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The paper discusses on the effect that the growth in our energy consumption as a species is having upon the planet, and how the global lighting and telecommunications industries are major contributors. We demonstrate that through the adoption of LED based lighting combined with visible light communications, substantial economical and power savings by orders of magnitude can be made over existing technologies, contributing towards a greener more sustainable future. The future of LED technology is also discussed with a focus on organic technology, promising increased savings.

Orchidaceae is one of the most valuable plant groups all over the world, and is also an impressively large and complex family of flowering plants. Effective molecular tools used for the identification of orchid species should be developed to support traditional morphological approaches. This study reviews most of the DNA fragments that have been used as taxon identifiers in researches conducted on Orchidaceae in order to assess potential molecular markers and metric measurements for the identification of orchid species.

In the context of global change, the rapidly increase of socio-economic development can trigger the negative impacts on coastal resources, environment and ecosystems, causing the deterioration of the system sustainability. Vietnam is one of the most vulnerable countries by climate change. Of which, the South Central Coast (SCC) is a highly vulnerable area to climate extreme events and sea level rise. This research aimed to build a theoretical framework for evaluating the level of sustainable development in the SCC. By using Delphi method, Interpolation method and Evaluation method, the research proposed a set of indicators, which consists of 32 indicators of the four dimensions (Economic; Social; Environmental; Infrastructure and Governance) in order to evaluate the sustainable development index (SDI) of the SCC provinces. Results show that the SDI had an increased tendency from 2010 to 2016. In general, Da Nang is the capital of the region and the SDI was highest which increased from 0.38 in 2010 to 0.61 in 2016 through the development of economic, infrastructure and urban governance dimensions. In contrast, during the survey, Binh Dinh and Phu Yen’s SDI were still lower than other provinces. Results suggested that it is necessary to enhance the policy and planning to implement the sustainable development goals together with the economy, society, environment and infrastructure development. The sustainable development indicators can potentially contribute to apply to monitor the sustainable development in other coastal regions in Vietnam.

Influenza A has caused several deadly pandemics throughout human history. The virus is often resistant to developed treatments because of its genetic drift or shift property. Broad-spectrum antibodies show a promising potential to overcome the resistance of influenza viruses. In silico studies on broad-reactive antibodies and their interactions with hemagglutinins might shed light on the rational design of a universal vaccine. In this study, 11 broad-spectrum antibodies (or antigen-binding fragments) and 14 hemagglutinins of H3N2 and H5N1 strains were docked and analyzed to provide information about the construction of the scaffold for using universal antibodies against the influenza A virus. Antigen-binding fragments that have high number of appearances in the top 3 within each H3 and H5 subtypes were chosen for protein-protein interaction analysis. The results show that while the hydrogen bond is important for Ab/Fab binding to H3, the H5-Ab/Fab system may need cation-pi interaction for a strong interaction.

Background Autism spectrum disorders (ASD) are characterized by abnormal neurodevelopment, genetic, and environmental risk factors, as well as immune dysfunctions. Several lines of evidence suggest alterations in innate immune responses in children with ASD. To address this question in adults with high-functioning ASD (hf-ASD), we sought to investigate the role of natural killer (NK) cells in the persistence of ASD. Methods NK cells from 35 adults with hf-ASD were compared to that of 35 healthy controls (HC), selected for the absence of any immune dysfunctions, at different time-points, and over a 2-year follow-up period for four patients. The phenotype and polyfunctional capacities of NK cells were explored according to infectious stigma and clinical parameters (IQ, social, and communication scores). Results As compared to HC, NK cells from patients with hf-ASD showed a high level of cell activation (p

Despite the high clinical burden, little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting-state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here, we addressed these questions in four large ASD cohorts. Using rs-fMRI, we identified functional connectivity alterations associated with ASD. We tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status, and clinical symptom severity. Our results showed reproducible patterns of ASD-associated functional hyper- and hypoconnectivity. Hypoconnectivity was primarily restricted to sensory-motor regions, whereas hyperconnectivity hubs were predominately located in prefrontal and parietal cortices. Shifts in cortico-cortical between-network connectivity from outside to within the identified regions were shown to be a key driver of these abnormalities. This reproducible pathophysiological phenotype was partially associated with core ASD symptoms related to communication and daily living skills and was not affected by age, sex, or medication status. Although the large effect sizes in standardized cohorts are encouraging with respect to potential application as a treatment and for patient stratification, the moderate link to clinical symptoms and the large overlap with healthy controls currently limit the usability of identified alterations as diagnostic or efficacy readout.

The Mekong Delta has made great progress in rice production over the past ten years. Intensive cultivation with multi-cropping brings many benefits to farmers as well as the food export industry. However, this is also an opportunity for raising epidemic outbreak, Brown Plant-hoppers can directly damage by sucking the rice’s vitality, and they can cause the wilting and complete drying of rice plants, a noncontagious disease known as “Hopper-burn”. In this article, we propose the CDNN model for insect classification based on Neural Network and Deep Learning approach. First, insect images are collected and extracted features based on Dense Scale-Invariant Feature Transform. Then, Bag of Features is used for image representation as feature vectors. Lastly, these feature vectors are trained and classified using CDNN model based on Deep Neural Network. The approach is demonstrated with experiments, and measured by a large amount of Brown Plant-hoppers and Ladybugs samples.

Autism spectrum disorder (ASD) is a developmental disorder underdiagnosed in adults. To date, no consistent evidence of alterations in brain structure has been reported in adults with ASD and few studies were conducted at that age. We analyzed structural magnetic resonance imaging data from 167 high functioning adults with ASD and 195 controls. We ran our analyses on a discovery (n = 301) and a replication sample (n = 61). The right caudal anterior cingulate cortical thickness was significantly thinner in adults with ASD compared to controls in both the discovery and the replication sample. Our work underlines the relevance of studying the brain anatomy of an adult ASD population.

Early detection and prediction of cardiac anomalies play an important role in the diagnosis and treatment of cardiovascular diseases. In medicine, electrocardiography provides valuable information for the doctors since they can accurately determine what is happening concerning the heart activities. Nevertheless, electrocardiography classification is a non-trivial challenge due to the specialties of these data as well as the reliability of manual data collection methods. With the recent advancement of the IoT technologies, some wearable IoT devices for electrocardiography monitoring have been developed. However, the data collected from those devices, though possibly automatic, pose more challenging issues for the problem of electrocardiography classification. In this paper, we propose a novel solution for electrocardiography signal classification based on Deep Learning by combining Auto Encoder and Long-Short Term Memory models to handle data collected from intelligent IoT devices Shimmer and VitalSigns Holter.

In the Internet of Things system, including wireless sensor networks, energy savings are a matter of prime concern today. While electronic devices as well as software have been developed ceaselessly, the technology of battery development does not keep pace. Beside that each sensor node usually uses batteries for power, these sensors often cannot change or recharge in the usual way. In all the operations of a sensor node, wireless communication is the most energy consuming operations. So, in order to extend the life of the battery in the sensor node, we need to limit the transmission of information over the wireless network, however, we must also ensure that the sensor node transmits enough information about the host. We can do this by data compression. We have to choose an efficient and simple data compression algorithm because energy, memory and computational resources of sensor node are very limited. In this article, we adjust a lossless data compression algorithm to suit the wireless sensor network and meet the requirements set forth above. Accompanying that, with the best knowledge we have, we also offer a comparison of the efficiency of this algorithm with two well-known compression algorithms, namely bzip2 and gzip.

Innovation performance is the fundamental factor for firms to survive and achieve competitive advantage in the context of increasing competitive pressure. The purpose of this paper is to investigate the impacts of two key components of transformational leadership (TL) namely idealized influence and individualized consideration on knowledge sharing (KS), as well as their influences on firm’s innovation performance. The paper used Structural Equations Modeling (SEM) to elaborate the relationship among these latent factors through using survey data gathered from 235 participants of 60 medium and small-sized firms in Vietnam. The findings reveal that KS activities of employees play a crucial role in improving firm’s innovation performance, and serve as a mediating role in the effects of TL on innovation performance. Moreover, the findings highlight the impact of individualized consideration on innovation performance in comparison with the impact of idealized influence on innovation performance. In general, the findings of this study have advanced the understanding and brought new initiatives for Vietnamese firms to follow and improve its innovation performance.

A high level of mutation enables the influenza A virus to resist antibiotics previously effective against the influenza A virus. A portion of the structure of hemagglutinin HA is assumed to be well-conserved to maintain its role in cellular fusion, and the structure tends to be more conserved than sequence. We designed peptide inhibitors to target the conserved residues on the HA surface, which were identified based on structural alignment. Most of the conserved and strongly similar residues are located in the receptor-binding and esterase regions on the HA1 domain In a later step, fragments of anti-HA antibodies were gathered and screened for the binding ability to the found conserved residues. As a result, Methionine amino acid got the best docking score within the −2.8 Å radius of Van der Waals when it is interacting with Tyrosine, Arginine, and Glutamic acid. Then, the binding affinity and spectrum of the fragments were enhanced by grafting hotspot amino acid into the fragments to form peptide inhibitors. Our peptide inhibitor was able to form in silico contact with a structurally conserved region across H1, H2, and H3 HA, with the binding site at the boundary between HA1 and HA2 domains, spreading across different monomers, suggesting a new target for designing broad-spectrum antibody and vaccine. This research presents an affordable method to design broad-spectrum peptide inhibitors using fragments of an antibody as a scaffold.

BACKGROUND Metabolic syndromes such as insulin resistance, type 2 diabetes and obesity share common pathogenic pathways with some age-related neurodegenerative disorders. Impaired insulin signaling, inflammation, mitochondrial dysfunction and ER stress can be both causatives and consequences in both groups of the diseases. Patients with chronic metabolic disorders therefore have potential risks to develop neurological diseases in late-age phase and vice versa those who with neurodegenerative diseases also have impairments in metabolic signaling. METHOD In this review, we summarize about the interrelation between pathogenic pathways, common drug targets as well as known and developing therapeutics for these "modern" diseases. RESULTS There are conventional medicines for insulin resistance associated metabolic disorders such as insulin analogues, insulin sensitizers and ER stress releasers which have been suggested in the treatments of some neurodegenerative diseases. Some used or tested therapeutics such as bromocriptine, memantine and α-2A adrenergic antagonists for Parkinson's and Alzheimer's diseases, vice versa, were promisingly shown as alternative or complementary drugs for metabolic syndromes. CONCLUSION Therefore, it is important and possible to consider contemporary control and intervention for both diseases.

Background Drug discovery is one important issue in medicine and pharmacology area. Traditional methods using target-based approach are usually time-consuming and ineffective. Recently, the problems are approached in a system-level view and therefore it is called systems pharmacology. This research field deals with the problems in drug discovery by integrating various kinds of biomedical and pharmacological data and using advanced computational methods. Ultimately, the problems are more effectively solved. One of the most important problem in systems pharmacology is prediction of drug-target interactions. Methods In this review, we are going to summarize various computational methods for this problem. Results More importantly, we formed a unified framework for the problem. In addition, to study human health and disease in a more systematically and effectively, we also presented an integrated scheme for a wider problem of prediction of disease-gene-drug associations. Conclusion By presenting the unified framework and the integrated scheme, underlying computational methods for problems in systems pharmacology can be understood and complex relationships among diseases, genes and drugs can be identified effectively.

Background: The influenza virus is one of the most critical threats to public health with major economic impact. Though annual influenza vaccination is currently the most effective prevention strategy against flu epidemics and pandemics, the mutational evolution of the influenza virus tends to reduce the effectiveness of strain-specific vaccines. Methods: For past decades, a broad spectrum of potentially universal influenza vaccines has been thoroughly investigated to suppress different strains and subtypes of influenza virus concomitantly. Universal influenza vaccines were attempted to be designed to target conserved regions of surface receptors to provide the necessary preventive strategy against new influenza outbreaks. Conclusion: Notably, the influenza hemagglutinin (HA) receptor has evolutionary conserved domains that can serve as basis for the rational design of a universal influenza vaccine. In this review, we examine recent studies on HA-based universal influenza vaccines and address their molecular mechanism.

The advance in genomics technology leads to the dramatic change in plant biology research. Plant biologists now easily access to enormous genomic data to deeply study plant high-density genetic variation at molecular level. Therefore, fully understanding and well manipulating bioinformatics tools to manage and analyze these data are essential in current plant genome research. Many plant genome databases have been established and continued expanding recently. Meanwhile, analytical methods based on bioinformatics are also well developed in many aspects of plant genomic research including comparative genomic analysis, phylogenomics and evolutionary analysis, and genome-wide association study. However, constantly upgrading in computational infrastructures, such as high capacity data storage and high performing analysis software, is the real challenge for plant genome research. This review paper focuses on challenges and opportunities which knowledge and skills in bioinformatics can bring to plant scientists in present plant genomics era as well as future aspects in critical need for effective tools to facilitate the translation of knowledge from new sequencing data to enhancement of plant productivity.

The aim of this work is to study the freezing process of water molecules surrounding Antarctic Gram-negative bacterium Marinomonas primoryensis antifreeze protein (MpAFP) and the MpAFP interactions to the surface of ice crystals under various marine environments (at different NaCl concentrations of 0.3, 0.6, and 0.8 mol/l). Our result indicates that activating temperature region of MpAFPs reduced as NaCl concentration increased. Specifically, MpAFP was activated and functioned at 0.6 mol/l with temperatures equal or larger 278 K, and at 0.8 mol/l with temperatures equal or larger 270 K. Additionally, MpAFP was inhibited by ice crystal network from 268 to 274 K and solid-liquid hybrid from 276 to 282 K at 0.3 mol/l concentration. Our results shed lights on structural dynamics of MpAFP among different marine environments.

In Vietnam, social media has become an emerging and popular communication platform. Despite the powerful effect of social media in conditioning a crisis, and the trend to integrate social media into crisis management strategies in many countries, Vietnamese companies have often ignored or underutilized these channels. Therefore, this study seeks to fill the gap, to understand how Vietnamese companies perceive the importance and use of social media in crisis communication. The focus of this study is to compare the perceptions of Vietnamese and American public relations practitioners. As America has always been considered a role model and primary influencer for Vietnam’s public relations practice, this study compares the perception of social media in Vietnam to that in the U.S. This study aims to understand the underlying factors contributed to that perception and to evaluate the perceived importance of social media in Vietnam’s crisis communication.Abstrak dalam Bahasa Indonesia. Di Vietnam, media sosial telah menjadi platform komunikasi yang berkembang dan populer. Terlepas dari pengaruh kuat media sosial dalam mengkondisikan krisis, dan tren untuk mengintegrasikan media sosial ke dalam strategi manajemen krisis di banyak negara, perusahaan Vietnam sering mengabaikan atau kurang memanfaatkan saluran ini. Oleh karena itu, studi ini betujuan untuk mencari tahu perihal permasalahan tersebut, untuk memahami bagaimana perusahaan Vietnam memandang pentingnya dan penggunaan media sosial dalam komunikasi krisis. Fokus studi ini adalah membandingkan persepsi praktis dari public relations di Vietnam dan Amerika. Karena Amerika selalu dianggap sebagai panutan dan pemberi pengaruh utama untuk praktik PR di Vietnam, penelitian ini membandingkan persepsi media sosial di Vietnam dengan di AS. Penelitian ini bertujuan untuk memahami faktor-faktor mendasar yang berkontribusi terhadap persepsi tersebut dan untuk mengevaluasi pentingnya persepsi media sosial dalam komunikasi krisis Vietnam.

The current theory implying local, short-range overconnectivity in autism spectrum disorder, contrasting with long-range underconnectivity, is based on heterogeneous results, on limited data involving functional connectivity studies, on heterogeneous paediatric populations and non-specific methodologies. In this work, we studied short-distance structural connectivity in a homogeneous population of males with high-functioning autism spectrum disorder and used a novel methodology specifically suited for assessing U-shaped short-distance tracts, including a recently developed tractography-based atlas of the superficial white matter fibres. We acquired diffusion-weighted MRI for 58 males (27 subjects with high-functioning autism spectrum disorder and 31 control subjects) and extracted the mean generalized fractional anisotropy of 63 short-distance tracts. Neuropsychological evaluation included Wechsler Adult Intelligence Scale IV (WAIS-IV), Communication Checklist-Adult, Empathy Quotient, Social Responsiveness Scale and Behaviour Rating Inventory of Executive Function-Adult (BRIEF-A). In contradiction with the models of short-range over-connectivity in autism spectrum disorder, we found that patients with autism spectrum disorder had a significantly decreased anatomical connectivity in a component comprising 13 short tracts compared to controls. Specific short-tract atypicalities in temporal lobe and insula were significantly associated with clinical manifestations of autism spectrum disorder such as social awareness, language structure, pragmatic skills and empathy, emphasizing their importance in social dysfunction. Short-range decreased anatomical connectivity may thus be an important substrate of social deficits in autism spectrum disorder, in contrast with current models.

Despite the high clinical burden little is known about pathophysiology underlying autism spectrum disorder (ASD). Recent resting state functional magnetic resonance imaging (rs-fMRI) studies have found atypical synchronization of brain activity in ASD. However, no consensus has been reached on the nature and clinical relevance of these alterations. Here we address these questions in the most comprehensive, large-scale effort to date comprising evaluation of four large ASD cohorts. We followed a strict exploration and replication procedure to identify core rs-fMRI functional connectivity (degree centrality) alterations associated with ASD as compared to typically developing (TD) controls (ASD: N=841, TD: N=984). We then tested for associations of these imaging phenotypes with clinical and demographic factors such as age, sex, medication status and clinical symptom severity. We find reproducible patterns of ASD-associated functional hyper- and hypo-connectivity with hypo-connectivity being primarily restricted to sensory-motor regions and hyper-connectivity hubs being predominately located in prefrontal and parietal cortices. We establish shifts in between-network connectivity from outside to within the identified regions as a key driver of these abnormalities. The magnitude of these alterations is linked to core ASD symptoms related to communication and social interaction and is not affected by age, sex or medication status. The identified brain functional alterations provide a reproducible pathophysiological phenotype underlying the diagnosis of ASD reconciling previous divergent findings. The large effect sizes in standardized cohorts and the link to clinical symptoms emphasize the importance of the identified imaging alterations as potential treatment and stratification biomarkers for ASD.

Expanded polystyrene lightweight concrete is a composite which can be made by adding expanded polystyrene aggregate in normalweight concrete (as matrix). The research was focused on the effect of properties and volume of the matrix on the properties of lightweight concrete. The results show that properties of structural polystyrene concrete, such as workability and compressive strength, depend on the aggregate size of the matrix. It also shows that decreasing aggregate size of the matrix is the effective way to increase workability and compressive strength of lightweight concrete. When the density of concretes decrease by 200 kg/m³, slump values decrease by about 20 to 30 mm with lightweight concrete mixtures using maximum particle size of 0.63 mm, while slump values decrease by about 40 mm with the mixtures using maximum particle size of 20 mm. At the same density, the compressive strength of the structural polystyrenre concrete significantly decreased when the coarse aggregate diameter greater than 10 mm. Therefore, coarse aggregates with diameter size are smaller than 10 mm was recommended to use for matrix. In the result, expanded polystyrene concrete with density from 1,400 kg/m³ to 2,000 kg/m³ and compressive strength more than 20 MPa for structural application was made.

Vietnam has an abundant of herbal traditional medicine with accumulated experience for thousands of years. They play an important role in the drug development. However, several therapeutic effects remain unknown among these plants. To explore active ingredients in the effective Vietnamese herbal medicine formulations for individual diseases and to understand therapeutic effects under scientific viewpoint, this project predicts therapeutic effects based on metabolite profiles. The herbal medicine database has been processed to get the useful information by the supporting of computational approach, particularly Random forest algorithm, Generalized Boosted Model and Support Vector Machine. Three specific therapeutic effects which are "Edema treatment", "Astrictive treatment" and "Cure sore eyes" - metabolites binary classification model to deal with multi-class classification and unbalanced class data problem. Since this project can reveal the main predictors of specific therapeutic effect, they are valuable information for further research of drug development.

The novel hyperactive antifreeze protein (AFP) of Antarctic sea ice bacterium Colwellia sp. provides a target for studying the protection of psychrophilic microgoranisms against freezing environment. Interestingly, the Colwellia sp. hyperactive antifreeze protein ( Col AFP) was crystallized without the structural dynamic characteristics. Here, the result indicated, through coarse grained simulation of Col AFP under various subfreezing temperature, that Col AFP remains active at temperature of equal and greater than 275 K (∼2 °C). Extensive simulation analyses also revealed the adaptive mechanism of Col AFP in subfreezing environment. Our result provides a structural dynamic understanding of the Col AFP.