Three-dimensional calculation of supersonic reacting flows using an LU scheme

A new three-dimensional numerical program incorporated with comprehensive real gas property models was developed to simulate supersonic reacting flows. The code employs an implicit finite volume, Lower-Upper (LU) time-marching method to solve the complete Navier-Stokes and species equations in a fully-coupled and very efficient manner. A chemistry model with nine species and eighteen reaction steps are adopted in the program to represent the chemical reaction of H2 and air. To demonstrate the capability of the program, flow fields of underexpanded hydrogen jets transversely injected into supersonic air stream inside the combustors of scramjets are calculated. Results clearly depict the flow characteristics, including the shock structure, separated flow regions around the injector, and the distribution of the combustion products

Three-dimensional calculations of supersonic reacting flows using an LU scheme

A 3-D numerical program that incorporates comprehensive real gas property models was developed to simulate supersonic reacting flows. The code employs an implicit, finite volume, Lower-Upper (LU), time-marching method to solve the complete Navier-Stokes and species equations in a fully-coupled and efficient manner. A chemistry model with 9 species and 18 reaction steps is adopted in the program to represent the chemical reactions of H2 and air. To demonstrate the capability of the program, flow fields of underexpanded hydrogen jets transversely injected into the supersonic airstream inside the combustors of scramjets are calculated. Results clearly depict the flow characteristics, including the shock structure, the separated flow regions around the injector, and the distribution of the combustion products

Discovery of dominant and dormant genes from expression data using a novel generalization of SNR for multi-class problems

<p>Abstract</p> <p>Background</p> <p>The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems.</p> <p>Results and discussion</p> <p>To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied; but when they are available, they can be used for authentication of diagnosis.</p> <p>Conclusion</p> <p>Since GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.</p

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases

Optimized evacuation route based on crowd simulation

Abstract An evacuation plan helps people move away from an area or a building. To assist rapid evacuation, we present an algorithm to compute the optimal route for each local region. The idea is to reduce congestion and maximize the number of evacuees arriving at exits in each time span. Our system considers crowd distribution, exit locations, and corridor widths when determining optimal routes. It also simulates crowd movements during route optimization. As a basis, we expect that neighboring crowds who take different evacuation routes should arrive at respective exits at nearly the same time. If this is not the case, our system updates the routes of the slower crowds. As crowd simulation is non-linear, the optimal route is computed in an iterative manner. The system repeats until an optimal state is achieved. In addition to directly computing optimal routes for a situation, our system allows the structure of the situation to be decomposed, and determines the routes in a hierarchical manner. This strategy not only reduces the computational cost but also enables crowds in different regions to evacuate with different priorities. Experimental results, with visualizations, demonstrate the feasibility of our evacuation route optimization method

Clinical and genetic characterization of NIPA1 mutations in a Taiwanese cohort with hereditary spastic paraplegia

Abstract Objective NIPA1 mutations have been implicated in hereditary spastic paraplegia (HSP) as the cause of spastic paraplegia type 6 (SPG6). The aim of this study was to investigate the clinical and genetic features of SPG6 in a Taiwanese HSP cohort. Methods We screened 242 unrelated Taiwanese patients with HSP for NIPA1 mutations. The clinical features of patients with a NIPA1 mutation were analyzed. Minigene‐based splicing assay, RT‐PCR analysis on the patients' RNA, and cell‐based protein expression study were utilized to assess the effects of the mutations on splicing and protein expression. Results Two patients were identified to carry a different heterozygous NIPA1 mutation. The two mutations, c.316G>A and c.316G>C, are located in the 3′ end of NIPA1 exon 3 near the exon–intron boundary and putatively lead to the same amino acid substitution, p.G106R. The patient harboring NIPA1 c.316G>A manifested spastic paraplegia, epilepsy and schizophrenia since age 17 years, whereas the individual carrying NIPA1 c.316G>C had pure HSP since age 12 years. We reviewed literature and found that epilepsy was present in multiple individuals with NIPA1 c.316G>A but none with NIPA1 c.316G>C. Functional studies demonstrated that both mutations did not affect splicing, but only the c.316G>A mutation was associated with a significantly reduced NIPA1 protein expression. Interpretation SPG6 accounted for 0.8% of HSP cases in the Taiwanese cohort. The NIPA1 c.316G>A and c.316G>C mutations are associated with adolescent‐onset complex and pure form HSP, respectively. The different effects on protein expression of the two mutations may be associated with their phenotypic discrepancy

SB-VQA: A Stack-Based Video Quality Assessment Framework for Video Enhancement

In recent years, several video quality assessment (VQA) methods have been developed, achieving high performance. However, these methods were not specifically trained for enhanced videos, which limits their ability to predict video quality accurately based on human subjective perception. To address this issue, we propose a stack-based framework for VQA that outperforms existing state-of-the-art methods on VDPVE, a dataset consisting of enhanced videos. In addition to proposing the VQA framework for enhanced videos, we also investigate its application on professionally generated content (PGC). To address copyright issues with premium content, we create the PGCVQ dataset, which consists of videos from YouTube. We evaluate our proposed approach and state-of-the-art methods on PGCVQ, and provide new insights on the results. Our experiments demonstrate that existing VQA algorithms can be applied to PGC videos, and we find that VQA performance for PGC videos can be improved by considering the plot of a play, which highlights the importance of video semantic understanding.Comment: CVPR NTIRE 202

Biallelic DDHD2 mutations in patients with adult‐onset complex hereditary spastic paraplegia

Abstract Objective Hereditary spastic paraplegias (HSPs) are a group of inherited neurodegenerative disorders characterized by slowly progressive lower limb spasticity and weakness. HSP type 54 (SPG54) is autosomal recessively inherited and caused by mutations in the DDHD2 gene. This study investigated the clinical characteristics and molecular features of DDHD2 mutations in a cohort of Taiwanese patients with HSP. Methods Mutational analysis of DDHD2 was performed for 242 unrelated Taiwanese patients with HSP. The clinical, neuroimaging, and genetic features of the patients with biallelic DDHD2 mutations were characterized. A cell‐based study was performed to assess the effects of the DDHD2 mutations on protein expression. Results SPG54 was diagnosed in three patients. Among them, two patients carried compound heterozygous DDHD2 mutations, p.[R112Q];[Y606*] and p.[R112Q];[p.D660H], and the other one was homozygous for the DDHD2 p.R112Q mutation. DDHD2 p.Y606* is a novel mutation, whereas DDHD2 p.D660H and p.R112Q have been reported in the literature. All three patients manifested adult onset complex HSP with additional cerebellar ataxia, polyneuropathy, or cognitive impairment. Brain proton magnetic resonance spectroscopy revealed an abnormal lipid peak in thalamus of all three patients. In vitro studies demonstrated that all the three DDHD2 mutations were associated with a considerably lower DDHD2 protein level. Interpretation SPG54 was detected in approximately 1.2% (3 of 242) of the Taiwanese HSP cohort. This study expands the known mutational spectrum of DDHD2, provides molecular evidence of the pathogenicity of the DDHD2 mutations, and underlines the importance of considering SPG54 as a potential diagnosis of adult‐onset HSP