Homogeneous point mutation detection by quantum dot-mediated two-color fluorescence coincidence analysis

This report describes a new genotyping method capable of detecting low-abundant point mutations in a homogeneous, separation-free format. The method is based on integration of oligonucleotide ligation with a semiconductor quantum dot (QD)-mediated two-color fluorescence coincidence detection scheme. Surface-functionalized QDs are used to capture fluorophore-labeled ligation products, forming QD-oligonucleotide nanoassemblies. The presence of such nanoassemblies and thereby the genotype of the sample is determined by detecting the simultaneous emissions of QDs and fluorophores that occurs whenever a single nanoassembly flows through the femtoliter measurement volume of a confocal fluorescence detection system. The ability of this method to detect single events enables analysis of target signals with a multiple-parameter (intensities and count rates of the digitized target signals) approach to enhance assay sensitivity and specificity. We demonstrate that this new method is capable of detecting zeptomoles of targets and achieve an allele discrimination selectivity factor >10(5)

Knowledge-guided multi-scale independent component analysis for biomarker identification

<p>Abstract</p> <p>Background</p> <p>Many statistical methods have been proposed to identify disease biomarkers from gene expression profiles. However, from gene expression profile data alone, statistical methods often fail to identify biologically meaningful biomarkers related to a specific disease under study. In this paper, we develop a novel strategy, namely knowledge-guided multi-scale independent component analysis (ICA), to first infer regulatory signals and then identify biologically relevant biomarkers from microarray data.</p> <p>Results</p> <p>Since gene expression levels reflect the joint effect of several underlying biological functions, disease-specific biomarkers may be involved in several distinct biological functions. To identify disease-specific biomarkers that provide unique mechanistic insights, a meta-data "knowledge gene pool" (KGP) is first constructed from multiple data sources to provide important information on the likely functions (such as gene ontology information) and regulatory events (such as promoter responsive elements) associated with potential genes of interest. The gene expression and biological meta data associated with the members of the KGP can then be used to guide subsequent analysis. ICA is then applied to multi-scale gene clusters to reveal regulatory modes reflecting the underlying biological mechanisms. Finally disease-specific biomarkers are extracted by their weighted connectivity scores associated with the extracted regulatory modes. A statistical significance test is used to evaluate the significance of transcription factor enrichment for the extracted gene set based on motif information. We applied the proposed method to yeast cell cycle microarray data and Rsf-1-induced ovarian cancer microarray data. The results show that our knowledge-guided ICA approach can extract biologically meaningful regulatory modes and outperform several baseline methods for biomarker identification.</p> <p>Conclusion</p> <p>We have proposed a novel method, namely knowledge-guided multi-scale ICA, to identify disease-specific biomarkers. The goal is to infer knowledge-relevant regulatory signals and then identify corresponding biomarkers through a multi-scale strategy. The approach has been successfully applied to two expression profiling experiments to demonstrate its improved performance in extracting biologically meaningful and disease-related biomarkers. More importantly, the proposed approach shows promising results to infer novel biomarkers for ovarian cancer and extend current knowledge.</p

BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps

Learning to follow instructions is of fundamental importance to autonomous agents for vision-and-language navigation (VLN). In this paper, we study how an agent can navigate long paths when learning from a corpus that consists of shorter ones. We show that existing state-of-the-art agents do not generalize well. To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially. A special design memory buffer is used by the agent to turn its past experiences into contexts for future steps. The learning process is composed of two phases. In the first phase, the agent uses imitation learning from demonstration to accomplish BabySteps. In the second phase, the agent uses curriculum-based reinforcement learning to maximize rewards on navigation tasks with increasingly longer instructions. We create two new benchmark datasets (of long navigation tasks) and use them in conjunction with existing ones to examine BabyWalk's generalization ability. Empirical results show that BabyWalk achieves state-of-the-art results on several metrics, in particular, is able to follow long instructions better. The codes and the datasets are released on our project page https://github.com/Sha-Lab/babywalk.Comment: Accepted by ACL 202

The novel ZIP4 regulation and its role in ovarian cancer

Our RNAseq analyses revealed that ZIP4 is a top gene up-regulated in more aggressive ovarian cancer cells. ZIP4's role in cancer stem cells has not been reported in any type of cancer. In addition, the role and regulation of ZIP4, a zinc transporter, have been studied in the context of extracellular zinc transporting. Factors other than zinc with ZIP4 regulatory effects are essentially unknown. ZIP4 expression and its regulation in epithelial ovarian cancer cells was assessed by immunoblotting, quantitative PCR, or immunohistochemistry staining in human ovarian tissues. Cancer stem cell-related activities were examined to evaluate the role of ZIP4 in human high-grade serous ovarian cancer cells in vitro and in vivo. RNAi and CRISPR techniques were used to knockdown or knockout ZIP4 and related genes. Ovarian cancer tissues overexpressed ZIP4 when compared with normal and benign tissues. ZIP4 knockout significantly reduced several cancer stem cell-related activities in EOC cells, including proliferation, anoikis-resistance, colony-formation, spheroid-formation, drug-resistance, and side-population in vitro. ZIP4-expressing side-population highly expressed known CSC markers ALDH1 and OCT4. ZIP4 knockout dramatically reduced tumorigenesis and ZIP4 overexpression increased tumorigenesis in vivo. In addition, the ZIP4-expressing side-population had the tumor initiating activity. Moreover, the oncolipid lysophosphatic acid effectively up-regulated ZIP4 expression via the nuclear receptor peroxisome proliferator-activated receptor gamma and lysophosphatic acid 's promoting effects in cancer stem cell-related activities in HGSOC cells was at least partially mediated by ZIP4 in an extracellular zinc-independent manner. Our critical data imply that ZIP4 is a new and important cancer stem cell regulator in ovarian cancer. Our data also provide an innovative interpretation for the apparent disconnection between low levels of zinc and up-regulation of ZIP4 in ovarian cancer tissues

Provider Behavior Under Global Budgeting and Policy Responses: An Observational Study on Eye Care Services in Taiwan

Third-party payer systems are consistently associated with health care cost escalation. Taiwan’s single-payer, universal coverage National Health Insurance (NHI) adopted global budgeting (GB) to achieve cost control. This study captures ophthalmologists’ response to GB, specifically service volume changes and service substitution between low-revenue and high-revenue services following GB implementation, the subsequent Bureau of NHI policy response, and the policy impact. De-identified eye clinic claims data for the years 2000, 2005, and 2007 were analyzed to study the changes in Simple Claim Form (SCF) claims versus Special Case Claims (SCCs). The 3 study years represent the pre-GB period, post-GB but prior to region-wise service cap implementation period, and the post-service cap period, respectively. Repeated measures multilevel regression analysis was used to study the changes adjusting for clinic characteristics and competition within each health care market. SCF service volume (low-revenue, fixed-price patient visits) remained constant throughout the study period, but SCCs (covering services involving variable provider effort and resource use with flexibility for discretionary billing) increased in 2005 with no further change in 2007. The latter is attributable to a 30% cap negotiated by the NHI Bureau with the ophthalmology association and enforced by the association. This study demonstrates that GB deployed with ongoing monitoring and timely policy responses that are designed in collaboration with professional stakeholders can contain costs in a health insurance–financed health care system