SARS-CoV-2 rapidly spreads among humans via social networks, with social mixing and network characteristics potentially facilitating transmission. However, limited data on topological structural features has hindered in-depth studies. Existing research is based on snapshot analyses, preventing temporal investigations of network changes. Comparing network characteristics over time offers additional insights into transmission dynamics. We examined confirmed COVID-19 patients from an eastern Chinese province, analyzing social mixing and network characteristics using transmission network topology before and after widespread interventions. Between the two time periods, the percentage of singleton networks increased from 38.9$ \% $ to 62.8$ \% $$ (p<0.001) $; the average shortest path length decreased from 1.53 to 1.14 $ (p<0.001) $; the average betweenness reduced from 0.65 to 0.11$ (p<0.001) $; the average cluster size dropped from 4.05 to 2.72 $ (p=0.004) $; and the out-degree had a slight but nonsignificant decline from 0.75 to 0.63 $ (p=0.099). $ Results show that nonpharmaceutical interventions effectively disrupted transmission networks, preventing further disease spread. Additionally, we found that the networks’ dynamic structure provided more information than solely examining infection curves after applying descriptive and agent-based modeling approaches. In summary, we investigated social mixing and network characteristics of COVID-19 patients during different pandemic stages, revealing transmission network heterogeneities.

Coronavirus disease 2019 (COVID-19) asymptomatic cases are hard to identify, impeding transmissibility estimation. The value of COVID-19 transmissibility is worth further elucidation for key assumptions in further modelling studies. Through a population-based surveillance network, we collected data on 1342 confirmed cases with a 90-days follow-up for all asymptomatic cases. An age-stratified compartmental model containing contact information was built to estimate the transmissibility of symptomatic and asymptomatic COVID-19 cases. The difference in transmissibility of a symptomatic and asymptomatic case depended on age and was most distinct for the middle-age groups. The asymptomatic cases had a 66.7% lower transmissibility rate than symptomatic cases, and 74.1% (95% CI 65.9–80.7) of all asymptomatic cases were missed in detection. The average proportion of asymptomatic cases was 28.2% (95% CI 23.0–34.6). Simulation demonstrated that the burden of asymptomatic transmission increased as the epidemic continued and could potentially dominate total transmission. The transmissibility of asymptomatic COVID-19 cases is high and asymptomatic COVID-19 cases play a significant role in outbreaks.

Nutritional Risk Screening index is a standard tool to assess nutritional risk, but epidemiological data are scarce on controlling nutritional status (CONUT) as a prognostic marker in acute haemorrhagic stroke (AHS). We aimed to explore whether the CONUT may predict a 3-month functional outcome in AHS. In total, 349 Chinese patients with incident AHS were consecutively recruited, and their malnutrition risks were determined using a high CONUT score of ≥ 2. The cohort patients were divided into high-CONUT (≥ 2) and low-CONUT (< 2) groups, and primary outcomes were a poor functional prognosis defined as the modified Rankin Scale (mRS) score of ≥ 3 at post-discharge for 3 months. Odds ratios (OR) with 95 % confidence intervals (CI) for the poor functional prognosis at post-discharge were estimated by using a logistic analysis with additional adjustments for unbalanced variables between the high-CONUT and low-CONUT groups. A total of 328 patients (60·38 ± 12·83 years; 66·77 % male) completed the mRS assessment at post-discharge for 3 months, with 172 patients at malnutrition risk at admission and 104 patients with a poor prognosis. The levels of total cholesterol and total lymphocyte counts were significantly lower in high-CONUT patients than low-CONUT patients (P = 0·012 and < 0·001, respectively). At 3-month post discharge, there was a greater risk for the poor outcome in the high-CONUT compared with the low-CONUT patients at admission (OR: 2·32, 95 % CI: 1·28, 4·17). High-CONUT scores independently predict a 3-month poor prognosis in AHS, which helps to identify those who need additional nutritional managements.

Microtubule-severing protein (MTSP) is critical for the survival of both mitotic and postmitotic cells. However, the study of MTSP during meiosis of mammalian oocytes has not been reported. We found that spastin, a member of the MTSP family, was highly expressed in oocytes and aggregated in spindle microtubules. After knocking down spastin by specific siRNA, the spindle microtubule density of meiotic oocytes decreased significantly. When the oocytes were cultured in vitro, the oocytes lacking spastin showed an obvious maturation disorder. Considering the microtubule-severing activity of spastin, we speculate that spastin on spindles may increase the number of microtubule broken ends by severing the microtubules, therefore playing a nucleating role, promoting spindle assembly and ensuring normal meiosis. In addition, we found the colocalization and interaction of collapsin response mediator protein 5 (CRMP5) and spastin in oocytes. CRMP5 can provide structural support and promote microtubule aggregation, creating transportation routes, and can interact with spastin in the microtubule activity of nerve cells (30). Knocking down CRMP5 may lead to spindle abnormalities and developmental disorders in oocytes. Overexpression of spastin may reverse the abnormal phenotype caused by the deletion of CRMP5. In summary, our data support a model in which the interaction between spastin and CRMP5 promotes the assembly of spindle microtubules in oocytes by controlling microtubule dynamics, therefore ensuring normal meiosis.

High-voltage power cables are important channels for power transmission systems. Their special geographical environment and harsh natural environment can lead to many different faults. At present, such special operations in dangerous and harsh environments are performed manually, which not only has high labor intensity and low work efficiency but also has great personal safety risks. In order to solve such difficult problems, this paper studies the power maintenance robot for insulator string replacement, spacer replacement, damper and drainage plate maintenance; the basic configuration and the operation motion planning have been proposed; and the virtual prototype of the inspection maintenance robots has been developed, and then the mechanical structure of the robots has been optimized by the robot kinematics modeling and analyzed the working space based on the Monte Carlo method. The system platform, operation function, structural characteristics and related key technologies involved in the robot system development were systematically summarized; the deep integration point for the robot technology with big data, cloud computing, artificial intelligence, and ubiquitous power Internet-of-Things technologies was also discussed. Finally, the physical prototype of the insulator replacement, drainage plate tightening, and damper replacement operation robot has been developed; several experimental tests on a 220 V live line have been conducted so as to verify the robot engineering practicality; and the main development and future research direction have also been pointed out at last.

Background: Although knowledge of established risk factors for Alzheimer's disease (AD) can logically contribute to the search for predictors of the progression of cognitive impairment, it has not yet been firmly established where in the cognitive impairment process these risk factors exert their effects and how to predict quantitatively for the progression of mild cognitive impairments (MCI) to AD. This study aimed to determine whether known risk factors increased the risk of progression from MCI to AD and to make prediction based on transition probabilities.

Methods: Based on ten examinations of 600 community-dwelling MCI residents and cognitive assessments to classify individuals into MCI, global impairment, and AD, a multi-state Markov Cox's regression model was used and the hazard ratios with their confidence intervals and transition probabilities were estimated.

Results: Multivariate analysis showed that gender, age, and hypertension were statistically significant predictors of transition from MCI to global impairment; age, education, and reading statistically influenced transition from global impairment to MCI; gender, age, hypertension, diabetes, and apolipoprotein E geneε4 status were statistically associated with transition from global impairment to AD. Subjects at MCI were more likely (67%) to remain in that cognitive state at the next cognitive assessment than to transition to cognitive deterioration. For global impairment, probability of remaining in the same state was only 18% and that of forward transition was three times more likely than that of backward transition.

Conclusions: Known risk factors influenced differently for different transitions. Transition from global impairment was more likely to worsen to severe cognitive deterioration than transition from MCI.