunteers, improved visualization of myocardial borders and delineation of suspected scar. Whereas second‐order motion compensation acquisitions were robust to motion‐induced signal dropout, zeroth‐ and first‐order motion compensation acquisitions suffered from severe signal loss and localized signal voids, respectively.ConclusionUltrahigh performance gradients (200 mT/m) enable high SNR DWIs of the heart and resultant improvements in diffusion tensor metrics. Despite reduced diffusion‐encoding duration, second‐order motion compensation is required to overcome sensitivity to cardiac motion.
Sex differences in the secular change in waist circumference relative to BMI in five countries from 1997 to 2020
Authors: Sánchez‐Romero Luz M. , Sagaceta‐Mejía Janine , Mindell Jennifer S. , Passi‐Solar Álvaro , Bernabé‐Ortiz Antonio , Tolentino‐Mayo Lizbeth , Moody Alison , Scholes Shaun 
Published: September 2024

AbstractObjectiveThe objective of this study was to quantify changes over time in waist circumference (WC) relative to BMI by sex in the Americas (United States, Mexico, Chile, and Peru) and England.MethodsData from adults aged 25 to 64 years between 1997 and 2020 were analyzed, and US data were stratified by race and ethnicity groups. Sex‐specific BMI and WC means and obesity and abdominal obesity prevalence were compared between the first and last surveys. Using data from all survey years, secular changes across the BMI and WC distributions were estimated, applying quantile regression models. BMI was added as a predictor of WC to estimate secular changes in WC relative to BMI. Interaction terms were included in all models to evaluate differences by sex.ResultsBMI and WC (except for Peru) showed larger secular increases at the upper‐tails of the distributions in both sexes. Increases at the 50th and 75th WC percentiles relative to BMI were more pronounced in women than in men, with larger increases in US non‐Hispanic White individuals and in England. In men, increases in WC independent of BMI were most evident in Mexico.ConclusionsDisease risk associated with visceral fat is potentially underestimated by national surveillance efforts that quantify only secular changes in BMI.