Valuable Resource Research Articles

Simultaneous single-nucleus RNA sequencing and single-nucleus ATAC sequencing of neuroblastoma cell lines

Neuroblastoma is the most common extracranial solid tumor in children, and a leading cause of childhood cancer deaths. All neuroblastomas arise from neural crest-derived sympathetic neuronal progenitors, but numerous mutations, the most common of which is MYCN amplification, give rise to these lesions. Epigenetic aberrations also play a role in oncogenesis and tumor progression. To better understand biologic diversity of neuroblastomas, we performed joint single-nucleus ATAC sequencing and single-nucleus RNA sequencing on six neuroblastoma cell lines, three of which are MYCN amplified. After standard filtering for high-quality nuclei, we obtained chromatin accessibility and transcript abundance data from 41,733 neuroblastoma tumor cells. Preliminary analysis reveals significant diversity in chromatin landscape and gene expression across neuroblastoma cell lines. This dataset is a valuable resource for studying the transcriptional and epigenetic mechanisms of this deadly childhood disease.

Optimization of Quadcopter Propeller Aerodynamics Using Blade Element and Vortex Theory

The optimization of quadcopter propeller aerodynamics is crucial for enhancing the efficiency and stability of unmanned aerial vehicles (UAVs), especially in their increasing applications across various sectors, including humanitarian relief and delivery services. This study focuses on optimizing quadcopter propeller performance using Blade Element Theory and Glauert Vortex Theory to analyze the aerodynamic properties and predict thrust generation accurately. Experimental methods, including wind tunnel testing and static thrust measurements, were used to validate the theoretical models. The results indicate a close correlation between theoretical predictions and experimental data, providing insights into improving propeller efficiency and mitigating aerodynamic losses. By understanding the propeller characteristics, this research contributes to the development of advanced drone propulsion systems with enhanced thrust, reduced drag, and better overall flight performance. The findings also highlight the impact of environmental factors such as turbulence and blade geometry on quadcopter stability, pointing toward areas for further improvement in propeller design. This paper serves as a valuable resource for drone hobbyists, engineers, and researchers seeking to enhance UAV performance through optimized propeller aerodynamics.

A Novel Machine Learning Model and a Web Portal for Predicting the Human Skin Sensitization Effects of Chemical Agents

Skin sensitization is a significant concern for chemical safety assessments. Traditional animal assays often fail to predict human responses accurately, and ethical constraints limit the collection of human data, necessitating a need for reliable in silico models of skin sensitization prediction. This study introduces HuSSPred, an in silico tool based on the Human Predictive Patch Test (HPPT). HuSSPred aims to enhance the reliability of predicting human skin sensitization effects for chemical agents to support their regulatory assessment. We have curated an extensive HPPT database and performed chemical space analysis and grouping. Binary and multiclass QSAR models were developed with Bayesian hyperparameter optimization. Model performance was evaluated via five-fold cross-validation. We performed model validation with reference data from the Defined Approaches for Skin Sensitization (DASS) app. HuSSPred models demonstrated strong predictive performance with CCR ranging from 55 to 88%, sensitivity between 48 and 89%, and specificity between 37 and 92%. The positive predictive value (PPV) ranged from 84 to 97%, versus negative predictive value (NPV) from 22 to 65%, and coverage was between 75 and 93%. Our models exhibited comparable or improved performance compared to existing tools, and the external validation showed the high accuracy and sensitivity of the developed models. HuSSPred provides a reliable, open-access, and ethical alternative to traditional testing for skin sensitization. Its high accuracy and reasonable coverage make it a valuable resource for regulatory assessments, aligning with the 3Rs principles. The publicly accessible HuSSPred web tool offers a user-friendly interface for predicting skin sensitization based on chemical structure.

Transcriptomic and de novo proteomic analyses of organotypic entorhino-hippocampal tissue cultures reveal changes in metabolic and signaling regulators in TTX-induced synaptic plasticity

Understanding the mechanisms of synaptic plasticity is crucial for elucidating how the brain adapts to internal and external stimuli. A key objective of plasticity is maintaining physiological activity states during perturbations by adjusting synaptic transmission through negative feedback mechanisms. However, identifying and characterizing novel molecular targets orchestrating synaptic plasticity remains a significant challenge. This study investigated the effects of tetrodotoxin (TTX)-induced synaptic plasticity within organotypic entorhino-hippocampal tissue cultures, offering insights into the functional, transcriptomic, and proteomic changes associated with network inhibition via voltage-gated sodium channel blockade. Our experiments demonstrate that TTX treatment induces substantial functional plasticity of excitatory synapses, as evidenced by increased miniature excitatory postsynaptic current (mEPSC) amplitudes and frequencies in both dentate granule cells and CA1 pyramidal neurons. Correlating transcriptomic and proteomic data, we identified novel targets for future research into homeostatic plasticity, including cytoglobin, SLIT-ROBO Rho GTPase Activating Protein 3, Transferrin receptor, and 3-Hydroxy-3-Methylglutaryl-CoA Synthase 1. These data provide a valuable resource for future studies aiming to understand the orchestration of homeostatic plasticity by metabolic pathways in distinct cell types of the central nervous system.

Leveraging NLP and web knowledge graphs to harmonize locations: A case study on US patent transactions

In the present study, we introduce a novel methodology for the harmonization and standardization of locations associated with patent transactions recorded at the USPTO from 2005 to 2022. Using natural language processing (NLP) techniques in conjunction with search engine-based web knowledge graphs, our method comprises four phases: data pre-processing, semantic clustering, exploitation of web-knowledge graphs, and API-driven harmonization. Initiating our analysis with a dataset of 63,838 unique locations, our methodology effectively reduces this number by more than 50 %. This approach exhibits an accuracy rate of approximately 92 %. The resulting geolocated dataset of companies’ patent transactions offers a valuable resource for fine-grained geographical analyses of the markets for technology; in particular, we provide examples of relevant economic insights which can be learned from looking at the geographical patterns of those transactions.

Discovery of the first Tn630 member and the closest homolog of IS630 from viruses

IS630/Tc1/mariner (ITm) represents the most widely distributed superfamily of DNA transposons in nature. Currently, bioinformatics research on ITm members primarily involves collecting data of existing and emerging members and organizing them into new groups or families. In the present study, our survey revealed that Tc1 and IS630 members have a broad host range, spanning across all six biological kingdoms (bacteria, fungi, plantae, animalia, archaea and protista) and viruses. The primary discoveries include the first Tn630 member—Tn630-NC1 and the closest homolog of IS630 from viruses—Tc1-C#1. By incorporating our discoveries into existing knowledge, we proposed a model to elucidate the formation of composite transposons. Organization of Tc1 and IS630 members into groups across biological kingdoms facilitates data collection for future research, particularly on their horizontal transfer between different kingdoms. The formation of composite transposons may result from asymmetric of terminal inverted repeats. IS630 should be merged with Tc1 into a single family IS630/Tc1. Furthermore, IS630 and its homologs constitute a valuable resource for studying horizontal gene transfer between gut bacteria and phages, opening up new avenues for research in this field.

The Queen Catherine Court Slum Clearance Scheme: A Turning Point in the History of Social Housing in London

Maps provide a valuable resource to historians and genealogists, but the neat rows of compact houses in the courtyard of the Queen Catherine pub in Ratcliff, home to four of my Irish great-great-grandparents, only hint at the living conditions of its occupants. This article draws together evidence from newspapers, archival sources, censuses, and other genealogical records to provide a conjectural reconstruction of Queen Catherine Court. The overdevelopment and overcrowding of the site resulted in it being declared unfit for human habitation in 1888, but owing to decades of laissez-faire ideology and fragmented local government, slum clearance and the provision of social housing in London had been beset by intractable problems. A flagship project of the London County Council, the Queen Catherine Court scheme was finally brought to completion in 1903 by Stepney Borough Council, marking a first in the capital's history of social housing.

Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques

Viral oncoproteins play crucial roles in transforming normal cells into cancer cells, representing a significant factor in the etiology of various cancers. Traditionally, identifying these oncoproteins is both time-consuming and costly. With advancements in computational biology, bioinformatics tools based on machine learning have emerged as effective methods for predicting biological activities. Here, for the first time, we propose an innovative approach that combines Generative Adversarial Networks (GANs) with supervised learning methods to enhance the accuracy and generalizability of viral oncoprotein prediction. Our methodology evaluated multiple machine learning models, including Random Forest, Multilayer Perceptron, Light Gradient Boosting Machine, eXtreme Gradient Boosting, and Support Vector Machine. In ten-fold cross-validation on our training dataset, the GAN-enhanced Random Forest model demonstrated superior performance metrics: 0.976 accuracy, 0.976 F1 score, 0.977 precision, 0.976 sensitivity, and 1.0 AUC. During independent testing, this model achieved 0.982 accuracy, 0.982 F1 score, 0.982 precision, 0.982 sensitivity, and 1.0 AUC. These results establish our new tool, VirOncoTarget, accessible via a web application. We anticipate that VirOncoTarget will be a valuable resource for researchers, enabling rapid and reliable viral oncoprotein prediction and advancing our understanding of their role in cancer biology.

β-1,3-glucan from Euglena gracilis: a promising epidrug targeting epigenetic regulators PRMTs and SIRTs for therapeutic applications in ovarian cancer

Natural products serve as a valuable resource in drug discovery and the identification of bioactive molecules in the field of epimedicine, which targets epigenetic regulator enzymes through epidrugs. In this study, β-1,3-glucan (BG), a natural storage polysaccharide in Euglena gracilis, a well-known immunostimulatory agent, is propounded as a promising epidrug. To elucidate the therapeutic efficacy of BG against ovarian cancer, the molecular interactions between BG and epigenetic regulators, Protein Arginine Methyltransferases (PRMTs) and Sirtuins (SIRTs) were investigated using computational methods followed by in vitro gene expression studies in SKOV-3 ovarian cancer cell line. The binding energies of PRMT5 and SIRT5 against BG were observed as −65.5 and −68.2 kcal/mol, respectively. The in vitro cytotoxic effects of BG against human ovarian cancer cell line, SKOV-3 showed an IC50 of 150 µg/mL at 48 h. Significant epigenetic modifications were observed to be influenced by BG which increased the gene expression of PRMT5, SIRT5 and Nrf2 to 0.3, 0.5, and 0.7 fold-change respectively, while the Nrf1/2 plasmid showed reduced reporter activity by 29%. Collectively, both in silico and in vitro studies provided valuable insights into the epigenetic regulation of PRMT5 and SIRT5 by BG via Nrf1/2. Nonetheless, further preclinical and clinical investigations are essential to validate the therapeutic properties of BG as an epidrug against ovarian cancer.

Phased T2T genome assemblies facilitate the mining of disease-resistance genes in Vitis davidii

Abstract Grape is an important fruit crop, and its production faces significant threat from diseases, resulting in substantial economic loss. Wild grape relatives are valuable resources for the restoration of disease-resistance loci. However, available resistance loci in wild grape genomes remain largely unexplored. In this study, we assembled two phased genomes, including a high resistant Chinese wild grape, Vitis davidii Föex, and a susceptible cultivar, V. vinifera L. cv. “Manicure Finger”. We detected a total of 36,688 structural variations (SVs), with the genes associated with heterozygous SVs showing an enrichment in allele-specific expression (ASE). Furthermore, we identified eight subgroups of R genes and found that 74.2% of R genes overlap with transposable elements (TEs). Among R genes, NBS-type genes exhibit higher expression profiles in the wild grape genome compared with those in the grape cultivar. Additionally, five specific NBS-type R gene clusters were identified in the wild grape genome that are absent in the cultivar. Through genetic mapping, we identified four quantitative trait loci (QTLs) associated with grape white rot resistance based on the Vitis davidii genome, within which six NBS-type R genes exhibit differential expression between wild and cultivated grapes. Overall, our study revealed the landscape of resistance genes in grape genomes, providing valuable genetic resources for further breeding programs.

Mechanism for developing brand equity of higher education institutes using intangible resources: a serial-multiple mediation analysis

PurposeThis study aims to build a comprehensive model for developing higher education institutes (HEIs) brand equity by examining the impact of HEIs intangible resources, brand management aspects, attachment strength and student’s contemporary citizenship behavior (CCB).Design/methodology/approachData were gathered through a self-administered questionnaire from Indian students and alumni (n = 703). The study examines the proposed comprehensive model through serial and multiple mediation analysis.FindingsThe study unfolded “service quality? brand image? attachment strength? word-of-mouth? brand equity” as the most significant path to develop HEI’s brand equity.Research limitations/implicationsThis study pioneers an empirically validated mechanism for developing brand equity in HEIs, addressing gaps identified by previous studies and offering a theoretical framework that elucidates how HEIs can leverage intangible resources through strategic brand management to foster attachment and influence positive student behaviors, thereby contributing to the development of HEI brand equity.Practical implicationsHEIs need to understand that relying solely on tangible aspects leads to short-lived effects. To maintain a lasting competitive advantage, HEIs should focus on crafting a rich historical narrative, retaining talented faculty and staff and earning respect from the public to build a lasting reputation.Originality/valueThis study develops the mechanism for developing brand equity of HEIs using its valuable, rare and inimitable intangible resources. Along with the introduction of novel constructs like competence, heritage and word-of-mouth to existing study, the proposed conceptual model is premised on the theory of self-congruence, social-exchange theory and CCB.

Growth Patterns of Small Pelagic Fish in West Africa

Small pelagic fishes play important ecological roles in marine ecosystems, constitute some of the most economically valuable fisheries resources, and play a vital role in West African food security. Often living in upwelling regions, these species appear to have developed mechanisms to cope with environmental variability, such as opportunistic reproductive tactics, enhancing their growth performance, or increasing their egg production by following the more predictable system attributes of seasonal cycles. To test this hypothesis, we investigated size-dependent patterns of the two growth mechanisms (i.e., growth rate) of two West African small pelagic populations (Ethmalosa fimbriata and Sardinella maderensis) in upwelling environments. These results were discussed with other areas in African tropical Atlantic waters. The monthly mean length of both species showed a large variation over the study period. Based on the fish length-frequency data and a coastal upwelling index, we found that the growth peaks of the species tended to occur during the most intense periods of upwelling (March–April). This study showed a significant decrease in size compared with other species found in other regions. It demonstrates how the geographical distribution of the same species, together with location-specific variation in temperature and food, can combine to determine local and regional growth responses in pelagic fish. Changes in growth rate may be an adaptive tactic in response to environmental change, as well as phenotypic plasticity in fish. This knowledge is essential to predict future changes in fish productivity and distribution vs. climate and to provide effective advice for ecosystem-based management.

Large-scale metagenomic assembly provide new insights into the genetic evolution of gut microbiomes in plateau ungulates

Trillions of microbes colonize the ungulate gastrointestinal tract, playing a pivotal role in enhancing host nutrient utilization by breaking down cellulose and hemicellulose present in plants. Here, through large-scale metagenomic assembly, we established a catalog of 131,416 metagenome-assembled genomes (MAGs) and 11,175 high-quality species-level genome bins (SGBs) from 17 species of ungulates in China. Our study revealed the convergent evolution of high relative abundances of carbohydrate-active enzymes (CAZymes) in the gut microbiomes of plateau-dwelling ungulates. Notably, two significant factors contribute to this phenotype: structural variations in their gut microbiome genomes, which contain more CAZymes, and the presence of novel gut microbiota species, particularly those in the genus Cryptobacteroides, which are undergoing independent rapid evolution and speciation and have higher gene densities of CAZymes. Furthermore, these enrichment CAZymes in the gut microbiomes are highly enrichment in known metabolic pathways for short-chain fatty acid (SCFA) production. Our findings not only provide a valuable genomic resource for understanding the gut microbiomes of ungulates but also offer fresh insights into the interaction between gut microbiomes and their hosts, as well as the co-adaptation of hosts and their gut microbiomes to their environments.

Biophysical and biochemical parameters of Sorghum associated to shoot fly resistance

The sorghum shoot fly, Atherigona soccata (Muscidae: Diptera), represents a significant biotic constraint to sorghum production, leading to considerable yield losses globally. This study aimed to systematically classify sorghum genotypes based on their resistance to A. soccata infestation. A total of 188 genotypes were subjected to rigorous evaluation employing standardized screening methodologies. The analysis revealed substantial variability in resistance levels across the genotypes. Based on damage assessments in field trials, 14 genotypes were selected for further investigation under controlled pot culture conditions. Comprehensive biochemical analyses were conducted on each genotype under both uninfested and infested scenarios. Among the evaluated genotypes, IS 10588 and IS 8380 exhibited high levels of resistance, IS 12787 demonstrated moderate resistance, while TNFS 230 was classified as moderately resistant to A. soccata infestation. Critical morphological and biochemical traits associated with resistance were identified, including trichome density, leaf glossiness, and enzyme activity levels of peroxidase (PO), polyphenol oxidase (PPO), tannins, and phenolic compounds. The study concludes that these morpho-physiological and biochemical characteristics contribute significantly to the resistance mechanisms in sorghum against A. soccata. Thus, these identified genotypes may serve as valuable genetic resources for breeding programs aimed at enhancing resistance to A. soccata in sorghum.

Unlocking financial innovation through strategic investments in information management: a systematic review

PurposeThe purpose of this study is to explore strategic investment in information management and its crucial role in driving financial innovation. By examining the integration of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Big Data Analytics, Cloud Computing, Large Language Models (LLMs), Robotic Process Automation (RPA), Internet of Things (IoT), Cybersecurity Technologies, and Quantum Computing, this research aims to highlight how these technologies enhance decision-making, operational effectiveness, risk management, and compliance within the financial sector.MethodologyThe study employs a comprehensive literature review of existing research to analyze the impact of strategic investment in information management on financial innovation. Key technologies are identified and their applications in finance are discussed. The methodology includes synthesizing findings from various sources to present a cohesive understanding of the relationship between information management, technology, and financial innovation.ResultsThe results indicate that strategic investment in information management significantly enhances financial innovation by leveraging advanced technologies. AI and ML improve predictive analytics and customer personalization, Blockchain ensures secure transactions and transparency, Big Data Analytics enables data-driven decision-making, and Cloud Computing provides scalable solutions. LLMs enhance natural language processing capabilities, RPA automates repetitive tasks, IoT facilitates real-time monitoring, Cybersecurity Technologies protect financial data, and Quantum Computing offers potential breakthroughs in financial modeling and encryption.ImplicationThe implications of this study suggest that financial institutions should prioritize strategic investments in information management and the adoption of advanced technologies to stay competitive and resilient in the evolving financial landscape. Effective information management practices enable better decision-making, improved operational efficiencies, enhanced risk management, and regulatory compliance, ultimately fostering financial innovation.ContributionThis study contributes to the existing body of knowledge by providing a detailed analysis of the role of strategic investment in information management and its impact on financial innovation. It highlights the importance of integrating advanced technologies in financial practices and offers insights into how these technologies can be leveraged to achieve innovative solutions and improvements in the financial sector. The findings serve as a valuable resource for financial institutions, policymakers, and researchers interested in the intersection of technology and finance.

Calcium Sulfide Based Nanophosphors-A Review on Synthesis Techniques, Characterization and Applications.

Calcium sulfide (CaS) is a widely investigated alkaline earth sulfide nanophosphor with promising applications in optoelectronics and biomedical fields due to its excellent photoluminescence properties. The selection of the synthesis method is a crucial factor in determining the efficacy of nanophosphors for various applications. This review provides a comprehensive overview of the various synthesis techniques employed to develop CaS nanophosphors, including solvothermal, alkoxide, sol-gel, microwave, wet chemical co-precipitation, solid-state diffusion, and single-source precursor methods. The structural and optical properties of CaS nanophosphors are discussed in detail, highlighting the influence of different dopants on the emission color, which can be tuned from blue to red. The review also explores the potential applications of CaS nanophosphors in optoelectronics and biomedicine. This review serves as a valuable resource for researchers interested in developing CaS nanophosphors for various optoelectronic and biomedical applications, providing insights into the latest advancements and future prospects in this field.

The effect of calculus and kinematics contexts on students’ understanding of graphs

In this study, we compare students’ understanding of the derivative as slope and the antiderivative as the area under the curve using isomorphic graph-based problems in both calculus and kinematics contexts. Drawing from previous research, we designed two isomorphic tests, each with 12 items, and administered them to 543 university students. Our findings show that students performed significantly better on the kinematics test, with a higher rate of correct answers in some items. We also identified the most frequent errors and general trends in students’ selection of incorrect answers across both contexts. Based on these results, we provide specific recommendations for improving the instruction of these concepts. The analyses, recommendations, and tests included in the study can serve as valuable resources for mathematics and science education researchers and instructors teaching these topics. This study offers insights into how context influences students’ conceptual understanding, with implications for enhancing calculus and physics education.

RBPWorld for exploring functions and disease associations of RNA-binding proteins across species.

RNA-binding proteins (RBPs) play key roles in a wide range of physiological and pathological processes. To facilitate the investigation of RBP functions and disease associations, we updated the EuRBPDB and renamed it as RBPWorld (http://research.gzsys.org.cn/rbpworld/#/home). Leveraging 998 RNA-binding domains (RBDs) and 87 RNA-binding Proteome (RBPome) datasets, we successfully identified 1 393 413 RBPs from 445 species, including 3030 human RBPs (hRBPs). RBPWorld includes primary RNA targets of diverse hRBPs, as well as potential downstream regulatory pathways and alternative splicing patterns governed by various hRBPs. These insights were derived from analyses of 1515 crosslinking immunoprecipitation-seq datasets and 616 RNA-seq datasets from cells with hRBP gene knockdown or knockout. Furthermore, we systematically identified 929 RBPs with multi-functions, including acting as metabolic enzymes and transcription factors. RBPWorld includes 838 disease-associated hRBPs and 970 hRBPs that interact with 12 disease-causing RNA viruses. This provision allows users to explore the regulatory roles of hRBPs within the context of diseases. Finally, we developed an intuitive interface for RBPWorld, facilitating users easily access all the included data. We believe that RBPWorld will be a valuable resource in advancing our understanding of the biological roles of RBPs across different species.

Genomes OnLine Database (GOLD) v.10: new features and updates.

The Genomes OnLine Database (GOLD; https://gold.jgi.doe.gov/) at the Department of Energy Joint Genome Institute is a comprehensive online metadata repository designed to catalog and manage information related to (meta)genomic sequence projects. GOLD provides a centralized platform where researchers can access a wide array of metadata from its four organization levels namely Study, Organism/Biosample, Sequencing Project and Analysis Project. GOLD continues to serve as a valuable resource and has seen significant growth and expansion since its inception in 1997. With its expanded role as a collaborative platform, it not only actively imports data from other primary repositories like National Center for Biotechnology Information but also supports contributions from researchers worldwide. This collaborative approach has enriched the database with diverse datasets, creating a more integrated resource to enhance scientific insights. As genomic research becomes increasingly integral to various scientific disciplines, more researchers and institutions are turning to GOLD for their metadata needs. To meet this growing demand, GOLD has expanded by adding diverse metadata fields, intuitive features, advanced search capabilities and enhanced data visualization tools, making it easier for users to find and interpret relevant information. This manuscript provides an update and highlights the new features introduced over the last 2 years.

A Comparison of the Impacts of Different Drying Methods on the Volatile Organic Compounds in Ginseng

Ginseng (Panax ginseng C. A. Meyer) is a valuable plant resource which has been used for centuries as both food and traditional Chinese medicine. It is popular in health research and markets globally. Fresh ginseng has a high moisture content and is prone to mold and rot, reducing its nutritional value without proper preservation. Drying treatments are effective for maintaining the beneficial properties of ginseng post-harvest. In this study, we investigated the effects of natural air drying (ND), hot-air drying (HAD), vacuum drying (VD), microwave vacuum drying (MVD), and vacuum freeze drying (VFD) on volatile organic compounds (VOCs) in ginseng. The results showed that the MVD time was the shortest, followed by the VFD, VD, and HAD times, whereas the ND time was the longest, but the VFD is the most beneficial to the appearance and color retention of ginseng. A total of 72 VOCs were obtained and 68 VOCs were identified using the five drying methods based on gas chromatography–ion mobility spectrometry (GC-IMS) technology, including 23 aldehydes, 19 alkenes, 10 alcohols, 10 ketones, 4 esters, 1 furan, and 1 pyrazine, and the ND method was the best for retaining VOCs. GC-IMS fingerprints, principal component analysis (PCA), Euclidean distance analysis, partial least squares discriminant analysis (PLS-DA), and cluster analysis (CA) can distinguish ginseng from different drying methods. A total of 29 VOCs can be used as the main characteristic markers of different drying methods in ginseng. Overall, our findings provide scientific theoretical guidance for optimizing ginseng’s drying methods, aromatic health effects, and flavor quality research.