Automatic Network Fingerprinting through Single-Node Motifs

Complex networks have been characterised by their specific connectivity patterns (network motifs), but their building blocks can also be identified and described by node-motifs---a combination of local network features. One technique to identify single node-motifs has been presented by Costa et al. (L. D. F. Costa, F. A. Rodrigues, C. C. Hilgetag, and M. Kaiser, Europhys. Lett., 87, 1, 2009). Here, we first suggest improvements to the method including how its parameters can be determined automatically. Such automatic routines make high-throughput studies of many networks feasible. Second, the new routines are validated in different network-series. Third, we provide an example of how the method can be used to analyse network time-series. In conclusion, we provide a robust method for systematically discovering and classifying characteristic nodes of a network. In contrast to classical motif analysis, our approach can identify individual components (here: nodes) that are specific to a network. Such special nodes, as hubs before, might be found to play critical roles in real-world networks.Comment: 16 pages (4 figures) plus supporting information 8 pages (5 figures

Networking - A Statistical Physics Perspective

Efficient networking has a substantial economic and societal impact in a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption require new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with non-linear large scale systems. This paper aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.Comment: (Review article) 71 pages, 14 figure

Novel Ancestry-Specific Primary Open-Angle Glaucoma Loci and Shared Biology With Vascular Mechanisms and Cell Proliferation

Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis

Indicated Truancy Interventions: Effects on School Attendance Among Chronic Truant Students.

BACKGROUNDTruancy is a significant problem in the U.S. and in other countries around the world. Truancy has been linked to serious immediate and far-reaching consequences for youth, families, and schools and communities, leading researchers, practitioners, and policy makers to try to understand and to address the problem. Although numerous and significant steps have been taken at the local, state, and national levels to reduce truancy, the rates of truancy have at best remained stable or at worst been on the rise, depending on the indicator utilized to assess truancy rates. The costs and impact of chronic truancy are significant, with both short- and long-term implications for the truant youth as well as for the family, school, and community. Although several narrative reviews and one meta-analysis of attendance and truancy interventions have attempted to summarize the extant research, there are a number of limitations to these reviews. It is imperative that we systematically synthesize and examine the evidence base to provide a comprehensive picture of interventions that are being utilized to intervene with chronic truants, to identify interventions that are effective and ineffective, and to identify gaps and areas in which more research needs to be conducted to better inform practice and policy.OBJECTIVESThe main objective of this systematic review was to examine the effects of interventions on school attendance to inform policy, practice, and research. The questions guiding this study were: 1) Do truancy programs with a goal of increasing student attendance for truant youth affect school attendance behaviors of elementary and secondary students with chronic attendance problems?2) Are there differences in the effects of school-based, clinic/community-based, and court-based programs?3) Are some modalities (i.e., family, group, multimodal) more effective than others in increasing student attendance? SEARCH STRATEGYA systematic and comprehensive search process was employed to locate all possible studies between 1990 and 2009, with every effort made to include both published and unpublished studies to minimize publication bias. A wide range of electronic bibliographic databases and research registers was searched, websites of relevant research centers and groups were mined for possible reports, over 200 e-mails and letters were sent to programs listed in large databases of truancy programs compiled by the National Center for School Engagement and the National Dropout Prevention Center, and contact with researchers in the field of truancy and absenteeism was attempted. In addition, we examined reference lists of all previous reviews as well as citations in research reports for potential studies.SELECTION CRITERIAStudies eligible for this review were required to meet several eligibility criteria. Studies must have utilized a randomized, quasi-experimental, or single-group pre-posttest design with the aim of evaluating the effectiveness of interventions with a stated primary goal of increasing student attendance (or decreasing absenteeism) among chronic truant students. Studies must have measured an attendance outcome and reported sufficient data to calculate an effect size. Finally, studies must have been published between 1990 and 2009 in the United States, United Kingdom, Australia, or Canada. DATA COLLECTION AND ANALYSISA total of 28 studies, reported in 26 reports, met final eligibility criteria and were included in this review and meta-analysis. Of the studies that were included, 5 utilized a randomized design (RCT), 11 utilized a quasi-experimental design (QED), and 12 utilized a single group pre-posttest design (SGPP). All eligible studies were coded using a structured coding instrument, with 20% of studies coded by a second coder. Descriptive analysis was conducted to examine and describe data related to the characteristics of the included studies. Analysis of the mean effect size, the heterogeneity of effect sizes, and the relationship between effect size and methodological and substantive characteristics of the interventions was also conducted separately for the RCT/QED studies and the SGPP studies. The effect sizes were calculated using the standardized mean difference effect size statistic, correcting for small sample size using Hedges’ g (Hedges, 1992). Assuming a mixed effects model, the analog to the ANOVA and bivariate meta-regression frameworks were used to examine potential moderating variables related to study, participant, and intervention characteristics. RESULTSThe meta-analytic findings demonstrated a significant overall positive and moderate mean effect of interventions on attendance outcomes. The mean effect size for interventions examined in the included RCT studies was .57 and the mean effect size for the QED studies was .43. No significant differences were observed between the RCT and QED studies in the magnitude of the treatment effect (Qb= .28, p \u3e.05). The mean effect size of interventions examined using an SGPP design was .95. A moderate effect on attendance outcomes is encouraging; however, the overall mean effect size is masked by a large amount of heterogeneity, indicating significant variance in effect sizes between studies. Moderator analyses found no significant differences in mean effects between studies on any moderating variable tested. No differences were found between school-, court-, or community-based programs or between different modalities of programs. The duration of the intervention also did not demonstrate any association with effect size. Collaborative programs and multimodal interventions produced statistically similar effects on attendance as non-collaborative and single-modality programs, which runs counter to the prevailing beliefs and recommendations for best practices in truancy reduction found in the literature.Other significant findings from this study relate to methodological shortcomings, the absence of important variables as well as gaps in the evidence base. These findings include the lack of inclusion of minority students and a lack of reporting and statistical analysis of demographic variables, particularly race/ethnicity and socioeconomic status (SES). Given that race and SES have been linked to absenteeism, the absence of this data was surprising. The majority of studies also lacked adequate descriptions of the interventions, making replication of the intervention difficult, and failed to measure and report long-term outcomes. AUTHORS’ CONCLUSIONSOverall, the findings from this study suggest that chronic truant students benefit from interventions targeting attendance behaviors; thus it is important and worthwhile to intervene with chronic truant youth. Given the minimal differences in effects across program types and modalities, no one program type or modality stands out as being more effective than any other. Although no statistically significant differences in effects were found between types and modalities of interventions included in this review, there was a lack of available evidence to support the general belief (and popular “best-practice” recommendations) that collaborative and multimodal interventions are more effective than programs that are not collaborative and single modal interventions. Due to the small sample size and large heterogeneity between studies and within groups of studies, caution must be used when interpreting and applying the findings from this meta-analysis. Overall, the studies included in the review improved attendance by an average of 4.69 days, almost a full school week. However, although the interventions included in this study were, overall, found to be effective, the mean rates of absenteeism at posttest in most studies remained above acceptable levels. This finding indicates the need for additional work and research. Developing more effective interventions and policies as well as studying outcomes of interventions, particularly with vulnerable and at-risk populations, is crucial to combating absenteeism. The gaps and deficiencies identified in this study also affirm the need for increasing and strengthening the evidence base on which current policies and practices rest. Although additional outcome research is necessary, more of the same is not sufficient. Significant improvements in the quality of truancy intervention research are required and identified gaps need to be addressed. Recommendations to improve the quality and fill gaps in truancy intervention research are discussed here. In addition, given the significant and pervasive deficiencies in the extant research, a critical analysis of the practices, assumptions, and sociopolitical contexts underlying truancy intervention research seems warranted

Global Biobank Meta-analysis Initiative:Powering genetic discovery across human disease

Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.</p

Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program

One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology.

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p