Cadmium toxicity: its' uptake and retaliation by plant defence system and ja signaling.

Cadmium (Cd+2) renders multifarious environmental stresses and highly toxic to nearly all living organisms including plants. Cd causes toxicity by unnecessary augmentation of ROS that targets essential molecules and fundamental processes in plants. In response, plants outfitted a repertory of mechanisms to offset Cd toxicity. The main elements of these are Cd chelation, sequestration into vacuoles, and adjustment of Cd uptake by transporters and escalation of antioxidative mechanism. Signal molecules like phytohormones and reactive oxygen species (ROS) activate the MAPK cascade, the activation of the antioxidant system andsynergistic crosstalk between different signal molecules in order to regulate plant responses to Cd toxicity. Transcription factors like WRKY, MYB, bHLH, bZIP, ERF, NAC etc., located downstream of MAPK, and are key factors in regulating Cd toxicity responses in plants. Apart from this, MAPK and Ca2+signaling also have a salient involvement in rectifying Cd stress in plants. This review highlighted the mechanism of Cd uptake, translocation, detoxification and the key role of defense system, MAPKs, Ca2+ signals and jasmonic acid in retaliating Cd toxicity via synchronous management of various other regulators and signaling components involved under stress condition.

Methyl jasmonate reduces cadmium toxicity by enhancing phenol and flavonoid metabolism and activating the antioxidant defense system in pigeon pea (Cajanus cajan).

Cadmium (Cd) is absorbed by plant roots from soil along with essential nutrients and affects plant growth and productivity. Methyl jasmonate (Me-JA) play important roles to mitigate Cd toxicity in plants. We have investigated the role of Me-JA to ameliorate Cd toxicity in Pigeon pea (Cajanus cajan). Plant root growth, biomass, cellular antioxidant defense system and expression of key regulatory genes in molecular and signaling process have been analyzed. Two Cajanus cajan varieties AL-882 and PAU-881 were grown at 25 °C, 16/8h light/dark conditions in three biological replicates at 5 mM Cd concentration, three concentration of Me-JA (0, 10 nM, 100 nM) and two concentrations in combination of Me-JA + Cd (10 nM Me-JA +5 mM Cd, 100 nM Me-JA +5 mM Cd). The seedlings were exposed to Cd stress consequently plants showed decrease in primary root growth (60.71%, in AL-882 and 8.33%, in PAU-881), shoot and root biomass and antioxidant enzymes activities. Me-JA treatment resulted in increased primary root growth (63.64%, in AL-882) and overall plant biomass. Oxidative stress generated due to Cd stress was counter balanced by Me-JA treatment. Me-JA reduced H2O2 free radicals formation and enhanced antioxidant enzyme activities and phenolic content in stressed seedlings. Me-JA treatment increased expression of CALM, IP3, CDPK2, MPKs (involved in calcium and kinase signaling pathways) and reduced expression of metal transporters (IRT1 and HMA3) genes. This reduction in metal transporters gene expression is a probable reason for low toxicity effect of Cd in root after Me-JA treatment which has potential implications in reducing the risk of Cd in the food chain.

Seed priming with methyl jasmonate mitigates copper and cadmium toxicity by modifying biochemical attributes and antioxidants in Cajanus cajan.

Contamination of agricultural soils with heavy metals (HMs) has posed major threat to the environment as well as human health. The aim of this study was to appraise the efficiency of key-antioxidant enzymes in enhancing plants' tolerance to HMs (heavy metals) like copper (Cu) and Cadmium (Cd), under the action of methyl jasmonate (Me-JA) in Cajanus cajan L. Seeds of C. cajan treated with Me-JA (0, 1 nM) were discretely subjected to noxious concentrations of Cu and Cd (0, 1, 5 mM) and raised for 12 days under controlled conditions in plant growth chamber for biochemical analysis. In contrast to Cd, Cu triggered oxidative stress more significantly (44.54% in 5 mM Cu increase in MDA as compared to control) and prominently thereby affecting plants' physiological and biochemical attributes. By activating the antioxidant machinery, Me-JA pre-treatment reduced HMs-induced oxidative stress, increased proline production, glutathione (41.95% under 5 mM Cu when treated with 1 nM Me-JA treatment) and ascorbic acid content by 160.4 % under aforemtioned treatments thus improving the redox status. Thus, in light of this our results put forward a firm basis of the positive role that Me-JA might play in the mitigation of oxidative stress caused due to HMs stress by stimulating antioxidant defense system leading to overall improvement of growth of C. cajan seedlings.

AI in Healthcare: Time-Series Forecasting Using Statistical, Neural, and Ensemble Architectures.

Both statistical and neural methods have been proposed in the literature to predict healthcare expenditures. However, less attention has been given to comparing predictions from both these methods as well as ensemble approaches in the healthcare domain. The primary objective of this paper was to evaluate different statistical, neural, and ensemble techniques in their ability to predict patients' weekly average expenditures on certain pain medications. Two statistical models, persistence (baseline) and autoregressive integrated moving average (ARIMA), a multilayer perceptron (MLP) model, a long short-term memory (LSTM) model, and an ensemble model combining predictions of the ARIMA, MLP, and LSTM models were calibrated to predict the expenditures on two different pain medications. In the MLP and LSTM models, we compared the influence of shuffling of training data and dropout of certain nodes in MLPs and nodes and recurrent connections in LSTMs in layers during training. Results revealed that the ensemble model outperformed the persistence, ARIMA, MLP, and LSTM models across both pain medications. In general, not shuffling the training data and adding the dropout helped the MLP models and shuffling the training data and not adding the dropout helped the LSTM models across both medications. We highlight the implications of using statistical, neural, and ensemble methods for time-series forecasting of outcomes in the healthcare domain.