Perfluorinated alkyl acids in the serum and follicular fluid of UK women with and without polycystic ovarian syndrome undergoing fertility treatment and associations with hormonal and metabolic parameters

© 2018 Women with polycystic ovarian syndrome (PCOS) undergoing treatment for infertility could be a sensitive subpopulation for endocrine effects of exposure to perfluorinated alkyl acids (PFAAs), persistent organic pollutants with potential endocrine activity. Women with, PCOS (n = 30) and age- and BMI-matched controls (n = 29) were recruited from a UK fertility clinic in 2015. Paired serum and follicular fluid samples were collected and analysed for 13 PFAAs. Sex steroid and thyroid hormones, and metabolic markers were measured and assessed for associations with serum PFAAs. Four PFAAs were detected in all serum and follicular fluid samples and concentrations in the two matrices were highly correlated (R2 > 0.95): perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA), perfluorohexane sulfonate (PFHxS), and perfluorononanoic acid (PFNA). Serum PFOS was positively associated with age (1 ng/mL per yr, p < 0.05) and was higher in PCOS cases than controls (geometric mean [GM] 3.9 vs. 3.1 ng/mL, p < 0.05) and in women with irregular vs. regular menstrual cycles (GM 3.9 vs. 3.0 ng/mL, p = 0.01). After adjustment for confounders, serum testosterone was significantly associated with PFOA, PFHxS, PFNA, and the molar sum of the four frequently detected serum PFAAs (approximately 50 percent increase per ln-unit) among controls but not PCOS cases. HbA1c in PCOS cases was inversely associated with serum PFOA, PFHxs, and sum of PFAAs (2–3 mmol/mol per ln-unit). In controls, fasting glucose was positively associated with serum PFOA and sum of PFAAs (0.25 nmol/L per ln-unit increase in PFAAs). Few other associations were observed. The analyses and findings here should be considered exploratory in light of the relatively small sample sizes and large number of statistical comparisons conducted. However, the data do not suggest increased sensitivity to potential endocrine effects of PFAAs in PCOS patients

Sensory Ataxic Neuropathy in Golden Retriever Dogs Is Caused by a Deletion in the Mitochondrial tRNATyr Gene

Sensory ataxic neuropathy (SAN) is a recently identified neurological disorder in golden retrievers. Pedigree analysis revealed that all affected dogs belong to one maternal lineage, and a statistical analysis showed that the disorder has a mitochondrial origin. A one base pair deletion in the mitochondrial tRNATyr gene was identified at position 5304 in affected dogs after re-sequencing the complete mitochondrial genome of seven individuals. The deletion was not found among dogs representing 18 different breeds or in six wolves, ruling out this as a common polymorphism. The mutation could be traced back to a common ancestor of all affected dogs that lived in the 1970s. We used a quantitative oligonucleotide ligation assay to establish the degree of heteroplasmy in blood and tissue samples from affected dogs and controls. Affected dogs and their first to fourth degree relatives had 0–11% wild-type (wt) sequence, while more distant relatives ranged between 5% and 60% wt sequence and all unrelated golden retrievers had 100% wt sequence. Northern blot analysis showed that tRNATyr had a 10-fold lower steady-state level in affected dogs compared with controls. Four out of five affected dogs showed decreases in mitochondrial ATP production rates and respiratory chain enzyme activities together with morphological alterations in muscle tissue, resembling the changes reported in human mitochondrial pathology. Altogether, these results provide conclusive evidence that the deletion in the mitochondrial tRNATyr gene is the causative mutation for SAN

Emotion detection from facial expression using image processing

Abstract: Facial expression recognition is a powerful tool for communicating our emotions, understanding, and intent with each other. It is an intelligent human-computer interaction technology. Various studies have been conducted to classify facial expressions. Six fundamental universal emotions can be expressed through facial expressions: happiness, sadness, anger, fearful, surprised, and neutral. In this project, emotion detection can be implemented in real time with the help of a webcam. Our work proposed a CNN-based VGG16 architecture for emotion detection systems. A model would be trained by using the FER-2013 dataset. Then the images from the dataset are first pre-processed, which includes operations such as image scaling, changing the colour mode, and so on. Following that, a CNN model with multiple layers was created. After that, the model would be trained with the specified dataset, resulting in the .h5 file, which is a pre-trained model file. Instead of repeatedly training the model, the results can be predicted using this file.&nbsp