Comparison of foodborne disease outbreaks data in China and the United States from 2011 to 2017 and its inspiration for the construction of monitoring system in China

The history of the establishment of foodborne disease outbreak monitoring system in the United States was introduced and the surveillance data of foodborne disease outbreaks in the United States from 2011 to 2017 were analyzed and compared with that in China. It was found that there were obvious differences in the characteristics of surveillance data of foodborne disease outbreaks between China and the United States in the same period, and microbial pathogenic factors were the main cause of foodborne disease outbreaks. Facing the challenges of global trade integration and post epidemic era of COVID-19,China's foodborne disease outbreak monitoring system should accelerate the use of new technologies to improve the ability of identification and early warning, and foodborne disease outbreak data results should further play the technical support role in the formulation of relevant food safety management measures in China.

CD4+ T cell-induced inflammatory killing controls immune evasive tumours (preprint)

Current clinically applied cancer immunotherapies largely focus on the ability of CD8+ cytolytic T-cells to directly recognise and kill tumour cells1–3. These strategies are limited by the emergence of MHC-I-deficient or IFN-unresponsive tumour cells and the development of an immunosuppressive tumour microenvironment4–6. CD4+ effector T-cells can contribute to tumour immune defence independent of CD8+ T-cells. However, the potential and the mechanisms of CD4+ T-cell-mediated anti-tumour immunity are incompletely understood7–12. Here, we show how an indirect CD4+ T-cell-mediated mode of action, that is fundamentally different from CD8+ T-cells, enables the eradication of tumours that would otherwise escape direct T-cell targeting. CD4+ effector T-cells preferentially cluster at tumour invasive margins where they engage in antigen-specific interactions with MHC-II+CD11c+ cells, while CD8+ T-cells briskly infiltrate tumour tissues. CD4+ T-cells and innate immune stimulation reprogram the tumour-associated inflammatory monocyte network towards IFN-activated antigen-presenting and tumouricidal effector phenotypes. This results in an amplification loop driving the release of T-cell-derived IFNγ and myeloid cell-derived nitric oxide which cooperatively induce apoptotic death of MHC-deficient and IFN-unresponsive tumour cells that escape cytolytic CD8+ T-cell therapy. Exploiting the ability of CD4+ T-cells to orchestrate indirect inflammatory killing of tumour cells complements the direct cytolytic activity of T-cells to advance cancer immunotherapies.

Clustering Spatially Correlated Functional Data With Multiple Scalar Covariates.

We propose a probabilistic model for clustering spatially correlated functional data with multiple scalar covariates. The motivating application is to partition the 29 provinces of the Chinese mainland into a few groups characterized by the epidemic severity of COVID-19, while the spatial dependence and effects of risk factors are considered. It can be regarded as an extension of mixture models, which allows different subsets of covariates to influence the component weights and the component densities by modeling the parameters of the mixture as functions of the covariates. In this way, provinces with similar spatial factors are a priori more likely to be clustered together. Posterior predictive inference in this model formalizes the desired prediction. Further, the identifiability of the proposed model is analyzed, and sufficient conditions to guarantee ``generic'' identifiability are provided. An L1-penalized estimator is developed to assist variable selection and robust estimation when the number of explanatory covariates is large. An efficient expectation-minimization algorithm is presented for parameter estimation. Simulation studies and real-data examples are presented to investigate the empirical performance of the proposed method. Finally, it is worth noting that the proposed model has a wide range of practical applications, e.g., health management, environmental science, ecological studies, and so on.

COVID-19 and early-stage lung cancer both featuring ground-glass opacities: a propensity score-matched study.

BACKGROUND: Radiological manifestations of coronavirus disease 2019 (COVID-19) featured ground-glass opacities (GGOs), especially in the early stage, which might create confusion in differential diagnosis with early lung cancer. We aimed to specify the radiological characteristics of COVID-19 and early lung cancer and to unveil the discrepancy between them. METHODS: One hundred and fifty-seven COVID-19 patients and 374 early lung cancer patients from four hospitals in China were retrospectively enrolled. Epidemiological, clinical, radiological, and pathological characteristics were compared between the two groups using propensity score-matched (PSM) analysis. RESULTS: COVID-19 patients had more distinct symptoms, tended to be younger (P<0.0001), male (P<0.0001), and had a higher body mass index (P=0.014). After 1:1 PSM, 121 matched pairs were identified. Regarding radiological characteristics, patients with a single lesion accounted for 17% in COVID-19 and 89% in lung cancer (P<0.0001). Most lesions were peripherally found in both groups. Lesions in COVID-19 involved more lobes (median 3.5 vs. 1; P<0.0001) and segments (median 6 vs. 1; P<0.0001) and tended to have multiple types (67%) with patchy form (54%). Early lung cancer was more likely to have a single type (92%) with oval form (66%). Also, COVID-19 and early lung cancer either had some distinctive features on computed tomography (CT) images. CONCLUSIONS: Both COVID-19 and early lung cancers showed GGOs, with similar but independent features. The imaging characteristics should be fully understood and combined with epidemiological history, pathogen detection, laboratory tests, short-term CT reexamination, and pathological results to aid differential diagnosis.

Post Traumatic Stress Disorder and Death Anxiety Symptoms During the Coronavirus Epidemic in 2019: A Cross-Sectional Study Based on the Affected Population (preprint)

Background Mental health is an important aspect of the management public health emergencies.While extensive research is being conducted on various aspects of the COVID-19 epidemic, one of the factors overlooked is death anxiety. Methods A cross-sectional study based on the results of an online survey. The survey was conducted one month after the outbreak (February 18–29, 2020) and repeated at the time of resuming activity (April 8–14, 2020). The 15-item Death Anxiety Scale (T-DAS) was used to assess the degree of death anxiety, and the revised 23-item Stanford Acute Stress Response Questionnaire (SASRQ) assessed PTSD symptom clusters. Through convenient sampling, a total of 7678 cases were collected. Results: Between the first and second surveys, the number of individuals with high death anxiety rose from 48.1–53.2%, while the incidence of PTSD increased from 7–10.4%. PTSD was found to be significantly associated with living community contact history, poor health status of participants, history of life-threatening experiences, high death anxiety level, and non-medical occupation. Compared with other occupations, medical staff suffer more lasting death anxiety during the COVID-19 epidemic. Conclusions: During the COVID-19 epidemic, adverse psychological symptoms were prevalent among the general population in China.High death anxiety also was an important factor affecting PTSD.Therefore, means to address death anxiety must be included in the plan for the management of psychological effects of public health emergency and high-risk groups such as medical personnel should receive targeted intervention.

Cross-Modal Alignment with Mixture Experts Neural Network for Intral-City Retail Recommendation (preprint)

In this paper, we introduce Cross-modal Alignment with mixture experts Neural Network (CameNN) recommendation model for intral-city retail industry, which aims to provide fresh foods and groceries retailing within 5 hours delivery service arising for the outbreak of Coronavirus disease (COVID-19) pandemic around the world. We propose CameNN, which is a multi-task model with three tasks including Image to Text Alignment (ITA) task, Text to Image Alignment (TIA) task and CVR prediction task. We use pre-trained BERT to generate the text embedding and pre-trained InceptionV4 to generate image patch embedding (each image is split into small patches with the same pixels and treat each patch as an image token). Softmax gating networks follow to learn the weight of each transformer expert output and choose only a subset of experts conditioned on the input. Then transformer encoder is applied as the share-bottom layer to learn all input features' shared interaction. Next, mixture of transformer experts (MoE) layer is implemented to model different aspects of tasks. At top of the MoE layer, we deploy a transformer layer for each task as task tower to learn task-specific information. On the real word intra-city dataset, experiments demonstrate CameNN outperform baselines and achieve significant improvements on the image and text representation. In practice, we applied CameNN on CVR prediction in our intra-city recommender system which is one of the leading intra-city platforms operated in China.