Dog-human dietary relationships in Yup'ik western Alaska : The stable isotope and zooarchaeological evidence from pre-contact Nunalleq

Acknowledgements This work was funded by a PhD studentship to EM from the Natural Environment Research Council (2210 GG005 RGA1521) and an Arts and Humanities Research Council (AH/K006029/1) grant to RK, KB and Charlotta Hillerdal (Aberdeen). Material was excavated from Nunalleq by staff and students from the University of Aberdeen, volunteer excavators and residents of Quinhagak. Logistical and planning support for the excavation was provided by Qanirtuuq Incorporated, Quinhagak, and the residents of Quinhagak.

A novel, automated nutrition screening system as a predictor of nutritional risk in an oncology day treatment unit (ODTU)

Purpose Paper-based nutrition screening tools can be challenging to implement in the ambulatory oncology setting. The aim of this study was to determine the validity of the Malnutrition Screening Tool (MST) and a novel, automated nutrition screening system compared to a ‘gold standard’ full nutrition assessment using the Patient-Generated Subjective Global Assessment (PG-SGA). Methods An observational, cross-sectional study was conducted in an outpatient oncology day treatment unit (ODTU) within an Australian tertiary health service. Eligibility criteria were as follows: ≥18 years, receiving outpatient anticancer treatment and English literate. Patients self-administered the MST. A dietitian assessed nutritional status using the PGSGA, blinded to the MST score. Automated screening system data were extracted from an electronic oncology prescribing system. This system used weight loss over 3 to 6 weeks prior to the most recent weight record or age-categorised body mass index (BMI) to identify nutritional risk. Sensitivity and specificity against PG-SGA (malnutrition) were calculated using contingency tables and receiver operating curves. Results There were a total of 300 oncology outpatients (51.7 % male, 58.6±13.3 years). The area under the curve (AUC) for weight loss alone was 0.69 with a cut-off value of ≥1 % weight loss yielding 63 % sensitivity and 76.7 % specificity. MST (score ≥2) resulted in 70.6 % sensitivity and 69.5 % specificity, AUC 0.77. Conclusions Both the MST and the automated method fell short of the accepted professional standard for sensitivity (~≥80 %) derived from the PG-SGA. Further investigation into other automated nutrition screening options and the most appropriate parameters available electronically is warranted to support targeted service provision.

Sweet Persimmon Information Kit. Agrilink, your growing guide to better farming guide

Each Agrilink kit has been designed to be both comprehensive and practical. As the kits are arranged to answer questions of increasing complexity, they are useful references for both new and experienced producers of specific crops. Agrilink integrates the technology of horticultural production with the management of horticultural enterprises. REPRINT INFORMATION - PLEASE READ! For updated information please call 13 25 23 or visit the website www.deedi.qld.gov.au (Select: Queensland Industries – Agriculture link) This publication has been reprinted as a digital book without any changes to the content published in 2005. We advise readers to take particular note of the areas most likely to be out-of-date and so requiring further research: see detailed information on first page of the kit. Even with these limitations we believe this information kit provides important and valuable information for intending and existing growers. This publication was last revised in 2005. The information is not current and the accuracy of the information cannot be guaranteed by the State of Queensland. This information has been made available to assist users to identify issues involved in the production of sweet persimmon. This information is not to be used or relied upon by users for any purpose which may expose the user or any other person to loss or damage. Users should conduct their own inquiries and rely on their own independent professional advice. While every care has been taken in preparing this publication, the State of Queensland accepts no responsibility for decisions or actions taken as a result of any data, information, statement or advice, expressed or implied, contained in this publication.

Baobabopsis, a new genus of graminicolous downy mildews from tropical Australia, with an updated key to the genera of downy mildews

So far 19 genera of downy mildews have been described, of which seven are parasitic to grasses. Here, we introduce a new genus, Baobabopsis, to accommodate two distinctive downy mildews, B. donbarrettii sp. nov., collected on Perotis rara in northern Australia, and B. enneapogonis sp. nov., collected on Enneapogon spp. in western and central Australia. Baobabopsis donbarrettii produced both oospores and sporangiospores that are morphologically distinct from other downy mildews on grasses. Molecular phylogenetic analyses showed that the two species of Baobabopsis occupied an isolated position among the known genera of graminicolous downy mildews. The importance of the Poaceae for the evolution of downy mildews is highlighted by the observation that more than a third of the known genera of downy mildews occur on grasses, while more than 90 % of the known species of downy mildews infect eudicots.

Identification of a novel FGFRL1 MicroRNA target site polymorphism for bone mineral density in meta-analyses of genome-wide association studies

MicroRNAs (miRNAs) are critical post-transcriptional regulators. Based on a previous genome-wide association (GWA) scan, we conducted a polymorphism in microRNAs' Target Sites (poly-miRTS)-centric multistage meta-analysis for lumbar spine (LS)-, total hip (HIP)-, and femoral neck (FN)-bone mineral density (BMD). In stage I, 41,102 poly-miRTSs were meta-analyzed in 7 cohorts with a genome-wide significance (GWS) α=0.05/41,102=1.22×10-6. By applying α=5×10-5 (suggestive significance), 11 poly-miRTSs were selected, with FGFRL1 rs4647940 and PRR5 rs3213550 as top signals for FN-BMD (P-value=7.67×10-6 and 1.58×10-5) in gender-combined sample. In stage II in silico replication (two cohorts), FGFRL1 rs4647940 was the only signal marginally replicated for FN-BMD (P-value=5.08×10-3) at α=0.10/11=9.09×10-3. PRR5 rs3213550 was also selected based on biological significance. In stage III de novo genotyping replication (two cohorts), FGFRL1 rs4647940 was the only signal significantly replicated for FN-BMD (P-value=7.55×10-6) at α=0.05/2=0.025 in gender-combined sample. Aggregating three stages, FGFRL1 rs4647940 was the single stage I-discovered and stages II- and III-replicated signal attaining GWS for FN-BMD (P-value=8.87×10-12). Dual-luciferase reporter assays demonstrated that FGFRL1 3' untranslated region harboring rs4647940 appears to be hsa-miR-140-5p's target site. In a zebrafish microinjection experiment, dre-miR-140-5p is shown to exert a dramatic impact on craniofacial skeleton formation. Taken together, we provided functional evidence for a novel FGFRL1 poly-miRTS rs4647940 in a previously known 4p16.3 locus, and experimental and clinical genetics studies have shown both FGFRL1 and hsa-miR-140-5p are important for bone formation. © The Author 2015. Published by Oxford University Press. All rights reserved.

Discriminative learning of Bayesian networks via factorized conditional log-likelihood

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (ˆfCLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achieving the same time and space complexity as the traditional log-likelihood scoring criterion. The resulting criterion has an information-theoretic interpretation based on interaction information, which exhibits its discriminative nature. To evaluate the performance of the proposed criterion, we present an empirical comparison with state-of-the-art classifiers. Results on a large suite of benchmark data sets from the UCI repository show that ˆfCLL-trained classifiers achieve at least as good accuracy as the best compared classifiers, using significantly less computational resources.