Quantum dots based photocatalytic hydrogen evolution

Photocatalytic production of hydrogen from water can directly convert solar energy into chemical energy storage, which has significant advantages and great promise. As an emerging photosensitizer, the efficiency of quantum dots (QDs) based artificial photosynthetic system have made breakthroughs. In this review, we will give a summary of the development of QDs based photocatalytic hydrogen evolution in these years. First, we highlight different types of hydrogen evolution catalyst combined with QDs; then we focus on the surface modification and heterostructure formation of QDs to improve the photocatalytic efficiency, respectively. In the end, we will propose some Cd‐free QDs and future development of photocatalytic hydrogen evolution

Profiling alternatively spliced mRNA isoforms for prostate cancer classification

BACKGROUND: Prostate cancer is one of the leading causes of cancer illness and death among men in the United States and world wide. There is an urgent need to discover good biomarkers for early clinical diagnosis and treatment. Previously, we developed an exon-junction microarray-based assay and profiled 1532 mRNA splice isoforms from 364 potential prostate cancer related genes in 38 prostate tissues. Here, we investigate the advantage of using splice isoforms, which couple transcriptional and splicing regulation, for cancer classification. RESULTS: As many as 464 splice isoforms from more than 200 genes are differentially regulated in tumors at a false discovery rate (FDR) of 0.05. Remarkably, about 30% of genes have isoforms that are called significant but do not exhibit differential expression at the overall mRNA level. A support vector machine (SVM) classifier trained on 128 signature isoforms can correctly predict 92% of the cases, which outperforms the classifier using overall mRNA abundance by about 5%. It is also observed that the classification performance can be improved using multivariate variable selection methods, which take correlation among variables into account. CONCLUSION: These results demonstrate that profiling of splice isoforms is able to provide unique and important information which cannot be detected by conventional microarrays

Curvature Effect of a Non-Power-Law Spectrum and Spectral Evolution of GRB X-Ray Tails

The apparent spectral evolution observed in the steep decay phase of many GRB early afterglows raises a great concern of the high-latitude "curvature effect" interpretation of this phase. However, previous curvature effect models only invoked a simple power law spectrum upon the cessation of the prompt internal emission. We investigate a model that invokes the "curvature effect" of a more general non-power-law spectrum and test this model with the Swift/XRT data of some GRBs. We show that one can reproduce both the observed lightcurve and the apparent spectral evolution of several GRBs using a model invoking a power-law spectrum with an exponential cut off. GRB 050814 is presented as an example.Comment: 12 pages, 4 figures, 1 table, . Accepted for publication in ApJ Letter

Quantum theory of electronic double-slit diffraction

The phenomena of electron, neutron, atomic and molecular diffraction have been studied by many experiments, and these experiments are explained by some theoretical works. In this paper, we study electronic double-slit diffraction with quantum mechanical approach. We can obtain the results: (1) When the slit width $a$ is in the range of $3\lambda\sim 50\lambda$ we can obtain the obvious diffraction patterns. (2) when the ratio of $\frac{d+a}{a}=n (n=1, 2, 3,\cdot\cdot\cdot)$, order $2n, 3n, 4n,\cdot\cdot\cdot$ are missing in diffraction pattern. (3)When the ratio of $\frac{d+a}{a}\neq n (n=1, 2, 3,\cdot\cdot\cdot)$, there isn't missing order in diffraction pattern. (4) We also find a new quantum mechanics effect that the slit thickness $c$ has a large affect to the electronic diffraction patterns. We think all the predictions in our work can be tested by the electronic double-slit diffraction experiment.Comment: 9pages, 14figure

Synthesis and Evaluation of a New Type of Small Molecule Epigenetic Modulator Containing Imidazo[1,2-b][1,2,4]triazole Motif

Epigenetic modifications such as DNA methylation is important for many cellular processes, such as cell differentiation and cell death. The disorder of epigenetic state is closely related to human diseases, especially cancers. DNA methylation is a well-characterized epigenetic modification which is related to gene silencing and is considered as a repressive epigenetic mark. DNA methylation caused gene repression can be derepressed by chemical agents. Small molecules targeting DNA methyltransferases, histone deacetylases, and other regulatory factors can activate genes silenced by DNA methylation. However, more and more studies have shown that histone deacetylation is not the only downstream event of DNA methylation. Some additional, unknown mechanisms that promote DNA methylation-mediated gene silencing may exist. Recently, through high-throughput screening using a 308,251-member chemical library to identify potent small molecules that derepress an EGFP reporter gene silenced by DNA methylation, we identified seven hit compounds that did not directly target bulk DNA methylation or histone acetylation. Three of them (LX-3, LX-4, LX-5) were proven to selectively activate the p38 MAPK pathway in multiple cell types. In order to identify the exact cellular targets of these compounds, we turn to work on the SAR study of LX-3 by constructing a structurally diverse chemical library based on the imidazo[1,2-b][1,2,4]triazole core structure via diversity-oriented synthesis. Our work provides a general approach to efficiently access diverse heterocyclic molecules with interesting epigenetic modulation activities

Preprocessing and Quality Control Strategies for Illumina DASL Assay-Based Brain Gene Expression Studies with Semi-Degraded Samples

Available statistical preprocessing or quality control analysis tools for gene expression microarray datasets are known to greatly affect downstream data analysis, especially when degraded samples, unique tissue samples, or novel expression assays are used. It is therefore important to assess the validity and impact of the assumptions built in to preprocessing schemes for a dataset. We developed and assessed a data preprocessing strategy for use with the Illumina DASL-based gene expression assay with partially degraded postmortem prefrontal cortex samples. The samples were obtained from individuals with autism as part of an investigation of the pathogenic factors contributing to autism. Using statistical analysis methods and metrics such as those associated with multivariate distance matrix regression and mean inter-array correlation, we developed a DASL-based assay gene expression preprocessing pipeline to accommodate and detect problems with microarray-based gene expression values obtained with degraded brain samples. Key steps in the pipeline included outlier exclusion, data transformation and normalization, and batch effect and covariate corrections. Our goal was to produce a clean dataset for subsequent downstream differential expression analysis. We ultimately settled on available transformation and normalization algorithms in the R/Bioconductor package lumi based on an assessment of their use in various combinations. A log2-transformed, quantile-normalized, and batch and seizure-corrected procedure was likely the most appropriate for our data. We empirically tested different components of our proposed preprocessing strategy and believe that our results suggest that a preprocessing strategy that effectively identifies outliers, normalizes the data, and corrects for batch effects can be applied to all studies, even those pursued with degraded samples

A systematic review and meta-analysis of the diagnostic accuracy of metagenomic next-generation sequencing for diagnosing tuberculous meningitis

ObjectiveThe utility of metagenomic next-generation sequencing (mNGS) in the diagnosis of tuberculous meningitis (TBM) remains uncertain. We performed a meta-analysis to comprehensively evaluate its diagnostic accuracy for the early diagnosis of TBM.MethodsEnglish (PubMed, Medline, Web of Science, Cochrane Library, and Embase) and Chinese (CNKI, Wanfang, and CBM) databases were searched for relevant studies assessing the diagnostic accuracy of mNGS for TBM. Review Manager was used to evaluate the quality of the included studies, and Stata was used to perform the statistical analysis.ResultsOf 495 relevant articles retrieved, eight studies involving 693 participants (348 with and 345 without TBM) met the inclusion criteria and were included in the meta-analysis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the summary receiver-operating characteristic curve of mNGS for diagnosing TBM were 62% (95% confidence interval [CI]: 0.46–0.76), 99% (95% CI: 0.94–1.00), 139.08 (95% CI: 8.54–2266), 0.38 (95% CI: 0.25–0.58), 364.89 (95% CI: 18.39–7239), and 0.97 (95% CI: 0.95–0.98), respectively.ConclusionsmNGS showed good specificity but moderate sensitivity; therefore, a more sensitive test should be developed to assist in the diagnosis of TBM

Lattice marginal reconstruction enabled high ambient-tolerance Perovskite Quantum Dots phototransistors

Perovskite quantum dots (PeQDs) have been developed rapidly as photoactive materials in hybrid phototransistors because of their strong light absorption, broad bandgap customizability, and defect-tolerance in charge-transport properties. The solvent treatment has been well recognized as a practical approach for improving the charge transport of PeQDs and the photoresponsivity of PeQD phototransistors. However, there is a lack of fundamental understanding of the origin of its impacts on the material’s ambient stability as well as phototransistor’s operational lifetime. Especially, the relationship between surface ligands dissociation and their microstructural reconstruction has not been fully elucidated so far. Herein, we report that a simultaneous enhancement of photoresponsivity and ambient tolerance for PeQD-based hybrid phototransistors can be realized via medium-polarity-solvent treatment on solid-state PeQDs. Our comprehensive optoelectronic characterization and electron microscopic study reveals that the crystal morphology, instead of surface ligands, is the dominating factor that results in the PeQD’s stability enhancement associated with the preservation of optical property and quantum confinement. Besides, we unveil a marginal reconstruction process occurred during solvent treatment, which opens up a new route for facets-oriented attachment of PeQDs along the zone axis to suppress the damage from water molecules penetration. Our study yields a new understanding of the solvent impact on PeQD microstructures reconstruction and suggests new routes for perovskite materials and corresponding device operational stability enhancement

Site-specific incorporation of phosphotyrosine using an expanded genetic code.

Access to phosphoproteins with stoichiometric and site-specific phosphorylation status is key to understanding the role of protein phosphorylation. Here we report an efficient method to generate pure, active phosphotyrosine-containing proteins by genetically encoding a stable phosphotyrosine analog that is convertible to native phosphotyrosine. We demonstrate its general compatibility with proteins of various sizes, phosphotyrosine sites and functions, and reveal a possible role of tyrosine phosphorylation in negative regulation of ubiquitination

The combined signatures of telomere and immune cell landscape provide a prognostic and therapeutic biomarker in glioma

BackgroundGliomas, the most prevalent primary malignant tumors of the central nervous system in adults, exhibit slow growth in lower-grade gliomas (LGG). However, the majority of LGG cases progress to high-grade gliomas, posing challenges for prognostication. The tumor microenvironment (TME), characterized by telomere-related genes and immune cell infiltration, strongly influences glioma growth and therapeutic response. Therefore, our objective was to develop a Telomere-TME (TM-TME) classifier that integrates telomere-related genes and immune cell landscape to assess prognosis and therapeutic response in glioma.MethodsThis study encompassed LGG patients from the TCGA and CCGA databases. TM score and TME score were derived from the expression signatures of telomere-related genes and the presence of immune cells in LGG, respectively. The TM-TME classifier was established by combining TM and TME scores to effectively predict prognosis. Subsequently, we conducted Kaplan-Meier survival estimation, univariate Cox regression analysis, and receiver operating characteristic curves to validate the prognostic prediction capacity of the TM-TME classifier across multiple cohorts. Gene Ontology (GO) analysis, biological processes, and proteomaps were performed to annotate the functional aspects of each subgroup and visualize the cellular signaling pathways.ResultsThe TM_low+TME_high subgroup exhibited superior prognosis and therapeutic response compared to other subgroups (P<0.001). This finding could be attributed to distinct tumor somatic mutations and cancer cellular signaling pathways. GO analysis indicated that the TM_low+TME_high subgroup is associated with the neuronal system and modulation of chemical synaptic transmission. Conversely, the TM_high+TME_low subgroup showed a strong association with cell cycle and DNA metabolic processes. Furthermore, the classifier significantly differentiated overall survival in the TCGA LGG cohort and served as an independent prognostic factor for LGG patients in both the TCGA cohort (P<0.001) and the CGGA cohort (P<0.001).ConclusionOverall, our findings underscore the significance of the TM-TME classifier in predicting prognosis and immune therapeutic response in glioma, shedding light on the complex immune landscape within each subgroup. Additionally, our results suggest the potential of integrating risk stratification with precision therapy for LGG