Linear Model-based Approach Research Articles

Improving ramification detection of St. Nicolas House Analysis

The St. Nicolas House Analysis (SNHA) is a new graph estimation method for detection of extensive interactions among variables. It operates by ranking absolute bivariate correlation coefficients in descending order thereby creating hierarchic association chains. The latter characterizes dependence structures of interacting variables which can be visualized in a corresponding network graph as a chain of end-to-end connected edges representing direct relationships between the connected nodes. The important advantage of this relatively new approach is that it produces less false positive edges resulting from indirect or transitive associations than expected with standard correlation or linear model-based approaches. Here we aim to improve the detection of ramifications in graphs by addition of different data processing layers to SNHA. They include the combinations of the extensions R-squared gaining(RSG) and linear model check(LMC). SNHA together with these so-called extensions were benchmarked against default SNHA and other reference methods available for the programming language R. In the end combinations of RSG, LMC and Bootstrapping improve SNHA performance across different network types, albeit at the cost of longer computation time.

Empirical Bayes functional models for hydrogen deuterium exchange mass spectrometry

Hydrogen deuterium exchange mass spectrometry (HDX-MS) is a technique to explore differential protein structure by examining the rate of deuterium incorporation for specific peptides. This rate will be altered upon structural perturbation and detecting significant changes to this rate requires a statistical test. To determine rates of incorporation, HDX-MS measurements are frequently made over a time course. However, current statistical testing procedures ignore the correlations in the temporal dimension of the data. Using tools from functional data analysis, we develop a testing procedure that explicitly incorporates a model of hydrogen deuterium exchange. To further improve statistical power, we develop an empirical Bayes version of our method, allowing us to borrow information across peptides and stabilise variance estimates for low sample sizes. Our approach has increased power, reduces false positives and improves interpretation over linear model-based approaches. Due to the improved flexibility of our method, we can apply it to a multi-antibody epitope-mapping experiment where current approaches are inapplicable due insufficient flexibility. Hence, our approach allows HDX-MS to be applied in more experimental scenarios and reduces the burden on experimentalists to produce excessive replicates. Our approach is implemented in the R-package “hdxstats”: https://github.com/ococrook/hdxstats.

Functional Connectivity Lateralisation Shift of Resting State Networks is Linked to Visuospatial Memory and White Matter Microstructure in Relapsing\u2013Remitting Multiple Sclerosis

Laterality patterns of resting state networks (RSN) change in various neuropsychiatric conditions. Multiple sclerosis (MS) causes neuro-cognitive symptoms involving dysfunctional large-scale brain networks. Yet, whether healthy laterality patterns of RSNs are maintained in MS and whether altered laterality patterns explain disease symptoms has not been explicitly investigated. We analysed functional MRI and diffusion tensor imaging data from 24 relapsing–remitting MS patients and 25 healthy participants. We performed group-level independent component analysis and used dual regression to estimate individual versions of well-established RSNs. Voxelwise laterality indices were calculated for each RSN. Group differences were assessed via a general linear model-based approach. The relationship between functional laterality and white matter microstructural asymmetry was assessed using Tract-Based Spatial Statistics. Spearman’s correlation was calculated between laterality indices and Brief International Cognitive Assessment for Multiple Sclerosis scores. Functional laterality of the dorsal attention network showed a significant leftward shift in the MS group in the posterior intraparietal sulcus (p < 0.033). Default-mode network laterality showed a significant leftward shift in the MS group in the angular gyrus (p < 0.005). Diminished dorsal attention network laterality was associated with increased fractional anisotropy asymmetry in the superior longitudinal fasciculus (p < 0.02). In the default-mode network, leftward laterality of the angular gyrus was associated with higher BVMT-R scores (R = − 0.52, p < 0.023). Our results confirm previous descriptions of RSN dysfunction in relapsing–remitting MS and show that altered functional connectivity lateralisation patterns of RSNs might contibute to cognitive performance and structural remodellation even in patients with mild clinical symptoms.

Respiration is a Confounder of the Closed Loop Relationship Between Mean Arterial Pressure and Mean Cerebral Blood Flow.

This study tested the hypothesis that respiration (RESP) is a confounder or suppressor of the closed loop relationship responsible for the cerebrovascular dynamical interactions as assessed from spontaneous variability of mean arterial pressure (MAP) and mean cerebral blood flow (MCBF). The evaluation was carried out in the information domain via transfer entropy (TE) estimated through a linear model-based approach comparing TE markers computed solely over MAP and MCBF series with TE indexes accounting for the eventual action of RESP over MAP and MCBF. We considered 11 patients (age: 76±5 yrs, 7 males) undergoing surgical aortic valve replacement (SAVR) at supine resting (REST) and during active standing (STAND) before and after SAVR surgery. The decrease of the predictive ability of MCBF to MAP when accounting for RESP compared to the one assessed when disregarding RESP suggested that RESP is a confounder of the link from MCBF to MAP along the Cushing reflex instead of being a suppressor. This result was more evident in POST when autonomic control was dramatically depressed and in an unchallenged condition such as REST. RESP did not affect significantly the link from MAP to MCBF along the pressure-to-flow relationship. Clarification of the type of RESP influence on the MAP-MCBF closed loop relationship could favor a deeper characterization of cerebrovascular interactions and the comprehension of cerebral autoregulation mechanisms.Clinical Relevance- This study suggests that respiration is a confounder of the closed loop relationship between MAP and MCBF, especially of the flow-to-pressure causal link. This result might open new possibilities in elucidating the mechanisms of cerebral autoregulation in healthy and pathological populations.

Cell type-aware analysis of RNA-seq data.

Most tissue samples are composed of different cell types. Differential expression analysis without accounting for cell type composition cannot separate the changes due to cell type composition or cell type-specific expression. We propose a computational framework to address these limitations: Cell Type Aware analysis of RNA-seq (CARseq). CARseq employs a negative binomial distribution that appropriately models the count data from RNA-seq experiments. Simulation studies show that CARseq has substantially higher power than a linear model-based approach and it also provides more accurate estimate of the rankings of differentially expressed genes. We have applied CARseq to compare gene expression of schizophrenia/autism subjects versus controls, and identified the cell types underlying the difference and similarities of these two neuron-developmental diseases. Our results are consistent with the results from differential expression analysis using single cell RNA-seq data.

Chain Reversion for Detecting Associations in Interacting Variables-St. Nicolas House Analysis.

(1) Background: We present a new statistical approach labeled as “St. Nicolas House Analysis” (SNHA) for detecting and visualizing extensive interactions among variables. (2) Method: We rank absolute bivariate correlation coefficients in descending order according to magnitude and create hierarchic “association chains” defined by sequences where reversing start and end point does not alter the ordering of elements. Association chains are used to characterize dependence structures of interacting variables by a graph. (3) Results: SNHA depicts association chains in highly, but also in weakly correlated data, and is robust towards spurious accidental associations. Overlapping association chains can be visualized as network graphs. Between independent variables significantly fewer associations are detected compared to standard correlation or linear model-based approaches. (4) Conclusion: We propose reversible association chains as a principle to detect dependencies among variables. The proposed method can be conceptualized as a non-parametric statistical method. It is especially suited for secondary data analysis as only aggregate information such as correlations matrices are required. The analysis provides an initial approach for clarifying potential associations that may be subject to subsequent hypothesis testing.

Anomaly detection of structural health monitoring data using the maximum likelihood estimation-based Bayesian dynamic linear model

Enormous data are continuously collected by the structural health monitoring system of civil infrastructures. The structural health monitoring data inevitably involve anomalies caused by sensors, transmission errors, or abnormal structural behaviors. It is important to identify the anomalies and find their origin (e.g. sensor fault or structural damage) to make correct interventions. Moreover, online anomaly identification of the structural health monitoring data is critical for timely structural condition assessment and decision-making. This study proposes an online approach for detecting anomalies of the structural health monitoring data based on the Bayesian dynamic linear model. In particular, Bayesian dynamic linear model, consisting of various components, is implemented to characterize the feature of real-time measurements. Expectation maximization algorithm and Kalman smoother are combined to estimate the Bayesian dynamic linear model parameters and generate log-likelihood functions. The subspace identification method is introduced to overcome the initialization issue of the expectation maximization algorithm. The log-likelihood difference of consecutive time steps is then used to determine thresholds without introducing extra anomaly detectors. The proposed Bayesian dynamic linear model-based approach is first illustrated by the simulation data and then applied to the structural health monitoring data collected from two long-span bridges. The results indicate that the proposed method exhibits good accuracy and high computational efficiency and also allows for reconstructing the strain measurements to replace anomalies.

Role of prefrontal cortex during Sudoku task: fNIRS study.

BackgroundSudoku is a popular cognitively stimulating leisure-time activity. Many studies have been directed toward finding an algorithm to solve Sudoku, but the investigation of the neural substrates involved in Sudoku has been challenging.MethodsSudoku task was divided into two steps to understand the differential function of the prefrontal cortex (PFC) while applying heuristic rules. PFC activity was recorded at 16 optode locations using functional near infrared spectroscopy. Classical two-way analysis of variance as well as general linear model-based approach was used to analyze the data from 28 noise-free recordings obtained from right-handed participants.Results Post hoc analysis showed a significant increase in oxyhemoglobin concentrations and decrease in deoxyhemoglobin concentrations at all 16 optode locations during step 1 (3 × 3 subgrids) and step 2 (easy level 9 × 9 Sudoku) when compared with the rest (p < 0.0001). Contrasting the step 2 – step 1 revealed that medial regions of PFC were preferentially activated.ConclusionBoth the medial and lateral regions of PFC are activated during Sudoku task. However, the medial regions of PFC play a differential role, especially when we consider searching and selecting the heuristic rules. Thus, Sudoku may be used for cognitive remediation training in neuropsychiatric disorders involving PFC.

RegSNPs-ASB: A Computational Framework for Identifying Allele-Specific Transcription Factor Binding From ATAC-seq Data.

Expression quantitative trait loci (eQTL) analysis is useful for identifying genetic variants correlated with gene expression, however, it cannot distinguish between causal and nearby non-functional variants. Because the majority of disease-associated SNPs are located in regulatory regions, they can impact allele-specific binding (ASB) of transcription factors and result in differential expression of the target gene alleles. In this study, our aim was to identify functional single-nucleotide polymorphisms (SNPs) that alter transcriptional regulation and thus, potentially impact cellular function. Here, we present regSNPs-ASB, a generalized linear model-based approach to identify regulatory SNPs that are located in transcription factor binding sites. The input for this model includes ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) raw read counts from heterozygous loci, where differential transposase-cleavage patterns between two alleles indicate preferential transcription factor binding to one of the alleles. Using regSNPs-ASB, we identified 53 regulatory SNPs in human MCF-7 breast cancer cells and 125 regulatory SNPs in human mesenchymal stem cells (MSC). By integrating the regSNPs-ASB output with RNA-seq experimental data and publicly available chromatin interaction data from MCF-7 cells, we found that these 53 regulatory SNPs were associated with 74 potential target genes and that 32 (43%) of these genes showed significant allele-specific expression. By comparing all of the MCF-7 and MSC regulatory SNPs to the eQTLs in the Genome-Tissue Expression (GTEx) Project database, we found that 30% (16/53) of the regulatory SNPs in MCF-7 and 43% (52/122) of the regulatory SNPs in MSC were also in eQTL regions. The enrichment of regulatory SNPs in eQTLs indicated that many of them are likely responsible for allelic differences in gene expression (chi-square test, p-value < 0.01). In summary, we conclude that regSNPs-ASB is a useful tool for identifying causal variants from ATAC-seq data. This new computational tool will enable efficient prioritization of genetic variants identified as eQTL for further studies to validate their causal regulatory function. Ultimately, identifying causal genetic variants will further our understanding of the underlying molecular mechanisms of disease and the eventual development of potential therapeutic targets.

Linear and non-linear brain–heart and brain–brain interactions during sleep

In this study, the physiological networks underlying the joint modulation of the parasympathetic component of heart rate variability (HRV) and of the different electroencephalographic (EEG) rhythms during sleep were assessed using two popular measures of directed interaction in multivariate time series, namely Granger causality (GC) and transfer entropy (TE). Time series representative of cardiac and brain activities were obtained in 10 young healthy subjects as the normalized high frequency (HF) component of HRV and EEG power in the δ, θ, α, σ, and β bands, measured during the whole duration of sleep. The magnitude and statistical significance of GC and TE were evaluated between each pair of series, conditional on the remaining series, using respectively a linear model-based approach exploiting regression models, and a nonlinear model-free approach combining nearest-neighbor entropy estimation with a procedure for dimensionality reduction. The contribution of nonlinear dynamics to the TE was also assessed using surrogate data. GC and TE consistently detected structured networks of physiological interactions, with links directed predominantly from HRV to the EEG waves in the brain–heart network, and from the σ and β EEG waves to the δ, θ, and α waves in the brain–brain network. While these common patterns supported the suitability of a linear model-based analysis, we also found a significant contribution of nonlinear dynamics, particularly involving the information transferred out of the δ node in the two networks. This suggested the importance of nonparametric TE estimation for evidencing the fine structure of the physiological networks underlying the autonomic regulation of cardiac and brain functions during sleep.

A general linear model-based approach for inferring selection to climate

BackgroundMany efforts have been made to detect signatures of positive selection in the human genome, especially those associated with expansion from Africa and subsequent colonization of all other continents. However, most approaches have not directly probed the relationship between the environment and patterns of variation among humans. We have designed a method to identify regions of the genome under selection based on Mantel tests conducted within a general linear model framework, which we call MAntel-GLM to Infer Clinal Selection (MAGICS). MAGICS explicitly incorporates population-specific and genome-wide patterns of background variation as well as information from environmental values to provide an improved picture of selection and its underlying causes in human populations.ResultsOur results significantly overlap with those obtained by other published methodologies, but MAGICS has several advantages. These include improvements that: limit false positives by reducing the number of independent tests conducted and by correcting for geographic distance, which we found to be a major contributor to selection signals; yield absolute rather than relative estimates of significance; identify specific geographic regions linked most strongly to particular signals of selection; and detect recent balancing as well as directional selection.ConclusionsWe find evidence of selection associated with climate (P < 10-5) in 354 genes, and among these observe a highly significant enrichment for directional positive selection. Two of our strongest 'hits’, however, ADRA2A and ADRA2C, implicated in vasoconstriction in response to cold and pain stimuli, show evidence of balancing selection. Our results clearly demonstrate evidence of climate-related signals of directional and balancing selection.

Sensor-based Navigation and Integrated Control of Ambient Intelligent Wheeled Robots with Tire-Ground Interaction Uncertainties

This paper regards the synthesis of intelligent non-visual sensor-based navigation, motion planning and the integrated control of indoor ambient adaptive wheel- based mobile robots in unknown environments with tire-ground interaction uncertainties. The problem relates to searching appropriate techniques how to navigate towards a target position in an unknown environment when the obstacles to avoid are discovered in real time, and how to maintain collision free motion of a high dynamic performance. Environments characterized by variable ground surface conditions with immobile obstacles of different shapes and sizes will be considered in the paper as unexpected disturbances, i.e. system uncertainties. The tools developed to address this issue thus consist of the combination of cognitive motion planning and control theory techniques, including a non- linear model-based approach. Two characteristic approaches to integrated control are evaluated in the paper: a kinematical as well as dynamic one, in the sense of control efficiency and robustness to the environmental and model uncertainties. Characteristic simulation tests are performed to verify the proposed algorithms.

Nonlinear analysis of Type 1 Diabetes Models by Differential Geometric Approach

The control of physiological systems is a highly demanding task. The requirements are strict and there is a little margin of error, since failure can directly endanger the patient's life. In the same time the performance of the available sensors and actuators are limited in most cases, leaving even higher burden on the control algorithm. Finally the models themselves, which can describe the biophysical and biochemical processes that are most significantly linked to the system we wish to regulate, are rather complex and nonlinear in nature. In general, linear model-based approaches are used, but linearization gives a first source of errors in the further development. The aim of this paper is to investigate two frequently used models describing the metabolism of the human body in case of Type 1 Diabetes Mellitus (T1DM) from nonlinear control perspective: the model presented by Magni et al. (2009) and Hovorka et al. (2004). These models will be investigated using differential geometric approach for the first time.

A Preliminary Study of Genetic Factors That Influence Susceptibility to Bovine Tuberculosis in the British Cattle Herd

Associations between specific host genes and susceptibility to Mycobacterial infections such as tuberculosis have been reported in several species. Bovine tuberculosis (bTB) impacts greatly the UK cattle industry, yet genetic predispositions have yet to be identified. We therefore used a candidate gene approach to study 384 cattle of which 160 had reacted positively to an antigenic skin test (‘reactors’). Our approach was unusual in that it used microsatellite markers, embraced high breed diversity and focused particularly on detecting genes showing heterozygote advantage, a mode of action often overlooked in SNP-based studies. A panel of neutral markers was used to control for population substructure and using a general linear model-based approach we were also able to control for age. We found that substructure was surprisingly weak and identified two genomic regions that were strongly associated with reactor status, identified by markers INRA111 and BMS2753. In general the strength of association detected tended to vary depending on whether age was included in the model. At INRA111 a single genotype appears strongly protective with an overall odds ratio of 2.2, the effect being consistent across nine diverse breeds. Our results suggest that breeding strategies could be devised that would appreciably increase genetic resistance of cattle to bTB (strictly, reduce the frequency of incidence of reactors) with implications for the current debate concerning badger-culling.

A Nonlinear Algorithm for Seasonal Adjustment in Multiplicative Component Decompositions

We propose a new model-based, nonlinear method for seasonally adjusting time series in a multiplicative components model. The method seeks to reduce the bias inherent in linear model-based approaches, while at the same time preserving the flexibility of parametric methods. We discuss the problem of bias and the concept of recovery, and demonstrate the favorable properties of the proposed algorithm on several synthetic series.

Maternal and fetal variation in genes of cholesterol metabolism is associated with preterm delivery

To examine the contribution of variants in fetal and maternal cholesterol metabolism genes in preterm delivery (PTD). A total of 40 single-nucleotide polymorphisms (SNPs) in 16 genes related to cholesterol metabolism were examined for 414 preterm infants (gestational ages 22 to 36 weeks; comprising 305 singletons and 109 twins) and at least 1 parent. Fetal effects were assessed using the transmission disequilibrium test (TDT) for each SNP, followed by a log linear model-based approach to utilize families with missing parental genotypes for those SNPs showing significance under TDT. Genetic variant effects were examined for a role in PTD, gestational age and birth weight. Maternal effects were estimated using a log linear model-based approach. Among singleton gestations, suggestive association (P<0.01 without adjusting for multiple comparisons) was found between birth weight and fetal DHCR7 gene/SNP combinations (rs1630498, P=0.002 and rs2002064, P=0.003). Among all gestations, suggestive associations were found between PTD and fetal HMGCR (rs2303152, P=0.002) and APOA1 (rs 5070, P=0.004). The result for HMGCR was further supported by the log linear model-based test in the single births (P=0.007) and in all births (P=0.006). New associations (APOE and ABCA1) were observed when birth weight was normalized for gestational age suggesting independent effects of variants on birth weight separate from effects on PTD. Testing for maternally mediated genetic effects has identified suggestive association between ABCA1 (rs4149313, P=0.004) and decreased gestational age. Variants in maternal and fetal genes for cholesterol metabolism were associated with PTD and decreased birth weight or gestational age in this study. Genetic markers may serve as one mechanism to identify high-risk mothers and fetuses for targeted nutritional treatment and/or prevention of low birth weight or PTD.

A Comparison of Predictive Methods in Extinction Risk Studies: Contrasts and Decision Trees

Over the last two decades an increasing emphasis has been placed on the importance of controlling for phylogeny when examining cross-species data; so-called comparative methods. These methods are appropriate for testing hypotheses about correlations between evolutionary events in the history of a clade and adaptive responses to those changes. When this approach is applied to extinction risk, possible correlations between evolutionary changes in, for example, body size or habitat specialisation and some measure(s) of current threat status are examined. However, there may be a mismatch here between the results of such studies, and the real, pragmatic needs of species conservation. This kind of approach certainly adds to our knowledge of some fundamental processes, but it is more difficult to see how this can be applied to conservation decision-making. For more practical purposes a decision-tree approach can be extremely useful. This paper illustrates the use of a contrasts based analysis of extinction risk compared with a decision-tree analysis for Galliformes (Aves). While the contrasts analyses concur with some general macroecological trends found in other studies, the decision-tree models provide lists of species predicted to be more at risk than current assessments would suggest. We argue that in practical terms, decision tree models might be more useful than a macroecological linear model-based approach.

Overcoming Test Setup Limitations by Applying Model-Based Testing to High-Precision ADCs

Test setup limitations, such as noise and parasitics, increasingly impede repeatable and accurate linearity measurements in high-volume production testing of high-precision data converters. Model-based testing has been shown to reduce the adverse effects of noise [14]. In this work, we present two enhancements of the linear model-based approach: one is a change of the modeling strategy in order to account for measurement errors induced, for example, by parasitics associated with the device contactor, and another is a Design-for-Test feature that significantly improves the model's ability to reduce the effect of measurement noise on the accuracy of the test outcome.