Clinical metagenomics.

Clinical metagenomic next-generation sequencing (mNGS), the comprehensive analysis of microbial and host genetic material (DNA and RNA) in samples from patients, is rapidly moving from research to clinical laboratories. This emerging approach is changing how physicians diagnose and treat infectious disease, with applications spanning a wide range of areas, including antimicrobial resistance, the microbiome, human host gene expression (transcriptomics) and oncology. Here, we focus on the challenges of implementing mNGS in the clinical laboratory and address potential solutions for maximizing its impact on patient care and public health

Genomic selection in rubber tree breeding: A comparison of models and methods for managing G×E interactions

Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs

Convergence of Euro Area Inflation Rates

We study the behavior of inflation rates among the 12 initial Euro countries in order to test whether and when the group convergence initially dictated by the Maastricht treaty and now by the ECB, occurs. We also assess the impact of events such as the advent of the Euro and the 2008 financial crisis. Due to the small size of the estimation sample, we propose a new procedure that increases the power of panel unit root tests when used to study group-wise convergence. Applying this new procedure to Euro area inflation, we find strong and lasting evidence of convergence among the inflation rates soon after the implementation of the Maastricht treaty and a dramatic decrease in the persistence of the differential after the occurrence of the single currency. After the 2008 crisis, Euro area inflation rates follow the ECB’s price stability benchmark, although Greece reports relatively higher inflation.groupwise convergence, inflation, Euro area, 2008 crisis.

Silicon Pad Detectors for the PHOBOS Experiment at RHIC

The PHOBOS experiment is well positioned to obtain crucial information about relativistic heavy ion collisions at RHIC, combining a multiplicity counter with a multi-particle spectrometer. The multiplicity arrays will measure the charged particle multiplicity over the full solid angle. The spectrometer will be able to identify particles at mid-rapidity. The experiment is constructed almost exclusively of silicon pad detectors. Detectors of nine different types are configured in the multiplicity and vertex detector (22,000 channels) and two multi-particle spectrometers (120,000 channels). The overall layout of the experiment, testing of the silicon sensors and the performance of the detectors during the engineering run at RHIC in 1999 are discussed.Comment: 7 pages, 7 figures, 1 table, Late