Adaptive mechanisms in quinoa for coping in stressful environments: an update.

Quinoa (Chenopodium quinoa) is a grain-like, genetically diverse, highly complex, nutritious, and stress-tolerant food that has been used in Andean Indigenous cultures for thousands of years. Over the past several decades, numerous nutraceutical and food companies are using quinoa because of its perceived health benefits. Seeds of quinoa have a superb balance of proteins, lipids, carbohydrates, saponins, vitamins, phenolics, minerals, phytoecdysteroids, glycine betaine, and betalains. Quinoa due to its high nutritional protein contents, minerals, secondary metabolites and lack of gluten, is used as the main food source worldwide. In upcoming years, the frequency of extreme events and climatic variations is projected to increase which will have an impact on reliable and safe production of food. Quinoa due to its high nutritional quality and adaptability has been suggested as a good candidate to offer increased food security in a world with increased climatic variations. Quinoa possesses an exceptional ability to grow and adapt in varied and contrasting environments, including drought, saline soil, cold, heat UV-B radiation, and heavy metals. Adaptations in salinity and drought are the most commonly studied stresses in quinoa and their genetic diversity associated with two stresses has been extensively elucidated. Because of the traditional wide-ranging cultivation area of quinoa, different quinoa cultivars are available that are specifically adapted for specific stress and with broad genetic variability. This review will give a brief overview of the various physiological, morphological and metabolic adaptations in response to several abiotic stresses.

COVID‐19 among health care workers and their impact on the health care system in a teaching hospital in Pakistan: A cross sectional observational study

Background and Aims Health care workers (HCWs) are thought to be high‐risk population for acquiring coronavirus disease (COVID‐19). The COVID‐19 emergence has had a profound effect on healthcare system. We sought to investigate the COVID‐19 among HCWs and their effects on the healthcare system. Methods A cross sectional observational study was conducted at Timergara teaching hospital. The study included HCWs with positive real time polymerase chain reaction (Q‐PCR) for severe acute respiratory syndrome coronavirus (SARS‐CoV‐2). The study duration was from April to September, 2020. The demographic profile of each recruited subject was collected through structured interview. The patient's admissions to hospital were collected for the 5 months before (October 2019–February 2020) and 5 months after lockdown (March–July 2020). Results A total of 72 out of 689 (10%) HCWs were tested positive for SARS‐CoV‐2, of whom 83% were front‐liners. The majority were male (72%), with comorbidities (14%) and no mortality. The structured interview of all participants showed that the healthcare setting was the major possible source of infection (97%). The patient admissions into the hospital were reduced by 42% during lockdown than prelockdown period. The patients admission was significantly decreased in the medical ward during lockdown (60% decrease;p < 0.01) with slightly similar trends in other departments. Conclusion In conclusion, we found increased risk of COVID‐19 for front‐line HCWs. Lack of mortality was the favorable outcome. Lack of replacing the infected HCWs possibly explained the marked decrease in hospital admissions, and potential inadequate healthcare delivery during the lockdown. Understanding SARS‐CoV‐2 among HCWs and their impact on health‐care system will be crucial for countries under COVID‐19 crises or in case of future pandemic to deliver proper health services.

COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm.

Many severe epidemics and pandemics have hit human civilizations throughout history. The recent Sever Actuate Respiratory disease SARS-CoV-2 known as COVID-19 became a global disease and is still growing around the globe. It has severely affected the world's economy and ways of life. It necessitates predicting the spread in advance and considering various control policies to avoid the country's complete closure. In this paper, we propose deep learning-based stacked Bi-directional long short-term memory (Stacked Bi-LSTM) network that forecasts COVID-19 more accurately for the country of South Korea. The paper's main objectives are to present a lightweight, accurate, and optimized model to predict the spread considering restriction policies such as school closure, workspace closing, and the canceling of public events. Based on the fourteen parameters (including control policies), we predict and forecast the future value of the number of positive, dead, recovered, and quarantined cases. In this paper, we use the dataset of South Korea comprised of several control policies implemented for minimizing the spread of COVID-19. We compare the performance of the stacked Bi-LSTM with the traditional time-series models and LSTM model using the performance metrics mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Moreover, we study the impact of control policies on forecasting accuracy. We further study the impact of changing the Bi-LSTM default activation functions Tanh with ReLU on forecasting accuracy. The research provides insight to policymakers to optimize the pooling of resources more optimally on the correct date and time prior to the event and to control the spread by employing various strategies in the meantime.

COVID-19 among health care workers and their impact on the health care system in a teaching hospital in Pakistan: A cross sectional observational study.

Background and Aims: Health care workers (HCWs) are thought to be high-risk population for acquiring coronavirus disease (COVID-19). The COVID-19 emergence has had a profound effect on healthcare system. We sought to investigate the COVID-19 among HCWs and their effects on the healthcare system. Methods: A cross sectional observational study was conducted at Timergara teaching hospital. The study included HCWs with positive real time polymerase chain reaction (Q-PCR) for severe acute respiratory syndrome coronavirus (SARS-CoV-2). The study duration was from April to September, 2020. The demographic profile of each recruited subject was collected through structured interview. The patient's admissions to hospital were collected for the 5 months before (October 2019-February 2020) and 5 months after lockdown (March-July 2020). Results: A total of 72 out of 689 (10%) HCWs were tested positive for SARS-CoV-2, of whom 83% were front-liners. The majority were male (72%), with comorbidities (14%) and no mortality. The structured interview of all participants showed that the healthcare setting was the major possible source of infection (97%). The patient admissions into the hospital were reduced by 42% during lockdown than prelockdown period. The patients admission was significantly decreased in the medical ward during lockdown (60% decrease; p < 0.01) with slightly similar trends in other departments. Conclusion: In conclusion, we found increased risk of COVID-19 for front-line HCWs. Lack of mortality was the favorable outcome. Lack of replacing the infected HCWs possibly explained the marked decrease in hospital admissions, and potential inadequate healthcare delivery during the lockdown. Understanding SARS-CoV-2 among HCWs and their impact on health-care system will be crucial for countries under COVID-19 crises or in case of future pandemic to deliver proper health services.

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model.

In this work, we propose a new mathematical modeling of the spread of COVID-19 infection in an arbitrary population, by modifying the SIQRD model as m-SIQRD model, while taking into consideration the eight governmental interventions such as cancellation of events, closure of public places etc., as well as the influence of the asymptomatic cases on the states of the model. We introduce robustness and improved accuracy in predictions of these models by utilizing a novel deep learning scheme. This scheme comprises of attention based architecture, alongside with Generative Adversarial Network (GAN) based data augmentation, for robust estimation of time varying parameters of m-SIQRD model. In this regard, we also utilized a novel feature extraction methodology by employing noise removal operation by Spline interpolation and Savitzky-Golay filter, followed by Principal Component Analysis (PCA). These parameters are later directed towards two main tasks: forecasting of states to the next 15 days, and estimation of best policy encodings to control the infected and deceased number within the framework of data driven synergetic control theory. We validated the superiority of the forecasting performance of the proposed scheme over countries of South Korea and Germany and compared this performance with 7 benchmark forecasting models. We also showed the potential of this scheme to determine best policy encodings in South Korea for 15 day forecast horizon.

Smart Antennas and Intelligent Sensors Based Systems: Enabling Technologies and Applications, 2020

[...]this special issue highlights the most up-to-date research in this field. The paper “Dimensionality Reduction for the Internet of Things Using the Cuckoo Search Algorithm: Reduced Implications of Mesh Sensor Technologies” highlights a problem in the Internet of Things network and presents a unique cuckoo search-based outdoor data management system. [...]of the low-dimensional data, classification accuracy is improved, while complexity and expense are lowered. The results of the suggested method’s simulation demonstrated that using intrusion detection systems based on cloud-fog in the Internet of Things can be extremely effective in recognizing attacks with the least number of errors in this network.

Mobile sensors based platform of Human Physical Activities Recognition for COVID-19 spread minimization.

The development of smartphones technologies has determined the abundant and prevalent computation. An activity recognition system using mobile sensors enables continuous monitoring of human behavior and assisted living. This paper proposes the mobile sensors-based Epidemic Watch System (EWS) leveraging the AI models to recognize a new set of activities for effective social distance monitoring, probability of infection estimation, and COVID-19 spread prevention. The research focuses on user activities recognition and behavior concerning risks and effectiveness in the COVID-19 pandemic. The proposed EWS consists of a smartphone application for COVID-19 related activities sensors data collection, features extraction, classifying the activities, and providing alerts for spread presentation. We collect the novel dataset of COVID-19 associated activities such as hand washing, hand sanitizing, nose-eyes touching, and handshaking using the proposed EWS smartphone application. We evaluate several classifiers such as random forests, decision trees, support vector machine, and Long Short-Term Memory for the collected dataset and attain the highest overall classification accuracy of 97.33%. We provide the Contact Tracing of the COVID-19 infected person using GPS sensor data. The EWS activities monitoring, identification, and classification system examine the infection risk of another person from COVID-19 infected person. It determines some everyday activities between COVID-19 infected person and normal person, such as sitting together, standing together, or walking together to minimize the spread of pandemic diseases.

Wearable IoTs and Geo-Fencing Based Framework for COVID-19 Remote Patient Health Monitoring and Quarantine Management to Control the Pandemic

The epidemic disease of Severe Acute Respiratory Syndrome (SARS) called COVID-19 has become a more frequently active disease. Managing and monitoring COVID-19 patients is still a challenging issue for advanced technologies. The first and foremost critical issue in COVID-19 is to diagnose it timely and cut off the chain of transmission by isolating the susceptible and patients. COVID-19 spreads through close interaction and contact with an infected person. It has affected the entire world, and every country is facing the challenges of having adequate medical facilities along with the availability of medical staff in rural and urban areas that have a high number of patients due to the pandemic. Due to the invasive method of treatment, SARS-COVID is spreading swiftly. In this paper, we propose an intelligent health monitoring framework using wearable Internet of Things (IoT) and Geo-fencing for COVID-19 susceptible and patient monitoring, and isolation and quarantine management to control the pandemic. The proposed system consists of four layers, and each layer has different functionality: a wearable sensors layer, IoT gateway layer, cloud server layer, and client application layer for visualization and analysis. The wearable sensors layer consists of wearable biomedical and GPS sensors for physiological parameters, and GPS and Wi-Fi Received Signal Strength Indicator acquisition for health monitoring and user Geo-fencing. The IoT gateway layer provides a Bluetooth and Wi-Fi based wireless body area network and IoT environment for data transmission anytime and anywhere. Cloud servers use Raspberry Pi and ThingSpeak cloud for data analysis and web-based application layers for remote monitoring based on user consent. The susceptible and patient conditions, real-time sensor’s data, and Geo-fencing enables minimizing the spread through close interaction. The results show the effectiveness of the proposed framework.