Detection of SARS-CoV-2-Specific Humoral and Cellular Immunity in COVID-19 Convalescent IndividualsHighlights d SARS-CoV-2-specific antibodies are detected in COVID-19 convalescent subjects d Most COVID-19 convalescent individuals have detectable neutralizing antibodies d Cellular immune responses to SARS-CoV-2 are found in COVID-19 convalescent subjects d Neutralization antibody titers correlate with the numbers of virus-specific T cells.

What are the novel findings of this work?In this study, we developed and validated an artificial intelligence system, the Prenatal ultrasound diagnosis Artificial Intelligence Conduct System (PAICS), to detect nine specific intracranial-malformation patterns in standard sonographic reference planes of the fetal central nervous system (CNS). The PAICS achieved excellent performance on both internal and external validation, with accuracy comparable to that of expert sonologists, while requiring significantly less time. What are the clinical implications of this work?The PAICS is a real-time artificial intelligence-aided image recognition system capable of detecting fetal intracranial malformations. This fast, accurate algorithm has the potential to be an effective and efficient tool in screening for congenital CNS malformations. It should be particularly useful in community hospitals, which often lack imaging expertise.

The WHO has declared SARS-CoV-2 outbreak a public health emergency of international concern. However, to date, there was hardly any study in characterizing the immune responses, especially adaptive immune responses to SARS-CoV-2 infection. In this study, we collected blood from COVID-19 patients who have recently become virus-free and therefore were discharged, and analyzed their SARS-CoV-2-specific antibody and T cell responses. We observed SARS-CoV-2-specific humoral and cellular immunity in the patients. Both were detected in newly discharged patients, suggesting both participate in immune-mediated protection to viral infection. However, follow-up patients (2 weeks post discharge) exhibited high titers of IgG antibodies, but with low levels of virus-specific T cells, suggesting that they may enter a quiescent state. Our work has thus provided a basis for further analysis of protective immunity to SARS-CoV-2, and understanding the pathogenesis of COVID-19, especially in the severe cases. It has also implications in designing an effective vaccine to protect and treat SARS-CoV-2 infection.

Wheel and rail wear seriously affects the safety and reliability of train operations. In this study single-carriage and double-carriage models considering the connecting unit of a high-speed train are developed to investigate the normal forces, lateral forces, and lateral displacements of wheelsets. Based on the results from these models, the Archard wear model is employed to predict the wheel wear. In addition, based on the daily measured data, a nonlinear autoregulatory (NAR) model and a wavelet neural network (WNN) model are developed to predict the wheel wear over a longer time period. The simulation results show that, compared with the single-carriage model, the normal forces, lateral forces, and lateral displacements of the wheelsets close to the connecting unit in the double-carriage model increase to a certain extent dependent on the speed. The wheel wear predictions show that the wheel wear on the wheelsets near the connecting unit is slightly larger than on the wheelsets far from the connecting unit. Based on the mean square error, the NAR model has somewhat better performance in the wheel wear prediction than the WNN model. The research results represent an important contribution to the maintenance and safe operation of high-speed trains.