Institutional determinants of construction safety management strategies of contractors in Hong Kong

published_or_final_versio

Differences Help Recognition: A Probabilistic Interpretation

<div><p>This paper presents a computational model to address one prominent psychological behavior of human beings to recognize images. The basic pursuit of our method can be concluded as that differences among multiple images help visual recognition. Generally speaking, we propose a statistical framework to distinguish what kind of image features capture sufficient category information and what kind of image features are common ones shared in multiple classes. Mathematically, the whole formulation is subject to a generative probabilistic model. Meanwhile, a discriminative functionality is incorporated into the model to interpret the differences among all kinds of images. The whole Bayesian formulation is solved in an Expectation-Maximization paradigm. After finding those discriminative patterns among different images, we design an image categorization algorithm to interpret how these differences help visual recognition within the bag-of-feature framework. The proposed method is verified on a variety of image categorization tasks including outdoor scene images, indoor scene images as well as the airborne SAR images from different perspectives.</p></div

Top 10 enriched HPO terms by the NOA method and hypergeometric enrichment.

<p>Both enrichment methods identify HPO terms related to neoplasm as the top hits. However, these two methods give different enriched terms and different ranks of terms.</p><p>Top 10 enriched HPO terms by the NOA method and hypergeometric enrichment.</p

HPOSim: An R Package for Phenotypic Similarity Measure and Enrichment Analysis Based on the Human Phenotype Ontology

<div><p>Background</p><p>Phenotypic features associated with genes and diseases play an important role in disease-related studies and most of the available methods focus solely on the Online Mendelian Inheritance in Man (OMIM) database without considering the controlled vocabulary. The Human Phenotype Ontology (HPO) provides a standardized and controlled vocabulary covering phenotypic abnormalities in human diseases, and becomes a comprehensive resource for computational analysis of human disease phenotypes. Most of the existing HPO-based software tools cannot be used offline and provide only few similarity measures. Therefore, there is a critical need for developing a comprehensive and offline software for phenotypic features similarity based on HPO.</p><p>Results</p><p>HPOSim is an R package for analyzing phenotypic similarity for genes and diseases based on HPO data. Seven commonly used semantic similarity measures are implemented in HPOSim. Enrichment analysis of gene sets and disease sets are also implemented, including hypergeometric enrichment analysis and network ontology analysis (NOA).</p><p>Conclusions</p><p>HPOSim can be used to predict disease genes and explore disease-related function of gene modules. HPOSim is open source and freely available at SourceForge (<a href="https://sourceforge.net/p/hposim/" target="_blank">https://sourceforge.net/p/hposim/</a>).</p></div

Disease modules of the cancer entries in OMIM.

<p>OMIM:191600 (URETER, CANCER OF) and OMIM:610644 (PALMOPLANTAR HYPERKERATOSIS WITH SQUAMOUS CELL CARCINOMA OF SKIN AND 46,XX SEX REVERSAL) could not be grouped into a certain module.</p><p>Disease modules of the cancer entries in OMIM.</p

Compactness of the Lithium Peroxide Thin Film Formed in Li–O₂ Batteries and Its Link to the Charge Transport Mechanism: Insights from Stochastic Simulations

We simulated the discharge process of Li–O₂ batteries and the growth of Li₂O₂ thin films at the mesoscale with a novel kinetic Monte Carlo model, which combined a stochastic description of mass transport and detailed elementary reaction kinetics. The simulation results show that the ordering of the Li₂O₂ thin film is determined by the interplay between diffusion and reaction kinetics. Due to the fast reaction kinetics on the catalyst, the Li₂O₂ formed in the presence of catalyst (cat-CNF) shows a low degree of ordering and is more likely to be amorphous. Moreover, the mobility of the LiO₂ ion pair, which depends largely on the nature of the electrolyte, also impacts the homogeneity of the compactness of the Li₂O₂ thin film. These results are of high importance for understanding the role of the catalyst and reaction kinetics in Li–O₂ batteries

Comparison of HPOSim and other HPO-based tools.

<p>* PhenomeNET only supports human genes included in OMIM.</p><p># Although OntoSIML and OwlSim provide functions for calculating semantic similarity, users are required to manually input the mapping from entities (gene or disease) to HPO terms, which entails additional preprocessing effort.</p><p>“√” means the tool provides the function and “×” means the tool does not.</p><p>Comparison of HPOSim and other HPO-based tools.</p

Gene modules of the aging network.

<p>* N/A indicates that there are no enriched KEGG pathway (p-value<0.05) for this module.</p><p>Module M5 only have two enriched KEGG pathway (p-value<0.05).</p><p>Gene FOXO4 (Entrez ID: 4303) could not be grouped into a certain module.</p><p>Gene modules of the aging network.</p

Framework of HPOSim.

<p>Users can use HPOSim to calculate semantic similarity for HPO terms, genes and diseases. HPOSim can also be used to identify enriched HPO terms for gene set and disease set.</p

Example of the structure of HPO.

<p>HPO term <i>Abnormality of the joints of the lower limbs</i> (HP:0100491) and all its ancestor terms are shown. Each term in the HPO describes a phenotypic abnormality. Terms are related to parent terms by “is a” relationships in the form of a directed acyclic graph. If a disease or a gene is annotated to a term, it will also be annotated to all of its ancestors.</p