Identification of Robust Antibiotic Subgroups by Integrating Multi-Species Drug–Drug Interactions

Previous studies have shown that antibiotics can be divided into groups, and drug–drug interactions (DDI) depend on their groups. However, these studies focused on a specific bacteria strain (i.e., Escherichia coli BW25113). Existing datasets often contain noise. Noisy labeled data may have a bad effect on the clustering results. To address this problem, we developed a multi-source information fusion method for integrating DDI information from multiple bacterial strains. Specifically, we calculated drug similarities based on the DDI network of each bacterial strain and then fused these drug similarity matrices to obtain a new fused similarity matrix. The fused similarity matrix was combined with the T-distributed stochastic neighbor embedding algorithm, and hierarchical clustering algorithm can effectively identify antibiotic subgroups. These antibiotic subgroups are strongly correlated with known antibiotic classifications, and group–group interactions are almost monochromatic. In summary, our method provides a promising framework for understanding the mechanism of action of antibiotics and exploring multi-species group–group interactions

HVEM Gene Polymorphisms Are Associated with Sporadic Breast Cancer in Chinese Women

<div><p>As a costimulatory molecule, Herpesvirus entry mediator (HVEM) can bind with several costimulatory members, thus HVEM plays different roles in T cell immunity. HVEM and its ligands have been involved in the pathogenesis of various autoimmune, inflammatory diseases and tumors. In the current study, we conducted a case-control study comparing polymorphisms of HVEM and breast cancer. Subjects included 575 females with breast cancer and 604 age-matched healthy controls. Six HVEM SNPs (rs2281852, rs1886730, rs2234163, rs11573979, rs2234165, and rs2234167) were genotyped by PCR-RFLP. The results showed significant differences in genotypes and alleles between rs1886730 and rs2234167 (<i>P</i><0.05). One haplotype (CTGCGG) that was associated with breast cancer was found via haplotype analysis. Our research also indicated an association between polymorphisms of HVEM and clinicopathologic features, including lymph node metastasis, estrogen receptor, progesterone receptor and P53. Our results primarily indicate that polymorphisms of the HVEM gene were associated with the risk of sporadic breast cancer in northeast Chinese females.</p></div

Haplotypes of HVEM gene.

<p>S1 = rs2281852, S2 = rs1886730, S3 = rs2234163, S4 = rs11573979, S5 = rs2234165, S6 = rs2234167.</p>*<p>correcting the P value for multiple testing by Haploview program using 10,000 permutations.</p

Primers and PCR programs for HVEM PCR-RFLP genotyping.

<p>Primers and PCR programs for HVEM PCR-RFLP genotyping.</p

Characterization and Catalytic Performance of Cu/ZnO/Al₂O₃ Water–Gas Shift Catalysts Derived from Cu–Zn–Al Layered Double Hydroxides

Cu/ZnO/Al₂O₃ catalysts with different compositions were prepared from Cu–Zn–Al layered double hydroxides (LDHs) and tested for the water–gas shift reaction. LDHs were synthesized by the coprecipitation method, and Cu–Zn–Al LDHs or Cu–Al LDHs could be formed depending on the (Cu + Zn)/Al atomic ratio. Upon calcination, LDHs decomposed to form mixed metal oxides consisting of CuO, ZnO, ZnAl₂O₄, CuAl₂O₄, and/or amorphous Al₂O₃. After reduction, well dispersed Cu metal particles with 18–48% dispersion and 2–6 nm size were formed. It was observed that the initial activity of Cu/ZnO/Al₂O₃ catalysts was proportional to the number of surface Cu⁰ atoms and the 30%Cu/Zn₁Al catalyst showed the highest activity. Moreover, this optimum catalyst exhibited better activity, thermal stability, and long-term stability than a commercial Cu/ZnO/Al₂O₃ catalyst. It was considered that a synergetic effect between Cu metal and ZnAl₂O₄ spinel might exist and play a key role for the high catalytic performance

Snagger: A user-friendly program for incorporating additional information for tagSNP selection-5

design scores as well as the location of HapMap-formatted data. The user can select the genomic region and population(s) to load into Snagger here. In addition, a minimum pairwise comparison distance and minimum genotype percentage for individuals can be chosen.<p><b>Copyright information:</b></p><p>Taken from "Snagger: A user-friendly program for incorporating additional information for tagSNP selection"</p><p>http://www.biomedcentral.com/1471-2105/9/174</p><p>BMC Bioinformatics 2008;9():174-174.</p><p>Published online 27 Mar 2008</p><p>PMCID:PMC2375134.</p><p></p

Snagger: A user-friendly program for incorporating additional information for tagSNP selection-3

Used in the selection of tagSNPs across multiple populations.<p><b>Copyright information:</b></p><p>Taken from "Snagger: A user-friendly program for incorporating additional information for tagSNP selection"</p><p>http://www.biomedcentral.com/1471-2105/9/174</p><p>BMC Bioinformatics 2008;9():174-174.</p><p>Published online 27 Mar 2008</p><p>PMCID:PMC2375134.</p><p></p

Snagger: A user-friendly program for incorporating additional information for tagSNP selection-4

Cross multiple populations.<p><b>Copyright information:</b></p><p>Taken from "Snagger: A user-friendly program for incorporating additional information for tagSNP selection"</p><p>http://www.biomedcentral.com/1471-2105/9/174</p><p>BMC Bioinformatics 2008;9():174-174.</p><p>Published online 27 Mar 2008</p><p>PMCID:PMC2375134.</p><p></p

Clinicopathologic information of breast cancer patients.

<p>IDC infiltrative ductal carcinoma, MC medullary carcinoma, LN lymph node, TZ tumor size, ER estrogen receptor, PR progesterone receptor.</p

Allele frequencies of HVEM polymorphisms and their associations with breast cancer risk.

*<p>P<0.01 after correcting the P value for multiple testing by Haploview program using 10,000 permutations.</p><p>Rs2281852: cases n = 575, missing n = 0; controls n = 604, missing n = 0.</p><p>Rs1886730: cases n = 564, missing n = 11; controls n = 600, missing n = 4.</p><p>Rs2234163: cases n = 570, missing n = 5; controls n = 602, missing n = 2.</p><p>Rs11573979: cases n = 571, missing n = 4; controls n = 601, missing n = 3.</p><p>Rs2234165: cases n = 574, missing n = 1; controls n = 601, missing n = 3.</p><p>Rs2234167: cases n = 574, missing n = 1; controls n = 603, missing n = 1.</p