The liver is one of the largest glands in the digestive system and performs 13 various functions, including the secretion of hormones and enzymes. The gallbladder serves as a storage reservoir for secretions before they are released into the digestive system through the duodenum. The bile ducts branch from the liver’s lobes and ultimately connect to the digestive system, making this structure significant and distinct among different animal species. This review focuses on the differences between dogs and cats, highlighting the importance of these differences from both health and pathological perspectives. After conducting a detailed scientific review of the biliary tree in dogs and cats, we concluded that cats are more susceptible to the d

The liver is one of the largest glands in the digestive system and performs 13 various functions, including the secretion of hormones and enzymes. The gallbladder serves as a storage reservoir for secretions before they are released into the digestive system through the duodenum. The bile ducts branch from the liver’s lobes and ultimately connect to the digestive system, making this structure significant and distinct among different animal species. This review focuses on the differences between dogs and cats, highlighting the importance of these differences from both health and pathological perspectives. After conducting a detailed scientific review of the biliary tree in dogs and cats, we concluded that cats are more susceptible

One of the important differences between multiwavelets and scalar wavelets is that each channel in the filter bank has a vector-valued input and a vector-valued output. A scalar-valued input signal must somehow be converted into a suitable vector-valued signal. This conversion is called preprocessing. Preprocessing is a mapping process which is done by a prefilter. A postfilter just does the opposite.

The most obvious way to get two input rows from a given signal is to repeat the signal. Two rows go into the multifilter bank. This procedure is called “Repeated Row” which introduces oversampling of the data by a factor of 2.

 For data compression, where one is trying to find compact transform representations for a

Spatial data observed on a group of areal units is common in scientific applications. The usual hierarchical approach for modeling this kind of dataset is to introduce a spatial random effect with an autoregressive prior. However, the usual Markov chain Monte Carlo scheme for this hierarchical framework requires the spatial effects to be sampled from their full conditional posteriors one-by-one resulting in poor mixing. More importantly, it makes the model computationally inefficient for datasets with large number of units. In this article, we propose a Bayesian approach that uses the spectral structure of the adjacency to construct a low-rank expansion for modeling spatial dependence. We propose a pair of computationally efficient estimati