Association study of SNP locus for color related traits in herbaceous peony (Paeonia lactiflora Pall.) using SLAF-seq

Paeonia lactiflora Pall. (P. lactiflora) is a famous ornamental plant with showy and colorful flowers that has been domesticated in China for 4,000 years. However, the genetic basis of phenotypic variation and genealogical relationships in P. lactiflora population is poorly understood due to limited genetic information, which brings about bottlenecks in the application of effective and efficient breeding strategies. Understanding the genetic basis of color-related traits is essential for improving flower color by marker-assisted selection (MAS). In this study, a high throughput sequencing of 99 diploid P. lactiflora accessions via specific-locus amplified fragment sequencing (SLAF-seq) technology was performed. In total, 4,383,645 SLAF tags were developed from 99 P. lactiflora accessions with an average sequencing depth of 20.81 for each SLAF tag. A total of 2,954,574 single nucleotide polymorphisms (SNPs) were identified from all SLAF tags. The population structure and phylogenetic analysis showed that P. lactiflora population used in this study could be divided into six divergent groups. Through association study using Mixed linear model (MLM), we further identified 40 SNPs that were significantly positively associated with petal color. Moreover, a derived cleaved amplified polymorphism (dCAPS) marker that was designed based on the SLAF tag 270512F co-segregated with flower colors in P. lactiflora population. Taken together, our results provide valuable insights into the application of MAS in P. lactiflora breeding programs

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

The 13th Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-IV Survey Mapping Nearby Galaxies at Apache Point Observatory

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) began observations in July 2014. It pursues three core programs: APOGEE-2,MaNGA, and eBOSS. In addition, eBOSS contains two major subprograms: TDSS and SPIDERS. This paper describes the first data release from SDSS-IV, Data Release 13 (DR13), which contains new data, reanalysis of existing data sets and, like all SDSS data releases, is inclusive of previously released data. DR13 makes publicly available 1390 spatially resolved integral field unit observations of nearby galaxies from MaNGA,the first data released from this survey. It includes new observations from eBOSS, completing SEQUELS. In addition to targeting galaxies and quasars, SEQUELS also targeted variability-selected objects from TDSS and X-ray selected objects from SPIDERS. DR13 includes new reductions ofthe SDSS-III BOSS data, improving the spectrophotometric calibration and redshift classification. DR13 releases new reductions of the APOGEE-1data from SDSS-III, with abundances of elements not previously included and improved stellar parameters for dwarf stars and cooler stars. For the SDSS imaging data, DR13 provides new, more robust and precise photometric calibrations. Several value-added catalogs are being released in tandem with DR13, in particular target catalogs relevant for eBOSS, TDSS, and SPIDERS, and an updated red-clump catalog for APOGEE.This paper describes the location and format of the data now publicly available, as well as providing references to the important technical papers that describe the targeting, observing, and data reduction. The SDSS website, http://www.sdss.org, provides links to the data, tutorials and examples of data access, and extensive documentation of the reduction and analysis procedures. DR13 is the first of a scheduled set that will contain new data and analyses from the planned ~6-year operations of SDSS-IV.PostprintPeer reviewe

Condition Monitoring for Roller Bearings of Wind Turbines Based on Health Evaluation under Variable Operating States

Condition monitoring (CM) is used to assess the health status of wind turbines (WT) by detecting turbine failure and predicting maintenance needs. However, fluctuating operating conditions cause variations in monitored features, therefore increasing the difficulty of CM, for example, the frequency-domain analysis may lead to an inaccurate or even incorrect prediction when evaluating the health of the WT components. In light of this challenge, this paper proposed a method for the health evaluation of WT components based on vibration signals. The proposed approach aimed to reduce the evaluation error caused by the impact of the variable operating condition. First, the vibration signal was decomposed into a set of sub-signals using variational mode decomposition (VMD). Next, the sub-signal energy and the probability distribution were obtained and normalized. Finally, the concept of entropy was introduced to evaluate the health condition of a monitored object to provide an effective guide for maintenance. In particular, the health evaluation for CM was based on a performance review over a range of operating conditions, rather than at a certain single operating condition. Experimental investigations were performed which verified the efficiency of the evaluation method, as well as a comparison with the previous method

Experimental studies on the interaction between vehicles and floodplain flows

Floodwater flows through urban floodplains with storm water systems are often inadequate during extreme storm events and/ or when the river flood inundation extent becomes extreme. Such flows may cause potential hazard risks to humans and their properties along the floodplains. Recently, flood hazards relating to vehicles have become more noticeable and it is vital to investigate the hydraulic behaviour of vehicles on urban floodplains. Therefore, this paper outlines a study of the theoretical and experimental aspects of the hydrodynamics of floodwater flows over urban floodplains with vehicles. A theoretical background study is discussed to establish an understanding of the hydrodynamics of floodwater flows over urban floodplains with vehicles; a condition which can be very important for extreme storm events, or even moderate storm events, when the storm water system is insufficient to drain away the surface runoff. Extensive investigations have been undertaken on stationary scaled die cast model vehicles in laboratory hydraulics flumes by conducting a series of physical experimental studies on: (i) the threshold of vehicle instability, (ii) the effects of vehicle orientation, (iii) the effects of ground surface gradient, (iv) the vehicle stability on urban floodplains, and (v) the influence of vehicles on floodwater flows. The results for all the test cases have been analysed to investigate the effects of vehicles on floodwater flow propagation over urban floodplains and, on the other hand, the influence of the floodwater flows on the stability of model vehicles. The two principal factors of hazards (i.e. the floodwater depth and flow velocity) that affect the stability of model vehicles in urban floodplains have been identified to confirm the significant impact of hydrodynamic processes in urban floodplains with vehicles. All experiments undertaken so far have only looked into the conditions under which the model vehicles begin to be moved. Observations have been made from the theory studied and experiments conducted to systematically look into the hydraulic behaviour of vehicles in urban floodplains. The main findings have highlighted that: (i) the model vehicles had a significant impact on the floodwater flow propagation and the hydrodynamic processes in the flooded area, (ii) if the incoming flow depth was less than the vehicle height, then the threshold velocity increased for a decease in the depth of flow; (iii) if the incoming flow depth was greater than the vehicle height, then the threshold velocity would rise with an increase in the depth of flow, and (iv) a flooded vehicle was more likely to move if the incoming depth just approached the vehicle chassis height due to the buoyancy effects. Based on these findings, an innovative approach of a straightforward three colour zone envelope curve has been developed, and first introduced herein, which has been defined as the Traffic Light of Hydraulic Stability (TLHS) system. This novel approach can be readily used to evaluate the degree of hydraulic stability for model vehicles, and it is also invaluable for assessing the vehicle hazard conditions in urban floodplains