Functional and psychosocial outcomes 1 year after mild stroke

Background Mild stroke survivors are generally discharged from acute care within a few days of the stroke event, often without rehabilitation follow-up. We aimed to examine the recovery trajectory for male patients and their wife-caregivers during the 12 months postdischarge. Methods A descriptive study was undertaken to examine functional outcomes, quality of life (QOL), depression, caregiver strain, and marital function in a prospective cohort of male survivors of mild stroke and their wife-caregivers during the 12 months postdischarge. Data from each point in time were summarized and repeated measures analyses undertaken. Logistic regression was used to determine which baseline demographic and biopsychosocial variables influenced or predicted marital functioning 1 year postdischarge. Results A total of 38 male patients (mean age 63.4 years) and their wife-caregivers (mean age 58.5 years) were examined. The median discharge National Institutes of Health Stroke Scale score was 1.5, modified Rankin Scale score was 1.0, Barthel Index was 100.0, and Stroke Impact Scale-16v2 score was 78.5. The patients' modified Rankin Scale (function) and QOL scores improved significantly over time (F (2) = 4.583, P = .017; and F (6) = 5.632, P < .001, respectively). However, the wife-caregiver QOL scores did not change. Multivariate analysis revealed overall worsening of depression for both the patient and wife-caregivers (F (6, 32) = 3.087, P = .017) and marital function (F (6, 32) = 3.961, P = .004), although the wife-caregivers' perceptions of caregiver strain improved (F (6, 32) = 3.923, P = .007). None of the measured variables were associated with marital functioning 1 year postdischarge. Conclusions Despite improvement in patients' functional status, other patient and wife-caregiver psychosocial outcomes during the 12 months postdischarge may be negatively affected. Thus, attention needs to focus on recovery beyond functional outcomes.

Using decision trees to understand structure in missing data

Objectives Demonstrate the application of decision trees – classification and regression trees (CARTs), and their cousins, boosted regression trees (BRTs) – to understand structure in missing data. Setting Data taken from employees at three different industry sites in Australia. Participants 7915 observations were included. Materials and Methods The approach was evaluated using an occupational health dataset comprising results of questionnaires, medical tests, and environmental monitoring. Statistical methods included standard statistical tests and the ‘rpart’ and ‘gbm’ packages for CART and BRT analyses, respectively, from the statistical software ‘R’. A simulation study was conducted to explore the capability of decision tree models in describing data with missingness artificially introduced. Results CART and BRT models were effective in highlighting a missingness structure in the data, related to the Type of data (medical or environmental), the site in which it was collected, the number of visits and the presence of extreme values. The simulation study revealed that CART models were able to identify variables and values responsible for inducing missingness. There was greater variation in variable importance for unstructured compared to structured missingness. Discussion Both CART and BRT models were effective in describing structural missingness in data. CART models may be preferred over BRT models for exploratory analysis of missing data, and selecting variables important for predicting missingness. BRT models can show how values of other variables influence missingness, which may prove useful for researchers. Conclusion Researchers are encouraged to use CART and BRT models to explore and understand missing data.

Micromorphology and anatomy of leaves of Syzygium floribundum (Myrtaceae: Syzygieae), a rainforest tree endemic to eastern Australia

Although species of Syzygium are abundant components of the rainforests in Queensland and New South Wales, little is known about the anatomy of the Australian taxa. Here we describe the foliar anatomy and micromorphology of Syzygium floribundum (syn: Waterhousea floribunda) using standard protocols for scanning electron microscopy (SEM) and light microscopy. Syzygium floribundum possesses dorsiventral leaves with cyclo-staurocytic stomata, single epidermis, internal phloem, rhombus-shaped calcium oxalate crystals and complex-open midrib. In general, leaf anatomical and micromorphological characters are common with some species of the tribe Syzygieae. However, this particular combination of leaf characters has not been reported in a species of the genus. The anatomy of the species is typical of mesophytic taxa.

Electromyography and sonomyography analysis of the tibialis anterior: a cross sectional study

Background Foot dorsiflexion plays an essential role in both controlling balance and human gait. Electromyography (EMG) and sonomyography (SMG) can provide information on several aspects of muscle function. The aim was to establish the relationship between the EMG and SMG variables during isotonic contractions of foot dorsiflexors. Methods Twenty-seven healthy young adults performed the foot dorsiflexion test on a device designed ad hoc. EMG variables were maximum peak and area under the curve. Muscular architecture variables were muscle thickness and pennation angle. Descriptive statistical analysis, inferential analysis and a multivariate linear regression model were carried out. The confidence level was established with a statistically significant p-value of less than 0.05. Results The correlation between EMG variables and SMG variables was r = 0.462 (p < 0.05). The linear regression model to the dependent variable “peak normalized tibialis anterior (TA)” from the independent variables “pennation angle and thickness”, was significant (p = 0.002) with an explained variance of R2 = 0.693 and SEE = 0.16. Conclusions There is a significant relationship and degree of contribution between EMG and SMG variables during isotonic contractions of the TA muscle. Our results suggest that EMG and SMG can be feasible tools for monitoring and assessment of foot dorsiflexors. TA muscle parameterization and assessment is relevant in order to know that increased strength accelerates the recovery of lower limb injuries.

Ability to discriminate between healthy and low back pain sufferers using ultrasound during maximum lumbar extension

Objective To analyze the ability to discriminate between healthy individuals and individuals with chronic nonspecific low back pain (CNLBP) by measuring the relation between patient-reported outcomes and objective clinical outcome measures of the erector spinae (ES) muscles using an ultrasound during maximal isometric lumbar extension. Design Cross-sectional study with screening and diagnostic tests with no blinded comparison. Setting University laboratory. Participants Healthy individuals (n=33) and individuals with CNLBP (n=33). Interventions Each subject performed an isometric lumbar extension. With the variables measured, a discriminate analysis was performed using a value ≥6 in the Roland and Morris disability questionnaire (RMDQ) as the grouping variable. Then, a logistic regression with the functional and architectural variables was performed. A new index was obtained from each subject value input in the discriminate multivariate analysis. Main Outcome Measures Morphologic muscle variables of the ES muscle were measured through ultrasound images. The reliability of the measures was calculated through intraclass correlation coefficients (ICCs). The relation between patient-reported outcomes and objective clinical outcome measures was analyzed using a discriminate function from standardized values of the variables and an analysis of the reliability of the ultrasound measurement. Results The reliability tests show an ICC value >.95 for morphologic and functional variables. The independent variables included in the analysis explained 42% (P=.003) of the dependent variable variance. Conclusions The relation between objective variables (electromyography, thickness, pennation angle) and a subjective variable (RMDQ ≥6) and the capacity of this relation to identify CNLBP within a group of healthy subjects is moderate. These results should be considered by clinicians when treating this type of patient in clinical practice.

Discovery and replication of gene influences on brain structure using LASSO regression

We implemented least absolute shrinkage and selection operator (LASSO) regression to evaluate gene effects in genome-wide association studies (GWAS) of brain images, using an MRI-derived temporal lobe volume measure from 729 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sparse groups of SNPs in individual genes were selected by LASSO, which identifies efficient sets of variants influencing the data. These SNPs were considered jointly when assessing their association with neuroimaging measures. We discovered 22 genes that passed genome-wide significance for influencing temporal lobe volume. This was a substantially greater number of significant genes compared to those found with standard, univariate GWAS. These top genes are all expressed in the brain and include genes previously related to brain function or neuropsychiatric disorders such as MACROD2, SORCS2, GRIN2B, MAGI2, NPAS3, CLSTN2, GABRG3, NRXN3, PRKAG2, GAS7, RBFOX1, ADARB2, CHD4, and CDH13. The top genes we identified with this method also displayed significant and widespread post hoc effects on voxelwise, tensor-based morphometry (TBM) maps of the temporal lobes. The most significantly associated gene was an autism susceptibility gene known as MACROD2.We were able to successfully replicate the effect of the MACROD2 gene in an independent cohort of 564 young, Australian healthy adult twins and siblings scanned with MRI (mean age: 23.8±2.2 SD years). Our approach powerfully complements univariate techniques in detecting influences of genes on the living brain.

The multivariate A/C/E model and the genetics of fiber architecture

We present a new algorithm to compute the voxel-wise genetic contribution to brain fiber microstructure using diffusion tensor imaging (DTI) in a dataset of 25 monozygotic (MZ) twins and 25 dizygotic (DZ) twin pairs (100 subjects total). First, the structural and DT scans were linearly co-registered. Structural MR scans were nonlinearly mapped via a 3D fluid transformation to a geometrically centered mean template, and the deformation fields were applied to the DTI volumes. After tensor re-orientation to realign them to the anatomy, we computed several scalar and multivariate DT-derived measures including the geodesic anisotropy (GA), the tensor eigenvalues and the full diffusion tensors. A covariance-weighted distance was measured between twins in the Log-Euclidean framework [2], and used as input to a maximum-likelihood based algorithm to compute the contributions from genetics (A), common environmental factors (C) and unique environmental ones (E) to fiber architecture. Quanititative genetic studies can take advantage of the full information in the diffusion tensor, using covariance weighted distances and statistics on the tensor manifold.

Multivariate variance-components analysis in DTI

Twin studies are a major research direction in imaging genetics, a new field, which combines algorithms from quantitative genetics and neuroimaging to assess genetic effects on the brain. In twin imaging studies, it is common to estimate the intraclass correlation (ICC), which measures the resemblance between twin pairs for a given phenotype. In this paper, we extend the commonly used Pearson correlation to a more appropriate definition, which uses restricted maximum likelihood methods (REML). We computed proportion of phenotypic variance due to additive (A) genetic factors, common (C) and unique (E) environmental factors using a new definition of the variance components in the diffusion tensor-valued signals. We applied our analysis to a dataset of Diffusion Tensor Images (DTI) from 25 identical and 25 fraternal twin pairs. Differences between the REML and Pearson estimators were plotted for different sample sizes, showing that the REML approach avoids severe biases when samples are smaller. Measures of genetic effects were computed for scalar and multivariate diffusion tensor derived measures including the geodesic anisotropy (tGA) and the full diffusion tensors (DT), revealing voxel-wise genetic contributions to brain fiber microstructure.

Identifying candidate gene effects by restricting search space in a multivariate genetic analysis of white matter microstructure

Several genetic variants are thought to influence white matter (WM) integrity, measured with diffusion tensor imaging (DTI). Voxel based methods can test genetic associations, but heavy multiple comparisons corrections are required to adjust for searching the whole brain and for all genetic variants analyzed. Thus, genetic associations are hard to detect even in large studies. Using a recently developed multi-SNP analysis, we examined the joint predictive power of a group of 18 cholesterol-related single nucleotide polymorphisms (SNPs) on WM integrity, measured by fractional anisotropy. To boost power, we limited the analysis to brain voxels that showed significant associations with total serum cholesterol levels. From this space, we identified two genes with effects that replicated in individual voxel-wise analyses of the whole brain. Multivariate analyses of genetic variants on a reduced anatomical search space may help to identify SNPs with strongest effects on the brain from a broad panel of genes.

Why do Queenslanders seek care in emergency departments? A population study

Objective This study aims to identify the main reasons for which first time and multiple users seek medical care through Queensland emergency departments (ED). Methods A cross-sectional survey was conducted at eight public EDs among presenting patients (n = 911). The questions measured the socio-demographic characteristics of patients, their beliefs and attitudes towards EDs services, and perceptions of health status. Bivariate and binary logistic regression analyses were performed to examine the differences between first time and multiple users of EDs. Results First time and multiple users accounted for 55.5% and 44.5%, respectively. Multiple users themselves believed to be sicker, have poorer health status, and additional and/or chronic health conditions. Multiple users more strongly believed that their condition required treatment at an ED and perceived their condition as being very serious. Multiple users reported weekly household incomes below $600, and half of the multiple users were not working as compared to 35% first time users. Multivariate analysis showed that multiple use was significantly associated with the existence of additional health problems, having chronic condition, lower self-efficacy, and need for ED treatment. Conclusions Patients who sought care for multiple times at EDs more often than first time users suffered from additional and chronic conditions. Their opinion of an ED as the most suitable place to address their current health problem was stronger than first time users. Any proposed demand management strategies need to address these beliefs together with the reasoning of patients to provide effective and appropriate care outside or within ED services.

Eccentric knee-flexor strength and risk of hamstring injuries in Rugby Union: A prospective study

BACKGROUND Hamstring strain injuries (HSIs) represent the most common cause of lost playing time in rugby union. Eccentric knee-flexor weakness and between-limb imbalance in eccentric knee-flexor strength are associated with a heightened risk of hamstring injury in other sports; however these variables have not been explored in rugby union. PURPOSE To determine if lower levels of eccentric knee-flexor strength or greater between-limb imbalance in this parameter during the Nordic hamstring exercise are risk-factors for hamstring strain injury in rugby union. STUDY DESIGN Cohort study; level of evidence, 3. METHODS This prospective study was conducted over the 2014 Super Rugby and Queensland Rugby Union seasons. In total, 178 rugby union players (age, 22.6 ± 3.8 years; height, 185 ± 6.8 cm; mass, 96.5 ± 13.1 kg) had their eccentric knee-flexor strength assessed using a custom-made device during the pre-season. Reports of previous hamstring, quadriceps, groin, calf and anterior cruciate ligament injury were also obtained. The main outcome measure was prospective occurrence of hamstring strain injury. RESULTS Twenty players suffered at least one hamstring strain during the study period. Players with a history of hamstring strain injury had 4.1 fold (RR = 4.1, 95% CI = 1.9 to 8.9, p = 0.001) greater risk of subsequent hamstring injury than players without such history. Between-limb imbalance in eccentric knee-flexor strength of ≥ 15% and ≥ 20% increased the risk of hamstring strain injury 2.4 fold (RR = 2.4, 95% CI = 1.1 to 5.5, p = 0.033) and 3.4 fold (RR = 3.4, 95% CI = 1.5 to 7.6, p = 0.003), respectively. Lower eccentric knee flexor strength and other prior injuries were not associated with increased risk of future hamstring strain. Multivariate logistic regression revealed that the risk of re-injury was augmented in players with strength imbalances. CONCLUSION Previous hamstring strain injury and between-limb imbalance in eccentric knee-flexor strength were associated with an increased risk of future hamstring strain injury in rugby union. These results support the rationale for reducing imbalance, particularly in players who have suffered a prior hamstring injury, to mitigate the risk of future injury.

Analysis of complex molecular systems: The impact of multivariate analysis for resolving the interactions of small molecules with biopolymers - a review

This review is focused on the impact of chemometrics for resolving data sets collected from investigations of the interactions of small molecules with biopolymers. These samples have been analyzed with various instrumental techniques, such as fluorescence, ultraviolet–visible spectroscopy, and voltammetry. The impact of two powerful and demonstrably useful multivariate methods for resolution of complex data—multivariate curve resolution–alternating least squares (MCR–ALS) and parallel factor analysis (PARAFAC)—is highlighted through analysis of applications involving the interactions of small molecules with the biopolymers, serum albumin, and deoxyribonucleic acid. The outcomes illustrated that significant information extracted by the chemometric methods was unattainable by simple, univariate data analysis. In addition, although the techniques used to collect data were confined to ultraviolet–visible spectroscopy, fluorescence spectroscopy, circular dichroism, and voltammetry, data profiles produced by other techniques may also be processed. Topics considered including binding sites and modes, cooperative and competitive small molecule binding, kinetics, and thermodynamics of ligand binding, and the folding and unfolding of biopolymers. Applications of the MCR–ALS and PARAFAC methods reviewed were primarily published between 2008 and 2013.

Predictors of physical activity in colorectal cancer survivors after participation in a telephone-delivered multiple health behavior change intervention

Purpose: Physical activity improves the health outcomes of colorectal cancer (CRC) survivors, yet few are exercising at levels known to yield health benefits. Baseline demographic, clinical, behavioral, and psychosocial predictors of physical activity at 12 months were investigated in CRC survivors. Methods: Participants were CRC survivors (n = 410) who completed a 12-month multiple health behavior change intervention trial (CanChange). The outcome variable was 12 month sufficient physical activity (≥150 min of moderate–vigorous physical activity/week). Baseline predictors included demographics and clinical variables, health behaviors, and psychosocial variables. Results: Multivariate linear regression revealed that baseline sufficient physical activity (p < 0.001), unemployment (p = 0.004), private health insurance (p = 0.040), higher cancer-specific quality of life (p = 0.031) and higher post-traumatic growth (p = 0.008) were independent predictors of sufficient physical activity at 12 months. The model explained 28.6 % of the variance. Conclusions: Assessment of demographics, health behaviors, and psychosocial functioning following a diagnosis of CRC may help to develop effective physical activity programs.

Global variation in the effects of ambient temperature on mortality: A systematic evaluation

Background: Studies have examined the effects of temperature on mortality in a single city, country, or region. However, less evidence is available on the variation in the associations between temperature and mortality in multiple countries, analyzed simultaneously. Methods: We obtained daily data on temperature and mortality in 306 communities from 12 countries/regions (Australia, Brazil, Thailand, China, Taiwan, Korea, Japan, Italy, Spain, United Kingdom, United States, and Canada). Two-stage analyses were used to assess the nonlinear and delayed relation between temperature and mortality. In the first stage, a Poisson regression allowing overdispersion with distributed lag nonlinear model was used to estimate the community-specific temperature-mortality relation. In the second stage, a multivariate meta-analysis was used to pool the nonlinear and delayed effects of ambient temperature at the national level, in each country. Results: The temperatures associated with the lowest mortality were around the 75th percentile of temperature in all the countries/regions, ranging from 66th (Taiwan) to 80th (UK) percentiles. The estimated effects of cold and hot temperatures on mortality varied by community and country. Meta-analysis results show that both cold and hot temperatures increased the risk of mortality in all the countries/regions. Cold effects were delayed and lasted for many days, whereas heat effects appeared quickly and did not last long. Conclusions: People have some ability to adapt to their local climate type, but both cold and hot temperatures are still associated with increased risk of mortality. Public health strategies to alleviate the impact of ambient temperatures are important, in particular in the context of climate change.

Rank regression for analyzing ordinal qualitative data for treatment comparison

Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the underlying assumptions such as normality and homogeneous variances are often violated for qualitative data. To this end, we investigated an alternative methodology, rank regression, for analyzing the ordinal data. The rank-based methods are essentially based on pairwise comparisons and, therefore, can deal with qualitative data naturally. They require neither normality assumption nor data transformation. Apart from robustness against outliers and high efficiency, the rank regression can also incorporate covariate effects in the same way as the ordinary regression. By reanalyzing a data set from a wheat Fusarium crown rot study, we illustrated the use of the rank regression methodology and demonstrated that the rank regression models appear to be more appropriate and sensible for analyzing nonnormal data and data with outliers.