bluuebunny/crossref_metadata_2025_split huggingface dataset:
UAV‐assisted multi‐hop D2D relaying for communication in large disaster area
Authors: Ghosh Sayanti , Bhowmick Abhijit , Roy Sanjay Dhar , Kundu Sumit 
Published: September 2023

SummaryAn unmanned aerial vehicle (UAV)‐assisted communication is a promising technique for enabling ground users in a non‐functional area (NFA) or disaster area to communicate. All the base stations in the disaster area may be partially or fully damaged due to the natural calamity. Device‐to‐device (D2D) communication can be a viable solution for immediate connection between users in an NFA. We propose a UAV‐assisted multi‐hop D2D communication following a hybrid power‐time switching (PTS) in this paper. Moreover, a D2D user of a cluster can communicate with another D2D user in a different cluster through UAVs. However, D2D users can harvest energy from their respective ad hoc energy stations and forward the information to the nearby D2D user following a hybrid PTS‐based strategy. We propose a time frame for the same and show a node‐based energy harvesting strategy. The expressions of outage probability, throughput, end‐to‐end energy consumption, and energy efficiency are developed for the Rician and Nakagami‐m faded channel. The impact of several network parameters such as energy harvesting factor, energy harvesting efficiency, and fading parameter on the network performance is also indicated.
Plasma ceramides relate to mild cognitive impairment in middle‐aged men: The Maastricht Study
Authors: van Kruining Daan , Losen Mario , Crivelli Simone M. , de Jong Joost J. A. , Jansen Jacobus F. A. , Backes Walter H. , Monereo‐Sánchez Jennifer , van Boxtel Martin P. J. , Köhler Sebastian , Linden David E. J. , Schram Miranda T. , Mielke Michelle M. , Martinez‐Martinez Pilar 
Published: September 2023

AbstractIntroductionThere is an urgent need for biomarkers identifying individuals at risk of early‐stage cognitive impairment. Using cross‐sectional data from The Maastricht Study, this study included 197 individuals with mild cognitive impairment (MCI) and 200 cognitively unimpaired individuals aged 40 to 75, matched by age, sex, and educational level.MethodsWe assessed the association of plasma sphingolipid and ceramide transfer protein (CERT) levels with MCI and adjusted for potentially confounding risk factors. Furthermore, the relationship of plasma sphingolipids and CERTs with magnetic resonance imaging brain volumes was assessed and age‐ and sex‐stratified analyses were performed.ResultsAssociations of plasma ceramide species C18:0 and C24:1 and combined plasma ceramide chain lengths (ceramide risk score) with MCI were moderated by sex, but not by age, and higher levels were associated with MCI in men. No associations were found among women. In addition, higher levels of ceramide C20:0, C22:0, and C24:1, but not the ceramide risk score, were associated with larger volume of the hippocampus after controlling for covariates, independent of MCI. Although higher plasma ceramide C18:0 was related to higher plasma CERT levels, no association of CERT levels was found with MCI or brain volumes.DiscussionOur results warrant further analysis of plasma ceramides as potential markers for MCI in middle‐aged men. In contrast to previous studies, no associations of plasma sphingolipids with MCI or brain volumes were found in women, independent of age. These results highlight the importance of accounting for sex‐ and age‐related factors when examining sphingolipid and CERT metabolism related to cognitive function.
Anxiety, aggression, reward sensitivity, and forebrain dopamine receptor expression in a laboratory rat model of early‐life disadvantage
Authors: Lonstein Joseph S. , Vitale Erika M. , Olekanma Doris , McLocklin Andrew , Pence Nathan , Bredewold Remco , Veenema Alexa H. , Johnson Alexander W. , Burt S. Alexandra 
Published: September 2023

AbstractDespite early‐life disadvantage (ELD) in humans being a highly heterogenous construct, it consistently predicts negative neurobehavioral outcomes. The numerous environmental contributors and neural mechanisms underlying ELD remain unclear, though. We used a laboratory rat model to evaluate the effects of limited resources and/or heavy metal exposure on mothers and their adult male and female offspring. Dams and litters were chronically exposed to restricted (1‐cm deep) or ample (4‐cm deep) home cage bedding postpartum, with or without lead acetate (0.1%) in their drinking water from insemination through 1‐week postweaning. Restricted‐bedding mothers showed more pup‐directed behaviors and behavioral fragmentation, while lead‐exposed mothers showed more nestbuilding. Restricted bedding‐raised male offspring showed higher anxiety and aggression. Either restricted bedding or lead exposure impaired goal‐directed performance in a reinforcer devaluation task in females, whereas restricted bedding alone disrupted it in males. Lead exposure, but not limited bedding, also reduced sucrose reward sensitivity in a progressive ratio task in females. D1 and D2 receptor mRNA in the medial prefrontal cortex and nucleus accumbens (NAc) were each affected by the early‐life treatments and differently between the sexes. Most notably, adult males (but not females) exposed to both early‐life treatments had greatly increased D1 receptor mRNA in the NAc core. These results illuminate neural mechanisms through which ELD threatens neurobehavioral development and highlight forebrain dopamine as a factor.
The successes and pitfalls: Deep‐learning effectiveness in a Chernobyl field camera trap application
Authors: Maile Rachel E. , Duggan Matthew T. , Mousseau Timothy A. 
Published: September 2023

AbstractCamera traps have become in situ sensors for collecting information on animal abundance and occupancy estimates. When deployed over a large landscape, camera traps have become ideal for measuring the health of ecosystems, particularly in unstable habitats where it can be dangerous or even impossible to observe using conventional methods. However, manual processing of imagery is extremely time and labor intensive. Because of the associated expense, many studies have started to employ machine‐learning tools, such as convolutional neural networks (CNNs). One drawback for the majority of networks is that a large number of images (millions) are necessary to devise an effective identification or classification model. This study examines specific factors pertinent to camera trap placement in the field that may influence the accuracy metrics of a deep‐learning model that has been trained with a small set of images. False negatives and false positives may occur due to a variety of environmental factors that make it difficult for even a human observer to classify, including local weather patterns and daylight. We transfer‐trained a CNN to detect 16 different object classes (14 animal species, humans, and fires) across 9576 images taken from camera traps placed in the Chernobyl Exclusion Zone. After analyzing wind speed, cloud cover, temperature, image contrast, and precipitation, there was not a significant correlation between CNN success and ambient conditions. However, a possible positive relationship between temperature and CNN success was noted. Furthermore, we found that the model was more successful when images were taken during the day as well as when precipitation was not present. This study suggests that while qualitative site‐specific factors may confuse quantitative classification algorithms such as CNNs, training with a dynamic training set can account for ambient conditions so that they do not have a significant impact on CNN success.
Diet and mitonuclear haplotype interactions affect growth rate in a slime mould
Authors: Nagarajan‐Radha Venkatesh , Cordina Natalie , Beekman Madeleine 
Published: September 2023

AbstractTrait expression in metazoans is strongly influenced by the balance of macronutrients (i.e. protein, carbohydrate and fat) in the diet. At the same time, an individual's genetic background seems to regulate the magnitude of phenotypic response to a particular diet. It needs to be better understood whether interactions between diet, genetic background and trait expression are found in unicellular eukaryotes. A protist—the slime mould, Physarum polycephalum can choose diets based on protein‐to‐carbohydrate (P:C) content to support optimal growth rate. Yet, the role of genetic background (variation in the mitochondrial and nuclear DNAs) in mediating growth rate response to dietary P:C ratios in the slime mould is unknown. Here, we studied the effects of interactions between mitochondrial and nuclear DNA haplotypes and diet (i.e. G × G × E interactions) on the growth rate of P. polycephalum. A genetic panel of six distinct strains of P. polycephalum that differ in their mitochondrial and nuclear DNA haplotypes was used to measure growth rate across five diets that varied in their P:C ratio and total calories. We first determined the strains' growth rate (total biomass and surface area) when grown on a set menu with access to a particular diet. We then assessed whether the growth rate of strains increased on a buffet menu with access to all diets. Our findings show that the growth rate of P. polycephalum is generally higher on diets containing more carbohydrates than protein and that total calories negatively affect the growth rate. Three‐way interactions between mitochondrial, nuclear haplotypes and dietary P:C ratios affected the strains' surface area of growth but not biomass. Intriguingly, strains did not increase their surface area and biomass when they had access to all diets on the buffet menu. Our findings have broad implications for our understanding of the effect of mitonuclear interactions on trait expression across diverse eukaryotic lineages.
Controllable factors affecting accuracy and precision of human identification of animals from drone imagery
Authors: Jones Landon R. , Elmore Jared A. , Krishnan B. Santhana , Samiappan Sathishkumar , Evans Kristine O. , Pfeiffer Morgan B. , Blackwell Bradley F. , Iglay Raymond B. 
Published: September 2023

AbstractSurveying animal populations using drones (unoccupied aircraft systems [UAS]) provides numerous advantages; however, few best practices exist to survey animal communities with drones. Among myriad factors that can affect human identification and counts of animals from drone images, we focused on three factors typically controlled in the study design or by the drone pilot: flight altitude, camera angle, and time of day. Identifying interactions and patterns among these three variables represents an important first step to determining best survey practices. We used a drone to survey known numbers of eight animal decoy species, representing a range of body sizes and colors, at four ground sampling distance (GSD) values (0.35, 0.70, 1.06, and 1.41 cm/pixel) representing equivalent flight altitudes (15.2, 30.5, 45.7, and 61.0 m) at two camera angles (45° and 90°) and across a range of times of day (morning to late afternoon). Expert human observers identified and counted animals from drone images to determine how the three controllable factors affected accuracy and precision. Observer precision was high and unaffected by tested factors. However, results for observer accuracy revealed an interaction among all three controllable factors. Increasing flight altitude resulted in decreased accuracy in animal counts overall; however, accuracy was best at midday than during the morning and afternoon, when decoy and structure shadows were present or more pronounced. Surprisingly, the 45° camera enhanced accuracy more than the 90° camera, but only when animals were most difficult to identify and count, such as at higher flight altitudes or during the early morning and late afternoon. We provide recommendations for improving human accuracy in identifying and counting animals from drone images used in monitoring animal populations and communities. These results should be incorporated into drone survey design in addition to considering funding, logistical, and animal behavior constraints.
Characteristics of overlying strata and mechanisms of arch beam failure in shallowly buried thick bedrock coal seams: A case study in western China
Authors: Wang Changxiang , Li Huaibin , Zhang Meng , Liao Changlong , Zhang Shaojie 
Published: September 2023

AbstractThe Yangjiacun coal mine, located in western China, exhibits distinctive characteristics, such as its shallow depth, thick bedrock, thin loose layer, and unique roof strata breaking movement. In this research, a comprehensive analysis of previous exploration data, subsequent mechanical testing, key stratum theory, natural equilibrium arch theory, and simulation tests and numerical simulations has been conducted to investigate the failure behavior of the overlying rock strata in the Yangjiacun Coal Mine. Specifically, the load‐bearing capacity of the key stratum structure in the bedrock and the morphological properties of the arch‐beam structure have been thoroughly examined. A mechanical model of the roof rock arch‐beam structure has been established, and an analytical formula for the limit size and stability criterion of the arch‐beam structure has been derived. The mining‐induced rock arch‐beam structure plays a crucial role in transferring the load of the upper strata to the surrounding goaf, thereby preventing subsidence and deformation of the overlying strata. However, the arch‐beam structure periodically experiences instability during the advancement of the working face, gradually progressing towards the upper layer until the main key stratum is breached and the arch‐beam structure collapses. Based on the characteristics of weathered rock and the arch‐beam failure model in the Yangjiacun Coal Mine, quantitative backfilling mining methods have been proposed to either compensate for the mining void created by coal extraction or limit the size of exposed key strata through strip mining, ensuring the stability of the R6 key stratum. This study represents a significant contribution to understanding the intricate failure behavior of overlying rock strata in coal mines and provides a valuable framework for the responsible and efficient extraction of coal resources in similar environments.
Energy‐efficient power control and altitude planning for UAV‐based vehicular communications in 5G NR‐V2X networks
Authors: Aslani Rojin , Saberinia Ebrahim 
Published: September 2023

AbstractUnmanned aerial vehicles (UAVs) are becoming increasingly popular in various industries, including telecommunications. In the realm of 5G new radio vehicle‐to‐everything (NR‐V2X) networks, UAVs can be used as mobile base stations (UAV‐BS) to provide vehicular communications. However, effective power control and altitude planning algorithms are required to maximize the energy efficiency (EE) of these UAV‐BS systems. In this article, we propose an energy‐efficient power control and altitude planning algorithm for UAV‐BS in NR‐V2X networks. We formulate the optimization problem of maximizing the system's EE while considering the reliability requirements of vehicular users, the limitations of UAV‐BS hovering altitude, and the feasibility of transmission power levels. The problem is nonconvex, and we address this by reformulating it into a convex problem using the Dinkelbach and alternating optimization methods. The simulation results demonstrate that the proposed algorithm closely achieves the performance of exhaustive search while it is more computationally efficient. Moreover, our algorithm by jointly optimizing transmission power and hovering altitude performs better than other schemes proposed in the literature, as shown through our simulation results.
Limited‐memory polynomial methods for large‐scale matrix functions
Authors: Güttel Stefan , Kressner Daniel , Lund Kathryn 
Published: September 2023

AbstractMatrix functions are a central topic of linear algebra, and problems requiring their numerical approximation appear increasingly often in scientific computing. We review various limited‐memory methods for the approximation of the action of a large‐scale matrix function on a vector. Emphasis is put on polynomial methods, whose memory requirements are known or prescribed a priori. Methods based on explicit polynomial approximation or interpolation, as well as restarted Arnoldi methods, are treated in detail. An overview of existing software is also given, as well as a discussion of challenging open problems.
Ganglioside GD3 regulates neural stem cell quiescence and controls postnatal neurogenesis
Authors: Fuchigami Takahiro , Itokazu Yutaka , Yu Robert K. 
Published: September 2023

AbstractThe postnatal neural stem cell (NSC) pool hosts quiescent and activated radial glia‐like NSCs contributing to neurogenesis throughout adulthood. However, the underlying regulatory mechanism during the transition from quiescent NSCs to activated NSCs in the postnatal NSC niche is not fully understood. Lipid metabolism and lipid composition play important roles in regulating NSC fate determination. Biological lipid membranes define the individual cellular shape and help maintain cellular organization and are highly heterogeneous in structure and there exist diverse microdomains (also known as lipid rafts), which are enriched with sugar molecules, such as glycosphingolipids. An often overlooked but key aspect is that the functional activities of proteins and genes are highly dependent on their molecular environments. We previously reported that ganglioside GD3 is the predominant species in NSCs and that the reduced postnatal NSC pools are observed in global GD3‐synthase knockout (GD3S‐KO) mouse brains. The specific roles of GD3 in determining the stage and cell‐lineage determination of NSCs remain unclear, since global GD3S‐KO mice cannot distinguish if GD3 regulates postnatal neurogenesis or developmental impacts. Here, we show that inducible GD3 deletion in postnatal radial glia‐like NSCs promotes NSC activation, resulting in the loss of the long‐term maintenance of the adult NSC pools. The reduced neurogenesis in the subventricular zone (SVZ) and the dentate gyrus (DG) of GD3S‐conditional‐knockout mice led to the impaired olfactory and memory functions. Thus, our results provide convincing evidence that postnatal GD3 maintains the quiescent state of radial glia‐like NSCs in the adult NSC niche.
ApoE4 expression disrupts tau uptake, trafficking, and clearance in astrocytes
Authors: Eisenbaum Maxwell , Pearson Andrew , Ortiz Camila , Mullan Michael , Crawford Fiona , Ojo Joseph , Bachmeier Corbin 
Published: September 2023

AbstractTauopathies are a collection of neurodegenerative diseases characterized by the accumulation of pathogenic aggregates of the microtubule‐associated protein tau. Despite the prevalence and diversity of tau astrogliopathy in tauopathies, the interactions between astrocytes and tau in the brain, and the influence of neurodegenerative genetic risk factors like the apolipoprotein E4 (apoE4) isoform, are largely unknown. Here, we leveraged primary and immortalized astrocytes expressing humanized apoE isoforms to characterize the mechanisms by which astrocytes interact with and eliminate extracellular tau, and the influence of apoE genotype on these processes. Our work indicates that astrocytes rapidly internalize, process, and release tau via an exosomal secretory mechanism under physiological conditions. However, we found that apoE4 disrupted these processes in comparison to apoE3, resulting in an astrocytic phenotype prone to intracellular tau accumulation. Furthermore, exposure to repetitive mild traumatic brain injuries exacerbated the apoE4‐induced impairments in tau processing and elimination by astrocytes in apoE4 targeted‐replacement mice. The diminished ability of apoE4 astrocytes to eliminate extracellular tau can lead to an accumulation of pathogenic tau, which induces mitochondrial dysfunction, as demonstrated by our studies. In total, our findings suggest that the apoE4 isoform lowers the threshold of astrocytic resilience to pathogenic tau, rendering them susceptible to bioenergetic deficits in the early stages of neurodegenerative diseases such as traumatic brain injury, potentially contributing to neurological decline.
Unsupervised contrastive graph learning for resting‐state functional MRI analysis and brain disorder detection
Authors: Wang Xiaochuan , Chu Ying , Wang Qianqian , Cao Liang , Qiao Lishan , Zhang Limei , Liu Mingxia 
Published: September 2023

AbstractResting‐state functional magnetic resonance imaging (rs‐fMRI) helps characterize regional interactions that occur in the human brain at a resting state. Existing research often attempts to explore fMRI biomarkers that best predict brain disease progression using machine/deep learning techniques. Previous fMRI studies have shown that learning‐based methods usually require a large amount of labeled training data, limiting their utility in clinical practice where annotating data is often time‐consuming and labor‐intensive. To this end, we propose an unsupervised contrastive graph learning (UCGL) framework for fMRI‐based brain disease analysis, in which a pretext model is designed to generate informative fMRI representations using unlabeled training data, followed by model fine‐tuning to perform downstream disease identification tasks. Specifically, in the pretext model, we first design a bi‐level fMRI augmentation strategy to increase the sample size by augmenting blood‐oxygen‐level‐dependent (BOLD) signals, and then employ two parallel graph convolutional networks for fMRI feature extraction in an unsupervised contrastive learning manner. This pretext model can be optimized on large‐scale fMRI datasets, without requiring labeled training data. This model is further fine‐tuned on to‐be‐analyzed fMRI data for downstream disease detection in a task‐oriented learning manner. We evaluate the proposed method on three rs‐fMRI datasets for cross‐site and cross‐dataset learning tasks. Experimental results suggest that the UCGL outperforms several state‐of‐the‐art approaches in automated diagnosis of three brain diseases (i.e., major depressive disorder, autism spectrum disorder, and Alzheimer's disease) with rs‐fMRI data.
Identification of karst spring hydrographs using laboratory and numerical simulations considering combined discrete‐continuum approaches
Authors: Chen Yuan , Shu Longcang , Lian Yanqing , Li Hu , Li Gang 
Published: September 2023

AbstractThe quantitative analysis of karst spring hydrographs aids in characterizing the hydrodynamic properties of aquifer systems. However, the relationships between spring hydrographs and hydrologic functions of different media lack in‐depth research. This study detected the rainfall‐discharge processes of covered karst systems using two combined discrete‐continuum models (a coupled continuum‐pipe model and a hybrid model) developed based on laboratory experiments. An improved exponential function was used to decompose spring recession hydrographs with a great performance, and the results indicate that the slow drainage controlled by the porous medium dominates the whole recession process. There is a shift in the contribution of the matrix to karst discharge systems with the increase of its hydraulic conductivity. Although spring hydrographs have a great dependency on the hydraulic process of fracture networks, the variation in permeability of the matrix significantly changes the storage capacity of fractured rocks and spring discharge behaviours. The changing process of water storage rate of the matrix and fractures with spring hydrographs reveals three stages: a period of steep rise and fall, a period with slight fluctuations, and a gentle descent stage. The parameter sensitivity analysis in both models shows that the covering porous layer has a strong effect on the patterns of karst spring hydrographs, and the specific yield is more sensitive than hydraulic conductivity. While these simulations were conducted at the laboratory scale, the findings provide insights into practical applications of spring protection and water resources management.