bluuebunny/crossref_metadata_2025 huggingface dataset:
p-tau/Aβ42 Ratio Associates with Cognitive Decline in Alzheimer’s disease, Mild Cognitive Impairment, and Cognitively Unimpaired Older Adults
Authors: Prakash Ruchika Shaurya , McKenna Michael R. , Gbadeyan Oyetunde , Andridge Rebecca , Scharre Douglas W. ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: October 2020

INTRODUCTION

The most well-studied biomarkers in AD are CSF amyloid beta-42 (Aβ42), tau, p-tau, and the ratio p-tau/Aβ42. The ratiometric measure of p-tau/Aβ42shows the best diagnostic accuracy, and correlates reliably with metrics of cognition in unimpaired participants. However, no study has examined the impact of the CSF p-tau/Aβ42ratio in predicting cognitive decline in both healthy and AD individuals in one sample. The goal of this study was to examine whether CSF-based p-tau/Aβ42predicts changes in global cognitive functioning, episodic memory, and executive functioning over a two-year period in cognitively impaired older adults (CU), and in individuals with Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD).

METHODS

This study involves secondary analysis of data from 1215 older adults available in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Neuropsychological variables, collected at baseline, 6-month, 12-month, and 24-month follow-ups, included the Preclinical Alzheimer’s Cognitive Composite (PACC) to assess global cognitive functioning, ADNI-MEM to assess episodic memory functioning, and ADNI-EF to assess executive functioning. Linear mixed models were constructed to examine the effect of CSF p-tau/Aβ42, diagnostic group, and change over time (baseline, 6-month, 12-month, and 24-month) on cognitive scores.

RESULTS

CSF p-tau/Aβ42ratios predicted worsening cognitive impairment, both on global cognition and episodic memory in individuals with MCI and AD, but not in CU older adults and predicted decline in executive functioning for all three diagnostic groups.

DISCUSSION

Our study, including CU, MCI, and AD individuals, provides evidence for differential cognitive consequences of accumulated AD pathology based on diagnostic groups.
m6A RNA methylation regulators contribute to progression and impact the prognosis of breast cancer
Authors: Jiang Wenjie , Dong Minglong , Hu Zebin , Wan Kaidi , Wang Han
Published: October 2020

N6-methyladenosine (m6A) is the most commonly modified form of mRNA.M6ARNA methylation regulators are proved to be expressed clearly in some cancers by plenty of studies. Moreover, they also are proved to be indirectly involved in the growth of cancers. However, it remains unclear that the role ofm6ARNA methylation regulator in the prognosis of breast cancer (BRCA). The data that we used in this study is the mRNA expression data obtained from the corresponding clinical information and the Tumor Genome Atlas (TCGA) database. And the goal we used the Wilcoxon rank-sum test was to evaluate the difference in the expression ofm6ARNA methylation regulators in the normal group and the tumor group, and analyze the correlation betweenm6ARNA methylation regulators. We identified two subgroups of BRCA (cluster1 and 2) by using the K-mean algorithm and analyzing the correlation between clinic information and subgroups. The LASSO regression model then was used to figure out threem6ARNA methylation regulators, namely YTHDF3, ZC3H13, and HNRNPC. The riskScore of each patient was calculated according to the regression coefficients of the threem6ARNA methylation regulators. Base on the riskScore, we divided the patients into two groups, the high-risk group, and the low-risk group. After analyzing, we found that the overall survival rate (OS) of the low-risk group was higher than that of the other group. We conducted a univariate and multi-factor independent prognostic analysis of riskScore and threem6ARNA methylation regulators, and found that riskScore has a significant correlation with BRCA.

In conclusion, them6ARNA methylation regulator is closely related to the development of BRCA, and the prognostic factor riskScore obtained from the regression of the expression of the threem6ARNA methylation regulators in the human body are likely to guide the individualization of BRCA patients A useful prognostic biomarker for treatment.
Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)
Authors: Kornblith Aaron E. , Addo Newton , Dong Ruolei , Rogers Robert , Grupp-Phelan Jacqueline , Butte Atul , Gupta Pavan , Callcut Rachael A , Arnaout Rima
Published: October 2020

The pediatric Focused Assessment with Sonography for Trauma (FAST) is a sequence of ultrasound views rapidly performed by the clinician to diagnose hemorrhage. One limitation of FAST is inconsistent acquisition of required views. We sought to develop a deep learning model and classify FAST views using a heterogeneous dataset of pediatric FAST. This study of diagnostic test developed and tested a deep learning model for view classification of archived real-world pediatric FAST studies collected from two pediatric emergency departments. FAST frames were randomly distributed to training, validation, and test datasets in a 70:20:10 ratio; each patient was represented in only one dataset to maintain sample independence. The outcome was the prediction accuracy of the model in classifying FAST frames and video clips. FAST studies performed by 30 different clinicians from 699 injured children included 4,925 videos representing 1,062,612 frames from children who were a median of 9 years old. On test dataset, the overall view classification accuracy for the model was 93.4% (95% CI: 93.3-93.6) for frames and 97.8% (95% CI: 96.0-99.0) for video clips. Frames were correctly classified with an accuracy of 96.0% (95% CI: 95.9-96.1) for cardiac, 99.8% (95% CI: 99.8-99.8) for thoracic, 95.2% (95% CI: 95.0-95.3) for abdominal upper quadrants, and 95.9% (95% CI: 95.8-96.0) for suprapubic. A deep learning model can be developed to accurately classify pediatric FAST views. Accurate view classification is the important first step to support developing a consistent and accurate multi-stage deep learning model for pediatric FAST interpretation.
Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning
Authors: Ogallo William , Speakman Skyler , Akinwande Victor , Varshney Kush R. , Walcott-Bryant Aisha , Wayua Charity , Weldemariam Komminist , Mershon Claire-Helene , Orobaton Nosa
Published: October 2020

This study aimed at identifying the factors associated with neonatal mortality. We analyzed the Demographic and Health Survey (DHS) datasets from 10 Sub-Saharan countries. For each survey, we trained machine learning models to identify women who had experienced a neonatal death within the 5 years prior to the survey being administered. We then inspected the models by visualizing the features that were important for each model, and how, on average, changing the values of the features affected the risk of neonatal mortality. We confirmed the known positive correlation between birth frequency and neonatal mortality and identified an unexpected negative correlation between household size and neonatal mortality. We further established that mothers living in smaller households have a higher risk of neonatal mortality compared to mothers living in larger households; and that factors such as the age and gender of the head of the household may influence the association between household size and neonatal mortality.
TREATMENT PROFILES AND CLINICAL OUTCOMES OF COVID-19 PATIENTS AT PRIVATE HOSPITAL IN JAKARTA
Authors: Ramatillah Diana Laila , Isnaini Suri
Published: October 2020

Background

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is a virus that causes COVID-19, which has become a worldwide pandemic. However, until now, there is no vaccine or specific drug to prevent or treat COVID-19.

Objectives

To find out the effective treatment as an antiviral agent for COVID-19, to determine the correlation between sociodemography with clinical outcomes and duration of treatment, and to determine the relationship between comorbidities with clinical outcomes and duration of treatment for COVID-19 patients.

Methods

A prospective cohort study was conducted in this study. This study included only confirmed COVID-19 patients who were admitted to the hospital during April-May 2020. Convenience sampling was used to select 103 patients, but only 72 patients were suitable for inclusion.

Results

The survival analysis for COVID-19 patients using the Kaplan Meier method showed that patients receiving Oseltamivir + Hydroxychloroquine had an average survival rate of about 83% after undergoing treatment for about ten days. Gender (p = 0.450) and age (p = 0.226) did not have a significant correlation with the duration of treatment for COVID-19 patients. Gender (p = 0.174) and age (p = 0.065) also did not have a significant correlation with clinical outcome of COVID-19 patients. Comorbidities showed a significant correlation with duration of treatment (p = 0.002) and clinical outcome (p = 0.014) of COVID-19 patients.

Conclusion

The most effective antiviral agent in this study based on treatment duration was the combination of Oseltamivir + Hydroxychloroquine. The higher the patient’s average treatment duration, the lower the average survival rate for COVID-19 patients.
SiGMoiD: A super-statistical generative model for binary data
Authors: Zhao Xiaochuan , Plata Germán , Dixit Purushottam D.
Published: October 2020

In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers’ identification of constraints and are computationally expensive to infer when the number of variables is large (N∼100). Here, we address both these issues withSuper-statisticalGenerativeModel for binaryData (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same ‘bath’ whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Notably, unlike current approaches, SiGMoiD allows to model collections of a very large number (N> 1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using several datasets spanning several time- and length-scales.
Sonogenetic control of mammalian cells using exogenous Transient Receptor Potential A1 channels
Authors: Duque Marc , Lee-Kubli Corinne A. , Tufail Yusuf , Magaram Uri , Patel Janki , Chakraborty Ahana , Lopez Jose Mendoza , Edsinger Eric , Vasan Aditya , Shiao Rani , Weiss Connor , Friend James , Chalasani Sreekanth H.
Published: October 2020

Ultrasound has been used to non-invasively manipulate neuronal functions in humans and other animals1–4. However, this approach is limited as it has been challenging to target specific cells within the brain or body5–8. Here, we identify human Transient Receptor Potential A1 (hsTRPA1) as a candidate that confers ultrasound sensitivity to mammalian cells. Ultrasound-evoked gating ofhsTRPA1 specifically requires its N-terminal tip region and cholesterol interactions; and target cells with an intact actin cytoskeleton, revealing elements of the sonogenetic mechanism. Next, we use calcium imaging and electrophysiology to show thathsTRPA1 potentiates ultrasound-evoked responses in primary neurons. Furthermore, unilateral expression ofhsTRPA1 in mouse layer V motor cortical neurons leads toc-fosexpression and contralateral limb responses in response to ultrasound delivered through an intact skull. Collectively, we demonstrate thathsTRPA1-based sonogenetics can effectively manipulate neurons within the intact mammalian brain, a method that could be used across species.
High dimensional immunotyping of the obese tumor microenvironment reveals model specific adaptation
Authors: Wogsland Cara E , Lien Hilde E , Pedersen Line , Hanjra Pahul , Grondal Sturla M , Brekken Rolf A , Lorens James B , Halberg Nils
Published: October 2020

Obesity is a disease characterized by chronic low-grade systemic inflammation and has been causally linked to the development of 13 cancer types. Several studies have been undertaken to determine if tumors evolving in obese environments adapt differential interactions with immune cells and if this can be connected to disease outcome. Most of these studies have been limited to single cell lines and tumor models and analysis of limited immune cell populations. Given the multicellular complexity of the immune system and its dysregulation in obesity, we applied high-dimensional suspension mass cytometry to investigate how obesity affects tumor immunity. We used a 36-marker immune-focused mass cytometry panel to interrogate the immune landscape of orthotopic syngeneic mouse models of pancreatic and breast cancer. Unanchored batch correction was implemented to enable simultaneous analysis of tumor cohorts to uncover the immunotypes of each cancer model and reveal remarkably model-specific immune regulation. In the E0771 breast cancer model, we demonstrate an important link to obesity with an increase in two T cell suppressive cell types and a decrease in CD8 T-cells.
TRex, a fast multi-animal tracking system with markerless identification, and 2D estimation of posture and visual fields
Authors: Walter Tristan , Couzin Iain D
Published: October 2020

Automated visual tracking of animals is rapidly becoming an indispensable tool for the study of behavior. It offers a quantitative methodology by which organisms’ sensing and decision-making can be studied in a wide range of ecological contexts. Despite this, existing solutions tend to be challenging to deploy in practice, especially when considering long and/or high-resolution video-streams. Here, we present TRex, a fast and easy-to-use solution for tracking a large number of individuals simultaneously using background-subtraction with real-time (60Hz) tracking performance for up to approximately 256 individuals and estimates 2D visual-fields, outlines, and head/rear of bilateral animals, both in open and closed-loop contexts. Additionally, TRex offers highly-accurate, deep-learning-based visual identification of up to approximately 100 unmarked individuals, where it is between 2.5-46.7 times faster, and requires 2-10 times less memory, than comparable software (with relative performance increasing for more organisms/longer videos) and provides interactive data-exploration within an intuitive, platform-independent graphical user-interface.
Ultrafast phasor-based hyperspectral snapshot microscopy for biomedical imaging
Authors: Hedde Per Niklas , Cinco Rachel , Malacrida Leonel , Kamaid Andrés , Gratton Enrico
Published: October 2020

Hyperspectral imaging is highly sought after in many fields including mineralogy and geology, environment and agriculture, astronomy and, importantly, biomedical imaging and biological fluorescence. We developed ultrafast phasor-based hyperspectral snapshot microscopy based on sine/cosine interference filters for biomedical imaging not feasible with conventional hyperspectral detection methods. Current approaches rely on slow spatial or spectral scanning limiting their application in living biological tissues, while faster snapshot methods such as image mapping spectrometry and multispectral interferometry are limited in spatial and/or spectral resolution, are computationally demanding, and imaging devices are very expensive to manufacture. Leveraging light sheet microscopy, phasor-based hyperspectral snapshot microscopy improved imaging speed 10-100 fold which, combined with minimal light exposure and high detection efficiency, enabled hyperspectral metabolic imaging of live, three-dimensional mouse tissues not feasible with other methods. As a fit-free method that does not require anya prioriinformation often unavailable in complex and evolving biological systems, the rule of linear combinations of the phasor could spectrally resolve subtle differences between cell types in the developing zebrafish retina and spectrally separate and track multiple organelles in 3D cultured cells over time. The sine/cosine snapshot method is adaptable to any microscope or imaging device thus making hyperspectral imaging and fit-free analysis based on linear combinations broadly available to researchers and the public.
Higher-order epistasis and phenotypic prediction
Authors: Zhou Juannan , Wong Mandy S. , Chen Wei-Chia , Krainer Adrian R. , Kinney Justin B. , McCandlish David M.
Published: October 2020

Contemporary high-throughput mutagenesis experiments are providing an increasingly detailed view of the complex patterns of genetic interaction that occur between multiple mutations within a single protein or regulatory element. By simultaneously measuring the effects of thousands of combinations of mutations, these experiments have revealed that the genotype-phenotype relationship typically reflects genetic interactions not only between pairs of sites, but also higher-order interactions among larger numbers of sites. However, modeling and understanding these higher-order interactions remains challenging. Here, we present a method for reconstructing sequence-to-function mappings from partially observed data that can accommodate all orders of genetic interaction. The main idea is to make predictions for unobserved genotypes that match the type and extent of epistasis found in the observed data. This information on the type and extent of epistasis can be extracted by considering how phenotypic correlations change as a function of mutational distance, which is equivalent to estimating the fraction of phenotypic variance due to each order of genetic interaction (additive, pairwise, three-way, etc.). Using these estimated variance components, we then define an empirical Bayes prior that in expectation matches the observed pattern of epistasis, and reconstruct the genotype-phenotype mapping by conducting Gaussian process regression under this prior. To demonstrate the power of this approach, we present an application to the antibody-binding domain GB1 and also provide a detailed exploration of a dataset consisting of high-throughput measurements for the splicing efficiency of human pre-mRNA5′ splice sites, for which we also validate our model predictions via additional low-throughput experiments.
Protein Structure Determination via Distance Geometry Structural Similarity based Initialization
Authors: Tasissa Abiy , Benavides Tiburon , Lai Rongjie , Wang Chunyu
Published: October 2020

The problem of finding the configuration of points given partial information on pairwise inter-point distances, the Euclidean distance geometry problem, appears in multiple applications. In this paper, we propose an approach that integrates structural similarity and a nonconvex distance geometry algorithm for the protein structure determination problem. When initialized with a homologous structure, reconstruction of ubiquitin structure with our non convex algorithm resulted in an RMSE of less than 2 Å with 1.5% available inter proton distance and up to 20% relative error in the input distances. To test the robustness of this algorithm with regard to initialization, we also initialized with a nonhomologous structure on a larger protein with pdb coordinate 1W2E. Even though the initialization structure 1JYB is far different from 1W2E with an RMSE of 25 Å, reconstruction generated structures with RMSE of close to 2 Å, using 1.7% available proton distances and up to 10% relative error in input distances. These results suggest EDG-based approach may be applied to fast NMR structure determination in the future.
CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation
Authors: Arthurs C.J. , Khlebnikov R. , Melville A. , Marčan M. , Gomez A. , Dillon-Murphy D. , Cuomo F. , Vieira M.S. , Schollenberger J. , Lynch S.R. , Tossas-Betancourt C. , Iyer K. , Hopper S. , Livingston E. , Youssefi P. , Noorani A. , Ahmed S. Ben , Nauta F.J.H. , van Bakel T.M.J. , Ahmed Y. , van Bakel P.A.J. , Mynard J. , Di Achille P. , Gharahi H. , Lau K. D. , Filonova V. , Aguirre M. , Nama N. , Xiao N. , Baek S. , Garikipati K. , Sahni O. , Nordsletten D. , Figueroa C.A.
Published: October 2020

In this work, we describe the CRIMSON (CardiovasculaR Integrated Modelling and SimulatiON) software environment. CRIMSON provides a powerful, customizable and user-friendly system for performing three-dimensional and reduced-order computational haemodynamics studies via a pipeline which involves: 1) segmenting vascular structures from medical images; 2) constructing analytic arterial and venous geometric models; 3) performing finite element mesh generation; 4) designing, and 5) applying boundary conditions; 6) running incompressible Navier-Stokes simulations of blood flow with fluid-structure interaction capabilities; and 7) post-processing and visualizing the results, including velocity, pressure and wall shear stress fields. A key aim of CRIMSON is to create a software environment that makes powerful computational haemodynamics tools accessible to a wide audience, including clinicians and students, both within our research laboratories and throughout the community. The overall philosophy is to leverage best-in-class open source standards for medical image processing, parallel flow computation, geometric solid modelling, data assimilation, and mesh generation. It is actively used by researchers in Europe, North and South America, Asia, and Australia. It has been applied to numerous clinical problems; we illustrate applications of CRIMSON to real-world problems using examples ranging from pre-operative surgical planning to medical device design optimization. CRIMSON binaries for Microsoft Windows 10, documentation and example input files are freely available for download fromwww.crimson.software, and the source code with compilation instructions is available on GitHubhttps://github.com/carthurs/CRIMSONFlowsolver(CRIMSON Flowsolver) under the GPL v3.0 license, andhttps://github.com/carthurs/CRIMSONGUI(CRIMSON GUI), under the AGPL v3.0 license. Support is available on the CRIMSON Google Groups forum, located athttps://groups.google.com/forum/#!forum/crimson-users.
A high throughput RNA displacement assay for screening SARS-CoV-2 nsp10-nsp16 complex towards developing therapeutics for COVID-19
Authors: Perveen Sumera , Yazdi Aliakbar Khalili , Devkota Kanchan , Li Fengling , Ghiabi Pegah , Hajian Taraneh , Loppnau Peter , Bolotokova Albina , Vedadi Masoud
Published: October 2020

SARS-CoV-2, the coronavirus that causes COVID-19, evades the human immune system by capping its RNA. This process protects the viral RNA and is essential for its replication. Multiple viral proteins are involved in this RNA capping process including the nonstructural protein 16 (nsp16) which is an S-adenosyl-L-methionine (SAM)-dependent 2’-O-methyltransferase. Nsp16 is significantly active when in complex with another nonstructural protein, nsp10, which plays a key role in its stability and activity. Here we report the development of a fluorescence polarization (FP)-based RNA displacement assay for nsp10-nsp16 complex in 384-well format with a Z′-Factor of 0.6, suitable for high throughput screening. In this process, we purified the nsp10-nsp16 complex to higher than 95% purity and confirmed its binding to the methyl donor SAM, product of the reaction, SAH, and a common methyltransferase inhibitor, sinefungin using Isothermal Titration Calorimetry (ITC). The assay was further validated by screening a library of 1124 drug-like compounds. This assay provides a cost-effective high throughput method for screening nsp10-nsp16 complex for RNA-competitive inhibitors towards developing COVID-19 therapeutics.
Inhibition of the phosphoinositide 3-kinase-AKT-cyclic GMP-c-Jun N-terminal kinase signaling pathway attenuates the development of morphine tolerance in a mouse model of neuropathic pain
Authors: Okerman T. , Jurgenson T. , Moore M. , Klein A. H.
Published: October 2020

Background

Opioid management of chronic pain can cause opioid-induced analgesic tolerance and hyperalgesia, complicating clinical pain-management treatments. Research presented here sought to determine if opioid induced tolerance is linked to activity changes within the PI3Kγ-AKT-cGMP-JNK intracellular signaling pathway in spinal cord or peripheral nervous systems.

Methods

Morphine or saline injections were given subcutaneously twice a day for five days (15 mg/kg) to male C57Bl6 mice. A separate cohort of mice received spinal nerve ligation (SNL) one week prior to the start of morphine tolerance. Afterwards, spinal cord, dorsal root ganglia, and sciatic nerves were isolated for quantifying total and phosphorylated-JNK levels, cGMP, and gene expression analysis.

Results

Gene expression for the PI3Kγ-AKT-cGMP-JNK signaling pathway including,Akt1, Akt2, Akt3, Pik3cg,Pten, Jnk3, andnNos1were decreased in the spinal cord with varied expression changes in the dorsal root ganglia and sciatic nerve of morphine tolerant and morphine tolerant mice after SNL. We observed significant increases in total and phosphorylated-JNK levels in the spinal cord, total JNK in dorsal root ganglia, and cGMP in the sciatic nerve of morphine tolerant mice with SNL. Pharmacological inhibition of PI3K, nNOS, or JNK, using thalidomide, quercetin, or SP600125, attenuated the development of morphine tolerance in mice with SNL as measured by thermal paw withdrawal.

Conclusions

Overall, the PI3K/AKT intracellular signaling pathway is a potential target for reducing the development of morphine tolerance. Continued research into this pathway will contribute to the development of new analgesic drug therapies.
Impulsivity and Thought Suppression in Behavioral Addiction: Associated Neural Connectivity and Neural Networks
Authors: Wan Li , Zha Rujing , Ren Jiecheng , Li Ying , Zhao Qian , Zuo Huilin , Zhang Xiaochu
Published: October 2020

Impulsivity and thought suppression are two psychological traits that have great variation in healthy population. In extreme cases, both are closely related to mental illness and play an important role in behavioral addiction. We have known the role of the top-down mechanism in impulsivity and thought suppression, but we do not know how the related neural nuclei are functionally connected and interact with each other. In the study, we selected excessive internet users (EIU) as our target population and investigated the relationship between thought suppression and impulsivity in the following aspects: their correlations to psychological symptoms; the associated neural networks; and the associated brain morphometric changes. We acquired data from 131 excessive internet users, with their psychological, resting-state fMRI and T1-MRI data collected. With the whole brain analysis, graph theory analysis, replication with additional brain atlas, replication with additional MRI data, and analysis of brain structure, we found that: (i) implusivity and thought suppression shared common neural connections in the top-down mechanism; (ii) thought suppression was associated with the neural network that connected to the occipital lobe in the resting-state brain but not the morphometric change of the occipital lobe. The study confirmed the overlap between impulsivity and thought suppression in terms of neural connectivity and suggested the role of thought suppression and the occipital network in behavioral addiction. Studying thought suppression provided a new insight into behavioral addiction research. The neural network study helped further understanding of behavioral addiction in terms of information interaction in the brain.
Identification of subfunctionalized aggregate-remodeling J-domain proteins in plants
Authors: Tak Yogesh , Lal Silviya S. , Gopan Shilpa , Balakrishnan Madhumitha , Verma Amit K. , Cole Sierra J. , Brown Rebecca E. , Hayward Rachel E. , Hines Justin K. , Sahi Chandan
Published: October 2020

Hsp70s and J-domain proteins (JDPs) are among the most critical components of the cellular protein quality control machinery, playing crucial roles in preventing and solubilizing cytotoxic protein aggregates. Bacteria, yeast and plants additionally have large, multimeric Hsp100-class disaggregases which, allow the resolubilization of otherwise “dead-end” aggregates, including amyloids. JDPs interact with aggregated proteins and specify the aggregate remodeling activities of Hsp70s and Hsp100s. Plants have a complex network of cytosolic Hsp70s and JDPs, however the aggregate remodeling properties of plant JDPs are not well understood. Here we identify evolutionary-conserved Class II JDPs in the model plantArabidopsis thalianawith distinct aggregate remodeling functionalities. We identify eight plant orthologs of the yeast protein, Sis1, the principal JDP responsible for directing the yeast chaperone machinery for remodeling protein aggregates. Expression patterns vary dramatically among the eight paralogous proteins under a variety of stress conditions, indicating their subfunctionalization to address distinct stressors. Consistent with a role in solubilizing cytotoxic protein aggregates, six of these plant JDPs associate with heat-induced protein aggregatesin vivoas well as colocalize with plant Hsp101 to distinct heat-induced protein aggregate centers. Finally, we show that these six JDPs can differentially remodel multiple model protein aggregates in yeast confirming their involvement in aggregate resolubilization. These results demonstrate that compared to complex metazoans, plants have a robust network of JDPs involved in aggregate remodeling activities with the capacity to process a variety of protein aggregate conformers.
COVID-19 and frontline health workers in West Africa: a scoping review
Authors: Pereko Kingsley K. A. , Ameyaw Edward Kwabena , Bukari Shaibu , Acquaye Victoria , Dai-Kosi Alfred Dickson
Published: October 2020

Introduction

The novel Coronavirus 2019 (COVID-19) has become a severe global health threat since its emergence. Overcoming the virus is partly dependent on the holistic wellbeing of frontline health workers. Implications of COVID-19 on frontline health workers in West Africa could be substantial given the limited resources and logistics. This scoping review maps available literature on the impact of COVID-19 on frontline health workers in West Africa.

Materials and methods

Literature on the impact of COVID-19 on frontline health workers in West Africa were searched in six databases namely Cochrane Library, PubMed, EMBASE, Google Scholar, Africa Journals Online (AJOL) and CINAHL. Further search was done across websites of the ministries of health of West African countries and notable organisations. We conducted a narrative synthesis of the findings taking cognisance of the overarching purpose of the study and the research question.

Results

Of the 67 studies identified, 19 were included in the final synthesis. Three main themes emerged and these are impact of COVID-19 on frontline health workers, drivers of susceptibility to COVID-19 and government/donor support. A greater number of the studies originated from Nigeria. Each study reported at least one impact of COVID-19 on frontline health workers in West Africa. The impacts included death, fear, unwillingness to attend to COVID-19 patients and stigmatisation. Some health workers were not adhering to the safety protocols coupled with periodic shortage of personal protective equipment (PPE) and thereby had an increased susceptibility.

Conclusion

Being the first scoping review on the impact of COVID-19 on frontline health workers in West Africa, the study has illustrated the urgent need for West African governments to enact laws/rules that would compel all frontline health workers to adhere to all the COVID-19 protocols at the workplace. To end intermittent shortage or issue of inadequate PPEs, governments ought to liaise with local industries by empowering them, providing financial support and creating a conducive atmosphere for them to produce cost effective PPEs using available local resources.

Scoping review registration

DOI 10.17605/OSF.IO/B9NXZ (Open Science Framework)
The Strand-biased Transcription of SARS-CoV-2 and Unbalanced Inhibition by Remdesivir
Authors: Zhao Yan , Sun Jing , Li Yunfei , Li Zhengxuan , Xie Yu , Feng Ruoqing , Zhao Jincun , Hu Yuhui
Published: October 2020

SARS-CoV-2, a positive single-stranded RNA virus, caused the COVID-19 pandemic. During the viral replication and transcription, the RNA dependent RNA polymerase (RdRp) “jumps” along the genome template, resulting in discontinuous negative-stranded transcripts. In other coronaviruses, the negative strand RNA was found functionally relevant to the activation of host innate immune responses. Although the sense-mRNA architectures of SARS-CoV-2 were reported, its negative strand was unexplored. Here, we deeply sequenced both strands of RNA and found SARS-CoV-2 transcription is strongly biased to form the sense strand. During negative strand synthesis, apart from canonical sub-genomic ORFs, numerous non-canonical fusion transcripts are formed, driven by 3-15 nt sequence homology scattered along the genome but more prone to be inhibited by SARS-CoV-2 RNA polymerase inhibitor Remdesivir. The drug also represses more of the negative than the positive strand synthesis as supported by a mathematic simulation model and experimental quantifications. Overall, this study opens new sights into SARS-CoV-2 biogenesis and may facilitate the anti-viral vaccine development and drug design.

One Sentence Summary

Strand-biased transcription of SARS-CoV-2.
Large-scale analysis ofDrosophilacore promoter function using synthetic promoters
Authors: Qi Zhan , Jung Christophe , Bandilla Peter , Ludwig Claudia , Heron Mark , Kiesel Anja Sophie , Philippou-Massier Julia , Nikolov Miroslav , Renna Alessio , Schnepf Max , Unnerstall Ulrich , Soeding Johannes , Gaul Ulrike
Published: October 2020

The core promoter, the region immediately surrounding the transcription start site, plays a central role in setting metazoan gene expression levels, but how exactly it ‘computes’ expression remains poorly understood. To dissect core promoter function, we carried out a comprehensive structure-function analysis to measure synthetic promoters’ activities, with and without an external stimulus (hormonal activation). By using robotics and a dual-luciferase reporter assay, we tested ∼3000 mutational variants representing 19 differentDrosophila melanogasterpromoter architectures. We explored the impact of different types of mutations, including knockout of individual sequence motifs and motif combinations, variations of motif strength, positioning, and flanking sequences. We observe strong effects of the mutations on activity, and a linear combination of the individual motif features can largely account for the combinatorial effects on core promoter activity. Our findings shed new light on the quantitative assessment of gene expression, a fundamental process in all metazoans.
Emergence of prefrontal neuron maturation properties by training recurrent neural networks in cognitive tasks
Authors: Liu Yichen Henry , Zhu Junda , Constantinidis Christos , Zhou Xin
Published: October 2020

Working memory and response inhibition are functions that mature relatively late in life, after adolescence, paralleling the maturation of the prefrontal cortex. The link between behavioral and neural maturation is not obvious, however, making it challenging to understand how neural activity underlies the maturation of cognitive function. To gain insights into the nature of observed changes in prefrontal activity between adolescence and adulthood, we investigated the progressive changes in unit activity of Recurrent Neural Networks (RNNs) as they were trained to perform working memory and response inhibition tasks. These included increased delay period activity during working memory tasks, and increased activation in antisaccade tasks. These findings reveal universal properties underlying the neuronal computations behind cognitive tasks and explicate the nature of changes that occur as the result of developmental maturation.
A multi-platform reference for somatic structural variation detection
Authors: Valle-Inclan Jose Espejo , Besselink Nicolle J.M. , de Bruijn Ewart , Cameron Daniel L. , Ebler Jana , Kutzera Joachim , van Lieshout Stef , Marschall Tobias , Nelen Marcel , Pang Andy Wing Chun , Priestley Peter , Renkens Ivo , Roemer Margaretha G.M. , van Roosmalen Markus J. , Wenger Aaron M. , Ylstra Bauke , Fijneman Remond J.A. , Kloosterman Wigard P. , Cuppen Edwin
Published: October 2020

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality gold standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines for comprehensive somatic SV detection. Here, we approached this challenge by genome-wide somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different technologies: Illumina HiSeq, Oxford Nanopore, Pacific Biosciences and 10x Genomics. Based on the evidence from multiple technologies combined with extensive experimental validation, including Bionano optical mapping data and targeted detection of candidate breakpoint junctions, we compiled a comprehensive set of true somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance of each technology as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects and data analysis tool evaluation. The reference truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.
Population scale nucleic acid delivery toCaenorhabditis elegansvia electroporation
Authors: Khodakova Anastasia S. , Vilchis Daniela Vidal , Amanor Ferdinand , Samuel Buck S.
Published: October 2020

The free-living nematodeC.elegansremains one of the most robust and flexible genetic systems for inter-rogating the complexities of animal biology. Targeted genetic manipulations, such as RNA interference (RNAi), CRISPR/Cas9- or array-based transgenesis, all depend on initial delivery of nucleic acids. Delivery of dsRNA by feeding can be effective, but expression inE. coliis not conducive to experiments intended to remain sterile or with defined microbial communities. Soaking-based delivery requires prolonged exposure of animals to high material concentrations without a food source and is of limited throughput. Last, microinjection of individual animals can precisely deliver materials to animals’ germlines, but is limited by the need to target and inject each animal one-by-one. Thus, we sought to address some of these challenges in nucleic acid delivery by developing a population-scale delivery method. We demonstrate efficient electroporation-mediated delivery of dsRNA throughout the worm and effective RNAi-based silencing, including in the germline. Finally, we show that guide RNA delivered by electroporation can be utilized by transgenic Cas9 expressing worms for population-scale genetic targeting. Together, these methods expand the scale and scope of genetic methodologies that can be applied to theC.eleganssystem.
Natural selection reverses the exaggeration of a male sexually selected trait, which increases female fitness
Authors: Okada Kensuke , Katsuki Masako , Sharma Manmohan D. , Kiyose Katsuya , Seko Tomokazu , Okada Yasukazu , Wilson Alastair J. , Hosken David J.
Published: October 2020

Theory shows how sexual selection can exaggerate male traits beyond naturally selected optima and also how natural selection can ultimately halt trait elaboration. Empirical evidence supports this theory, but to date, there have been no experimental evolution studies directly testing this logic, and little examination of possible associated effects on female fitness. Here we used experimental evolution of replicate populations of broad-horned flour-beetles to test for evolutionary effects of sex-specific predation on an exaggerated sexually selected male trait, while also testing for effects on female lifetime reproductive success. We found that populations subjected to male-specific predation evolved smaller sexually selected traits and this indirectly increased female fitness, seemingly through intersexual genetic correlations we documented. Predation solely on females had no effects. Our findings support fundamental theory, but also reveal novel outcomes when natural selection targets sex-limited sexually selected characters.
Brain mapping across 16 autism mouse models reveals a spectrum of functional connectivity subtypes
Authors: Zerbi V. , Pagani M. , Markicevic M. , Matteoli M. , Pozzi D. , Fagiolini M. , Bozzi Y. , Galbusera A. , Scattoni ML , Provenzano G. , Banerjee A. , Helmchen F. , Albert Basson M. , Ellegood J. , Lerch J. P. , Rudin M. , Gozzi A. , Wenderoth N.
Published: October 2020

Autism Spectrum Disorder (ASD) is characterized by substantial, yet highly heterogeneous abnormalities in functional brain connectivity. However, the origin and significance of this phenomenon remain unclear. To unravel ASD connectopathy and relate it to underlying etiological heterogeneity, we carried out a bi-center cross-etiological investigation of fMRI-based connectivity in the mouse, in which specific ASD-relevant mutations can be isolated and modelled minimizing environmental contributions. By performing brain-wide connectivity mapping across 16 mouse mutants, we show that different ASD-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse, often diverging, connectivity signatures are recognizable. Despite this heterogeneity, the identified connectivity alterations could be classified into four subtypes characterized by discrete signatures of network dysfunction. Our findings show that etiological variability is a key determinant of connectivity heterogeneity in ASD, hence reconciling conflicting findings in clinical populations. The identification of etiologically-relevant connectivity subtypes could improve diagnostic label accuracy in the non-syndromic ASD population and paves the way for personalized treatment approaches.
Nanoscape, a data-driven 3D real-time interactive virtual cell environment
Authors: Kadir Shereen R. , Lilja Andrew , Gunn Nick , Strong Campbell , Hughes Rowan T. , Bailey Benjamin J. , Rae James , Parton Robert G. , McGhee John
Published: October 2020

Knowledge of cellular and structural biology has reached unprecedented levels of detail. In conjunction with advances in 3D computer visualisation techniques this has allowed exploration of cellular ultrastructure and environments by a virtual user. The extraction and integration of relevant scientific information, along with consideration of the best representation of data, is often a bottleneck in the visualisation process for many 3D biomedical artists. Here we introduce ‘Nanoscape’, a collaborative project between 3D computer artists, computer graphics developers, and cell biologists that enables a user to navigate a cell in a complex 3D computer visualised environment. We combine actual data from various scientific disciplines (including structural biology, cell biology and multiple microscopic techniques) and apply artistic expression and design aesthetics to create a unique new experience where a real-time virtual explorer can traverse a cell surface, observe and interact with a more scientifically accurate cell surface environment.
Integrated characterization of SARS-CoV-2 genome, microbiome, antibiotic resistance and host response from single throat swabs
Authors: Lu Bo , Yan Yi , Dong Liting , Han Lingling , Liu Yawei , Yu Junping , Chen Jianjun , Yi Danyang , Zhang Meiling , Wang Chao , Wang Runkun , Wang Dengpeng , Wei Hongping , Liu Di , Yi Chengqi
Published: October 2020

The ongoing coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, poses a severe threat to humanity. Rapid and comprehensive analysis of both pathogen and host sequencing data is critical to track infection and inform therapies. In this study, we performed unbiased metatranscriptomic analysis of clinical samples from COVID-19 patients using a newly-developed RNA-seq library construction method (TRACE-seq), which utilizes tagmentation activity of Tn5 on RNA/DNA hybrids. This approach avoids the laborious and time-consuming steps in traditional RNA-seq procedure, and hence is fast, sensitive and convenient. We demonstrated that TRACE-seq allowed integrated characterization of full genome information of SARS-CoV-2, putative pathogens causing coinfection, antibiotic resistance and host response from single throat swabs. We believe that the integrated information will deepen our understanding of pathogenesis and improve diagnostic accuracy for infectious diseases.
Latent Feature Representations for Human Gene Expression Data Improve Phenotypic Predictions
Authors: Pantazis Yannis , Tselas Christos , Lakiotaki Kleanthi , Lagani Vincenzo , Tsamardinos Ioannis
Published: October 2020

High-throughput technologies such as microarrays and RNA-sequencing (RNA-seq) allow to precisely quantify transcriptomic profiles, generating datasets that are inevitably high-dimensional. In this work, we investigate whether the whole human transcriptome can be represented in a compressed, low dimensional latent space without loosing relevant information. We thus constructed low-dimensional latent feature spaces of the human genome, by utilizing three dimensionality reduction approaches and a diverse set of curated datasets. We applied standard Principal Component Analysis (PCA), kernel PCA and Autoencoder Neural Networks on 1360 datasets from four different measurement technologies. The latent feature spaces are tested for their ability to (a) reconstruct the original data and (b) improve predictive performance on validation datasets not used during the creation of the feature space. While linear techniques show better reconstruction performance, nonlinear approaches, particularly, neural-based models seem to be able to capture non-additive interaction effects, and thus enjoy stronger predictive capabilities. Our results show that low dimensional representations of the human transcriptome can be achieved by integrating hundreds of datasets, despite the limited sample size of each dataset and the biological / technological heterogeneity across studies. The created space is two to three orders of magnitude smaller compared to the raw data, offering the ability of capturing a large portion of the original data variability and eventually reducing computational time for downstream analyses.