A LOSSY CODING SCHEME FOR IMAGES BY USING THE HAAR WAVELET TRANSFORM AND THE THEORY OF IDEAL CROSS-POINTS REGIONS

This paper presents Lossy Coding Scheme for Images by Using The Haar Wavelet Transform and The Theory of Cross-points Regions with Ideal Cross-points Regions (HWTICR). The base of this statement is the effect of Gray coding on cross-points which are neighbor to the points of grey levels 2n. After Gray coding these regions always contain only 1-bits or 0-bits depending on the number of each bit plane after bit plane decomposition. The optimization of probability in each bit plane has important effects on encoding and decoding processes of lossless image compression for data transmission. The framework itself is founded upon a wavelet transformed domain, the scheme will show how The Haar Wavelet Transform combines with the theory of Ideal Cross-points Regions to become a lossy coding scheme for images. The goal of the method is to build a lossy coding scheme for images with high compression ratio and low distortion factor in comparison with some other methods. Finally, some initial results of the scheme are also presented and compared to the other methods. The algorithm can be used in medical and photographic imaging

MODELING USING 2-D AREAS OF IDEAL CROSS-POINT REGIONS FOR LOSSLESS IMAGES COMPRESSION

This paper presents 2-D areas of ideal cross-point regions which are the new part in the theory of cross-point regions. Actually for using cross-point regions we need an algorithm for determining cross-point maps; this takes a long time and a big space for storing these maps, and brings about not high compression ratio when using one dimensional cross-point regions because many coordinates of data points need to be saved for decoding. When these 2-D areas are used, the scheme of 2-DICRIC (2-D Ideal Cross-point Regions for lossless Image Compression) for losslessly encoding and decoding images with the optimization of probability of cross points which are neighbor to the points of grey levels 2n is improved to get higher compression ratio. The base idea of this method is the effect of Gray coding on cross points, and there are many cross-point regions. Before Gray coding data sets of cross points are determined, they are called the ideal cross point regions (ICRs). After Gray coding these regions always contain only 1 bits or 0 bits depending on the number of bit plane after the operation of bit plane decomposition. This is the characteristic of images, the data do not change much in a specific area, especially in medical images which have many regions with the approximate grey levels. So, the way to determine 2-D areas of cross-point regions so that the cross-point maps are small is important for the theory. The theory with these 2-D areas has important effects on the compression ratio when encoding and decoding processes of lossless image compression for data transmission are proceeded

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Socializing One Health: an innovative strategy to investigate social and behavioral risks of emerging viral threats

In an effort to strengthen global capacity to prevent, detect, and control infectious diseases in animals and people, the United States Agency for International Development’s (USAID) Emerging Pandemic Threats (EPT) PREDICT project funded development of regional, national, and local One Health capacities for early disease detection, rapid response, disease control, and risk reduction. From the outset, the EPT approach was inclusive of social science research methods designed to understand the contexts and behaviors of communities living and working at human-animal-environment interfaces considered high-risk for virus emergence. Using qualitative and quantitative approaches, PREDICT behavioral research aimed to identify and assess a range of socio-cultural behaviors that could be influential in zoonotic disease emergence, amplification, and transmission. This broad approach to behavioral risk characterization enabled us to identify and characterize human activities that could be linked to the transmission dynamics of new and emerging viruses. This paper provides a discussion of implementation of a social science approach within a zoonotic surveillance framework. We conducted in-depth ethnographic interviews and focus groups to better understand the individual- and community-level knowledge, attitudes, and practices that potentially put participants at risk for zoonotic disease transmission from the animals they live and work with, across 6 interface domains. When we asked highly-exposed individuals (ie. bushmeat hunters, wildlife or guano farmers) about the risk they perceived in their occupational activities, most did not perceive it to be risky, whether because it was normalized by years (or generations) of doing such an activity, or due to lack of information about potential risks. Integrating the social sciences allows investigations of the specific human activities that are hypothesized to drive disease emergence, amplification, and transmission, in order to better substantiate behavioral disease drivers, along with the social dimensions of infection and transmission dynamics. Understanding these dynamics is critical to achieving health security--the protection from threats to health-- which requires investments in both collective and individual health security. Involving behavioral sciences into zoonotic disease surveillance allowed us to push toward fuller community integration and engagement and toward dialogue and implementation of recommendations for disease prevention and improved health security

A Deep Bottleneck U-Net Combined With Saliency Map For Classifying Diabetic Retinopathy In Fundus Images

Early detection of retinopathy plays an important role in the care of people with diabetes. Classification of diabetic retinopathy in fundus images is very challenging because the blood vessels in the retinal images are too small. Morphology of objects with multi-level saliency is the recent choice because of the activation of feature extraction. However, the challenges of the input models are very complex with the blood. The color, lighting or context can become the reasons that create the decline of the primary key for training. This paper proposes a method for classification of diabetic retinopathy using saliency and shape detection of objects based on a deep Bottleneck U-Net (DbU-Net) and support vector machines  in retinal blood vessels. The proposed method includes four stages: preprocessing, feature extraction using DbU-Net, saliency prediction and classification based on the support vector machine. To evaluate this method, its results are compared to the results of the other methods by using the same datasets of STARE and DRIVE for testing with evaluation criteria such as sensitivity, specificity, and accuracy. The accuracy of the proposed method is about 97.1% in these datasets. To assess the levels of diabetes, the diagnostician must initially identify the retinal image with diabetes or not. The result of this paper may help the diagnostician to easily do this

Palladium-Catalyzed Direct Mono- and Diarylation of Diphenydithienylethenes: A Useful Method for Enhancing Fluorescence Intensity and Aggregation-Induced Emission

In this study we report efficient method for the syntheses of mono- and diarylated diphenyldithienylethene (DPDTE) via a palladium-catalyzed C–H arylation reaction. These new derivatives showed amplified luminescent properties thanks to a change in polarity, particularly in the presence of an electron-withdrawing groups (EWG). Moreover, the arylated DPDTEs showed dual-emissive phenomena, including fluorescence in organic solvents and aggregation-induced emission

PHÂN LẬP CÁC CHỦNG VI KHUẨN HIẾU KHÍ CÓ KHẢ NĂNG PHÂN HỦY CHLORPYRIFOS TRONG ĐẤT TRỒNG RAU MÀU Ở TỈNH LÂM ĐỒNG

This study aims to isolate aerobic bacteria strains for decomposing chlorpyrifos in soil in Lam Dong.  The number of microorganisms for decomposing chlorpyrifos can be increased by incubating the soil samples in MSM medium supplemented with chlorpyrifos (20 mg/L) as the only carbon source. Three aerobic bacteria strains were isolated after the separating each bacterial strain and investigating the decomposition ability chlorpyrifos processing, which were further named as T1, W3 and B2, respectively.  In particular, T1, W3 and B2 strains removed 50.4%, 59.3% and 62.2% of chlorpyrifos after 14 days of culture in MSM medium in supplementation with 20 mg/L chlorpyrifos, respectively. Acinetobacter calcoaceticus, Bacillus megaterium and Sphingomonas pseudosanguinis were identified by 16S rRNA gene sequencing. The three strains of bacteria exhibit ability to degrade chlorpyrifos when added to the soil environment. These results suggested that the isolated bacteria strains can be applied to treat contaminated soil with pesticides and irritate the plant growing.Nghiên cứu này được thực hiện với mục đích phân lập được các chủng vi khuẩn hiếu khí có khả năng phân hủy dư lượng thuốc bảo vệ thực vật chlorpyrifos tồn dư trong đất tại Lâm Đồng. Tiến hành làm giàu dòng vi khuẩn hiếu khí bản địa tại Lâm Đồng có khả năng phân hủy chlorpyrifos bằng cách ủ dịch chiết các mẫu đất trong môi trường MSM bổ sung chlorpyrifos đạt nồng độ 20 mg/L làm nguồn cacbon duy nhất. Tách riêng từng dòng vi khuẩn và khảo sát khả năng phân hủy chlorpyrifos đã chọn ra được 3 dòng vi khuẩn hiếu khí kí hiệu là T1, W3 và B2. Kết quả thí nghiệm cho thấy cả 3 dòng này đều có khả năng phân hủy chlorpyrifos, lần lượt là 50,4%, 59,3% và 62,2% hàm lượng chlorpyrifos sau 14 ngày nuôi cấy trong môi trường MSM bổ sung 20 mg/L chlorpyrifos. Ba dòng vi khuẩn được định danh lần lượt là Acinetobacter calcoaceticus, Bacillus megaterium và Sphingomonas pseudosanguinis bằng phương pháp giải trình tự gen 16S rRNA. Cả ba dòng vi khuẩn này đều thể hiện khả năng phân hủy chlorpyrifos tốt khi bổ sung vào môi trường đất, từ đó cho thấy chúng có tiềm năng ứng dụng cao để sản xuất tạo ra các chế phẩm vi sinh giúp xử lý đất ô nhiễm

Non-annual seasonality of influenza-like illness in a tropical urban setting.

BACKGROUND In temperate and sub-tropical climates, respiratory diseases exhibit seasonal peaks in winter. In the tropics, with no winter, peak timings are irregular. METHODS To obtain a detailed picture of influenza-like illness (ILI) patterns in the tropics, we established an mHealth study in community clinics in Ho Chi Minh City (HCMC). During 2009-2015, clinics reported daily case numbers via SMS, with a subset performing molecular diagnostics for influenza virus. This real-time epidemiology network absorbs 6,000 ILI reports annually, one or two orders of magnitude more than typical surveillance systems. A real-time online ILI indicator was developed to inform clinicians of the daily ILI activity in HCMC. RESULTS From August 2009 to December 2015, 63 clinics were enrolled and 37,676 SMS reports were received, covering approximately 1.8M outpatient visits. Approximately 10.6% of outpatients met the ILI case definition. ILI activity in HCMC exhibited strong non-annual dynamics with a dominant periodicity of 206 days. This was confirmed by time-series decomposition, step-wise regression, and a forecasting exercise showing that median forecasting errors are 30%-40% lower when using a 206-day cycle. In ILI patients from whom naso-pharyngeal swabs were taken, 31.2% were positive for influenza. There was no correlation between the ILI time series and the time series of influenza, influenza A, or influenza B (all p > 0.15). CONCLUSION This suggests, for the first-time, that a non-annual cycle may be an essential driver of respiratory disease dynamics in the tropics. An immunological interference hypothesis is discussed as a potential underlying mechanism. This article is protected by copyright. All rights reserved