This study develops a newly designed, patented, bidirectional dc/dc converter (BDC) that interfaces a main energy storage (ES1), an auxiliary energy storage (ES2), and dc-bus of different voltage levels, for application in hybrid electric vehicle systems. The proposed converter can operate in a step-up mode (i.e., low-voltage dual-source-powering mode) and a step-down (i.e., high-voltage dc-link energy-regenerating mode), both with bidirectional power flow control. In addition, the model can independently control power flow between any two low-voltage sources (i.e., low-voltage dual-source buck/boost mode). Herein, the circuit configuration, operation, steady-state analysis, and closed-loop control of the proposed BDC are discussed according to its three modes of power transfer. Moreover, the simulation and experimental results for a 1 kW prototype system are provided to validate the proposed converter.

Many single nucleotide polymorphisms (SNPs) have been found to be associated with breast cancer, but their SNP interactions are seldom addressed. In this study, we focused on the joint effect for SNP combinations of seven CXCL12-related genes involved in major cancer-related pathways. SNP genotyping was determined by PCR-restriction fragment length polymorphism (RFLP) in this study (case = 220, control = 334). Different numbers of combinational SNPs with genotypes called the SNP barcodes from different chromosomes were used to evaluate their joint effect on breast cancer risk. Except for vascular endothelial growth factor (VEGF) rs3025039-CT, none of these SNPs were found to individually contribute to breast cancer risk. However, for two combined SNPs, the proportion of subjects with breast cancer was significantly low in the SNP barcode with CC–GG genotypes in rs2228014–1801157 (CXCR4–CXCL12) compared to those with non-CC–GG genotypes. Similarly, the SNP barcode of rs12812942–rs2228014–rs3025039 (CD4–CXCR4–VEGF) and rs12812942–rs3136685–rs2228014–rs1801157 (CD4– CCR7–CXCR4–CXCL12) with specific genotype patterns (AT-CC-CC and AT-AG-CC-GG) among three and four combinational SNPs were significantly low in breast cancer occurrence. More SNP combinations larger than five SNPs were also addressed, and these showed similar effects. After controlling for age, and comparing their corresponding non-SNP barcodes, the estimated odds ratios for breast cancer ranged between 0.20 and 0.71 for specific SNP barcodes with two to seven SNPs. In conclusion, we have associated the potential combined CXCL12-related SNPs with genotypes that were protective against breast cancer, and that may contribute to identification of a low-risk population for the development of breast cancer.

Background: The restriction fragment length polymorphism (RFLP) is a common laboratory method for the genotyping of single nucleotide polymorphisms (SNPs). Here, we describe a web-based software, named SNP-RFLPing, which provides the restriction enzyme for RFLP assays on a batch of SNPs and genes from the human, rat, and mouse genomes. Results: Three user-friendly inputs are included: 1) NCBI dbSNP "rs" or "ss" IDs; 2) NCBI Entrez gene ID and HUGO gene name; 3) any formats of SNP-in-sequence, are allowed to perform the SNP-RFLPing assay. These inputs are auto-programmed to SNP-containing sequences and their complementary sequences for the selection of restriction enzymes. All SNPs with available RFLP restriction enzymes of each input genes are provided even if many SNPs exist. The SNP-RFLPing analysis provides the SNP contig position, heterozygosity, function, protein residue, and amino acid position for cSNPs, as well as commercial and non-commercial restriction enzymes. Conclusion: This web-based software solves the input format problems in similar softwares and greatly simplifies the procedure for providing the RFLP enzyme. Mixed free forms of input data are friendly to users who perform the SNP-RFLPing assay. SNP-RFLPing offers a time-saving application for association studies in personalized medicine and is freely available at http://bio.kuas.edu.tw/snp-rflp/.

Background: PCR-restriction fragment length polymorphism (RFLP) assay is a cost-effective method for SNP genotyping and mutation detection, but the manual mining for restriction enzyme sites is challenging and cumbersome. Three years after we constructed SNP-RFLPing, a freely accessible database and analysis tool for restriction enzyme mining of SNPs, significant improvements over the 2006 version have been made and incorporated into the latest version, SNP-RFLPing 2. Results: The primary aim of SNP-RFLPing 2 is to provide comprehensive PCR-RFLP information with multiple functionality about SNPs, such as SNP retrieval to multiple species, different polymorphism types (bi-allelic, tri-allelic, tetra-allelic or indels), gene-centric searching, HapMap tagSNPs, gene ontology-based searching, miRNAs, and SNP500Cancer. The RFLP restriction enzymes and the corresponding PCR primers for the natural and mutagenic types of each SNP are simultaneously analyzed. All the RFLP restriction enzyme prices are also provided to aid selection. Furthermore, the previously encountered updating problems for most SNP related databases are resolved by an on-line retrieval system. Conclusions: The user interfaces for functional SNP analyses have been substantially improved and integrated. SNP-RFLPing 2 offers a new and user-friendly interface for RFLP genotyping that can be used in association studies and is freely available at http://bio.kuas.edu.tw/snp-rflping2.

Artificial Intelligence (AI) and related technologies have been employed to simulate human decision-making processes to improve people's lives. Accordingly, AI knowledge and related competence are crucial, especially for students pursuing information technology (IT) and computer-related degrees, since they will eventually be the next generation of AI designers or users. While the importance of AI technology and its applications have been widely discussed and explored, AI technology in Vietnam is in a nascent stage due to the shortage of skilled experts and the reluctance to adopt AI applications by businesses. Thus, the primary objective of this study was to explore the knowledge of and competence in AI among Vietnamese university IT students. A total of 206 university students from software engineering and computer science programs participated in this study. The results indicate the need for a focused effort to establish a strong foundation for a comprehensive and accessible AI mandatory course plan for IT students.