Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the Diversity of Barbonymus (Cypriniformes, Cyprinidae) in Sundaland Using DNA-Based Species Delimitation Methods

Biodiversity hotspots often suffer from a lack of taxonomic knowledge, particularly those in tropical regions. However, accurate taxonomic knowledge is needed to support sustainable management of biodiversity, especially when it is harvested for human sustenance. Sundaland, the biodiversity hotspot encompassing the islands of Java, Sumatra, Borneo, and Peninsular Malaysia, is one of those. With more than 900 species, its freshwater ichthyofauna includes a large number of mediumto large-size species, which are targeted by inland fisheries. Stock assessment requires accurate taxonomy; however, several species groups targeted by inland fisheries are still poorly known. One of those cases is the cyprinid genus Barbonymus. For this study, we assembled a consolidated DNA barcode reference library for Barbonymus spp. of Sundaland, consisting of mined sequences from BOLD, as well as newly generated sequences for hitherto under-sampled islands such as Borneo. A total of 173 sequences were analyzed using several DNA-based species delimitation methods. We unambiguously detected a total of 6 Molecular Operational Taxonomic Units (MOTUs) and were able to resolve several conflicting assignments to the species level. Furthermore, we clarified the identity of MOTUs occurring in Java.
Identification of Burkholderia mallei isolates with PCR- Restriction Fragment Length Polymorphism

Burkholderia mallei is the main cause of glanders as a dangerous contagious zoonosis disease that is mostly seen in monogamous animals, especially in horses. Modern molecular techniques have been recently employed to improve epidemiology for identifying and searching for strains of this bacterium in different times and locations. Due to the unknown number of circulating strains and the lack of prevention of glanders, the disease is still observed in the form of short-term and point epidemics. The aim of this study was to evaluate six clinical isolates, one standard Borkolderia mallei strain and one Burkholderia pseudomallei strain using PCR-Restriction fragment length polymorphism (PCR-RFLP) method. Standard isolates and strains of Burkholderia pseudomallei were microbially cultured in the glycerol nutrient and glycerol agar media. The individually grown colonies of the bacterium were used in the biochemical tests. The DNA of isolates was extracted by boiling and the PCR-RFLP test was conducted on their genome. Eventually, the bacterium was injected to guinea pigs to induce the Straus reaction. The biochemical assays (or bioassays) confirmed the isolates as Burkholderia pseudomallei. The PCR-RFLP assay showed a product for Burkholderia mallei with a length of 650 bp. However, 250 and 400 bp were produced for Burkholderia pseudomallei. The swollen scrotum was suggestive of the occurrence of the Straus reaction. PCR-RFLP is a proper differential diagnosis technique for B. mallei. It is also a suitable method for differentiating Burkholderia mallei and Burkholderia pseudomallei. This technique detects Burkholderia mallei in a short time with high precision and sensitivity.
Clinical Outcomes of Ablation Compared with Resection for Single Hepatocellular Carcinoma Lesions, as a Primary Treatment or Bridging to Liver Transplantation: A Retrospective Comparative Study

Background Ablative therapies (AT) are widely utilized as bridging treatment for liver transplantation (LT) candidates with hepatocellular carcinoma (HCC) who are on the transplant waiting list to minimize dropout rate. We aimed to investigate whether AT could be considered a primary treatment modality for LT candidates with single, small HCC lesions. Material/Methods We retrospectively investigated the outcomes of patients with AT for single HCC lesions as primary treatment or bridging to LT between 2010 and 2017, compared with surgical resection (SR) during the same time period as control. Univariate and multivariate survival analyses were performed. Matched analysis, after propensity score matching (PSM), was performed to minimize the selection bias confounding effect on outcomes. Results Of 162 patients identified, 92 received AT and 70 had SR. PSM identified 38 paired matches in each group. Overall survival (OS) and disease-free survival (DFS) before matching showed comparable outcomes for each treatment after 1, 3, and 5 years. Multivariate analysis using Cox regression models adjusting the study confounders showed lesion size (>30 mm), not treatment received, was associated with worse DFS (hazard ratio, 2.21 [95% confidence interval, 1.14–4.28]). In the matched groups, OS and DFS were equivalent and consistent with the whole-cohort survival outcomes. Explant histopathology of patients having AT as a bridge to LT showed complete pathological response in 85.7% of patients. Conclusions This study supports the use of AT with curative intent for single ≤3-cm HCCs, particularly in LT candidates, with salvage transplantation kept as a backup in case of recurrence.
Quality and Shelf Life of White Shrimp (Litopenaeus vannamei) Processed with High-Pressure Carbon Dioxide (HPCD) at Subcritical and Supercritical States

Phase changes of carbon dioxide (supercritical or subcritical) depend on its proximity to a pressure of 7.35MPa and temperature of 31.1°C. Carbon dioxide becomes supercritical and subcritical when it is above and slightly below its critical point, respectively. (is study aims to determine the effect of high-pressure CO2 treatments at a pressure of 900 psi, 950 psi (subcritical), and 1100 psi (supercritical) and at holding times of 5, 10, and 15min on the quality parameters of white shrimp (Litopenaeus vannamei) and to determine the shelf life of white shrimp processed with the best treatment. (e results showed that the interaction between pressure and holding time had a significant (p< 0.05) effect on cholesterol, protein, moisture content, and b∗ value, but pressure had a significant effect on carotene content. (e best treatment was a supercritical CO2 treatment at 1100 psi for 10min, which was determined based on a significant reduction in the number of microorganisms and no significant changes in color, texture, and fat content were observed compared with control. (e best treatment was applied to process shrimps, which were then stored at 4°C to evaluate the effectiveness of scCO2 treatment on the shelf life. No significant changes were found in PV and lipid in treated and scCO2-treated shrimps during storage, but the treatment significantly affected pH, TVBN, and microbial counts. Among the samples, there was no hedonic difference in all sensory attributes. Supercritical CO2 treatment at 1100 psi for 10min can be an alternative method for preservation of shrimps.
A Hybrid Encryption Solution to Improve Cloud Computing Security using Symmetric and Asymmetric Cryptography Algorithms

—Ensuring the security of cloud computing is one of the most critical challenges facing the cloud services sector. Dealing with data in a cloud environment, which uses shared resources, and providing reliable and secure cloud services, requires a robust encryption solution with no or low negative impact on performance. Thus, this study proposes an effective cryptography solution to improve security in cloud computing with a very low impact on performance. A complex cryptography algorithm is not helpful in cloud computing as computing speed is essential in this environment. Therefore, this solution uses an improved Blowfish algorithm in combination with an elliptic-curve-based algorithm. Blowfish will encrypt the data, and the elliptic curve algorithm will encrypt its key, which will increase security and performance. Moreover, a digital signature technique is used to ensure data integrity. The solution is evaluated, and the results show improvements in throughput, execution time, and memory consumption parameters.
A review on Deep Learning for content-based image retrieval

Content based image retrieval system has a crucial parameter as effective feature and similarity measures. Amy researcher work very hard to find out the solution for effective retrieval and still the work is continuing. The main objective is to remove the semantic gap exist between low level and high level real time pixel value. Machine learning became an outstanding solution to cope up with this problem and various success ratio has been achieved through it. And that can be achieved by implementing by various technical and representation features with it to improve the performance of the system.
Lung Cancer Detection from CT Scan Images Using Deep Convolutional Neural Networks

Deep Convolutional Neural Networks (DCNNs) are the key ingredient in the recent progress in computer vision, medical image processing, and many other machine learning areas. In this project, we participated in the Data Science Bowl 2017 (DSB-17) challenge [1] on lung cancer detection. We built a lung cancer detection model based on deep convolutional neural networks to predict from CT scan images whether a patient has lung cancer. To build an effective model for this task, one needs to address several challenges. First, the raw CT scan images need to be preprocessed to extract the lung regions of interest. Second, compared with the typical use-case of deep convolutional neural networks on two-dimensional images, CT scan images are three-dimensional and require signiﬁcantly more computational resources, imposing restrictions on network architecture and training batch size. Third, the limited size of the provided DSB-17 dataset also introduces difﬁculties and risks of overﬁtting in training deep convolutional neural networks. To handle the above challenges, we explored different model designs and techniques, and used LUNA-16 as our external dataset. In the end, we achieved (after the challenge deadline) 0.55 loss and 76.7% accuracy on the DSB-17 test set, which is comparable to the top-50 performances on the Data Science Bowl leader-board.
IMPROVING BUG TRIAGING WITH HIGH CONFIDENCE PREDICTIONS AT ERICSSON

: Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modeling algorithm named multi-feature topic model (MTM) which extends Latent Dirichlet Allocation (LDA) for bug triaging. MTM considers product and component information of bug reports to map the term space to the topic space. Finally, we propose an incremental learning method named TopicMiner which considers the topic distribution of a new bug report to assign an appropriate fixer based on the affinity of the fixer to the topics. We pair Topic Miner with MTM (TopicMiner MTM). We have evaluated our solution on 5 large bug report datasets including GCC, Open Office, Mozilla, Netbeans, and Eclipse containing a total of 227,278 bug reports. We show that Topic Miner MTM can achieve top-1 and top-5 prediction accuracies of 0.4831 - 0.6868, and 0.7686 - 0.9084, respectively. We also compare TopicMinerMTM with Bugzie, LDA-KL, SVM-LDA, LDA-Activity, and Yang et al. ’ s approach. The results show that TopicMinerMTM on average improves top-1 and top-5 prediction accuracies of Bugzie by 128.48% and 53.22%, LDA-KL by 262.91% and 105.97%, SVM-LDA by 205.89% and 110.48%, LDA-Activity by 377.60% and 176.32%, and Yang et al. ’ s approach by 59.88% and 13.70%, respectively.
Compiler-Assisted Compaction/Restoration of SIMD Instructions

Vector processors (e.g., SIMD or GPUs) are ubiquitous in high performance systems. All the supercomputers in the world exploit data-level parallelism (DLP), for example by using single instructions to operate over several data elements. Improving vector processing is therefore key for exascale computing. However, despite its potential, vector code generation and execution have significant challenges. Among these challenges, control flow divergence is one of the main performance limiting factors. Most modern vector instruction sets, including SIMD, rely on predication to support divergence control. Nevertheless, the performance and energy consumption in predicated codes is usually insensitive to the number of active elements in a predicated mask. Since the trend is that vector register size increases, the energy efficiency of exascale computing systems will become sub-optimal. This article proposes a novel approach to improve execution efficiency in predicated vector codes, the Compiler-Assisted Compaction/Restoration (CACR) technique. Baseline CR delays predicated SIMD instructions with inactive elements, compacting active elements from instances of the same instruction of consecutive loop iterations. Compacted elements form an equivalent dense vector instruction. After executing the dense instructions, their results are restored to the original instructions. However, CR has a significant performance and energy penalty when it fails to find active elements, either due to lack of resources when unrolling or because of inter-loop dependencies. In CACR, the compiler analyzes the code looking for key information required to configure CR. Then, it passes this information to the processor via new instructions inserted in the code. This prevents CR from waiting for active elements on scenarios when it would fail to form dense instructions. Simulated results (gem5) show that CACR improves performance by up to 29 percent and reduces dynamic energy by up to 24.2 percent on average, for a a set of applications with predicated execution. The baseline CR only achieves 18.6 percent performance and 14 percent energy improvements for the same configuration and applications.
A New Feature Filtering Approach by Integrating IG and T-Test Evaluation Metrics for Text Classification

High dimensionality is one of the main issues associated with text classification, such as selecting the most discrepant features subset for classifier's effective utilization is a difficult task. This significant preprocessing stage of selecting the relevant features is often called feature selection or feature filtering. Eliminating the non-relevant and noise features from the original feature set will drastically reduce the size of the feature set and the time complexity of the classification models and also improve or maintain their performance. Most of the existing filtering method produced a subset with relatively high number of features without much significant impact on running time, or produced subset with lesser number of features but results in performance degradation. In this paper, we proposed a new bi-strategy filtering approach that integrates Information Gain with t-test that selects a subset of informative features by considering both the score and ranking of respective features. Our approach considers the results' disparity produced by the benchmark metrics used in order to maximized and lessen their advantage and disadvantage. The approach set a new threshold parameter by computing V-score of the features with minimum scores present in both the two subsets and further refined the selected features. Hence, it reduces the size of the features subset without losing much informative features. Experiment results conducted on three different text datasets have shown that the proposed method is able to select features that are highly discrepant and at the same time achieves a significant improvement in terms of classification accuracy and F-score at the cost of a minimum running time. Keywords—Dimensional reduction; feature filtering; feature selection; t-test; information gain; V-score
DETERMINANTS OF TRANSPARENCY AND DISCLOSURE IN ANNUAL REPORTS: EVIDENCE FROM IRAQI COMPANIES

The present study aims to empirically verify the factors affecting the level of transparency and disclosure provided in the annual reports of Iraqi companies listed in Iraq Stock Exchange. The level of transparency and disclosure was measured in the published annual reports of 30 Iraqi companies listed on the market for the period 2013-2017 distributed across five sectors using the Standard and Poor's methodology. The results showed that the average rate of transparency and disclosure for all companies for the five years was approximately 42%. A regression analysis conducted on the same sample indicated five factors (determinants) that influence transparency and disclosure levels. Three factors were positively related: size, age and activity of the company, while leverage and auditor type were negatively related. Moreover, no significant effect of profitability ratios was identified on transparency and disclosure levels. Such findings are not only useful in providing support for some previous studies, but also suitable for those who need to learn about the determinants of transparency and disclosure in Iraq from local and foreign investors. Furthermore the regulators can benefit from these results to improve the level of disclosure and transparency.
Taking Wireless Underground: A Comprehensive Summary

The tremendous potential of sensing and communication technologies has been explored and implemented for different remote event monitoring applications over the past two decades. However, the applicability of sensing and communication technologies is not necessarily limited to aboveground environments—it is also implementable and applicable for subterranean, underground scenarios. However, as opposed to air medium, underground communication medium is quite harsh due to the presence of heterogeneous underground materials along with underground aqueous components. In this article, we provide a technical overview of different underground wireless communication technologies, namely radio, acoustic, magnetic, and visible light, along with their potentials and challenges for several underground applications. We also lay out a detailed comparison among these technologies along with their pros and cons using detailed experimental results.
Towards a European unifying lab for Kennedy ’ s Disease 15-17 th February , 2019 Hoofddorp , The Netherlands

1) Department of Neuromuscular Diseases, UCL Institute of Neurology, London, UK 2) Département de Neurologie, AP-HP, Groupe hospitalier Pitié-Salpêtrière, F-75013, Paris, France 3) Sorbonne Université, UPMC University Paris 06, CNRS, INSERM, Laboratoire d’Imagerie Biomédicale (LIB), 75013, Paris, France 4) Department of Neurosciences, University of Padova, Padova, Italy 5) Department of Biomedical Sciences (DBS), University of Padova, 35131, Padova, Italy 6) Veneto Institute of Molecular Medicine (VIMM), Padova 35100, Italy 7) Myology Center (Cir-Myo), University of Padova, 35100 Padova, Italy 8) Padova Neuroscience Center (PNC), Padova 35100, Italy
Feasibility Study of a Small-Scale Grid-Connected PV Power Plants in Egypt; Case Study: New Valley Governorate

The construction of photovoltaic power plants (PVPPs) in the right place is an important task when planning the development of the power system and choosing investors. In this paper, the technical, environmental, the economic feasibility of installing a 50kW solar power plant in different places in the New Valley Governorate in Egypt has been presented using RETScreen Expert software. The input data used in the current study are obtained from the database of the Surface Meteorology and Solar Energy Dataset of NASA. In general, five sites for the construction of 50 kW power stations were assessed which represent the five administrative regions of the New Vally governorate. The study is based on annual electricity production, greenhouse gas (GHG) emissions, and financial analysis. With the proposed PV power plant, up to 100 MWh of electricity can be produced and a minimum of 43.3 tons of GHG emission can be prevented from the exhaust into the local atmosphere annually The obtained results from the RETScreen program proved the viability of installing the proposed 50kW photovoltaic power plant in any of the proposed locations. This study could give a piece of important information and feedback that can be utilized as a database for upcoming investments in the photovoltaic generation projects in Egypt. Keywords—RETScreen; new valley; solar energy; energy cost; feasibility analysis; greenhouse gases
The Importance of Wort Composition for Yeast Metabolism during Accelerated Brewery Fermentations1

The volumetric productivity of the beer fermentation process can be increased by using a higher pitching rate (i.e., higher inoculum level). Next to this, high-gravity brewing has become a standard strategy for brewers to increase their productivity. However, both technologies can have a significant effect on yeast metabolism. In this study, high-gravity and high-cell-density fermentations were combined, and their impact on yeast physiology and flavor compound production was evaluated. Additionally, an attempt was made to decrease the production of total diacetyl during accelerated fermentation systems by optimization of the free amino nitrogen content of the wort. Higher wort density resulted in decreased yeast viability, which correlated to increased expression levels of stress-related genes and higher levels of trehalose. More than wort density, the relative amounts of different assimilable sugars have a drastic effect on both yeast fermentation performance and flavor compound production. Worts with high amounts of sucrose had a stimulating effect on the uptake of amino acids, yeast growth, glycogen formation, recycling of trehalose, ethyl ester synthesis, and the turnover rate of total diacetyl. Yeast exposed to high levels of sucrose experienced more osmotic stress and stress related to stationary phase than other high-gravity worts. Although sucrose, compared with maltose, had a stimulating effect on ATF1 expression, a remarkable decrease in acetate esters was observed. Considering the adverse effects of sucrose on the yeast performance, it is advisable to avoid high concentrations of these fast-food sugars and to use a combination of sucrose and maltose syrups as a way to increase the wort density.