Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that was caused by a novel bunyavirus, SFTSV. The study aimed to disclose the epidemiological and clinical characteristics of SFTSV infection in China so far. An integrated clinical database comprising 1920 SFTS patients was constructed by combining first-hand clinical information collected from SFTS sentinel hospitals (n = 1159) and extracted data (n = 761) from published literature. The considered variables comprised clinical manifestations, routine laboratory tests of acute infection, hospitalization duration and disease outcome. SFTSV-IgG data from 19 119 healthy subjects were extracted from the published papers. The key clinical variables, case-fatality rate (CFR) and seroprevalence were estimated by meta-analysis. The most commonly seen clinical manifestations of SFTSV infection were fever, anorexia, myalgia, chill and lymphadenopathy. The major laboratory findings were elevated lactate dehydrogenase, aminotransferase, followed by thrombocytopenia, lymphocytopenia, elevated alanine transaminase and creatine kinase. A CFR of 12·2% was estimated, significantly higher than that obtained from national reporting data, but showing no geographical difference. In our paper, the mortality rate was about 1·9 parts per million. Older age and longer delay to hospitalization were significantly associated with fatal outcome. A pooled seroprevalence of 3·0% was obtained, which increased with age, while comparable for gender. This study represents a clinical characterization on the largest group of SFTS patients up to now. A higher than expected CFR was obtained. A wider spectrum of clinical index was suggested to be used to identify SFTSV infection, while the useful predictor for fatal outcome was found to be restricted.

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An integrated physical diffractive optical neural network (DONN) is proposed based on a standard silicon-on-insulator (SOI) substrate. This DONN has compact structure and can realize the function of machine learning with whole-passive fully-optical manners. The DONN structure is designed by the spatial domain electromagnetic propagation model, and the approximate process of the neuron value mapping is optimized well to guarantee the consistence between the pre-trained neuron value and the SOI integration implementation. This model can better ensure the manufacturability and the scale of the on-chip neural network, which can be used to guide the design and manufacturing of the real chip. The performance of our DONN is numerically demonstrated on the prototypical machine learning task of prediction of coronary heart disease from the UCI Heart Disease Dataset, and accuracy comparable to the state-of-the-art is achieved.

Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have demonstrated extraordinary potential for executing deep learning processes that involve complex calculations. In this work, an on-chip diffractive optical neural network (DONN) based on a silicon-on-insulator platform is proposed to perform machine learning tasks with high integration and low power consumption characteristics. To validate the proposed DONN, we fabricated 1-hidden-layer and 3-hidden-layer on-chip DONNs with footprints of 0.15 mm2 and 0.3 mm2 and experimentally verified their performance on the classification task of the Iris plants dataset, yielding accuracies of 86.7% and 90%, respectively. Furthermore, a 3-hidden-layer on-chip DONN is fabricated to classify the Modified National Institute of Standards and Technology handwritten digit images. The proposed passive on-chip DONN provides a potential solution for accelerating future artificial intelligence hardware with enhanced performance.

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