Expression of flagellin FLjB derived from Salmonella enterica serovar typhimurium in Escherichia coli BL21

Flagellin FljB composes flagellar antigen (H:1,2) of S. Typhimurium. This kind of antigen increases immunogenicity of any conjugated antigen upon administration. Thus, it is supposed to have an enormous potentiality for vaccine development against bacterial infections and cancer diseases. fljB gene (1515 nucleotides) coding for mature FljB was amplified by PCR from genomic DNA of S. Typhimurium and inserted into pET32a(+) for expression in E. coli BL21. The protein FljB was well expressed under the fusion form with Trx, S-tag at N terminal and hexa-histidine at C terminal, thus the recombinant protein was abbreviated to TrxFljB. Study on the impact of temperature on the gene expression showed that TrxFljB was synthesized at lower level at 37oC comparing to the levels at 22oC and 25oC. 13% of the protein synthesized at 37oC was inclusion body. Lower temperatures used during induction phase increased the solubility of the recombinant protein. About 97% of TrxFljB synthesized at 25oC was soluble. IPTG concentration had a strong effect on the growth of freshly transformed cells but did not affect on the growth of stored and re-cultivated cells. The increase of IPTG concentration resulted in the decrease of the growth of freshly transformed cells and the TrxFljB productivity. However, 0.05 mM IPTG concentration was found to gain the full TrxFljB expression. TrxFljB productivity declined during storage of cells at 4oC and re-cultivation. At optimal condition, volumetric productivity of TrxFljB was about 300 mg/ l broth.

Chiral force of guided light on an atom

We calculate the force of a near-resonant guided light field of an ultrathin optical fiber on a two-level atom. We show that, if the atomic dipole rotates in the meridional plane, the magnitude of the force of the guided light depends on the field propagation direction. The chirality of the force arises as a consequence of the directional dependencies of the Rabi frequency of the guided driving field and the spontaneous emission from the atom. This provides a unique method for controlling atomic motion in the vicinity of an ultrathin fiber.Comment: text and figures were revised, and a new discussion was adde

Developing students’ practical competence in teaching natural science for 8th graders using STEM education orientation

STEM education is a direction chosen by the Ministry of Education and Training in recent years to train learners with the necessary skills of the 21st century, which helps learners meet the increasing demand for human resources. In the 4.0 era, the problem of developing students’ abilities is an urgent matter. The competencies that students need to develop include general and specialized competencies. In the specialized competencies, the capacity to practice has a particularly important role. Developing the capacity to practise natural sciences of 8th graders is an urgent issue to improve the quality of current education in Vietnam to meet the reform’s requirements of the general education program

Using Event-Based Style for Developing M2M Applications

International audienceIn this paper, we introduce how to write M2M applications by using INI, a programming language specified and implemented by ourselves that supports event-based style. With event-based programming, all M2M communication can be handled and scheduled. Programmers may use existing built-in events or define their own events. We apply our approach in a real M2M gateway, which allows gathering and exchanging information between sensors and machines in the network. The results shows that our work proposes a concise and elegant alternative and complement to industrial state-of-the-art languages such as Java or C/C++

Unsupervised Learning for Robust Fitting:A Reinforcement Learning Approach

Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for datasets highly contaminated with outliers is, however, still challenging due to the underlying computational complexity. Recent literature has focused on learning-based algorithms. However, most approaches are supervised which require a large amount of labelled training data. In this paper, we introduce a novel unsupervised learning framework that learns to directly solve robust model fitting. Unlike other methods, our work is agnostic to the underlying input features, and can be easily generalized to a wide variety of LP-type problems with quasi-convex residuals. We empirically show that our method outperforms existing unsupervised learning approaches, and achieves competitive results compared to traditional methods on several important computer vision problems.Comment: The preprint of paper accepted to CVPR 202

The contribution of free radicals in paracetamol degradation by UV/NaClO

UV/Chlorine is an emerging advanced oxidation process which forms several reactive species including •OH, •Cl, •OCl. This study investigated the contribution of three main free radicals: •OH, •Cl, •OCl on Paracetamol degradation under different conditions. Benzoic acid (BA), Nitro benzene (NB) and DMOB were used as probe compounds. The second rate constant of •OH, •Cl, •OCl with PRC were determined: 4.19 (±0.15) ×109 M-1s-1; 3.71 1010 M-1s-1; 3.532×109 M-1s-1, respectively. The formation of free radicals depends on pH. In particular, at pH 5: the contribution of •OH and     (-•OCl, •Cl) are 45 %, 41 %, respectively, at pH 8.5, the contribution of free radicals increases up to 63 %. Keywords. Paracetamol, UV/Chlorine process, reactive species

VulCurator: A Vulnerability-Fixing Commit Detector

Open-source software (OSS) vulnerability management process is important nowadays, as the number of discovered OSS vulnerabilities is increasing over time. Monitoring vulnerability-fixing commits is a part of the standard process to prevent vulnerability exploitation. Manually detecting vulnerability-fixing commits is, however, time consuming due to the possibly large number of commits to review. Recently, many techniques have been proposed to automatically detect vulnerability-fixing commits using machine learning. These solutions either: (1) did not use deep learning, or (2) use deep learning on only limited sources of information. This paper proposes VulCurator, a tool that leverages deep learning on richer sources of information, including commit messages, code changes and issue reports for vulnerability-fixing commit classifica- tion. Our experimental results show that VulCurator outperforms the state-of-the-art baselines up to 16.1% in terms of F1-score. VulCurator tool is publicly available at https://github.com/ntgiang71096/VFDetector and https://zenodo.org/record/7034132#.Yw3MN-xBzDI, with a demo video at https://youtu.be/uMlFmWSJYOE.Comment: accepted to ESEC/FSE 2022, Tool Demos Trac

Tuberculosis among economic migrants: a cross-sectional study of the risk of poor treatment outcomes and impact of a treatment adherence intervention among temporary residents in an urban district in Ho Chi Minh City, Viet Nam.

BACKGROUND Tuberculosis (TB) remains a major cause of avoidable deaths. Economic migrants represent a vulnerable population due to their exposure to medical and social risk factors. These factors expose them to higher risks for TB incidence and poor treatment outcomes. METHODS This cross-sectional study evaluated WHO-defined TB treatment outcomes among economic migrants in an urban district of Ho Chi Minh City, Viet Nam. We measured the association of a patient's government-defined residency status with treatment success and loss to follow-up categories at baseline and performed a comparative interrupted time series (ITS) analysis to assess the impact of community-based adherence support on treatment outcomes. Key measures of interest of the ITS were the differences in step change (β) and post-intervention trend (β). RESULTS Short-term, inter-province migrants experienced lower treatment success (aRR = 0.95 [95% CI: 0.92-0.99], p = 0.010) and higher loss to follow-up (aOR = 1.98 [95% CI: 1.44-2.72], p  55 years of age (aRR = 0.93 [95% CI: 0.89-0.96], p < 0.001), relapse patients (aRR = 0.89 [95% CI: 0.84-0.94], p < 0.001), and retreatment patients (aRR = 0.62 [95% CI: 0.52-0.75], p < 0.001) had lower treatment success rates. TB/HIV co-infection was also associated with lower treatment success (aRR = 0.77 [95% CI: 0.73-0.82], p < 0.001) and higher loss to follow-up (aOR = 2.18 [95% CI: 1.55-3.06], p < 0.001). The provision of treatment adherence support increased treatment success (IRR(β) = 1.07 [95% CI: 1.00, 1.15], p = 0.041) and reduced loss to follow-up (IRR(β) = 0.17 [95% CI: 0.04, 0.69], p = 0.013) in the intervention districts. Loss to follow-up continued to decline throughout the post-implementation period (IRR(β) = 0.90 [95% CI: 0.83, 0.98], p = 0.019). CONCLUSIONS Economic migrants, particularly those crossing provincial borders, have higher risk of poor treatment outcomes and should be prioritized for tailored adherence support. In light of accelerating urbanization in many regions of Asia, implementation trials are needed to inform evidence-based design of strategies for this vulnerable population

Two-Phase Defect Detection Using Clustering and Classification Methods

Autonomous fault management of network and distributed systems is a challenging research problem and attracts many research activities. Solving this problem heavily depends on expertise knowledge and supporting tools for monitoring and detecting defects automatically. Recent research activities have focused on machine learning techniques that scrutinize system output data for mining abnormal events and detecting defects. This paper proposes a two-phase defect detection for network and distributed systems using log messages clustering and classification. The approach takes advantage of K-means clustering method to obtain abnormal messages and random forest method to detect the relationship of the abnormal messages and the existing defects. Several experiments have evaluated the performance of this approach using the log message data of Hadoop Distributed File System (HDFS) and the bug report data of Bug Tracking System (BTS). Evaluation results have disclosed some remarks with lessons learned