Cancer is a serious health concern and a global threat. Designing therapies and overcoming the disease has been challenging due to its multifactorial complexity. High-throughput technology advancements have made it possible to build extensive biological networks, which calls for integrative computational methods in order to derive significant regulatory insights. The identification of conserved network elements, such as network motifs defined as statistically overrepresented functional subgraphs help gain important insight into underlying biological processes by revealing the constrained aspects of these complex networks. Understanding the molecular basis of carcinogenesis requires an integrative approach capable of dissecting complex signaling relationships. Analyzing complicated cancer pathways to better understand their disease relationship remains a laborious procedure. In this study, we applied an integrative system-component approach to analyze tissue-specific cancer pathways and DNA repair networks across 12 cancers, focusing on the identification of network motifs. In addition to applying commonly used statistical restrictions, including z-scores, p-values, and significance profiles, the important signatures from these pathways were identified using several novel metrics that reflected overrepresented sub-structures, narrowing the search to essential regulatory players. By employing statistical and network-based parameters, our analysis prioritizes prostate cancer regulatory proteins MDM2, CHUK, GSK3B, AKT3, and CDKN1B as highly connected regulatory candidate proteins. These proteins are functionally linked to DNA repair, genomic maintenance, and pathway regulation. The proposed multi-step computational workflow gives a basis for leading subsequent experimental validation and mechanistic modeling, as well as a tool for generating hypotheses for systems-level analysis of cancer signaling networks.

A new and efficient metal-free method for the visible-light-driven oxidative cyclodesulfurization of phenolic thioureas to 2-aminobenzoxazoles is reported using tetrabutylammonium tribromide (TBATB) as the sustainable promoter. This approach eliminates the need of additional metallic or nonmetallic bases. It overcomes the limitations of existing methods that rely on organic photocatalysts requiring multi-step synthesis or on costly and potentially hazardous polypyridyl organometallic complexes. The reaction proceeds at room temperature employing visible light and TBATB without external oxidants, or inert atmospheres. This study highlights another synthetic utility of TBATB-mediated visible-light-driven organic transformations, demonstrating its potential as an efficient promoter for visible-light-driven reactions in organic synthesis. Thus, this work may open new avenues for developing TBATB-mediated green synthesis methodologies.

An efficient, photo-catalytic synthesis of thioamidoguanidine was developed through oxidative sulfurization of thioureas. The protocol involves the use of Ru(bpy)3Cl2 (bpy = 2,2′-bipyridine)as a photo-catalyst, carbon tetrabromide (CBr4) as the oxidizing reagent, and visible light as the source of energy at room temperature. Present reaction offers a practical, base-free, economically cheap reagent, an environmentally benign and safer approach for a variety of anti-Hugelchoff products.

Introduction In recent years, the integration of renewable energy sources, particularly Photovoltaic (PV) systems, into the grid has garnered considerable attention. However, the distributed nature of these grid-integrated PV systems has introduced challenges concerning grid faults and maintenance.Methods This paper aims to present a pioneering approach to augment the monitoring of grid-integrated PV systems by integrating intelligent methods, specifically machine learning and feature extraction techniques. The primary focus of this approach is on islanding detection, which involves promptly identifying grid faults or maintenance challenges and initiating the grid's transition to an isolated mode of operation. To accomplish this, an intelligent signaling method is employed, capitalizing on the capabilities of Distributed Generation (DG) networks. By recording critical signals such as voltage, current, and frequency at common coupling points, fault conditions can be accurately detected. Signals obtained from the grid are subjected to wavelet transformation to extract pertinent information that characterizes fault conditions. These extracted features are then utilized as inputs to machine learning methods, facilitating the proposal of intelligent islanding scenarios. To assess the efficacy of the proposed approach, simulations are conducted on a grid-connected PV system. The recorded signals are meticulously analyzed, and the extracted features are employed to train machine learning models. The performance of these models is evaluated based on their ability to detect fault conditions and initiate appropriate islanding scenarios accurately.Results The results obtained demonstrate the immense potential of the proposed approach in bolstering the monitoring of grid-integrated PV systems.Conclusion By synergizing machine learning techniques with feature extraction and intelligent signaling methods with the KNN-Confusion Matrix, all predicted labels match the true labels, resulting in a 100% accuracy. The detection of grid faults and maintenance challenges can be substantially improved, thereby fostering more efficient and dependable operation of these systems.

Background: Adverse drug reaction (ADR) reporting is the integrity of healthcare professionals (HCPs) to counteract fatal outcomes aroused by the use of medications during therapy. The underreporting of ADRs imposes major problems globally. The pharmacovigilance program plays a key role in detecting and preventing ADRs. Targeting the upcoming younger generation of HCPs and sensitizing them towards ADR reporting in their clinical practice is essential. Aims and Objectives: The main objective of our study is to measure the knowledge, attitude, and practice of interns and postgraduates at our institute towards ADR reporting and pharmacovigilance. Materials and Methods: This study was a cross-sectional study conducted on interns and postgraduates of a tertiary care teaching hospital in Udaipur, Rajasthan. A set of 28 standard questions related to knowledge, attitude, and practice of pharmacovigilance and ADR reporting were distributed among 106 students after taking the ethical approval from the institute. The collected data were analyzed using SPSS (version 20) software in percentage. The Chi-square test (χ2) was used, and P < 0.01* was considered significant statistically. Results: In our study, we found most of the participants were in 21–30 age groups (84.19%); male participants were (67%) in comparison with females (33%). The majority of interns showed their interest in participation (66%) rather than postgraduates (34%) in this study. The overall knowledge (14.645, P = 0.01), attitude (14.64, P = 0.01), and practice (35.712, P = 0.01) of HCPs were found to be significant. Conclusion: We conclude from this study that both interns and postgraduates have good knowledge of ADR reporting, but interns lack practice in their daily duties due to fear of heavy paper work, and postgraduates lack attitude towards ADR reporting due to heavy duty schedules. Most of the participants, i.e., interns and postgraduates, felt ADR reporting was their primary responsibility along with patient safety. Recurrent training and sensitization of upcoming medical professionals about ADR reporting will impart better patient compliance and eradicate fatal outcomes. They also suggest a separate working body needs to keep by institutions to collect the ADR reports during their busy duty schedules.

A solar cell is the basic unit of PV modules, for which the silicon semiconductor is widely utilized. In the present scenario of an exponentially growing market of solar PV technology-based electricity generation, researchers have to think of another Solar PV technology for customers to fulfill the future demand. As of now, the maximum share is of silicon-based solar PV technology. In this paper, a series of experiments are conducted with the help of a solar PV emulator, and collected data is analyzed to obtain comparative performance parameters. Therefore, so that the customer can concentrate other than silicon technology-based PV modules. The performance of several PV module technologies is estimated on emulated data for various locations. It is found that for all locations mono, polycrystalline silicon, and HIT (hetero-junction with intrinsic thin layer), have average performance ratio 78.97%, 71.99% and 96.92% respectively, with CIS (copper indium gallium selenide solar cell) having 65.46% which is also of interest with a tradeoff between performance and cost. Average return amount for the above technologies across the locations ranging from 2539.9 USD to 6349.9 USD approximately, with a key focus on CIS and CdTe (cadmium telluride). It is also seen that at most of the locations, the CIS and CdTe Technology-based PV modules have a payback period very close to fatigue life, so there is no problem about the guaranteed output.

BackgroundWe investigated the associations of micronutrients and lipids with prediabetes, glycemic parameters, and glycemic indices among the adolescent girls of the DERVAN (aDolescent and prEconception health peRspectiVe of Adult Non-communicable diseases) cohort study from rural India.MethodsWe recruited 1,520 adolescent girls aged 16–18 years. We measured glycemic parameters (glucose, insulin and HbA1C), lipids (total cholesterol, high-density lipoprotein [HDL], low-density lipoprotein [LDL], and triglycerides), and micronutrients (vitamin B12, folate, and vitamin D). Prediabetes was defined using American Diabetes Association criteria (fasting glucose ≥100 mg/dL or HbA1C ≥5.7%). Glycemic indices (insulin resistance, insulin sensitivity, and β cell function) were calculated using the homeostasis model. Associations of prediabetes, glycemic parameters and glycemic indices with micronutrients and lipids were analyzed by multiple logistic regressions.ResultsThe median age and Body Mass Index (BMI) were 16.6 years and 17.6 kg/m2, respectively. Overall, 58% of girls had a low BMI. Median vitamin B12, folate, and vitamin D concentrations were 249.0 pg/mL, 6.1 ng/mL, and 14.2 ng/mL, respectively. The deficiencies observed were 32.1% for vitamin B12, 11.8% for folate, and 33.0% for vitamin D. Median total cholesterol, LDL, HDL, and triglyceride concentrations were 148.0 mg/dL, 81.5 mg/dL, 50.8 mg/dL, and 61.5 mg/dL, respectively. Elevated total cholesterol, LDL, and triglycerides were observed in 4.8, 4.0, and 3.8%, respectively, while low HDL was observed in 12.8%. Prediabetes was observed in 39.7% of the girls. Among lipids, total cholesterol and LDL were higher in girls with prediabetes (p < 0.01 for both). In a multivariate model containing cholesterol and vitamin B12/folate/vitamin D, prediabetes was associated with high cholesterol. Prediabetes was also associated with high LDL, independent of folate and vitamin D. Poor insulin secretion was high in those with low vitamin B12. Elevated insulin resistance was associated with low HDL. The likelihood of high insulin sensitivity was reduced in those with high triglycerides. The likelihood of poor β cell function was high in those with high LDL. Statistical interactions between micronutrients and lipids for prediabetes and glycemic outcomes were not significant.ConclusionThere was a substantial deficiency of micronutrients and an absence of dyslipidemia. Our results indicate the need for lipid and micronutrient-based interventions in adolescence to improve glycemic outcomes. Maintaining adequate storage of not only micronutrients but also lipids in adolescent girls is likely to reduce diabetes risk in adulthood.

Protein methyltransferases (PMTs) are a group of enzymes that help catalyze the transfer of a methyl group to its substrates. These enzymes play an important role in epigenetic regulation and can methylate various substrates with DNA, RNA, protein, and small-molecule secondary metabolites. Dysregulation of methyltransferases is implicated in various human cancers. However, in light of the well-recognized significance of PMTs, reliable and efficient identification methods are essential. In the present work, we propose a machine-learning-based method for the identification of PMTs. Various sequence-based features were calculated, and prediction models were trained using various machine-learning algorithms using a tenfold cross-validation technique. After evaluating each model on the dataset, the SVM-based CKSAAP model achieved the highest prediction accuracy with balanced sensitivity and specificity. Also, this SVM model outperformed deep-learning algorithms for the prediction of PMTs. In addition, cross-database validation was performed to ensure the robustness of the model. Feature importance was assessed using shapley additive explanations (SHAP) values, providing insights into the contributions of different features to the model’s predictions. Finally, the SVM-based CKSAAP model was implemented in a standalone tool, PMTPred, due to its consistent performance during independent testing and cross-database evaluation. We believe that PMTPred will be a useful and efficient tool for the identification of PMTs. The PMTPred is freely available for download at https://github.com/ArvindYadav7/PMTPred and http://www.bioinfoindia.org/PMTPred/home.html for research and academic use.

Emergence of compact design power converters for applications such as photovoltaic system, active power filter, and high-frequency AC system has introduced multi-level inverters (MLIs). In the last decade, rigorous research has been carried out to transform multi-input MLIs to single-input step-up type that will be extremely beneficial in different applications. The proposed MLI in this work is a reduced components based switched-capacitor MLI. It consists of one input source and using the capacitor voltage, it can produce a four-time step-up output. One of the capacitors is charged to magnitude the input voltage and the other two capacitors are charged to two-time the input voltage magnitude. The charging of all the capacitors is executed by utilizing them in series-parallel combination with the switching operation. By maintaining charging-discharging effectively within each cycle, the voltages are naturally balanced. A thorough comparison is carried to justify the new development in terms of reduction in number of components and voltage stress. The power loss is estimated in the proposed circuit in detail. Extensive analysis is carried out to verify the operational ability and results are provided under different scenarios such as change in modulation index, load, and supply side variations.

Electric and hybrid electric vehicles are becoming more popular today. Typically, batteries serve as the major energy source. Battery management is used to optimize battery use and protection. This battery management system provides cell balancing and guards against overcharging and over-discharging of batteries. For these purposes, a precise state of charge assessment is required. The many techniques used to determine state of charge (SOC) can be categorized as direct measurement techniques, accounting techniques, adaptive techniques, and hybrid techniques. This article discusses the benefits and drawbacks of the most prominent state-of-charge estimation methodologies. The review also outlines the critical reaction factors required for calculating the battery SOC precisely. This will help make sure that the SOC assessment is precise. It will help a lot when deciding on the best method for making an EV's energy storage and control strategy secure and reliable.

Abstract An electric vehicle (EV) system is a transportation solution that relies on electric propulsion rather than traditional internal combustion engines. Induction motors are well-suited for EVs, offering high torque at low speeds, ideal for city driving. To enhance the sustainability of the EV system, photovoltaic (PV) panels are integrated to directly power the induction motor using renewable solar energy. The PV panels' optimal sizing and placement are achieved through the modified sandpiper optimization (MSO) algorithm, maximizing their efficiency. Additionally, an improved competitive swarm optimization (ICSO) algorithm is employed to optimize power interfaces in the PV-powered EV system, addressing issues with traditional maximum power point tracking (MPPT) techniques. To facilitate bidirectional power flow and mitigate voltage unbalance, a bidirectional DC-DC converter is implemented. Through extensive simulation scenarios, the proposed induction motor-driven EV system is thoroughly validated and compared to state-of-the-art EV systems utilizing brushless DC (BLDC) motors. The comparative analysis assesses system efficiency, power output, torque characteristics, and overall performance, providing valuable insights into the suitability and advantages of using induction motors in EV systems. This research contributes to advancing sustainable and efficient EV technologies, offering a greener mode of transportation for a more environmentally friendly future.

Present study focuses on improving maize productivity, economics, and energy efficiency in the Indo-Gangetic Plains through the integration of CA, precision nitrogen and water management. Maize grain yield significantly differed among treatments, with CA outperforming CT by 13.3%, recording the highest yield with optimal N application (N3) and irrigation at 25% DASM. The CA incurred 23.7% lower cultivation costs (₹30,421/ha) compared to CT. Gross returns and net returns were higher under CA (₹1,16,007/ha and ₹85,586/ha) with a net benefit ratio of 2.78, showcasing its economic viability. Energy efficiency was a crucial aspect considered, with CA proving to be 33.1% more energy-efficient than CT. In different irrigation regimes, CA with W2 treatment exhibited superior energy parameters. The study also highlighted the significance of optimal N scheduling (N3) in achieving higher economic returns (₹97,927/ha) compared to conventional N splits (N1) and its integration. The most effective integration involved combining CA with precision N management (75% basal, GreenSeekerTM-guided top dressing) and irrigation at 25% DASM, resulting in higher grain yield (7.21 t/ ha), gross returns (₹132,497/ha), and impressive energy output (230,831 MJ/ha). In conclusion, CA, especially when combined with optimal irrigation and nitrogen management, not only enhances maize yield and economic returns but also proves to be more energy-efficient, promoting sustainable and resource-efficient agricultural practices. The study recommends this integrated approach for enhancing maize productivity, energy efficiency and economic returns.

The importance of food security cannot be emphasized enough, as food is one of the necessities for human survival. The future of human race will depend upon its ability to meet food security and nutritional security of burgeoning population through crop improvement. The advancement of omics-based technologies such as genomics, transcriptomics, proteomics, metabolomics, and phenomics has been applied for crop improvement. These omics-based technologies help to improve crop yield, improve nutritional content, solve problems of food insecurity, and improve global hunger index. Moreover, integrating these multiomics approaches is now an effective approach to understanding gene expression network and other important plant traits that help improve crop productivity. Therefore, this chapter, provides a comprehensive overview of multiple omics technologies, network, and systems biology approaches with a special focus on crop improvement. This chapter also focused on advances in sequencing technologies and how omics technologies coupled with artificial intelligence, play a role in combating food insecurity. Challenges, problems, and complexities associated with these approaches in crop improvement have also been discussed here.

The Dopa Decarboxylase (DDC) gene plays an important role in the synthesis of biogenic amines such as dopamine, serotonin, and histamine. Non-synonymous single nucleotide polymorphisms (nsSNPs) in the DDC gene have been linked with various neurodegenerative disorders. In this study, a comprehensive in silico analysis of nsSNPs in the DDC gene was conducted to assess their potential functional consequences and associations with disease outcomes. Using publicly available databases, a complete list of nsSNPs in the DDC gene was obtained. 29 computational tools and algorithms were used to characterise the effects of these nsSNPs on protein structure, function, and stability. In addition, the population-based association studies were performed to investigate possible associations between specific nsSNPs and arthritis. Our research identified four novel DDC gene nsSNPs that have a major impact on the structure and function of proteins. Through molecular dynamics simulations (MDS), we observed changes in the stability of the DDC protein induced by specific nsSNPs. Furthermore, population-based association studies have revealed potential associations between certain DDC nsSNPs and various neurological disorders, including Parkinson's disease and dementia. The in silico approach used in this study offers insightful information about the functional effects of nsSNPs in the DDC gene. These discoveries provide insight into the cellular processes that underlie cognitive disorders. Furthermore, the detection of disease-associated nsSNPs in the DDC gene may facilitate the development of tailored and targeted therapy approaches.Communicated by Ramaswamy H. Sarma

FACTS based devices are mostly used in power systems due to their capacity to improve system stability. The Static Synchronous Compensator (STATCOM), a shunt-connected device in the FACTS device family, is used for power compensation, power balancing, and enhancing dynamic stability in contemporary power systems. In the proposed work, STATCOM based novel nine level switched capacitor based multi-level inverter (MLI) is used to reduce power quality issues. The proposed novel inverter has a few number of switches with one voltage source. The placement of STATCOM based inverters in the wrong places may have detrimental effects on power loss and electricity quality. To overcome these issues, green anaconda optimization (GAO) is used to allocate the proposed system in an optimal place. The performance of the proposed inverter is examined using the MATLAB/Simulink tool. This inverter requires fewer switches and achieves DC link voltage balances in comparison to any other existing conventional topologies. STATCOM based inverter is validated under different faulty conditions to show the effectiveness of this inverter. The proposed new inverter has nine levels and compensates for the poor state while improving power quality. The proposed method has a substantially lower total harmonic distortion (THD) of 1.04 % while maintaining an efficiency of 99.02 %. Experimental validation is established using a dSPACE RTI1104 controller to validate the proposed method. Experimental results obtained 1.04 % of harmonics with the same output voltage as the simulation.

The integration of nanotechnology into agriculture has garnered significant interest due to its potential to revolutionize agricultural practices. This paper explores the application of nanotechnology in crop protection, nutrient delivery, soil management, and environmental sustainability. Nanopesticides and nanofertilizers represent notable advancements, offering enhanced efficacy, reduced environmental impact, and improved targeted delivery of active ingredients. Nanomaterials such as nanoparticles and nanocapsules encapsulate nutrients and agrochemicals, ensuring gradual release and optimized plant uptake. Nanosensors, based on materials like carbon nanotubes and quantum dots, enable real-time monitoring of soil health, crop growth, and environmental conditions, facilitating precision agriculture. Nanomaterials also play a role in soil remediation and pollution control by degrading pollutants and enhancing soil fertility. Additionally, nanobiotechnology offers eco-friendly solutions for pest and disease management through nanoscale delivery systems for biocontrol agents and plant vaccines. Despite the transformative potential of nanotechnology in agriculture, considerations of safety, regulatory oversight, and ethical implications are essential. Interdisciplinary collaboration and stakeholder engagement will be crucial to harness the full benefits of nanotechnology for sustainable agriculture.

This paper introduces a novel Multi-Level Inverter (MLI) design which utilizes a single input and leverages capacitor voltages source to generate a four-fold increase in output voltage as the main problem that stays with the inverters is their low boost ability and efficiency while maintaining power quality at the same time. One capacitor is charged to match the input voltage magnitude, while the other two capacitors store twice this magnitude. Through a series-parallel combination with switching operations, all capacitors are effectively charged and discharged within each cycle, ensuring natural voltage balance. A comprehensive comparative analysis is conducted to highlight the advantages of this innovative approach, particularly in terms of component reduction and mitigation of voltage stress. Detailed assessment of power losses within the proposed circuit is undertaken, simulation studies are first carried out while extensive experimentation verifies its operational efficiency under diverse conditions such as varying modulation indices, loads, and supply-side fluctuations with an impressive maximum efficiency of 96.9 % at a 200 W power rating, our research contributes to advancing compact power converters, addressing crucial challenges in modern power electronics applications, and paving the way for enhanced performance and reliability in such systems.

The meaningful data extraction from the biological big data or omics data is a remaining challenge in bioinformatics. The deep learning methods, which can be used for the prediction of hidden information from the biological data, are widely used in the industry and academia. The authors have discussed the similarity and differences in the widely utilized models in deep learning studies. They first discussed the basic structure of various models followed by their applications in biological perspective. They have also discussed the suggestions and limitations of deep learning. They expect that this chapter can serve as significant perspective for continuous development of its theory, algorithm, and application in the established bioinformatics domain.