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Background: Due to more mosquito habitats and the lack of basic mosquito control facilities, construction sites are more likely to have secondary cases after case importation, which may increase the number of cases entering the community and the chance of community transmission. For example, the outbreak in Zhanjiang Prefecture, China, 2018, started at a construction site, then occurred in the surrounding communities. This study aims to investigate how to effectively reduce the dengue transmission risk related to construction sites and the neighboring communities.Methods: The study was based on an outbreak of dengue fever (DF) occurred in Zhanjiang Prefecture, Guangdong Province in 2018. We simulated the transmission of dengue virus between human and mosquitoes at the construction site and in the neighboring community separately, as well between them by assuming human movement between the construction site and the neighboring community. Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model was used for the human, while SEI model was used for the mosquitoes. We obtained the transmissibility parameters by fitting the model to observed number of locally acquired cases before local interventions started and the coefficient of determination (R2) was used to quantify the significance of the fit. We then evaluated the effectiveness of different intervention scenarios targeting at reducing the transmissibility between different human and mosquito subpopulations at different locations (i.e. construction sites and community) quantified by the total attack rate (TAR) and duration of the outbreak (DO).Results: A total of 467 DF cases were reported in Zhanjiang Prefecture in 2018, among which 102 were located at a construction site and 131 were located in the surrounding community. The values of R2 were 0.829 (P < 0.001) and 0.878 (P < 0.001) for the construction site and the community, respectively. The modelling outcome indicated that without interventions, the number of cases on the construction site would reach to 156, yielding a total attack rate (TAR) of 31.25% (95% confidence interval [CI]: 27.18%- 35.31%). In the community, the number of the cases would be much more than the reported data and up to 10796, yielding a TAR of 21.59% (95%CI: 21.23%- 21.95%). When the transmission route from mosquitoes to people is cut off in the community, the number of cases in the community would decrease to a minimum of 33 compared with other situations we simulated, yielding a TAR of 0.068% (95%CI: 0.05%- 0.09%) and a duration of outbreak (DO) of 60 days. When the transmission route from infectious mosquitoes in the community and the construction site to susceptible people on the construction site is cut off at the same time, the number of cases in the construction site would drop to a minimum of 74, yielding a TAR of 14.88% (95%CI: 11.76%- 18.00%) and a DO of 66 days.Conclusions: Without intervention, DF could spread rapidly in the densely populated communities around the construction site. To control the outbreak effectively for both the construction site and the community, interventions need to be taken to reduce the transmission within the community and from the community to the construction site. Only controlling the transmission within the construction site could not reduce the number of cases on the construction site, and controlling the transmission route within the construction site or between the construction site and the community could not lead to a reduction in the number of cases in the community.

Disease surveillance data are important for monitoring disease burden and occurrence, and for informing a wide range of efforts to improve population health. Surveillance for infectious diseases may be conducted passively, relying on reports from healthcare facilities, or actively, involving surveys of the population at risk. Passive surveillance typically provides wide spatial coverage, but is subject to biases arising from differences in care-seeking behavior, diagnostic practices, and under-reporting. Active surveillance minimizes these biases, but is typically constrained to small areas and subpopulations due to resource limitations. Methods based on linkage of individual records between passive and active surveillance datasets provide a means to estimate and correct for the biases of each system, leveraging the size and coverage of passive surveillance and the quality of data in active surveillance. We develop a spatial Bayesian hierarchical model for bias-correcting data from both systems to yield an improved estimate of disease measures after adjusting for under-ascertainment. We apply the framework to data from a passive and an active surveillance system for pulmonary tuberculosis (PTB) in Sichuan, China, and estimate the average sensitivity of the active surveillance system at 70% (95% credible interval: 62%, 78%), and the passive system at 30% (95% CI: 24%, 35%). Passive surveillance sensitivity exhibited considerable spatial variability, and was positively associated with a site’s gross domestic product per capita. Bias-corrected estimates of county-level PTB prevalence in the province in 2010 identified regions in the southeast with the highest PTB burden, yielding different geographic priorities than previous reports.

Global food security is a major driver of population health, and food system collapse may have complex and long-lasting effects on health outcomes. We examined the effect of prenatal exposure to the Great Chinese Famine (1958-1962)-the largest famine in human history-on pulmonary tuberculosis (PTB) across consecutive generations in a major center of ongoing transmission in China. We analyzed >1 million PTB cases diagnosed between 2005 and 2018 in Sichuan Province using age-period-cohort analysis and mixed-effects metaregression to estimate the effect of the famine on PTB risk in the directly affected birth cohort (F1) and their likely offspring (F2). The analysis was repeated on certain sexually transmitted and blood-borne infections (STBBI) to explore potential mechanisms of the intergenerational effects. A substantial burden of active PTB in the exposed F1 cohort and their offspring was attributable to the Great Chinese Famine, with more than 12,000 famine-attributable active PTB cases (>1.23% of all cases reported between 2005 and 2018). An interquartile range increase in famine intensity resulted in a 6.53% (95% confidence interval [CI]: 1.19-12.14%) increase in the ratio of observed to expected incidence rate (incidence rate ratio, IRR) in the absence of famine in F1, and an 8.32% (95% CI: 0.59-16.6%) increase in F2 IRR. Increased risk of STBBI was also observed in F2. Prenatal and early-life exposure to malnutrition may increase the risk of active PTB in the exposed generation and their offspring, with the intergenerational effect potentially due to both within-household transmission and increases in host susceptibility.

Cheng-hao Qu,1,2 Tong Li,1,2 Zhan-peng Tang,1,2 Xi-Rui Zhu,1,2 Jing-yi Han,1,2 Hui Tian2 1Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, People’s Republic of China; 2Department of Thoracic Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, People’s Republic of ChinaCorrespondence: Hui TianDepartment of Thoracic Surgery, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, People’s Republic of ChinaTel +86 18560080016Email tianhuiql@126.comPurpose: Emerging studies have revealed that platelets are involved in tumor metastasis in lung adenocarcinoma (ADC). The solid pathological subtype of lung ADC is associated with metastasis, recurrence, and poor prognosis. However, there is no study exploring the relationship between platelets and different lung pathological subtypes.Patients and Methods: The association between platelet counts and lymph node metastasis was analyzed in 852 patients with lung ADC who underwent surgery and lymph node dissection. Multivariate logistic analysis was conducted to identify the risk factors of lymph node metastasis. Then, lymph node metastasis and other factors were analyzed to determine their correlation with platelet count and histological subtype.Results: We found that the platelet count was associated with lymph node metastasis (P = 0.01) in multivariable analysis, independent of tumor size, predominant subtype, visceral pleural invasion, and microvessel invasion. In patients with a platelet count ≥ 300 × 109/L, the rate of lymph node metastasis was 38.5%, almost twice as high as that in patients with a platelet count < 300 × 109/L (23.2%). Additionally, elevated platelet counts, even those within the normal range, were significantly associated with a higher rate of lymph node metastasis. The mean platelet count in patients with solid-predominant histology (269.70 ± 69.38 × 109/L) was significantly higher than that in patients with other histologies (P < 0.001).Conclusion: Elevated platelet counts are significantly associated with a higher rate of lymph node metastasis, even if the platelet counts are within the reference range. Platelet counts were significantly higher in patients with solid-predominant histology than in patients with other histologies. In addition, VEGF-C may play an important role in lymphatic metastasis in patients with lung ADC. We hypothesize that antiplatelet therapy may reduce lymph node metastasis in lung ADC patients.Keywords: lung adenocarcinoma, platelets, lymph node, solid-predominant, pathological subtype

Temperature is widely known to influence the spatio-temporal dynamics of vector-borne disease transmission, particularly as temperatures vary across critical thermal thresholds. When temperature conditions exhibit such ‘transcritical variation’, abrupt spatial or temporal discontinuities may result, generating sharp geographical or seasonal boundaries in transmission. Here, we develop a spatio-temporal machine learning algorithm to examine the implications of transcritical variation for West Nile virus (WNV) transmission in the Los Angeles metropolitan area (LA). Analysing a large vector and WNV surveillance dataset spanning 2006–2016, we found that mean temperatures in the previous month strongly predicted the probability of WNV presence in pools of Culex quinquefasciatus mosquitoes, forming distinctive inhibitory (10.0–21.0°C) and favourable (22.7–30.2°C) mean temperature ranges that bound a narrow 1.7°C transitional zone (21–22.7°C). Temperatures during the most intense months of WNV transmission (August/September) were more strongly associated with infection probability in Cx. quinquefasciatus pools in coastal LA, where temperature variation more frequently traversed the narrow transitional temperature range compared to warmer inland locations. This contributed to a pronounced expansion in the geographical distribution of human cases near the coast during warmer-than-average periods. Our findings suggest that transcritical variation may influence the sensitivity of transmission to climate warming, and that especially vulnerable locations may occur where present climatic fluctuations traverse critical temperature thresholds.

BackgroundDue to an increase in mosquito habitats and the lack facilities to carry out basic mosquito control, construction sites in China are more likely to experience secondary dengue fever infection after importation of an initial infection, which may then increase the number of infections in the neighboring communities and the chance of community transmission. The aim of this study was to investigate how to effectively reduce the transmission of dengue fever at construction sites and the neighboring communities.MethodsThe Susceptible-Exposed-Infectious/Asymptomatic-Recovered (SEIAR) model of human and SEI model of mosquitoes were developed to estimate the transmission of dengue virus between humans and mosquitoes within the construction site and within a neighboring community, as well between each of these. With the calibrated model, we further estimated the effectiveness of different intervention scenarios targeting at reducing the transmissibility at different locations (i.e. construction sites and community) with the total attack rate (TAR) and the duration of the outbreak (DO).ResultsA total of 102 construction site-related and 131 community-related cases of dengue fever were reported in our area of study. Without intervention, the number of cases related to the construction site and the community rose to 156 (TAR: 31.25%) and 10,796 (TAR: 21.59%), respectively. When the transmission route from mosquitoes to humans in the community was cut off, the number of community cases decreased to a minimum of 33 compared with other simulated scenarios (TAR: 0.068%, DO: 60 days). If the transmission route from infectious mosquitoes in the community and that from the construction site to susceptible people on the site were cut off at the same time, the number of cases on the construction site dropped to a minimum of 74 (TAR: 14.88%, DO: 66 days).ConclusionsTo control the outbreak of dengue fever effectively on both the construction site and in the community, interventions needed to be made both within the community and from the community to the construction site. If interventions only took place within the construction site, the number of cases on the construction site would not be reduced. Also, interventions implemented only within the construction site or between the construction site and the community would not lead to a reduction in the number of cases in the community.Graphical Abstract

Infectious disease surveillance systems provide vital data for guiding disease prevention and control policies, yet the formalization of methods to optimize surveillance networks has largely been overlooked. Decisions surrounding surveillance design parameters—such as the number and placement of surveillance sites, target populations, and case definitions—are often determined by expert opinion or deference to operational considerations, without formal analysis of the influence of design parameters on surveillance objectives. Here we propose a simulation framework to guide evidence-based surveillance network design to better achieve specific surveillance goals with limited resources. We define evidence-based surveillance design as a constrained, multi-dimensional, multi-objective, dynamic optimization problem, acknowledging the many operational constraints under which surveillance systems operate, the many dimensions of surveillance system design, the multiple and competing goals of surveillance, and the complex and dynamic nature of disease systems. We describe an analytical framework for the identification of optimal designs through mathematical representations of disease and surveillance processes, definition of objective functions, and the approach to numerical optimization. We then apply the framework to the problem of selecting candidate sites to expand an existing surveillance network under alternative objectives of: (1) improving spatial prediction of disease prevalence at unmonitored sites; or (2) estimating the observed effect of a risk factor on disease. Results of this demonstration illustrate how optimal designs are sensitive to both surveillance goals and the underlying spatial pattern of the target disease. The findings affirm the value of designing surveillance systems through quantitative and adaptive analysis of network characteristics and performance. The framework can be applied to the design of surveillance systems tailored to setting-specific disease transmission dynamics and surveillance needs, and can yield improved understanding of tradeoffs between network architectures.Author summaryDisease surveillance systems are essential for understanding the epidemiology of infectious diseases and improving population health. A well-designed surveillance system can achieve a high level of fidelity in estimates of interest (e.g., disease trends, risk factors) within its operational constraints. Currently, design parameters that define surveillance systems (e.g., number and placement of the surveillance sites, target populations, case definitions) are selected largely by expert opinion and practical considerations. Such an informal approach is less tenable when multiple aspects of surveillance design—or multiple surveillance objectives— need to be considered simultaneously, and are subject to resource or logistical constraints. Here we propose a framework to optimize surveillance system design given a set of defined surveillance objectives and a dynamical model of the disease system under study. The framework provides a platform to conduct in silico surveillance system design, and allows the formulation of surveillance guidelines based on quantitative evidence, tailored to local realities and priorities. The approach facilitates greater collaboration between health planners and computational and data scientists to advance surveillance science and strengthen the architecture of surveillance networks.

The Loschmidt amplitude of the purified states of mixed-state density matrices is shown to have zeros when the system undergoes a quasistatic, quench, or Uhlmann process. While the Loschmidt-amplitude zero of a quench process corresponds to a dynamical quantum phase transition (DQPT) accompanied by the diverging dynamical free energy, the Loschmidt-amplitude zero of the Uhlmann process corresponds to a topological phase transition (TQPT) accompanied by a jump of the Uhlmann phase. Although the density matrix remains intact in a quasistatic process, the Loschmidt amplitude can have zeros not associated with a phase transition. We present examples of two-level and three-level systems exhibiting finite- or infinite- temperature DQPTs and finite-temperature TQPTs associated with the Loschmidt-amplitude zeros. Moreover, the dynamical phase or geometrical phase of mixed states can be extracted from the Loschmidt amplitude. Those phases may become quantized or exhibit discontinuity at the Loschmidt-amplitude zeros. A spinor representation of the purified states of a general two-level system is presented to offer more insights into the change of purification in different processes. The quasistatic process, for example, is shown to cause a rotation of the spinor., Comment: 15 pages, 3 figures, submitted

Climate exerts complex influences on leptospirosis transmission, affecting human behavior, zoonotic host population dynamics, and survival of the pathogen in the environment. Here, we describe the spatiotemporal distribution of leptospirosis incidence reported to China’s National Infectious Disease Surveillance System from 2004–2014 in an endemic region in western China, and employ distributed lag models at annual and sub-annual scales to analyze its association with hydroclimatic risk factors and explore evidence for the potential role of a soil reservoir in the transmission of Leptospira spp. More than 97% of the 2,934 reported leptospirosis cases occurred during the harvest season between August and October, and most commonly affected farmers (83%). Using a distributed lag Poisson regression framework, we characterized incidence rate ratios (IRRs) associated with interquartile range increases in precipitation of 3.45 (95% confidence interval 2.57–4.64) over 0-1-year lags, and 1.90 (1.18–3.06) over 0-15-week lags. Adjusting for soil moisture decreased IRRs for precipitation at both timescales (yearly adjusted IRR: 1.05, 0.74–1.49; weekly adjusted IRR: 1.36, 0.72–2.57), suggesting precipitation effects may be mediated through soil moisture. Increased soil moisture was positively associated with leptospirosis at both timescales, suggesting that the survival of pathogenic Leptospira spp. in moist soils may be a critical control on harvest-associated leptospirosis transmission in the study region. These results support the hypothesis that soils may serve as an environmental reservoir and may play a significant yet underrecognized role in leptospirosis transmission., Author summary Leptospirosis is among the leading causes of morbidity from zoonotic infections worldwide, affecting populations that are exposed to contaminated water. The disease is caused by Leptospira spp. bacteria, which are transmitted to humans either through direct contact with infected animals, or indirectly through the environment. Climatic conditions can influence transmission by altering human exposure, animal host population dynamics, and environmental conditions that allow Leptospira spp. to persist in the environment (e.g., moist environments, warm temperatures). Here, we investigated the spatiotemporal distribution of leptospirosis cases in a rural setting in western China and estimated the association between hydroclimatic conditions and leptospirosis incidence. We found that incidence of leptospirosis—especially high amongst farmers—may be associated with rice harvest, and modulated by prior bacterial accumulation within the soil under moist conditions. These results corroborate previous findings that soils may be underrecognized environmental reservoirs of pathogenic Leptospira spp., and that their role in explaining leptospirosis incidence should be considered when developing prevention programs.

Vinasse is the waste liquid of molasses fermentation for alcohol production from sugarcane mills. The adequate utilization and treatment for vinasse have received increasing attention. Among the alternatives considered for the reuse of vinasse, irrigation in sugarcane fields is most common solution. However, few researchers consider the impact on the soil fertility, soil heavy metals content and soil microbial diversity by long-term application of vinasse in the fields. In order to evaluate the cumulative impact arising from the long-term application of vinasse, different treatments, viz., (1) soils not irrigated with vinasse, (2) soil irrigated with vinasse for 2 years, (3) soil irrigated with vinasse for 7 years, (4) soil irrigated with vinasse for 13 years, (5) soil irrigated with vinasse for 18 years, have been applied to sugarcane fields in Shangsi County, Guangxi, China. The pH, organic matter, total and available N, P, K, the concentration of Cu, Pb, Cr, Zn, Ni, Cd and As, as well as the diversity of microbial community of the soil samples were determined. The results showed that after long-term irrigation by vinasse, soil acidification was observed, organic matter, N and K content increased, but no significant changes in the increase in P content were observed. The potential ecological risk of Cd, Cr, Pb, Zn, Cu, Ni and As was low, indicating that accumulation of heavy metals in the soil was negligible when long-term irrigation with vinasse was practiced. The diversity of microbial community expressed as relative abundance of the predominant species in irrigated soil was slightly reduced due to influence by the physicochemical properties of soil, especially soil pH. Consequently, the proper management of vinasse irrigation and periodic monitoring of soil quality parameters are required to ensure safe and long-term irrigation.

Dengue is the fastest spreading mosquito-transmitted disease in the world. In China, Guangzhou City is believed to be the most important epicenter of dengue outbreaks although the transmission patterns are still poorly understood. We developed an autoregressive integrated moving average model incorporating external regressors to examine the association between the monthly number of locally acquired dengue infections and imported cases, mosquito densities, temperature and precipitation in Guangzhou. In multivariate analysis, imported cases and minimum temperature (both at lag 0) were both associated with the number of locally acquired infections (P < 0.05). This multivariate model performed best, featuring the lowest fitting root mean squared error (RMSE) (0.7520), AIC (393.7854) and test RMSE (0.6445), as well as the best effect in model validation for testing outbreak with a sensitivity of 1.0000, a specificity of 0.7368 and a consistency rate of 0.7917. Our findings suggest that imported cases and minimum temperature are two key determinants of dengue local transmission in Guangzhou. The modelling method can be used to predict dengue transmission in non-endemic countries and to inform dengue prevention and control strategies.

We demonstrate a CuS/GeSe dual-layer selector with excellent performance. This CuS/GeSe dual-layer selector exhibits ultrahigh selectivity ( $\text {1.25}\times \text {10}^{\text {9}}$ ), high on-current drive ( $600~\mu \text{A}$ ), ultralow off current (~100 fA), and extremely steep switching slope (

Objective: The purpose of this meta-analysis was to explore the effect of corticosteroids on atrial fibrillation (AF) following catheter ablation. Methods: We searched PubMed, Embase, and the Cochrane Central Register of Controlled Trials for published articles describing the effect of corticosteroids in preventing AF recurrence after catheter ablation. Data on study and patient were extracted. Risk ratios (RRs) and 95% confidence intervals (CIs) were calculated by use of a random-effect model, and P values of