Previous studies have demonstrated that the hypothalamus regulates neuroendocrine and autonomic function and behavior. Within the hypothalamus, the paraventricular nucleus (PVN) is an integratory node that contains neurons associated with the control of neuroendocrine and autonomic responses. The PVN also has one of the highest density of blood vessels within the brain. Alterations of normal PVN angiogenesis by dexamethasone could potentially result in long-term modifications of brain and endocrine functions.
Timed-pregnant Sprague Dawley female rats received DEX on gestational days 18-21 and the resulting progeny were sacrificed at Postnatal Day (PND) 0, 4, 14, and 21. A tomato lectin, Lycopersicon Esculentum labeled with DyLight594 was used to stain blood vessels in the PVN and scanning confocal microscopy was used to analyze the experimental brains for PVN blood vessel density
Analysis of data using a 3-way analysis of variance (ANOVA) with age, sex and treatment as main factors, showed a significant age effect in vascular density. Analysis of female data by 2-way ANOVA demonstrated a significant effect of age, but no treatment or interaction effects. Post-hoc analysis shows significant differences at PND 2, 4, 14, and 21 compared to PND0. A Student‘s t-test of a planned comparison on PND2 showed a significant reduction by DEX treatment (p < 0.05). Analysis of data from females, using 2-way ANOVA demonstrated a significant effect of age, but no treatment or interaction effects. Post-hoc analysis shows significant differences at PND 2, 4, 14, and 21 compared to PND0. A planned comparison at PND 2 using Student’s t-test indicated a significant reduction by dex treatment.
The results of these studies demonstrate that there is significant postnatal angiogenic programming and that the vascular density of the PVN is altered by prenatal dexamethasone administration at PND2. The time-course shows developmental fluctuations in vessel density that may prove to be physiologically significant for normal brain function and developmental programming of brain and behavior.

Human activity recognition is the task of identifying a person’s movement from sensors in a wearable device, such as a smartphone, smartwatch, or a medical-grade device. A great method for this task is machine learning, which is the study of algorithms that learn and improve on their own with the help of massive amounts of useful data. These classification models can accurately classify activities with the time-series data from accelerometers and gyroscopes. A significant way to improve the accuracy of these machine learning models is preprocessing the data, essentially augmenting data to make the identification of each activity, or class, easier for the model. <br/>On this topic, this paper explains the design of SigNorm, a new web application which lets users conveniently transform time-series data and view the effects of those transformations in a code-free, browser-based user interface. The second and final section explains my take on a human activity recognition problem, which involves comparing a preprocessed dataset to an un-augmented one, and comparing the differences in accuracy using a one-dimensional convolutional neural network to make classifications.