Search Results (121)

The worldwide agriculture industry is facing increasing problems due to rapid population increase and increasingly unfavorable weather patterns. In order to reach the projected food production targets, which are essential for guaranteeing global food security, innovative and sustainable agricultural methods must be adopted. Conventional approaches, including traditional breeding procedures, often cannot handle the complex and simultaneous effects of biotic pressures such as pest infestations, disease attacks, and nutritional imbalances, as well as abiotic stresses including heat, salt, drought, and heavy metal toxicity. Applying phytohormonal approaches, particularly those involving hormonal crosstalk, presents a viable way to increase crop resilience in this context. Abscisic acid (ABA), gibberellins (GAs), auxin, cytokinins, salicylic acid (SA), jasmonic acid (JA), ethylene, and GA are among the plant hormones that control plant stress responses. In order to precisely respond to a range of environmental stimuli, these hormones allow plants to control gene expression, signal transduction, and physiological adaptation through intricate networks of antagonistic and constructive interactions. This review focuses on how the principal hormonal signaling pathways (in particular, ABA-ET, ABA-JA, JA-SA, and ABA-auxin) intricately interact and how they affect the plant stress response. For example, ABA-driven drought tolerance controls immunological responses and stomatal behavior through antagonistic interactions with ET and SA, while using SnRK2 kinases to activate genes that react to stress. Similarly, the transcription factor MYC2 is an essential node in ABA–JA crosstalk and mediates the integration of defense and drought signals. Plants’ complex hormonal crosstalk networks are an example of a precisely calibrated regulatory system that strikes a balance between growth and abiotic stress adaptation. ABA, JA, SA, ethylene, auxin, cytokinin, GA, and BR are examples of central nodes that interact dynamically and context-specifically to modify signal transduction, rewire gene expression, and change physiological outcomes. To engineer stress-resilient crops in the face of shifting environmental challenges, a systems-level view of these pathways is provided by a combination of enrichment analyses and STRING-based interaction mapping. These hormonal interactions are directly related to the United Nations Sustainable Development Goals (SDGs), particularly SDGs 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 13 (Climate Action). This review emphasizes the potential of biotechnologies to use hormone signaling to improve agricultural performance and sustainability by uncovering the molecular foundations of hormonal crosstalk. Increasing our understanding of these pathways presents a strategic opportunity to increase crop resilience, reduce environmental degradation, and secure food systems in the face of increasing climate unpredictability. Full article

In this study, camel milk (CM) and Gir cow milk (GCM) were fermented through cofermentation via yeast–lactic cultures, i.e., Lacticaseibacillus rhamnosus (M9, MTCC 25516) and Saccharomyces cerevisiae (WBS2A, MG101828), and their antioxidant and antidiabetic effectiveness were studied. To optimize the growth conditions, the level of proteolysis was evaluated by exploring various inoculation levels (1.5, 2.0 and 2.5%) as well as incubation durations (0, 12, 24, 36 and 48 h). Peptides were extracted and purified through 2D gel electrophoresis as well as SDS–PAGE. Water-soluble extracts (WSEs) of ultrafiltered (UF) peptide fractions were evaluated via reversed-phase high-performance liquid chromatography (RP-HPLC) to identify the peptide segments. By applying the Peakview tool, peptide sequences obtained from liquid chromatography–mass spectrometry (LC/MS) were reviewed by comparison with those in the BIOPEP database. Furthermore, the elevated levels of TNF-α, IL-6, IL-1β and nitric oxide (NO) in RAW 267.4 cells treated with lipopolysaccharide (LPS) are considerably lower than those in cultured CM and GCM. Protein macromolecules in CMs and GCMs have been captured via confocal laser scanning microscopy (CLSM) and Fourier transform infrared (FTIR) spectroscopy both before and after fermentation. Full article

This study investigates the impact on the Earth’s ionosphere of a severe geomagnetic storm (Dst $\sim -212$ nT) that began on 23 April 2023 at around 17:37 UT according to very low-frequency (VLF, 3–30 kHz) or low-frequency (LF, 30–300 kHz) radio signals and ionosonde data. We analyze VLF/LF signals received by SuperSID monitors located in mid-latitude (Europe) and low-latitude (South America, Colombia) areas across nine different propagation paths in the Northern Hemisphere. Mid-latitude regions exhibited a daytime amplitude perturbation, mostly an increase, by ∼3–5 dB during the storm period, with a subsequent recovery after 7–8 days post April 23. In contrast, signals received in low-latitude regions (UTP, Colombia) did not show significant variation during the storm-disturbed days. We also observe that the 3-hour average of foF2 data declined by up to 3 MHz on April 23 and April 24 at the European Digisonde stations. However, no significant variation in foF2 was observed at the low-latitude Digisonde stations in Brazil. Both the VLF and ionosonde data exhibited anomalies during the storm period in the European regions, confirming that both D- and F-region ionospheric perturbation was caused by the severe geomagnetic storm. Full article

In transfer learning, domain adaptation is one of the key research areas. For domain adaptation, domain shift is a known problem when the data distribution of the source domain, from which the training data is fetched, and the target domain, from which the test data is fetched, vary significantly. Aligning the source and target domains is a solution, but due to alignment, the intrinsic properties of the data may be altered. To address this issue of domain shift, we introduce a novel method, called “A Unified Deep-Domain Adaptation Framework: Advancing Feature Separability and Local Alignment” (DDASLA) that incorporates an attention mechanism into the ResNet18 model to improve its feature extraction capability. Apart from self-attention, a combined loss function consisting of angular loss, Local Maximum Mean Discrepancy (LMMD), and entropy minimization is used. Angular loss enhances feature discrimination through angular alignment, whereas LMMD equalizes local data distributions across domains, and entropy minimization refines the decision boundaries. A comprehensive experiment on the Office and remote sensing datasets shows that DDASLA outperforms several state-of-the-art methods. These findings show that DDASLA improves model generalization and robustness across domains, paving the way for future domain adaptation research. Full article

The miniaturization of smart materials has become a new trend in the modern manufacturing industry due to its enormous application in the aerospace, biomedical, and automobile sectors. Nickel–titanium (NiTi)-based binary shape memory alloys (SMAs) are one of the smart materials with certain supreme features like shape memory effect, pseudo-elasticity, high ductility, strong corrosion-resistance, and elevated wear resistance. For this, several micro-machining processes have been developed to machine NiTi SMAs. This paper summarizes all of the conventional and non-conventional micro-machining processes employed to machine NiTi SMAs. In this review process, the surface integrity, dimensional accuracy of the machined surface, cutting force and tool wear analysis during conventional and non-conventional micro-machining of NiTi SMA are evaluated mostly with the aid of input process variables like cutting speed, depth of cut, width of cut, types of coolants, tool coating, discharge voltage, capacitance, laser fluence, pulse duration, scan speed, electrolysis concentration and gap voltage. The optimization of process parameters using different methods during conventional and non-conventional micro-machining of NiTi SMAs is also analyzed. The problems faced during conventional micro-machining of NiTi SMAs are overcome by non-conventional micro-machining processes as discussed. The present study aims to recognize potential developments in the improvement of the micro-machinability of NiTi SMAs. Full article

Ginger is an important spice crop in the north-eastern region of India. Rhizome rot, also called soft rot, is one of the most devastating diseases found in ginger that causes yield losses of up to 100% under favourable conditions. Initially, the disease symptoms appear as a light yellowing of the leaf tips that gradually spreads down to the leaf blade of lower leaves and the leaf sheath along the margin. Under favourable environmental conditions, the disease spreads rapidly, potentially causing significant crop damage. The pathogen can infect at any stage of crop growth, and under favourable environmental conditions, the disease spreads rapidly, failing the crop. Current research emphasises mitigating the losses caused by the devastating disease by using management strategies and biocontrol agents (BCAs). Results revealed that the average highest percent rhizome germination, lowest mean disease incidence, lowest mean disease severity index, lowest coefficient of disease index value, highest rhizome yield and benefit–cost ratio were recorded with Trichoderma harzianum (10 g/kg of rhizomes) + soil application of T. harzianum-enriched well-decomposed farm yard manure (3 kg of T. harzianum mixed with 100 kg FYM at 10–15 days before sowing) + soil drenching with T. harzianum at the rate 10 kg/ha, compared to the untreated control. Furthermore, soil chemical properties such as pH, electrical conductivity, soil organic carbon, total available nitrogen, total available phosphorus, and total available potassium play critical roles in rhizome rot disease severity. BCAs can suppress the phytopathogenic fungi and modulate different functions in plants. Full article

Background/Objectives: Developing a treatment strategy that effectively prolongs the lives of people with brain tumors requires an accurate diagnosis of the condition. Therefore, improving the preoperative classification of meningiomas is a priority. Machine learning (ML) has made great strides thanks to the development of convolutional neural networks (CNNs) and computer-aided tumor detection systems. The deep convolutional layers automatically extract important and dependable information from the input space, in contrast to more traditional neural network layers. One recent and promising advancement in this field is ML. Still, there is a dearth of studies being carried out in this area. Methods: Therefore, starting with the analysis of magnetic resonance images, we have suggested in this research work a tried-and-tested and methodical strategy for real-time meningioma diagnosis by image segmentation using a very deep transfer learning CNN model or DNN model (VGG-16) with CUDA. Since the VGGNet CNN model has a greater level of accuracy than other deep CNN models like AlexNet, GoogleNet, etc., we have chosen to employ it. The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. Our VGGNet model takes in an sMRI FLAIR image input. The VGG’s convolutional layers leverage a minimal receptive field, i.e., 3 × 3, the smallest possible size that still captures up/down and left/right. Moreover, there are also 1 × 1 convolution filters acting as a linear transformation of the input. This is followed by a ReLU unit. The convolution stride is fixed at 1 pixel to keep the spatial resolution preserved after convolution. All the hidden layers in our VGG network also use ReLU. A dataset consisting of 264 3D FLAIR sMRI image segments from three different classes (meningioma, tuberculoma, and normal) was employed. The number of epochs in the Sequential Model was set to 10. The Keras layers that we used were Dense, Dropout, Flatten, Batch Normalization, and ReLU. Results: According to the simulation findings, our suggested model successfully classified all of the data in the dataset used, with a 99.0% overall accuracy. The performance metrics of the implemented model and confusion matrix for tumor classification indicate the model’s high accuracy in brain tumor classification. Conclusions: The good outcomes demonstrate the possibility of our suggested method as a useful diagnostic tool, promoting better understanding, a prognostic tool for clinical outcomes, and an efficient brain tumor treatment planning tool. It was demonstrated that several performance metrics we computed using the confusion matrix of the previously used model were very good. Consequently, we think that the approach we have suggested is an important way to identify brain tumors. Full article

Oxidative stress, a pivotal driver of neurodegenerative diseases, results from an imbalance between the generation of reactive oxygen species (ROS) and cellular antioxidant defenses. This review provides a comprehensive analysis of key oxidative stress sources, focusing on NADPH oxidase (NOX) hyperactivity and mitochondrial Uncoupling Protein (UCP) downregulation. Critically, we examine the therapeutic potential of phytochemicals in mitigating NOX-mediated ROS generation through direct enzyme inhibition, including impacts on NOX subunit assembly and gene expression. Furthermore, we explore the ability of phytochemicals to bolster cellular antioxidant defenses by activating the Kelch-like ECH-associated protein 1 (KEAP1)/nuclear factor erythroid 2-related factor 2 (Nrf2)/antioxidant response element (ARE) signaling pathway, elucidating the upregulation of antioxidant genes, such as GPx, SOD, CAT, and HO-1. This review expands beyond confined overviews; emphasizes specific molecular interactions between phytochemicals and target proteins, including NOX isoforms; and provides an in-depth analysis of the specific antioxidant genes upregulated via Nrf2. This approach aims to pave the way for targeted and translatable therapeutic strategies in neurodegenerative diseases. Ultimately, this review illuminates the intricate molecular dynamics of oxidative stress in neurodegenerative diseases; underscores the potential of phytochemicals to restore redox homeostasis and reverse pathological conditions through precise modulation of key signaling pathways. Full article

Alligator weed (AW; Alternanthera philoxeroides) can have severe ecological and economic consequences by impacting water quality, flow and the growth of native flora and fauna. Plants, both edible and inedible, contain phenolics, compounds that can serve as antioxidants. Using this background information, the study aimed to explore the possible antioxidative nature of AW by incorporating it as a supplement on production performance, gene expression, and antioxidant levels during the summer and winter seasons in improved-variety rural chicks. A total of 400 improved-variety Vanaraja chicks (35 days old), were used in each of the two seasons (summer and winter), making a combined total of 800 birds (n = 400 for each summer and winter season). The chicks were subjected to four experimental dietary treatments over a 35-day period during both seasons. The experimental diet consisted of the following: a control diet without any supplements (C); basal diet + 1% AW (T₁); basal diet + 2% AW(T₂); and basal diet + 4% AW(T₃). The production performance, cytokine gene expression (IFN-γ, IL-1β, IL-6, IL-12 and iNOS) and serum antioxidants, viz. catalase (CAT) and superoxide dismutase (SOD), were evaluated. The results indicated that body weight, average body weight gain and weekly feed intake in the T₁ group was significantly (p < 0.05) higher as compared to the other groups. The FCR in group T₁ was significantly (p < 0.05) lower during winter than in summer. A significant (p < 0.001) upregulation in the expression of IL-6, IL-1β and IL-12 in T₁ as compared to the other groups was reported. IFN-γ, IL-1β, IL-6 and iNOS were significantly (p < 0.001) upregulated in winter. SOD and CAT activity was significantly (p < 0.001) higher in T₁ compared to C, and both were significantly (p < 0.05) higher during winter than in summer. The results suggested that AW has the potential to mitigate the consequences of cold stress on growth, immune response, and antioxidant function during winter. We propose adding 1% AW, which can possibly function as an antioxidant, to the diet of chicks to enhance their production performance and immunity levels. Full article

Bos frontalis (Mithun) and Bos grunniens (yak) are crucial to the culture, food security, and economy of Southeast Asia, especially in India and China, respectively. Their genetic closeness to Bos indicus (indicine cattle) and Bos taurus (taurine cattle) necessitates precise methods for meat origin authentication. This study introduces a DNA-based technique to distinguish Mithun and yak species using the alkaline lysis (AL) protocol for DNA extraction, followed by species-specific polymerase chain reaction (PCR) to amplify unique mitochondrial D-loop regions, yielding 489 bp and 422 bp amplicons, respectively. The AL-PCR method showed high specificity for both species, with no cross-amplification with other related species. The method’s effectiveness was validated across various sample preparations, including raw, cooked, autoclaved, microwaved, and fried samples. The AL-PCR assay is highly sensitive, detecting as little as 1 pg of Mithun DNA and 100 pg of yak DNA, and can identify down to 0.1% of these species in binary mixtures. This approach is rapid and cost-effective, offering significant benefits for consumer protection, promoting Mithun and yak farming, and addressing food safety and traceability issues. Full article

This study introduces two bivariate extensions of the recently proposed weighted discretized Fréchet–Weibull distribution, termed as bivariate weighted discretized Fréchet–Weibull (BWDFW) distributions. These models are specifically designed for analyzing two-dimensional discrete datasets and are developed using two distinct structural approaches: the minimum operator (BWDFW-I) and the maximum operator (BWDFW-II). A rigorous mathematical formulation is presented, encompassing the joint cumulative distribution function, joint probability mass function, and joint (reversed) hazard rate function. The dependence structure of the models is investigated, demonstrating their capability to capture positive quadrant dependence. Additionally, key statistical measures, including covariance, Pearson’s correlation coefficient, Spearman’s rho, and Kendall’s tau, are derived using the joint probability-generating function. For robust statistical inferences, the parameters of the proposed models are estimated via the maximum likelihood estimation method, with extensive simulation studies conducted to assess the efficiency and accuracy of the estimators. The practical applicability of the BWDFW distributions is demonstrated through their implementation in two real-world datasets: one from the aviation sector and the other from the security and safety domain. Comparative analyses against four existing discrete bivariate Weibull extensions reveal the superior performance of the BWDFW models, with BWDFW-I (minimum operator based) exhibiting greater flexibility and predictive accuracy than BWDFW-II (maximum operator based). These findings underscore the potential of the BWDFW models as effective tools for modeling and analyzing bivariate discrete data in diverse applied contexts. Full article

Microgreens are tender, edible seedlings harvested 7–21 days after germination containing a central stem, cotyledons, and true leaves. Known as a fresh, ready-to-eat functional food, they are mostly rich in vitamins, antioxidants, bioactive compounds, and minerals, with distinctive flavors, colors, and textures. These attributes make microgreens a valuable component in nutrition and health research. In countries like India, where low-income households spend 50–80% of their income on food, micronutrient deficiencies are common, particularly among women. Indian women, facing a double burden of malnutrition, experience both underweight (18.7%) and obesity (24.0%) issues, with 57% suffering from anemia. Women’s unique health requirements vary across life stages, from infancy to their elderly years, and they require diets rich in vitamins and minerals to ensure micronutrient adequacy. Microgreens, with their high nutrient density, hold promise in addressing these deficiencies. Fresh and processed microgreens based products can enhance food variety, nutritive value, and appeal. Rethinking agriculture and horticulture as tools to combat malnutrition and reduce the risk of non-communicable diseases (NCDs) is vital for achieving nutritional security and poverty reduction. This review compiles recent research on microgreens, focusing on their nutrient profiles, health benefits, suitable crops, substrates, seed density, growing methods, sensory characteristics, and applications as fresh and value-added products. It offers valuable insights into sustainable agriculture and the role of microgreens in enhancing human nutrition and health. Full article

It is becoming harder to manage the growing amounts of waste generated daily at an increasing rate. These problems require an efficient solution that guarantees effectiveness and transparency and maintains trust within the community. To improve the process of traditional waste management, we proposed a unique solution, “GREENLINK”, which uses a combination of blockchain technology with the concept of zero-knowledge proofs (ZKPs), non-fungible tokens (NFTs), and Walrasian equilibrium. Zero-knowledge proofs (cryptographic protocols) are used to verify organizations and prove compliance (e.g., certification, recycling capacity) without disclosing sensitive information. Through an iterative bidding process, the proposed framework employs Walrasian equilibrium, a technique to balance supply and demand, guaranteeing equitable pricing and effective resource distribution among participants. The transactions and waste management activities are securely recorded on an immutable ledger, ensuring accountability, traceability, and transparency. The performance of the proposed model is evaluated. Parameters like average latency, TPS, and memory consumption are calculated using Hyperledger Caliper (a blockchain performance benchmark framework). Full article

The northeastern region of India is one of the six most seismically active convergent plate tectonic areas in the world. The north–south convergence along the Indo-Tibetan Himalayan Ranges and the east–west subduction within the Indo-Burma Ranges create a complex stress regime, resulting in significant seismic activity and a history of great/large earthquakes. The region’s intricate strain patterns, active faults, and potential seismic gaps underscore the need for detailed subsurface studies to effectively assess seismic hazards and impending seismicity. Geophysical research is essential for understanding the region’s geodynamic evolution, seismotectonics, and mineral resources. This manuscript reviews the geological and tectonic settings of the region and summarizes recent geophysical studies, including seismic, gravity, magnetic, and magnetotelluric surveys conducted in the Assam Valley and adjacent areas (within latitudes 24.5–28.5° N and longitudes 89–97.5° E). The review highlights key findings on hydrocarbon-bearing sediments, the configuration of the crystalline basement, the heterogeneous structures of the crust and upper mantle, and seismic discontinuities. By synthesizing these results, the review aims to enhance the understanding of seismic hazards in Northeast India, guide mitigation strategies, and identify key knowledge gaps to direct future research efforts. Full article

To understand the impact of climate change on water resources, this research investigates long-term rainfall trends and anomalies across Northeastern India (NEI), covering Assam and Meghalaya (A&M); Nagaland, Manipur, Mizoram, and Tripura (NMMT); and Sub-Himalayan West Bengal and Sikkim (SHWB&S) using different statistical tests including innovative trend analysis (ITA). The study scrutinizes 146 years of rainfall statistics, trend analyses, variability, and probability distribution changes to comprehend its implications. Furthermore, the change in the assured rainfall probabilities was also worked out to understand the impact on rainfed agriculture of Northeastern India. Comparative analysis between all India (AI) and NEI reveals that NEI receives nearly double the annual rainfall compared to AI (2051 mm and 1086 mm, respectively). Despite resembling broad rainfall patterns, NEI displays intra-regional variations, underscoring the necessity for region-specific adaptation strategies. Statistical characteristics like the coefficient of skewness (CS) and coefficient of kurtosis indicate skewed rainfall distributions, notably during the winter seasons in NMMT (CS~1.6) and SHWB&S (CS~1.5). Trend analyses reveal declining rainfall trends, especially conspicuous in NEI’s winter (−1.88) and monsoon (−2.9) seasons, where the rate of decrease was higher in the last three decades. The return periods of assured rainfall at 50% and 75% probability levels also increased sharply during the winter and monsoon seasons by over 30% during the recent half, posing challenges for rainfed upland hill farming. Furthermore, this study highlights increasing variability and negative anomalies in monsoon rainfall over NEI, exacerbating decreasing rainfall trends and significantly impacting agricultural productivity. These findings underscore the urgency for adaptive measures tailored to evolving rainfall patterns, ensuring sustainable agricultural practices and efficient water resource management. Full article