McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks.

Haplotype networks are graphs used to represent evolutionary relationships between a set of taxa and are characterized by intuitiveness in analyzing genealogical relationships of closely related genomes. We here propose a novel algorithm termed McAN that considers mutation spectrum history (mutations in ancestry haplotype should be contained in descendant haplotype), node size (corresponding to sample count for a given node) and sampling time when constructing haplotype network. We show that McAN is two orders of magnitude faster than state-of-the-art algorithms without losing accuracy, making it suitable for analysis of a large number of sequences. Based on our algorithm, we developed an online web server and offline tool for haplotype network construction, community lineage determination, and interactive network visualization. We demonstrate that McAN is highly suitable for analyzing and visualizing massive genomic data and is helpful to enhance the understanding of genome evolution. Availability: Source code is written in C/C++ and available at https://github.com/Theory-Lun/McAN and https://ngdc.cncb.ac.cn/biocode/tools/BT007301 under the MIT license. Web server is available at https://ngdc.cncb.ac.cn/bit/hapnet/. SARS-CoV-2 dataset are available at https://ngdc.cncb.ac.cn/ncov/. Contact: songshh@big.ac.cn (Song S), zhaowm@big.ac.cn (Zhao W), baoym@big.ac.cn (Bao Y), zhangzhang@big.ac.cn (Zhang Z), ybxue@big.ac.cn (Xue Y).

Carryover Contamination-Controlled Amplicon Sequencing Workflow for Accurate Qualitative and Quantitative Detection of Pathogens: a Case Study on SARS-CoV-2.

Carryover contamination during amplicon sequencing workflow (AMP-Seq) put the accuracy of the high-throughput detection for pathogens at risk. The purpose of this study is to develop a carryover contaminations-controlled AMP-Seq (ccAMP-Seq) workflow to enable accurate qualitative and quantitative detection for pathogens. By using the AMP-Seq workflow to detect SARS-CoV-2, Aerosols, reagents and pipettes were identified as potential sources of contaminations and ccAMP-Seq was then developed. ccAMP-Seq used filter tips and physically isolation of experimental steps to avoid cross contamination, synthetic DNA spike-ins to compete with contaminations and quantify SARS-CoV-2, dUTP/uracil DNA glycosylase system to digest the carryover contaminations, and a new data analysis procedure to remove the sequencing reads from contaminations. Compared to AMP-Seq, the contamination level of ccAMP-Seq was at least 22-folds lower and the detection limit was also about an order of magnitude lower-as low as one copy/reaction. By testing the dilution series of SARS-CoV-2 nucleic acid standard, ccAMP-Seq showed 100% sensitivity and specificity. The high sensitivity of ccAMP-Seq was further confirmed by the detection of SARS-CoV-2 from 62 clinical samples. The consistency between qPCR and ccAMP-Seq was 100% for all the 53 qPCR-positive clinical samples. Seven qPCR-negative clinical samples were found to be positive by ccAMP-Seq, which was confirmed by extra qPCR tests on subsequent samples from the same patients. This study presents a carryover contamination-controlled, accurate qualitative and quantitative amplicon sequencing workflow that addresses the critical problem of pathogen detection for infectious diseases. IMPORTANCE Accuracy, a key indicator of pathogen detection technology, is compromised by carryover contamination in the amplicon sequencing workflow. Taking the detection of SARS-CoV-2 as case, this study presents a new carryover contamination-controlled amplicon sequencing workflow. The new workflow significantly reduces the degree of contamination in the workflow, thereby significantly improving the accuracy and sensitivity of the SARS-CoV-2 detection and empowering the ability of quantitative detection. More importantly, the use of the new workflow is simple and economical. Therefore, the results of this study can be easily applied to other microorganism, which has great significance for improving the detection level of microorganism.

The Effects of Loneliness on Depressive Symptoms Among Older Adults During COVID-19: Longitudinal Analyses of the Canadian Longitudinal Study on Aging.

ObjectivesThis paper examines the longitudinal effects of changes in the association between loneliness and depressive symptoms during the pandemic among older adults (65+). Methods Baseline (2011-2015) and Follow-up 1 (2015-2018) from the Canadian Longitudinal Study on Aging (CLSA), and the Baseline and Exit waves of the CLSA COVID-19 study (April-December, 2020) (n = 12,469) were used. Loneliness was measured using the 3-item UCLA Loneliness Scale and depression using the CES_D- 9. Results Loneliness is associated with depressive symptoms pre-pandemic; and changes in level of loneliness between FUP1 and the COVID Exit survey, adjusting for covariates. No interaction between loneliness and caregiving, and with multimorbidity, on depressive symptoms were observed, and several covariates exhibited associations with depressive symptoms. Discussion Strong support is found for an association between loneliness on depressive symptoms among older adults during the pandemic. Public health approaches addressing loneliness could reduce the burden of depression on older populations.

Predicting Managers' Mental Health Across Countries: Using Country-Level COVID-19 Statistics.

Background: There is limited research focusing on publicly available statistics on the Coronavirus disease 2019 (COVID-19) pandemic as predictors of mental health across countries. Managers are at risk of suffering from mental disorders during the pandemic because they face particular hardship. Objective: We aim to predict mental disorder (anxiety and depression) symptoms of managers across countries using country-level COVID-19 statistics. Methods: A two-wave online survey of 406 managers from 26 countries was performed in May and July 2020. We used logistic panel regression models for our main analyses and performed robustness checks using ordinary least squares regressions. In the sample, 26.5% of managers reached the cut-off levels for anxiety (General Anxiety Disorder-7; GAD-7) and 43.5% did so for depression (Patient Health Questionnaire-9; PHQ-9) symptoms. Findings: We found that cumulative COVID-19 statistics (e.g., cumulative cases, cumulative cases per million, cumulative deaths, and cumulative deaths per million) predicted managers' anxiety and depression symptoms positively, whereas daily COVID-19 statistics (daily new cases, smoothed daily new cases, daily new deaths, smoothed daily new deaths, daily new cases per million, and smoothed daily new cases per million) predicted anxiety and depression symptoms negatively. In addition, the reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor. Individually, we found that the cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms. Conclusions: Cumulative COVID-19 statistics predicted managers' anxiety and depression symptoms positively, while non-cumulative daily COVID-19 statistics predicted anxiety and depression symptoms negatively. Cumulative count of deaths is the most suitable single predictor of both anxiety and depression symptoms. Reproduction rate was a positive predictor, while stringency of governmental lockdown measures was a negative predictor.

Multimorbidity, COVID-19 and Mental Health: Canadian Longitudinal Study on Aging (CLSA) Longitudinal Analyses.

OBJECTIVES: This paper examines the longitudinal effects of the COVID-19 pandemic on older adults (65+) with multimorbidity on levels of depression, anxiety, and perceived global impact on their lives. METHODS: Baseline (2011-2015) and Follow-up 1 (2015-2018) data from the Canadian Longitudinal Study on Aging (CLSA), and the Baseline and Exit waves of the CLSA COVID-19 study (April-December, 2020) (n = 18,099). Multimorbidity was measured using: a) an additive scale of chronic conditions; and b) six chronic disease clusters. Linear Mixed Models were employed to test hypotheses. RESULTS: Number of chronic conditions pre-pandemic was associated with pandemic levels of depression (estimate = 0.40, 95% CI: [0.37,0.44]); anxiety (estimate = 0.20, 95% CI: [0.18, 0.23]); and perceived negative impact of the pandemic (OR = 1.04, 95% CI: [1.02, 1.06]). The associations between multimorbidity and anxiety decreased during the period of the COVID-19 surveys (estimate = -0.02, 95% CI: [-0.05, -0.01]); whereas the multimorbidity association with perceived impact increased (OR = 1.03, 95% CI: [1.01, 1.05]). CONCLUSIONS: This study demonstrates that pre-pandemic multimorbidity conditions are associated with worsening mental health. CLINICAL IMPLICATIONS: Clinicians treating mental health of older adults need to consider the joint effects of multimorbidity conditions and pandemic experiences to tailor counseling and other treatment protocols.

Multimorbidity resilience and COVID-19 pandemic self-reported impact and worry among older adults: a study based on the Canadian Longitudinal Study on Aging (CLSA).

BACKGROUND: The Coronavirus Disease-2019 (COVID-19) pandemic has created a spectrum of adversities that have affected older adults disproportionately. This paper examines older adults with multimorbidity using longitudinal data to ascertain why some of these vulnerable individuals coped with pandemic-induced risk and stressors better than others - termed multimorbidity resilience. We investigate pre-pandemic levels of functional, social and psychological forms of resilience among this sub-population of at-risk individuals on two outcomes - self-reported comprehensive pandemic impact and personal worry. METHODS: This study was conducted using Follow-up 1 data from the Canadian Longitudinal Study on Aging (CLSA), and the Baseline and Exit COVID-19 study, conducted between April and December in 2020. A final sub-group of 9211 older adults with two or more chronic health conditions were selected for analyses. Logistic regression and Generalized Linear Mixed Models were employed to test hypotheses between a multimorbidity resilience index and its three sub-indices measured using pre-pandemic Follow-up 1 data and the outcomes, including covariates. RESULTS: The multimorbidity resilience index was inversely associated with pandemic comprehensive impact at both COVID-19 Baseline wave (OR = 0.83, p < 0.001, 95% CI: [0.80,0.86]), and Exit wave (OR = 0.84, p < 0.001, 95% CI: [0.81,0.87]); and for personal worry at Exit (OR = 0.89, p < 0.001, 95% CI: [0.86,0.93]), in the final models with all covariates. The full index was also associated with comprehensive impact between the COVID waves (estimate = - 0.19, p < 0.001, 95% CI: [- 0.22, - 0.16]). Only the psychological resilience sub-index was inversely associated with comprehensive impact at both Baseline (OR = 0.89, p < 0.001, 95% CI: [0.87,0.91]) and Exit waves (OR = 0.89, p < 0.001, 95% CI: [0.87,0.91]), in the final model; and between these COVID waves (estimate = - 0.11, p < 0.001, 95% CI: [- 0.13, - 0.10]). The social resilience sub-index exhibited a weak positive association (OR = 1.04, p < 0.05, 95% CI: [1.01,1.07]) with personal worry, and the functional resilience measure was not associated with either outcome. CONCLUSIONS: The findings show that psychological resilience is most pronounced in protecting against pandemic comprehensive impact and personal worry. In addition, several covariates were also associated with the outcomes. The findings are discussed in terms of developing or retrofitting innovative approaches to proactive coping among multimorbid older adults during both pre-pandemic and peri-pandemic periods.

Levels of Depression and Anxiety Among Informal Caregivers During the COVID-19 Pandemic: A Study Based on the Canadian Longitudinal Study on Aging.

OBJECTIVES: Studies on informal caregiving during the coronavirus disease 2019 (COVID-19) pandemic have mainly focused on subgroups of caregivers using cross-sectional or convenience samples, limiting the generalizability of findings. Conversely, this longitudinal study examines the effects of the pandemic and caregiving factors on depressive symptoms and anxiety over 9 months among informal caregivers in Canada. METHODS: This study uses data from the Baseline (2011-2015), Follow-up 1 (2015-2018), and COVID-19 Study Baseline survey (April to May 2020) and Exit surveys (September to December 2020) of the Canadian Longitudinal Study on Aging (CLSA). A total of 14,118 CLSA participants who were caregivers at Follow-up 1 and participated in the COVID-19 studies were selected. Linear mixed models were used to examine the effect of sex of caregiver, changes in caregiving (increase in caregiving hours and inability to care), and location of care (same household, another household, and health care institution) on depressive symptoms and anxiety from COVID-19 studies Baseline to Exit surveys (about 6-7 months apart). RESULTS: Informal caregivers reported more frequent depressive symptoms from the COVID-19 Baseline to Exit surveys, but not anxiety. Female caregivers reported greater depressive symptoms and anxiety, and male caregivers exhibited a greater increase in depressive symptoms and anxiety over time. More caregiving hours and inability to provide care were significantly positively associated with depressive symptoms and anxiety. Also, in-home caregivers reported more depressive symptoms and anxiety than those who cared for someone in health care institution, and more anxiety than those who cared for some in another household. DISCUSSION: The findings shed light on the change in mental health among informal caregivers during the outset of the pandemic. The demonstrated associations between studied variables and mental health among informal caregivers provide empirical evidence for intervention programs aiming to support caregivers, particularly those who are female, and providing intensive care at home.

Role of astroglial toll-like receptors (TLRs) in central nervous system infections, injury and neurodegenerative diseases.

Central nervous system (CNS) innate immunity plays essential roles in infections, neurodegenerative diseases, and brain or spinal cord injuries. Astrocytes and microglia are the principal cells that mediate innate immunity in the CNS. Pattern recognition receptors (PRRs), expressed by astrocytes and microglia, sense pathogen-derived or endogenous ligands released by damaged cells and initiate the innate immune response. Toll-like receptors (TLRs) are a well-characterized family of PRRs. The contribution of microglial TLR signaling to CNS pathology has been extensively investigated. Even though astrocytes assume a wide variety of key functions, information about the role of astroglial TLRs in CNS disease and injuries is limited. Because astrocytes display heterogeneity and exhibit phenotypic plasticity depending on the effectors present in the local milieu, they can exert both detrimental and beneficial effects. TLRs are modulators of these paradoxical astroglial properties. The goal of the current review is to highlight the essential roles played by astroglial TLRs in CNS infections, injuries and diseases. We discuss the contribution of astroglial TLRs to host defense as well as the dissemination of viral and bacterial infections in the CNS. We examine the link between astroglial TLRs and the pathogenesis of neurodegenerative diseases and present evidence showing the pivotal influence of astroglial TLR signaling on sterile inflammation in CNS injury. Finally, we define the research questions and areas that warrant further investigations in the context of astrocytes, TLRs, and CNS dysfunction.