Allelic Imbalance Detection in Crohn’s Disease Patients

The exact cause of Crohn’s Disease (CD), one of the main types of inflammatory bowel disease (IBD), remains an open question. Previous Genome-Wide Association Studies (GWAS) have associated Crohn’s disease with >240 genetic loci, but the mechanism behind these associations is still unknown. Many of the identified sites lie within the non-coding region of human genome, suggesting gene regulatory element as one of the contributors to Crohn’s Disease. From this observation, we posed the question that motivated this research: How does genetic variation influence chromatin accessibility that may contribute to developing Crohn’s disease? Allelic imbalance is a phenomenon where the two alleles of a given gene are expressed at different levels in a given cell, either because of epigenetic inactivation of one of the two alleles, or because of genetic variation in regulatory regions (Wagner, Pokholok, Gunderson, Pastinen, & Blanchette, 2010). We developed a computational pipeline to perform allelic imbalance analysis on both Crohn’s disease patients and non-IBD control individuals to discover the correlation between chromatin accessibility with different copies of SNP alleles. We performed the analysis based on patients’ genotype information and genome-wide, sequencing-based chromatin accessibility assay results. 239 sites of allelic imbalance were detected in chromatin accessibility data from the CD group, and 273 imbalance sites from the non-IBD group. By comparing the identified sites of imbalance with GWAS results and reviewing the function of genes adjacent to these imbalanced sites, we identified several sites potentially contributing to Crohn’s disease. These identified sites/regions provide interesting targets for future studies to determine the cause of Crohn’s disease.Bachelor of Scienc

An Evaluation Study on the Level of China's Low-Carbon Manufacturing Based on Factor Analysis

AbstractBased on the absorption of some relevant research results, this paper establishes an evaluation model of the level of China's low-carbon manufacturing and uses the factor analysis method for empirical research. Then it analyses the exist problems of China's low-carbon manufacturing and puts forward some relevant countermeasures and suggestions

“Ask Everyone?” Understanding How Social Q&A Feedback Quality Influences Consumers\u27 Purchase Intentions

Social question & answer (Q&A) feedback is a novel form of electronic word-of-mouth that allows customers to ask questions and share opinions with peer customers. Based on the stimulus-organism-response framework, this paper proposes a model to describe how social Q&A feedback quality affects consumers\u27 willingness to purchase by influencing their perceived risk, perceived usefulness, and use intention. We focused on the social Q&A feature named Ask Everyone in Taobao and collected 153 valid responses through an online survey. Canonical correlation analysis was used to identify the association between feedback characteristics and feedback quality. Then, PLS-SEM was conducted to test the proposed research model. Results show that feedback quality negatively associated with perceived risk, but had a positive impact on perceived usefulness, use intention, and purchase intention. Findings of this research has both theoretical and practical implications for facilitating social Q&A design in e-commerce platforms

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping

Exploring Boarding Strategies for High-Speed Railway

In light of the increasing demand for passenger transportation on high-speed railway (HSR), the pedestrian flow at HSR stations has become quite crowded in many countries, which has attracted researchers to study the HSR boarding behavior. In this paper, we propose three boarding strategies based on the features of the boarding behavior at an origin HSR station; we then use a cellular automaton (CA) model to study the impacts of boarding strategies on each passenger’s motion during the boarding process at HSR station. The simulation results indicate that some of the three strategies can optimize some passengers’ boarding time and relieve the congestion degree, and the positive impacts on the boarding process are the most prominent when the three strategies are used simultaneously. The results can help administrators to effectively organize the boarding process at the origin HSR station

Learning single-cell chromatin accessibility profiles using meta-analytic marker genes

MOTIVATION: Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a valuable resource to learn cis-regulatory elements such as cell-type specific enhancers and transcription factor binding sites. However, cell-type identification of scATAC-seq data is known to be challenging due to the heterogeneity derived from different protocols and the high dropout rate. RESULTS: In this study, we perform a systematic comparison of seven scATAC-seq datasets of mouse brain to benchmark the efficacy of neuronal cell-type annotation from gene sets. We find that redundant marker genes give a dramatic improvement for a sparse scATAC-seq annotation across the data collected from different studies. Interestingly, simple aggregation of such marker genes achieves performance comparable or higher than that of machine-learning classifiers, suggesting its potential for downstream applications. Based on our results, we reannotated all scATAC-seq data for detailed cell types using robust marker genes. Their meta scATAC-seq profiles are publicly available at https://gillisweb.cshl.edu/Meta_scATAC. Furthermore, we trained a deep neural network to predict chromatin accessibility from only DNA sequence and identified key motifs enriched for each neuronal subtype. Those predicted profiles are visualized together in our database as a valuable resource to explore cell-type specific epigenetic regulation in a sequence-dependent and -independent manner

Machine-Learning-Enabled Virtual Screening for Inhibitors of Lysine-Specific Histone Demethylase 1

A machine learning approach has been applied to virtual screening for lysine specific demethylase 1 (LSD1) inhibitors. LSD1 is an important anti-cancer target. Machine learning models to predict activity were constructed using Morgan molecular fingerprints. The dataset, consisting of 931 molecules with LSD1 inhibition activity, was obtained from the ChEMBL database. An evaluation of several candidate algorithms on the main dataset revealed that the support vector regressor gave the best model, with a coefficient of determination (R2) of 0.703. Virtual screening, using this model, identified five predicted potent inhibitors from the ZINC database comprising more than 300,000 molecules. The virtual screening recovered a known inhibitor, RN1, as well as four compounds where activity against LSD1 had not previously been suggested. Thus, we performed a machine-learning-enabled virtual screening of LSD1 inhibitors using only the structural information of the molecules

In situ interface engineering for probing the limit of quantum dot photovoltaic devices.

Quantum dot (QD) photovoltaic devices are attractive for their low-cost synthesis, tunable band gap and potentially high power conversion efficiency (PCE). However, the experimentally achieved efficiency to date remains far from ideal. Here, we report an in-situ fabrication and investigation of single TiO2-nanowire/CdSe-QD heterojunction solar cell (QDHSC) using a custom-designed photoelectric transmission electron microscope (TEM) holder. A mobile counter electrode is used to precisely tune the interface area for in situ photoelectrical measurements, which reveals a strong interface area dependent PCE. Theoretical simulations show that the simplified single nanowire solar cell structure can minimize the interface area and associated charge scattering to enable an efficient charge collection. Additionally, the optical antenna effect of nanowire-based QDHSCs can further enhance the absorption and boost the PCE. This study establishes a robust 'nanolab' platform in a TEM for in situ photoelectrical studies and provides valuable insight into the interfacial effects in nanoscale solar cells

Translation and cross-cultural validation of a precision health tool, the Suboptimal Health Status Questionnaire-25, in Korean

Background Suboptimal health status (SHS) is a reversible stage between health and illness that is characterized by health complaints, low energy, general weakness, and chronic fatigue. The Suboptimal Health Status Questionnaire-25 (SHSQ-25) has been validated in three major populations (African, Asian, and Caucasian) and is internationally recognized as a reliable and robust tool for health estimation in general populations. This study focused on the development of K-SHSQ-25, a Korean version of the SHSQ-25, from its English version. Methods The SHSQ-25 was translated from English to Korean according to international guidelines set forth by the World Health Organization (WHO) for health instrument translation between different languages. A subsequent cross-sectional survey involved 460 healthy South Korean participants (aged 18-83 years; 65.4 % females) to answer the 25 questions focusing on the health perspectives of 5 domains, 1) fatigue, 2) cardiovascular health, 3) digestive tract, 4) immune system and 5) mental health. The K-SHSQ-25 was further validated using tests for reliability, internal consistency, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). Results The version of K-SHSQ-25 achieved linguistic, cultural, and conceptual equivalence to the English version. The intraclass correlation coefficient (ICC) of test-retest reliability for individual items ranged from 0.88 to 0.99. Reliability estimates based on internal consistency reached a Cronbach’s α of 0.953; the Cronbach’s α for each domain ranged from 0.76 to 0.94. Regarding construct validity, the EFA of the K-SHSQ-25 generally replicated the multidimensional structure (fatigue, cardiovascular, digestive, immune system, and mental health) and 25 questions. The CFA revealed that the root mean square error of approximation (RMSEA), goodness-of-fit index (GFI) and adjusted goodness of fit index (AGFI) were excellent (RMSEA = 0.069 \u3c 0.08, GFI = 0.929 \u3e 0.90, AGFI = 0.907 \u3e 0.90). The five domains of the K-SHSQ-25 showed significant correlations with each other (r = 0.59-0.81, P \u3c 0.001). The cut-off point of K-SHSQ-25 for SHS was determined as an SHS score of 25. The prevalence of SHS in this study was 60.0 % (276/460), with 47.8 % (76/159) for males and 58.5 % for females (176/301). Conclusions Our results indicate that the Korean version of SHSQ-25, K-SHSQ-25, is a transcultural equivalent, robust, valid, and reliable assessment tool for evaluating SHS in the Korean-speaking population