Identification of meat spoilage by FTIR spectroscopy and neural networks

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid determination of meat spoilage, Fourier transform infrared (FTIR) spectroscopy technique, with the help of advanced learning-based methods, was attempted in this work. FTIR spectra were obtained from the surface of beef samples during aerobic storage at various temperatures, while a microbiological analysis had identified the population of Total viable counts. A fuzzy principal component algorithm has been also developed to reduce the dimensionality of the spectral data. The results confirmed the superiority of the adopted scheme compared to the partial least squares technique, currently used in food microbiology.

Diagnostic value of H3F3A mutations in giant cell tumour of bone compared to osteoclast-rich mimics

Driver mutations in the two histone 3.3 (H3.3) genes, H3F3A and H3F3B, were recently identified by whole genome sequencing in 95% of chondroblastoma (CB) and by targeted gene sequencing in 92% of giant cell tumour of bone (GCT). Given the high prevalence of these driver mutations, it may be possible to utilise these alterations as diagnostic adjuncts in clinical practice. Here, we explored the spectrum of H3.3 mutations in a wide range and large number of bone tumours (n 5 412) to determine if these alterations could be used to distinguish GCT from other osteoclast-rich tumours such as aneurysmal bone cyst, nonossifying fibroma, giant cell granuloma, and osteoclast-rich malignant bone tumours and others. In addition, we explored the driver landscape of GCT through whole genome, exome and targeted sequencing (14 gene panel). We found that H3.3 mutations, namely mutations of glycine 34 in H3F3A, occur in 96% of GCT. We did not find additional driver mutations in GCT, including mutations in IDH1, IDH2, USP6, TP53. The genomes of GCT exhibited few somatic mutations, akin to the picture seen in CB. Overall our observations suggest that the presence of H3F3A p.Gly34 mutations does not entirely exclude malignancy in osteoclast-rich tumours. However, H3F3A p.Gly34 mutations appear to be an almost essential feature of GCT that will aid pathological evaluation of bone tumours, especially when confronted with small needle core biopsies. In the absence of H3F3A p.Gly34 mutations, a diagnosis of GCT should be made with caution.

3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.

Protein deiminases: new players in the developmentally regulated loss of neural regenerative ability

Spinal cord regenerative ability is lost with development, but the mechanisms underlying this loss are still poorly understood. In chick embryos, effective regeneration does not occur after E13, when spinal cord injury induces extensive apoptotic response and tissue damage. As initial experiments showed that treatment with a calcium chelator after spinal cord injury reduced apoptosis and cavitation, we hypothesized that developmentally regulated mediators of calcium-dependent processes in secondary injury response may contribute to loss of regenerative ability. To this purpose we screened for such changes in chick spinal cords at stages of development permissive (E11) and non-permissive (E15) for regeneration. Among the developmentally regulated calcium-dependent proteins identified was PAD3, a member of the peptidylarginine deiminase (PAD) enzyme family that converts protein arginine residues to citrulline, a process known as deimination or citrullination. This post-translational modification has not been previously associated with response to injury. Following injury, PAD3 up-regulation was greater in spinal cords injured at E15 than at E11. Consistent with these differences in gene expression, deimination was more extensive at the non-regenerating stage, E15, both in the gray and white matter. As deimination paralleled the extent of apoptosis, we investigated the effect of blocking PAD activity on cell death and deiminated-histone 3, one of the PAD targets we identified by mass-spectrometry analysis of spinal cord deiminated proteins. Treatment with the PAD inhibitor, Cl-amidine, reduced the abundance of deiminated-histone 3, consistent with inhibition of PAD activity, and significantly reduced apoptosis and tissue loss following injury at E15. Altogether, our findings identify PADs and deimination as developmentally regulated modulators of secondary injury response, and suggest that PADs might be valuable therapeutic targets for spinal cord injury.

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