A CdZnTeSe Gamma Spectrometer Trained by Deep Convolutional Neural Network for Radioisotope Identification

We report the implementation of a deep convolutional neural network to train a high-resolution room-temperature CdZnTeSe based gamma ray spectrometer for accurate and precise determination of gamma ray energies for radioisotope identification. The prototype learned spectrometer consists of a NI PCI 5122 fast digitizer connected to a pre-amplifier to recognize spectral features in a sequence of data. We used simulated preamplifier pulses that resemble actual data for various gamma photon energies to train a CNN on the equivalent of 90 seconds worth of data and validated it on 10 seconds worth of simulated data

Inferential Model for pH Control

586-590An inferential model pH control strategy based on fundamental relations for pH neutralization has been developed. The inferential model was simulated to analyze performance of the pH control scheme and then compared with an experimental acidbase neutralization system

Replication of mycoplasmavirus MVL51: VI. acriflavine stimulates growth of this single-stranded DNA virus

Acriflavine inhibits the growth of a double stranded DNA mycoplasmavirus, but stimulates the growth of a single stranded DNA mycoplasmavirus. Maximal stimulation occurs when acriflavine is added late during infection and reflects an increased synthesis of viral relative to cellular DNA

Activation of Mitochondrial Promoter PH-binding Protein in a Radio-Resistant Chinese Hamster Cell Strain Associated with Bcl-2

The cellular response to ionizing radiation is mediated by a complex interaction of number of proteins involving different pathways. Previously, we have shown that up regulation of mitochondrial genes ND1, ND4, and COX1 transcribed from the heavy strand promoter (PH) has been increased in a radio-resistant cell strain designated as M5 in comparison with the parental Chinese hamster V79 cells. These genes are also up regulated in Chinese hamster V79 cells VB13 that express exogenous human Bcl2. In the present study, the expression of the gene ND6 that is expressed from the light strand promoter (PL) was found to be similar in both the cell lines, as determined by RT-PCR. To test the possibility that this differential expression of mitochondrial genes under these two promoters was mediated by differences in proteins’ affinity to interact with these promoters, we have carried out electrophoretic mobility shift assay (EMSA) using mitochondrial cell extracts from these two cell lines. Our result of these experiments revealed that two different proteins formed complex with the synthetic promoters and higher amount of protein from M5 cell extracts interacted with the PH promoter in comparison to that observed with cell extracts from Chinese hamster V79 cells. The promoter-specific differential binding of proteins was also observed in VB13. These results showed that differential mitochondrial gene expression observed earlier in the radio-resistant M5 cells was due to enhanced interaction proteins with the promoters PH and mediated by the expression of Bcl2

Unconstrained Bengali handwriting recognition with recurrent models

International audienceThis paper presents a pioneering attempt for developing a recurrent neural net based connectionist system for unconstrained Bengali offline handwriting recognition. The major challenge in configuring such a classification system for a complex script like Bengali is to effectively define the character classes. A novel way of defining character classes is introduced making the recognition problem suitable for using a recurrent model. Indeed, it has to deal with more than nine hundred character classes for which the occurrence probability is very skewed in the language. An off-the-shelf BLSTM-CTC recognizer is used. An open-source dataset is developed for unconstrained Bengali offline handwriting recognition. The dataset contains 2,338 handwritten text lines consisting of about 21,000 word. Experiment shows that with the new definition of character classes the BLSTM-CTC provides an impressive performance for unconstrained Bengali offline handwriting recognition. The character level recognition accuracy is 75.40% without doing any post-processing on the BLSTM-CTC output. Among the 24.60% character level errors, the substitution, deletion and insertion errors are 18.91%, 4.69% and 0.98%, respectively

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Role of grain boundary ferrite layer in dynamic recrystallization of semi-solid processed type 304L austenitic stainless steel

A semi-solid processed steel with well arranged delta-ferrite layer around the austenite-solid globules is found to show different dynamic recrystallization (DRX) pattern compared to its conventionally processed counterpart with ferrite stringers across the austenite grains. Analysis of experimental results indicates that the presence of delta-ferrite layer around austenite-globules delays the nucleation of new DRX grains. On the other hand, with increase in temperature, the delta-ferrite layer contributes to increase in DRX fraction by restricting the grain growth of the austenite matrix prior to deformation. (C) 2016 Elsevier B.V. All rights reserved