bluuebunny/crossref_metadata_2025_split huggingface dataset:
A functional-structural connectivity metric detects ipsilateral connections with distinct functional specialisation in each hemisphere
Authors: Civier Oren , Sourty Marion , Calamante Fernando 
Published: December 2020

AbstractWe introduce a connectomics metric that integrates information on structural connectivity (SC) from diffusion MRI tractography and functional connectivity (FC) from resting-state functional MRI, at individual subject level. The metric is based on the ability of SC to broadly predict FC using a simple linear predictive model; for each connection in the brain, the metric quantifies the deviation from that model. For the metric to capture underlying physiological properties, we minimise systematic measurement errors and processing biases in both SC and FC, and address several challenges with the joint analysis. This also includes a data-driven normalisation approach. The combined metric may provide new information by indirectly assessing white matter structural properties that cannot be inferred from diffusion MRI alone, and/or complex interregional neural interactions that cannot be inferred from functional MRI alone. To demonstrate the utility of the metric, we used young adult data from the Human Connectome Project to examine all bilateral pairs of ipsilateral connections, i.e. each left-hemisphere connection in the brain was paired with its right-hemisphere homologue. We detected a minority of bilateral pairs where the metric value is significantly different across hemispheres, which we suggest reflects cases of ipsilateral connections that have distinct functional specialisation in each hemisphere. The pairs with significant effects spanned all cortical lobes, and also included several cortico-subcortical connections. Our findings highlight the potential in a joint analysis of structural and functional measures of connectivity, both for clinical applications and to help in the interpretation of results from standard functional connectivity analysis.Significance StatementBased on the notion that structure predicts function, the scientific community sought to demonstrate that structural information on fibre bundles that connect brain regions is sufficient to estimate the strength of interregional interactions. However, an accurate prediction using MRI has proved elusive. This paper posits that the failure to predict function from structure originates from limitations in measurement or interpretation of either diffusion MRI (to assess fibre bundles), fMRI (to assess functional interactions), or both. We show that these limitations can be nevertheless beneficial, as the extent of divergence between the two modalities may reflect hard-to-measure properties of interregional connections, such as their functional role in the brain. This provides many insights, including into the division of labour between hemispheres.
Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox
Authors: Kuntzelman Karl M. , Williams Jacob M. , Lim Phui Cheng , Samal Ashok , Rao Prahalada K. , Johnson Matthew R. 
Published: December 2020

AbstractIn recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.
Accessible Analysis of Longitudinal Data with Linear Mixed Effects Models
Authors: Murphy Jessica I. , Weaver Nicholas E. , Hendricks Audrey E. 
Published: December 2020

ABSTRACTLongitudinal mouse models are commonly used to study possible causal factors associated with human health and disease. However, the statistical models, such as two-way ANOVA, often applied in these studies do not appropriately model the experimental design, resulting in biased and imprecise results. Here, we describe the linear mixed effects (LME) model and how to use it for longitudinal mice studies. We re-analyze a dataset published by Blanton et al (Science 2016) that modeled mice growth trajectories after microbiome implantation from nourished or malnourished children. We compare the fit and stability of different parameterizations of ANOVA and LME models; most models found the nourished vs. malnourished growth trajectories differed significantly. We show through simulation that the results from the two-way ANOVA and LME models are not always consistent. Incorrectly modeling correlated data can result in increased rates of false positives or false negatives, supporting the need to model correlated data correctly. We provide an interactive Shiny App to enable accessible and appropriate analysis of longitudinal data using LME models.
AStreptococcusquorum sensing system enables suppression of innate immunity
Authors: Rahbari Kate , Chang Jennifer , Federle Michael 
Published: December 2020

AbstractSome bacterial pathogens utilize cell-cell communication systems, such as quorum sensing (QS), to coordinate genetic programs during host colonization and infection. The human-restricted pathosymbiontStreptococcus pyogenes(Group A Streptococcus, GAS) uses the Rgg2/Rgg3 QS system to modify the bacterial surface, enabling biofilm formation and lysozyme resistance. Here, we demonstrate that innate immune cell responses to GAS are substantially altered by the QS status of the bacteria. We found that macrophage activation, stimulated by multiple agonists and assessed by cytokine production and NFκB activity, was substantially suppressed upon interaction with QS-active GAS but not QS-inactive bacteria. Neither macrophage viability nor bacterial adherence were seen as different between QS activity states, yet TNFα, IL-6, and IFNβ levels and NFκB reporter activity were drastically lower following infection with QS-active GAS. Suppression required contact between viable bacteria and macrophages. A QS-regulated biosynthetic gene cluster (BGC) in the GAS genome, encoding several putative enzymes, was also required for macrophage modulation. Our findings suggest a model wherein upon contact with macrophages, QS-active GAS produce a BGC-derived factor capable of suppressing inflammatory responses. The suppressive capability of QS-active GAS is abolished after treatment with a specific QS inhibitor. These observations suggest that interfering with the ability of bacteria to collaborate via QS can serve as a strategy to counteract microbial efforts to manipulate host defenses.ImportanceStreptococcus pyogenesis restricted to human hosts and commonly causes superficial diseases such as pharyngitis; it can also cause severe and deadly manifestations including necrotizing skin disease or severe post-infectious sequelae like rheumatic heart disease. Understanding the complex mechanisms used by this pathogen to manipulate host defenses could aid in developing new therapeutics to treat infections. Here, we examine the impact of a bacterial cell-cell communication system, which is highly conserved acrossS. pyogenes, on host innate immune responses. We find thatS. pyogenesuses this system to suppress macrophage pro-inflammatory cytokine responses. Interference with this communication system could serve as a strategy to disarm bacteria and maintain an effective immune response.
Insulinase like protease 1 contributes to macrogamont formation inCryptosporidium parvum
Authors: Xu Rui , Feng Yaoyu , Xiao Lihua , Sibley L. David 
Published: December 2020

AbstractThe apicomplexan parasiteCryptosporidium parvumcontains an expanded family of 22 insulinase like proteases (INS), a feature that contrasts with their otherwise streamlined genome. Here we examined the function of INS1, which is most similar to the human insulinase protease that cleaves a variety of small peptide substrates. INS1 is a M16A clan member and contains a signal peptide, an N-terminal domain with the HxxEH active site, followed by three inactive domains. Unlike previously studiedC. parvumINS proteins that are expressed in sporozoites and during merogony, INS1 was expressed exclusively in macrogamonts, where it was localized in small cytoplasmic vesicles. Although INS1 did not colocalize with the oocyst wall protein recognized by the antibody OW50, immune-electron microscopy indicated that INS1 resides in small vesicles in the secretory system. Notably, these small INS1 positive vesicles often subtend large vacuoles resembling wall forming bodies, which contain precursors for oocyst wall formation. Genetic deletion of INS1, or replacement with an active site mutant, resulted in lower formation of macrogamontsin vitroand reduced oocyst sheddingin vivo. Our findings reveal that INS1 functions in formation or maturation of macrogamonts and that its loss results in attenuated virulence in immunocompromised mice.ImportanceCryptosporidiosis is a debilitating diarrheal disease in young children in developing countries. Absence of effective treatments or vaccines makes this infection very difficult to manage in susceptible populations. Although the oral dose of oocysts needed to cause infection is low, infected individuals shed very high numbers of oocysts, hence readily contaminating the environment. Our studies demonstrate that the protease INS1 is important for formation of female sexual stages and that in its absence, parasites produce fewer oocysts and are attenuated in immunocompromised mice. These findings suggest that mutants lacking INS1, or related proteases, may be useful for producing attenuated vaccines to induce immunity without causing disease.
Development and optimization of a new method for direct extraction of SARS-CoV-2 RNA from municipal wastewater using magnetic beads
Authors: Parra Guardado Ana L. , Sweeney Crystal L. , Hayes Emalie , Trueman Benjamin F. , Huang Yannan , Jamieson Rob C. , Rand Jennie L. , Gagnon Graham A. , Stoddart Amina K. 
Published: December 2020

AbstractThe use of magnetic beads in the extraction of nucleic acids from wastewater is presented as an approach to simplify extraction techniques for the detection of SARS-CoV-2 viral fragments in wastewater. In particular, this paper describes the development and optimization of a direct method for extracting SARS-CoV-2 RNA from municipal wastewater using magnetic beads. The recovery efficiency of the method using Accuplex SARS-CoV-2 Positive Reference Material (ASCV-2) was examined. Method factors assessed were sample volume, concentration of magnetic bead mix, elution temperature, and water matrix (deionized (DI) water and wastewater). The combination of optimized method parameters that resulted in the highest RNA recovery in both DI water (26.0 ± 0.8%) and wastewater (11.8 ± 1.4%) was a sample volume of 1.0 mL, a magnetic beads concentration of 100 µL mL-1 sample, and an elution temperature of 60 °C. The performance of this optimized method was further assessed in recovery experiments using wastewater samples spiked at 1.8×106 and 1.8×104 gene copies L-1 (GU L-1) with Gamma Inactivated SARS-COV-2 (GI-SCV-2) and 1.0×106 and 1.0×104 infectious units L-1 of Human Coronavirus 229E (HCV 229E) as viral surrogates. Recoveries of 86.1 and 4.6% were achieved for wastewater samples spiked with GI-SCV-2 at low and high concentrations, respectively. In assessing the effects of wastewater pre-filtration and addition of DL-Dithiothreitol (DTT, used to inactivate RNases that may degrade RNA) on recovery efficiency of ASCV-2, the magnetic bead-based extraction protocol performed optimally with unfiltered wastewater without DTT (recovery = 17.4 ± 0.4%). The method limit of detection (MLOD) for ASCV-2 recovered from pre-filtered wastewater was determined to be 4.6×104 GU L-1 (95% degree of confidence). Using this optimized magnetic bead-based extraction protocol, the presence of SARS-CoV-2 RNA was verified in wastewater collected from sewershed locations in Atlantic Canada. This emerging RNA extraction method is direct, rapid, and does not require the use of specialized equipment, thus offering advantageous application for laboratories with limited resources. As such, this method is an indispensable tool in the monitoring of wastewater for SARS-CoV-2 to potentially understand COVID-19 infection occurrence within communities and inform public health leaders.Graphical Abstract
Initial vancomycin versus metronidazole for the treatment of first-episode non-severe Clostridioides difficile infection
Authors: Zhang Kevin , Beckett Patricia , Abouanaser Salaheddin , Smieja Marek 
Published: December 2020

AbstractObjectiveClostridioides difficile infection (CDI) is the leading cause of infectious nosocomial diarrhea. Although initial fidaxomicin or vancomycin treatment is recommended by most major guidelines to treat severe CDI, there exists varied recommendations for first-episode non-severe CDI. Given the discrepancy in current treatment guidelines, we sought to evaluate the use of initial vancomycin versus metronidazole for first-episode non-severe CDI.MethodsWe conducted a retrospective cohort study of all adult inpatients with first-episode CDI at our institution from January 2013 to May 2018. The initial vancomycin versus initial metronidazole cohorts were examined using a multivariate logistic regression model.ResultsPatients (n = 737) had a median age of 72.3 years and 357 (48.4%) had hospital-acquired infection. Among patients with non-severe CDI (n = 326), recurrence, new incident infection, and 30-day mortality rates were 16.2%, 10.9%, and 5.3%, respectively, when treated with initial metronidazole, compared to 20.0%, 1.4%, and 10.0%, respectively, when treated with initial vancomycin. In an adjusted multivariable analysis, the use of initial vancomycin for the treatment of non-severe CDI was associated with a reduction in new incident infection (ORadj: 0.11; 95% CI: 0.02–0.86; P=0.035), compared to initial metronidazole.ConclusionsInitial vancomycin was associated with a reduced rate of new incident infection in the treatment of adult inpatients with first-episode non-severe CDI. These findings support the use of initial vancomycin for all inpatients with CDI, when fidaxomicin is unavailable.
Correction of human forehead temperature variations measured by non-contact infrared thermometer
Authors: Shajkofci Adrian 
Published: December 2020

AbstractElevated body temperature (fever) can be a common symptom of a medical condition, such as a viral or bacterial infection, including SARS-CoV-2 or influenza. Non-contact infrared thermometers are able to measure forehead temperature in a timely manner and were used to perform a fast fever screening in a population. However, forehead temperature measurements differ greatly from basal body temperatures, and are the target of massive perturbations from the environment. Here we gathered a dataset of N=18024 measurements using the same precision infrared sensor in different locations while tracking both outside temperature, room temperature, time of measurement, and identity. Herein, we propose a method able to extract and remove the influence of external perturbations and set the threshold for fever based on local statistics to 37.38 °C, after calibration and temperature correction. This method can help manufacturers and decision-makers to build and use more accurate tools so as to maximize both sensitivity and specificity of the screening protocol.Abstract Figure
The influence of body side and sex on neck muscle responses to left-frontal-oblique impacts
Authors: Mühlbeier Andreas , Boström Kim Joris , de Lussanet Marc H. E. , Kalthoff Wolfram , Kraaijenbrink Cassandra , Hagenfeld Lena , Natrup Jens , Castro William H. M. , Wagner Heiko 
Published: December 2020

ABSTRACTLow-velocity motor vehicle crashes frequently induce chronic neck disorders also referred to as whiplash-associated disorders (WAD). The etiology of WAD is still not fully understood. Women are affected more often and more severely than men. A frontal-oblique collision direction leads to WAD relatively frequently, but is poorly investigated as compared to rear-end or frontal collision directions. An oblique impact direction is assumed to strain the contralateral neck side more than the ipsilateral side, provoking an asymmetrical response pattern of the left and right cervical muscles. In this study, we examined the muscle reflex responses of the sternocleidomastoid, the paraspinal, and the trapezius muscles as well as the kinematic responses of 60 subjects during left-frontal-oblique collisions. Neither the reflex delay nor the peak head acceleration revealed significant sex differences. Thus, the elsewhere reported higher injury risk of females as compared to males cannot be explained by the electromyographic and kinematic results of this study. In females as well as in males the right muscle responses revealed shorter onset times than the left muscle responses. Thus, cervical muscles seem to be activated depending on the specific direction of the impact. Moreover, the difference between right and left muscle responses cannot be explained by a startle reflex. The movement of the head in relation to the torso (female mean ± SD: 92 ± 16 ms; male: 87 ± 16 ms) started almost simultaneously to the onset of the first muscle activation (right paraspinal muscles: female mean: 95 ms and 95%-CI 82 − 109; male mean: 95 ms and 95%-CI 84 − 108) indicating that the initial muscle activity seems not to be triggered by a stretch reflex in the PARA muscles.
Nectar non-protein amino acids (NPAAs) do not change nectar palatability but enhance learning and memory in honey bees
Authors: Carlesso Daniele , Smargiassi Stefania , Pasquini Elisa , Bertelli Giacomo , Baracchi David 
Published: December 2020

AbstractFloral nectar is a pivotal element of the intimate relationship between plants and pollinators and its chemical composition is likely to have been shaped by strong selective pressures. Nectars are composed of a plethora of nutritionally valuable compounds but also hundreds of secondary metabolites (SMs) whose ecological role is still not completely understood. Here we performed a set of behavioural experiments to study whether five ubiquitous nectar non-protein amino acids (NPAAs: β-alanine, GABA, citrulline, ornithine and taurine) interact with gustation, feeding preference, and learning and memory in the pollinatorApis mellifera. We showed that harnessed foragers were unable to discriminate NPAAs from water when only accessing antennal chemo-tactile information and that freely moving bees did not exhibit innate feeding preferences for NPAA-laced sucrose solutions. Also, dietary consumption of NPAAs did not alter food consumption or longevity in caged bees over 10 days. Taken together our data suggest that ecologically relevant concentrations of NPAAs did not alter nectar palatability to bees. Olfactory conditioning assays showed that honey bees were more likely to learn a scent when it signalled a sucrose reward containing either β-alanine or GABA, and that GABA also enhanced specific memory retention. Conversely, when ingested two hours prior to conditioning, GABA, β-alanine, and taurine weakened bees’ acquisition performances but not specific memory retention, which was enhanced in the case of β-alanine and taurine. Neither citrulline nor ornithine affected learning and memory. Our study suggests that NPAAs in nectars may represent a cooperative strategy adopted by plants to attract beneficial pollinators, while simultaneously enhancing pollen transfer among conspecific flowers. Future work should validate these results in more ecological scenarios and extend the study to as many nectar SMs as possible, alone and in combination, as well as to other species of pollinators.
Machine Learning-Assisted Directed Evolution Navigates a Combinatorial Epistatic Fitness Landscape with Minimal Screening Burden
Authors: Wittmann Bruce J. , Yue Yisong , Arnold Frances H. 
Published: December 2020

AbstractDue to screening limitations, in directed evolution (DE) of proteins it is rarely feasible to fully evaluate combinatorial mutant libraries made by mutagenesis at multiple sites. Instead, DE often involves a single-step greedy optimization in which the mutation in the highest-fitness variant identified in each round of single-site mutagenesis is fixed. However, because the effects of a mutation can depend on the presence or absence of other mutations, the efficiency and effectiveness of a single-step greedy walk is influenced by both the starting variant and the order in which beneficial mutations are identified—the process is path-dependent. We recently demonstrated a path-independent machine learning-assisted approach to directed evolution (MLDE) that allowsin silicoscreening of full combinatorial libraries made by simultaneous saturation mutagenesis, thus explicitly capturing the effects of cooperative mutations and bypassing the path-dependence that can limit greedy optimization. Here, we thoroughly investigate and optimize an MLDE workflow by testing a number of design considerations of the MLDE pipeline. Specifically, we (1) test the effects of different encoding strategies on MLDE efficiency, (2) integrate new models and a training procedure more amenable to protein engineering tasks, and (3) incorporate training set design strategies to avoid information-poor low-fitness protein variants (“holes”) in the training data. When applied to an epistatic, hole-filled, four-site combinatorial fitness landscape of protein G domain B1 (GB1), the resulting focused training MLDE (ftMLDE) protocol achieved the global fitness maximum up to 92% of the time at a total screening burden of 470 variants. In contrast, minimal-screening-burden single-step greedy optimization over the GB1 fitness landscape reached the global maximum just 1.2% of the time; ftMLDE matching this minimal screening burden (80 total variants) achieved the global optimum up to 9.6% of the time with a 49% higher expected maximum fitness achieved. To facilitate further development of MLDE, we present the MLDE software package (https://github.com/fhalab/MLDE), which is designed for use by protein engineers without computational or machine learning expertise.