Building local capacity for hand pump maintenance

Building local capacity for hand pump maintenanc

Expression profile of mucins (MUC2, MUC5AC and MUC6) in Helicobacter pylori infected pre-neoplastic and neoplastic human gastric epithelium

BACKGROUND: Helicobacter pylori (H. pylori) causes gastritis and intestinal metaplasia (IM) that may evolve to gastric carcinoma. The objective of this study was to compare the profile of mucins in the progressive stages of H. pylori infected pre-neoplastic and neoplastic human gastric epithelium. We used a panel of monoclonal antibodies with well-defined specificities of MUC2, MUC5AC and MUC6 to characterize the expression pattern of mucins by immunohistochemistry. METHODS: RUT and ELISA were down for H. pylori confirmation. Human gastric biopsy sections were stained using immunohistochemistry with MUC2, MUC5AC and MUC6 antibodies. RESULTS: MUC5AC was expressed in the superficial epithelium and the upper part of the gastric pits. MUC6 expression was detected in the lower part of the gastric glands. MUC2 was expressed in intestinal metaplasia, mostly in goblet cells. The mucin expression profile in the progressive stages of H. pylori infected human gastric epithelium allows the identification of intestinal metaplasia, which is characterized by a decreased expression of the gastric mucins (MUC5AC and MUC6) and de novo expression of MUC2. CONCLUSION: In conclusion, our results suggest that there is altered expression of MUC5AC and MUC6 together with the aberrant expression of MUC2 in intestinal metaplasia, during the process of gastric carcinogenesis. The present study indicates that the MUC2 mucin expression pattern is a reliable marker of intestinal metaplasia, which appears in the context of H. pylori infected individuals

Image based Plant leaf disease detection using Deep learning

Agriculture is important for India. Every year growing variety of crops is at loss due to inefficiency in shipping, cultivation, pest infestation in crop and storage of government-subsidized crops.  There is reduction in production of good crops in both quality and quantity due to Plants being affected by diseases. Hence it is important for early detection and identification of diseases in plants. The proposed methodology consists of collection of Plant leaf dataset, Image preprocessing, Image Augmentation and Neural network training. The dataset is collected from ImageNet for training phase. The CNN technique is used to differentiate the healthy leaf from disease affected leaf. In image preprocessing resizing the image is carried out to reduce the training phase time. Image augmentation is performed in training phase by applying various transformation function on Plant images. The Network is trained by Caffenet deep learning framework. CNN is trained with ReLu (Rectified Linear Unit). The convolution base of CNN generates features from image through the multiple convolution layers and pooling layers. The classifier part of CNN classifies the image based on the features extracted from the convolution base. The classification is performed through the fully connected layers. The performance is measured using 10-fold cross validation function. The final layer uses activation function like softmax to categorize the outputs

Poincar\'{e} cycle of a multibox Ehrenfest urn model with directed transport

We propose a generalized Ehrenfest urn model of many urns arranged periodically along a circle. The evolution of the urn model system is governed by a directed stochastic operation. Method for solving an $N$-ball, $M$-urn problem of this model is presented. The evolution of the system is studied in detail. We find that the average number of balls in a certain urn oscillates several times before it reaches a stationary value. This behavior seems to be a peculiar feature of this directed urn model. We also calculate the Poincar\'{e} cycle, i.e., the average time interval required for the system to return to its initial configuration. The result can be easily understood by counting the total number of all possible microstates of the system.Comment: 10 pages revtex file with 7 eps figure

Interplay of air and sand: Faraday heaping unravelled

We report on numerical simulations of a vibrated granular bed including the effect of the ambient air, generating the famous Faraday heaps known from experiment. A detailed analysis of the forces shows that the heaps are formed and stabilized by the airflow through the bed while the gap between bed and vibrating bottom is growing, confirming the pressure gradient mechanism found experimentally by Thomas and Squires [Phys. Rev. Lett. 81, 574 (1998)], with the addition that the airflow is partly generated by isobars running parallel to the surface of the granular bed. Importantly, the simulations also explain the heaping instability of the initially flat surface and the experimentally observed coarsening of a number of small heaps into a larger one

An Inverse Approximation and Saturation Order for Kantorovich Exponential Sampling Series

In the present article, an inverse approximation result and saturation order for the Kantorovich exponential sampling series $I_{w}^{\chi}$ are established. First we obtain a relation between the generalized exponential sampling series $S_{w}^{\chi}$ and $I_{w}^{\chi}$ for the space of all uniformly continuous and bounded functions on $\mathbb{R}^{+}.$ Next, a Voronovskaya type theorem for the sampling series $S_{w}^{\chi}$ is proved. The saturation order for the series $I_{w}^{\chi}$ is obtained using the Voronovskaya type theorem. Further, an inverse result for $I_{w}^{\chi}$ is established for the class of log-H\"{o}lderian functions. Moreover, some examples of kernels satisfying the conditions, which are assumed in the hypotheses of the theorems, are discussed