Carbon Quantum Dot Conjugated Copper(II) Phthalocyanine Integrating BiVO₄ Semiconductor for Photocatalytic Water Oxidation

Photocatalytic water splitting as one of the most promising strategies has attracted widespread attention to solve the energy crisis, in which water oxidation was the bottleneck because of the complex four-electron reaction process. Bismuth vanadate (BiVO4), as a widely studied light-harvesting semiconductor in photocatalytic water oxidation, suffers from a low separated rate of photogenerated charge carriers and poor stability. Herein, carbon quantum dots (CQDs) and copper­(II) phthalocyanine (CuPc) form tight conjugate systems by π–π electron stacking, which then coupled with BiVO4 by a hydrothermal method to construct a water oxidation photocatalyst BiVO4/CQDs/CuPc. The hybrid catalyst exhibits efficient photocatalytic water oxidation activity due to the presence of a Z-scheme transfer mechanism, in which the O2-evolved amount for an optimal sample is about 5.1 times (371.8 μmol g–1 h–1) higher than that of bare BiVO4. The apparent quantum efficiency (AQE) in 1 h is 36.8%. Additionally, in this Z-scheme system, CuPc coupled with BiVO4 enhances the separation efficiency of photogenerated charge carries, where CQDs play a role in the electron shuttle, promoting the electron transfer rate between CuPc and BiVO4. Our study demonstrates that CQDs and CuPc are introduced to couple with the inorganic semiconductor BiVO4 to fabricate efficient ternary composite water oxidation photocatalysts

Additional file 5: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

Parameters discussion. This file discusses the performance of MGOGP under different parameter settings. (DOCX 65 kb

Additional file 2: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

GSEA gene module. This file is all the gene modules downloaded from GSEA website. (TXT 2837 kb

Data_Sheet_1_A new pathogenic isolate of Kocuria kristinae identified for the first time in the marine fish Larimichthys crocea.pdf

In recent years, new emerging pathogenic microorganisms have frequently appeared in animals, including marine fish, possibly due to climate change, anthropogenic activities, and even cross-species transmission of pathogenic microorganisms among animals or between animals and humans, which poses a serious issue for preventive medicine. In this study, a bacterium was clearly characterized among 64 isolates from the gills of diseased large yellow croaker Larimichthys crocea that were raised in marine aquaculture. This strain was identified as K. kristinae by biochemical tests with a VITEK 2.0 analysis system and 16S rRNA sequencing and named K. kristinae_LC. The potential genes that might encode virulence-factors were widely screened through sequence analysis of the whole genome of K. kristinae_LC. Many genes involved in the two-component system and drug-resistance were also annotated. In addition, 104 unique genes in K. kristinae_LC were identified by pan genome analysis with the genomes of this strain from five different origins (woodpecker, medical resource, environment, and marine sponge reef) and the analysis results demonstrated that their predicted functions might be associated with adaptation to living conditions such as higher salinity, complex marine biomes, and low temperature. A significant difference in genomic organization was found among the K. kristinae strains that might be related to their hosts living in different environments. The animal regression test for this new bacterial isolate was carried out using L. crocea, and the results showed that this bacterium could cause the death of L. crocea and that the fish mortality was dose-dependent within 5 days post infection, indicating the pathogenicity of K. kristinae_LC to marine fish. Since K. kristinae has been reported as a pathogen for humans and bovines, in our study, we revealed a new isolate of K. kristinae_LC from marine fish for the first time, suggesting the potentiality of cross-species transmission among animals or from marine animals to humans, from which we would gain insight to help in future public prevention strategies for new emerging pathogens.</p

Additional file 1: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

A step by step example of Rank Fusion process. This file provides an example of how to get the final gene rank. (DOCX 275 kb

Additional file 3: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

Breast-Cancer-Gene. This is the known breast cancer-related genes downloaded from SNP4Disease. (TXT 2 kb

Additional file 4: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

Final module list. This is the refined module list after removing irrelevant genes. (TXT 2736 kb

Additional file 7: of MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization

Sourcecode. Some core code of our method. (TXT 5 kb

Differences in <i>CCBL2</i> expression shown in boxplots.

The subgroups included type (A), molecular subtype (B), histological type (C), clinical stage (D), T classification (E), and M classification (F). (p < 0.05).</p

Pan-cancer analysis of <i>CCBL2</i>.

Differential expression of CCBL2 between normal and tumor tissues(A). Low CCBL2 expression had relations with the low survival probability of renal cancer (B) and ovarian cancer (C) and head and neck cancer (D) (p proteinatlas.org and TIMER databases.</p