Recent publications
In this study, wild apricot kernels from the Himalayan region of India were studied for their physicochemical and textural properties. The wild apricot kernels had 3.45% moisture content, 28.35% protein and 45.16% fat. The length, width, thickness, equivalent diameter, thousand kernels and shape index weight determined were 15.90 mm, 9.10 mm, 4.40 mm, 8.57 mm, 0.341 kg and 0.42, respectively. The surface area, seed volume, sphericity, aspect ratio, porosity, bulk and true density were 196.58 mm², 182.68 mm³, 53.90%, 57.0%, 37.63%, 0.58 and 0.94 g/ml. Wild apricot kernels’ hardness, adhesiveness, fracturability, cohesiveness, springiness, chewiness, gumminess and resilience were 33.35 kg, -1.09 kg. sec, 14.59 kg, 0.41, 0.53, 7.50 kg, 13.89 kg and 0.23, respectively. Wild apricot kernels contained 5673.09 mg/100 g of amygdalin, 1569.0 mg CN/kg of cyanide content and 82.52 mg GAE/100 g of total phenolic content. DPPH scavenging (IC50) and Ferric reducing ability (EC50) of wild apricot kernel were 3.64 mg/ml and 0.166 mg/ml, respectively. Apricot kernel was rich in glutamic acid and arginine, major amino acids and unsaturated fatty acids with a significant proportion of oleic acid and linoleic acid.
Common mycorrhizal networks (CMNs) facilitate nutrient transfer between plants, but their role in supporting non-mycorrhizal species remains largely unexplored. This study investigates the effect of CMNs on the growth and nutrient uptake of the non-mycorrhizal plant Chenopodium album, in association with the mycorrhizal plant Parthenium hysterophorus. The treatments included: C. album alone, C. album grown with P. hysterophorus to allow CMN formation, and C. album treated with fungicide to inhibit mycorrhizal activity. Results showed that CMN treatment significantly enhanced the plant growth and uptake of nutrient content (N and P) in C. album compared to the control. There was no mycorrhizal colonization in C. album, while high colonization in P. hysterophorus suggested that the enhanced growth in C. album was due to nutrient uptake transfer through the CMNs. The fungicide treatment resulted in reduced growth and uptake of nutrient content, providing further evidence that CMNs and mycorrhizal associations of mycorrhizal plants, enhanced the growth of C. album. These findings provide the first evidence that CMNs can enhance the growth and nutrient uptake of non-mycorrhizal plants through associations with mycorrhizal partners.
Plant growth promoting (PGP) rhizobacteria are crucial source of fertilizer, which have the potential to boost crop productivity and encourage plant development. Plant nutrition status can be improved by microbial inoculation with naturally occurring nutrients or by adding chemical fertilizers. The purpose of present study was to assess fertilization and multifunctional PGP inoculant on growth promotion of garlic (Allium sativum L.). A total of 65 bacterial strains were isolated from rhizospheric region of garlic and screened for various PGP attributes. On the basic of multifunctional PGP attributes, the rhizobacterial strain EU-GRN-46 was selected and identified as Sporosarcina globispora and evaluated on garlic crop under in-vitro and in-vivo conditions. The strain EU-GRN-46 showed efficient gibberellin producer GA1 (45 ng mL− 1) along with other PGP attributes. To the best of our knowledge, the present study is the first report on gibberellin producing novel bacterial strain Sporosarcina globispora EU-GRN-46 for growth enhancement of garlic. Moreover, the inoculation of this identified strain improves the growth and physiological parameters of garlic as compared to cattle dung manure and chemical fertilizer. The efficient strain EU-GRN-46 could be used as bioinoculants for horticultural crops grown in mountainous regions.
Mushrooms have evolved as a nutritional powerhouse, harnessing a diverse spectrum of bioactive molecules to fortify human health. Hypsizygus ulmarius represents a pioneering species within the oyster mushrooms distinguished by its unique characteristics and potential abilities. It is characterized by its large fruiting bodies, which have a meaty flavor and excellent taste. Additionally, this mushroom has a high yield and biological efficiency. This mushroom also holds significant importance globally and is cultivated in China, Japan and other Asian nations due to its favorable growth conditions, exceptional nutritional value, and medicinal attributes. This review focuses on the nutrition and bioactive molecules present in this mushroom species and their further implications in medicine, agriculture, biotechnology for the development of new anti-bacterial agents and their potential industrial uses for human health. This review aims to provide more recent information on the above aspects. Hypsizygus ulmarius shows great potential as a valuable source of several nutrients and bioactive chemicals that may have therapeutic qualities. The immunomodulatory, anti-oxidant, anti-inflammatory and potential anti-cancer properties of this mushroom provide opportunities for further future research in the creation of beneficial functional food, dietary supplements and pharmaceutical interventions to enhance human health.
Keywords: Bioactive properties; Elm oyster mushroom; Functional food; Hypsizygus ulmarius; Medicinal properties, Nutrition
In this research work, we developed a mathematical model of heat transfer for different profiles of fins. This paper investigates heat transfer dynamics in continuously moving fins (rectangular, trapezoidal and concave parabolic), focusing on temperature‐dependent thermal conductivity, heat transfer coefficients, internal heat generation and emissivity that varies with temperature and wavelength. The different values of the heat transfer coefficient capture various types of convection, nucleate boiling, condensation, and radiation effects, while treating thermal conductivity as a linear function of temperature. This problem is converted into a dimensionless form, and we adopted the Legendre wavelet collocation method (LWCM) to get the solution of the fin problem for various profiles. An exact solution in specific cases shows congruence up to seven to eight decimal places with LWCM results. Moreover, our analysis explores the effect of numerous non‐dimensional parameters such as thermal conductivity parameter A A, Peclet number Pe , surface emissivity parameter B, convention‐conduction parameter N cc , radiation‐conduction parameter N rc , internal heat generation Q Q, D fin taper ratio on the temperature profile and fin efficiency were studied in detail. As N cc , N rc , and B, D increases in magnitude, the temperature inside the fin decreases, while higher values of Peclet number (P e ), A, m, and Q cause lower heat transfer rate inside the fin. The results provide significant insights into the complex interplay of thermal processes across different fin geometries, emphasizing the importance of these dependencies for accurate modeling and optimization in thermal management systems.
The world has changed exponentially from the time of famine to the existent, when food is produced globally to feed a population that is expanding at an exponential rate. The intensification of agriculture through the introduction of mechanical, industrial, and economic inputs has been a hallmark of major agricultural revolutions, which have enabled this transformation. However, the explosion of agricultural inputs consisting of fertilizers, pesticides, and irrigation brought about by this quick development in agriculture has also resulted in long-term environmental crises. These challenges have highlighted the pressing need to safeguard our communal spaces, especially the environment, by means of a participatory strategy that engages nations everywhere, irrespective of their level of development. One notable effort in this area is Zero Budget Natural Farming (ZBNF), which emphasizes the value of utilizing the complementary effects of plant and animal products to improve soil fertility, encourage the growth of beneficial microbes, and improve development of crops. The development of self-sustaining agro-ecosystems is the ultimate goal. Consequently, the world is depending on the use of microbial formulations in agriculture to address the "5F" crisis; food, feed, fuel, fertilizer, and finance. Despite the fact that there are now many studies being conducted in this area, the market need for effective microbial formulations outweigh the supply. Many different microbes have been considered so far for their potential as plant stimulants, but there are still innumerable soil microorganisms that need to be found in order to play a useful role in the formulation companies. The present review deals with improving the broad range of mechanism of microbial formulations, delivery methods, challenges and biosafety issue and assessment for agricultural sustainability that support the sustainable development goals (SDGs).
Recycling leftovers from food processing presents a significant challenge, with apricot kernels being one such by-product with immense untapped potential. This study investigates the impact of microwave treatment at 100W, 180W, 300W, 450W, and 600W for 6 min on the compositional attributes, antioxidant activity, and anti-nutritional components of apricot kernels. Results revealed that microwave heating led to a reduction in moisture content and a simultaneous increase in protein and oil content. Notably, treatment at 450W for 6 min significantly enhanced fatty acid profile, total phenolic content (1.16-fold), total flavonoid content (1.52-fold), and antioxidant potential (1.12-fold). Importantly, this treatment also reduced anti-nutritional factors significantly, such as amygdalin content by 53.73%, hydrocyanic acid by 56.94%, cyanogenic glycosides by 33.50%, phytic acid by 34.61%, and tannins by 31.88%. However, prolonged heating at 600W for 6 min negatively impacted these quality attributes. Color alterations, including darker hues due to pyrolysis and browning reactions, were noticed at 450W for 6 min. The findings highlight the potential of microwave treatment as a green detoxification method to enhance the value and safety of apricot kernels, promoting their utilization in various industrial applications and contributing to waste reduction in the food processing industry.
Landslide susceptibility assessment has been a comprehensive tool for decision makers. However, the efficacy of susceptibility model depends on factor selection and the scientific trustworthiness of the results yielded is varying. This research was objectified to select the factors for model construction through an ensemble of genetic algorithm and Boruta algorithm. 1,888 landslides and 1,888 non-landslides points were collected and randomly split into 70:30 ratio for model training and validation purpose. Twenty selected environmental factors were utilized for model construction. Six advanced machine learning models, Sparse Partial Least Square, Bayesian Generalized Linear Model, Neural Network with Principal Component Analysis, Multivariate Adaptive Regression Spline, Boosted Decision Tree and Extreme Gradient Boosting, were used for susceptibility map preparation with their hyperparameters optimized through Particle Swarm Optimization. The models attained astounding prediction results with testing dataset having AUCROC score of 0.84, 0.85, 0.89, 0.89, 0.87, and 0.95 respectively. Following AUCROC, the model performances were validated through the Quality Sum Index (Q’s), which resulted highest quantification for XGBoost model (3.54), which proved the model excellence. The model’s discrimination capability was quantified through Kolmogorov-Smirnov (KS) statistics, which showed XGBoost as the most efficient model having a KS value of 95.8%, following which came the MARS model with KS value of 65.9%. Furthermore, the uncertainty of the model was computed and confidence map (CNFM) was generated for actual susceptibility map. The regional policy makers for disaster mitigation will be greatly benefitted from the findings of this research.
Wolff-Parkinson-White (WPW) syndrome was first described by Louis Wolff, John Parkinson, and Paul Dudley White in 1930.[1] This cardiac condition is characterized by an abnormal accessory pathway that predisposes patients to tachyarrhythmias. A 45-year-old female patient with chief complaints of chest pain and palpitations for one day. An ECG revealed a short PR interval, broad QRS complex and delta wave, leading to a diagnosis of WPW syndrome. Advanced imaging and electrophysiological studies confirmed the presence of an accessory pathway. Treatment options were discussed, including antiarrhythmic medications and radiofrequency catheter ablation (RFCA). WPW syndrome is associated with a risk of sudden cardiac death, but advancements in diagnostic and therapeutic techniques have significantly improved the prognosis. RFCA is particularly noted for its high success rate and low complication profile, offering a potential curative solution for many patients.
Indigenous groups emerge as essential guardians of a priceless natural heritage. These groups have developed good insights from generations coexisting with nature firmly rooted in their natural environment. Their commitment to nature preservation creates a complex network of understanding and practices that protect their local environment, flora, and wildlife. Also, Indigenous groups have insights into climate change adaptation by exhibiting several practices that help them to understand the variations in average temperature, wind, solar radiation, rainfall intensity, and patterns. This is pertinent because local communities and Indigenous people are experiencing real, continuing effects of climate change that are affecting many different aspects of their socio-ecological systems. Since agriculture has been the bulk of the population’s primary source of income, these effects tend to render the Indigenous population and the community vulnerable. Notwithstanding challenges and information shortages, these approaches may be strengthened by accepting and modifying newly imported technology from other societies or contemporary science. Therefore, it is necessary to identify these behaviours since community-based adaptation strategies could successfully mainstream them.
Natural Disasters like landslides affect livelihood and nature. To mitigate this hazard, scientists developed Landslide Susceptibility Mapping (LSM), which helps to identify landslide-prone zones. With the advancements in Geographical Information Systems, machine learning approaches have taken over heuristic techniques for LSM. However, model uncertainty has yet to be considered. This study focused to use the advantage the uncertainty analysis to generate more precise LSM. The present study considered twenty-one geo-environmental factors to evaluate LSM in the Darjeeling Himalayas. 1,888 landslide locations were used to prepare the landslide inventory, and 1,888 non-landslide points were carefully created for model training purposes. Seven advanced machine learning methods, viz., naive Bayes, boosted decision tree, linear discriminant analysis, flexible discriminant analysis, monotone multilayer perceptron, gradient boosting machine, and extreme gradient boosting, were utilized for preparing landslide susceptibility maps. The constructed maps were then categorized into five susceptibility classes, viz., very low, low, moderate, high, and very high, and these were validated through the Area Under Receiver Operating Characteristics curve, Kolmogorov–Smirnov statistics, and Quality Sum method. The machine learning model's performance was evaluated through classification metrics, viz., overall accuracy, sensitivity (recall), specificity, precision, and F1-score. With AUCROC values greater than 0.90 for both the training and testing datasets, KS statistics values of 94.6 and 74.5, respectively, and Quality sum index of 2.671 and 2.058, respectively, XGBoost and GBM were found to be better performing than the rest of the utilized models. An uncertainty analysis was attempted using the coefficient-of-variation method and aleatoric uncertainty (lowest value of 0.024 for XGBoost and highest value of 0.25 for LDA). A confidence map for each susceptibility map was generated, which can be utilized as a reference for policymakers to formulate landslide mitigation strategies on a regional scale.
It is still unclear what contribution transition metals like iron play in the room-temperature ferromagnetism (RTFM) of Fe-doped ZnO nanoparticles. Although some research concluded that the magnetic interaction involves the Fe ions, conversely, other people do not think that Fe directly contributes to the magnetic exchange interaction. We have extensively examined the structural, optical, vibrational, dielectric, and magnetic properties of (FexZn1-xO: x = 0.00, 0.10, 0.20, and 0.30) nanoparticles synthesized with sol–gel route to contribute to the understanding of this problem. Pristine ZnO samples showed compressive microstrain, which changed to tensile behavior when Fe ions of various weight percentages were added. In FESEM images, cauliflower-like surface morphology was observed with particle aggregation. The Maxwell–Wagner model explains the observed dielectric behavior. According to Tauc plot estimates, direct optical band gaps were discovered in the range of 2.89–3.24 eV. Ac power law fit on ac conductivity shows that the hopping of charges is responsible for the conduction phenomenon in the samples under investigation. With the Fe-ions doping, a good ferromagnetic signature as compared to non-magnetic ZnO nanoparticles was also recorded at ambient temperature.
Arbuscular mycorrhizal fungi (AMF) can improve water-deficit tolerance in tomatoes, although very few studies have examined the AMF contribution to the metabolism of proline under water-deficit stress. In our study, we investigated the effects of AMF inoculation on plant growth and drought tolerance in tomatoes (Solanum lycopersicum) under well-watered and drought conditions. AMF inoculations were applied in treatments with or without AMF, and with Rhizophagus intraradices, Funneliformis mosseae, or both. Our results evident that AMF colonization significantly increased the plant growth of tomatoes despite soil water conditions and significant with dually inoculated plants and R. intraradices was more effective than F. mosseae. During AMF inoculation and water stress conditions, photosynthesis increased significantly, while proline levels showed no significant change under these conditions. AMF could enhance the growth of the crop, drought tolerance through changes in morphological, physiological, and biochemical qualities of tomato crops. It summarized that AMF enhances the higher SLA, LAR, RGR, and photosynthetic yield under both watered and drought conditions. AMF enhanced the nutritional status, combined with leaf relative water content (RWC), which assists the plant’s translocation of minerals and alleviates the impact of drought on tomato growth.
The increasing industrialization, road and air transport, and intensive agriculture practices to meet the demands of a growing population have become a threat to the environment globally. The utilization of agrochemicals in an inappropriate way to enhance productivity has depleted the soil fertility, and biodiversity and negatively affected the climate. Keeping in view the present scenario, adoption of sustainable, economically and socially viable approaches are of utmost importance. Azolla is a free floating fern of great potential as a step towards achieving agro-environmental sustainability. Carbon neutral system has been the need of the day. Various species of Azolla have been reported from tropical and subtropical areas of the world. The symbiotic association of Azolla with nitrogen fixing blue green alga, Anabaena is an important co-evolved system and important contributor to biotechnological fields. Along with biological nitrogen fixation, cyanobacteria and Azolla promote plant growth as their extract contains different plant growth-promoting substances such as auxins, cytokinins, and gibberellins. Different species of Azolla play an amazing role for plant growth promotion as biofertilizers and for amelioration of abiotic stress. The use of Azolla is thus an innovative technology towards clean and green environment as they can sequester a lot of carbon dioxide in their bodies. The present review deals with diversity, distribution, and biotechnological applications of Azolla in different sectors, including agriculture, industry, and the environment.
Titanium dioxide (TiO2) nanoparticles are being extensively used in a wide range of industrial applications for producing a variety of different consumer products, including medicines and even food items. The consumption of these products is increasing at an alarming rate, and this results in the release of these nanoparticles in the environment, causing a threat to organisms thriving in aquatic as well as terrestrial ecosystems. That is why screening such materials for their geno-toxic effects, if any, becomes essential. A toxicity assay was performed to determine the LD20 of these nanoparticles for the mosquito Culex quinquefaciatus by Probit analysis. Early fourth instar larvae were exposed to the selected dose of 50 µg/mL, which is < LD20 value, for 24 h treatment. Chromo-somal slides were prepared from lacto-aceto-orcein-stained gonads of adult mosquitoes developed from treated and control larvae. These nanoparticles were reported cytotoxic as a statistically significant decline in mitotic index in treated mosquitoes than controls were observed. The nanoparticles were also found to induce various structural and numerical chromosomal aberrations in the treated lot. In the end, it can be concluded that these nanoparticles do have a genotoxic effect. The present study provides a caution against further use of these nanoparticles. There must be the development of strategies for the safe, sustainable use as well as proper disposal of these nanoparticles so as to protect both biotic and non-biotic components of the environment.
Artificial intelligence (AI) is a technology that combines machine learning (ML) and deep learning. It has numerous usages in the domains of medicine and other sciences. Artificial intelligence can forecast the behavior of a drug's target protein and predict its desired physicochemical qualities. AI's potential to enhance healthcare services offerings formerly unheard-of opportunities for cost reserves, enhanced overall clinical and patient outcomes. The recent development of research in the biomedical field, encompassing fields such as genomics, computational medicine, AI, and algorithms for learning, has led to the demand for novel technology, a fresh workforce, and new standards of practice set the stage for the revolution in healthcare. By connecting these health statistics with cutting-edge AI technologies, precise insights into patient treatment can be obtained. Moreover, AI can aid in the search for new drugs by foretelling the target protein's two-dimensional structure. In the current review, an overview of the latest AI-based technologies and how they may be employed to reduce product development time to market and snowballing product quality, cost-effectiveness, as well as security throughout the manufacturing process is detailed.
Weather parameters, such as precipitation and temperature predictions, could be advantages in to making decisions, risk management, and water resource optimization. These two variables have an indisputable impact on the hydrological cycle, crop production cycles, agricultural water usage, and the environment. This paper inspects the time series analysis of the monthly precipitation and mean temperatures for the Punjab and Haryana states of India. For Punjab state, data was taken for the years 1951–2020 and for Haryana state, data was collected for the years 1957–2020. In order to anticipate the subsequent 15 years (2016–2030), using the seasonal ARIMA (SARIMA) model and fitted to the data up to the year 2015. For precipitation, ARIMA (1,1,0) (0,1,2) 12 and ARIMA (1,1,2) (0,1,1) 12 were the most suitable model for precipitation data analysis of Haryana and Punjab states, respectively. For temperature, best selected model was ARIMA (2,1,2) (0,1,1) 12, and ARIMA (2,1,2) (2,1,2) 12 for Haryana and Punjab states, respectively. The predicted outcomes establish that the predicted data closely matches the data’s pattern. For certain years, however, overestimations of precipitation in July have been discovered.
Endophytic microbes are an outstanding bioresources of bioactive metabolites. Cellulases have a great potential in industrial sectors including textile wet processing, biostoning of denim fabric, biopolishing of textile fibers, softening of garments, and removal of excess dye from the fabrics. In the present investigation, endophytic mushroom was isolated from leaves of the medicinal plant Cannabis sativa. The strain EU-FB-14 was screened for production of extra-cellular hydrolytic enzymes including cellulases, amylases, pectinases and proteases as well as secondary metabolites including siderophores, IAA and pigments. The isolated macrofungus showed cellulase activity and produced other enzymes as well. The isolated endophyte Psilocybe ovoideocystidiata EU-FB-14was checked for its anti-microbial activity against pathogenic Bacillus cereus (MTCC-430), E. coli (MTCC-1687) and Yersinia enterocolitica (MTCC-4912).Psilocybe ovoideocystidiata EU-FB-14 thus could be used for bioactive compounds production for industrial sustainability.
Nitrogen, phosphorus, and potassium are the three most essential micronutrients which play major roles in plant survivability by being a structural or non-structural component of the cell. Plants acquire these nutrients from soil in the fixed (NO3¯, NH4+) and solubilized forms (K+, H2PO4− and HPO42−). In soil, the fixed and solubilized forms of nutrients are unavailable or available in bare minimum amounts; therefore, agrochemicals were introduced. Agrochemicals, mined from the deposits or chemically prepared, have been widely used in the agricultural farms over the decades for the sake of higher production of the crops. The excessive use of agrochemicals has been found to be deleterious for humans, as well as the environment. In the environment, agrochemical usage resulted in soil acidification, disturbance of microbial ecology, and eutrophication of aquatic and terrestrial ecosystems. A solution to such devastating agro-input was found to be substituted by macronutrients-availing microbiomes. Macronutrients-availing microbiomes solubilize and fix the insoluble form of nutrients and convert them into soluble forms without causing any significant harm to the environment. Microbes convert the insoluble form to the soluble form of macronutrients (nitrogen, phosphorus, and potassium) through different mechanisms such as fixation, solubilization, and chelation. The microbiomes having capability of fixing and solubilizing nutrients contain some specific genes which have been reported in diverse microbial species surviving in different niches. In the present review, the biodiversity, mechanism of action, and genomics of different macronutrients-availing microbiomes are presented.
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