Esub8: A novel tool to predict protein subcellular localizations in eukaryotic organisms

BACKGROUND: Subcellular localization of a new protein sequence is very important and fruitful for understanding its function. As the number of new genomes has dramatically increased over recent years, a reliable and efficient system to predict protein subcellular location is urgently needed. RESULTS: Esub8 was developed to predict protein subcellular localizations for eukaryotic proteins based on amino acid composition. In this research, the proteins are classified into the following eight groups: chloroplast, cytoplasm, extracellular, Golgi apparatus, lysosome, mitochondria, nucleus and peroxisome. We know subcellular localization is a typical classification problem; consequently, a one-against-one (1-v-1) multi-class support vector machine was introduced to construct the classifier. Unlike previous methods, ours considers the order information of protein sequences by a different method. Our method is tested in three subcellular localization predictions for prokaryotic proteins and four subcellular localization predictions for eukaryotic proteins on Reinhardt's dataset. The results are then compared to several other methods. The total prediction accuracies of two tests are both 100% by a self-consistency test, and are 92.9% and 84.14% by the jackknife test, respectively. Esub8 also provides excellent results: the total prediction accuracies are 100% by a self-consistency test and 87% by the jackknife test. CONCLUSIONS: Our method represents a different approach for predicting protein subcellular localization and achieved a satisfactory result; furthermore, we believe Esub8 will be a useful tool for predicting protein subcellular localizations in eukaryotic organisms

Commerce as a Service Solution Accelerates Transition to E-commerce for Traditional Manufacturing Enterprises and Retailers

Using internet to operate business has become very important for traditional manufacturing enterprises and retailers. These enterprises are facing great risk when entering e-commerce due to lack of experiences and large volume of initial IT investment. This study analyzed the Commerce as a Service (CaaS) solution which can perfectly solve these problems. The solution is based on leading e-commerce platform and cloud computing technology, which provides small to medium sized clients with a low cost alternative e-commerce. Clients will be relieved of the responsibility of managing an IT shop while still maintaining full control over their site through business user tools

Kernel Nutrient Composition and Antioxidant Ability of Corylus spp. in China

Hazelnut (Corylus) is an important woody oil tree species in economic forests. China, as one of the original countries of native Corylus species, had 8 species and 2 varieties. However, little information is available on the hazelnut nutritional quality of these Chinese Corylus species. In this study, four main wild Corylus species (C. heterophylla Fisch., C. mandshurica Maxim., C. kweichowensis Hu., and C. yunnanensis Franch.) originating in China and one main cultivar of hybrid hazelnut (Corylus heterophylla Fisch. × C. avellana L.) cv. ‘Dawei’ from China were used to analyze the basic nutritional composition (content of oil, fatty acid, protein, saccharide, aminao acid, vitamin C, tocopherol, total phenols, and total flavonoids) and antioxidant ability. The results showed that oil content ranged from 52.97 to 60.88 g/100 g DW and highly unsaturated fatty acid (UFA) content was over 91%. Oleic was the most dominant UFA in these hazelnut kernels, and the relative content was ranging from 71.32 to 85.19%. Compared with other four hazelnut kernels, C. heterophylla Fisch. was the lowest oil content of hazelnut with lower oleic acid content and higher linoleic acid content, obviously. The total protein content ranged from 13.15 to 18.35 g/100 g DW, and all amino acids were detected as hydrate amino acids, but Tryptophan, an essential amino acid, was not detected as free amino acid in these hazelnut kernels. Kernel of C. heterophylla Fisch. was with the highest content of protein and amino acid. Saccharose was the most essential and abundant disaccharide in the hazelnut kernels. C. mandshurica Maxim. was the highest saccharide content among these hazelnut kernels. α-tocopherol was the main type of tocopherol found in the hazelnut kernels. Wild hazelnut kernels generally had higher bioactivity substance content (vitamin C, total tocopherol, total phenol and total flavonoid) and antioxidant capacity. Compared to the four wild hazelnut kernels, the hybrid hazelnut cv. ‘Dawei’ had higher content of oil, oleic acid, α-tocopherol and sugar. Overall, there were great differences in the nutritional composition of different hazelnut species. Wild species are a good source of breeding materials because of their own characteristics in nutrition composition, and the hybrid hazelnut cv. ‘Dawei’ with good quality has the value of commercial promotion

Edge and corner states in 2D non-Abelian topological insulators from an eigenvector frame rotation perspective

We propose the concept of 2D non-Abelian topological insulator which can explain the energy distributions of the edge states and corner states in systems with parity-time symmetry. From the viewpoint of non-Abelian band topology, we establish the constraints on the 2D Zak phase and polarization. We demonstrate that the corner states in some 2D systems can be explained as the boundary mode of the 1D edge states arising from the multi-band non-Abelian topology of the system. We also propose the use of off-diagonal Berry phase as complementary information to assist the prediction of edge states in non-Abelian topological insulators. Our work provides an alternative approach to study edge and corner modes and this idea can be extended to 3D systems