Understanding the Influence of Turbulence in Imaging Fourier-Transform Spectrometry of Smokestack Plumes

An imaging Fourier-transform spectrometer (IFTS) was used to collect infrared hyper-spectral imagery of smokestack plume of a coal-burning power facility to assess the influence of turbulence on spectral retrieval of temperature (T) and pollutant concentrations (Ci). Radiance fluctuations due to plume turbulence introduced scene change artifacts (SCA) in the spectra. Time-averaging the spectra minimizes SCA magnitudes, but accurate T and Ci retrieval would require a priori knowledge of the statistical distribution of temperature and other stochastic flow field parameters. A method of quantile sorting in interferogram space that would allow the retrieval of the temperature distribution is presented and used to identify turbulence throughout the plume. Immediately above the stack exit, T and Ci estimates compared well to in situ measurements, and turbulence introduced only small systematic errors. Systematic errors, however, were more significant in other parts of the plume. While more complicated radiance fluctuations precluded retrieval of the temperature distribution, the results demonstrate the utility of additional information associated with interferogram quantiles and suggest the utility of IFTS in non-intrusive flow field analysis

Genomic Selection and Association Mapping in Rice (<i>Oryza sativa</i>): Effect of Trait Genetic Architecture, Training Population Composition, Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite, Tropical Rice Breeding Lines

<div><p>Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.</p></div

Elevated rates of mild cognitive impairment in HIV disease

With the rising number of individuals in their 50s and 60s who are infected with HIV, concerns have emerged about possible increases in the rates of non-HIV-associated dementias. The current study examined the prevalence of mild cognitive impairment (MCI) in older HIV-infected adults, since MCI is an intermediate state between typical cognitive aging and dementia that emerges in this age range. Participants included 75 adults with HIV disease aged 50 years and older who were on cART and had undetectable plasma viral loads and 80 demographically similar HIV seronegative comparison subjects. Participants completed a research neuropsychological evaluation that was used to classify MCI according to the comprehensive diagnostic scheme described by Bondi et al. (2014). HIV-infected persons were over seven times more likely to have an MCI designation (16%) than their seronegative counterparts (2.5%). Within the HIV+ cohort, MCI had minimal overlap with diagnoses of Asymptomatic Neurocognitive Impairment and was significantly associated with older age, lower Karnofsky Scale of Performance Scores, and mild difficulties performing instrumental activities of daily living (iADLs). HIV infection in older adults is associated with a notably elevated concurrent risk of MCI, which may increase the likelihood of developing non-HIV-associated dementias as this population ages further

Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies LRRC4C, LHX5-AS1 and nominates ancestry-specific loci PTPRK , GRB14 , and KIAA0825 as novel risk loci for Alzheimer’s disease: the Alzheimer’s Disease Genetics Consortium

Limited ancestral diversity has impaired our ability to detect risk variants more prevalent in non-European ancestry groups in genome-wide association studies (GWAS). We constructed and analyzed a multi-ancestry GWAS dataset in the Alzheimer’s Disease (AD) Genetics Consortium (ADGC) to test for novel shared and ancestry-specific AD susceptibility loci and evaluate underlying genetic architecture in 37,382 non-Hispanic White (NHW), 6,728 African American, 8,899 Hispanic (HIS), and 3,232 East Asian individuals, performing within-ancestry fixed-effects meta-analysis followed by a cross-ancestry random-effects meta-analysis. We identified 13 loci with cross-ancestry associations including known loci at/near CR1 , BIN1 , TREM2 , CD2AP , PTK2B , CLU , SHARPIN , MS4A6A , PICALM , ABCA7 , APOE and two novel loci not previously reported at 11p12 ( LRRC4C ) and 12q24.13 ( LHX5-AS1 ). Reflecting the power of diverse ancestry in GWAS, we observed the SHARPIN locus using 7.1% the sample size of the original discovering single-ancestry GWAS (n=788,989). We additionally identified three GWS ancestry-specific loci at/near ( PTPRK ( P =2.4×10 -8 ) and GRB14 ( P =1.7×10 -8 ) in HIS), and KIAA0825 ( P =2.9×10 -8 in NHW). Pathway analysis implicated multiple amyloid regulation pathways (strongest with P adjusted =1.6×10 -4 ) and the classical complement pathway ( P adjusted =1.3×10 -3 ). Genes at/near our novel loci have known roles in neuronal development ( LRRC4C, LHX5-AS1 , and PTPRK ) and insulin receptor activity regulation ( GRB14 ). These findings provide compelling support for using traditionally-underrepresented populations for gene discovery, even with smaller sample sizes