Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feed-forward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power plant detection on middle-resolution satellite images. To express the strength of the top-down connections, we introduce feedback CNN network (FB-Net) to a baseline CNN model used for solar power plant classification on multi-spectral satellite data. Moreover, we introduce a method to improve class activation mapping (CAM) to our FB-Net, which takes advantage of multi-channel pulse coupled neural network (m-PCNN) for weakly-supervised localization of the solar power plants from the features of proposed FB-Net. For the proposed FB-Net CAM with m-PCNN, experimental results demonstrated promising results on both solar-power plant image classification and detection task.Comment: 9 pages, 9 figures, 4 table

The development of fluorescent protein tracing vectors for multicolor imaging of clinically isolated Staphylococcus aureus

Recent advances in fluorescent protein technology provide a wide variety of biological imaging applications; however current tools for bio-imaging in the Gram-positive bacterium Staphylococcus aureus has necessitated further developments for fluorescence intensity and for a multicolor palette of fluorescent proteins. To enhance the expression of multicolor fluorescent proteins in clinical S. aureus strains, we developed new fluorescent protein expression vectors, containing the blaZ/sodp promoter consisting of the β-lactamase gene (blaZ) promoter and the ribosome binding site (RBS) of superoxide dismutase gene (sod). We found S. aureus-adapted GFP (GFPsa) driven by the blaZ/sodp promoter was highly expressed in the S. aureus laboratory strain RN4220, but not in the clinical strains, MW2 and N315, harboring the endogenous blaI gene, a repressor of the blaZ gene promoter. We therefore constructed a constitutively induced blaZ/sodp promoter (blaZ/sodp(Con)) by introducing substitution mutations into the BlaI binding motif, and this modification allowed enhanced expression of the multicolor GFP variants (GFPsa, EGFP, mEmerald, Citrine, Cerulean, and BFP) as well as codon-optimized reef coral fluorescent proteins (mCherry and AmCyan) in the S. aureus clinical strains. These new fluorescent probes provide new tools to enhance expression of multicolor fluorescent proteins and facilitate clear visualization of clinical S. aureus strains.This study was supported by Grant-in-Aid for Young Scientists (B) Grant Number JP25861744 and Grant-in-Aid for Scientific Research (C) Grant Number JP25460533 from the Japan Society for the promotion of Science (JSPS). A confocal laser scanning microscopy was performed at the Analysis Center of Life Science, Natural Science Center for Basic Research and Development, Hiroshima University

Lattice QCD and High Baryon Density State

We report our recent studies on the finite density QCD obtained from lattice QCD simulation with clover-improved Wilson fermions of two flavor and RG-improved gauge action. We approach the subject from two paths, i.e., the imaginary and real chemical potentials.Comment: 10 pages, 16 figures. Proceedings of BARYONS'10 on Decemberg 7-11, 2010, Osaka, Japan. MSN-01

CIG-DB: the database for human or mouse immunoglobulin and T cell receptor genes available for cancer studies

<p>Abstract</p> <p>Background</p> <p>Immunoglobulin (IG or antibody) and the T-cell receptor (TR) are pivotal proteins in the immune system of higher organisms. In cancer immunotherapy, the immune responses mediated by tumor-epitope-binding IG or TR play important roles in anticancer effects. Although there are public databases specific for immunological genes, their contents have not been associated with clinical studies. Therefore, we developed an integrated database of IG/TR data reported in cancer studies (the Cancer-related Immunological Gene Database [CIG-DB]).</p> <p>Description</p> <p>This database is designed as a platform to explore public human and murine IG/TR genes sequenced in cancer studies. A total of 38,308 annotation entries for IG/TR proteins were collected from GenBank/DDBJ/EMBL and the Protein Data Bank, and 2,740 non-redundant corresponding MEDLINE references were appended. Next, we filtered the MEDLINE texts by MeSH terms, titles, and abstracts containing keywords related to cancer. After we performed a manual check, we classified the protein entries into two groups: 611 on cancer therapy (Group I) and 1,470 on hematological tumors (Group II). Thus, a total of 2,081 cancer-related IG and TR entries were tabularized. To effectively classify future entries, we developed a computational method based on text mining and canonical discriminant analysis by parsing MeSH/title/abstract words. We performed a leave-one-out cross validation for the method, which showed high accuracy rates: 94.6% for IG references and 94.7% for TR references. We also collected 920 epitope sequences bound with IG/TR. The CIG-DB is equipped with search engines for amino acid sequences and MEDLINE references, sequence analysis tools, and a 3D viewer. This database is accessible without charge or registration at <url>http://www.scchr-cigdb.jp/</url>, and the search results are freely downloadable.</p> <p>Conclusions</p> <p>The CIG-DB serves as a bridge between immunological gene data and cancer studies, presenting annotation on IG, TR, and their epitopes. This database contains IG and TR data classified into two cancer-related groups and is able to automatically classify accumulating entries into these groups. The entries in Group I are particularly crucial for cancer immunotherapy, providing supportive information for genetic engineering of novel antibody medicines, tumor-specific TR, and peptide vaccines.</p