PIG WELFARE THROUGH BEHAVIOR LEARNING FROM CAMERA RECORDINGS

Animal Science students along with the farm staff have to monitor the behavior of pigs in order to assure their welfare. The video systems are used by our educational software and new methods of pig observation, evaluation and treatment are applied much faster and more efficient compared to the classical intervention. Each recording is stored as a media file and each frame taken at 0.1 seconds is stored as a Bitmap image. The Bitmap images are processed in parallel using the MapReduce programming model from Apache Hadoop. The contour of the image is automatically analyzed and based on it the presence of pigs is detected, as well as their location can be determined. The location is important because it can be denoting that the pig eats or that it stays aside. Pig limp was also detected. It was observed based on the recordings that 83% of the time the pigs spend it lying down, 7% is spent eating and 10% of the time they walk and sit. Video monitoring and automatic interpretation facilitates the learning of new intervention approaches and boosts the responsiveness among the students. The students can learn from the critical situations and benefit from these cases while learning

AUTOMATIC RECOGNITION OF DENTAL PATHOLOGIES AS PART OF A CLINICAL DECISION SUPPORT PLATFORM

The current work is done within the context of Romanian National Program II (PNII) research project "Application for Using Image Data Mining and 3D Modeling in Dental Screening" (AIMMS). The AIMMS project aims to design a program that can detect anatomical information and possible pathological formations from a collection of digital imaging and communications in medicine (DICOM) images. The main function of the AIMMS platform is to provide the user with the opportunity to use an integrated dental support platform, using image processing techniques and 3D modeling. From the literature review, it can be found that for the detection and classification of teeth and dental pathologies existing studies are in their infancy. Therefore, the work reported in this article makes a scientific contribution in this field. In this article it is presented the relevant literature review and algorithms that were created for detection of dental pathologies in the context of research project AIMMS

Comprehensive analysis of normal adjacent to tumor transcriptomes.

Histologically normal tissue adjacent to the tumor (NAT) is commonly used as a control in cancer studies. However, little is known about the transcriptomic profile of NAT, how it is influenced by the tumor, and how the profile compares with non-tumor-bearing tissues. Here, we integrate data from the Genotype-Tissue Expression project and The Cancer Genome Atlas to comprehensively analyze the transcriptomes of healthy, NAT, and tumor tissues in 6506 samples across eight tissues and corresponding tumor types. Our analysis shows that NAT presents a unique intermediate state between healthy and tumor. Differential gene expression and protein-protein interaction analyses reveal altered pathways shared among NATs across tissue types. We characterize a set of 18 genes that are specifically activated in NATs. By applying pathway and tissue composition analyses, we suggest a pan-cancer mechanism of pro-inflammatory signals from the tumor stimulates an inflammatory response in the adjacent endothelium

Multiple breast cancer risk variants are associated with differential transcript isoform expression in tumors.

Genome-wide association studies have identified over 70 single-nucleotide polymorphisms (SNPs) associated with breast cancer. A subset of these SNPs are associated with quantitative expression of nearby genes, but the functional effects of the majority remain unknown. We hypothesized that some risk SNPs may regulate alternative splicing. Using RNA-sequencing data from breast tumors and germline genotypes from The Cancer Genome Atlas, we tested the association between each risk SNP genotype and exon-, exon-exon junction- or transcript-specific expression of nearby genes. Six SNPs were associated with differential transcript expression of seven nearby genes at FDR < 0.05 (BABAM1, DCLRE1B/PHTF1, PEX14, RAD51L1, SRGAP2D and STXBP4). We next developed a Bayesian approach to evaluate, for each SNP, the overlap between the signal of association with breast cancer and the signal of association with alternative splicing. At one locus (SRGAP2D), this method eliminated the possibility that the breast cancer risk and the alternate splicing event were due to the same causal SNP. Lastly, at two loci, we identified the likely causal SNP for the alternative splicing event, and at one, functionally validated the effect of that SNP on alternative splicing using a minigene reporter assay. Our results suggest that the regulation of differential transcript isoform expression is the functional mechanism of some breast cancer risk SNPs and that we can use these associations to identify causal SNPs, target genes and the specific transcripts that may mediate breast cancer risk

Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer.

Expression of the oncogenic transcription factor MYC is disproportionately elevated in triple-negative breast cancer (TNBC), as compared to estrogen receptor-, progesterone receptor- or human epidermal growth factor 2 receptor-positive (RP) breast cancer. We and others have shown that MYC alters metabolism during tumorigenesis. However, the role of MYC in TNBC metabolism remains mostly unexplored. We hypothesized that MYC-dependent metabolic dysregulation is essential for the growth of MYC-overexpressing TNBC cells and may identify new therapeutic targets for this clinically challenging subset of breast cancer. Using a targeted metabolomics approach, we identified fatty acid oxidation (FAO) intermediates as being dramatically upregulated in a MYC-driven model of TNBC. We also identified a lipid metabolism gene signature in patients with TNBC that were identified from The Cancer Genome Atlas database and from multiple other clinical data sets, implicating FAO as a dysregulated pathway that is critical for TNBC cell metabolism. We found that pharmacologic inhibition of FAO catastrophically decreased energy metabolism in MYC-overexpressing TNBC cells and blocked tumor growth in a MYC-driven transgenic TNBC model and in a MYC-overexpressing TNBC patient-derived xenograft. These findings demonstrate that MYC-overexpressing TNBC shows an increased bioenergetic reliance on FAO and identify the inhibition of FAO as a potential therapeutic strategy for this subset of breast cancer