An Investigation into the Pedagogical Features of Documents

Characterizing the content of a technical document in terms of its learning utility can be useful for applications related to education, such as generating reading lists from large collections of documents. We refer to this learning utility as the "pedagogical value" of the document to the learner. While pedagogical value is an important concept that has been studied extensively within the education domain, there has been little work exploring it from a computational, i.e., natural language processing (NLP), perspective. To allow a computational exploration of this concept, we introduce the notion of "pedagogical roles" of documents (e.g., Tutorial and Survey) as an intermediary component for the study of pedagogical value. Given the lack of available corpora for our exploration, we create the first annotated corpus of pedagogical roles and use it to test baseline techniques for automatic prediction of such roles.Comment: 12th Workshop on Innovative Use of NLP for Building Educational Applications (BEA) at EMNLP 2017; 12 page

The normal-auxeticity mechanical phase transition in graphene

When a solid object is stretched, in general, it shrinks transversely. However, the abnormal ones are auxetic, which exhibit lateral expansion, or negative Poisson ratio. While graphene is a paradigm 2D material, surprisingly, graphene converts from normal to auxetic at certain strains. Here, we show via molecular dynamics simulations that the normal-auxeticity mechanical phase transition only occurs in uniaxial tension along the armchair direction or the nearest neighbor direction. Such a characteristic persists at temperatures up to 2400 K. Besides monolayer, bilayer and multi-layer graphene also possess such a normal-auxeticity transition. This unique property could extend the applications of graphene to new horizons

Protein subcellular localization prediction based on compartment-specific features and structure conservation

BACKGROUND: Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. RESULTS: We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. CONCLUSION: Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes

Evaluation of PM2.5 Surface Concentration Simulated by Version 1 of the Nasa's MERRA Aerosol Reanalysis Over Israel and Taiwan

Version 1 of the NASA MERRA Aerosol Reanalysis (MERRAero) assimilates bias-corrected 18 aerosol optical depth (AOD) data from MODIS-Terra and MODIS-Aqua, and simulates particulate 19 matter (PM) concentration data to reproduce a consistent database of AOD and PM concentration around 20 the world from 2002 to the end of 2015. The purpose of this paper is to evaluate MERRAeros simulation 21 of fine PM concentration against surface measurements in two regions of the world with relatively high 22 levels of PM concentration but with profoundly different PM composition, those of Israel and Taiwan. 23 Being surrounded by major deserts, Israels PM load is characterized by a significant contribution of 24 mineral dust, and secondary contributions of sea salt particles, given its proximity to the Mediterranean 25 Sea, and sulfate particles originating from Israels own urban activities and transported from Europe. 26 Taiwans PM load is composed primarily of anthropogenic particles (sulfate, nitrate and carbonaceous 27 particles) locally produced or transported from China, with an additional contribution of springtime 28 transport of mineral dust originating from Chinese and Mongolian deserts. The evaluation in Israel 29 produced favorable results with MERRAero slightly overestimating measurements by 6 on average 30 and reproducing an excellent year-to-year and seasonal fluctuation. The evaluation in Taiwan was less 31 favorable with MERRAero underestimating measurements by 42 on average. Two likely reasons 32 explain this discrepancy: emissions of anthropogenic PM and their precursors are largely uncertain in 33 China, and MERRAero doesnt include nitrate particles in its simulation, a pollutant of predominately 34 anthropogenic sources. MERRAero nevertheless simulates well the concentration of fine PM during the 35 summer, when Taiwan is least affected by the advection of pollution from China

2015-2016 Dean\u27s Showcase No. 1

https://spiral.lynn.edu/conservatory_deansshowcase/1006/thumbnail.jp

Fenofibrate reduces the severity of neuroretinopathy in a type 2 model of diabetes without inducing peroxisome proliferator-activated receptor alpha-dependent retinal gene expression

Fenofibrate slows the progression of clinical diabetic retinopathy (DR), but its mechanism of action in the retina remains unclear. Fenofibrate is a known agonist of peroxisome proliferator-activated receptor alpha (PPARα), a transcription factor critical for regulating metabolism, inflammation and oxidative stress. Using a DR mouse model