Biopolymer Materials in Triboelectric Nanogenerators: A Review.

In advancing the transition of the energy sector toward heightened sustainability and environmental friendliness, biopolymers have emerged as key elements in the construction of triboelectric nanogenerators (TENGs) due to their renewable sources and excellent biodegradability. The development of these TENG devices is of significant importance to the next generation of renewable and sustainable energy technologies based on carbon-neutral materials. This paper introduces the working principles, material sources, and wide-ranging applications of biopolymer-based triboelectric nanogenerators (BP-TENGs). It focuses on the various categories of biopolymers, ranging from natural sources to microbial and chemical synthesis, showcasing their significant potential in enhancing TENG performance and expanding their application scope, while emphasizing their notable advantages in biocompatibility and environmental sustainability. To gain deeper insights into future trends, we discuss the practical applications of BP-TENG in different fields, categorizing them into energy harvesting, healthcare, and environmental monitoring. Finally, the paper reveals the shortcomings, challenges, and possible solutions of BP-TENG, aiming to promote the advancement and application of biopolymer-based TENG technology. We hope this review will inspire the further development of BP-TENG towards more efficient energy conversion and broader applications.

From Body Monitoring to Biomolecular Sensing: Current Progress and Future Perspectives of Triboelectric Nanogenerators in Point-of-Care Diagnostics.

In the constantly evolving field of medical diagnostics, triboelectric nanogenerators (TENGs) stand out as a groundbreaking innovation for simultaneously harnessing mechanical energy from micromovements and sensing stimuli from both the human body and the ambient environment. This advancement diminishes the dependence of biosensors on external power sources and paves the way for the application of TENGs in self-powered medical devices, especially in the realm of point-of-care diagnostics. In this review, we delve into the functionality of TENGs in point-of-care diagnostics. First, from the basic principle of how TENGs effectively transform subtle physical movements into electrical energy, thereby promoting the development of self-powered biosensors and medical devices that are particularly advantageous for real-time biological monitoring. Then, the adaptable design of TENGs that facilitate customization to meet individual patient needs is introduced, with a focus on their biocompatibility and safety in medical applications. Our in-depth analysis also covers TENG-based biosensor designs moving toward exceptional sensitivity and specificity in biomarker detection, for accurate and efficient diagnoses. Challenges and future prospects such as the integration of TENGs into wearable and implantable devices are also discussed. We aim for this review to illuminate the burgeoning field of TENG-based intelligent devices for continuous, real-time health monitoring; and to inspire further innovation in this captivating area of research that is in line with patient-centered healthcare.

Plant Adaptation to Flooding Stress under Changing Climate Conditions: Ongoing Breakthroughs and Future Challenges.

Climate-change-induced variations in temperature and rainfall patterns are a serious threat across the globe. Flooding is the foremost challenge to agricultural productivity, and it is believed to become more intense under a changing climate. Flooding is a serious form of stress that significantly reduces crop yields, and future climatic anomalies are predicted to make the problem even worse in many areas of the world. To cope with the prevailing flooding stress, plants have developed different morphological and anatomical adaptations in their roots, aerenchyma cells, and leaves. Therefore, researchers are paying more attention to identifying developed and adopted molecular-based plant mechanisms with the objective of obtaining flooding-resistant cultivars. In this review, we discuss the various physiological, anatomical, and morphological adaptations (aerenchyma cells, ROL barriers (redial O2 loss), and adventitious roots) and the phytohormonal regulation in plants under flooding stress. This review comprises ongoing innovations and strategies to mitigate flooding stress, and it also provides new insights into how this knowledge can be used to improve productivity in the scenario of a rapidly changing climate and increasing flood intensity.

Weed infestation and productivity of wheat crop sown in various cropping systems under conventional and conservation tillage.

Introduction: Climate change, pest infestation, and soil degradation are significantly reducing wheat (Triticum aestivum L.) yield. Wheat is cultivated in rice-wheat and cotton-wheat cropping systems and escalating global population is exerting substantial pressure on the efficiency of these systems. Conservation tillage and crop rotation could help in lowering soil degradation and pest infestation, and improving wheat yield. Methods: This three-year study evaluated soil properties, weed infestation and wheat yield under various tillage and cropping systems. Six different cropping systems, i.e., cotton-wheat, sorghum-wheat, mungbean-wheat, rice-wheat, sunflower-wheat, and fallow-wheat (control) and three tillage systems, i.e., conventional tillage (CT), zero-tillage (ZT) and minimum tillage (MT) were included in the study. Results: The individual and interactive effects of tillage and cropping systems significantly affected soil properties, weed infestation and yield of wheat crop. Overall, CT resulted in lower soil bulk density and higher porosity, while ZT behaved oppositely at all locations in this regard. Similarly, mungbean-wheat cropping system resulted in lower bulk density and higher porosity and nitrogen (N) contents, while fallow-wheat cropping system resulted in higher bulk density, and lower soil porosity and N contents. Similarly, ZT and CT resulted in higher and lower weed infestation, respectively. Likewise, lower and higher weed density and biomass were recorded in wheat-sorghum and wheat-fallow cropping systems, respectively at all locations. In the same way higher number of productive tillers, number of grains per spike, 1000-grain weight, grain yield, and economic returns of wheat crop were recorded for CT, whereas ZT resulted in lower values of these traits. Regarding interactions, wheat-mungbean cropping system with CT resulted in lower bulk density and higher porosity and N contents, whereas wheat-fallow system with ZT behaved oppositely at all locations in this regard. Similarly, higher and lower values for yield-related traits and economic returns of wheat crop were noted for mungbean-wheat cropping system under CT and fallow-wheat and sorghum-wheat cropping systems under ZT, respectively. It is concluded that the mungbean-wheat cropping system improved wheat productivity and soil health and sorghum-wheat cropping system could lower weed infestation. Therefore, these cropping systems can be practiced to lower weed infestation and improve wheat yield and economic returns.

Soil drenching of paclobutrazol: An efficient way to improve quinoa performance under salinity.

Salinity extent and severity is rising because of poor management practices on agricultural lands, possibility lies to grow salt-tolerant crops with better management techniques. Therefore, a highly nutritive salt-tolerant crop quinoa with immense potential to contribute for future food security was selected for this investigation. Soil drenching of paclobutrazol (PBZ; 20 mg l-1 ) was used to understand the ionic relations, gaseous exchange characteristics, oxidative defense system and yield under saline conditions (400 mM NaCl) including normal (0 mM NaCl) and no PBZ (0 mg l-1 ) as controls. The results revealed that salinity stress reduced the growth and yield of quinoa through perturbing ionic homeostasis with the consequences of overproduction of reactive oxygen species (ROS), oxidative damages and reduced photosynthesis. PBZ improved the quinoa performance through regulation of ionic homeostasis by decreasing Na+ , Cl- , while improving K+ , Mg2+ and Ca2+ concentration. It also enhanced the antioxidative system including ascorbic acid, phenylalanine ammonia-lyase, polyphenol oxidase and glutathione peroxidase, which scavenged the ROS (H2 O2 and O2 •- ) and lowered the oxidative damages (malondialdehyde level) under salinity in roots and more specifically in leaf tissues. The photosynthetic rate and stomatal conductance consequently improved (16 and 21%, respectively) in salt-stressed quinoa PBZ-treated compared to the non-treated ones and contributed to the improvement of panicle length (33%), 100-grain weight (8%) and grain yield (38%). Therefore, PBZ can be opted as a shotgun approach to improve quinoa performance and other crops under high saline conditions.

A Bayesian approach for estimating protein-protein interactions by integrating structural and non-structural biological data.

Accurate elucidation of genome wide protein-protein interactions is crucial for understanding the regulatory processes of the cell. High-throughput techniques, such as the yeast-2-hybrid (Y2H) assay, co-immunoprecipitation (co-IP), mass spectrometric (MS) protein complex identification, affinity purification (AP) etc., are generally relied upon to determine protein interactions. Unfortunately, each type of method is inherently subject to different types of noise and results in false positive interactions. On the other hand, precise understanding of proteins, especially knowledge of their functional associations is necessary for understanding how complex molecular machines function. To solve this problem, computational techniques are generally relied upon to precisely predict protein interactions. In this work, we present a novel method that combines structural and non-structural biological data to precisely predict protein interactions. The conceptual novelty of our approach lies in identifying and precisely associating biological information that provides substantial interaction clues. Our model combines structural and non-structural information using Bayesian statistics to calculate the likelihood of each interaction. The proposed model is tested on Saccharomyces cerevisiae's interactions extracted from the DIP and IntAct databases and provides substantial improvements in terms of accuracy, precision, recall and F1 score, as compared with the most widely used related state-of-the-art techniques.

A three-way approach for protein function classification.

The knowledge of protein functions plays an essential role in understanding biological cells and has a significant impact on human life in areas such as personalized medicine, better crops and improved therapeutic interventions. Due to expense and inherent difficulty of biological experiments, intelligent methods are generally relied upon for automatic assignment of functions to proteins. The technological advancements in the field of biology are improving our understanding of biological processes and are regularly resulting in new features and characteristics that better describe the role of proteins. It is inevitable to neglect and overlook these anticipated features in designing more effective classification techniques. A key issue in this context, that is not being sufficiently addressed, is how to build effective classification models and approaches for protein function prediction by incorporating and taking advantage from the ever evolving biological information. In this article, we propose a three-way decision making approach which provides provisions for seeking and incorporating future information. We considered probabilistic rough sets based models such as Game-Theoretic Rough Sets (GTRS) and Information-Theoretic Rough Sets (ITRS) for inducing three-way decisions. An architecture of protein functions classification with probabilistic rough sets based three-way decisions is proposed and explained. Experiments are carried out on Saccharomyces cerevisiae species dataset obtained from Uniprot database with the corresponding functional classes extracted from the Gene Ontology (GO) database. The results indicate that as the level of biological information increases, the number of deferred cases are reduced while maintaining similar level of accuracy.

Seed Priming with Selenium: Consequences for Emergence, Seedling Growth, and Biochemical Attributes of Rice.

The present study was undertaken to appraise the role of selenium priming for improving emergence and seedling growth of basmati rice. Seeds of two fine rice cultivars (Super and Shaheen Basmati) were primed with concentrations of 15, 30, 45, 60, 75, 90, and 105 µmol L(-1) selenium. Untreated dry- and hydro-primed seeds were maintained as the control and positive control, respectively. Selenium priming resulted in early commencement of emergence, triggered seedling growth irrespective of rice cultivar over untreated control, and was more effective than hydro-priming except at higher concentrations. Lower electrical conductivity of seed leachates, reduced lipid peroxidation, greater α-amylase activity, higher soluble sugars, and enhanced activities of enzymatic antioxidants (superoxide dismutase (SOD), peroxidase (POX), catalase (CAT), and glutathione peroxidase (GPX)) were observed in seeds primed with selenium. Rice seedlings derived from selenium-primed seeds exhibited more chlorophyll contents, while total phenolics were comparable with those of the control seedlings. The improved starch metabolism, greater membrane stability, and increased activity of antioxidants were considered as possible mechanisms responsible for such improvements in emergence and seedling vigor of rice mediated by selenium priming. Priming with selenium (15-60 µmol L(-1)) favored rice emergence and seedling growth. Nevertheless, soaking seeds in relatively concentrated (90 and 105 µmol L(-1)) selenium solution had overall detrimental effects.

A combined approach for genome wide protein function annotation/prediction.

BACKGROUND: Today large scale genome sequencing technologies are uncovering an increasing amount of new genes and proteins, which remain uncharacterized. Experimental procedures for protein function prediction are low throughput by nature and thus can't be used to keep up with the rate at which new proteins are discovered. On the other hand, proteins are the prominent stakeholders in almost all biological processes, and therefore the need to precisely know their functions for a better understanding of the underlying biological mechanism is inevitable. The challenge of annotating uncharacterized proteins in functional genomics and biology in general motivates the use of computational techniques well orchestrated to accurately predict their functions. METHODS: We propose a computational flow for the functional annotation of a protein able to assign the most probable functions to a protein by aggregating heterogeneous information. Considered information include: protein motifs, protein sequence similarity, and protein homology data gathered from interacting proteins, combined with data from highly similar non-interacting proteins (hereinafter called Similactors). Moreover, to increase the predictive power of our model we also compute and integrate term specific relationships among functional terms based on Gene Ontology (GO). RESULTS: We tested our method on Saccharomyces Cerevisiae and Homo sapiens species proteins. The aggregation of different structural and functional evidence with GO relationships outperforms, in terms of precision and accuracy of prediction than the other methods reported in literature. The predicted precision and accuracy is 100% for more than half of the input set for both species; overall, we obtained 85.38% precision and 81.95% accuracy for Homo sapiens and 79.73% precision and 80.06% accuracy for Saccharomyces Cerevisiae species proteins.