Factors affecting the decision to choose a university of high school students: A study in An Giang Province, Vietnam

It is important to provide high school students with the necessary information for them to consult and make a decision to choose a university. The study aims to identify and evaluate the influence of factors in the decision to choose a university for high school students. The questionnaire survey method was used to collect data from 393 students from eight high schools in An Giang Province, Vietnam. Exploratory factor analysis and linear regression were used to analyze the data. The research results show that students are quite satisfied and quite certain with their decision to choose a university, while there are six important factors affecting the decision to choose a university. Influential factors with decreasing order of magnitude are: i) Factors consulted by teachers, family, friends, and relatives; ii) Factors of future job opportunities; iii) Factors of media activities; iv) Factors of learning conditions; v) Factors of university reputation; vi) Factors belong to the students themselves. The findings of the study show that there is no statistically significant difference between the group of males and females, between grades 10, 11, and 12. Besides, there is a statistically significant difference between students in high schools. The findings of this study have theoretical and practical implications for university admissions in Vietnam. Proposals made to university administrators were discussed. From the research results, we want to help students find the right university, and support universities to improve the efficiency of admissions

HYBRID END-TO-END APPROACH INTEGRATING ONLINE LEARNING WITH FACE-IDENTIFICATION SYSTEM

To date, facial recognition has been one of the most intriguing, interesting research topics over years. It requires some specific face-based algorithms such as facial detection, facial alignment, facial representation, and facial recognition as well; however, all of these algorithms derive from heavy deep learning architectures that cause limitations for development, scalability, flawed accuracy, and deployment into publicity with mere CPU servers. It also calls for large datasets containing hundreds of thousands of records for training purposes. In this paper, we propose a full pipeline for an effective face recognition application which only uses a small Vietnamese celebrity dataset and CPU for training that can solve the leakage of data and the need for GPU devices. It is based on a face vector-to-string tokens algorithm then saves face’s properties into Elasticsearch for future retrieval, so the problem of online learning in Facial Recognition is also tackled. Comparison with another popular algorithm on the dataset, our proposed pipeline not only outweighs the accuracy counterpart, but it also achieves a very speedy time inference for a real-time face recognition application

Targeting Mannitol Metabolism as an Alternative Antimicrobial Strategy Based on the Structure-Function Study of Mannitol-1-Phosphate Dehydrogenase in Staphylococcus aureus

Mannitol-1-phosphate dehydrogenase (M1PDH) is a key enzyme in Staphylococcus aureus mannitol metabolism, but its roles in pathophysiological settings have not been established. We performed comprehensive structure-function analysis of M1PDH from S. aureus USA300, a strain of community-associated methicillin-resistant S. aureus, to evaluate its roles in cell viability and virulence under pathophysiological conditions. On the basis of our results, we propose M1PDH as a potential antibacterial target. In vitro cell viability assessment of ΔmtlD knockout and complemented strains confirmed that M1PDH is essential to endure pH, high-salt, and oxidative stress and thus that M1PDH is required for preventing osmotic burst by regulating pressure potential imposed by mannitol. The mouse infection model also verified that M1PDH is essential for bacterial survival during infection. To further support the use of M1PDH as an antibacterial target, we identified dihydrocelastrol (DHCL) as a competitive inhibitor of S. aureus M1PDH (SaM1PDH) and confirmed that DHCL effectively reduces bacterial cell viability during host infection. To explain physiological functions of SaM1PDH at the atomic level, the crystal structure of SaM1PDH was determined at 1.7-Å resolution. Structure-based mutation analyses and DHCL molecular docking to the SaM1PDH active site followed by functional assay identified key residues in the active site and provided the action mechanism of DHCL. Collectively, we propose SaM1PDH as a target for antibiotic development based on its physiological roles with the goals of expanding the repertory of antibiotic targets to fight antimicrobial resistance and providing essential knowledge for developing potent inhibitors of SaM1PDH based on structure-function studies.IMPORTANCE Due to the shortage of effective antibiotics against drug-resistant Staphylococcus aureus, new targets are urgently required to develop next-generation antibiotics. We investigated mannitol-1-phosphate dehydrogenase of S. aureus USA300 (SaM1PDH), a key enzyme regulating intracellular mannitol levels, and explored the possibility of using SaM1PDH as a target for developing antibiotic. Since mannitol is necessary for maintaining the cellular redox and osmotic potential, the homeostatic imbalance caused by treatment with a SaM1PDH inhibitor or knockout of the gene encoding SaM1PDH results in bacterial cell death through oxidative and/or mannitol-dependent cytolysis. We elucidated the molecular mechanism of SaM1PDH and the structural basis of substrate and inhibitor recognition by enzymatic and structural analyses of SaM1PDH. Our results strongly support the concept that targeting of SaM1PDH represents an alternative strategy for developing a new class of antibiotics that cause bacterial cell death not by blocking key cellular machinery but by inducing cytolysis and reducing stress tolerance through inhibition of the mannitol pathway

HYBRID END-TO-END APPROACH INTEGRATING ONLINE LEARNING WITH FACE-IDENTIFICATION SYSTEM

peer reviewedFacial recognition has been one of the most intriguing and exciting research topics over the last few years. It involves multiple face-based algorithms such as facial detection, facial alignment, facial representation, and facial recognition. However, all of these algorithms are derived from large deep-learning architectures, leading to limitations in development, scalability, accuracy, and deployment for public use with mere CPU servers. Also, large data sets that contain hundreds of thousands of records are often required for training purposes. In this paper, we propose a complete pipeline for an effective face-recognition application that requires only a small dataset of Vietnamese celebrities and a CPU for training, solving the problem of data leakage, and the need for GPU devices.The pipeline is based on the combination of a conversion algorithm from face vectors to string tokens and the indexing & retrieval process by Elasticsearch, thereby tackling the problem of online learning in facial recognition. Compared with other popular algorithms on the same data set, our proposed pipeline not only outperforms the counterpart in terms of accuracy but also delivers faster inference, which is essential to real-time applications

Metronomic oral vinorelbine in previously untreated advanced non-small-cell lung cancer patients unfit for platinum-based chemotherapy: results of the randomized phase II Tempo Lung trial

[Background] To assess the efficacy and safety of a metronomic schedule of oral vinorelbine (mVNR) in advanced non-small-cell lung cancer (NSCLC) in patients unfit for platinum-based combination chemotherapy.[Patients and methods] This was a multicenter, prospective, randomized, open-label phase II study in treatment-naive patients with TNM stage IIIB/IV NSCLC. Patients received mVNR at a fixed dose of 50 mg × 3 or standard schedule 60-80 mg/m2 weekly until disease progression or unacceptable toxicity. The primary endpoint was progression-free survival (PFS) without grade 4 toxicity (G4PFS; NCI-CTC v4). Main secondary objectives were safety, disease control rate (DCR) without grade 4 toxicity (G4DCR), DCR, PFS, overall survival (OS) and quality of life (QoL).[Results] A total of 167 patients were included, 83 and 84 patients in the mVNR and standard arms, respectively. The median G4PFS was 4.0 months [95% confidence interval (CI): 2.6-4.3] and 2.2 months (95% CI: 1.5-2.9), hazard ration (HR) = 0.63 (95% CI: 0.45-0.88), P = 0.0068 in favor of metronomic arm; G4DCR was 45.8% and 26.8% in the mVNR and standard arms, respectively. Grade 3-4 treatment-related adverse events were less frequent in the mVNR arm (25.3% versus 54.4%) mainly owing to a reduction in all grades (15.7% versus 51.9%) and grade 3-4 neutropenia (10.8% versus 42%). PFS was 4.3 (95% CI: 3.3-5.1) and 3.9 months (95% CI: 2.8-5.2) in mVNR and standard arms, respectively. No difference in median OS was observed. QoL was comparable between arms.[Conclusions] Metronomic oral vinorelbine significantly prolonged median G4PFS in advanced NSCLC patients unfit for platinum combinations as first-line treatment. It was associated with a clear reduction in toxicity and may be considered as an important option in this challenging population.Pierre Fabre Médicament was the Sponsor of the study. The study was funded by Pierre Fabre Médicament. Conduct of the study: Pierre Fabre Médicament with the support of Clinipace clinical research organization (CRO) for the monitoring, of C-Med (CRO) for the data management and statistical analyses.Peer reviewe