Thickness estimation, automated classification and novelty detection in ultrasound images of the plantar fascia tissues

The plantar fascia (PF) tissue plays an important role in the movement and the stability of the foot during walking and running. Thus it is possible for the overuse and the associated medical problems to cause injuries and some severe common diseases. Ultrasound (US) imaging offers significant potential in diagnosis of PF injuries and monitoring treatments. Despite the advantages of US, the generated PF images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. This limits the use of US in clinical practice and therefore impacts on patient services for what is a common problem and a major cause of foot pain and discomfort. It is therefore a requirement to devise an automated system that allows better and easier interpretation of PF US images during diagnosis. This study is concerned with developing a computer-based system using a combination of medical image processing techniques whereby different PF US images can be visually improved, segmented, analysed and classified as normal or abnormal, so as to provide more information to the doctors and the clinical treatment department for early diagnosis and the detection of the PF associated medical problems. More specifically, this study is required to investigate the possibility of a proposed model for localizing and estimating the PF thickness a cross three different sections (rearfoot, midfoot and forefoot) using a supervised ANN segmentation technique. The segmentation method uses RBF artificial neural network module in order to classify small overlapping patches into PF and non-PF tissue. Feature selection technique was performed as a post-processing step for feature extraction to reduce the number of the extracted features. Then the trained RBF-ANN is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and a proposed area-length calculation algorithm. Additionally, different machine learning approaches were investigated and applied to the segmented PF region in order to distinguish between symptomatic and asymptomatic PF subjects using the best normalized and selected feature set. This aims to facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Finally, a novelty detection framework for detecting the symptomatic PF samples (with plantar fasciitis disorder) using only asymptomatic samples is proposed. This model implies the following: feature analysis, building a normality model by training the one-class SVDD classifier using only asymptomatic PF training datasets, and computing novelty scores using the trained SVDD classifier, training and testing asymptomatic datasets, and testing symptomatic datasets of the PF dataset. The performance evaluation results showed that the proposed approaches used in this study obtained favourable results compared to other methods reported in the literature

SiC polytypes and doping nature effects on electrical properties of ZnO-SiC Schottky diodes

Electrical properties of ZnO/SiC Schottky diodes with two SiC polytypes and N and P doping are investigated. Characterization was performed through I–V and C–V–f measurements. Schottky barrier height (Φb), ideality factor (n), and series resistance (Rs) were extracted from forward I–V characteristics. (Φb), carrier’s concentrations (Nd-Na) and (Rs) frequency dependence were extracted from C–V–f characteristics. The extracted n values suggest that current transport is dominated by interface generation-recombination and/or barrier tunneling mechanisms. When changing SiC polytypes, the rectifying ratio of ZnO/n-4HSiC is found to be twice that of ZnO/n-6HSiC. A change in doping nature gave a leakage current ratio of 40 between ZnO/p-4HSiC and ZnO/n- 4HSiC. These results indicate that ZnO/p-4HSiC diodes have a complex current transport compared to diodes on n-type SiC. From I-V measurements, barrier height values are 0.63eV, 0.65eV and 0.71 eV for heterojunction grown on n-6HSiC, n-4HSiC and p-4HSiC, respectively. C-V measurements gave higher values indicating the importance of interface density of states. Nss values at 1MHz frequency are 4.54×1011 eV-1 cm-2, 3×1012 eV-1 cm-2 and 8.13×1010 eV-1 cm-2 for ZnO/n-6HSiC, ZnO/n-4HSiC and ZnO/p-4HSiC, respectively. Results indicate the importance of SiC polytypes and its doping natur

Plantar fascia ultrasound images characterization and classification using support vector machine

The examination of plantar fascia (PF) ultrasound (US) images is subjective and based on the visual perceptions and manual biometric measurements carried out by medical experts. US images feature extraction, characterization and classification have been widely introduced for improving the accuracy of medical assessment, reducing its subjective nature and the time required by medical experts for PF pathology diagnosis. In this paper, we develop an automated supervised classification approach using the Support Vector Machine (Linear and Kernel) to distinguishes between symptomatic and asymptomatic PF cases. Such an approach will facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Six feature sets were extracted from the segmented PF region. Additionally, features normalization, features ranking and selection analysis using an unsupervised infinity selection method were introduced for the characterization and the classification of symptomatic and asymptomatic PF subjects. The performance of the classifiers was assessed using confusion matrix attributes and some derived performance measures including recall, specificity, balanced accuracy, precision, F-score and Matthew’s correlation coefficient. Using the best selected features sets, Linear SVM and Kernel SVM achieved an F-Score of 97.06 and 98.05 respectively

The D4Z4 Macrosatellite Repeat Acts as a CTCF and A-Type Lamins-Dependent Insulator in Facio-Scapulo-Humeral Dystrophy

Both genetic and epigenetic alterations contribute to Facio-Scapulo-Humeral Dystrophy (FSHD), which is linked to the shortening of the array of D4Z4 repeats at the 4q35 locus. The consequence of this rearrangement remains enigmatic, but deletion of this 3.3-kb macrosatellite element might affect the expression of the FSHD-associated gene(s) through position effect mechanisms. We investigated this hypothesis by creating a large collection of constructs carrying 1 to >11 D4Z4 repeats integrated into the human genome, either at random sites or proximal to a telomere, mimicking thereby the organization of the 4q35 locus. We show that D4Z4 acts as an insulator that interferes with enhancer–promoter communication and protects transgenes from position effect. This last property depends on both CTCF and A-type Lamins. We further demonstrate that both anti-silencing activity of D4Z4 and CTCF binding are lost upon multimerization of the repeat in cells from FSHD patients compared to control myoblasts from healthy individuals, suggesting that FSHD corresponds to a gain-of-function of CTCF at the residual D4Z4 repeats. We propose that contraction of the D4Z4 array contributes to FSHD physio-pathology by acting as a CTCF-dependent insulator in patients

High-resolution CT phenotypes in pulmonary sarcoidosis: a multinational Delphi consensus study

One view of sarcoidosis is that the term covers many different diseases. However, no classification framework exists for the future exploration of pathogenetic pathways, genetic or trigger predilections, patterns of lung function impairment, or treatment separations, or for the development of diagnostic algorithms or relevant outcome measures. We aimed to establish agreement on high-resolution CT (HRCT) phenotypic separations in sarcoidosis to anchor future CT research through a multinational two-round Delphi consensus process. Delphi participants included members of the Fleischner Society and the World Association of Sarcoidosis and other Granulomatous Disorders, as well as members' nominees. 146 individuals (98 chest physicians, 48 thoracic radiologists) from 28 countries took part, 144 of whom completed both Delphi rounds. After rating of 35 Delphi statements on a five-point Likert scale, consensus was achieved for 22 (63%) statements. There was 97% agreement on the existence of distinct HRCT phenotypes, with seven HRCT phenotypes that were categorised by participants as non-fibrotic or likely to be fibrotic. The international consensus reached in this Delphi exercise justifies the formulation of a CT classification as a basis for the possible definition of separate diseases. Further refinement of phenotypes with rapidly achievable CT studies is now needed to underpin the development of a formal classification of sarcoidosis

Identification of an enhancer that increases miR-200b~200a~429 gene expression in breast cancer cells

The miR-200b~200a~429 gene cluster is a key regulator of EMT and cancer metastasis, however the transcription-based mechanisms controlling its expression during this process are not well understood. We have analyzed the miR-200b~200a~429 locus for epigenetic modifications in breast epithelial and mesenchymal cell lines using chromatin immunoprecipitation assays and DNA methylation analysis. We discovered a novel enhancer located approximately 5.1kb upstream of the miR-200b~200a~429 transcriptional start site. This region was associated with the active enhancer chromatin signature comprising H3K4me1, H3K27ac, RNA polymerase II and CpG dinucleotide hypomethylation. Luciferase reporter assays revealed the upstream enhancer stimulated the transcription of the miR-200b~200a~429 minimal promoter region approximately 27-fold in breast epithelial cells. Furthermore, we found that a region of the enhancer was transcribed, producing a short, GC-rich, mainly nuclear, non-polyadenylated RNA transcript designated miR-200b eRNA. Over-expression of miR-200b eRNA had little effect on miR-200b~200a~429 promoter activity and its production did not correlate with miR-200b~200a~429 gene expression. While additional investigations of miR-200b eRNA function will be necessary, it is possible that miR-200b eRNA may be involved in the regulation of miR-200b~200a~429 gene expression and silencing. Taken together, these findings reveal the presence of a novel enhancer, which contributes to miR-200b~200a~429 transcriptional regulation in epithelial cells.Joanne L. Attema, Andrew G. Bert, Yat-Yuen Lim, Natasha Kolesnikoff, David M. Lawrence, Katherine A. Pillman, Eric Smith, Paul A. Drew, Yeesim Khew-Goodall, Frances Shannon, Gregory J. Goodal