Selection of HyspIRI optimal band positions for the earth compositional mapping using HyTES data

The National Aeronautics and Space Administration (NASA) has proposed the launch of a new space-borne sensor called HyspIRI (Hyperspectral and Infrared Imager) which will cover the spectral range from 0.4–12 μm. Two instruments will be mounted on HyspIRI platform: 1) a hyperspectral instrument which can sense earth surface between 0.4 and 2.5 μm at 10 nm intervals and 2) a multispectral infrared sensor will acquire images between 3 and 12 μm in eight spectral bands (one in Mid infrared (MIR) and seven in Thermal Infrared (TIR)). The TIR spectral wavebands will be positioned based on their importance in various applications. This study aimed to identify HyspIRI optimal TIR wavebands position for earth compositional mapping. A Genetic Algorithm coupled with the Spectral Angle Mapper (GA-SAM) was used as a spectral bands selector. High dimensional HyTES (Hyperspectral Thermal Emission Spectrometer) emissivity spectra comprised of 202 spectral bands of Cuprite and Death Valley regions were used to select meaningful subsets of bands for earth compositional mapping. The GA-SAM was trained for fifteen mineral classes and the algorithms were run iteratively 50 times. High calibration (> 95%) and validation (> 90%) accuracies were achieved with a limited number (seven) of spectral bands selected by GA-SAM. The knowledge of important band positions will help the scientists of the HyspIRI group to place spectral bands in regions where accuracies of earth compositional mapping can be enhanced

Genomewide Analysis of Inherited Variation Associated with Phosphorylation of PI3K/AKT/mTOR Signaling Proteins

While there exists a wealth of information about genetic influences on gene expression, less is known about how inherited variation influences the expression and post-translational modifications of proteins, especially those involved in intracellular signaling. The PI3K/AKT/mTOR signaling pathway contains several such proteins that have been implicated in a number of diseases, including a variety of cancers and some psychiatric disorders. To assess whether the activation of this pathway is influenced by genetic factors, we measured phosphorylated and total levels of three key proteins in the pathway (AKT1, p70S6K, 4E-BP1) by ELISA in 122 lymphoblastoid cell lines from 14 families. Interestingly, the phenotypes with the highest proportion of genetic influence were the ratios of phosphorylated to total protein for two of the pathway members: AKT1 and p70S6K. Genomewide linkage analysis suggested several loci of interest for these phenotypes, including a linkage peak for the AKT1 phenotype that contained the AKT1 gene on chromosome 14. Linkage peaks for the phosphorylated:total protein ratios of AKT1 and p70S6K also overlapped on chromosome 3. We selected and genotyped candidate genes from under the linkage peaks, and several statistically significant associations were found. One polymorphism in HSP90AA1 was associated with the ratio of phosphorylated to total AKT1, and polymorphisms in RAF1 and GRM7 were associated with the ratio of phosphorylated to total p70S6K. These findings, representing the first genomewide search for variants influencing human protein phosphorylation, provide useful information about the PI3K/AKT/mTOR pathway and serve as a valuable proof of concept for studies integrating human genomics and proteomics

Novel MLR-RF-Based Geospatial Techniques: A Comparison with OK

Geostatistical estimation methods rely on experimental variograms that are mostly erratic, leading to subjective model fitting and assuming normal distribution during conditional simula-tions. In contrast, Machine Learning Algorithms (MLA) are (1) free of such limitations, (2) can in-corporate information from multiple sources and therefore emerge with increasing interest in real-time resource estimation and automation. However, MLAs need to be explored for robust learning of phenomena, better accuracy, and computational efficiency. This paper compares MLAs, i.e., Multiple Linear Regression (MLR) and Random Forest (RF), with Ordinary Kriging (OK). The techniques were applied to the publicly available Walkerlake dataset, while the exhaustive Walker Lake dataset was validated. The results of MLR were significant (p \u3c 10 × 10−5), with correlation coeffi-cients of 0.81 (R-square = 0.65) compared to 0.79 (R-square = 0.62) from the RF and OK methods. Additionally, MLR was automated (free from an intermediary step of variogram modelling as in OK), produced unbiased estimates, identified key samples representing different zones, and had higher computational efficiency