Adherence to antiplatelet therapy after coronary intervention among patients with myocardial infarction attending Vietnam National Heart Institute

Adherence to antiplatelet therapy is critical to successful treatment of cardiovascular conditions. However, little has been known about this issue in the context of constrained resources such as in Vietnam. The objective of this study was to examine the adherence to antiplatelet therapy among patients receiving acute myocardial infarction interventions and its associated factors. In a cross-sectional survey design, 175 adult patients revisiting Vietnam National Heart Institute diagnosed with acute myocardial infarction were approached for data collection from October 2014 to June 2015. Adherence to antiplatelet therapy was assessed by asking patients whether they took taking antiplatelet regularly as per medication (do not miss any dose at the specified time) for any type of antiplatelet (aspirin, clopidogrel, ticlopidine.) during the last month before the participants came back to take re-examinations. The results indicated that the adherence to antiplatelet therapy among patients was quite high at 1 month; it begins to decline by 6 months, 12 months, and more than 12 months (less than 1 month was 90.29%; from 1 to 6 months 88.0%, from 6 to 12 months 75.43%, and after 12 months only 46.29% of patients). Multivariable logistic regression was utilized to detect factors associated with the adherence to antiplatelet therapy. It showed that patients with average income per month of $300 or more (OR=2.92, 95% CI=1.24-6.89), distance to the hospital of less than 50km (OR=2.48, 95% CI: 1.12-5.52), taking medicine under doctor's instructions (OR=3.65; 95% CI=1.13-11.70), and timely re-examination (OR=3.99, 95% CI=1.08-14.73) were more likely to follow the therapy. In general, the study suggested that to increase the likelihood of adherence to antiplatelet therapy it is important to establish a continuous care system after discharging from hospital. © 2019 Ngoc Minh Luu et al

Soft Robotic Link with Controllable Transparency for Vision-based Tactile and Proximity Sensing

Robots have been brought to work close to humans in many scenarios. For coexistence and collaboration, robots should be safe and pleasant for humans to interact with. To this end, the robots could be both physically soft with multimodal sensing/perception, so that the robots could have better awareness of the surrounding environment, as well as to respond properly to humans' action/intention. This paper introduces a novel soft robotic link, named ProTac, that possesses multiple sensing modes: tactile and proximity sensing, based on computer vision and a functional material. These modalities come from a layered structure of a soft transparent silicon skin, a polymer dispersed liquid crystal (PDLC) film, and reflective markers. Here, the PDLC film can switch actively between the opaque and the transparent state, from which the tactile sensing and proximity sensing can be obtained by using cameras solely built inside the ProTac link. In this paper, inference algorithms for tactile proximity perception are introduced. Evaluation results of two sensing modalities demonstrated that, with a simple activation strategy, ProTac link could effectively perceive useful information from both approaching and in-contact obstacles. The proposed sensing device is expected to bring in ultimate solutions for design of robots with softness, whole-body and multimodal sensing, and safety control strategies.Comment: Submitted to RoboSoft 2023 for review. Final content subjected to chang

Federated Deep Reinforcement Learning-based Bitrate Adaptation for Dynamic Adaptive Streaming over HTTP

In video streaming over HTTP, the bitrate adaptation selects the quality of video chunks depending on the current network condition. Some previous works have applied deep reinforcement learning (DRL) algorithms to determine the chunk's bitrate from the observed states to maximize the quality-of-experience (QoE). However, to build an intelligent model that can predict in various environments, such as 3G, 4G, Wifi, \textit{etc.}, the states observed from these environments must be sent to a server for training centrally. In this work, we integrate federated learning (FL) to DRL-based rate adaptation to train a model appropriate for different environments. The clients in the proposed framework train their model locally and only update the weights to the server. The simulations show that our federated DRL-based rate adaptations, called FDRLABR with different DRL algorithms, such as deep Q-learning, advantage actor-critic, and proximal policy optimization, yield better performance than the traditional bitrate adaptation methods in various environments.Comment: 13 pages, 1 colum

N-Methyl-N-styrylcinnamamide (lansamide) from Clausena lansium in Vietnam

The title compound, C18H17NO, was isolated from the seeds of Clausena lansium (wampee) (Rutaceae). The X-ray crystal structure analysis confirmed its chemical identity and revealed that it is solvent-free, in contrast to the previously reported monohydrate [Huang, Ou & Tang (2006 ▶). Acta Cryst. E62, o1987–o1988]. The mol­ecular structures are practically identical but the mol­ecules pack differently. In contrast to the monohydrate in which the water molecule generates two hydrogen bonds, no such intermolecular contacts are present in the title compound. The dihedral angle between the cinnamamide and the styryl group is 53.1 (1)°

Biocontrol of Alternaria alternata YZU, a causal of stem end rot disease on pitaya, with soil phosphate solubilizing bacteria

Stem end rot is the most destructive disease caused by Alternaria alternata YZU in pitaya-growing regions of Vietnam. This study was conducted to characterize antagonistic phosphate-solubilizing bacteria (PSB) from rhizosphere soil for their biocontrol activities against A. alternata YZU and evaluate the effect of temperature, pH, and water activity on that antagonism. Among seven PSB isolated from 45 rhizosphere soil samples, PSB31 (identified as Bacillus sp. strain IMAU61039, Accession number: MF803700.1) exhibited the highest antagonistic activity against A. alternata YZU with an average inhibition diameter of 0.65 ± 0.05 cm. The results also show that the strain PSB31 controlled the mycelial growth of A. alternata YZU by secreting antifungal metabolites. The most potent inhibitory activity was identified under in vitro conditions of 25 °C, pH 7, and aw 1. The isolated PSB31 could be a potential biological control agent against A. alternata YZU

Z-GMOT: Zero-shot Generic Multiple Object Tracking

Despite the significant progress made in recent years, Multi-Object Tracking (MOT) approaches still suffer from several limitations, including their reliance on prior knowledge of tracking targets, which necessitates the costly annotation of large labeled datasets. As a result, existing MOT methods are limited to a small set of predefined categories, and they struggle with unseen objects in the real world. To address these issues, Generic Multiple Object Tracking (GMOT) has been proposed, which requires less prior information about the targets. However, all existing GMOT approaches follow a one-shot paradigm, relying mainly on the initial bounding box and thus struggling to handle variants e.g., viewpoint, lighting, occlusion, scale, and etc. In this paper, we introduce a novel approach to address the limitations of existing MOT and GMOT methods. Specifically, we propose a zero-shot GMOT (Z-GMOT) algorithm that can track never-seen object categories with zero training examples, without the need for predefined categories or an initial bounding box. To achieve this, we propose iGLIP, an improved version of Grounded language-image pretraining (GLIP), which can detect unseen objects while minimizing false positives. We evaluate our Z-GMOT thoroughly on the GMOT-40 dataset, AnimalTrack testset, DanceTrack testset. The results of these evaluations demonstrate a significant improvement over existing methods. For instance, on the GMOT-40 dataset, the Z-GMOT outperforms one-shot GMOT with OC-SORT by 27.79 points HOTA and 44.37 points MOTA. On the AnimalTrack dataset, it surpasses fully-supervised methods with DeepSORT by 12.55 points HOTA and 8.97 points MOTA. To facilitate further research, we will make our code and models publicly available upon acceptance of this paper

Burden of diarrheal diseases from biogas wastewater exposure among smallholder farmers in Ha Nam province, Vietnam

Livestock production has developed rapidly in Vietnam in recent years, particularly at the small-scale which account for 65% of the total livestock production. Biogas systems are commonly used to treat livestock waste, however, the health risks from biogas wastewater exposure at smallholder farms are not yet well understood. A quantitative microbial risk assessment approach was applied to estimate the burden of diarrheal diseases from biogas wastewater exposure among 451 smallholder farmers using biogas systems in Ha Nam province. A total of 150 biogas wastewater samples were collected and analysed for E. coli, Giardia, and Cryptosporidium. The study showed that farmers faced diarrheal disease risks due to exposure to biogas wastewater at different exposure scenarios. The calculated annual risk of diarrheal disease by E. coli ranked from 0.15 to 0.21; by Giardia ranked from 0.022 to 0.095; and by Cryptosporidium ranked from 0.006 to 0.015. The estimated diarrheal diseases burden from pathogens in all exposure scenarios largely exceeded the reference level of health outcome target of 10-6DALYs loss per person per year recommended by WHO. The results suggest the importance in reducing concentrations of pathogens in biogas wastewater before use in the fields as a means for mitigating public health impacts