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Key message: Genome-wide association study identified 205 significant marker–trait associations for chlorophyll fluorescence parameters in wheat. Candidate gene mining, in silico expression, and promoter analyses revealed the potential candidate genes associated with the studied parameters. The present study investigated the effect of varied sowing conditions (viz., early, timely, and late) on different chlorophyll fluorescence parameters in diverse wheat germplasm set comprising of 198 lines over two cropping seasons (2020–2021 and 2021–2022). Further, a genome-wide association study was conducted to identify potential genomic regions associated with these parameters. The results revealed significant impacts of sowing conditions on all fluorescence parameters, with the maximum and minimum effects on FI (26.64%) and FV/FM (2.12%), respectively. Among the 205 marker–trait associations (MTAs) identified, 11 high-confidence MTAs were chosen, exhibiting substantial effects on multiple fluorescence parameters, and each explaining more than 10% of the phenotypic variation. Through gene mining of genomic regions encompassing high-confidence MTAs, we identified a total of 626 unique gene models. In silico expression analysis revealed 42 genes with an expression value exceeding 2 TPM. Among them, 10 genes were identified as potential candidate genes with functional relevance to enhanced photosynthetic efficiency. These genes mainly encoded for the following important proteins/products—ankyrin repeat protein, 2Fe-2S ferredoxin-type iron-sulfur-binding domain, NADH-ubiquinone reductase complex-1 MLRQ subunit, oxidoreductase FAD/NAD(P)-binding, photosystem-I PsaF, and protein kinases. Promoter analysis revealed the presence of light-responsive (viz., GT1-motif, TCCC-motif, I-box, GT1-motif, TCT-motif, and SP-1) and stress-responsive (viz., ABRE, AuxRR-core, GARE-motif, and ARE) cis-regulatory elements, which may be involved in the regulation of identified putative candidate genes. Findings from this study could directly help wheat breeders in selecting lines with favorable alleles for chlorophyll fluorescence, while the identified markers will facilitate marker-assisted selection of potential genomic regions for improved photosynthesis. [ABSTRACT FROM AUTHOR]

Introduction: The nephrotic syndrome is one of the best known clinical presentations of adult or pediatric nephropathy. Nephrotic syndrome is characterized by the association of heavy proteinuria with pedal edema, hypoalbuminemia, and hypercholesterolemia. Aims: To compare and correlate bone mineral density with biochemical parameters like serum calcium, phosphorus, vitamin-D, alkaline phosphatase, albumin and parathyroid hormone, in adults with nephrotic syndrome. Methodology: The study was Cross sectional observational study. The conducted in adult patients with nephrotic syndrome above 18 years of age with no primary bone pathology or metastatic disease. Biochemical parameters were assessed and correlated with bone mineral density by DXA scan of lumbar spine and neck of femur. Fifty patients of command hospital Kolkata (EC) were selected and compared with equal number of age and sex matched controls. All patients were selected from a period of 01 Jan 18 to 31 May 19. All underwent detailed clinical examination and investigations were done at the time of recruitment. Results: DXA scan results in cases of nephrotic syndrome show significantly low BMD for both lumbar spine and neck of femur with mean Z-scores less than -1.0 (P<0.001). Lumbar spine (L1-L4): Low BMD was found in 88 percent cases and 32 percent of controls (P < 0.001). Neck of femur: Low BMD was found in 44 percent cases and 10 percent controls (P<0.001). Cases with normal BMD had statistically significant lower levels of blood urea with mean 23 ± 4.53 mg/dl. Cases with osteopenia and osteoporosis had higher levels of blood urea with mean 54.13 ± 32.4 mg/dl and 66.85 ± 29.64 mg/dl respectively. Cases with normal BMD had higher levels of serum calcium with mean 7.67 ± 1.01 mg/dl. Cases with osteopenia and osteoporosis had serum calcium with mean 7.22 ± 0.88mg/dl and 7.26 ± 0.68 mg/dl respectively. However this comparison did not reach any statistical significance. Conclusion: Adults with nephrotic syndrome are at risk for low bone mineral density. These patients should undergo regular evaluations for mineral bone diseases and appropriate therapeutic interventions should be planned. [ABSTRACT FROM AUTHOR]

Cloud is a pay-per use infra-structed which has invited huge clients to cloud, in order to get reliable services without extra maintenance or infrastructure cost. Growing cloud services and migration of small business to cloud have led to high load on cloud service providers, which leads to the need of better optimization algorithm in order to manage the machine better performance and meet better quality of services to the client. Cloud broker or agent plays an important role to achieve this using intelligent task scheduling algorithm to manage the task in such a way to optimize the performance of the cloud services and data center. Currently various optimization algorithms are proposed but most of them take execution time into consideration but not the network delay between the client and the data center. Hence, to overcome this, an optimization algorithm is proposed in this work using execution time and network delay as the optimization parameters. The nature inspired grasshopper optimization is proposed which is compared with the exiting PSO and ACO models to study the performance. The results show that the proposed algorithm out performs the existing models with execution time, total time and network delay as performance metrics. It demonstrates how the suggested, naturally inspired GOA algorithm beats the existing ACO and PSO algorithms for task scheduling in the cloud with scaling loads requiring 5 virtual machines and 2 data centers. More objective functions, such as power and cost-effective algorithms, can be added to the work to further expand it. This study compares the efficacy of several algorithms based on the predetermined criteria while also examining related algorithms. To determine the best algorithm possible, it is intended to offer each approach individually, analyze the results, and plot the resulting graphs. [ABSTRACT FROM AUTHOR]

Background: Yellow or stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. Results: Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker–trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5, Yr7, Yr16, Yr26, Yr30, Yr43, Yr44, Yr64, YrCH52, and YrH52. Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. Conclusion: The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches. [ABSTRACT FROM AUTHOR]

Crime prediction is an effort to cut down the crime rate and criminal activities in the society. This paper concentrates on monitoring the crime trends in India using data from 2001 to 2019. The work analyzes the crime condition in India by following a top-to bottom approach and implementing visualization and machine learning algorithms. Heat map visualization of India is done to focus on specific crime types for all states. Furthermore, various demography of human population like age-groups, sex, caste, etc., are considered to have a larger perspective of the issue. The model also suggest proactive measures based on these findings. [ABSTRACT FROM AUTHOR]

We recently developed a database for hexaploid wheat QTL (WheatQTLdb; www.wheatqtldb.net), which included 11,552 QTL affecting various traits of economic importance. However, that database did not include valuable QTL from other wheat species and/or progenitors of hexaploid wheat. Therefore, an updated and improved version of wheat QTL database (WheatQTLdb V2.0) was developed, which now includes information on hexaploid wheat (Triticum aestivum) and the following seven other related species: T. durum, T. turgidum, T. dicoccoides, T. dicoccum, T. monococcum, T. boeoticum, and Aegilops tauschii. WheatQTLdb V2.0 includes a much-improved list of QTL, including 27,518 main effect QTL, 202 epistatic QTL, and 1321 metaQTL. This newly released WheatQTLdb V2.0 also has additional valuable options to search and choose the QTL, category-wise, and trait-wise data for their use in research or breeding programs. [ABSTRACT FROM AUTHOR]

A meta-QTL analysis was conducted in Indian mustard to identify robust and stable meta-QTLs (MQTLs) by utilizing 1504 available QTLs, which included 891 QTLs for yield-related traits and 613 QTLs for quality traits. For yield-related traits, a total of 57 MQTLs (YRTs_MQTLs) were uncovered from the clustering of 560 projected QTLs, which had a 4.18-fold smaller confidence interval (CI) than that of the initial QTLs, whereas, for quality traits, as many as 51 MQTLs (Quality_MQTLs) were derived from 324 projected QTLs, which had a 2.65-fold smaller CI than that of the initial QTLs. Sixteen YRTs_MQTLs were observed to share chromosomal positions with 16 Quality_MQTLs. Moreover, four most promising YRTs_MQTLs and eight Quality-MQTLs were also selected and recommended for use in breeding programs. Four of these selected MQTLs were also validated with significant SNPs that were identified in previously published genome-wide association studies. Further, in silico functional analysis of some promising MQTLs allowed the detection of as many as 1435 genes, which also involved 15 high-confidence candidate genes (CGs) for yield-related traits and 46 high-confidence CGs for quality traits. After validation, the identified CGs can also be exploited to model the plant architecture and to improve quality traits through marker-assisted breeding, genetic engineering, and genome editing approaches. [ABSTRACT FROM AUTHOR]

In wheat, a meta-analysis was performed using previously identified QTLs associated with drought stress (DS), heat stress (HS), salinity stress (SS), water-logging stress (WS), pre-harvest sprouting (PHS), and aluminium stress (AS) which predicted a total of 134 meta-QTLs (MQTLs) that involved at least 28 consistent and stable MQTLs conferring tolerance to five or all six abiotic stresses under study. Seventy-six MQTLs out of the 132 physically anchored MQTLs were also verified with genome-wide association studies. Around 43% of MQTLs had genetic and physical confidence intervals of less than 1 cM and 5 Mb, respectively. Consequently, 539 genes were identified in some selected MQTLs providing tolerance to 5 or all 6 abiotic stresses. Comparative analysis of genes underlying MQTLs with four RNA-seq based transcriptomic datasets unravelled a total of 189 differentially expressed genes which also included at least 11 most promising candidate genes common among different datasets. The promoter analysis showed that the promoters of these genes include many stress responsiveness cis-regulatory elements, such as ARE, MBS, TC-rich repeats, As-1 element, STRE, LTR, WRE3, and WUN-motif among others. Further, some MQTLs also overlapped with as many as 34 known abiotic stress tolerance genes. In addition, numerous ortho-MQTLs among the wheat, maize, and rice genomes were discovered. These findings could help with fine mapping and gene cloning, as well as marker-assisted breeding for multiple abiotic stress tolerances in wheat. [ABSTRACT FROM AUTHOR]

The service-oriented computing paradigm changes the way of computing. Emerging technologies like grid computing, cloud computing, and smart health care application have changed the way we compute and communicate. Cloud computing has made computing huge data on the fly and uses flexible resources according to the requirement for real-time applications. Cloud computing comes with pay per use model to pay for only those resources that you have used. Inside the cloud there lie many issues related to efficient and cost-effective models to improve cloud performance and complete the client task with the least cost and high performance. E-Health care services are one of the most computational intensive services in the cloud, they require real-time computing which can only be achieved if the computational resources can compute it in the least time. Cloud can accomplish this using an efficient scheduling algorithm. This manuscript focuses on the task scheduling technique which enhances the performance in real-time with the least execution time, network cost, and execution cost. The presented model is inspired by Big Bang-Big Crunch algorithm in astronomy. The presented algorithm enhances the quality of service by reducing the scheduling delay, network delay with the least resource cost to complete the task in the least cost to the user with high quality of service. [ABSTRACT FROM AUTHOR]

Key message: In wheat, 2852 major QTLs of 8998 QTLs available for yield and related traits were used for meta-analysis; 141 meta-QTLs were identified, which included 13 breeder's MQTLs and 24 ortho-MQTLs; 1202 candidate genes and 50 homologues of genes for yield from other cereals were also identified. Meta-QTL analysis was conducted using 2852 of the 8998 known QTLs, retrieved from 230 reports published during 1999–2020 (including 19 studies on tetraploid wheat) for grain yield (GY) and the following ten component traits: (i) grain weight (GWei), (ii) grain morphology-related traits (GMRTs), (iii) grain number (GN), (iv) spikes-related traits (SRTs), (v) plant height (PH), (vi) tiller number (TN), (vii) harvest index (HI), (viii) biomass yield (BY), (ix) days to heading/flowering and maturity (DTH/F/M), and (x) grain filling duration (GFD). The study resulted in the identification of 141 meta-QTLs (MQTLs), with an average confidence interval (CI) of 1.4 cM as against a CI of > 12.1 cM (8.8 fold reduction) in the QTLs that were used. The corresponding physical length of CI ranged from 0.01 Mb to 661.9 Mb (mean, 31.5 Mb). Seventy-seven (77) of these 141 MQTLs overlapped marker-trait associations (MTAs) reported in genome-wide association studies. Also, 63 MQTLs (each based on at least 10 QTLs) were considered stable and robust, with 13 MQTLs described as breeder's MQTLs (selected based on small CI, large LOD, and high level of phenotypic variation explained). Thirty-five yield-related genes from rice, barley, and maize were also utilized to identify 50 wheat homologues in MQTLs. Further, the use of synteny and collinearity allowed the identification of 24 ortho-MQTLs which were common among the wheat, barley, rice, and maize. The results of the present study should prove useful for wheat breeding and future basic research in cereals including wheat, barley, rice, and maize. [ABSTRACT FROM AUTHOR]

In wheat, meta-QTLs (MQTLs) and candidate genes (CGs) were identified for multiple disease resistance (MDR). For this purpose, information was collected from 58 studies for mapping QTLs for resistance to one or more of the five diseases. As many as 493 QTLs were available from these studies, which were distributed in five diseases as follows: septoria tritici blotch (STB) 126 QTLs; septoria nodorum blotch (SNB), 103 QTLs; fusarium head blight (FHB), 184 QTLs; karnal bunt (KB), 66 QTLs; and loose smut (LS), 14 QTLs. Of these 493 QTLs, only 291 QTLs could be projected onto a consensus genetic map, giving 63 MQTLs. The CI of the MQTLs ranged from 0.04 to 15.31 cM with an average of 3.09 cM per MQTL. This is a ~ 4.39 fold reduction from the CI of QTLs, which ranged from 0 to 197.6 cM, with a mean of 13.57 cM. Of 63 MQTLs, 60 were anchored to the reference physical map of wheat (the physical interval of these MQTLs ranged from 0.30 to 726.01 Mb with an average of 74.09 Mb). Thirty-eight (38) of these MQTLs were verified using marker–trait associations (MTAs) derived from genome-wide association studies. As many as 874 CGs were also identified which were further investigated for differential expression using data from five transcriptome studies, resulting in 194 differentially expressed candidate genes (DECGs). Among the DECGs, 85 genes had functions previously reported to be associated with disease resistance. These results should prove useful for fine mapping and cloning of MDR genes and marker-assisted breeding. [ABSTRACT FROM AUTHOR]

The thermal battery has been fabricated using a fin and tube heat exchanger comprising copper tubes and aluminum fins. The present manuscript investigated paraffin wax with water to enhance thermal performance using HYTHERM 600 as heat transfer fluid (HTF). Conduction occurs during the initial charging phases, and natural convection takes precedence over conduction once the paraffin wax has liquefied. Conduction becomes dominant throughout the discharging process. The charging and discharging efficiency enhanced by 15.90%, 28.40%, 50.67% and 5.37%, 15.50%, 25.39% at HTF temperatures of 80 °C, 85 °C, and 90 °C compared to 75 °C. The highest energy storage/rejection was obtained as 312.97 kJ/kg and 279.22 kJ/kg during charging and discharging at HTF temperature of 90 °C. The shorter charging, longer discharging time, and higher efficiency were obtained at higher HTF temperatures in hot HTF tank. The water weight percentage increases the heat transfer rate as water only stores sensible heat below 100 °C. The 100% water requires a minimum time (146 min), whereas 100% PW requires the maximum time (258 min) for discharging. The 40% Paraffin wax +60% water composite exhibits maximum charging efficiency (66.98%) and discharging efficiency (87.11%), minimum charging time (72 min), energy stored (260.63 kJ/kg), and energy rejected (240.05 kJ/kg). • Fabricate a RTB incorporating a fin and tube-type heat exchanger. • Thermal performance of PW is investigated in the RTB. • The heat exchanger and PWWC improve the heat transfer rate. • Effect of different hot HTF temperatures on the PW is investigated. • The 40% PW and 60% W composite sample exhibits the maximum efficiencies. [ABSTRACT FROM AUTHOR]

The versatile use of biopigments in food, feed, cosmetic, pharmaceutical and analytical industries emphasized to find different and renewable sources of biopigments. Microalgae, including cyanobacteria, are becoming a potential candidate for pigment production as these have fast-growing ability, high pigment content, highly variable and also have "Generally recognized as safe" status. These algal groups are known to produce different metabolites that include hormones, vitamins, biopolythene and biochemicals. We discuss here the potential use of microalgal biopigments in our daily life as well as in food and cosmetic industries. Pigment like carotenoids has many health benefits such as antioxidant, anti-inflammatory properties and also provide photo-protection against UV radiation. This review details the effect of various abiotic and biotic factors such as temperature, light, nutrition on maximizing the pigment content in the microalgal cell. This review also highlights the potential of microalgae, whether in present native or engineered strain including the many metabolic strategies which are used or can be used to produce a higher amount of these valuable biopigments. Additionally, future challenges in the context of pigment production have also been discussed. [ABSTRACT FROM AUTHOR]

• Eggplant is widely cultivated for its fleshy fruits. • Innovative developments in biotechnology have opened new horizons for eggplant. • We reviewed the role of biotechnology in supporting eggplant ideotype. Eggplant is a member of family Solanaceae, and it is commonly cultivated in many parts of the world. Eggplant is susceptible to a number of biotic and abiotic stresses, therefore, there is a continuous demand of varieties with disease and insect pest resistance, better nutraceutical capacity, and adaption to climate change. Biotechnological approaches/tools have helped in the expansion of eggplant ideotype. In this direction, tissue culture techniques for organogenesis and somatic embryogenesis are standardized in eggplant. Also, plant transformation techniques like agrobacterium mediated gene transfer has been established in eggplant. Even if, the information of eggplant from a biotechnology perspective is increasing yet there is a lack of knowledge. Techniques like gene editing have not been tried in eggplant, further, eggplant is still remains unexplored from the molecular farming outlook. In this review, we compile the information regarding tissue culture, genetic engineering, and genome editing advancements so far accomplished in the eggplant. [ABSTRACT FROM AUTHOR]

Abstract Cyanobacteria are the oxygenic photosynthesis performing prokaryotes and show a connecting link between plastids of eukaryotic autotrophs and prokaryotes. A variety of pigments, like chlorophyll, carotenoids and phycobiliproteins which exhibit different colors are present in cyanobacteria. Increasing consciousness about the harmful effects of synthetic or chemical dyes encouraged people to give more preference towards the usage of natural products, such as plant or microbial-derived colors in food and cosmetics. That is why cyanobacteria are exploited as a source of natural colors and have high commercial value in many industries. This review mainly focuses on different cyanobacterial pigments, their applications and modern biotechnological approaches such as genetic engineering, systems biology to enhance the production of biopigments for their potential use in pharmaceuticals, food, research, and cosmetics industries. Graphical abstract Image 1 Highlights • Coloration provides product attractiveness; so many coloring agents are used in textile, food and cosmetic industries. • Pigments are light absorbing compounds and cyanobacteria are rich source of variety of pigments. • Carotenoids have commercial value because of their brilliant colors, antioxidant property and health promoting factors. • Phycobiliprotein production is directly affected by the environmental factors and the quality of the light. • Cyanobacterial structural similarity with plastids elucidates their potential for genetic and metabolic engineering. [ABSTRACT FROM AUTHOR]

Cloud computing is a computing technology that is expeditiously evolving. Cloud is a type of distributed computing system that provides a scalable computational resource on demand including storage, processing power and applications as a service via Internet. Cloud computing, with the assistance of virtualization, allows for transparent data and service sharing across cloud users, as well as access to thousands of machines in a single event. Virtual machine (VM) allocation is a difficult job in virtualization that is governed as an important aspect of VM migration. This process is performed to discover the optimum way to place VMs on physical machines (PMs) since it has clear implications for resource usage, energy efficiency, and performance of several applications, among other things. Hence an efficient VM placement problem is required. This paper presents a VM allocation technique based on the elephant herd optimization scheme. The proposed method is evaluated using real-time workload traces and the empirical results show that the proposed method reduces energy consumption, and maximizes resource utilization when compared to the existing methods. [ABSTRACT FROM AUTHOR]

Metaheuristic techniques have been utilized extensively to predict industrial systems' optimum availability. This prediction phenomenon is known as the NP-hard problem. Though, most of the existing methods fail to attain the optimal solution due to several limitations like slow rate of convergence, weak computational speed, stuck in local optima, etc. Consequently, in the present study, an effort has been made to develop a novel mathematical model for power generating units assembled in sewage treatment plants. Markov birth-death process is adopted for model development and generation of Chapman-Kolmogorov differential-difference equations. The global solution is discovered using metaheuristic techniques, namely genetic algorithm and particle swarm optimization. All time-dependent random variables associated with failure rates are considered exponentially distributed, while repair rates follow the arbitrary distribution. The repair and switch devices are perfect and random variables are independent. The numerical results of system availability have been derived for different values of crossover, mutation, several generations, damping ratio, and population size to attain optimum value. The results were also shared with plant personnel. Statistical investigation of availability results justifies that particle swarm optimization outdoes genetic algorithm in predicting the availability of power-generating systems. In present study a Markov model is proposed and optimized for performance evaluation of sewage treatment plant. The developed model is one that can be useful for sewage treatment plant designers in establishing new plants and purposing maintenance policies. The same procedure of performance optimization can be adopted in other process industries too. [ABSTRACT FROM AUTHOR]

Root system architecture (RSA), also known as root morphology, is critical in plant acquisition of soil resources, plant growth, and yield formation. Many QTLs associated with RSA or root traits in maize have been identified using several bi-parental populations, particularly in response to various environmental factors. In the present study, a meta-analysis of QTLs associated with root traits was performed in maize using 917 QTLs retrieved from 43 mapping studies published from 1998 to 2020. A total of 631 QTLs were projected onto a consensus map involving 19,714 markers, which led to the prediction of 68 meta-QTLs (MQTLs). Among these 68 MQTLs, 36 MQTLs were validated with the marker-trait associations available from previous genome-wide association studies for root traits. The use of comparative genomics approaches revealed several gene models conserved among the maize, sorghum, and rice genomes. Among the conserved genomic regions, the ortho-MQTL analysis uncovered 20 maize MQTLs syntenic to 27 rice MQTLs for root traits. Functional analysis of some high-confidence MQTL regions revealed 442 gene models, which were then subjected to in silico expression analysis, yielding 235 gene models with significant expression in various tissues. Furthermore, 16 known genes viz., DXS2, PHT, RTP1, TUA4, YUC3, YUC6, RTCS1, NSA1, EIN2, NHX1, CPPS4, BIGE1, RCP1, SKUS13, YUC5, and AW330564 associated with various root traits were present within or near the MQTL regions. These results could aid in QTL cloning and pyramiding in developing new maize varieties with specific root architecture for proper plant growth and development under optimum and abiotic stress conditions. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a nonlinear mathematical model is proposed and analyzed to study the effect of malicious object on the immune response of the computer network. Criteria for local stability, instability and global stability are obtained. It is shown that the immune response of the system decreases as the concentration of malicious objects increases, and certain criteria’s are obtained under which it settles down at its equilibrium level. This paper shows that the malicious objects have a grave effect on cyber defense mechanism. The paper has two parts – (i) in the first part a mathematical model is proposed in which dynamics of pathogen, immune response and relative characteristic of the damaged node in the network is investigated, (ii) in second part the effect of malicious object on the immune response of the network has been examined. Finally how and where to use this modeling is discussed. [Copyright &y& Elsevier]

Abstract: The present study was undertaken to determine the optimum effective dose, dose reduction factor (DRF) and possible mechanism of action of Aloe gel. Three different doses of gel (250, 500 and 750mg/kg body weight) were tested against 8Gy induced damage in Swiss albino mice. A dose of 750mg/kg body weight of Aloe was found the most effective while, 250mg/kg body weight was the least effective in providing protection, as observed in the form of higher concentrations of blood GSH and vitamin C and lower level of serum LPO than irradiated animals at 1h post irradiation and higher percent of survivors up to day 30 post irradiation. Treatment of mice with Aloe before irradiation with different doses of gamma radiation (6–12Gy) delayed the onset and reduced the severity of signs of radiation sickness. The LD50/30 was calculated as 6.77 and 10Gy for untreated irradiated and Aloe treated irradiated animals, respectively and its dose reduction factor was also calculated as 1.47. Aloe gel scavenged the free radicals, DPPH:+ n a concentration dependent manner in vitro and therefore, scavenging of free radicals seems to be its important mechanism of action. [Copyright &y& Elsevier]

Abstract: An epidemic transmission model SEIRS of malicious objects in the computer network is formulated, with death rate other than attack of malicious object is constant and an excess death rate constant for infective nodes. Deaths of a node in computer network equivalently mean to say the isolation of that node from the computer network which even on continuous run by anti malicious software spread malicious objects. Latent and immune periods are assumed to be constants, and the force of infection is assumed to be of standard form, namely proportional to I(t)/N(t), where N(t) is the total (variable) population size and I(t) is the size of infective population. The model consists of a set of integro-differential equations. When a node is recovered from the infected class, it recovers temporarily, acquiring temporary immunity with probability p (0⩽ p ⩽1) and dies from the attack of malicious object with probability (1− p). Malicious objects free equilibrium is investigated and the stability of the results are stated in terms of threshold parameter. [Copyright &y& Elsevier]

[Display omitted] • A hybrid machine learning optimization for phycobiliproteins (PBPs) reported. • Novel multi-objective based optimization for PBPs in Nostoc sp. CCC 403. • An increase of 61.76% (340.5 µg/ml) total PBPs and 90% in dry cell biomass stated. • Genome-scale metabolic network (GSMN) clued potential metabolic fluxes for PBPs. The cyanobacterial phycobiliproteins (PBPs) are an important natural colorant for nutraceutical industries. Here, a multi-objective hybrid machine learning-based optimization approach was used for enhanced cell biomass and PBPs production simultaneously in Nostoc sp. CCC-403. A central composite design (CCD) was employed to design an experimental setup for four input parameters, including three BG-11 medium components and pH. We achieved a 61.76% increase in total PBPs production and an almost 90% increase in cell biomass by our prediction model. We also established a test genome-scale metabolic network (GSMN) for Nostoc sp. and identified potential metabolic fluxes contributing to PBPs enhanced production. This study highlights the advantage of the hybrid machine learning approach and GSMN to achieve optimization for more than one objective and serves as the foundation for future efforts to convert cyanobacteria as an economically viable source for biofuels and natural products. [ABSTRACT FROM AUTHOR]

Summary Cellular vehicular‐to‐everything (C‐V2X), as a key technology of Internet of Vehicles (IoV), is promised to be enhanced and strengthened to improve road traffic safety and achieve intelligent transportation in 6G era. However, computation‐intensive and latency‐sensitive computation tasks of autonomous driving create a great challenge for the computation and storage limited vehicles. Fortunately, mobile edge computing (MEC) architecture offers a possible solution for the challenge. In this paper, a joint computation offloading and resource allocation algorithm is proposed to solve the computation tasks offloading problem in the scenario of vehicular edge computing network. First, the computation offloading and resource allocation are jointly modeled as a mixed integer nonlinear optimization problem. Aiming at minimizing the total system cost (weighted sum of delay and energy consumption), a particle encoding method and a particle refinement algorithm are proposed based on the compression factor particle swarm optimization, and multilevel penalty function is adopted to deal with the constraints of the objective. Finally, experimental performances validate the effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Key message: Unraveling genetic markers for MYMIV resistance in urdbean, with 8 high-confidence marker-trait associations identified across diverse environments, provides crucial insights for combating MYMIV disease, informing future breeding strategies. Globally, yellow mosaic disease (YMD) causes significant yield losses, reaching up to 100% in favorable environments within major urdbean cultivating regions. The introgression of genomic regions conferring resistance into urdbean cultivars is crucial for combating YMD, including resistance against mungbean yellow mosaic India virus (MYMIV). To uncover the genetic basis of MYMIV resistance, we conducted a genome-wide association study (GWAS) using three multi-locus models in 100 diverse urdbean genotypes cultivated across six individual and two combined environments. Leveraging 4538 high-quality single nucleotide polymorphism (SNP) markers, we identified 28 unique significant marker-trait associations (MTAs) for MYMIV resistance, with 8 MTAs considered of high confidence due to detection across multiple GWAS models and/or environments. Notably, 4 out of 28 MTAs were found in proximity to previously reported genomic regions associated with MYMIV resistance in urdbean and mungbean, strengthening our findings and indicating consistent genomic regions for MYMIV resistance. Among the eight highly significant MTAs, one localized on chromosome 6 adjacent to previously identified quantitative trait loci for MYMIV resistance, while the remaining seven were novel. These MTAs contain several genes implicated in disease resistance, including four common ones consistently found across all eight MTAs: receptor-like serine-threonine kinases, E3 ubiquitin-protein ligase, pentatricopeptide repeat, and ankyrin repeats. Previous studies have linked these genes to defense against viral infections across different crops, suggesting their potential for further basic research involving cloning and utilization in breeding programs. This study represents the first GWAS investigation aimed at identifying resistance against MYMIV in urdbean germplasm. [ABSTRACT FROM AUTHOR]

Key message: In wheat, multiple disease resistance meta-QTLs (MDR-MQTLs) and underlying candidate genes for the three rusts were identified which may prove useful for development of resistant cultivars. Rust diseases in wheat are a major threat to global food security. Therefore, development of multiple disease-resistant cultivars (resistant to all three rusts) is a major goal in all wheat breeding programs worldwide. In the present study, meta-QTLs and candidate genes for multiple disease resistance (MDR) involving all three rusts were identified using 152 individual QTL mapping studies for resistance to leaf rust (LR), stem rust (SR), and yellow rust (YR). From these 152 studies, a total of 1,146 QTLs for resistance to three rusts were retrieved, which included 368 QTLs for LR, 291 QTLs for SR, and 487 QTLs for YR. Of these 1,146 QTLs, only 718 QTLs could be projected onto the consensus map saturated with 2, 34,619 markers. Meta-analysis of the projected QTLs resulted in the identification of 86 MQTLs, which included 71 MDR-MQTLs. Ten of these MDR-MQTLs were referred to as the 'Breeders' MQTLs'. Seventy-eight of the 86 MQTLs could also be anchored to the physical map of the wheat genome, and 54 MQTLs were validated by marker-trait associations identified during earlier genome-wide association studies. Twenty MQTLs (including 17 MDR-MQTLs) identified in the present study were co-localized with 44 known R genes. In silico expression analysis allowed identification of several differentially expressed candidate genes (DECGs) encoding proteins carrying different domains including the following: NBS-LRR, WRKY domains, F-box domains, sugar transporters, transferases, etc. The introgression of these MDR loci into high-yielding cultivars should prove useful for developing high yielding cultivars with resistance to all the three rusts. [ABSTRACT FROM AUTHOR]

Wheat is an essential constituent of cereal-based diets, and one of the most significant sources of calories. However, modern wheat varieties are low in proteins and minerals. Biofortification is a method for increasing the availability of essential elements in the edible portions of crops through agronomic or genetic and genomic interventions. Wheat biofortification, as a research topic, has become increasingly prevalent. Recent accomplishments in genomic biofortification could potentially be helpful for the development of biofortified wheat grains, as a sustainable solution to the issue of "hidden hunger". Genomic interventions mainly include quantitative trait loci (QTL) mapping, marker-assisted selection (MAS), and genomic selection (GS). Developments in the identification of QTL and in the understanding of the physiological and molecular bases of the QTLs controlling the biofortification traits in wheat have revealed new horizons for the improvement of modern wheat varieties. Markers linked with the QTLs of desirable traits can be identified through QTL mapping, which can be employed for MAS. Besides MAS, a powerful tool, GS, also has great potential for crop improvement. We have compiled information from QTL mapping studies on wheat, carried out for the identification of the QTLs associated with biofortification traits, and have discussed the present status of MAS and different prospects of GS for wheat biofortification. Accelerated mapping studies, as well as MAS and GS schemes, are expected to improve wheat breeding efficiency further. [ABSTRACT FROM AUTHOR]

Epigenetic modifications act as conductors of inheritable alterations in gene expression, all while keeping the DNA sequence intact, thereby playing a pivotal role in shaping plant growth and development. This review article presents an overview of techniques employed to investigate and manipulate epigenetic diversity in crop plants, focusing on both naturally occurring and artificially induced epialleles. The significance of epigenetic modifications in facilitating adaptive responses is explored through the examination of how various biotic and abiotic stresses impact them. Further, environmental chemicals are explored for their role in inducing epigenetic changes, particularly focusing on inhibitors of DNA methylation like 5-AzaC and zebularine, as well as inhibitors of histone deacetylation including trichostatin A and sodium butyrate. The review delves into various approaches for generating epialleles, including tissue culture techniques, mutagenesis, and grafting, elucidating their potential to induce heritable epigenetic modifications in plants. In addition, the ground breaking CRISPR/Cas is emphasized for its accuracy in targeting specific epigenetic changes. This presents a potent tools for deciphering the intricacies of epigenetic mechanisms. Furthermore, the intricate relationship between epigenetic modifications and non-coding RNA expression, including siRNAs and miRNAs, is investigated. The emerging role of exo-RNAi in epigenetic regulation is also introduced, unveiling its promising potential for future applications. The article concludes by addressing the opportunities and challenges presented by these techniques, emphasizing their implications for crop improvement. Conclusively, this extensive review provides valuable insights into the intricate realm of epigenetic changes, illuminating their significance in phenotypic plasticity and their potential in advancing crop improvement. • Epigenetic changes impact plant growth without altering DNA. • Crop plant epigenetic diversity, including natural and induced variations. • Creation of epialleles: tissue culture, mutagenesis, grafting, and precise CRISPR/Cas targeting. • Opportunities and obstacles in epigenetic techniques for crop improvement. • Epigenetic research's promise for improving crop yields and resilience to changing environments. [ABSTRACT FROM AUTHOR]

Meta-QTL analysis for thermotolerance in wheat was conducted to identify robust meta-QTLs (MQTLs). In this study, 441 QTLs related to 31 heat-responsive traits were projected on the consensus map with 50,310 markers. This exercise resulted in the identification of 85 MQTLs with confidence interval (CI) ranging from 0.11 to 34.9 cM with an average of 5.6 cM. This amounted to a 2.96-fold reduction relative to the mean CI (16.5 cM) of the QTLs used. Seventy-seven (77) of these MQTLs were also compared and verified with the results of recent genome-wide association studies (GWAS). The 85 MQTLs included seven MQTLs that are particularly useful for breeding purposes (we called them breeders' MQTLs). Seven ortho-MQTLs between wheat and rice genomes were also identified using synteny and collinearity. The MQTLs were used for the identification of 1,704 candidate genes (CGs). In silico expression analysis of these CGs permitted identification of 182 differentially expressed genes (DEGs), which included 36 high confidence CGs with known functions previously reported to be important for thermotolerance. These high confidence CGs encoded proteins belonging to the following families: protein kinase, WD40 repeat, glycosyltransferase, ribosomal protein, SNARE associated Golgi protein, GDSL lipase/esterase, SANT/Myb domain, K homology domain, etc. Thus, the present study resulted in the identification of MQTLs (including breeders' MQTLs), ortho-MQTLs, and underlying CGs, which could prove useful not only for molecular breeding for the development of thermotolerant wheat cultivars but also for future studies focused on understanding the molecular basis of thermotolerance. [ABSTRACT FROM AUTHOR]

Background: Due to rising costs, water shortages, and labour shortages, farmers across the globe now prefer a direct seeding approach. However, submergence stress remains a major bottleneck limiting the success of this approach in rice cultivation. The merger of accumulated rice genetic resources provides an opportunity to detect key genomic loci and candidate genes that influence the flooding tolerance of rice. Results: In the present study, a whole-genome meta-analysis was conducted on 120 quantitative trait loci (QTL) obtained from 16 independent QTL studies reported from 2004 to 2023. These QTL were confined to 18 meta-QTL (MQTL), and ten MQTL were successfully validated by independent genome-wide association studies from diverse natural populations. The mean confidence interval (CI) of the identified MQTL was 3.44 times narrower than the mean CI of the initial QTL. Moreover, four core MQTL loci with genetic distance less than 2 cM were obtained. By combining differentially expressed genes (DEG) from two transcriptome datasets with 858 candidate genes identified in the core MQTL regions, we found 38 common differentially expressed candidate genes (DECGs). In silico expression analysis of these DECGs led to the identification of 21 genes with high expression in embryo and coleoptile under submerged conditions. These DECGs encode proteins with known functions involved in submergence tolerance including WRKY, F-box, zinc fingers, glycosyltransferase, protein kinase, cytochrome P450, PP2C, hypoxia-responsive family, and DUF domain. By haplotype analysis, the 21 DECGs demonstrated distinct genetic differentiation and substantial genetic distance mainly between indica and japonica subspecies. Further, the MQTL7.1 was successfully validated using flanked marker S2329 on a set of genotypes with phenotypic variation. Conclusion: This study provides a new perspective on understanding the genetic basis of submergence tolerance in rice. The identified MQTL and novel candidate genes lay the foundation for marker-assisted breeding/engineering of flooding-tolerant cultivars conducive to direct seeding. [ABSTRACT FROM AUTHOR]

Despite the longstanding importance of silage as a critical feed source for ruminants, its quality improvement has been largely overlooked. Although numerous quantitative trait loci (QTL) and genes affecting silage quality in maize have been reported, only a few have been effectively incorporated into breeding programs. Addressing this gap, the present study undertook a comprehensive meta-QTL (MQTL) analysis involving 523 QTL associated with silage-quality traits collected from 14 published studies. Of the 523 QTL, 405 were projected onto a consensus map comprising 62,424 genetic markers, resulting in the identification of 60 MQTL and eight singletons. The average confidence interval (CI) of the MQTL was 3.9-fold smaller than that of the source QTL. Nine of the 60 identified MQTL were classified as breeder’s MQTL owing to their small CIs, involvement of more QTL, and large contribution to phenotypic variation. One-third of the MQTL co-localized with DNA marker-trait associations identified in previous genomewide association mapping studies. A set of 78 high-confidence candidate genes influencing silage quality were identified in the MQTL regions. These genes and associated markers may advance marker-assisted breeding for maize silage quality. [ABSTRACT FROM AUTHOR]

The work reported in present study deals with the development of a novel stochastic model and estimation of parameters to assess reliability characteristics for a turbogenerator unit of thermal power plant under classical and Bayesian frameworks. Turbogenerator unit consists of five components namely turbine lubrication, turbine governing, generator oil system, generator gas system and generator excitation system. The concepts of cold standby redundancy and Weibull distributed random variables are used in development of stochastic model. The shape parameter for all the random variables is same while scale parameter is different. Regenerative point technique and semi-Markov approach are used for evaluation of reliability characteristics. Sufficient repair facility always remains available in plant as well as repair done by the repairman is considered perfect. As the life testing experiments are time consuming, so to highlight the importance of proposed model Monte Carlo simulation study is carried out. A comparative analysis is done between true, classical and Bayesian results of MTSF, availability and profit function. [ABSTRACT FROM AUTHOR]

Cloud infrastructure provides a real time computing environment to customers and had wide applicability in healthcare, medical facilities, business, and several other areas. Most of the health data recorded and saved on the cloud. But the cloud infrastructure is configured using several components and that makes it a complex structure. And the high value of availability and reliability is essential for satisfactory operation of such systems. So, the present study is conducted with the prominent objective of assessing the optimum availability of the cloud infrastructure. For this purpose, a novel stochastic model is proposed and optimized using dragonfly algorithm (DA) and Grey Wolf optimization (GWO) algorithms. The Markovian approach is employed to develop the Chapman-Kolmogorov differential difference equations associate with the system. It is considered that all failure and repair rates are exponentially distributed. The repairs are perfect. The numerical results are derived to highlight the importance of the study and identify the best algorithm. The system attains its optimum availability 0.9998649 at population size 120 with iteration 700 by GWO. It is revealed that grey wolf optimization algorithm performed better than the Dragonfly algorithm in assessing the availability, best fitted parametric values and execution time. [ABSTRACT FROM AUTHOR]

The biomass of the lignocellulosic weed P. hysterophorus is abundant and wasteful in India. This study aimed to investigate the lignocellulosic weed P. hysterophorus for reducing sugar production. Therefore, several pretreatment methods, physical (Steam and Microwave), and chemical (alkalis and acids) were investigated to determine the statistically best conditions for enzymatic hydrolysis. The results revealed that alkali pretreatment (sodium hydroxide, 1.0% w/v) had the greatest impact on the production of reducing sugar (110.36 ± 0.05 mg/g substrate) from P. hysterophorus. The decrease in the lignin content and increase in carbohydrates from pretreated biomass was further validated using an ultrastructural technique (Fourier-transform infrared spectroscopy and X-ray diffraction). The quantity of reducing sugars released was optimized using a response surface methodology, and the experimental results with optimized process variables gave 385.44 mg/g substrate reducing sugar. The investigations showed that P. hysterophorus can be utilized as a promising and low-cost biomass source for commercial ethanol production. [ABSTRACT FROM AUTHOR]

The waste management of an evolving smart city environment is one of the most important tasks as the living conditions and health of the population depend on proper waste management. Currently deployed systems are failing to monitor the garbage production as they use IoT-based pipelines to monitor the production in a locality, but often the device is used to get destroyed by the frequent use of dustbin. This leads to an increase in expenditure and affects the sustainability of the system. In this work, we propose an efficient and scalable garbage monitoring and collection methodology based on time-series forecasting techniques. The proposed system is also cost-effective because of the iterative deployment of rented IoT sensors, which are used to collect time series format data and then used to train the forecasting module to learn the temporal representation of the data that can produce accurate results for monitoring the fill-up time of the garbage collector. We also propose an efficient collection in-routing technique based on the ranking of bin stations on the basis of temporal and spatial data of the fill-up time and route location to minimize the collection time by making an efficient routing algorithm for garbage collection. This concept of garbage collection will be very useful for smart city planners. [ABSTRACT FROM AUTHOR]

Crop lodging, either stem breaking/stem bending or root lodging, is a major concern for farmers worldwide. In the present study, meta-QTLs, ortho-MQTLs and candidate genes related to lodging and its associated traits were identified using 836 of the 1269 known QTLs retrieved from 107 QTL mapping studies published during 1998–2022. The study resulted in the identification of 86 MQTLs with an average confidence interval (CI) of 1.66 cM (4.26-fold reduction) as against a CI of > 7.08 cM in the initial QTLs. Out of 86 MQTLs, a subset of 12 MQTLs demonstrated stability and robustness, earning them the title of breeders' MQTLs. These selections were made based on criteria such as the number of initial QTLs involved, small CI, high LOD scores, and PVE values. Furthermore, leveraging synteny and collinearity facilitated the identification of 10 ortho-MQTLs shared between wheat and maize. Further, in silico expression analysis allowed the detection of 53 significantly expressed candidate genes encoding different proteins/products including the following: F-box protein, GDSL lipase/esterases, SANT/Myb domain transcription factor, pectin esterase inhibitor domain, glycoside hydrolase family, protein kinase domain, zinc finger domain, cytochrome P450, UDP-glucuronosyl/UDP-glucosyltransferase, glycosyltransferase 61 etc. In addition, 26 lodging tolerance genes from Arabidopsis, rice, and maize were also utilized to identify 34 wheat homologues in wheat MQTL regions. Among these, three genes TraesCS2D02G256600 (TaEREB114 -2D) , TraesCS7B02G340100 (TaIRX14- 7B), and TraesCS1B02G347600 (TaGA2ox4- 1B) were prioritized for qRT-PCR-based expression analysis in one lodging-tolerant (i.e., WH-1105) and one lodging-susceptible (i.e., WH-1184) variety. When comparing these two varieties, we found higher relative expression levels of these three genes in the second basal internode at the milky endosperm stage of the lodging-tolerant variety. The MQTLs, ortho-MQTLs, and candidate genes (CGs) identified in this study can be utilized for marker-assisted selection to improve lodging tolerance and enhance our understanding of the molecular basis of lodging tolerance in wheat. [Display omitted] • Eighty-six MQTLs with significantly reduced confidence intervals were identified. • Twelve MQTLs were recommended for breeding programs for improving lodging resistancein wheat. • Ten ortho-MQTLs were identified between the wheat and maize genomes. • Three genes viz., TraesCS1B02G347600, TraesCS2D02G256600, and TraesCS7B02G340100 were validated using qRT-PCR analysis. [ABSTRACT FROM AUTHOR]

In plants, the enzyme CCD8 (carotenoid cleavage dioxygenase 8) is involved in the synthesis of an important hormone, strigolactone, and therefore, plays an important role in controlling growth and development. Using cDNA and protein sequence derived from the gene ZmCCD8 from maize, we identified putative orthologs of the gene encoding CCD8 in six other monocots and eight dicots; the sequence similarity ranged from 52–75.9% at the gene level and 60.9–93.7% at the protein level. The average length of the gene was ~3.3 kb (range: 2.08 to 3.98 kb), although the number of introns within the genes differed (4 or 5 in dicots and 3 or 4 in monocots, except in T. urartu with 6 introns). Several cis-acting regulatory elements were identified in the promoters of CCD8 genes, which are known to respond to biotic and abiotic stresses. The N-terminal end (up to ~70 amino acids) of CCD8 proteins was highly variable due to insertions, deletions and mismatches. The variation in genes and proteins were particularly conspicuous in T. urartu and Ae. tauschii among the monocots and A. thaliana and P. persica among the dicots. In CCD8 proteins, 12 motifs were also identified, of which 6 were novel; 4 of these novel motifs occurred in all the 15 species. The 3D structures of proteins had the characteristic features of the related enzyme apocarotenoid oxygenase (ACO) of Synechocystis (a representative of cyanobacteria). The results of qRT-PCR in wheat revealed that under phosphorous (P)-starved condition (relative to expression under optimum P used as control), the expression of TaCCD8 genes increased ~37 fold in root tissue of the cultivar C306 and ~33 fold in shoot tissue of the cultivar HUW468 (the two cultivars differed in their P-use efficiency). This suggested that expression of TaCCD8 genes is genotype-dependent and tissue-specific and is regulated under different levels of P supply. [ABSTRACT FROM AUTHOR]

Highlights • A self-contained dynamic goal-seeking mechanism using behavior-based approach has been implemented for searching targets in an unknown environment. • Bio-inspired dynamic virtual leaders are used, who steer and drive the swarm from current goal to the reference goal. • An avoid past behavior has been designed to help goal-seeking behavior more efficiently. Abstract Search and rescue (SAR) is one of the foremost issues in disaster management. A robust SAR mechanism can significantly reduce the number of causalities. This paper presents a behavior-based model for a swarm of small robots to perform an efficient SAR operation in an unknown environment. The swarm is guided by a dynamically selected virtual leader (VL). A self-contained dynamic goal-seeking mechanism, using behavior-based approach, is designed to search targets (victims). Under the leadership of VL, the proposed model retains the integrity of the swarm while driving it from its current position to referenced goals. Fuzzy logic has been used to design constituent behavioral modules, namely obstacle avoidance, alignment, and inter-robot cohesion. The model has been simulated to validate its efficiency and the findings reveal that robots moving as a swarm are more effective in the SAR process as compared to multiple single robots. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

This study explores the impact of public school location/distribution and surrounding build environment based on its distance from the main street, service road, petrol stations in Sohar, Oman using GIS. Data have been collected from various official sources to fulfil the requirements of this study, which helped in constructing the geodatabase for this project. The results of this study demonstrated that there is an apparent inconsistency in public schools distribution, as most public schools are concentrated in one location. The study concludes with suggestions and recommendations to use GIS and spatial planning by the decision makers to make future sound decisions of building public schools. It is desirable to build schools in areas with lower percentages of highways and commercial sectors. [ABSTRACT FROM AUTHOR]