Search

Motivation It has become routine in neuroscience studies to measure brain networks for different individuals using neuroimaging. These networks are typically expressed as adjacency matrices, with each cell containing a summary of connectivity between a pair of brain regions. There is an emerging statistical literature describing methods for the analysis of such multi-network data in which nodes are common across networks but the edges vary. However, there has been essentially no consideration of the important problem of outlier detection. In particular, for certain subjects, the neuroimaging data are so poor quality that the network cannot be reliably reconstructed. For such subjects, the resulting adjacency matrix may be mostly zero or exhibit a bizarre pattern not consistent with a functioning brain. These outlying networks may serve as influential points, contaminating subsequent statistical analyses. We propose a simple Outlier DetectIon for Networks (ODIN) method relying on an influence measure under a hierarchical generalized linear model for the adjacency matrices. An efficient computational algorithm is described, and ODIN is illustrated through simulations and an application to data from the UK Biobank. Results ODIN was successful in identifying moderate to extreme outliers. Removing such outliers can significantly change inferences in downstream applications. Availability and implementation ODIN has been implemented in both Python and R and these implementations along with other code are publicly available at github.com/pritamdey/ODIN-python and github.com/pritamdey/ODIN-r, respectively. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Joint analysis of multiple correlated phenotypes for genome-wide association studies (GWAS) can identify and interpret pleiotropic loci which are essential to understand pleiotropy in diseases and complex traits. Meanwhile, constructing a network based on associations between phenotypes and genotypes provides a new insight to analyze multiple phenotypes, which can explore whether phenotypes and genotypes might be related to each other at a higher level of cellular and organismal organization. In this paper, we first develop a bipartite signed network by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). The GPN can be constructed by a mixture of quantitative and qualitative phenotypes and is applicable to binary phenotypes with extremely unbalanced case-control ratios in large-scale biobank datasets. We then apply a powerful community detection method to partition phenotypes into disjoint network modules based on GPN. Finally, we jointly test the association between multiple phenotypes in a network module and a single nucleotide polymorphism (SNP). Simulations and analyses of 72 complex traits in the UK Biobank show that multiple phenotype association tests based on network modules detected by GPN are much more powerful than those without considering network modules. The newly proposed GPN provides a new insight to investigate the genetic architecture among different types of phenotypes. Multiple phenotypes association studies based on GPN are improved by incorporating the genetic information into the phenotype clustering. Notably, it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. Author summary: Biological pleiotropy refers to a SNP or gene that has a direct biological influence on more than one phenotypic trait, which can offer significant insights in understanding the complex genotype-phenotype relationships. Network analyses provide an integrative approach to characterize complex genomic associations by linking phenotypes and genotypes into a Genotype and Phenotype Network (GPN). Jointly analyzing multiple phenotypes and incorporating the genetic information into the phenotype clustering may increase the statistical power to discover the cross-phenotype association and pleiotropy. We evaluate our proposed multiple phenotype association tests based on network modules detected by GPN for 72 EHR-derived phenotypes in the diseases of the musculoskeletal system and connective tissue in the UK Biobank. From the post-GWAS analyses, we observe that the test based on GPN can identify more significantly enriched biological pathways than that without considering the network modules. Meanwhile, some of the uniquely identified SNPs by the test based on GPN are also colocalized in the eQTL study of the gene expression in the Muscle Skeletal tissue. [ABSTRACT FROM AUTHOR]

Motivation Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Results Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities. Availability and implementation The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN. [ABSTRACT FROM AUTHOR]

Motivation Genome-wide association studies is an essential tool for analyzing associations between phenotypes and single nucleotide polymorphisms (SNPs). Most of binary phenotypes in large biobanks are extremely unbalanced, which leads to inflated type I error rates for many widely used association tests for joint analysis of multiple phenotypes. In this article, we first propose a novel method to construct a Multi-Layer Network (MLN) using individuals with at least one case status among all phenotypes. Then, we introduce a computationally efficient community detection method to group phenotypes into disjoint clusters based on the MLN. Finally, we propose a novel approach, MLN with Omnibus (MLN-O), to jointly analyse the association between phenotypes and a SNP. MLN-O uses the score test to test the association of each merged phenotype in a cluster and a SNP, then uses the Omnibus test to obtain an overall test statistic to test the association between all phenotypes and a SNP. Results We conduct extensive simulation studies to reveal that the proposed approach can control type I error rates and is more powerful than some existing methods. Meanwhile, we apply the proposed method to a real data set in the UK Biobank. Using phenotypes in Chapter XIII (Diseases of the musculoskeletal system and connective tissue) in the UK Biobank, we find that MLN-O identifies more significant SNPs than other methods we compare with. Availability and implementation https://github.com/Hongjing-Xie/Multi-Layer-Network-with-Omnibus-MLN-O. [ABSTRACT FROM AUTHOR]

Automatic calf feeders (AMFs) that supply milk for pre-weaning calves require group housing. This observational study assessed calf growth, health, feeding patterns and social behaviours prior to, during and following weaning in groups on the AMF. Calves were recruited from a single UK dairy farm and placed into two groups on the AMF between 3 and5 weeks of age (n = 19 and 13). They were fed milk near-ad libitum until 8 weeks of age when they entered a 2-week step-down weaning period. Daily milk consumption and the numbers of successful and refusal visits were collected from the AMF, along with weekly weights and health checks. Calf behaviour around the AMF was monitored through video analysis, and activity was assessed using tri-axial accelerometers. On average, the calves consumed approximately 3 L of milk three times a day from the AMF, confirming that limiting calves to twice daily feeding will restrict their desired milk consumption. The ADLG pre-weaning was 0.74 kg/d (SD 0.41 kg), rising to 0.99 kg/d (SD 0.46 kg) over weaning and 1.05 kg/d (SD 0.12 kg) post weaning. Refusal visits to the AMF, social interactions, displacements and cross sucking all increased over the weaning periods, suggesting that the calves were experiencing frustration due to a lack of expected milk rewards rather than hunger as ADLG continued to increase. Female calves also had more successful and refusal visits to the AMF than males, along with higher displacement indexes (p = 0.052), suggesting single-sex groups may be beneficial to AMFs. Calf activity had a diurnal pattern, with the highest standing times being during mid-morning and early evening, which may put pressure on limited resources if all calves are active during these particular time periods. [ABSTRACT FROM AUTHOR]

Obesity-induced adipose tissue dysfunction can cause low-grade inflammation and downstream obesity comorbidities. Although preadipocytes may contribute to this pro-inflammatory environment, the underlying mechanisms are unclear. We used human primary preadipocytes from body mass index (BMI) -discordant monozygotic (MZ) twin pairs to generate epigenetic (ATAC-sequence) and transcriptomic (RNA-sequence) data for testing whether increased BMI alters the subnuclear compartmentalization of open chromatin in the twins' preadipocytes, causing downstream inflammation. Here we show that the co-accessibility of open chromatin, i.e. compartmentalization of chromatin activity, is altered in the higher vs lower BMI MZ siblings for a large subset (~ 88.5 Mb) of the active subnuclear compartments. Using the UK Biobank we show that variants within these regions contribute to systemic inflammation through interactions with BMI on C-reactive protein. In summary, open chromatin co-accessibility in human preadipocytes is disrupted among the higher BMI siblings, suggesting a mechanism how obesity may lead to inflammation via gene-environment interactions. Preadipocytes contribute to the pro-inflammatory environment in obesity, via unknown mechanisms. Here, comparing monozygotic twin pairs, the authors show that co-accessibility of chromatin in preadipocytes is altered in siblings with higher compared to lower BMI, and that variants in these regions contribute to systemic inflammation via interactions with BMI. [ABSTRACT FROM AUTHOR]

Network analysis provides a rich framework to model complex phenomena, such as human brain connectivity. It has proven efficient to understand their natural properties and design predictive models. In this paper, we study the variability within groups of networks, i.e., the structure of connection similarities and differences across a set of networks. We propose a statistical framework to model these variations based on manifold-valued latent factors. Each network adjacency matrix is decomposed as a weighted sum of matrix patterns with rank one. Each pattern is described as a random perturbation of a dictionary element. As a hierarchical statistical model, it enables the analysis of heterogeneous populations of adjacency matrices using mixtures. Our framework can also be used to infer the weight of missing edges. We estimate the parameters of the model using an Expectation-Maximization-based algorithm. Experimenting on synthetic data, we show that the algorithm is able to accurately estimate the latent structure in both low and high dimensions. We apply our model on a large data set of functional brain connectivity matrices from the UK Biobank. Our results suggest that the proposed model accurately describes the complex variability in the data set with a small number of degrees of freedom. [ABSTRACT FROM AUTHOR]

There is an increasing expectation that advanced, computationally expensive machine learning (ML) techniques, when applied to large population‐wide neuroimaging datasets, will help to uncover key differences in the human brain in health and disease. We take a comprehensive approach to explore how multiple aspects of brain structural connectivity can predict sex, age, general cognitive function and general psychopathology, testing different ML algorithms from deep learning (DL) model (BrainNetCNN) to classical ML methods. We modelled N = 8183 structural connectomes from UK Biobank using six different structural network weightings obtained from diffusion MRI. Streamline count generally provided the highest prediction accuracies in all prediction tasks. DL did not improve on prediction accuracies from simpler linear models. Further, high correlations between gradient attribution coefficients from DL and model coefficients from linear models suggested the models ranked the importance of features in similar ways, which indirectly suggested the similarity in models' strategies for making predictive decision to some extent. This highlights that model complexity is unlikely to improve detection of associations between structural connectomes and complex phenotypes with the current sample size. [ABSTRACT FROM AUTHOR]

Human Action Recognition is a challenging task used in many applications. It interacts with many aspects of Computer Vision, Machine Learning, Deep Learning and Image Processing in order to understand human behaviours as well as identify them. It makes a significant contribution to sport analysis, by indicating players' performance level and training evaluation. The main purpose of this study is to investigate how the content of three-dimensional data influences on classification accuracy of four basic tennis strokes: forehand, backhand, volley forehand, and volley backhand. An entire player's silhouette and its combination with a tennis racket were taken into consideration as input to the classifier. Three-dimensional data were recorded using the motion capture system (Vicon Oxford, UK). The Plug-in Gait model consisting of 39 retro-reflective markers was used for the player's body acquisition. A seven-marker model was created for tennis racket capturing. The racket is represented in the form of a rigid body; therefore, all points associated with it changed their coordinates simultaneously. The Attention Temporal Graph Convolutional Network was applied for these sophisticated data. The highest accuracy, up to 93%, was achieved for the data of the whole player's silhouette together with a tennis racket. The obtained results indicated that for dynamic movements, such as tennis strokes, it is necessary to analyze the position of the whole body of the player as well as the racket position. [ABSTRACT FROM AUTHOR]

Background Spinal stenosis is a neurological disorder related to the compression of the spinal cord or nerve roots, and its incidence increases yearly. We aimed to use Mendelian randomization (MR) to investigate the causal relationship between several modifiable risk factors and the risk of spinal stenosis. Methods We obtained genome-wide association study summary data of large-sample projects (more than 100,000 individuals) from public databases. The data were associated with traits, including years of schooling (educational attainment) from the IEU OpenGWAS Project, smoking behavior (never vs. initiation) from the IEU OpenGWAS Project, body mass index (BMI) from the UK Biobank, length of mobile phone use from the UK Biobank, time spent watching television (TV) from the UK Biobank, and spinal stenosis from FinnGen biobank. Spinal stenosis was used as the outcome, whereas the other four traits were used as exposures. Inverse variance weighted (IVW) regressions were used as a primary to estimate the causal-effect size. Several sensitive analyses (including consistency, heterogenicity, and pleiotropy analyses) were conducted to test the stability and reliability of causal estimates. Results Univariable MR analyses showed that genetically predicted higher educational attainment (IVW; odds ratio (OR) = 0.606; 95% confidence interval (CI): 0.507–0.724; P = 3.37 × 10−8) and never smoking (IVW; OR = 1.388; 95% CI [1.135–1.697]; P = 0.001) were negatively correlated with the risk of spinal stenosis. Meanwhile, a higher BMI (IVW; OR = 1.569; 95% CI [1.403–1.754]; P = 2.35 × 10−8), longer time spent using a mobile phone (IVW; OR = 1.895; 95% CI [1.306–2.750]; P = 0.001), and watching TV (IVW; OR = 1.776; 95% CI [1.245–2.532]; P = 0.002) were positively associated with the risk of spinal stenosis. Multivariable MR analysis indicated that educational attainment (IVW; OR = 0.670; 95% CI [0.465–0.967]; P = 0.032) and BMI (IVW; OR = 1.365; 95% CI [1.179–1.580]; P = 3.12 × 10−5) were independently causally related to the risk of spinal stenosis. Conclusion Our findings supported the potential causal associations of the five factors (educational attainment, smoking behavior, BMI, length of mobile phone use, and watching TV) with the risk for spinal stenosis. While replication studies are essential, these findings may provide a new perspective on prevention and intervention strategies directed toward spinal stenosis. [ABSTRACT FROM AUTHOR]

The aim of this paper is first to examine, through a qualitative analysis of statistics syllabi, the current state of statistical education in a sample of universities in Colombia. The focus is on statistics teaching in degrees for economics and business administration students. The results from the qualitative analysis reflect a preponderance of traditional and didactic teaching methods centered on the teacher, not on the student. The second aim is to present findings from a case study that has developed an innovative pedagogical intervention, called a data fellows program, from the University of Manchester, United Kingdom, which evidences opportunities for how statistics can be taught effectively to non-STEM majors. Further, the data fellows model has also been explored in the context of developing statistical and data skills capacities in Latin America. We reflect on how the lessons from the UK case study could open up opportunities for rethinking the teaching of statistics in Colombia through developing data projects and experiential learning to practice statistics in the real world. [ABSTRACT FROM AUTHOR]

Socioeconomic segregation has an important role in the emergence of large-scale inequalities in urban areas. Most of the available measures of spatial segregation depend on the scale and size of the system under study, or neglect large-scale spatial correlations, or rely on ad-hoc parameters, making it hard to compare different systems on equal grounds. We propose here a family of non-parametric measures for spatial distributions, based on the statistics of the trajectories of random walks on graphs associated to a spatial system. These quantities provide a consistent estimation of segregation in synthetic spatial patterns, and we use them to analyse the ethnic segregation of metropolitan areas in the US and the UK. We show that the spatial diversity of ethnic distributions, as measured through diffusion on graphs, allow us to compare the ethnic segregation of urban areas having different size, shape, or peculiar microscopic characteristics, and exhibits a strong association with socio-economic deprivation. Socioeconomic segregation is one of the main factors behind large-scale inequalities in urban areas and its characterisation remains challenging. The authors propose a family of non-parametric measures to quantify spatial heterogeneity through diffusion, and show how this relates to segregation and deprivation [ABSTRACT FROM AUTHOR]

Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities. [ABSTRACT FROM AUTHOR]

Background: With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide. Methods: Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005–2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs based on single snapshots of closed cohorts, we adopt a longitudinal approach to examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality. Results: The proportion of the population with multimorbidity has increased by approximately 2.5 percentage points over the last decade, with more than 17% having at least two chronic morbidities. We find that the prevalence and the severity of multimorbidity, as quantified by the number of co-occurring chronic conditions, increase progressively with age. Stratifying by socioeconomic status, we find that people living in more deprived areas are more likely to be multimorbid compared to those living in more affluent areas at all ages. The same trend holds consistently for all years in our data. In general, hypertension, diabetes, and respiratory-related diseases demonstrate high in-degree centrality and eigencentrality, while cardiac disorders show high out-degree centrality. Conclusions: We use data-driven methods to characterize multimorbidity patterns in different demographic groups and their evolution over the past decade. In addition to a number of strongly associated comorbid pairs (e.g., cardiac-vascular and cardiac-metabolic disorders), we identify three principal clusters: a respiratory cluster, a cardiovascular cluster, and a mixed cardiovascular-renal-metabolic cluster. These are supported by established pathophysiological mechanisms and shared risk factors, and largely confirm and expand on the results of existing studies in the medical literature. Our findings contribute to a more quantitative understanding of the epidemiology of multimorbidity, an important pre-requisite for developing more effective medical care and policy for multimorbid patients. [ABSTRACT FROM AUTHOR]

Rock fractures organize as networks, exhibiting natural variation in their spatial arrangements. Therefore, identifying, quantifying, and comparing variations in spatial arrangements within network geometries are of interest when explicit fracture representations or discrete fracture network models are chosen to capture the influence of fractures on bulk rock behaviour. Treating fracture networks as spatial graphs, we introduce a novel approach to quantify spatial variation. The method combines graph similarity measures with hierarchical clustering and is applied to investigate the spatial variation within large-scale 2-D fracture networks digitized from the well-known Lilstock limestone pavements, Bristol Channel, UK. We consider three large, fractured regions, comprising nearly 300 000 fractures spread over 14 200 m2 from the Lilstock pavements. Using a moving-window sampling approach, we first subsample the large networks into subgraphs. Four graph similarity measures – fingerprint distance, D-measure, Network Laplacian spectral descriptor (NetLSD), and portrait divergence – that encapsulate topological relationships and geometry of fracture networks are then used to compute pair-wise subgraph distances serving as input for the statistical hierarchical clustering technique. In the form of hierarchical dendrograms and derived spatial variation maps, the results indicate spatial autocorrelation with localized spatial clusters that gradually vary over distances of tens of metres with visually discernable and quantifiable boundaries. Fractures within the identified clusters exhibit differences in fracture orientations and topology. The comparison of graph similarity-derived clusters with fracture persistence measures indicates an intra-network spatial variation that is not immediately obvious from the ubiquitous fracture intensity and density maps. The proposed method provides a quantitative way to identify spatial variations in fracture networks, guiding stochastic and geostatistical approaches to fracture network modelling. [ABSTRACT FROM AUTHOR]

Nowadays, increase of analyzing stock markets as complex systems lead graph theory to play a key role. For instance, detecting graph communities is an important task in the analysis of stocks, and as planar maximally filtered graphs let us to get important information for the topology of the market. In this study, we first obtain correlation network representation of UK's leading stock market network by using a novel threshold method. Then, we determine vertex clusters by using modularity and analyze clusters in planar maximally filtered graph substructures. Our analyze include a new measure called weighted Gini index for measuring the sparsity. The main goal of this paper is to study the hierarchical evolution of the market communities throughout the Brexit referendum, which is known as the stress period for the stock market. Hence, the overall sample is divided into two sub-periods of pre-referendum, and post-referendum to obtain communities and hierarchical structures. Our results indicate that financial companies are leading elements of the clusters. Moreover, the significant changes within the network topologies are observed for insurance, consumer goods, consumer services, mining, and technology sectors whereas oil and gas and health care sectors have not been affected by Brexit stress. [ABSTRACT FROM AUTHOR]

Notable cross-country differences exist in the diffusion of the Covid-19 and in its lethality. Contact patterns in populations, and in particular intergenerational contacts, have been argued to be responsible for the most vulnerable, the elderly, getting infected more often and thus driving up mortality in some context, like in the southern European one. This paper asks a simple question: is it between whom contacts occur that matters or is it simply how many contacts people have? Due to the high number of confounding factors, it is extremely difficult to empirically assess the impact of single network features separately. This is why we rely on a simulation exercise in which we counterfactually manipulate single aspects of countries' age distribution and network structures. We disentangle the contributions of the kind and of the number of contacts while holding constant the age structure. More precisely, we isolate the respective effects of inter-age contact patterns, degree distribution and clustering on the virus propagation across age groups. We use survey data on face-to-face contacts for Great Britain, Italy, and Germany, to reconstruct networks that mirror empirical contact patterns in these three countries. It turns out that the number of social contacts (degree distribution) largely accounts for the higher infection rates of the elderly in the Italian context, while differences in inter-age contacts patterns are only responsible for minor differences. This suggests that policies specifically targeting inter-age contacts would be little effective. [ABSTRACT FROM AUTHOR]

Background: Public health policies and recommendations aim to be informed by the best available evidence. Evidence underpinning e-cigarettes policy recommendations has been necessarily limited due to the novelty of the technology and the lack of long-term epidemiological studies and trials. Some public health bodies have actively encouraged e-cigarette use whilst others have raised concerns over introducing new health risks and renormalising tobacco smoking. Using citation network analysis we investigated the author conflicts of interest and study funding statements within sources of evidence used by public health bodies when making recommendations about e-cigarette policy. Methods: We conducted citation network analysis of public health recommendation documents across four purposively selected diverse jurisdictions: WHO, UK, Australia, and USA. We extracted all citations from 15 public health recommendation documents, with more detailed data collected for influential citations (used in 3+ recommendation documents). We analysed the relationships between the sources of evidence used across jurisdictions using block modelling to determine if similar groups of documents were used across different jurisdictions. We assessed the frequency and nature of conflicts of interest. Results: 1700 unique citations were included across the 15 public health recommendation documents, with zero to 923 citations per document (median = 63, IQR = 7.5–132). The evidence base underpinning public health recommendations did not systematically differ across jurisdictions. Of the 1700 citations included, the majority were journal articles (n = 1179). Across 1081 journal articles published between 1998–2018, 200 declared a conflict of interest, 288 contained no mention of conflicts of interest, and 593 declared none. Conflicts of interest were reported with tobacco (3%; n = 37 journal articles of 1081), e-cigarette (7%; n = 72), and pharmaceutical companies (12%; n = 127), with such conflicts present even in the most recent years. There were 53 influential citations, the most common study type was basic science research without human subjects (e.g. examination of aerosols and e-liquids) (n = 18) followed by systematic review (n = 10); with randomised control trial being least common (n = 4). Network analysis identified clusters of highly-cited articles with a higher prevalence of conflicts of interest. Conclusion: Public health bodies across different jurisdictions drew upon similar sources of evidence, despite articulating different policy approaches to e-cigarettes. The evidence drawn upon, including the most influential evidence, contained substantial conflicts of interest (including relationships with e-cigarette and tobacco industries). Processes to explicitly manage conflicts of interest arising from the underlying evidence base may be required when developing public health recommendations. [ABSTRACT FROM AUTHOR]

Here, we investigated the genetics of weighted functional brain network graph theory measures from 18,445 participants of the UK Biobank (44–80 years). The eighteen measures studied showed low heritability (mean h2SNP = 0.12) and were highly genetically correlated. One genome-wide significant locus was associated with strength of somatomotor and limbic networks. These intergenic variants were located near the PAX8 gene on chromosome 2. Gene-based analyses identified five significantly associated genes for five of the network measures, which have been implicated in sleep duration, neuronal differentiation/development, cancer, and susceptibility to neurodegenerative diseases. Further analysis found that somatomotor network strength was phenotypically associated with sleep duration and insomnia. Single nucleotide polymorphism (SNP) and gene level associations with functional network measures were identified, which may help uncover novel biological pathways relevant to human brain functional network integrity and related disorders that affect it. [ABSTRACT FROM AUTHOR]

Microbial infections have been linked to the pathogenesis and pathophysiology of Alzheimer's disease (AD) and other neurodegenerative diseases. The present study aimed to synthesise and assess global evidence of microbial pathogenesis and pathophysiology in AD (MPP-AD) and associated neurodegenerative conditions using integrated science mapping and content analytics to explore the associated research landscape. Relevant MPP-AD documents were retrieved from Web of Science and Scopus according to PRISMA principles and analysed for productivity/trend linked to authors/countries, thematic conceptual framework, and international collaborative networks. A total of 258 documents published from 136 sources to 39.42 average citations/document were obtained on MPP-AD. The co-authors per document were 7.6, and the collaboration index was 5.71. The annual research outputs increased tremendously in the last 6 years from 2014 to 2019, accounting for 66% compared with records in the early years from 1982 to 1990 (16%). The USA (n = 71, freq. = 30.34%), United Kingdom (n = 32, freq. = 13.68%) and China (n = 27, 11.54%) ranked in first three positions in term of country's productivity. Four major international collaboration clusters were found in MPP-AD research. The country collaboration network in MPP-AD was characteristic of sparse interaction and acquaintanceship (density = 0.11, diameter = 4). Overall, international collaboration is globally inadequate [centralisation statistics: degree (40.5%), closeness (4%), betweenness (23%), and eigenvector (76.7%)] against the robust authors' collaboration index of 5.71 in MPP-AD research. Furthermore, four conceptual thematic frameworks (CTF) namely, CTF#1, roles of microbial/microbiome infection and dysbiosis in cognitive dysfunctions; CTF#2, bacterial infection specific roles in dementia; CTF#3, the use of yeast as a model system for studying MPP-AD and remediation therapy; and CFT#4, flow cytometry elucidation of amyloid-beta and aggregation in Saccharomyces cerevisiae model. Finally, aetiology-based mechanisms of MPP-AD, namely, gut microbiota, bacterial infection, and viral infection, were comprehensively discussed. This study provides an overview of MPP-AD and serves as a stepping stone for future preparedness in MPP-AD-related research. [ABSTRACT FROM AUTHOR]

COVID-19 caseloads in England have passed through a first peak, and at the time of this analysis appeared to be gradually increasing, potentially signalling the emergence of a second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths at small-area resolution, identify localised areas in space–time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at high spatial resolution in coming weeks. We applied a Bayesian hierarchical space–time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England [Middle Layer Super Output Area (MSOA), 6791 units] and by week (using observed data from week 5 to 34 of 2020), including key determinants, the modelled transmission dynamics and spatial–temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA. Reductions in population mobility during the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates. While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have significantly contributed to the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced. [ABSTRACT FROM AUTHOR]

This paper presents a fault tree analysis (FTA) method for the unavailability of supply of gas networks. The method is based on the automatic generation of fault trees, which estimate the probability of disruption of the gas delivery from terminals/storages to the demand nodes. Moreover, it allows probabilistic analyses of the availability of gas supply to individual demand nodes and to the overall gas network. To assess the importance of each network component, the risk achievement worth and the risk reduction worth importance measures are utilized. The developed method can identify weakness in the gas network and can guide expansion planning and maintenance scheduling activities. Furthermore, the developed framework is enriched by steady-state analysis of the gas network operations performed using a physical flow/pressure model. The impact of a component failure on the gas supply interruption at different demand nodes is assessed by the physical model in retrospective to the FTA results. The framework is exemplified with reference to the reduced gas network of Great Britain. The results provide insights to support a robust reliability assessment of the gas network. Moreover, the probability mapping of the most important components in the gas network informs optimal strategies for maintenance scheduling as well for prioritizing improvements of the gas network aimed at significant risk reduction. Remarkably, the failure of components identified as most important according to the FTA importance measures may not have significant effects on the gas supply. A discussion on the range of applicability and limitations of the two methodologies is provided. [ABSTRACT FROM AUTHOR]

The European Emission Trading System (EU ETS) is commonly regarded as the key pillar of the European climate policy and as the main unifying tool to create a unique carbon price all over Europe. The UK has always played a crucial role in the EU ETS, being one of the most active national registry and a crucial hub for the exchange of allowances in the market. Brexit, therefore, could deeply modify the number and directions of such exchanges as well as the centrality of the other countries in this system. To investigate these issues, the present paper exploits network analysis tools to compare the structure of the EU ETS market in its first two phases with and without the UK, investigating a few different scenarios that might emerge from a possible reallocation of the transactions that have involved UK partners. We find that without the UK the EU ETS network would become in general much more homogeneous, though results may change focusing on the type of accounts involved in the transactions. [ABSTRACT FROM AUTHOR]

Various company interactions can be described by networks, for instance the ownership networks and the board membership networks. To understand the ecosystem of companies, these interactions cannot be seen in isolation. For this purpose we construct a new multiplex network of interactions between companies in Germany and in the United Kingdom, combining ownership links, social ties through joint board directors, R&D collaborations and stock correlations in one linked multiplex dataset. We describe the features of this network and show there exists a non-trivial overlap between these different types of networks, where the different types of connections complement each other and make the overall structure more complex. This highlights that corporate control, boardroom influence and other connections have different structures and together make an even smaller corporate world than previously reported. We have a first look at the relation between company performance and location in the network structure. [ABSTRACT FROM AUTHOR]

Katharina Pistor proposed the Legal Theory of Finance (LTF), based on the premise that finance is legally constructed. In this paper, we apply network science to provide empirical evidence from global financial networks (GFN) to support the argument of the LFT. Using data from the World Bank and IMF, we confirm that the GFN is indeed hierarchical. We also show that the depth of interconnectedness in the GFN is increasing. The United States is the most important node in the GFN but temporarily lost its position to the United Kingdom in 2008. This paper shows that the most important node in the GFN can temporarily shift during major global financial events. The United Kingdom has also lost its position in the GFN to Switzerland on several measures of centrality. We further confirm that there is no evidence of a flattening of the GFN. Although some emerging economies have improved significantly in terms of GDP and international reserves, these improvements have not reflected in their positions in the GFN. We propose that the approach to regulating the global financial system should focus on more stringent rules for the most central countries in the GFN. This could be more effective in ensuring stability in the global financial system. [ABSTRACT FROM AUTHOR]

Radical environmental groups and their members have a wide and varied agenda which often encompasses both local and global issues. In their efforts to call attention to environmental problems, communicate with like-minded groups, and mobilize support for their activities, radical environmental organizations also produce an enormous amount of text, which can be used to estimate the complex communications and task-based networks that underlie these organizations. Moreover, the tactics employed to garnish attention for these groups’ agenda can range from peaceful activities such as information dissemination to violent activities such as fire-bombing buildings. To obtain these varied objectives, radical environmental organizations must harness their networks, which have an important spatial component that structures their ability to communicate, coordinate and act on any given agenda item. Here, we analyze a network built from communications and information provided by the semi-annual “Do or Die” (DoD) magazine published in the UK over a 10 year period in the late 1990s and early 2000s. We first employ structural topic model methods to discover violent and nonviolent actors within the larger environmental community. Using this designation, we then compare the spatial structure of these groups, finding that violent groups are especially likely to engage in coordination and/or communication if they are sufficiently close, but exhibit a quickly decreasing probability of interaction over even a few kilometers. Further, violent and nonviolent groups each have a higher probability of coordination with their own group than across groups over even short distances. In these respects, we see that violent groups are especially local in their organization and that their geographic reach is likely very limited. This suggests that nonviolent environmental groups seek each other out over both large and short distances for communication and coordination, but violent groups tend to be highly localized. [ABSTRACT FROM AUTHOR]

In 2002, the UK government launched the Advanced Institute of Management Research, a major initiative intended to raise the quality of research in business schools. Rather than offering research grants in open competition, AIM deliberately funded a select few leading lights in management. Insufficient allowance was made for the Research Assessment Exercise, which measured research excellence in terms of papers published in top journals. The AIM's elite exploited its existing publishing advantage, and AIM provided further resources to aid their efforts. The AIM recruited willing acolytes to work with its elite in fashioning the sort of papers required by the top journals of management - positive papers, consensual and endlessly citable. Analysis of the publishing patterns of AIM senior fellows reveals research cliques and publication silos rather than a network organization. Much as the elite saw its AIM funding as recognition of its own excellence, so AIM itself came to be seen as acknowledgement of the excellence of management research as a whole. That AIM existed to raise management research from intellectual poverty was forgotten. The AIM was wound up in 2012, having spent £30 million, most of it on the subject's elite. The problems that beset management research remain. [ABSTRACT FROM AUTHOR]

Although urban birth, upbringing, and living are associated with increased risk of nonaffective psychotic disorders, few studies have used appropriate multilevel techniques accounting for spatial dependency in risk to investigate social, economic, or physical determinants of psychosis incidence. We adopted Bayesian hierarchical modeling to investigate the sociospatial distribution of psychosis risk in East London for DSM-IV nonaffective and affective psychotic disorders, ascertained over a 2-year period in the East London first-episode psychosis study. We included individual and environmental data on 427 subjects experiencing first-episode psychosis to estimate the incidence of disorder across 56 neighborhoods, having standardized for age, sex, ethnicity, and socioeconomic status. A Bayesian model that included spatially structured neighborhood-level random effects identified substantial unexplained variation in nonaffective psychosis risk after controlling for individual-level factors. This variation was independently associated with greater levels of neighborhood income inequality (SD increase in inequality: Bayesian relative risks [RR]: 1.25; 95% CI: 1.04–1.49), absolute deprivation (RR: 1.28; 95% CI: 1.08–1.51) and population density (RR: 1.18; 95% CI: 1.00–1.41). Neighborhood ethnic composition effects were associated with incidence of nonaffective psychosis for people of black Caribbean and black African origin. No variation in the spatial distribution of the affective psychoses was identified, consistent with the possibility of differing etiological origins of affective and nonaffective psychoses. Our data suggest that both absolute and relative measures of neighborhood social composition are associated with the incidence of nonaffective psychosis. We suggest these associations are consistent with a role for social stressors in psychosis risk, particularly when people live in more unequal communities. [ABSTRACT FROM PUBLISHER]

Few people would deny an intuitive sense of increased wellbeing when spending time in beautiful locations. Here, we ask: can we quantify the relationship between environmental aesthetics and human health? We draw on data from Scenic-Or-Not, a website that crowdsources ratings of 'scenicness' for geotagged photographs across Great Britain, in combination with data on citizen-reported health from the Census for England and Wales. We find that inhabitants of more scenic environments report better health, across urban, suburban and rural areas, even when taking core socioeconomic indicators of deprivation into account, such as income, employment and access to services. Our results provide evidence in line with the striking hypothesis that the aesthetics of the environment may have quantifiable consequences for our wellbeing. [ABSTRACT FROM AUTHOR]