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Background: Transfer entropy (TE) is a measure for the detection of directed interactions. Transfer entropy is an information theoretic implementation of Wiener's principle of observational causality. It offers an approach to the detection of neuronal interactions that is free of an explicit model of the interactions. Hence, it offers the power to analyze linear and nonlinear interactions alike. This allows for example the comprehensive analysis of directed interactions in neural networks at various levels of description. Here we present the open-source MATLAB toolbox TRENTOOL that allows the user to handle the considerable complexity of this measure and to validate the obtained results using non-parametrical statistical testing. We demonstrate the use of the toolbox and the performance of the algorithm on simulated data with nonlinear (quadratic) coupling and on local field potentials (LFP) recorded from the retina and the optic tectum of the turtle (Pseudemys scripta elegans) where a neuronal one-way connection is likely present., Results: In simulated data TE detected information flow in the simulated direction reliably with false positives not exceeding the rates expected under the null hypothesis. In the LFP data we found directed interactions from the retina to the tectum, despite the complicated signal transformations between these stages. No false positive interactions in the reverse directions were detected., Conclusions: TRENTOOL is an implementation of transfer entropy and mutual information analysis that aims to support the user in the application of this information theoretic measure. TRENTOOL is implemented as a MATLAB toolbox and available under an open source license (GPL v3). For the use with neural data TRENTOOL seamlessly integrates with the popular FieldTrip toolbox.

Background: Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s) of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s), and sigma = 1, are hallmark features of self-organized critical (SOC) systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s) and sigma by imposing subsampling on three different SOC models. We then compared f(s) and sigma of the subsampled models with those of multielectrode local field potential (LFP) activity recorded in three macaque monkeys performing a short term memory task., Results: Neither the LFP nor the subsampled SOC models showed a power law for f(s). Both, f(s) and sigma, depended sensitively on the subsampling geometry and the dynamics of the model. Only one of the SOC models, the Abelian Sandpile Model, exhibited f(s) and sigma similar to those calculated from LFP activity., Conclusion: Since subsampling can prevent the observation of the characteristic power law and sigma in SOC systems, misclassifications of critical systems as sub- or supercritical are possible. Nevertheless, the system specific scaling of f(s) and sigma under subsampling conditions may prove useful to select physiologically motivated models of brain function. Models that better reproduce f(s) and sigma calculated from the physiological recordings may be selected over alternatives.

Background: Conflicting evidence exists regarding the effects of ursodeoxycholic acid (UDCA) on coronavirus disease 2019 (COVID-19). This study investigates the association between UDCA administration and COVID-19 infection and its related outcomes in individuals with chronic liver disease (CLD). Methods: A customized COVID-19 research database (n = 3,485,376) was created by integrating data from the National Health Insurance Service (NHIS) and the Korea Disease Control and Prevention Agency's COVID-19 databases. The study focused on patients diagnosed with COVID-19 in 2021, using the NHIS data from 365 days before diagnosis. To create comparable groups with and without UDCA administration before COVID-19, we used propensity score matching. The primary endpoint was the first confirmed positive result for severe acute respiratory syndrome coronavirus-2. In addition, we identified severe COVID-19-related outcomes. Subgroup analysis were conducted based on the dose of UDCA exposure. Results: Data from 74,074 individuals with CLD was analyzed. The participants' average age was 57.5 years, and 52.1% (19,277) of those in each group were male. Those with prior UDCA exposure had a significantly lower risk of COVID-19 infection (adjusted OR: 0.80, 95% CI [0.76–0.85]) compared to the non-UDCA group. Additionally, the UDCA group had a lower risk of severe COVID-19 outcomes (adjusted OR: 0.67, 95% CI [0.46–0.98]). Subgroup analyses indicated that there was a decrease in COVID-19 infection and its related outcomes with increasing UDCA exposure dose. Conclusions: Our large observational study highlights the potential use of readily available UDCA as an adjunctive therapy for COVID-19 in individuals with CLD. [ABSTRACT FROM AUTHOR]

Background: Long-lasting symptoms with a possible relation to psychosomatic comorbidity have been described following COVID-19. However, data is sparse in general practice. The trial's objective was to investigate the time-dependent frequency of disease symptoms and relation to psychosomatic comorbidity and daily life impairment (DLI). Methods: Comparative cohort study of patients reporting a previous SARS-CoV-2 infection and uninfected controls in general practice. Participants were recruited in 14 general practices in the greater Munich area. Data collection was questionnaire based with a 12 months follow-up. Descriptive statistics, multivariable regression and bivariate correlations were used for analysis. Results: A total of n = 204 cases infected up to 42 months ago (n = 141 Omicron, n = 63 earlier variants), and n = 119 controls were included. Disease symptoms were substantially more prevalent in cases (55–79% vs. 43% within one year of infection). This difference also appeared in the multivariable analysis adjusting for socio-demographics and psychosomatic comorbidity with odds ratios (OR) of 4.15 (p < 0.001) and 3.51 (p = 0.054) for the cohorts with Omicron or earlier variants infection (vs. controls), respectively. It was persistent with earlier variants (OR 1.00 per month, p = 0.903), while a decreasing trend was observed for Omicron (OR 0.89 per month, p < 0.001). DLI was especially correlated with fatigue (r = 0.628). Conclusion: DLI, psychosomatic comorbidity and independently increased disease symptoms require holistic treatment of the patient in general practice according to the bio-psycho-social model. A key role in restoring the daily life capability may be attributed to the symptom fatigue. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has presented multiple psychological challenges for healthcare workers, such as anxiety, depression, burnout, and substance use disorders. In this research, we investigate the different ways Romanian physicians dealt with the difficult period of the COVID-19 pandemic. We also analyze how positive and negative stress-reducing strategies, as well as demographic variables, affect their psychological resilience and quality of life. Our goal is to provide a comprehensive overview of how physicians coped with the unprecedented global health challenges. Methods: We carried out a national cross-sectional study of 265 physicians in Romania between January 2021 and January 2022 using a web-based questionnaire. The study employed a web-based questionnaire to assess coping mechanisms using the COPE inventory, resilience through the Connor-Davidson Resilience Scale 25 (CD-RISC 25), and quality of life via the WHOQOL-BREF scale. The COPE inventory, consisting of 60 items across 15 subscales, categorizes coping strategies into problem-focused, emotion-focused, and dysfunctional types, with each item rated on a 4-point scale. The CD-RISC 25 measures resilience on a 5-point Likert scale, with total scores ranging from 0 to 100. WHOQOL-BREF assesses quality of life through 26 items in 4 domains: physical, mental, social relations, and environmental, scored from 1 to 5 and converted to a 0–100 scale for domain scores. Univariate and multivariate linear regression models were employed to discern the intricate relationships between coping strategies, resilience levels, quality of life dimensions, and pertinent demographic factors. Results: The average CD-RISC score among participants was 66.2. The mean scores for the values for the QOL subscales were 64.0 for physical well-being, 61.7 for psychological well-being, 61.2 for social relationships, and 64.7 for environment. Individuals tend to use problem-focused and emotion-focused coping more than dysfunctional mechanisms, according to the COPE inventory. Problem-focused and emotion-focused coping are positively correlated with resilience, while dysfunctional coping is negatively correlated. Resilience is significantly influenced by gender and professional status, with males and senior specialists reporting higher levels while younger physicians and residents reporting lower levels. Conclusions: Our data points to specific protective characteristics and some detrimental factors on physicians' resilience and quality of life during the pandemic. [ABSTRACT FROM AUTHOR]

Background: Targeting receptor-interacting serine/threonine protein kinase 1 could mitigate the devastating sequelae of the hyperinflammatory state observed in severe cases of COVID-19. This study explored the immunomodulatory and clinical effects of the receptor-interacting serine/threonine protein kinase 1 inhibitor SAR443122 (eclitasertib) in patients with severe COVID-19. Methods: In this Phase 1b, double-blinded, placebo-controlled study (NCT04469621) a total of 82 patients were screened, of whom 68 patients were eligible and randomized (2:1) to receive eclitasertib 600 mg (300 mg twice daily) or placebo up to 14 days. Primary outcome was relative change in C-reactive protein from baseline to Day 7. Time to clinical improvement using 7-point ordinal scale, ventilator/respiratory failure-free days, change in SpO2/FiO2 ratio, and biomarkers of severe COVID-19 were explored. Results: Geometric mean ratio (point estimate [90% confidence interval]) of the relative change from baseline in C-reactive protein with eclitasertib vs. placebo on Day 7 was 0.85 (0.49–1.45; p = 0.30). Median time to 50% decrease in C-reactive protein from baseline was 3 days vs. 5 days (p = 0.056) with eclitasertib vs. placebo. Median time to ≥ 2-point improvement on 7-point clinical symptoms scale was 8 days vs. 10 days with eclitasertib vs. placebo (p = 0.38). Mean ventilator/respiratory failure-free days, change in baseline-adjusted SpO2/FiO2 ratio, and clinical biomarkers showed consistent numerical improvements with eclitasertib vs. placebo. The most frequently reported treatment-emergent adverse events were gastrointestinal disorders and condition aggravated/worsened COVID-19 pneumonia. Conclusions: Eclitasertib was well tolerated with consistent trends toward more rapid resolution of inflammatory biomarkers and clinical improvement in severe COVID-19 patients than placebo. ClinicalTrials.gov identifier: NCT04469621, first posted on clinicaltrials.gov on July 14, 2020. [ABSTRACT FROM AUTHOR]

In Parkinson's disease, pathogenic factors such as the intraneuronal accumulation of the protein α-synuclein affect key metabolic processes. New approaches are required to understand how metabolic dysregulations cause degeneration of vulnerable subtypes of neurons in the brain. Here, we apply correlative electron microscopy and NanoSIMS isotopic imaging to map and quantify 13C enrichments in dopaminergic neurons at the subcellular level after pulse-chase administration of 13C-labeled glucose. To model a condition leading to neurodegeneration in Parkinson's disease, human α-synuclein was unilaterally overexpressed in the substantia nigra of one brain hemisphere in rats. When comparing neurons overexpressing α-synuclein to those located in the control hemisphere, the carbon anabolism and turnover rates revealed metabolic anomalies in specific neuronal compartments and organelles. Overexpression of α-synuclein enhanced the overall carbon turnover in nigral neurons, despite a lower relative incorporation of carbon inside the nucleus. Furthermore, mitochondria and Golgi apparatus showed metabolic defects consistent with the effects of α-synuclein on inter-organellar communication. By revealing changes in the kinetics of carbon anabolism and turnover at the subcellular level, this approach can be used to explore how neurodegeneration unfolds in specific subpopulations of neurons. [ABSTRACT FROM AUTHOR]

Background: Non-invasive recordings of gross neural activity in humans often show responses to omitted stimuli in steady trains of identical stimuli. This has been taken as evidence for the neural coding of prediction or prediction error. However, evidence for such omission responses from invasive recordings of cellular-scale responses in animal models is scarce. Here, we sought to characterise omission responses using extracellular recordings in the auditory cortex of anaesthetised rats. We profiled omission responses across local field potentials (LFP), analogue multiunit activity (AMUA), and single/multi-unit spiking activity, using stimuli that were fixed-rate trains of acoustic noise bursts where 5% of bursts were randomly omitted. Results: Significant omission responses were observed in LFP and AMUA signals, but not in spiking activity. These omission responses had a lower amplitude and longer latency than burst-evoked sensory responses, and omission response amplitude increased as a function of the number of preceding bursts. Conclusions: Together, our findings show that omission responses are most robustly observed in LFP and AMUA signals (relative to spiking activity). This has implications for models of cortical processing that require many neurons to encode prediction errors in their spike output. [ABSTRACT FROM AUTHOR]

Background: One critical variable in the time series analysis is the change point, which is the point where an abrupt change occurs in chronologically ordered observations. Existing parametric models for change point detection, such as the linear regression model and the Bayesian model, require that observations are normally distributed and that the trend line cannot have extreme variability. To overcome the limitations of the parametric model, we apply a nonparametric method, the Mann-Kendall-Sneyers (MKS) test, to change point detection for the state-level COVID-19 case time series data of the United States in the early outbreak of the pandemic.Methods: The MKS test is implemented for change point detection. The forward sequence and the backward sequence are calculated based on the new weekly cases between March 22, 2020 and January 31, 2021 for each of the 50 states. Points of intersection between the two sequences falling within the 95% confidence intervals are identified as the change points. The results are compared with two other change point detection methods, the pruned exact linear time (PELT) method and the regression-based method. Also, an open-access tool by Microsoft Excel is developed to facilitate the model implementation.Results: By applying the MKS test to COVID-19 cases in the United States, we have identified that 30 states (60.0%) have at least one change point within the 95% confidence intervals. Of these states, 26 states have one change point, 4 states (i.e., LA, OH, VA, and WA) have two change points, and one state (GA) has three change points. Additionally, most downward changes appear in the Northeastern states (e.g., CT, MA, NJ, NY) at the first development stage (March 23 through May 31, 2020); most upward changes appear in the Western states (e.g., AZ, CA, CO, NM, WA, WY) and the Midwestern states (e.g., IL, IN, MI, MN, OH, WI) at the third development stage (November 19, 2020 through January 31, 2021).Conclusions: This study is among the first to explore the potential of the MKS test applied for change point detection of COVID-19 cases. The MKS test is characterized by several advantages, including high computational efficiency, easy implementation, the ability to identify the change of direction, and no assumption for data distribution. However, due to its conservative nature in change point detection and moderate agreement with other methods, we recommend using the MKS test primarily for initial pattern identification and data pruning, especially in large data. With modification, the method can be further applied to other health data, such as injuries, disabilities, and mortalities. [ABSTRACT FROM AUTHOR]

Background: In Italy, the beginning of 2021 was characterized by the emergence of new variants of SARS-CoV-2 and by the availability of effective vaccines that contributed to the mitigation of non-pharmaceutical interventions and to the avoidance of hospital collapse.Methods: We analyzed the COVID-19 propagation in Italy starting from September 2021 with a Susceptible-Exposed-Infected-Recovered (SEIR) model that takes into account SARS-CoV-2 lineages, intervention measures and efficacious vaccines. The model was calibrated with the Bayesian method Conditional Robust Calibration (CRC) using COVID-19 data from September 2020 to May 2021. Here, we apply the Conditional Robustness Analysis (CRA) algorithm to the calibrated model in order to identify model parameters that most affect the epidemic diffusion in the long-term scenario. We focus our attention on vaccination and intervention parameters, which are the key parameters for long-term solutions for epidemic control.Results: Our model successfully describes the presence of new variants and the impact of vaccinations and non-pharmaceutical interventions in the Italian scenario. The CRA analysis reveals that vaccine efficacy and waning immunity play a crucial role for pandemic control, together with asymptomatic transmission. Moreover, even though the presence of variants may impair vaccine effectiveness, virus transmission can be kept low with a constant vaccination rate and low restriction levels.Conclusions: In the long term, a policy of booster vaccinations together with contact tracing and testing will be key strategies for the containment of SARS-CoV-2 spread. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has led to a high interest in mathematical models describing and predicting the diverse aspects and implications of the virus outbreak. Model results represent an important part of the information base for the decision process on different administrative levels. The Robert-Koch-Institute (RKI) initiated a project whose main goal is to predict COVID-19-specific occupation of beds in intensive care units: Steuerungs-Prognose von Intensivmedizinischen COVID-19 Kapazitäten (SPoCK). The incidence of COVID-19 cases is a crucial predictor for this occupation. Methods: We developed a model based on ordinary differential equations for the COVID-19 spread with a time-dependent infection rate described by a spline. Furthermore, the model explicitly accounts for weekday-specific reporting and adjusts for reporting delay. The model is calibrated in a purely data-driven manner by a maximum likelihood approach. Uncertainties are evaluated using the profile likelihood method. The uncertainty about the appropriate modeling assumptions can be accounted for by including and merging results of different modelling approaches. The analysis uses data from Germany describing the COVID-19 spread from early 2020 until March 31st, 2021. Results: The model is calibrated based on incident cases on a daily basis and provides daily predictions of incident COVID-19 cases for the upcoming three weeks including uncertainty estimates for Germany and its subregions. Derived quantities such as cumulative counts and 7-day incidences with corresponding uncertainties can be computed. The estimation of the time-dependent infection rate leads to an estimated reproduction factor that is oscillating around one. Data-driven estimation of the dark figure purely from incident cases is not feasible. Conclusions: We successfully implemented a procedure to forecast near future COVID-19 incidences for diverse subregions in Germany which are made available to various decision makers via an interactive web application. Results of the incidence modeling are also used as a predictor for forecasting the need of intensive care units. [ABSTRACT FROM AUTHOR]

Background: Despite the vaccination process in Germany, a large share of the population is still susceptible to SARS-CoV-2. In addition, we face the spread of novel variants. Until we overcome the pandemic, reasonable mitigation and opening strategies are crucial to balance public health and economic interests.Methods: We model the spread of SARS-CoV-2 over the German counties by a graph-SIR-type, metapopulation model with particular focus on commuter testing. We account for political interventions by varying contact reduction values in private and public locations such as homes, schools, workplaces, and other. We consider different levels of lockdown strictness, commuter testing strategies, or the delay of intervention implementation. We conduct numerical simulations to assess the effectiveness of the different intervention strategies after one month. The virus dynamics in the regions (German counties) are initialized randomly with incidences between 75 and 150 weekly new cases per 100,000 inhabitants (red zones) or below (green zones) and consider 25 different initial scenarios of randomly distributed red zones (between 2 and 20% of all counties). To account for uncertainty, we consider an ensemble set of 500 Monte Carlo runs for each scenario.Results: We find that the strength of the lockdown in regions with out of control virus dynamics is most important to avoid the spread into neighboring regions. With very strict lockdowns in red zones, commuter testing rates of twice a week can substantially contribute to the safety of adjacent regions. In contrast, the negative effect of less strict interventions can be overcome by high commuter testing rates. A further key contributor is the potential delay of the intervention implementation. In order to keep the spread of the virus under control, strict regional lockdowns with minimum delay and commuter testing of at least twice a week are advisable. If less strict interventions are in favor, substantially increased testing rates are needed to avoid overall higher infection dynamics.Conclusions: Our results indicate that local containment of outbreaks and maintenance of low overall incidence is possible even in densely populated and highly connected regions such as Germany or Western Europe. While we demonstrate this on data from Germany, similar patterns of mobility likely exist in many countries and our results are, hence, generalizable to a certain extent. [ABSTRACT FROM AUTHOR]

Background: Despite widespread COVID-19 vaccination programs, there is an ongoing need for targeted disease prevention and control efforts in high-risk occupational settings. This study aimed to develop, pilot, and validate an instrument for surveying occupational COVID-19 infection prevention and control (IPC) measures available to workers in diverse geographic and occupational settings.Methods: A 44-item online survey was developed in English and validated for face and content validity according to literature review, expert consultation, and pre-testing. The survey was translated and piloted with 890 workers from diverse industries in Canada, Ireland, Argentina, Poland, Nigeria, China, the US, and the UK. Odds ratios generated from univariable, and multivariable logistic regression assessed differences in 'feeling protected at work' according to gender, age, occupation, country of residence, professional role, and vaccination status. Exploratory factor analysis (EFA) was conducted, and internal consistency reliability verified with Cronbach's alpha. Hypothesis testing using two-sample t-tests verified construct validity (i.e., discriminant validity, known-groups technique), and criterion validity.Results: After adjustment for occupational sector, characteristics associated with feeling protected at work included being male (AOR = 1.88; 95% CI = 1.18,2.99), being over 55 (AOR = 2.17; 95% CI = 1.25,3.77) and working in a managerial position (AOR = 3.1; 95% CI = 1.99,4.83). EFA revealed nine key IPC domains relating to: environmental adjustments, testing and surveillance, education, costs incurred, restricted movements, physical distancing, masking, isolation strategies, and areas for improvement. Each domain showed sufficient internal consistency reliability (Cronbach's alpha ≥0.60). Hypothesis testing revealed differences in survey responses by country and occupational sector, confirming construct validity (p < 0.001), criterion validity (p = 0.04), and discriminant validity (p < 0.001).Conclusions: The online survey, developed in English to identify the COVID-19 protective measures used in diverse workplace settings, showed strong face validity, content validity, internal consistency, criterion validity, and construct validity. Translations in Chinese, Spanish, French, Polish, and Hindi demonstrated adaptability of the survey for use in international working environments. The multi-lingual tool can be used by decision makers in the distribution of IPC resources, and to guide occupational safety and health (OSH) recommendations for preventing COVID-19 and future infectious disease outbreaks. [ABSTRACT FROM AUTHOR]

Background: Government responses to the pandemic varied in terms of timing, duration, and stringency, seeking to protect healthcare systems, whose pre-pandemic state varied significantly. Therefore, the severity of Covid-19 and, thus, excess mortality have been unequal across counties. This paper explores the geography of excess mortality and its underlying factors in 2020, highlighting the effects of health policies pre-pandemic and strategies devised by governments to cope with Covid-19.Methods: Excess mortality is estimated for 79 high, medium and low-income countries. The factors of excess mortality are examined employing median quantile regression analysis.Results: Health privatization, healthcare underfunding, and late implementation of containment and mitigation strategies were powerful drivers of excess mortality. By contrast, the results suggest a negative association of excess mortality with health expenditure, number of doctors and hospital beds, share of population covered by health insurance and test and trace capacity.Conclusions: The evidence highlights the importance of sufficiently funded healthcare systems with universal access and strong primary healthcare in the battle against the pandemic. An early response to Covid-19, including borders' controls and a strong test and trace capacity, could improve epidemiological surveillance and minimize excess mortality, with stringent and lengthy lockdowns not providing a significant benefit. [ABSTRACT FROM AUTHOR]

Presently, the developments of COVID-19 situation in different countries and regions have clearly differentiated. Due to differences in resources, infrastructure, and awareness of epidemic prevention and control, capabilities for COVID-19 prevention and control in various regions have also shown a significant imbalance as the COVID-19 epidemic is entering a new normal. The objectives of this study are to provide dynamically adjusted strategies in response to developments in the COVID-19 pandemic as a new normal. In the face of the new normal, one key is normalizing epidemic prevention and control. As part of this, we should implement precise policies based on the dynamics of the COVID-19 epidemic and particular response needs. In ongoing COVID-19 prevention and control, we must pay attention to new vulnerabilities and new features in the dynamics of the epidemic. In this study, health and government officials can benefit from insights of preparing ourselves for long-term challenges and both certainties and uncertainties in a future facing COVID-19. [ABSTRACT FROM AUTHOR]

Background: The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. In contrast to the case fatality rate (CFR), the IFR depends on the total number of infected individuals - not just on the number of confirmed cases. In order to estimate the IFR, several seroprevalence studies have been or are currently conducted.Methods: Using German COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections.Results: Effective IFRs in Germany are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the time-dependent variability in observed mortality can be explained by changes in the age distribution of infections. Furthermore, a vanishing gap between effective IFRs and observed CFRs is apparent after the first infection wave, while an increasing gap can be observed during the second wave.Conclusions: The development of estimated effective IFR and observed CFR reflects the changing age distribution of infections over the course of the COVID-19 pandemic in Germany. Further research is warranted to obtain timely age-stratified IFR estimates, particularly in light of new variants of the virus. [ABSTRACT FROM AUTHOR]

Background: Developing a safe and effective vaccine will be the principal way of controlling the COVID-19 pandemic. However, current COVID-19 vaccination trials are not adequately representing a diverse participant population in terms of age, ethnicity and comorbidities. Achieving the representative recruitment targets that are adequately powered to the study remains one of the greatest challenges in clinical trial management. To ensure accuracy and generalisability of the safety and efficacy conclusions generated by clinical trials, it is crucial to recruit patient cohorts as representative as possible of the future target population. Missing these targets can lead to reduced validity of the study results and can often slow down drug development leading to costly delays.Objective: This study explores the key factors related to perceptions and participation in vaccination trials.Methods: This study involved an anonymous cross-sectional online survey circulated across the UK. Statistical analysis was done in six phases. Multi-nominal logistic models examined demographic and geographic factors that may impact vaccine uptake.Results: The survey had 4884 participants of which 9.44% were Black Asian Minority Ethnic (BAME). Overall, 2020 (41.4%) respondents were interested in participating in vaccine trials; 27.6% of the respondents were not interested and 31.1% were unsure. The most interested groups were male (OR = 1.29), graduates (OR = 1.28), the 40-49 and 50-59 age groups (OR = 1.88 and OR = 1.46 respectively) and those with no health issues (OR = 1.06). The least interested groups were BAME (OR = 0.43), those from villages and small towns (OR = 0.66 and 0.54 respectively) and those aged 70 and above (OR = 1.11).Conclusions: In order to have a vaccination that is generalisable to the entire population, greater work needs to be done in engaging a diverse cohort of participants. Public health campaigns need to be targeted in improving trial recruitment rates for the elderly, BAME community and the less educated rural population. [ABSTRACT FROM AUTHOR]

Deficits in synaptic transmission and plasticity are thought to contribute to the pathophysiology of Alzheimer's disease (AD) and Parkinson's disease (PD). Several brain stimulation techniques are currently available to assess or modulate human neuroplasticity, which could offer clinically useful interventions as well as quantitative diagnostic and prognostic biomarkers. In this review, we discuss several brain stimulation techniques, with a special emphasis on transcranial magnetic stimulation and deep brain stimulation (DBS), and review the results of clinical studies that applied these techniques to examine or modulate impaired neuroplasticity at the local and network levels in patients with AD or PD. The impaired neuroplasticity can be detected in patients at the earlier and later stages of both neurodegenerative diseases. However, current brain stimulation techniques, with a notable exception of DBS for PD treatment, cannot serve as adequate clinical tools to assist in the diagnosis, treatment, or prognosis of individual patients with AD or PD. Targeting the impaired neuroplasticity with improved brain stimulation techniques could offer a powerful novel approach for the treatment of AD and PD. [ABSTRACT FROM AUTHOR]

Background: Despite widespread use, the accuracy of the diagnostic test for SARS-CoV-2 infection is poorly understood. The aim of our work was to better quantify misclassification errors in identification of true cases of COVID-19 and to study the impact of these errors in epidemic curves using publicly available surveillance data from Alberta, Canada and Philadelphia, USA.Methods: We examined time-series data of laboratory tests for SARS-CoV-2 viral infection, the causal agent for COVID-19, to try to explore, using a Bayesian approach, the sensitivity and specificity of the diagnostic test.Results: Our analysis revealed that the data were compatible with near-perfect specificity, but it was challenging to gain information about sensitivity. We applied these insights to uncertainty/bias analysis of epidemic curves under the assumptions of both improving and degrading sensitivity. If the sensitivity improved from 60 to 95%, the adjusted epidemic curves likely falls within the 95% confidence intervals of the observed counts. However, bias in the shape and peak of the epidemic curves can be pronounced, if sensitivity either degrades or remains poor in the 60-70% range. In the extreme scenario, hundreds of undiagnosed cases, even among the tested, are possible, potentially leading to further unchecked contagion should these cases not self-isolate.Conclusion: The best way to better understand bias in the epidemic curves of COVID-19 due to errors in testing is to empirically evaluate misclassification of diagnosis in clinical settings and apply this knowledge to adjustment of epidemic curves. [ABSTRACT FROM AUTHOR]

Background: Characterising programs of gene regulation by studying individual protein-DNA and protein-protein interactions would require a large volume of high-resolution proteomics data, and such data are not yet available. Instead, many gene regulatory network (GRN) techniques have been developed, which leverage the wealth of transcriptomic data generated by recent consortia to study indirect, gene-level relationships between transcriptional regulators. Despite the popularity of such methods, previous methods of GRN inference exhibit limitations that we highlight and address through the lens of information theory. Results: We introduce new model-free and non-linear information theoretic measures for the inference of GRNs and other biological networks from continuous-valued data. Although previous tools have implemented mutual information as a means of inferring pairwise associations, they either introduce statistical bias through discretisation or are limited to modelling undirected relationships. Our approach overcomes both of these limitations, as demonstrated by a substantial improvement in empirical performance for a set of 160 GRNs of varying size and topology. Conclusions: The information theoretic measures described in this study yield substantial improvements over previous approaches (e.g. ARACNE) and have been implemented in the latest release of NAIL (Network Analysis and Inference Library). However, despite the theoretical and empirical advantages of these new measures, they do not circumvent the fundamental limitation of indeterminacy exhibited across this class of biological networks. These methods have presently found value in computational neurobiology, and will likely gain traction for GRN analysis as the volume and quality of temporal transcriptomics data continues to improve. [ABSTRACT FROM AUTHOR]

The article presents abstracts on neuroscience topics including "Dynamics and biomarkers of mental disorders," by Mitsuo Kawato, "Input-location dependent gain modulation in cerebellar nucleus neurons," by Maria Psarrou and colleagues, and "Analytic solution of cable energy function for cortical axons and dendrites," by Huiwen Ju and colleagues.

Background Cortical neurons display network-level dynamics with unique spatiotemporal patterns that construct the backbone of processing information signals and contribute to higher functions. Recent years have seen a wealth of research on the characteristics of neuronal networks that are sufficient conditions to activate or cease network functions. Local field potentials (LFPs) exhibit a scale-free and unique event size distribution (i.e., a neuronal avalanche) that has been proven in the cortex across species, including mice, rats, and humans, and may be used as an index of cortical excitability. In the present study, we induced seizure activity in the anterior cingulate cortex (ACC) with medial thalamic inputs and evaluated the impact of cortical excitability and thalamic inputs on network-level dynamics. We measured LFPs from multi-electrode recordings in mouse cortical slices and isoflurane-anesthetized rats. Results The ACC activity exhibited a neuronal avalanche with regard to avalanche size distribution, and the slope of the power-law distribution of the neuronal avalanche reflected network excitability in vitro and in vivo. We found that the slope of the neuronal avalanche in seizurelike activity significantly correlated with cortical excitability induced by γ-aminobutyric acid system manipulation. The thalamic inputs desynchronized cingulate seizures and affected the level of cortical excitability, the modulation of which could be determined by the slope of the avalanche size. Conclusions We propose that the neuronal avalanche may be a tool for analyzing cortical activity through LFPs to determine alterations in network dynamics. [ABSTRACT FROM AUTHOR]

Background: How living neural networks retain information is still incompletely understood. Two prominent ideas on this topic have developed in parallel, but have remained somewhat unconnected. The first of these, the "synaptic hypothesis," holds that information can be retained in synaptic connection strengths, or weights, between neurons. Recent work inspired by statistical mechanics has suggested that networks will retain the most information when their weights are distributed in a skewed manner, with many weak weights and only a few strong ones. The second of these ideas is that information can be represented by stable activity patterns. Multineuron recordings have shown that sequences of neural activity distributed over many neurons are repeated above chance levels when animals perform well-learned tasks. Although these two ideas are compelling, no one to our knowledge has yet linked the predicted optimum distribution of weights to stable activity patterns actually observed in living neural networks. Results: Here, we explore this link by comparing stable activity patterns from cortical slice networks recorded with multielectrode arrays to stable patterns produced by a model with a tunable weight distribution. This model was previously shown to capture central features of the dynamics in these slice networks, including neuronal avalanche cascades. We find that when the model weight distribution is appropriately skewed, it correctly matches the distribution of repeating patterns observed in the data. In addition, this same distribution of weights maximizes the capacity of the network model to retain stable activity patterns. Thus, the distribution that best fits the data is also the distribution that maximizes the number of stable patterns. Conclusions: We conclude that local cortical networks are very likely to use a highly skewed weight distribution to optimize information retention, as predicted by theory. Fixed distributions impose constraints on learning, however. The network must have mechanisms for preserving the overall weight distribution while allowing individual connection strengths to change with learning. [ABSTRACT FROM AUTHOR]

Background: Pulsed transcranial ultrasound stimulation (pTUS) can modulate the neuronal activity of motor cortex and elicit muscle contractions. Cortico-muscular coupling (CMC) can serve as a tool to identify interaction between the oscillatory activity of the motor cortex and effector muscle. This research aims to explore the neuromodulatory effect of low-intensity, pTUS with different number of tone burst to neural circuit of motor-control system by analyzing the coupling relationship between motor cortex and tail muscle in mouse. The motor cortex of mice was stimulated by pulsed transcranial ultrasound with different number of tone bursts (NTB = 100 150 200 250 300). The local field potentials (LFPs) in tail motor cortex and electromyography (EMG) in tail muscles were recorded simultaneously during pTUS. The change of integral coupling strength between cortex and muscle was evaluated by mutual information (MI). The directional information interaction between them were analyzed by transfer entropy (TE).Results: Almost all of the MI and TE values were significantly increased by pTUS. The results of MI showed that the CMC was significantly enhanced with the increase of NTB. The TE results showed the coupling strength of CMC in descending direction (from LFPs to EMG) was significantly higher than that in ascending direction (from EMG to LFPs) after stimulation. Furthermore, compared to NTB = 100, the CMC in ascending direction were significantly enhanced when NTB = 250, 300, and CMC in descending direction were significantly enhanced when NTB = 200, 250, 300.Conclusion: These results confirm that the CMC between motor cortex and the tail muscles in mouse could be altered by pTUS. And by increasing the NTB (i.e. sonication duration), the coupling strength within the cortico-muscular circuit could be increased, which might further influence the motor function of mice. It demonstrates that, using MI and TE method, the CMC could be used for quantitatively evaluating the effect of pTUS with different NTBs, which might provide a new insight into the effect of pTUS neuromodulation in motor cortex. [ABSTRACT FROM AUTHOR]

An abstract of the article "Learning more by sampling less: subsampling effects are model specific" by Viola Priesemann and colleagues is presented.

An abstract of the article "Neuronal avalanches change from wakefulness to deep sleep - a study of intracranial depth recordings in humans" by Viola Priesemann, Mario Valderrama, Michael Wibral and Michel Le Van Quyen is presented.

An abstract of the article "Signal propagation and neuronal avalanches analysis in networks of formal neurons," by several researchers including, Mauricio Girardi-Schappo, Marcelo H. R. Tragtenberg, and Osame Kinouchi is presented.