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Networks of intermediate filaments (IFs) need to constantly reorganize to fulfil their functions at different locations within the cell. The mechanism of IF assembly is well described and involves filament end-to-end annealing. By contrast, the mechanisms involved in IF disassembly are far less understood.In vitro, IFs are assumed to be very stable and their disassembly negligible. IF fragmentation has been observed in many cell types, but it has been suggested to be associated with active processes such as IF post-translational modifications. In this article, we uncover the contribution of filament spontaneous fragmentation in the assembly dynamics of type III vimentin IF using a combination ofin vitroreconstitution probed by fluorescence imaging and theoretical modeling. We first show that vimentin assembly at low concentrations results in an equilibrium between filament annealing and fragmentation at times ≥24 hours. At higher concentrations, entanglements kinetically trap the system out of equilibrium, and we show that this trapping is reversible upon dilution. Taking into account both fragmentation and entanglement, we estimate that the mean bond breaking time is ∼18 hours. This translates into a mean breaking time of ∼ 5 hours for a 1 μm long filament, which is a relevant time scale for IF reorganization in live cells. Finally, we provide direct evidence through dual-color imaging that filament fragmentation and annealing coexist during assembly. By showing that IF fragmentation can occur without cofactors or post-translational modifications, our study provides new insights into the physical understanding of the IF length regulation.

Summary Background Vietnam has emerged as one of the world's leading success stories in responding to COVID-19. After a prolonged period of little to no transmission, there was an outbreak of unknown source in July, 2020, in the Da Nang region, but the outbreak was quickly suppressed. We aimed to use epidemiological, behavioural, demographic, and policy data from the COVID-19 outbreak in Da Nang to calibrate an agent-based model of COVID-19 transmission for Vietnam, and to estimate the risk of future outbreaks associated with reopening of international borders in the country. Methods For this modelling study, we used comprehensive data from June 15 to Oct 15, 2020, on testing, COVID-19 cases, and quarantine breaches within an agent-based model of SARS-CoV-2 transmission to model a COVID-19 outbreak in Da Nang in July, 2020. We applied this model to quantify the risk of future outbreaks in Vietnam in the 3 months after the reopening of international borders, under different behavioural scenarios, policy responses (ie, closure of workplaces and schools), and ongoing testing. Findings We estimated that the outbreak in Da Nang between July and August, 2020, resulted in substantial community transmission, and that higher levels of symptomatic testing could have mitigated this transmission. We estimated that the outbreak peaked on Aug 2, 2020, with an estimated 1060 active infections (95% projection interval 890–1280). If the population of Vietnam remains highly compliant with mask-wearing policies, our projections indicate that the epidemic would remain under control even if a small but steady flow of imported infections escaped quarantine into the community. However, if complacency increases and testing rates are relatively low (10% of symptomatic individuals are tested), the epidemic could rebound again, resulting in an estimated 2100 infections (95% projected interval 1050–3610) in 3 months. These outcomes could be mitigated if the behaviour of the general population responds dynamically to increases in locally acquired cases that exceed specific thresholds, but only if testing of symptomatic individuals is also increased. Interpretation The successful response to COVID-19 in Vietnam could be improved even further with higher levels of symptomatic testing. If the previous approaches are used in response to new COVID-19 outbreaks, epidemic control is possible even in the presence of low levels of imported cases. Funding Ministry of Science and Technology (Vietnam). Translation For the Vietnamese translation of the abstract see Supplementary Materials section.

Deformable registration is a crucial step in many medical procedures such as image-guided surgery and radiation therapy. Most recent learning-based methods focus on improving the accuracy by optimizing the non-linear spatial correspondence between the input images. Therefore, these methods are computationally expensive and require modern graphic cards for real-time deployment. In this paper, we introduce a new Light-weight Deformable Registration network that significantly reduces the computational cost while achieving competitive accuracy. In particular, we propose a new adversarial learning with distilling knowledge algorithm that successfully leverages meaningful information from the effective but expensive teacher network to the student network. We design the student network such as it is light-weight and well suitable for deployment on a typical CPU. The extensively experimental results on different public datasets show that our proposed method achieves state-of-the-art accuracy while significantly faster than recent methods. We further show that the use of our adversarial learning algorithm is essential for a time-efficiency deformable registration method. Finally, our source code and trained models are available at: https://github.com/aioz-ai/LDR_ALDK.

In our inter-connected world, health-care professionals are the first line of defence to identify emerging diseases and public health events for rapid response. In Vietnam, event-based surveillance (EBS), critical for the early detection of emerging disease outbreaks and acute public health events, has been limited to media-based EBS until recently. In 2017–2018, the Ministry of Health of Vietnam, in collaboration with the World Health Organisation and the US Centres for Disease Control and Prevention, designed, implemented, and evaluated a hospital EBS demonstration pilot in six hospitals in two provinces in Vietnam. After the 9-month implementation period, we conducted a logbook review, eight interviews, and six focus group discussions with hospital and preventive medicine staff, and conducted thematic and descriptive analysis. During the implementation period, 11 signals were reported and confirmed as true events. Of the 11 signals, majority (N = 8, 72.7%) were detected in ICU, followed by the outpatient department (N = 2, 18.2%). The most common signal were clusters of food poisoning (N = 4, 36.4%). All (100%) signals were reported, risk-assessed, and responded to within 24 hours of signal detection. The hospital and preventive medicine staff reported that one of the main benefits of the pilot was their improved mutual relationship. This pilot formalised hospital event-based surveillance through a legal framework, standard operating procedures, a formal feedback mechanism to hospitals to facilitate a two-way conversation, and providing additional training and continued sensitisation. Most importantly, it fostered a trusting relationship between the curative medicine and public health sectors, marking an important step towards advancing the national event-based surveillance system in Vietnam.

Intermediate filaments (IF) are involved in key cellular functions including polarization, migration, and protection against large deformations. These functions are related to their remarkable ability to extend without breaking, a capacity that should be determined by the molecular organization of subunits within filaments. However, this structure-mechanics relationship remains poorly understood at the molecular level. Here, using super-resolution microscopy (SRM), we show that vimentin filaments exhibit a ~49 nm axial repeat both in cells and in vitro. As unit-length-filaments (ULFs) were measured at ~59 nm, this demonstrates a partial overlap of ULFs during filament assembly. Using an SRM-compatible stretching device, we also provide evidence that the extensibility of vimentin is due to the unfolding of its subunits and not to their sliding, thus establishing a direct link between the structural organization and its mechanical properties. Overall, our results pave the way for future studies of IF assembly, mechanical and structural properties in cells.

Video streaming is a multimedia service that continuously transmits the data over the Internet and presents the content on user screens without predownloading the entire video. Augmented video streaming is an advanced version of video streaming, where the video is enriched with additional embedded information in video frames. These additional data aim to provide a better user experience. Using QR code is one of the efficient approaches to incorporate information into video streams in this context. However, receiving the data in the embedded QR code is considered a challenging task owing to video quality and view angles. This paper proposes a lightweight two-stage QR code decoder for augmented video streaming using deep learning technologies. In the first stage, the position of the embedded QR code is detected using an online object detection algorithm. In the second stage, the detected region of the QR code is fed into a QR code reader to extract the embedded data. The experimental results show that the proposed decoder achieves high performances in terms of response time and decoding accuracy while being very lightweight, which is promising to be implemented in smartphones.

Market analysis reports suggest that the mobile virtual network operator (MVNO) market will experience subscriber growth at a compound annual growth rate (CAGR) of 10.7% from 2014 to 2020. This optimistic news presents great opportunities as well as many challenges. The major issue lies in the MVNOs' lack of spectrum allocation and an end-to-end network infrastructure, which means they must rely on mobile network operators (MNOs) for wireless access network connectivity to service their customers. In this article, we propose an approach that provides alternative wireless access methods to enable MVNOs to minimize their dependency on MNO networks. This leverages the fifth-generation (5G) softwarization technologies to provide selective multipath device-to-infrastructure (D2I) connections and service assortments. Supervised by software-defined networking (SDN)-based edge controllers, the multipath D2I connections provide flexible user data delivery over available communication resources without strict consideration of the MNO's infrastructure restraints. Additionally, a priority-based classification policy is applied to user data providing service assortments before it reaches the network infrastructure. Multiple pilot scenarios are implemented to verify the feasibility and performance tradeoff between the communication metrics of the proposed approach.

Introduction Hospitals are a key source of information for the early identification of emerging disease outbreaks and acute public health events for risk assessment, decision-making and public health response. The objective of this study was to identify potential facilitators and barriers for event reporting from the curative sector to the preventive medicine sector in Viet Nam. Methods In 2016, we conducted 18 semi-structured, in-depth interviews, as well as nine focus group discussions, with representatives from the curative and preventive medicine sectors in four provinces. We transcribed the interviews and focus group discussions and used thematic analysis to identify the factors that appeared to affect public health event reporting. Results We identified five major themes. First, the lack of a legal framework to guide reporting meant hospital staff relied on internal procedures that varied from hospital to hospital, which sometimes delayed reporting. Second, participants stated the importance of an enabling environment, such as leadership support and having focal points for reporting, to facilitate reporting. Third, participants described the potential benefits of reporting, such as support provided during outbreaks and information received about local outbreaks. Fourth, some challenges prohibited timely reporting such as not perceiving reporting to be the task of the curative sector and hesitancy to report without laboratory confirmation. Finally, limited resources and specialist capacities in remote areas hindered timely detection and reporting of unusual events. Discussion This study identified potential opportunities to promote the detection and reporting of unusual events from health-care workers to the public health sector, and thus to improve the overall health security system in Viet Nam.The influenza virus is a respiratory pathogen that is transmitted through respiratory droplets. 1 During seasonal influenza epidemics, high attack rates cause a significant public health burden. 2 The infection is usually self-limited in young adults but can lead to severe infections in people in high-risk groups, including elderly people (> 65 years old), pregnant women, children aged 6-59 months and adults with chronic illnesses. 3.

Phytophthora species cause diseases in a large variety of plants and represent a serious agricultural threat, leading, every year, to multibillion dollar losses. Infection occurs when these biflagellated zoospores move across the soil at their characteristic high speed and reach the roots of a host plant. Despite the relevance of zoospore spreading in the epidemics of plant diseases, characteristics of individual swimming of zoospores have not been fully investigated. It remains unknown about the characteristics of two opposite beating flagella during translation and turning, and the roles of each flagellum on zoospore swimming. Here, combining experiments and modeling, we show how these two flagella contribute to generate thrust when beating together, and identify the mastigonemes-attached anterior flagellum as the main source of thrust. Furthermore, we find that turning involves a complex active process, in which the posterior flagellum temporarily stops, while the anterior flagellum keeps on beating and changes its pattern from sinusoidal waves to power and recovery strokes, similar to Chlamydomonas’s breaststroke, to reorient its body to a new direction. Our study is a fundamental step towards a better understanding of the spreading of plant pathogens’ motile forms, and shows that the motility pattern of these biflagellated zoospores represents a distinct eukaryotic version of the celebrated “run-and-tumble” motility class exhibited by peritrichous bacteria.

Autonomous navigation is a long-standing field of robotics research, which provides an essential capability for mobile robots to execute a series of tasks on the same environments performed by human everyday. In this chapter, we present a set of algorithms to train and deploy deep networks for autonomous navigation of mobile robots using the Robot Operation System (ROS). We describe three main steps to tackle this problem: (i) collecting data in simulation environments using ROS and Gazebo; (ii) designing deep network for autonomous navigation, and (iii) deploying the learned policy on mobile robots in both simulation and real-world. Theoretically, we present deep learning architectures for robust navigation in normal environments (e.g., man-made houses, roads) and complex environments (e.g., collapsed cities, or natural caves). We further show that the use of visual modalities such as RGB, Lidar, and point cloud is essential to improve the autonomy of mobile robots. Our project website and demonstration video can be found at https://sites.google.com/site/autonomousnavigationros.

Transfer learning is an important step to extract meaningful features and overcome the data limitation in the medical Visual Question Answering (VQA) task. However, most of the existing medical VQA methods rely on external data for transfer learning, while the meta-data within the dataset is not fully utilized. In this paper, we present a new multiple meta-model quantifying method that effectively learns meta-annotation and leverages meaningful features to the medical VQA task. Our proposed method is designed to increase meta-data by auto-annotation, deal with noisy labels, and output meta-models which provide robust features for medical VQA tasks. Extensively experimental results on two public medical VQA datasets show that our approach achieves superior accuracy in comparison with other state-of-the-art methods, while does not require external data to train meta-models. Source code available at: https://github.com/aioz-ai/MICCAI21_MMQ.

Autonomous navigation in complex environments is a crucial task in time-sensitive scenarios such as disaster response or search and rescue. However, complex environments pose significant challenges for autonomous platforms to navigate due to their challenging properties: constrained narrow passages, unstable pathway with debris and obstacles, or irregular geological structures and poor lighting conditions. In this work, we propose a multimodal fusion approach to address the problem of autonomous navigation in complex environments such as collapsed cites, or natural caves. We first simulate the complex environments in a physics-based simulation engine and collect a large-scale dataset for training. We then propose a Navigation Multimodal Fusion Network (NMFNet) which has three branches to effectively handle three visual modalities: laser, RGB images, and point cloud data. The extensively experimental results show that our NMFNet outperforms recent state of the art by a fair margin while achieving real-time performance. We further show that the use of multiple modalities is essential for autonomous navigation in complex environments. Finally, we successfully deploy our network to both simulated and real mobile robots., Accepted to IROS 2020

Background Simulation exercises can functionally validate World Health Organization (WHO) International Health Regulations (IHR 2005) core capacities. In 2018, the Vietnam Ministry of Health (MOH) conducted a full-scale exercise (FSX) in response to cases of severe viral pneumonia with subsequent laboratory confirmation for Middle East Respiratory Syndrome Coronavirus (MERS-CoV) to evaluate the country’s early warning and response capabilities for high-risk events. Methods An exercise planning team designed a complex fictitious scenario beginning with one case of severe viral pneumonia presenting at the hospital level and developed all the materials required for the exercise. Actors, controllers and evaluators were trained. In August 2018, a 3-day exercise was conducted in Quang Ninh province and Hanoi city, with participation of public health partners at the community, district, province, regional and national levels. Immediate debriefings and an after-action review were conducted after all exercise activities. Participants assessed overall exercise design, conduction and usefulness. Results FSX findings demonstrated that the event-based surveillance component of the MOH surveillance system worked optimally at different administrative levels. Detection and reporting of signals at the community and health facility levels were appropriate. Triage, verification and risk assessment were successfully implemented to identify a high-risk event and trigger timely response. The FSX identified infection control, coordination with internal and external response partners and process documentation as response challenges. Participants positively evaluated the exercise training and design. Conclusions This exercise documents the value of exercising surveillance capabilities as part of a real-time operational scenario before facing a true emergency. The timing of this exercise and choice of disease scenario was particularly fortuitous given the subsequent appearance of COVID-19. As a result of this exercise and subsequent improvements made by the MOH, the country may have been better able to deal with the emergence of SARS-CoV-2 and contain it.

By considering an edge computing platform as one of the main computing tiers in a fully comprehensive cloudisation model, this chapter first discusses the distinguished features of edge computing compared to other platforms from the perspectives of service, operation and control. In addition, a local view of the intrinsic architecture of an edge computing host is analysed. Subsequently, a standard reference architecture of the edge computing platform is presented, which defines the internal and external interfaces among components at the computing host and system levels. The reference architecture is matched to the network function virtualisation management and orchestration (NFV MANO) model for consideration as a virtualised network function (VNF). In particular, the position and roles of VNF edge computing in the standard mobile network reference model are demonstrated. Finally, the open issues and challenges are presented by way of conclusion.

Background In 2016–2017, Vietnam’s Ministry of Health (MoH) implemented an event-based surveillance (EBS) pilot project in six provinces as part of Global Health Security Agenda (GHSA) efforts. This manuscript describes development and design of tools for monitoring and evaluation (M&E) of EBS in Vietnam. Methods A strategic EBS framework was developed based on the EBS implementation pilot project’s goals and objectives. The main process and outcome components were identified and included input, activities, outputs, and outcome indicators. M&E tools were developed to collect quantitative and qualitative data. The tools included a supervisory checklist, a desk review tool, a key informant interview guide, a focus group discussion guide, a timeliness form, and an online acceptability survey. An evaluation team conducted field visits for assessment of EBS 5–9 months after implementation. Results The quantitative data collected provided evidence on the number and type of events that were being reported, the timeliness of the system, and the event-to-signal ratio. The qualitative and subjective data collected helped to increase understanding of the system’s field utility and acceptance by field staff, reasons for non-compliance with established guidelines, and other factors influencing implementation. Conclusions The use of M&E tools for the EBS pilot project in Vietnam provided data on signals and events reported, timeliness of reporting and response, perceptions and opinions of implementers, and fidelity of EBS implementation. These data were valuable for Vietnam’s MoH to understand the function of the EBS program, and the success and challenges of implementing this project in Vietnam.

An increasing number of university-K-12 partnerships are addressing the out-of-school challenges that can often constitute barriers to learning for students. This chapter describes the role that universities have played in shaping models of schooling that are intentionally designed to support the whole child and integrate services and enrichment opportunities to support students. It outlines how this university-K-12 collaboration has occurred in three major phases in which universities have: 1) helped to develop and/or implement new approaches on the ground, 2) refine these approaches utilizing developmental science and best practice, and 3) deepened the measurement of the effectiveness and efficiency of the models, as well as strengthening their scalability and sustainability.

Different approaches have been proposed to Visual Question Answering (VQA). However, few works are aware of the behaviors of varying joint modality methods over question type prior knowledge extracted from data in constraining answer search space, of which information gives a reliable cue to reason about answers for questions asked in input images. In this paper, we propose a novel VQA model that utilizes the question-type prior information to improve VQA by leveraging the multiple interactions between different joint modality methods based on their behaviors in answering questions from different types. The solid experiments on two benchmark datasets, i.e., VQA 2.0 and TDIUC, indicate that the proposed method yields the best performance with the most competitive approaches.

Surveillance and outbreak reporting systems in Vietnam required improvements to function effectively as early warning and response systems. Accordingly, the Ministry of Health of Vietnam, in collaboration with the US Centers for Disease Control and Prevention, launched a pilot project in 2016 focusing on community and hospital event–based surveillance. The pilot was implemented in 4 of Vietnam’s 63 provinces. The pilot demonstrated that event-based surveillance resulted in early detection and reporting of outbreaks, improved collaboration between the healthcare facilities and preventive sectors of the ministry, and increased community participation in surveillance and reporting.

Movie summarization and indexing is the study which takes into account the understanding of the audiences. Besides, movie summarization focuses on reducing the length of a movie. Regarding this work, we propose a character network analysis to index and summarize the given movie. The method is based on the discovery and analysis of characters with respect to their appearance and the relationships among them in the movie. The strategy analysis is used to detect scenes, to segment the sub-plots of the story, and to extract character network and the main storyline of the movie. As a result, the social strength of each character in the social network is measured using measurement techniques that will then be used to mine the main plotline of the movie. In the final stage, the main storyline is used to provide a summarized version of the movie based on the social power of the characters. Experiments were carried out with 17 series of the Star Wars, the Lord of the Rings and the Harry Potter. The experimental evaluation results show that this study should index and extract summarization versions while keeping the understanding of the audiences.

We address the problem of jointly learning vision and language to understand the object in a fine-grained manner. The key idea of our approach is the use of object descriptions to provide the detailed understanding of an object. Based on this idea, we propose two new architectures to solve two related problems: object captioning and natural language-based object retrieval. The goal of the object captioning task is to simultaneously detect the object and generate its associated description, while in the object retrieval task, the goal is to localize an object given an input query. We demonstrate that both problems can be solved effectively using hybrid end-to-end CNN-LSTM networks. The experimental results on our new challenging dataset show that our methods outperform recent methods by a fair margin, while providing a detailed understanding of the object and having fast inference time. The source code will be made available.

In Visual Question Answering (VQA), answers have a great correlation with question meaning and visual contents. Thus, to selectively utilize image, question and answer information, we propose a novel trilinear interaction model which simultaneously learns high level associations between these three inputs. In addition, to overcome the interaction complexity, we introduce a multimodal tensor-based PARALIND decomposition which efficiently parameterizes trilinear interaction between the three inputs. Moreover, knowledge distillation is first time applied in Free-form Opened-ended VQA. It is not only for reducing the computational cost and required memory but also for transferring knowledge from trilinear interaction model to bilinear interaction model. The extensive experiments on benchmarking datasets TDIUC, VQA-2.0, and Visual7W show that the proposed compact trilinear interaction model achieves state-of-the-art results when using a single model on all three datasets., Comment: Accepted in ICCV 2019