Next-generation sequencing is highly sensitive for the detection of beta-catenin mutations in desmoid-type fibromatoses

Desmoid-type fibromatoses are locally aggressive and frequently recurrent tumours, and an accurate diagnosis is essential for patient management. The majority of sporadic lesions harbour beta-catenin (CTNNB1) mutations. We used next-generation sequencing to detect CTNNB1 mutations and to compare the sensitivity and specificity of next-generation sequencing with currently employed mutation detection techniques: mutation-specific restriction enzyme digestion and polymerase chain reaction amplification. DNA was extracted from formalin-fixed paraffin-embedded needle biopsy or resection tissue sections from 144 patients with sporadic desmoid-type fibromatoses, four patients with syndrome-related desmoid-type fibromatoses and 11 morphological mimics. Two primer pairs were designed for CTNNB1 mutation hotspots. Using ≥10 ng of DNA, libraries were generated by Fluidigm and sequenced on the Ion Torrent Personal Genome Machine. Next-generation sequencing had a sensitivity of 92.36 % (133/144, 95 % CIs: 86.74 to 96.12 %) and a specificity of 100 % for the detection of CTNNB1 mutations in desmoid-type fibromatoses-like spindle cell lesions. All mutations detected by mutation-specific restriction enzyme digestion were identified by next-generation sequencing. Next-generation sequencing identified additional mutations in 11 tumours that were not detected by mutation-specific restriction enzyme digestion, two of which have not been previously described. Next-generation sequencing is highly sensitive for the detection of CTNNB1 mutations. This multiplex assay has the advantage of detecting additional mutations compared to those detected by mutation-specific restriction enzyme digestion (sensitivity 82.41 %). The technology requires minimal DNA and is time- and cost-efficient.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

Investigating the causal role of rightOFA in holistic detection of Mooney faces and objects: an fMRI-guided TMS study

Background The right occipital face area (rOFA) is known to be involved in face discrimination based on local featural information. Whether this region is involved in global, holistic stimulus processing is not known. Objective We used fMRI-guided transcranial magnetic stimulation (TMS) to investigate whether rOFA is causally implicated in stimulus detection based on holistic processing, by the use of Mooney stimuli. Methods Two studies were carried out: In Experiment 1, participants performed a detection task involving Mooney faces and Mooney objects; Mooney stimuli lack distinguishable local features and can be detected solely via holistic processing (i.e. at a global level) with top-down guidance from previously stored representations. Experiment 2 required participants to detect shapes which are recognized via bottom-up integration of local (collinear) Gabor elements and was performed to control for specificity of rightOFA's implication in holistic detection. Results In Experiment 1, TMS over rOFA and rLO impaired detection of all stimulus categories, with no category-specific effect. In Experiment 2, shape detection was impaired when TMS was applied over rLO but not over rOFA. Conclusions Our results demonstrate that rOFA is causally implicated in the type of top-down holistic detection required by Mooney stimuli and that such role is not face-selective. In contrast, rOFA does not appear to play a causal role in in detection of shapes based on bottom-up integration of local components, demonstrating that its involvement in processing non-face stimuli is specific for holistic processing.

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology