Interpretation Of Data Research Articles

Performance in professional soccer players: normative data and benchmarks from official matches for metabolic power and high-intensity activities.

The opportunity to compare data among players using normative data and benchmarks may represent a helpful tool for coaches to assess the players' physical performance easily and quickly. This study aimed to create normative data and benchmarks that can be exploited to compare professional soccer players' physical capacity competing in the Premier Division Championship (series A). Match data from six professional soccer teams competing in the Premier Division championship (Serie A and Italy Cup matches) were collected during the entire season. Players (N.=112) were divided based on the role positioning as follows: forwards and wingers (FW), midfielders (MF), side-backs (SB), and center-backs (CB). All the teams analyzed competed with a 4-3-3 formation, and only players who played for the entire match (85-95 minutes) were considered. The video analysis system STATS SportVU was used to collect the data during official matches. Average metabolic power (AP) was considered to estimate the energy cost. The number of power events (PE), distance (m) covered at more than 25 W/kg (D25), and finally, the distance covered at v>25 km/h (VHS) were considered as high-intensity assessments. Standardized T-scores (scaled from 0 to 100) were calculated for each role and variable. T-score data was used to create performance bands combined with qualitative description (ranging from extremely poor to excellent), and a traffic light system approach was implemented to simplify the data's interpretation. The results could be used to compare different athletes' performance quickly and effectively, to detect symptoms of overtraining, and to give helpful insights to coaches on what the training should be focused on.

Acylphloroglucinol derivatives from the aerial parts of Syzygium borneense and their cytotoxic activities

Four new acylphloroglucinol derivatives, borneensinones A-D (1–4) along with five known compounds (5-9) were isolated from the aerial parts of Syzygium borneense. Their structures were elucidated based on the interpretation of spectrometric and spectroscopic data (HRESI-MS, 1D- and 2D-NMR) and by comparison with literature data for known compounds. All acylphloroglucinol derivatives (1–6) were tested for their cytotoxic activities against four human cancer cell lines (KB, MCF-7, HepG-2, and A549). Among the six tested compounds, compound 6 exhibited the strongest inhibitory activity against four tested cancer cell lines, with IC50 values ranging from 6.21 to 26.96 μM. Compound 1 was more active against KB and HepG2 comparing to the A549 and MCF7 cells. Compound 5 had a moderate cytotoxicity with IC50 values ranging from 27.71 to 97.32 μM.

Rates of positive vs negative studies in the spine literature

BackgroundAccuracy in the interpretation of data, and publication of studies regardless of outcomes are vital to the development of the scientific literature. ObjectiveTo determine the proportion of studies in the spine literature that report positive results. Study designReview article of studies published in nine major spine, pain, and physical medicine and rehabilitation (PM&R) journals from January 1, 2018–December 31, 2022. Patient sampleNot applicable. MethodsArticles that reported on pain and/or function from 2018 to 2022 in nine major journals were reviewed by two independent evaluators. The articles were graded as either positive or negative based on the authors’ own conclusions about their work. ResultsOverall, 91 % [95 % CI 88–94 %] of all articles were reported to have positive results. No significant differences were found between the broad categories of spine, pain, and PM&R journals. When comparing different categories of treatments, there were lower rates of positive results from medication/supplement studies (54 % [95 % CI 27–81 %]) compared to studies of spine injections/interventions (95 % [95 % CI 91–99 %]) and those of surgery (100 % [95 % CI 96–100 %]), and a lower rate of positive results from studies on physical treatments (85 % [95 % CI 75–95 %]) compared to those of surgery (100 % [95 % CI 96–100 %]). Studies with placebo controls were less likely to report positive results (60 % [95 % CI 44–76 %]) compared to those that did not use placebo controls (96 % [95 % CI 94–98 %]). ConclusionsDespite the vast majority of studies in the spine literature concluding positive results, the high disease prevalence of spine conditions and the enormous burden on the healthcare system remain.

Genome-wide analysis and visualization of copy number with CNVpytor in igv.js.

Copy number variation (CNV) and alteration (CNA) analysis is a crucial component in many genomic studies and its applications span from basic research to clinic diagnostics and personalized medicine. CNVpytor is a tool featuring a read depth-based caller and combined read depth and B-allele frequency (BAF) based 2D caller to find CNVs and CNAs. The tool stores processed intermediate data and CNV/CNA calls in a compact HDF5 file-pytor file. Here, we describe a new track in igv.js that utilizes pytor and whole genome variant files as input for on-the-fly read depth and BAF visualization, CNV/CNA calling and analysis. Embedding into HTML pages and Jupiter Notebooks enables convenient remote data access and visualization simplifying interpretation and analysis of omics data. The CNVpytor track is integrated with igv.js and available at https://github.com/igvteam/igv.js. The documentation is available at https://github.com/igvteam/igv.js/wiki/cnvpytor. Usage can be tested in the IGV-Web app at https://igv.org/app and also on https://github.com/abyzovlab/CNVpytor. Supplementary data are available at Bioinformatics online.

Structure Determination of 1,3-Dioxolane-Containing Lipids from the Marine Sponge Leucetta sp. Using Chiral 1H NMR Analysis of Model Systems.

Chemical investigation of n-hexane extract from the marine sponge Leucetta sp. led to the isolation of five new lipids, 1-5, each characterized by a substituted dioxolane core. The structures of 1-5 were established based on the interpretation of NMR and HRESIMS data. To assign the absolute configuration at C-1', model systems consisting of diastereomers at C-2, C-4, and C-1' of the dioxolane core were prepared from a chiral glycerol dimethylacetal. 1H NMR inspection of model compounds revealed that a pair of C-1' epimers, 11a/c and 11b/d, was indistinguishable, restricting structural assignment by direct comparison of NMR data. In addition, the lack of chromophores in the dioxolane core resulted in unreliable ECD results, with Cotton effects appearing below 190 nm. As an alternative, a chiral NMR method using Eu(hfc)3 revealed notable lanthanide-induced shifts, allowing the spectroscopic discrimination of 11a/c and ent-11a/c. Therefore, the absolute configuration of all five new lipids was determined to be 2S, 4S, 1'S by direct comparison with the Eu(hfc)3-induced 1H NMR data.

Continuous Glucose Monitoring for Diabetes Management During Pregnancy: Evidence, Practical Tips, and Common Pitfalls.

Continuous glucose monitoring (CGM) has the potential to revolutionize diabetes management during pregnancy by providing detailed and real-time data to patients and clinicians, overcoming many of the limitations of self-monitoring of blood glucose. Although there are limited data on the role of CGM to improve pregnancy outcomes in patients with type 2 diabetes or gestational diabetes, CGM has been shown to reduce pregnancy complications in patients with type 1 diabetes. Despite the limited data in some populations, given its ease of use and recent U.S. Food and Drug Administration approval with expanding insurance coverage, CGM has gained widespread popularity among pregnant patients with all types of diabetes. It is critical for obstetric clinicians to understand how CGM can be successfully integrated into clinical practice. We present a practical, step-wise approach to CGM data interpretation that can be incorporated into diabetes management during pregnancy and common CGM pitfalls and solutions. Although technology will continue to advance with newer-generation CGM devices and diabetes technology such as automated insulin delivery (not covered here), these key principles form a basic foundation for understanding CGM technology and its utility for pregnant people.

MetaboLink: A web application for Streamlined Processing and Analysis of Large-Scale Untargeted Metabolomics Data.

The post-processing and analysis of large-scale untargeted metabolomics data face significant challenges due to the intricate nature of correction, filtration, imputation, and normalization steps. Manual execution across various applications often leads to inefficiencies, human-induced errors, and inconsistencies within the workflow. Addressing these issues, we introduce MetaboLink, a novel web application designed to process LC-MS metabolomics datasets combining established methodologies and offering flexibility and ease of implementation. It offers visualization options for data interpretation, an interface for statistical testing, and integration with PolySTest for further tests and with VSClust for clustering analysis. Fully functional tool is publicly available at https://computproteomics.bmb.sdu.dk/Metabolomics/. The source code is available at https://github.com/anitamnd/MetaboLink and a detailed description of the app can be found at https://github.com/anitamnd/MetaboLink/wiki. A tutorial video can be found at https://youtu.be/ZM6j10S6Z8Q. Supplementary data are available at Bioinformatics online.

First Australian estimates of incidence and prevalence of uterine fibroids: a data linkage cohort study 2000-2022.

What is the estimated prevalence and incidence of uterine fibroids diagnosed in Australian women of reproductive age? An estimated 7.3% of Australian women had a diagnosis of uterine fibroids by the age of 45-49 years, with age-specific incidence highest in women aged 40-44 years (5.0 cases per 1000 person-years). Uterine fibroids are associated with a high symptom burden and may affect overall health and quality of life. Studies in different countries show a wide variation in both the prevalence (4.5-68%) and incidence (2.2-37.5 per 1000 person-years) of uterine fibroids, which may be partly explained by the type of investigation, method of case ascertainment, or the age range of the study population, necessitating the reporting of country-specific estimates. This observational prospective cohort study using self-report survey and linked administrative data (2000-2022) included 8066 women, born between 1973 and 1978, in the Australian Longitudinal Study on Women's Health. A combination of self-report survey and linked administrative health data (hospital, emergency department, the Medicare Benefits Schedule, and the Pharmaceutical Benefits Scheme) were used to identify women with a report of a diagnosis of uterine fibroids between 2000 and 2022. Of the 8066 Australian women followed for 22 years, an estimated 7.3% of women (95% CI 6.9, 7.6) had a diagnosis of uterine fibroids by the age of 45-49 years. The incidence increased with age and was highest in women aged 40-44 years (5.0 cases per 1000 person-years, 95% CI 4.3, 5.7 cases per 1000 person-years). Women with uterine fibroids were more likely to experience heavy or painful periods. They were also more likely to report low iron levels, endometriosis, and poor self-rated health and to have two or more annual visits to their general practitioner. Our estimates are based on self-report of doctor diagnosis or treatment for fibroids and/or data linked to treatment and procedure administrative records. This predominantly captures women with symptomatic fibroids, but has the potential for misclassification of asymptomatic women and an underestimate of overall prevalence and incidence. In addition, questions on fibroids were only asked in surveys when women were 37-42 years of age to 43-48 years of age, so cases at younger ages may have been underestimated (particularly in women with less severe symptoms) as these were only ascertained through data linkage. These are the first population-based estimates of the prevalence and incidence of uterine fibroids in women of reproductive age in Australia. Establishing these first estimates will help inform health policy and health care provision in the Australian context. The ALSWH is funded by the Australian Government Department of Health and Aged Care. L.FW. was supported by an Australian National Health and Medical Research Council (NHMRC) Centres for Research Excellence grant (APP1153420) and G.D.M. was supported by an NHMRC Leadership Fellowship (APP2009577). The funding bodies played no role in the design, the collection, analysis or interpretation of data, the writing of the manuscript, or the decision to submit the manuscript for publication. There are no competing interests. N/A.

Understanding magnetic hyperthermia performance within the "Brezovich criterion": beyond the uniaxial anisotropy description.

Careful determination of the heating performance of magnetic nanoparticles under AC fields is critical for magnetic hyperthermia applications. However, most interpretations of experimental data are based on the uniaxial anisotropy approximation, which in the first instance can be correlated with the particle aspect ratio. This is to say, the intrinsic magnetocrystalline anisotropy is discarded, under the assumption that the shape contribution dominates. We show in this work that such a premise, generally valid for large field amplitudes, does not hold for describing hyperthermia experiments carried out under small field values. Specifically, given its relevance for in vivo applications, we focus our analysis on the so-called "Brezovich criterion", H·f = 4.85 × 108 A m-1 s-1. By means of a computational model, we show that the intrinsic magnetocrystalline anisotropy plays a critical role in defining the heat output, determining also the role of the shape and aspect ratio of the particles on the SLP. Our results indicate that even small deviations from spherical shape have an important impact on optimizing the heating performance. The influence of interparticle interactions on the dissipated heat is also evaluated. Our results call, therefore, for an improvement in the theoretical models used to interpret magnetic hyperthermia performance.

Theory for proton-coupled energy transfer.

In the recently discovered proton-coupled energy transfer (PCEnT) mechanism, the transfer of electronic excitation energy between donor and acceptor chromophores is coupled to a proton transfer reaction. Herein, we develop a general theory for PCEnT and derive an analytical expression for the nonadiabatic PCEnT rate constant. This theory treats the transferring hydrogen nucleus quantum mechanically and describes the PCEnT process in terms of nonadiabatic transitions between reactant and product electron-proton vibronic states. The rate constant is expressed as a summation over these vibronic states, and the contribution of each pair of vibronic states depends on the square of the vibronic coupling as well as the spectral convolution integral, which can be viewed as a generalization of the Förster-type spectral overlap integral for vibronic rather than electronic states. The convolution integral also accounts for the common vibrational modes shared by the donor and acceptor chromophores for intramolecular PCEnT. We apply this theory to model systems to investigate the key features of PCEnT processes. The excited vibronic states can contribute significantly to the total PCEnT rate constant, and the common modes can either slow down or speed up the process. Because the pairs of vibronic states that contribute the most to the PCEnT rate constant may correspond to spectroscopically dark states, PCEnT could occur even when there is no apparent overlap between the donor emission and acceptor absorption spectra. This theory will assist in the interpretation of experimental data and will guide the design of additional PCEnT systems.

Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models

AbstractThe development of biophysical models for clinical applications is rapidly advancing in the research community, thanks to their predictive nature and their ability to assist the interpretation of clinical data. However, high-resolution and accurate multi-physics computational models are computationally expensive and their personalisation involves fine calibration of a large number of parameters, which may be space-dependent, challenging their clinical translation. In this work, we propose a new approach, which relies on the combination of physics-informed neural networks (PINNs) with three-dimensional soft tissue nonlinear biomechanical models, capable of reconstructing displacement fields and estimating heterogeneous patient-specific biophysical properties and secondary variables such as stresses and strains. The proposed learning algorithm encodes information from a limited amount of displacement and, in some cases, strain data, that can be routinely acquired in the clinical setting, and combines it with the physics of the problem, represented by a mathematical model based on partial differential equations, to regularise the problem and improve its convergence properties. Several benchmarks are presented to show the accuracy and robustness of the proposed method with respect to noise and model uncertainty and its great potential to enable the effective identification of patient-specific, heterogeneous physical properties, e.g. tissue stiffness properties. In particular, we demonstrate the capability of PINNs to detect the presence, location and severity of scar tissue, which is beneficial to develop personalised simulation models for disease diagnosis, especially for cardiac applications.

Importance of Conducting Indoor and Outdoor Events at Secondary Schools in Nepal

Many secondary schools conduct indoor and outdoor events at schools to improve the holistic development of learners. Both events not only boost academically for students but also improve their self-confidence. The purpose of the study was to find out the importance of conducting indoor and outdoor events in secondary schools and find the strengths behind organizing such events at schools. The research paradigm of this study was positivism, and this quantitative study employed the survey research design to investigate the importance of conducting these events in schools. I selected 75 students and 25 teachers from secondary schools using random sampling for this study. I distributed the questionnaire into two sections to my respondents online, that is, via messenger, to collect the primary data for the study. I used a correlation analysis for the data interpretation of the collected from this study. I divided the responses received into students’ responses and teachers’ responses. The findings of the students’ response revealed that a fixed schedule for extracurricular activities, trained and experienced coordinators, and enough materials for the events are essential to conduct indoor and outdoor events in schools. The findings of the teachers’ responses show that extracurricular activities improve the academic performance of students developing different language skills. The findings of this study have implications for school administrators, teachers, parents, students, and textbook writers.

Drug-drug interactions and synergy: from pharmacological models to clinical application.

This review explores the concept of synergy in pharmacology, emphasizing its importance in optimizing treatment outcomes through the combination of drugs with different mechanisms of action. Synergy, defined as an effect greater than the expected additive effect elicited by individual agents according to specific predictive models, offers a promising approach to enhance therapeutic efficacy while minimizing adverse events. The historical evolution of synergy research, from ancient civilizations to modern pharmacology, highlights the ongoing quest to understand and harness synergistic interactions. Key concepts such as concentration-response curves, additive effects, and predictive models are discussed in detail, emphasizing the need for accurate assessment methods throughout translational drug development. While various mathematical models exist for synergy analysis, selecting the appropriate model and software tools remains a challenge, necessitating careful consideration of experimental design and data interpretation. Furthermore, this review addresses practical considerations in synergy assessment, including preclinical and clinical approaches, mechanism of action, and statistical analysis. Optimizing synergy requires attention to concentration/dose ratios, target site localization, and timing of drug administration, ensuring that the benefits of combination therapy detected at bench-side are translatable into clinical practice. Overall, the review advocates for a systematic approach to synergy assessment, incorporating robust statistical analysis, effective and simplified predictive models, and collaborative efforts across pivotal sectors such as academic institutions, pharmaceutical companies, and regulatory agencies. By overcoming critical challenges and maximizing therapeutic potential, effective synergy assessment in drug development holds promise for advancing patient care. Significance Statement Combining drugs with different mechanisms of action for synergistic interactions optimizes treatment efficacy and safety. Accurate interpretation of synergy requires the identification of the expected additive effect. Despite innovative models to predict the additive effect, consensus in drug interaction research is lacking, hindering the bench-to-bedside development of combination therapies. Collaboration among science, industry, and regulation is crucial for advancing combination therapy development, ensuring rigorous application of predictive models in clinical settings.

Creep Failure Characteristics and Mathematical Modeling of High-Density Polyethylene Geomembranes under High Stress Levels

To explore the creep characteristics of geomembrane under different tensile stresses, a series of creep tests were carried out on high-density polyethylene (HDPE) geomembrane specimens. For the interpretation and fitting of the experimental data, refined approximation functions were proposed. Particular attention was paid to the creep failure behavior under high tensile stresses, i.e., 70%, 80%, and 90% of maximum peak stress. To investigate the effects of size on the mechanical response, experiments with two different membrane thicknesses were conducted. The results obtained under high stress levels were compared with creep tests at medium and low stress levels. Depending on load level, different creep characteristics can be distinguished. In the secondary creep state, the creep velocity is higher for higher load levels. In contrast to the medium and low load levels, the geomembrane under high stresses underwent the tertiary creep stage after instantaneous deformation and primary and secondary creep stages. In some tests, it was observed that under very high stress levels, creep velocity does not necessarily follow the expected trend and creep rupture can occur within a short time. For numerical simulation, an improved mathematical model was proposed to reproduce in a unified manner the experimental data of the whole non-linear evolution of creep elongation under different stress levels.

A fusion approach using GIS, green area detection, weather API and GPT for satellite image based fertile land discovery and crop suitability

Proper utilization of agricultural land is a big challenge as they often laid over as waste lands. Farming is a significant occupation in any country and improving it further by promoting more farming opportunities will take the country towards making a huge leap forward. The issue in achieving this would be the lack of knowledge of cultivable land for food crops. The objective of this work is to utilize modern computer vision technology to identify and map cultivable land for agricultural needs. With increasing population and demand for food, improving the farming sector is crucial. However, the challenge lies in the lack of suitable land for food crops cultivation. To tackle this issue, we propose to use sophisticated image processing techniques on satellite images of the land to determine the regions that are capable of growing food crops. The solution architecture includes enhancement of satellite imagery using sophisticated pan sharpening techniques, notably the Brovey transformation, aiming to transform dull satellite images into sharper versions, thereby improving the overall quality and interpretability of the visual data. Making use of the weather data on the location observed and taking into factors like the soil moisture, weather, humidity, wind, sunlight times and so on, this data is fed into a generative pre-trained transformer model which makes use of it and gives a set of crops that are suitable to be grown on this piece of land under the said conditions. The results obtained by the proposed fusion approach is compared with the dataset provided by the government for different states in India and the performance was measured. We achieved an accuracy of 80% considering the crop suggested by our model and the predominant crop of the region. Also, the classification report detailing the performance of the proposed model is presented.

The challenges of assessing adiposity in a clinical setting.

To tackle the burden of obesity-induced cardiometabolic disease, the scientific community relies on accurate and reproducible adiposity measurements in the clinic. These measurements guide our understanding of underlying biological mechanisms and clinical outcomes of human trials. However, measuring adiposity and adipose tissue distribution in a clinical setting can be challenging, and different measurement methods pose important limitations. BMI is a simple and high-throughput measurement, but it is associated relatively poorly with clinical outcomes when compared with waist-to-hip and sagittal abdominal diameter measurements. Body composition measurements by dual energy X-ray absorptiometry or MRI scans would be ideal due to their high accuracy, but are not high-throughput. Another important consideration is that adiposity measurements vary between men and women, between adults and children, and between people of different ethnic backgrounds. In this Perspective article, we discuss how these critical challenges can affect our interpretation of research data in the field of obesity and the design and implementation of clinical guidelines.

Review on 3D electromagnetic modeling and inversion for Mineral Exploration

Many mineral deposits demonstrate low-resistivity characteristics. This property makes the electromagnetic (EM) method a very useful tool for mineral exploration. In the past decades, the application of EM exploration technologies has been reviewed in many case studies. However, most reviews focused on EM exploration methods, the development of equipment, or their applications. The three-dimensional (3D) forward modeling and inversions are high-accuracy EM interpretation techniques that have made great progress in recent years in mineral explorations. In this paper, we make a review on the development of EM technology for mineral exploration with focus on 3D EM forward modeling, inversion technology, and its applications. We will first briefly introduce the EM methods for mineral explorations from the methodology. After that, we will give a comprehensive review of 3D EM forward modeling, inversions, and data interpretations, with special attention paid to the development of EM theory and applications in mineral explorations. We hope this review can promote the application of 3D EM numerical simulation and inversion in mineral explorations.

Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review

This review article offers a comprehensive overview of the current understanding of using metagenomic tools in food microbiome research. It covers the scientific foundation and practical application of genetic analysis techniques for microbial material from food, including bioinformatic analysis and data interpretation. The method discussed in the article for analyzing microorganisms in food without traditional culture methods is known as food metagenomics. This approach, along with other omics technologies such as nutrigenomics, proteomics, metabolomics, and transcriptomics, collectively forms the field of foodomics. Food metagenomics allows swift and thorough examination of bacteria and potential metabolic pathways by utilizing foodomic databases. Despite its established scientific basis and available bioinformatics resources, the research approach of food metagenomics outlined in the article is not yet widely implemented in industry. The authors believe that the integration of next-generation sequencing (NGS) with rapidly advancing digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), and big data will facilitate the widespread adoption of this research strategy in microbial analysis for the food industry. This adoption is expected to enhance food safety and product quality in the near future.

Prompt response variation in learner corpus research: Implications for data interpretation

While general first language corpora are composed of samples from various naturalistic sources (e.g., websites, books), language samples in most written learner corpora (LC) are texts produced in response to prompts. In this context, LC users need to develop a clear awareness of the affordances and limitations of specific prompts and how responses to said prompts may affect the investigation of their intended object(s) of study. Through an analysis of the presence/absence of specific Spanish verb tenses in texts written in response to two supposedly narrative prompts in a Spanish LC (COWS-L2H; Yamada et al., 2020), this article illustrates the impact of inter- and intra-prompt response variation on LC data interpretation. Based on this evidence, we caution against rapid assumptions about text content based solely on the superficial phrasing of LC writing prompts. Instead, we recommend that LC users perform in-depth quantitative and qualitative analyses of learners’ samples written in response to each prompt they aim to include in their study prior to running statistical models on those data.

Neutralizing Antibody Sample Testing and Report Harmonization

Immunogenicity testing and characterization is an important part of understanding the immune response to administration of a protein therapeutic. Neutralizing antibody (NAb) assays are used to characterize a positive anti-drug antibody (ADA) response. Harmonization of reporting of NAb assay performance and results enables efficient communication and expedient review by industry and health authorities. Herein, a cross-industry group of NAb assay experts have harmonized NAb assay reporting recommendations and provided a bioanalytical report (BAR) submission editable template developed to facilitate agency filings. This document addresses key bioanalytical reporting gaps and provides a report structure for documenting clinical NAb assay performance and results. This publication focuses on the content and presentation of the NAb sample analysis report including essential elements such as the method, critical reagents and equipment, data analysis, study samples, and results. The interpretation of immunogenicity data, including the evaluation of the impact of NAb on safety, exposure, and efficacy, is out of scope of this publication.Graphical