bluuebunny/crossref_metadata_2025_split huggingface dataset:
Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics
Authors: Winner Taniel S. , Rosenberg Michael C. , Jain Kanishk , Kesar Trisha M. , Ting Lena H. , Berman Gordon J. 
Published: December 2022

AbstractLocomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics – i.e., gait signatures – for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations.Author SummaryIn this manuscript, we introduce a novel, machine learning-based framework for quantifying, characterizing, and modifying the underlying neuromechanical dynamics that drive unique gait patterns. Standard methods for evaluating movement typically focus on extracting discrete gait variables ignoring the complex inter-limb and inter-joint spatiotemporal dependencies that occur during gait. Popular physiologically realistic modeling approaches encode these spatiotemporal dependencies but are too complex to characterize individual differences in the factors driving unique gait patterns or disorders. To circumvent these modeling complications, we develop a phenomenological model of gait that enables more holistic and interpretable characterizations of gait, encoding these complex spatiotemporal dependencies between humans’ joint angles arising from joint neural and biomechanical constraints. Our coined ‘gait signature’ framework provides a path towards understanding the neuromechanics of locomotion. This framework has potential utility for clinical researchers prescribing individualized therapies for pathologies or biomechanists interested in animal locomotion or other periodic movements assessed across different pathologies, neural perturbations, and or conditions.
Identification and Elimination of Antifungal Tolerance inCandida auris
Authors: Koohi Samira Rasouli , Shankarnarayan Shamanth A. , Galon Clare Maristela , Charlebois Daniel A. 
Published: December 2022

AbstractAntimicrobial resistance is a global health crisis to which pathogenic fungi make a substantial contribution. The human fungal pathogenC. aurisis of particular concern due to its rapid spread across the world and its evolution of multidrug resistance. Fluconazole failure inC. aurishas been recently attributed to antifungal “tolerance”. Tolerance is a phenomenon whereby a slow growing subpopulation of tolerant cells, which are genetically identical to susceptible cells, emerges during drug treatment. We use microbroth dilution and disk diffusion assays together with image analysis to investigate antifungal tolerance inC. auristo all three classes of antifungal drugs used to treat invasive candidiasis. We find that 1)C. aurisis tolerant to several common fungistatic and fungicidal drugs, which in some cases can be visually detected after 24 hours, as well as after 48 hours, of antifungal drug exposure; 2) the tolerant phenotype reverts to the susceptible phenotype inC. auris; and 3) combining azole, polyene, and echinocandin antifungal drugs with the adjuvant chloroquine reduces or eliminates tolerance and resistance in patient-derivedC. aurisisolates. These results suggest that tolerance contributes to treatment failure inC. aurisinfections for a broad range of antifungal drugs and that antifungal adjuvants may improve treatment outcomes for patients infected with antifungal-tolerant or antifungal-resistant fungal pathogens.
History-dependent muscle resistance to stretch remains high after small, posturally-relevant pre-movements
Authors: Horslen Brian C. , Milburn Gregory N. , Blum Kyle P. , Simha Surabhi N. , Campbell Kenneth S. , Ting Lena H. 
Published: December 2022

AbstractThe contributions of intrinsic muscle fiber resistance during mechanical perturbations to standing and other postural behaviors are unclear. Muscle stiffness, a traditional metric for estimating muscle’s intrinsic resistance to stretch, is known to vary depending on the current level and history of the muscle’s activation, as well as the muscle’s recent movement history; this property has been referred to as history dependence or muscle thixotropy. However, we currently lack sufficient data about the degree to which muscle stiffness is modulated across posturally-relevant characteristics of muscle stretch and activation. Here, we characterized the history dependence of muscle’s resistance to stretch in single, permeabilized, activated, muscle fibers in posturally-relevant stretch conditions and activation levels. We used a classic paired muscle stretch paradigm, varying the amplitude of a “conditioning” triangular stretch-shorten cycle followed by a “test” ramp-and-hold imposed after a variable inter-stretch interval. We tested low (<15%), intermediate (15-50%) and high (>50%) muscle fiber activation levels, evaluating short-range stiffness and total impulse in the test stretch. Muscle fiber resistance to stretch remained high at conditioning amplitudes of <1% L0 and inter-stretch intervals of >1 s, characteristic of healthy standing postural sway. A ~70% attenuation of muscle resistance to stretch was reached at conditioning amplitudes of >3% L0 and inter-stretch intervals of <0.1s, characteristic of larger, faster postural sway in balance-impaired individuals. Overall, amplitude and inter-stretch interval interact to disrupt myofilaments such that intrinsic resistance to stretch is attenuated if the stretch is large enough and/or frequent enough.Summary StatementIntrinsic muscle fiber resistance to stretch is preserved after small, slow pre-movements based on healthy postural sway, but markedly reduced as pre-movements increase to emulate abnormal postural sway.
LSD use in the United States: Examining user demographics and their evolution from 2015-2019
Authors: Weleff Jeremy , Anand Akhil , Dewey Elizabeth N. , Barnett Brian S. 
Published: December 2022

AbstractBackgroundLysergic acid (LSD) use has risen in the United States (US) in recent years amid increased interest in therapeutic applications of psychedelics. Despite this, contemporary epidemiological investigations of LSD users are few. To expand the literature on this topic, we sought to characterize past-year LSD users in the US and investigate recent demographic evolution within this population.MethodsUsing National Survey on Drug Use and Health (NSDUH) data from 2015-2019, we investigated correlates of past-year LSD use and associated changes over the study period.ResultsPast-year LSD use increased by 47% from 2015 to 2019 (0.59% to 0.87%). However, among people reporting past-year hallucinogen use disorder there was no significant proportional increase in LSD users. Notable correlates of LSD use on multivariable analysis were: increased LSD access, lower perceived risk of trying LSD, Asian race, low income, fewer children in the home, history of ever selling drugs, being approached by someone selling drugs in the past month, lack of influence of religious beliefs on decision-making, and past-year suicide attempt among people age 18 and older. We found no associations with unemployment, arrest history, past-year psychological distress, or sexually transmitted infections. From 2015 to 2019, the proportion of respondents reporting past-year LSD use who were pregnant, age 26-34, and married increased. Past-year LSD use among lifetime users of methamphetamine also rose.ConclusionsThough still uncommonly used in the US, LSD’s societal acceptance may be increasing. Overall, LSD does not appear to contribute significantly to the country’s public health problems.Highlights:-For the last few years, NSDUH data has shown an increase in LSD use-Despite this, rates of reported hallucinogen use disorder have not proportionally increased-Evidence shows increasing societal acceptance for LSD use-The proportion of respondents reporting past-year LSD use who were pregnant, age 26-34, and married increased-Past-year LSD use among lifetime users of methamphetamine also rose
Dopaminergic Medication Accentuates Fecal Gut Microbiome Changes in Parkinson’s Disease
Authors: Boertien Jeffrey M , Pereira Pedro AB , Laine Pia , Paulin Lars , van der Zee Sygrid , Auvinen Petri , Scheperjans Filip , van Laar Teus 
Published: December 2022

AbstractFecal gut microbiota changes are associated with Parkinson’s disease (PD). However, disease related changes cannot readily be discerned from medication effects, as almost all participants in previous studies were using PD medication, and conclusive longitudinal data related to treatment initiation is lacking. Here, fecal gut microbiota composition was assessed in 62de novoPD participants who were untreated at baseline and used PD medication at one-year follow-up, by means of 16S-sequencing. In addition, participants were stratified for the type of dopaminergic medication.Overall gut microbiota composition did not differ between baseline and one-year follow-up, but was associated with levodopa dose and levodopa equivalent daily dose (LEDD). Several differentially abundant taxa are in line with previously described changes in PD. These included reduced levels of amplicon sequence variants (ASVs) belonging toFaecalibacterium prausnitziiand Lachnospiraceae in all participants at follow-up, and increased levels of an ASV belonging toBifidobacteriumin dopamine agonist users. The family Bifidobacteriaceae was increased in dopamine agonist users who only used pramipexole. Levodopa dose was inversely related to the abundance of the families Ruminococcaceae and Lachnospiraceae, and the genusLachnospiraceae ND3007 group. PD medications exert a measurable and dose-dependent effect on gut microbiota composition and accentuate several previously described gut microbiota changes in PD. Detailed knowledge of medication effects should be part of future trial designs of gut microbiome studies in PD and are necessary to interpret previously published data.
Bird song comparison using deep learning trained from avian perceptual judgments
Authors: Zandberg Lies , Morfi Veronica , George Julia , Clayton David F. , Stowell Dan , Lachlan Robert F. 
Published: December 2022

AbstractOur understanding of bird song, a model system for animal communication and the neurobiology of learning, depends critically on making reliable, validated comparisons between the complex multidimensional syllables that are used in songs. However, most assessments of song similarity are based on human inspection of spectrograms, or computational methods developed from human intuitions. Using a novel automated operant conditioning system, we collected a large corpus of zebra finches’ (Taeniopygia guttata) decisions about song syllable similarity. We use this dataset to compare and externally validate similarity algorithms in widely-used publicly available software (Raven, Sound Analysis Pro, Luscinia). Although these methods all perform better than chance, they do not closely emulate the avian assessments. We then introduce a novel deep learning method that can produce perceptual similarity judgements trained on such avian decisions. We find that this new method outperforms the established methods in accuracy and more closely approaches the avian assessments. Inconsistent (hence ambiguous) decisions are a common occurrence in animal behavioural data; we show that a modification of the deep learning training that accommodates these leads to the strongest performance. We argue this approach is the best way to validate methods to compare song similarity, that our dataset can be used to validate novel methods, and that the general approach can easily be extended to other species.
A large-scale serological survey in pets from October 2020 through June 2021 in France shows significantly higher exposure to SARS-CoV-2 in cats
Authors: Fritz Matthieu , Elguero Eric , Becquart Pierre , de Fonclare Daphné de Riols , Garcia Déborah , Beurlet Stephanie , Denolly Solène , Boson Bertrand , Rosolen Serge G. , Cosset François-Loïc , Briend-Marchal Alexandra , Legros Vincent , Leroy Eric M. 
Published: December 2022

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can infect many animals, including pets such as dogs and cats. Many studies have documented infection in companion animals by bio-molecular and serological methods. However, only a few have compared seroprevalence in cats and dogs from the general population, and these studies were limited by small sample sizes and collections over short periods. Our goal was to obtain a more accurate evaluation of seroprevalence in companion animals in France and to determine whether cats and dogs differ in their exposure to SARS-CoV-2. For this purpose, we conducted an extensive SARS-CoV-2 cross-sectional serological survey of 2036 cats and 3577 dogs sampled by veterinarians during medical examinations in clinics throughout France. Sampling was carried out from October 2020 through June 2021, a period encompassing the second and third waves of SARS-CoV-2 infections in humans in the country. Using a microsphere immunoassay targeting the receptor binding domain and trimeric spike protein, we found 7.1% seroprevalence in pets. In a subset of 308 seropositive samples, 26.3% had neutralizing antibodies. We found that cats were significantly more likely to test positive than dogs, with seropositivity rates of 9.3% and 5.9% in cats and dogs, respectively. Finally, data for both species showed that seroprevalence was lower in older animals and was not associated with the date of sampling or the sex of the animal. Our results show that cats are significantly more sensitive to SARS-CoV-2 than dogs, in line with experimental studies. Our large sample size provides for a reliable, statistically robust estimate of the frequency of infection of pets from their owners and offers strong support for the notion that cats are more sensitive to SARS-CoV-2 than dogs. Our findings emphasise the importance of a One-Health approach to the SARS-CoV-2 pandemic and raise the question of whether companion animals in close contact with humans should be vaccinated.
Variables associated with owner perceptions of the health of their dog: further analysis of data from a large international survey
Authors: Barrett-Jolley Richard , German Alexander J. 
Published: December 2022

AbstractIn a recent study (doi: 10.1371/journal.pone.0265662), associations were identified between owner-reported dog health status and diet, whereby those fed a vegan diet were perceived to be healthier. However, the study was limited because it did not consider possible confounding from variables not included in the analysis. The aim of the current study was to extend these earlier findings, using different modelling techniques and including multiple variables, to identify the most important predictors of owner perceptions of dog health.From the original dataset, two binary outcome variables were created: the‘any health problem’distinguished dogs that owners perceived to be healthy (“no”) from those perceived to have illness of any severity; the‘significant illness’variable distinguished dogs that owners perceived to be either healthy or having mild illness (“no”) from those perceived to have significant or serious illness (“yes”). Associations between these health outcomes and both owner-animal metadata and healthcare variables were assessed using logistic regression and machine learning predictive modelling using XGBoost.For theany health problemoutcome, best-fit models for both logistic regression (area under curve [AUC] 0.842) and XGBoost (AUC 0.836) contained the variables dog age, veterinary visits and received medication, whilst owner age and breed size category also featured. For thesignificant illnessoutcome, received medication, veterinary visits, dog age and were again the most important predictors for both logistic regression (AUC 0.903) and XGBoost (AUC 0.887), whilst breed size category, education and owner age also featured in the latter. Any contribution from the dog vegan diet variable was negligible.The results of the current study extend the previous research using the same dataset and suggest that diet has limited impact on owner-perceived dog health status; instead, dog age, frequency of veterinary visits and receiving medication are most important.
Assessing the brain representation of conscious and unconscious visual contents using encoding based representational similarity analysis
Authors: Mei Ning , Santana Roberto , Soto David 
Published: December 2022

AbstractThe development of novel frameworks to understand the properties of unconscious representations and how they differ from the conscious counterparts may be critical to make progress in the neuroscience of vision consciousness. Here we re-analysed data from a within-subject, high-precision, highly-sampled fMRI study (N=7) coupled with model-based representational similarity analysis (RSA) in order to provide an information-based approach to study the representation of conscious and unconscious visual contents The standard whole-brain searchlight RSA revealed that the hidden representations of convolutional neural network models explained brain activity patterns in response to unconscious contents in the ventral visual pathway in the majority of the observers, particularly for models that ranked high in explaining the variance of the visual cortex (i.e., VGGNet and ResNet50). Also five of seven subjects showed brain activity patterns that correlated with the model in frontoparietal areas in the unconscious trials. However, the results of an encoding-based RSA analyses in the unconscious condition were mixed and somehow difficult to interpret, including negative correlations between the representations of the computer vision models and the brain activity in frontal areas in a substantial amount of the observers.
Emerging variants of SARS-CoV-2 NSP10 highlight strong functional conservation of its binding to two non-structural proteins, NSP14 and NSP16
Authors: Wang Huan , Rizvi Syed R A , Dong Danni , Lou Jiaqi , Wang Qian , Sopipong Watanyoo , Su Yufeng , Najar Fares , Agarwal Pratul K , Kozielski Frank , Haider Shozeb 
Published: December 2022

AbstractThe coronavirus SARS-CoV-2 protects its RNA from being recognized by host immune responses by methylation of its 5’ end, also known as capping. This process is carried out by two enzymes, non-structural protein 16 (NSP16) containing 2’-O-methyltransferase and NSP14 through its N7 methyltransferase activity, which are essential for the replication of the viral genome as well as evading the host’s innate immunity. NSP10 acts as a crucial cofactor and stimulator of NSP14 and NSP16. To further understand the role of NSP10, we carried out a comprehensive analysis of >13 million globally collected whole-genome sequences (WGS) of SARS-CoV-2 obtained from the Global Initiative Sharing All Influenza Data (GISAID) and compared it with the reference genome Wuhan/WIV04/2019 to identify all currently known variants in NSP10. T12I, T102I, and A104V in NSP10 have been identified as the three most frequent variants and characterized using X-ray crystallography, biophysical assays and enhanced sampling simulations. In contrast to other proteins such as spike and NSP6, NSP10 is significantly less prone to mutation due to its crucial role in replication. The functional effects of the variants were examined for their impact on the binding affinity and stability of both NSP14-NSP10 and NSP16-NSP10 complexes. These results highlight the limited changes induced by variant evolution in NSP10 and reflect on the critical roles NSP10 plays during the SARS-CoV-2 life cycle. These results also indicate that there is limited capacity for the virus to overcome inhibitors targeting NSP10 via the generation of variants in inhibitor binding pockets.Significance StatementThe SARS-CoV-2 proteins have constantly been evolving. These variants assist the virus to survive, adapt and evade the host immune responses. While the main focus has been on structural proteins like Spike, there is very limited structural and functional information on the effects of emerging mutations on other essential non-structural viral proteins. One such protein is NSP10, an essential cofactor for NSP14 and NSP16. This study demonstrates that NSP10 is more resistant to genetic variations than other SARS-CoV-2 non-structural proteins and that the presence of mutations conserve structural and dynamic changes in NSP10. The effects of naturally occurring mutations reflect the evolutionary relationship between structurally conserved essential cofactors, their function and the role they play in the survival of the virus.
In Vitro Inhibition of SARS-CoV-2 Infection by Bromhexine hydrochloride
Authors: Martins Ronaldo , Ferreira Iasmin , Jorge Daniel M. M. , Almeida Leticia , Souza Juliano P. , Pontelli Marjorie , Castro Italo A. , Lima Thais M. , Viana Rosa M. M. , Zamboni Dario , Marcato Priscyla D. , Arruda Eurico 
Published: December 2022

ABSTRACTThe world is enduring the SARS CoV-2 pandemic, and although extensive research has been conducted on the issue, only a few antivirals have been approved to treat patients with COVID-19. Bromhexine hydrochloride was previously identified as a potent inhibitor of TMPRSS2, an essential protease for ACE-2 virus receptor interactions. In the present study, we investigated whether bromhexine treatment could reduce SARS CoV-2 replication in vitro. To evaluate bromhexine’s effectiveness against SARS COV-2 infection, viral load was measured using Caco-2 cell lines expressing TMPRSS2. Our molecular docking results indicate that bromhexine displays an affinity with the active site of TMPRSS2. The drug significantly inhibited SARS CoV-2, both parental and P1 variant strains, infection in the Caco-2 cell line, reducing about 40% of SARS-CoV-2 entrance and about 90% of viral progeny in the supernatant 48h post-infection. Furthermore, bromhexine did not exhibit any direct virucidal activity on SARS CoV-2. In conclusion, bromhexine hydrochloride efficiently disrupts SARS CoV-2 infection in vitro and has the potential to become an effective antiviral agent in COVID-19 treatment.