DeepSignals: Predicting Intent of Drivers Through Visual Signals

Detecting the intention of drivers is an essential task in self-driving, necessary to anticipate sudden events like lane changes and stops. Turn signals and emergency flashers communicate such intentions, providing seconds of potentially critical reaction time. In this paper, we propose to detect these signals in video sequences by using a deep neural network that reasons about both spatial and temporal information. Our experiments on more than a million frames show high per-frame accuracy in very challenging scenarios.Comment: To be presented at the IEEE International Conference on Robotics and Automation (ICRA), 201

Application of DETECTER, an evolutionary genomic tool to analyze genetic variation, to the cystic fibrosis gene family

BACKGROUND: The medical community requires computational tools that distinguish missense genetic differences having phenotypic impact within the vast number of sense mutations that do not. Tools that do this will become increasingly important for those seeking to use human genome sequence data to predict disease, make prognoses, and customize therapy to individual patients. RESULTS: An approach, termed DETECTER, is proposed to identify sites in a protein sequence where amino acid replacements are likely to have a significant effect on phenotype, including causing genetic disease. This approach uses a model-dependent tool to estimate the normalized replacement rate at individual sites in a protein sequence, based on a history of those sites extracted from an evolutionary analysis of the corresponding protein family. This tool identifies sites that have higher-than-average, average, or lower-than-average rates of change in the lineage leading to the sequence in the population of interest. The rates are then combined with sequence data to determine the likelihoods that particular amino acids were present at individual sites in the evolutionary history of the gene family. These likelihoods are used to predict whether any specific amino acid replacements, if introduced at the site in a modern human population, would have a significant impact on fitness. The DETECTER tool is used to analyze the cystic fibrosis transmembrane conductance regulator (CFTR) gene family. CONCLUSION: In this system, DETECTER retrodicts amino acid replacements associated with the cystic fibrosis disease with greater accuracy than alternative approaches. While this result validates this approach for this particular family of proteins only, the approach may be applicable to the analysis of polymorphisms generally, including SNPs in a human population

Realistic Saliency Guided Image Enhancement

Common editing operations performed by professional photographers include the cleanup operations: de-emphasizing distracting elements and enhancing subjects. These edits are challenging, requiring a delicate balance between manipulating the viewer's attention while maintaining photo realism. While recent approaches can boast successful examples of attention attenuation or amplification, most of them also suffer from frequent unrealistic edits. We propose a realism loss for saliency-guided image enhancement to maintain high realism across varying image types, while attenuating distractors and amplifying objects of interest. Evaluations with professional photographers confirm that we achieve the dual objective of realism and effectiveness, and outperform the recent approaches on their own datasets, while requiring a smaller memory footprint and runtime. We thus offer a viable solution for automating image enhancement and photo cleanup operations.Comment: For more info visit http://yaksoy.github.io/realisticEditing

Moisture Desorption Studies on Polymer Hydrated and Vacuum Extruded Bentonite Clay Mat

Moisture desorption observations from two bentonite clay mats subjected to ten environmental zones with individually different combinations of laboratory-controlled constant temperatures (between 20 °C and 40 °C) and relative humidity (between 15% and 70%) are presented. These laboratory observations are compared with predictions from mathematical models, such as thin-layer drying equations and kinetic drying models proposed by Page, Wang and Singh, and Henderson and Pabis. The quality of fit of these models is assessed using standard error (SE) of estimate, relative percent of error, and coefficient of correlation. The Page model was found to better predict the drying kinetics of the bentonite clay mats for the simulated tropical climates. Critical study on the drying constant and moisture diffusion coefficient helps to assess the efficacy of a polymer to retain moisture and control desorption through water molecule bonding. This is further substantiated with the Guggenheim–Anderson–De Boer (GAB) desorption isotherm model which is presented

Possible Influence of δ-Aminolevulinic Acid Dehydratase Polymorphism and Susceptibility to Renal Toxicity of Lead: A Study of a Vietnamese Population

We examined six newly identified polymorphisms in the δ-aminolevulinic acid dehydratase (ALAD) single-nucleotide polymorphisms (SNPs) to determine if these SNPs could modify the relationship between blood lead (PbB) and some renal parameters. This is a cross-sectional study of 276 lead-exposed workers in Vietnam. All workers were measured for PbB, urinary retinol-binding protein (URBP), urinary α(1)-microglobulin (Uα1m), urinary β(2)-microglobulin (Uβ2m), urinary N-acetyl-β-d-glucosaminidase (NAG), urinary aminolevulinic acid (ALAU), serum α(1)-microglobulin (Sα1m), serum β(2)-microglobulin (Sβ2m), and urinary albumin (Ualb). The six SNPs were Msp and Rsa in exon 4, Rsa39488 in exon 5, HpyIV and HpyCH4 in intron 6, and Sau3A in intron 12. Analysis of covariance (ANCOVA) with interaction of PbB × SNPs were applied to examine modifying effect of the SNPs on the association of renal parameters and PbB, adjusting for potential confounders of age, gender, body mass index, and exposure duration. HpyCH4 was found to be associated with certain renal parameters. For HpyCH4 1-1, an increase of 1 μg/dL PbB caused an increase of 1.042 mg/g creatinine (Cr) Uα1m, 1.069 mg/g Cr Uβ2m, 1.038 mg/g Cr URBP, and 1.033 mg/g Cr Ualb, whereas in HpyCH4 1-2, an increase of 1 μg/dL PbB resulted in an increase of only 1.009 mg/g Cr Uα1m, 1.012 mg/g Cr Uβ2m, 1.009 mg/g Cr URBP, and 1.007 mg/g Cr Ualb. HpyCH4 SNP appeared to modify the lead toxicity to kidney with wild-type allele being more susceptible than variants. The mechanism for this effect is not clear. Further studies are needed to confirm this observation

EXPRSS: an Illumina based high-throughput expression-profiling method to reveal transcriptional dynamics

Background: Next Generation Sequencing technologies have facilitated differential gene expression analysis through RNA-seq and Tag-seq methods. RNA-seq has biases associated with transcript lengths, lacks uniform coverage of regions in mRNA and requires 10–20 times more reads than a typical Tag-seq. Most existing Tag-seq methods either have biases or not high throughput due to use of restriction enzymes or enzymatic manipulation of 5’ ends of mRNA or use of RNA ligations.  Results: We have developed EXpression Profiling through Randomly Sheared cDNA tag Sequencing (EXPRSS) that employs acoustic waves to randomly shear cDNA and generate sequence tags at a relatively defined position (~150-200 bp) from the 3′ end of each mRNA. Implementation of the method was verified through comparative analysis of expression data generated from EXPRSS, NlaIII-DGE and Affymetrix microarray and through qPCR quantification of selected genes. EXPRSS is a strand specific and restriction enzyme independent tag sequencing method that does not require cDNA length-based data transformations. EXPRSS is highly reproducible, is high-throughput and it also reveals alternative polyadenylation and polyadenylated antisense transcripts. It is cost-effective using barcoded multiplexing, avoids the biases of existing SAGE and derivative methods and can reveal polyadenylation position from paired-end sequencing.  Conclusions: EXPRSS Tag-seq provides sensitive and reliable gene expression data and enables high-throughput expression profiling with relatively simple downstream analysis