Individual Clusters Research Articles

Using Multilevel Models and Generalized Estimating Equation Models to Account for Clustering in Neurology Clinical Research.

In clinical and health services research, clustered data (also known as data with a multilevel or hierarchical structure) are frequently encountered. For example, patients may be clustered or nested within hospitals. Understanding when data have a multilevel structure is important because clustering of individuals can induce a homogeneity in outcomes within clusters, so that, even after adjusting for measured covariates, outcomes for 2 individuals in the same cluster are more likely to be similar than outcomes for 2 individuals from different clusters. Using conventional statistical regression models to analyze clustered data can result in incorrect conclusions being drawn. In particular, estimated CIs may be artificially narrow, and significance levels may be artificially low. As a result, one may conclude that there is a statistically significant association when there is none. To avoid this problem, investigators should ensure that their analyses use techniques that account for clustering of data. Generalized linear models estimated using generalized estimating equation (GEE) methods and multilevel regression models (also known as hierarchical regression models, mixed-effects models, or random-effects models) are two such techniques. We provide an introduction to clustered or multilevel data and describe how GEE models or multilevel models can be used for the analysis of multilevel data.

Characterization of droplet clusters in a turbulent multiphase gas cloud using a model cough simulator

This study aims to investigate the transport of droplets ejected from an artificial cough simulator, which releases a turbulent puff of droplets into the surrounding air, closely resembling the human coughing process. The focus is on understanding droplet clustering within the multiphase gas cloud across various operating conditions that emulate the wide variation in the spray characteristics in actual human subjects owing to infection severity, age, and gender. Time-resolved particle image velocimetry (PIV) technique was employed to measure the velocity of both droplet and gas phases. It also facilitates the identification and characterization of droplet clusters through Voronoi analysis of the PIV images. The area and length scale of individual droplet clusters were measured, and the degree of droplet clustering was quantified using the clustering index and relative droplet number density within the clusters. Additionally, the interferometric laser imaging for droplet sizing technique was utilized for planar measurement of individual droplet sizes. The range of Stokes number indicated partial to poor response of the droplets to the turbulent eddies. The results reported, for the first time, the presence of droplet clusters in the simulated coughing process. The wide spectrum of cluster size and self-similar evolution of droplet clusters unveil a multi-scale clustering phenomenon, shedding light on the intricate dynamics of the respiratory droplet dispersion process. The study comprehensively investigates the role of injection pressure on droplet clustering and the spatial development of the clusters, revealing some interesting findings, which are discussed.

MVCLST: A spatial transcriptome data analysis pipeline for cell type classification based on multi-view comparative learning

Recent advancements in spatial transcriptomics sequencing technologies can not only provide gene expression within individual cells or cell clusters (spots) in a tissue but also pinpoint the exact location of this expression and generate detailed images of stained tissue sections, which offers invaluable insights into cell type identification and cell function exploration. However, effectively integratingthegene expression data, spatial location information, and tissue images from spatial transcriptomics data presents a significant challenge for computational methodsin cell classification. In this work, we propose MVCLST, a multi-view comparative learningmethod to analyze spatial transcriptomicsdata for accurate cell type classification. MVCLSTconstructs two views based on gene expression profiles, cell coordinates and image features. The multi-view method we proposed can significantly enhance the effectiveness of feature extraction while avoiding the impact of erroneous information in organizing image or gene expression data. The model employs four separate encoders to capture shared and unique features within each view. To ensure consistency and facilitate information exchange between the two views, MVCLST incorporates a contrastive learning loss function. The extracted shared and private features from both views are fused using corresponding decoders. Finally, the model utilizes the Leiden algorithm to clusterthe learned featuresfor cell type identification. Additionally, we establish a framework called MVCLST-CCFS for spatial transcriptomicsdata analysis based on MVCLST and consistent clustering. Our method achieves excellent results in clustering on human dorsolateral prefrontal cortex data and the mouse brain tissue data. Italso outperforms state-of-the-art techniques in the subsequent search for highly variable genes across cell types on the mouse olfactory bulbdata.

Visual Search Interactive Model for Artificial Intelligence Robotics Model forthe Agricultural Field Analysis

The Visual Search Interactive Model for Artificial Intelligence (AI) is designed to enhance the efficiency and effectiveness of visual data analysis across various applications. By leveraging advanced computer vision techniques and machine learning algorithms, this model enables AI systems to interpret and analyze visual information in real-time, facilitating tasks such as object recognition, image classification, and scene understanding. The interactive nature of the model allows users to engage with the AI, refining searches and improving outcomes through iterative feedback. This paper introduces the Auxiliary Clustering k-means Machine Learning (AC k-means ML) model, designed to enhance agricultural efficiency through advanced data analysis and robotic integration. The study evaluates the performance of the AC k-means ML model using a dataset comprising 1,950 samples, achieving an overall accuracy of 91.5% and a precision of 89.2%. Key performance metrics such as F1 scores averaged 88.6%, with the highest individual cluster accuracy reaching 96% for Cluster 10. In addition to data classification, the model facilitated the completion of 250 tasks with a remarkable success rate of 92%, while maintaining an average task completion time of 15.4 minutes and an energy consumption of just 0.5 kWh per task. The implementation of the AC k-means ML model resulted in a 15% increase in crop yield and substantial cost savings estimated at $2,000. With a user satisfaction score averaging 8.7 and an adaptability score of 9.0, the findings indicate that the integration of machine learning and robotics significantly optimizes agricultural processes, promoting sustainability and efficiency in farming practices.

Determinants of Citizens' Intention to Participate in Self-Led Contact Tracing: Cross-Sectional Online Questionnaire Study.

Contact tracing (CT) is a key intervention to contain outbreaks of communicable diseases. During large-scale outbreaks, public health services may lack the resources required to perform CT effectively. One way of mitigating this issue is to shift some of the tasks in CT normally performed by public health services to cases and their contacts, supported by digital tools. We refer to this as "self-led CT." However, while the effectiveness of the self-led CT inherently depends on the willingness and skills of citizens to participate, the determinants of citizens' intention to participate in self-led CT are not yet fully understood. We aimed to identify determinants of Dutch citizens' intention to participate in self-led CT and assess their potential for behavioral change, so as to identify "behavior change targets," which may be used in the development and implementation of self-led CT to increase citizens' intention to participate. In March 2022, we performed an online cross-sectional questionnaire study. The questionnaire was developed based on findings from a previous exploratory semistructured interview study and distributed among a Dutch consumer panel. Using all questionnaire items as potential predictors, we performed a random forest analysis to identify determinants of citizens' intention to participate in self-led CT. We then performed an Agglomerative Hierarchical Cluster Analysis to identify groups of related determinants that may be considered overarching behavior change targets. Finally, we used Confidence Interval-Based Estimation of Relevance and calculated the Potential for Change Indices to compare the potential for behavioral change of the selected individual determinants and determinant clusters. The questionnaire was completed by 3019 respondents. Our sample is representative of the Dutch population in terms of age, gender, educational level, and area of residence. Out of 3019 respondents, 2295 (76%) had a positive intention to participate in self-led CT. We identified 20 determinants of citizens' intention that we grouped into 9 clusters. In general, increasing citizens' trust in the digital tools developed for self-led CT has the highest potential to increase citizens' intention, followed by increasing the belief that using digital tools makes participating in self-led CT easier, reducing privacy-related concerns, and increasing citizens' willingness-and sense of responsibility-to cooperate in CT in general. Overall, Dutch citizens are positive toward participating in self-led CT. Our results provide directions for the development and implementation of self-led CT, which may be particularly useful in preparing for future, large-scale outbreaks.

Receptor determinants for ß-arrestin functional specificity at C-X-C chemokine receptor 5 (CXCR5).

ß-arrestins are multi-faceted adaptor proteins that mediate G protein-coupled receptor (GPCR) desensitization, internalization, and signaling. It is emerging that receptor-specific determinants specify these divergent functions at GPCRs, yet this remains poorly understood. Here, we set out to identify the receptor determinants responsible for ß-arrestin-mediated regulation of the chemokine receptor CXCR5. Using bioluminescence resonance energy transfer (BRET) we show that ß-arrestin1 and ß-arrestin2 are dose-dependently recruited to CXCR5 by its cognate ligand CXCL13. The carboxy-terminal tail of CXCR5 contains several Ser/Thr residues that can be divided into 3 discrete phospho-site clusters based on their position relative to transmembrane domain 7. Mutagenesis experiments revealed that the distal and medial phospho-site clusters, but not the proximal, are required for agonist-stimulated ß-arrestin1 or ß-arrestin2 recruitment to CXCR5. Consistent with this, we provide evidence that the distal and medial, but not proximal, phospho-site clusters are required for receptor desensitization. Surprisingly, the individual phospho-site clusters are not required for agonist-stimulated internalization of CXCR5. Further, we show that CXCL13-stimulated CXCR5 internalization and ERK1/2 phosphorylation, but not desensitization, remain intact in HEK293 cells lacking ß-arrestin1 and ß-arrestin2. Our study provides evidence that b-arrestins are recruited to CXCR5 and are required for desensitization but are dispensable for internalization or signaling, suggesting that discrete receptor determinants specify the divergent functions of ß-arrestins. Significance Statement CXCL13 and CXCR5 are important in the immune system and are linked to diseases, yet regulation of CXCR5 signaling remains poorly understood. We provide evidence that a phospho-site cluster located at the extreme distal carboxyl-terminal tail of the receptor is responsible for ß-arrestin recruitment and receptor desensitization. ß-arrestins are not required for CXCL13-stimulated internalization or signaling, indicating that ß-arrestins perform only one of their functions at CXCR5 and that discrete receptor determinants specify the divergent functions of ß-arrestins.

Assessment of conservation status of Ferula huber‐morathii: association with population genetic structure and regional climate

Ferula huber‐morathii is an endemic and medicinally important plant. This species is distributed in eight Turkish localities, including three newly identified ones. Its extent of occurrence and area of occupancy is determined to be 3963 km2 and 32 km2 respectively. All localities are characterized by East Mediterranean and sub‐Mediterranean precipitation regimes; however, temperatures increase excessively and precipitation decreases during the flowering period of the species. The population sizes are quite small, and the number of reproducing individuals in some populations is below ten. Analyses of ISSR markers showed the percentage of polymorphic loci to be 94% at the species level and 56% at the population level. The level of genetic differentiation (measured by GST) was 0.37 and the estimated level of gene flow among populations (NM) was 0.84. The percentage of variance occurring within and among populations, determined by AMOVA, was 75% and 25%, respectively. STRUCTURE analysis revealed two genetic clusters of individuals with a geographic structure, similar to that found in UPGMA and an ordination analysis. Some populations turned out to have both low numbers of individuals and low genetic diversity. Since many of the populations are subject to anthropogenic disturbance, the species should remain in the EN category. At the same time, it is suggested that a new in‐situ conservation area should be created around nearby dams, situated in the same climate area as the currently known populations.

Analysis of group evaporation characteristics of droplet clusters in an evaporating spray

Droplet clustering in sprays refers to the dynamic evolution of highly concentrated regions due to the preferential accumulation of the polydisperse droplets in the turbulent airflow entrained by the spray. In the current study, we aim to experimentally investigate the collective vaporization of the droplets in droplet clusters in an air-assisted acetone spray characterized by the Group number, $G$ . The magnitude of $G$ depends on the cluster length scale and interdroplet spacing, and it indicates the vaporization mode that may vary from the isolated mode ( $G \ll 1$ ) to external group mode ( $G \gg 1$ ). The droplet measurements were obtained under atmospheric conditions at different axial and radial locations within the spray. Application of the Voronoi analysis to particle image velocimetry images of the spray droplets facilitated the identification and characterization of the droplet clusters, which allowed the measurement of $G$ for each cluster. The results highlighted that multiscale clustering of the evaporating droplets leads to multimode group evaporation of the clusters (characterized by a wide range of $G$ : 0.001–10). The trend of interdroplet spacing versus cluster area allowed the classification of the droplet clusters into small-scale clusters (which are of the order of the Kolmogorov length scale) and large-scale clusters (that scale with the large-scale turbulent eddies), that are found to exhibit distinct group evaporation behaviour. A theoretical model is invoked to correlate $G$ with the droplet evaporation rate for individual clusters, and some interesting observations are identified, which are explained in the paper.

Right-Siting Care and Decongesting Specialist Outpatient Clinics - A Singapore Case Study

Abstract Issue Globally, Specialist Outpatient Clinics (SOCs) face a growing burden due to rising chronic diseases. This strain on resources leads to long appointment wait times (LAWTs), delaying diagnoses and treatments. This is particularly of concern in countries with rapidly ageing populations such as Singapore. Description The Right-Site Care Program is a healthcare transformation initiative implemented by individual healthcare clusters within Singapore to improve access to timely SOC care by directing patients to the most appropriate care level (right-siting). This transitions stable chronic disease patients to primary care providers (PCPs) for ongoing management. A standardised approach utilises evidence-based guidelines for patient selection, followed by identification and transfer of suitable patients to PCPs. This multi-center program offers a real-world example of healthcare system optimisation for chronic disease management. Results A multi-source data analysis, including program reports and peer-reviewed studies from participating healthcare clusters, was conducted to evaluate program impact. The analysis focused on LAWTs, patient outcomes and implementation challenges. Right-siting yielded mixed results across various conditions. Right-sited patients had better survival rates, likely due to closer monitoring and management of chronic conditions with higher total outpatient attendance frequencies. (1) Improvements were particularly significant for diabetes mellitus patients who successfully transitioned to PCPs. (2) However, challenges such as patient scepticism about PCP competence and a preference for specialist care have been identified. (3) Lessons learned Right-siting care can improve patient outcomes for certain chronic diseases. The program’s impact varied across different chronic diseases, highlighting the need for tailored strategies. Educational interventions addressing patient concerns can facilitate smoother patient transitions and program success. Key messages • Right-siting care shows promise, with improved patient outcomes for conditions like diabetes mellitus. • Patient education is crucial to overcome patient scepticism and preference for specialists.

Diagnostic classification systems for disorders of gut-brain interaction should include psychological symptoms.

The group of disorders known as Disorders of Gut Brain Interaction (DGBI) were originally labeled functional GI disorders and were thought to be disorders of the gastrointestinal tract that had several psychological conditions as comorbidities. Despite mounting evidence that psychological morbidity plays an innate role in the etiology and maintenance of DGBI, none of the Rome IV criteria include any measure of psychological symptoms. This study tested the hypothesis that individuals would cluster differently if GI symptoms alone were considered versus GI symptoms combined with measures of psychological symptoms. Data were obtained from the Rome Foundation Global Epidemiology Study measuring Rome IV GI symptoms, psychological measures and demographic characteristics. Latent profile models were used to cluster individuals based on (i) GI symptoms only (GI only) and then (ii) GI and psychological measures (GI + Psych). Individuals clustering into the same group of individuals whether formed via GI only or GI + Psych, ranged from 96% for a 2-class solution (the most simplistic) to 76% with 6 classes (the parsimonious system) and 59% with twenty-two classes (mimicking Rome IV). The generalisability of this finding between six geographic regions was confirmed with agreement varying between 95%-97% for 2 clusters and 71-79% for 6 classes and 51%-63% for 22 classes. These findings were also consistent between DGBI (range 94% with 2 classes to 50% with 22 classes) and non-DGBI (range 97% with 2 clusters to 65% with 22 classes) groups. Our data suggest that considering psychological as well as gastrointestinal symptoms would lead to a different clustering of individuals in more complex, and accurate, classification systems. For this reason, future work on DGBI classification should consider inclusion of psychological traits.

Religiosity and Attitudes towards Robots: Results from a Global Survey

Religion is one lens in which people understand the world around them and interpret the world around them. However, it is unclear whether religion has an impact upon attitudes towards robots around the world. In this article, the authors investigate how an individual’s religiosity impacts upon perceptions of robots. The article investigates how an individual’s religiosity impacts attitudes towards robots, using data from a large-scale global survey of attitudes towards robots (N=1263). In order to investigate how religion impacts upon such perceptions, cluster, factor and regression analyses were used for data analysis. The findings illustrate that there are discernible clusters of individuals exhibiting different levels of religiosity and that these clusters have different perceptions of robots, showing as hypothesized, that more religious individuals are more likely to be robophobic than robophilic. The relationship between religiosity and attitudes towards robots is negative: less religious people show more positive attitudes towards robots. While there is clear indication that there is a relationship between religiosity and attitudes towards robots, religiosity does not have a particularly strong impact upon perceptions of robots. The analysis of the data illustrates that there are factors more clearly associated with perceptions of robots, meaning that while religiosity may have some impact on perceptions of robots, that there are more important attitudes and other factors that should be taken into consideration to encourage users to use robots in various service environments. In addition, in this particular research, there seems to be no notable relationship between perceptions of robots and demographics of individuals, illustrating that the age, gender, wealth, and education of an individual may not play a strong role in shaping perceptions of robots in service industries.

The glow of axion quark nugget dark matter

Context. The existence of axion quark nuggets is a potential consequence of the axion field, which provides a possible solution to the charge-conjugation parity violation in quantum chromodynamics. In addition to explaining the cosmological discrepancy of matter-antimatter asymmetry and a visible-to-dark-matter ratio of Ωdark/Ωvisible ≃ 5, these composite compact objects are expected to represent a potentially ubiquitous electromagnetic background radiation by interacting with ordinary baryonic matter. We conducted an in-depth analysis of axion quark nugget-baryonic matter interactions in the environment of the intracluster medium in the constrained cosmological Simulation of the LOcal Web (SLOW). Aims. Here, we aim to provide upper limit predictions on electromagnetic counterparts of axion quark nuggets in the environment of galaxy clusters by inferring their thermal and non-thermal emission spectrum originating from axion quark nugget-cluster gas interactions. Methods. We analyzed the emission of axion quark nuggets in a large sample of 161 simulated galaxy clusters using the SLOW simulation. These clusters are divided into a sub-sample of 150 galaxy clusters, ordered in five mass bins ranging from 0.8 to 31.7 × 1014 M⊙, along with 11 cross-identified galaxy clusters from observations. We investigated dark matter-baryonic matter interactions in galaxy clusters in their present stage at the redshift of z = 0 by assuming all dark matter consists of axion quark nuggets. The resulting electromagnetic signatures were compared to thermal Bremsstrahlung and non-thermal cosmic ray (CR) synchrotron emission in each galaxy cluster. We further investigated individual frequency bands imitating the observable range of the WMAP, Planck, Euclid, and XRISM telescopes for the most promising cross-identified galaxy clusters hosting detectable signatures of axion quark nugget emission. Results. We observed a positive excess in the low- and high-energy frequency windows, where thermal and non-thermal axion quark nugget emission can significantly contribute to (or even outshine) the emission of the intracluster medium (ICM) in frequencies up to νT ≲ 3842.19 GHz and νT ϵ [3.97, 10.99] × 1010GHz, respectively. Emission signatures of axion quark nuggets are found to be observable if CR synchrotron emission of individual clusters is sufficiently low. The degeneracy in the parameters contributing to an emission excess makes it challenging to offer predictions with respect to pinpointing specific regions of a positive axion quark nugget excess; however, a general increase in the total galaxy cluster emission is expected based on this dark matter model. Axion quark nuggets constitute an increment of 4.80% of the total galaxy cluster emission in the low-energy regime of νT ≲ 3842.19 GHz for a selection of cross-identified galaxy clusters. We propose that the Fornax and Virgo clusters represent the most promising candidates in the search for axion quark nugget emission signatures. Conclusions. The results from our simulations point towards the possibility of detecting an axion quark nugget excess in galaxy clusters in observations if their signatures can be sufficiently disentangled from the ICM radiation. While this model proposes a promising explanation for the composition of dark matter, with the potential to have this outcome verified by observations, we propose further changes that are aimed at refining our methods. Our ultimate goal is to identify the extracted electromagnetic counterparts of axion quark nuggets with even greater precision in the near future.

Defect crystal structure of α-Na0.5-xR0.5+xF2+2x (R = Dy-Lu, Y) on X-Ray and electron diffraction data. I. MethoD of Defect structure modelling on the α-Na0.35Dy0.65F2.30 example

For the first time, the crystal α-Na0.35Dy0.65F2.30 was studied using X-ray diffraction at 293 K and 85 K and electron diffraction at 293 K. A unified cluster model of the defective structure of nanostructured crystals with a fluorite-type structure, based on the polymorphism of ordered phases KR3F10 (R = Er, Yb), was expanded with a matrix part model based on the structure of the KYF4 compound. The unified cluster model was applied to construct the defective structure of α-Na0.35Dy0.65F2.30. It was found that the matrix part of the crystal contains Na+ и Dy3+ cations in a 1:1 ratio. Some of the anions in the matrix are displaced to the 32f positions (space group Fm3m). The excess Dy3+ forms octahedral-cubic clusters with Na+ [Na14–nDynF64+n] with cores in the form of distorted and regular cuboctahedrons {F12}. These are composed of interstitial anions in two 32f positions and one 48i position. The cluster component of the α-Na0.35Dy0.65F2.30 crystal contains octahedral-cubic clusters of f-, f–i- and i-types. Electron diffraction showed that α-Na0.35Dy0.65F2.30 is a nanostructured crystal. Its cluster component is in the form of plate-like inclusions about 5 nm thick with superstructural ordering and individual octahedral-cubic clusters. A model of their structure was proposed. Lowering the temperature to 85 K increases the number of interstitial F(32f)1 anions in the matrix component of the crystal.

Cluster Assembly Dynamics Drive Fidelity of Planar Cell Polarity Polarization.

The planar cell polarity (PCP) signaling pathway polarizes epithelial cells in the tissue plane by segregating distinct molecular subcomplexes to opposite sides of each cell, where they interact across intercellular junctions to form asymmetric clusters. The role of clustering in this process is unknown. We hypothesized that protein cluster size distributions could be used to infer the underlying molecular dynamics and function of cluster assembly and polarization. We developed a method to count the number of monomers of core PCP proteins within individual clusters in live animals, and made measurements over time and space in wild type and in strategically chosen mutants. The data demonstrate that clustering is required for polarization, and together with mathematical modeling provide evidence that cluster assembly dynamics dictate that larger clusters are more likely to be strongly asymmetric and correctly oriented. We propose that cluster assembly dynamics thereby drive fidelity of cell- and tissue-level polarization.

Initial Symptom Severity and Recovery of Sport-Related Concussion in Team Versus Individual Sport Athletes.

Recovery after sport-related concussion is variable, and potential differences between team vs individual sport athletes are not fully understood. In a cohort of athletes with concussions, we sought to compare these groups across (1) symptom severity score, (2) individual symptom cluster scores, and (3) recovery metrics. A retrospective, cohort study of 13 to 23-year-old athletes treated at a regional sport concussion center between November 2017 and April 2022 was conducted. Athletes were categorized into team vs individual sport athletes, with additional classification of individual sports based on strong vs minimal team elements (ie, the degree of interdependence displayed by athletes). The primary outcomes were symptom severity score, measured by either the Sport Concussion Assessment Tool-5th Edition or the Immediate Post-Concussion Assessment and Cognitive Testing, and physical, cognitive, emotional, and sleep symptom cluster scores. Secondary outcomes were recovery metrics, including time to return-to-learn, symptom resolution, and return-to-play. Of the 1051 athletes, 954 (90.8%) were in team sports and 97 (9.2%) in individual sports. In multivariable linear regression, individual sport athletes had higher emotional symptom severity compared with team sport athletes (β = 0.09 [0.01, 0.17], P = .034) when adjusting for sex, attention-deficit/hyperactivity disorder, psychological disorders, time to clinic, on-field evaluation, and Immediate Post-Concussion Assessment and Cognitive Testing vs Sport Concussion Assessment Tool-5th Edition scale. There were no significant differences in return-to-learn (P = .104), symptom resolution (P = .941), or return-to-play (P = .279) on univariate regressions. In the current single-center, pilot study of athletes with a sport-related concussion, individual sport athletes exhibited more emotional symptoms than team sport athletes. These preliminary data lend early support that individual sport athletes may be more at risk of emotional symptoms than team sport athletes after a sport-related concussion. Clinicians may use these findings to identify athletes who may be particularly vulnerable to emotional symptoms.

Comparative Study of Cox Proportional Hazard and Accelerated Failure Time Models on Survival of Diabetes Mellitus and Hypertension Patients

Comparative analysis of survival models for hypertension and diabetics carried out in this study. The study compared the performance of Cox Proportional Hazard (CPH) and Accelerated Failure Time (AFT) models with and without random effects in analyzing hypertension and diabetes survival data obtained from General Hospital Keffi and General Hospital Nasarawa for the period of five years (2018-2022). Models evaluated include CPH, AFT (Exponential, Weibull, Log-Logistic) and their corresponding random effect counterparts. The Weibull AFT random effect model outperformed all other models in terms of goodness-of-fit, predictive accuracy and interpretability. This model accounted for individual heterogeneity and clustering effects, providing a more nuanced understanding of risk factors’ impact on survival rates. The Weibull AFT random effects model is the most suitable choice for analyzing hypertension and diabetes survival data, offering improved predictive power and insight into the complex relationships between risk factors and time-to-event outcomes. These findings have significant implications for healthcare professionals, policymakers, and researchers seeking to optimize survival analysis methodologies.

Analysis of the Impact of Clusters on Productivity of Multi-Fracturing Horizontal Well in Shale Gas

Shale gas reservoirs with nanoporous media have become one of the primary resources for natural gas development. The nanopore diameters of shale reservoirs range from 5 to 200 nm, with permeability ranging from 1 × 10−9 to 1 × 10−6 μm2. The natural gas production from shale gas reservoirs is low, necessitating the use of multi-stage hydraulic fracturing in horizontal wells. Segmented multi-cluster perforation fracturing is an effective method for shale gas extraction in these wells. The number of clusters significantly impacts the productivity of horizontal wells. Therefore, it is essential to analyze the impact of cluster numbers on fracture productivity in shale gas reservoir development. In this study, the equivalent flow resistance method was applied to establish a productivity model for multi-stage hydraulic fracturing horizontal wells in shale gas reservoirs considering diffusion and slip. An approximate analytical solution was obtained, and the effects of cluster length, diffusion coefficient, and fracture network permeability on productivity were analyzed. The results show that gas production gradually increases with the increase in the number of clusters and cluster length. However, as the number of clusters increases, the interference between clusters leads to a decrease in the productivity of individual clusters. As the fracture permeability, fracture network permeability, and diffusion coefficient increase, shale gas production also gradually increases. The permeability of the fracture network has the greatest impact on productivity. These research results are beneficial for the design of clusters in horizontal well fracturing and are of great importance for the development and production of shale gas reservoirs.

Association of psychosocial state with subsequent risk of dementia: a prospective cohort study based on the UK Biobank

BackgroundMultiple psychosocial factors have been associated with dementia, while the individual or joint effects of various psychosocial states on dementia remain unrevealed due to the complex interplay between those factors. Here, the authors examined the associations of psychosocial factors and patterns with subsequent risk of dementia, and if the associations could be modified by genetic susceptibility to dementia.MethodsUK Biobank dementia-free participants were followed from one year after recruitment (median date: 24 January, 2010) until 31 October, 2022. Psychosocial states were measured by 22 items related to five dimensions, including psychiatric history, recent stressful life events, current psychiatric symptoms, social contact, and individual socioeconomic state. We identified clusters of individuals with distinct psychosocial patterns using latent class analysis. Cox proportional hazards models were used to evaluate the association between psychosocial items, as well as psychosocial patterns, and risk of dementia. We further performed stratification analyses by apolipoprotein E (APOE) genotype, polygenic risk score (PRS) of dementia, and family history of dementia.ResultsOf 497,787 included participants, 54.54% were female. During a median follow-up of 12.70 years, we identified 9,858 (1.98%) patients with newly diagnosed dementia. We identified seven clusters with distinct psychosocial patterns. Compared to individuals with a pattern of ‘good state’, individuals with other unfavorable patterns, featured by varying degrees of poor psychological state (‘fair state’ and ‘mildly, moderately, and extremely poor psychological state’), low social contact or socioeconomic state (‘living alone’ and ‘short education years’), were all at an increased risk of dementia (hazard ratios [HR] between 1.29 and 2.63). The observed associations showed no significant differences across individuals with varying APOE genotypes, levels of PRS, and family histories of dementia.ConclusionUnfavorable psychosocial patterns are associated with an increased risk of dementia, independent of genetic susceptibility. The findings highlight the importance of surveillance and prevention of cognitive decline among individuals with suboptimal psychosocial state.

The Vaginal Penetration Skills Scale (VPSS): a paradigm shift in genito-pelvic pain/penetration disorder screening, assessment, and stratification.

Receptive vaginal penetration skills have been implicated in the etiology, explanatory models, and treatment of genito-pelvic pain penetration disorder (GPPPD). However, there are no psychometric skills measures designed to screen, assess, and stratify GPPPD. We aimed to develop and psychometrically evaluate a new scale-the Vaginal Penetration Skills Scale (VPSS)-to screen, assess, and stratify GPPPD. This study included 148 Brazilian females with GPPPD symptoms (113 lifelong and 35 acquired) and 251 Brazilian females without sexual complaints. We conducted factor analyses considering all participants (n= 399). Then, we conducted latent class analysis within the GPPPD group to identify clusters of individuals with similar VPSS profiles. We assessed convergent validity through intercorrelation with the Brazilian versions of the Female Genital Self-Image Scale (FGSIS) and the 6-item Female Sexual Function Index (FSFI-6). We developed complete and short-form versions of the VPSS (VPSS-29 and VPSS-SF11, respectively), each with 3 dimensions, to screen, assess, and stratify GPPPD. Factor analysis yielded a 3-factor VPSS model with the "Nonsexual Genital Self-Exploration," "Nonsexual Vaginal Penetration Skills," and "Sexual Vaginal Penetration Skills" dimensions for both VPSS versions. The reliability was excellent for the VPSS-29 (ω= 0.981, α= 0.981) and the VPSS-SF11 (ω= 0.959, α= 0.961). All 3 dimensions could detect significant differences between patients with GPPPD and healthy females. They also differentiated the patients with GPPPD, distinguishing gradient levels. For convergent validity, we found moderate to strong correlations (rho = 0.715-0.745) between the VPSS, FGSIS, and FSFI-6. The VPSS can be applied easily in both clinical and research settings. The VPSS provides a concise and thorough evaluation of receptive vaginal penetration skills in both sexual and nonsexual contexts among patients with GPPPD. The sample had limited diversity regarding gender and sexual orientation; therefore, it is important to validate the use of this scale in populations beyond the cisgender heterosexual female population to ensure its applicability in diverse settings. These results support the reliability and psychometric validity of the VPSS as a self-report measure to screen, assess, and stratify GPPPD symptoms.

Dispersal Limitation Governs Bacterial Community Assembly in the Northern Pitcher Plant (Sarracenia purpurea) at the Continental Scale

ABSTRACTAimEcological theory suggests that dispersal limitation and selection by climatic factors influence bacterial community assembly at a continental scale, yet the conditions governing the relative importance of each process remains unclear. The carnivorous pitcher plant Sarracenia purpurea provides a model aquatic microecosystem to assess bacterial communities across the host plant's north–south range in North America. This study determined the relative influences of dispersal limitation and environmental selection on the assembly of bacterial communities inhabiting S. purpurea pitchers at the continental scale.LocationEastern United States and Canada.Time Period2016.Major Taxa StudiedBacteria inhabiting S. purpurea pitchers.MethodsPitcher morphology, fluid, inquilines and prey were measured, and pitcher fluid underwent DNA sequencing for bacterial community analysis. Null modelling of β‐diversity provided estimates for the contributions of selection and dispersal limitation to community assembly, complemented by an examination of spatial clustering of individuals. Phylogenetic and ecological associations of co‐occurrence network module bacteria was determined by assessing the phylogenetic diversity and habitat preferences of member taxa.ResultsDispersal limitation was evident from between‐site variation and spatial aggregation of individual bacterial taxa in the S. purpurea pitcher system. Selection pressure was weak across the geographic range, yet network module analysis indicated environmental selection within subgroups. A group of aquatic bacteria held traits under selection in warmer, wetter climates, and midge abundance was associated with selection for traits held by a group of saprotrophs. Processes that increased pitcher fluid volume weakened selection in one module, possibly by supporting greater bacterial dispersal.ConclusionDispersal limitation governed bacterial community assembly in S. purpurea pitchers at a continental scale (74% of between‐site comparisons) and was significantly greater than selection across the range. Network modules showed evidence for selection, demonstrating that multiple processes acted concurrently in bacterial community assembly at the continental scale.