EviPrompt: A Training-Free Evidential Prompt Generation Method for Segment Anything Model in Medical Images

Medical image segmentation has immense clinical applicability but remains a challenge despite advancements in deep learning. The Segment Anything Model (SAM) exhibits potential in this field, yet the requirement for expertise intervention and the domain gap between natural and medical images poses significant obstacles. This paper introduces a novel training-free evidential prompt generation method named EviPrompt to overcome these issues. The proposed method, built on the inherent similarities within medical images, requires only a single reference image-annotation pair, making it a training-free solution that significantly reduces the need for extensive labeling and computational resources. First, to automatically generate prompts for SAM in medical images, we introduce an evidential method based on uncertainty estimation without the interaction of clinical experts. Then, we incorporate the human prior into the prompts, which is vital for alleviating the domain gap between natural and medical images and enhancing the applicability and usefulness of SAM in medical scenarios. EviPrompt represents an efficient and robust approach to medical image segmentation, with evaluations across a broad range of tasks and modalities confirming its efficacy

Controlled Assembly of Sb₂S₃ Nanoparticles on Silica/Polymer Nanotubes:Insights into the Nature of Hybrid Interfaces

Silica nanotubes can serve as high aspect ratio templates for the deposition of inorganic nanoparticles to form novel hybrids. However, the nature of the interfacial binding is still an unresolved challenge when considered at the atomic level. In this work, novel nanocomposites have been successfully fabricated by the controlled nucleation and assembly of Sb(2)S(3) nanoparticles on the surface of mercaptopropyl-functionalized silica/polymer hybrid nanotubes (HNTs). The Sb(2)S(3) nanoparticles were strongly attached to the HNTs surface by interactions between the pendent thiol groups and inorganic sulfur atoms. Detailed analysis of the geometric and electronic structure using first–principle density functional theory demonstrates charge transfer from the nanoparticles to the underlying HNTs at the Sb(2)S(3)/HNTs interfaces. Formation of a packed array of Sb(2)S(3) nanoparticles on the HNTs results in mixing of the electronic states of the components, and is mediated by the mercaptopropyl bridges between Sb(2)S(3) and the outer layer of the HNTs

The Myosin Va Head Domain Binds to the Neurofilament-L Rod and Modulates Endoplasmic Reticulum (ER) Content and Distribution within Axons

The neurofilament light subunit (NF-L) binds to myosin Va (Myo Va) in neurons but the sites of interaction and functional significance are not clear. We show by deletion analysis that motor domain of Myo Va binds to the NF-L rod domain that forms the NF backbone. Loss of NF-L and Myo Va binding from axons significantly reduces the axonal content of ER, and redistributes ER to the periphery of axon. Our data are consistent with a novel function for NFs as a scaffold in axons for maintaining the content and proper distribution of vesicular organelles, mediated in part by Myo Va. Based on observations that the Myo Va motor domain binds to intermediate filament (IF) proteins of several classes, Myo Va interactions with IFs may serve similar roles in organizing organelle topography in different cell types

Mining phenotypes and informative genes from gene expression data

Mining microarray gene expression data is an important research topic in bioinformatics with broad applications. While most of the previous studies focus on clustering either genes or samples, it is interesting to ask whether we can partition the complete set of samples into exclusive groups (called phenotypes) and find a set of informative genes that can manifest the phenotype structure. In this paper, we propose a new problem of simultaneously mining phenotypes and informative genes from gene expression data. Some statistics-based metrics are proposed to measure the quality of the mining results. Two interesting algorithms are developed: the heuristic search and the mutual reinforcing adjustment method. We present an extensive performance study on both real-world data sets and synthetic data sets. The mining results from the two proposed methods are clearly better than those from the previous methods. They are ready for the real-world applications. Between the two methods, the mutual reinforcing adjustment method is in general more scalable, more effective and with better quality of the mining results

BIBE2001: 2nd IEEE International Symposium on Bioinformatics and Bioengineering Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis

DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Currently most research focuses on the interpretation of the meaning of the data. However, majority methods are supervised-based, less attention has been paid on unsupervised approaches which is important when domain knowledge is incomplete or hard to obtain. In this paper, we present a new framework for unsupervised analysis of gene expression data which applies an interrelated two-way clustering approach on the gene expression matrices. The goal of clustering is to find important gene patterns and perform cluster discovery on samples. The advantage of this approach is that we can dynamically use the relationships between the groups of the genes and samples while iteratively clustering through both gene-dimension and sample-dimension. We illustrate the method on gene expression data from a study of multiple sclerosis patients. The experiments demonstrate the effectiveness of this approach.

Using Keyblock Statistics to Model Image Retrieval

WWW demo page:http://vangogh.cse.buffalo.edu:8080/ Abstract. Keyblock, which is a new framework we proposed for the contentbased image retrieval, is a generalization of the text-based information retrieval technology in the image domain. In this framework, keyblocks, which are analogous to keywords in text document retrieval, can be constructed by exploiting a clustering approach. Then an image can be represented as a list of keyblocks similar to a text document which can be considered as a list of keywords. Based on this image representation, various feature models can be constructed for supporting image retrieval. In this paper, we will conduct keyblock statistic analysis and propose keyblock importance vector to improve the retrieval performance. The statistic analysis is based on the keyblock entropy as well as the keyblock frequency in the image database.