TechnoFile: Viscosity

The article focuses on the effect the viscosity of a glaze or slip has on a piece of pottery. The article explains the term and provides tests that can be performed to determine the viscosity of a substance. It goes on to describe how one can manipulate the viscosity of a glaze or slip through the addition of water or other aids and includes step-by-step instructions for making a slip

Chasing the Craze: When the Right Variables are Off-Stage

A smooth white glaze, (Figure 1) with a buttery surface and smooth breaking on edges, just enough change of whiteness in a crevice pooling, seemingly opaque when thicker, but with a certain glow, a slight grey showing through. It crazes slightly, a fine webbing of cracks. Not enough to be decorative crazing, and not enough crazing to make me abandon the glaze, but enough crazing that I would like it to be gone. I prefer a system-oriented testing approach as a kind of universal order. A simple Unity Molecular Formula grid mapping method typically shows a boundary line of crazed and non-crazed surfaces and trends in surface quality. Sounds simple enough. But, as has been said, there is no need to seek the ceramic troubles. They will find you. (excerpt

Techno File: Pyrometric Cones

A pyrometer measures temperature, but pyrometric cones measure heatwork. What is a cone, how does it work, and what does any of this have to do with synchronized swimmers? [excerpt

Lawsonia intracellularis infection and proliferative enteropathy in foals.

Equine proliferative enteropathy (EPE) is a disease of foals caused by the obligate intracellular organism Lawsonia intracellularis. This organism is unique in that it causes proliferation of infected enterocytes, resulting in thickening of the intestinal epithelium, most often the small intestine. This disease affects mainly weanling foals and causes fever, lethargy, peripheral edema, diarrhea, colic and weight loss. The diagnosis of EPE may be challenging and relies on the presence of hypoproteinemia, thickening of segments of the small intestinal wall observed on abdominal ultrasonography, positive serology and molecular detection of L. intracellularis in feces. The epidemiology and genetic basis for pathogenesis for this disease is beginning to be elucidated. Phenotypic traits, genomic features, and gene expression profiles during L. intracellularis infection in vitro and in vivo are presented. In addition, this article reviews the epidemiology, pathological and clinicopathological findings, diagnosis, and control of EPE

Equine proliferative enteropathy--a review of recent developments.

Equine proliferative enteropathy (EPE) is a disease of foals caused by the obligate intracellular organism Lawsonia intracellularis. This emerging disease affects mainly weanling foals and causes fever, lethargy, peripheral oedema, diarrhoea, colic and weight loss. The diagnosis of EPE may be challenging and relies on the presence of hypoproteinaemia, thickening of segments of the small intestinal wall observed upon abdominal ultrasonography, positive serology and molecular detection of L. intracellularis in faeces. Although the clinical entity, diagnostic approach and treatment of EPE are well established and described, the epidemiology for this disease has remained largely unaddressed. This article focuses on new developments in the field of EPE, including epidemiology, pathophysiology, clinical signs, diagnosis, treatment and prevention

Characterizing the Shape of Activation Space in Deep Neural Networks

The representations learned by deep neural networks are difficult to interpret in part due to their large parameter space and the complexities introduced by their multi-layer structure. We introduce a method for computing persistent homology over the graphical activation structure of neural networks, which provides access to the task-relevant substructures activated throughout the network for a given input. This topological perspective provides unique insights into the distributed representations encoded by neural networks in terms of the shape of their activation structures. We demonstrate the value of this approach by showing an alternative explanation for the existence of adversarial examples. By studying the topology of network activations across multiple architectures and datasets, we find that adversarial perturbations do not add activations that target the semantic structure of the adversarial class as previously hypothesized. Rather, adversarial examples are explainable as alterations to the dominant activation structures induced by the original image, suggesting the class representations learned by deep networks are problematically sparse on the input space

Effect of Standing Estrus Prior to an Injection of GnRH on Steriodogenic Enzyme Expression in Luteal Tissue

Cows detected in estrus around the time of fixed-time AI had increased pregnancy success and progesterone concentrations. Additionally, GnRH following onset of estrus influenced LH pulse frequency and CL formation/function. Therefore our objective was to determine steriodogenic enzyme expression within luteal tissue of cows that were or were not detected in standing estrus prior to an injection of GnRH. Cows were synchronized with the CO-Synch protocol (day -9 100 mg GnRH; day -2 25 mg PGF2α; day 0 100 mg GnRH). Estrus was detected with the HeatWatch system. Location and size of the ovulatory follicle was determined on day 0 at time of GnRH by transrectal ultrasonography; blood samples were collected on day 3,4,5,7, and 9; and luteal tissue was collected on d 10 (n = 3 estrus and n = 8 no estrus) from CL originating from similar sized follicles (13.5 to 16 mm). Total cellular RNA was extracted and relative mRNA levels were determined by real-time RT-PCR and corrected for GAPDH. There was no effect of estrus on CL weight (P = 0.83). There was no effect of estrus by time (P = 0.17) or estrus (P = 0.97) on progesterone concentrations, but there was an effect of time (P \u3c 0.01). In addition, there was no effect of estrus, follicle size, or CL weight on LH receptor expression (P = 0.97, 0.94, and 0.85), StAR expression (P = 0.87, 0.92, and 0.86), CYP11A1 expression (P = 0.49, 0.27, and 0.99), or 3HSD expression (P = 0.49, 0.61, and 0.91). However, there was a correlation between follicle size and CL weight (P = 0.01; R2 = 0.51); for every increase of 1 mm in follicle size, CL weight increased by 1.1 g. In addition, there was an effect of CL weight by time (P = 0.01) on progesterone concentrations and an effect of time (P \u3c 0.01) with a tendency for an effect of CL weight (P = 0.06). In summary, estrus did not influence CL weight, progesterone concentrations, or expression of steriodogenic enzymes. However, as follicle size increased, CL weight increased, and CL weight influenced progesterone concentrations