Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017.

BACKGROUND: Malaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogeneity in sociodemographic and environmental risk factors over time and across diverse geographical and climatological regions make outbreak prediction challenging. Data-driven approaches accounting for spatiotemporal variability could offer potential for location-specific early warning tools for malaria. METHODS: In this case study, we developed and internally validated a data fusion approach to predict malaria incidence in Pakistan, India, and Bangladesh using geo-referenced environmental factors. For 2000-17, district-level malaria incidence rates for each country were obtained from the US Agency for International Development's Demographic and Health Survey datasets. Environmental factors included average annual temperature, rainfall, and normalised difference vegetation index, obtained from the Advancing Research on Nutrition and Agriculture (known as AReNA) project conducted by the International Food Policy Research Institute in 2020. Data on night-time light intensity was derived from two satellites of the National Oceanic and Atmospheric Administration Defense Meteorological Satellite Program-Operational Linescan System: Nighttime Lights Time Series Version 4, and VIIRS Nighttime Day/Night Band Composites version 1. A multi-dimensional spatiotemporal long short-term memory (M-LSTM) model was developed using data from 2000-16 and internally validated for the year 2017. The M-LSTM model consisted of four hidden layers, each with 100 LSTM units; a fully connected layer was used, followed by linear regression, to predict the malaria incidence rate for 2017 using spatiotemporal partitioning. Model performance was measured using accuracy and root mean squared error. Country-specific models were produced for Bangladesh, India, and Pakistan. Bivariate geospatial heatmaps were produced for a qualitative comparison of univariate environmental factors with malaria rates. FINDINGS: Malaria incidence was predicted with 80·6% accuracy in districts across Pakistan, 76·7% in districts across India, and 99·1% in districts across Bangladesh. The root mean squared error was 7 × 10-4 for Pakistan, 4·86 × 10-6 for India, and 1·32 × 10-5 for Bangladesh. Bivariate maps showed an inverse relationship between night-time lights and malaria rates; whereas high malaria rates were found in areas with high temperature, rainfall, and vegetation. INTERPRETATION: Malaria outbreaks could be forecasted using remotely measured environmental factors. Modelling techniques that enable simultaneously forecasting ahead in time as well as across large geographical areas might potentially empower regional decision makers to manage outbreaks early. FUNDING: NIHR Oxford Biomedical Research Centre Programme and The Higher Education Commission of Pakistan.

Assessment of the changes in growth, photosynthetic traits and gene expression in Cynodon dactylon against drought stress.

Drought stress considered a key restrictive factor for a warm-season bermudagrass growth during summers in China. Genotypic variation against drought stress exists among bermudagrass (Cynodon sp.), but the selection of highly drought-tolerant germplasm is important for its growth in limited water regions and for future breeding. Our study aimed to investigate the most tolerant bermudagrass germplasm among thirteen, along latitude and longitudinal gradient under a well-watered and drought stress condition. Current study included high drought-resistant germplasm, "Tianshui" and "Linxiang", and drought-sensitive cultivars; "Zhengzhou" and "Cixian" under drought treatments along longitude and latitudinal gradients, respectively. Under water deficit conditions, the tolerant genotypes showed over-expression of a dehydrin gene cdDHN4, antioxidant genes Cu/ZnSOD and APX which leads to higher antioxidant activities to scavenge the excessive reactive oxygen species and minimizing the membrane damage. It helps in maintenance of cell membrane permeability and osmotic adjustment by producing organic osmolytes. Proline an osmolyte has the ability to keep osmotic water potential and water use efficiency high via stomatal conductance and maintain transpiration rate. It leads to optimum CO2 assimilation rate, high chlorophyll contents for photosynthesis and elongation of leaf mesophyll, palisade and thick spongy cells. Consequently, it results in elongation of leaf length, stolon and internode length; plant height and deep rooting system. The CdDHN4 gene highly expressed in "Tianshui" and "Youxian", Cu/ZnSOD gene in "Tianshui" and "Linxiang" and APX gene in "Shanxian" and "Linxiang". The genotypes "Zhongshan" and "Xiaochang" showed no gene expression under water deficit conditions. Our results indicate that turfgrass show morphological modifications firstly when subjected to drought stress; however the gene expression is directly associated and crucial for drought tolerance in bermudagrass. Hence, current research has provided excellent germplasm of drought tolerant bermudagrass for physiological and molecular study and future breeding.

Design and Evaluation of Asphalt Concrete Incorporating Plastic Aggregates Fabricated Using 3D Printing Technology.

Environmental protection and sustainability have been a serious challenge due to the excess amount of waste plastic, and the inefficient methods of its disposal. This study aims at exploring the feasibility of using plastic material in asphalt concrete followed by the design and evaluation of modified asphalt mix. Four different types of asphalt mixtures were made by mixing mineral aggregates with plastic aggregates of different shapes and sizes. The conventional dense-graded asphalt concrete incorporating mineral aggregates was selected as control material, whereas the other three types of asphalt mixtures were produced by substituting 3D-printed plastic aggregates for mineral aggregates. Marshall characteristics were utilized to evaluate the volumetric and mechanical properties of the asphalt mixtures. Test results show that the asphalt mixture containing solid plastic aggregate appears to have a 14.3% reduction in bulk density, thereby potentially reducing the haul costs. Due to the angular shape of the aggregate, the stability for the asphalt mixture containing the 3D-printed aggregate is 1.95 times higher than that for the asphalt mixture incorporating all mineral aggregates. The addition of the plastic printed aggregate enhances the strain capacity to achieve failure of the asphalt mixture. However, caution should be taken when incorporating the hollow or fine printed aggregates into the asphalt mixture due to their variation of volumetric properties.

An Open Label Trial to Assess Safety of Losartan for Treating Worsening Respiratory Illness in COVID-19.

Rationale: Coronavirus disease 2019 (COVID-19) can cause disruption of the renin-angiotensin system in the lungs, possibly contributing to pulmonary capillary leakage. Thus, angiotensin receptor blockers (ARBs) may improve respiratory failure. Objective: Assess safety of losartan for use in respiratory failure related to COVID-19 (NCT04335123). Methods: Single arm, open label trial of losartan in those hospitalized with respiratory failure related to COVID-19. Oral losartan (25 mg daily for 3 days, then 50 mg) was administered from enrollment until day 14 or hospital discharge. A post-hoc external control group with patients who met all inclusion criteria was matched 1:1 to the treatment group using propensity scores for comparison. Measures: Primary outcome was cumulative incidence of any adverse events. Secondary, explorative endpoints included measures of respiratory failure, length of stay and vital status. Results: Of the 34 participants enrolled in the trial, 30 completed the study with a mean age SD of 53.8 ± 17.7 years and 17 males (57%). On losartan, 24/30 (80%) experienced an adverse event as opposed to 29/30 (97%) of controls, with a lower average number of adverse events on losartan relative to control (2.2 vs. 3.3). Using Poisson regression and controlling for age, sex, race, date of enrollment, disease severity at enrollment, and history of high-risk comorbidities, the incidence rate ratio of adverse events on losartan relative to control was 0.69 (95% CI: 0.49-0.97) Conclusions: Losartan appeared safe for COVID-19-related acute respiratory compromise. To assess true efficacy, randomized trials are needed.

The rise of 3D Printing entangled with smart computer aided design during COVID-19 era.

During the current Pandemic, seven and a half million flights worldwide were canceled which disrupted the supply chain of all types of goods such as, personal protective gears, medical health devices, raw materials, food, and other essential equipments. The demand for health and medical related goods increased during this period globally, while the production using classical manufacturing techniques were effected because of the lockdowns and disruption in the transporation system. This created the need of geo scattered, small, and rapid manufacturing units along with a smart computer aided design (CAD) facility. The availability of 3D printing technologies and open source CAD design made it possible to overcome this need. In this article, we present an extensive review on the utilization of 3D printing technology in the days of pandamic. We observe that 3D printing together with smart CAD design show promise to overcome the disruption caused by the lockdown of classical manufacturing units specially for medical and testing equipment, and protective gears. We observe that there are several short communications, commentaries, correspondences, editorials and mini reviews compiled and published; however, a systematic state-of-the-art review is required to identify the significance of 3D printing, design for additive manufacturing (AM), and digital supply chain for handling emergency situations and in the post-COVID era. We present a review of various benefits of 3DP particularly in emergency situations such as a pandemic. Furthermore, some relevant iterative design and 3DP case studies are discussed systematically. Finally, this article highlights the areas that can help to control the emergency situation such as a pandemic, and critically discusses the research gaps that need further research in order to exploit the full potential of 3DP in pandemic and post-pandemic future era.

Empathy among medical students: A cross-sectional survey.

BACKGROUND: Empathy is regarded as one of the leading elements of good physician-patient relationships, having a strong association with improved patient outcomes. It is an aspect of personality, which is considered to be an essential component in the development of interpersonal understanding and in assisting proficiency in communication. Developing empathy among medical students who are going to become tomorrow's physicians is an important part of medical education. The objective of this study was to measure the mean empathy score of medical students at a private medical college and identify associated factors. METHODS: This cross-sectional study was conducted from January to June 2018 among 569 medical students at a private medical college in Lahore. Toronto empathy questionnaire (TEQ) was used to determine empathy levels among students. Data was analysed in IBM SPSS-20. Mean TEQ scores were calculated. Independent sample t-test and one-way ANOVA were used to determine association between mean Toronto empathy score and socio-demographic variables, significant at a p-value of <0.05. RESULTS: The age of 569 respondents ranged from 17 to 27 years with a mean of 20.82±1.757. There were 317 (55.71%) females; and 312 (54.8%) were residing at home with parents. The overall mean TEQ score was calculated to be 42.57±7.513. The Cronbach's alpha was 0.710. There was a significant association between empathy levels and year of study of the respondents (p<0.001), gender (p<0.001) and students' permanent area of residence (p=0.018). CONCLUSION: Overall mean TEQ score was found to be 42.57±7.513. Statistically significant difference was found between gender, place of residence, year of study and mean empathy scores.

Crop Production under Drought and Heat Stress: Plant Responses and Management Options.

Abiotic stresses are one of the major constraints to crop production and food security worldwide. The situation has aggravated due to the drastic and rapid changes in global climate. Heat and drought are undoubtedly the two most important stresses having huge impact on growth and productivity of the crops. It is very important to understand the physiological, biochemical, and ecological interventions related to these stresses for better management. A wide range of plant responses to these stresses could be generalized into morphological, physiological, and biochemical responses. Interestingly, this review provides a detailed account of plant responses to heat and drought stresses with special focus on highlighting the commonalities and differences. Crop growth and yields are negatively affected by sub-optimal water supply and abnormal temperatures due to physical damages, physiological disruptions, and biochemical changes. Both these stresses have multi-lateral impacts and therefore, complex in mechanistic action. A better understanding of plant responses to these stresses has pragmatic implication for remedies and management. A comprehensive account of conventional as well as modern approaches to deal with heat and drought stresses have also been presented here. A side-by-side critical discussion on salient responses and management strategies for these two important abiotic stresses provides a unique insight into the phenomena. A holistic approach taking into account the different management options to deal with heat and drought stress simultaneously could be a win-win approach in future.