Palygorskite-bearing claystones and mudstones were deposited in a salt lake in the middle and lower parts of the Neogene Baiyanghe Formation in the Yangtaiwatan Basin, China. The petrological, mineralogical and geochemical characteristics of the sediments were investigated to determine the factors controlling palygorskite formation. The palygorskite claystones and mudstones have distinctly varying mineral compositions. The claystones are composed of detrital minerals, palygorskite and illite, whereas the mudstones consist mainly of mixed-layer illite/smectite and illite. The palygorskite crystals were intact with sharp edges and interwoven with other minerals, indicating an authigenic origin. The chemical characteristics indicate that the palygorskite claystones in the middle part of the Baiyanghe Formation were deposited in a salt lake environment in an arid and hot climate. As the salinity of the lake gradually increased, the detrital minerals such as quartz, feldspar, dolomite and detrital clay minerals dissolved in the alkaline medium, thus providing Si4+, Mg2+ and Al3+ for the crystallization of palygorskite. The palygorskite coexists with certain amounts of detrital quartz and feldspar with limited roundness and sorting, indicating that the shallow lake of the basin under an oxidation environment may represent a favourable environment for the crystallization of palygorskite.

In December 2019, the first confirmed case of pneumonia caused by a novel coronavirus was reported. Coronavirus disease 2019 (COVID-19) is currently spreading around the world. The relationships among the pandemic and its associated travel restrictions, social distancing measures, contact tracing, mask-wearing habits and medical consultation efficiency have not yet been extensively assessed. Based on the epidemic data reported by the Health Commission of Wenzhou, we analysed the developmental characteristics of the epidemic and modified the Susceptible-Exposed-Infectious-Removed (SEIR) model in three discrete ways. (1) According to the implemented preventive measures, the epidemic was divided into three stages: initial, outbreak and controlled. (2) We added many factors, such as health protections, travel restrictions and social distancing, close-contact tracing and the time from symptom onset to hospitalisation (TSOH), to the model. (3) Exposed and infected people were subdivided into isolated and free-moving populations. For the parameter estimation of the model, the average TSOH and daily cured cases, deaths and imported cases can be obtained through individual data from epidemiological investigations. The changes in daily contacts are simulated using the intracity travel intensity (ICTI) from the Baidu Migration Big Data platform. The optimal values of the remaining parameters are calculated by the grid search method. With this model, we calculated the sensitivity of the control measures with regard to the prevention of the spread of the epidemic by simulating the number of infected people in various hypothetical situations. Simultaneously, through a simulation of a second epidemic, the challenges from the rebound of the epidemic were analysed, and prevention and control recommendations were made. The results show that the modified SEIR model can effectively simulate the spread of COVID-19 in Wenzhou. The policy of the lockdown of Wuhan, the launch of the first-level Public Health Emergency Preparedness measures on 23 January 2020 and the implementation of resident travel control measures on 31 January 2020 were crucial to COVID-19 control.

The aim of the present study was to explore the influence of tea consumption on diabetes mellitus in the Chinese population. This multi-centre, cross-sectional study was conducted in eight sites from south, east, north, west and middle regions in China by enrolling 12 017 subjects aged 20–70 years. Socio-demographic and general information was collected by a standardised questionnaire. A standard procedure was used to measure anthropometric characteristics and to obtain blood samples. The diagnosis of diabetes was determined using a standard 75-g oral glucose tolerance test. In the final analysis, 10 825 participants were included and multiple logistic models and interaction effect analysis were applied for assessing the association between tea drinking with diabetes. Compared with non-tea drinkers, the multivariable-adjusted OR for newly diagnosed diabetes were 0·80 (95 % CI 0·67, 0·97), 0·88 (95 % CI 0·71, 1·09) and 0·86 (95 % CI 0·67, 1·11) for daily tea drinkers, occasional tea drinkers and seldom tea drinkers, respectively. Furthermore, drinking tea daily was related to decreased risk of diabetes in females by 32 %, elderly (>45 years) by 24 % and obese (BMI > 30 kg/m2) by 34 %. Moreover, drinking dark tea was associated with reduced risk of diabetes by 45 % (OR 0·55; 95 % CI 0·42, 0·72; P < 0·01). The results imply that drinking tea daily was negatively related to risk of diabetes in female, elderly and obese people. In addition, drinking dark tea was associated with decreased risk of type 2 diabetes mellitus.

Reynolds, McCauley, Tsacoumis, and the Jeanneret Symposium Participants (2018) present outcomes of the 2-day Jeanneret Symposium describing the state of the science and practice related to the assessment and development of senior leaders. They call for cross-disciplinary and/or organization-level research that examines “how organizations mature their assessment and development practices toward an integrated system embedded in a development culture” (p. 646). The purpose of this commentary is to answer this call and add to their work by proposing a systems thinking framework of organizational development where a corporate university plays a pivotal role.

The performance of Global Navigation Satellite System (GNSS) and Micro-Electro-Mechanical System (MEMS)-based Inertial Navigation System (INS) integrated navigation is reduced during GNSS outages. To bridge the period during GNSS outages, a novel hybrid intelligent algorithm incorporating a Discrete Grey Predictor (DGP) and a Multilayer Perceptron (MLP) neural network (DGP-MLP) is proposed. The DGP-MLP is used to provide a pseudo-GNSS position to correct the INS errors during GNSS outages; the DGP uses the GNSS position information of the latest few moments to predict the position of future moments; in the process of DGP-MLP, the MLP is used to modify the prediction errors of DGP, and the MLP is improved by adding momentum terms and adaptively adjusting the learning rate and momentum factor. To evaluate the effectiveness of the proposed methodology, four GNSS outages in different cases over a real field test data were employed. The experimental results demonstrate that the proposed methodology can significantly improve positioning accuracy during GNSS outages.

Global Navigation Satellite Systems (GNSSs) are easily influenced by the external environment. Signals may be lost or become abnormal thereby causing outliers. The filter gain of the standard Kalman filter of a loosely coupled GNSS/inertial navigation system cannot change with the outliers of the GNSS, causing large deviations in the filtering results. In this paper, a method based on a χ2-test and a dynamically adjusting filter gain method are proposed to detect and separately to suppress GNSS observation outliers in integrated navigation. An indicator of an innovation vector is constructed, and a χ2-test is performed for this indicator. If it fails the test, the corresponding observation value is considered as an outlier. A scale factor is constructed according to this outlier, which is then used to lower the filter gain dynamically to decrease the influence of outliers. The simulation results demonstrate that the observation outlier processing method does not affect the normal values under normal circumstances; it can also discriminate between single and continuous outliers without errors or omissions. The impact time of outliers is greatly reduced, and the system performance is improved by more than 90%. Experimental results indicate that the proposed methods are effective in suppressing GNSS observation outliers in integrated navigation.

Elevated levels of blood interleukin-1β (IL-1β), interleukin-6 (IL-6) and tumour necrosis factor–α (TNF–α) increase insulin resistance and result in inflammation. It is not clear whether elevated blood level of acetoacetate (ACAC) and decreased blood level of glucose, which are the predominant characteristics of clinical biochemistry in ketotic dairy cows, increase proinflammatory cytokines and subsequent inflammation. The objective of this study was to test the hypothesis that ACAC and glucose activate the NF-κB signalling pathway to regulate cytokines expression in bovine hepatocytes. Bovine hepatocytes were cultured with ACAC (0–4·8 mm) and glucose (0–5·55 mm) with or without NF-κB inhibitor PDTC for 24 h. The secretion and mRNA levels of cytokines were determined by enzyme-linked immunosorbent assay (ELISA) and real-time fluorescence quantitative polymerase chain reaction (qRT-PCR). The NF-κB signalling pathway activation was evaluated by western blotting. Results showed that the secretion and expression of IL-1β, IL-6 and TNF-α increased in an ACAC dose-dependent manner. Additionally, there was an increase in the secretion and mRNA expression of these three cytokines in glucose treatment group, which increased significantly when the glucose concentrations exceed 3·33 mm. Furthermore, both ACAC and glucose upregulated NF-κB p65 protein expression and IκBα phosphorylation levels. However, these effects were reduced by PDTC. These results demonstrate that elevated levels of ACAC and glucose increase the synthesis and expression of proinflammatory factors by activating NF-κB signalling pathway in hepatocytes, which may contribute to inflammation injury in ketotic dairy cows.