Counting actin in contractile rings reveals novel contributions of cofilin and type II myosins to fission yeast cytokinesis

Cytokinesis by animals, fungi and amoebas depends on actomyosin contractile rings, which are stabilized by continuous turnover of actin filaments. Remarkably little is known about the amount of polymerized actin in contractile rings, so we used low concentration of GFP-Lifeact to count total polymerized actin molecules in the contractile rings of live fission yeast cells. Contractile rings of wild-type cells accumulated polymerized actin molecules at 4,900/min to a peak number of ∼198,000 followed by a loss of actin at 5,400/min throughout ring constriction. In adf1-M3 mutant cells with cofilin that severs actin filaments poorly, contractile rings accumulated polymerized actin at twice the normal rate and eventually had almost two-fold more actin along with a proportional increase in type II myosins Myo2, Myp2 and formin Cdc12. Although 30% of adf1-M3 mutant cells failed to constrict their rings fully, the rest lost actin from the rings at the wild-type rates. Mutations of type II myosins Myo2 and Myp2 reduced contractile ring actin filaments by half and slowed the rate of actin loss from the rings.

Counting actin in contractile rings reveals novel contributions of cofilin and type II myosins to fission yeast cytokinesis

Cytokinesis by animals, fungi and amoebas depends on actomyosin contractile rings, which are stabilized by continuous turnover of actin filaments. Remarkably little is known about the amount of polymerized actin in contractile rings, so we used low concentration of GFP-Lifeact to count total polymerized actin molecules in the contractile rings of live fission yeast cells. Contractile rings of wild-type cells accumulated polymerized actin molecules at 4,900/min to a peak number of ~198,000 followed by a loss of actin at 5,400/min throughout ring constriction. In adf1-M3 mutant cells with cofilin that severs actin filaments poorly, contractile rings accumulated polymerized actin at twice the normal rate and eventually had almost two-fold more actin along with a proportional increase in type II myosins Myo2, Myp2 and formin Cdc12. Although 30% of adf1-M3 mutant cells failed to constrict their rings fully, the rest lost actin from the rings at the wild-type rates. Mutations of type II myosins Myo2 and Myp2 reduced contractile ring actin filaments by half and slowed the rate of actin loss from the rings.

The relationship between time to a high COVID-19 response level and timing of peak daily incidence: an analysis of governments’ Stringency Index from 148 countries

Background The transmission dynamics and severity of coronavirus disease 2019 (COVID-19) pandemic is different across countries or regions. Differences in governments’ policy responses may explain some of these differences. We aimed to compare worldwide government responses to the spread of COVID-19, to examine the relationship between response level, response timing and the epidemic trajectory. Methods Free publicly-accessible data collected by the Coronavirus Government Response Tracker (OxCGRT) were used. Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1, 2020. The sub-indicators were scored and were aggregated into a common Stringency Index (SI, a value between 0 and 100) that reflects the overall stringency of the government’s response in a daily basis. Group-based trajectory modelling method was used to identify trajectories of SI. Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases. Results Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated: before January 13, from January 13 to February 12, from February 12 to March 11, and the last stage—from March 11 (the day WHO declared a pandemic of COVID-19) on going. Governments’ responses were upgraded with further spread of COVID-19 but varied substantially across countries. After the adjustment of SI level, geographical region and initiation stages, each day earlier to a high SI level (SI > 80) from the start of response was associated with 0.44 (standard error: 0.08, P < 0.001, R2 = 0.65) days earlier to the peak number of daily new case. Also, each day earlier to a high SI level from the date of first reported case was associated with 0.65 (standard error: 0.08, P < 0.001, R2 = 0.42) days earlier to the peak number of daily new case. Conclusions Early start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases. This may help to reduce the delays in flattening the epidemic curve to the low spread level. Graphic abstract

A Comparison of The Predictive Ability between Logistic and Gompertz Model on COVID-19 Outbreak

A predictive model can be learned using historical information. Thereafter, information about a running case is combined with a predictive model to estimate the case's remaining flow time. The predictive model is based on data from past events, which can be used to make predictions for current operating situations. For example, the case of coronavirus disease 2019 (COVID-19), which is currently infecting the whole world, including Indonesia, have influenced various aspects, ranging from the educational environment, business, economy, to the companies. Data scientists are urgently needed who can help organizations improve their operational processes. Therefore, this journal discusses the prediction of the peak number of COVID-19 cases in Indonesia, using two prediction models, logistic and Gompertz. The results obtained show that the Gompertz model has higher accuracy than the logistic model, with an accuracy of 99.85%. This journal's results are expected to help organizations estimate the time to rebuild themselves after being affected by COVID-19.

Flattening the Curve: The effects of intervention strategies during COVID-19

COVID-19 has plagued countries worldwide due to its infectious nature. Social distancing and the use of personal protective equipment (PPE) are two main strategies employed to prevent its spread. A SIR model with a time-dependent transmission rate is implemented to examine the effect of social distancing and PPE use in hospitals. These strategies’ effect on the size and timing of the peak number of infectious individuals are examined as well as the total number of individuals infected by the epidemic. The effect on the epidemic of when social distancing is relaxed is also examined. Overall, social distancing was shown to cause the largest impact in the number of infections. Studying this interaction between social distancing and PPE use is novel and timely. We show that decisions made at the state level on implementing social distancing and acquiring adequate PPE have dramatic impact on the health of its citizens.

COVID-19 Case Prediction and Outbreak Control of Navy Cluster in Sri Lanka: Effectiveness of SIR Model

Introduction: Infectious diseases such as coronavirus disease 2019 (COVID-19) can spread contagiously fast in semi-confined places, which demand prompt public health interventions such as isolation and quarantine for their effective control. An outbreak of COVID-19 was reported within a cluster of Navy personnel in the Western Province of Sri Lanka commencing from 22nd April 2020. In response, an aggressive outbreak management program was launched by the Epidemiology Unit of the Ministry of Health supported by the Sri Lanka Navy. The objective of this research was to predict possible number of cases within the susceptible population in Sri Lanka Navy. Methods: COVID-19 Hospital Impact Model for Epidemics (CHIME) developed by Predictive Health Care Team at Penn Medicine, Philadelphia, USA, which was a Susceptibility, Infected and Removed (SIR) model was used. The model was run on 20.05.2020 for a susceptible population of 10400, with number of hospitalized patients on the day of running the model being 357, first case hospitalized on 22.04.2020 and social distancing being implemented on 26.04.2020. Social distancing scenarios of 0, 25, 50 and 74% were run with 10 days of infectious period and 30 days of projection period. Results: With increasing social distancing measures, the peak number of infected persons decreased, and the duration of the curve extended. With increasing social distancing from 0% to 74%, the date on which the peak number of infected cases was reported increased from 49th day to the 54th day, the doubling time increased from 3.1 days to 4.1 days, the Ro decreased from 3.54 to 2.83, and expected daily growth rate decreased from 25.38% to 18.53%. The number of COVID-19 cases prevented as per the model ranged from 2.3 – 21.1 %, compared to the base line prediction of no social distancing. When comparing the observed number of cases with the baseline model with no social distancing, a 90.3% reduction was observed. Conclusion: The research demonstrated the practical use of a prediction model made readily available through an online open-source platform for the operational aspects of controlling outbreaks such as COVID-19 in a closed community. Predictive modelling is a useful tool for outbreak management.

Neuromechanical Assessment of Activated vs. Resting Leg Rigidity Using the Pendulum Test Is Associated With a Fall History in People With Parkinson’s Disease

Leg rigidity is associated with frequent falls in people with Parkinson’s disease (PD), suggesting a potential role in functional balance and gait impairments. Changes in the neural state due to secondary tasks, e.g., activation maneuvers, can exacerbate (or “activate”) rigidity, possibly increasing the risk of falls. However, the subjective interpretation and coarse classification of the standard clinical rigidity scale has prohibited the systematic, objective assessment of resting and activated leg rigidity. The pendulum test is an objective diagnostic method that we hypothesized would be sensitive enough to characterize resting and activated leg rigidity. We recorded kinematic data and electromyographic signals from rectus femoris and biceps femoris during the pendulum test in 15 individuals with PD, spanning a range of leg rigidity severity. From the recorded data of leg swing kinematics, we measured biomechanical outcomes including first swing excursion, first extension peak, number and duration of the oscillations, resting angle, relaxation index, maximum and minimum angular velocity. We examined associations between biomechanical outcomes and clinical leg rigidity score. We evaluated the effect of increasing rigidity through activation maneuvers on biomechanical outcomes. Finally, we assessed whether either biomechanical outcomes or changes in outcomes with activation were associated with a fall history. Our results suggest that the biomechanical assessment of the pendulum test can objectively quantify parkinsonian leg rigidity. We found that the presence of high rigidity during clinical exam significantly impacted biomechanical outcomes, i.e., first extension peak, number of oscillations, relaxation index, and maximum angular velocity. No differences in the effect of activation maneuvers between groups with clinically assessed low rigidity were observed, suggesting that activated rigidity may be independent of resting rigidity and should be scored as independent variables. Moreover, we found that fall history was more common among people whose rigidity was increased with a secondary task, as measured by biomechanical outcomes. We conclude that different mechanisms contributing to resting and activated rigidity may play an important yet unexplored functional role in balance impairments. The pendulum test may contribute to a better understanding of fundamental mechanisms underlying motor symptoms in PD, evaluating the efficacy of treatments, and predicting the risk of falls.

Modelling the impact of presemester testing on COVID-19 outbreaks in university campuses

ObjectivesUniversities are exploring strategies to mitigate the spread of COVID-19 prior to reopening their campuses. National guidelines do not currently recommend testing students prior to campus arrival. However, the impact of presemester testing has not been studied.DesignDynamic SARS-CoV-2 transmission models are used to explore the effects of three presemester testing interventions.InterventionsTesting of students 0, 1 and 2 times prior to campus arrival.Primary outcomesNumber of active infections and time until isolation bed capacity is reached.SettingWe set on-campus and off-campus populations to 7500 and 17 500 students, respectively. We assumed 2% prevalence of active cases at the semester start, and that one-third of infected students will be detected and isolated throughout the semester. Isolation bed capacity was set at 500. We varied disease transmission rates (R0=1.5, 2, 3, 4) to represent the effectiveness of mitigation strategies throughout the semester.ResultsWithout presemester screening, peak number of active infections ranged from 4114 under effective mitigation strategies (R0=1.5) to 10 481 under ineffective mitigation strategies (R0=4), and exhausted isolation bed capacity within 10 (R0=4) to 25 days (R0=1.5). Mandating at least one test prior to campus arrival delayed the timing and reduced the size of the peak, while delaying the time until isolation bed capacity was reached. Testing twice in conjunction with effective mitigation strategies (R0=1.5) was the only scenario that did not exhaust isolation bed capacity during the semester.ConclusionsPresemester screening is necessary to avert early and large surges of active COVID-19 infections. Therefore, we recommend testing within 1 week prior to and on campus return. While this strategy is sufficient for delaying the timing of the peak outbreak, presemester testing would need to be implemented in conjunction with effective mitigation strategies to significantly reduce outbreak size and preserve isolation bed capacity.

Evaluating the impact of stay-at-home orders on the time to reach the peak burden of Covid-19 cases and deaths: does timing matter?

Background The economic, psychological, and social impact of pandemics and social distancing measures prompt the urgent need to determine the efficacy of non-pharmaceutical interventions (NPIs), especially those considered most stringent such as stay-at-home and self-isolation mandates. This study focuses specifically on the impact of stay-at-home orders, both nationally and internationally, on the control of COVID-19. Methods We conducted an observational analysis from April to May 2020 and included both countries and US states with known stay-at-home orders. Our primary exposure was the time between the date of the first reported case of COVID-19 to an implemented stay-at-home mandate for each region. Our primary outcomes were the time from the first reported case to the highest number of daily cases and daily deaths. We conducted linear regression analyses, controlling for the case rate of the outbreak in each respective region. Results For countries and US states, a longer period of time between the first reported case and stay-at-home mandates was associated with a longer time to reach both the peak daily case and death counts. The largest effect was among regions classified as the latest 10% to implement a mandate, which in the US, predicted an extra 35.3 days (95% CI: 18.2, 52.5) to the peak number of cases, and 38.3 days (95% CI: 23.6, 53.0) to the peak number of deaths. Conclusions Our study supports the association between the timing of stay-at-home orders and the time to peak case and death counts for both countries and US states. Regions in which mandates were implemented late experienced a prolonged duration to reaching both peak daily case and death counts.