Microclimate control to increase productivity and nutritional quality of leafy vegetables in a cost-effective manner

Food security is one of the key global challenges in this century. In Singapore, our research team has been using novel aeroponic technology to produce fresh vegetables since 1997. Aeroponic systems allow for year-round production of not only tropical, but also sub-tropical and temperate fresh vegetables, by simply cooling the roots suspended in aeroponic systems while the aerial parts grow under tropical ambient environments. It has also been used to investigate the impacts of root-zone CO2 on vegetables by enriching root-zone CO2 while their aerial portions were subjected to constant atmospheric CO2. To compensate for the lack of available land, Singapore also needs to develop a farming system that can increase productivity per unit land area by many-fold. Over the past 10 years, my research team has established a commercially viable LED integrated vertical aeroponic farming system to grow different leafy vegetables under different LED spectra, intensities, and durations in the tropical greenhouse. The results demonstrate that it is possible to increase shoot production and rate of shoot production of leafy vegetables by increasing light intensity and extending the photoperiod under effective LED lighting. Furthermore, temperate vegetable crops such as lettuce were able to acclimate to high light intensity under supplementary LED lights to natural sunlight in the greenhouse. Supplementary LED lightings promote both leaf initiation and expansion with increased photo synthetic pigments, higher Cyt b6f and Rubisco protein contents on a per area basis and thus improve photosynthetic capacity and enhance productivity. Plants sense and respond to changes in their immediate environments (microclimate), manipulating the root zone temperature (RZT) and water supply will impact not only their growth and development but also their nutritional quality. Our on-going research aims to investigate if the nutritional quality of leafy vegetables could be improved under suboptimal RZT and mild water deficit through deficit irrigation. If substantial energy and water savings in urban farming can be achieved without substantial yield penalty but with higher nutritional quality, the amount of water and energy saved can bring substantial benefits to society.

Health Impairments in Children and Adolescents After Hospitalization for Acute COVID-19 or MIS-C.

OBJECTIVES: To evaluate risk factors for postdischarge sequelae in children and adolescents hospitalized for acute coronavirus disease 2019 (COVID-19) or multisystem inflammatory syndrome in children (MIS-C). METHODS: Multicenter prospective cohort study conducted in 25 United States pediatric hospitals. Patients <21-years-old, hospitalized May 2020 to May 2021 for acute COVID-19 or MIS-C with follow-up 2 to 4 months after admission. We assessed readmissions, persistent symptoms or activity impairment, and new morbidities. Multivariable regression was used to calculate adjusted risk ratios (aRR) and 95% confidence intervals (CI). RESULTS: Of 358 eligible patients, 2 to 4 month survey data were available for 119 of 155 (76.8%) with acute COVID-19 and 160 of 203 (78.8%) with MIS-C. Thirteen (11%) patients with acute COVID-19 and 12 (8%) with MIS-C had a readmission. Thirty-two (26.9%) patients with acute COVID-19 had persistent symptoms (22.7%) or activity impairment (14.3%) and 48 (30.0%) with MIS-C had persistent symptoms (20.0%) or activity impairment (21.3%). For patients with acute COVID-19, persistent symptoms (aRR, 1.29 [95% CI, 1.04-1.59]) and activity impairment (aRR, 1.37 [95% CI, 1.06-1.78]) were associated with more organ systems involved. Patients with MIS-C and pre-existing respiratory conditions more frequently had persistent symptoms (aRR, 3.09 [95% CI, 1.55-6.14]) and those with obesity more frequently had activity impairment (aRR, 2.52 [95% CI, 1.35-4.69]). New morbidities were infrequent (9% COVID-19, 1% MIS-C). CONCLUSIONS: Over 1 in 4 children hospitalized with acute COVID-19 or MIS-C experienced persistent symptoms or activity impairment for at least 2 months. Patients with MIS-C and respiratory conditions or obesity are at higher risk of prolonged recovery.

The Influence of a School Social Network Intervention on Adolescent's Health Behaviors: A Gender-Specific Agent-Based Model.

Introduction: Adolescence is a crucial stage for health behavior development, which is associated with health in adulthood. School closures caused by the coronavirus disease 2019 (COVID-19) pandemic have exposed adolescents to an increased risk of obesity due to a lack of physical activity. Although social network interventions provide an effective approach for promoting health-related behavior, current practices neglect gender differences in adolescent behavioral patterns and emotional preferences. The aim of this study was to examine the effectiveness of centrality-based methods integrated with of gender contexts in a social network intervention to improve adolescent's health behavior. Methods: We developed an agent-based model (ABM) that supports the small-world characteristics of adolescent social networks. Health-related data for junior middle school students (n = 234, 48% girls) were collected in November 2018, 2019 and 2020 in Tianjin, China. We simulated multiple network-based interventions with different criteria for influential agents (i.e., betweenness centrality, closeness centrality, eigenvector centrality, and PageRank) and a random condition. The rules for generating peer influence and accelerating behavioral changes were based on the diffusion of innovations theory, with gender specifications. Results: After the school closures, there was a significant increase in the prevalence of overweight and obesity among adolescents, with a greater increase in girls than in boys (+8.85% vs. +1.65%, p < 0.001). Simulations showed that centrality-based network interventions were more effective than the random condition (average 6.17% per tick vs. 5.22% per tick, p < 0.05), with a higher efficiency in girls than boys (average 3.68% vs. 2.99% per tick, p < 0.05). PageRank outperformed other centrality conditions at the population level (6.37% per tick, p < 0.05). In girls, betweenness centrality was the best method (3.85% per tick, p < 0.05), while in boys, PageRank still had the greatest efficiency (3.21% per tick, p < 0.05). Conclusions: We found evidence for gender differences in the negative impact of COVID-19-related school closures and the potential for centrality-based social network interventions to affect adolescent health behavior. Therefore, we emphasize the importance of gender-specific targeting strategies to further promote health-related school programs in the post-pandemic era.

Highly sensitive and portable mRNA detection platform for early cancer detection.

Pancreatic cancer, at unresectable advanced stages, presents poor prognoses, which could be prevented by early pancreatic cancer diagnosis methods. Recently, a promising early-stage pancreatic cancer biomarker, extracellular vesicles (EVs) related glypican-1 (GPC1) mRNA, is found to overexpress in pancreatic cancer cells. Current mRNA detection methods usually require expensive machinery, strict preservation environments, and time-consuming processes to guarantee detection sensitivity, specificity, and stability. Herein, we propose a novel two-step amplification method (CHAGE) via the target triggered Catalytic Hairpin Assembly strategy combined with Gold-Enhanced point-of-care-testing (POCT) technology for sensitive visual detection of pancreatic cancer biomarker. First, utilizing the catalyzed hairpin DNA circuit, low expression of the GPC1 mRNA was changed into amplification product 1 (AP1, a DNA duplex) as the next detection targets of the paper strips. Second, the AP1 was loaded onto a lateral flow assay and captured with the gold signal nanoparticles to visualize results. Finally, the detected results can be further enhanced by depositing gold to re-enlarge the sizes of gold nanoparticles in detection zones. As a result, the CHAGE methodology lowers the detection limit of mRNA to 100 fM and provides results within 2 h at 37 °C. Furthermore, we demonstrate the successful application in discriminating pancreatic cancer cells by analyzing EVs' GPC1 mRNA expression levels. Hence, the CHAGE methodology proposed here provides a rapid and convenient POCT platform for sensitive detection of mRNAs through unique probes designs (COVID, HPV, etc.).

On being warm and friendly: The effect of socially responsible human resource management on employee fears of the threats of COVID-19

Purpose: This paper aims to investigate the influence of socially- responsible human resource management (SRHRM) on employee fears of external threats during the COVID-19 outbreak, based on social support and event system theories. COVID-19 caused sharp profit declines and bankruptcies of hotels, restaurants and travel agencies. In addition, employees faced threats to their health and job security. How to overcome employee anxieties and fears about the negative impacts of this crisis and promote psychological recovery is worthy of attention from researchers and practitioners. This research investigated the impacts of SRHRM on employee fears through organizational trust, with the COVID-19 pandemic playing a moderating role between SRHRM and employee fears. Design/methodology/approach: The hypotheses were tested through multiple linear regression analysis based on a survey of 408 employees in hospitality and tourism firms in China. Qualitative data were also gathered through interviews with selected managers. Findings: The results showed that SRHRM had a negative influence on employee fears of external threats by enhancing trust in their organizations. In addition, the strength of the COVID-19 pandemic positively moderated the effect of SRHRM on employee fears. When the pandemic strength was more robust, the negative effects of SRHRM on employee fears were more significant. Research limitations/implications: This research illustrated the contribution of SRHRM in overcoming employee fears of external threats in the context of COVID-19. It shed light on the organizational contribution of SRHRM to hospitality and tourism employee psychological recovery during the crisis. Originality/value: This research explored strategic HRM by examining the effects of SRHRM on employee fears in the midst of a severe crisis, specifically COVID-19. The moderation effect of event strength and mediation effect of organizational trust were tested. It is of great value for hospitality and tourism firms to foster employee psychological recovery during a crisis such as COVID-19. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

A dynamic physical-distancing model to evaluate spatial measures for prevention of Covid-19 spread.

Motivated by the global pandemic of COVID-19, this study investigates the spatial factors influencing physical distancing, and how these affect the transmission of the SARS-CoV-2 virus, by integrating pedestrian dynamics with a modified susceptible-exposed-infectious model. Contacts between infected and susceptible pedestrians are examined by determining physical-distancing pedestrian dynamics in three types of spaces, and used to estimate the proportion of newly infected pedestrians in these spaces. Desired behaviour for physical distancing can be observed from simulation results, and aggregated simulation findings reveal that certain layouts enable physical distancing to reduce the transmission of SARS-CoV-2. We also provide policymakers with several design guidelines on how to proactively design more effective and resilient space layouts in the context of pandemics to keep low transmission risks while maintaining a high pedestrian volume. This approach has enormous application potential for other infectious-disease transmission and space assessments.

Combined clinical and imaging features better predict the critical outcomes of patients with SARS-COV-2.

ABSTRACT: The purpose of this study was to investigate the predictive value of combined clinical and imaging features, compared with the clinical or radiological risk factors only. Moreover, the expected results aimed to improve the identification of severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) patients who may have critical outcomes.This retrospective study included laboratory-confirmed SARS-COV-2 cases between January 18, 2020, and February 16, 2020. The patients were divided into 2 groups with noncritical illness and critical illness regarding severity status within the hospitalization. Univariable and multivariable logistic regression models were used to explore the risk factors associated with clinical and radiological outcomes in patients with SARS-COV-2. The ROC curves were performed to compare the prediction performance of different factors.A total of 180 adult patients in this study included 20 critical patients and 160 noncritical patients. In univariate logistic regression analysis, 15 risk factors were significantly associated with critical outcomes. Of importance, C-reactive protein (1.051, 95% confidence interval 1.024-1.078), D-dimer (1.911, 95% CI, 1.050-3.478), and CT score (1.29, 95% CI, 1.053-1.529) on admission were independent risk factors in multivariate analysis. The combined model achieved a better performance in disease severity prediction (P = .05).CRP, D-dimer, and CT score on admission were independent risk factors for critical illness in adults with SARS-COV-2. The combined clinical and radiological model achieved better predictive performance than clinical or radiological factors alone.

An epidemiological forecast model and software assessing interventions on the COVID-19 epidemic in China

We develop a health informatics toolbox that enables timely analysis and evaluation of the time-course dynamics of a range of infectious disease epidemics. As a case study, we examine the novel coronavirus (COVID-19) epidemic using the publicly available data from the China CDC. This toolbox is built upon a hierarchical epidemiological model in which two observed time series of daily proportions of infected and removed cases are generated from the underlying infection dynamics governed by a Markov Susceptible-Infectious-Removed (SIR) infectious disease process. We extend the SIR model to incorporate various types of time-varying quarantine protocols, including government-level 'macro' isolation policies and community-level 'micro' social distancing (e.g. self-isolation and self-quarantine) measures. We develop a calibration procedure for underreported infected cases. This toolbox provides forecasts, in both online and offline forms, as well as simulating the overall dynamics of the epidemic. An R software package is made available for the public, and examples on the use of this software are illustrated. Some possible extensions of our novel epidemiological models are discussed.

A Review of Multi-Compartment Infectious Disease Models.

Multi-compartment models have been playing a central role in modelling infectious disease dynamics since the early 20th century. They are a class of mathematical models widely used for describing the mechanism of an evolving epidemic. Integrated with certain sampling schemes, such mechanistic models can be applied to analyse public health surveillance data, such as assessing the effectiveness of preventive measures (e.g. social distancing and quarantine) and forecasting disease spread patterns. This review begins with a nationwide macromechanistic model and related statistical analyses, including model specification, estimation, inference and prediction. Then, it presents a community-level micromodel that enables high-resolution analyses of regional surveillance data to provide current and future risk information useful for local government and residents to make decisions on reopenings of local business and personal travels. r software and scripts are provided whenever appropriate to illustrate the numerical detail of algorithms and calculations. The coronavirus disease 2019 pandemic surveillance data from the state of Michigan are used for the illustration throughout this paper.

Evaluation of the Secondary Transmission Pattern and Epidemic Prediction of COVID-19 in the Four Metropolitan Areas of China.

Understanding the transmission dynamics of COVID-19 is crucial for evaluating its spread pattern, especially in metropolitan areas of China, as its spread could lead to secondary outbreaks. In addition, the experiences gained and lessons learned from China have the potential to provide evidence to support other metropolitan areas and large cities outside China with their emerging cases. We used data reported from January 24, 2020, to February 23, 2020, to fit a model of infection, estimate the likely number of infections in four high-risk metropolitan areas based on the number of cases reported, and increase the understanding of the COVID-19 spread pattern. Considering the effect of the official quarantine regulations and travel restrictions for China, which began January 23~24, 2020, we used the daily travel intensity index from the Baidu Maps app to roughly simulate the level of restrictions and estimate the proportion of the quarantined population. A group of SEIR model statistical parameters were estimated using Markov chain Monte Carlo (MCMC) methods and fitting on the basis of reported data. As a result, we estimated that the basic reproductive number, R 0, was 2.91 in Beijing, 2.78 in Shanghai, 2.02 in Guangzhou, and 1.75 in Shenzhen based on the data from January 24, 2020, to February 23, 2020. In addition, we inferred the prediction results and compared the results of different levels of parameters. For example, in Beijing, the predicted peak number of cases was 467 with a peak time of March 01, 2020; however, if the city were to implement different levels (strict, moderate, or weak) of travel restrictions or regulation measures, the estimation results showed that the transmission dynamics would change and that the peak number of cases would differ by between 54% and 209%. We concluded that public health interventions would reduce the risk of the spread of COVID-19 and that more rigorous control and prevention measures would effectively contain its further spread, and awareness of prevention should be enhanced when businesses and social activities return to normal before the end of the epidemic. Further, the experiences gained and lessons learned from China offer the potential to provide evidence supporting other metropolitan areas and big cities with their emerging cases outside China.