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\"There is a drastic growth in multimedia data. Even during the Covid-19 pandemic, we observed that the images helped doctors immensely in fast detection of Covid-19 infection in patients. There are many critical applications where images play a vital role. These applications use raw image data to extract some useful information about the world around us. Quick extraction of valuable information from raw images is one challenge that academicians and professionals face nowadays. This is where image processing comes into action. Image processing's primary purpose is to get an enhanced image or extract some useful information from it. Therefore, there is a major need for some technique or system that addresses this challenge. Intelligent Systems have emerged as a solution to address quick image information extraction. In simple words, an Intelligent System can be defined as a mathematical model that adapts itself to deal with the problems' dynamicity. These systems learn how to act so it can reach their objectives. Intelligent System helps accomplish various image processing functions like enhancement, segmentation, reconstruction, object detection, and morphing. The advent of Intelligent Systems in the image processing field has leveraged many critical applications for humankind. These critical applications include factory automation, biomedical imaging analysis, and decision-econometrics, Intelligent Systems and challenges\"-- Provided by publisher.

The purpose of the present study was to evaluate the anti-hypercholesterolaemic effects of two putative probiotic bile salt hydrolase (Bsh)-producing Lactobacillusplantarum strains, i.e. Lp91 and Lp21, in rats. L. plantarum Lp91 exhibited excellent tolerance to low pH and high bile salt concentrations as well as showed potential Bsh activity, cholesterol assimilation and cholesterol co-precipitation ability along with L. plantarum Lp21 and NCDO82 strains. Furthermore, the potential effect of L. plantarum Lp91 on plasma cholesterol level was evaluated in Sprague–Dawley rats. Five treatment groups of rats (n 6) were fed experimental diets: normal diet, hypercholesterolaemic diet (HD), HD plus L. plantarum Lp91 (HD91) at ≥ 1·0 × 108 colony-forming units (cfu)/g, HD plus microencapsulated L. plantarum Lp91 (HDCap91) at ≥ 1·0 × 108 cfu/g and HD plus L. plantarum Lp21 (HD21) at ≥ 1·0 × 108 cfu/g for 3 weeks. Feed intake and feed efficiency differed significantly among the five groups. After 21 d of dietary treatment, comparative analysis revealed 23·26, 15·71 and 15·01 % reduction in total cholesterol, 21·09, 18·77 and 18·17 % reduction in TAG, 38·13, 23·22 and 21·42 % reduction in LDL-cholesterol, and the corresponding HDL-cholesterol values increased at the rate of 18·94, 10·30 and 7·78 % in treated groups HD91, HDCap91 and HD21, respectively. Faecal excretion of cholic acid and faecal lactobacilli counts were significantly higher in the probiotic treatment groups than in the control groups. In conclusion, these results suggest that the indigenous L. plantarum Lp91 strain has the potential to be explored as a probiotic in the management of hypercholesterolaemia.

PurposeDiabetes and obesity are characterized by glucose intolerance, fat deposition, inflammation, and dyslipidemia. Recent reports postulated that distinct gut microbiota alterations were observed in obese/diabetic subjects and modulating gut microbiota beneficially through specific probiotics could be a potential therapeutic option for type 2 diabetes/obesity. Therefore, we attempted to study the efficacy of probiotics of Indian gut origin (Lactobacillus plantarum MTCC5690 and Lactobacillus fermentum MTCC5689) along with a positive control, Lactobacillus rhamnosus (LGG) on glucose/lipid homeostasis in high-fat-diet-induced diabetic animal model.MethodsC57BL/6J male mice were divided into seven groups (n = 6 per group) comprising feeding on: (1) Normal Pellet Diet (NPD), (2) High-Fat Diet (HFD), (3) HFD with LGG, (4) HFD with MTCC5690, (5) HFD with MTCC5689, (6) HFD with metformin, and 7) HFD with vildagliptin for a period of 6 months. Biochemical markers, glucose tolerance, insulin resistance, and GLP-1 and LPS levels were assessed by standard protocols. Gut integrity was measured by intestinal permeability test. Transcriptional levels of tight junction proteins (TJPs) were probed in small intestinal tissues while inflammatory signals and other pathway specific genes were profiled in liver, visceral adipose tissue, and skeletal muscle.ResultsMice fed with HFD became insulin resistant, glucose intolerant, hyperglycemic, and dyslipidemic. Diabetic mice were characterized to exhibit decreased levels of GLP-1, increased gut permeability, increased circulatory levels of LPS, decrease in the gene expression patterns of intestinal tight junction markers (occludin and ZO-1), and increased proinflammatory gene markers (TNFα and IL6) in visceral fat along with decreased mRNA expression of FIAF and adiponectin. Diabetic mice also exhibited increased mRNA expression of ER stress markers in skeletal muscle. In addition, liver from HFD-fed diabetic mice showed increased gene expressions of proinflammation, lipogenesis, and gluconeogenesis. Probiotic interventions (most prominently the MTCC5689) resisted insulin resistance and development of diabetes in mice under HFD feeding and beneficially modulated all the biochemical and molecular alterations in a mechanistic way in several tissues. The metabolic benefits offered by the probiotics were also more or less similar to that of standard drugs such as metformin and vildagliptin.ConclusionNative probiotic strains MTCC 5690 and MTCC 5689 appear to have potential against insulin resistance and type 2 diabetes with mechanistic, multiple tissue-specific mode of actions.

Rapid increasing urbanization and resource scarcity are global phenomena nowadays, leading to the urban transformation of cities into smart cities. This article explores sustainability by using the lens of the spirit of place (SOP) for smart city development by proposing a model for the transformation of the cities into smart cities and attainment of the sustainable development simultaneously based on Interpretive Structure Modelling (ISM) and Analytic Hierarchy Process (AHP). This study followed a systematic approach by utilizing an analytical framework that included an extensive literature review and urban experts' opinions for the identification of a pool of indicators and its evaluation for validity, pilot testing, and administration of a questionnaire to a population sample. The study utilizes a sample of 142 participants who have witnessed the transformation of their city over the years. The research showed that every place has its own identity known to be the ‘spirit of place’ that helps in assessing the sustainable characteristics and utilizing that in the path of planning and development for the attainment of sustainable development. It also showed that urban developers should consider local populations’ views and important aspects in designing and planning development projects to achieve sustainable development with resilient infrastructure. This study will help facilitate sustainability at a local level for urban developers, planners, and decision-makers while crafting strategic plans.

Yield gap analysis is used to characterize the untapped production potential of cropping systems. With emerging large- n agronomic datasets and data science methods, pathways for narrowing yield gaps can be identified that provide actionable insights into where and how cropping systems can be sustainably intensified. Here we characterize the contributing factors to rice yield gaps across seven Indian states, with a case study region used to assess the power of intervention targeting. Primary yield constraints in the case study region were nitrogen and irrigation, but scenario analysis suggests modest average yield gains with universal adoption of higher nitrogen rates. When nitrogen limited fields are targeted for practice change (47% of the sample), yield gains are predicted to double. When nitrogen and irrigation co-limitations are targeted (20% of the sample), yield gains more than tripled. Results suggest that analytics-led strategies for crop intensification can generate transformative advances in productivity, profitability, and environmental outcomes. Rice production in India is a foundation for global food security, but strategies for sustainable intensification are uncertain. By combining large-scale surveys with predictive modeling, the authors identify efficient pathways for achieving productivity gains while enhancing economic and environmental goals.

Large changes have taken place in smallholder farming systems in South Asia’s coastal areas in recent decades, particularly related to cropping intensity, input availability, climate risks, and off-farm activities. However, few studies have investigated the extent to which these changes have impacted farm-level crop productivity, which is a key driver of food security and poverty in rainfed, low-input, rice-based systems. The objective of this study was to conduct an integrated assessment of variables related to socioeconomic status, farm characteristics, and crop management practices to understand the major factors influencing crop productivity and identify promising leverage points for sustainable development in coastal Bangladesh. Using a panel survey dataset of 32 variables from 502 farm households located within polder (coastal embankment) and outside polder systems during 2005–2015, we employed statistical factor analysis to characterize five independent latent factors named here as Farming Challenges , Economic Status , Crop Management Practices , Asset Endowment , and Farm Characteristics . The factor Farming Challenges explained the most variation among households (31%), with decreases observed over time, specifically households located outside polders. Individual variables contributing to this factor included perceived cyclone severity, household distance to main roads and input-output markets, cropping intensity, and access to extension services. The most important factors for increasing crop productivity on a household and per unit area basis were Asset Endowment and Crop Management Practices , respectively. The former highlights the need for increasing gross cropped area, which can be achieved through greater cropping intensity, while the latter was associated with increased fertilizer, labor, and pesticide input use. Despite the importance of these factors, household poverty trajectory maps showed that changes in off-farm income had played the strongest role in improving livelihoods in this coastal area. This study can help inform development efforts and policies for boosting farm-level crop productivity, specifically through agricultural intensification (higher cropping intensity combined with appropriate and efficient use of inputs) and expanding opportunities for off-farm income as key pathways to bring smallholder households out of poverty.

Genome-editing, a recent technological advancement in the field of life sciences, is one of the great examples of techniques used to explore the understanding of the biological phenomenon. Besides having different site-directed nucleases for genome editing over a decade ago, the CRISPR/Cas (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein) based genome editing approach has become a choice of technique due to its simplicity, ease of access, cost, and flexibility. In the present review, several CRISPR/Cas based approaches have been discussed, considering recent advances and challenges to implicate those in the crop improvement programs. Successful examples where CRISPR/Cas approach has been used to improve the biotic and abiotic stress tolerance, and traits related to yield and plant architecture have been discussed. The review highlights the challenges to implement the genome editing in polyploid crop plants like wheat, canola, and sugarcane. Challenges for plants difficult to transform and germline-specific gene expression have been discussed. We have also discussed the notable progress with multi-target editing approaches based on polycistronic tRNA processing, Csy4 endoribonuclease, intron processing, and Drosha ribonuclease. Potential to edit multiple targets simultaneously makes it possible to take up more challenging tasks required to engineer desired crop plants. Similarly, advances like precision gene editing, promoter bashing, and methylome-editing will also be discussed. The present review also provides a catalog of available computational tools and servers facilitating designing of guide-RNA targets, construct designs, and data analysis. The information provided here will be useful for the efficient exploration of technological advances in genome editing field for the crop improvement programs.

Api5, is a known anti-apoptotic and nuclear protein that is responsible for inhibiting cell death in serum-starved conditions. The only known post-translational modification of Api5 is acetylation at lysine 251 (K251). K251 acetylation of Api5 is responsible for maintaining its stability while the de-acetylated form of Api5 is unstable. This study aimed to find out the enzymes regulating acetylation and deacetylation of Api5 and the effect of acetylation on its function. Our studies suggest that acetylation of Api5 at lysine 251 is mediated by p300 histone acetyltransferase while de-acetylation is carried out by HDAC1. Inhibition of acetylation by p300 leads to a reduction in Api5 levels while inhibition of deacetylation by HDAC1 results in increased levels of Api5. This dynamic switch between acetylation and deacetylation regulates the localisation of Api5 in the cell. This study also demonstrates that the regulation of acetylation and deacetylation of Api5 is an essential factor for the progression of the cell cycle.

HIV‐1 infects lymphoid and myeloid cells, which can harbor a latent proviral reservoir responsible for maintaining lifelong infection. Glycolytic metabolism has been identified as a determinant of susceptibility to HIV‐1 infection, but its role in the development and maintenance of HIV‐1 latency has not been elucidated. By combining transcriptomic, proteomic, and metabolomic analyses, we here show that transition to latent HIV‐1 infection downregulates glycolysis, while viral reactivation by conventional stimuli reverts this effect. Decreased glycolytic output in latently infected cells is associated with downregulation of NAD + /NADH. Consequently, infected cells rely on the parallel pentose phosphate pathway and its main product, NADPH, fueling antioxidant pathways maintaining HIV‐1 latency. Of note, blocking NADPH downstream effectors, thioredoxin and glutathione, favors HIV‐1 reactivation from latency in lymphoid and myeloid cellular models. This provides a “shock and kill effect” decreasing proviral DNA in cells from people living with HIV/AIDS. Overall, our data show that downmodulation of glycolysis is a metabolic signature of HIV‐1 latency that can be exploited to target latently infected cells with eradication strategies. SYNOPSIS The upregulation of glycolysis in activated cells favors HIV‐1 infection and initial viral replication. This study discovers that, to transit into a latent form, which can shield the virus from immunity and antiretroviral drugs, HIV‐1 needs to downregulate glycolysis. Restoration of glycolytic activity is required for HIV‐1 reactivation from latency. Latently infected cells rely on pentose phosphate metabolism and its downstream effectors, i.e., the antioxidant glutathione and thioredoxin pathways, for their survival. Preferential targeting of latently infected cells with drugs inhibiting thioredoxin and glutathione pathways leads to both HIV‐1 reactivation from latency and death of infected cells. Graphical Abstract The upregulation of glycolysis in activated cells favors HIV‐1 infection and initial viral replication. This study discovers that, to transit into a latent form, which can shield the virus from immunity and antiretroviral drugs, HIV‐1 needs to downregulate glycolysis.

Vehicular Ad-hoc Networks (VANETs) face significant challenges in ensuring reliable data delivery due to the highly dynamic mobility of vehicles and frequent link disruptions. Traditional clustering approaches often suffer from unstable Cluster Head (CH) selection, which degrades packet delivery, increases communication overhead, and other relative metrics also get effected. To address these limitations, this paper proposes an Enhanced Clustering-based Efficient Relay Vehicle (ECERV) selection framework that integrates a dual-strategy CH selection mechanism with predictive relay support from Road Side Units (RSUs). The proposed method combines stability and proximity factors to improve cluster lifetime, while RSUs dynamically predict optimal relay vehicles for data forwarding in uncovered regions. Extensive NS-2 simulations demonstrate that the proposed ECERV selection scheme outperforms its competitive approaches and achieves 23% higher throughput, 12% higher packet delivery ratio, 25% higher requested data completeness, and 14% lower delay compared to baseline protocol, while also reducing control overhead by 24%, energy consumption by 8.5%, and extending the cluster stability period by 50% under a 100-vehicles scenario. These results confirm that the proposed ECERV selection scheme provides a scalable and robust solution for enhancing data dissemination in highly mobile vehicular communication scenarios.