Multi-faceted insights of entrepreneurship facing a fast-growing economy: A literature review

This study explores entrepreneurship research in Vietnam, a lower-middle-income country in Southeast Asia that has witnessed rapid economic growth since the 1990s but has nonetheless been absent in the relevant Western-centric literature. Using an exclusively developed software, the study presents a structured dataset on entrepreneurship research in Vietnam from 2008 to 2018, highlighting: low research output, low creativity level, inattention to entrepreneurship theories, and instead, a focus on practical business matters. The scholarship remains limited due to the detachment between the academic and entrepreneur communities. More important are the findings that Vietnamese research on entrepreneurship, still in its infancy, diverges significantly from those in developed and emerging economies in terms of their content and methods. These studies are contextualized to a large extent to reflect the concerns of a developing economy still burdened by the high financial and nonfinancial costs

A Comparative Study of Two Prediction Models for Brain Tumor Progression

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images. We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. 2013) for medical image analysis. This paper presents a comparative study of an incremental manifold learning scheme (Tran. et al. 2013) versus the deep learning model (Hinton et al. 2006) in the application of brain tumor progression prediction. The incremental manifold learning is a variant of manifold learning algorithm to handle large-scale datasets in which a representative subset of original data is sampled first to construct a manifold skeleton and remaining data points are then inserted into the skeleton by following their local geometry. The incremental manifold learning algorithm aims at mitigating the computational burden associated with traditional manifold learning methods for large-scale datasets. Deep learning is a recently developed multilayer perceptron model that has achieved start-of-the-art performances in many applications. A recent technique named Dropout can further boost the deep model by preventing weight coadaptation to avoid over-fitting (Hinton et al. 2012). We applied the two models on multiple MRI scans from four brain tumor patients to predict tumor progression and compared the performances of the two models in terms of average prediction accuracy, sensitivity, specificity and precision. The quantitative performance metrics were calculated as average over the four patients. Experimental results show that both the manifold learning and deep neural network models produced better results compared to using raw data and principle component analysis (PCA), and the deep learning model is a better method than manifold learning on this data set. The averaged sensitivity and specificity by deep learning are comparable with these by the manifold learning approach while its precision is considerably higher. This means that the predicted abnormal points by deep learning are more likely to correspond to the actual progression region

TRAPID : an efficient online tool for the functional and comparative analysis of de novo RNA-Seq transcriptomes

Transcriptome analysis through next-generation sequencing technologies allows the generation of detailed gene catalogs for non-model species, at the cost of new challenges with regards to computational requirements and bioinformatics expertise. Here, we present TRAPID, an online tool for the fast and efficient processing of assembled RNA-Seq transcriptome data, developed to mitigate these challenges. TRAPID offers high-throughput open reading frame detection, frameshift correction and includes a functional, comparative and phylogenetic toolbox, making use of 175 reference proteomes. Benchmarking and comparison against state-of-the-art transcript analysis tools reveals the efficiency and unique features of the TRAPID system

Nutrigenomics and immune function in fish : new insights from omics technologies

This study was funded by BBSRC grant BB/M026604/1.Peer reviewedPublisher PD

Orthology guided transcriptome assembly of Italian ryegrass and meadow fescue for single-nucleotide polymorphism discovery

Single-nucleotide polymorphisms (SNPs) represent natural DNA sequence variation. They can be used for various applications including the construction of high-density genetic maps, analysis of genetic variability, genome-wide association studies, and mapbased cloning. Here we report on transcriptome sequencing in the two forage grasses, meadow fescue (Festuca pratensis Huds.) and Italian ryegrass (Lolium multiflorum Lam.), and identification of various classes of SNPs. Using the Orthology Guided Assembly (OGA) strategy, we assembled and annotated a total of 18,952 and 19,036 transcripts for Italian ryegrass and meadow fescue, respectively. In addition, we used transcriptome sequence data of perennial ryegrass (L. perenne L.) from a previous study to identify 16,613 transcripts shared across all three species. Large numbers of intraspecific SNPs were identified in all three species: 248,000 in meadow fescue, 715,000 in Italian ryegrass, and 529,000 in perennial ryegrass. Moreover, we identified almost 25,000 interspecific SNPs located in 5343 genes that can distinguish meadow fescue from Italian ryegrass and 15,000 SNPs located in 3976 genes that discriminate meadow fescue from both Lolium species. All identified SNPs were positioned in silico on the seven linkage groups (LGs) of L. perenne using the GenomeZipper approach. With the identification and positioning of interspecific SNPs, our study provides a valuable resource for the grass research and breeding community and will enable detailed characterization of genomic composition and gene expression analysis in prospective Festuca Lolium hybrids

Heat transfer correlation for flow boiling in small to micro tubes

This article is available open access under a Creative Commons license (http://creativecommons.org/licenses/by-nc-nd/3.0/) Copyright © 2013 The Authors. Published by Elsevier Ltd. All rights reserved.There is a large discrepancy in the open literature about the comparative performance of the existing macro and microscale heat transfer models and correlations when applied to small/micro flow boiling systems. This paper presents a detailed comparison of the flow boiling heat transfer coefficient for R134a in stainless steel micro tubes with 21 macro and microscale correlations and models. The experimental database that was used in the comparison includes the data for 1.1 and 0.52 mm diameter tubes, mass flux range of 100–500 kg/m2 s and system pressure range 6–10 bar obtained in the course of this study. The effect of the evaporator heated length on the comparative performance of the correlations and models was investigated using three different lengths of the 1.1 mm diameter tube (L = 150, 300 and 450 mm). This comparative study demonstrated that none of the assessed models and correlations could predict the experimental data with a reasonable accuracy. Also, the predictability of most correlations becomes worse as the heated length increases. This may contribute in explaining the discrepancy in the comparative performance of the correlations from one study to another. A new correlation is proposed in the present study based on the superposition model of Chen. The database used in developing the correlation consists of 5152 data points including the current experimental data and data obtained previously with the same test rig, fluid and methodology for tubes of diameter 4.26, 2.88, 2.01 mm. The new correlation predicted 92% of the data within the ±30% error bands with a MAE value of 14.3%

Expression of PEG11 and PEG11AS transcripts in normal and callipyge sheep

BACKGROUND: The callipyge mutation is located within an imprinted gene cluster on ovine chromosome 18. The callipyge trait exhibits polar overdominant inheritance due to the fact that only heterozygotes inheriting a mutant paternal allele (paternal heterozygotes) have a phenotype of muscle hypertrophy, reduced fat and a more compact skeleton. The mutation is a single A to G transition in an intergenic region that results in the increased expression of several genes within the imprinted cluster without changing their parent-of-origin allele-specific expression. RESULTS: There was a significant effect of genotype (p < 0.0001) on the transcript abundance of DLK1, PEG11, and MEG8 in the muscles of lambs with the callipyge allele. DLK1 and PEG11 transcript levels were elevated in the hypertrophied muscles of paternal heterozygous animals relative to animals of the other three genotypes. The PEG11 locus produces a single 6.5 kb transcript and two smaller antisense strand transcripts, referred to as PEG11AS, in skeletal muscle. PEG11AS transcripts were detectable over a 5.5 kb region beginning 1.2 kb upstream of the PEG11 start codon and spanning the entire open reading frame. Analysis of PEG11 expression by quantitative PCR shows a 200-fold induction in the hypertrophied muscles of paternal heterozygous animals and a 13-fold induction in homozygous callipyge animals. PEG11 transcripts were 14-fold more abundant than PEG11AS transcripts in the gluteus medius of paternal heterozygous animals. PEG11AS transcripts were expressed at higher levels than PEG11 transcripts in the gluteus medius of animals of the other three genotypes. CONCLUSIONS: The effect of the callipyge mutation has been to alter the expression of DLK1, GTL2, PEG11 and MEG8 in the hypertrophied skeletal muscles. Transcript abundance of DLK1 and PEG11 was highest in paternal heterozygous animals and exhibited polar overdominant gene expression patterns; therefore, both genes are candidates for causing skeletal muscle hypertrophy. There was unique relationship of PEG11 and PEG11AS transcript abundance in the paternal heterozygous animals that suggests a RNA interference mechanism may have a role in PEG11 gene regulation and polar overdominance in callipyge sheep

A Comparative Analysis of STM Approaches to Reduction Operations in Irregular Applications

As a recently consolidated paradigm for optimistic concurrency in modern multicore architectures, Transactional Memory (TM) can help to the exploitation of parallelism in irregular applications when data dependence information is not available up to run- time. This paper presents and discusses how to leverage TM to exploit parallelism in an important class of irregular applications, the class that exhibits irregular reduction patterns. In order to test and compare our techniques with other solutions, they were implemented in a software TM system called ReduxSTM, that acts as a proof of concept. Basically, ReduxSTM combines two major ideas: a sequential-equivalent ordering of transaction commits that assures the correct result, and an extension of the underlying TM privatization mechanism to reduce unnecessary overhead due to reduction memory updates as well as unnecesary aborts and rollbacks. A comparative study of STM solutions, including ReduxSTM, and other more classical approaches to the parallelization of reduction operations is presented in terms of time, memory and overhead.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech