bluuebunny/crossref_metadata_2025 huggingface dataset:
Multifaceted strategies for the control of COVID-19 outbreaks in long-term care facilities in Ontario, Canada
Authors: Vilches Thomas N. , Nourbakhsh Shokoofeh , Zhang Kevin , Juden-Kelly Lyndon , Cipriano Lauren E. , Langley Joanne M. , Sah Pratha , Galvani Alison P. , Moghadas Seyed M.
Published: December 2020

The novel coronavirus disease 2019 (COVID-19) has caused severe outbreaks in Canadian long-term care facilities (LTCFs). In Canada, over 80% of COVID-19 deaths during the first pandemic wave occurred in LTCFs. We sought to evaluate the effect of mitigation measures in LTCFs including frequent testing of staff, and vaccination of staff and residents. We developed an agent-based transmission model and parameterized it with disease-specific estimates, temporal sensitivity of nasopharyngeal and saliva testing, results of vaccine efficacy trials, and data from initial COVID-19 outbreaks in LTCFs in Ontario, Canada. Characteristics of staff and residents, including contact patterns, were integrated into the model with age-dependent risk of hospitalization and death. Estimates of infection and outcomes were obtained and 95% credible intervals were generated using a bias-corrected and accelerated bootstrap method. Weekly routine testing of staff with 2-day turnaround time reduced infections among residents by at least 25.9% (95% CrI: 23.3% - 28.3%), compared to baseline measures of mask-wearing, symptom screening, and staff cohorting alone. A similar reduction of hospitalizations and deaths was achieved in residents. Vaccination averted 2-4 times more infections in both staff and residents as compared to routine testing, and markedly reduced hospitalizations and deaths among residents by 95.9% (95% CrI: 95.4% - 96.3%) and 95.8% (95% CrI: 95.5% - 96.1%), respectively, over 200 days from the start of vaccination. Vaccination could have a substantial impact on mitigating disease burden among residents, but may not eliminate the need for other measures before population-level control of COVID-19 is achieved.
Active Neural Networks to Detect Mentions of Changes to Medication Treatment in Social Media
Authors: Weissenbacher Davy , Ge Suyu , Klein Ari , O’Connor Karen , Gross Robert , Hennessy Sean , Gonzalez-Hernandez Graciela
Published: December 2020

Objective

We address a first step towards using social media data to supplement current efforts in monitoring population-level medication non-adherence: detecting changes to medication treatment. Medication treatment changes, like changes to dosage or to frequency of intake, that are not overseen by a physician are, by that, non-adherence to medication. Despite the consequences, including worsening health conditions or death, 50% of patients are estimated to not take medications as indicated. Current methods to identify non-adherence have major limitations. Direct observation may be intrusive or expensive, and indirect observation through patient surveys relies heavily on patients’ memory and candor. Using social media data in these studies may address these limitations.

Methods

We annotated 9,835 tweets mentioning medications and trained a convolutional neural network (CNN) to find mentions of medication treatment changes, regardless of whether the change was recommended by a physician. We used active and transfer learning from 12,972 reviews we annotated from WebMD to address the class imbalance of our Twitter corpus. To validate our CNN and explore future directions, we annotated 1,956 positive tweets as to whether they reflect non-adherence and categorized the reasons given.

Results

Our CNN achieved state-of-the-art performance with 0.50 F1-score. The manual analysis of positive tweets revealed that non-adherence is evident in a subset with nine categories of reasons for non-adherence.

Conclusion

We showed that social media users publicly discuss medication treatment changes and may explain their reasons including when it constitutes non-adherence. This approach may be useful to supplement current efforts in adherence monitoring.
MeFSAT: A curated natural product database specific to secondary metabolites of medicinal fungi
Authors: Vivek-Ananth R.P. , Sahoo Ajaya Kumar , Kumaravel Kavyaa , Mohanraj Karthikeyan , Samal Areejit
Published: December 2020

Fungi are a rich source of secondary metabolites which constitutes a valuable and diverse chemical space of natural products. Medicinal fungi have been used in traditional medicine to treat human ailments for centuries. To date, there is no devoted resource on secondary metabolites and therapeutic uses of medicinal fungi. Such a dedicated resource compiling dispersed information on medicinal fungi across published literature will facilitate ongoing efforts towards natural product based drug discovery. Here, we present the first comprehensive manually curated database onMedicinalFungiSecondary metabolitesAndTherapeutics (MeFSAT) that compiles information on 184 medicinal fungi, 1830 secondary metabolites and 149 therapeutics uses. Importantly, MeFSAT contains a non-redundantin siliconatural product library of 1830 secondary metabolites along with information on their chemical structures, computed physicochemical properties, drug-likeness properties, predicted ADMET properties, molecular descriptors and predicted human target proteins. By comparing the physicochemical properties of secondary metabolites in MeFSAT with other small molecules collections, we find that fungal secondary metabolites have high stereochemical complexity and shape complexity similar to other natural product libraries. Based on multiple scoring schemes, we have filtered a subset of 228 drug-like secondary metabolites in MeFSAT database. By constructing and analyzing chemical similarity networks, we show that the chemical space of secondary metabolites in MeFSAT is highly diverse. The compiled information in MeFSAT database is openly accessible at:https://cb.imsc.res.in/mefsat/.
Selectively caring for the most severe COVID-19 patients delays ICU bed shortages more than increasing hospital capacity
Authors: Roberts Miles , Seymour Helena Duplechin ,   Faculty Advisor, Dimitrov Alexander
Published: December 2020

1

SARS-CoV-2, the virus responsible for COVID-19, has killed hundreds of thousands of Americans. Although physical distancing measures played a key role in slowing COVID-19 spread in early 2020, infection rates are now peaking at record levels across the country. Hospitals in several states are threatened with overwhelming numbers of patients, compounding the death toll of COVID-19. Implementing strategies to minimize COVID-19 hospitalizations will be key to controlling the toll of the disease, but non-physical distancing strategies receive relatively little attention. We present a novel system of differential equations designed to predict the relative effects of hospitalizing fewer COVID-19 patients vs increasing ICU bed availability on delaying ICU bed shortages. This model, which we call SEAHIRD, was calibrated to mortality data on two US states with different peak infection times from mid-March – mid-May 2020. It found that hospitalizing fewer COVID-19 patients generally delays ICU bed shortage more than a comparable increase in ICU bed availability. This trend was consistent across both states and across wide ranges of initial conditions and parameter values. We argue that being able to predict which patients will develop severe COVID-19 symptoms, and thus require hospitalization, should be a key objective of future COVID-19 research, as it will allow limited hospital resources to be allocated to individuals that need them most and prevent hospitals from being overwhelmed by COVID-19 cases.
Antibiotic prescriptions in children with COVID-19 and Multisystem Inflammatory Syndrome: a multinational experience in 990 cases from Latin America
Authors: Yock-Corrales Adriana , Lenzi Jacopo , Ulloa-Gutiérrez Rolando , Gómez-Vargas Jessica , Yassef Antúnez-Montes Omar , Rios Aida Jorge Alberto , del Aguila Olguita , Arteaga-Menchaca Erick , Campos Francisco , Uribe Fadia , Buitrago Andrea Parra , Betancur Londoño Lina Maria , Brizuela Martin , Buonsenso Danilo
Published: December 2020

Background

To date, there are no comprehensive data on antibiotic use in children with COVID-19 and Multisystem Inflammatory Syndrome (MIS-C).

Methods

Multicenter cohort study from 5 Latin American countries. Children 17 years of age or younger with microbiologically confirmed SARS-CoV-2 infection or fulfilling MIS-C definition were included. Antibiotic prescriptions were collected and factors associated with their use were calculated.

Findings

990 children were included, with a median age of 3 years (interquartile range 1–9). Of these, 69 (7.0%) were diagnosed with MIS-C. The prevalence of antibiotic use was 24.5% (n = 243). MIS-C with (OR = 45.48) or without (OR = 10.35) cardiac involvement, provision of intensive care (OR = 9.60), need for hospital care (OR = 6.87), pneumonia and/or ARDS detected through chest X-rays (OR = 4.40), administration of systemic corticosteroids (OR = 4.39), oxygen support, mechanical ventilation or CPAP (OR = 2.21), pyrexia (OR = 1.84), and female sex (OR = 1.50) were independently associated with increased use of antibiotics. On the contrary, lower respiratory tract infections without radiologic evidence of pneumonia/ARDS and not requiring respiratory support (OR = 0.34) were independently associated with decreased use of antibiotics. There was significant variation in antibiotic use across the hospitals.

Conclusions

Our study showed a relatively high rate of antibiotic prescriptions in children with COVID-19 and in particular in those with severe disease or MIS-C. Importantly, we found a significant variation in reasons for prescriptions of antibiotics and type of chosen therapies, as well in hospital practices, highlighting current uncertainties and lack of guidelines for the recognition of bacterial infections in children with COVID-19. Prospective studies are needed to provide better evidence on the recognition and management of bacterial infections in COVID-19 children.

What is known

COVID-19 may worsen antibiotic prescription practices

What this new

COVID-19 and MIS-C children frequently received antibiotics

There was a wide variation in antibiotic prescriptions among institutions, highlighting the lack of practicle guidelines in the use of antibiotics in children with COVID-19
A new, simple method of describing the COVID-19 trajectory and dynamics in any country based on Johnson Cumulative Distribution Function fitting.
Authors: Cmiel Adam M , Cmiel Bogdan
Published: December 2020

A simple method is described to study and compare COVID-19 infection dynamics between countries, based on curve fitting to publicly shared data of confirmed COVID-19 infections in them. The method was tested using data from 80 countries in 6 regions. We found that Johnson Cumulative Distribution Functions (CDF) were extremely well fitted to the data (R2>0.99) and that Johnson CDFs were much better fitted to the data at their tails than either the commonly used Normal or Lognormal CDFs. Fitted Johnson CDFs can be used to obtain basic parameters of the infection wave, such as the percentage of the population infected during an infection wave, the days of the start, peak and end of the infection wave, as well as the durations of the infection wave of the wave's increase and decrease. These parameters can be easily interpreted biologically and used both for describing the infection wave dynamics and in further statistical analysis. The usefulness of the parameters obtained was analysed with respect to the relation between the Gross Domestic Product (GDP) per capita and the population density, and the percentage of the population infected during an infection wave, the starting day and the duration of the infection wave in the 80 countries. We found that all the above parameters were significantly dependent on the GDP per capita, but only the percentage of the population infected was significantly dependent on the population density in these countries. If used with caution, this method has a limited ability to predict the future trajectory and parameters of an ongoing infection wave.
Rt2: computing and visualising COVID-19 epidemics temporal reproduction number
Authors: Reyné Bastien , Danesh Gonché , Alizon Samuel , Sofonea Mircea T.
Published: December 2020

Analysing the spread of COVID-19 epidemics in a timely manner is essential for public health authorities. However, raw numbers may be misleading because of spatial and temporal variations. We introduce Rt2, an R-program with a shiny interface, which uses incidence data, i.e. number of new cases per day, to compute variations in the temporal reproduction number (ℛt), which corresponds to the average number of secondary infections caused by an infected person. This number is computed with theR0package, which better captures past variations, and theEpiEstimpackage, which provides a more accurate estimate of current values. ℛtcan be computed in different countries using either the daily number of new cases or of deaths. For France, these numbers can also be computed at the regional and departmental level using also daily numbers of hospital and ICU admissions. Finally, in addition to ℛt, we represent the incidence using a one-week sliding window to buffer daily variations. Overall, Rt2 provides an accurate and timely overview of the state and speed of spread of COVID-19 epidemics at different scales, using different metrics.
Does higher BMI increase COVID-19 severity?: a systematic review and meta-analysis
Authors: Chowdhury Akibul Islam , Rabbi Md. Fazley , Rahman Tanjina , Reza Sompa , Alam Mohammad Rahanur
Published: December 2020

Introduction

COVID-19 pandemic has caused havoc worldwide, and different comorbidities have been seen to exacerbate the condition. Obesity is one of the leading comorbidities, which is associated with many other diseases. In this paper, we present a systematic review and meta-analysis estimating the effects of overweight and obesity on COVID-19 disease severity.

Methodology

Two electronic databases (Medline and Cochrane library) and one grey literature database (Grey Literature Report) were searched using the following keywords: overweight, obesity, body mass index, respiratory disease, coronavirus, COVID-19. The risks of bias of the selected studies were assessed by using the Navigation Guide method for human data. Both random and fixed effect meta-analysis were determined using Review Manager (RevMan) software version 5.4.

Results

After initial screening, 12 studies (7 cohort studies, four case-control studies, and one cross-sectional study) were fulfilled the eligibility criteria, comprising a total of 405359 patients and included in the systematic review. The pooled risk of disease severity was 1.31 times higher based on both fixed and random effect model among those overweight patients,I20% and 2.09 and 2.41 times higher based on fixed and random effect respectively among obese patients,I242% compared to healthy individuals.

Conclusion

Overweight and obesity are common risk factors for disease severity of COVID-19 patients. However, further assessment of metabolic parameters included BMI, waist-hip ratio, and insulin levels, are required to estimate the risk factors of COVID-19 patients and understanding the mechanism between COVID-19 and body mass index.
Aripiprazole as a candidate treatment of COVID-19 identified through genomic analysis
Authors: Crespo-Facorro Benedicto , Ruiz-Veguilla Miguel , Vázquez-Bourgon Javier , Sánchez-Hidalgo Ana C. , Garrido-Torres Nathalia , Cisneros Jose M. , Prieto Carlos , Sainz Jesus
Published: December 2020

Background

Antipsychotics suppress expression of inflammatory cytokines and inducible inflammatory enzymes. Elopiprazole (a phenylpiperazine antipsychotic drug in phase 1) has been characterized as a therapeutic drug to treat SARS-CoV-2 infection in a repurposing study. We aim to investigate the potential effects of aripiprazole (an FDA approved phenylpiperazine) on COVID19-related immunological parameters.

Methods

Differential gene expression profiles of non-COVID versus COVID RNA-Seq samples (CRA002390 project in GSA database) and drug-naïve patients with psychosis at baseline and after three months of aripiprazole treatment was identified. An integrative analysis between COVID and aripiprazole immunomodulatory antagonist effects was performed.

Findings

82 out the 377 genes (21.7%) with expression significantly altered by aripiprazole have also their expression altered in COVID-19 patients and in 93.9% of these genes their expression is reverted by aripiprazole. The number of common genes with expression altered in both analyses is significantly higher than expected (Fisher’s Exact Test, two tail; P value=3.2e-11). 11 KEGG pathways were significantly enriched with genes with altered expression both in COVID-19 patients and aripiprazole medicated schizophrenia patients (P adj<0.05). The most significant pathways were associated to the immune system such as the “inflammatory bowel disease (IBD)” (the most significant pathway with a P adj of 0.00021), “Th1 and Th2 cell differentiation” and “B cell receptor signaling pathway”, all three related to the defense against infections.

Interpretation

This exploratory investigation may provide further support to the notion that protective effect is exerted by phenylpiperazine by modulating the immunological dysregulation associated to COVID-19. Along with many ongoing studies and clinical trials, repurposing available medications could be of use in countering SARS-CoV-2 infection, but require further studies and trials.
Attention-dependent physiological correlates in sleep deprived healthy humans
Authors: Cesari Valentina , Marinari Elena , Laurino Marco , Gemignani Angelo , Menicucci Danilo
Published: December 2020

Distinct cognitive functions are based on specific brain networks, but they are also affected by workload. The workload is a common factor affecting cognitive functioning that, by activating the Central Autonomic Network, modulates heart rate peripheral correlates of cognitive functioning. Based on these premises, we expected that the peripheral patterns associated with different attentional systems would have common (workload-related) and specific (task-dependent) components. To disentangling the components, a profile of peripheral physiological correlates of cognitive functioning was derived by studying healthy volunteers while performing different cognitive tasks during baseline and post-sleep deprivation conditions. Post-sleep deprivation condition was introduced to increase workload during tasks, allowing the investigation of the same participant at different levels of workload in different conditions. We performed, in each condition, physiological recordings of heart pulse, facial temperature and head movements during tasks assessing attentional networks efficiency (ANT - Attentional Network Task; CCT - Continuous Compensatory Tracker). We assessed perceived workload after the execution of these tasks. Physiological correlates of cognitive performance were identified by associating changes of task indices with the corresponding changes in physiological measures from baseline to post-sleep deprivation condition. Correlation analyses were performed after correction for the between-conditions workload changes: indeed, mental and physical demands of perceived workload increased after sleep deprivation. We found that alerting/vigilance has specific physiological correlates as indicated by the negative correlation between changes in ANT-alerting score and changes in amplitude of head movements and the positive one between changes in CCT-visuomotor speed indexing alertness and changes in facial temperature.
Attentional Modulation of Hierarchical Speech Representations in a Multitalker Environment
Authors: Kiremitçi Ibrahim , Yilmaz Özgür , Çelik Emin , Shahdloo Mo , Huth Alexander G , Çukur Tolga
Published: December 2020

Humans are remarkably adept in listening to a desired speaker in a crowded environment, while filtering out non-target speakers in the background. Attention is key to solving this difficult cocktail-party task, yet a detailed characterization of attentional effects on speech representations is lacking. It remains unclear across what levels of speech features and how much attentional modulation occurs in each brain area during the cocktail-party task. To address these questions, we recorded whole-brain BOLD responses while subjects either passively listened to single-speaker stories, or selectively attended to a male or a female speaker in temporally-overlaid stories in separate experiments. Spectral, articulatory, and semantic models of the natural stories were constructed. Intrinsic selectivity profiles were identified via voxelwise models fit to passive listening responses. Attentional modulations were then quantified based on model predictions for attended and unattended stories in the cocktail-party task. We find that attention causes broad modulations at multiple levels of speech representations while growing stronger towards later stages of processing, and that unattended speech is represented up to the semantic level in parabelt auditory cortex. These results provide insights on attentional mechanisms that underlie the ability to selectively listen to a desired speaker in noisy multi-speaker environments.
Causal relations between cortical network oscillations and breathing frequency
Authors: Tort Adriano BL , Hammer Maximilian , Zhang Jiaojiao , Brankačk Jurij , Draguhn Andreas
Published: December 2020

Nasal breathing generates a rhythmic signal which entrains cortical network oscillations in widespread brain regions on a cycle-to-cycle time scale. It is unknown, however, how respiration and neuronal network activity interact on a larger time scale: are breathing frequency and typical neuronal oscillation patterns correlated? Is there any directionality or causal relationship? To address these questions, we recorded field potentials from the posterior parietal cortex of mice together with respiration during REM sleep. In this state, the parietal cortex exhibits prominent theta and gamma oscillations while behavioral activity is minimal, reducing confounding signals. We found that the instantaneous breathing rate strongly correlates with the instantaneous frequency and amplitude of both theta and gamma oscillations. Granger causality analysis revealed specific directionalities for different rhythms: changes in theta activity precede and cause changes in breathing rate, suggesting control of breathing frequency by the functional state of the brain. On the other hand, the instantaneous breathing rate Granger-causes changes in gamma oscillations, suggesting that gamma is influenced by a peripheral reafference signal. These findings show that breathing causally relates to different patterns of rhythmic brain activity, revealing new and complex interactions between elementary physiological functions and neuronal information processing.

Significance Statement

The study of the interactions between respiration and brain activity has been focused on phase-entrainment relations, in which cortical networks oscillate phase-locked to breathing cycles. Here we discovered new and much broader interactions which link respiration rate (frequency) to different patterns of oscillatory brain activity. Specifically, we show that the instantaneous breathing rate strongly correlates with the instantaneous frequency and amplitude of theta and gamma oscillations, two major network patterns associated with cognitive functions. Interestingly, causality analyses reveal that changes in breathing rate follow theta, suggesting a central drive, while in contrast, gamma activity follows changes in breathing rate, suggesting the role of a reafferent signal. Our results reveal new mechanisms by which nasal breathing patterns may influence brain functions.
The Quercetin and Quercetin Derivatives Interaction With Cyclooxygenase-1 and Cyclooxygenase-2
Authors: Manukyan A.E. , Hovhannisyan A.A.
Published: December 2020

The cyclooxygenase (COX) enzymes are tumor markers, the inhibition of which can be used in the prevention and therapy of carcinogenesis. It was found that COX-1 and COX-2 are considered as targets for tumor inhibition. In anticancer therapy, plant compounds are considered that can inhibit their activity. Modeling of the COX-1 and COX-2 enzymes was carried out on the basis of molecular models of three-dimensional structures from the PDB database [PDB ID: 3KK6, 5f19] RCSB. For docking analysis, 3D ligand models were created using MarvinSketch based on the PubChem database [CID: 5280343, 5281654].In silicoexperiments, for the first time, revealed the possible interaction and inhibition of COX-1 and COX-2 by quercetin and quercetin derivatives. Aspirin and Celecoxib [CID: 2244, 2662] were taken to compare the results. Possible biological activities and possible side effects of the ligands have been identified. It is noteworthy that Celecoxib is not active on the studied cell lines, while quercetin and quercetin derivatives are more active than Aspirin.
The social shape of sperm: Using an integrative machine-learning approach to examine sperm ultrastructure and collective motility
Authors: Hook Kristin A. , Yang Qixin , Campanello Leonard , Losert Wolfgang , Fisher Heidi S.
Published: December 2020

Sperm are one of the most morphologically diverse cell types in nature, yet they also exhibit remarkable behavioral variation, including the formation of collective groups of cells that swim together for motility or transport through the female reproductive tract. Here we take advantage of the natural variation in sperm traits observed acrossPeromyscusmice to test the hypothesis that the morphology of the rodent sperm head influences their sperm aggregation behavior. Using machine learning and traditional morphometric approaches to quantify and analyze their complex shapes, we show that the elongation of the sperm head is the most distinguishing morphological trait in these rodents and, as predicted, significantly associates with collective sperm movements obtained fromin vitroobservations. We then successfully use neural network analysis to predict the size and proportion of sperm aggregates from sperm head morphology and show that species whose sperm feature relatively wider heads aggregate more often and form larger groups, providing support for the theoretical prediction that an adhesive region around the equatorial region of the sperm head mediates these unique gametic interactions. Together these findings advance our understanding of how even subtle variation in sperm design can drive differences in sperm function and performance.

Subject Areas

Evolution, Cellular Biology, Behavior
The cost of attentional reorienting on conscious visual perception: an MEG study
Authors: Spagna Alfredo , Bayle Dimitri J. , Romeo Zaira , Seidel-Malkinson Tal , Liu Jianghao , Yahia-Cherif Lydia , Chica Ana B. , Bartolomeo Paolo
Published: December 2020

How do attentional networks influence conscious perception? To answer this question, we used magnetoencephalography (MEG) in human participants, and assessed the effects of spatially nonpredictive or predictive supra-threshold peripheral cues on the conscious perception of near-threshold Gabors. Three main results emerged. (1) As compared with invalid cues, both nonpredictive and predictive valid cues increased conscious detection. Yet, only predictive cues shifted the response criterion towards a more liberal decision (i.e., willingness to report the presence of a target under conditions of greater perceptual uncertainty) and affected target contrast leading to 50% detections. (2) Conscious perception following valid predictive cues was associated to enhanced activity in frontoparietal networks. These responses were lateralized to the left hemisphere during attentional orienting, and to the right hemisphere during target processing. The involvement of frontoparietal networks occurred earlier in valid than in invalid trials, a possible neural marker of the cost of re-orienting attention. (3) When detected targets were preceded by invalid predictive cues, and thus reorienting to the target was required, neural responses occurred in left hemisphere temporo-occipital regions during attentional orienting, and in right hemisphere anterior insular and temporo-occipital regions during target processing. These results confirm and specify the role of frontoparietal networks in modulating conscious processing, and detail how invalid orienting of spatial attention disrupts conscious processing.

Significance Statement

Do we need to pay attention to external objects in order to become aware of them? Characterizing the spatiotemporal dynamics of attentional effects on visual perception is critical to understand how humans process and select relevant information. Participants detected near-threshold visual targets preceded by supra-threshold spatial cues with varying degrees of predictivity, while their brain activity was recorded using magnetoencephalography. Results demonstrated that valid predictive cues biased participants’ conscious perception through an early recruitment of frontoparietal regions, and that attentional costs associated to invalid predictive cues were related to activation of the right hemisphere ventral network. This work characterizes the neural dynamics associated with the cost of attentional reorienting on conscious processing.
Patient-Derived Orthotopic Xenografts and Cell Lines from Pediatric High-Grade Glioma Recapitulate the Heterogeneity of Histopathology, Molecular Signatures, and Drug Response
Authors: He Chen , Xu Ke , Zhu Xiaoyan , Dunphy Paige S. , Gudenas Brian , Lin Wenwei , Twarog Nathaniel , Hover Laura D. , Kwon Chang-Hyuk , Kasper Lawryn H. , Zhang Junyuan , Li Xiaoyu , Dalton James , Jonchere Barbara , Mercer Kimberly S. , Currier Duane G. , Caufield William , Wang Yingzhe , Broniscer Alberto , Wetmore Cynthia , Upadhyaya Santhosh A. , Qaddoumi Ibrahim , Klimo Paul , Boop Frederick , Gajjar Amar , Zhang Jinghui , Orr Brent A. , Robinson Giles W. , Monje Michelle , Freeman Burgess B. , Roussel Martine F. , Northcott Paul A. , Chen Taosheng , Rankovic Zoran , Wu Gang , Chiang Jason , Tinkle Christopher L. , Shelat Anang A. , Baker Suzanne J.
Published: December 2020

Pediatric high-grade glioma (pHGG) is a major contributor to cancer-related death in children.In vitroandin vivodisease models reflecting the intimate connection between developmental context and pathogenesis of pHGG are essential to advance understanding and identify therapeutic vulnerabilities. We established 21 patient-derived pHGG orthotopic xenograft (PDOX) models and eight matched cell lines from diverse groups of pHGG. These models recapitulated histopathology, DNA methylation signatures, mutations and gene expression patterns of the patient tumors from which they were derived, and included rare subgroups not well-represented by existing models. We deployed 16 new and existing cell lines for high-throughput screening (HTS).In vitroHTS results predicted variablein vivoresponse to inhibitors of PI3K/mTOR and MEK signaling pathways. These unique new models and an online interactive data portal to enable exploration of associated detailed molecular characterization and HTS chemical sensitivity data provide a rich resource for pediatric brain tumor research.
The Importance of Individual Beliefs in Assessing Treatment Efficacy: Insights from Neurostimulation Studies
Authors: Fassi Luisa , Hochman Shachar , Daskalakis Zafiris J. , Blumberger Daniel M. , Kadosh Roi Cohen
Published: December 2020

In recent years, there has been debate about the effectiveness of treatments from different fields, such as neurostimulation, neurofeedback, brain training, and pharmacotherapy. This debate has been fuelled by contradictory and nuanced experimental findings. Notably, the effectiveness of a given treatment is commonly evaluated by comparing the effect of the active treatment versus the placebo on human health and/or behaviour. However, this approach neglects the individual’s subjective experience of the type of treatment s/he received in establishing treatment efficacy. Here, we show that individual differences insubjective treatment—the thought of receiving the active or placebo condition during an experiment—can explain variability in outcomes better than the actual treatment. We analysed four independent datasets (N=387 participants), including clinical patients and healthy adults from different age groups who were exposed to different neurostimulation treatments (transcranial magnetic stimulation: Study 1 & 2; transcranial direct current stimulation: Study 3 & 4). Our findings show that the inclusion ofsubjective treatmentcan provide a better model fit, either alone or in interaction withobjective treatment(defined as the condition to which participants are assigned in the experiment). These results demonstrate the significant contribution of subjective experience in explaining the variability of clinical, cognitive and behavioural outcomes. We advocate for existing and future studies in clinical and non-clinical research to start accounting for participants’ subjective beliefs and their interplay with objective treatment when assessing the efficacy of treatments. This approach will be crucial in providing a more accurate estimation of the treatment effect and its source, allowing the development of effective and reproducible interventions.

Significance statement

We demonstrate that individual differences insubjective treatment—the belief of receiving the active or placebo condition during an experiment—can explain variability in research outcomes better thanobjective treatment, the actual treatment to which participants are assigned. Even though it is a standard practice for intervention studies to collect data onsubjective treatment, its contribution to research outcomes has been overlooked. By demonstrating the explanatory power ofsubjective treatmentbeyondobjective treatmentin four independent datasets, we show its potential to provide further insights into the effectiveness of different interventions. We, therefore, encourage researchers to adopt our approach in existing and new studies, to improve experimental design and ultimately increase the rigour and robustness of clinical and non-clinical interventions.
Somatic Cell Nuclear Transfer Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways
Authors: Long Chunshen , Li Hanshuang , Li Xinru , Zuo Yongchun
Published: December 2020

Transcription is the most fundamental molecular event that occurs with zygotic genome activation (ZGA) during embryo development. However, the potential association between transcription pathways and low cloning efficiency of nuclear transfer (NT) embryos remains elusive. Here, we integrated a series of RNA-seq data on NT embryos to deciphering the molecular barriers of NT embryo development. Comparative transcriptome analysis indicated that incomplete activation of transcription pathways functions as a barrier for NT embryos. Then, the gene regulatory network (GRN) identified that crucial factors responsible for transcription play a coordinated role in epigenome erasure and pluripotency regulation during normal embryo development. But in NT embryos, massive genes involved in transcription pathways were varying degrees of inhibition. Our study therefore provides new insights into understanding the barriers to NT embryo reprogramming.
Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures
Authors: Khatami Sepehr Golriz , Mubeen Sarah , Bharadhwaj Vinay Srinivas , Kodamullil Alpha Tom , Hofmann-Apitius Martin , Domingo-Fernández Daniel
Published: December 2020

The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and a novel scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for three different cancers. We also show how this approach can deconvolute a drugs’ mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs’ effect on a given patient.
The cellular architecture of memory modules inDrosophilasupports stochastic input integration
Authors: Hafez Omar A. , Escribano Benjamin , Ziegler Rouven L. , Hirtz Jan J. , Niebur Ernst , Pielage Jan
Published: December 2020

The ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within theDrosophilamushroom body (MB), the fly’s center for learning and memory. Our model combines the electron microscopy-based architecture of one MB output neuron (MBON-α3), the synaptic connectivity of its 948 presynaptic Kenyon cells (KCs), and its membrane properties obtained from patch-clamp recordings. We show that this neuron is electrotonically compact and that synaptic input corresponding to simulated odor input robustly drives its spiking behavior. Therefore, sparse innervation by KCs can efficiently control and modulate MBON activity in response to learning with minimal requirements on the specificity of synaptic localization. This architecture allows efficient storage of large numbers of memories using the flexible stochastic connectivity of the circuit.
Grid cell firing fields in a volumetric space
Authors: Grieves Roddy M. , Jedidi-Ayoub Selim , Mishchanchuk Karyna , Liu Anyi , Renaudineau Sophie , Duvelle Éléonore , Jeffery Kate J.
Published: December 2020

We investigated how entorhinal grid cells represent volumetric (three-dimensional) space. On a flat surface, grid cell firing fields are circular and arranged in a close-packed hexagonal array. In three dimensions, theoretical and computational work suggests that the most efficient configuration would be a regular close packing of spherical fields. We report that in rats exploring a cubic lattice, grid cells were spatially stable and maintained normal directional modulation, theta modulation and spike dynamics. However, while the majority of grid fields were spherical, they were irregularly arranged, even when only fields abutting the lower surface (equivalent to the floor) were considered. Thus, grid organization is shaped by the environment’s movement affordances, and may not need to be regular to support spatial computations.

One Sentence Summary

In rats exploring a volumetric space, grid cells are spatially modulated but their firing fields are irregularly arranged.
Suboptimal human inference inverts the bias-variance trade-off for decisions with asymmetric evidence
Authors: Eissa Tahra L , Gold Joshua I , Josić Krešimir , Kilpatrick Zachary P
Published: December 2020

Solutions to challenging inference problems are often subject to a fundamental trade-off between bias (being systematically wrong) that is minimized with complex inference strategies and variance (being oversensitive to uncertain observations) that is minimized with simple inference strategies. However, this trade-off is based on the assumption that the strategies being considered are optimal for their given complexity and thus has unclear relevance to the frequently suboptimal inference strategies used by humans. We examined inference problems involving rare, asymmetrically available evidence, which a large population of human subjects solved using a diverse set of strategies that were suboptimal relative to the Bayesian ideal observer. These suboptimal strategies reflected an inversion of the classic bias-variance trade-off: subjects who used more complex, but imperfect, Bayesian-like strategies tended to have lower variance but high bias because of incorrect tuning to latent task features, whereas subjects who used simpler heuristic strategies tended to have higher variance because they operated more directly on the observed samples but displayed weaker, near-normative bias. Our results yield new insights into the principles that govern individual differences in behavior that depends on rare-event inference, and, more generally, about the information-processing trade-offs that are sensitive to not just the complexity, but also the optimality of the inference process.
A Budding Yeast Model for Human Disease Mutations in theEXOSC2Cap Subunit of the RNA Exosome
Authors: Sterrett Maria C. , Enyenihi Liz , Leung Sara W. , Hess Laurie , Strassler Sarah E. , Farchi Daniela , Lee Richard S. , Withers Elise S. , Kremsky Isaac , Baker Richard E. , Basrai Munira A. , van Hoof Ambro , Fasken Milo B. , Corbett Anita H.
Published: December 2020

RNA exosomopathies, a growing family of tissue-specific diseases, are linked to missense mutations in genes encoding the structural subunits of the conserved 10-subunit exoribonuclease complex, the RNA exosome. Such mutations in the cap subunit geneEXOSC2cause the novel syndrome SHRF (Short stature,Hearing loss,Retinitis pigmentosa and distinctiveFacies). In contrast, exosomopathy mutations in the cap subunit geneEXOSC3cause pontocerebellar hypoplasia type 1b (PCH1b). Though having strikingly different disease pathologies,EXOSC2andEXOSC3exosomopathy mutations result in amino acid substitutions in similar, conserved domains of the cap subunits, suggesting that these exosomopathy mutations have distinct consequences for RNA exosome function. We generated the firstin vivomodel of the SHRF pathogenic amino acid substitutions using budding yeast by introducing theEXOSC2mutations in the orthologousS. cerevisiaegeneRRP4. The resultingrrp4mutant cells have defects in cell growth and RNA exosome function. We detect significant transcriptomic changes in both coding and non-coding RNAs in therrp4variant,rrp4-G226D, which modelsEXOSC2p.Gly198Asp. Comparing thisrrp4-G226Dmutant to the previously studiedS. cerevisiaemodel ofEXOSC3PCH1b mutation,rrp40-W195R, reveals that these mutants have disparate effects on certain RNA targets, providing the first evidence for different mechanistic consequences of these exosomopathy mutations. Congruently, we detect specific negative genetic interactions between RNA exosome cofactor mutants andrrp4-G226Dbut notrrp40-W195R. These data provide insight into how SHRF mutations could alter the function of the RNA exosome and allow the first direct comparison of exosomopathy mutations that cause distinct pathologies.
Scientific publications and COVID-19 “research pivots” during the pandemic: An initial bibliometric analysis
Authors: Shapira Philip
Published: December 2020

An examination is presented of scientific research publication trends during the global coronavirus (COVID-19) pandemic in 2020. After reviewing the timing of the emergence of the pandemic in 2020 and the growth of governmental responses, available secondary sources are used to highlight impacts of COVID-19 on scientific research. A bibliometric analysis is then undertaken to analyze developments in COVID-19 related scientific publications through to October of 2020 by broad trends, fields, countries, and organizations. Two publication data sources are used: PubMed and the Web of Science.

While there has been a massive absolute increase in PubMed and Web of Science papers directly focused on COVID-19 topics, especially in medical, biological science, and public health fields, this is still a relatively small proportion of publication outputs across all fields of science. Using Web of Science publication data, the paper examines the extent to which researchers across all fields of science have pivoted their research outputs to focus on topics related to COVID-19. A COVID-19 research pivot is defined as the extent to which the proportion of output in a particular research field has shifted to a focus on COVID-19 topics in 2020 (to date) compared with 2019. Significant variations are found by specific fields (identified by Web of Science Subject Categories). In a top quintile of fields, not only in medical specialties, biomedical sciences, and public health but also in subjects in social sciences and arts and humanities, there are relatively high to medium research pivots. In lower quintiles, including other subjects in science, social science, and arts and humanities, low to zero COVID-19 research pivoting is identified.

In a new Appendix to the paper, an updated analysis is provided through to mid-April 2022.

Citation

Shapira, P. “Scientific publications and COVID-19 “research pivots” during the pandemic: An initial bibliometric analysis,”bioRxiv2020.12.06.413682; doi:https://doi.org/10.1101/2020.12.06.413682

Version Notes

Version 1: Original paper, completed on December 6, 2020; posted atbioRxivon December 7, 2020.

Version 2: Minor grammar items corrected.

Version 3: Updated bibliometric analysis through to mid-April 2022 added on April 29, 2022, as new Appendix 2.
MCOLN1gene-replacement therapy corrects neurologic dysfunction in the mouse model of mucolipidosis IV
Authors: DeRosa Samantha , Salani Monica , Smith Sierra , Sangster Madison , Miller-Browne Victoria , Wassmer Sarah , Xiao Ru , Vandenberghe Luk , Slaugenhaupt Susan , Misko Albert , Grishchuk Yulia
Published: December 2020

Mucolipidosis IV (MLIV, OMIM 252650) is an orphan disease leading to debilitating psychomotor deficits and vision loss. It is caused by loss-of-function mutations in theMCOLN1gene that encodes thethe lysosomal transient receptor potential channel mucolipin 1 (TRPML1). With no existing therapy, the unmet need in this disease is very high. Here we show that AAV-mediated gene transfer of the humanMCOLN1gene rescues motor function and alleviates brain pathology in theMcoln1−/−MLIV mouse model. Using the AAV-PHP.b vector for initial proof-of-principle experiments in symptomatic mice, we showed long-term reversal of declined motor function and significant delay of paralysis. Next, we designed self-complimentary AAV9 vector for clinical use and showed that its intracerebroventricular administration in post-natal day 1 mice significantly improved motor function and myelination and reduced lysosomal storage load in the MLIV mouse brain. We also showed that CNS targeted gene transfer is necessary to achieve therapeutic efficacy in this disease. Based on our data and general advancements in the gene therapy field, we propose scAAV9-mediated CSF-targetedMCOLN1gene transfer as a therapeutic strategy in MLIV.