In this paper, we propose an effective saliency detection method based on dense and sparse representation in-terms of an optimized background template. Firstly, the input image is divided into compact and uniform super-pixels. Then, the optimized background template is produced by introducing boundary conductivity measurement to improve the dense and sparse representation of the image's super-pixels in terms of the optimized background, where the reconstruction error represents a saliency measure. Based on the optimized template, two saliency maps are generated by dense and sparse

This Electronic Health (E-Health) is a broad expression that enables the communication between healthcare professionals in handling patient information through the cloud. Exchanging medical data over the public cloud requires securing transferring for the data that's direct many researchers in proposing different secure schemes to enable users to handle data safely without hacking or alternating. In this study, one of the most common encryption algorithms called Triple Data Encryption Standard (Triple-DES) has been implemented with the aid of the hash function and DNA cryptography base to

Transforming growth factor beta (TGF-β) acts as a tumor-suppressing cytokine in healthy tissues and non-malignant tumors. Yet, in malignancy, TGF-β can exert the opposite effects that can promote proliferation of cancer cells. C-Kit plays a prominent role in stem cell activation and liver regeneration after injury. However, little is known about the cross-talk between TGF-β and C-Kit and its role in the progression of hepatocellular carcinoma (HCC). Here, we studied the effect of increasing doses of TGF-β1 on CD44+CD90+ liver stem cells (LSCs) and C-Kit gene expression in malignant and

Human Microbiome plays a critical role in health and the environment. Colorectal cancer (CRC) is the most common cause of death in many countries, and hence early diagnosis of CRC may help in increasing the survival rate. Tracking changes in the microbiome structure of human gut opens new gates towards the detection and prediction of the risk of CRC. Recently, machine learning became a powerful technique in many bioinformatics fields, one of which is metagenomics. Metagenomics is defined as the study of a collection of microbial genomes isolated directly and sequenced from its natural habitats

Recent findings suggest an implication of the gut microbiome in Parkinson’s disease (PD) patients. PD onset and progression has also been linked with various environmental factors such as physical activity, exposure to pesticides, head injury, nicotine, and dietary factors. In this study, we used a mouse model, overexpressing the complete human SNCA gene (SNCA-TG mice) modeling familial and sporadic forms of PD to study whether environmental conditions such as standard vs. enriched environment changes the gut microbiome and influences disease progression. We performed 16S rRNA DNA sequencing

Microbial nitrogen transformation processes such as denitrification represent major sources of the potent greenhouse gas nitrous oxide (N2O). Soil biochar amendment has been shown to significantly decrease N2O emissions in various soils. However, the effect of biochar on the structure and function of microbial communities that actively perform nitrogen redox transformations has not been studied in detail yet. To analyse the community composition of actively denitrifying and N2O-reducing microbial communities, we collected RNA samples at different time points from a soil microcosm experiment

Assessment of corneal biomechanics for pre- and post-refractive surgery is of great clinical importance. Corneal biomechanics affect vision quality of human eye. Many factors affect corneal biomechanics such as, age, corneal diseases and corneal refractive surgery. There is a need for non-invasive in-vivo measurement of corneal biomechanics due to corneal shape preserving as opposed to ex-vivo measurements that destructs corneal tissue. In this study, a new approach for assessing corneal biomechanics in-vivo non-invasively using ultrasound estimation method with 100 KHz frame rate is proposed

The objective of this research is to build a 'Whole Slide Images' classification system using Convolutional Neural Network (CNN). This system is capable of classifying Thyroid tumors into three types: Follicular adenoma, follicular carcinoma, and papillary carcinoma. Furthermore, the cascaded CNN technique is additionally employed to classify the classified follicular carcinoma into four subclasses: follicular carcinoma, papillary follicular variant, well-differentiated follicular carcinoma, and Poorly-differentiated follicular carcinoma. Results of the proposed CNN architecture showed

The scientific literature is full of studies that provide evidence highlighting the role of microbiome in type 2 diabetes (T2D) development and progression, still, discrepancies are evident when studying the link between certain taxonomic groupings and T2D, thus, eliminating the discrepancy between such studies is crucial to build on a robust systematic approach to identify the possible linkage between such taxonomic groups and diabetes development and progression, hence developing a potential treatment. Here we aimed to use a publicly available data set of gut and nares microbiome of

The human β-coronavirus SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China. It causes Covid-19 disease which has become pandemic. Each of the five-known human β-coronaviruses has four major structural proteins (E, M, N and S) and 16 non-structural proteins encoded by ORF1a and ORF1b together (ORF1ab) that are involved in virus pathogenicity and infectivity. Here, we performed detailed positive selection analyses for those six genes among the four previously known human β-coronaviruses and within 38 SARS-CoV-2 genomes to assess signatures of adaptive evolution using maximum