Root-to-shoot signaling positively mediates source-sink relation in late growth stages in diploid and tetraploid wheat.

Non-hydraulic root source signaling (nHRS) is a unique positive response to soil drying in the regulation of plant growth and development. However, it is unclear how the nHRS mediates the tradeoff between source and sink at the late growth stages and its adaptive mechanisms in primitive wheat. To address this issue, a root-splitting design was made by inserting solid partition in the middle of the pot culture to induce the occurrence of nHRS using four wheat cultivars (MO1 and MO4, diploid; DM22 and DM31, tetraploid) as materials. Three water treatments were designed as 1) both halves watered (CK), 2) holistic root system watered then droughted (FS), 3) one-half of the root system watered and half droughted (PS). FS and PS were designed to compare the role of the full root system and split root system to induce nHRS. Leaves samples were collected during booting and anthesis to compare the role of nHRS at both growth stages. The data indicated that under PS treatment, ABA concentration was significantly higher than FS and CK, demonstrating the induction of nHRS in split root design and nHRS decreased cytokinin (ZR) levels, particularly in the PS treatment. Soluble sugar and proline accumulation were higher in the anthesis stage as compared to the booting stage. POD activity was higher at anthesis, while CAT was higher at the booting stage. Increased ABA (nHRS) correlated with source-sink relationships and metabolic rate (i.e., leaf) connecting other stress signals. Biomass density showed superior resource acquisition and utilization capabilities in both FS and PS treatment as compared to CK in all plants. Our findings indicate that nHRS-induced alterations in phytohormones and their effect on source-sink relations were allied with the growth stages in primitive wheat.

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review.

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

Comparative study of machine learning methods integrated with genetic algorithm and particle swarm optimization for bio-char yield prediction.

In this study, Machine learning (ML) models integrated with genetic algorithm (GA) and particle swarm optimization (PSO) have been developed to predict, evaluate, and analyze biochar yield using biomass properties and process operating conditions. Comparative study of different ML algorithms integrated with GA and PSO were performed to improve the ML models architecture and parameters selection. The results proposed that Ensembled Learning Tree (ELT-PSO) model outperformed all other models and is favored for biochar yield prediction (R2 = 0.99, RMSE = 2.33). The partial dependence plots (PDPs) analysis shows the potential effects of each influencing parameter impact on the biochar yield and as well as shows that how these factors will interact during the pyrolysis process. A user-friendly software was developed based on the ELT-PSO model to avoid extensive and expensive experimentations without requiring considerable ML understanding. Difference recorded by GUI was less than 2% with experimental yield.