Fabrication of 0D/1D S-scheme CoO-CuBi2O4 heterojunction for efficient photocatalytic degradation of tetracycline by activating peroxydisulfate and product risk assessment.

The step-scheme (S-scheme) heterojunction has excellent redox capability, effectively degrading organic pollutants in wastewater. Combining S-scheme heterojunction with activated persulfate advanced oxidation process reasonably can further enhance the degradation of Emerging Contaminants. Herein, a novel zero-dimensional/one-dimensional (0D/1D) CoO-CuBi2O4 (CoO-CBO) photocatalyst with S-scheme heterojunction was designed by hydrothermal and solvothermal methods. The band structure and electron and hole transfer pathway of CoO-CBO were analyzed using the ex-situ and in-situ X-ray photoelectron spectroscopy (XPS), Ultraviolet and Visible Spectrophotometer (UV-Vis) and optical radiation Kelvin probe force microscope (KPFM), and the formation of S-scheme heterojunction was demonstrated. The photocatalytic activity of ·S-scheme CoO-CBO heterojunction was carried out by degrading tetracycline (TC) with activating potassium monopersulfate triple salt under visible light. Compared with pure CuBi2O4 and pure CoO, 30%CoO/CuBi2O4 catalyst exhibited the highest TC degradation performance after activating persulfate, degrading 89.5% of TC within 90 min. On the one hand, the S-scheme heterojunction formed between CoO and CBO had a high redox potential. On the other hand, the activation of persulfate by Co and Cu could accelerate redox cycles and facilitate the generation of active radicals such as SO4-, O2- and OH, promoting the separation of the photogenerated e- and h+ in the composite, enhancing the peroxydisulfate (PDS) activation performance and improving the degradation effect of TC. Then, a gradual decrease in the toxicity of the intermediates in the TC degradation process was detected by ECOCER. In all, this study provided an S-scheme CoO/CuBi2O4 heterojunction that can activate PDS to degrade TC efficiently, which provided a new idea for the study of novel pollutant degradation and environmental toxicology.

Comparison of the predictive value of intrinsic capacity and comorbidity on adverse health outcome in community-dwelling older adults.

OBJECTIVES: To compare the predictive value of intrinsic capacity (IC) and comorbidity on all-cause mortality and falls. METHODS: This prospective cohort study included 220 older adults (84.0±4.4 years) followed for 3 years in a community in Beijing. The methodology recommended by the World Health Organization was used to assess IC, and comorbidity was assessed by the Cumulative Illness Rating Scale for Geriatrics. RESULTS: The areas under the characteristic curve of IC and comorbidity were 0.78 vs. 0.67 (p=0.033), respectively, in predicting all-cause mortality, and 0.69 vs. 0.61 in predicting falls (p=0.032). The vitality domain impairment (odds ratio [OR]=3.28, p=0.013), and cognition domain impairment (OR=3.97, p=0.004) were significantly associated with all-cause mortality. Locomotion domain impairment (OR=2.35, p=0.010) was associated with higher fall risk. CONCLUSION: IC might be a better predictor than comorbidity in community-dwelling older adults, in which the vitality, locomotion and cognition domains should be given more attention.

Assessment of immune status of laryngeal squamous cell carcinoma can predict prognosis and guide treatment.

BACKGROUND: In the past few years, immunotherapy has changed the way we treat solid tumors. People pay more and more attention to the immune microenvironment of laryngeal squamous cell carcinoma (LSCC). In this study, our immunotherapy research took advantage of the clinical database and focused our in-depth analysis on the tumor microenvironment (TME). METHODS: This study evaluated the relationship between the clinical outcome and the local tissue and overall immune status in 412 patients with primary LSCC. We constructed and validated a risk model that could predict prognosis, assess immune status, identify high-risk patients, and develop personalized treatment plans through bioinformatics. In addition, through immunohistochemical analysis, we verified the differential expression of CTSL and KDM5D genes with the largest weight coefficients in the model in LSCC tissues and their influence on the prognosis and tumor-infiltrating lymphocytes (TILs). RESULTS: We found that interstitial tumor-infiltrating lymphocytes, tumor parenchymal-infiltrating lymphocyte volume, tumor infiltrates lymphocytes of frontier invasion, and the platelet-to-lymphocyte ratio (PLR) were independent factors affecting the prognosis of patients with LSCC. A novel risk model can guide clinicians to accurately predict prognosis, identify high-risk patients, and formulate personalized treatment plans. The differential expression of genes such as CTSL and KDM5D has a significant correlation with the TILs of LSCC and the prognosis of patients. CONCLUSION: Local and systemic inflammatory markers in patients with laryngeal squamous cell carcinoma are reliable prognostic factors. The risk model and CTSL, KDM5D gene have important potential research value.

Deterministic and stochastic models for the epidemic dynamics of COVID-19 in Wuhan, China.

In this paper, deterministic and stochastic models are proposed to study the transmission dynamics of the Coronavirus Disease 2019 (COVID-19) in Wuhan, China. The deterministic model is formulated by a system of ordinary differential equations (ODEs) that is built upon the classical SEIR framework. The stochastic model is formulated by a continuous-time Markov chain (CTMC) that is derived based on the ODE model with constant parameters. The nonlinear CTMC model is approximated by a multitype branching process to obtain an analytical estimate for the probability of a disease outbreak. The local and global dynamics of the disease are analyzed by using the deterministic model with constant parameters, and the result indicates that the basic reproduction number $ \mathcal{R}_0 $ serves as a sharp disease threshold: the disease dies out if $ \mathcal{R}_0\le 1 $ and persists if $ \mathcal{R}_0 > 1 $. In contrast to the deterministic dynamics, the stochastic dynamics indicate that the disease may not persist when $ \mathcal{R}_0 > 1 $. Parameter estimation and validation are performed to fit our ODE model to the public reported data. Our result indicates that both the exposed and infected classes play an important role in shaping the epidemic dynamics of COVID-19 in Wuhan, China. In addition, numerical simulations indicate that a second wave of the ongoing pandemic is likely to occur if the prevention and control strategies are not implemented properly.

Intent-to-treat analysis of cluster randomized trials when clusters report unidentifiable outcome proportions.

BACKGROUND: Cluster randomized trials are designed to evaluate interventions at the cluster or group level. When clusters are randomized but some clusters report no or non-analyzable data, intent-to-treat analysis, the gold standard for the analysis of randomized controlled trials, can be compromised. This article presents a very flexible statistical methodology for cluster randomized trials whose outcome is a cluster-level proportion (e.g. proportion from a cluster reporting an event) in the setting where clusters report non-analyzable data (which in general could be due to nonadherence, dropout, missingness, etc.). The approach is motivated by a previously published stratified randomized controlled trial called, "The Randomized Recruitment Intervention Trial (RECRUIT)," designed to examine the effectiveness of a trust-based continuous quality improvement intervention on increasing minority recruitment into clinical trials (ClinicalTrials.gov Identifier: NCT01911208). METHODS: The novel approach exploits the use of generalized estimating equations for cluster-level reports, such that all clusters randomized at baseline are able to be analyzed, and intervention effects are presented as risk ratios. Simulation studies under different outcome missingness scenarios and a variety of intra-cluster correlations are conducted. A comparative analysis of the method with imputation and per protocol approaches for RECRUIT is presented. RESULTS: Simulation results show the novel approach produces unbiased and efficient estimates of the intervention effect that maintain the nominal type I error rate. Application to RECRUIT shows similar effect sizes when compared to the imputation and per protocol approach. CONCLUSION: The article demonstrates that an innovative bivariate generalized estimating equations framework allows one to implement an intent-to-treat analysis to obtain risk ratios or odds ratios, for a variety of cluster randomized designs.

Stochastic Model of Bovine Babesiosis with Juvenile and Adult Cattle.

A stochastic model for Bovine Babesiosis (BB) including ticks, and both juvenile and adult cattle is developed. This model is formulated by a system of continuous-time Markov chains (CTMCs) that is derived based on an extension of the deterministic ordinary differential equation model developed by Saad-Roy et al. (Bull Math Biol 77:514-547, 2015). The nonlinear CTMC model is approximated by a multitype branching process, giving a theoretical estimate of the probability of an outbreak of BB. Unlike the deterministic dynamics where the basic reproduction number is a sharp threshold parameter, the stochastic model indicates that there is always a positive probability of disease extinction within the cattle population. For parameter values from Colombia data, conditional probability distributions are numerically obtained for the time to disease extinction or outbreak, and are found to depend on the host type at the initiation of infection. The models with and without the inclusion of juvenile cattle are compared, and our result highlights that neglecting juvenile bovine in the models may lead to faulty predictions of critical disease statistics: particularly, it may underestimate the risk of infection. Endemic disease prevalence in adult cattle is examined for certain parameter values in the corresponding deterministic model. Notably, with long-lasting immunity, increased tick to juvenile infectivity decreases the proportion of infectious adults.

Assessment of potential false positives via orbitrap-based untargeted lipidomics from rat tissues.

Untargeted lipidomics is increasingly popular due to the broad coverage of lipid species. Data dependent MS/MS acquisition is commonly used in order to acquire sufficient information for confident lipid assignment. However, although lipids are identified based on MS/MS confirmation, a number of false positives are still observed. Here, we discuss several causes of introducing lipid false identifications in untargeted analysis. Phosphotidylcholines and cholesteryl esters generate in-source fragmentation to produce dimethylated phosphotidylethanolamine and free cholesterol. Dimerization of fatty acid results in false identification of fatty acid ester of hydroxyl fatty acid. Realizing these false positives is able to improve confidence of results acquired from untargeted analysis. Besides, thresholds are established for lipids identified using LipidSearch v4.1.16 software to reduce unreliable results.

A quantitative risk-assessment system (QR-AS) evaluating operation safety of Organic Rankine Cycle using flammable mixture working fluid.

Mixture of hydrocarbon and carbon dioxide shows excellent cycle performance in Organic Rankine Cycle (ORC) used for engine waste heat recovery, but the unavoidable leakage in practical application is a threat for safety due to its flammability. In this work, a quantitative risk assessment system (QR-AS) is established aiming at providing a general method of risk assessment for flammable working fluid leakage. The QR-AS covers three main aspects: analysis of concentration distribution based on CFD simulations, explosive risk assessment based on the TNT equivalent method and risk mitigation based on evaluation results. A typical case of propane/carbon dioxide mixture leaking from ORC is investigated to illustrate the application of QR-AS. According to the assessment results, proper ventilation speed, safe mixture ratio and location of gas-detecting devices have been proposed to guarantee the security in case of leakage. The results revealed that this presented QR-AS was reliable for the practical application and the evaluation results could provide valuable guidance for the design of mitigation measures to improve the safe performance of ORC system.

Modeling the within-host dynamics of cholera: bacterial-viral interaction.

Novel deterministic and stochastic models are proposed in this paper for the within-host dynamics of cholera, with a focus on the bacterial-viral interaction. The deterministic model is a system of differential equations describing the interaction among the two types of vibrios and the viruses. The stochastic model is a system of Markov jump processes that is derived based on the dynamics of the deterministic model. The multitype branching process approximation is applied to estimate the extinction probability of bacteria and viruses within a human host during the early stage of the bacterial-viral infection. Accordingly, a closed-form expression is derived for the disease extinction probability, and analytic estimates are validated with numerical simulations. The local and global dynamics of the bacterial-viral interaction are analysed using the deterministic model, and the result indicates that there is a sharp disease threshold characterized by the basic reproduction number [Formula: see text]: if [Formula: see text], vibrios ingested from the environment into human body will not cause cholera infection; if [Formula: see text], vibrios will grow with increased toxicity and persist within the host, leading to human cholera. In contrast, the stochastic model indicates, more realistically, that there is always a positive probability of disease extinction within the human host.

Stochastic models for the Trojan Y-Chromosome eradication strategy of an invasive species.

The Trojan Y-Chromosome (TYC) strategy, an autocidal genetic biocontrol method, has been proposed to eliminate invasive alien species. In this work, we develop a Markov jump process model for this strategy, and we verify that there is a positive probability for wild-type females going extinct within a finite time. Moreover, when sex-reversed Trojan females are introduced at a constant population size, we formulate a stochastic differential equation (SDE) model as an approximation to the proposed Markov jump process model. Using the SDE model, we investigate the probability distribution and expectation of the extinction time of wild-type females by solving Kolmogorov equations associated with these statistics. The results indicate how the probability distribution and expectation of the extinction time are shaped by the initial conditions and the model parameters.