Alternative Methods of Estimating Snow-Water Parameters

A recurrence analysis technique using probability and contingency relationships of snow depth, water equivalent, and snow density is presented. Three methods of estimating snow water parameters at site A by recurrence and the presently used regression techniques are based on (1) the value from the previous month at site A, (2) the value from a reference site, and (3) the month to previous month contingency parameter of the reference course. The recurrence technique (Pearson type 3) when it was tested on three central Idaho snow courses was most useful when method 3 was used to estimate snow depth and either method 1 or 3 was used to estimate the water equivalent. Correlation of estimated values to measured values indicated equal reliability of recurrence and regression analysis when the three methods were used. The recurrence technique can successfully be used in estimating snow water parameters and their probability of occurrence. This technique like the regression technique requires a basic data set before it can be applied

Defining Yield Goals and Management Zones to Minimize Yield and Nitrogen and Phosphorus Fertilizer Recommendation Errors

Three general approaches (minimize soil nutrient variability, yield, and fertilizer recommendation errors) have been used to assess nutrient management zone boundaries. The objective of this study was to determine the influence of different approaches to define management zones and yield goals on minimizing yield variability and fertilizer recommendation errors. This study used soil nutrient and yield information collected from two east-central South Dakota fields between 1995 and 2000. The crop rotation was corn (Zea mays L.) followed by soybean [Glycine max (L.) Merr.]. The four management zone delineation approaches tested were to: (i) sample areas impacted by old homesteads separately from the rest of the field; (ii) separate the field into grid cells; (iii) use geographic information systems or cluster analysis of apparent electrical conductivity, elevation, aspect, and connectedness to identify zones; and (iv) use the Order 1 soil survey. South Dakota fertilizer N and P recommendations were used to calculate fertilizer requirements. This study showed that management zones based on a 4-ha grid cell and an Order 1 soil survey had lower within-zone yield variability than the other methods tested. The best approaches for minimizing recommendation errors were nutrient specific. Nitrogen and P recommendations were improved using multiple years of yield monitor data to develop landscape-specific yield goals, sampling old homesteads separately from the rest of the field, and grid cell soil sampling to fine-tune N and P recommendations

Genome-Wide Analysis of Human Disease Alleles Reveals That Their Locations Are Correlated in Paralogous Proteins

The millions of mutations and polymorphisms that occur in human populations are potential predictors of disease, of our reactions to drugs, of predisposition to microbial infections, and of age-related conditions such as impaired brain and cardiovascular functions. However, predicting the phenotypic consequences and eventual clinical significance of a sequence variant is not an easy task. Computational approaches have found perturbation of conserved amino acids to be a useful criterion for identifying variants likely to have phenotypic consequences. To our knowledge, however, no study to date has explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying polymorphisms with likely phenotypic consequences. In order to investigate the potential of this approach, we have assembled a unique collection of known disease-causing variants from OMIM and the Human Genome Mutation Database (HGMD) and used them to identify and characterize pairs of sequence variants that occur at homologous positions within paralogous human proteins. Our analyses demonstrate that the locations of variants are correlated in paralogous proteins. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, information about variant-pairs can be used to identify potentially disease-causing variants, extend existing procedures for polymorphism prioritization, and provide a suite of candidates for further diagnostic and therapeutic purposes

MEASUREMENTS of the SUNYAEV-ZEL'DOVICH EFFECT in MACS J0647.7+7015 and MACS J1206.2-0847 at HIGH ANGULAR RESOLUTION with MUSTANG

We present high resolution (9?) imaging of the Sunyaev-Zel'dovich Effect (SZE) toward two massive galaxy clusters, MACS J0647.7+7015 (z = 0.591) and MACS J1206.2-0847 (z = 0.439). We compare these 90 GHz measurements, taken with the Multiplexed Squid/TES Array at Ninety Gigahertz (MUSTANG ) receiver on the Green Bank Telescope, with generalized Navarro-Frenk-White (gNFW) models derived from Bolocam 140 GHz SZE data as well as maps of the thermal gas derived from Chandra X-ray observations. We adopt a serial-fitting approach, in which gNFW models are first fit to the Bolocam data and then compared to the MUSTANG data to determine an overall best-fit model. For MACS J0647.7+7015, we find a gNFW profile with core slope parameter ? = 0.9 fits the MUSTANG image with and probability to exceed (PTE) = 0.34. For MACS J1206.2-0847, we find , , and PTE = 0.70. In addition, we find a significant (>3s) residual SZE feature in MACS J1206.2-0847 coincident with a group of galaxies identified in Very Large Telescope data and filamentary structure found in a weak-lensing mass reconstruction. We suggest the detected sub-structure may be the SZE decrement from a low mass foreground group or an infalling group. Giant Metrewave Radio Telescope measurements at 610 MHz reveal diffuse extended radio emission to the west, which we posit is either an active galactic nucleus-driven radio lobe, a bubble expanding away from disturbed gas associated with the SZE signal, or a bubble detached and perhaps re-accelerated by sloshing within the cluster. Using the spectroscopic redshifts available, we find evidence for a foreground (z = 0.423) or infalling group, coincident with the residual SZE feature

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics

Variations in the NBN/NBS1 gene and the risk of breast cancer in non-BRCA1/2 French Canadian families with high risk of breast cancer

<p>Abstract</p> <p>Background</p> <p>The Nijmegen Breakage Syndrome is a chromosomal instability disorder characterized by microcephaly, growth retardation, immunodeficiency, and increased frequency of cancers. Familial studies on relatives of these patients indicated that they also appear to be at increased risk of cancer.</p> <p>Methods</p> <p>In a candidate gene study aiming at identifying genetic determinants of breast cancer susceptibility, we undertook the full sequencing of the <it>NBN </it>gene in our cohort of 97 high-risk non-<it>BRCA1 </it>and -<it>BRCA2 </it>breast cancer families, along with 74 healthy unrelated controls, also from the French Canadian population. <it>In silico </it>programs (ESEfinder, NNSplice, Splice Site Finder and MatInspector) were used to assess the putative impact of the variants identified. The effect of the promoter variant was further studied by luciferase gene reporter assay in MCF-7, HEK293, HeLa and LNCaP cell lines.</p> <p>Results</p> <p>Twenty-four variants were identified in our case series and their frequency was further evaluated in healthy controls. The potentially deleterious p.Ile171Val variant was observed in one case only. The p.Arg215Trp variant, suggested to impair NBN binding to histone γ-H2AX, was observed in one breast cancer case and one healthy control. A promoter variant c.-242-110delAGTA displayed a significant variation in frequency between both sample sets. Luciferase reporter gene assay of the promoter construct bearing this variant did not suggest a variation of expression in the MCF-7 breast cancer cell line, but indicated a reduction of luciferase expression in both the HEK293 and LNCaP cell lines.</p> <p>Conclusion</p> <p>Our analysis of <it>NBN </it>sequence variations indicated that potential <it>NBN </it>alterations are present, albeit at a low frequency, in our cohort of high-risk breast cancer cases. Further analyses will be needed to fully ascertain the exact impact of those variants on breast cancer susceptibility, in particular for variants located in <it>NBN </it>promoter region.</p

Finding the missing honey bee genes: lessons learned from a genome upgrade

BACKGROUND: The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes. RESULTS: Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes ~5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data. CONCLUSIONS: Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination.Funding for the project was provided by a grant to RG from the National Human Genome Research Institute, National Institutes of Health (NHGRI, NIH) U54 HG003273. Contributions from members of the CGE lab were supported by Agriculture and Food Research Initiative Competitive grant no. 2010- 65205-20407 from the USDA National Institute of Food Agriculture. AKB was supported by a Clare Luce Booth Fellowship at Georgetown University