ABSTRACTyS T cells are a distinct lymphocyte population that can exhibit reactivity with heat shock proteins over-
RESULTSAccumulation of y6T Cells in Acute MS Plques. a4and y8 T-cell populations were examined in frozen tissue specimens from five postmortem cases with MS, one case with subacute sclerosing panencephalitis (SSPE), and five cases without neurological disease. Frozen sections were stained with hematoxylin/eosin, oil red-O (ORO), and mAbs specific for CD2 and the interleukin 2 receptor. Both the clinical data and a detailed immunohistological analysis of these cases are published in another article with the same designations for CNS samples as in this report (10). Plaques with perivenular inflammation (Fig. 1), hypercellularity, and foamy macrophages containing ORO-positive degenerating myelin throughout the lesion were considered to be actively demyelinating (cases 285 and 194), while hypocellular ORO-and galactocerebroside-negative demyelinated plaques were classified as chronic lesions. Subacute plaques had ORO-positive cells only in the borders of demyelinated lesions (
We propose using faster regions with convolutional neural network features (faster R-CNN) in the TensorFlow tool package to detect and number teeth in dental periapical films. To improve detection precisions, we propose three post-processing techniques to supplement the baseline faster R-CNN according to certain prior domain knowledge. First, a filtering algorithm is constructed to delete overlapping boxes detected by faster R-CNN associated with the same tooth. Next, a neural network model is implemented to detect missing teeth. Finally, a rule-base module based on a teeth numbering system is proposed to match labels of detected teeth boxes to modify detected results that violate certain intuitive rules. The intersection-over-union (IOU) value between detected and ground truth boxes are calculated to obtain precisions and recalls on a test dataset. Results demonstrate that both precisions and recalls exceed 90% and the mean value of the IOU between detected boxes and ground truths also reaches 91%. Moreover, three dentists are also invited to manually annotate the test dataset (independently), which are then compared to labels obtained by our proposed algorithms. The results indicate that machines already perform close to the level of a junior dentist.
Background: Breast cancer predisposition genes identified to date (e.g., BRCA1 and BRCA2) are responsible for less than 5% of all breast cancer cases. Many studies have shown that the cancer risks associated with individual commonly occurring single nucleotide polymorphisms (SNPs) are incremental. However, polygenic models suggest that multiple commonly occurring low to modestly penetrant SNPs of cancer related genes might have a greater effect on a disease when considered in combination.
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