bluuebunny/crossref_metadata_2025 huggingface dataset:
How important are parents in the development of child anxiety and depression? A genomic analysis of parent-offspring trios in the Norwegian Mother Father and Child Cohort Study (MoBa)
Authors: Cheesman Rosa , Eilertsen Espen Moen , Ahmadzadeh Yasmin I. , Gjerde Line C. , Hannigan Laurie J. , Havdahl Alexandra , Young Alexander I. , Eley Thalia C. , Njølstad Pål R. , Magnus Per , Andreassen Ole A. , Ystrom Eivind , McAdams Tom A.
Published: April 2020

Background

Many studies detect associations between parent behaviour and child symptoms of anxiety and depression. However, most do not account for shared genetic risk. Quantitative genetic designs provide a means of controlling for shared genes, but rely on observed putative exposure variables, and require data from highly specific family structures.

Methods

The intergenerational genomic method, Relatedness Disequilibrium Regression (RDR), indexes environmental effects of parents on child traits using measured genotypes. RDR estimates how much the parent genome influences the child indirectly via the environment, over and above effects of genes acting directly in the child. This ‘genetic nurture’ effect is agnostic to parent phenotype and captures unmeasured heritable parent behaviours. We applied RDR in a sample of 11,598 parent-offspring trios from the Norwegian Mother, Father and Child Cohort Study (MoBa) to estimate parental genetic nurture separately from direct child genetic effects on anxiety and depression symptoms at age 8. We also tested for mediation of genetic nurture via maternal emotional symptoms. Results were compared to a complementary non-genomic pedigree model.

Results

Parental genetic nurture significantly influenced depression symptoms at age 8, explaining 14% of the phenotypic variance. Subsequent analyses suggested that maternal anxiety and depression partially mediated the parental genetic nurture effect. The genetic nurture effect was mirrored by the finding of shared family environmental influence in our complementary pedigree model. In contrast, variance in anxiety symptoms was not significantly influenced by common genetic variation in children or parents, despite a moderate pedigree heritability.

Conclusions

Genomic methods like RDR represent new opportunities for genetically sensitive family research in humans, which until now has been largely confined to adoption, twin and other pedigree designs. Our results are relevant to debates about the role of parents in the development of emotional problems in children, and possibly where to intervene to reduce problems.
Estimating the end of the first wave of epidemic for COVID-19 outbreak in mainland China
Authors: Griette Quentin , Liu Zhihua , Magal Pierre
Published: April 2020

Our main aim is to estimate the end of the first wave epidemic of COVID-19 outbreak in mainland China. We developed mathematical models to predict reasonable bounds on the date of end of the COVID-19 epidemics in mainland China with strong quarantine and testing measures for a sufficiently long time. We used reported data in China from January 20, 2020 to April 9, 2020. We firstly used a deterministic approach to obtain a formula to compute the probability distribution of the extinction date by combining the models and continuous-time Markov processes. Then we present the individual based model (IMB) simulations to compare the result by deterministic approach and show the absolute difference between the estimated cumulative probability distribution computed by simulations and formula. We provide the predictions of the end of the first wave epidemic for different fractions f of asymptomatic infectious that become reported symptomatic infectious.
Estimate of Covid prevalence using imperfect data
Authors: Rudnicki Witold R. , Piliszek Radosław
Published: April 2020

The real number of people who were truly infected with SARS-CoV-2, is certainly significantly larger than the official record. Few countries have tracking and testing procedures that are sufficiently robust to discover nearly all infections. In most countries they are inadequate, hence the true extent of the pandemic is unknown. The current study proposes the estimate of the COVID-19 extent for countries with sufficiently high number of deaths and cases. The estimate is based on a simple model of mortality. This model was developed for a reference country with a large number of cases and high intensity of COVID-19 testing. The model is then applied to compute apparent mortality in the target and reference countries. The number of cases in the target country is then estimated assuming constant underlying true mortality. The estimate of cases in most countries is significantly higher than the official record. As of April 12, 2020, the global estimate is 5.2 million compared to 1.8 million in the official record. The models developed in this study are available at covid-model.net. The model ignores several factors that are known to influence mortality, such as the demographics and health condition of population, state of epidemic and sociological differences between countries. While the model is rough, it nevertheless provides a unified approach to producing a systematic global estimate of the extent of the COVID-19 epidemic and can be useful for its monitoring.
Effects of temperature and humidity on the spread of COVID-19: A systematic review
Authors: Mecenas Paulo , da Rosa Moreira Bastos Renata Travassos , Rosário Vallinoto Antonio Carlos , Normando David
Published: April 2020

Background

Faced with the global pandemic of COVID-19, declared by World Health Organization (WHO) on March 11th2020, and the need to better understand the seasonal behavior of the virus, our team conducted this systematic review to describe current knowledge about the emergence and replicability of the virus and its correlation with different weather factors such as temperature and relative humidity.

Methods

The review was registered with the PROSPERO database. The electronic databases PubMed, Scopus, Web of Science, Cochrane Library, LILACS, OpenGrey and Google Scholar were examined with the searches restricted to the years 2019 and 2020. Risk of bias assessment was performed using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist tool. The GRADE tool was used to assess the quality of the evidence.

Results

The initial screening identified 517 articles. After examination of the full texts, seventeen studies met the review’s eligibility criteria. Great homogeneity was observed in the findings regarding the effect of temperature and humidity on the seasonal viability and transmissibility of COVID-19. Cold and dry conditions were potentiating factors on the spread of the virus. After quality assessment, four studies had a high risk of bias and thirteen studies were scored as moderate risk of bias. The certainty of evidence was graded as low for both outcomes evaluated.

Conclusion

Considering the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. The certainty of the evidence generated was graded as low. However, these variables alone could not explain most of the variability in disease transmission.
Novel Coronavirus in Nigeria: Epidemiological analysis of the first 45 days of the pandemic
Authors: Adegboye Oyelola A. , Adekunle Adeshina I. , Gayawan Ezra
Published: April 2020

Background

On December 31, 2019, the World Health Organization (WHO) was notified of a novel coronavirus in China that was later named COVID-19. On March 11, 2020, the outbreak of COVID-19 was declared a pandemic. The first instance of the virus in Nigeria was documented on February 27, 2020.

Methods

This study provides a preliminary epidemiological analysis of the first 45 days of COVID-19 outbreak in Nigeria quantifying. We estimated the early transmissibility via time-varying reproduction number based on Bayesian method that incorporates uncertainty in the distribution of serial interval (time interval between symptoms onset in an infected individual and the infector) and adjusted for disease importation.

Findings

By April 11, 2020, 318 confirmed cases and 10 deaths from COVID-19 have occurred in Nigeria. At day 45, the exponential growth rate was 0.07 (95% Confidence Interval (CI): 0.05 – 0.10) with doubling time of 9.84 days (95% CI: 7.28 – 15.18). Separately for travel related and local cases the doubling time was 12.88 days and 2.86 days, respectively. Furthermore, we estimated the reproduction number for each day of the outbreak using three-weekly window while adjusting for travel related cases. The estimated reproduction number was 4.98 (95% CrI: 2.65 – 8.41) at day 22 (March 19, 2020), peaking at 5.61 (95% CrI: 3.83 –7.88) at day 25 (March 22, 2020). The median reproduction number over the study period was 2.71 and the latest value at April 11, 2020 was 1.42 (95% CI: 1.26 – 1.58).

Interpretation

These 45-day estimates suggested that cases of COVID-19 in Nigeria have been remarkably lower than expected and the preparedness to detect needs to be shifted to stop local transmission.

Funding

None
Multinational modeling of SARS-CoV-2 spreading dynamics: Insights on the heterogeneity of COVID-19 transmission and its potential healthcare burden
Authors: Martinez-Loran Erick R. , Jesús Naveja J. , Bello-Chavolla Omar Y. , Contreras-Torres Flavio F.
Published: April 2020

Background

Modelling and projections of COVID-19 using a single set of transmission parameters can be an elaborated because the application of different levels of containment measures at different stages of the worldwide COVID-19 outbreak.

Methods

We developed a piecewise fitting SEIR methodology to fit the progress of the COVID-19 that can be applied on any of the 185 countries listed in John Hopkins Coronavirus Resource Center. The contagious contact rate, the rate of removal and the initially exposed population were obtained at three different stages of the pandemic for a set of 18 countries, and globally for the total number of cases worldwide. The active number of infections and the removed populations were fitted simultaneously to validate the SEIR model against the available time series reports on the number of confirmed infections, recoveries and deaths. We evaluate the effect of a reduction of contagious contact rate on the level of burden put on local healthcare infrastructure considering different levels of intervention. As a guideline for future public health interventions, we also estimated the maximum number of future cases and its potential peak date.

Findings

We project that the peak in the number of infections worldwide will take place after the third quarter of 2020 with a decline rate that might extend beyond 2020. For 12 out of the 18 countries analyzed, we observe that, following the trend at the date of this study, the number of severe infections will surpass their healthcare capacity. For a 90% reduction scenario of the contagious contact rate, four out of the 18 countries analyzed will undergo a significant delay in the peak of infection, extending the course of the epidemic further than our simulation window (365 days).

Interpretation

We identify three stages for the COVID-19 transmission dynamics, which suggest that it is highly heterogeneous between countries and its contagious contact rate, is currently affected by both local responses of the public health interventions and to the population’s adherence to the measures.

Funding

No funding received.
Refined compartmental models, asymptomatic carriers and COVID-19
Authors: Oliveira Gonçalo
Published: April 2020

Abstract

The goal of this article is to analyze some compartmental models specially designed to model the spread of a disease whose transmission has the same features as COVID-19. The major contributions of this article are: (1) Rigorously find sufficient conditions for the outbreak to only have one peak, i.e. for no second wave of infection to form; (2) Investigate the formation of other waves of infection when such conditions are not met; (3) Use numerical simulations to analyze the different roles asymptomatic carriers can have. We also argue that dividing the population into non-interacting groups leads to an effective reduction of the transmission rates.

As in any compartmental model, the goal of this article is to provide qualitative understanding rather than exact quantitative predictions.
The Easter and Passover Blip in New York City: How exceptions can cause detrimental effects in pandemic times
Authors: Vierlboeck Maximilian , Nilchiani Roshanak R. , Edwards Christine M.
Published: April 2020

Abstract and Executive Summary

When it comes to pandemics such as the currently present COVID-19 [1], various issues and problems arise for infrastructures and institutions. Due to possible extreme effects, such as hospitals potentially running out of beds or medical equipment, it is essential to lower the infection rate to create enough space to attend to the affected people and allow enough time for a vaccine to be developed. Unfortunately, this requires that measures put into place are upheld long enough to reduce the infection rate sufficiently.

In this paper, we describe research simulating the influences of the contact rate on the spread of the pandemic using New York City as an example (Section IV) and especially already observed effects of contact rate increases during holidays [2-4] (Section V). In multiple simulations scenarios for Passover and Easter holidays, we evaluated 25%, 50%, 75%, and 100% temporary increases in contact rates using a scenario close to the currently reported numbers as reference and contact rates based on bioterrorism research as a “normal” baseline for NYC.

The first general finding from the simulations is that singular events of increased visits/contacts amplify each other disproportionately if they are happening in close proximity (time intervals) together. The second general observation was that contact rate spikes leave a permanently increased and devastating infection rate behind, even after the contact rate returns to the reduced one. In case of a temporary sustained increase of contact rate for just three days in a row, the aftermath results in an increase of infection rate up to 40%, which causes double the fatalities in the long run.

In numbers, given that increases of 25% and 50% seem to be most likely given the data seen in Germany for the Easter weekend for example [2, 3], our simulations show the following increases (compared to the realistic reference run): for a temporary 25% surge in contact rate, the total cases grew by 215,880, the maximum of required hospitalizations over time increased to 63,063, and the total fatalities climbed by 8,844 accumulated over 90 days. As for the 50% surge, we saw the total number of cases rise by 461,090, the maximum number of required hospitalizations increase to 79,733, and the total number of fatalities climb by 19,125 over 90 days in NYC.

All in all, we conclude that even very short, temporary increases in contact rates can have disproportionate effects and result in unrecoverable phenomena that can hardly be reversed or managed later. The numbers show possible phenomena before they might develop effects in reality. This is important because phenomena such as the described blip can impact the hospitals in reality. Therefore, we warn that a wave of infections due to increased contact rates during Passover/Easter might come as a result!
Estimate of COVID-19 case prevalence in India based on surveillance data of patients with severe acute respiratory illness
Authors: Venkatesan Padmanaban
Published: April 2020

In absence of extensive testing for SARS-CoV-2, true prevalence of COVID-19 cases in India remain unknown. In this study, a conservative estimate of prevalence of COVID-19 is calculated based on the age wise COVID-19 positivity rate among patients with severe respiratory illness as reported by Indian Council of Medical Research. Calculations in the study estimates a cumulative number of 17151 COVID-19 positive cases by the end of April 2, 2020.
Clinical academic research in the time of Corona: a simulation study in England and a call for action
Authors: Banerjee Amitava , Katsoulis Michail , Lai Alvina G. , Pasea Laura , Treibel Thomas A. , Manisty Charlotte , Denaxas Spiros , Quarta Giovanni , Hemingway Harry , Cavalcante Joao , Noursadeghi Mahdad , Moon James C
Published: April 2020

Background

Coronavirus (COVID-19) poses health system challenges in every country. As with any public health emergency, a major component of the global response is timely, effective science. However, particular factors specific to COVID-19 must be overcome to ensure that research efforts are optimised. We aimed to model the impact of COVID-19 on the clinical academic response in the UK, and to provide recommendations for COVID-related research.

Methods

We constructed a simple stochastic model to determine clinical academic capacity in the UK in four policy approaches to COVID-19 with differing population infection rates: “Italy model” (6%), “mitigation” (10%), “relaxed mitigation” (40%) and “do-nothing” (80%) scenarios. The ability to conduct research in the COVID-19 climate is affected by the following key factors: (i) infection growth rate and population infection rate (from UK COVID-19 statistics and WHO); (ii) strain on the healthcare system (from published model); and (iii) availability of clinical academic staff with appropriate skillsets affected by frontline clinical activity and sickness (from UK statistics).

Findings

In “Italy model”, “mitigation”, “relaxed mitigation” and “do-nothing” scenarios, from 5 March 2020 the duration (days) and peak infection rates (%) are 95(2.4%), 115(2.5%), 240(5.3%) and 240(16.7%) respectively. Near complete attrition of academia (87% reduction, <400 clinical academics) occurs 35 days after pandemic start for 11, 34, 62, 76 days respectively – with no clinical academics at all for 37 days in the “do-nothing” scenario. Restoration of normal academic workforce (80% of normal capacity) takes 11,12, 30 and 26 weeks respectively.

Interpretation

Pandemic COVID-19 crushes the science needed at system level. National policies mitigate, but the academic community needs to adapt. We highlight six key strategies: radical prioritisation (eg 3-4 research ideas per institution), deep resourcing, non-standard leadership (repurposing of key non-frontline teams), rationalisation (profoundly simple approaches), careful site selection (eg protected sites with large academic backup) and complete suspension of academic competition with collaborative approaches.
Investigating spatial variability in COVID-19 pandemic severity across 19 geographic areas, Spain, 2020
Authors: Dahal Sushma , Mizumoto Kenji , Rothenberg Richard , Chowell Gerardo
Published: April 2020

Introduction

Spain has been disproportionately affected by the COVID-19 pandemic, ranking fifth in the world in terms of both total cases and total deaths due to COVID-19 as of May 20, 2020. Here we derived estimates of pandemic severity and assessed its relationship with socio-demographic and healthcare factors.

Methods

We retrieved the daily cumulative numbers of laboratory-confirmed COVID-19 cases and deaths in Spain from February 20, 2020 to May 20, 2020. We used statistical methods to estimate the time-delay adjusted case fatality risk (aCFR) for 17 autonomous communities and 2 autonomous cities of Spain. We then assessed how transmission and sociodemographic variables were associated with the aCFR across areas using multivariate regression analysis.

Results

We estimated the highest aCFR for Madrid (25.9%) and the average aCFR in Spain (18.2%). Our multivariate regression analysis revealed three statistically significant predictor variables: population size, population density, and the unemployment rate.

Conclusions

The estimated aCFR for 10 autonomous communities/cities in Spain are significantly higher than those previously estimated for other geographic regions including China and Korea. Our results suggest that public health interventions focused on densely populated areas and low socioeconomic groups can ameliorate the mortality burden of the COVID-19 pandemic in Spain.
The Longevity-Frailty Hypothesis: Evidence from COVID-19 Death Rates in Europe
Authors: Zahran Sammy , Altringer Levi , Prasad Ashok
Published: April 2020

By the end of spring (May 31st), the COVID-19 death rate was remarkably unevenly distributed across the countries Europe. While the risk of COVID-19 mortality is known to increase with age, age-specific COVID-19 death rates across Europe were similarly aberrantly distributed, implying that differences in age structure is an unlikely source of European variation in COVID-19 mortality. To explain these mortality distributions, we present a simple model where more favorable survival environments promote longevity and the accumulation of health frailty among the elderly while less favorable survival environments induce a mortality selection process that results in lower health frailty. Because the age-related conditions of frailty render the elderly less resistant to SARS-CoV-2, pre-existing survival environments may be non-obviously positively related to the COVID-19 death rate. To quantify thesurvival environmentparameter of our model, we collected historic cohort- and period-based age-specific probabilities of death across Europe. We find strong positive relationships between survival indicators and COVID-19 death rates across Europe, a result that is robust to statistical control for the capacity of a healthcare system to treat and survive infected persons, the timing and stringency of non-pharmaceutical interventions, and the volume of inbound international travelers, among other factors. To address possible concerns over reporting heterogeneity across countries, we show that results are robust to the substitution of our response variable for a measure of cumulative excess mortality. Consistent with the intuition of our model, we also show a strong negative association between age-specific COVID-19 death rates and pre-existing all-cause age-specific mortality rates for a subset of European countries. Overall, results support the notion that variation in pre-existing frailty, resulting from heterogeneous survival environments, partially caused striking differences in COVID-19 death during the first wave of the pandemic.
Using Feedback on Symptomatic Infections to Contain the Coronavirus Epidemic: Insight from a SPIR Model
Authors: Nikolaou Michael
Published: April 2020

A study is presented on the use of real-time information about symptomatic infectious individuals to adjust restrictions of human contacts at two basic levels, the stricter being on the symptomatic infectious group. Explicit analytical formulas as well as numerical results are presented to rapidly elucidate what-if questions on averting resurgence of the coronavirus epidemic after the first wave wanes. Implementation of related ideas would rely on a mix of several factors, including personal initiative and sophisticated technology for monitoring and testing. For robust decision making on the subject, detailed multidisciplinary studies remain indispensable.
CoroNet: A Deep Network Architecture for Semi-Supervised Task-Based Identification of COVID-19 from Chest X-ray Images
Authors: Khobahi Shahin , Agarwal Chirag , Soltanalian Mojtaba
Published: April 2020

In late 2019, a new Coronavirus disease, referred to as Corona virus disease 2019 (COVID-19), emerged in Wuhan city, Hubei, China, and resulted in a global pandemic—claiming a large number of lives and affecting billions all around the world. The current global standard used in diagnosis of COVID-19 in suspected cases is the real-time polymerase chain reaction (RT-PCR) test. Although the RT-PCR remains the standard reference for diagnosis purposes, it is a time-consuming and expensive test, and moreover, it usually suffers from high rates of false-negatives. Several early works have reported that the sensitivity of the chest Computed Tomography (CT) and the chest X-ray imaging are noticeably greater than that of the RT-PCR test at the initial representations of the disease, making them great candidates for developing new and sophisticated methodologies for analysis and classification of COVID-19 cases. In this paper, we establish the use of a rapid, non-invasive and cost-effective X-ray-based method as a key diagnosis and screening tool for COVID-19 at early and intermediate stages of the disease. To this end, we develop a novel and sophisticated deep learning-based signal and image processing technique as well as classification methodology for analyzing X-ray images specific to COVID-19 disease. Specifically, we consider a semi-supervised learning methodology based on AutoEncoders to first extract the infected legions in chest X-ray manifestation of COVID-19 and other Pneumonia-like diseases (as well as healthy cases). Then, we utilize this highly-tailored deep architecture to extract the relevant features specific to each class (i.e., healthy, non-COVID pneumonia, and COVID-19) and train a powerful yet efficient classifier to perform the task of automatic diagnosis. Furthermore, the semi-supervised nature of the proposed framework enables us to efficiently exploit the limited available dataset on COVID-19 while exploiting the vast amount of available X-ray dataset for healthy and non-COVID classes. Moreover, such a semi-supervised approach does not require an expert-annotated lesion area for each class. Our numerical investigations demonstrate that the proposed framework outperforms the state-of-the-art methods for COVID-19 identification while employing approximately ten times fewer training parameters as compared to other existing methodologies for classification of the COVID-19 from X-ray images (facilitating efficient training in a limited data regime). We further develop explainable artificial intelligence tools that can explain the diagnosis by using attribution maps while providing an indispensable tool for the radiologist in triage state. We have made the codes of our proposed framework publicly available to the research and healthcare community1.
A systematic review of antibody mediated immunity to coronaviruses: antibody kinetics, correlates of protection, and association of antibody responses with severity of disease
Authors: Huang Angkana T. , Garcia-Carreras Bernardo , Hitchings Matt D.T. , Yang Bingyi , Katzelnick Leah , Rattigan Susan M , Borgert Brooke , Moreno Carlos , Solomon Benjamin D. , Rodriguez-Barraquer Isabel , Lessler Justin , Salje Henrik , Burke Donald S. , Wesolowski Amy , Cummings Derek A.T.
Published: April 2020

The duration and nature of immunity generated in response to SARS-CoV-2 infection is unknown. Many public health responses and modeled scenarios for COVID-19 outbreaks caused by SARS-CoV-2 assume that infection results in an immune response that protects individuals from future infections or illness for some amount of time. The timescale of protection is a critical determinant of the future impact of the pathogen. The presence or absence of protective immunity due to infection or vaccination (when available) will affect future transmission and illness severity. The dynamics of immunity and nature of protection are relevant to discussions surrounding therapeutic use of convalescent sera as well as efforts to identify individuals with protective immunity. Here, we review the scientific literature on antibody immunity to coronaviruses, including SARS-CoV-2 as well as the related SARS-CoV-1, MERS-CoV and human endemic coronaviruses (HCoVs). We reviewed 1281 abstracts and identified 322 manuscripts relevant to 5 areas of focus: 1) antibody kinetics, 2) correlates of protection, 3) immunopathogenesis, 4) antigenic diversity and cross-reactivity, and 5) population seroprevalence. While studies of SARS-CoV-2 are necessary to determine immune responses to it, evidence from other coronaviruses can provide clues and guide future research.
Forecasting Hospital Staff Availability During The COVID-19 Epidemic
Authors: Schooling Chloe N. , Gyenge Norbert , Kadirkamanathan Visakan , Alix James J.P.
Published: April 2020

The COVID-19 pandemic poses two challenges to healthcare providers. Firstly, a high number of patients require hospital admission. Second, a high number of healthcare staff are either falling ill with the infection, or self-isolating. This poses significant problems for the staffing of busy hospital departments. We have created a simple model which allows users to stress test their rota. The model provides plots of staff availability over time using either a constant infection rate, or a changing infection rate fitted to population-based infection curves. It allows users to gauge the extent and timing of dips in staff availability. The basic constant infection rate model is available within an on-line web application (https://covid19.shef.ac.uk). As for any model, our work is imperfect. However, it allows a range of infection rates to be simulated quickly across different work patterns. We hope it will be useful to those planning staff deployment and will stimulate debate on the most effective patterns of work during the COVID-19 epidemic.
Amyloid precursor protein causes fusion of promyelocytic leukemia nuclear bodies in human hippocampal areas with high plaque load
Authors: Marks David , Heinen Natalie , Bachmann Lisa , Meermeyer Sophia , Werner Michelle , Gallego Lucia , Nolte Stephanie , Hemmerich Peter , Bader Verian , Winklhofer Konstanze , Schröder Elisabeth , Knauer Shirley K. , Müller Thorsten
Published: April 2020

The amyloid precursor protein (APP) is a type I transmembrane protein with unknown physiological function but potential impact in neurodegeneration. The current study demonstrates that APP signals to the nucleus causing the generation of aggregates comprising its adapter protein FE65 and the tumour suppressor proteins p53 and PML. The PML nuclear body generation, known to be of relevance in virus defence and cell division, is induced and fusion occurs over time depending on APP signalling. We further show that the nuclear aggregates of APP C-terminal (APP-CT) fragments together with PML and FE65 are present in the aged human brain but not in cerebral organoids differentiated from iPS cells. Notably, human Alzheimer’s disease brains reveal a highly significant loss of these nuclear aggregates in areas with high plaque load compared to plaque-free areas of the same individual. Based on these results we conclude that APP-CT signalling to the nucleus takes place in the aged human brain and is potentially involved in the pathophysiology of AD. Taken the current knowledge on PML bodies into account, we hypothesize a new role for APP as a twofold virus response protein. The APP-dependent defence strategy includes Aß-virus interaction at the extracellular matrix and APP-CT driven PML aggregation in the nucleus to encapsulate the viral nucleic acid. This defence strategy preferentially occurs in high-plaque regions of the human brain and overstimulation of this pathway results in a pyrrhic victory.
ASolanum lycopersicoidesreference genome facilitates biological discovery in tomato
Authors: Powell Adrian F. , Courtney Lance E. , Schmidt Maximilian H.-W. , Feder Ari , Vogel Alexander , Xu Yimin , Lyon David A. , Dumschott Kathryn , McHale Marcus , Sulpice Ronan , Bao Kan , Duhan Asha , Hallab Asis , Denton Alisandra K. , Mueller Lukas A. , Alseekh Saleh , Lie Jie , Martin Cathie , Fernie Alisdair R. , Hind Sarah R. , Martin Gregory B. , Fei Zhangjun , Giovannoni James J. , Strickler Susan R. , Usadel Björn
Published: April 2020

Wild relatives of tomato are a valuable source of natural variation in tomato breeding, as many can be hybridized to the cultivated species (Solanum lycopersicum). Several, includingSolanum lycopersicoides, have been crossed toS. lycopersicumfor the development of ordered introgression lines (ILs). Despite the utility of these wild relatives and their associated ILs, limited finished genomes have been produced to aid genetic and genomic studies. We have generated a chromosome-scale genome assembly forSolanum lycopersicoidesLA2951 using PacBio sequencing, Illumina, and Hi-C. We identified 37,938 genes based on Illumina and Isoseq and compared gene function to the available cultivated tomato genome resources, in addition to mapping the boundaries of theS. lycopersicoidesintrogressions in a set of cv. VF36 x LA2951 introgression lines (IL). The genome sequence and IL map will support the development ofS. lycopersicoidesas a model for studying fruit nutrient/quality, pathogen resistance, and environmental stress tolerance traits that we have identified in the IL population and are known to exist inS. lycopersicoides.
Big dairy data to disentangle the effect of geo-environmental, physiological and morphological factors on milk production of mountain-pastured Braunvieh cows
Authors: Duruz Solange , Vajana Elia , Burren Alexander , Flury Christine , Joost Stéphane
Published: April 2020

1.

The transhumance system, which consists in moving animals to high mountain pastures during summer, plays a considerable role in preserving both local biodiversity and traditions, as well as protecting against natural hazard. In cows, particularly, milk production is observed to decline as a response to food shortage and climatic stress, leading to atypical lactation curves that are barely described by current lactation models. Here, we relied on five million monthly milk records from over 200,000 Braunvieh and Original Braunvieh cows to devise a new model accounting for transhumance, and test the influence of environmental, physiological, and morphological factors on cattle productivity. Counter to expectations, environmental conditions in the mountain showed a globally limited impact on milk production during transhumance, with cows in favourable conditions producing only 10% less compared to cows living in adverse conditions, and with precipitation in spring and altitude revealing to be the most production-affecting variables. Conversely, physiological factors as lactation number and pregnancy stage presented an important impact over the whole lactation cycle with 20% difference in milk production, and may therefore alter the way animals respond to transhumance. Finally, the considered morphological factors (cow height and foot angle) presented a smaller impact during the whole lactation cycle (10% difference in milk production). The present findings can help farmers to establish sustainable strategies for alleviating the negative effects of transhumance on productivity and preserving this important livestock practice.
Investigating the genomic landscape of novel coronavirus (2019-nCoV) to identify non-synonymous mutations for use in diagnosis and drug design
Authors: Tiwari Manish , Mishra Divya
Published: April 2020

Novel coronavirus has wrecked medical and health care facilities claiming ~5% death tolls globally. All efforts to contain the pathogenesis either using inhibitory drugs or vaccines largely remained futile due to a lack of better understanding of the genomic feature of this virus. In the present study, we compared the 2019-nCoV with other coronaviruses, which indicated that bat-SARS like coronavirus could be a probable ancestor of the novel coronavirus. The protein sequence similarity of pangolin-hCoV and bat-hCoV with human coronavirus was higher as compared to their nucleotide similarity denoting the occurrence of more synonymous mutations in the genome. Phylogenetic and alignment analysis of 591 novel coronaviruses of different clades from Group I to Group V revealed several mutations and concomitant amino acid changes. Detailed investigation on nucleotide substitution unfolded 100 substitutions in the coding region of which 43 were synonymous and 57 were of non-synonymous type. The non-synonymous substitutions resulting into 57 amino acid changes were found to be distributed over different hCoV proteins with maximum on spike protein. An important diamino acid change RG to KR was observed in ORF9 protein. Additionally, several interesting features of the novel coronavirus genome have been highlighted in respect to various other human infecting viruses which may explain extreme pathogenicity, infectivity and simultaneously the reason behind failure of the antiviral therapies.

This study presents a comprehensive phylogenetic analysis of SARS-CoV2 isolates to understand discrete mutations that are occurring between patient samples. This analysis will provide an explanation for varying treatment efficacies of different inhibitory drugs and a future direction towards a combinatorial treatment therapies based on the kind of mutation in the viral genome.
G3BP1 tethers the TSC complex to lysosomes and suppresses mTORC1 in the absence of stress granules
Authors: Prentzell Mirja T. , Rehbein Ulrike , Sandoval Marti Cadena , De Meulemeester Ann-Sofie , Baumeister Ralf , Brohée Laura , Berdel Bianca , Bockwoldt Mathias , Carroll Bernadette , von Deimling Andreas , Demetriades Constantinos , Figlia Gianluca , Heberle Alexander M. , Heiland Ines , Holzwarth Birgit , Huber Lukas A. , Jaworski Jacek , Kern Katharina , Kopach Andrii , Korolchuk Viktor I. , van ’t Land-Kuper Ineke , Macias Matylda , Nellist Mark , Pusch Stefan , Reil Michele , Reintjes Anja , Reuter Friederike , Scheldeman Chloë , Stefan Eduard , Teleman Aurelio , Torres-Quesada Omar , Trump Saskia , de Witte Peter , Yordanov Teodor , Opitz Christiane A. , Thedieck Kathrin
Published: April 2020

G3BP1 (Ras GTPase-activating protein-binding protein 1) is widely recognized as a core component of stress granules (SG), non-membranous RNA-protein-assemblies required for cellular survival under stress. We report that in the absence of SG, G3BP1 acts as lysosomal anchor of the Tuberous Sclerosis Complex (TSC) protein complex. By tethering the TSC complex to lysosomes, G3BP1 suppresses signaling through the metabolic master regulator mTORC1 (mechanistic target of rapamycin complex 1). Like the known TSC complex subunits, G3BP1 suppresses phenotypes related to mTORC1 hyperactivity in the context of tumors and neuronal dysfunction. Thus, G3BP1 is not only a core component of SG but also a key element of lysosomal TSC-mTORC1 signaling.

Highlights

Thebona fidestress granule component G3BP1

is a key element of the TSC-mTORC1 signaling axis.

tethers the TSC complex to lysosomes.

prevents mTORC1 hyperactivation by metabolic stimuli.

suppresses mTORC1-driven cancer cell motility and epileptiform activity.

Graphical Abstract
Metagenomic Next-Generation Sequencing of Rectal Swabs for the Surveillance of Antimicrobial Resistant Organisms on the Illumina Miseq and Oxford MinION Platforms
Authors: Yee Rebecca , Breitwieser Florian P. , Hao Stephanie , Opene Belita N.A. , Workman Rachael E. , Tamma Pranita D. , Dien-Bard Jennifer , Timp Winston , Simner Patricia J.
Published: April 2020

Purpose

Antimicrobial resistance (AMR) is a public health threat where efficient surveillance is needed to prevent outbreaks. Existing methods for detection of gastrointestinal colonization of multidrug-resistant organisms (MDRO) are limited to specific organisms or resistance mechanisms. Metagenomic next-generation sequencing (mNGS) is a more rapid and agnostic diagnostic approach for microbiome and resistome investigations. We determined if mNGS can detect MDRO from rectal swabs in concordance with standard microbiology results.

Methods

We performed and compared mNGS performance on short-read Illumina MiSeq (N=10) and long-read Nanopore MinION (N=4) platforms directly from peri-rectal swabs to detect vancomycin-resistant enterococci (VRE) and carbapenem-resistant Gram-negative organisms (CRO).

Results

We detectedE. faecium(N=8) andE. faecalis(N=2) with associatedvangenes (9/10) in concordance with VRE culture-based results. We studied the microbiome and identified CRO organisms,P. aeruginosa(N=1),E. cloacae(N=1), and KPC-producingK. pneumoniae(N=1). Nanopore real-time detection detected theblaKPCgene in 2.5 minutes and provided genetic context (blaKPCharbored on pKPC_Kp46 IncF plasmid). Illumina sequencing provided accurate allelic variant determination (i.e.,blaKPC-2) and strain typing of theK. pneumoniae(ST-15). Conclusions: We demonstrated an agnostic approach for surveillance of MDRO, examining advantages of both short and long-read mNGS methods for AMR detection.
The white matter network in cognitive normal elderly predict the rate of cognitive decline
Authors: Huang Weijie , Shu Ni
Published: April 2020

White matter degradation has been proposed as one possible explanation for age-related cognitive decline. The human brain is, however, a network and it may be more appropriate to relate cognitive functions to properties of the network rather than specific brain regions. Cognitive domains were measured annually (mean follow-up = 1.25 ± 0.61 years), including processing speed, memory, language, visuospatial, and executive functions. Diffusion tensor imaging was performed at baseline in 90 clinically normal older adults (aged 54–86). We report on graph theory-based analyses of diffusion tensor imaging tract-derived connectivity. The machine learning approach was used to predict the rate of cognitive decline from white matter connectivity data. The reduced efficacy of white matter networks could predict the performance of these cognitive domains except memory. The predicted scores were significantly correlated with the real scores. For the local regions for predicting the cognitive changes, the right precuneus, left inferior parietal lobe and cuneus are the most important regions for predicting monthly change of executive function; some left partial and occipital regions are the most important for the changed of attention; the right frontal and temporal regions are the most important for the changed of language. Our findings suggested that the global white matter connectivity characteristics are the valuable predictive index for the longitudinal cognitive decline. For the first time, topological efficiency of white matter connectivity maps which related to special domains of cognitive decline in the elderly are identified.
A mathematical model-based approach to optimize loading schemes of isometric resistance training sessions
Authors: Herold Johannes L. , Sommer Andreas
Published: April 2020

Individualized resistance training (RT) is necessary to optimize training results. A model-based optimization of loading schemes could provide valuable impulses for practitioners and complement the predominant manual program design by customizing the loading schemes to the trainee and the training goals. We compile a literature overview of model-based approaches used to simulate or optimize the response to single RT sessions or to longterm RT plans in terms of strength, power, muscle mass, or local muscular endurance by varying the loading scheme. To the best of our knowledge, contributions employing a predictive model to algorithmically optimize loading schemes for different training goals are nonexistent in the literature. Thus, we propose to set up optimal control problems as follows. For the underlying dynamics, we use a phenomenological model of the time course of maximum voluntary isometric contraction force. Then, we provide mathematical formulations of key performance indicators for loading schemes identified in sport science and use those as objective functionals or constraints. We then solve those optimal control problems using previously obtained parameter estimates for the elbow flexors. We discuss our choice of training goals, analyze the structure of the computed solutions, and give evidence of their real-life feasibility. The proposed optimization methodology is independent from the underlying model and can be transferred to more elaborate physiological models once suitable ones become available.
Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals
Authors: Kanzler Christoph M. , Schwarz Anne , Held Jeremia P.O. , Luft Andreas R. , Gassert Roger , Lambercy Olivier
Published: April 2020

Background

Assessing arm and hand sensorimotor impairments that are functionally relevant is essential to optimize the impact of neurorehabilitation interventions. Technology-aided assessments should provide a sensitive and objective characterization of upper limb impairments, but often provide arm weight support and neglect the importance of the hand, thereby questioning their functional relevance. The Virtual Peg Insertion Test (VPIT) addresses these limitations by quantifying arm movements and grip forces during a goal-directed manipulation task without arm weight support. The aim of this work was to evaluate the potential and robustness of the VPIT metrics to inform on sensorimotor impairments in arm and hand, and especially identify the functional relevance of the detected impairments.

Methods

Arm and hand sensorimotor impairments were systematically characterized in 30 chronic stroke patients using conventional clinical scales and the VPIT. For the latter, ten previously established kinematic and kinetic core metrics were extracted and compared to conventional clinical scales of impairment and activity limitations. Additionally, the robustness of the VPIT metrics was investigated by analyzing their clinimetric properties (test-retest reliability, measurement error, and learning effects).

Results

Twenty-three of the participants, the ones with mild to moderate sensorimotor impairments and without strong cognitive deficits, were able to successfully complete the VPIT protocol (duration 16.6 min). The VPIT metrics detected impairments in arm and hand in 90.0% of the participants, and were sensitive to increased muscle tone and pathological joint coupling. Most importantly, moderate to high significant correlations between conventional scales of activity limitations and the VPIT metrics were found, thereby indicating their functional relevance when grasping and transporting lightweight objects as well as dexterous finger manipulations. Lastly, the robustness of three out of the ten VPIT core metrics in post-stroke individuals was confirmed.

Conclusions

This work provides evidence that technology-aided assessments requiring goal-directed manipulations without arm weight support can provide an objective, robust, and clinically feasible way to assess functionally relevant sensorimotor impairments in arm and hand in chronic post-stroke individuals with mild to moderate deficits. This allows better identifying impairments with high functional relevance and can contribute to optimizing the functional benefits of neurorehabilitation interventions.

Retrospectively registered: clinicaltrials.gov/ct2/show/NCT03135093
Axonal transport of Hrs is activity-dependent and rate limiting for synaptic vesicle protein degradation
Authors: Birdsall Veronica , Zhu Mei , Kirwan Konner , Imoto Yuuta , Watanabe Shigeki , Waites Clarissa L.
Published: April 2020

Turnover of synaptic vesicle (SV) proteins is vital for the maintenance of healthy and functional synapses. SV protein turnover is driven by neuronal activity in an ESCRT (endosomal sorting complex required for transport)-dependent manner. Here, we characterize a critical step in this process: axonal transport of ESCRT-0 component Hrs, necessary for sorting proteins into the ESCRT pathway and recruiting downstream ESCRT machinery to catalyze multivesicular body (MVB) formation. We find that neuronal activity stimulates the formation of presynaptic endosomes and MVBs, as well as the motility of Hrs+ vesicles in axons and their delivery to SV pools. Hrs+ vesicles co-transport ESCRT-0 component STAM1 and comprise a subset of Rab5+ vesicles, likely representing pro-degradative early endosomes. Furthermore, we identify kinesin motor protein KIF13A as essential for the activity-dependent transport of Hrs to SV pools and the degradation of SV membrane proteins. Together, these data demonstrate a novel activity- and KIF13A-dependent mechanism for mobilizing axonal transport of ESCRT machinery to facilitate the degradation of SV membrane proteins.