Generic structure and APPRAISAL resources in the editorial article Free money

Editorial is one of news genres aiming for opinion making and persuading. These functions determine that there are abundant evaluative resources in this genre. Exploring evaluative resources in the editorial could be conducive to understanding editorial text better and providing sensible suggestions for English learners to produce effectively persuasive writings. In view of this, the present study sets out to analyze the generic structure of the editorial Free money, then examine usage patterns of APPRAISAL resources in this text, and finally explore variations of APPRAISAL resources at different stages of the genre of this text. All APPRAISAL resources were coded based on APPRAISAL system and analyzed from quantitative and qualitative perspectives. It shows that Free money employed discussion genre with exposition and challenge embedded in the Background stage. An investigation into the usage of APPRAISAL resources found that negative ATTITUDE resources were mainly used to form the prosody of the text; more negation and concession resources within ENGAGEMENT were deployed to contract the dialogue; far more force raising GRADUATION resources were applied to amplify the evaluation. The APPRAISAL resources used at different stages of the editorial demonstrate distinct features with the aim of serving specific function of each stage. For instance, attribution resources were used in Issue stage to expand the dialogue and engage the readers; invoked resources were primarily employed in Background stage to make the statement objective; far more negative impressions in Side stage indicated the author’s concern, and more inscribed resources in Resolution stage manifested author’s attitude and made the conclusion impressive

The function of the Saccharomyces Cerevisiae ribonucleotide reductase second [beta] subunit in DNA repair

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Evolution-aware Protein Structure Comparison and Applications in Protein-Protein Interaction Prediction

Comparison of protein structures provide insights into the function and interactions of proteins and enhance our understanding of biomolecular mechanisms driving life and disease. Available protein structure comparison methods are based solely on the 3D geometric similarity, limiting their ability to detect functionally relevant correspondences between the residues of the proteins, especially for distantly related homologous proteins. However, non-geometric features contained in primary sequence and evolutionary history of proteins contain valuable information that can enhance detection of such similarities. In this study, we introduced a new method to incorporate additional biochemical and evolutionary features of the proteins being compared. We proposed UniScore as a new structure similarity score, which integrates geometric similarity, sequence similarity, and evolutionary profiles of the proteins. We further developed a corresponding Unialign algorithm for finding structural alignment of proteins with near-optimal UniScore. We evaluated Unialign in terms of the consistency between the alignments it produces with human-curated alignments, calculated by the fraction of correctly aligned residues. Experimental results show that UniAlign outperforms other structural programs in aligning proteins from the NCBI's human-curated Conserved Domain Database. Unialign's ability in detecting functionally important structural similarities is utilized in an application to discover interactions between HIV-1 ENV protein (gp41 and gp120) and human proteins. Structural compatibility of an HIV-human interaction pairs are evaluated via geometric, biochemical, and evolutionary features and a prediction model is developed using a Support Vector Machine. This provides the first model for prediction of interactions that can also generate a protein-protein 3D complex. The results of the HIV-human interaction study have discovered novel virus-host interactions as well as potential clinical targets for therapeutic intervention.Ph.D., Biomedical Engineering -- Drexel University, 201

Evaluation of Performance of Different Methods in Detecting Abrupt Climate Changes

We compared and evaluated the performance of five methods for detecting abrupt climate changes using a time series with artificially generated abrupt characteristics. Next, we analyzed these methods using annual mean surface air temperature records from the Shenyang meteorological station. Our results show that the moving t-test (MTT), Yamamoto (YAMA), and LePage (LP) methods can correctly and effectively detect abrupt changes in means, trends, and dynamic structure; however, they cannot detect changes in variability. We note that the sample size of the subseries used in these tests can affect their results. When the sample size of the subseries ranges from one-quarter to three-quarters of the jump scale, these methods can effectively detect abrupt changes; they perform best when the sample size is one-half of the jump scale. The Cramer method can detect abrupt changes in the mean and trend of a series but not changes in variability or dynamic structure. Finally, we found that the Mann-Kendall test could not detect any type of abrupt change. We found no difference in the results of any of the methods following removal of the mean, creation of an anomaly series, or normalization. However, detrending and study period selection affected the results of the Cramer and Mann-Kendall methods; in the latter case, they could lead to a completely different result

Towards Reliable Image Outpainting: Learning Structure-Aware Multimodal Fusion with Depth Guidance

Image outpainting technology generates visually plausible content regardless of authenticity, making it unreliable to be applied in practice. Thus, we propose a reliable image outpainting task, introducing the sparse depth from LiDARs to extrapolate authentic RGB scenes. The large field view of LiDARs allows it to serve for data enhancement and further multimodal tasks. Concretely, we propose a Depth-Guided Outpainting Network to model different feature representations of two modalities and learn the structure-aware cross-modal fusion. And two components are designed: 1) The Multimodal Learning Module produces unique depth and RGB feature representations from the perspectives of different modal characteristics. 2) The Depth Guidance Fusion Module leverages the complete depth modality to guide the establishment of RGB contents by progressive multimodal feature fusion. Furthermore, we specially design an additional constraint strategy consisting of Cross-modal Loss and Edge Loss to enhance ambiguous contours and expedite reliable content generation. Extensive experiments on KITTI and Waymo datasets demonstrate our superiority over the state-of-the-art method, quantitatively and qualitatively