Meanwhile, Fe3O4/VAN@MIL-101(Fe) maintains the excellent catalytic activity in OERs and HERs after recycling for 5 times. Moreover, the introduction of magnetic Fe3O4 nanoplates into Fe3O4/VAN@MIL-101(Fe) can make it easily recyclable by magnetic separation, which can maximize its performance.Intense ns pulse laser excitation to nanoparticle colloids of a photochromic diarylethene induced an amplified cycloreversion reaction. The mechanism was explained as a ‘photosynergetic response’ coupled with nanoscale laser heating and the photochemical reaction in nanoparticles.Three-dimensional (3D) printing has recently emerged as a cost-effective alternative for rapid prototyping of microfluidic devices. The feature resolution of stereolithography-based 3D printing is particularly well suited for manufacturing of continuous flow cell culture platforms. Poor cell adhesion or material-induced cell death may, however, limit the introduction of new materials to microfluidic cell culture. In this work, we characterized four commercially available materials commonly used in stereolithography-based 3D printing with respect to long-term (2 month) cell survival on native 3D printed surfaces. Cell proliferation rates, along with material-induced effects on apoptosis and cell survival, were examined in mouse embryonic fibroblasts. Additionally, the feasibility of Dental SG (material with the most favored properties) for culturing of human hepatocytes and human-induced pluripotent stem cells was evaluated. The strength of cell adhesion to Dental SG was further examined over a shear force gradient of 1-89 dyne per cm2 by using a custom-designed microfluidic shear force assay incorporating a 3D printed, tilted and tapered microchannel sealed with a polydimethylsiloxane lid. According to our results, autoclavation of the devices prior to cell seeding played the most important role in facilitating long-term cell survival on the native 3D printed surfaces with the shear force threshold in the range of 3-8 dyne per cm2.Polymorphisms associated with BIN1 confer the second greatest risk for developing late onset Alzheimer’s disease. The biological consequences of this genetic variation are not fully understood, however BIN1 is a binding partner for tau. Tau is normally a highly soluble cytoplasmic protein, but in Alzheimer’s disease tau is abnormally phosphorylated and accumulates at synapses to exert synaptotoxicity. The purpose of this study was to determine if alterations to BIN1 and tau in Alzheimer’s disease promote the damaging redistribution of tau to synapses, as a mechanism by which BIN1 polymorphisms may increase risk of developing Alzheimer’s disease. We show that BIN1 is lost from the cytoplasmic fraction of Alzheimer’s disease cortex, and this is accompanied by the progressive mislocalization of phosphorylated tau to synapses. We confirmed proline 216 in tau as critical for tau interaction with the BIN1-SH3 domain and show that phosphorylation of tau disrupts this binding, suggesting that tau phosphorylation in Alzheimer’s disease disrupts tau-BIN1 associations. Moreover, we show that BIN1 knockdown in rat primary neurons to mimic BIN1 loss in Alzheimer’s disease brain, causes the damaging accumulation of phosphorylated tau at synapses and alterations in dendritic spine morphology. We also observed reduced release of tau from neurons upon BIN1 silencing, suggesting that BIN1 loss disrupts the function of extracellular tau. Together, these data indicate that polymorphisms associated with BIN1 that reduce BIN1 protein levels in the brain likely act synergistically with increased tau phosphorylation to increase risk of Alzheimer’s disease by disrupting cytoplasmic tau-BIN1 interactions, promoting the damaging mis-sorting of phosphorylated tau to synapses to alter synapse structure, and by reducing the release of physiological forms of tau to disrupt tau function.Genome-wide association studies have identified lung disease-associated loci; however, the functions of such loci are not well understood in part because the majority of such loci are located at non-coding regions. Hi-C, ChIP-seq and eQTL data predict potential roles (e.g. enhancer) of such loci; however, they do not elucidate the molecular function. To determine whether these loci function as gene-regulatory regions, CRISPR interference (CRISPRi; CRISPR/dCas9-KRAB) has been recently used. Here, we applied CRISPRi along with Hi-C, ChIP-seq and eQTL to determine the functional roles of loci established as highly associated with asthma, cystic fibrosis (CF), chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF). Notably, Hi-C, ChIP-seq and eQTL predicted that non-coding regions located at chromosome 19q13 or chromosome 17q21 harboring single-nucleotide polymorphisms (SNPs) linked to asthma/CF/COPD and chromosome 11p15 harboring an SNP linked to IPF interact with nearby genes and function as enhancers; however, CRISPRi indicated that the regions with rs1800469, rs2241712, rs12603332 and rs35705950, but not others, regulate the expression of nearby genes (single or multiple genes). These data indicate that CRISPRi is useful to precisely determine the roles of non-coding regions harboring lung disease-associated loci as to whether they function as gene-regulatory regions at a genomic level.Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. CCG-203971 research buy The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPacket descriptions supported by GA4GH (http//phenopackets.org/), calculate a prioritized gene list based on a probabilistic model and output gene-disease relationships with great accuracy.