Uniform Local Amenability

The main results of this paper show that various coarse (`large scale') geometric properties are closely related. In particular, we show that property A implies the operator norm localisation property, and thus that norms of operators associated to a very large class of metric spaces can be effectively estimated. The main tool is a new property called uniform local amenability. This property is easy to negate, which we use to study some `bad' spaces. We also generalise and reprove a theorem of Nowak relating amenability and asymptotic dimension in the quantitative setting

Observing requirements for long-term climate records at the ocean surface

Observations of conditions at the ocean surface have been made for centuries, contributing to some of the longest instrumental records of climate change. Most prominent is the climate data record (CDR) of sea surface temperature (SST), which is itself essential to the majority of activities in climate science and climate service provision. A much wider range of surface marine observations is available however, providing a rich source of data on past climate. We present a general error model describing the characteristics of observations used for the construction of climate records, illustrating the importance of multi-variate records with rich metadata for reducing uncertainty in CDRs. We describe the data and metadata requirements for the construction of stable, multi-century marine CDRs for variables important for describing the changing climate: SST, mean sea level pressure, air temperature, humidity, winds, clouds, and waves. Available sources of surface marine data are reviewed in the context of the error model. We outline the need for a range of complementary observations, including very high quality observations at a limited number of locations and also observations that sample more broadly but with greater uncertainty. We describe how high-resolution modern records, particularly those of high-quality, can help to improve the quality of observations throughout the historical record. We recommend the extension of internationally-coordinated data management and curation to observation types that do not have a primary focus of the construction of climate records. Also recommended is reprocessing the existing surface marine climate archive to improve and quantify data and metadata quality and homogeneity. We also recommend the expansion of observations from research vessels and high quality moorings, routine observations from ships and from data and metadata rescue. Other priorities include: field evaluation of sensors; resources for the process of establishing user requirements and determining whether requirements are being met; and research to estimate uncertainty, quantify biases and to improve methods of construction of CDRs. The requirements developed in this paper encompass specific actions involving a variety of stakeholders, including funding agencies, scientists, data managers, observing network operators, satellite agencies, and international co-ordination bodies

Validation of Global Diet Quality Score Among Nonpregnant Women of Reproductive Age in India: Findings from the Andhra Pradesh Children and Parents Study (APCAPS) and the Indian Migration Study (IMS).

BACKGROUND: In India, there is a need to monitor population-level trends in changes in diet quality in relation to both undernutrition and noncommunicable diseases. OBJECTIVES: We conducted a study to validate a novel diet quality score in southern India. METHODS: We included data from 3041 nonpregnant women of reproductive age (15-49 years) from 2 studies in India. Diet was assessed using a validated food frequency questionnaire (FFQ). The Global Diet Quality Score (GDQS) was calculated from 25 food groups (16 healthy; 9 unhealthy), with points for each group based on the frequency and quantity of items consumed in each group. We used Spearman correlations to examine correlations between the GDQS and several nutrient intakes of concern. We examined associations between the GDQS [overall, healthy (GDQS+), and unhealthy (GDQS-) submetrics] and overall nutrient adequacy, micro- and macronutrients, body mass index (BMI), midupper arm circumference, hemoglobin, blood pressure, high density lipoprotein (HDL), and total cholesterol (TC). RESULTS: The mean GDQS was 23 points (SD, 3.6; maximum, 46.5). In energy-adjusted models, positive associations were found between the overall GDQS and GDQS+ and intakes of calcium, fiber, folate, iron, monounsaturated fatty acid (MUFA), protein, polyunsaturated fatty acid (PUFA), saturated fatty acid (SFA), total fat, and zinc (ρ = 0.12-0.39; P < 0.001). Quintile analyses showed that the GDQS was associated with better nutrient adequacy. At the same time, the GDQS was associated with higher TC, lower HDL, and higher BMI. We found no associations between the GDQS and hypertension. CONCLUSIONS: The GDQS was a useful tool for reflecting overall nutrient adequacy and some lipid measures. Future studies are needed to refine the GDQS for populations who consume large amounts of unhealthy foods, like refined grains, along with healthy foods included in the GDQS

Exploration of Machine Learning and Statistical Techniques in Development of a Low-Cost Screening Method Featuring the Global Diet Quality Score for Detecting Prediabetes in Rural India.

BACKGROUND: The prevalence of type 2 diabetes has increased substantially in India over the past 3 decades. Undiagnosed diabetes presents a public health challenge, especially in rural areas, where access to laboratory testing for diagnosis may not be readily available. OBJECTIVES: The present work explores the use of several machine learning and statistical methods in the development of a predictive tool to screen for prediabetes using survey data from an FFQ to compute the Global Diet Quality Score (GDQS). METHODS: The outcome variable prediabetes status (yes/no) used throughout this study was determined based upon a fasting blood glucose measurement ≥100 mg/dL. The algorithms utilized included the generalized linear model (GLM), random forest, least absolute shrinkage and selection operator (LASSO), elastic net (EN), and generalized linear mixed model (GLMM) with family unit as a (cluster) random (intercept) effect to account for intrafamily correlation. Model performance was assessed on held-out test data, and comparisons made with respect to area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. RESULTS: The GLMM, GLM, LASSO, and random forest modeling techniques each performed quite well (AUCs >0.70) and included the GDQS food groups and age, among other predictors. The fully adjusted GLMM, which included a random intercept for family unit, achieved slightly superior results (AUC of 0.72) in classifying the prediabetes outcome in these cluster-correlated data. CONCLUSIONS: The models presented in the current work show promise in identifying individuals at risk of developing diabetes, although further studies are necessary to assess other potentially impactful predictors, as well as the consistency and generalizability of model performance. In addition, future studies to examine the utility of the GDQS in screening for other noncommunicable diseases are recommended

A call for new approaches to quantifying biases in observations of sea-surface temperature

Global surface-temperature changes are a fundamental expression of climate change. Recent, much-debated, variations in the observed rate of surface-temperature change have highlighted the importance of uncertainty in adjustments applied to sea-surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface-temperature change and provide higher- quality gridded SST fields for use in many applications. Bias adjustments have been based either on physical models of the observing processes or on the assumption of an unchanging relationship between SST and a reference data set such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and timescales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method. New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and of high-quality observations for validation and bias model development are likely to remain major challenges

Main nutrient patterns and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition study.

BACKGROUND: Much of the current literature on diet-colorectal cancer (CRC) associations focused on studies of single foods/nutrients, whereas less is known about nutrient patterns. We investigated the association between major nutrient patterns and CRC risk in participants of the European Prospective Investigation into Cancer and Nutrition (EPIC) study. METHODS: Among 477 312 participants, intakes of 23 nutrients were estimated from validated dietary questionnaires. Using results from a previous principal component (PC) analysis, four major nutrient patterns were identified. Hazard ratios (HRs) and 95% confidence intervals (CIs) were computed for the association of each of the four patterns and CRC incidence using multivariate Cox proportional hazards models with adjustment for established CRC risk factors. RESULTS: During an average of 11 years of follow-up, 4517 incident cases of CRC were documented. A nutrient pattern characterised by high intakes of vitamins and minerals was inversely associated with CRC (HR per 1 s.d.=0.94, 95% CI: 0.92-0.98) as was a pattern characterised by total protein, riboflavin, phosphorus and calcium (HR (1 s.d.)=0.96, 95% CI: 0.93-0.99). The remaining two patterns were not significantly associated with CRC risk. CONCLUSIONS: Analysing nutrient patterns may improve our understanding of how groups of nutrients relate to CRC

Prospective cohort study reveals unexpected aetiologies of livestock abortion in northern Tanzania

Livestock abortion is an important cause of productivity losses worldwide and many infectious causes of abortion are zoonotic pathogens that impact on human health. Little is known about the relative importance of infectious causes of livestock abortion in Africa, including in subsistence farming communities that are critically dependent on livestock for food, income, and wellbeing. We conducted a prospective cohort study of livestock abortion, supported by cross-sectional serosurveillance, to determine aetiologies of livestock abortions in livestock in Tanzania. This approach generated several important findings including detection of a Rift Valley fever virus outbreak in cattle; high prevalence of C. burnetii infection in livestock; and the first report of Neospora caninum, Toxoplasma gondii, and pestiviruses associated with livestock abortion in Tanzania. Our approach provides a model for abortion surveillance in resource-limited settings. Our findings add substantially to current knowledge in sub-Saharan Africa, providing important evidence from which to prioritise disease interventions

A Genome Scan for Positive Selection in Thoroughbred Horses

Thoroughbred horses have been selected for exceptional racing performance resulting in system-wide structural and functional adaptations contributing to elite athletic phenotypes. Because selection has been recent and intense in a closed population that stems from a small number of founder animals Thoroughbreds represent a unique population within which to identify genomic contributions to exercise-related traits. Employing a population genetics-based hitchhiking mapping approach we performed a genome scan using 394 autosomal and X chromosome microsatellite loci and identified positively selected loci in the extreme tail-ends of the empirical distributions for (1) deviations from expected heterozygosity (Ewens-Watterson test) in Thoroughbred (n = 112) and (2) global differentiation among four geographically diverse horse populations (FST). We found positively selected genomic regions in Thoroughbred enriched for phosphoinositide-mediated signalling (3.2-fold enrichment; P<0.01), insulin receptor signalling (5.0-fold enrichment; P<0.01) and lipid transport (2.2-fold enrichment; P<0.05) genes. We found a significant overrepresentation of sarcoglycan complex (11.1-fold enrichment; P<0.05) and focal adhesion pathway (1.9-fold enrichment; P<0.01) genes highlighting the role for muscle strength and integrity in the Thoroughbred athletic phenotype. We report for the first time candidate athletic-performance genes within regions targeted by selection in Thoroughbred horses that are principally responsible for fatty acid oxidation, increased insulin sensitivity and muscle strength: ACSS1 (acyl-CoA synthetase short-chain family member 1), ACTA1 (actin, alpha 1, skeletal muscle), ACTN2 (actinin, alpha 2), ADHFE1 (alcohol dehydrogenase, iron containing, 1), MTFR1 (mitochondrial fission regulator 1), PDK4 (pyruvate dehydrogenase kinase, isozyme 4) and TNC (tenascin C). Understanding the genetic basis for exercise adaptation will be crucial for the identification of genes within the complex molecular networks underlying obesity and its consequential pathologies, such as type 2 diabetes. Therefore, we propose Thoroughbred as a novel in vivo large animal model for understanding molecular protection against metabolic disease

Biomarkers of Dietary Omega-6 Fatty Acids and Incident Cardiovascular Disease and Mortality: An Individual-Level Pooled Analysis of 30 Cohort Studies

BACKGROUND: Global dietary recommendations for and cardiovascular effects of linoleic acid, the major dietary omega-6 fatty acid, and its major metabolite, arachidonic acid, remain controversial. To address this uncertainty and inform international recommendations, we evaluated how in vivo circulating and tissue levels of linoleic acid (LA) and arachidonic acid (AA) relate to incident cardiovascular disease (CVD) across multiple international studies. METHODS: We performed harmonized, de novo, individual-level analyses in a global consortium of 30 prospective observational studies from 13 countries. Multivariable-adjusted associations of circulating and adipose tissue LA and AA biomarkers with incident total CVD and subtypes (coronary heart disease, ischemic stroke, cardiovascular mortality) were investigated according to a prespecified analytic plan. Levels of LA and AA, measured as the percentage of total fatty acids, were evaluated linearly according to their interquintile range (ie, the range between the midpoint of the first and fifth quintiles), and categorically by quintiles. Study-specific results were pooled using inverse-variance–weighted meta-analysis. Heterogeneity was explored by age, sex, race, diabetes mellitus, statin use, aspirin use, omega-3 levels, and fatty acid desaturase 1 genotype (when available). RESULTS: In 30 prospective studies with medians of follow-up ranging 2.5 to 31.9 years, 15 198 incident cardiovascular events occurred among 68 659 participants. Higher levels of LA were significantly associated with lower risks of total CVD, cardiovascular mortality, and ischemic stroke, with hazard ratios per interquintile range of 0.93 (95% CI, 0.88–0.99), 0.78 (0.70–0.85), and 0.88 (0.79–0.98), respectively, and nonsignificantly with lower coronary heart disease risk (0.94; 0.88–1.00). Relationships were similar for LA evaluated across quintiles. AA levels were not associated with higher risk of cardiovascular outcomes; in a comparison of extreme quintiles, higher levels were associated with lower risk of total CVD (0.92; 0.86–0.99). No consistent heterogeneity by population subgroups was identified in the observed relationships. CONCLUSIONS: In pooled global analyses, higher in vivo circulating and tissue levels of LA and possibly AA were associated with lower risk of major cardiovascular events. These results support a favorable role for LA in CVD prevention