3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation

Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements. In this paper we propose 3DQ, a ternary quantization method, applied for the first time to 3D Fully Convolutional Neural Networks (F-CNNs), enabling 16x model compression while maintaining performance on par with full precision models. We extensively evaluate 3DQ on two datasets for the challenging task of whole brain segmentation. Additionally, we showcase our method's ability to generalize on two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety of baselines, the proposed method is capable of compressing large 3D models to a few MBytes, alleviating the storage needs in space critical applications.Comment: Accepted to MICCAI 201

Improvement in the Production of L-Lysine by Overexpression of Aspartokinase (ASK) in C. glutamicum ATCC 21799

Purpose: To clone Corynebacterium glutamicum ATCC21799 aspartokinase gene (EC 2.7.2.4) using shuttle expression vector pEKEx2 in order to increase lysine production.Methods: C. glutamicum DNA was extracted and used for amplification of aspartokinase gene (ask) by cloning into an E. coli/C. glutamicum shuttle expression vector, pEKEx2. Initially, the recombinant vector transformed into E. coli DH5á and then into C. glutamicum.Results: Electrophoresis of recombinant protein by SDS-PAGE showed that the molecular weight of the recombinant protein was 42 KD. The induction of recombinant vector by IPTG had an inhibitory effect on cell growth due to over-expression of the cloned gene. The results of lysine assay by Chinard method showed that lysine production increased about two-fold, compared with the parent strain, as a result of increased copy numbers of lysC gene in recombinant strain.Conclusion: A two-fold increase in lysine production was observed by cloning of the ASK gene in C. glutamicum rather than in E. coli, due to the presence of lysine exporter channel which facilitates lysine extraction.Keywords: LysC gene, Corynebacterium glutamicum, L- lysine, Cloning, Aspartokinase, E. col

Resistance of wheat cultivars to bird cherry-oat aphid, Rhopalosiphum padi (Hem.: Aphididae)

The bird cherry-oat aphid, Rhopalosiphum padi (L.), is polyphagous with a nearly worldwide distribution and known as an important pest of wheat and the main vector of barley yellow dwarf virus. In this study, the possibility of antixenosis, antibiosis and tolerance of six common wheat cultivars of Chamran, Darab 2, Shiraz, Ghods, Marvdasht and Niknezhad was investigated at 2-3 leaf growth stage in the Fars province, Iran. The experiments were conducted at 24 ± 5°C, 65 ± 5% R.H. and natural light in a greenhouse, using a randomized complete design. The analysis of variance in regard to the number of adult aphids attracted to each cultivar, was indicative of significant differences among the cultivars (P < 0.01). The highest (21 ± 0.71) and the lowest (11.6 ± 0.51) mean number of adult aphids attracted per plant was observed on Shiraz and Darab 2, respectively. The antibiosis test, based on nymphs per female was significantly different among the cultivars (P < 0.01) whose average values were 62.05, 55.84, 49.89, 47.63, 42.76 and 40.65 nymphs per female on Niknezhad, Shiraz, Ghods, Marvdash, Chamran, and Darab 2, respectively. The tolerance studies based on the damage index, showed that Chamran and Darab 2, with the lowest damage index (1.33), were the most tolerant cultivars while Shiraz and Niknezhad, with the highest damage indexes of 3.00 and 3.67 respectively, were the most susceptible cultivars. The cutivars Niknezhad and Shiraz are found to be susceptible, Ghods and Marvdasht partially resistant, and Chamran and Darab 2 resistant to the bird cherry - oat aphid

Intelligent mining of large-scale bio-data: bioinformatics applications

Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given