bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring facilitators for a transition from alternative and complementary therapies to evidence-based treatments in Ugandan first-episode psychosis patients
Authors: Mwesiga Emmanuel K. , Ssemata Andrew S. , Nakitende Ann Jacquelline , Ongeri Linnet , Semeere Aggrey , Loewy Rachel , Meffert Susan , Nakasujja Noeline 
Published: February 2022

AbstractIntroductionMost patients with psychotic disorders in Africa initially use alternative and complementary therapies leading to delays accessing evidence-based treatments. This delay, known as the duration of untreated psychosis (DUP), is associated with suboptimal response and reduced efficacy in short-term and long-term outcomes. In this study, we explore facilitators for transitioning from alternative and complementary therapies to evidence-based treatments for psychotic disorders, comparing these facilitators between patients and their caregivers.MethodsThe study was conducted at Butabika hospital in Kampala, Uganda. Participants included patients with first-episode psychosis who had used alternative and complementary therapies and their caregivers. An exploratory qualitative design using in-depth interviews was performed with thematic analysis used to analyze the data.ResultsWe interviewed eight patients and eight caregivers. The key themes that led to a switch were the high cost of alternative therapies, a desire to know the cause of the illness, seeking better care and the influence of friends and community members. Themes were similar among the participants and their caregivers except for stigma, which was only found among the caregivers.ConclusionsThe reasons for transitioning from alternative and complementary therapies to evidence-based treatments are similar between patients and caregivers, except for stigma that is a more important factor for transition among caregivers. Since this was an exploratory study, additional research with a larger sample and more diverse population would enable a deeper understanding of these factors and guide the development of interventions to reduce DUP and improve outcomes for psychotic disorders.
SARS-CoV-2 RNA wastewater settled solids surveillance frequency and impact on predicted COVID-19 incidence using a distributed lag model
Authors: Schoen Mary E. , Wolfe Marlene K. , Li Linlin , Duong Dorothea , White Bradley J. , Hughes Bridgette , Boehm Alexandria B. 
Published: February 2022

AbstractSARS-CoV-2 RNA concentrations in wastewater settled solids correlate well with COVID-19 incidence rates (IRs). Here, we develop distributed lag models (DLMs) to estimate IRs using concentrations of SARS-CoV-2 RNA from wastewater solids and investigate the impact of sampling frequency on model performance. SARS-CoV-2 N gene and PMMoV RNA concentrations were measured daily at four wastewater treatment plants in California. Artificially reduced datasets were produced for each plant with sampling frequencies of once every 2, 3, 4, and 7 days. Sewershed-specific models that related daily N/PMMoV to IR were fit for each sampling frequency with data from mid-Nov 2020 through mid-July 2021, which included the period of time during which Delta emerged. Models were used to predict IRs during a subsequent out-of-sample time period. When sampling occurred at least once every 4 days, the in- and out-of-sample root mean square error (RMSE) changed less than 7 cases/100,000 compared to daily sampling across sewersheds. This work illustrates that real-time, daily predictions of IR are possible with small error, despite changes in circulating variants, when sampling frequency is once every 4 days or more. However, reduced sampling frequency may not serve other important wastewater surveillance use cases.
Discovery and functional interrogation of SARS-CoV-2 protein-RNA interactions
Authors: Xiang Joy S. , Mueller Jasmine R. , Luo En-Ching , Yee Brian A. , Schafer Danielle , Schmok Jonathan C. , Tan Frederick E. , Rothamel Katherine , McVicar Rachael N. , Kwong Elizabeth M. , Jones Krysten L. , Her Hsuan-Lin , Chen Chun-Yuan , Vu Anthony Q. , Jin Wenhao , Park Samuel S. , Le Phuong , Brannan Kristopher W. , Kofman Eric R. , Li Yanhua , Tankka Alexandra T. , Dong Kevin D. , Song Yan , Carlin Aaron F. , Van Nostrand Eric L. , Leibel Sandra L. , Yeo Gene W. 
Published: February 2022

AbstractThe COVID-19 pandemic is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The betacoronvirus has a positive sense RNA genome which encodes for several RNA binding proteins. Here, we use enhanced crosslinking and immunoprecipitation to investigate SARS-CoV-2 protein interactions with viral and host RNAs in authentic virus-infected cells. SARS-CoV-2 proteins, NSP8, NSP12, and nucleocapsid display distinct preferences to specific regions in the RNA viral genome, providing evidence for their shared and separate roles in replication, transcription, and viral packaging. SARS-CoV-2 proteins expressed in human lung epithelial cells bind to 4773 unique host coding RNAs. Nine SARS-CoV-2 proteins upregulate target gene expression, including NSP12 and ORF9c, whose RNA substrates are associated with pathways in protein N-linked glycosylation ER processing and mitochondrial processes. Furthermore, siRNA knockdown of host genes targeted by viral proteins in human lung organoid cells identify potential antiviral host targets across different SARS-CoV-2 variants. Conversely, NSP9 inhibits host gene expression by blocking mRNA export and dampens cytokine productions, including interleukin-1α/β. Our viral protein-RNA interactome provides a catalog of potential therapeutic targets and offers insight into the etiology of COVID-19 as a safeguard against future pandemics.
An in-silico study of the mutation-associated effects on the spike protein of SARS-CoV-2, Omicron variant
Authors: Shishir Tushar Ahmed , Jannat Taslimun , Naser Iftekhar Bin 
Published: February 2022

AbstractThe emergence of Omicron (B.1.1.529), a new Variant of Concern in the COVID-19 outbreak, while accompanied by the ongoing Delta variant infection, has once again fueled fears of a new infection wave and global health concern. In the Omicron variant, the receptor-binding domain (RBD) of its spike glycoprotein is heavily mutated, a feature critical for the transmission rate of the virus by interacting with hACE2. In this study, we used a combination of conventional and advanced neural network-based in silico approaches to predict how these mutations would affect the spike protein. The results demonstrated a decrease in the electrostatic potentials of residues corresponding to receptor recognition sites, an increase in the alkalinity of the protein, a change in hydrophobicity, variations in functional residues, and an increase in the percentage of alpha-helix structure. Our next step was to predict the structural changes of the spike protein using the AI-based tool Alphafold2 and dock it with hACE2. The results revealed that the RBD of the Omicron variant had a higher affinity than the reference. Moreover, all-atom molecular dynamics simulations concluded that the RBD of the Omicron variant exhibits a more dispersed interaction network since mutations resulted in an increased number of hydrophobic interactions and hydrogen bonds with hACE2 compared to the reference RBD. In summary, our current study highlighted the potential structural basis for the enhanced transmissibility and pathogenicity of the Omicron variant, although further research is needed to investigate its epidemiological and biological implications.
Genome-wide significant risk loci for mood disorders in the Old Order Amish founder population
Authors: Humphries Elizabeth M. , Ahn Kwangmi , Kember Rachel L. , Lopes Fabiana L. , Mocci Evelina , Peralta Juan M. ,   Bipolar Sequencing Consortium, Blangero John , Glahn David C. , Goes Fernando S. , Zandi Peter P. , Kochunov Peter , Van Hout Cristopher , Shuldiner Alan R. , Pollin Toni I. , Mitchell Braxton D. , Bucan Maja , Hong L. Elliot , McMahon Francis J. , Ament Seth A. 
Published: February 2022

ABSTRACTBackgroundGenome-wide association studies (GWAS) of mood disorders in large case-control cohorts have identified numerous risk loci, yet pathophysiological mechanisms remain elusive, primarily due to the very small effects of common variants.MethodsWe sought to discover risk variants with larger effects by conducting a genome-wide association study of mood disorders in a founder population, the Old Order Amish (OOA, n=1,672).ResultsOur analysis revealed four genome-wide significant risk loci, all of which were associated with >2-fold relative risk. Quantitative behavioral and neurocognitive assessments (n=314) revealed effects of risk variants on sub-clinical depressive symptoms and information processing speed. Network analysis suggested that OOA-specific risk loci harbor novel risk-associated genes that interact with known neuropsychiatry-associated genes via gene interaction networks. Annotation of the variants at these risk loci revealed population-enriched, non-synonymous variants in two genes encoding neurodevelopmental transcription factors,CUX1andCNOT1.ConclusionsOur findings provide insight into the genetic architecture of mood disorders and a substrate for mechanistic and clinical studies.
Impact of key meteorological parameters on the spread of COVID-19 in Mumbai: Correlation and Regression Analysis
Authors: Shetty Sushant , Gawade Amit , Deolekar Subodh , Patil Vaishali , Pandharkar Rohit , Salunkhe Uday 
Published: February 2022

AbstractPurposeTo understand key meteorological parameters that influence the spread of COVID-19 in Mumbai, India (based on data from April 2020 – April 2021).MethodsThe meteorological parameters chosen were Temperature, Dew Temperature, Humidity, Pressure, Wind Speed. The underlying basic relationships between meteorological parameters and COVID-19 information for Mumbai was understood using Spearman’s rank correlation coefficients. After establishing basic relationships, Linear analysis and Generalized Additive Model’s (GAM) were used to figure out statistically significant weather parameters and model them to explain the best possible variance in the pandemic data.ResultsA model of temperature and windspeed could explain 17.3% and 8.3% of variance in Daily new cases and Daily recoveries respectively. As for deaths occurring due to the virus, a model comprising of only pressure best explains a variance of 17.3% in the data. Non-Linear modelling based on GAM confirms the findings of linear analysis and establishes certain non-linear relationships as well.ConclusionSARS-CoV-2 belongs to the class of Human Coronaviruses (HCoV) which show seasonality depending on weather conditions. The above article focuses on understanding the underlying relationships between SARS-CoV-2 and meteorological parameters that would help progress basic research and formulation of policies around the disease for each weather/season.Competing interestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Patterns of connectome variability in autism across five functional activation tasks. Findings from the LEAP project
Authors: Looden Tristan , Floris Dorothea L. , Llera Alberto , Chauvin Roselyne J. , Charman Tony , Banaschewski Tobias , Murphy Declan , Marquand Andre. F. , Buitelaar Jan K. , Beckmann Christian F. ,   the AIMS-2-TRIALS group
Published: February 2022

AbstractBackgroundAutism spectrum disorder (autism) is a complex neurodevelopmental condition with pronounced behavioural, cognitive, and neural heterogeneities across individuals. Here, our goal was to characterise heterogeneity in autism by identifying patterns of neural diversity as reflected in BOLD fMRI in the way individuals with autism engage with a varied array of cognitive tasks.MethodsAll analyses were based on the EU-AIMS/AIMS-2-TRIALS multisite Longitudinal European Autism Project (LEAP) with participants with autism and typically developing controls (TD) between 6 and 30 years of age. We employed a novel task-potency approach which combines the unique aspects of both resting-state fMRI and task-fMRI to quantify task-induced variations in the functional connectome. Normative modelling was used to map atypicality of features on an individual basis with respect to their distribution in neurotypical control participants. We applied robust out-of-sample canonical correlation analysis (CCA) to relate connectome data to behavioural data.ResultsDeviation from the normative ranges of global functional connectivity was greater for individuals with autism compared to TD in each fMRI task paradigm (all tasks p<0.001). The similarity across individuals of the deviation pattern was significantly increased in autistic relative to TD individuals (p<0.002). The CCA identified significant and robust brainbehavior covariation between functional connectivity atypicality and autism-related behavioral features.ConclusionsIndividuals with autism engage with tasks in a globally atypical way, but the particular spatial pattern of this atypicality is nevertheless similar across tasks. Atypicalities in the tasks originate mostly from prefrontal cortex and default mode network regions, but also speech and auditory networks. We show, moving forward, sophisticated modeling methods such as task-potency and normative modeling will prove key to unravelling complex heterogeneous conditions like autism.
Deep learning-based system for real-time behavior recognition and closed-loop control of behavioral mazes using depth sensing
Authors: Gerós Ana , Cruz Ricardo , de Chaumont Fabrice , Cardoso Jaime S. , Aguiar Paulo 
Published: February 2022

ABSTRACTRobust quantification of animal behavior is fundamental in experimental neuroscience research. Systems providing automated behavioral assessment are an important alternative to manual measurements avoiding problems such as human bias, low reproducibility and high cost. Integrating these tools with closed-loop control systems creates conditions to correlate environment and behavioral expressions effectively, and ultimately explain the neural foundations of behavior. We present an integrated solution for automated behavioral analysis of rodents using deep learning networks on video streams acquired from a depth-sensing camera. The use of depth sensors has notable advantages: tracking/classification performance is improved and independent of animals’ coat color, and videos can be recorded in dark conditions without affecting animals’ natural behavior. Convolutional and recurrent layers were combined in deep network architectures, and both spatial and temporal representations were successfully learned for a 4- classes behavior classification task (standstill, walking, rearing and grooming). Integration with Arduino microcontrollers creates an easy-to-use control platform providing low-latency feedback signals based on the deep learning automatic classification of animal behavior. The complete system, combining depth-sensor camera, computer, and Arduino microcontroller, allows simple mapping of input-output control signals using the animal’s current behavior and position. For example, a feeder can be controlled not by pressing a lever but by the animal behavior itself. An integrated graphical user interface completes a user-friendly and cost-effective solution for animal tracking and behavior classification. This open-software/open-hardware platform can boost the development of customized protocols for automated behavioral research, and support ever more sophisticated, reliable and reproducible behavioral neuroscience experiments.
A model of colour appearance based on efficient coding of natural images
Authors: Troscianko Jolyon , Osorio Daniel 
Published: February 2022

AbstractAn object’s colour, brightness and pattern are all influenced by its surroundings, and a number of visual phenomena and “illusions” have been discovered that highlight these often dramatic effects. Explanations for these phenomena range from low-level neural mechanisms to high-level processes that incorporate contextual information or prior knowledge. Importantly, few of these phenomena can currently be accounted for when measuring an object’s perceived colour. Here we ask to what extent colour appearance is predicted by a model based on the principle of coding efficiency. The model assumes that the image is encoded by noisy spatio-chromatic filters at one octave separations, which are either circularly symmetrical or oriented. Each spatial band’s lower threshold is set by the contrast sensitivity function, and the dynamic range of the band is a fixed multiple of this threshold, above which the response saturates. Filter outputs are then reweighted to give equal power in each channel for natural images. We demonstrate that the model fits human behavioural performance in psychophysics experiments, and also primate retinal ganglion responses. Next we systematically test the model’s ability to qualitatively predict over 35 brightness and colour phenomena, with almost complete success. This implies that contrary to high-level processing explanations, much of colour appearance is potentially attributable to simple mechanisms evolved for efficient coding of natural images, and is a basis for modelling the vision of humans and other animals.
Anatomy of the heart of the leatherback turtle
Authors: Jensen Bjarke , Lauridsen Henrik , Webb Grahame , Wang Tobias 
Published: February 2022

AbstractNon-crocodylian reptiles have hearts with a single ventricle, which is partially separated by a muscular ridge that provide some separation of blood flows. An exceptional situation exists in monitor lizards and pythons, where the ventricular left side generates a much higher systolic blood pressure than the right side, thus resembling mammals and birds. This functional division of the ventricle depends on a large muscular ridge and may relate to high metabolic demand. The large leatherback turtle (<1000 kg), with its active ocean-going lifestyle and elevated body temperatures, may have similar adaptations. Here, we report on the anatomy the hearts of two leatherback turtles. One stranded in Ballum, Denmark in 2020, and was examined in detail, supplemented by observations and photos of an additional stranding specimen from Canada. The external morphology of the leatherback heart resembles that of other turtles, but it is large. We made morphometric measurements of the Ballum heart and created an interactive 3D model using high resolution MRI. The volume of the ventricle was 950 ml, from a turtle of 300 kg, which is almost twice as large as in other reptiles. The Ballum heart was compared to MRI scans of the hearts of a tortoise, a python, and a monitor lizard. Internally, the leatherback heart is typical of non-crocodylian reptiles, and did not contain the well-developed septation found in pythons and monitor lizards. We conclude that if leatherback turtles have exceptional circulation needs, they are sustained with a relatively large but otherwise typical non-crocodylian reptile heart.
Characterization of various remdesivir-resistant mutations of SARS-CoV-2 by mathematical modeling and molecular dynamics simulation
Authors: Torii Shiho , Kim Kwang Su , Koseki Jun , Suzuki Rigel , Iwanami Shoya , Fujita Yasuhisa , Jeong Yong Dam , Matsuura Yoshiharu , Shimamura Teppei , Iwami Shingo , Fukuhara Takasuke 
Published: February 2022

AbstractMutations continue to accumulate within the SARS-CoV-2 genome, and the ongoing epidemic has shown no signs of ending. It is critical to predict problematic mutations that may arise in clinical environments and assess their properties in advance to quickly implement countermeasures against future variant infections. In this study, we identified mutations resistant to remdesivir, which is widely administered to SARS-CoV-2-infected patients, and discuss the cause of resistance. First, we simultaneously constructed eight recombinant viruses carrying the mutations detected in in vitro serial passages of SARS-CoV-2 in the presence of remdesivir. Time course analyses of cellular virus infections showed significantly higher infectious titers and infection rates in mutant viruses than wild type virus under treatment with remdesivir. Next, we developed a mathematical model in consideration of the changing dynamic of cells infected with mutant viruses with distinct propagation properties and defined that mutations detected in in vitro passages canceled the antiviral activities of remdesivir without raising virus production capacity. Finally, molecular dynamics simulations of the NSP12 protein of SARS-CoV-2 revealed that the molecular vibration around the RNA-binding site was increased by the introduction of mutations on NSP12. Taken together, we identified multiple mutations that affected the flexibility of the RNA binding site and decreased the antiviral activity of remdesivir. Our new insights will contribute to developing further antiviral measures against SARS-CoV-2 infection.Significance StatementConsidering the emerging Omicron strain, quick characterization of SARS-CoV-2 mutations is important. However, owing to the difficulties in genetically modifying SARS-CoV-2, limited groups have produced multiple mutant viruses. Our cutting-edge reverse genetics technique enabled construction of eight reporter-carrying mutant SARS-CoV-2 in this study. We developed a mathematical model taking into account sequential changes and identified antiviral effects against mutant viruses with differing propagation capacities and lethal effects on cells. In addition to identifying the positions of mutations, we analyzed the structural changes in SARS-CoV-2 NSP12 by computer simulation to understand the mechanism of resistance. This multidisciplinary approach promotes the evaluation of future resistance mutations.
Preferred synonymous codons are translated more accurately: Proteomic evidence, among-species variation, and mechanistic basis
Authors: Sun Mengyi , Zhang Jianzhi 
Published: February 2022

ABSTRACTA commonly stated cause of the widespread phenomenon of unequal uses of synonymous codons is their differential translational accuracies. However, this long-standing translational accuracy hypothesis (TAH) of codon usage bias has had no direct evidence beyond anecdotes. Analyzing proteomic data from Escherichia coli, we observe higher translational accuracies of more frequently used synonymous codons, offering direct, global evidence for the TAH. The experimentally measured codon-specific translational accuracies validate a sequence-based proxy; this proxy provides support for the TAH from the vast majority of over 1000 taxa surveyed in all domains of life. We find that the relative translational accuracies of synonymous codons vary substantially among taxa and are strongly correlated with the amounts of cognate tRNAs relative to those of near-cognate tRNAs. These and other observations suggest a model in which selections for translational efficiency and accuracy drive codon usage bias and its coevolution with the tRNA pool.