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Introduction: Functional magnetic resonance imaging (fMRI) has become a fundamental tool for studying brain function. However, the presence of serial correlations in fMRI data complicates data analysis, violates the statistical assumptions of analyses methods, and can lead to incorrect conclusions in fMRI studies. Methods: In this paper, we show that conventional whitening procedures designed for data with longer repetition times (TRs) (>2 s) are inadequate for the increasing use of short-TR fMRI data. Furthermore, we comprehensively investigate the shortcomings of existing whitening methods and introduce an iterative whitening approach named "IDAR" (Iterative Data-adaptive Autoregressivemodel) to address these shortcomings. IDAR employs high-order autoregressive (AR) models with flexible and data-driven orders, offering the capability to model complex serial correlation structures in both short-TR and long-TR fMRI datasets. Results: Conventional whiteningmethods, such as AR(1), ARMA(1,1), and higher-order AR, were effective in reducing serial correlation in long-TR data but were largely ineffective in even reducing serial correlation in short-TR data. In contrast, IDAR significantly outperformed conventional methods in addressing serial correlation, power, and Type-I error for both long-TR and especially short-TR data. However, IDAR could not simultaneously address residual correlations and inflated Type-I error effectively. Discussion: This study highlights the urgent need to address the problem of serial correlation in short-TR (<1 s) fMRI data, which are increasingly used in the field. Although IDAR can address this issue for a wide range of applications and datasets, the complexity of short-TR data necessitates continued exploration and innovative approaches. These efforts are essential to simultaneously reduce serial correlations and control Type-I error rates without compromising analytical power. [ABSTRACT FROM AUTHOR]

We investigated non-motor symptoms in ALS using sequential questionnaires; here we report the findings of the second questionnaire. A social media platform (Twitter, now known as X) was used to publicize the questionnaires. Data were downloaded from SurveyMonkey and analyzed by descriptive statistics, comparison of means, and regression models. There were 182 people with ALS and 57 controls. The most important non-motor symptoms were cold limbs (60.4% cases, 14% controls, p = 9.67 x 10−10) and appetite loss (29.7% cases, 5.3% controls, p = 1.6 x 10−4). The weaker limb was most likely to feel cold (p = 9.67 x 10−10), and symptoms were more apparent in the evening and night. Appetite loss was reported as due to feeling full and the time taken to eat. People with ALS experienced medium-intensity pain, more usually shock-like pain than burning or cold-like pain, although the most prevalent type of pain was non-differentiated. Non-motor symptoms are an important feature of ALS. Further investigation is needed to understand their physiological basis and whether they represent phenotypic differences useful for subtyping ALS. [ABSTRACT FROM AUTHOR]

Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub‐population. Of particular interest in many biomedical settings are absence/presence qualitative interactions, which occur when an effect is present in one sub‐population but absent in another. Absence/presence interactions arise in emerging applications in precision medicine, where the objective is to identify a set of predictive biomarkers that have prognostic value for clinical outcomes in some sub‐population but not others. They also arise naturally in gene regulatory network inference, where the goal is to identify differences in networks corresponding to diseased and healthy individuals, or to different subtypes of disease; such differences lead to identification of network‐based biomarkers for diseases. In this paper, we argue that while the absence/presence hypothesis is important, developing a statistical test for this hypothesis is an intractable problem. To overcome this challenge, we approximate the problem in a novel inference framework. In particular, we propose to make inferences about absence/presence interactions by quantifying the relative difference in effect size, reasoning that when the relative difference is large, an absence/presence interaction occurs. The proposed methodology is illustrated through a simulation study as well as an analysis of breast cancer data from the Cancer Genome Atlas. [ABSTRACT FROM AUTHOR]

Context Autoantibodies directed against the 65-kilodalton isoform of glutamic acid decarboxylase (GAD65Abs) are markers of autoimmune type 1 diabetes (T1D) but are also present in patients with Latent Autoimmune Diabetes of Adults and autoimmune neuromuscular diseases, and also in healthy individuals. Phenotypic differences between these conditions are reflected in epitope-specific GAD65Abs and anti-idiotypic antibodies (anti-Id) against GAD65Abs. We previously reported that 7.8% of T2D patients in the GRADE study have GAD65Abs but found that GAD65Ab positivity was not correlated with beta-cell function, glycated hemoglobin (HbA1c), or fasting glucose levels. Context In this study, we aimed to better characterize islet autoantibodies in this T2D cohort. This is an ancillary study to NCT01794143. Methods We stringently defined GAD65Ab positivity with a competition assay, analyzed GAD65Ab-specific epitopes, and measured GAD65Ab-specific anti-Id in serum. Results Competition assays confirmed that 5.9% of the patients were GAD65Ab positive, but beta-cell function was not associated with GAD65Ab positivity, GAD65Ab epitope specificity or GAD65Ab-specific anti-Id. GAD65-related autoantibody responses in GRADE T2D patients resemble profiles in healthy individuals (low GAD65Ab titers, presence of a single autoantibody, lack of a distinct epitope pattern, and presence of anti-Id to diabetes-associated GAD65Ab). In this T2D cohort, GAD65Ab positivity is likely unrelated to the pathogenesis of beta-cell dysfunction. Conclusion Evidence for islet autoimmunity in the pathophysiology of T2D beta-cell dysfunction is growing, but T1D-associated autoantibodies may not accurately reflect the nature of their autoimmune process. [ABSTRACT FROM AUTHOR]

The article focuses on determining the etiology of infection in patients from the Ubon-Sepsis study, categorizing bacteremias and non-bacteremic cases of melioidosis, along with identifying culture-negative cases. Topics include the methodology of metabolomics analysis conducted by Metabolon, Inc., involving sample preparation, chromatography, mass spectrometry, and quality control procedures to analyze plasma samples obtained from study participants.

Rationale: The global burden of sepsis is greatest in low-resource settings. Melioidosis, infection with the gram-negative bacterium Burkholderia pseudomallei, is a frequent cause of fatal sepsis in endemic tropical regions such as Southeast Asia. Objectives: To investigate whether plasma metabolomics would identify biological pathways specific to melioidosis and yield clinically meaningful biomarkers. Methods: Using a comprehensive approach, differential enrichment of plasma metabolites and pathways was systematically evaluated in individuals selected from a prospective cohort of patients hospitalized in rural Thailand with infection. Statistical and bioinformatics methods were used to distinguish metabolomic features and processes specific to patients with melioidosis and between fatal and nonfatal cases. Measurements and Main Results: Metabolomic profiling and pathway enrichment analysis of plasma samples from patients with melioidosis (n = 175) and nonmelioidosis infections (n = 75) revealed a distinct immuno-metabolic state among patients with melioidosis, as suggested by excessive tryptophan catabolism in the kynurenine pathway and significantly increased levels of sphingomyelins and ceramide species. We derived a 12-metabolite classifier to distinguish melioidosis from other infections, yielding an area under the receiver operating characteristic curve of 0.87 in a second validation set of patients. Melioidosis nonsurvivors (n = 94) had a significantly disturbed metabolome compared with survivors (n = 81), with increased leucine, isoleucine, and valine metabolism, and elevated circulating free fatty acids and acylcarnitines. A limited eight-metabolite panel showed promise as an early prognosticator of mortality in melioidosis. Conclusions: Melioidosis induces a distinct metabolomic state that can be examined to distinguish underlying pathophysiological mechanisms associated with death. A 12-metabolite signature accurately differentiates melioidosis from other infections and may have diagnostic applications. [ABSTRACT FROM AUTHOR]

While motor symptoms are well-known in ALS, non-motor symptoms are often under-reported and may have a significant impact on quality of life. In this study, we aimed to examine the nature and extent of non-motor symptoms in ALS. A 20-item questionnaire was developed covering the domains of autonomic function, sleep, pain, gastrointestinal disturbance, and emotional lability, posted online and shared on social media platforms to target people with ALS and controls. A total of 1018 responses were received, of which 927 were complete from 506 people with ALS and 421 unaffected individuals. Cold limbs (p 1.66 × 10−36), painful limbs (p 6.14 × 10−28), and urinary urgency (p 4.70 × 10−23) were associated with ALS. People with ALS were more likely to report autonomic symptoms, pain, and psychiatric symptoms than controls (autonomic symptoms B = 0.043, p 6.10 × 10−5, pain domain B = 0.18, p 3.72 × 10−11 and psychiatric domain B = 0.173, p 1.32 × 10−4). Non-motor symptoms in ALS are common. The identification and management of non-motor symptoms should be integrated into routine clinical care for people with ALS. Further research is warranted to investigate the relationship between non-motor symptoms and disease progression, as well as to develop targeted interventions to improve the quality of life for people with ALS. [ABSTRACT FROM AUTHOR]

Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p < 4.9E-5, n up to 7289). Of these, 45 were replicated using SomaScan data, and three were replicated using Olink data at Bonferroni-corrected significance. Enrichment analysis linked the proteins associated with general cognitive function to cell signaling pathways and synapse architecture. Mendelian randomization analysis implicated higher levels of NECTIN2, a protein mediating viral entry into neuronal cells, with higher Alzheimer's disease (AD) risk (p = 2.5E-26). Levels of 14 other protein measures were implicated as consequences of AD susceptibility (p < 2.0E-4). Proteins implicated as causes or consequences of AD susceptibility may provide new insight into the potential relationship between immunity and AD susceptibility as well as potential therapeutic targets. Analysis of circulating proteins across multiple cohorts identifies proteins that are associated with cognitive function and may serve as targets to understand the relationship between immunity and Alzheimer's disease risk. [ABSTRACT FROM AUTHOR]

Amyotrophic lateral sclerosis and Parkinson's disease are neurodegenerative diseases of the motor system which are now recognized also to affect non-motor pathways. Non-motor symptoms have been acknowledged as important determinants of quality of life in Parkinson's disease, and there is increasing interest in understanding the extent and role of non-motor symptoms in amyotrophic lateral sclerosis. We therefore reviewed what is known about non-motor symptoms in amyotrophic lateral sclerosis, using lessons from Parkinson's disease. [ABSTRACT FROM AUTHOR]

The objective of this study was to compare two estrus induction protocols in Shal ewes (Iranian native breed) during the non-breeding season using progesterone injections and an intravaginal sponge. Non pregnant ewes (n=180), considering their age and BCS were stratified randomly into two groups: progesterone injection (n=54; age: 33.0±3.21 months; BCS, 2.5±0.11) and vaginal sponge (n: 54; age: 37.5±3.58; BCS: 2.6±0.11). The progesterone injection group received four consecutive injections of progesterone (50 mg, SC), 3 days apart. Ewes in the sponge group received an intravaginal sponge (40 mg Flugestone acetate) for 14 days. An intramuscular injection of eCG (300 IU) was given concurrent with sponge withdrawal or 48 hrs after the last progesterone injection. Twelve hours after eCG, estrus was detected by rams equipped with harnesses. Laparoscopic AI was performed 50-56 hrs after eCG injection by fresh semen (100x106 sperm in 0.2 ml per uterine horn). Pregnancy was diagnosed by rectal ultrasonography, 35 days after insemination. The incidence of estrus (74.1%) was greater in the sponge group than injection group (38.9%). There were no differences between the two groups in terms of time to estrus and time to AI after eCG injection. There was no differences between the two groups in the interval from estrus to AI, fertility, prolificacy, and fecundity. Fertility was greater in ewes that were inseminated within 24 hrs after estrous detection in the progesterone injected group (76.2%) compared to the sponge group (42.5%). In summary, due to the similarity in fecundity, as the final indicator of reproductive performance, and less cost, availability and no intervention of reproductive tract, progesterone injection method could be advised for inducing estrus in ewes during the non-breeding season. [ABSTRACT FROM AUTHOR]

While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear. In these cases, using linear models may lead to inconsistent estimation of Granger causal interactions. We propose a class of nonlinear methods by applying structured multilayer perceptrons (MLPs) or recurrent neural networks (RNNs) combined with sparsity-inducing penalties on the weights. By encouraging specific sets of weights to be zero—in particular, through the use of convex group-lasso penalties—we can extract the Granger causal structure. To further contrast with traditional approaches, our framework naturally enables us to efficiently capture long-range dependencies between series either via our RNNs or through an automatic lag selection in the MLP. We show that our neural Granger causality methods outperform state-of-the-art nonlinear Granger causality methods on the DREAM3 challenge data. This data consists of nonlinear gene expression and regulation time courses with only a limited number of time points. The successes we show in this challenging dataset provide a powerful example of how deep learning can be useful in cases that go beyond prediction on large datasets. We likewise illustrate our methods in detecting nonlinear interactions in a human motion capture dataset. [ABSTRACT FROM AUTHOR]

Islet autoimmunity may contribute to β-cell dysfunction in type 2 diabetes (T2D). Its prevalence and clinical significance have not been rigorously determined. In this ancillary study to the Glycemia Reduction Approaches in Diabetes-A Comparative Effectiveness (GRADE) Study, we investigated the prevalence of cellular and humoral islet autoimmunity in patients with T2D duration 4·0±3·0 y, HbA1c 7·5±0·5% on metformin alone. We measured T cell autoreactivity against islet proteins, islet autoantibodies against GAD65, IA2, ZnT8, and β-cell function. Cellular islet autoimmunity was present in 41·3%, humoral islet autoimmunity in 13·5%, and both in 5·3%. β-cell function calculated as iAUC-CG and ΔC-peptide(0- 30)/Δglucose(0-30) from an oral glucose tolerance test was lower among T cell-positives (T+) than T cell-negatives (T-) using two different adjustments for insulin sensitivity (iAUC-CG: 13·2% [95% CI 0·3, 24·4%] or 11·4% [95% CI 0·4, 21·2%] lower; ΔC-peptide(0-30)/Δglucose(0-30)) 19% [95% CI 3·1, 32·3%] or 17·7% [95% CI 2·6, 30·5%] lower). T+ patients had 17% higher HbA1c (95% CI 0·07, 0·28) and 7·7 mg/dL higher fasting plasma glucose levels (95% CI 0·2,15·3) than T- patients. We conclude that islet autoimmunity is much more prevalent in T2D patients than previously reported. T cell-mediated autoimmunity is associated with diminished β-cell function and worse glycemic control. [ABSTRACT FROM AUTHOR]

Differences between biological networks corresponding to disease conditions can help delineate the underlying disease mechanisms. Existing methods for differential network analysis do not account for dependence of networks on covariates. As a result, these approaches may detect spurious differential connections induced by the effect of the covariates on both the disease condition and the network. To address this issue, we propose a general covariate-adjusted test for differential network analysis. Our method assesses differential network connectivity by testing the null hypothesis that the network is the same for individuals who have identical covariates and only differ in disease status. We show empirically in a simulation study that the covariate-adjusted test exhibits improved type-I error control compared with naïve hypothesis testing procedures that do not account for covariates. We additionally show that there are settings in which our proposed methodology provides improved power to detect differential connections. We illustrate our method by applying it to detect differences in breast cancer gene co-expression networks by subtype. [ABSTRACT FROM AUTHOR]

Estimation of density functions supported on general domains arises when the data are naturally restricted to a proper subset of the real space. This problem is complicated by typically intractable normalizing constants. Score matching provides a powerful tool for estimating densities with such intractable normalizing constants but as originally proposed is limited to densities on |$\mathbb{R}^m$| and |$\mathbb{R}_+^m$|⁠. In this paper, we offer a natural generalization of score matching that accommodates densities supported on a very general class of domains. We apply the framework to truncated graphical and pairwise interaction models and provide theoretical guarantees for the resulting estimators. We also generalize a recently proposed method from bounded to unbounded domains and empirically demonstrate the advantages of our method. [ABSTRACT FROM AUTHOR]

Assuming stationarity is unrealistic in many time series applications. A more realistic alternative is to assume piecewise stationarity, where the model can change at potentially many change points. We propose a three-stage procedure for simultaneous estimation of change points and parameters of high-dimensional piecewise vector autoregressive (VAR) models. In the first step, we reformulate the change point detection problem as a high-dimensional variable selection one, and solve it using a penalized least square estimator with a total variation penalty. We show that the penalized estimation method over-estimates the number of change points, and propose a selection criterion to identify the change points. In the last step of our procedure, we estimate the VAR parameters in each of the segments. We prove that the proposed procedure consistently detects the number and location of change points, and provides consistent estimates of VAR parameters. The performance of the method is illustrated through several simulated and real data examples. for this article are available online. [ABSTRACT FROM AUTHOR]

The PC and FCI algorithms are popular constraint-based methods for learning the structure of directed acyclic graphs (DAGs) in the absence and presence of latent and selection variables, respectively. These algorithms (and their order-independent variants, PC-stable and FCI-stable) have been shown to be consistent for learning sparse high-dimensional DAGs based on partial correlations. However, inferring conditional independences from partial correlations is valid if the data are jointly Gaussian or generated from a linear structural equation model—an assumption that may be violated in many applications. To broaden the scope of high-dimensional causal structure learning, we propose nonparametric variants of the PC-stable and FCI-stable algorithms that employ the conditional distance covariance (CdCov) to test for conditional independence relationships. As the key theoretical contribution, we prove that the high-dimensional consistency of the PC-stable and FCI-stable algorithms carry over to general distributions over DAGs when we implement CdCov-based nonparametric tests for conditional independence. Numerical studies demonstrate that our proposed algorithms perform nearly as good as the PC-stable and FCI-stable for Gaussian distributions, and offer advantages in non-Gaussian graphical models. [ABSTRACT FROM AUTHOR]

Background: Differential correlation networks are increasingly used to delineate changes in interactions among biomolecules. They characterize differences between omics networks under two different conditions, and can be used to delineate mechanisms of disease initiation and progression. Results: We present a new R package, CorDiffViz, that facilitates the estimation and visualization of differential correlation networks using multiple correlation measures and inference methods. The software is implemented in R, HTML and Javascript, and is available at https://github.com/sqyu/CorDiffViz. Visualization has been tested for the Chrome and Firefox web browsers. A demo is available at https://diffcornet.github.io/CorDiffViz/demo.html. Conclusions: Our software offers considerable flexibility by allowing the user to interact with the visualization and choose from different estimation methods and visualizations. It also allows the user to easily toggle between correlation networks for samples under one condition and differential correlations between samples under two conditions. Moreover, the software facilitates integrative analysis of cross-correlation networks between two omics data sets. [ABSTRACT FROM AUTHOR]

Existing software tools for topology-based pathway enrichment analysis are either computationally inefficient, have undesirable statistical power, or require expert knowledge to leverage the methods' capabilities. To address these limitations, we have overhauled NetGSA, an existing topology-based method, to provide a computationally-efficient user-friendly tool that offers interactive visualization. Pathway enrichment analysis for thousands of genes can be performed in minutes on a personal computer without sacrificing statistical power. The new software also removes the need for expert knowledge by directly curating gene-gene interaction information from multiple external databases. Lastly, by utilizing the capabilities of Cytoscape, the new software also offers interactive and intuitive network visualization. Author summary: With the increase in publicly available pathway topology information, topology-based pathway enrichment methods have become effective tools to analyze omics data. While many different methods are available, none are uniformly best. This paper focused on overhauling an existing topology-based method, NetGSA. The three key improvements included dramatically reduced computation time so pathway enrichment can be performed within minutes on a personal computer, integration of publicly available pathway topology databases so users can easily leverage the entire capabilities of the NetGSA method, and facilitating interactive visualization of results through an interface with Cytoscape, a popular network visualization tool. The improved NetGSA was compared to the previous version as well as other similar pathway topology-based methods and achieves competitive statistical power. With these improvements and NetGSA's flexibility to address a diverse set of problems and data types, we believe that the new NetGSA can be a useful tool for practitioners. The most recent software is available on GitHub at https://github.com/mikehellstern/netgsa. [ABSTRACT FROM AUTHOR]

Networks effectively capture interactions among components of complex systems, and have thus become a mainstay in many scientific disciplines. Growing evidence, especially from biology, suggest that networks undergo changes over time, and in response to external stimuli. In biology and medicine, these changes have been found to be predictive of complex diseases. They have also been used to gain insight into mechanisms of disease initiation and progression. Primarily motivated by biological applications, this article provides a review of recent statistical machine learning methods for inferring networks and identifying changes in their structures. This article is categorized under:Data: Types and Structure > Graph and Network DataStatistical Models > Graphical Models [ABSTRACT FROM AUTHOR]

We present a general approach for computing the convergence rates of nonparametric penalized regression estimators under misspecified smoothness, where the true regression function lies in the closure, but not the interior, of the space of smooth functions characterized by the penalty. The proposed approach uses an approximating/representative sequence that has a finite (but growing) penalty value. Here, to establish consistency, we balance the rate at which the penalty grows, with the approximation error of the representative sequence. We apply these ideas to the two most commonly used nonparametric penalties: total variation and Sobolev semi-norms. We give an upper bound for the rate at which we can estimate a function that exhibits bounded l + 1th-order total-variation or Sobolev complexity, using a kth-order total-variation or Sobolev penalty (for k > l + 1) respectively. Our bounds have a simple form that depends on k and l. In particular, we show that using total-variation penalties, we will achieve a rate better than n-1/2 for any l ≥ 0 and k ≥ 1. We evaluate the sharpness of our bounds based on total-variation penalties using a simulation. Empirically, for l = 0 our bound appears to be sharp; however for l ≥ 1, there appears to be a small gap between our upper bound and the empirical rate. [ABSTRACT FROM AUTHOR]

Background: Dietary patterns low in glycemic load are associated with reduced risk of cardiometabolic diseases. Improvements in serum lipid concentrations may play a role in these observed associations. Objective: We investigated how dietary patterns differing in glycemic load affect clinical lipid panel measures and plasma lipidomics profiles. Methods: In a crossover, controlled feeding study, 80 healthy participants (n = 40 men, n = 40 women), 18–45 y were randomized to receive low-glycemic load (LGL) or high glycemic load (HGL) diets for 28 days each with at least a 28-day washout period between controlled diets. Fasting plasma samples were collected at baseline and end of each diet period. Lipids on a clinical panel including total-, VLDL-, LDL-, and HDL-cholesterol and triglycerides were measured using an auto-analyzer. Lipidomics analysis using mass-spectrometry provided the concentrations of 863 species. Linear mixed models and lipid ontology enrichment analysis were implemented. Results: Lipids from the clinical panel were not significantly different between diets. Univariate analysis showed that 67 species on the lipidomics panel, predominantly in the triacylglycerol class, were higher after the LGL diet compared to the HGL (FDR < 0.05). Three species with FA 17:0 were lower after LGL diet with enrichment analysis (FDR < 0.05). Conclusion: In the context of controlled eucaloric diets with similar macronutrient distribution, these results suggest that there are relative shifts in lipid species, but the overall pool does not change. Further studies are needed to better understand in which compartment the different lipid species are transported in blood, and how these shifts are related to health outcomes. This trial was registered at clinicaltrials.gov as NCT00622661. [ABSTRACT FROM AUTHOR]

In most organisms, dietary restriction (DR) increases lifespan. However, several studies have found that genotypes within the same species vary widely in how they respond to DR. To explore the mechanisms underlying this variation, we exposed 178 inbred Drosophila melanogaster lines to a DR or ad libitum (AL) diet, and measured a panel of 105 metabolites under both diets. Twenty four out of 105 metabolites were associated with the magnitude of the lifespan response. These included proteinogenic amino acids and metabolites involved in α-ketoglutarate (α-KG)/glutamine metabolism. We confirm the role of α-KG/glutamine synthesis pathways in the DR response through genetic manipulations. We used covariance network analysis to investigate diet-dependent interactions between metabolites, identifying the essential amino acids threonine and arginine as "hub" metabolites in the DR response. Finally, we employ a novel metabolic and genetic bipartite network analysis to reveal multiple genes that influence DR lifespan response, some of which have not previously been implicated in DR regulation. One of these is CCHa2R, a gene that encodes a neuropeptide receptor that influences satiety response and insulin signaling. Across the lines, variation in an intronic single nucleotide variant of CCHa2R correlated with variation in levels of five metabolites, all of which in turn were correlated with DR lifespan response. Inhibition of adult CCHa2R expression extended DR lifespan of flies, confirming the role of CCHa2R in lifespan response. These results provide support for the power of combined genomic and metabolomic analysis to identify key pathways underlying variation in this complex quantitative trait. Author summary: Dietary restriction extends lifespan across most organisms in which it has been tested. However, several studies have now demonstrated that this effect can vary dramatically across different genotypes within a population. Within a population, dietary restriction might be beneficial for some, yet detrimental for others. Here, we measure the metabolome of 178 genetically characterized fly strains on fully fed and restricted diets. The fly strains vary widely in their lifespan response to dietary restriction. We then use information about each strain's genome and metabolome (a measure of small molecules circulating in flies) to pinpoint cellular pathways that govern this variation in response. We identify a novel pathway involving the gene CCHa2R, which encodes a neuropeptide receptor that has not previously been implicated in dietary restriction or age-related signaling pathways. This study demonstrates the power of leveraging systems biology and network biology methods to understand how and why different individuals vary in their response to health and lifespan-extending interventions. [ABSTRACT FROM AUTHOR]

For multiple testing problems, Benjamini and Hochberg (1995) proposed the false discovery rate (FDR) as an alternative to the family-wise error rate (FWER). Since then, researchers have provided many proofs to control the FDR under different assumptions. Storey et al. (2004) showed that the rejection threshold of a BH step-up procedure is a stopping time with respect to the reverse filtration generated by the pvalues and proposed a new proof based on the martingale theory. Following this work, martingale methods have been widely used to establish FDR control in various settings, but have been primarily applied to reverse filtration only. However, forward filtration can be more amenable for generalized and adaptive FDR controlling procedures. In this paper, we present a new proof, based on forward filtration, for step-down FDR controlling procedures that start from small p-values and update the rejection regions as larger p-values are observed. [ABSTRACT FROM AUTHOR]

Background: Low–glycemic load dietary patterns, characterized by consumption of whole grains, legumes, fruits, and vegetables, are associated with reduced risk of several chronic diseases. Methods: Using samples from a randomized, controlled, crossover feeding trial, we evaluated the effects on metabolic profiles of a low-glycemic whole-grain dietary pattern (WG) compared with a dietary pattern high in refined grains and added sugars (RG) for 28 d. LC-MS-based targeted metabolomics analysis was performed on fasting plasma samples from 80 healthy participants (n = 40 men, n = 40 women) aged 18–45 y. Linear mixed models were used to evaluate differences in response between diets for individual metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG)– defined pathways and 2 novel data-driven analyses were conducted to consider differences at the pathway level. Results: There were 121 metabolites with detectable signal in >98% of all plasma samples. Eighteen metabolites were significantly different between diets at day 28 [false discovery rate (FDR) < 0.05]. Inositol, hydroxyphenylpyruvate, citrulline, ornithine, 13-hydroxyoctadecadienoic acid, glutamine, and oxaloacetate were higher after the WG diet than after the RG diet, whereas melatonin, betaine, creatine, acetylcholine, aspartate, hydroxyproline, methylhistidine, tryptophan, cystamine, carnitine, and trimethylamine were lower. Analyses using KEGG-defined pathways revealed statistically significant differences in tryptophan metabolism between diets, with kynurenine and melatonin positively associated with serum C-reactive protein concentrations. Novel data-driven methods at the metabolite and network levels found correlations among metabolites involved in branched-chain amino acid (BCAA) degradation, trimethylamine-N-oxide production, and β oxidation of fatty acids (FDR < 0.1) that differed between diets, with more favorable metabolic profiles detected after the WG diet. Higher BCAAs and trimethylamine were positively associated with homeostasis model assessment-insulin resistance. Conclusions: These exploratory metabolomics results support beneficial effects of a low–glycemic load dietary pattern characterized by whole grains, legumes, fruits, and vegetables, compared with a diet high in refined grains and added sugars on inflammation and energy metabolism pathways. [ABSTRACT FROM AUTHOR]

We consider the problem of nonparametric regression with a potentially large number of covariates. We propose a convex, penalized estimation framework that is particularly well suited to high-dimensional sparse additive models and combines the appealing features of finite basis representation and smoothing penalties. In the case of additive models, a finite basis representation provides a parsimonious representation for fitted functions but is not adaptive when component functions possess different levels of complexity. In contrast, a smoothing spline-type penalty on the component functions is adaptive but does not provide a parsimonious representation. Our proposal simultaneously achieves parsimony and adaptivity in a computationally efficient way. We demonstrate these properties through empirical studies and show that our estimator converges at the minimax rate for functions within a hierarchical class. We further establish minimax rates for a large class of sparse additive models. We also develop an efficient algorithm that scales similarly to the lasso with the number of covariates and sample size. [ABSTRACT FROM AUTHOR]

Suppose we want to infer the conditional independence relationships among components of a random vector Graph HT ht . Then Graph HT ht if either Graph HT ht or Graph HT ht is nonzero. Alternatively, one may choose to include an edge only when both Graph HT ht and Graph HT ht are nonzero. [Extracted from the article]

MicroRNAs (miRNAs) are short non-coding RNAs which bind to mRNAs and regulate their expression. MiRNAs have been found to be associated with initiation and progression of many complex diseases. Investigating miRNAs and their targets can thus help develop new therapies by designing anti-miRNA oligonucleotides. While existing computational approaches can predict miRNA targets, these predictions have low accuracy. In this paper, we propose a two-step approach to refine the results of sequence-based prediction algorithms. The first step, which is based on our previous work, uses an ensemble learning approach that combines multiple existing methods. The second step utilizes support vector machine (SVM) classifiers in one- and two-class modes to infer miRNA-mRNA interactions based on both binding features, as well as network features extracted from gene regulatory network. Experimental results using two real data sets from TCGA indicate that the use of two-class SVM classification significantly improves the precision of miRNA-mRNA prediction. [ABSTRACT FROM AUTHOR]

Complex diseases such as cancers usually result from accumulated disturbance of pathways instead of the disruptions of one or a few major genes. As opposed to single-platform analyses, it is likely that integrating diverse molecular regulatory elements and their interactions can lead to more insights on pathway-level disturbances of biological systems and their potential consequences in disease development and progression. To explore the benefit of pathway-based analysis, we focus on multi-platform genomics, epigenomics, and transcriptomics (-omics, for short) from 11 cancer types collected by The Cancer Genome Atlas project. Specifically, we use a well-studied oncogenic pathway, the BRAF pathway, to investigate the relevant copy number variants (CNVs), methylations, and gene expressions, and quantify their effects on discovering tumor-specific aberrations across multiple tumor lineages. We also perform simulation studies to further investigate the effects of network topology and multiple omics on dissecting pathway disturbances. Our analysis shows that adding molecular regulatory elements such as CNVs and/or methylations to the baseline mRNA molecules can improve our power of discovering tumorous aberrances. Also, incorporating CNVs with the baseline mRNA molecules can be more beneficial than incorporating methylations. Moreover, employing regulatory topologies can improve the discoveries of tumorous aberrances. Finally, our analysis reveals similarities and differences among diverse cancer types based on disturbance of the BRAF pathway. [ABSTRACT FROM AUTHOR]

We consider the task of learning a dynamical systemfromhigh-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. We model the dynamical system nonparametrically as a systemof additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient. We propose a novel approach that does not involve derivative estimation. We show that the proposedmethod can consistently recover the true network structure even in high dimensions, and we demonstrate empirical improvement over competing approaches. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Despite recent and growing interest in using Twitter to examine human behavior and attitudes, there is still significant room for growth regarding the ability to leverage Twitter data for social science research. In particular, gleaning demographic information about Twitter users—a key component of much social science research—remains a challenge. This article develops an accurate and reliable data processing approach for social science researchers interested in using Twitter data to examine behaviors and attitudes, as well as the demographic characteristics of the populations expressing or engaging in them. Using information gathered from Twitter users who state an intention to not vote in the 2012 presidential election, we describe and evaluate a method for processing data to retrieve demographic information reported by users that is not encoded as text (e.g., details of images) and evaluate the reliability of these techniques. We end by assessing the challenges of this data collection strategy and discussing how large-scale social media data may benefit demographic researchers. [ABSTRACT FROM AUTHOR]

Long-term use of aspirin is associated with lower risk of colorectal cancer and other cancers; however, the mechanism of chemopreventive effect of aspirin is not fully understood. Animal studies suggest that COX-2, NFκB signaling and Wnt/β-catenin pathways may play a role, but no clinical trials have systematically evaluated the biological response to aspirin in healthy humans. Using a high-density antibody array, we assessed the difference in plasma protein levels after 60 days of regular dose aspirin (325 mg/day) compared to placebo in a randomized double-blinded crossover trial of 44 healthy non-smoking men and women, aged 21–45 years. The plasma proteome was analyzed on an antibody microarray with ~3,300 full-length antibodies, printed in triplicate. Moderated paired t-tests were performed on individual antibodies, and gene-set analyses were performed based on KEGG and GO pathways. Among the 3,000 antibodies analyzed, statistically significant differences in plasma protein levels were observed for nine antibodies after adjusting for false discoveries (FDR adjusted p-value<0.1). The most significant protein was succinate dehydrogenase subunit C (SDHC), a key enzyme complex of the mitochondrial tricarboxylic acid (TCA) cycle. The other statistically significant proteins (NR2F1, MSI1, MYH1, FOXO1, KHDRBS3, NFKBIE, LYZ and IKZF1) are involved in multiple pathways, including DNA base-pair repair, inflammation and oncogenic pathways. None of the 258 KEGG and 1,139 GO pathways was found to be statistically significant after FDR adjustment. This study suggests several chemopreventive mechanisms of aspirin in humans, which have previously been reported to play a role in anti- or pro-carcinogenesis in cell systems; however, larger, confirmatory studies are needed. [ABSTRACT FROM AUTHOR]

Rationale: Understanding mechanisms of resistance to M. tuberculosis (M.tb) infection in humans could identify novel therapeutic strategies as it has for other infectious diseases, such as HIV. Objectives: To compare the early transcriptional response of M.tb-infected monocytes between Ugandan household contacts of tuberculosis patients who demonstrate clinical resistance to M.tb infection (cases) and matched controls with latent tuberculosis infection. Methods: Cases (n = 10) and controls (n = 18) were selected from a long-term household contact study in which cases did not convert their tuberculin skin test (TST) or develop tuberculosis over two years of follow up. We obtained genome-wide transcriptional profiles of M.tb-infected peripheral blood monocytes and used Gene Set Enrichment Analysis and interaction networks to identify cellular processes associated with resistance to clinical M.tb infection. Measurements and main results: We discovered gene sets associated with histone deacetylases that were differentially expressed when comparing resistant and susceptible subjects. We used small molecule inhibitors to demonstrate that histone deacetylase function is important for the pro-inflammatory response to in-vitro M.tb infection in human monocytes. Conclusions: Monocytes from individuals who appear to resist clinical M.tb infection differentially activate pathways controlled by histone deacetylase in response to in-vitro M.tb infection when compared to those who are susceptible and develop latent tuberculosis. These data identify a potential cellular mechanism underlying the clinical phenomenon of resistance to M.tb infection despite known exposure to an infectious contact. [ABSTRACT FROM AUTHOR]

The consumption pattern in the energy sector could be modified by adopting innovative strategies on both sides of energy supply and energy demand. The energy consumption could be given a great cut by relying on research-based strategies, target-oriented investments, and modern technologies. Also an effective way to reduce the energy consumption is efficient management of energy demand, which has many implications including fair pricing. By fair pricing or fairer pricing we mean each consumer to pay according to their amount of consumption on a progressive trend--the more energy the unit uses, the more it has to pay on average. A way to get closer to fair pricing is applying the nonlinear reward functions. The present article seeks to calculate E[Zρ(t)], the mean of consumption cost till the time t, and E[Tz], the mean time of passing from a certain price level to the next price level of z, with Z(t) as a reward process and ρ as the nonlinear reward function. [ABSTRACT FROM AUTHOR]

The "within diet" metabolomics data, as well as the lifespan data, are unaffected by this coding error. The authors discovered a coding error in one of the scripts used to generate the R objects for downstream analysis of the dataset used in this article. (B) Volcano plot of significance of the difference between metabolite abundance with DR reveals that almost all metabolites are highly significantly changed with DR. [Extracted from the article]

Motivation: Pathway enrichment analysis has become a key tool for biomedical researchers to gain insight into the underlying biology of differentially expressed genes, proteins and metabolites. It reduces complexity and provides a system-level view of changes in cellular activity in response to treatments and/or in disease states. Methods that use existing pathway network information have been shown to outperform simpler methods that only take into account pathway membership. However, despite significant progress in understanding the association amongst members of biological pathways, and expansion of data bases containing information about interactions of biomolecules, the existing network information may be incomplete or inaccurate and is not cell-type or disease condition-specific. Results: We propose a constrained network estimation framework that combines network estimation based on cell- and condition-specific high-dimensional Omics data with interaction information from existing data bases. The resulting pathway topology information is subsequently used to provide a framework for simultaneous testing of differences in expression levels of pathway members, as well as their interactions. We study the asymptotic properties of the proposed network estimator and the test for pathway enrichment, and investigate its small sample performance in simulated and real data settings. [ABSTRACT FROM AUTHOR]

Graph-constrained estimation methods encourage similarities among neighboring covariates presented as nodes of a graph, and can result in more accurate estimates, especially in high-dimensional settings. Variable selection approaches can then be utilized to select a subset of variables that are associated with the response. However, existing procedures do not provide measures of uncertainty of estimates. Further, the vast majority of existing approaches assume that available graph accurately captures the association among covariates; violations to this assumption could severely hurt the reliability of the resulting estimates. In this article, we present a new inference framework, called the Grace test, which produces coefficient estimates and corresponding p-values by incorporating the external graph information. We show, both theoretically and via numerical studies, that the proposed method asymptotically controls the type-I error rate regardless of the choice of the graph. We also show that when the underlying graph is informative, the Grace test is asymptotically more powerful than similar tests that ignore the external information. We study the power properties of the proposed test when the graph is not fully informative and develop a more powerful Grace-ridge test for such settings. Our numerical studies show that as long as the graph is reasonably informative, the proposed inference procedures deliver improved statistical power over existing methods that ignore external information. [ABSTRACT FROM AUTHOR]

Biomarkers that indicate the severity of hypoxic-ischemic brain injury and response to treatment and that predict neurodevelopmental outcomes are urgently needed to improve the care of affected neonates. We hypothesize that sequentially obtained plasma metabolomes will provide indicators of brain injury and repair, allowing for the prediction of neurodevelopmental outcomes. A total of 33 Macaca nemestrina underwent 0, 15 or 18 min of in utero umbilical cord occlusion (UCO) to induce hypoxic-ischemic encephalopathy and were then delivered by hysterotomy, resuscitated and stabilized. Serial blood samples were obtained at baseline (cord blood) and at 0.1, 24, 48, and 72 h of age. Treatment groups included nonasphyxiated controls (n = 7), untreated UCO (n = 11), UCO + hypothermia (HT; n = 6), and UCO + HT + erythropoietin (n = 9). Metabolites were extracted and analyzed using comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry and quantified by PARAFAC (parallel factor analysis). Using nontargeted discovery-based methods, we identified 63 metabolites as potential biomarkers. The changes in metabolite concentrations were characterized and compared between treatment groups. Further comparison determined that 8 metabolites (arachidonic acid, butanoic acid, citric acid, fumaric acid, lactate, malate, propanoic acid, and succinic acid) correlated with early and/or long-term neurodevelopmental outcomes. The combined outcomes of death or cerebral palsy correlated with citric acid, fumaric acid, lactate, and propanoic acid. This change in circulating metabolome after UCO may reflect cellular metabolism and biochemical changes in response to the severity of brain injury and have potential to predict neurodevelopmental outcomes. © 2015 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

We consider the problem of estimating the parameters in a pairwise graphical model in which the distribution of each node, conditioned on the others, may have a different exponential family form. We identify restrictions on the parameter space required for the existence of a well-defined joint density, and establish the consistency of the neighbourhood selection approach for graph reconstruction in high dimensions when the true underlying graph is sparse. Motivated by our theoretical results, we investigate the selection of edges between nodes whose conditional distributions take different parametric forms, and show that efficiency can be gained if edge estimates obtained from the regressions of particular nodes are used to reconstruct the graph. These results are illustrated with examples of Gaussian, Bernoulli, Poisson and exponential distributions. Our theoretical findings are corroborated by evidence from simulation studies. [ABSTRACT FROM PUBLISHER]

In recent years, there has been considerable interest in estimating conditional independence graphs in high dimensions. Most previous work assumed that the variables are multivariate Gaussian or that the conditional means of the variables are linearly related. Unfortunately, if these assumptions are violated, the resulting conditional independence estimates can be inaccurate. We propose a semiparametric method, graph estimation with joint additive models, which allows the conditional means of the features to take an arbitrary additive form. We present an efficient algorithm for computation of our estimator, and prove that it is consistent. We extend our method to estimation of directed graphs with known causal ordering. Using simulated data, we show that our method performs better than existing methods when there are nonlinear relationships among the features, and is comparable to methods that assume multivariate normality when the conditional means are linear. We illustrate our method on a cell signalling dataset. [ABSTRACT FROM AUTHOR]

In the high-dimensional regression setting, the elastic net produces a parsimonious model by shrinking all coefficients toward the origin. However, in certain settings, this behavior might not be desirable: if some features are highly correlated with each other and associated with the response, then we might wish to perform less shrinkage on the coefficients corresponding to that subset of features. We propose thecluster elastic net, which selectively shrinks the coefficients for such variables toward each other, rather than toward the origin. Instead of assuming that the clusters are known a priori, the cluster elastic net infers clusters of features from the data, on the basis of correlation among the variables as well as association with the response. These clusters are then used to more accurately perform regression. We demonstrate the theoretical advantages of our proposed approach, and explore its performance in a simulation study, and in an application to HIV drug resistance data. Supplementary materials are available online. [ABSTRACT FROM AUTHOR]

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g., wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network. [ABSTRACT FROM AUTHOR]