A structured overview of simultaneous component based data integration

<p>Abstract</p> <p>Background</p> <p>Data integration is currently one of the main challenges in the biomedical sciences. Often different pieces of information are gathered on the same set of entities (e.g., tissues, culture samples, biomolecules) with the different pieces stemming, for example, from different measurement techniques. This implies that more and more data appear that consist of two or more data arrays that have a shared mode. An integrative analysis of such coupled data should be based on a simultaneous analysis of all data arrays. In this respect, the family of simultaneous component methods (e.g., SUM-PCA, unrestricted PCovR, MFA, STATIS, and SCA-P) is a natural choice. Yet, different simultaneous component methods may lead to quite different results.</p> <p>Results</p> <p>We offer a structured overview of simultaneous component methods that frames them in a principal components setting such that both the common core of the methods and the specific elements with regard to which they differ are highlighted. An overview of principles is given that may guide the data analyst in choosing an appropriate simultaneous component method. Several theoretical and practical issues are illustrated with an empirical example on metabolomics data for <it>Escherichia coli </it>as obtained with different analytical chemical measurement methods.</p> <p>Conclusion</p> <p>Of the aspects in which the simultaneous component methods differ, pre-processing and weighting are consequential. Especially, the type of weighting of the different matrices is essential for simultaneous component analysis. These types are shown to be linked to different specifications of the idea of a fair integration of the different coupled arrays.</p

Development of the Adolescent Preoccupation with Screens Scale

Abstract Background Although public health concerns have been raised regarding the detrimental health effects of increasing rates of electronic screen use among adolescents, such effects have been small. Instruments currently available tend to be lengthy, have a clinical research focus, and assess young people’s screen use on specific screen-based activities (e.g., TV, computer, or internet). None appear to address screen use across a broad range of screens, including mobile devices and screen-based activities. The objective was to develop a new and short self-report scale for investigating adolescents’ screen use across all screens and screen-based activities in non-clinical settings. Methods The Adolescent Preoccupation with Screens Scale (APSS) was developed over a three stage process. First, a review of the current literature and existing instruments was undertaken and suitable items identified. Second, the draft APSS was piloted with adolescents and item affectivity and discrimination indices were calculated. Third, a cross sectional school based online survey of 1967 Australian adolescents in grades 5 (10 years old), 7 (13 years) and 9 (15 years) from 25 randomly selected schools was conducted. Results Factor Analysis on a sub-sample of the data (n = 782) and Confirmatory Factor Analysis on the remaining sub-sample (n = 1185), supported a two-factor model. The first factor reflects adolescents’ mood management with screen use, and the second reflects a behavioural preoccupation. The measure demonstrated strong invariance across sex and across Grades 5, 7, and 9. Both factors displayed good internal consistency (α = .91 and .87, respectively). Sex and grade differences on both scales were investigated and boys in Grade 5 reported higher levels of both mood management and behavioural preoccupation with screens. There were no sex differences on mood management in Grades 7 and 9, but girls reported higher behavioural preoccupation in both these later grades. Conclusion The APSS provides researchers with a new, brief and robust measure of potentially problematic screen use across a wide array of screens, including mobile devices, so readily accessed during adolescence

Plasma fibrin D-dimer levels correlate with tumour volume, progression rate and survival in patients with metastatic breast cancer

Plasma levels of D-dimer are elevated in cancer patients. Activation of the extrinsic coagulation system and the fibrinolytic cascade within a tumour is thought to be related with growth, invasion and metastasis. We have investigated the relationship between these markers of fibrin metabolism, standard clinicopathological variables and serum levels of angiogenic cytokines in three cohorts: group A (n=30) consisted of 30 healthy female volunteers, group B (n=23) of consecutive patients with operable breast cancer and group C (n=84) of patients with untreated or progressive metastatic breast cancer. Plasma D-dimers, fibrinogen, IL-6, vascular endothelial growth factor and calculated vascular endothelial growth factor load in platelets are clearly increased in patients with breast cancer. D-dimers were increased in nearly 89% of patients with progressive metastatic disease. The level of D-dimers was positively correlated with tumour load (P<0.0001), number of metastatic sites (P=0.002), progression kinetics (P<0.0001) and the cytokines related to angiogenesis: serum vascular endothelial growth factor (P=0.0016, Spearman correlation=0.285), calculated vascular endothelial growth factor load in platelets (P<0.0001, Spearman correlation=0.37) and serum interleukin-6 (P<0.0001, Spearman correlation=0.59). Similarly increased D-dimer levels were positively correlated with increased fibrinogen levels (P<0.0001, Spearman correlation=0.38). The association between markers of fibrin degradation in patients with progressive breast cancer suggests that the D-dimer level is a clinically important marker for progression and points towards a relation between haemostasis and tumour progression. A role of interleukin-6, by influencing both angiogenesis and haemostasis, is suggested by these observations

DISCO-SCA and Properly Applied GSVD as Swinging Methods to Find Common and Distinctive Processes

BACKGROUND: In systems biology it is common to obtain for the same set of biological entities information from multiple sources. Examples include expression data for the same set of orthologous genes screened in different organisms and data on the same set of culture samples obtained with different high-throughput techniques. A major challenge is to find the important biological processes underlying the data and to disentangle therein processes common to all data sources and processes distinctive for a specific source. Recently, two promising simultaneous data integration methods have been proposed to attain this goal, namely generalized singular value decomposition (GSVD) and simultaneous component analysis with rotation to common and distinctive components (DISCO-SCA). RESULTS: Both theoretical analyses and applications to biologically relevant data show that: (1) straightforward applications of GSVD yield unsatisfactory results, (2) DISCO-SCA performs well, (3) provided proper pre-processing and algorithmic adaptations, GSVD reaches a performance level similar to that of DISCO-SCA, and (4) DISCO-SCA is directly generalizable to more than two data sources. The biological relevance of DISCO-SCA is illustrated with two applications. First, in a setting of comparative genomics, it is shown that DISCO-SCA recovers a common theme of cell cycle progression and a yeast-specific response to pheromones. The biological annotation was obtained by applying Gene Set Enrichment Analysis in an appropriate way. Second, in an application of DISCO-SCA to metabolomics data for Escherichia coli obtained with two different chemical analysis platforms, it is illustrated that the metabolites involved in some of the biological processes underlying the data are detected by one of the two platforms only; therefore, platforms for microbial metabolomics should be tailored to the biological question. CONCLUSIONS: Both DISCO-SCA and properly applied GSVD are promising integrative methods for finding common and distinctive processes in multisource data. Open source code for both methods is provided

Sero-epidemiological evaluation of changes in Plasmodium falciparum and Plasmodium vivax transmission patterns over the rainy season in Cambodia

<p>Abstract</p> <p>Background</p> <p>In Cambodia, malaria transmission is low and most cases occur in forested areas. Sero-epidemiological techniques can be used to identify both areas of ongoing transmission and high-risk groups to be targeted by control interventions. This study utilizes repeated cross-sectional data to assess the risk of being malaria sero-positive at two consecutive time points during the rainy season and investigates who is most likely to sero-convert over the transmission season.</p> <p>Methods</p> <p>In 2005, two cross-sectional surveys, one in the middle and the other at the end of the malaria transmission season, were carried out in two ecologically distinct regions in Cambodia. Parasitological and serological data were collected in four districts. Antibodies to <it>Plasmodium falciparum </it>Glutamate Rich Protein (GLURP) and <it>Plasmodium vivax </it>Merozoite Surface Protein-1₁₉(MSP-1₁₉) were detected using Enzyme Linked Immunosorbent Assay (ELISA). The force of infection was estimated using a simple catalytic model fitted using maximum likelihood methods. Risks for sero-converting during the rainy season were analysed using the Classification and Regression Tree (CART) method.</p> <p>Results</p> <p>A total of 804 individuals participating in both surveys were analysed. The overall parasite prevalence was low (4.6% and 2.0% for <it>P. falciparum </it>and 7.9% and 6.0% for <it>P. vivax </it>in August and November respectively). <it>P. falciparum </it>force of infection was higher in the eastern region and increased between August and November, whilst <it>P. vivax </it>force of infection was higher in the western region and remained similar in both surveys. In the western region, malaria transmission changed very little across the season (for both species). CART analysis for <it>P. falciparum </it>in the east highlighted age, ethnicity, village of residence and forest work as important predictors for malaria exposure during the rainy season. Adults were more likely to increase their antibody responses to <it>P. falciparum </it>during the transmission season than children, whilst members of the Charay ethnic group demonstrated the largest increases.</p> <p>Discussion</p> <p>In areas of low transmission intensity, such as in Cambodia, the analysis of longitudinal serological data enables a sensitive evaluation of transmission dynamics. Consecutive serological surveys allow an insight into spatio-temporal patterns of malaria transmission. The use of CART enabled multiple interactions to be accounted for simultaneously and permitted risk factors for exposure to be clearly identified.</p

MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

Objective: Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes. Methods: We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask–WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA). Results: Radiomic features were predictive of WMH burden (R2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-valuesCV1–6 < 0.001, p-valueCV7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes. Conclusion: Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients’ brain health

Outcome after acute ischemic stroke is linked to sex-specific lesion patterns

Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke

Excessive White Matter Hyperintensity Increases Susceptibility to Poor Functional Outcomes After Acute Ischemic Stroke.

Objective: To personalize the prognostication of post-stroke outcome using MRI-detected cerebrovascular pathology, we sought to investigate the association between the excessive white matter hyperintensity (WMH) burden unaccounted for by the traditional stroke risk profile of individual patients and their long-term functional outcomes after a stroke. Methods: We included 890 patients who survived after an acute ischemic stroke from the MRI-Genetics Interface Exploration (MRI-GENIE) study, for whom data on vascular risk factors (VRFs), including age, sex, atrial fibrillation, diabetes mellitus, hypertension, coronary artery disease, smoking, prior stroke history, as well as acute stroke severity, 3- to−6-month modified Rankin Scale score (mRS), WMH, and brain volumes, were available. We defined the unaccounted WMH (uWMH) burden via modeling of expected WMH burden based on the VRF profile of each individual patient. The association of uWMH and mRS score was analyzed by linear regression analysis. The odds ratios of patients who achieved full functional independence (mRS < 2) in between trichotomized uWMH burden groups were calculated by pair-wise comparisons. Results: The expected WMH volume was estimated with respect to known VRFs. The uWMH burden was associated with a long-term functional outcome (β = 0.104, p < 0.01). Excessive uWMH burden significantly reduced the odds of achieving full functional independence after a stroke compared to the low and average uWMH burden [OR = 0.4, 95% CI: (0.25, 0.63), p < 0.01 and OR = 0.61, 95% CI: (0.42, 0.87), p < 0.01, respectively]. Conclusion: The excessive amount of uWMH burden unaccounted for by the traditional VRF profile was associated with worse post-stroke functional outcomes. Further studies are needed to evaluate a lifetime brain injury reflected in WMH unrelated to the VRF profile of a patient as an important factor for stroke recovery and a plausible indicator of brain health