CRC-ICM: Colorectal Cancer Immune Cell Markers Pattern Dataset

Colorectal Cancer (CRC) is the second most common cause of cancer death in the world, ad can be identified by the location of the primary tumor in the large intestine: right and left colon, and rectum. Based on the location, CRC shows differences in chromosomal and molecular characteristics, microbiomes incidence, pathogenesis, and outcome. It has been shown that tumors on left and right sides also have different immune landscape, so the prognosis may be different based on the primary tumor locations. It is widely accepted that immune components of the tumor microenvironment (TME) plays a critical role in tumor development. One of the critical regulatory molecules in the TME is immune checkpoints that as the gatekeepers of immune responses regulate the infiltrated immune cell functions. Inhibitory immune checkpoints such as PD-1, Tim3, and LAG3, as the main mechanism of immune suppression in TME overexpressed and result in further development of the tumor. The images of this dataset have been taken from colon tissues of patients with CRC, stained with specific antibodies for CD3, CD8, CD45RO, PD-1, LAG3 and Tim3. The name of this dataset is CRC-ICM and contains 1756 images related to 136 patients. The initial version of CRC-ICM is published on Elsevier Mendeley dataset portal, and the latest version is accessible via: https://databiox.com

A Novel Ramp Metering Approach Based on Machine Learning and Historical Data

The random nature of traffic conditions on freeways can cause excessive congestions and irregularities in the traffic flow. Ramp metering is a proven effective method to maintain freeway efficiency under various traffic conditions. Creating a reliable and practical ramp metering algorithm that considers both critical traffic measures and historical data is still a challenging problem. In this study we use machine learning approaches to develop a novel real-time prediction model for ramp metering. We evaluate the potentials of our approach in providing promising results by comparing it with a baseline traffic-responsive ramp metering algorithm.