The precise detection and segmentation of cells in microscopic image sequences is an essential task in biomedical research, such as drug discovery and studying the development of tissues, organs, or entire organisms. However, the detection and segmentation of cells in phase contrast images with a halo and shade-off effects is still challenging. Lately, Mask Regional Convolutional Neural Network (Mask R-CNN) has been introduced for object detection and instance segmentation of natural images. This study investigates the efficacy of the Mask R-CNN to instantly detect and segment label-free

Pediatric septic shock is generally considered as a devastating clinical syndrome that can lead to tissue damage and organ failure due to the over exaggerated immune response to an infection. Therefore, in this paper, we attempted to early identify the clinical course of such disease with the aid of peripheral blood T-cells of 181 pediatric patients who admitted to Intensive Care Unit (ICU), Accordingly, 34 differential expressed genes have been identified as biological genetic biomarkers. Minimum redundancy and maximum relevance feature selection strategy has been proposed for the discovery

Metagenomics is a rapidly emerging field that is concerned with the study of microbial communities 'microbiomes' on both levels of taxonomic classification and functional annotation. Targeted amplicon (16S rRNA) and whole genome shotgun (WGS) sequencing are the two main sequencing strategies in metagenomics. As amplicon sequencing provides a cheap way to classify the composition of a microbial community, it lacks the ability to identify microbial genes and annotate its corresponding functions. On the other hand, WGS sequencing allows further investigation of the complete genomes with all

Taxonomic assignment is the core of targeted metagenomics approaches that aims to assign sequencing reads to their corresponding taxonomy. Sequence similarity searching and machine learning (ML) are two commonly used approaches for taxonomic assignment based on the 16S rRNA. Similarity based approaches require high computation resources, while ML approaches dont need these resources in prediction. The majority of these ML approaches depend on k-mer frequency rather than direct sequence, which leads to low accuracy on short reads as k-mer frequency doesnt consider k-mer position. Moreover

In 2017, pneumonia was the primary diagnosis for 1.3 million visits to the Emergency Department (ED) in the United States. The mortality rate was estimated to be 5%-10% of hospitalized patients, whereas it rises to 30% for severe cases admitted to the Intensive Care Unit (ICU). Among all cases admitted to ED, 30% were misdiagnosed, and they did not suffer from pneumonia, which raises a flag for the need for more accurate diagnosis methods. Several methods for pneumonia detection were recently developed using AI in general and more specifically, using deep neural networks. Even though it worth

One of the key challenges in applying deep learning to solve real-life problems is the lack of large annotated datasets. Furthermore, for a deep learning model to perform well on the test set, all samples in the training and test sets should be independent and identically distributed (i.i.d.), which means that test samples should be similar to the samples that were used to train the model. In many cases, however, the underlying training and test set distributions are different. In such cases, it is common to adapt the test samples by transforming them to their equivalent counterparts in the

Although many cross platform mobile development software used a trans-compiler-based approach, it was very difficult to generalize it to work in both directions. For example, to convert between Java for Android Development and Swift for iOS development and vice versa. This is due to the need of writing a specific parser for each source language, and a specific code generator for each destination language. Neural network-based models are used successfully to translate between natural languages, including English, French, German any many others by providing enough datasets and without the need

As the common understanding of Cloud Computing is continuously evolving, the terminology and concepts used to define it often need clarifying. Therefore, Cloud customers and Cloud Providers are used to dispute about Service Level Agreements, Service Level Objectives and Quality of Service. Simultaneously, SLAs/SLOs/QoS represent other related technical problems such as Security, Privacy, Compliancy and others. Technical problems are usually defined within technical context, where both parties ignore analyzing problem's legally related causes. In fact, these problems are stemming from the

Intelligent camera systems provide an effective solution for road traffic monitoring with traffic stream characteristics, such as volumes and densities, continuously computed and relayed to control stations. However, developing a functional vision-based traffic monitoring system is a complex task that entails the creation of appropriate visual sensing platforms with on-board visual analytics algorithms, integration of versatile technologies for data provision and stream management, and development of data visualization techniques suitable for end-users. This paper describes TraffiSense, a

In this work, we investigate the effect of using spatio-tepmoral features on a regional basis on the liver focal lesions classification performance in the multiphase CT images. Texture, Density, and temporal feature set and their different combinations along spatial partitioned ROI were investigated to better characterizing five hepatic pathologies from multiphase contrast-enhanced CT scans. Embedded feature selection followed by decision tree ensembles classification with ten folds cross-validation were employed to classify a total of 180 ROI includes normal tissues, cyst, haemangioma