Functional data analysis: Application to daily observation of COVID-19 prevalence in France

<abstract> <p>In this paper we use the technique of functional data analysis to model daily hospitalized, deceased, Intensive Care Unit (ICU) cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments in France while our response variables are numbers of vaccinations, deaths, infected, recovered and tests in France. These sets of data were considered before and after vaccination started in France. After smoothing our data set, analysis based on functional principal components method was performed. Then, a clustering using k-means techniques was done to understand the dynamics of the pandemic in different French departments according to their geographical location on France map. We also performed canonical correlations analysis between variables. Finally, we made some predictions to assess the accuracy of the method using functional linear regression models.</p> </abstract>

Weak Coupling Between Spontaneous Local Cortical Activity State Switches Under Anesthesia Leads to Strongly Correlated Global Cortical States

Under anesthesia, neural dynamics deviate dramatically from those seen during wakefulness. During recovery from this perturbation, thalamocortical activity abruptly switches among a small set of metastable intermediate states. These metastable states and structured transitions among them form a scaffold that guides the brain back to the waking state. Here, we investigate the mechanisms that constrain cortical activity to discrete states and give rise to abrupt transitions among them. If state transitions were imposed onto the thalamocortical system by changes in the subcortical modulation, different cortical sites should exhibit near-synchronous state transitions. To test this hypothesis, we quantified state synchrony at different cortical sites in anesthetized rats. States were defined by compressing spectra of layer-specific local field potentials (LFPs) in visual and motor cortices. Transition synchrony, mutual information, and canonical correlations all demonstrate that most state transitions in the cortex are local and that coupling between sites is weak. Fluctuations in the LFP in the thalamic input layer 4 were particularly dissimilar from those in supra- and infra-granular layers. Thus, our results suggest that the discrete global cortical states are not imposed by the ascending modulatory pathways but emerge from the multitude of weak pairwise interactions within the cortex.

Canonical interrelationships in morphological characters, yield and nutritional components of corn

The increase in the world population, the need to increase food production, both in quantity and quality, becomes increasingly prominent. The objective of this work was to identify the canonical correlations between yield components, morphological characters, micronutrients, bioactive compounds and amino acids in corn. The experimental design used was a randomized block containing 11 treatments arranged in three replications. The treatments consisted of 11 Top Crosses hybrid genotypes, these being made through crosses directed between a narrow genetic base tester hybrid for specific combining ability with 11 S5 inbred lines. It is inferred that groups considered yield components, secondary traits, bioactive compounds, micronutrients and amino acids are dependent. Promising characters are identified for the corn breeding for high yields, nutritional and energetic quality of corn grains. The indirect selection of grains with additions in essential amino acids can be directed to plants with superiority in height, mass and width of grains, phenols, flavonoids, soluble solids and zinc content.

Functional Data Analysis: Transition from Daily Observation of COVID-19 Prevalence in France to Functional Curves

In this paper we use the technique of functional data analysis to model daily hospitalized, deceased, ICU cases and return home patient numbers along the COVID-19 outbreak, considered as functional data across different departments in France while our response variables are numbers of vaccinations, deaths, infected, recovered and tests in France. These sets of data were considered before and after vaccination started in France. We used some smoothing techniques to smooth our data set, then analysis based on functional principal components method was performed, clustering using k-means techniques was done to understand the dynamics of the pandemic in different French departments according to their geographical location on France map and we also performed canonical correlations analysis between variables. Finally, we made some predictions to assess the accuracy of the method using functional linear regression models.

Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images

A novel feature generation algorithm for the synthetic aperture radar image is designed in this study for automatic target recognition. As an adaptive 2D signal processing technique, bidimensional empirical mode decomposition is employed to generate multiscale bidimensional intrinsic mode functions from the original synthetic aperture radar images, which could better capture the broad spectral information and details of the target. And, the combination of the original image and decomposed bidimensional intrinsic mode functions could promisingly provide more discriminative information for correct target recognition. To reduce the high dimension of the original image as well as bidimensional intrinsic mode functions, multiset canonical correlations analysis is adopted to fuse them as a unified feature vector. The resultant feature vector highly reduces the high dimension while containing the inner correlations between the original image and decomposed bidimensional intrinsic mode functions, which could help improve the classification accuracy and efficiency. In the classification stage, the support vector machine is taken as the basic classifier to determine the target label of the test sample. In the experiments, the 10-class targets in the moving and stationary target acquisition and recognition dataset are classified to investigate the performance of the proposed method. Several operating conditions and reference methods are setup for comprehensive evaluation.

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

ObjectiveNeuroimaging 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.MethodsWe 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).ResultsRadiomic 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 &lt; 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.ConclusionRadiomics 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.

NONUNIFORM COURSE OF ACUTE PERIOD OF HEMORRHAGIC STROKE: ANALYSIS USING METHODS OF MULTIVARIATE STATISTICS

Background. Hemorrhagic stroke is an important medical and social problem both in the world and in the Russian Federation due to high parameters of morbidity, mortality and disability. Aim. To compare expert assessments and formalized multivariate statistical procedures in analysis of clinical inhomogeneity of patients with intracranial hematoma of supratentorial location. Materials and Methods. 75 Patients who took treatment in the neurovascular department of Ryazan Regional Clinical Hospital with the diagnosis of hemorrhagic stroke, were examined. Of them, there were 40 men and 35 women with the mean age 68.1 years. Results. Primarily, on the basis of expert assessments, the patients were divided to 2 groups: with unfavorable course and with relatively favorable course – satisfactory condition, regress of symptoms, recovery of the level of consciousness. In the first stage, in result of the primary expert assessment, the patients were divided to subgroups with favorable and unfavorable course. After that, significant for selection of groups variables were used in selection of clusters: method of hierarchial tree for determination of the number of groups; k-means method for identification of their elements. Discrimination analysis was used for selection of variables in NIHSS and GCS, and also for assessment of canonical correlations. After that, cluster analysis of NIHSS and GCS dynamics was conducted on the 1st, 3rd, 21st day for selection of groups in high-dimensional space of signs with exclusion of subjective expert assessments. Three main groups of patients were selected. In the second stage, in accordance with the number of groups, patients belonging to the corresponding clusters were identified using the k-means method. Cluster 1 included patients with a poor prognosis, clusters 2 and 3 suggested a more favorable course of the acute period with worse parameters in cluster 2. Conclusions. Use of discriminant functions confirms the role of severity of depression of consciousness and of the volume of hematoma in the unfavorable course.

Conditional canonical correlation estimation based on covariates with random forests

Motivation Investigating the relationships between two sets of variables helps to understand their interactions and can be done with canonical correlation analysis (CCA). However, the correlation between the two sets can sometimes depend on a third set of covariates, often subject-related ones such as age, gender or other clinical measures. In this case, applying CCA to the whole population is not optimal and methods to estimate conditional CCA, given the covariates, can be useful. Results We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables given subject-related covariates. The individual trees in the forest are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. We also propose a significance test to detect the global effect of the covariates on the relationship between two sets of variables. The performance of the proposed method and the global significance test is evaluated through simulation studies that show it provides accurate canonical correlation estimations and well-controlled Type-1 error. We also show an application of the proposed method with EEG data. Availability and implementation RFCCA is implemented in a freely available R package on CRAN (https://CRAN.R-project.org/package=RFCCA). Supplementary information Supplementary data are available at Bioinformatics online.

Abstract 10: Radiomic Signature of the White Matter Hyperintensity Burden Correlates With Clinical Phenotypes

Introduction: Structural integrity of cerebral parenchyma is an essential radiographic equivalent of brain health; but its assessment usually requires dedicated advanced image acquisitions. Radiomics analyses bear the potential to describe radiophenotypes beyond what meets the naked eye. We sought to: 1) evaluate this novel approach to predict white matter hyperintensity (WMH) burden and 2) uncover latent clinico-radiological associations. Methods: An international, multi-site cohort of 4,164 acute ischemic stroke (AIS) patients with FLAIR MRI (MRI-GENIE study) underwent total brain and WMH lesion segmentation using convolutional neural networks. Radiomic features (n=1905) were extracted from clinical FLAIR images outside of the WMH (brain mask - WMH mask). Prediction of the WMH burden using radiomics was done using LASSO regression. Radiomic signature of WMH was built with the most stable selected features, then compared to the clinical variables using canonical correlation analysis. Results: In this cohort, (mean age=62.8±15.0, median WMH volume=4.2cc IQR 1.4-11.2), radiomic features were highly predictive of WMH burden (R2=0.8±0.012). Radiomic signature of WMH included 68 features. All 7 pairs of extracted canonical variates (CV) were statistically significant with respective canonical correlations of 0.79, 0.64, 0.44, 0.21, 0.16, 0.15 (Bonferroni corrected p-values CV1-6 <.001, p-value CV7 =.003). Upon examination, CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes mellitus (DM), CV4 by hypertension, CV5 by atrial fibrillation (AF) and DM, CV6 by coronary artery disease (CAD) and CV7 by CAD and DM. Conclusion: Radiomics extracted from clinical grade FLAIR images of AIS patients seem able to capture structural integrity of the cerebral parenchyma and to correlate with clinical phenotypes. Further research could evaluate radiomics to predict the progression of cerebral small vessel disease on longitudinal data.

Lay Definitions of Cultural Appropriation in U.S. Community and College Student Samples

In three studies we examined lay definitions of cultural appropriation in U.S. community and college student samples. In a fourth study we examined correlates of perceptions of cultural appropriation. Using community and undergraduate student samples (Studies 1-2), and popular media articles (Study 3) we examined definitions of cultural appropriation following Rogers’ (2006) typology of cultural exchange, dominance, and subordination. The results across three studies revealed that cultural appropriation was defined predominantly as cultural exploitation. In Study 4 we examined political correctness (emotion and activism), social justice, empathy (perspective taking and empathic concern), and political orientation as correlates of cultural appropriation perceptions. Using canonical correlations, we found that cultural exploitation was the primary contributor to the synthetic predictor, and political orientation, emotion political correctness, activism political correctness, and social justice made significant contributions to the criterion variable, but not empathy. Implication and further research directions are discussed.