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The fast-growing urbanization and slow progress in the field of waste management have led to the accumulation of large quantities of animal wastes. Present work focused on the synthesis of low-cost and eco-friendly chicken bile juice mediated silver nanoparticles (BJ-AgNP). Results reveals that bile juices have enough potentiality towards the synthesis of almost uniform sizes (average size < 50 nm) of BJ-AgNPs which remains stable for more than 6 months. Response surface methodology (RSM) successfully demonstrated the optimised condition of AgNP-BJ synthesis. Factor like concentration of salt and bile extract and temperature are significantly responsible for the synthesis of nanoparticle which was further characterized using UV-Vis, TEM, FESEM, XRD, FTIR, TGA, and EDS. The synthesised nanoparticle showed excellent bactericidal activity against both Gram positive and Gram negative bacteria with MIC and MBC of 40 and 50 µg/mL for Bacillus subtilis (MTCC-441) and 60 and 60 µg/mL for Eschecheria coli (MTCC-1687) respectively. The synthesized nanoparticle also acted as an antibiofilm agent against Bacillus subtilis, with ~89% biofilm inhibition efficacy at 4 X MIC, having optimal bacterial concentration of 106 CFU/mL. Therefore, present findings clearly demonstrated that an absolute animal waste could be a valuable ingredient in the field of therapeutic nanoscience.

Plants generate a wide variety of organic components during their different growth phases. The majority of those compounds have been classified as primary and secondary metabolites. Secondary metabolites are essential in plants' adaptation to new changing environments and in managing several biotic and abiotic stress. It also invests some of its photosynthesized carbon as secondary metabolites to establish a mutual relationship with soil microorganisms in that specific niche. As soil harbors both pathogenic and beneficial microorganisms, it is essential to identify some specific metabolites that can discriminate beneficial and pathogenic ones. Thus, a detailed understanding of metabolite's architectures that interact with beneficial microorganisms could open a new horizon of ecology and agricultural research. Flavonoids are used as classic examples of secondary metabolites in this study to demonstrate recent developments in understanding and realizing how these valuable metabolites can be controlled at different levels. Most of the research was focused on plant flavonoids, which shield the host plant against competitors or predators, as well as having other ecological implications. Thus, in the present review, our goal is to cover a wide range of functional and signalling activities of secondary metabolites especially, flavonoids mediated selective cross-talk between plant and its beneficial soil microbiome. Here, we have summarized recent advances in understanding the interactions between plant species and their rhizosphere microbiomes through root exudates (flavonoids), with a focus on how these exudates facilitate rhizospheric associations.The online version contains supplementary material available at 10.1007/s11101-022-09806-3.

Dorpalen-Barry et al. proved Elser's conjecture about sign of Elser's number by interpreting them as certain sums of reduced Euler characteristics of an abstract simplicial complex known as $U$-nucleus complex. We prove a conjecture posed by them regarding the homology of $U$-nucleus complex., 8 pages, 8 figures

Two-dimensional molybdenum disulfide (MoS

Present work is highlighted on the conversion of waste PET plastics to carbon nanosphere, their characterization by SEM-EDX, XRD and FTIR and finally their application in the field of germination of Cicer arietinum and biochemical analysis. SEM and XRD results revealed that PET plastic are comfortably converted to carbon microsphere with a diameter ranges between 2-8 µm with amorphous nature and FTIR study suggested that the existence of aromatic C-H and aromatic ring along with carbonyl groups. Root morphology suggested that both root length and seminal root number gradually decrease with increasing carbon microsphere dose. Biochemical results revealed that the level of proline, catalase and MDA levels significantly (p < 0.0001) increase with increasing the dose of carbon microsphere. Finally, it can be concluded that lower dose of carbon microsphere could be beneficial for both seed germination and seedling growth, but higher dose may have adverse effect on plant community.

Flavour of tea is mainly contributed by a group of polyphenols - flavonoids. However, the content of flavonoid fluctuates seasonally and is found to be higher in the first flush of tea, when compared to the second flush. This disparity in the flavonoid content, and hence taste, incurs heavy economic losses to the tea plantation industry each harvest season. For our present study, four key product-specific enzymes (PAL, FNS, FLS and ANS) of the tea-specific flavonoid pathway were selected to perform molecular docking studies with specific virtually screened allosteric modulators. Results of docking analyses showed Naringenin, 2-Morpholin-4-ium-4-ylethanesulfonate, 6-C-Glucosylquercetin, 2-Oxoglutaric acid, 3,5,7,3',4'-pentahydroxyflavone to be capable of improving the spontaneity of the enzyme-substrate reactions in terms of docking score, RMSD values, and non-covalent interactions (H-bond,hydrophobic interaction, Π-stacking, salt bridge, etc.). Further, the evolutionary relationship of tea flavonoid pathway enzymes was constructed and compared with related taxa. The codon usage-based of tea flavonoid biosynthetic genes indicated the non-biasness of their nucleotide composition. Overall this study will provide a direction towards putative ligand-dependent enhancement of flavonoid content, irrespective of seasonal variation.

Tea (Camellia sinensis) is perhaps the most popular and economic beverage in the globe due to its distinctive fragrance and flavour generated by the leaves of commercially farmed tea plants. The tea microbiome has now become a prominent topic of attention for microbiologists in recent years as it can help the plant for soil nutrient acquisition as well as stress management. Tea roots are well known to be colonized by Arbuscular Mycorrhizal Fungi (AMF) and many other beneficial microorganisms that boost the growth of the tea which increases leaf amino acids, protein, caffeine, and polyphenols content. One of the primary goals of rhizosphere microbial biology is to aid in the establishment of agricultural systems that provide high quantities of the food supply while minimizing environmental effects and anthropogenic activities. The present review is aimed to highlight the importance of microbes (along with their phylogeny) derived from cultivated and natural tea rhizospheres to understand the role of AMF and rhizospheric bacterial population to improve plant growth, enhancement of tea quality, and protecting tea plants from pathogens. This review also summarizes recent advances in our understanding of the diversity and profile of tea-associated bacteria. The utilization of the tea microbiome as a “natural resource” could provide holistic development in tea cultivation to ensure sustainability, highlighting knowledge gaps and future microbiome research.

The present study is focused on synthesis of silver nanoparticles from weeds and an assessment of their mosquito larvicidal efficacy. This study also presented the toxicological effects as well as the stability of these nanoparticles in aquatic mesocosms. The weed Digiteria sanguinallis was first time used for the synthesis of silver nanoparticles. The synthesized nanoparticles were characterized by various analytical techniques, such as UV-VIS, TEM, FESEM, EDX, XRD, FTIR, and zeta potential study. The result revealed that the nanoparticles are crystalline, spherical shape with band gap 2.44 eV, and average size 18 nm. The LC

Health-care services are implanted by deploying roots of information extraction techniques. This extraction process is laborious and time-consuming due to unavailability of medical experts. Thus, in the present task, we were motivated to develop an automated extraction system for identifying medical and non-medical concepts. These concepts help to extract the key information from medical corpora. Not only medical concepts but also their non-medical counterparts are equally important for diagnosis purposes. Hence, we have employed three different approaches such as unsupervised, supervised, and their combined ensemble version to identify both medical and non-medical terms (words/phrases). The unsupervised module consists of two phases: parts-of-speech (POS) tagging followed by searching in a domain-specific lexicon, namely WordNet of Medical Event (WME 3.0). On the other hand the supervised module is designed by two machine learning classifiers, namely Naive Bayes and Conditional Random Field (CRF) along with various features like category, POS, sentiment, etc. Finally, we have combined the important outcomes of unsupervised and supervised modules and developed two versions of ensemble module (Ensemble-I and Ensemble-II). All the modules identify uni-gram, bi-gram, tri-gram, and more than tri-gram medical concepts and separate non-medical words or phrases in a context. In order to evaluate all modules of concept identification system, we have prepared an experimental dataset. It has been split into three parts, namely training, development, and test. We observed that ensemble module provides better output in contrast with individual modules and Ensemble-I outperforms Ensemble-II in identifying medical concepts consisting of all possible n-grams. The result analysis shows that the F-measures of 0.91 and 0.94 have been obtained for identifying medical concepts and non-medical words/phrases using both of the ensemble modules, respectively. The present research reports the initial steps to build an automated concept identification framework in health-care. This system assists in designing various domain-specific applications like annotation, categorization, recommendation system, etc.

The recent trends of Internet help to produce the sentiment and emotion-based opinion at the time of conversation between human beings. During the Coronavirus disease (COVID-19) pandemic, we are spending maximum time of the day on Internet especially on social networking Web sites. The Web sites are Twitter, Facebook, and WhatsApp, which are taking a crucial role to communicate with each other in the form of messages. The messages are representing such as short-texts and micro-texts and carrying sentiment. The sentiment identification is challenging due to the length of the message. In the present paper, we are motivated to design a sentiment analysis system for Twitter micro-texts in the topic of Coronavirus disease (COVID-19). Hence, we have scrawled a dataset from Twitter using Twitter API and presented as our experimental dataset. Additionally, we have developed two state-of-the-art techniques, viz. unsupervised and supervised to build this system. The unsupervised technique helps to understand the characteristic of the dataset, whereas supervised technique assists in improving the accuracy of the system. The developed system may help to design various domain-specific applications such as annotation and emotion identification system for micro-texts in future.

NTD are prevalent in economically backward society of the developing countries. NTD has become a major threat around the globe, resulting in millions of deaths per year. Accurate, rapid, and cost-effective tools for disease detection and control are required. Conventional diagnostic methods have limitations such as time consuming, less sensitive, laborious, and cost effective. The drug resistance mechanism has become a crucial challenge which has compelled the scientist to hunt for alternative strategies to combat these diseases. Nanodiagnostics have brought the revolution in this field, exploring various multiplexed methods for disease detection. This chapter deals with various multiplexed detection approaches used in nanodiagnostic tools, its advancements, and future challenges.