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Objective To compare the 7-point subjective global assessment (SGA) and the protein energy wasting (PEW) score with nutrition evaluations conducted by registered dietitian nutritionists in identifying PEW risk in stage 5 chronic kidney disease patients on maintenance hemodialysis. Design and Methods This study is a secondary analysis of a cross-sectional study entitled "Development and Validation of a Predictive energy Equation in Hemodialysis". PEW risk identified by the 7-point SGA and the PEW score was compared against the nutrition evaluations conducted by registered dietitian nutritionists through data examination from the original study (reference standard). Subjects A total of 133 patients were included for the analysis. Main Outcome Measures The sensitivity, specificity, positive and negative predictive value (PPV and NPV), positive and negative likelihood ratio (PLR and NLR) of both scoring tools were calculated when compared against the reference standard. Results The patients were predominately African American (n = 112, 84.2%), non-Hispanic (n = 101, 75.9%), and male (n = 80, 60.2%). Both the 7-point SGA (sensitivity = 78.6%, specificity = 59.1%, PPV = 33.9%, NPV = 91.2%, PLR = 1.9, and NLR = 0.4) and the PEW score (sensitivity = 100%, specificity = 28.6%, PPV = 27.2%, NPV = 100%, PLR = 1.4, and NLR = 0) were more sensitive than specific in identifying PEW risk. The 7-point SGA may miss 21.4% patients having PEW and falsely identify 40.9% of patients who do not have PEW. The PEW score can identify PEW risk in all patients, but 71.4% of patients identified may not have PEW risk. Conclusions Both the 7-point SGA and the PEW score could identify PEW risk. The 7-point SGA was more specific, and the PEW score was more sensitive. Both scoring tools were found to be clinically confident in identifying patients who were actually not at PEW risk.

Cancers arise through the process of somatic evolution fueled by the inception of somatic mutations. We lack a complete understanding of the sources of these somatic mutations. Humans host a vast repertoire of microbes collectively known as the microbiota. The microbiota plays a role in altering the tumor microenvironment and proliferation. In addition, microbes have been shown to elicit DNA damage which provides the driver for somatic mutations. An understanding of microbiota-driven mutational mechanisms would contribute to a more complete understanding of the origins of the cancer genome. Here, we review the modes by which microbes stimulate DNA damage and the effect of these phenomena upon the cancer genomic architecture, specifically in the form of mutational spectra and mutational signatures.

Considerable research has focused on identifying risk factors for intramammary infections, yet mastitis remains a pervasive disease on dairy farms. Increasingly, researchers are appreciating the role of dairy producer mindset in determining management style and thus udder health status of the herd. The objective of this study was to explore the attitudes and motivations of Ontario dairy farmers toward udder health in herds with varying bulk milk somatic cell count (BMSCC). In December 2011, 5 focus groups were conducted across Ontario, Canada, with independent groups of dairy producers representing low, medium, and high BMSCC herds. Groups were established based on producer's weighted BMSCC levels as recorded over the summer of 2011. A semi-structured interview guide was followed to discuss topics relating to udder health. Thematic analysis was performed on the interview transcripts. Generally, producers noted management techniques (specifically culling infected cows and monitoring BMSCC), a perceived wealth of information on mastitis control, and a proactive whole-herd management approach engender the perception of control over mastitis. Producers in the low BMSCC group were confident in their level of knowledge and control of mastitis in their herds, whereas high BMSCC producers generally felt lower levels of control. Several areas were identified by producers that counteract this perception, contributing to perceived low levels of control over mastitis. Participants identified that at certain times they do not understand the cause of BMSCC on their farm. This attitude was especially prominent in the high BMSCC group. Other times, producers cited improper sample handling, seasonal issues, perceived milk culture shortcomings, and low herd size as factors that limited their control over mastitis in their herds. Though producers generally have high levels of self-efficacy beliefs when it comes to udder health management, the perception still exists that, under certain situations, mastitis is uncontrollable. This highlights the fact that educational and extension efforts need to focus on ensuring that producers employ proven mastitis diagnostic, prevention, and treatment practices in a systematic manner, with realistic expectations.

Evidence-based practice in medical nutrition therapy for kidney stones involves staying up-to-date on evidence-based practice guidelines. However, because there are limited practice guidelines in this area, it may be necessary to search and evaluate the primary literature to make practice decisions. Searching the literature starts with writing a question in PICO (population, intervention, comparison, outcome) format. Search results are then evaluated based on the study design, with randomized controlled trials considered to provide the best evidence. When primary literature is unavailable, strategies are reviewed for practitioners to systematically collect outcomes from their own practice in order to advance knowledge in this area.

Estimating the effective population size and effective number of breeders per year (Nb ) can facilitate early detection of population declines. We used computer simulations to quantify bias and precision of the one-sample LDNe estimator of Nb in age-structured populations using a range of published species life history types, sample sizes, and DNA markers. Nb estimates were biased by ~5%-10% when using SNPs or microsatellites in species ranging from fishes to mosquitoes, frogs, and seaweed. The bias (high or low) was similar for different life history types within a species suggesting that life history variation in populations will not influence Nb estimation. Precision was higher for 100 SNPs (H ≈ 0.30) than for 15 microsatellites (H ≈ 0.70). Confidence intervals (CIs) were occasionally too narrow, and biased high when Nb was small (Nb

Climate change vulnerability assessments (CCVAs) are important tools to plan for and mitigate potential impacts of climate change. However, CCVAs often lack scientific rigor, which can ultimately lead to poor conservation prioritization and associated ecological and economic costs. We discuss the need to improve comparability and consistency of CCVAs and either validate their findings or improve assessment of CCVA uncertainty and sensitivity to methodological assumptions.

Effective population size (Ne) is among the most important metrics in evolutionary biology. In natural populations, it is often difficult to collect adequate demographic data to calculate Ne directly. Consequently, genetic methods to estimate Ne have been developed. Two Ne estimators based on sibship reconstruction using multi-locus genotype data have been developed in recent years; sibship assignment and parentage analysis without parents. In this study, we evaluated the accuracy of sibship reconstruction using a large empirical dataset from five hatchery steelhead populations with known pedigrees and using 95 single nucleotide polymorphism (SNP) markers. We challenged the software COLONY with 2,599,961 known relationships and demonstrated that reconstruction of full-sib and unrelated pairs were greater than 95% and 99% accurate, respectively. However, reconstruction of half-sib pairs was poor (< 5% accurate). Despite poor half-sib reconstruction, both estimators provided accurate estimates of the effective number of breeders (Nb) when sample sizes were near or greater than the true Nb and when assuming a monogamous mating system. We further demonstrated that both methods provide roughly equivalent estimates of Nb. Our results indicate that sibship reconstruction and current SNP panels provide promise for estimating Nb in steelhead populations in the region. This article is protected by copyright. All rights reserved.

The objectives of this study were to describe networks of Ontario swine sites and their service providers (including trucking, feed, semen, gilt and boar companies); to categorize swine sites into clusters based on site-level centrality measures, and to investigate risk factors for porcine reproductive and respiratory syndrome (PRRS) using information gathered from the above-mentioned analyses. All 816 sites included in the current study were enrolled in the PRRS area regional control and elimination projects in Ontario. Demographics, biosecurity and network data were collected using a standardized questionnaire and PRRS status was determined on the basis of available diagnostic tests and assessment by site veterinarians. Two-mode networks were transformed into one-mode dichotomized networks. Cluster and risk factor analyses were conducted separately for breeding and growing pig sites. In addition to the clusters obtained from cluster analyses, other explanatory variables of interest included: production type, type of animal flow, use of a shower facility, and number of neighboring swine sites within 3km. Unadjusted univariable analyses were followed by two types of adjusted models (adjusted for production systems): a generalizing estimation equation model (GEE) and a generalized linear mixed model (GLMM). Results showed that the gilt network was the most fragmented network, followed by the boar and truck networks. Considering all networks simultaneously, approximately 94% of all swine sites were indirectly connected. Unadjusted risk factor analyses showed significant associations between almost all predictors of interest and PRRS positivity, but these disappeared once production system was taken into consideration. Finally, the vast majority of the variation on PRRS status was explained by production system according to GLMM, which shows the highly correlated nature of the data, and raises the point that interventions at this level could potentially have high impact in PRRS status change and/or maintenance.

High ambient heat and humidity have profound effects on the production, health, profitability, and welfare of dairy cattle. To describe the relationship between summer temperature and relative humidity in the barn and determine the appropriateness of using meteorological station data as a surrogate for on-farm environmental monitoring, a study was conducted on 48 farms in Ontario, Canada, over the summer (May through September) of 2013. Within-barn environmental conditions were recorded using remote data loggers. These values were compared with those of the closest official meteorological station. In addition, farm-level characteristics and heat-abatement strategies were recorded for each farm. Environmental readings within the barn were significantly higher than those of the closest meteorological station; however, this relationship varied greatly by herd. Daily temperature-humidity index (THI) values within the barn tended to be 1 unit higher than those of the closest meteorological station. Numerically, 1.5 times more mean daily THI readings were in excess of 68 (heat stress threshold for lactating dairy cows) in the barn, relative to the closest meteorological station. In addition, tiestalls, herds that were allowed access to pasture, and herds that had no permanent cooling strategy for their cows had the highest mean and maximum daily THI values. Minimum daily THI values were almost 4 units higher for tiestall relative to freestall herds. Overall, due to farm-specific and unpredictable variability in magnitude of environmental differences between on-farm and meteorological station readings, researchers attempting to study the effects of environment on dairy cows should not use readings from meteorological stations because these will often underestimate the level of heat stress to which cows are exposed.

Understanding how environmental variation influences population genetic structure is important for conservation management because it can reveal how human stressors influence population connectivity, genetic diversity and persistence. We used riverscape genetics modelling to assess whether climatic and habitat variables were related to neutral and adaptive patterns of genetic differentiation (population-specific and pairwise FST ) within five metapopulations (79 populations, 4583 individuals) of steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, USA. Using 151 putatively neutral and 29 candidate adaptive SNP loci, we found that climate-related variables (winter precipitation, summer maximum temperature, winter highest 5% flow events and summer mean flow) best explained neutral and adaptive patterns of genetic differentiation within metapopulations, suggesting that climatic variation likely influences both demography (neutral variation) and local adaptation (adaptive variation). However, we did not observe consistent relationships between climate variables and FST across all metapopulations, underscoring the need for replication when extrapolating results from one scale to another (e.g. basin-wide to the metapopulation scale). Sensitivity analysis (leave-one-population-out) revealed consistent relationships between climate variables and FST within three metapopulations; however, these patterns were not consistent in two metapopulations likely due to small sample sizes (N = 10). These results provide correlative evidence that climatic variation has shaped the genetic structure of steelhead populations and highlight the need for replication and sensitivity analyses in land and riverscape genetics.

Preterm birth (infants born at

Objective Patient to staff ratios vary based on facility characteristics, and therefore have been proposed as an explanatory factor for the variation in dialysis facility outcomes. This analysis tested that hypothesis. Design and Methods Observational study using Dialysis Facility Report data. Reported staff numbers from the Annual Facility Survey were converted to full time equivalents (FTE). Subsequently, ratios were created for patients per FTE registered dietitian (RD), social worker, nurse, and patient care technician. Bivariate associations and structural equation modeling (SEM) were used to explore relationships between these ratios and patient outcomes: standardized mortality ratio and standardized hospitalization rate, when also considering the impact of non-modifiable facility characteristics (region, chain, profit status). Our focus was on RD staffing; therefore we also included serum phosphorus and normalized protein catabolic ratio in the model, and also conducted a sub-analysis of the 198 facilities that exceeded the KDOQI maximum of 150 patients:FTE RD. Subjects Dialysis centers in the US with at least 30 adult patients and no pediatric patients. 4035 facilities had complete data for the proposed variables. Main Outcome Measure Standardized mortality ratio and standardized hospitalization rate were the primary outcomes. Results The mean and standard deviation for patients per FTE staff were 90.0 ± 34.0, 88.7 ± 32.8, 17.1 ± 20.5 and 11.9 ± 7.0 for RDs, social workers, nurses, and technicians, respectively. Facility characteristics impacted staffing in bivariate analyses and SEM. The only significant paths from staffing ratio to outcomes were for patient:FTE social worker to SMR (standardized beta=−0.09, 95% CI −0.13, −0.04) and Patients:FTE RD to SHR Days (standardized beta=0.04, 95% CI 0.001, 0.09). In the sub-analysis, there were no significant paths from staffing to outcomes. Conclusions This study did not provide evidence that patient per staff ratios explain variation in dialysis facility outcomes. While there are some important bivariate relationships, these disappear in more complex models. Future research should investigate the impacts of staffing ratios on individual patients, to overcome the possible ecological fallacy.

Population genomics is advancing our understanding of evolution, ecology, conservation, agriculture, forestry, and human health by allowing new and long-standing questions to be addressed with unprecedented power and accuracy. These advances result from plummeting costs for DNA sequencing, which makes genotyping feasible for hundreds to millions of individuals and loci, and also allows for the study of variation in gene expression, epigenetic variation, and proteins. The increased power also results from the development of innovative software, statistical approaches, and models to extract information from massive next-generation sequencing datasets. Among the most exciting developments are conceptually novel approaches that are advancing understanding about inbreeding and outbreeding depression, adaptive gene flow, population demographic history, and the genomic basis of local adaptation and speciation. Remaining challenges in applying genomics to natural and managed populations include the limited understanding and availability of validated bioinformatics pipelines for genotyping and analyzing genomic data. We also lack knowledge of best practices and general guidelines for filtering and genotyping genomic data including restriction site-associated DNA sequences (RAD), targeted DNA capture, and pooled sequencing. Finally, we emphasize the need for continued rigorous teaching of population genetics theory, so that the next generation of population genomicists can ask well-informed questions and interpret next-generation sequence datasets.

Population genomic tools have revolutionized many aspects of biology, as detailed throughout the chapters of this volume. In particular, population genomics has provided key insights into ecological and evolutionary processes in natural and managed populations. These studies address a wide range of questions, including demography, phylogeny, genetics of ecologically relevant traits, and adaptation. They have also facilitated the conservation and management of biodiversity and harvested populations. Rather than exhaustively document the applications of population genomics in ecology and evolution, in this chapter we provide perspectives on a few key issues confronting researchers seeking to use population genomic tools in non-model systems. A wide variety of molecular and computational genomic approaches are available and have been used in ecological and evolutionary studies. There is no single best approach; rather, the genomic approach used should be tailored to best address the particular study goals and guided by the biology of the system. A large number of trade-offs, costs, and benefits distinguish genomic approaches, which we discuss below. To illustrate these issues, we focus on several published case studies and assess how the research questions were addressed.

Objective To better define the prevalence of protein-energy wasting (PEW) in kidney disease is poorly defined. Methods We performed a meta-analysis of PEW prevalence from contemporary studies including more than 50 subjects with kidney disease, published during 2000-2014 and reporting on PEW prevalence by subjective global assessment or malnutrition-inflammation score. Data were reviewed throughout different strata: (1) acute kidney injury (AKI), (2) pediatric chronic kidney disease (CKD), (3) nondialyzed CKD 3-5, (4) maintenance dialysis, and (5) subjects undergoing kidney transplantation (Tx). Sample size, period of publication, reporting quality, methods, dialysis technique, country, geographical region, and gross national income were a priori considered factors influencing between-study variability. Results Two studies including 189 AKI patients reported a PEW prevalence of 60% and 82%. Five studies including 1776 patients with CKD stages 3-5 reported PEW prevalence ranging from 11% to 54%. Finally, 90 studies from 34 countries including 16,434 patients on maintenance dialysis were identified. The 25th-75th percentiles range in PEW prevalence among dialysis studies was 28-54%. Large variation in PEW prevalence across studies remained even when accounting for moderators. Mixed-effects meta-regression identified geographical region as the only significant moderator explaining 23% of the observed data heterogeneity. Finally, two studies including 1067 Tx patients reported a PEW prevalence of 28% and 52%, and no studies recruiting pediatric CKD patients were identified. Conclusion By providing evidence-based ranges of PEW prevalence, we conclude that PEW is a common phenomenon across the spectrum of AKI and CKD. This, together with the well-documented impact of PEW on patient outcomes, justifies the need for increased medical attention.

Landscape genomics investigates how spatial and environmental factors influence geographic patterns of genome-wide genetic variation. Adaptive landscape genomics focuses on how these spatial and environmental processes structure the amount and distribution of selection-driven genetic variation among populations, which ultimately determines how phenotypic variation is arrayed across landscapes. This adaptive landscape genomics approach can be used to identify the causal factors underlying local adaptation and has great potential to guide decision-making in applied wildlife research, especially in light of anthropogenic climate and land use change. Conservation and management applications include delineating conservation units, designing conservation monitoring programs, and predicting changes in the spatial distribution and potential loss of adaptive genomic variation under environmental change. However, there remains great untapped potential for the application of adaptive landscape genomics to wildlife research, including moving beyond correlative genotype-environment association tests. In this chapter, we explore and discuss the potential of adaptive landscape genomics for improving wildlife research, including case studies that illustrate its application in wildlife management and conservation. We also present a comprehensive workflow for adaptive landscape genomics studies in wildlife, including sampling design, genomic and environmental data production, and data analysis. We conclude with avenues and perspectives for future work and ongoing challenges in adaptive landscape genomics.

Regionally aggregated bulk milk somatic cell count (BMSCC) data from around the world shows a repeatable cyclicity, with the highest levels experienced during warm, humid seasons. No studies have evaluated this seasonal phenomenon at the herd level. The objectives of this study were to define summer seasonality in BMSCC on an individual herd basis, and subsequently to describe the characteristics and dynamics of herds with increased BMSCC in the summer. The data used for this analysis were from all dairy farms in Ontario, Canada, between January 2000 and December 2011 (n ≈ 4,000 to 6,000 herds/yr). Bulk milk data were obtained from the milk marketing board and consisted of bulk milk production, components (fat, protein, lactose, other solids), and quality (BMSCC, bacterial count, inhibitor presence, freezing point), total milk quota of the farm, and milk quota and incentive fill percentage. A time-series linear mixed model, with random slopes and intercepts, was constructed using sine and cosine terms as predictors to describe seasonality, with herd as a random effect. For each herd, seasonality was described with reference to 1 cosine function of variable amplitude and phase shift. The predicted months of maximal and minimal BMSCC were then calculated. Herds were assigned as low, medium, and high summer increase (LSI, MSI, and HSI, respectively) based on percentiles of amplitude in BMSCC change for each of the 4 seasons. Using these seasonality classifications, 2 transitional repeated measures logistic regression models were built to assess the characteristics of MSI and HSI herds, using LSI herds as controls. Based on the analyses performed, a history of summer BMSCC increases increased the odds of experiencing a subsequent increase. As herd size decreased, the odds of experiencing HSI to MSI in BMSCC increased. Herds with more variability in daily BMSCC were at higher odds of experiencing MSI and HSI in BMSCC, as were herds with lower annual mean BMSCC. Finally, a negative association was noted between filling herd production targets and experiencing MSI to HSI in BMSCC. These findings provide farm advisors direction for predicting herds likely to experience increases in SCC over the summer, allowing them to proactively focus udder health prevention strategies before the high-risk summer period.

As a proactive measure toward controlling the nontreatable and contagious Johne's disease in cattle, the Ontario dairy industry launched the voluntary Ontario Johne's Education and Management Assistance Program in 2010. The objective of this study was to describe the results of the first 4 yr of the program and to investigate the variability in Risk Assessment and Management Plan (RAMP) scores associated with the county, veterinary clinic, and veterinarian. Of 4,158 Ontario dairy farms, 2,153 (51.8%) participated in the program between January 2010 and August 2013. For this study, RAMP scores and whole-herd milk or serum ELISA results were available from 2,103 farms. Herd-level ELISA-positive prevalence (herds with one or more test-positive cows were considered positive) was 27.2%. Linear mixed model analysis revealed that the greatest RAMP score variability was at the veterinarian level (24.2%), with relatively little variability at the county and veterinary clinic levels. Consequently, the annual RAMP should be done by the same veterinarian to avoid misleading or discouraging results.

Understanding how climatic variation influences ecological and evolutionary processes is crucial for informed conservation decision-making. Nevertheless, few studies have measured how climatic variation influences genetic diversity within populations or how genetic diversity is distributed across space relative to future climatic stress. Here, we tested whether patterns of genetic diversity (allelic richness) were related to climatic variation and habitat features in 130 bull trout (Salvelinus confluentus) populations from 24 watersheds (i.e., ~4-7th order river subbasins) across the Columbia River Basin, USA. We then determined whether bull trout genetic diversity was related to climate vulnerability at the watershed scale, which we quantified on the basis of exposure to future climatic conditions (projected scenarios for the 2040s) and existing habitat complexity. We found a strong gradient in genetic diversity in bull trout populations across the Columbia River Basin, where populations located in the most upstream headwater areas had the greatest genetic diversity. After accounting for spatial patterns with linear mixed models, allelic richness in bull trout populations was positively related to habitat patch size and complexity, and negatively related to maximum summer temperature and the frequency of winter flooding. These relationships strongly suggest that climatic variation influences evolutionary processes in this threatened species and that genetic diversity will likely decrease due to future climate change. Vulnerability at a watershed scale was negatively correlated with average genetic diversity (r = -0.77; P

Invasive hybridization and introgression pose a serious threat to the persistence of many native species. Understanding the effects of hybridization on native populations (e.g., fitness consequences) requires numerous species-diagnostic loci distributed genome-wide. Here we used RAD sequencing to discover thousands of single-nucleotide polymorphisms (SNPs) that are diagnostic between rainbow trout (RBT, Oncorhynchus mykiss), the world’s most widely introduced fish, and native westslope cutthroat trout (WCT, O. clarkii lewisi) in the northern Rocky Mountains, USA. We advanced previous work that identified 4,914 species-diagnostic loci by using longer sequence reads (100 bp vs. 60 bp) and a larger set of individuals (n = 84). We sequenced RAD libraries for individuals from diverse sampling sources, including native populations of WCT and hatchery broodstocks of WCT and RBT. We also took advantage of a newly released reference genome assembly for RBT to align our RAD loci. In total, we discovered 16,788 putatively diagnostic SNPs, 10,267 of which we mapped to anchored chromosome locations on the RBT genome. A small portion of previously discovered putative diagnostic loci (325 of 4,914) were no longer diagnostic (i.e., fixed between species) based on our wider survey of non-hybridized RBT and WCT individuals. Our study suggests that RAD loci mapped to a draft genome assembly could provide the marker density required to identify genes and chromosomal regions influencing selection in admixed populations of conservation concern and evolutionary interest.

to guide the development of nutrition education programs, or to evaluate program plans or proposals. GENIE has been validated across a wide range of nutrition education program proposals, 12 giving this tool the unique ability to compare disparate programs. This article describes a literature review in which GENIE was used to assess and compare published program descriptions and to: 1) identify qualities of nutrition education programs commonly present or absent in both high-scoring and lower-scoring interventions when assessed with GENIE; 2) provide examples of high-scoring programs in each category; and 3) determine the relationship between GENIE score and program outcomes.

Dietary and physical activity habits are developed early in life and are influenced by family environments. We describe and evaluate an intervention for low-income families to encourage healthy habits. The RD Parent Empowerment Program (http://www.eatright.org/programs/kidseatright/activities/content.aspx?id=6442477891) consists of four workshops centered on the 8 Habits of Healthy Children and Families (Academy of Nutrition and Dietetics Foundation). Registered dietitian nutritionists conduct the workshops in school and community settings using a structured leader guide and tailor the communication and interactive activities to the audience. Participants are parents of young children. Our goals were to use a phenomenologic approach to elicit participant feedback, determine whether participants in the RD Parent Empowerment Program made healthier choices for their families after attending the workshops, and identify which elements of the program participants believed contributed most to its success. The evaluation design used a pragmatic, mixed-methods approach utilizing postintervention focus groups and pre-post intervention scores on the Family Nutrition and Physical Activity (FNPA) survey. All workshop attendees aged 18 years or older were eligible to participate in the evaluation. One hundred twenty-three parents participated in the intervention across seven sites. Focus group results were analyzed using thematic analysis methods to match themes to the main intervention goals. t Tests were used to compare pre- and postintervention FNPA scores and demographic characteristics pooled across sites. FNPA scores significantly improved from pre- to postintervention by a mean of 4.3 FNPA points (6.5%; P0.01). Focus group participants reported behavior changes as a result of the program and identified the site leaders as integral to the program's success, triangulating the results. The RD Parent Empowerment Program generates meaningful self-reported behavior change in parents. Long-term sustainability of the changes must be investigated.

Andersen-Tawil syndrome (ATS) is a rare autosomal dominant potassium channelopathy characterized by a triad of periodic paralysis, ventricular arrhythmias, and distinctive dysmorphic abnormalities. We present a 19-year-old man with characteristic skeletal dysmorphic features of ATS, early nonfluctuating proximal lower limb weakness from childhood, and neonatal focal seizures. He later developed fluctuating weakness in addition to a fixed proximal myopathy. A 12-lead electrocardiogram showed prominent "U" waves, and McManis protocol prolonged exercise test showed an unusually early decline in the compound motor action potential amplitude by 51%. Genetic testing revealed a de novo heterozygous mutation (R218W) in KCNJ2 associated with ATS. This is the first reported case of ATS in an Irish population with an unusual fixed myopathy from early childhood.

Puritz et al. provide a review of several RADseq methodological approaches in response to our 'Population Genomic Data Analysis' workshop (Sept 2013) review (Andrews & Luikart 2014). We agree with Puritz et al. on the importance for researchers to thoroughly understand RADseq library preparation and data analysis when choosing an approach for answering their research questions. Some of us are currently using multiple RADseq protocols, and we agree that the different methods may offer advantages in different cases. Our workshop review did not intend to provide a thorough review of RADseq because the workshop covered a broad range of topics within the field of population genomics. Similarly, neither the response of Puritz et al. nor our comments here provide sufficient space to thoroughly review RADseq. Nonetheless, here we address some key points that we find unclear or potentially misleading in their evaluation of techniques.

We agree with Shafer et al. [1] that there is a need for well-documented case studies of the application of genomics in conservation and management as well as increased communication between academics and natural resource managers. However, we challenge Shafer et al.’s [1] relatively pessimistic assertion that ‘conservation genomics is far from seeing regular application’. Here we illustrate by examples that conservation practitioners utilize more genomic research than is often apparent. In addition, we highlight the work of nonacademic laboratories [government and nongovernmental organizations (NGOs)], some of which are not always well represented in peer-reviewed literature.

Aims To evaluate the effect of a single early high-dose vitamin D supplement on fracture union in patients with hypovitaminosis D and a long bone fracture. Patients and Methods Between July 2011 and August 2013, 113 adults with a long bone fracture were enrolled in a prospective randomised double-blind placebo-controlled trial. Their serum vitamin D levels were measured and a total of 100 patients were found to be vitamin D deficient (< 20 ng/ml) or insufficient (< 30 ng/mL). These were then randomised to receive a single dose of vitamin D3 orally (100 000 IU) within two weeks of injury (treatment group, n = 50) or a placebo (control group, n = 50). We recorded patient demographics, fracture location and treatment, vitamin D level, time to fracture union and complications, including vitamin D toxicity. Outcomes included union, nonunion or complication requiring an early, unplanned secondary procedure. Patients without an outcome at 15 months and no scheduled follow-up were considered lost to follow-up. The t-test and cross tabulations verified the adequacy of randomisation. An intention-to-treat analysis was carried out. Results In all, 100 (89%) patients had hypovitaminosis D. Both treatment and control groups had similar demographics and injury characteristics. The initial median vitamin D levels were 16 ng/mL (interquartile range 5 to 28) in both groups (p = 0.885). A total of 14 patients were lost to follow-up (seven from each group), two had fixation failure (one in each group) and one control group patient developed an infection. Overall, the nonunion rate was 4% (two per group). No patient showed signs of clinical toxicity from their supplement. Conclusions Despite finding a high level of hypovitaminosis D, the rate of union was high and independent of supplementation with vitamin D3. Cite this article: Bone Joint J 2017;99-B:1520–5.

Quantifying the effects of landscape change on population connectivity is compounded by uncertainties about population size and distribution and a limited understanding of dispersal ability for most species. In addition, the effects of anthropogenic landscape change and sensitivity to regional climatic conditions interact to strongly affect habitat fragmentation and loss. To further develop conservation theory and to understand the interplay between all of these factors, we simulated habitat fragmentation and loss across the Western United States for several hypothetical species associated with four biome types, and a range of habitat requirements and dispersal abilities. We found dispersal ability and population size of the focal species to be equally sensitive to habitat extent, while dispersal ability is more sensitive to habitat fragmentation. There were also strong critical threshold effects where habitat connectivity decreased disproportionately to decreases in life-history traits making these species near these thresholds more sensitive to changes in habitat loss and fragmentation. Overall, grassland and forest associated species are also most at risk from habitat loss and fragmentation driven by human related land-use. These two largest biome types were most sensitive at large contiguous patch sizes which is often considered most important for metapopulation viability and biodiversity conservation. Hypothetical simulation studies such as this can be of great value to scientists in further conceptualizing and developing conservation theory, and evaluating spatially-explicit scenarios of habitat connectivity. Our results are available for download in a web-based interactive mapping prototype useful for accessing the results of this study.

A previous pilot test on an online nutrition care algorithm for patients on hemodialysis showed that the algorithm was able to track nutrition changes over time; however, use of the algorithm took time beyond usual care. This study piloted methodology to compare outcomes between patients who receive

We quantified patterns of population genetic structure to help understand gene flow among elk populations across the Greater Yellowstone Ecosystem. We sequenced 596 base pairs of the mitochondrial control region of 380 elk from eight populations. Analysis revealed high mitochondrial DNA variation within populations, averaging 13.0 haplotypes with high mean gene diversity (0.85). The genetic differentiation among populations for mitochondrial DNA was relatively high (FST = 0.161; P = 0.001) compared to genetic differentiation for nuclear microsatellite data (FST = 0.002; P = 0.332), which suggested relatively low female gene flow among populations. The estimated ratio of male to female gene flow (mm/mf = 46) was among the highest we have seen reported for large mammals. Genetic distance (for mitochondrial DNA pairwise FST) was not significantly correlated with geographic (Euclidean) distance between

Epidemics of powdery mildew, the most important disease of grapevines in California, are driven by moderate temperatures (21 to 30°C). High temperatures can delay or stop the growth and sporulation of Erysiphe necator. Using controlled conditions, we investigated the response of the pathogen's colony growth, conidiospore production, and germination to eight temperatures (30 to 44°C) at 12 exposure times (0.25 to 24 h). The pathogen survived, grew, and reproduced at higher temperatures than previously reported; exposure time was as important as temperature in defining lethal and sublethal effects. Lethal effects started at 36 to 38°C. Lethal exposure times decreased with increasing temperature. Based on this new information on the pathogen's biology, we tested revisions to the high-temperature threshold of the UC Davis Powdery Mildew Risk Index in field settings. No differences in the total number of fungicide applications resulted from the use of either the original or the revised models. However, use of the 38°C for 2 h threshold consistently showed equal (leaves) or better (fruit) disease control compared with the original model, as well as equal disease control compared with a calendar schedule, but with five fewer applications over the 2 years of the study.

Objective This study was conducted to determine how often renal dietitians assess patients' diets and what methods they use to collect and analyze the data. Design and Methods Data were collected through an anonymous online survey. The survey was distributed to renal dietitians during the winter of 2012. Subjects Currently practicing renal dietitian members of the International Society for Renal Nutrition and Metabolism, the National Kidney Foundation Council on Renal Nutrition, the Academy of Nutrition and Dietetics Renal Practice Group, and the RenalRD listserve were invited to participate. Of 599 usable responses received, 91% worked in the United States. The main outcome measure was dietitian compliance with Kidney Disease Outcomes Quality Initiative (KDOQI) nutrition assessment guidelines. Results Most dietitians collect dietary intake data when certain laboratory results are abnormal (70%) whereas 6.5% biannually collect dietary intake data (recommended by KDOQI). The most common reasons for not collecting dietary intake data more frequently were lack of time (42%) and software (25%). Most dietitians determine the frequency of dietary intake assessment on their own (60%) whereas 10% follow the KDOQI guidelines. The most common method of dietary intake data collection was a typical day recall (50%), although 8% reported using a 3-day food record as recommended by KDOQI. The most common method for analyzing dietary intake data was "estimate in my head" (62%) followed by calculate by hand (24.5%). Conclusion Renal dietitians do not follow the KDOQI guidelines for diet assessment because of time constraints. Research must explore which nutrition assessment tools may improve patient outcomes and can also be completed within the time allotted for nutrition care.

Background Hypermetabolism is theorized in patients diagnosed with chronic kidney disease who are receiving maintenance hemodialysis (MHD). We aimed to distinguish key disease-specific determinants of resting energy expenditure to create a predictive energy equation that more precisely establishes energy needs with the intent of preventing protein-energy wasting. Materials and methods For this 3-year multisite cross-sectional study (N = 116), eligible participants were diagnosed with chronic kidney disease and were receiving MHD for at least 3 months. Predictors for the model included weight, sex, age, C-reactive protein (CRP), glycosylated hemoglobin, and serum creatinine. The outcome variable was measured resting energy expenditure (mREE). Regression modeling was used to generate predictive formulas and Bland-Altman analyses to evaluate accuracy. Results The majority were male (60.3%), black (81.0%), and non-Hispanic (76.7%), and 23% were ≥65 years old. After screening for multicollinearity, the best predictive model of mREE (R2 = 0.67) included weight, age, sex, and CRP. Two alternative models with acceptable predictability (R2 = 0.66) were derived with glycosylated hemoglobin or serum creatinine. Based on Bland-Altman analyses, the maintenance hemodialysis equation that included CRP had the best precision, with the highest proportion of participants' predicted energy expenditure classified as accurate (61.2%) and with the lowest number of individuals with underestimation or overestimation. Conclusions This study confirms disease-specific factors as key determinants of mREE in patients on MHD and provides a preliminary predictive energy equation. Further prospective research is necessary to test the reliability and validity of this equation across diverse populations of patients who are receiving MHD.