bluuebunny/crossref_metadata_2025 huggingface dataset:
Engagement and adherence trade-offs for SARS-CoV-2 contact tracing
Authors: Lucas Tim C. D. , Davis Emma L. , Ayabina Diepreye , Borlase Anna , Crellen Thomas , Pi Li , Medley Graham F. , Yardley Lucy , Klepac Petra , Gog Julia , Déirdre Hollingsworth T.
Published: May 2021

Contact tracing is an important tool for allowing countries to ease lockdown policies introduced to combat SARS-CoV-2. For contact tracing to be effective, those with symptoms must self-report themselves while their contacts must self-isolate when asked. However, policies such as legal enforcement of self-isolation can create trade-offs by dissuading individuals from self-reporting. We use an existing branching process model to examine which aspects of contact tracing adherence should be prioritized. We consider an inverse relationship between self-isolation adherence and self-reporting engagement, assuming that increasingly strict self-isolation policies will result in fewer individuals self-reporting to the programme. We find that policies which increase the average duration of self-isolation, or that increase the probability that people self-isolate at all, at the expense of reduced self-reporting rate, will not decrease the risk of a large outbreak and may increase the risk, depending on the strength of the trade-off. These results suggest that policies to increase self-isolation adherence should be implemented carefully. Policies that increase self-isolation adherence at the cost of self-reporting rates should be avoided.

This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.
Clinical Evaluation of In House Produced 3D Printed Nasopharyngeal Swabs for COVID-19 Testing
Authors: Lapierre Simon Grandjean , Bedwani Stéphane , DeBlois François , Fortin Audray , Cuervo Natalia Zamorano , Zerouali Karim , Caron Elise , Potvin Philippe Morency , Gagnon Simon , Nguissan Nakome , Arlotto Pascale , Hardy Isabelle , Boutin Catherine-Audrey , Tremblay Cécile , Coutlée François , Guise Jacques de , Grandvaux Nathalie
Published: May 2021

3D printed alternatives to standard flocked swabs were rapidly developed to provide a response to the unprecedented and sudden need for an exponentially growing amounts of diagnostic tools to fight the pandemics of COVID-19. In light of the anticipated shortage, an hospital-based 3D printing platform was implemented in our institution for the production of swabs for nasopharyngeal and oropharyngeal sampling based on the freely available open-sourced design made available to the community by University of South Florida’s Health Radiology and Northwell Health System teams as replacement for locally used commercial swabs. Validation of our 3D printed swabs was performed by a head-to-head diagnostic accuracy study of the 3D printed “Northwell model” with the cobas PCR Media swabs sample kit. We observed an excellent concordance (total agreement 96.8%, Kappa 0.936) in results obtained with the 3D printed and flocked swabs indicating that the in-house 3D printed swab can be used reliably in a context of shortage of flocked swabs. To our knowledge, this is the first study to report on autonomous hospital-based production and clinical validation of 3D printed swabs.
MALDI-ToF Protein Profiling as Potential Rapid Diagnostic Platform for COVID-19
Authors: Chivte Prajkta , LaCasse Zane , Seethi Venkata Devesh R. , Bharti Pratool , Bland Joshua , Kadkol Shrihari S. , Gaillard Elizabeth R.
Published: May 2021

More than a year after the COVID-19 pandemic has been declared, the need still exists for accurate, rapid, inexpensive and non-invasive diagnostic methods that yield high specificity and sensitivity towards the current and newly emerging SARS-CoV-2 strains. Several studies have since established saliva as a more amenable specimen type for early detection of SARS-CoV-2 as compared to nasopharyngeal swabs. Considering the limitations and high demand for COVID-19 testing, we employed MALDI-ToF mass spectrometry for the analysis of 60 gargle samples from human donors and compared the spectra with their COVID-19 status. Several standards including isolated human serum immunoglobulins and controls such as pre-COVID-19 saliva and heat inactivated SARS-CoV-2 virus were simultaneously analyzed to provide a relative view of the saliva and viral proteome as they would appear in this works methodology. Five potential biomarker peaks were established that demonstrated high concordance with COVID-19 positive individuals. Overall, the agreement of these results with RT-qPCR testing on NP swabs was no less than 90% for the studied cohort, which consisted of young and largely asymptomatic student athletes. From a clinical standpoint, the results from this pilot study are promising and suggest that MALDI-ToF can be used to develop a relatively rapid and inexpensive COVID-19 assay.
Risk Factors, Clinical Outcomes and Prognostic Factors of Bacterial Keratitis: The Nottingham Infectious Keratitis Study
Authors: Ting Darren S. J. , Cairns Jessica , Gopal Bhavesh P. , Ho Charlotte Shan , Krstic Lazar , Elsahn Ahmad , Lister Michelle , Said Dalia G. , Dua Harminder S.
Published: May 2021

Background/aim

To examine the risk factors, clinical characteristics, outcomes and prognostic factors of bacterial keratitis (BK) in Nottingham, UK.

Methods

This was a retrospective study of patients who presented to the Queen’s Medical Centre, Nottingham, with suspected BK during 2015-2019. Relevant data, including the demographic factors, risk factors, clinical outcomes, and potential prognostic factors, were analysed.

Results

A total of 283 patients (n=283 eyes) were included; mean age was 54.4±21.0 years and 50.9% were male. Of 283 cases, 128 (45.2%) cases were culture-positive. Relevant risk factors were identified in 96.5% patients, with ocular surface diseases (47.3%), contact lens wear (35.3%) and systemic immunosuppression (18.4%) being the most common factors. Contact lens wear was most commonly associated withP. aeruginosawhereasStaphylococci spp. were most commonly implicated in non-contact lens-related BK cases (p=0.017). At presentation, culture-positive cases were associated with older age, worse presenting corrected-distance-visual-acuity (CDVA), larger epithelial defect and infiltrate, central location and hypopyon (all p<0.01), when compared to culture-negative cases. Hospitalisation was required in 57.2% patients, with a mean length of stay of 8.0±8.3 days. Surgical intervention was required in 16.3% patients. Significant complications such as threatened/actual corneal perforation (8.8%), loss of perception of light vision (3.9%), and evisceration/enucleation (1.4%) were noted. Poor visual outcome (final corrected-distance-visual-acuity of <0.6 logMAR) and delayed corneal healing (>30 days from initial presentation) were significantly affected by age >50 years, infiltrate size >3mm, and reduced presenting vision (all p<0.05).

Conclusion

BK represents a significant ocular morbidity in the UK. Culture positivity is associated with more severe disease at presentation but has no significant influence on the final outcome. Older age, large infiltrate, and poor presenting vision were predictive of poor visual outcome and delayed corneal healing, highlighting the importance of primary prevention and early intervention for BK.
Pharmacological Management of Acute Spinal Cord Injury: A longitudinal multi-cohort observational study
Authors: Jutzeler Catherine R. , Bourguignon Lucie , Tong Bobo , Ronca Elias , Bailey Eric , Harel Noam Y. , Geisler Fred , Ferguson Adam R. , Kwon Brian K. , Cragg Jacquelyn J. , Grassner Lukas , Kramer John L.K.
Published: May 2021

Background

Nearly every individual sustaining traumatic spinal cord injury receives multiple types and classes of medications to manage a litany of secondary complications. Prior clinical studies and evidence from animal models suggest that several of these medications could enhance or impede endogenous neurological recovery. However, there is a knowledge gap surrounding the spectrum of pharmacologic agents typically administered in the routine management of spinal cord injury.

Objective

To systematically determine the types of medications commonly administered, alone or in combination, in the acute to subacute phase of spinal cord injury.

Methods

We conducted an analysis of two largescale cohorts (the Sygen interventional trial and the SCIRehab observational cohort study) to determine what constitutes “ standards of acute pharmacological care” after spinal cord injury. Concomitant medication use, including dosage, timing and reason for administration, was tracked. Descriptive statistics were used to describe the medications administered within the first 60 days after spinal cord injury.

Results

Across 2040 individuals with spinal cord injury, 775 unique medications were administered within the two months after injury. On average, patients enrolled in the Sygen trial received 9.9 ± 4.9 (range 0-34), 14.3 ± 6.3 (range 1-40), 18.6 ± 8.2 (range 0-58), and 21.5 ± 9.7 (range 0-59) medications within the first 7, 14, 30, and 60 days post-injury, respectively. Patients enrolled in the SCIRehab cohort study received on average 1.7 ± 1.7 (range 0-11), 3.7 ± 3.7 (range 0-24), 8.5 ± 6.3 (range 0-42), and 13.5 ± 8.3 (range 0-52) medications within the first 7, 14, 30, and 60 days post-injury, respectively. Polypharmacy was commonplace (up to 43 medications per day per patient). Approximately 10% of medications were administered acutely as prophylaxis (e.g., against the development of pain or infections).

Conclusions

To our knowledge, this was the first time acute pharmacological practices have been comprehensively examined after spinal cord injury. Our study revealed a high degree of polypharmacy in the acute stages of spinal cord injury, with potential to both positively and negatively impact neurological recovery. This data may provide key insight to achieve better understanding of how the acute pharmacological management of spinal cord injury affects long-term recovery. All results can be interactively explored on theRXSCIweb site (https://jutzelec.shinyapps.io/RxSCI/) and GitHub repository (https://github.com/jutzca/Acute-Pharmacological-Treatment-in-SCI/).
Generic Patient-Reported Outcomes and Outcome Measures: A Systematic Review Study Protocol
Authors: Seidler Yuki , Mosor Erika , Andrews Margaret , Watson Carolina , Bott Nick , Gross Evelyn , Hazelzet Jan A , Omara Maisa , Ritschl Valentin , Stamm Tanja
Published: May 2021

Background

Patient-reported outcomes (PROs) are an essential part of health outcome measurement and vital to patient-centricity and valued-based care. Several international consortia have developed core outcome sets and many of them include PROs. PROs are measured by patient-reported outcome measures (PROMs). PROs and PROMs can be generic or specific to certain diseases or conditions. While the characteristics of generic PROs and PROMs are well recognised as widely relevant and applicable across different domains, diseases and conditions, there is a lack of knowledge on the types of PROs measured by generic PROMs. We also do not know in which disease areas generic PROs and PROMs are commonly used. To date, there has been no systematic review solely focusing on generic PROMs, what they measure and their areas of application.

Objectives

This systematic review will identify core PROs measured by generic PROMs used in adult populations and the areas in which they are applied.

Methods

We will conduct a systematic review of reviews. The screening process and the reporting will comply with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) 2020 Statement. We will use four databases, Medline [PubMed], CINHAL [Ebsco], Cochrane [Cochrane Library], and PsycINFO [Ovid], and reports from international consortia. Inclusion criteria are systematic reviews, meta-analysis or patient-reported outcome sets developed by international consortia reporting on generic PROMs in adult populations. Articles primarily focusing on patient-reported experience measures (PREMs), children or adolescents, or those not written in English will be excluded. Risk of bias will be assessed by checking if the included articles comply with established guidelines for systematic reviews such as the PRISMA statement. We will extract generic PROMs and PROs measured by these PROMs, and the areas applied from the selected articles and reports. Extracted data and information will be quantitatively and qualitatively synthesised without statistical interference. The quality of the synthesised evidences will be assessed by clarifying the strengths, limitations and possible biases in our review.
Insulinopathies of the brain? Genetic overlap between somatic insulin-related and neuropsychiatric disorders
Authors: Fanelli Giuseppe , Franke Barbara , De Witte Ward , Ruisch I. Hyun , Haavik Jan , van Gils Veerle , Jansen Willemijn J. , Vos Stephanie J. B. , Lind Lars , Buitelaar Jan K. , Banaschewski Tobias , Dalsgaard Søren , Serretti Alessandro , Mota Nina Roth , Poelmans Geert , Bralten Janita
Published: May 2021

The prevalence of Alzheimer’s disease (AD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD) is higher among patients with somatic insulinopathies, like metabolic syndrome (MetS), obesity, and type 2 diabetes mellitus (T2DM). Dysregulation of insulin signalling has been implicated in these neuropsychiatric disorders, and shared genetic factors might partly underlie these observed comorbidities. We investigated genetic overlap between AD, ASD, and OCD with MetS, obesity, and T2DM by estimating pairwise genetic correlations using the summary statistics of the largest available genome-wide association studies for these diseases. Stratified covariance analyses were performed to investigate the contribution of insulin-related gene-sets. Having tested these hypotheses, novel brain “insulinopathies” were explored by estimating the genetic relationship of six additional neuropsychiatric disorders with nine insulin-related diseases/traits. Significant genetic correlations were found between OCD and MetS (rg=-0.315, p=3.9e-8), OCD and obesity (rg=-0.379, p=3.4e-5), and OCD and T2DM (rg=-0.172, p=3e-4). Stratified analyses showed negative genetic covariances between ASD and MetS/T2DM through gene-sets comprising insulin signalling and/or insulin processing genes, and between AD/OCD and MetS/T2DM through an insulin receptor recycling gene-set (p<6.17e-4). Significant genetic correlations with insulin-related phenotypes were also found for anorexia nervosa, attention-deficit/hyperactivity disorder, major depression, and schizophrenia (p<6.17e-4). Our findings suggest the existence of two clusters of neuropsychiatric disorders, in which the genetics of insulin-related diseases and traits may exert divergent pleiotropic effects. These results represent a starting point for a new research line on “insulinopathies” of the brain, which may support the development of more effective/tolerated treatment strategies for neuropsychiatric disorders.
Risk-Averse Multi-Stage Stochastic Programming to Optimizing Vaccine Allocation and Treatment Logistics for Effective Epidemic Response
Authors: Yin Xuecheng , Büyüktahtakın İ. Esra
Published: May 2021

Existing compartmental-logistics models in epidemics control are limited in terms of optimizing the allocation of vaccines and treatment resources under a risk-averse objective. In this paper, we present a data-driven, mean-risk, multi-stage, stochastic epidemics-vaccination-logistics model that evaluates various disease growth scenarios under the Conditional Value-at-Risk (CVaR) risk measure to optimize the distribution of treatment centers, resources, and vaccines, while minimizing the total expected number of infections, deaths, and close contacts of infected people under a limited budget. We integrate a new ring vaccination compartment into a Susceptible-Infected-Treated-Recovered-Funeral-Burial epidemics-logistics model. Our formulation involves uncertainty both in the vaccine supply and the disease transmission rate. Here, we also consider the risk of experiencing scenarios that lead to adverse outcomes in terms of the number of infected and dead people due to the epidemic. Combining the risk-neutral objective with a risk measure allows for a trade-off between the weighted expected impact of the outbreak and the expected risks associated with experiencing extremely disastrous scenarios. We incorporate human mobility into the model and develop a new method to estimate the migration rate between each region when data on migration rates is not available. We apply our multi-stage stochastic mixed-integer programming model to the case of controlling the 2018-2020 Ebola Virus Disease (EVD) in the Democratic Republic of the Congo (DRC) using real data. Our results show that increasing the risk-aversion by emphasizing potentially disastrous outbreak scenarios reduces the expected risk related to adverse scenarios at the price of the increased expected number of infections and deaths over all possible scenarios. We also find that isolating and treating infected individuals are the most efficient ways to slow the transmission of the disease, while vaccination is supplementary to primary interventions on reducing the number of infections. Furthermore, our analysis indicates that vaccine acceptance rates affect the optimal vaccine allocation only at the initial stages of the vaccine rollout under a tight vaccine supply.
Estimation of incidence from aggregated current status data without differential mortality
Authors: Brinks Ralph
Published: May 2021

We use a historical data about breathlessness in British coal miners to compare two methods centered around a differential equation for deriving the age-specific incidence from aggregated current status data with age information, i.e. age-specific prevalence data. Special focus is put on estimating confidence bounds. For this, we derive a maximum likelihood (ML) estimator for estimating the age-specific incidence from the prevalence data and confidence bounds are calculated based on classical ML theory. Second, we construct a Markov-Chain-Monte-Carlo (MCMC) algorithm to estimate confidence bounds, which implements a weighted version of the differential equation into the prior of the MCMC algorithm. The confidence bounds for both methods are compared and it turns out that the MCMC estimates approach the ML estimates if the prior gives strong weight to the differential equation.
GlyNet: A Multi-Task Neural Network for Predicting Protein-Glycan Interactions
Authors: Carpenter Eric J. , Seth Shaurya , Yue Noel , Greiner Russell , Derda Ratmir
Published: May 2021

Advances in diagnostics, therapeutics, vaccines, transfusion, and organ transplantation build on a fundamental understanding of glycan-protein interactions. To aid this, we developed GlyNet, a model that accurately predicts interactions (relative binding strengths) between mammalian glycans and 352 glycan-binding proteins, many at multiple concentrations. For each glycan input, our model produces 1257 outputs, each representing the relative interaction strength between the input glycan and a particular protein sample. GlyNet learns these continuous values using relative fluorescence units (RFUs) measured on 599 glycans in the Consortium for Functional Glycomics glycan arrays and extrapolates these to RFUs from additional, untested glycans. GlyNet’s output of continuous values provides more detailed results than classification models. Such continuous outputs are easily converted by a following classifier, and in this form GlyNet outperforms reported classifiers. GlyNet is the first multi-output regression model for protein-glycan interactions and will serve as an important benchmark, facilitating development of quantitative computational glycobiology.
Distinct behavior of the little finger during the vertical translation of an unsteady thumb platform while grasping
Authors: Rajakumar Banuvathy , Varadhan SKM
Published: May 2021

Object stabilization while grasping is a common topic of research in motor control and robotics. Forces produced by the peripheral fingers (index and little) play a crucial role in sustaining the rotational equilibrium of a handheld object. In this study, we examined the contribution of the peripheral fingers towards object stabilization when the rotational equilibrium is disturbed. For this purpose, the thumb was placed over an unsteady platform and vertically translated. The task was to trace a trapezoid or an inverted trapezoid pattern by moving the thumb platform in the vertical direction. The thumb displacement data served as visual feedback to trace the pattern displayed. Participants were instructed to maintain the handle in static equilibrium at all times. We observed that the change in the normal force of the little finger due to the downward translation of the thumb was significantly greater than the change in the normal force of the index finger due to the upward translation. We speculate that morphological correlations (between thumb and little finger) during the displacement of the thumb might be a reason for such large increases in the little finger forces.
Beyond category-supervision: instance-level contrastive learning models predict human visual system responses to objects
Authors: Konkle Talia , Alvarez George A.
Published: May 2021

Anterior regions of the ventral visual stream have substantial information about object categories, prompting theories that category-level forces are critical for shaping visual representation. The strong correspondence between category-supervised deep neural networks and ventral stream representation supports this view, but does not provide a viable learning model, as these deepnets rely upon millions of labeled examples. Here we present a fully self-supervised model which instead learns to represent individual images, where views of the same image are embedded nearby in a low-dimensional feature space, distinctly from other recently encountered views. We find category information implicitly emerges in the feature space, and critically that these models achieve parity with category-supervised models in predicting the hierarchical structure of brain responses across the human ventral visual stream. These results provide computational support for learning instance-level representation as a viable goal of the ventral stream, offering an alternative to the category-based framework that has been dominant in visual cognitive neuroscience.
Mutational hotspot in the SARS-CoV-2 Spike protein N-terminal domain conferring immune escape potential
Authors: Kubik Slawomir , Arrigo Nils , Bonet Jaume , Xu Zhenyu
Published: May 2021

Global efforts are being taken to monitor the evolution of SARS-CoV-2, aiming at early identification of mutations with the potential of increasing viral infectivity or virulence. We report a striking increase in the frequency of recruitment of diverse substitutions at a critical residue (W152), positioned in the N-terminal domain (NTD) of the Spike protein, observed repeatedly across independent phylogenetic and geographical contexts. We investigate the impact these mutations might have on the evasion of neutralizing antibodies. Finally, we uncover that NTD is a region exhibiting particularly high frequency of mutation recruitments, suggesting an evolutionary path on which the virus maintains optimal efficiency of ACE2 binding combined with the flexibility facilitating the immune escape.
Digital PCR to quantify ChAdOx1 nCoV-19 copies in blood and tissues
Authors: Badbaran Anita , Mailer Reiner , Dahlke Christine , Woens Jannis , Fathi Anahita , Mellinghoff Sibylle C. , Renné Thomas , Addo Marylyn M. , Riecken Kristoffer , Fehse Boris
Published: May 2021

Vaccination with the adenoviral-vector based Astra Zeneca ChAdOx1 nCov-19 vaccine is efficient and safe. However, in rare cases vaccinated individuals developed life-threatening thrombotic complications, including thrombosis in cerebral sinus and splanchnic veins. Monitoring of the applied vector in vivo represents an important precondition to study the molecular mechanisms underlying vaccine-driven adverse effects now referred to as vaccine-induced immune thrombotic thrombocytopenia (VITT). We previously have shown that digital PCR is an excellent tool to quantify transgene copies in vivo. Here we present a highly sensitive digital PCR for in-situ quantification of ChAdOx1 nCoV-19 copies. Using this method, we quantified vector copies in human serum 24, 72 and 168 hours post vaccination, and in a variety of murine tissues in an experimental vaccination model 30 minutes post injection. We describe a method for high-sensitivity quantitative detection of ChAdOx1 nCoV-19 with possible implications to elucidate the mechanisms of severe ChAdOx1 nCov-19 vaccine complications.
Sustainability of agricultural systems of indigenous people in Hidalgo, Mexico
Authors: Leyva Doris , de la Torre Mayra , Coronado Yaxk’in
Published: May 2021

Agricultural sustainability depends on complex relationships between environmental, economic and social aspects, in particular with the small farm holders from indigenous communities. This work was centered in two municipalities of Hidalgo State in Mexico, Ixmiquilpan (mainly irrigated systems) and El Cardonal (rainfed systems). Our objective was to understand the relationships between the small farm-holders and their agricultural systems. We evaluated the sustainability of their agricultural systems and did some recommendations. We applied the Framework for the Evaluation of Management Systems using Indicators (MESMIS, Spanish acronym), thirty-one indicators were identified, and the quantitative indexes were established to assess sustainability. The results showed that adaptability was a critical factor for irrigated and rainfed systems, the main problem identified was youth migration. Additionally, the access to water and economic resources, as well as environmental resources management, are imperious needs to increase the yield of agriculture crops. Therefore, a holistic approach taking into account the organization of small producers and synergy between indigenous knowledge and modern technologies, are required for the territorial development of the communities.
Denoising-based Image Compression for Connectomics
Authors: Minnen David , Januszewski Michał , Blakely Tim , Shapson-Coe Alexander , Schalek Richard L. , Ballé Johannes , Lichtman Jeff W. , Jain Viren
Published: May 2021

Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of 3d reconstruction and synaptic detection accuracy.
Non-coding mutations reveal cancer driver cistromes in luminal breast cancer
Authors: Ghamrasni Samah El , Quevedo Rene , Hawley James , Mazrooei Parisa , Hanna Youstina , Cirlan Iulia , Zhu Helen , Bruce Jeff , Oldfield Leslie E. , Yang S. Y. Cindy , Guilhamon Paul , Reimand Jüri , Cescon Dave , Done Susan J. , Lupien Mathieu , Pugh Trevor J
Published: May 2021

Whole genome sequencing of primary breast tumors enabled the identification of cancer driver genes 1,2 and non-coding cancer driver plexuses from somatic mutations 3–6. However, differentiating driver and passenger events among non-coding genetic variants remains a challenge to understand the etiology of cancer and inform delivery of personalized cancer medicine. Herein, we reveal an enrichment of non-coding mutations in cis-regulatory elements that cover a subset of transcription factors linked to tumor progression in luminal breast cancers. Using a cohort of 26 primary luminal ER+PR+ breast tumors, we compiled a catalogue of ∼100,000 unique cis-regulatory elements from ATAC-seq data. Integrating this catalogue with somatic mutations from 350 publicly available breast tumor whole genomes, we identified four recurrently mutated individual cis-regulatory elements. By then partitioning the non-coding genome into cistromes, defined as the sum of binding sites for a transcription factor, we uncovered cancer driver cistromes for ten transcription factors in luminal breast cancer, namely CTCF, ELF1, ESR1, FOSL2, FOXA1, FOXM1 GATA3, JUND, TFAP2A, and TFAP2C in luminal breast cancer. Nine of these ten transcription factors were shown to be essential for growth in breast cancer, with four exclusive to the luminal subtype. Collectively, we present a strategy to find cancer driver cistromes relying on quantifying the enrichment of non-coding mutations over cis-regulatory elements concatenated into a functional unit drawn from an accessible chromatin catalogue derived from primary cancer tissues.
Magnetic Resonance Spectroscopy Frequency and Phase Correction Using Convolutional Neural Networks
Authors: Ma David J. , Le Hortense A-M. , Ye Yuming , Laine Andrew F. , Lieberman Jeffery A. , Rothman Douglas L. , Small Scott A. , Guo Jia
Published: May 2021

We introduce DeepSPEC, a novel convolutional neural network (CNN) -based approach for frequency-and-phase correction (FPC) of MRS spectra to achieve fast and accurate FPC of single-voxel PRESS MRS and MEGA-PRESS data. In DeepSPEC, two neural networks, including one for frequency correction and one for phase correction were trained and validated using published simulated and in vivo PRESS and MEGA-PRESS MRS dataset with wide-range artificial frequency and phase offsets applied. DeepSPEC was subsequently tested and compared to the current deep learning solution - a “vanilla” neural network approach using multilayer perceptrons (MLP). Furthermore, random noise was added to the original simulated dataset to further investigate the model performance with noise at varied signal-to-noise (SNR) levels (i.e., 6 dB, 3 dB, and 1.5 dB). The testing showed that DeepSPEC is more robust to noise compared to the MLP-based approach due to having a smaller absolute error in both frequency and phase offset prediction. The DeepSPEC framework was capable of correcting frequency offset with 0.01±0.01 Hz and phase offset with 0.12±0.09° absolute errors on average for unseen simulated data at a high SNR (12 dB) and correcting frequency offset with 0.01±0.02 Hz and phase offset within -0.07±0.44° absolute errors on average at very low SNR (1.5 dB). Furthermore, additional frequency and phase offsets (i.e., small, moderate, large) were applied to the in vivo dataset, and DeepSPEC demonstrated better performance for FPC when compared to the MLP-based approach. Results also show DeepSPEC has superior performance than the model-based SR implementation (mSR) in FPC by having higher accuracy in a wider range of additional offsets. These results represent a proof of concept for the use of CNNs for preprocessing MRS data and demonstrate that DeepSPEC accurately predicts frequency and phase offsets at varying noise levels with state-of-the-art performance.
Cryptic speciation in freshwaters: are speciation in lentic species shaped by paleohydrological events?
Authors: Sil Maitreya , Roy Abhishikta , Palden Tenzin , Karanth K. Praveen , Aravind N. A.
Published: May 2021

The Indian subcontinent is extremely diverse in terms of its flora and fauna. However, there is a severe underestimation of the biotic diversity in invertebrate groups as well as a lack of understanding of the processes generating diversity in these groups. Here we aimed to jointly estimate the cryptic diversity of two freshwater snail species complexes – Pila globosa and Pila virens, and uncover the processes behind the speciation in these groups. We employed phylogenetic, morphometric, population genetic and niche modelling technique to address these questions. We found out that both species complexes consist of several genetically and ecologically distinct putative species. The speciation was primarily driven by allopatric isolation into different river basins. A combination of paleoclimatic and paleohydrological changes during the Miocene have shaped the speciation events. Local climatic adaptation also could have contributed towards some of the speciation events. The study sheds light on the complex interaction between the biology of the species and the environment that shape the diversification patterns in a group.
Brain structural correlates of autistic traits across the diagnostic divide: A grey matter and white matter microstructure study
Authors: Chandran Varun Arunachalam , Pliatsikas Christos , Neufeld Janina , O’Connell Garret , Haffey Anthony , DeLuca Vincent , Chakrabarti Bhismadev
Published: May 2021

Autism Spectrum Disorders (ASD) are a set of neurodevelopmental conditions characterised by difficulties in social interaction and communication as well as stereotyped and restricted patterns of interest. Autistic traits exist in a continuum across the general population, whilst the extreme end of this distribution is diagnosed as clinical ASD. While many studies have investigated brain structure in autism using a case-control design, few have used a dimensional approach. To add to this growing body of literature, we investigated the structural brain correlates of autistic traits in a mixed sample of adults (N=91) with and without a clinical diagnosis of autism. We examined regional brain volumes (using voxel-based morphometry and surface-based morphometry) and white matter microstructure properties (using Diffusion Tensor Imaging). Our findings show widespread grey matter differences, including in the social brain regions, and some evidence for white matter microstructure differences related to higher autistic traits. These grey matter and white matter microstructure findings from our study are consistent with previous reports and support the brain structural differences in ASD. These findings provide further support for shared aetiology for autistic traits across the diagnostic divide.
A Reference-free Approach for Cell Type Classification with scRNA-seq
Authors: Sun Qi , Peng Yifan , Liu Jinze
Published: May 2021

The single-cell RNA sequencing (scRNA-seq) has become a revolutionary technology to detect and characterize distinct cell populations under different biological conditions. Unlike bulk RNA-seq, the expression of genes from scRNA-seq is highly sparse due to limited sequencing depth per cell. This is worsened by tossing away a significant portion of reads that cannot be mapped during gene quantification. To overcome data sparsity and fully utilize original sequences, we propose scSimClassify, a reference-free and alignment-free approach to classify cell types withk-mer level features derived from raw reads in a scRNA-seq experiment. The major contribution of scSimClassify is the simhash method compressingk-mers with similar abundance profiles into groups. The compressedk-mer groups (CKGs) serve as the aggregatedk-mer level features for cell type classification. We evaluate the performance of CKG features for predicting cell types in four scRNA-seq datasets comparing four state-of-the-art classification methods as well as two scRNA-seq specific algorithms. Our experiments demonstrate that the CKG features lend themselves to better performance than traditional gene expression features in scRNA-seq classification accuracy in the majority of cases. Because CKG features can be efficiently derived from raw reads without a resource-intensive alignment process, scSimClassify offers an efficient alternative to help scientists rapidly classify cell types without relying on reference sequences. The current version of scSimClassify is implemented in python and can be found athttps://github.com/digi2002/scSimClassify.
Kite-shaped molecules block SARS-CoV-2 cell entry at a post-attachment step
Authors: Chan Shiu-Wan , Shafi Talha , Ford Robert C.
Published: May 2021

Anti-viral small molecules are currently lacking for treating coronavirus infection. The long development timescales for such drugs are a major problem, but could be shortened by repurposing existing drugs. We therefore screened a small library of FDA-approved compounds for potential severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antivirals using a pseudovirus system that allows a sensitive read-out of infectivity. A group of structurally-related compounds, showing moderate inhibitory activity with IC50values in the 1-5µM range, were identified. Further studies demonstrated that these ‘kite-shaped’ molecules were surprisingly specific for SARS-CoV and SARS-CoV-2 and that they acted early in the entry steps of the viral infectious cycle, but did not affect virus attachment to the cells. Moreover the compounds were able to prevent infection in both kidney- and lung-derived human cell lines. The structural homology of the hits allowed the production of a well-defined pharmacophore that was found to be highly accurate in predicting the anti-viral activity of the compounds in the screen. We discuss the prospects of repurposing these existing drugs for treating current and future coronavirus outbreaks.
The role of memory and perspective shifts in systematic biases during object location estimation
Authors: Segen Vladislava , Colombo Giorgio , Avraamides Marios , Slattery Timothy , Wiener Jan M.
Published: May 2021

Our previous research highlighted a systematic bias in a spatial memory task, with participants correctly detecting object movements in the same direction as the perspective shift, whilst misjudging the direction of object movements if those were in the opposite direction to the perspective shift. The aim of the current study was to investigate if the introduction of perspective shifts results in systematic biases in object location estimations. To do so, we asked participants to encode the position of an object in a virtual room and to then estimate the object’s position following a perspective shift. In addition, by manipulating memory load (perception and memory condition) we investigated if the bias in object position estimates results from systematic distortions introduced in spatial memory. Overall, our results show that participants make systematic errors in estimating object positions in the same direction as the perspective shift. This bias was present in both the memory and the perception condition. We propose that the systematic bias in the same direction as the perspective shift is driven by difficulties in understanding the perspective shifts that may lead participants to use an egocentric representation of object positions as an anchor when estimating the object location following a perspective shift, thereby giving rise to a systematic shift in errors in the same direction as the perspective shift.
Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment
Authors: Fu Yilei , Mahmoud Medhat , Muraliraman Viginesh Vaibhav , Sedlazeck Fritz J. , Treangen Todd J.
Published: May 2021

Background

Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hotspots reduces read alignment accuracy and impedes structural variant detection.

Findings

We tested our hypothesis by implementing a read mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via e.g. minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long read mapper (NGMLR). In support of our hypothesis, we show Vulcan improves the alignments for Oxford Nanopore Technology (ONT) long-reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read mapping methods alone.

Conclusions

Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes, resulting in improved structural variant recall and precision. Vulcan is open-source and available under the MIT License athttps://gitlab.com/treangenlab/vulcan
Influence of age on functional memory T cell diversity
Authors: Fang Fengqin , Cao Wenqiang , Zhu Weikang , Lam Nora , Li Lingjie , Gaddam Sadhana , Wang Yong , Kim Chulwoo , Lambert Simon , Zhang Huimin , Hu Bin , Farber Donna L. , Weyand Cornelia M. , Goronzy Jörg J.
Published: May 2021

Memory T cells exhibit considerable diversity that determines their ability to be protective and their durability. Here, we examined whether changes in T cell heterogeneity contribute to the age-associated failure of immune memory. By screening for age-dependent T cell surface markers, we have identified CD4 and CD8 memory T cell subsets that are unrelated to previously defined subsets of central and effector memory cells. Memory T cells expressing the ecto-5’-nucleotidase CD73 constitute a functionally distinct subset of memory T cells that declines with age. They exhibit many features favorable for immune protection, including longevity and polyfunctionality. They have a low turnover, but are poised to display effector functions and to develop into cells resembling tissue-resident memory T cells (TRM). Upstream regulators of differential chromatin accessibility and transcriptomes include transcription factors that are characteristic for conferring these superior memory features as well as facilitating CD73 expression. CD73 is not just a surrogate marker of these regulatory networks but is directly involved in T cell survival and TRMdifferentiation Interventions preventing the decline of this T cell subset or increasing CD73 expression have the potential to improve immune memory in older adults.
A connectivity-constrained computational account of topographic organization in primate high-level visual cortex
Authors: Blauch Nicholas M. , Behrmann Marlene , Plaut David C.
Published: May 2021

Inferotemporal cortex (IT) in humans and other primates is topo-graphically organized, containing multiple hierarchically-organized areas selective for particular domains, such as faces and scenes. This organization is commonly viewed in terms of evolved domain-specific visual mechanisms. Here, we develop an alternative, domain-general and developmental account of IT cortical organization. The account is instantiated as an Interactive Topographic Network (ITN), a form of computational model in which a hierarchy of model IT areas, subject to connectivity-based constraints, learns high-level visual representations optimized for multiple domains. We find that minimizing a wiring cost on spatially organized feedforward and lateral connections within IT, combined with constraining the feedforward processing to be strictly excitatory, results in a hierarchical, topographic organization. This organization replicates a number of key properties of primate IT cortex, including the presence of domain-selective spatial clusters preferentially involved in the representation of faces, objects, and scenes, columnar responses across separate excitatory and inhibitory units, and generic spatial organization whereby the response correlation of pairs of units falls off with their distance. We thus argue that domain-selectivity is an emergent property of a visual system optimized to maximize behavioral performance while minimizing wiring costs.

Significance Statement

We introduce the Interactive Topographic Network, a framework for modeling high-level vision, to demonstrate in computational simulations that the spatial clustering of domains in late stages of the primate visual system may arise from the demands of visual recognition under the constraints of minimal wiring costs and excitatory between-area neuronal communication. The learned organization of the model is highly specialized but not fully modular, capturing many of the properties of organization in primates. Our work is significant for cognitive neuroscience, by providing a domain-general developmental account of topo-graphic functional specialization, and for computational neuroscience, by demonstrating how well-known biological details can be successfully incorporated into neural network models in order to account for critical empirical findings.
Antibody Display of cell surface receptor Tetraspanin12 and SARS-CoV-2 spike protein
Authors: Hsieh Fu-Lien , Chang Tao-Hsin
Published: May 2021

In previous work, Hsieh and Higgins presented a novel structure of antibodies identified from malaria-exposed individuals, in which the extracellular immunoglobulin (Ig)-like domain of leukocyte-associated immunoglobulin-like receptor 1 (LAIR1) is presented on the third complementarity determining regions (CDR3) of the Ig heavy chain. Here we develop an Antibody Display technology based on this LAIR1-containing antibody, by grafting proteins of interest (POI) onto the heavy chain CDR3 while retaining the biological properties of the POI. As a proof of principle, we displayed the second extracellular domain of Tetraspanin12 (Tspan12EC2) and the receptor-binding domain (RBD) of SARS-CoV-2 spike protein on the heavy chain CDR3. Our data revealed that Antibody Display Tspan12EC2bound to Norrie Disease Protein (Norrin) and Antibody Display SARS-CoV-2 RBD bound to angiotensin-converting enzyme 2 (ACE2) and neutralizing nanobodies. Collectively, Antibody Display technology offers the general strategy of designing novel antibodies by grafting POI onto the CDR3.
ppx: Programmatic access to proteomics data repositories
Authors: Fondrie William E , Bittremieux Wout , Noble William S
Published: May 2021

The volume of proteomics and mass spectrometry data available in public repositories continues to grow at a rapid pace as more researchers embrace open science practices. Open access to the data behind scientific discoveries has become critical to validate published findings and develop new computational tools. Here, we present ppx, a Python package that provides easy, programmatic access to the data stored in ProteomeXchange repositories, such as PRIDE and MassIVE. The ppx package can either be used as a command line tool or a Python package to retrieve the files and metadata associated with a project when provided its identifier. To demonstrate how ppx enhances reproducible research, we used ppx within a Snakemake workflow to reanalyze a published dataset with the open modification search tool ANN-SoLo and compared our reanalysis to the original results. We show that ppx readily integrates into workflows and our reanalysis produced results consistent with the original analysis. We envision that ppx will be a valuable tool for creating reproducible analyses, providing tool developers easy access to data for development, testing, and benchmarking, and enabling the use of mass spectrometry data in data-intensive analyses. The ppx package is freely available and open source under the MIT license at: https://github.com/wfondrie/ppx