Relationship between BMI and prognosis of chronic heart failure outpatients in Vietnam: a single-center study.

Background: Insufficient data exists regarding the relationship between body mass index (BMI) and the prognosis of chronic heart failure (CHF) specifically within low- and middle-income Asian countries. The objective of this study was to evaluate the impact of BMI on adverse outcomes of ambulatory patients with CHF in Vietnam. Methods: Between 2018 and 2020, we prospectively enrolled consecutive outpatients with clinically stable CHF in an observational cohort, single-center study. The participants were stratified according to Asian-specific BMI thresholds. The relationships between BMI and adverse outcomes (all-cause death and all-cause hospitalization) were analyzed by Kaplan-Meier survival curves and Cox proportional-hazards model. Results: Among 320 participants (age 63.5 ± 13.3 years, 57.9% male), the median BMI was 21.4 kg/m2 (IQR 19.5-23.6), and 10.9% were underweight (BMI <18.50 kg/m2). Over a median follow-up time of 32 months, the cumulative incidence of all-cause mortality and hospitalization were 5.6% and 19.1%, respectively. After multivariable adjustment, underweight patients had a significantly higher risk of all-cause mortality than patients with normal BMI (adjusted hazard ratios = 3.03 [95% CI: 1.07-8.55]). Lower BMI remained significantly associated with a worse prognosis when analyzed as a continuous variable (adjusted hazard ratios = 1.27 [95% CI: 1.03-1.55] per 1 kg/m2 decrease for all-cause mortality). However, BMI was not found to be significantly associated with the risk of all-cause hospitalization (p > 0.05). Conclusion: In ambulatory patients with CHF in Vietnam, lower BMI, especially underweight status (BMI < 18.5 kg/m2), was associated with a higher risk of all-cause mortality. These findings suggest that BMI should be considered for use in risk classification, and underweight patients should be managed by a team consisting of cardiologists, nutritionists, and geriatricians.

Homophily-Based Social Group Formation in a Spin Glass Self-Assembly Framework.

Homophily, the tendency of humans to attract each other when sharing similar features, traits, or opinions, has been identified as one of the main driving forces behind the formation of structured societies. Here we ask to what extent homophily can explain the formation of social groups, particularly their size distribution. We propose a spin-glass-inspired framework of self-assembly, where opinions are represented as multidimensional spins that dynamically self-assemble into groups; individuals within a group tend to share similar opinions (intragroup homophily), and opinions between individuals belonging to different groups tend to be different (intergroup heterophily). We compute the associated nontrivial phase diagram by solving a self-consistency equation for "magnetization" (combined average opinion). Below a critical temperature, there exist two stable phases: one ordered with nonzero magnetization and large clusters, the other disordered with zero magnetization and no clusters. The system exhibits a first-order transition to the disordered phase. We analytically derive the group-size distribution that successfully matches empirical group-size distributions from online communities.

Heavy metals in cultured oysters (Saccostrea glomerata) and clams (Meretrix lyrata) from the northern coastal area of Vietnam.

Oysters and clams are abundant and popularly consumed seafood in Viet Nam. These bivalves were proved to be suitable bioindicators to assess the heavy metal accumulation in the aquatic environment. The study is to investigate heavy metals such as As, Cd, Hg and Pb in the cultured oysters Saccostrea glomerata and clams Meretrix lyrata collected in VanDon - Quang Ninh, and CatBa - Hai Phong. The results showed that the metal heavy concentrations in the oysters Saccostrea glomerata and clams Meretrix lyrata presented in the order of As > Cd > Pb > Hg. The most polluted concentration with As among four heavy metals studied in six sampling sites was 2.81 ± 1.07 and 1.6 ± 0.62 mg/kg wet weight for clams and oysters, respectively. Investigated heavy metals in cultured oysters and clams indicated potential risks for human health in future by assessment of the heavy metals.

Empirical social triad statistics can be explained with dyadic homophylic interactions.

The remarkable robustness of many social systems has been associated with a peculiar triangular structure in the underlying social networks. Triples of people that have three positive relations (e.g., friendship) between each other are strongly overrepresented. Triples with two negative relations (e.g., enmity) and one positive relation are also overrepresented, and triples with one or three negative relations are drastically suppressed. For almost a century, the mechanism behind these very specific ("balanced") triad statistics remained elusive. Here, we propose a simple realistic adaptive network model, where agents tend to minimize social tension that arises from dyadic interactions. Both opinions of agents and their signed links (positive or negative relations) are updated in the dynamics. The key aspect of the model resides in the fact that agents only need information about their local neighbors in the network and do not require (often unrealistic) higher-order network information for their relation and opinion updates. We demonstrate the quality of the model on detailed temporal relation data of a society of thousands of players of a massive multiplayer online game where we can observe triangle formation directly. It not only successfully predicts the distribution of triangle types but also explains empirical group size distributions, which are essential for social cohesion. We discuss the details of the phase diagrams behind the model and their parameter dependence, and we comment on to what extent the results might apply universally in societies.

Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings.

The building sector is the largest energy consumer accounting for 40% of global energy usage. An energy forecast model supports decision-makers to manage electric utility management. Identifying optimal values of hyperparameters of prediction models is challenging. Therefore, this study develops a novel time-series Wolf-Inspired Optimized Support Vector Regression (WIO-SVR) model to predict 48-step-ahead energy consumption in buildings. The proposed model integrates the support vector regression (SVR) and the grey wolf optimizer (GWO) in which the SVR model serves as a prediction engine while the GWO is used to optimize the hyperparameters of the SVR model. The 30-min energy data from various buildings in Vietnam were adopted to validate model performance. Buildings include one commercial building, one hospital building, three authority buildings, three university buildings, and four office buildings. The dataset is divided into the learning data and the test data. The performance of the WIO-SVR was superior to baseline models including the SVR, random forests (RF), M5P, and decision tree learner (REPTree). The WIO-SVR model obtained the highest value of correlation coefficient (R) with 0.90. The average root-mean-square error (RMSE) of the WIO-SVR was 2.02 kWh which was more accurate than those of the SVR model with 10.95 kWh, the RF model with 16.27 kWh, the M5P model with 17.73 kWh, and the REPTree model with 26.44 kWh. The proposed model improved 442.0-1207.9% of the predictive accuracy in RMSE. The reliable WIO-SVR model provides building managers with useful references in efficient energy management.

Optimizing Traffic Engineering for Resilient Services in NFV-Based Connected Autonomous Vehicles.

The massive amount of data generated daily by various sensors equipped with connected autonomous vehicles (CAVs) can lead to a significant performance issue of data processing and transfer. Network Function Virtualization (NFV) is a promising approach to improving the performance of a CAV system. In an NFV framework, Virtual Network Function (VNF) instances can be placed in edge and cloud servers and connected together to enable a flexible CAV service with low latency. However, protecting a service function chain composed of several VNFs from a failure is challenging in an NFV-based CAV system (VCAV). We propose an integer linear programming (ILP) model and two approximation algorithms for resilient services to minimize the service disruption cost in a VCAV system when a failure occurs. The ILP model, referred to as TERO, allows us to obtain the optimal solution for traffic engineering, including the VNF placement and routing for resilient services with regard to dynamic routing. Our proposed algorithms based on heuristics (i.e., TERH) and reinforcement learning (i.e., TERA) provide an approximation solution for resilient services in a large-scale VCAV system. Evaluation results with real datasets and generated network topologies show that TERH and TERA can provide a solution close to the optimal result. It also suggests that TERA should be used in a highly dynamic VCAV system.

Efficient Traffic Engineering in an NFV Enabled IoT System.

The Internet of Things (IoT) is increasingly creating new market possibilities in several industries' sectors such as smart homes, smart manufacturing, and smart cities, to link the digital and physical worlds. A key challenge in an IoT system is to ensure network performance and cost-efficiency when a plethora of data is generated and proliferated. The adoption of Network Function Virtualization (NFV) technologies within an IoT environment enables a new approach of providing services in a more agile and cost-efficient way. We address the problem of traffic engineering with multiple paths for an NFV enabled IoT system (vIoT), taking into account the fluctuation of traffic volume in various time periods. We first formulate the problem as a mixed linear integer programming model for finding the optimal solution of link-weight configuration and traffic engineering. We then develop heuristic algorithms for a vIoT system with a large number of devices. Our solution enables a controller to adjust a link weight system and update a flow table at an NFV switch for directing IoT traffic through a service function chain in a vIoT system. The evaluation results under both synthetic and real-world datasets of network traffic and topologies show that our approach to traffic engineering with multiple paths remarkably improves several performance metrics for a vIoT system.

Predicting collapse of adaptive networked systems without knowing the network.

The collapse of ecosystems, the extinction of species, and the breakdown of economic and financial networks usually hinges on topological properties of the underlying networks, such as the existence of self-sustaining (or autocatalytic) feedback cycles. Such collapses can be understood as a massive change of network topology, usually accompanied by the extinction of a macroscopic fraction of nodes and links. It is often related to the breakdown of the last relevant directed catalytic cycle within a dynamical system. Without detailed structural information it seems impossible to state, whether a network is robust or if it is likely to collapse in the near future. Here we show that it is nevertheless possible to predict collapse for a large class of systems that are governed by a linear (or linearized) dynamics. To compute the corresponding early warning signal, we require only non-structural information about the nodes' states such as species abundances in ecosystems, or company revenues in economic networks. It is shown that the existence of a single directed cycle in the network can be detected by a "quantization effect" of node states, that exists as a direct consequence of a corollary of the Perron-Frobenius theorem. The proposed early warning signal for the collapse of networked systems captures their structural instability without relying on structural information. We illustrate the validity of the approach in a transparent model of co-evolutionary ecosystems and show this quantization in systems of species evolution, epidemiology, and population dynamics.

Iodine Absorption Cells Purity Testing.

This article deals with the evaluation of the chemical purity of iodine-filled absorption cells and the optical frequency references used for the frequency locking of laser standards. We summarize the recent trends and progress in absorption cell technology and we focus on methods for iodine cell purity testing. We compare two independent experimental systems based on the laser-induced fluorescence method, showing an improvement of measurement uncertainty by introducing a compensation system reducing unwanted influences. We show the advantages of this technique, which is relatively simple and does not require extensive hardware equipment. As an alternative to the traditionally used methods we propose an approach of hyperfine transitions' spectral linewidth measurement. The key characteristic of this method is demonstrated on a set of testing iodine cells. The relationship between laser-induced fluorescence and transition linewidth methods will be presented as well as a summary of the advantages and disadvantages of the proposed technique (in comparison with traditional measurement approaches).

Fractional kinetics emerging from ergodicity breaking in random media.

We present a modeling approach for diffusion in a complex medium characterized by a random length scale. The resulting stochastic process shows subdiffusion with a behavior in qualitative agreement with single-particle tracking experiments in living cells, such as ergodicity breaking, p variation, and aging. In particular, this approach recapitulates characteristic features previously described in part by the fractional Brownian motion and in part by the continuous-time random walk. Moreover, for a proper distribution of the length scale, a single parameter controls the ergodic-to-nonergodic transition and, remarkably, also drives the transition of the diffusion equation of the process from nonfractional to fractional, thus demonstrating that fractional kinetics emerges from ergodicity breaking.

Recombinase and translesion DNA polymerase decrease the speed of replication fork progression during the DNA damage response in Escherichia coli cells.

The SOS response is a DNA damage response pathway that serves as a general safeguard of genome integrity in bacteria. Extensive studies of the SOS response in Escherichia coli have contributed to establishing the key concepts of cellular responses to DNA damage. However, how the SOS response impacts on the dynamics of DNA replication fork movement remains unknown. We found that inducing the SOS response decreases the mean speed of individual replication forks by 30-50% in E. coli cells, leading to a 20-30% reduction in overall DNA synthesis. dinB and recA belong to a group of genes that are upregulated during the SOS response, and encode the highly conserved proteins DinB (also known as DNA polymerase IV) and RecA, which, respectively, specializes in translesion DNA synthesis and functions as the central recombination protein. Both genes were independently responsible for the SOS-dependent slowdown of replication fork progression. Furthermore, fork speed was reduced when each gene was ectopically expressed in SOS-uninduced cells to the levels at which they are expressed in SOS-induced cells. These results clearly indicate that the increased expression of dinB and recA performs a novel role in restraining the progression of an unperturbed replication fork during the SOS response.

A single-molecule approach to DNA replication in Escherichia coli cells demonstrated that DNA polymerase III is a major determinant of fork speed.

The replisome catalyses DNA synthesis at a DNA replication fork. The molecular behaviour of the individual replisomes, and therefore the dynamics of replication fork movements, in growing Escherichia coli cells remains unknown. DNA combing enables a single-molecule approach to measuring the speed of replication fork progression in cells pulse-labelled with thymidine analogues. We constructed a new thymidine-requiring strain, eCOMB (E. coli for combing), that rapidly and sufficiently incorporates the analogues into newly synthesized DNA chains for the DNA-combing method. In combing experiments with eCOMB, we found the speed of most replication forks in the cells to be within the narrow range of 550-750 nt s(-1) and the average speed to be 653 ± 9 nt s(-1) (± SEM). We also found the average speed of the replication fork to be only 264 ± 9 nt s(-1) in a dnaE173-eCOMB strain producing a mutant-type of the replicative DNA polymerase III (Pol III) with a chain elongation rate (300 nt s(-1) ) much lower than that of the wild-type Pol III (900 nt s(-1) ). This indicates that the speed of chain elongation by Pol III is a major determinant of replication fork speed in E. coli cells.