Scientific Data Analysis Research Articles

Biological Potential and Therapeutic Effectiveness of Artemetin from Traditional to Modern Medicine: An Update on Pharmacological Activities and Analytical Aspects.

Plant products derived from natural sources have been used in medicine as a raw material and newer kinds of drug molecules in pharmaceuticals and other allied health sectors. Phytochemicals have numerous medicinal potentials, including anti- ageing, anti-carcinogenic, anti-microbial, anti-oxidant, and anti-inflammatory activity in medicine. Development and biological application of herbal products in modern medicine signified the value of traditional medicinal plants in health care systems. The objective of the present study was to explore the scientific knowledge of the medicinal importance and therapeutic potential of artemetin in medicine. However, scientific investigations for their pharmacological activities in medicine have been done through scientific data analysis of different scientific research work collected from PubMed, Google, Science Direct and Google Scholar in order to know the biological importance of artemetin in medicine. Moreover, analytical data of artemetin have also been discussed in the present work. The present work and scientific data signified the biological potential of artemetin in medicine. Artemetin has been derived from numerous medicinal plants and dietary herbs, including Artemisia absinthium, Artemisia argyi, Achillea millefolium, and Vitex trifolia. Artemetin has anti-malarial, anti-oxidant, anti-apoptotic, anti-microbial, antitumoral, anti-atherosclerotic, anti-inflammatory, hypotensive and hepatoprotective effects. Further, the biological role of artemetin on lipid oxidation, cytokine production, lipoxygenase, and estrogen-like effects was also investigated in the present work. Analytical data on artemetin in the present paper signified their important role in the isolation, separation, and identification of different classes of pure phytochemicals, including artemetin in medicine. Scientific data analysis of artemetin signified its therapeutic potential in medicine for the development of newer scientific approaches for different human disorders.

Clustering of artificial intelligence derived plaque features for risk stratification for future atrial fibrillation development

Abstract Introduction We previously demonstrated that artificial intelligence (AI)-derived coronary artery plaque features are associated with future development of atrial fibrillation (AF) among patients undergoing CCTA(1). Our results suggest that phenotyping a large dataset of plaque features using data science techniques may reveal associations not visible from univariate analysis. Purpose Use data science analysis of AI-CT plaque characteristics to identify subpopulations at higher risk of AF. Methods We applied dimensionality reduction and visual cluster identification to phenotype our population of patients based on their AI-CT plaque features. We used a single center CCTA registry of patients with prior clinically-indicated CCTA (N=2700) from 2017-2020. Cases of new AF were defined by onset of AF on ECG or telemetry after CCTA via EMR search. Patients with AF prior to CCTA, history of mitral valve surgery, or postoperative AF were excluded. Controls were selected 1:1 by nearest neighbor propensity matching (R. v. 4.0.3.) based on age, sex, tobacco use, HTN, HLD, and diabetes. Atherosclerotic plaques were quantitatively analyzed using a commercially available FDA-approved AI coronary analysis software yielding stenosis area and diameter, remodeling index, volume of calcified/non-calcified/low-density plaques, and number of 2 or more high risk plaques. Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction with cosine distance metric was used to reduce the plaque feature space from 80 dimensions (8 AI measurements per each of 10 anatomical vessel segments in all patients) to 2 dimensions on a scatter plot. Two non-contiguous clusters (i.e. plaque phenotypes) were identified based on visual analysis of UMAP feature reduction (Figure 1). Results 94 cases comprised the study cohort (47 cases and 47 controls). Mean age was 67±11 years. 38% were female. Mean CADRADS score was 1.8±1.52 and mean CAC was 325±622. Mean time from CT to AF was 281±300 days. The majority of AF patients (67%) fell into Cluster 1 (N=36), while Cluster 2 (N=58) contained most of the control subjects (75%). Treating Cluster 1 as a predictor of AF, the PPV and specificity of the UMAP reduction was 67% and 75% respectively, with NPV of 60% and sensitivity of 51% (Table 1). Left atrial volume index (LAVI) was significantly different between the 2 clusters, with mean 56.15±12.42 ml/m2 in Cluster 1 and 46.96±16.03 in Cluster 2 (p=0.0046). LAVI was also significantly different between control subjects in Cluster 1 vs 2, mean 54.84±11.6 vs 41.62±7.92 (p<0.001). Conclusions We demonstrated that UMAP-derived clustering of AI-CT plaque characteristics identifies novel phenotypes for future risk of AF among patients undergoing CCTA. Further, control patients in Cluster 1 were found to have a significantly higher LAVI compared to control subjects in Cluster 2, which supports that membership in Cluster 1 may increase risk of future development of AF.

AnVILWorkflow: A runnable workflow package for Cloud-implemented bioinformatics analysis pipelines

Advancements in sequencing technologies and the development of new data collection methods produce large volumes of biological data. The Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) provides a cloud-based platform for democratizing access to large-scale genomics data and analysis tools. However, utilizing the full capabilities of AnVIL can be challenging for researchers without extensive bioinformatics expertise, especially for executing complex workflows. We present the AnVILWorkflow R package, which enables the convenient execution of bioinformatics workflows hosted on AnVIL directly from an R environment. AnVILWorkflow simplifies the setup of the cloud computing environment, input data formatting, workflow submission, and retrieval of results through intuitive functions. We demonstrate the utility of AnVILWorkflow for three use cases: bulk RNA-seq analysis with Salmon, metagenomics analysis with bioBakery, and digital pathology image processing with PathML. The key features of AnVILWorkflow include user-friendly browsing of available data and workflows, seamless integration of R and non-R tools within a reproducible analysis pipeline, and accessibility to scalable computing resources without direct management overhead. AnVILWorkflow lowers the barrier to utilizing AnVIL’s resources, especially for exploratory analyses or bulk processing with established workflows. This empowers a broader community of researchers to leverage the latest genomics tools and datasets using familiar R syntax. This package is distributed through the Bioconductor project (https://bioconductor.org/packages/AnVILWorkflow), and the source code is available through GitHub (https://github.com/shbrief/AnVILWorkflow).

Perspective: on the importance of extensive, high-quality and reliable deposition of biomolecular NMR data in the age of artificial intelligence.

Artificial intelligence (AI) models are revolutionising scientific data analysis but are reliant on large training data sets. While artificial training data can be used in the context of NMR processing and data analysis methods, relating NMR parameters back to protein sequence and structure requires experimental data. In this perspective we examine what the biological NMR community needs to do, in order to store and share its data better so that we can make effective use of AI methods to further our understanding of biological molecules. We argue, first, that the community should be depositing much more of its experimental data. In particular, we should be depositing more spectra and dynamics data. Second, the NMR data deposited needs to capture the full information content required to be able to use and validate it adequately. The NMR Exchange Format (NEF) was designed several years ago to do this. The widespread adoption of NEF combined with a new proposal for dynamics data specifications come at the right time for the community to expand its deposition of data. Third, we highlight the importance of expanding and safeguarding our experimental data repository, the Biological Magnetic Resonance Data Bank (BMRB), not only in the interests of NMR spectroscopists, but biological scientists more widely. With this article we invite others in the biological NMR community to champion increased (possibly mandatory) data deposition, to get involved in designing new NEF specifications, and to advocate on behalf of the BMRB within the wider scientific community.

Design of pattern image generation technique based on self-attentive residual conditional generative adversarial network

Image generation techniques have made remarkable progress in the digital image processing and computer vision. However, traditional generation models cannot meet the complexity and diversity requirements of patterned images. In view of this, the study aims to enhance the quality of generated pattern images, which uses improved residual block, and introduces a self-attention mechanism to compute the weight parameters of the input features to enhance the accuracy. Comparing with three image generation models, the research model shows lower Frechette initial distance, which is better than the other three methods, and the average Frechette initial distance values in the four scenes are 175.23, 176.41, 174.41, and 165.23. Generated mouths and eyes: the average values of Frechette initial distances reach 98.23 and 97.24, respectively. For emotion classification, the Frechette initial distance averages for sad, excited, and calm emotions were 82.34, 75.63, and 70.21, respectively. The model was trained up to 2500 iterations, and the loss value was reduced to 0.54, with an accuracy of 98.23%, confirming its effectiveness and high performance. The self attention residual network enhances the model's ability to capture image details, effectively improving the quality and accuracy of image generation, and providing a new technological path for radiation imaging data processing and analysis in radiation science.

Economic and social impacts of armed conflicts: Assessing the effects of military operations on development

The paper highlights the impact of military conflicts on society's socio-economic development. A review of scientific publications on the subject is carried out. The impact of the Russia-Ukraine military conflict on the socio-economic development of Ukraine is considered based on regression analysis. The main consequences of military operations in Ukraine are outlined. The approaches of the leading international organisations to accounting and auditing the consequences of conflicts and wars are determined. Examples are given, and the main aspects of accounting for losses caused by military conflict in different countries are compared. The study aims to comprehensively analyse and assess the impact of military conflicts on economic development and social aspects of society. Regression analysis methods were used to determine the relationship between military conflicts and economic indicators such as GDP, inflation, unemployment, public debt, defence spending, scientific data analysis, synthesis and comparison, and geospatial and comparative analysis, which helped to assess the direct and indirect effects of conflicts on economic development and the social sphere in Ukraine. The article analyses the social consequences of the military conflict in Ukraine in 2014–2024. An empirical analysis of economic losses from military conflicts is carried out. The need for an integrated approach to assessing and overcoming the consequences of wars and conflicts and coordination between government organisations, local communities and international partners to achieve sustainable development and restore the country's socio-economic situation is established.

Spectral Performance of the Follow-up X-Ray Telescope on Board the EP Satellite

The Follow-up X-ray Telescope (FXT) is one of the two main scientific instruments on board the Einstein Probe astronomical satellite, which was launched in 2024 January. FXT focuses on the energy range of 0.3–10 keV and mainly conducts follow-up observations of transients and burst sources. It consists of two units of completely independent optical system and detector system (FXT-A and FXT-B). The focal plane detector adopts PNCCD provided by Max Planck Institute for Extraterrestrial Physics. FXT was designed to have three operating modes with different integration times and readout schemes, namely full-frame mode, partial-window mode and timing mode. We conducted a detailed calibration for PNCCD at the Institute of High Energy Physics before launch. Our results demonstrate that both FXT-A and FXT-B exhibit excellent spectral performance. The energy resolution (Full Width at Half Maximum) of FXT-A and FXT-B are both better than 85 eV at 1.487 keV. We determined a mean equivalent noise charge around 2.8 e− for FXT-A and FXT-B in three operating modes at −90°C ± 0.5°C, except for a few noisy pixels in full-frame mode. In addition, we measured the relation of charge transfer inefficiency as function of photon energy and confirmed the ability to detect photons in the energy range of 0.3–10 keV. These calibration results have been ingested into the initial version of calibration database and applied to the analysis of scientific data acquired by FXT.

From crisis to prevention: mining big data for public health insights during the flint water crisis

This study investigates the utility of Google Trends and Google Search data in retrospectively analyzing the Flint Water Crisis, a significant public health event. By examining keywords relevant to the crisis, such as “lead,” “bottled water,” “water filters,” “pneumonia,” and “Legionnaires’ Disease,” we aimed to uncover patterns in public awareness and response during a public crisis and explore how such awareness and responses could potentially aid in reducing the risk of similar crises in the future. Our analysis reveals a clear correlation between search frequencies and the crisis timeline, with spikes in search terms corresponding to key events. This suggests that such data can serve as a valuable tool for understanding public sentiment and behavior in the face of environmental disasters. The research underscores the potential of “Big Data,” led by search engines and social media platforms, in shaping public policy and informed decision-making. However, it also addresses the limitations and challenges in using these data sources, including issues of data consistency, replicability, and the influence of sociocultural contexts on search behaviors. The study advocates for the combined use of Google Trends and Google Search data, complemented by other datasets, for a more comprehensive understanding of public engagement in environmental crises. This work contributes to the growing field of infodemiology, emphasizing the importance of big data analysis in environmental science and public health research.

Phenotypic and genotypic perspectives on detection methods for bacterial antimicrobial resistance in a One Health context: research progress and prospects.

The widespread spread of bacterial antimicrobial resistance (AMR) and multidrug-resistant bacteria poses a significant threat to global public health. Traditional methods for detecting bacterial AMR are simple, reproducible, and intuitive, requiring long time incubation and high labor intensity. To quickly identify and detect bacterial AMR is urgent for clinical treatment to reduce mortality rate, and many new methods and technologies were required to be developed. This review summarizes the current phenotypic and genotypic detection methods for bacterial AMR. Phenotypic detection methods mainly include antimicrobial susceptibility tests, while genotypic detection methods have higher sensitivity and specificity and can detect known or even unknown drug resistance genes. However, most of the current tests are either genotypic or phenotypic and rarely combined. Combining the advantages of phenotypic and genotypic methods, combined with the joint application of multiple rapid detection methods may be the trend for future AMR testing. Driven by rapid diagnostic technology, big data analysis, and artificial intelligence, detection methods of bacterial AMR are expected to constantly develop and innovate. Adopting rational detection methods and scientific data analysis can better address the challenges of bacterial AMR and ensure human health and social well-being.

A feasibility study on predicting cow calving time over 40 h in advance using heart rate and financial technical indicators

In dairy farming, the uncertainty of cow calving date often imposes waiting costs for days on farmers. Improving the accuracy of calving date prediction would mitigate these costs, specifically before a few days of the event. We monitored and analyzed the heart rate patterns of eight pregnant cows in the days leading up to calving using a dedicated monitoring device. We decomposed the heart rate data into three distinct components: trend, daily cycle, and the remainder, and discovered that the heart rate trend exhibited a sharp decline more than 40 h before the calving event via the trend turning point. To detect the turning point, we applied common financial technical indicators traditionally used to identify turning points of asset prices in trading markets for the extracted heart rate trend. This study remains a feasibility study because of the limited observations, but it indicates that these indicators can effectively capture the trend’s turning point in real time, offering a promising approach for enhanced calving prediction. In addition to discussing the practical implications for cow management, we also contemplate the broader utility of these technical indicators in the context of various dynamic scientific data analyses.

Genetic engineering in oncology based on CRISPR-Cas9 technology

Purpose of the study: analysis of modern scientific data on the molecular mechanisms of the CRISPR-Cas9 system in gene editing, advantages and disadvantages in cancer research and the development of new treatment methods. Material and Methods. A comprehensive electronic search of relevant published studies was conducted in the scientific databases PubMed/MEDLINE, ScienceDirect, Wiley and Google Scholar published between 2014 and 2024. The search was tailored to the specific requirements of each database based on the following keywords: CRISPR-Cas9, sgRNA, genome editing, cancer immunotherapy, CAR-T. The search yielded 487 studies on the topic of interest, of which 54 were used to write the literature review. Additionally, the article discretely highlights the importance and challenges of CRISPR-Cas9 in the production of genetically engineered T cells for potential use in treating certain types of cancer. Results. Accordingly, CAR-T (chimeric antigen receptor T-cell) therapy is widely used as one of the main components of immunotherapy in the treatment of leukemia, lymphoma and some solid tumors. The development of programmed single guide RNAs (sgRNAs) and new modifications of the Cas9 protein has made the technology flexible and universal. CRISPR-Cas9 is often used to modify T and NK cells by designing antigen receptors to improve their sensory circuits with complex functionality capable of recognizing and killing tumor cells. At the same time, delivery of the finished ribonucleoprotein (Cas9+sgRNA) complex into the cell avoids the constitutive processes of transcription and translation, which ensures the fastest possible gene editing. Conclusion. In this review, we reviewed the scientific evidence highlighting the promising impact of CRISPR technologies in cancer research and treatment. CRISPR-Cas9 is considered a unique and effective technology in the field of genetic and biomolecular engineering.

The Foundation to Advance Brain Rehabilitation: Organization, History, and Collaborative Database.

To describe the history, organization, goals, and data management procedures of the Foundation to Advance Brain Rehabilitation (FABR). Postacute brain injury rehabilitation following acute inpatient care. FABR was incorporated in 2019 with a primary mission to advance brain rehabilitation through scientific and strategic analysis of industry-wide data. Contributing FABR member organizations include Bancroft NeuroRehab, Collage Rehabilitation Partners, On With Life, Pate NeuroRehabilitation, and Shepherd Center. These organizations contribute demographic, admission, discharge, and follow-up data on persons served in 5 types of intensive posthospital brain injury rehabilitation programs (neurobehavioral residential, neurorehabilitation residential, home and community neurorehabilitation, day treatment, and outpatient) and 3 types of supported living programs (neurobehavioral residential, residential, and community-based) to a collaborative database managed by Inventive Software Solutions. Inventive Software Solutions provides FABR researchers with a dataset that is de-identified both for participant and organizational identifying information.

Methodological foundations and content-based methodological approaches to the formation of dominant metacognitive competencies of schoolchildren based on an integrative approach

Introduction. The dominant metacognitive competencies of students include critical, reflective and creative thinking. Their systemic development is the most important target of education. The essence of the dominant metacognitive competencies of schoolchildren and the proven expediency of their conjugate formation objectifies the assumption that reliance on an integrative approach to building education, upbringing and development of schoolchildren is the fundamental basis for designing the educational process. The purpose of the study: substantiation of the key importance of the integrative approach for building the process of formation of dominant metacognitive competencies of schoolchildren and the definition of methodological and content-methodical elements included in the circle of pedagogical integration in this context. Materials and methods. The study involved employees of Kuban State University (doctors and candidates of pedagogical and psychological sciences) in the number of 30 people. Methods: theoretical analysis, synthesis and concretization of scientific data; comparison and modeling; expert assessment. During the examination, specially designed forms of expert assessment were used. The results of the study. The validation of the constructed set of methodological foundations for the formation of dominant MCCS of schoolchildren was carried out; according to the results of expert assessment: the integrative approach was recognized as the key, system–forming methodological guideline of the specified process (90% of experts); contextual, subjective, optimization approaches were included in the integrated set of methodological guidelines (73.3% – 93.3% of experts); the content and methodological tools of vitagenic, interactive, problem-based, activity–based approaches (73.3% – 90% of experts). Conclusion. The results of the study are consistent with the general vectors of the research conducted by scientists, complementing them with regard to ensuring the conjugate formation of clusters of metacognitive competencies and specifying certain provisions regarding the content-methodical ways of implementing methodological principles and conditions.

Therapeutic Potential and Pharmacological Activities of Moscatilin in Medicine for the Treatment of Cancers and other Human Complication: A Review of the Active Components of Dendrobium Species

Background: Plant-derived byproducts have been used to treat numerous kinds of human complications in medicine since a very early age. Moscatilin is a bibenzyl compound found to be present in Dendrobium. Moscatilin, also called 4,4′-dihydroxyl-3,3′,5-trimethoxybibenzyl has potential benefits in medicine for the treatment of ovarian, lung, breast, esophageal, hepatic, colorectal, pancreatic and neck squamous cancer. Methods: The present work summarized the health-beneficial aspects of moscatilin for its effectiveness against numerous kinds of cancerous disorders in medicine. Pharmacological activities and analytical aspects of moscatilin have been analyzed in the present work through available scientific data on Google, Scopus, Science Direct, and PubMed. Results: Scientific data analysis of moscatilin signified their therapeutic effectiveness against ovarian cancer, lung cancer, breast cancer, esophageal cancer, hepatic cancer, colorectal cancer, pancreatic cancer, neck squamous cell cancer, apoptosis, and angiogenesis. Further, moscatilin has a significant effect on inflammation, Alzheimer's disease, diabetic neuropathy, and retinal ischemia. However, analytical data on moscatilin were also discussed in the present work in order to know the effective separation, isolation and identification of moscatilin. Conclusion: Scientific information on moscatilin presented in this work will be helpful to all scientific people to understand the biological importance and therapeutic potential of moscatilin in medicine.

Standardised and structured reporting in fetal magnetic resonance imaging: recommendations from the Fetal Task Force of the European Society of Paediatric Radiology.

Over the last decades, magnetic resonance imaging (MRI) has emerged as a valuable adjunct to prenatal ultrasound for evaluating fetal malformations. Several radiological societies advocate for standardised and structured reporting practices to enhance the uniformity of imaging language. Compared to narrative formats, standardised and structured reports offer enhanced content quality, minimise reader variability, have the potential to save reporting time, and streamline the communication between specialists by employing a shared lexicon. Structured reporting holds promise for mitigating medico-legal liability, while also facilitating rigorous scientific data analyses and the development of standardised databases. While structured reporting templates for fetal MRI are already in use in some centres, specific recommendations and/or guidelines from international societies are scarce in the literature. The purpose of this paper is to propose a standardised and structured reporting template for fetal MRI to assist radiologists, particularly those with less experience, in delivering systematic reports. Additionally, the paper aims to offer an overview of the anatomical structures that necessitate reporting and the prevalent normative values for fetal biometrics found in current literature.

Electrokinetics with Surface-Enhanced Raman Spectroscopy for Bacterial Identification in Wastewater

Bacterial presence in wastewater serves as a crucial bioindicator for monitoring population-level infections and offering early disease outbreak warnings [1,2]. However, identifying bacteria in wastewater presents outstanding challenges; notably, current culturing or fluorescence-based methods [3] to identify bacteria are slow and expensive, unsuitable for high-throughput screening of diverse bacterial species, and may not work well in the complex wastewater matrix. Here, we harness the potential of surface chemistry and electrokinetics to introduce an innovative electro-optical approach that has the promise to detect a broad spectrum of pathogenic bacteria in wastewater.We combine surface-enhanced Raman spectroscopy (SERS) [4] with alternating current electric fields, complemented by machine learning models [5], to enable rapid and amplification-free bacteria detection in filtered wastewater, at cell concentrations as low as 104 cells/mL. First, we synthesize gold nanorods that can electrostatically bind to bacteria surfaces, allowing for SERS from cell surfaces. We collect SERS from bacteria spiked into filtered wastewater, including Staphylococcus aureus, Staphylococcus epidermidis, and Escherichia coli, spanning concentrations from 109 cells/mL to 104 cells/mL. Spectral clustering analysis shows that bacteria signals become more indistinguishable from wastewater as the concentrations decrease. Further, we incorporate electrokinetic effects into SERS by employing gold microelectrodes to apply electric fields, utilizing dielectrophoresis [6] to rapidly displace and concentrate bacteria based on their dielectric responses within minutes. The enrichment of bacteria with nanorods is directly visualized through microscopy, resulting in Raman signal intensities increased by up to tenfold under electrical fields for bacterial concentrations ranging from 106 down to 104 cells/mL. Such enhancement may enable the detection sensitivity to reach environmentally-relevant concentrations. Moreover, employing data science analysis, we identify biologically-relevant “fingerprint” Raman peaks characterizing proteins, nucleic acids, and lipids from bacteria surfaces, allowing for rapid identification of bacteria species in wastewater. This novel method exhibits potential for generalized pathogen detection and molecular recognition in complex liquid samples, such as wastewater, blood, and seawater.[1] Hellmér, et al. Applied and environmental microbiology. (2014).[2] Keshaviah, et al. The Lancet Global Health. (2023).[3] Jahn, et al. Nature Microbiology. (2022).[4] Tadesse, et al., Nano Lett. (2020).[5] Ho, et al., Nat. Comm. (2019).[6] Pethig. John Wiley & Sons (2010).

Herbal Medicines Genkwadaphnin as Therapeutic Agent for Cancers and Other Human Disorders: A Review of Pharmacological Activities through Scientific Evidence

Backgrounds: Daphnane-type diterpenes are an important class of phytochemicals found to be present in the family Euphorbiaceae and Thymelaeaceae. It has anti-leukaemic, antihyperglycaemic, and anti-fertility activities in medicine. Daphne genkwa is indigenous to the Yangtze River and Yellow River of China. Daphne genkwa contains significant amounts of daphnane- type diterpenes. Phytochemical analysis of Daphne genkwa led to the isolation of flavonoids, lignins, coumarins, caffeotannic acids, and genkwadaphnin. Methods: Present review highlighted the biological potential of genkwadaphnin in medicine. All the scientific data of Daphne genkwa, and genkwadaphnin were collected from Google, Google Scholar, Science Direct, Scopus, and PubMed and analyzed in the present work to know the therapeutic potential of genkwadaphnin in medicine. Detailed pharmacological activities of genkwadaphnin were analyzed in the present work through scientific data analysis of various research works. Results: Genkwadaphnin is a daphnane diterpene ester molecule mainly isolated from the Daphne genkwa, Dendrostellera lessertii, Daphne odorata, Gnidia latifolia, and Gnidia glaucus. Genkwadaphnin has been reported to exert therapeutic potential against hepatocellular carcinoma, human colon cancer, squamous cell carcinoma, and leukemia. Further, it has a significant role in innate immunity, melanogenesis, skeletal diseases, inflammatory cytokines, and natural killer cell. However, pharmacokinetics and metabolomics aspects of genkwadaphnin were also discussed in the present work. Further, more scientific data on human clinical trials is needed to ensure the safety and efficacy of genkwadaphnin in medicine. Conclusion: In the present work, a successful review had been achieved by the above-mentioned scientific data, which signified the therapeutic potential of genkwadaphnin in medicine.

Game Engines for Immersive Visualization: Using Unreal Engine Beyond Entertainment

Abstract One core aspect of immersive visualization labs is to develop and provide powerful tools and applications that allow for efficient analysis and exploration of scientific data. As the requirements for such applications are often diverse and complex, the same applies to the development process. This has led to a myriad of different tools, frameworks, and approaches that grew and developed over time. The steady advance of commercial off-the-shelf game engines such as Unreal Engine has made them a valuable option for development in immersive visualization labs. In this work, we share our experience of migrating to Unreal Engine as a primary developing environment for immersive visualization applications. We share our considerations on requirements, present use cases developed in our lab to communicate advantages and challenges experienced, discuss implications on our research and development environments, and aim to provide guidance for others within our community facing similar challenges.

Research on Influencing Factors and Strategies of Art Collection Investment Decision

The realm of art collection holds an irreplaceable position in the long-standing cultural heritage, showcasing the taste and status of the holders from ancient nobility to modern high-net-worth individuals. However, with the development of economic globalization, art collection is not merely a symbol of identity, but also a hot investment target sought after by numerous investors. In recent years, the market value of the global art industry has rapidly climbed, making art gradually an essential component of diversified investment portfolios for investors. The development of the art market has sparked widespread attention from various sectors, prompting scholars and companies to explore, through scientific methods and data analysis, how to maximize the investment returns of art collection. However, art differs significantly from other investment tools in the financial market, involving not only the intrinsic value judgment of the art itself but also complex market environments and individual judgment and psychological expectations of investors. Its unpredictability and unique value assessment system make art collection investment more challenging and research-worthy compared to other types of investments.

Quantitative assessment of runoff change and its drivers in a multi-scale framework

ABSTRACT Identifying runoff changes and quantifying the impacts of climate change and human activities are important for water resources planning and management in river basins. The impacts of climate change and human activities on runoff can be more accurately assessed through scientific hydrological modeling and data analysis methods. In this study, an integrated assessment framework was established to quantitatively separate the driving mechanisms of runoff at different time scales. The results show that the runoff of Wu River has shown a decreasing trend since 2004, with a change degree of 56%, and the monthly average flow indexes of August and September have changed significantly, both are over 90%. The NSE coefficient of the SWAT simulation effect is above 0.8, we validate the simulation results based on the LSTM model. It was found that climate change was the main factor affecting the runoff of Wu River, with the contribution rates reaching 60 and 57%, respectively. Pearson correlation analysis found that rainfall was the most important factor affecting the runoff. The results of this study are helpful to formulate effective water resources management policies and measures to ensure the sustainable utilization and management of water resources in the Wu River Basin.