A Comparison of Wavelet, Curvelet and Contourlet based Texture Classification Algorithms for Characterization of Bone Quality in Dental CT

Abstract: The objective of this paper is to design and implement classifier framework to assist the surgeon for preoperative assessment of bone quality from Dental Computed Tomography images. This article focuses on comparing the discriminating power of several multiresolution texture analysis methods to evaluate the quality of the bone based on the texture variations of the images obtained from the implant site using wavelet, curvelet and contourlet.The approach consists of three steps: automatic extraction of the most discriminative texture features from regions of interest, creation of a classifier that automatically grades the bone depends on the quality. Since this is medical domain, the validation against the human experts is carried out. The results indicate that the combination of the statistical and multiscale representation of the bone image gives adequate information to classify the different bone groups compared to gray level features at single scale

A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio [HR] = 2.4; 95% CI = 1.4–3.8; p<0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1–2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4–2.8; p<0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04–1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy

Conversion of levulinic acid to valuable chemicals: a review

Levulinic acid (LA), a class of important chemical intermediates and new energy chemicals, has been considered one of the top-12 platform compounds that can be converted from biomass resources. Substantial progress on the conversion of lignocellulose biomass to LA and the further conversion of LA to high value downstream chemicals have been achieved recently. This review summarizes the preparation and separation processes of LA firstly, and then, emphatically discusses the catalytic system for LA conversion to valuable downstream products, including levulinate esters, 2-methyl tetrahydrofuran (MTHF), Gamma-valerolactone (GVL), 5-aminolevulinic acid (DALA), and diphenolic acid (DPA). An outlook is provided at the end of this paper to highlight the challenges and opportunities for the comprehensive utilization of lignocellulose biomass. (c) 2021 Society of Chemical Industry (SCI)