Translational bioinformatics in mental health: open access data sources and computational biomarker discovery.

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data.

Biologic modelling of periodontal disease progression.

AIM: To investigate the synergistic role of biologic markers from saliva, serum and plaque in modelling periodontitis disease progression. MATERIAL AND METHODS: This longitudinal study evaluated characteristics of disease progression in 114 patients with generalized moderate to severe periodontitis. The primary outcome was detection of sites with progressing attachment loss sites over 6 months in patients who received scaling and root planing or oral hygiene only. The predictive potential of 27 biomarkers in serum, whole saliva and subgingival plaque was evaluated using three classification algorithms (Support Vector Machines; Naïve Bayes Classifier; and Linear Discriminant Analysis) within an ensemble predictive modelling framework. RESULTS: Disease progression occurred in 24.6% of subjects (28/114). Predictive modelling using Naïve Bayes Classifier identified progressors best with sensitivity of ~89%. The use of the three classification algorithms revealed the concerted role of salivary matrix metalloproteinase-8, serum biomarkers (serum amyloid P, matrix metalloproteinase 1, bactericidal permeability-increasing protein, isoprostane) along with levels of Porphryomonas gingivalis and Tannerella forsythia in plaque in predicting progressors. CONCLUSIONS: Synergistic utility of baseline bacterial and inflammatory biomarkers from saliva, serum and plaque predicted disease progression.

An ensemble predictive modeling framework for breast cancer classification.

Molecular changes often precede clinical presentation of diseases and can be useful surrogates with potential to assist in informed clinical decision making. Recent studies have demonstrated the usefulness of modeling approaches such as classification that can predict the clinical outcomes from molecular expression profiles. While useful, a majority of these approaches implicitly use all molecular markers as features in the classification process often resulting in sparse high-dimensional projection of the samples often comparable to that of the sample size. In this study, a variant of the recently proposed ensemble classification approach is used for predicting good and poor-prognosis breast cancer samples from their molecular expression profiles. In contrast to traditional single and ensemble classifiers, the proposed approach uses multiple base classifiers with varying feature sets obtained from two-dimensional projection of the samples in conjunction with a majority voting strategy for predicting the class labels. In contrast to our earlier implementation, base classifiers in the ensembles are chosen based on maximal sensitivity and minimal redundancy by choosing only those with low average cosine distance. The resulting ensemble sets are subsequently modeled as undirected graphs. Performance of four different classification algorithms is shown to be better within the proposed ensemble framework in contrast to using them as traditional single classifier systems. Significance of a subset of genes with high-degree centrality in the network abstractions across the poor-prognosis samples is also discussed.

The periodontal war: microbes and immunity.

Maintenance of periodontal health or transition to a periodontal lesion reflects the continuous and ongoing battle between the vast microbial ecology in the oral cavity and the array of resident and emigrating inflammatory/immune cells in the periodontium. This war clearly signifies many 'battlefronts' representing the interface of the mucosal-surface cells with the dynamic biofilms composed of commensal and potential pathogenic species, as well as more recent knowledge demonstrating active invasion of cells and tissues of the periodontium leading to skirmishes in connective tissue, the locality of bone and even in the local vasculature. Research in the discipline has uncovered a concerted effort of the microbiome, using an array of survival strategies, to interact with other bacteria and host cells. These strategies aid in colonization by 'ambushing, infiltrating and outflanking' host cells and molecules, responding to local environmental changes (including booby traps for host biomolecules), communicating within and between genera and species that provide MASINT (Measurement and Signature Intelligence) to enhance sustained survival, sabotage the host inflammatory and immune responses and by potentially adopting a 'Fabian strategy' with a war of attrition and resulting disease manifestations. Additionally, much has been learned regarding the ever-increasing complexity of the host-response armamentarium at both cellular and molecular levels that is addressed in this review. Knowledge regarding how these systems fully interact requires both new laboratory and clinical tools, as well as sophisticated modeling of the networks that help maintain homeostasis and are dysregulated in disease. Finally, the triggers resulting in a 'coup de main' by the microbiome (exacerbation of disease) and the characteristics of susceptible hosts that can result in 'pyrrhic victories' with collateral damage to host tissues, the hallmark of periodontitis, remains unclear. While much has been learned, substantial gaps in our understanding of the 'parameters of this war' remain elusive toward fulfilling the Sun Tzu adage: 'If you know the enemy and know yourself, you need not fear the result of a hundred battles.'

Integrated biomarker profiling of smokers with periodontitis.

BACKGROUND: In the context of precision medicine, understanding patient-specific variation is an important step in developing targeted and patient-tailored treatment regimens for periodontitis. While several studies have successfully demonstrated the usefulness of molecular expression profiling in conjunction with single classifier systems in discerning distinct disease groups, the majority of these studies do not provide sufficient insights into potential variations within the disease groups. AIM: The goal of this study was to discern biological response profiles of periodontitis and non-periodontitis smoking subjects using an informed panel of biomarkers across multiple scales (salivary, oral microbiome, pathogens and other markers). MATERIAL & METHODS: The investigation uses a novel ensemble classification approach (SVA-SVM) to differentiate disease groups and patient-specific biological variation of systemic inflammatory mediators and IgG antibody to oral commensal and pathogenic bacteria within the groups. RESULTS: Sensitivity of SVA-SVM is shown to be considerably higher than several traditional independent classifier systems. Patient-specific networks generated from SVA-SVM are also shown to reveal crosstalk between biomarkers in discerning the disease groups. High-confidence classifiers in these network abstractions comprised of host responses to microbial infection elucidated their critical role in discerning the disease groups. CONCLUSIONS: Host adaptive immune responses to the oral colonization/infection contribute significantly to creating the profiles specific for periodontitis patients with potential to assist in defining patient-specific risk profiles and tailored interventions.

An approach for deciphering patient-specific variations with application to breast cancer molecular expression profiles.

Several studies have successfully used molecular expression profiling in conjunction with classification techniques for discerning distinct disease groups. However, a majority of these studies do not provide sufficient insights into potential patient-specific variations within the disease groups. Such variations are ubiquitous and manifests across multiple scales with varying resolution. There is an urgent need for novel approaches that falls within the objective of precision medicine and provide novel insights into patient-specific variations and sub-populations within disease groups while discerning the disease groups of interest so as to enable timely and targeted intervention of select subjects. This study presents a selective-voting ensemble classification approach (SVA) for discerning good and poor-prognosis breast cancer samples from their 70-gene molecular expression profile revealing patient-specific variations within the poor-prognosis group. In contrast to traditional classification, SVA adapts the feature sets in a sample-specific manner capturing the proclivity of the samples to each of the disease groups. Correlation between normalized vote counts from SVA and clinical outcomes of the subjects is elucidated. Performance of Support Vector Machine and Naïve Bayes classifier is investigated within the SVA framework and compared to established clinical criteria (Nottingham Prognostic Index, Adjuvant Online, St. Gallen) and Mammaprint approach. Weighted undirected graph abstractions of the ensemble sets of the poor-prognosis test samples is also shown to exhibit markedly different topologies with varying proclivities. These patient-specific networks may reflect inherent variations in underlying signaling mechanisms in the poor-prognosis subjects and reveal potential targets for personalized therapeutic intervention.

3D tumor tissue analogs and their orthotopic implants for understanding tumor-targeting of microenvironment-responsive nanosized chemotherapy and radiation.

An appropriate representation of the tumor microenvironment in tumor models can have a pronounced impact on directing combinatorial treatment strategies and cancer nanotherapeutics. The present study develops a novel 3D co-culture spheroid model (3D TNBC) incorporating tumor cells, endothelial cells and fibroblasts as color-coded murine tumor tissue analogs (TTA) to better represent the tumor milieu of triple negative breast cancer in vitro. Implantation of TTA orthotopically in nude mice, resulted in enhanced growth and aggressive metastasis to ectopic sites. Subsequently, the utility of the model is demonstrated for preferential targeting of irradiated tumor endothelial cells via radiation-induced stromal enrichment of galectin-1 using anginex conjugated nanoparticles (nanobins) carrying arsenic trioxide and cisplatin. Demonstration of a multimodal nanotherapeutic system and inclusion of the biological response to radiation using an in vitro/in vivo tumor model incorporating characteristics of tumor microenvironment presents an advance in preclinical evaluation of existing and novel cancer nanotherapies. FROM THE CLINICAL EDITOR: Existing in-vivo tumor models are established by implanting tumor cells into nude mice. Here, the authors described their approach 3D spheres containing tumor cells, enodothelial cells and fibroblasts. This would mimic tumor micro-environment more realistically. This interesting 3D model should reflect more accurately tumor response to various drugs and would enable the design of new treatment modalities.

Evidence of community structure in biomedical research grant collaborations.

Recent studies have clearly demonstrated a shift towards collaborative research and team science approaches across a spectrum of disciplines. Such collaborative efforts have also been acknowledged and nurtured by popular extramurally funded programs including the Clinical Translational Science Award (CTSA) conferred by the National Institutes of Health. Since its inception, the number of CTSA awardees has steadily increased to 60 institutes across 30 states. One of the objectives of CTSA is to accelerate translation of research from bench to bedside to community and train a new genre of researchers under the translational research umbrella. Feasibility of such a translation implicitly demands multi-disciplinary collaboration and mentoring. Networks have proven to be convenient abstractions for studying research collaborations. The present study is a part of the CTSA baseline study and investigates existence of possible community-structure in Biomedical Research Grant Collaboration (BRGC) networks across data sets retrieved from the internally developed grants management system, the Automated Research Information Administrator (ARIA) at the University of Arkansas for Medical Sciences (UAMS). Fastgreedy and link-community community-structure detection algorithms were used to investigate the presence of non-overlapping and overlapping community-structure and their variation across years 2006 and 2009. A surrogate testing approach in conjunction with appropriate discriminant statistics, namely: the modularity index and the maximum partition density is proposed to investigate whether the community-structure of the BRGC networks were different from those generated by certain types of random graphs. Non-overlapping as well as overlapping community-structure detection algorithms indicated the presence of community-structure in the BRGC network. Subsequent, surrogate testing revealed that random graph models considered in the present study may not necessarily be appropriate generative mechanisms of the community-structure in the BRGC networks. The discrepancy in the community-structure between the BRGC networks and the random graph surrogates was especially pronounced at 2009 as opposed to 2006 indicating a possible shift towards team-science and formation of non-trivial modular patterns with time. The results also clearly demonstrate presence of inter-departmental and multi-disciplinary collaborations in BRGC networks. While the results are presented on BRGC networks as a part of the CTSA baseline study at UAMS, the proposed methodologies are as such generic with potential to be extended across other CTSA organizations. Understanding the presence of community-structure can supplement more traditional network analysis as they're useful in identifying research teams and their inter-connections as opposed to the role of individual nodes in the network. Such an understanding can be a critical step prior to devising meaningful interventions for promoting team-science, multi-disciplinary collaborations, cross-fertilization of ideas across research teams and identifying suitable mentors. Understanding the temporal evolution of these communities may also be useful in CTSA evaluation.

Immunoglobulin gene expression profiles and microbiome characteristics in periodontitis in nonhuman primates.

Colonization of mucosal tissues throughout the body occurs by a wide array of bacteria in the microbiome that stimulate the cells and tissues, as well as respond to changes in the local milieu. A feature of periodontitis is the detection of adaptive immune responses to members of the oral microbiome that show specificity and changes with disease and treatment. Thus, variations in antibody responses are noted across the population and affected by aging, albeit, data are still unclear as to how these differences relate to disease risk and expression. This study used a nonhuman primate model of experimental periodontitis to track local microbiome changes as they related to the use and expression of a repertoire of immunoglobulin genes in gingival tissues. Gingival tissue biopsies from healthy tissues and following ligature-placement for disease initiation and progression provided gene expression analysis. Additionally, following removal of the ligatures, clinical healing occurs with gene expression in disease resolved tissues. Groups of 9 animals (young: <3 yrs., adolescent: 3-7 yrs., adult -12 to 15 yrs.; aged: 17-22 yrs) were used in the investigation. In healthy tissues, young and adolescent animals showed levels of expression of 78 Ig genes that were uniformly less than adults. In contrast, ⅔ of the Ig genes were elevated by > 2-fold in the aged samples. Specific increases in an array of the Ig gene transcripts were detected in adults at disease initiation and throughout progression, while increases in young and adolescent animals were observed only with disease progression, and in aged samples primarily late in disease progression. Resolved lesions continued to demonstrate elevated levels of Ig gene expression in only young, adolescent and adult animals. The array of Ig genes significantly correlated with inflammatory, tissue biology and hypoxia genes in the gingival tissues, with variations associated with age. In the young group of animals, specific members of the oral microbiome positively correlated with Ig gene expression, while in the older animals, many of these correlations were negative. Significant correlations were observed with a select assortment of bacterial OTUs and multiple Ig genes in both younger and older animal samples, albeit the genera/species showed little overlap. Incorporating this array of microbes and host responses clearly discriminated the various time points in transition from health to disease and resolution in both the young and adult animals. The results support a major importance of adaptive immune responses in the kinetics of periodontal lesion formation, and support aging effects on the repertoire of Ig genes that may relate to the increased prevalence and severity of periodontitis with age.

Variations in temporal trends in non-traumatic dental condition related emergencies.

OBJECTIVE: The objective of the study was to investigate temporal trends in non-traumatic dental condition (NTDC) related emergency visits at Emergency Department (ED), urgent care (UC), and at a Federally Qualified Health Center (FQHC) that providing dental services to a mid-sized rural community. METHODS: Temporal trends over a 9-year period (2008-2016) in NTDC rates at ED, UC, FQHC and in a region around the FQHC were determined. Statistically significant changes (α = 0.05) in the proportion of NTDC visits between FQHC and UC across each of the time points were investigated. RESULTS: Proportion of NTDC ED visits was relatively stable over the study period; whereas those at FQHC exceeded those at UC site beginning 2012 and were significantly (α = 0.05) higher than that of UC subsequently (2015-2016). CONCLUSIONS: NTDCs are preventable dental conditions and the care provided in treating NTDCs in emergency settings is palliative and does not address the underlying conditions resulting in poor outcomes. The results presented elucidate the critical role of FQHCs in significantly reducing NTDC visits. These might be precursors to a potential shift in NTDC care seeking behavior and expected to favorably impact oral health outcomes.

Impaired Wnt signaling in embryonal rhabdomyosarcoma cells from p53/c-fos double mutant mice.

Rhabdomyosarcoma is a primitive neoplasm with a poorly understood etiology that exhibits features of fetal skeletal muscle. It represents the most frequent malignant soft tissue sarcoma affecting the pediatric population and is often treated very aggressively. Embryonal rhabdomyosarcoma (ERMS) and alveolar rhabdomyosarcoma constitute the two major subtypes and exhibit different molecular features. We investigated one potential molecular basis for ERMS by using cells derived from tumors produced in p53(-/-)/c-fos(-/-) mice. This model closely recapitulates the timing, location, molecular markers, and histology seen in human ERMS. A combined chromatin immunoprecipitation/promoter microarray approach was used to identify promoters bound by the c-Jun-containing AP-1 complex in the tumor-derived cells that lacked c-Fos. Identification of the Wnt2 gene and its overexpression in ERMS cells was confirmed in human rhabdomyosarcoma cell lines and prompted further analysis of the Wnt signaling pathway. Contrary to our expectations, the canonical Wnt/ß-catenin signaling pathway was down-regulated in ERMS cells compared with normal myoblasts, and activating this pathway promoted myogenic differentiation. Furthermore, the identification of both survivin and sfrp2 through promoter and expression analyses suggested that increased resistance to apoptosis was associated with the inhibition of the Wnt signaling pathway. These results suggest that altered AP-1 activity that leads to the down-regulation of the Wnt pathway may contribute to the inhibition of myogenic differentiation and resistance to apoptosis in ERMS cases.

Granger causality analysis of human cell-cycle gene expression profiles.

Granger causality (GC) tests are ideally suited to investigate time series data generated by bivariate vector autoregressive (VAR) processes. Recent studies have applied GC analysis and its extensions for modeling functional relationships and network structure from temporal gene expression profiles. The present study investigates GC analysis of human cell-cycle gene expression profiles that can be modeled as a first-order bivariate VAR. Analytical results presented establish the contribution of the VAR process parameters, including auto-regulatory feedback and noise variance to the mean-squared forecast error, as a critical component in identifying statistically significant GC relationships. These results in turn discourage blind inference of functional relationship between a given pair of genes solely based on the result of the statistical tests for GC. The presence of significant auto-regulatory feedback and discrepancy in noise variance is demonstrated across the cell-cycle gene expression profiles by VAR parameter estimation. It is emphasized that discrepancies in noise variance can be due to artifacts and can lead to spurious existence of functional relationship between a given pair of genes. VAR parameter estimation is encouraged for better of GC interpretation of the results. Published case studies on GC analysis of the same publicly available cell-cycle gene expression data are reinvestigated for transparency.

Oral Microbiome and Gingival Gene Expression of Inflammatory Biomolecules With Aging and Periodontitis.

Although data describe the presence and increase of inflammatory mediators in the local environment in periodontitis vs. health in humans, details regarding how these responses evolve in the transition from health to disease, changes during disease progression, and features of a resolved lesion remain unknown. This study used a nonhuman primate model of ligature-induced periodontitis in young, adolescent, adult, and aged animals to document features of inflammatory response affected by age. Rhesus monkeys had ligatures tied and provided gingival tissue biopsy specimens at baseline, 0.5, 1, and 3 months of disease and at 5 months of the study, which was 2 months post-ligature removal for clinically resolved tissues. The transcriptome was assessed using microarrays for chemokine (n = 41), cytokine (n = 45), chemokine receptor (n = 21), cytokine receptor (n = 37), and lipid mediator (n = 31) genes. Limited differences were noted in healthy tissues for chemokine expression with age; however, chemokine receptor genes were decreased in young but elevated in aged samples. IL1A, IL36A, and IL36G cytokines were decreased in the younger groups, with IL36A elevated in aged animals. IL10RA/IL10RB cytokine receptors were altered with age. Striking variation in the lipid mediator genes in health was observed with nearly 60% of these genes altered with age. A specific repertoire of chemokine and chemokine receptor genes was affected by the disease process, predominated by changes during disease initiation. Cytokine/cytokine receptor genes were also elevated with disease initiation, albeit IL36B, IL36G, and IL36RN were all significantly decreased throughout disease and resolution. Significant changes were observed in similar lipid mediator genes with disease and resolution across the age groups. Examination of the microbiome links to the inflammatory genes demonstrated that specific microbes, including Fusobacterium, P. gingivalis, F. alocis, Pasteurellaceae, and Prevotella are most frequently significantly correlated. These correlations were generally positive in older animals and negative in younger specimens. Gene expression and microbiome patterns from baseline were distinctly different from disease and resolution. These results demonstrate patterns of inflammatory gene expression throughout the phases of the induction of a periodontal disease lesion. The patterns show a very different relationship to specific members of the oral microbiome in younger compared with older animals.

Transcriptomic phases of periodontitis lesions using the nonhuman primate model.

We used a nonhuman primate model of ligature-induced periodontitis to identify patterns of gingival transcriptomic after changes demarcating phases of periodontitis lesions (initiation, progression, resolution). A total of 18 adult Macaca mulatta (12-22 years) had ligatures placed (premolar, 1st molar teeth) in all 4 quadrants. Gingival tissue samples were obtained (baseline, 2 weeks, 1 and 3 months during periodontitis and at 5 months resolution). Gene expression was analyzed by microarray [Rhesus Gene 1.0 ST Array (Affymetrix)]. Compared to baseline, a large array of genes were significantly altered at initiation (n = 6049), early progression (n = 4893), and late progression (n = 5078) of disease, with the preponderance being up-regulated. Additionally, 1918 genes were altered in expression with disease resolution, skewed towards down-regulation. Assessment of the genes demonstrated specific profiles of epithelial, bone/connective tissue, apoptosis/autophagy, metabolism, regulatory, immune, and inflammatory responses that were related to health, stages of disease, and tissues with resolved lesions. Unique transcriptomic profiles occured during the kinetics of the periodontitis lesion exacerbation and remission. We delineated phase specific gene expression profiles of the disease lesion. Detection of these gene products in gingival crevicular fluid samples from human disease may contribute to a better understanding of the biological dynamics of the disease to improve patient management.

Variations in Schedule III prescription patterns in a Medicaid population pre- and post-policy.

The present study investigated variations in patient movement patterns between prescribers before and after House Bill 1 (HB1) implementation in Kentucky using network abstractions (PPN: prescriber-prescriber networks) from a one-month cross-sectional Schedule III prescription data in a Medicaid population. Network characteristics such as degree centrality distribution of PPN was positively skewed and revealed Dental Practitioners to be the highly connected specialty with opioid analgesic hydrocodone-acetaminophen to be the most commonly prescribed drug. Taxonomy enrichment of the prescriber specialties in PPN using chi-square test revealed a reduction in the enriched taxonomies Post-HB1 compared to Pre-HB1 with Dental practitioners being constitutively enriched (p < 0.05). PPNs were also found to exhibit rich community structure revealing inherent clustering of prescribers as a result of patient movement, and were markedly different from those generated by random graph models. The magnitude of deviation from random graphs decreased Post-HB1 relative to Pre-HB1. The proposed network approach provides system-level insights into prescribers with potential to complement classical reductionist approaches and aggregate statistical measures used in assessing changes in prescription patterns pre- and post- policy implementation. It can provide preliminary cues into drug seeking behavior, and facilitate targeted surveillance of prescriber communities.

NATbox: a network analysis toolbox in R.

BACKGROUND: There has been recent interest in capturing the functional relationships (FRs) from high-throughput assays using suitable computational techniques. FRs elucidate the working of genes in concert as a system as opposed to independent entities hence may provide preliminary insights into biological pathways and signalling mechanisms. Bayesian structure learning (BSL) techniques and its extensions have been used successfully for modelling FRs from expression profiles. Such techniques are especially useful in discovering undocumented FRs, investigating non-canonical signalling mechanisms and cross-talk between pathways. The objective of the present study is to develop a graphical user interface (GUI), NATbox: Network Analysis Toolbox in the language R that houses a battery of BSL algorithms in conjunction with suitable statistical tools for modelling FRs in the form of acyclic networks from gene expression profiles and their subsequent analysis. RESULTS: NATbox is a menu-driven open-source GUI implemented in the R statistical language for modelling and analysis of FRs from gene expression profiles. It provides options to (i) impute missing observations in the given data (ii) model FRs and network structure from gene expression profiles using a battery of BSL algorithms and identify robust dependencies using a bootstrap procedure, (iii) present the FRs in the form of acyclic graphs for visualization and investigate its topological properties using network analysis metrics, (iv) retrieve FRs of interest from published literature. Subsequently, use these FRs as structural priors in BSL (v) enhance scalability of BSL across high-dimensional data by parallelizing the bootstrap routines. CONCLUSION: NATbox provides a menu-driven GUI for modelling and analysis of FRs from gene expression profiles. By incorporating readily available functions from existing R-packages, it minimizes redundancy and improves reproducibility, transparency and sustainability, characteristic of open-source environments. NATbox is especially suited for interdisciplinary researchers and biologists with minimal programming experience and would like to use systems biology approaches without delving into the algorithmic aspects. The GUI provides appropriate parameter recommendations for the various menu options including default parameter choices for the user. NATbox can also prove to be a useful demonstration and teaching tool in graduate and undergraduate course in systems biology. It has been tested successfully under Windows and Linux operating systems. The source code along with installation instructions and accompanying tutorial can be found at http://bioinformatics.ualr.edu/natboxWiki/index.php/Main_Page.

Asia Optical Fiber and Optoelectronic 2008 Exposition and Conference: introduction to the special issue.

This Applied Optics special issue focuses on optical fibers, fiber components and optoelectronic devices, and materials, highlighting the research presented at the 2008 Asia Optical Fiber and Optoelectronic Exposition and Conference.

Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks.

Surrogate testing techniques have been used widely to investigate the presence of dynamical nonlinearities, an essential ingredient of deterministic chaotic processes. Traditional surrogate testing subscribes to statistical hypothesis testing and investigates potential differences in discriminant statistics between the given empirical sample and its surrogate counterparts. The choice and estimation of the discriminant statistics can be challenging across short time series. Also, conclusion based on a single empirical sample is an inherent limitation. The present study proposes a recurrent neural network classification framework that uses the raw time series obviating the need for discriminant statistic while accommodating multiple time series realizations for enhanced generalizability of the findings. The results are demonstrated on short time series with lengths (L = 32, 64, 128) from continuous and discrete dynamical systems in chaotic regimes, nonlinear transform of linearly correlated noise and experimental data. Accuracy of the classifier is shown to be markedly higher than ≫50% for the processes in chaotic regimes whereas those of nonlinearly correlated noise were around ~50% similar to that of random guess from a one-sample binomial test. These results are promising and elucidate the usefulness of the proposed framework in identifying potential dynamical nonlinearities from short experimental time series.

Network Abstractions of Prescription Patterns in a Medicaid Population.

Understanding prescription patterns have relied largely on aggregate statistical measures. Evidence of doctor- shopping, inappropriate prescribing, drug diversion and patient seeking prescription drugs across multiple prescribers demand understanding the concerted working of prescribers and prescriber communities as opposed to treating them as independent entities. We model potential associations between prescribers as prescriber-prescriber network (PPN) and subsequently investigate its properties across Schedule II, III, IV drugs in a single month in a Medicaid population. Community structure detection algorithms and geo-spatial layouts revealed characteristic patterns in PPN markedly different from their random graph surrogate counterparts rejecting them as potential generative mechanism. Outlier detection with recommended thresholds also revealed a subset of prescriber specialties to be constitutively flagged across Schedule II, III, IV drugs. Presence of prescriber communities may assist in targeted monitoring and their deviation from random graphs may serve as a metric in assessing PPN evolution temporally and pre-/post- interventions.

Delay estimation in a two-node acyclic network.

Linear measures such as cross-correlation have been used successfully to determine time delays from the given processes. Such an analysis often precedes identifying possible causal relationships between the observed processes. The present study investigates the impact of a positively correlated driver whose correlation function decreases monotonically with lag on the delay estimation in a two-node acyclic network with one and two-delays. It is shown that cross-correlation analysis of the given processes can result in spurious identification of multiple delays between the driver and the dependent processes. Subsequently, delay estimation of increment process as opposed to the original process under certain implicit constraints is explored. Short-range and long-range correlated driver processes along with those of their coarse-grained counterparts are considered.