Integration of transcriptomics, metabolomics, and hormone analysis revealed the formation of lesion spots inhibited by GA and CTK was related to cell death and disease resistance in bread wheat (Triticum aestivum L.).

BACKGROUND: Wheat is one of the important grain crops in the world. The formation of lesion spots related to cell death is involved in disease resistance, whereas the regulatory pathway of lesion spot production and resistance mechanism to pathogens in wheat is largely unknown. RESULTS: In this study, a pair of NILs (NIL-Lm5W and NIL-Lm5M) was constructed from the BC1F4 population by the wheat lesion mimic mutant MC21 and its wild genotype Chuannong 16. The formation of lesion spots in NIL-Lm5M significantly increased its resistance to stripe rust, and NIL-Lm5M showed superiour agronomic traits than NIL-Lm5W under stripe rust infection.Whereafter, the NILs were subjected to transcriptomic (stage N: no spots; stage S, only a few spots; and stage M, numerous spots), metabolomic (stage N and S), and hormone analysis (stage S), with samples taken from normal plants in the field. Transcriptomic analysis showed that the differentially expressed genes were enriched in plant-pathogen interaction, and defense-related genes were significantly upregulated following the formation of lesion spots. Metabolomic analysis showed that the differentially accumulated metabolites were enriched in energy metabolism, including amino acid metabolism, carbohydrate metabolism, and lipid metabolism. Correlation network diagrams of transcriptomic and metabolomic showed that they were both enriched in energy metabolism. Additionally, the contents of gibberellin A7, cis-Zeatin, and abscisic acid were decreased in leaves upon lesion spot formation, whereas the lesion spots in NIL-Lm5M leaves were restrained by spaying GA and cytokinin (CTK, trans-zeatin) in the field. CONCLUSION: The formation of lesion spots can result in cell death and enhance strip rust resistance by protein degradation pathway and defense-related genes overexpression in wheat. Besides, the formation of lesion spots was significantly affected by GA and CTK. Altogether, these results may contribute to the understanding of lesion spot formation in wheat and laid a foundation for regulating the resistance mechanism to stripe rust.

An Amiable Design of Cobalt Single Atoms as the Active Sites for Oxygen Evolution Reaction in Desalinated Seawater.

Green fuel from water splitting is hardcore for future generations, and the limited source of fresh water (<1%) is a bottleneck. Seawater cannot be used directly as a feedstock in current electrolyzer techniques. Until now single atom catalysts were reported by many synthetic strategies using notorious chemicals and harsh conditions. A cobalt single-atom (CoSA) intruding cobalt oxide ultrasmall nanoparticle (Co3 O4 USNP)-intercalated porous carbon (PC) (CoSA-Co3 O4 @PC) electrocatalyst was synthesized from the waste orange peel as a single feedstock (solvent/template). The extended X-ray absorption fine structure spectroscopy (EXAFS) and theoretical fitting reveal a clear picture of the coordination environment of the CoSA sites (CoSA-Co3 O4 and CoSA-N4 in PC). To impede the direct seawater corrosion and chlorine evolution the seawater has been desalinated (Dseawater) with minimal cost and the obtained PC is used as an adsorbent in this process. CoSA-Co3 O4 @PC shows high oxygen evolution reaction (OER) activity in transitional metal impurity-free (TMIF) 1 M KOH and alkaline Dseawater. CoSA-Co3 O4 @PC exhibits mass activity that is 15 times higher than the commercial RuO2 . Theoretical interpretations suggest that the optimized CoSA sites in Co3 O4 USNPs reduce the energy barrier for alkaline water dissociation and simultaneously trigger an excellent OER followed by an adsorbate evolution mechanism (AEM).

Multistage temporal transcriptomic atlas unveils major contributor to reproductive phase in upland cotton.

Flowering is a major developmental transition in plants, but asynchronous flowering hinders the utilization of wild cotton relatives in breeding programs. We performed comparative transcriptomic profiling of early- and late-flowering Gossypium hirsutum genotypes to elucidate genetic factors influencing reproductive timing. Shoot apices were sampled from the photoperiod-sensitive landrace G. hirsutum purpurascens (GhP) and early-maturing variety ZhongMianSuo (ZMS) at five time points following the emergence of sympodial nodes. RNA-sequencing revealed extensive transcriptional differences during floral transition. Numerous flowering-associated genes exhibited genotype-specific expression, including FLOWERING LOCUS T (FT) homologs upregulated in ZMS. FT-interacting factors like SOC1 and CO-like also showed higher expression in ZMS, implicating florigen pathways in early flowering. Additionally, circadian clock and light signalling components were misregulated between varieties, suggesting altered photoperiod responses in GhP. Weighted co-expression network analysis specifically linked a module enriched for circadian-related genes to GhP's late flowering. Through an integrated transcriptome analysis, we defined a regulatory landscape of reproductive phase change in cotton. Differentially expressed genes related to photoperiod, circadian clock, and light signalling likely contribute to delayed flowering in wild cottons. Characterization of upstream flowering regulators will enable modifying photoperiod sensitivity and expand germplasm use for cotton improvement. This study provides candidate targets for elucidating interactive mechanisms that control cotton flowering time across diverse genotypes.

Endophytic Fungi-Mediated Defense Signaling in Maize: Unraveling the Role of WRKY36 in Regulating Immunity against Spodoptera frugiperda.

Seed priming with beneficial endophytic fungi is an emerging sustainable strategy for enhancing plant resistance against insect pests. This study examined the effects of Beauvaria bassiana Bb20091317 and Metarhizium rileyi MrCDTLJ1 fungal colonization on maize growth, defence signalling, benzoxazinoid levels and gene expression. The colonization did not adversely affect plant growth but reduced larval weights of Spodoptera frugiperda. Maize leaves treated with M. rileyi exhibited higher levels of jasmonic acid, jasmonoyl-Isoleucine, salicylic acid, and indole acetic acid compared to control. B. bassiana and M. rileyi accelerated phytohormone increase upon S. frugiperda herbivory. Gene expression analysis revealed modulation of benzoxazinoid biosynthesis genes. We further elucidated the immune regulatory role of the transcription factor zmWRKY36 using virus-induced gene silencing (VIGS) in maize. zmWRKY36 positively regulates maize immunity against S. frugiperda, likely by interacting with defense-related proteins. Transient overexpression of zmWRKY36 in tobacco-induced cell death, while silencing in maize reduced chitin-triggered reactive oxygen species burst, confirming its immune function. Overall, B. bassiana and M. rileyi successfully colonized maize, impacting larval growth, defense signalling, and zmWRKY36-mediated resistance. This sheds light on maize-endophyte-insect interactions for sustainable plant protection.

Nanoarchitecture Frameworks for Electrochemical miRNA Detection.

With revolutionary advances in next-generation sequencing, the human transcriptome has been comprehensively interrogated. These discoveries have highlighted the emerging functional and regulatory roles of a large fraction of RNAs suggesting the potential they might hold as stable and minimally invasive disease biomarkers. Although a plethora of molecular-biology- and biosensor-based RNA-detection strategies have been developed, clinical application of most of these is yet to be realized. Multifunctional nanomaterials coupled with sensitive and robust electrochemical readouts may prove useful in these applications. Here, we summarize the major contributions of engineered nanomaterials-based electrochemical biosensing strategies for the analysis of miRNAs. With special emphasis on nanostructure-based detection, this review also chronicles the needs and challenges of miRNA detection and provides a future perspective on the presented strategies.

Construction and application of a new watermelon germplasm with the phenotype of dwarf and branchless.

Watermelon (Citrullus lanatus) is a widely cultivated cucurbitaceae crop appreciated by consumers worldwide. However, the long vine and abundant lateral branches of currently cultivated watermelon varieties hinder light simplification and mechanized cultivation, affecting plant spacing and row spacing requirements. To address this, the development of watermelon with dwarf and branchless traits has become a crucial direction for the industry. In previous studies, the genes controlling dwarf (Cldw-1) and branchless (Clbl) traits were mapped and cloned. Marker-assisted selection markers, dCAPS3 and dCAPS10, were developed for these traits, respectively. In this study, the dwarf germplasm WM102 and the branchless germplasm WCZ were crossed to obtain F1 .Further self-crossing of the F1 individuals resulted in the F2 population. Through multiple generations of self-pollination, a new watermelon germplasm DM with double mutation (dwarf and branchless) was obtained. DM exhibited stable inheritance without segregation. Moreover, DM was used as a donor parent for crossing with commercial watermelon materials, and near-isogenic lines (NILs) with the dwarf and branchless traits were developed. These NILs carry additional desirable agronomic traits and provide valuable genetic resources for future watermelon breeding programs, particularly in improving plant architecture and overall quality. The development and application of DM and NILs hold great potential for advancing the watermelon industry toward industrialization, large-scale cultivation, and enhanced plant architecture.

Genome-wide identification and expression analysis of terpene synthases in Gossypium species in response to gossypol biosynthesis.

Cottonseed is an invaluable resource, providing protein, oil, and abundant minerals that significantly contribute to the well-being and nutritional needs of both humans and livestock. However, cottonseed also contains a toxic substance called gossypol, a secondary metabolite in Gossypium species that plays an important role in cotton plant development and self-protection. Herein, genome-wide analysis and characterization of the terpene synthase (TPS) gene family identified 304 TPS genes in Gossypium. Bioinformatics analysis revealed that the gene family was grouped into six subgroups TPS-a, TPS-b, TPS-c, TPS-e, TPS-f, and TPS-g. Whole-genome, segmental, and tandem duplication contributed to the evolution of TPS genes. According to the analysis of selection pressure, it was predicted that TPS genes experience predominantly negative selection, with positive selection occurring subsequently. RT-qPCR analysis in TM-1 and CRI-12 lines revealed GhTPS48 gene as the candidate gene for silencing experiments. To summarize, comprehensive genome-wide studies, RT-qPCR, and gene silencing experiments have collectively demonstrated the involvement of the TPS gene family in the biosynthesis of gossypol in cotton.

Insights to Gossypium defense response against Verticillium dahliae: the Cotton Cancer.

The soil-borne pathogen Verticillium dahliae, also referred as "The Cotton Cancer," is responsible for causing Verticillium wilt in cotton crops, a destructive disease with a global impact. To infect cotton plants, the pathogen employs multiple virulence mechanisms such as releasing enzymes that degrade cell walls, activating genes that contribute to virulence, and using protein effectors. Conversely, cotton plants have developed numerous defense mechanisms to combat the impact of V. dahliae. These include strengthening the cell wall by producing lignin and depositing callose, discharging reactive oxygen species, and amassing hormones related to defense. Despite the efforts to develop resistant cultivars, there is still no permanent solution to Verticillium wilt due to a limited understanding of the underlying molecular mechanisms that drive both resistance and pathogenesis is currently prevalent. To address this challenge, cutting-edge technologies such as clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9), host-induced gene silencing (HIGS), and gene delivery via nano-carriers could be employed as effective alternatives to control the disease. This article intends to present an overview of V. dahliae virulence mechanisms and discuss the different cotton defense mechanisms against Verticillium wilt, including morphophysiological and biochemical responses and signaling pathways including jasmonic acid (JA), salicylic acid (SA), ethylene (ET), and strigolactones (SLs). Additionally, the article highlights the significance of microRNAs (miRNAs), circular RNAs (circRNAs), and long non-coding RNAs (lncRNAs) in gene expression regulation, as well as the different methods employed to identify and functionally validate genes to achieve resistance against this disease. Gaining a more profound understanding of these mechanisms could potentially result in the creation of more efficient strategies for combating Verticillium wilt in cotton crops.

Dynamic characteristics and functional analysis provide new insights into the role of GauERF105 for resistance against Verticillium dahliae in cotton.

BACKGROUND: The cotton industry suffers significant yield losses annually due to Verticillium wilt, which is considered the most destructive disease affecting the crop. However, the precise mechanisms behind this disease in cotton remain largely unexplored. METHODS: Our approach involved utilizing transcriptome data from G. australe which was exposed to Verticillium dahliae infection. From this data, we identified ethylene-responsive factors and further investigated their potential role in resistance through functional validations via Virus-induced gene silencing (VIGS) in cotton and overexpression in Arabidopsis. RESULTS: A total of 23 ethylene response factors (ERFs) were identified and their expression was analyzed at different time intervals (24 h, 48 h, and 72 h post-inoculation). Among them, GauERF105 was selected based on qRT-PCR expression analysis for further investigation. To demonstrate the significance of GauERF105, VIGS was utilized, revealing that suppressing GauERF105 leads to more severe infections in cotton plants compared to the wild-type. Additionally, the silenced plants exhibited reduced lignin deposition in the stems compared to the WT plants, indicating that the silencing of GauERF105 also impacts lignin content. The overexpression of GauERF105 in Arabidopsis confirmed its pivotal role in conferring resistance against Verticillium dahliae infection. Our results suggest that WT possesses higher levels of the oxidative stress markers MDA and H2O2 as compared to the overexpressed lines. In contrast, the activities of the antioxidant enzymes SOD and POD were higher in the overexpressed lines compared to the WT. Furthermore, DAB and trypan staining of the overexpressed lines suggested a greater impact of the disease in the wild-type compared to the transgenic lines. CONCLUSIONS: Our findings provide confirmation that GauERF105 is a crucial candidate in the defense mechanism of cotton against Verticillium dahliae invasion, and plays a pivotal role in this process. These results have the potential to facilitate the development of germplasm resistance in cotton.

Decoding the guardians of cotton resilience: A comprehensive exploration of the ßCA genes and its role in Verticillium dahliae resistance.

Plant Carbonic anhydrases (Cas) have been shown to be stress-responsive enzymes that may play a role in adapting to adverse conditions. Cotton is a significant economic crop in China, with upland cotton (Gossypium hirsutum) being the most widely cultivated species. We conducted genome-wide identification of the ßCA gene in six cotton species and preliminary analysis of the ßCA gene in upland cotton. In total, 73 ßCA genes from six cotton species were identified, with phylogenetic analysis dividing them into five subgroups. GHßCA proteins were predominantly localized in the chloroplast and cytoplasm. The genes exhibited conserved motifs, with motifs 1, 2, and 3 being prominent. GHßCA genes were unevenly distributed across chromosomes and were associated with stress-responsive cis-regulatory elements, including those responding to light, MeJA, salicylic acid, abscisic acid, cell cycle regulation, and defence/stress. Expression analysis indicated that GHßCA6, GHßCA7, GHßCA10, GHßCA15, and GHßCA16 were highly expressed under various abiotic stress conditions, whereas GHßCA3, GHßCA9, GHßCA10, and GHßCA18 had higher expression patterns under Verticillium dahliae infection at different time intervals. In Gossypium thurberi, GthßCA1, GthßCA2, and GthßCA4 showed elevated expression across stress conditions and tissues. Silencing GHßCA10 through VIGS increased Verticillium wilt severity and reduced lignin deposition compared to non-silenced plants. GHßCA10 is crucial for cotton's defense against Verticillium dahliae. Further research is needed to understand the underlying mechanisms and develop strategies to enhance resistance against Verticillium wilt.

Transcriptomic analysis reveals the beneficial effects of salt priming on enhancing defense responses in upland cotton under successive salt stress.

Priming-mediated stress tolerance in plants stimulates defense mechanisms and enables plants to cope with future stresses. Seed priming has been proven effective for tolerance against abiotic stresses; however, underlying genetic mechanisms are still unknown. We aimed to assess upland cotton genotypes and their transcriptional behaviors under salt priming and successive induced salt stress. We pre-selected 16 genotypes based on previous studies and performed morpho-physiological characterization, from which we selected three genotypes, representing different tolerance levels, for transcriptomic analysis. We subjected these genotypes to four different treatments: salt priming (P0), salt priming with salinity dose at 3-true-leaf stage (PD), salinity dose at 3-true-leaf stage without salt priming (0D), and control (CK). Although the three genotypes displayed distinct expression patterns, we identified common differentially expressed genes (DEGs) under PD enriched in pathways related to transferase activity, terpene synthase activity, lipid biosynthesis, and regulation of acquired resistance, indicating the beneficial role of salt priming in enhancing salt stress resistance. Moreover, the number of unique DEGs associated with G. hirsutum purpurascens was significantly higher compared to other genotypes. Coexpression network analysis identified 16 hub genes involved in cell wall biogenesis, glucan metabolic processes, and ribosomal RNA binding. Functional characterization of XTH6 (XYLOGLUCAN ENDOTRANSGLUCOSYLASE/HYDROLASE) using virus-induced gene silencing revealed that suppressing its expression improves plant growth under salt stress. Overall, findings provide insights into the regulation of candidate genes in response to salt stress and the beneficial effects of salt priming on enhancing defense responses in upland cotton.

An Intelligent Framework for Cyber-Physical Satellite System and IoT-Aided Aerial Vehicle Security Threat Detection.

The small-drone technology domain is the outcome of a breakthrough in technological advancement for drones. The Internet of Things (IoT) is used by drones to provide inter-location services for navigation. But, due to issues related to their architecture and design, drones are not immune to threats related to security and privacy. Establishing a secure and reliable network is essential to obtaining optimal performance from drones. While small drones offer promising avenues for growth in civil and defense industries, they are prone to attacks on safety, security, and privacy. The current architecture of small drones necessitates modifications to their data transformation and privacy mechanisms to align with domain requirements. This research paper investigates the latest trends in safety, security, and privacy related to drones, and the Internet of Drones (IoD), highlighting the importance of secure drone networks that are impervious to interceptions and intrusions. To mitigate cyber-security threats, the proposed framework incorporates intelligent machine learning models into the design and structure of IoT-aided drones, rendering adaptable and secure technology. Furthermore, in this work, a new dataset is constructed, a merged dataset comprising a drone dataset and two benchmark datasets. The proposed strategy outperforms the previous algorithms and achieves 99.89% accuracy on the drone dataset and 91.64% on the merged dataset. Overall, this intelligent framework gives a potential approach to improving the security and resilience of cyber-physical satellite systems, and IoT-aided aerial vehicle systems, addressing the rising security challenges in an interconnected world.

Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System.

With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient's activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.

Genome-Wide Association Study of Lint Percentage in Gossypium hirsutum L. Races.

Lint percentage is one of the most essential yield components and an important economic index for cotton planting. Improving lint percentage is an effective way to achieve high-yield in cotton breeding worldwide, especially upland cotton (Gossypium hirsutum L.). However, the genetic basis controlling lint percentage has not yet been systematically understood. Here, we performed a genome-wide association mapping for lint percentage using a natural population consisting of 189 G. hirsutum accessions (188 accessions of G. hirsutum races and one cultivar TM-1). The results showed that 274 single-nucleotide polymorphisms (SNPs) significantly associated with lint percentage were detected, and they were distributed on 24 chromosomes. Forty-five SNPs were detected at least by two models or at least in two environments, and their 5 Mb up- and downstream regions included 584 makers related to lint percentage identified in previous studies. In total, 11 out of 45 SNPs were detected at least in two environments, and their 550 Kb up- and downstream region contained 335 genes. Through RNA sequencing, gene annotation, qRT-PCR, protein-protein interaction analysis, the cis-elements of the promotor region, and related miRNA prediction, Gh_D12G0934 and Gh_A08G0526 were selected as key candidate genes for fiber initiation and elongation, respectively. These excavated SNPs and candidate genes could supplement marker and gene information for deciphering the genetic basis of lint percentage and facilitate high-yield breeding programs of G. hirsutum ultimately.

Utilization Of The Complete Blood Count In Diagnosing Endemic Diseases In Pakistan.

Numerous infectious diseases, including enteric fever, malaria, dengue fever, and, most recently, coronavirus disease-2019, are prevalent in Pakistan. All these diseases have overlapping clinical symptoms and can present a diagnostic challenge for the general practitioner. Since definitive testing for these disorders is time-consuming and expensive, basic clinical testing, such as a complete blood count, should be utilised to supplement clinical diagnosis, when possible. The current narrative review was planned to present specific alterations in haematological parameters for each of these disorders. The review was intended as a guide for practising physicians in their decision-making.

Respectful Maternal Care Experience in Low- and Middle-Income Countries: A Systematic Review.

Background and Objectives: Respectful maternity care promotes practices that acknowledge women's preferences and women and newborns' needs. It is an individual-centered strategy founded on ethical and human rights principles. The objective of this systematic review is to identify the impact of income on maternal care and respectful maternity care in low- and middle-income countries. Materials and Methods: Data were searched from Google Scholar, PubMed, Web of Science, NCBI, CINAHL, National Library of Medicine, ResearchGate, MEDLINE, EMBASE database, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), and Maternity and Infant Care database. This review followed PRISMA guidelines. The initial search for publications comparing low- and middle-income countries with respectful maternity care yielded 6000 papers, from which 700 were selected. The review articles were further analyzed to ensure they were pertinent to the comparative impact of income on maternal care. A total of 24 articles were included, with preference given to those published from 2010 to 2023 during the last fourteen years. Results: Considering this study's findings, respectful maternity care is a crucial component of high-quality care and human rights. It can be estimated that there is a direct association between income and maternity care in LMICs, and maternity care is substandard compared to high-income countries. Moreover, it is determined that the evidence for medical tools that can enhance respectful maternity care is sparse. Conclusions: This review highlights the significance of improving maternal care experiences, emphasizing the importance of promoting respectful practices and addressing disparities in low- and middle-income countries.

Overexpression of cotton GhNAC072 gene enhances drought and salt stress tolerance in transgenic Arabidopsis.

BACKGROUND: Crops face several environmental stresses (biotic and abiotic), thus resulting in severe yield losses. Around the globe abiotic stresses are the main contributors of plant damages, primarily drought and salinity. Many genes and transcription factors are involved in abiotic and biotic stress responses. NAC TF (Transcription Factors) improves tolerance to stresses by controlling the physiological and enzyme activities of crops. RESULTS: In current research, GhNAC072 a highly upregulated TF in RNA-Seq was identified as a hub gene in the co-expression network analysis (WGCNA). This gene was transformed to Arabidopsis thaliana to confirm its potential role in drought and salt stress tolerance. Significant variations were observed in the morpho-physiological traits with high relative leaf water contents, chlorophyll contents, higher germination and longer root lengths of the overexpressed lines and low excised leaf loss and ion leakage as compared to the wildtype plants. Besides, overexpressed lines have higher amounts of antioxidants and low oxidant enzyme activities than the wildtype during the period of stress exposure. CONCLUSIONS: In summary, the above analysis showed that GhNAC072 might be the true candidate involved in boosting tolerance mechanisms under drought and salinity stress.

Seed Myco-priming improves crop yield and herbivory induced defenses in maize by coordinating antioxidants and Jasmonic acid pathway.

BACKGROUND: Seed Myco-priming based on consortium of entomopathogenic fungi is very effective seed treatment against Ostrinia furnacalis herbivory. Maize regulates defense responses against herbivory by the production of defense-related enzymatic and non-enzymatic antioxidants, phytohormones, and their corresponding genes. Jasmonic acid (JA) plays a key role in plant-entomopathogenic fungi-herbivore interaction. RESULTS: To understand how a consortium of the entomopathogenic fungi Beauveria bassiana and Trichoderma asperellum induce changes in the response of maize to herbivory and increase the crop yield, 2-year field experiment, antioxidant enzymes, leaf transcriptome, and phytohormone were performed. Fungal inoculation enhanced the production of antioxidant enzymes and JA signaling pathway more than the normal herbivory. The comparison between single inoculated, consortium inoculated, and non-inoculated plants resulted in distinct transcriptome profiles representing a considerable difference in expression of antioxidant- and JA- responsive genes identified through Weighted gene co-expression network analysis (WGCNA) and expression analysis, respectively. Seed priming with a consortium of B. bassiana and T. asperellum significantly enhanced the expression of genes involved in antioxidants production and JA biosynthesis cascade, with the highest expression recorded at 24-h post O. furnacalis larval infestation. They reduced the larval nutritional indices and survival up to 87% and enhancing crop yield and gross return up to 82-96% over the year 2018 and 2019. CONCLUSION: From our results we suggest that a consortium of B. bassiana and T. asperellum can be used synergistically against O. furnacalis in maize under field condition and can mediate antioxidants- and JA- associated maize defense response by boosting up the expression of their responsive genes, thereby enhancing crop yield.

IoT Based Smart Monitoring of Patients' with Acute Heart Failure.

The prediction of heart failure survivors is a challenging task and helps medical professionals to make the right decisions about patients. Expertise and experience of medical professionals are required to care for heart failure patients. Machine Learning models can help with understanding symptoms of cardiac disease. However, manual feature engineering is challenging and requires expertise to select the appropriate technique. This study proposes a smart healthcare framework using the Internet-of-Things (IoT) and cloud technologies that improve heart failure patients' survival prediction without considering manual feature engineering. The smart IoT-based framework monitors patients on the basis of real-time data and provides timely, effective, and quality healthcare services to heart failure patients. The proposed model also investigates deep learning models in classifying heart failure patients as alive or deceased. The framework employs IoT-based sensors to obtain signals and send them to the cloud web server for processing. These signals are further processed by deep learning models to determine the state of patients. Patients' health records and processing results are shared with a medical professional who will provide emergency help if required. The dataset used in this study contains 13 features and was attained from the UCI repository known as Heart Failure Clinical Records. The experimental results revealed that the CNN model is superior to other deep learning and machine learning models with a 0.9289 accuracy value.

Predicting Students' Academic Performance with Conditional Generative Adversarial Network and Deep SVM.

The availability of educational data obtained by technology-assisted learning platforms can potentially be used to mine student behavior in order to address their problems and enhance the learning process. Educational data mining provides insights for professionals to make appropriate decisions. Learning platforms complement traditional learning environments and provide an opportunity to analyze students' performance, thus mitigating the probability of student failures. Predicting students' academic performance has become an important research area to take timely corrective actions, thereby increasing the efficacy of education systems. This study proposes an improved conditional generative adversarial network (CGAN) in combination with a deep-layer-based support vector machine (SVM) to predict students' performance through school and home tutoring. Students' educational datasets are predominantly small in size; to handle this problem, synthetic data samples are generated by an improved CGAN. To prove its effectiveness, results are compared with and without applying CGAN. Results indicate that school and home tutoring combined have a positive impact on students' performance when the model is trained after applying CGAN. For an extensive evaluation of deep SVM, multiple kernel-based approaches are investigated, including radial, linear, sigmoid, and polynomial functions, and their performance is analyzed. The proposed improved CGAN coupled with deep SVM outperforms in terms of sensitivity, specificity, and area under the curve when compared with solutions from the existing literature.