Random Permutation Test Research Articles

Neural Decoding of the Speech Envelope: Effects of Intelligibility and Spectral Degradation.

During continuous speech perception, endogenous neural activity becomes time-locked to acoustic stimulus features, such as the speech amplitude envelope. This speech-brain coupling can be decoded using non-invasive brain imaging techniques, including electroencephalography (EEG). Neural decoding may provide clinical use as an objective measure of stimulus encoding by the brain-for example during cochlear implant listening, wherein the speech signal is severely spectrally degraded. Yet, interplay between acoustic and linguistic factors may lead to top-down modulation of perception, thereby complicating audiological applications. To address this ambiguity, we assess neural decoding of the speech envelope under spectral degradation with EEG in acoustically hearing listeners (n = 38; 18-35 years old) using vocoded speech. We dissociate sensory encoding from higher-order processing by employing intelligible (English) and non-intelligible (Dutch) stimuli, with auditory attention sustained using a repeated-phrase detection task. Subject-specific and group decoders were trained to reconstruct the speech envelope from held-out EEG data, with decoder significance determined via random permutation testing. Whereas speech envelope reconstruction did not vary by spectral resolution, intelligible speech was associated with better decoding accuracy in general. Results were similar across subject-specific and group analyses, with less consistent effects of spectral degradation in group decoding. Permutation tests revealed possible differences in decoder statistical significance by experimental condition. In general, while robust neural decoding was observed at the individual and group level, variability within participants would most likely prevent the clinical use of such a measure to differentiate levels of spectral degradation and intelligibility on an individual basis.

Conditional Independence Test Based on Residual Similarity

Recently, many regression-based conditional independence (CI) test methods have been proposed to solve the problem of causal discovery. These methods provide alternatives to test CI of x,y given Z by first removing the information of the controlling set Z from x and y , and then testing the independence between the two residuals R x,Z and R y,Z . When the residuals are linearly uncorrelated, the independence test between them is nontrivial. With the ability to calculate inner product in high-dimensional space, kernel-based methods are usually used to achieve this goal, but they are considerably time-consuming. In this paper, we test the independence between two linear combinations under linear structural equation model. We show that the dependence between the two residuals can be captured by the difference between the similarity of R x,Z and R y,Z and that of R x,Z and R r ( R r is an independent copy of R y,Z ) in high-dimensional space. With this result, we provide a new way to test CI based on the similarity between residuals, which is called SCIT — the abbreviation of Similarity-based CI Testing. Furthermore, we develop two versions of the proposal, called Kernel-SCIT and Neural-SCIT, respectively. Kernel-SCIT calculates the similarity by using kernel functions, while Neural-SCIT approximates the upper bound of the similarity by using deep neural networks. In both algorithms, random permutation tests are performed to control Type I error rate. The proposed tests are evaluated on (conditional) independence test and causal discovery with both synthetic and real datasets. Experimental results show that Kernel-SCIT is simpler yet more efficient and effective than the typical existing kernel-based methods HSIC and KCIT in the cases of small sample size, and Neural-SCIT can significantly boost the performance of CI testing when sufficient samples are available. The source code is available at https://github.com/xyw5vplus1/SCIT .

Assessment of Accuracy, User Engagement, and Themes of Eating Disorder Content in Social Media Short Videos

Social media is simultaneously home to communities of users who promote eating disorders as a lifestyle and users who advocate for recovery. As studies have confirmed an association between exposure to pro-eating disorder content and engaging in disordered eating behaviors, an examination of the accuracy of and interactions with information shared in these complex and contradictory communities can provide insights into content available to users at risk. To determine the associations among themes, accuracy of information, and user engagement of eating disorder content on a short video-sharing social media platform. This qualitative study included a thematic analysis of a sample of 200 TikTok videos, as well as user engagement metrics and content creator characteristics, between February and June 2022. Data were analyzed from March to June 2022. Content themes, accuracy of information, user engagement, and the associations among these factors were identified in a sample of eating disorder videos on a social media platform. Data were analyzed with Pearson χ2, analysis of variance, linear regression, and random permutation tests. Among the 200 videos assessed, 124 (62.0%) covered prorecovery content, 59 (29.5%) included pro-eating disorder content, and 17 (8.5%) contained anti-eating disorder content. Thematic analysis revealed 4 high-level themes: (1) encouraging the development or sustainment of eating disorders, (2) sharing physical or emotional experiences with eating disorders, (3) sharing narratives of recovery, and (4) social support. While the results of Pearson χ2 test indicated that videos in the prorecovery domain contained more accurate content than videos categorized in the pro-eating disorder and anti-eating disorder domains (χ24 = 157.92; P < .001), the results of analysis of variance demonstrated that there was no significant difference in user engagement between informative and misleading content (likes: F = 0.110; P = .95; comments: F = 2.031; P = .13; views: F = 0.534; P = .59; shares: F = 0.691; P = .50). Results of the 10 000 random permutation tests, in which all P values were between .40 and .60 regardless of the distances, suggested that there was no significant difference in user engagement among the 3 domains. This mixed-methods qualitative analysis of misleading eating disorder information on social media found that pro-eating disorder and prorecovery communities were prevalent. However, social media users in the prorecovery community created more informative than misleading content. Furthermore, the study found no significant difference between users' engagement with accurate vs misleading videos, which may suggest that false information alone does not influence the virality of videos.

Controlling the familywise error rate in widefield optical neuroimaging of functional connectivity in mice.

Statistical inference in functional neuroimaging is complicated by the multiple testing problem and spatial autocorrelation. Common methods in functional magnetic resonance imaging to control the familywise error rate (FWER) include random field theory (RFT) and permutation testing. The ability of these methods to control the FWER in optical neuroimaging has not been evaluated. We attempt to control the FWER in optical intrinsic signal imaging resting-state functional connectivity using both RFT and permutation inference at a nominal value of 0.05. The FWER was derived using a mass empirical analysis of real data in which the null is known to be true. Data from normal mice were repeatedly divided into two groups, and differences between functional connectivity maps were calculated with pixel-wise -tests. As the null hypothesis was always true, all positives were false positives. Gaussian RFT resulted in a higher than expected FWER with either cluster-based (0.15) or pixel-based (0.62) methods. -distribution RFT could achieve FWERs of 0.05 (cluster-based or pixel-based). Permutation inference always controlled the FWER. RFT can lead to highly inflated FWERs. Although -distribution RFT can be accurate, it is sensitive to statistical assumptions. Permutation inference is robust to statistical errors and accurately controls the FWER.

Geology, climate, and hydrochemistry shape the spatial patterns of multiple diversity facets and functional traits of fishes

AbstractIn a large river with significant environmental variations in space (i.e., microhabitat) or over time (i.e., seasonality and/or long‐term trend), discovering the spatial changes in fish diversity, distributions, and functional traits would provide an important foundation for the understanding of ecosystem structure and function. In the Pearl River in China, three datasets as fish distribution, species functional traits, and environmental variables were constructed and sorted to (1) test the relationships between and congruence of taxonomic diversity, functional diversity, and phylogenetic diversity by a randomisation procedure, (2) map the spatial pattern of diversity and the correlation to environmental variables by maximal information coefficient, and (3) determine the fish traits tendency under environmental changes by the fourth‐corner method. The Pearl River possesses the highest fish richness among all the rivers in China, with 438 freshwater fish species including 119 cave species. At basin scale, phylogenetic diversity showed faster change than functional diversity, suggesting a little divergence of functional traits despite high phenotypic or genetic diversity across species, as parallel evolution. However, at a small scale, when only considering cave fishes, the variation rate of species traits was faster than that of species increase, as evolutionary radiation in a variety of microhabitats. For all three aspects of diversity, random permutation tests confirmed that congruence values in each grid cell were significantly different from the random expectations. Besides widely accepted variables such as altitude, precipitation, and slope concerned with fish biogeography, hydrochemical variables showed influential impacts on the morphological and growth traits of fishes in the Pearl River characterised by the dominance of the karst habitat.

Novel application of 1H NMR spectroscopy coupled with chemometrics for the authentication of dark chocolate

The applicability of 1H NMR spectroscopy coupled with chemometric in the quality control of dark chocolate was investigated for the first time to detect cocoa-butter equivalents (CBEs) above the allowed limit by European regulation. Blends of chocolate-fats with CBEs in the range 0–50 % were prepared and analyzed by 1H NMR spectroscopy. Datasets composed of peaks’ areas or spectral variables (fingerprinting) in glycerol region were tested for the creation of multivariate statistical models. Partial least-squares discriminant analysis (PLS-DA) and regression (PLS-R) methods were used to correctly identify the type of CBE and quantify its concentration respectively. The performances of the models created on the two datasets were evaluated in terms of chemometric indicators and compared. The robustness of models was investigated through the analysis of test sets and random permutation tests. Fingerprinting models revealed fruitful results in classifying and quantifying CBEs in blends demonstrating the applicability of NMR in chocolate quality control.

S794 Pathway Enrichment Analysis Reveals Ulcerative Colitis Patients With Non-Response to TNFi Therapy May Have More Biological Dysregulation

Introduction: Tumor necrosis factor-α inhibitors (TNFi) are essential to ulcerative colitis (UC) management, however only 50% of patients will achieve a clinical response during induction therapy. There is an unmet need to identify and predict UC therapy response. This study aims to analyze potential etiologies for lack of TNFi response using pathway enrichment analysis. Methods: Differential expression analysis between TNFi responders, non-responders, and healthy controls was conducted from Gene Expression Omnibus UC patient cohorts (Table) using the limma package. All datasets contained post-treatment mucosal gene expression data and corresponding response, defined as endoscopic healing at 4 to 8 weeks after treatment initiation. Enrichment analysis of signaling pathways from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database was performed to compare the gene expression profile dysregulation severity between responders and non-responders. Genes exclusive to each group for select significantly enriched pathways were identified. The difference between the number of responder and non-responder exclusive genes was computed and assessed using the random label permutation test. Significantly different pathways between non-responder-exclusive genes and responder-exclusive genes were reported. Results: Several differentially expressed genes between responders and healthy controls (R-set) and between non-responders and healthy controls (NR-set) were identified (Figure a). Multiple overlapping dysregulated genes were observed between the two groups. Non-responders had significantly more differentially expressed genes (Figure b). Pathway enrichment analysis on the R- and NR-sets demonstrated that 40 of 282 KEGG pathways were significantly enriched with non-responder genes (p< 0.05). Of the 40 pathways, 28 had significantly more NR-exclusive genes than R-exclusive genes (p< 0.05) (Figure c). The NR-set included unique differentially expressed genes involved in cytokine signaling, receptor mediation, and signal transduction. Conclusion: UC-relevant KEGG pathways are significantly more enriched and disrupted in non-responders compared to responders, suggesting that non-responders have more dysregulated biological pathways. Other enriched pathways highlight the role of inflammation, barrier integrity, and the intestinal microbiome in UC. This study illustrates the potential value of precision medicine to predict clinical response to TNFi in UC patients.Figure 1.: KEGG pathway enrichment analysis for genes differentially expressed in responders and non-responders at baseline with respect to healthy controls. (a) schematic illustration of the differential expression gene sets obtained by comparing different pairs of responders, non-responders, and healthy controls; (b) Venn diagram showing responders’ and non-responders’ differentially expressed genes at baseline with respect to healthy controls after merging infliximab- and golimumab-based cohorts. Merging is done by taking the intersection of differential expression gene sets from infliximab- and golimumab-based cohorts; (c) KEGG pathways significantly enriched with NR-gene set that also have significantly more NR-exclusive genes than R-exclusive genes. UC-related pathways are shown here. Table 1. - Number of patients from Gene Expression Omnibus UC patient cohort datasets included in analyses Cohort Dataset Control Responders Non-Responders Infliximab GSE16879 6 8 16 GSE23597 - 24 7 Golimumab GSE92415 21 32 27

Long-Term Effects of Yugoslav War

ABSTRACT We examine the long-term effects of civil war in former Yugoslavia on economic growth and development. To this end, we employ the synthetic control methodology, and estimate the missing counterfactual scenario for the long-run growth and development of former Yugoslav republics, in the absence of the civil war by matching their growth and development characteristics with non-Yugoslav countries at the similar stages of development. The results suggest that the civil war led to unprecedented long-run growth and development losses. In the absence of the war, per capita income levels of former Yugoslav republics would improve markedly with notable differences in the magnitude of the growth and development gains across the affected countries. The estimated effects of the civil war are robust to a large number of placebo checks, and falsification-based random permutation tests. The results show that not a single former Yugoslav republic down to the present day has achieved the level of per capita income predicted by their synthetic control groups in the years preceding the civil war. Our results show that the Yugoslav civil war appears to be a permanent institutional shock with long-lasting implications for economic growth and development.

A systematic evaluation of learning rate policies in training CNNs for brain tumor segmentation

Convolutional neural networks (CNNs) have recently emerged as a powerful approach for automatic segmentation of brain tumor subregions on 3D multi-parametric MRI scans. Learning rate is a crucial hyperparameter in the training of CNNs, impacting the performance of the learned model. Different learning rate policies trace unique trajectories in the optimization landscape that converge to local minima with varying generalization properties. In this work, we empirically evaluated nine learning rate policy-optimizer pairs with two state-of-the-art architectures, namely 2D slice-based U-Net and 3D DeepMedicRes, on an augmented brain tumor dataset of 534 subjects. Segmentation performance was quantified in terms of Dice similarity coefficient and Hausdorff distance metrics. The policies were ranked based on the final ranking score (FRS) employed by the BraTS challenge, with the statistical significance of the rankings evaluated by random permutation test. For 2D slice-based U-Net architecture, an overall ranking of learning rate policies showed that the polynomial decay policy with Adam optimizer significantly outperformed other policies for the task of individual and hierarchical segmentation of tumor subregions (p < 10−4). For 3D segment-based DeepMedicRes architecture, polynomial decay policy with Adam optimizer performed significantly better than all other policies, with the exception of polynomial decay with SGD optimizer for the same task (p < 10−4). Based on the FRS, polynomial decay policy with Adam and SGD optimizer occupied the top two positions respectively, but the difference was not statistically significant (p > 0.3). These findings were also validated on the BraTS 2019 Validation dataset which comprised of an additional 125 subjects.

Searchlight Classification Informative Region Mixture Model (SCIM): Identification of Cortical Regions Showing Discriminable BOLD Patterns in Event-Related Auditory fMRI Data.

The investigation of abstract cognitive tasks, e.g., semantic processing of speech, requires the simultaneous use of a carefully selected stimulus design and sensitive tools for the analysis of corresponding neural activity that are comparable across different studies investigating similar research questions. Multi-voxel pattern analysis (MVPA) methods are commonly used in neuroimaging to investigate BOLD responses corresponding to neural activation associated with specific cognitive tasks. Regions of significant activation are identified by a thresholding operation during multivariate pattern analysis, the results of which are susceptible to the applied threshold value. Investigation of analysis approaches that are robust to a large extent with respect to thresholding, is thus an important goal pursued here. The present paper contributes a novel statistical analysis method for fMRI experiments, searchlight classification informative region mixture model (SCIM), that is based on the assumption that the whole brain volume can be subdivided into two groups of voxels: spatial voxel positions around which recorded BOLD activity does convey information about the present stimulus condition and those that do not. A generative statistical model is proposed that assigns a probability of being informative to each position in the brain, based on a combination of a support vector machine searchlight analysis and Gaussian mixture models. Results from an auditory fMRI study investigating cortical regions that are engaged in the semantic processing of speech indicate that the SCIM method identifies physiologically plausible brain regions as informative, similar to those from two standard methods as reference that we compare to, with two important differences. SCIM-identified regions are very robust to the choice of the threshold for significance, i.e., less “noisy,” in contrast to, e.g., the binomial test whose results in the present experiment are highly dependent on the chosen significance threshold or random permutation tests that are additionally bound to very high computational costs. In group analyses, the SCIM method identifies a physiologically plausible pre-frontal region, anterior cingulate sulcus, to be involved in semantic processing that other methods succeed to identify only in single subject analyses.

High Dimensional Tissue-Based Spatial Analysis of the Tumor Microenvironment of Follicular Lymphoma Reveals Unique Immune Niches inside Malignant Follicles

High Dimensional Tissue-Based Spatial Analysis of the Tumor Microenvironment of Follicular Lymphoma Reveals Unique Immune Niches inside Malignant Follicles

Metabolomic profiling of dried blood spots reveals gender-specific discriminant models for the diagnosis of small cell lung cancer.

The accurate diagnosis of small cell lung cancer (SCLC) at initial presentation is essential to ensure appropriate treatment. No validated blood biomarkers that could distinguish SCLC from non-small cell lung cancer (NSCLC) has yet been developed. Dried blood spot (DBS) microsampling has gained increasing interest in biomarkers discovery. In this study, we first performed metabolomic profiling of DBS samples from 37 SCLC, 40 NSCLC, and 37 controls. Two gender-specific multianalyte discriminant models were established for males and females, respectively to distinguish SCLC from NSCLC and controls. The receiver operator characteristic (ROC) curve analysis showed the diagnostic accuracy of 95% (95% CI: 83%-100%) in males SCLC using five metabolites in DBS and 94% (95% CI: 74%-100%) for females using another set of five metabolites. The robustness of the models was confirmed by the random permutation tests (P < 0.01 for both). The performance of the discriminant models was further evaluated using a validation cohort with 78 subjects. The developed discriminant models yielded an accuracy of 91% and 81% for males and females, respectively, in the validation cohort. Our results highlighted the potential clinical utility of the metabolomic profiling of DBS as a convenient and effective approach for the diagnosis of SCLC.

Long-Term Effects of Yugoslav War

In this paper, we examine the long-term effects of civil war in former Yugoslavia on economic growth and development. To this end, we employ the synthetic control methodology, and construct the missing counterfactual scenario for the long-run growth and development of former Yugoslav countries in the absence of the civil war by matching their growth and development characteristics with non-Yugoslav countries at the parallel stages of development. The results suggest that the civil war led to unprecedented long-run growth and development losses. In the absence of the war, per capita income levels of former Yugoslav republics would improve markedly with notable differences in the magnitude of the growth and development gains. The estimated effects of the civil war are robust to a large number of placebo checks, and falsification-based random permutation tests. The results show that not a single former Yugoslav country down to the present day has achieved the level of per capita income predicted by their synthetic control groups in the years preceding the civil war. Our results show that the Yugoslav civil war appears to be a permanent institutional shock with long-lasting implications for economic growth and development.

Propylene Glycol in Free-Ranging Green Sea Turtle Plasma

Abstract Metabolomics is the study of metabolites, the small-molecular-weight end products of metabolism. Propylene glycol is a synthetic diol commonly used as antifreeze, as a humectant, and in the production of polyester compounds. In otherwise healthy animals, propylene glycol has generally been considered a contaminant, iatrogenic, or unexplained. We demonstrate the presence of propylene glycol in plasma of free-ranging apparently healthy green sea turtles Chelonia mydas and individuals impacted by cold stun syndrome, without iatrogenic administration or known sample processing contamination, using one- and two-dimensional proton nuclear magnetic resonance spectroscopy techniques. There was not a statistically significant difference in relative propylene glycol concentration between the two cohorts (two-sided random sampling two-sample permutation test, P = 0.842, R = 1,000). The presence of this metabolite raises important questions about sea turtle physiology and potential latent environmental contamination and serves as a starting point for future characterization of lipid metabolism and glycolysis in green sea turtles.

Random Permutation Testing Applied to Measurement Invariance Testing with Ordered-Categorical Indicators

We describe and evaluate a random permutation test of measurement invariance with ordered-categorical data. To calculate a p-value for the observed (∆)χ2, an empirical reference distribution is built by repeatedly shuffling the grouping variable, then saving the χ2 from a configural model, or the ∆χ2 between configural and scalar-invariance models, fitted to each permuted dataset. The current gold standard in this context is a robust mean- and variance-adjusted ∆χ2 test proposed by Satorra (2000), which yields inflated Type I errors, particularly when thresholds are asymmetric, unless samples sizes are quite large (Bandalos, 2014; Sass et al., 2014). In a Monte Carlo simulation, we compare permutation to three implementations of Satorra’s robust χ2 across a variety of conditions evaluating configural and scalar invariance. Results suggest permutation can better control Type I error rates while providing comparable power under conditions that the standard robust test yields inflated errors.

Sevoflurane-medicated the pathway of chemokine receptors bind chemokines in patients undergoing CABG

AbstractBackgroundWe aim to identify sevoflurane-induced modules and pathways in patients following coronary artery bypass graft (CABG) surgery, and to further elucidate the molecular mechanisms of the cardioprotective effects of sevoflurane.MethodsDifferential co-expression network (DCN) was constructed. Candidate modules were identified via three steps: selection of seed genes, search of modules using snowball sampling, and refinement of modules. Afterwards, the significance of the candidate modules was assessed. Ultimately, pathway analyses for genes in differential modules were implemented to illuminate the biological processes.ResultsOverall, 122 genes were identified to serve as seed genes. From every seed gene, we extracted 122 modules and the mean node size in a module was 3. By setting the classification accuracy cutoff at 0.9 and the number of nodes in a module at 5, 7 candidate modules were identified, including module 80, 82, 82, 84, 85, 86 and 89. Based on the random permutation test, we found that these 7 candidate modules were all differential ones. Moreover, pathway analysis showed that genes in the differential modules 80, 82, and 85 were all enriched in the pathway of chemokine receptors bind chemokines.ConclusionSevoflurane might exert cardioprotective functions in patients following CABG, partially through regulating the pathway of chemokine receptors bind chemokines.

Screening feature modules and pathways in glioma using EgoNet

AbstractBackgroundTo investigate differential egonetwork modules and pathways in glioma using EgoNet algorithm.MethodologyBased on microarray data, EgoNet algorithm mainly comprised three stages: construction of differential co-expression network (DCN); EgoNet algorithm used to identify candidate ego-network modules based on the increased classification accuracy; statistical significance for candidate modules using random permutation testing. After that, pathway enrichment analysis for differential ego-network modules was implemented to illuminate the biological processes.ResultsWe obtained 109 ego genes. From every ego gene, we progressively grew the ego-networks by levels; we extracted 109 ego-networks and the mean node size in an ego-network was 6. By setting the classification accuracy threshold at 0.90 and the count of nodes in an ego-network module at 10, we extracted 8 candidate ego-network modules. After random permutation test with 1000 times, 5 modules including module 59, 72, 78, 86, and 90 were identified to be significant. Of note, the genes of module 90 and 86 were enriched in the pathway of resolution of sister chromatid cohesion and mitotic prometaphase, respectively.ConclusionThe identified modules and their corresponding ego genes might be beneficial in revealing the pathology underlying glioma and give insight for future research of glioma.

Association of ARHGAP18 polymorphisms with schizophrenia in the Chinese-Han population.

Numerous developmental genes have been linked to schizophrenia (SZ) by case-control and genome-wide association studies, suggesting that neurodevelopmental disturbances are major pathogenic mechanisms. However, no neurodevelopmental deficit has been definitively linked to SZ occurrence, likely due to disease heterogeneity and the differential effects of various gene variants across ethnicities. Hence, it is critical to examine linkages in specific ethnic populations, such as Han Chinese. The newly identified RhoGAP ARHGAP18 is likely involved in neurodevelopment through regulation of RhoA/C. Here we describe four single nucleotide polymorphisms (SNPs) in ARHGAP18 associated with SZ across a cohort of >2000 cases and controls from the Han population. Two SNPs, rs7758025 and rs9483050, displayed significant differences between case and control groups both in genotype (P = 0.0002 and P = 7.54×10−6) and allelic frequencies (P = 4.36×10−5 and P = 5.98×10−7), respectively. The AG haplotype in rs7758025−rs9385502 was strongly associated with the occurrence of SZ (P = 0.0012, OR = 0.67, 95% CI = 0.48–0.93), an association that still held following a 1000-times random permutation test (P = 0.022). In an independently collected validation cohort, rs9483050 was the SNP most strongly associated with SZ. In addition, the allelic frequencies of rs12197901 remained associated with SZ in the combined cohort (P = 0.021), although not in the validation cohort alone (P = 0.251). Collectively, our data suggest the ARHGAP18 may confer vulnerability to SZ in the Chinese Han population, providing additional evidence for the involvement of neurodevelopmental dysfunction in the pathogenesis of schizophrenia.

Microarray and network-based identification of functional modules and pathways of active tuberculosis.

Diagnose of active tuberculosis (TB) is challenging and treatment response is also difficult to efficiently monitor. The aim of this study was to use an integrated analysis of microarray and network-based method to the samples from publically available datasets to obtain a diagnostic module set and pathways in active TB. Towards this goal, background protein-protein interactions (PPI) network was generated based on global PPI information and gene expression data, following by identification of differential expression network (DEN) from the background PPI network. Then, ego genes were extracted according to the degree features in DEN. Next, module collection was conducted by ego gene expansion based on EgoNetalgorithm. After that, differential expression of modules between active TB and controls was evaluated using random permutation test. Finally, biological significance of differential modules was detected by pathways enrichment analysis based on Reactome database, and Fisher's exact test was implemented to extract differential pathways for active TB. Totally, 47 ego genes and 47 candidate modules were identified from the DEN. By setting the cutoff-criteria of gene size >5 and classification accuracy ≥0.9, 7 ego modules (Module 4, Module 7, Module 9, Module 19, Module 25, Module 38 and Module 43) were extracted, and all of them had the statistical significance between active TB and controls. Then, Fisher's exact test was conducted to capture differential pathways for active TB. Interestingly, genes in Module 4, Module 25, Module 38, and Module 43 were enriched in the same pathway, formation of a pool of free 40S subunits. Significant pathway for Module 7 and Module 9 was eukaryotic translation termination, and for Module 19 was nonsense mediated decay enhanced by the exon junction complex (EJC). Accordingly, differential modules and pathways might be potential biomarkers for treating active TB, and provide valuable clues for better understanding of molecular mechanism of active TB.

Understanding Dominant Factors for Precipitation over the Great Lakes Region

Statistical modeling of local precipitation involves understanding local, regional and global factors informative of precipitation variability in a region. Modern machine learning methods for feature selection can potentially be explored for identifying statistically significant features from pool of potential predictors of precipitation. In this work, we consider sparse regression, which simultaneously performs feature selection and regression, followed by random permutation tests for selecting dominant factors. We consider average winter precipitation over Great Lakes Region in order to identify its dominant influencing factors.Experiments show that global climate indices, computed at different temporal lags, offer predictive information for winter precipitation. Further, among the dominant factors identified using randomized permutation tests, multiple climate indices indicate the influence of geopotential height patterns on winter precipitation.Using composite analysis, we illustrate that certain patterns are indeed typical in high and low precipitation years, and offer plausible scientific reasons for variations in precipitation.Thus, feature selection methods can be useful in identifying influential climate processes and variables, and thereby provide useful hypotheses over physical mechanisms affecting local precipitation.