bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?
Authors: Tian Ye , Zalesky Andrew 
Published: May 2021

AbstractCognitive performance can be predicted from an individual’s functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC<0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC ≈ 0.5). Larger sample sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC<0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-sample, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.
Cysteine Mutants of the Major Facilitator Superfamily-Type Transporter CcoA Provide insight into Copper Import
Authors: Khalfaoui-Hassani Bahia , Trasnea Petru-Iulian , Steimle Stefan , Koch Hans-Georg , Daldal Fevzi 
Published: May 2021

AbstractCcoA belongs to the widely distributed bacterial copper (Cu) importer subfamily CalT (CcoA-like Transporters) of the Major Facilitator Superfamily (MFS), and provides cytoplasmic Cu needed for cbb3-type cytochrome c oxidase (cbb3-Cox) biogenesis. Earlier studies have supported a 12 transmembrane helices (TMH) topology of CcoA with the well-conserved Met233xxxMet237 and His261xxxMet265 motifs in its TMH7 and TMH8, respectively. Of these residues, Met233 and His261 are essential for Cu uptake and cbb3-Cox production, whereas Met237 and Met265 contribute partly to these processes. CcoA also contains five Cys residues of unknown role, and remarkably, its structural models predict that three of these are exposed to the highly oxidizing periplasm. Here, we first demonstrate that elimination of both Met237 and Met265 completely abolishes Cu uptake and cbb3-Cox production, indicating that CcoA requires at least one of these two Met residues for activity. Second, using scanning mutagenesis to probe plausible metal-interacting Met, His and Cys residues of CcoA we found that the periplasm-exposed Cys49 located at the end of TMH2, the Cys247 on a surface loop between TMH7 and THM8, and the C367 located at the end of TMH11 are important for CcoA function. Analyses of the single and double Cys mutants revealed the occurrence of a disulfide bond in CcoA in vivo, possibly related to conformational changes it undergoes during Cu import as MFS-type transporter. Our overall findings suggested a model linking Cu import for cbb3-Cox biogenesis with a thiol: disulfide oxidoreduction step, advancing our understanding of the mechanisms of CcoA function.ImportanceCopper (Cu) is a redox-active micronutrient that is both essential and toxic. Its cellular homeostasis is critical for supporting cuproprotein maturation while avoiding excessive oxidative stress. The Cu importer CcoA is the prototype of the widespread CalT subfamily of the MFS-type transporters. Hence, understanding its molecular mechanism of function is significant. Here we show that CcoA undergoes a thiol: disulfide oxidoreduction cycle, which is important for its Cu import activity.
Antibody Response to CoronaVac Vaccine in Indonesian COVID-19 Survivor
Authors: Kemal Rahmat Azhari , Sari Dita Kartika , Paulina Ariza Julia 
Published: May 2021

ABSTRACTSeveral studies have shown that individuals with previous history of SARS-CoV-2 infection had boosted antibody response after single dose of mRNA or adenovirus-vectored vaccines. We wondered whether single dose CoronaVac, a whole-inactivated vaccine, could be considered for COVID-19 survivors in Indonesia. We measured IgG anti-RBD titre among 18 survivors and 37 non-survivors. Among survivors, there were 9 survivors with positive antibody titre (seropositive) before vaccination and 9 seronegative survivors. All respondents received two doses of CoronaVac vaccine at 14-days interval. We found no significant antibody titre difference between non-survivor at 14 or 28 days after second dose as well as seronegative survivor at at 14 days after second dose. Seropositive survivors were rapidly boosted after first dose with higher antibody titer than non-survivors and seronegative survivors after second dose. However, antibody titer did not differ between first and second dose among seropositive survivors. Seropositive COVID-19 survivors could receive single dose of CoronaVac vaccine which could potentially ease the vaccine supply constrain. A long-term follow-up must be conducted to observe difference in antibody response and persistence.Article Summary LineSeropositive COVID-19 survivors had significantly higher antibody response after first dose of CoronaVac vaccine compared to non-survivors and seronegative survivors after second dose of vaccine.
Fear, Anxiety, Stress, and Depression of novel coronavirus (COVID-19) pandemic among patients and their healthcare workers
Authors: Parchani Ashwin , Vidhya K , Panda Prasan Kumar , Rawat Vikram Singh , Bahurupi Yogesh Arvind , Kalita Deepjyoti , Kumar Harsh , Dr Naveen 
Published: May 2021

ABSTRACTBackgroundDisease pandemics are known to cause psychological distress. The ensuing mental health issues are not only restricted to the patients and their relatives/friends but affect the healthcare workers (HCWs) as well. Our study aims to assess these psychological trends during the COVID-19 pandemic between the two most affected population groups, that is, patients and frontline healthcare workers.MethodsA survey questionnaire including scales to assess fear, anxiety, stress, depression - PSS 10, and DASS 21 was distributed and sent to all COVID-19 suspected/confirmed individuals and healthcare workers at a tertiary care center along with a second visit after 14 days of answering the first questionnaire and this continued as follow up. Data were analyzed with the SPSS Version 23 using various tests of significance.ResultsIn the community, COVID-19 patients in the age group 41-50 with respiratory tract symptoms and those who were home isolated/quarantined experienced a greater tendency of mental health problems. Healthcare workers posted in COVID-19 designated areas of the hospital displayed higher levels of stress, anxiety, and depression.ConclusionThe high degree of uncertainty associated with novel pathogens has a profound effect on the psychological state of suspected/confirmed cases as well as healthcare workers. Within the community, individuals suspected of having COVID-19 display a significant mental health burden, while HCWs also experience an unprecedented amount of stress during such enduring situations.Key pointsQuestionWhat is the psychological impact among patients and their healthcare workers during the COVID-19 pandemic?FindingsIn this observational study based on PSS 10 and DASS 21 questionnaire that included 156 patients and 226 health care workers, the patients in the age group 41-50 with respiratory tract symptoms and those who were home isolated/quarantined experienced a greater tendency of mental health problems. Similar burden was observed among health care workers.MeaningIn a COVID-19 pandemic both population groups displayed higher levels of fear, anxiety, stress, and depression.
Subcortical brain segmentation in 5-year-old children: validation of FSL-FIRST and FreeSurfer against manual segmentation
Authors: Lidauer Kristian , Pulli Elmo P. , Copeland Anni , Silver Eero , Kumpulainen Venla , Hashempour Niloofar , Merisaari Harri , Saunavaara Jani , Parkkola Riitta , Lähdesmäki Tuire , Saukko Ekaterina , Nolvi Saara , Kataja Eeva-Leena , Karlsson Linnea , Karlsson Hasse , Tuulari Jetro J. 
Published: May 2021

AbstractDeveloping accurate subcortical volumetric quantification tools is crucial for neurodevelopmental studies, as they could reduce the need for challenging and time-consuming manual segmentation. In this study the accuracy of two automated segmentation tools, FSL-FIRST (with three different boundary correction settings) and FreeSurfer were compared against manual segmentation of subcortical nuclei, including the hippocampus, amygdala, thalamus, putamen, globus pallidus, caudate and nucleus accumbens, using volumetric and correlation analyses in 80 5-year-olds.Both FSL-FIRST and FreeSurfer overestimated the volume on all structures except the caudate, and the accuracy varied depending on the structure. Small structures such as the amygdala and nucleus accumbens, which are visually difficult to distinguish, produced significant overestimations and weaker correlations with all automated methods. Larger and more readily distinguishable structures such as the caudate and putamen produced notably lower overestimations and stronger correlations. Overall, the segmentations performed by FSL-FIRST’s Default pipeline were the most accurate, while FreeSurfer’s results were weaker across the structures.In line with prior studies, the accuracy of automated segmentation tools was imperfect with respect to manually defined structures. However, apart from amygdala and nucleus accumbens, FSL-FIRST’s agreement could be considered satisfactory (Pearson correlation > 0.74, Intraclass correlation coefficient (ICC) > 0.68 and Dice Score coefficient (DSC) > 0.87) with highest values for the striatal structures (putamen, globus pallidus and caudate) (Pearson correlation > 0.77, ICC > 0.87 and DSC > 0.88, respectively). Overall, automated segmentation tools do not always provide satisfactory results, and careful visual inspection of the automated segmentations is strongly advised.
Combination of a Sindbis-SARS-CoV-2 spike vaccine and αOX40 antibody elicits protective immunity against SARS-CoV-2 induced disease and potentiates long-term SARS-CoV-2-specific humoral and T-cell immunity
Authors: Scaglione Antonella , Opp Silvana , Hurtado Alicia , Lin Ziyan , Pampeno Christine , Noval Maria G , Thannickal Sara A. , Stapleford Kenneth A. , Meruelo Daniel 
Published: May 2021

AbstractThe COVID-19 pandemic caused by the coronavirus SARS-CoV-2 is a major global public threat. Currently, a worldwide effort has been mounted to generate billions of effective SARS-CoV-2 vaccine doses to immunize the world’s population at record speeds. However, there is still demand for alternative effective vaccines that rapidly confer long-term protection and rely upon cost-effective, easily scaled-up manufacturing. Here, we present a Sindbis alphavirus vector (SV), transiently expressing the SARS-CoV-2 spike protein (SV.Spike), combined with the OX40 immunostimulatory antibody (αOX40) as a novel, highly effective vaccine approach. We show that SV.Spike plus αOX40 elicits long-lasting neutralizing antibodies and a vigorous T-cell response in mice. Protein binding, immunohistochemical and cellular infection assays all show that vaccinated mice sera inhibits spike functions. Immunophenotyping, RNA Seq transcriptome profiles and metabolic analysis indicate a reprogramming of T-cells in vaccinated mice. Activated T-cells were found to mobilize to lung tissue. Most importantly, SV.Spike plus αOX40 provided robust immune protection against infection with authentic coronavirus in transgenic mice expressing the human ACE2 receptor (hACE2-Tg). Finally, our immunization strategy induced strong effector memory response, potentiating protective immunity against re-exposure to SARS-CoV-2 spike protein. Our results show the potential of a new Sindbis virus-based vaccine platform to counteract waning immune response that can be used as a new candidate to combat SARS-CoV-2. Given the strong T-cell responses elicited, our vaccine is likely to be effective against variants that are proving challenging, as well as, serve as a platform to develop a broader spectrum pancoronavirus vaccine. Similarly, the vaccine approach is likely to be applicable to other pathogens.
Evaluating the risk of SARS-CoV-2 transmission to bats using a decision analytical framework
Authors: Cook Jonathan D. , Campbell Grant Evan H. , Coleman Jeremy T. H. , Sleeman Jonathan M. , Runge Michael C. 
Published: May 2021

ABSTRACTPreventing wildlife disease outbreaks is a priority issue for natural resource agencies, and management decisions can be urgent, especially in epidemic circumstances. With the emergence of SARS-CoV-2, wildlife agencies were concerned whether the activities they authorize might increase the risk of viral transmission from humans to North American bats but had a limited amount of time in which to make decisions. We provide a description of how decision analysis provides a powerful framework to analyze and re-analyze complex natural resource management problems as knowledge evolves. Coupled with expert judgment and avenues for the rapid release of information, risk assessment can provide timely scientific information for evolving decisions. In April 2020, the first rapid risk assessment was conducted to evaluate the risk of transmission of SARS-CoV-2 from humans to North American bats.Based on the best available information, and relying heavily on formal expert judgment, the risk assessment found a small possibility of transmission during summer work activities. Following that assessment, additional knowledge and data emerged, such as bat viral challenge studies, that further elucidated the risks of human-to-bat transmission and culminated in a second risk assessment in the fall of 2020. We update the first SARS-CoV-2 risk assessment with new estimates of little brown bat (Myotis lucifugus) susceptibility and new management alternatives, using findings from the prior two risk assessments and other empirical studies. We highlight the strengths of decision analysis and expert judgment not only to frame decisions and produce useful science in a timely manner, but also to serve as a framework to reassess risk as understanding improves. For SARS-CoV-2 risk, new knowledge led to an 88% decrease in the median number of bats estimated to be infected per 1000 encountered when compared to earlier results. The use of facemasks during, or a negative COVID-19 test prior to, bat encounters further reduced those risks. Using a combination of decision analysis, expert judgment, rapid risk assessment, and efficient modes of information distribution, we provide timely science support to decision makers for summer bat work in North America.
A Biologically Interpretable Graph Convolutional Network to Link Genetic Risk Pathways and Neuroimaging Markers of Disease
Authors: Ghosal Sayan , Chen Qiang , Pergola Giulio , Goldman Aaron L. , Ulrich William , Weinberger Daniel R. , Venkataraman Archana 
Published: May 2021

AbstractWe propose a novel end-to-end framework for whole-brain and whole-genome imaging-genetics. Our genetics network uses hierarchical graph convolution and pooling operations to embed subject-level data onto a low-dimensional latent space. The hierarchical network implicitly tracks the convergence of genetic risk across well-established biological pathways, while an attention mechanism automatically identifies the salient edges of this network at the subject level. In parallel, our imaging network projects multimodal data onto a set of latent embeddings. For interpretability, we implement a Bayesian feature selection strategy to extract the discriminative imaging biomarkers; these feature weights are optimized alongside the other model parameters. We couple the imaging and genetic embeddings with a predictor network, to ensure that the learned representations are linked to phenotype. We evaluate our framework on a schizophrenia dataset that includes two functional MRI paradigms and gene scores derived from Single Nucleotide Polymorphism data. Using repeated 10-fold cross-validation, we show that our imaging-genetics fusion achieves the better classification performance than state-of-the-art baselines. In an exploratory analysis, we further show that the biomarkers identified by our model are reproducible and closely associated with deficits in schizophrenia.
Historical biogeography and phylogeography ofIndoplanorbis exustus
Authors: Sil Maitreya , Mahveen Juveriya , Roy Abhishikta , Praveen Karanth K. , Aravind Neelavara Ananthram 
Published: May 2021

AbstractThe history of a lineage is intertwined with the history of the landscape it resides in. Here we showcase how the geo-tectonic and climatic evolution in South Asia and surrounding landmasses have shaped the biogeographic history ofIndoplanorbis exustus, a tropical Asian, freshwater, pulmonated snail. We amplified partial COI gene fragment from all over India and combined this with a larger dataset from South and Southeast Asia to carry out phylogenetic reconstruction, species delimitation analysis, and population genetic analyses. Two nuclear genes were also amplified from one individual per putative species to carry out divergence dating and ancestral area reconstruction analyses. The results suggest thatIndoplanorbisdispersed out of Africa into India during Eocene. Furthermore, molecular data suggestsIndoplanorbisis a species complex consisting of multiple putative species. The primary diversification took place in Northern Indian plains or the Northeast India. The speciation events appear to be primarily allopatric caused by a series of aridification events starting from late Miocene to early Pleistocene. None of the species seemed to have any underlying genetic structure suggestive of high vagility. All the species underwent population fluctuations during the Pleistocene likely driven by the Quaternary climatic fluctuations.
Impact of ploidy and pathogen life cycle on resistance durability
Authors: Saubin Méline , De Mita Stéphane , Zhu Xujia , Sudret Bruno , Halkett Fabien 
Published: May 2021

AbstractThe breeding of resistant hosts based on the gene-for-gene interaction is crucial to address epidemics of plant pathogens in agroecosystems. Resistant host deployment strategies are developed and studied worldwide to decrease the probability of resistance breakdown and increase the resistance durability in various pathosystems. A major component of deployment strategies is the proportion of resistant hosts in the landscape. However, the impact of this proportion on resistance durability remains unclear for diploid pathogens with complex life cycles. In this study, we modelled pathogen population dynamics and genetic evolution at the virulence locus to assess the impact of the ploidy (haploid or diploid) and the pathogen’s life cycle (with or without host alternation) on resistance durability. Ploidy has a strong impact on evolutionary trajectories, with much greater stochasticity and delayed times of resistance breakdown for diploids. This result emphasises the importance of genetic drift in this system: as the virulent allele is recessive, positive selection on resistant hosts only applies to homozygous (virulent) individuals, which may lead to population collapse at low frequencies of the virulent allele. We also observed differences in the effect of host deployment depending on the pathogen’s life cycle. With host alternation, the probability that the pathogen population collapses strongly increases with the proportion of resistant hosts in the landscape. Therefore, resistance breakdown events occurring at high proportions of resistant hosts frequently amount to evolutionary rescue. Last, life cycles correspond to two selection regimes: without host alternation (soft selection) the resistance breakdown is mainly driven by the migration rate. Conversely, host alternation (hard selection) resembles an all-or-nothing game, with stochastic trajectories caused by the recurrent allele redistributions on the alternate host.
Federated Multi-Site Normative Modeling using Hierarchical Bayesian Regression
Authors: Kia Seyed Mostafa , Huijsdens Hester , Rutherford Saige , Dinga Richard , Wolfers Thomas , Mennes Maarten , Andreassen Ole A. , Westlye Lars T. , Beckmann Christian F. , Marquand Andre F. 
Published: May 2021

AbstractClinical neuroimaging data availability has grown substantially in the last decade, providing the potential for studying heterogeneity in clinical cohorts on a previously unprecedented scale. Normative modeling is an emerging statistical tool for dissecting heterogeneity in complex brain disorders. However, its application remains technically challenging due to medical data privacy issues and difficulties in dealing with nuisance variation, such as the variability in the image acquisition process. Here, we introduce a federated probabilistic framework using hierarchical Bayesian regression (HBR) for multi-site normative modeling. The proposed method completes the life-cycle of normative modeling by providing the possibilities to learn, update, and adapt the model parameters on decentralized neuroimaging data. Our experimental results confirm the superiority of HBR in deriving more accurate normative ranges on large multi-site neuroimaging datasets compared to the current standard methods. In addition, our approach provides the possibility to recalibrate and reuse the learned model on local datasets and even on datasets with very small sample sizes. The proposed federated framework closes the technical loop for applying normative modeling across multiple sites in a decentralized manner. This will facilitate applications of normative modeling as a medical tool for screening the biological deviations in individuals affected by complex illnesses such as mental disorders.