A mini-review on heavy metals accumulation in seaweed of India: prospects for phyco-remediation.

Heavy metals are major contaminants introduced through various anthropogenic and geogenic activities, affecting and altering the quality of coastal water bodies where seaweed inhabits. Metal contamination poses a significant threat to aquatic and human health. Among different remediation methods for these pollutants, phyco-remediation is emerging as an effective strategy due to the metal's accumulation potential in the seaweed and its feasibility.

OPHash: learning of organ and pathology context-sensitive hashing for medical image retrieval.

Purpose: Retrieving images of organs and their associated pathologies is essential for evidence-based clinical diagnosis. Deep neural hashing (DNH) has demonstrated the ability to retrieve images fast on large datasets. Conventional pairwise DNH methods can focus on semantic similarity between either organs or pathology of an image pair but not on both simultaneously.

A wrapper method for finding optimal subset of multimodal Magnetic Resonance Imaging sequences for ischemic stroke lesion segmentation.

Multimodal data, while being information-rich, contains complementary as well as redundant information. Depending on the target problem some modalities are more informative and thus relevant for decision-making. Identifying the optimal subset of modalities best suited to solve a particular task significantly reduces the complexity of acquisition without compromising performance.