The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is highly contagious, and the coronavirus disease 2019 (COVID-19) pandemic caused by it has forced many countries to adopt ‘lockdown’ measures to prevent the spread of the epidemic through social isolation of citizens. Some countries proposed universal mask wearing as a protection measure of public health to strengthen national prevention efforts and to limit the wider spread of the epidemic. In order to reveal the epidemic prevention efficacy of masks, this paper systematically evaluates the experimental studies of various masks and filter materials, summarises the general characteristics of the filtration efficiency of isolation masks with particle size, and reveals the actual efficacy of masks by combining the volume distribution characteristics of human exhaled droplets with different particle sizes and the SARS-CoV-2 virus load of nasopharynx and throat swabs from patients. The existing measured data show that the filtration efficiency of all kinds of masks for large particles and extra-large droplets is close to 100%. From the perspective of filtering the total number of pathogens discharged in the environment and protecting vulnerable individuals from breathing live viruses, the mask has a higher protective effect. If considering the weighted average filtration efficiency with different particle sizes, the filtration efficiencies of the N95 mask and the ordinary mask are 99.4% and 98.5%, respectively. The mask can avoid releasing active viruses to the environment from the source of infection, thus maximising the protection of vulnerable individuals by reducing the probability of inhaling a virus. Therefore, if the whole society strictly implements the policy of publicly wearing masks, the risk of large-scale spread of the epidemic can be greatly reduced. Compared with the overall cost of social isolation, limited personal freedoms and forced suspension of economic activities, the inconvenience for citizens caused by wearing masks is perfectly acceptable.

Vertex-primitive self-complementary graphs were proved to be affine or in product action by Guralnick et al. [‘On orbital partitions and exceptionality of primitive permutation groups’, Trans. Amer. Math. Soc. 356 (2004), 4857–4872]. The product action type is known in some sense. In this paper, we provide a generic construction for the affine case and several families of new self-complementary Cayley graphs are constructed.

The association between dietary Fe intake and diabetes risk remains inconsistent. We aimed to explore the association between dietary Fe intake and type 2 diabetes mellitus (T2DM) risk in middle-aged and older adults in urban China. This study used data from the Guangzhou Nutrition and Health Study, an on-going community-based prospective cohort study. Participants were recruited from 2008 to 2013 in Guangzhou community. A total of 2696 participants aged 40–75 years without T2DM at baseline were included in data analyses, with a median of 5·6 (interquartile range 4·1–5·9) years of follow-up. T2DM was identified by self-reported diagnosis, fasting glucose ≥ 7·0 mmol/l or glycosylated Hb ≥ 6·5 %. Cox proportional hazard models were used to estimate hazard ratios (HR) and 95 % CI. We ascertained 205 incident T2DM cases during 13 476 person-years. The adjusted HR for T2DM risk in the fourth quartile of haem Fe intake was 1·92 (95 % CI 1·07, 3·46; P trend = 0·010), compared with the first quartile intake. These significant associations were found in haem Fe intake from total meat (HR 2·74; 95 % CI 1·22, 6·15; P trend = 0·011) and haem Fe intake from red meat (HR 1·86; 95 % CI 1·01, 3·44; P trend = 0·034), but not haem Fe intake from processed meat, poultry or fish/shellfish. The association between dietary intake of total Fe or non-haem Fe with T2DM risk had no significance. Our findings suggested that higher dietary intake of haem Fe (especially from red meat), but not total Fe or non-haem Fe, was associated with greater T2DM risk in middle-aged and older adults.

To evaluate a large portfolio of variable annuity (VA) contracts, many insurance companies rely on Monte Carlo simulation, which is computationally intensive. To address this computational challenge, machine learning techniques have been adopted in recent years to estimate the fair market values (FMVs) of a large number of contracts. It is shown that bootstrapped aggregation (bagging), one of the most popular machine learning algorithms, performs well in valuing VA contracts using related attributes. In this article, we highlight the presence of prediction bias of bagging and use the bias-corrected (BC) bagging approach to reduce the bias and thus improve the predictive performance. Experimental results demonstrate the effectiveness of BC bagging as compared with bagging, boosting, and model points in terms of prediction accuracy.

The clinical characteristics of patients with COVID-19 were analysed to determine the factors influencing the prognosis and virus shedding time to facilitate early detection of disease progression. Logistic regression analysis was used to explore the relationships among prognosis, clinical characteristics and laboratory indexes. The predictive value of this model was assessed with receiver operating characteristic curve analysis, calibration and internal validation. The viral shedding duration was calculated using the Kaplan–Meier method, and the prognostic factors were analysed by univariate log-rank analysis and the Cox proportional hazards model. A retrospective study was carried out with patients with COVID-19 in Tianjin, China. A total of 185 patients were included, 27 (14.59%) of whom were severely ill at the time of discharge and three (1.6%) of whom died. Our findings demonstrate that patients with an advanced age, diabetes, a low PaO2/FiO2 value and delayed treatment should be carefully monitored for disease progression to reduce the incidence of severe disease. Hypoproteinaemia and the fever duration warrant special attention. Timely interventions in symptomatic patients and a time from symptom onset to treatment <4 days can shorten the duration of viral shedding.

Dolostones are widely developed in the middle Permian rocks of East Yunnan, China, mainly in the shoal-facies Maokou Formation. The previously reported dolostone formation mechanisms cannot explain the distribution and geochemical characteristics of these dolostones, in particular their strontium, magnesium and oxygen isotope signatures. To help predict the distribution of dolostone reservoirs and reduce the exploration risk and cost, this study proposes a new model of dolomitization: open thermal convection dolomitization. In this new dolomitization model, Mg2+ in dolomitizing fluids originates mostly from seawater, with a minor component coming from deep hydrothermal fluids. Elevated heat flux (in this case due to the nearby Emei mantle plume) causes spatial temperature variations in the fluid along the circulation flow pathways, resulting in fast and pervasive dolomitization of limestone. The proposed model not only explains the characteristics and distribution of dolostones in the study area but also serves as a reference for predicting the distribution of dolostones in other areas subjected to thermal convection.

In this paper, we solve exit problems for a one-sided Markov additive process (MAP) which is exponentially killed with a bivariate killing intensity $\omega(\cdot,\cdot)$ dependent on the present level of the process and the current state of the environment. Moreover, we analyze the respective resolvents. All identities are expressed in terms of new generalizations of classical scale matrices for MAPs. We also remark on a number of applications of the obtained identities to (controlled) insurance risk processes. In particular, we show that our results can be applied to the Omega model, where bankruptcy takes place at rate $\omega(\cdot,\cdot)$ when the surplus process becomes negative. Finally, we consider Markov-modulated Brownian motion (MMBM) as a special case and present analytical and numerical results for a particular choice of piecewise intensity function $\omega(\cdot,\cdot)$ .

In this process-based study, we introduced a collocation tool with a new interface and advanced search features and examined how a class of EFL college students interacted with it. To elicit their tool consultation behaviors, a vocabulary test with collocation questions was designed. The students’ use of the tool to answer the vocabulary questions was screen-recorded for further analysis, serving as the major data source. One-on-one interviews with selected students were then conducted to clarify issues related to the study and their experience in using the tool. The findings indicated that the pattern-based tool was efficient in helping students solve collocation problems. This paper concludes with some pedagogical implications and suggestions for further research.

Ketosis is a metabolic disease of dairy cows often characterized by high concentrations of ketone bodies and fatty acids, but low milk protein and milk production. The Janus kinase 2 (JAK2)-signal transducer and activator of transcription 5 (STAT5) and the mechanistic target of rapamycin (mTOR) signaling pathways are central for the regulation of milk protein synthesis. The effect of high levels of fatty acids on these pathways and β-casein synthesis are unknown in dairy cows with clinical ketosis. Mammary gland tissue and blood samples were collected from healthy (n = 15) and clinically-ketotic (n = 15) cows. In addition, bovine mammary epithelial cells (BMEC) were treated with fatty acids, methionine (Met) or prolactin (PRL), respectively. In vivo, the serum concentration of fatty acids was greater (P > 0.05) and the percentage of milk protein (P > 0.05) was lower in cows with clinical ketosis. The JAK2-STAT5 and mTOR signaling pathways were inhibited and the abundance of β-casein was lower in mammary tissue of cows with clinical ketosis (P > 0.05). In vitro, high levels of fatty acids inhibited the JAK2-STAT5 and mTOR signaling pathways (P > 0.05) and further decreased the β-casein synthesis (P > 0.05) in BMEC. Methionine or PRL treatment, as positive regulators, activated the JAK2-STAT5 and mTOR signaling pathways to increase the β-casein synthesis. Importantly, the high concentration of fatty acids attenuated the positive effect of Met or PRL on mTOR, JAK2-STAT5 pathways and the abundance of β-casein (P > 0.05). Overall, these data indicate that the high concentrations of fatty acids that reach the mammary cells during clinical ketosis inhibit mTOR and JAK2-STAT5 signaling pathways, and further suppress β-casein synthesis.

Findings for the roles of dairy products, Ca and vitamin D on ovarian cancer risk remain controversial. We aimed to assess these associations by using an updated meta-analysis. Five electronic databases (e.g. PubMed and Embase) were searched from inception to 24 December 2019. Pooled relative risks (RR) with 95 % CI were calculated. A total of twenty-nine case–control or cohort studies were included. For comparisons of the highest v. lowest intakes, higher whole milk intake was associated with increased ovarian cancer risk (RR 1·35; 95 % CI 1·15, 1·59), whereas decreased risks were observed for higher intakes of low-fat milk (RR 0·84; 95 % CI 0·73, 0·96), dietary Ca (RR 0·71; 95 % CI 0·60, 0·84) and dietary vitamin D (RR 0·80; 95 % CI 0·67, 0·95). Additionally, for every 100 g/d increment, increased ovarian cancer risks were found for total dairy products (RR 1·03; 95 % CI 1·01, 1·04) and for whole milk (RR 1·07; 95 % CI 1·03, 1·11); however, decreased risks were found for 100 g/d increased intakes of low-fat milk (RR 0·95; 95 % CI 0·91, 0·99), cheese (RR 0·87; 95 % CI 0·76, 0·98), dietary Ca (RR 0·96; 95 % CI 0·95, 0·98), total Ca (RR 0·98; 95 % CI 0·97, 0·99), dietary vitamin D (RR 0·92; 95 % CI 0·87, 0·97) and increased levels of circulating vitamin D (RR 0·84; 95 % CI 0·72, 0·97). These results show that whole milk intake might contribute to a higher ovarian cancer risk, whereas low-fat milk, dietary Ca and dietary vitamin D might reduce the risk.

Terrorist attacks can occur anywhere. As the threat of terrorism develops, the China-Eurasia Expo held in Ürümqi, China is attracting fewer potential visitors. A nationwide survey of 2034 residents from 31 provinces and municipalities in China was conducted to examine the relation between the distance to respondents’ city of residence from Ürümqi and their levels of concern for safety and security concerning the Expo. The two were found to be positively related: the closer the respondents lived to Ürümqi, the less concerned they were with the safety and security of the Expo. This is consistent with the “psychological typhoon eye” effect, which states that people living closer to the center of an unfortunate event (whether natural or man-made hazards) are less concerned with the event’s negative consequences. This effect appears to hold for terrorism. There are implications of this finding for international counter-terrorism practice, tourism, and research.