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While melatonin, a vital player in plant physiology, initially attracted recognition due to its involvement in animal circadian rhythms, the molecule appears to be a multifunctional molecule requiring substantial attention for prospective applications in sustainable horticulture. It has been identified and recorded in numerous fruit crops, and its significance in physiological functions is critical for crop productivity. It is critical in safeguarding plants in response to reactive oxygen species in oxidative stress, one of the most damaging stressors to plant life in adverse conditions. Melatonin also cooperates with plants in boosting stress resistance, which concerns abiotic stress factors, e.g. low and high temperature, drought stress, toxicity of heavy metals, and biotic stress factors, including pests and pathogens. The anti-senescence properties of melatonin in aging leaves may be explained by its widespread antioxidant activity and its function in maintaining chlorophyll. The function of melatonin in controlling the production of genes linked to ethylene to modify postharvest fruit ripening has been the subject of an astounding amount of research. Additionally, recent research has shown that melatonin works with other phytohormones and well-known chemicals like nitric oxide and reactive oxygen species to assist plants in responding to biotic stress.The present review emphasizes a perspective that examining the role of melatonin in fruit crop physiology and stress responses may be a promising research direction in prospective fruit crop yield. In particular, this perspective is well supported by the following: melatonin is involved in the antioxidant response of fruit crops and can thus be used to mitigate the stressful impact of various environmental conditions; melatonin influences the development of plants and, consequently, affects fruit yield and quality; and applying melatonin is feasible for mitigating the impact of abiotic factors, such as cold, drought, heavy metals, and biotic factors, pests, and pathogens.

Lack of factual correctness is an issue that still plagues state-of-the-art summarization systems despite their impressive progress on generating seemingly fluent summaries. In this paper, we show that factual inconsistency can be caused by irrelevant parts of the input text, which act as confounders. To that end, we leverage information-theoretic measures of causal effects to quantify the amount of confounding and precisely quantify how they affect the summarization performance. Based on insights derived from our theoretical results, we design a simple multi-task model to control such confounding by leveraging human-annotated relevant sentences when available. Crucially, we give a principled characterization of data distributions where such confounding can be large thereby necessitating the use of human annotated relevant sentences to generate factual summaries. Our approach improves faithfulness scores by 20\\% over strong baselines on AnswerSumm \\citep{fabbri2021answersumm}, a conversation summarization dataset where lack of faithfulness is a significant issue due to the subjective nature of the task. Our best method achieves the highest faithfulness score while also achieving state-of-the-art results on standard metrics like ROUGE and METEOR. We corroborate these improvements through human evaluation.

Natural language generation (NLG) is a critical component in conversational systems, owing to its role of formulating a correct and natural text response. Traditionally, NLG components have been deployed using template-based solutions. Although neural network solutions recently developed in the research community have been shown to provide several benefits, deployment of such model-based solutions has been challenging due to high latency, correctness issues, and high data needs. In this paper, we present approaches that have helped us deploy data-efficient neural solutions for NLG in conversational systems to production. We describe a family of sampling and modeling techniques to attain production quality with light-weight neural network models using only a fraction of the data that would be necessary otherwise, and show a thorough comparison between each. Our results show that domain complexity dictates the appropriate approach to achieve high data efficiency. Finally, we distill the lessons from our experimental findings into a list of best practices for production-level NLG model development, and present them in a brief runbook. Importantly, the end products of all of the techniques are small sequence-to-sequence models (2Mb) that we can reliably deploy in production.

Lightweighting of automobiles by use of novel low-cost, high strength-to-weight ratio structural materials can reduce the consumption of fossil fuels and in turn CO 2 emission. Working towards this goal we achieved high strength in a low cost β -titanium alloy, Ti–1Al–8V–5Fe (Ti185), by hierarchical nanostructure consisting of homogenous distribution of micron-scale and nanoscale α -phase precipitates within the β -phase matrix. The sequence of phase transformation leading to this hierarchical nanostructure is explored using electron microscopy and atom probe tomography. Our results suggest that the high number density of nanoscale α -phase precipitates in the β -phase matrix is due to ω assisted nucleation of α resulting in high tensile strength, greater than any current commercial titanium alloy. Thus hierarchical nanostructured Ti185 serves as an excellent candidate for replacing costlier titanium alloys and other structural alloys for cost-effective lightweighting applications. Lightweight materials with high strength are desirable for applications where they could reduce energy consumption. Here, the authors develop a low cost beta-titanium alloy that uses a hierarchical nanostructure of precipitates with different sizes to achieve high strength.

The ex-vivo biochemical changes of different body fluids also referred as aging of fluids are potential marker for the estimation of Time since deposition. Infrared spectroscopy has great potential to reveal the biochemical changes in these fluids as previously reported by several researchers. The present study is focused to analyze the spectral changes in the ATR-FTIR spectra of three body fluids, commonly encountered in violent crimes i.e., semen, saliva, and urine as they dry out. The whole analytical timeline is divided into relatively slow phase I due to the major contribution of water and faster Phase II due to significant evaporation of water. Two spectral regions i.e., 3200–3400 cm −1 and 1600–1000 cm −1 are the major contributors to the spectra of these fluids. Several peaks in the spectral region between 1600 and 1000 cm −1 showed highly significant regression equation with a higher coefficient of determination values in Phase II in contrary to the slow passing Phase I. Principal component and Partial Least Square Regression analysis are the two chemometric tool used to estimate the time since deposition of the aforesaid fluids as they dry out. Additionally, this study potentially estimates the time since deposition of an offense from the aging of the body fluids at the early stages after its occurrence as well as works as the precursor for further studies on an extended timeframe.

Beyond the most crucial roles of RNA molecules as a messenger, ribosomal, and transfer RNAs, the regulatory role of many non-coding RNAs (ncRNAs) in plant biology has been recognized. ncRNAs act as riboregulators by recognizing specific nucleic acid targets through homologous sequence interactions to regulate plant growth, development, and stress responses. Regulatory ncRNAs, ranging from small to long ncRNAs (lncRNAs), exert their control over a vast array of biological processes. Based on the mode of biogenesis and their function, ncRNAs evolved into different forms that include microRNAs (miRNAs), small interfering RNAs (siRNAs), miRNA variants (isomiRs), lncRNAs, circular RNAs (circRNAs), and derived ncRNAs. This article explains the different classes of ncRNAs and their role in plant development and stress responses. Furthermore, the applications of regulatory ncRNAs in crop improvement, targeting agriculturally important traits, have been discussed.

Capabilities in continuous monitoring of key physiological parameters of disease have never been more important than in the context of the global COVID-19 pandemic. Soft, skin-mounted electronics that incorporate high-bandwidth, miniaturized motion sensors enable digital, wireless measurements of mechanoacoustic (MA) signatures of both core vital signs (heart rate, respiratory rate, and temperature) and underexplored biomarkers (coughing count) with high fidelity and immunity to ambient noises. This paper summarizes an effort that integrates such MA sensors with a cloud data infrastructure and a set of analytics approaches based on digital filtering and convolutional neural networks for monitoring of COVID-19 infections in sick and healthy individuals in the hospital and the home. Unique features are in quantitative measurements of coughing and other vocal events, as indicators of both disease and infectiousness. Systematic imaging studies demonstrate correlations between the time and intensity of coughing, speaking, and laughing and the total droplet production, as an approximate indicator of the probability for disease spread. The sensors, deployed on COVID-19 patients along with healthy controls in both inpatient and home settings, record coughing frequency and intensity continuously, along with a collection of other biometrics. The results indicate a decaying trend of coughing frequency and intensity through the course of disease recovery, but with wide variations across patient populations. The methodology creates opportunities to study patterns in biometrics across individuals and among different demographic groups.

Cancer-associated fibroblasts (CAFs) demonstrate the characteristics of myofibroblast differentiation by often expressing the ultrastructure of alpha-smooth muscle actin (αSMA). However, heterogeneity among cancer-associated fibroblasts (CAFs), with respect to αSMA expression, has been demonstrated in several clinical studies of oral cancer. Like normal stem cells, stem-like cancer cells (SLCCs) are also regulated extrinsically by its microenvironment; therefore, we postulated that the heterogeneous oral-CAFs would differently regulate oral-SLCCs. Using transcriptomics, we clearly demonstrated that the gene expression differences between oral tumor-derived CAFs were indeed the molecular basis of heterogeneity. This also grouped these CAFs in two distinct clusters, which were named as C1 and C2. Interestingly, the oral-CAFs belonging to C1 or C2 clusters showed low or high αSMA-score, respectively. Our data with tumor tissues and in vitro co-culture experiments interestingly demonstrated a negative correlation between αSMA-score and cell proliferation, whereas, the frequency of oral-SLCCs was significantly positively correlated with αSMA-score. The oral-CAF-subtype with lower score for αSMA (C1-type CAFs) was more supportive for cell proliferation but suppressive for the self-renewal growth of oral-SLCCs. Further, we found the determining role of BMP4 in C1-type CAFs-mediated suppression of self-renewal of oral-SLCCs. Overall, we have discovered an unexplored interaction between CAFs with lower-αSMA expression and SLCCs in oral tumors and provided the first evidence about the involvement of CAF-expressed BMP4 in regulation of self-renewal of oral-SLCCs.

Carbonaceous aerosols play an important role in affecting human health, radiative forcing, hydrological cycle, and climate change. As our current understanding about the carbonaceous aerosols, the source(s) and process(es) associated with them in the ecologically sensitive North-Western Himalayas are limited; this systematic study was planned to understand inherent dynamics in the mass concentration and source contribution of carbonaceous aerosols in the Dhauladhar region. During four winter months (January 2015–April 2015), 24-h PM10 samples were collected every week simultaneously at the rural site of Pohara (32.19° N, 76.20° E; 750 m amsl) and the urban location of Dharamshala (32.20° N, 76.32° E; 1350 m amsl). These samples were analyzed by using thermal/optical carbon analyzer for different carbon forms. Organic carbon (OC) dominated over elemental carbon (EC) and was found to be 59.3 and 64.1% in total carbon (TC) at Pohara and Dharamshala, respectively. The respective mass concentrations of OC and EC were higher at Pohara (6.8 ± 2.3 and 4.8 ± 2.0 μg.m−3) in comparison to that observed in Dharamshala (5.0 ± 3.1 and 2.5 ± 0.6 μg.m−3). The OC/EC ratio at Pohara (1.51 ± 0.41) indicates the dominance of fossil fuel combustion (coal and vehicular exhaust), while at Dharamshala, an OC/EC of 2.01 ± 1.07 signified additional contribution from secondary organic carbon (SOC). Diagnostic ratios (OC/EC and char-EC/soot-EC) suggested dominance of emissions from fossil fuel combustion sources over biomass burning sources in the region. Estimated non-sea salt (nss)K+/OC and nssK+/EC ratios indicated heterogeneity within the biomass burning sources over low and high altitude locations. A strong correlation between nssK+ and SOC over a high altitude urban location further suggested possible conversion of gaseous precursors to carbonaceous particles during coniferous wood burning.

A comprehensive literature review of the optimization techniques used for the process parameter optimization of Abrasive Jet Machining (AJM), Ultrasonic Machining (USM), Laser Beam Machining (LBM), Electrochemical Machining (ECM), and Plasma Arc Machining (PAM) are presented in this review article. This review article is an extension of the review work carried out by previous researchers for the process parameter optimization of non-traditional machining processes using various advanced optimization algorithms. The review period considered for the same is from 2012 to 2022. The prime motive of this review article is to find out the sanguine effects of various optimization techniques used for the optimization of various considered objectives of selected non-traditional machining processes in addition to deemed materials and foremost process parameters. It is found that most of the researchers have more inclination towards the minimization of Surface Roughness (SR) compared to the maximization of the Material Removal Rate (MRR) as their objective function for AJM and PAM. Similarly, for USM and ECM, researchers are more inclined towards the maximization of MRR compared to the minimization of SR. Minimization of the Heat-Affected Zone (HAZ) and SR are the two most considered response parameters for the LBM and its allied processes. This study provides ready-to-use details on the use of various advanced optimization techniques for AJM, USM, LBM, ECM, and PAM, with the considered workpiece material, process parameters, and imposed limitations. This review work is carried out on such a large scale that it will help future researchers and industrialists to decide their research direction.