bluuebunny/crossref_metadata_2025_split huggingface dataset:
The treatment gap for mental disorders in adults enrolled in HIV treatment programs in South Africa: A cohort study using linked electronic health records
Authors: Ruffieux Yann , Efthimiou Orestis , Van den Heuvel Leigh L. , Joska John A. , Cornell Morna , Seedat Soraya , Mouton Johannes P. , Prozesky Hans , Lund Crick , Maxwell Nicola , Tlali Mpho , Orrell Catherine , Davies Mary-Ann , Maartens Gary , Haas Andreas D. 
Published: August 2020

AbstractBackgroundMental disorders are common in people living with HIV (PLWH) but often remain untreated. We aimed to explore the gap in access to treatment (treatment gap) for mental disorders in adults followed-up in antiretroviral therapy (ART) programs in South Africa and disparities in access to mental health services.MethodsWe conducted a cohort study using ART program data and linked pharmacy and hospitalization data to estimate the 12-month prevalence of treatment for mental disorders (pharmacological or inpatient) and to examine factors associated with the rate of treatment for mental disorders among adults, aged 15-49 years, followed-up from January 1, 2012 to December 31, 2017 at one private care, two pubic primary care, and one public tertiary care ART programs in South Africa. We calculated the treatment gap for mental disorders as the discrepancy between the 12-month prevalence of mental disorders in PLWH (aged 15-49 years) in South Africa (estimated based on data from the Global Burden of Disease study) and the 12-month prevalence of treatment for mental disorders in ART programs. We calculated adjusted rate ratios (aRR) for factors associated with the rate of treatment of mental disorders using Poisson regression.Results182,285 ART patients were followed-up over 405,153 person-years. In 2017, the estimated treatment gap for mental disorders was 40.5% (95% CI 19.5%-52.9%) for patients followed-up in private care, 96.5% (95% CI 95.0%-97.5%) for patients followed-up in public primary care, and 65.0% (95% CI 36.5%-85.1%) for patients followed-up in public tertiary-care ART programs. Rates of treatment with antidepressants, anxiolytics and antipsychotics were 17 (aRR 0.06, 95% CI 0.06-0.07), 50 (aRR 0.02 95% CI 0.01-0.03), and 2.6 (aRR 0.39, 95% CI 0.35-0.43) times lower in public primary-care programs than in the private-sector ART program.InterpretationThere is a large treatment gap for mental disorders in PLWH in South Africa and substantial disparities in access to mental health service between patients receiving ART in the public vs. the private sector. In the public sector and especially in public primary care, PLWH with common mental disorders remain largely untreated.
Chemical-genetic interactions with the proline analog L-azetidine-2-carboxylic acid inSaccharomyces cerevisiae
Authors: Berg Matthew D. , Zhu Yanrui , Isaacson Joshua , Genereaux Julie , Loll-Krippleber Raphaël , Brown Grant W. , Brandl Christopher J. 
Published: August 2020

ABSTRACTNon-proteinogenic amino acids, such as the proline analog L-azetidine-2-carboxylic acid (AZC), are detrimental to cells because they are mis-incorporated into proteins and lead to proteotoxic stress. Our goal was to identify genes that show chemical-genetic interactions with AZC inSaccharomyces cerevisiaeand thus also potentially define the pathways cells use to cope with amino acid mis-incorporation. Screening the yeast deletion and temperature sensitive collections, we found 72 alleles with negative synthetic interactions with AZC treatment and 12 alleles that suppress AZC toxicity. Many of the genes with negative synthetic interactions are involved in protein quality control pathways through the proteasome. Genes involved in actin cytoskeleton organization and endocytosis also had negative synthetic interactions with AZC. Related to this, the number of actin patches per cell increases upon AZC treatment. Many of the same cellular processes were identified to have interactions with proteotoxic stress caused by two other amino acid analogs, canavanine and thialysine, or a mistranslating tRNA variant that mis-incorporates serine at proline codons. Alleles that suppressed AZC-induced toxicity functioned through the amino acid sensing TOR pathway or controlled amino acid permeases required for AZC uptake.
Diffusive dynamics of Aspartateα-decarboxylase (ADC) liganded with D-serine in aqueous solution
Authors: Raskar Tushar , Niebling Stephan , Devos Juliette M. , Yorke Briony A. , Härtlein Michael , Huse Nils , Forsyth V. Trevor , Seydel Tilo , Pearson Arwen R. 
Published: August 2020

AbstractIncoherent neutron spectroscopy, in combination with dynamic light scattering was used to investigate the effect of ligand binding on the center-of-mass self-diffusion and internal diffusive dynamics ofE.coliaspartateα-decarboxylase (ADC). The X-ray crystal structure of the D-serine inhibitor complex with ADC was also determined, and molecular dynamics simulations used to further probe the structural rearrangements that occur as a result of ligand binding. These experiments reveal the existence of higher order oligomers of the ADC tetramer on ns-ms time-scales, and also show that ligand binding both affects the ADC internal diffusive dynamics and appears to further increase the size of the higher order oligomers.
Ever-increasing viral diversity associated with the red imported fire ant Solenopsis invicta (Formicidae: Hymenoptera)
Authors: Xavier César A.D. , Allen Margaret L. , Whitfield Anna E. 
Published: August 2020

AbstractBackgroundAdvances in sequencing and analysis tools have facilitated discovery of many new viruses from invertebrates, including ants. Solenopsis invicta is an invasive ant that has quickly spread around world causing significant ecological and economic impacts. Its virome has begun to be characterized pertaining to potential use of viruses as natural enemies. Although the S. invicta virome is best characterized among ants, most studies have been performed in its native range, with little information from invaded areas.MethodsUsing a metatranscriptome approach, we further characterized viruses associated with S. invicta, in two introduced areas, U.S and Taiwan. The data set used here was obtained from different stages (larvae, pupa, and adults) of S. invicta life cycle. Publicly available RNA sequences from GenBank’s Sequence Read Archive were downloaded and de novo assembled using CLC Genomics Workbench 20.0.1. Contigs were compared against the non-redundant protein sequences and those showing similarity to viral sequences were further analyzed.ResultsWe characterized five putative new viruses associated with S. invicta transcriptomes. Sequence comparisons revealed extensive divergence across ORFs and genomic regions with most of them sharing less than 40% amino acid identity with those closest homologous sequences previously characterized. The first negative-sense single-stranded RNA viruses included in the orders Bunyavirales and Mononegavirales are reported. In addition, two positive single-strand viruses and one single strand DNA virus were also characterized. While the presence of a putative tenuivirus associated with S. invicta was previously suggested to be a contamination, here we characterized and present strong evidence that Solenopsis invicta virus 14 (SINV-14) is a tenui-like virus that has a long-term association with the ant. Furthermore, based on virus abundance compared to housekeeping genes, phylogenetic relationships, and completeness of viral coding sequences, our results suggest that four of five viruses reported, those being SINV-14, SINV-15, SINV-16 and SINV-17, replicate in the ant S. invicta.ConclusionsThe present study expands our knowledge about viral diversity associated with S. invicta in introduced areas with potential to be used as biological control agents, which will require further biological characterization.
PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies
Authors: Vantini Michele , Mannerström Henrik , Rautio Sini , Ahlfors Helena , Stockinger Brigitta , Lähdesmäki Harri 
Published: August 2020

AbstractWe propose PairGP, a non-stationary Gaussian process method to compare gene expression timeseries across several conditions that can account for paired longitudinal study designs and can identify groups of conditions that have different gene expression dynamics. We demonstrate the method on both simulated data and previously unpublished RNA-seq time-series with five conditions. The results show the advantage of modeling the pairing effect to better identify groups of conditions with different dynamics. The implementations is available athttps://github.com/michelevantini/PairGP
FAM19A5l affects mustard oil-induced peripheral nociception in zebrafish
Authors: Jeong Inyoung , Yun Seongsik , Shahapal Anu , Cho Eun Bee , Hwang Sun Wook , Seong Jae Young , Park Hae-Chul 
Published: August 2020

SUMMARYFamily with sequence similarity 19 (chemokine (C-C motif)-like), member A5 (FAM19A5) is a chemokine-like secretory protein recently identified to be involved in the regulation of osteoclast formation, post-injury neointima formation, and depression. Here, we identifiedFAM19A5l, an orthologous zebrafish gene that originated from a common ancestralFAM19A5gene.FAM19A5lwas expressed in trigeminal and dorsal root ganglion neurons as well as distinct neuronal subsets of the central nervous system of zebrafish. Interestingly,FAM19A5l+trigeminal neurons were nociceptors that co-localized with TRPA1b and TRPV1, and responded to mustard-oil treatment. Behavioral analysis revealed that the nociceptive response to mustard oil decreased inFAM19A5l-knockout zebrafish larvae. In addition,TRPA1bandNGFamRNA levels were down- and up-regulated inFAM19A5l-knockout and - overexpressing transgenic zebrafish, respectively. Together, our data suggested that FAM19A5l played a role in nociceptive responses to mustard oil by regulatingTRPA1bandNGFaexpression in zebrafish.
Spec2Vec: Improved mass spectral similarity scoring through learning of structural relationships
Authors: Huber Florian , Ridder Lars , Verhoeven Stefan , Spaaks Jurriaan H. , Diblen Faruk , Rogers Simon , van der Hooft Justin J.J. 
Published: August 2020

AbstractSpectral similarity is used as a proxy for structural similarity in many tandem mass spectrometry (MS/MS) based metabolomics analyses such as library matching and molecular networking. Although weaknesses in the relationship between spectral similarity scores and the true structural similarities have been described, little development of alternative scores has been undertaken. Here, we introduce Spec2Vec, a novel spectral similarity score inspired by a natural language processing algorithm -- Word2Vec. Spec2Vec learns fragmental relationships within a large set of spectral data to derive abstract spectral embeddings that can be used to assess spectral similarities. Using data derived from GNPS MS/MS libraries including spectra for nearly 13,000 unique molecules, we show how Spec2Vec scores correlate better with structural similarity than cosine-based scores. We demonstrate the advantages of Spec2Vec in library matching and molecular networking. Spec2Vec is computationally more scalable allowing structural analogue searches in large databases within seconds.
Metamers of Bayesian computation
Authors: Sohn Hansem , Jazayeri Mehrdad 
Published: August 2020

AbstractThere are two sharply debated views on how humans make decisions under uncertainty. Bayesian decision theory posits that humans optimize their behavior by establishing and integrating internal models of past sensory experiences (priors) and decision outcomes (cost functions). An alternative model-free hypothesis posits that decisions are optimized through trial and error without explicit internal models for priors and cost functions. To distinguish between these possibilities, we introduce a novel paradigm that probes sensitivity of humans to transitions between prior-cost pairs that demand the same optimal policy (metamers) but distinct internal models. We demonstrate the utility of our approach in two experiments that were classically explained by model-based Bayesian theory. Our approach validates the model-based strategy in an interval timing task but not in a visuomotor rotation task. More generally, our work provides a domain-general approach for testing the circumstances under which humans implement model-based Bayesian computations.
Individual heterogeneity and its importance for metapopulation dynamics
Authors: Masier Stefano , Dahirel Maxime , Mortier Frederik , Bonte Dries 
Published: August 2020

AbstractLandscape connectedness shapes the exchange of individuals among patches, and hence metapopulation connectivity and dynamics. Connectedness, and its resulting effects on connectivity are therefore rightfully central in conservation biology. However, besides determining demographic fluxes of individuals between patches, connectedness also generates phenotypic sorting and thus impacts local and regional eco-evolutionary dynamics. Despite the central role of connectedness, its effects on individual phenotypic heterogeneity and spatial organization are so far neglected in theory and applications.Through experimental metapopulations ofTetranychus urticae(two-spotted spider mite) with three levels of landscape connectedness and by regularly removing phenotypic structure in a subset of these populations, we tested how regional and local population dynamics are determined both by network connectedness and phenotypic spatial organization.We find that the self-organizing phenotypic spatial structure increases local equilibrium population sizes and variability. It in contrast dampens the effects of imposed connectedness differences on population sizes and is therefore anticipated to improve metapopulation persistence. Contrary to theoretical expectations, the most locally connected patches within the network showed an overall reduced local population size, possibly originating from a faster depletion of resources from immigrants or transiting individuals.This experiment shows how metapopulation dynamics can significantly deviate from theoretical expectations due to individual heterogeneity. This potential rescue effect stemming from phenotypical self-organization in space is a key point to consider for conservation actions, especially based on translocations.
Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation
Authors: Song Seungmoon , Kidziński Łukasz , Peng Xue Bin , Ong Carmichael , Hicks Jennifer , Levine Sergey , Atkeson Christopher G. , Delp Scott L. 
Published: August 2020

AbstractModeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Despite advances in neuroscience techniques, it is still difficult to measure and interpret the activity of the millions of neurons involved in motor control. Thus, researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This “Learn to Move” competition, which we have run annually since 2017 at the NeurIPS conference, has attracted over 1300 teams from around the world. Top teams adapted state-of-art deep reinforcement learning techniques to produce complex motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research.
Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues
Authors: DeVilbiss Andrew W. , Zhao Zhiyu , Martin-Sandoval Misty S. , Ubellacker Jessalyn M. , Tasdogan Alpaslan , Agathocleous Michalis , Mathews Thomas P. , Morrison Sean J. 
Published: August 2020

Little is known about the metabolic regulation of rare cell populations because most metabolites are hard to detect in small numbers of cells. We previously described a method for metabolomic profiling of flow cytometrically-isolated hematopoietic stem cells (HSCs) that detects approximately 60 metabolites in 10,000 cells (Agathocleous et al., 2017). Here we describe a new method involving hydrophilic liquid interaction chromatography (HILIC) and high-sensitivity orbitrap mass spectrometry that detected approximately 160 metabolites in 10,000 HSCs, including many more glycolytic and lipid intermediates. We improved chromatographic separation, increased mass resolution, minimized ion suppression, extracted with acetonitrile, and eliminated sample drying. Most metabolites did not significantly change during cell preparation and sorting. We used this method to profile HSCs and circulating melanoma cells. HSCs exhibited increased glycerophospholipid metabolites relative to unfractionated bone marrow cells and altered purine biosynthesis after methotrexate treatment in vivo. Circulating melanoma cells were depleted for purine intermediates relative to subcutaneous tumors, suggesting they decrease purine synthesis during metastasis. These methods facilitate the routine metabolomic analysis of rare cell populations from tissues.Impact statementWe developed a method for metabolomic analysis of small numbers of flow cytometrically isolated cells from rare cell populations such as hematopoietic stem cells and circulating cancer cells.
The relative coding strength of object identity and nonidentity features in human occipito-temporal cortex and convolutional neural networks
Authors: Xu Yaoda , Vaziri-Pashkam Maryam 
Published: August 2020

ABSTRACTAny given visual object input is characterized by multiple visual features, such as identity, position and size. Despite the usefulness of identity and nonidentity features in vision and their joint coding throughout the primate ventral visual processing pathway, they have so far been studied relatively independently. Here we document the relative coding strength of object identity and nonidentity features in a brain region and how this may change across the human ventral visual pathway. We examined a total of four nonidentity features, including two Euclidean features (position and size) and two non-Euclidean features (image statistics and spatial frequency content of an image). Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with identity outweighed the non-Euclidean features, but not the Euclidean ones, in higher levels of visual processing. A similar analysis was performed in 14 convolutional neural networks (CNNs) pretrained to perform object categorization with varying architecture, depth, and with/without recurrent processing. While the relative coding strength of object identity and nonidentity features in lower CNN layers matched well with that in early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Similar results were obtained regardless of whether a CNN was trained with real-world or stylized object images that emphasized shape representation. Together, by measuring the relative coding strength of object identity and nonidentity features, our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.SIGNIFICANCE STATEMENTThis study documented the relative coding strength of object identity compared to four types of nonidentity features along the human ventral visual processing pathway and compared brain responses with those of 14 CNNs pretrained to perform object categorization. Overall, identity representation increased and nonidentity feature representation decreased along the ventral visual pathway, with the coding strength of the different nonidentity features differed at higher levels of visual processing. While feature coding in lower CNN layers matched well with that of early human visual areas, the match between higher CNN layers and higher human visual regions were limited. Our approach provided a new tool to characterize feature coding in the human brain and the correspondence between the brain and CNNs.
Geographic potential of the world’s largest hornet,Vespa mandariniaSmith (Hymenoptera: Vespidae), worldwide and particularly in North America
Authors: Nuñez-Penichet Claudia , Osorio-Olvera Luis , Gonzalez Victor H. , Cobos Marlon E. , Jiménez Laura , DeRaad Devon A. , Alkishe Abdelghafar , Contreras-Díaz Rusby G. , Nava-Bolaños Angela , Utsumi Kaera , Ashraf Uzma , Adeboje Adeola , Peterson A. Townsend , Soberón Jorge 
Published: August 2020

ABSTRACTThe Asian giant hornet (AGH,Vespa mandarinia) is the world’s largest hornet, occurring naturally in the Indomalayan region, where it is a voracious predator of pollinating insects including honey bees. In September 2019, a nest of Asian giant hornets was detected outside of Vancouver, British Columbia and in May 2020 an individual was detected nearby in Washington state, indicating that the AGH successfully overwintered in North America. Because hornets tend to spread rapidly and become pests, reliable estimates of the potential invasive range ofV. mandariniain North America are needed to assess likely human and economic impacts, and to guide future eradication attempts. Here, we assess climatic suitability for AGH in North America, and suggest that, without control, this species could establish populations across the Pacific Northwest and much of eastern North America. Predicted suitable areas for AGH in North America overlap broadly with areas where honey production is highest, as well as with species-rich areas for native bumble bees and stingless bees of the genusMeliponain Mexico, highlighting the economic and environmental necessity of controlling this nascent invasion.