Relative Contribution Of Individual Factors Research Articles

Predictors of Metformin Failure: Repurposing Electronic Health Record Data to Identify High-Risk Patients.

Metformin is the first-line drug for treating diabetes but has a high failure rate. To identify demographic and clinical factors available in the electronic health record (EHR) that predict metformin failure. A cohort of patients with at least 1 abnormal diabetes screening test that initiated metformin was identified at 3 sites (Arizona, Mississippi, and Minnesota). We identified 22 047 metformin initiators (48% female, mean age of 57 ± 14 years) including 2141 African Americans, 440 Asians, 962 Other/Multiracial, 1539 Hispanics, and 16 764 non-Hispanic White people. We defined metformin failure as either the lack of a target glycated hemoglobin (HbA1c) (<7%) within 18 months of index or the start of dual therapy. We used tree-based extreme gradient boosting (XGBoost) models to assess overall risk prediction performance and relative contribution of individual factors when using EHR data for risk of metformin failure. In this large diverse population, we observed a high rate of metformin failure (43%). The XGBoost model that included baseline HbA1c, age, sex, and race/ethnicity corresponded to high discrimination performance (C-index of 0.731; 95% CI 0.722, 0.740) for risk of metformin failure. Baseline HbA1c corresponded to the largest feature performance with higher levels associated with metformin failure. The addition of other clinical factors improved model performance (0.745; 95% CI 0.737, 0.754, P < .0001). Baseline HbA1c was the strongest predictor of metformin failure and additional factors substantially improved performance suggesting that routinely available clinical data could be used to identify patients at high risk of metformin failure who might benefit from closer monitoring and earlier treatment intensification.

Global landscape of gut microbiome diversity and antibiotic resistomes across vertebrates

Multiple factors influence gut microbiome diversity in vertebrate hosts. Most previous studies have only investigated specific factors and certain host species or taxa. However, a comprehensive assessment of the relative contributions of individual factors towards gut microbial diversity within a broader evolutionary context remains lacking. Here, 2202 16S rRNA gene sequencing samples of gut bacterial communities collected from 452 host species across seven classes were analyzed together to understand the factors broadly affecting vertebrate gut microbiomes across hosts with different diets, threatened status, captivity status, and habitat environmental factors. Among wild vertebrates, diet was most significantly associated with gut microbiome alpha diversity, while host phylogeny and diet were significantly associated with beta diversity, consistent with a previous study. Host threatened status and habitat environmental factors (e.g., geography and climate) were also associated with gut bacterial community beta diversity. Subsequent ecological modeling revealed a strong association between stochastic assembly processes and patterns of gut bacterial diversity among free-ranging vertebrates. In addition, metagenomic analysis of gut microbiomes from 62 captive vertebrates and sympatric humans revealed similar diversity and resistome profiles despite differences in host phylogeny, diet, and threatened status. These results thus suggest that captivity diminishes the effects of host phylogeny, diet, and threatened status on the diversity of vertebrate gut bacterial communities. The most overrepresented antibiotic resistant genes (ARGs) observed in these samples are involved in resistance to β-lactams, aminoglycosides, and tetracycline. These results also revealed potential horizontal transfers of ARGs between captive animals and humans, thereby jointly threatening public health and vertebrate conservation. Together, this study provides a comprehensive overview of the diversity and resistomes of vertebrate gut microbiomes. These combined analyses will help guide future vertebrate conservation via the rational manipulation of microbial diversity and reducing antibiotic usage.

Exploring Probabilistic Network-Based Modeling of Multidimensional Factors Associated with Country Risk.

Assessment of country risk provides a vital source of information to organizations for expanding and globalizing their operations. Various rating agencies are involved in developing models for assessing country risk, which utilize different statistical techniques for establishing the overall impact of individual factors on country risk. The main limitation of existing studies on country risk is their limited focus on exploring the relative contribution of individual factors to country risk in a probabilistic network setting. Utilizing real data, we develop a probabilistic network model that captures dependencies among multidimensional factors associated with country risk. Further, we assess the network-wide vulnerability and resilience potential of individual factors to identify critical factors. The findings of this study provide policy-makers with some unique insights into prioritizing strategies to mitigate country risk. Further, this study provides the context for multinational enterprises to utilize the proposed methodology for prioritizing key factors associated with the relative variables of interest such as regional stability and business environment among others.

Code-Switching in Linguistics: A Position Paper

This paper provides a critical review of the state of the art in code-switching research being conducted in linguistics. Three issues of theoretical and practical importance are explored: (a) code-switching vs. borrowing; (b) grammaticality; and (c) variability vs. uniformity, and I take a position on all three issues. Regarding switching vs. borrowing, I argue that not all lone other-language items are borrowings once more subtle measures of integration are used. I defend the use of empirical data to compare competing theoretical frameworks of grammaticality, and I exemplify quantitative research on variability in code-switching, showing that it also reveals uniformity and the possible influence of community norms. I conclude that more research is needed on a range of bilingual communities in order to determine the relative contribution of individual factors, processing and community norms to the variability and uniformity of code-switching.

Linking park users' socio-demographic characteristics and visit-related preferences to improve urban parks

The characteristics, behaviors and preferences of urban-park users are seldom gleaned and analyzed to improve park planning and management. This study addresses the research gap using Hong Kong case studies, aiming at four objectives: firstly, to establish socio-demographic profiles of park users; secondly, to evaluate their visit-related preferences; thirdly, to examine the effect of socio-demographic variables on visit-related preferences; and fourthly, to apply the findings to improve urban parks. A questionnaire survey covered 872 users in eight urban parks in four districts. Non-Chinese respondents, married with children and less educated visited parks more often. Users were mainly residents of the district, usually visiting at least once per week, in all four seasons, and preferably in good weather. Three habitual companions were family or relatives, friends, and partner or boyfriend or girlfriend. A factor importance index (FII), summing Cramer's V statistic adjusted by significance level, was computed to indicate relative contributions of individual factors. Six of the 15 socio-demographic variables were significantly associated with visit-related variables, including in order of FII: economic activity status, age, number of children, ethnicity, educational attainment, and marital status. Seven of the ten visit-related variables were significantly associated with socio-demographic variables, including habitual companions, residential years, park-visiting frequency, and familiarity with other park users. The findings allowed formulation of a comprehensive, socially-relevant and community-sensitive strategy to improve urban parks. Applicability of the findings highlighted the pertinence of objective research data to match parks with the leisure and recreational needs of urban residents.

Observer Variability in CT Perfusion Parameters in Primary and Metastatic Tumors in the Lung.

Purpose:Evaluate observer variability in computed tomography perfusion measurements in lung tumors and assess the relative contributions of individual factors to overall variability.Materials and Methods:Four observers independently delineated tumor and defined arterial input function region of interests (tumor region of interest and arterial input function region of interest) on each of 4 contiguous slice levels of computed tomography perfusion images (arterial input function level), in 12 computed tomography perfusion data sets containing lung tumors (>2.5 cm size), on 2 separate occasions. Computed tomography perfusion parameters (blood flow, blood volume, mean transit time, and permeability surface area product) for tumor volumes of interest were computed for all combinations of these factors, totaling up to 1024 combinations per patient. Overall, inter- and intraobserver variability were assessed by within-patient coefficient of variation, variance components analyses, and intraclass correlation.Results:Overall observer within-patient coefficient of variations for tumor blood flow, blood volume, mean transit time, and permeability surface area product were 20.3%, 11.9%, 6.3%, and 31.7%, and intraclass correlations were 0.94, 0.91, 0.82, and 0.72, respectively. Interobserver tumor volume of interest and arterial input function level were the highest contributors to overall variance for blood flow, blood volume, and mean transit time. Overall intraobserver wCVs for blood flow, blood volume, mean transit time, and permeability surface area product (4.3%, 2.4%, 0.9%, and 3.1%) were smaller than interobserver within-patient coefficient of variations (9.5%, 5.6%, 1.6%, and 7.0%), respectively.Conclusion:The largest contributors to observer variability were interobserver tumor volume of interest and arterial input function level. Overall variability in computed tomography perfusion studies can potentially be minimized by using a single observer and a consistent level for arterial input function, which would be important considerations in longitudinal and multicenter studies. Methods to reliably define arterial input function and delineate tumor volumes would help to reduce variability in estimations of computed tomography perfusion parameter values.

The Advantages of Structural Equation Modeling to Address the Complexity of Spatial Reference Learning.

Background: Cognitive performance is a complex process influenced by multiple factors. Cognitive assessment in experimental animals is often based on longitudinal datasets analyzed using uni- and multi-variate analyses, that do not account for the temporal dimension of cognitive performance and also do not adequately quantify the relative contribution of individual factors onto the overall behavioral outcome. To circumvent these limitations, we applied an Autoregressive Latent Trajectory (ALT) to analyze the Morris water maze (MWM) test in a complex experimental design involving four factors: stress, age, sex, and genotype. Outcomes were compared with a traditional Mixed-Design Factorial ANOVA (MDF ANOVA).Results: In both the MDF ANOVA and ALT models, sex, and stress had a significant effect on learning throughout the 9 days. However, on the ALT approach, the effects of sex were restricted to the learning growth. Unlike the MDF ANOVA, the ALT model revealed the influence of single factors at each specific learning stage and quantified the cross interactions among them. In addition, ALT allows us to consider the influence of baseline performance, a critical and unsolved problem that frequently yields inaccurate interpretations in the classical ANOVA model.Discussion: Our findings suggest the beneficial use of ALT models in the analysis of complex longitudinal datasets offering a better biological interpretation of the interrelationship of the factors that may influence cognitive performance.

Multi-factor controls on terrestrial carbon dynamics in urbanized areas

Abstract. As urban land expands rapidly across the globe, much concern has been raised that urbanization may alter the terrestrial carbon cycle. Urbanization involves complex changes in land structure and multiple environmental factors. Little is known about the relative contribution of these individual factors and their interactions to the terrestrial carbon dynamics, however, which is essential for assessing the effectiveness of carbon sequestration policies focusing on urban development. This study developed a comprehensive analysis framework for quantifying relative contribution of individual factors (and their interactions) to terrestrial carbon dynamics in urbanized areas. We identified 15 factors belonging to five categories, and we applied a newly developed factorial analysis scheme to the southern United States (SUS), a rapidly urbanizing region. In all, 24 numeric experiments were designed to systematically isolate and quantify the relative contribution of individual factors. We found that the impact of land conversion was far larger than other factors. Urban managements and the overall interactive effects among major factors, however, created a carbon sink that compensated for 42% of the carbon loss in land conversion. Our findings provide valuable information for regional carbon management in the SUS: (1) it is preferable to preserve pre-urban carbon pools than to rely on the carbon sinks in urban ecosystems to compensate for the carbon loss in land conversion. (2) In forested areas, it is recommendable to improve landscape design (e.g., by arranging green spaces close to the city center) to maximize the urbanization-induced environmental change effect on carbon sequestration. Urbanization-induced environmental change will be less effective in shrubland regions. (3) Urban carbon sequestration can be significantly improved through changes in management practices, such as increased irrigation and fertilizer and targeted use of vehicles and machinery with least-associated carbon emissions.

Spatio-temporal dynamics of maize yield water constraints under climate change in Spain.

Many studies have analyzed the impact of climate change on crop productivity, but comparing the performance of water management systems has rarely been explored. Because water supply and crop demand in agro-systems may be affected by global climate change in shaping the spatial patterns of agricultural production, we should evaluate how and where irrigation practices are effective in mitigating climate change effects. Here we have constructed simple, general models, based on biological mechanisms and a theoretical framework, which could be useful in explaining and predicting crop productivity dynamics. We have studied maize in irrigated and rain-fed systems at a provincial scale, from 1996 to 2009 in Spain, one of the most prominent “hot-spots” in future climate change projections. Our new approach allowed us to: (1) evaluate new structural properties such as the stability of crop yield dynamics, (2) detect nonlinear responses to climate change (thresholds and discontinuities), challenging the usual linear way of thinking, and (3) examine spatial patterns of yield losses due to water constraints and identify clusters of provinces that have been negatively affected by warming. We have reduced the uncertainty associated with climate change impacts on maize productivity by improving the understanding of the relative contributions of individual factors and providing a better spatial comprehension of the key processes. We have identified water stress and water management systems as being key causes of the yield gap, and detected vulnerable regions where efforts in research and policy should be prioritized in order to increase maize productivity.

A model predicting fluindione dose requirement in elderly inpatients including genotypes, body weight, and amiodarone

Indandione VKAs have been widely used for decades, especially in Eastern Europe and France. Contrary to coumarin VKAs, the relative contribution of individual factors to the indandione-VKA response is poorly known. In the present multicentre study, we sought to develop and validate a model including genetic and non-genetic factors to predict the daily fluindione dose requirement in elderly patients in whom VKA dosing is challenging. We prospectively recorded clinical and therapeutic data in 230 Caucasian inpatients mean aged 85 ± 6 years, who had reached international normalized ratio stabilisation (range 2.0-3.0) on fluindione. In the derivation cohort (n=156), we analysed 13 polymorphisms in seven genes potentially involved in the pharmacological effect or vitamin-K cycle (VKORC1, CYP4F2, EPHX1) and fluindione metabolism/transport (CYP2C9, CYP2C19, CYP3A5, ABCB1). We built a regression model incorporating non-genetic and genetic data and evaluated the model performances in a separate cohort (n=74).Body-weight, amiodarone intake, VKORC1, CYP4F2, ABCB1 genotypes were retained in the final model, accounting for 31.5% of dose variability. None influence of CYP2C9 was observed. Our final model showed good performances: in 83.3% of the validation cohort patients, the dose was accurately predicted within 5 mg, i.e.the usual step used for adjusting fluindione dosage. In conclusion, in addition to body-weight and amiodarone-intake, pharmacogenetic factors (VKORC1, CYP4F2, ABCB1) related to the pharmacodynamic effect and transport of fluindione significantly influenced the dose requirement in elderly patients while CYP2C9 did not. Studies are required to know whether fluindione could be an alternative VKA in carriers of polymorphic CYP2C9 alleles, hypersensitive to coumarins.

A Review of the Pathogenesis of Canine Cranial Cruciate Ligament Disease as a Basis for Future Preventive Strategies

Cranial cruciate ligament (CCL) deficiency is the leading cause of lameness of the canine stifle and has important consequences in terms of morbidity and cost associated with its management. In spite of this impact, development of preventive strategies remains in its infancy, largely because of gaps in our understanding of the complex and likely multifactorial origin of CCL deficiency. The purpose of this article is to provide a critical review of the literature related to the pathogenesis of CCL deficiency and place this evidence in the context of potential preventive measures. Trauma accounts for a minority of CCL ruptures in dogs, whereas progressive degeneration of the ligament has been attributed to a variety of factors that may be broadly classified as genetic, conformational, environmental, immune-mediated, and inflammatory. Genetic screening appears promising as a long-term option in selected breeds while immunomodulating therapies may be implemented in the nearer future to reduce the incidence of contralateral disease in dogs with unilateral CCL deficiency. Preventive modification of conformation factors may be applicable to most breeds predisposed to CCL deficiency but requires further investigations into the relative contribution of individual factors, as well as into the feasibility, and potential risks of large-scale implementation.

Antecedents of Work Disability Absence Among Young People: A Prospective Study

This study examined the relative contribution of individual factors, job characteristics, and temporal factors to the likelihood of lost days of work due to a work-related disability or illness among Canadians 16 to 24 years old. Using a prospective Canadian survey with up to 6 years of follow-up, the job-based analyses included 45,125 job episodes generated from a representative sample of young workers. A hazard model on work disability absence included the following predictors: age, gender, physical demands of the job (manual, nonmanual, and mixed), hours worked, highest education achieved, multiple concurrent job, job tenure, school activity, and living in a rural or urban area. The overall 1-week work disability absence rate was 0.78 per 1000 person-months. In the multivariate model, young workers holding manual jobs were 2.65 times more likely to have a work disability absence compared with young workers with nonmanual jobs. Also, those with less than a high school education were almost 3 times more likely to have a work disability absence. Other demographic factors such as gender were not independently associated with work disability absences. This prospective study finds that job characteristics are the predominant risk factors for work disability absences for young workers. Young workers with less education appear to be particularly vulnerable, possibly because of inadequate job skills or particularly dangerous job tasks.

Biogenic amines and polyamines in liver, kidney and spleen of roe deer and European brown hare

Cadaverine (CAD), histamine (HIS), tyramine (TYR) and the polyamines putrescine (PUT), spermidine (SPD) and spermine (SPM) were determined in liver, kidney and spleen of hunted roe deer (n = 39) and European brown hare (n = 20) 3 and 6 h post mortem, respectively. Median concentrations (mg/kg fresh weight) of CAD and TYR were <5 mg/kg for organs of both species, whereas median HIS concentrations were higher in spleen (14.6 and 11.6 mg/kg for hare and roe deer, respectively) and lowest in liver. Maximum PUT concentration was 72.9 mg/kg, but median values were ≤7.8 mg/kg. Mean SPD concentrations were 8.5, 10.7 and 42.9 mg/kg for liver, kidney and spleen of roe deer, and 37.2, 24.4 and 52.0 mg/kg for hare. Mean SPM concentrations were higher (94.6, 79.9, 102.2 and 111.2, 82.8, 91.1 mg/kg, respectively). SPM concentrations in the organs of the male roe deer were significantly higher than in those of females. SPM:SPD ratios (weight base) were in the order of 2:1 for spleen, and higher for liver and kidney. SPM and SPD were correlated significantly (r = 0.42–0.47). Although variations of polyamine concentrations are partially associated with species, organ, age and gender, the relative contribution of individual factors deserves further study.

Assessing the Relative Contribution of Individual Risk Factors on Surgical Outcome for Gastric Bypass Surgery: A Baseline Probability Analysis

Background. Multiple regression is the best technique for the simultaneous analysis of the contributions of multiple risk factors to a surgical outcome. A probability analysis is used to determine the relative contribution of individual factors to the overall outcome being assessed. We used these techniques to determine which of the potential risk factors had the greatest impact on adverse outcomes following gastric bypass surgery.Methods. Records from 1067 consecutive patients undergoing Roux-Y gastric bypass at the UCLA Medical Center from December 1993 until June 2000 were reviewed. Major complications were used as the dependent variable in a multivariate logistic regression analysis, and 10 risk factors served as the independent variables. Based on the analysis, an average (i.e., baseline) patient was defined. Variations in the preoperative risk factors were then analyzed individually and in various combinations and their effect on the predicted probability for complication development was assessed.Results. The overall major complication rate in this series was 5.8%. The average patient was defined as a 334-pound woman who was 42.3 years of age. For this patient, the predicted complication rate by probability analysis was 3.9%. The greatest increase in the anticipated complication rate was attributable to revisional procedures that increased the rate to 6.5%. Many patients have a combination of risk factors; to this end, a 62-year-old, male patient with a 2SD increase in weight (i.e., 464 pounds) who was undergoing a revision operation and had a history of smoking, hypertension, diabetes, and sleep apnea had a predicted complication rate of 33.7%.Conclusion. Probability analysis is a useful tool for determining the relative contribution of individual and combinations of risk factors for predicting the outcomes for surgical procedures. The four most influential factors for predicting a complication after gastric bypass surgery were; (1) male gender, (2) revisional surgery, (3) increasing age, and (4) increasing weight. These factors increased the predicted complication rate by 56, 67, 28 and 28%, respectively.

Statistical Simulation of Psychological Perception of Clothing Sensory Comfort

Comfort is one of the most important attributes of clothing demanded by modern consumers. The objective of this paper is to simulate psychological sensory perceptions of clothing comfort by using ten sensations (clammy, clingy, sticky, damp, heavy, prickly, scratchy, fit, breathable and thermal). Twenty-two professional athletes were asked to participate in a series of trials wearing four different garments, cycling in a controlled climatic chamber. Subjective ratings on these sensory perceptions were recorded before, during and after their 90-minute cycling session. By using factor analysis with Quartimax rotation, 3- and 5-Factors models were generated. In the 5-Factors model, the sensations clammy, clingy, damp and sticky, formed the first factor, which related to moisture comfort and contributed around 41% of total variance of overall comfort. Prickly and scratchy formed the second factor, which related to tactile comfort and contributed about 20% of overall comfort variance. The remaining sensations, fit, breathable and thermal, were classified as three individual factors, each contributing around 10% of the total comfort variance. In the 3-Factors model, the sensation breathable was grouped into moisture comfort, while the sensations thermal and fit formed another factor, thermal-fit comfort. The 3- and 5-Factors models explained around 74% and 90% of the overall variance, respectively. A linear predictable model to overall comfort was developed using 3- and 5-Factors models and the relative contributions of individual factors as weights. The comfort score predicted from the models was compared with the actual comfort score rated by the subjects. The relationship between the simulation result and the actual comfort score was in good agreement, with r = 0.893 at p < 0.001 level.