Storage stability of whole and nibbed, conventional and high oleic peanuts (<i>Arachis hypogeae </i>L.)

Peanuts are increasingly being used as nibbed ingredients in cereal bars, confectionery and breakfast cereals. However, studies on their oxidative stability in this format are limited. Storage trials to determine the stability to oxidation were carried out on whole and nibbed kernels of conventional (CP) and high oleic (HOP) peanuts, with respect to temperature and modified atmosphere packaging. HOP exhibited the highest oxidative stability, with a lag phase in whole kernels of 12–15 weeks before significant oxidation occurred. HOP also showed higher levels of intrinsic antioxidants, a trolox equivalent antioxidant capacity (TEAC) of 70 mMol equivalence and radical scavenging percentage (RSP) of 99.8 % at the beginning of storage trials, whereas CP showed values of 40 mMol and 81.2 %, respectively. The intrinsic antioxidants at the beginning of these storage trials were shown to affect the peroxide value (PV), where RSP and TEAC decreased, and PV increased. Therefore, in peanuts the processing format (nibbed or whole) had the highest influence on susceptibility of lipid oxidation, highest to lowest importance: processing format &gt; temperature &gt; atmospheric conditions

Conference report:Improving outcomes for gastrointestinal cancer in the UK

Substantial steps are being made towards early diagnosis. A range of tools are available to help GPs appropriately categorise early symptoms during routine consultations. Various promising new tests and devices are being explored, especially for cancers that frequently present at late stages. The continuing increase in demand on endoscopy services is a major concern, not least because of the shortage of trained practitioners and other healthcare staff. However, screening and collaborative streamlining initiatives might help to improve the relevance of referrals. The question posed in the title of the conference was rhetorical, but a positive answer seems potentially achievable, even in austere times, through facilitating uptake of screening, working to develop the primary-secondary care interface, educating the public and by protecting funds for research

Deep roots and soil structure

In this opinion article we examine the relationship between penetrometer resistance and soil depth in the field. Assuming that root growth is inhibited at penetrometer resistances > 2.5 MPa, we conclude that in most circumstances the increases in penetrometer resistance with depth are sufficiently great to confine most deep roots to elongating in existing structural pores. We suggest that deep rooting is more likely related to the interaction between root architecture and soil structure than it is to the ability of a root to deform strong soil. Although the ability of roots to deform strong soil is an important trait, we propose it is more closely related to root exploration of surface layers than deep rooting

Instrumental performance and results from testing of the BLAST-TNG receiver, submillimeter optics, and MKID arrays

Polarized thermal emission from interstellar dust grains can be used to map magnetic fields in star forming molecular clouds and the diffuse interstellar medium (ISM). The Balloon-borne Large Aperture Submillimeter Telescope for Polarimetry (BLASTPol) flew from Antarctica in 2010 and 2012 and produced degree-scale polarization maps of several nearby molecular clouds with arcminute resolution. The success of BLASTPol has motivated a next-generation instrument, BLAST-TNG, which will use more than 3000 linear polarization sensitive microwave kinetic inductance detectors (MKIDs) combined with a 2.5m diameter carbon fiber primary mirror to make diffraction-limited observations at 250, 350, and 500 $\mu$m. With 16 times the mapping speed of BLASTPol, sub-arcminute resolution, and a longer flight time, BLAST-TNG will be able to examine nearby molecular clouds and the diffuse galactic dust polarization spectrum in unprecedented detail. The 250 $\mu$m detector array has been integrated into the new cryogenic receiver, and is undergoing testing to establish the optical and polarization characteristics of the instrument. BLAST-TNG will demonstrate the effectiveness of kilo-pixel MKID arrays for applications in submillimeter astronomy. BLAST-TNG is scheduled to fly from Antarctica in December 2017 for 28 days and will be the first balloon-borne telescope to offer a quarter of the flight for "shared risk" observing by the community.Comment: Presented at SPIE Millimeter, Submillimeter, and Far-Infrared Detectors and Instrumentation for Astronomy VIII, June 29th, 201

Choosing sensitivity analyses for randomised trials: principles

Background Sensitivity analyses are an important tool for understanding the extent to which the results of randomised trials depend upon the assumptions of the analysis. There is currently no guidance governing the choice of sensitivity analyses. Discussion We provide a principled approach to choosing sensitivity analyses through the consideration of the following questions: 1) Does the proposed sensitivity analysis address the same question as the primary analysis? 2) Is it possible for the proposed sensitivity analysis to return a different result to the primary analysis? 3) If the results do differ, is there any uncertainty as to which will be believed? Answering all of these questions in the affirmative will help researchers to identify relevant sensitivity analyses. Treating analyses as sensitivity analyses when one or more of the answers are negative can be misleading and confuse the interpretation of studies. The value of these questions is illustrated with several examples. Summary By removing unreasonable analyses that might have been performed, these questions will lead to relevant sensitivity analyses, which help to assess the robustness of trial results

The structural variation landscape in 492 Atlantic salmon genomes

Structural variants (SVs) are a major source of genetic and phenotypic variation, but remain challenging to accurately type and are hence poorly characterized in most species. We present an approach for reliable SV discovery in non-model species using whole genome sequencing and report 15,483 high-confidence SVs in 492 Atlantic salmon (Salmo salar L.) sampled from a broad phylogeographic distribution. These SVs recover population genetic structure with high resolution, include an active DNA transposon, widely affect functional features, and overlap more duplicated genes retained from an ancestral salmonid autotetraploidization event than expected. Changes in SV allele frequency between wild and farmed fish indicate polygenic selection on behavioural traits during domestication, targeting brain-expressed synaptic networks linked to neurological disorders in humans. This study offers novel insights into the role of SVs in genome evolution and the genetic architecture of domestication traits, along with resources supporting reliable SV discovery in non-model species.Peer reviewe

Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Goldstein, E. B., Buscombe, D., Lazarus, E. D., Mohanty, S. D., Rafique, S. N., Anarde, K. A., Ashton, A. D., Beuzen, T., Castagno, K. A., Cohn, N., Conlin, M. P., Ellenson, A., Gillen, M., Hovenga, P. A., Over, J.-S. R., Palermo, R., Ratliff, K. M., Reeves, I. R. B., Sanborn, L. H., Straub, J. A., Taylor, L. A., Wallace E. J., Warrick, J., Wernette, P., Williams, H. E. Labeling poststorm coastal imagery for machine learning: measurement of interrater agreement. Earth and Space Science, 8(9), (2021): e2021EA001896, https://doi.org/10.1029/2021EA001896.Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research.The authors gratefully acknowledge support from the U.S. Geological Survey (G20AC00403 to EBG and SDM), NSF (1953412 to EBG and SDM; 1939954 to EBG), Microsoft AI for Earth (to EBG and SDM), The Leverhulme Trust (RPG-2018-282 to EDL and EBG), and an Early Career Research Fellowship from the Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine (to EBG). U.S. Geological Survey researchers (DB, J-SRO, JW, and PW) were supported by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program as part of the response and recovery efforts under congressional appropriations through the Additional Supplemental Appropriations for Disaster Relief Act, 2019 (Public Law 116-20; 133 Stat. 871)

Fine-mapping of the HNF1B multicancer locus identifies candidate variants that mediate endometrial cancer risk.

Common variants in the hepatocyte nuclear factor 1 homeobox B (HNF1B) gene are associated with the risk of Type II diabetes and multiple cancers. Evidence to date indicates that cancer risk may be mediated via genetic or epigenetic effects on HNF1B gene expression. We previously found single-nucleotide polymorphisms (SNPs) at the HNF1B locus to be associated with endometrial cancer, and now report extensive fine-mapping and in silico and laboratory analyses of this locus. Analysis of 1184 genotyped and imputed SNPs in 6608 Caucasian cases and 37 925 controls, and 895 Asian cases and 1968 controls, revealed the best signal of association for SNP rs11263763 (P = 8.4 × 10(-14), odds ratio = 0.86, 95% confidence interval = 0.82-0.89), located within HNF1B intron 1. Haplotype analysis and conditional analyses provide no evidence of further independent endometrial cancer risk variants at this locus. SNP rs11263763 genotype was associated with HNF1B mRNA expression but not with HNF1B methylation in endometrial tumor samples from The Cancer Genome Atlas. Genetic analyses prioritized rs11263763 and four other SNPs in high-to-moderate linkage disequilibrium as the most likely causal SNPs. Three of these SNPs map to the extended HNF1B promoter based on chromatin marks extending from the minimal promoter region. Reporter assays demonstrated that this extended region reduces activity in combination with the minimal HNF1B promoter, and that the minor alleles of rs11263763 or rs8064454 are associated with decreased HNF1B promoter activity. Our findings provide evidence for a single signal associated with endometrial cancer risk at the HNF1B locus, and that risk is likely mediated via altered HNF1B gene expression