Assessing the genetic and pathogenic variability of Pyrenophora teres f. maculata (spot form net blotch of barley) and its ability to overcome currently-used sources of resistance on the Canadian prairies

Non-Peer ReviewedEighty two isolates of the fungus Pyrenophora teres f. maculata, causal agent of spot form net blotch (SFNB) of barley, were collected across the Canadian prairies. Following genetic analysis using 13 microsatellite DNA markers, 27 isolates were selected to assess the degree of pathogenic variation in the fungus. Pathogenic variability was evaluated by inoculating isolates onto 11 barley genotypes as differential hosts at the seedling stage. One week following inoculation, the second and third leaves of each plant were rated on to a 1–9 disease severity scale, and plants with scores of 1–3 and >3 were scored as resistant and susceptible, respectively. The entire experiment was repeated. Cluster analysis revealed 13 distinct pathotype groups (virulence patterns) among the 27 representative isolates. Disease severity ratings ranged from 2.2 to 6.1 with a mean of 4.6. To assess the risk of resistance breakdown, the resistance of four barley cultivars (‘AAC Synergy’, ‘CDC Meredith’, ‘Cerveza’ and ‘Major’), which had been previously identified as resistant to SFNB on the Canadian prairies, was evaluated at the seedling stage against six isolates of P. teres f. maculata representing four different pathotypes. The resistance in ‘CDC Meredith’ was clearly overcome by all isolates tested, while ‘AAC Synergy’, ‘Cerveza’ and ‘Major’ still exhibited resistance against all of the pathotypes. The identification of fungal isolates virulent on ‘CDC Meredith’ suggests that producers should avoid growing the same resistant barley variety in short rotation, and avoid relying on resistance as the sole approach to disease management. Judicious use of fungicides, coupled with rotations of at least two years between barley crops and diversity in the barley varieties grown, will promote effective and sustainable management of spot form net blotch

Groundwater resources in the Indo-Gangetic Basin : resilience to climate change and abstraction

Groundwater within the Indo‐Gangetic Basin (IGB) alluvial aquifer system forms one of the world’s most important and heavily exploited reservoirs of freshwater. In this study we have examined the groundwater system through the lens of its resilience to change – both from the impact of climate change and increases in abstraction. This has led to the development of a series of new maps for the IGB aquifer, building on existing datasets held in Pakistan, India, Nepal and Bangladesh, a review of approximately 500 reports and papers, and three targeted field studies on under‐researched topics within the region. The major findings of the study are described below. The IGB groundwater system 1. The IGB alluvial aquifer system comprises a large volume of heterogeneous unconsolidated sediment in a complex environmental setting. Annual rainfall varies from 2000mm in the Bengal basin, and the system is dissected by the major river systems of the Indus, Ganges and Brahmaputra. The groundwater system has been modified by the introduction of large scale canal irrigation schemes using water from the Indus and Ganges since the 19th and early 20th centuries. 2. High yielding tubewells can be sustained in most parts of the alluvial aquifer system; permeability is often in the range of 10 – 60 m/d and specific yield (the drainable porosity) varies from 5 – 20%, making it highly productive. 3. High salinity and elevated arsenic concentrations exist in parts of the basin limiting the usefulness of the groundwater resource. Saline water predominates in the Lower Indus, and near to the coast in the Bengal Delta, and is also a major concern in the Middle Ganges and Upper Ganges (covering much of the Punjab Region in Pakistan, southern Punjab, Haryana and parts of Uttar Pradesh in India). Arsenic severely impacts the development of shallow groundwater in the fluvial influenced deltaic area of the Bengal Basin. 4. Recharge to the IGB aquifer system is substantial and dynamic, controlled by monsoonal rainfall, leakage from canals, river infiltration and irrigation returns. Recharge from rainfall can occur even with low annual rainfall (350 mm) and appears to dominate where rainfall is higher (> 750 mm). Canal leakage is also highly significant and constitutes the largest proportion of groundwater recharge in the drier parts of the aquifer, partially mitigating the effects of abstraction on groundwater storage. 5. Deep groundwater (>150 m) in the Bengal basin has strategic value for water supply, health and economic development. Excessive abstraction poses a greater threat to the quality of this deep groundwater than climate change. Heavy pumping may induce the downward migration of arsenic in parts of Bangladesh, and of saline water in coastal regions, but field evidence and modelling both suggest that deep groundwater abstraction for public water supply in southern Bangladesh is in general secure against widespread ingress of arsenic and saline water for at least 100 years

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Superluminous supernovae from the Dark Energy Survey

We present a sample of 21 hydrogen-free superluminous supernovae (SLSNe-I) and one hydrogen-rich SLSN (SLSN-II) detected during the five-year Dark Energy Survey (DES). These SNe, located in the redshift range 0.220 < z < 1.998, represent the largest homogeneously selected sample of SLSN events at high redshift. We present the observed g, r, i, z light curves for these SNe, which we interpolate using Gaussian processes. The resulting light curves are analysed to determine the luminosity function of SLSNe-I, and their evolutionary timescales. The DES SLSN-I sample significantly broadens the distribution of SLSN-I light-curve properties when combined with existing samples from the literature. We fit a magnetar model to our SLSNe, and find that this model alone is unable to replicate the behaviour of many of the bolometric light curves. We search the DES SLSN-I light curves for the presence of initial peaks prior to the main light-curve peak. Using a shock breakout model, our Monte Carlo search finds that 3 of our 14 events with pre-max data display such initial peaks. However, 10 events show no evidence for such peaks, in some cases down to an absolute magnitude of<−16, suggesting that such features are not ubiquitous to all SLSN-I events. We also identify a red pre-peak feature within the light curve of one SLSN, which is comparable to that observed within SN2018bsz

First cosmology results using SNe Ia from the dark energy survey: analysis, systematic uncertainties, and validation

International audienceWe present the analysis underpinning the measurement of cosmological parameters from 207 spectroscopically classified type Ia supernovae (SNe Ia) from the first three years of the Dark Energy Survey Supernova Program (DES-SN), spanning a redshift range of 0.01

First cosmology results using type Ia supernovae from the Dark Energy Survey: constraints on cosmological parameters

We present the first cosmological parameter constraints using measurements of type Ia supernovae (SNe Ia) from the Dark Energy Survey Supernova Program (DES-SN). The analysis uses a subsample of 207 spectroscopically confirmed SNe Ia from the first three years of DES-SN, combined with a low-redshift sample of 122 SNe from the literature. Our "DES-SN3YR" result from these 329 SNe Ia is based on a series of companion analyses and improvements covering SN Ia discovery, spectroscopic selection, photometry, calibration, distance bias corrections, and evaluation of systematic uncertainties. For a flat LCDM model we find a matter density Omega_m = 0.331 +_ 0.038. For a flat wCDM model, and combining our SN Ia constraints with those from the cosmic microwave background (CMB), we find a dark energy equation of state w = -0.978 +_ 0.059, and Omega_m = 0.321 +_ 0.018. For a flat w0waCDM model, and combining probes from SN Ia, CMB and baryon acoustic oscillations, we find w0 = -0.885 +_ 0.114 and wa = -0.387 +_ 0.430. These results are in agreement with a cosmological constant and with previous constraints using SNe Ia (Pantheon, JLA)

Geographic origin of the Y Chromosomes in “old” inbred strains of mice

Six distinct Y Chromosomes (Chr) were identified among 39 standard inbred strains of mice with five probes that identified Y Chr-specific restriction fragments on Southern blots. Three Y Chr types, distributed among 31 strains, were of Asian Mus musculus origin. The remaining three Y Chr types, distributed among eight strains, were of M. domesticus origin. The Asian source of the M. musculus Y Chr was confirmed by determining the DNA sequence of 221 bp from an open reading frame within the Sry (sex determining region Y) gene (Gubbay et al., Nature 346 245–250, 1990) in three inbred strains (C57BL/6J, AKR/J, and SWR/J) and comparing the sequence to the homologous sequences derived from wild caught European and Asian M. musculus males. These data indicate that a minimum of six male mice contributed to the formation of the old inbred strains.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46993/1/335_2004_Article_BF00292153.pd

Driver Fusions and Their Implications in the Development and Treatment of Human Cancers.

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated fusions in 9,624 tumors across 33 cancer types using multiple fusion calling tools. We identified a total of 25,664 fusions, with a 63% validation rate. Integration of gene expression, copy number, and fusion annotation data revealed that fusions involving oncogenes tend to exhibit increased expression, whereas fusions involving tumor suppressors have the opposite effect. For fusions involving kinases, we found 1,275 with an intact kinase domain, the proportion of which varied significantly across cancer types. Our study suggests that fusions drive the development of 16.5% of cancer cases and function as the sole driver in more than 1% of them. Finally, we identified druggable fusions involving genes such as TMPRSS2, RET, FGFR3, ALK, and ESR1 in 6.0% of cases, and we predicted immunogenic peptides, suggesting that fusions may provide leads for targeted drug and immune therapy

Pushing automated morphological classifications to their limits with the Dark Energy Survey

We present morphological classifications of ~27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-type galaxies (LTGs); and (b) face-on galaxies from edge-on. Our convolutional neural networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≤17.7 mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well-determined classifications would look like if they were at higher redshifts. The CNNs reach 97 per cent accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalogue comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ~87 per cent and 73 per cent of the catalogue for the ETG versus LTG and edge-on versus face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ϵ), and spectral type, even for the fainter galaxies. This is the largest multiband catalogue of automated galaxy morphologies to date