Ultrasound k-nearest neighbor entropy imaging: Theory, algorithm, and applications

Ultrasound information entropy is a flexible approach for analyzing ultrasound backscattering. Shannon entropy imaging based on probability distribution histograms (PDHs) has been implemented as a promising method for tissue characterization and diagnosis. However, the bin number affects the stability of entropy estimation. In this study, we introduced the k-nearest neighbor (KNN) algorithm to estimate entropy values and proposed ultrasound KNN entropy imaging. The proposed KNN estimator leveraged the Euclidean distance between data samples, rather than the histogram bins by conventional PDH estimators. We also proposed cumulative relative entropy (CRE) imaging to analyze time-series radiofrequency signals and applied it to monitor thermal lesions induced by microwave ablation (MWA). Computer simulation phantom experiments were conducted to validate and compare the performance of the proposed KNN entropy imaging, the conventional PDH entropy imaging, and Nakagami-m parametric imaging in detecting the variations of scatterer densities and visualizing inclusions. Clinical data of breast lesions were analyzed, and porcine liver MWA experiments ex vivo were conducted to validate the performance of KNN entropy imaging in classifying benign and malignant breast tumors and monitoring thermal lesions, respectively. Compared with PDH, the entropy estimation based on KNN was less affected by the tuning parameters. KNN entropy imaging was more sensitive to changes in scatterer densities and performed better visualizable capability than typical Shannon entropy (TSE) and Nakagami-m parametric imaging. Among different imaging methods, KNN-based Shannon entropy (KSE) imaging achieved the higher accuracy in classification of benign and malignant breast tumors and KNN-based CRE imaging had larger lesion-to-normal contrast when monitoring the ablated areas during MWA at different powers and treatment durations. Ultrasound KNN entropy imaging is a potential quantitative ultrasound approach for tissue characterization.

Phylogenetic and conservation analyses of MFS transporters

Major facilitator superfamily is one of the largest superfamily of secondary transporters present across the kingdom of life. Considering the physiological and clinical importance of MFS proteins, we attempted to explore the phylogenetic and structural aspects of the superfamily. To achieve the objectives, we performed global sequence-based analyses of MFS proteins encompassing multiple taxa. Notably, phylogenetic analysis of MFS proteins resulted in the clustering of MFS proteins based on their function, rather than lineage of the respective organisms. Additionally, we employed information theoretic measures, Relative entropy (RE) and Cumulative relative entropy (CRE) to decipher fold-specific and function-specific residues, respectively, in the MFS proteins. The residues with high RE score when mapped on to the 3D-structure of MFS transporter LacY, were found to be distributed throughout the tertiary structure of the protein. On the other hand, CRE calculation was employed to contrast two subfamilies Drug H⁺ antiporter 1 and 3 (DHA1 and DHA3). The particular analysis unveiled certain differentially conserved residues in DHA1 as compared to DHA3 highlighting family-specific importance of them. Remarkably, a number of high scoring CRE residues have already established functional roles, for instance, the arginine residue present in TMH4. Altogether, the current study apart from providing an insight into the functional clustering of MFS proteins also identifies residues with established or plausible roles in the transport mechanism. Thus, the study lays a platform for future structure-function studies of these proteins.