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BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) is perceived as an emerging environmental endocrine disruptor, which have been linked to children neurodevelopment. However, the potential mechanisms are not clear. Brain-derived neurotrophic factor (BDNF) is a vital protein in neurodevelopment, and the associations between PFAS exposure and BDNF require exploration. OBJECTIVE: We aimed to explore the relationships between PFAS exposure and the levels of BDNF in cord serum. METHODS: A total of 1,189 mother-infant dyads from the Sheyang Mini Birth Cohort Study (SMBCS) were enrolled. The levels of 12 PFAS and BDNF were measured in cord serum. We utilized generalized linear models (GLMs), quantile-based g-computation (QGC) models, and Bayesian Kernel Machine Regression (BKMR) models to explore the relationships between single and mixed PFAS exposure and BDNF concentration. Additionally, the potential sex differences were explored by sex-stratified analysis. RESULTS: Median concentrations of the included 10 PFAS ranged from 0.04 to 3.97 µg/L. In the single chemical models, four PFAS congeners, namely perfluorononanoic acid (PFNA), perfluorooctane sulfonic acid (PFOS), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), were negatively associated with BDNF levels in cord serum among females only (ß: -0.116 to -0.062, p < 0.05). In the BKMR models of total mother-infant dyads and female fetuses, the significant negative relationships between PFAS mixtures and BDNF were observed, and PFUnDA was identified as an important contributor (Posterior inclusion probability, PIP = 0.8584 for the total subjects; PIP = 0.8488 for the females). PFOS was another important driver based on the mixture approaches. CONCLUSIONS: We found that PFNA, PFOS, PFDA, and PFUnDA were associated with decreased BDNF concentration in the females, although the causal inference might be limited. PFAS mixtures were also negatively linked with BDNF levels in the total mother-infant pairs and female fetuses. The adverse effect of PFAS exposure on fetal BDNF levels might be sex-specific.

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Transcranial electrical stimulation (TES) is a non-invasive neuromodulation technique with great potential. Electrode optimization methods based on simulation models of individual TES field could provide personalized stimulation parameters according to individual variations in head tissue structure, significantly enhancing the stimulation accuracy of TES. However, the existing electrode optimization methods suffer from prolonged computation times (typically exceeding 1 d) and limitations such as disregarding the restricted number of output channels from the stimulator, further impeding their clinical applicability. Hence, this paper proposes an efficient and practical electrode optimization method. The proposed method simultaneously optimizes both the intensity and focality of TES within the target brain area while constraining the number of electrodes used, and it achieves faster computational speed. Compared to commonly used electrode optimization methods, the proposed method significantly reduces computation time by 85.9% while maintaining optimization effectiveness. Moreover, our method considered the number of available channels for the stimulator to distribute the current across multiple electrodes, further improving the tolerability of TES. The electrode optimization method proposed in this paper has the characteristics of high efficiency and easy operation, potentially providing valuable supporting data and references for the implementation of individualized TES.

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In online video understanding, which has a wide range of real-world applications, inference speed is crucial. Many approaches involve frame-level visual feature extraction, which often represents the biggest bottleneck. We propose RetinaViT, an efficient method for extracting frame-level visual features in an online video stream, aiming to fundamentally enhance the efficiency of online video understanding tasks. RetinaViT is composed of efficiently approximated Transformer blocks that only take changed tokens (event tokens) as queries and reuse the already processed tokens from the previous timestep for the others. Furthermore, we restrict keys and values to the spatial neighborhoods of event tokens to further improve efficiency. RetinaViT involves tuning multiple parameters, which we determine through a multi-step process. During model training, we randomly vary these parameters and then perform black-box optimization to maximize accuracy and efficiency on the pre-trained model. We conducted extensive experiments on various online video recognition tasks, including action recognition, pose estimation, and object segmentation, validating the effectiveness of each component in RetinaViT and demonstrating improvements in the speed/accuracy trade-off compared to baselines. In particular, for action recognition, RetinaViT built on ViT-B16 reduces inference time by approximately 61.9% on the CPU and 50.8% on the GPU, while achieving slight accuracy improvements rather than degradation.

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Antimicrobial resistance (AMR) emerges when disease-causing microorganisms develop the ability to withstand the effects of antimicrobial therapy. This phenomenon is often fueled by the human-to-human transmission of pathogens and the overuse of antibiotics. Over the past 50 years, increased computational power has facilitated the application of Bayesian inference algorithms. In this comprehensive review, the basic theory of Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods are explained. These inference algorithms are instrumental in calibrating complex statistical models to the vast amounts of AMR-related data. Popular statistical models include hierarchical and mixture models as well as discrete and stochastic epidemiological compartmental and agent based models. Studies encompassed multi-drug resistance, economic implications of vaccines, and modeling AMR in vitro as well as within specific populations. We describe how combining these topics in a coherent framework can result in an effective antimicrobial stewardship. We also outline recent advancements in the methodology of Bayesian inference algorithms and provide insights into their prospective applicability for modeling AMR in the future.

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Two-stage stratified sampling is a complex design that involves nested sampling units and stratification. This complexity increases when the strata have too few sampled units for variance estimation, necessitating the use of collapsed strata, where multiple strata are combined to ensure an adequate sample size. When collapsing strata, two cases can be distinguished depending on whether a size variable associated with the variable of interest is available at the stratum level.•We present computer-implementable formulas for total, mean, and ratio estimators, along with their corresponding sampling variance estimators, for stratified two-stage simple random sampling without replacement, and we provide ready-to-use algorithms.•We introduce two methods for grouping strata: (1) a deterministic approach that uses stratum codes to define an ordinal variable, which orders the strata, and (2) a stochastic method that aims to minimize within-group inertia, which measures the heterogeneity within the newly formed groups of strata.•We emphasize that, unlike the correlation between a size variable and the variable of interest at the stratum level, the bias of the sampling variance estimator for the collapsed strata technique is not invariant to linear transformations. It follows that a high correlation does not ensure a low-bias estimator of the sampling variance.

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Animals are born with extensive innate behavioral capabilities, which arise from neural circuits encoded in the genome. However, the information capacity of the genome is orders of magnitude smaller than that needed to specify the connectivity of an arbitrary brain circuit, indicating that the rules encoding circuit formation must fit through a "genomic bottleneck" as they pass from one generation to the next. Here, we formulate the problem of innate behavioral capacity in the context of artificial neural networks in terms of lossy compression of the weight matrix. We find that several standard network architectures can be compressed by several orders of magnitude, yielding pretraining performance that can approach that of the fully trained network. Interestingly, for complex but not for simple test problems, the genomic bottleneck algorithm also captures essential features of the circuit, leading to enhanced transfer learning to novel tasks and datasets. Our results suggest that compressing a neural circuit through the genomic bottleneck serves as a regularizer, enabling evolution to select simple circuits that can be readily adapted to important real-world tasks. The genomic bottleneck also suggests how innate priors can complement conventional approaches to learning in designing algorithms for AI.

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BACKGROUND: Previous studies have shown significant associations between individual fat-soluble vitamins (FSVs) and metabolic syndromes (MetS). However, evidence on the multiple FSVs co-exposure and MetS odds is limited. Given that individuals are typically exposed to different levels of FSVs simultaneously, and FSVs can interact with each other. It's necessary to explore the association between multiple FSVs co-exposure and MetS odds. This study aims to address this gap in general U.S. adults aged ≥ 20 years. METHODS: We conducted a cross-sectional study utilizing data from the National Health and Nutrition Examination Surveys (NHANESs) 2003-2006 and 2017-2018. Three FSV, including vitamin A (VA), vitamin E (VE), and vitamin D (VD), and MetS diagnosed according to the ATP III guidelines were selected as exposure and outcome, respectively. Multivariable-adjusted logistic model was used to explore the associations of individual FSV exposure with MetS odds and MetS components. Restricted cubic splines were performed to explore the dose-response relationships among them. The quantile g-computation method was adopted to explore the associations of multiple FSVs co-exposure with MetS odds and MetS components. RESULTS: The presented study included a total of 13,975 individuals, with 2400 (17.17%) were diagnosed with MetS. After adjusting for various confounders, a positive linear pattern was observed for serum VA and VE and MetS associations. Serum VD was found to be negatively associated with MetS in a linear dose-response way. For each component of MetS, higher serum VA and VE were associated with higher triglyceride and high-density lipoprotein; higher serum VD was negatively associated with triglyceride, blood pressure, and fasting plasma glucose. MetS odds increased by 15% and 13%, respectively, in response to one quartile increase in FSVs co-exposure index (qgcomp) in the conditional model (OR = 1.15, 95%CI: 1.06, 1.24) and the marginal structural model (OR = 1.13, 95%CI: 1.06, 1.20). Besides, co-exposure to VA, VE, and VD was positively associated with triglyceride, high-density lipoprotein, and blood pressure levels. CONCLUSION: Findings in the present study revealed that high serum VA and VE levels were associated with elevated MetS odds, while serum VD was inversely associated with MetS odds. FSVs co-exposure was positively associated with MetS odds.

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A two-step methodology has been developed utilizing the models of Paul and Takayanagi to determine the modulus of polymer halloysite nanotube (HNT) products. Initially, HNTs and the adjacent interphase are considered as pseudoparticles, and their modulus is evaluated using the Paul model. Subsequently, the modulus of a nanocomposite, consisting of a polymer medium and pseudoparticles, is predicted by Takayanagi equation. The impacts of various factors on the modulus of the products are analyzed, and the results from the two-step method are compared with experimental data from different samples. It has been observed that the modulus of samples progressively increases with an increase in interphase depth. Also, a higher interphase modulus contributes to an enhanced modulus of samples. Nevertheless, excessively high interphase moduli (Ei > 60 GPa) result in only a marginal improvement in the modulus of nanocomposites. Additionally, narrower HNTs are advantageous for producing stronger samples, though the modulus of the nanocomposites slightly diminishes at very high HNT radii (R > 55 nm). The outputs of two-step method agree with the experimental moduli of various HNT-filled systems.

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As the size of water distribution network (WDN) models continues to grow, developing and applying real-time models or digital twins to simulate hydraulic behaviors in large-scale WDNs is becoming increasingly challenging. The long response time incurred when performing multiple hydraulic simulations in large-scale WDNs can no longer meet the current requirements for the efficient and real-time application of WDN models. To address this issue, there is a rising interest in accelerating hydraulic calculations in WDN models by integrating new model structures with abundant computational resources and mature parallel computing frameworks. This paper presents a novel and efficient framework for steady-state hydraulic calculations, comprising a joint topology-calculation decomposition method that decomposes the hydraulic calculation process and a high-performance decomposed gradient algorithm that integrates with parallel computation. Tests in four WDNs of different sizes with 8 to 85,118 nodes demonstrate that the framework maintains high calculation accuracy consistent with EPANET and can reduce calculation time by up to 51.93 % compared to EPANET in the largest WDN model. Further investigation found that factors affecting the acceleration include the decomposition level, consistency of sub-model sizes and sub-model structures. The framework aims to help develop rapid-responding models for large-scale WDNs and improve their efficiency in integrating multiple application algorithms, thereby supporting the water supply industry in achieving more adaptive and intelligent management of large-scale WDNs.

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Gold is generally considered a noble metal since it is inherently inert in its bulk state. However, gold demonstrates reactivity when it is in its ionic state. The inherent inertness of bulk gold has resulted in its widespread recognition as a vital raw material in various biomedical processes. The applications of these technologies include drug delivery microchips, dental prostheses, reconstructive surgery, culinary additives, and cardiovascular stents. Gold can also exist in molecules or ions, particularly gold ions, which facilitates the production of gold nanomaterials. In this paper, we have computed differential and integral operators by using the M -Polynomial of gold crystals and by utilizing this polynomial, we have also computed eleven topological indices like 1 s t Zagreb, 2 n d Zagreb, Hyper, Sigma, Second Modified, General Randic, General Reciprocal Randic, 3 r d Redefined Zagreb, Symmetric Division Degree, Harmonic, Inverse Sum indices for the structure of Gold crystal.

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Monitoring the data sources for possible changes is an important consumption requirement for applications running in interaction with the Web of Data. In this article, MonARCh which is an architecture for monitoring the result changes of registered SPARQL queries in the Linked Data environment, is proposed. MonARCh can be comprehended as a publish/subscribe system in the general sense. However, it differs in how communication with the data sources is realized. Data sources in the Linked Data environment do not publish the changes in the data. MonARCh provides the necessary communication infrastructure between the data sources and the consumers for the notification of changes. Users subscribe SPARQL queries to the system which are then converted to federated queries. MonARCh periodically checks for updates by re-executing SERVICE clauses and notifying users in case of any result change. In addition, to provide scalability, MonARCh takes the advantage of concurrent computation of the actor model. The parallel join algorithm utilized speeds up query execution and result generation processes. The design science methodology is used during the design, implementation and evaluation of the architecture. When compared to the literature MonARCh meets all the sufficient requirements from the linked data monitoring and state of the art perspectives while having many outstanding features from both points of view. The evaluation results show that even while working under the limited two-node cluster setting MonARCh could reach from 300 to 25,000 query monitoring capacity according to the diverse query selectivities executed within our test bench.

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Molecular robotics is challenging, so it seems best to keep it simple. We consider an abstract molecular robotics model based on simple folding instructions that execute asynchronously. Turning Machines are a simple 1D to 2D folding model, also easily generalisable to 2D to 3D folding. A Turning Machine starts out as a line of connected monomers in the discrete plane, each with an associated turning number. A monomer turns relative to its neighbours, executing a unit-distance translation that drags other monomers along with it, and through collective motion the initial set of monomers eventually folds into a programmed shape. We provide a suite of tools for reasoning about Turning Machines by fully characterising their ability to execute line rotations: executing an almost-full line rotation of 5 π / 3 radians is possible, yet a full 2 π rotation is impossible. Furthermore, line rotations up to 5 π / 3 are executed efficiently, in O ( log n ) expected time in our continuous time Markov chain time model. We then show that such line-rotations represent a fundamental primitive in the model, by using them to efficiently and asynchronously fold shapes. In particular, arbitrarily large zig-zag-rastered squares and zig-zag paths are foldable, as are y-monotone shapes albeit with error (bounded by perimeter length). Finally, we give shapes that despite having paths that traverse all their points, are in fact impossible to fold, as well as techniques for folding certain classes of (scaled) shapes without error. Our approach relies on careful geometric-based analyses of the feats possible and impossible by a very simple robotic system, and pushes conceptional hardness towards mathematical analysis and away from molecular implementation.

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The rapidly growing scale and variety of biomedical data repositories raise important privacy concerns. Conventional frameworks for collecting and sharing human subject data offer limited privacy protection, often necessitating the creation of data silos. Privacy-enhancing technologies (PETs) promise to safeguard these data and broaden their usage by providing means to share and analyze sensitive data while protecting privacy. Here, we review prominent PETs and illustrate their role in advancing biomedicine. We describe key use cases of PETs and their latest technical advances and highlight recent applications of PETs in a range of biomedical domains. We conclude by discussing outstanding challenges and social considerations that need to be addressed to facilitate a broader adoption of PETs in biomedical data science.

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Quantum machine learning has made remarkable progress in many important tasks. However, the gate complexity of the initial state preparation is seldom considered in lots of quantum machine learning algorithms, making them non-end-to-end. Herein, we propose a quantum algorithm for the node embedding problem that maps a node graph's topological structure to embedding vectors. The resulting quantum embedding state can be used as an input for other quantum machine learning algorithms. With O ( log ( N ) ) qubits to store the information of N nodes, our algorithm will not lose quantum advantage for the subsequent quantum information processing. Moreover, owing to the use of a parameterized quantum circuit with O ( poly ( log ( N ) ) ) depth, the resulting state can serve as an efficient quantum database. In addition, we explored the measurement complexity of the quantum node embedding algorithm, which is the main issue in training parameters, and extended the algorithm to capture high-order neighborhood information between nodes. Finally, we experimentally demonstrated our algorithm on an nuclear magnetic resonance quantum processor to solve a graph model.

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Many studies have indicated that individual exposure to phthalates (PAEs) or polycyclic aromatic hydrocarbons (PAHs) affects pregnancy outcomes. However, combined exposure to PAEs and PAHs presents a more realistic situation, and research on the combined effects of PAEs and PAHs on gestational age and newborn size is still limited. This study aimed to assess the effects of combined exposure to PAEs and PAHs on neonatal gestational age and birth size. Levels of 9 PAE and 10 PAH metabolites were measured from the urine samples of 1030 women during early pregnancy from the Zunyi Birth Cohort in China. Various statistical models, including linear regression, restricted cubic spline, Bayesian kernel machine regression, and quantile g-computation, were used to study the individual effects, dose-response relationships, and combined effects, respectively. The results of this prospective study revealed that each ten-fold increase in the concentration of monoethyl phthalate (MEP), 2-hydroxynaphthalene (2-OHNap), 2-hydroxyphenanthrene (2-OHPhe), and 1-hydroxypyrene (1-OHPyr) decreased gestational age by 1.033 days (95 % CI: -1.748, -0.319), 0.647 days (95 % CI: -1.076, -0.219), 0.845 days (95 % CI: -1.430, -0.260), and 0.888 days (95 % CI: -1.398, -0.378), respectively. Moreover, when the concentrations of MEP, 2-OHNap, 2-OHPhe, and 1-OHPyr exceeded 0.528, 0.039, 0.012, and 0.002 µg/g Cr, respectively, gestational age decreased in a dose-response manner. Upon analyzing the selected PAE and PAH metabolites as a mixture, we found that they were significantly negatively associated with gestational age, birth weight, and the ponderal index, with 1-OHPyr being the most important contributor. These findings highlight the adverse effects of single and combined exposure to PAEs and PAHs on gestational age. Therefore, future longitudinal cohort studies with larger sample sizes should be conducted across different geographic regions and ethnic groups to confirm the impact of combined exposure to PAEs and PAHs on birth outcomes.

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Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.

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The role of experience in the organization of cortical feedback (FB) remains unknown. We measured the effects of manipulating visual experience on the retinotopic specificity of supragranular and infragranular projections from the lateromedial (LM) visual area to layer (L)1 of the mouse primary visual cortex (V1). LM inputs were, on average, retinotopically matched with V1 neurons in normally and dark-reared mice, but visual exposure reduced the fraction of spatially overlapping inputs to V1. FB inputs from L5 conveyed more surround information to V1 than those from L2/3. The organization of LM inputs from L5 depended on their orientation preference and was disrupted by dark rearing. These observations were recapitulated by a model where visual experience minimizes receptive field overlap between LM inputs and V1 neurons. Our results provide a mechanism for the dependency of surround modulations on visual experience and suggest how expected interarea coactivation patterns are learned in cortical circuits.

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The cerebral cortex performs computations via numerous six-layer modules. The operational dynamics of these modules were studied primarily in early sensory cortices using bottom-up computation for response selectivity as a model, which has been recently revolutionized by genetic approaches in mice. However, cognitive processes such as recall and imagery require top-down generative computation. The question of whether the layered module operates similarly in top-down generative processing as in bottom-up sensory processing has become testable by advances in the layer identification of recorded neurons in behaving monkeys. This review examines recent advances in laminar signaling in these two computations, using predictive coding computation as a common reference, and shows that each of these computations recruits distinct laminar circuits, particularly in layer 5, depending on the cognitive demands. These findings highlight many open questions, including how different interareal feedback pathways, originating from and terminating at different layers, convey distinct functional signals.

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Two key challenges in the multidisciplinary field of sequence-controlled polymers are their efficient synthesis and the establishment of correlation with polymer properties. In this context, in this paper, gradient architecture in the hydrophobic tail of an amphiphile is implemented and synthesized for a fixed hydrophilic unit (polyethylene glycol, PEG), by means of two monomers (2-hydroxypropyl methacrylate, HPMA, and diacetone acrylamide, DAAM) of contrasting reactivities. The resulting non-biochemical gradient sequence-controlled polymers are generated from a one-pot, homogeneous mixture through a PET-RAFT-PISA (photoinduced electron/energy transfer-reversible addition-fragmentation chain transfer-polymerization-induced self-assembly) method. In addition, the initial concentration ratio of the monomers in the gradient is varied as an input for a set of fixed experimental parameters and conditions, and its correlation with kinetics, gradient and self-assembled morphologies is established, as the output of the process. These results are extensively corroborated via nuclear magnetic resonance (NMR) spectroscopy analysis, together with transmission electron microscopy (TEM) images, dynamic light scattering (DLS), and gel permeation chromatography (GPC) experiments. These results have implications for chemical computation carried out by PISA, programmable self-assembly, information storage, biomimetics, origins of life and synthetic protocell studies.

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Critically endangered Hooded Vultures (Necrosyrtes monachus Temminck, 1823), like many vulture species globally, are experiencing rapid population declines due to anthropogenic factors such as poisonings, human persecution, trading for belief-based use, and habitat loss/degradation. The Hooded Vulture is widespread across sub-Saharan Africa. Although it is considered one of the most abundant vultures in West Africa, this vulture species is less common in East and southern Africa, with the population at the southern-most edge of the distribution (in South Africa and Eswatini) estimated at only 100-200 mature individuals. The distribution of Hooded Vultures has contracted dramatically in southern Africa, with breeding populations largely confined to protected areas such as the Greater Kruger National Park. This study aimed to investigate the genetic diversity of the southern African range-edge population and assess if the recent contraction in the distribution has resulted in the population experiencing a genetic bottleneck. Sixteen microsatellite loci were amplified for samples collected along the Olifants River in the Greater Kruger National Park (n = 30). The genetic diversity in the South African population was compared to samples (n = 30) collected in Ghana, where Hooded Vultures are more abundant. Contrary to expectations, the South African peripheral Hooded Vulture population showed higher levels of heterozygosity (HO = 0.495) than the Ghanaian population (HO = 0.315). Neither population showed signs of recent bottleneck events when tested using demographic modelling and Approximate Bayesian computation (ABC). However, both populations showed high levels of inbreeding and relatedness. Our results suggest that despite being a small peripheral population, the South African Hooded Vulture population showed a similar level of genetic diversity as individuals sampled from a core population within the species distribution (in Ghana). This study supports the need for Hooded Vulture conservation efforts in the southern African region and highlights the evolutionary importance of range-edge populations.

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