Stochastic Weighted Graphs: Flexible Model Specification and Simulation

In most domains of network analysis researchers consider networks that arise in nature with weighted edges. Such networks are routinely dichotomized in the interest of using available methods for statistical inference with networks. The generalized exponential random graph model (GERGM) is a recently proposed method used to simulate and model the edges of a weighted graph. The GERGM specifies a joint distribution for an exponential family of graphs with continuous-valued edge weights. However, current estimation algorithms for the GERGM only allow inference on a restricted family of model specifications. To address this issue, we develop a Metropolis--Hastings method that can be used to estimate any GERGM specification, thereby significantly extending the family of weighted graphs that can be modeled with the GERGM. We show that new flexible model specifications are capable of avoiding likelihood degeneracy and efficiently capturing network structure in applications where such models were not previously available. We demonstrate the utility of this new class of GERGMs through application to two real network data sets, and we further assess the effectiveness of our proposed methodology by simulating non-degenerate model specifications from the well-studied two-stars model. A working R version of the GERGM code is available in the supplement and will be incorporated in the gergm CRAN package.Comment: 33 pages, 6 figures. To appear in Social Network

Statistical Modeling of the Default Mode Brain Network Reveals a Segregated Highway Structure

We investigate the functional organization of the Default Mode Network (DMN) - an important subnetwork within the brain associated with a wide range of higher-order cognitive functions. While past work has shown the whole-brain network of functional connectivity follows small-world organizational principles, subnetwork structure is less well understood. Current statistical tools, however, are not suited to quantifying the operating characteristics of functional networks as they often require threshold censoring of information and do not allow for inferential testing of the role that local processes play in determining network structure. Here, we develop the correlation Generalized Exponential Random Graph Model (cGERGM) - a statistical network model that uses local processes to capture the emergent structural properties of correlation networks without loss of information. Examining the DMN with the cGERGM, we show that, rather than demonstrating small-world properties, the DMN appears to be organized according to principles of a segregated highway - suggesting it is optimized for function-specific coordination between brain regions as opposed to information integration across the DMN. We further validate our findings through assessing the power and accuracy of the cGERGM on a testbed of simulated networks representing various commonly observed brain architectures

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants

Distribution and seasonality of rhinovirus and other respiratory viruses in a cross-section of asthmatic children in Trinidad, West Indies

<p>Abstract</p> <p>Background</p> <p>Childhood asthma in the Caribbean is advancing in prevalence and morbidity. Though viral respiratory tract infections are reported triggers for exacerbations, information on these infections with asthma is sparse in Caribbean territories. We examined the distribution of respiratory viruses and their association with seasons in acute and stable asthmatic children in Trinidad.</p> <p>Methods</p> <p>In a cross-sectional study of 70 wheezing children attending the emergency department for nebulisation and 80 stable control subjects (2 to 16 yr of age) in the asthma clinic, nasal specimens were collected during the dry (<it>n </it>= 38, January to May) and rainy (<it>n </it>= 112, June to December) seasons. A multitarget, sensitive, specific high-throughput Respiratory MultiCode assay tested for respiratory-virus sequences for eight distinct groups: human rhinovirus, respiratory syncytial virus, parainfluenza virus, influenza virus, metapneumovirus, adenovirus, coronavirus, and enterovirus.</p> <p>Results</p> <p>Wheezing children had a higher [χ²= 5.561, <it>p </it>= 0.018] prevalence of respiratory viruses compared with stabilized asthmatics (34.3% (24) versus (vs.) 17.5% (14)). Acute asthmatics were thrice as likely to be infected with a respiratory virus (OR = 2.5, 95% CI = 1.2 – 5.3). The predominant pathogens detected in acute versus stable asthmatics were the rhinovirus (RV) (<it>n </it>= 18, 25.7% vs. <it>n </it>= 7, 8.8%; <it>p </it>= 0.005), respiratory syncytial virus B (RSV B) (<it>n </it>= 2, 2.9% vs. <it>n </it>= 4, 5.0%), and enterovirus (<it>n </it>= 1, 1.4% vs. <it>n </it>= 2, 2.5%). Strong odds for rhinoviral infection were observed among nebulised children compared with stable asthmatics (<it>p </it>= 0.005, OR = 3.6, 95% CI = 1.4 – 9.3,). RV was prevalent throughout the year (Dry, <it>n </it>= 6, 15.8%; Rainy, <it>n </it>= 19, 17.0%) and without seasonal association [χ²= 0.028, <it>p </it>= 0.867]. However it was the most frequently detected virus [Dry = 6/10, (60.0%); Rainy = 19/28, (67.9%)] in both seasons.</p> <p>Conclusion</p> <p>Emergent wheezing illnesses during childhood can be linked to infection with rhinovirus in Trinidad's tropical environment. Viral-induced exacerbations of asthma are independent of seasons in this tropical climate. Further clinical and virology investigations are recommended on the role of infections with the rhinovirus in Caribbean childhood wheeze.</p

Perioperative Quality Initiative (POQI) consensus statement on the physiology of blood pressure control as applied to perioperative medicine.

Background: A multi-disciplinary, international working subgroup of the Third Perioperative Quality Initiative (POQI) consensus meeting reviewed the (patho)physiology and measurement of arterial blood pressure (ABP), as applied to perioperative medicine. Methods: We addressed predefined questions by undertaking a modified Delphi analysis, in which primary clinical research and review articles were identified using MEDLINE. Strength of recommendations, where applicable, were graded by NICE guidelines. Results: Perioperative ABP management is a physiologically-complex challenge influenced by multiple factors: (i) ABP is the input pressure to organ blood flow, but is not the sole determinant of perfusion pressure; (ii) blood flow is often independent of changes in perfusion pressure, due to autoregulatory changes in vascular resistance; (iii) microvascular dysfunction uncouples microvascular blood flow from ABP (haemodynamic incoherence) From a practical clinical perspective, we identified that: (i) ambulatory measurement is the optimal method to establish baseline ABP; (ii) automated and invasive ABP measurements have inherent physiological and technical limitations; (iii) individualised ABP targets may change over time, especially during the perioperative period. There remains a need for research in non-invasive, continuous arterial pressure measurements, macro- and microcirculatory control, regional perfusion pressure measurement and the development of sensitive, specific and continuous measures of cellular function to evaluate blood pressure management in a physiologically coherent manner. Conclusion: The multivariable, complex physiology contributing to dynamic changes in perioperative ABP may be underappreciated clinically. The frequently unrecognised dissociation between ABP, organ blood flow, microvascular and cellular function requires further research that develops a more refined, contextualized clinical approach to this routine measurement

RA-MAP, molecular immunological landscapes in early rheumatoid arthritis and healthy vaccine recipients

Rheumatoid arthritis (RA) is a chronic inflammatory disorder with poorly defined aetiology characterised by synovial inflammation with variable disease severity and drug responsiveness. To investigate the peripheral blood immune cell landscape of early, drug naive RA, we performed comprehensive clinical and molecular profiling of 267 RA patients and 52 healthy vaccine recipients for up to 18 months to establish a high quality sample biobank including plasma, serum, peripheral blood cells, urine, genomic DNA, RNA from whole blood, lymphocyte and monocyte subsets. We have performed extensive multi-omic immune phenotyping, including genomic, metabolomic, proteomic, transcriptomic and autoantibody profiling. We anticipate that these detailed clinical and molecular data will serve as a fundamental resource offering insights into immune-mediated disease pathogenesis, progression and therapeutic response, ultimately contributing to the development and application of targeted therapies for RA.</p

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report