In a Nicaraguan population-based cohort, SARS-CoV-2 seroprevalence reached 28% in the first 6 months of the country's epidemic and reached 35% 6 months later. Immune waning was uncommon. Individuals with a seropositive household member were over three times as likely to be seropositive themselves, suggesting the importance of household transmission.

Accurate prediction of laminar-turbulent transition is a critical element of computational fluid dynamics simulations for aerodynamic design across multiple flow regimes. Traditional methods of transition prediction cannot be easily extended to flow configurations where the transition process depends on a large set of parameters. In comparison, neural network methods allow higher dimensional input features to be considered without compromising the efficiency and accuracy of the traditional data-driven models. Neural network methods proposed earlier follow a cumbersome methodology of predicting instability growth rates over a broad range of frequencies, which are then processed to obtain the N-factor envelope, and then, the transition location based on the correlating N-factor. This paper presents an end-to-end transition model based on a recurrent neural network, which sequentially processes the mean boundary-layer profiles along the surface of the aerodynamic body to directly predict the N-factor envelope and the transition locations over a two-dimensional airfoil. The proposed transition model has been developed and assessed using a large database of 53 airfoils over a wide range of chord Reynolds numbers and angles of attack. The large universe of airfoils encountered in various applications causes additional difficulties. As such, we provide further insights on selecting training datasets from large amounts of available data. Although the proposed model has been analyzed for two-dimensional boundary layers in this paper, it can be easily generalized to other flows due to embedded feature extraction capability of convolutional neural network in the model.

We prove and generalise a conjecture in [MPP4] about the asymptotics of $\frac{1}{\sqrt{n!}} f^{\lambda/\mu}$, where $f^{\lambda/\mu}$ is the number of standard Young tableaux of skew shape $\lambda/\mu$ which have stable limit shape under the $1/\sqrt{n}$ scaling. The proof is based on the variational principle on the partition function of certain weighted lozenge tilings.

Bloodstream fungal infections have a high mortality rate. There is little data about the long-term mortality rate of fungaemia.This study aimed to explore the mortality of fungaemia and the influencing factors associated with death. In total, 204 intensive care unit (ICU) patients with fungaemia from Multi-parameter Intelligent Monitoring in Intensive Care-III (MIMIC-III) Database were studied. Age, gender, major underlying diseases, data about vital signs and blood test results were analysed to identify the predictors of the mortality and prognosis of fungaemia in ICU patients. Cox regression models were constructed, together with Kaplan−Meier survival curves. The 30-day, 1-year, 2-year, 3-year and 4-year mortality rates were 41.2%, 62.3%, 68.1%, 72.5% and 75%, respectively. Age (P ＜ 0.001, OR = 1.530; P ＜ 0.001, OR = 1.485)，serum bilirubin (P = 0.016, OR = 2.125；P = 0.001, OR = 1.748) and international normalised ratio (INR) (P = 0.001, OR = 2.642; P ＜ 0.001 OR = 2.065) were predictors of both the 30-day and 4-year mortality rates. Renal failure (P = 0.009, OR = 1.643) performed good in prediction of the 4-year mortality. The mortality of fungaemia is high. Age，the serum bilirubin and INR are good predictors of the 30-day and 4-year mortality rates of fungaemia. Renal failure has good performance in predicting the long-term mortality.

Vaccine hesitancy remains a serious global threat to achieve herd immunity, and this study aimed to assess the magnitude and associated factors of coronavirus disease-19 (COVID-19) vaccine hesitancy among healthcare workers (HCWs) in Amhara regional referral hospitals. A web-based anonymised survey was conducted among 440 HCWs in the Amhara region referral hospitals. The questionnaire was designed using Google Forms and distributed using telegram and e-mail from 15 May to 10 June 2021 to the randomly selected participants in each hospital. The data were analysed with Stata 14.0 and described using frequency tables. A multivariable binary logistic regression model was fitted and model fitness was checked with the Hosmer–Lemeshow goodness of fit test. Out of 440 participants, 418 were willing to participate in the study and the mean age was about 30 years. Overall, 45.9% (n = 192) of participants reported vaccine hesitancy. After applying multivariate analysis, age ≤25 years (adjusted odds ratio (aOR) = 5.6); do not wear a mask (aOR = 2.4); not compliance with physical distancing (aOR = 3.6); unclear information by public health authorities (aOR = 2.5); low risk of getting COVID-19 infection (aOR = 2.8); and not sure about the tolerability of the vaccine (aOR = 3.76) were associated with COVID-19 vaccine hesitancy. A considerable proportion of HCWs were hesitant towards COVID-19 vaccine, and this can be tackled with the provision of clear information about the vaccine.