Browse Articles

Common misconceptions held by health researchers when interpreting linear regression assumptions, a cross-sectional study

Background: Statistical models are valuable tools for interpreting complex relationships within health systems. These models rely on a framework of statistical assumptions that, when correctly addressed, enable valid inferences and conclusions. However, failure to appropriately address these assumptions can lead to flawed analyses, resulting in misleading conclusions and contributing to the adoption of ineffective or harmful treatments and poorer health outcomes. This study examines researchers’ understanding of the widely used linear regression model, focusing on assumptions, common misconceptions, and recommendations for improving research practices. Methods: One hundred papers were randomly sampled from the journal PLOS ONE , which used linear regression in the materials and methods section and were from the health and biomedical field in 2019. Two independent volunteer statisticians rated each paper for the reporting of linear regression assumptions. The prevalence of assumptions reported by authors was described using frequencies, percentages, and 95% confidence intervals. The agreement of statistical raters was assessed using Gwet’s statistic. Results: Of the 95 papers that met the inclusion and exclusion criteria, only 37% reported checking any linear regression assumptions, 22% reported checking one assumption, and no authors checked all assumptions. The biggest misconception was that the Y variable should be checked for normality, with only 5 of the 28 papers correctly checking the residuals for normality. Conclusion: The reporting of linear regression assumptions is alarmingly low. When assumptions are checked, the reporting is often inadequate or incorrectly checked. Addressing these issues requires a cultural shift in research practices, including improved statistical training, more rigorous journal review processes, and a broader understanding of regression as a unifying framework. Greater emphasis must be placed on evaluating model assumptions and their implications rather than the rote application of statistical methods. Careful consideration of assumptions helps improve the reliability of statistical conclusions, reducing the risk of misleading findings influencing clinical practice and potentially affecting patient outcomes.

Analyzing NBA player positions and interactions with density-functional fluctuation theory

Abstract The increasing availability of high-precision player-tracking data in sports—centimeter-precision positional information of athletes captured dozens of times per second—has the potential to improve the quantification of player abilities and overall team strategies. Working toward achieving this quantification, we adapt density-functional fluctuation theory (DFFT) to infer spatial preferences and player-to-player interactions in National Basketball Association (NBA) basketball. We first demonstrate several foundational results, including the ability of DFFT to predict the location of a player to within 3% of the half-court area roughly half the time, and to provide a team-position-based metric that correlates strongly with play outcomes. Building on these results, we demonstrate that it is possible to improve player positioning and identify player-specific tendencies, such as the consistency with which a player positions himself to help his team collectively defend against 2-point or 3-point shots. Finally, we quantify how particular players attract the opposing team, with and without the ball, constituting the first advanced quantification of ‘player gravity’ that explicitly deconfounds the influence of teammate positioning.

Picuris Pueblo oral history and genomics reveal continuity in US Southwest

Abstract Indigenous groups often encounter significant challenges when asserting ancestral claims and cultural affiliations based on oral histories, particularly in the USA where such narratives have historically been undervalued. Although ancient DNA offers a tool to complement traditional knowledge and address gaps in oral history, longstanding disregard for Indigenous sovereignty and beliefs has understandably led many Indigenous communities to distrust DNA studies1–4. Earlier research often focused on repatriation claims5–7, whereas more recent work has increasingly moved towards enhancing Tribal histories8,9. Here we present a collaborative study initiated by a federally recognized Native American tribe, the sovereign nation of Picuris Pueblo in the Northern Rio Grande region of New Mexico, USA, to address gaps in traditional knowledge and further their understanding of their population history and ancestry. We generated genomes from 16 ancient Picuris individuals and 13 present-day members of Picuris Pueblo, providing genomic data spanning the last millennium. We show genetic continuity between ancient and present-day Picuris, and more broadly with Ancestral Puebloans from Pueblo Bonito in Chaco Canyon10, 275 km to the west. This suggests a firm spatiotemporal link among these Puebloan populations of the North American Southwest. Furthermore, we see no evidence of population decline before European arrival11–13, and no Athabascan ancestry in individuals predating 1500 ce, challenging earlier migration hypotheses14–16. This work prioritizes Indigenous control of genetic data and brings together oral tradition, archaeology, ethnography and genetics.

Mechanism of human α3β GlyR regulation by intracellular M3/M4 loop phosphorylation and 2,6-di-tert-butylphenol interaction

The protective effect of iron isomaltoside on myocardial ischemia-reperfusion injury via the suppression of KLF4/NF-κB signaling

This study aimed to investigate the beneficial effects of iron isomaltoside (IIM) on myocardial function and the associated mechanisms in rats with myocardial ischemia/reperfusion (I/R)-induced damage and hypoxia/reoxygenation (H/R)-induced H9C2 cells. Changes in cardiac pathology after myocardial infarction (MI) were analyzed with hematoxylin-eosin staining. Myocardial cell apoptosis in the heart tissues of rats with MI was assessed using TUNEL staining. In H/R-induced H9C2 cells, cell viability and lactate dehydrogenase (LDH) and adenosine 5’-triphosphate levels were detected. Apoptosis and MMP in H9C2 cells were detected with flow cytometry. Our results demonstrated that IIM treatment reduced myocardial injury induced by ischemia-reperfusion (I/R) and suppressed cardiomyocyte apoptosis, inflammation, and autophagy induced by I/R in rats. Moreover, we confirmed that IIM repressed apoptosis and regulated MMP in H9C2 cells exposed to H/R. IIM relieved the inflammatory response and autophagy in H/R-treated H9C2 cells. In addition, IIM inhibited the Krüpple-like factor 4 (KLF4)/NF-κB pathway in H/R-induced H9C2 cells. Interestingly, the function of IIM on apoptosis, MMP, inflammation and autophagy were abolished by KLF4 overexpression in H/R-induced H9C2 cells. In conclusion, IIM could repress cardiomyocyte apoptosis, inflammation and autophagy through the inhibition of the KLF4/NF-κB pathway and thus reduced myocardial injury in vivo and in vitro.

Chimpanzees yawn when observing an android yawn

The TRIP12 E3 ligase induces SWI/SNF component BRG1-β-catenin interaction to promote Wnt signaling

Upper extremity joint tenderness as a practical indicator for assessing presenteeism in rheumatoid arthritis patients: A cross-sectional observational study

Objective Rheumatoid arthritis (RA) causes chronic polyarthritis and joint dysfunction, reducing work productivity. This reduction is mainly due to presenteeism, characterized by impaired work performance despite being present at work. This study aims to investigate the impact of specific joint involvement, particularly in the upper extremities, on work disability in RA patients. Methods Annual surveys assessing work disability were conducted among RA outpatients enrolled in the Nagahama Riumachi Cohort at Nagahama City Hospital, using the Work Productivity and Activity Impairment Questionnaire (WPAI). A multivariate regression analysis was performed to examine the cross-sectional and longitudinal associations between self-reported presenteeism and the tender joint count (TJC) in the extremities across two WPAI surveys. Results The analysis included 201 patients, 52% of whom reported presenteeism. Cross-sectional analysis revealed a significant positive correlation between three or more TJCs of the upper extremity and presenteeism, with a regression coefficient (β) = 17.9 (95% confidence interval [CI]: 9.85–25.9). Among the joints evaluated, the sum of TJCs in the shoulder area (β = 9.55, CI: 5.39–13.7) and the fingers (β = 1.60, CI: 0.35–2.85) were significantly correlated with presenteeism. Additionally, change in presenteeism was significantly correlated with change in upper extremity TJCs (β = 1.41, CI: 0.05–2.77). No significant correlation was observed between lower extremity TJCs and presenteeism in these multivariate regression analyses. Conclusions The upper extremity TJC is strongly associated with presenteeism in RA patients. Minimizing TJC in the upper extremities, particularly in the shoulders and fingers, could be important treatment goal to reduce work disability in RA patients.

Genome-wide transcriptomic response of whole blood to radiation

Abstract Blood cells are affected in nearly all ionizing radiation exposure scenarios. Whole transcriptome data offer detailed insights into blood’s radiation response, crucial for radiotherapy and biodosimetry. We conducted genome-wide RNA-seq analysis on blood from three donors irradiated ex vivo with X-rays and incubated for 2 h and 6 h. Gene expression was subject to strong inter-donor variation and time post-exposure. After 0.5, 1, 2, and 4 Gy X-rays, 5, 33, 84, and 364 genes (2 h) and 72, 99, 274, and 607 genes (6 h) were differentially expressed (DEG), compared to 0 Gy. The corresponding number of the inferred transcription factors was 255, 253, 274, and 292 after 2 h and 214, 245, 262, and 279 after 6 h. In sham-irradiated blood, 924 DEGs and 165 transcription factors were affected by ex vivo incubation alone. We identified 34 radioresponsive DEGs not previously described, 8 and 9 showing significant positive or negative correlations with dose, respectively, including GPN1, MRM2, G0S2, and PTPRS. DNA damage signaling pathways were affected from the lowest dose, with doses ≥ 2 Gy additionally triggering proinflammatory responses. This genome-wide RNA-seq study of ex vivo X-ray-exposed human blood reveals novel radiosensitive genes, transcription factors, and pathways, enhancing the understanding of the consequences of diagnostic, therapeutic, or accidental exposures on the highly radioresponsive blood system.

Changing aerosol chemistry is redefining HONO sources

Identifying dynamic regulation with machine learning using adversarial surrogates

Biological systems maintain stability of their function in spite of external and internal perturbations. An important challenge in studying biological regulation is to identify the control objectives based on empirical data. Very often these objectives are time-varying, and require the regulation system to follow a dynamic set-point. For example, the sleep-wake cycle varies according to the 24 hours solar day, inducing oscillatory dynamics on the regulation set-point; nutrient availability fluctuates in the organism, inducing time-varying set-points for metabolism. In this work, we introduce a novel data-driven algorithm capable of identifying internal regulation objectives that are maintained with respect to a dynamic reference value. This builds on a previous algorithm that identified variables regulated with respect to fixed set-point values. The new algorithm requires adding a prediction component that not only identifies the internally regulated variables, but also predicts the dynamic set-point as part of the process. To the best of our knowledge, this is the first algorithm that is able to achieve this. We test the algorithm on simulation data from realistic biological models, demonstrating excellent empirical results.

Perspectives on electron transfer kinetics across graphene-family nanomaterials and interplay of electronic structure with defects and quantum capacitance

Identification and characterisation of vaginal bacteria-glycan interactions implicated in reproductive tract health and pregnancy outcomes

Abstract Lactobacillus displacement from the vaginal microbiome associates with adverse health outcomes and is linked to increased risk of preterm birth. Glycans mediate bacterial adhesion events involved in colonisation and infection. Using customised glycan microarrays, we establish glycan interaction profiles of vaginal bacteria implicated in reproductive health. Glycan binding signatures of the opportunistic pathogens Escherichia coli, Fusobacterium nucleatum and Streptococcus agalactiae to oligomannose N-glycans, galactose-terminating glycans and hyaluronic acid, respectively are highly distinct from Lactobacillus commensals. Binding to sulphated glycosaminoglycans by vaginal bacteria is pH dependent, as is binding to neutral and sialic acid-terminating glycans by F. nucleatum. Adhesion of Lactobacillus crispatus, Lactobacillus iners, Gardnerella vaginalis, S. agalactiae and F. nucleatum to vaginal epithelial cells is partially mediated by chondroitin sulphate. S. agalactiae binding to chondroitin sulphate C oligosaccharides is inhibited by L. crispatus. This study highlights glycans as mediators of vaginal bacterial binding events involved in reproductive health and disease.

Adaptive network steganography using deep learning and multimedia video analysis for enhanced security and fidelity

This study presents an advanced adaptive network steganography paradigm that integrates deep learning methodologies with multimedia video analysis to enhance the universality and security of network steganography practices. The proposed approach utilizes a deep convolutional generative adversarial network-based architecture capable of fine-tuning steganographic parameters in response to the dynamic foreground, stable background, and spatio-temporal complexities of multimedia videos. Empirical evaluations using the MPII and UCF101 video repositories demonstrate that the proposed algorithm outperforms existing methods in terms of steganographic success and resilience. The framework achieves a 95% steganographic success rate and a peak signal-to-noise ratio (PSNR) of 48.3 dB, showing significant improvements in security and steganographic fidelity compared to contemporary techniques. These quantitative results underscore the potential of the approach for practical applications in secure multimedia communication, marking a step forward in the field of network steganography.

Synergistic integration of VSe2 and CuS nanostructures for advanced energy storage applications

Terahertz field effect in a two-dimensional semiconductor

Abstract Layered two-dimensional (2D) materials offer many promising avenues for advancing modern electronics, thanks to their tunable optical, electronic, and magnetic properties. Applying a strong electric field perpendicular to the layers, typically at the MV/cm level, is a highly effective way to control these properties. However, conventional methods to induce such fields employ electric circuit - based gating techniques, which are restricted to microwave response rates and face challenges in achieving device-compatible ultrafast, sub-picosecond control. Here, we demonstrate an ultrafast field effect in atomically thin MoS 2 embedded within a hybrid 3D-2D terahertz nanoantenna. This nanoantenna transforms an incoming terahertz electric field into a vertical ultrafast gating field in MoS 2 , simultaneously enhancing it to the MV/cm level. The terahertz field effect is observed as a coherent terahertz-induced Stark shift of exciton resonances in MoS 2 . Our results offer a promising strategy to tune and operate ultrafast optoelectronic devices based on 2D materials.

The influence of resource use on yield versus sale price trade-off in Australian vineyards

Strategies for achieving sustainability in the winegrowing industry require balancing resource investment against the economic outcomes of resultant yields and sale price of the produce. Although there has been much research into forecasting outcomes and resource use, little has been done to illustrate their effects on one another and the consequential economic outcomes. This analysis uses statistical models to observe relationships between resource use, yield, and sale price. The dataset used for this analysis includes data collected for the past 10 years from 1261 vineyards located over a diverse range of Australian winegrowing regions. Yield and sale price were evaluated regarding resource use factors, such as water use and Greenhouse Gas (GHG) emissions. The analysis confirmed a strong relationship between area and resource use, with the overall area of a vineyard and its access to resources significantly determining the upper limit of yield. However, the area was also negatively related to the average sale price of grapes; we find that higher average sale prices were connected to high resource inputs per area rather than to the overall expenditure of resources. Regional and temporal effects on vineyard yield and average sales price were also identified. The analysis highlighted the importance of considering a vineyard’s business goal, region, external pressures, and economies of scale when pursuing higher yields verse higher average sales prices.

Microclimate-driven strategies for thermally comfortable university open spaces in cold regions of China

Two major ecological shifts shaped 60 million years of ungulate faunal evolution

Abstract The fossil record provides direct evidence for the behavior of biological systems over millions of years, offering a vital source for studying how ecosystems evolved and responded to major environmental changes. Using network analysis on a dataset of over 3000 fossil species spanning the past 60 Myr, we find that ungulate continental assemblages exhibit prolonged ecological stability interrupted by irreversible reorganizations associated with abiotic events. During the early Cenozoic, continental assemblages are dominated by mid-sized browsers with low-crowned teeth, which show increasing functional diversity. Around 21 Ma, the formation of a land bridge between Eurasia and Africa triggers the first major global transition towards a new functional system featuring a prevalence of large browsers with mid- to high-crowned molars. Functional diversity continues to increase, peaking around 10 Ma. Shortly after, aridification and the spread of C4-dominated vegetation lead to a second tipping point towards a fauna characterized by grazers and browsers with high and low crowned teeth. A global decline in ungulate functional diversity begins 10 Ma ago and accelerates around 2.5 Ma, yet the functional structure of these faunas remains stable in the latest Cenozoic. Large mammal evolutionary history reflects two key transitions, aligning with major tectonic and climatic events.

AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients

Introduction In older patients, postoperative delirium (POD) is a major complication that can result in greater morbidity, longer hospital stays, and higher healthcare expenses. Accurate prediction models for POD can enhance patient outcomes by guiding preventative strategies. This study utilizes advanced machine learning techniques to develop a predictive model for POD using comprehensive perioperative data. Methods We examined information from the National Surgical Quality Improvement Program (NSQIP), which included 17,000 patients who were over 65 and undergoing different types of surgery. The dataset included variables such as patient demographics (age, sex), comorbidities (diabetes, cardiovascular diseases, pre-existing dementia), surgical details (type, duration), anesthesia type and dosage, and postoperative outcomes. Categorical variables were encoded numerically, and data standardization was applied to ensure normal distribution. A range of machine learning approaches were assessed such as Decision Trees and Random Forests. Based on the greatest Area Under the Curve (AUC) from Receiver Operating Characteristic (ROC) analysis, the final model was chosen. Hyperparameter tuning was performed using GridSearchCV, optimizing parameters like max_depth, min_child_weight, and gamma for XGBoost model. Results The optimized XGBoost model demonstrated superior performance, achieving an AUC of 0.85. Key hyperparameters included min_child_weight = 1, max_depth = 5, gamma = 0.3, subsample = 0.9, colsample_bytree = 0.7, reg_alpha = 0.0007, learning_rate = 0.14, and n_estimators = 123. The model exhibited an accuracy of 0.926, recall of 0.945, precision of 0.934, and an F1-score of 0.939, depicting a higher level of predictive accuracy & balance between sensitivity and specificity. Conclusion This study proposes a strong XGBoost-based model to predict POD in older surgical patients, demonstrating the potential of Machine Learning (ML) in clinical risk assessment. With the help of the model’s balanced performance indicators and high accuracy, physicians may identify high-risk patients and promptly execute interventions in clinical settings. Subsequent investigations ought to concentrate on integration into clinical workflows and external validation.