English Spelling Knowledge and Word Reading Skills of Arabic and Japanese ESL Learners

Previous research has demonstrated that L1 orthographic features and literacy experiences may influence some lower-level processing skills in L2 literacy development. The goal of this study is to expand understanding of this influence on the development of ESL word reading and spelling skills among a group of 49 intermediate-level Arabic learners of English as a Second Language (ESL) and a corresponding comparison group of 50 Japanese ESL learners. Data were collected on a spelling test, a reading comprehension test, and a series of word reading tasks which include reading words with a missing vowel, reading words with a missing consonant, reading a regular wordlist, and reading pseudo-words. The results indicated that at the same level of reading comprehension, the Japanese ESL group performed significantly better than the Arabic group on spelling and all the word reading measures except the accuracy and speed in reading words with a missing vowel. The study also found that the Arabic ESL learners were more adversely affected in both accuracy and speed of reading words with a missing consonant compared with reading words with a missing vowel. Furthermore, accuracy in reading words with a missing consonant was found to be the best predictor of reading comprehension for the Arabic group but for the Japanese group, spelling and accuracy in reading words with a missing consonant were both significant predictors of ESL reading comprehension. The findings were discussed in relation to previous research. Pedagogical implications were also addressed

Lower-Level Processing Skills in English-as-a-Second-Language Reading Comprehension: Possible Influence of First Language Orthography

Cross-linguistic studies on second language (L2) reading reveal that component skills of reading such as word recognition, phonemic decoding, spelling, and oral text reading are prone to the influence of first language (L1) orthography but few empirical studies have examined the possible influence of L1 orthography on these skills. This study investigates how adult ESL learners of two different L1 backgrounds (Spanish and Chinese) compare in their performances on word recognition efficiency, phonemic decoding efficiency, spelling, and oral text reading fluency and how these skills are related to their overall ability in reading comprehension. The differences in the learners’ performances on the component skills and the variations in the role of these skills in ESL reading comprehension indicated possible influence of the orthographic features of learners’ first language

Effects of discourse structure graphic organizers of EFL reading comprehension

This study investigated the effects of a 16-week reading instruction program with discourse structure graphic organizers (DSGOs) on the development of English reading comprehension among college-level English as a Foreign Language (EFL) students. A total of 340 first and third semester students of non-English majors at a Chinese university participated in this study. A DSGO completion test and a TOEFL (Test of English as a Foreign Language) reading comprehension test were administered before, immediately after, and 7 weeks following the instructional treatment. The results showed that the DSGO instruction significantly improved discourse comprehension as measured by the DSGO completion task, and the effect was retained 7 weeks after the instructional treatment. Significant improvement was also observed in the general reading ability as measured by TOEFL reading comprehension in the immediate posttest, but the effect did not persist in the delayed posttest. These findings apply to both the first and third semester students. Pedagogical implications of the DSGO instruction are discussed

An effective biomedical document classification scheme in support of biocuration: addressing class imbalance.

Published literature is an important source of knowledge supporting biomedical research. Given the large and increasing number of publications, automated document classification plays an important role in biomedical research. Effective biomedical document classifiers are especially needed for bio-databases, in which the information stems from many thousands of biomedical publications that curators must read in detail and annotate. In addition, biomedical document classification often amounts to identifying a small subset of relevant publications within a much larger collection of available documents. As such, addressing class imbalance is essential to a practical classifier. We present here an effective classification scheme for automatically identifying papers among a large pool of biomedical publications that contain information relevant to a specific topic, which the curators are interested in annotating. The proposed scheme is based on a meta-classification framework using cluster-based under-sampling combined with named-entity recognition and statistical feature selection strategies. We examined the performance of our method over a large imbalanced data set that was originally manually curated by the Jackson Laboratory\u27s Gene Expression Database (GXD). The set consists of more than 90 000 PubMed abstracts, of which about 13 000 documents are labeled as relevant to GXD while the others are not relevant. Our results, 0.72 precision, 0.80 recall and 0.75 f-measure, demonstrate that our proposed classification scheme effectively categorizes such a large data set in the face of data imbalance

Integrating image caption information into biomedical document classification in support of biocuration.

Gathering information from the scientific literature is essential for biomedical research, as much knowledge is conveyed through publications. However, the large and rapidly increasing publication rate makes it impractical for researchers to quickly identify all and only those documents related to their interest. As such, automated biomedical document classification attracts much interest. Such classification is critical in the curation of biological databases, because biocurators must scan through a vast number of articles to identify pertinent information within documents most relevant to the database. This is a slow, labor-intensive process that can benefit from effective automation. We present a document classification scheme aiming to identify papers containing information relevant to a specific topic, among a large collection of articles, for supporting the biocuration classification task. Our framework is based on a meta-classification scheme we have introduced before; here we incorporate into it features gathered from figure captions, in addition to those obtained from titles and abstracts. We trained and tested our classifier over a large imbalanced dataset, originally curated by the Gene Expression Database (GXD). GXD collects all the gene expression information in the Mouse Genome Informatics (MGI) resource. As part of the MGI literature classification pipeline, GXD curators identify MGI-selected papers that are relevant for GXD. The dataset consists of ~60 000 documents (5469 labeled as relevant; 52 866 as irrelevant), gathered throughout 2012-2016, in which each document is represented by the text of its title, abstract and figure captions. Our classifier attains precision 0.698, recall 0.784, f-measure 0.738 and Matthews correlation coefficient 0.711, demonstrating that the proposed framework effectively addresses the high imbalance in the GXD classification task. Moreover, our classifier\u27s performance is significantly improved by utilizing information from image captions compared to using titles and abstracts alone; this observation clearly demonstrates that image captions provide substantial information for supporting biomedical document classification and curation. Database URL

CIS UDEL Working Notes on ImageCLEF 2015: Compound figure detection task

Abstract. Figures that are included in biomedical publications play an important role in understanding essential aspects of the paper. Much work over the past few years has focused on figure analysis and classification in biomedical documents. As many of the figures appearing in biomedical documents comprise multiple panels (subfigures), the first step in the analysis requires identification of compound figures and their segmentation into subfigures. There is a wide variety ways to detect compound figures. In this paper, we utilize only visual information to identify compound vs non-compound figures. We have tested the proposed approach on the ImageCLEF 2015 benchmark of 10, 434 images; our approach has achieved an accuracy of 82.82%, thus demonstrating the best performance when compared to other systems that use only visual information for addressing the compound figure detection task

Nonlinear dynamic analysis of composite piezoelectric plates with graphene skin

This paper studies the nonlinear dynamical characteristic of a composite plate made of new three-phase materials which include the graphene (GP) combined with macro fiber composite (MFC) in the polymer. The GP is supposed to be uniformly dispersed in the upper and lower surfaces of the composite laminated plate with 1–3 mode of macro fiber. The cross-ply MFC composite laminated plate is subjected to transversal excitations. The constitutive laws for the MFC-GP composite material are obtained based on the rule of mixture for multi-components of composite material. The nonlinear governing equations of motion of the MFC-GP plate are derived by Hamilton's principle and the von Kármán geometrical kinematics. Galerkin's approach is employed to discretize the partial differential governing equations into a two-degree-of-freedom nonlinear system. Then, stability analysis is conducted to investigate the influences of various parameters on natural frequencies of the MFC-GP plate, with a particular focus on the effects of GP volume fraction, initial conditions and damping coefficients on nonlinear vibration behaviours of the composite plate

Utilizing image and caption information for biomedical document classification.

MOTIVATION: Biomedical research findings are typically disseminated through publications. To simplify access to domain-specific knowledge while supporting the research community, several biomedical databases devote significant effort to manual curation of the literature-a labor intensive process. The first step toward biocuration requires identifying articles relevant to the specific area on which the database focuses. Thus, automatically identifying publications relevant to a specific topic within a large volume of publications is an important task toward expediting the biocuration process and, in turn, biomedical research. Current methods focus on textual contents, typically extracted from the title-and-abstract. Notably, images and captions are often used in publications to convey pivotal evidence about processes, experiments and results. RESULTS: We present a new document classification scheme, using both image and caption information, in addition to titles-and-abstracts. To use the image information, we introduce a new image representation, namely Figure-word, based on class labels of subfigures. We use word embeddings for representing captions and titles-and-abstracts. To utilize all three types of information, we introduce two information integration methods. The first combines Figure-words and textual features obtained from captions and titles-and-abstracts into a single larger vector for document representation; the second employs a meta-classification scheme. Our experiments and results demonstrate the usefulness of the newly proposed Figure-words for representing images. Moreover, the results showcase the value of Figure-words, captions and titles-and-abstracts in providing complementary information for document classification; these three sources of information when combined, lead to an overall improved classification performance. AVAILABILITY AND IMPLEMENTATION: Source code and the list of PMIDs of the publications in our datasets are available upon request

PGC-1α Inhibits Oleic Acid Induced Proliferation and Migration of Rat Vascular Smooth Muscle Cells

BACKGROUND: Oleic acid (OA) stimulates vascular smooth muscle cell (VSMC) proliferation and migration. The precise mechanism is still unclear. We sought to investigate the effects of peroxisome proliferator-activated receptor gamma (PPARgamma) coactivator-1 alpha (PGC-1alpha) on OA-induced VSMC proliferation and migration. PRINCIPAL FINDINGS: Oleate and palmitate, the most abundant monounsaturated fatty acid and saturated fatty acid in plasma, respectively, differently affect the mRNA and protein levels of PGC-1alpha in VSMCs. OA treatment resulted in a reduction of PGC-1alpha expression, which may be responsible for the increase in VSMC proliferation and migration caused by this fatty acid. In fact, overexpression of PGC-1alpha prevented OA-induced VSMC proliferation and migration while suppression of PGC-1alpha by siRNA enhanced the effects of OA. In contrast, palmitic acid (PA) treatment led to opposite effects. This saturated fatty acid induced PGC-1alpha expression and prevented OA-induced VSMC proliferation and migration. Mechanistic study demonstrated that the effects of PGC-1alpha on VSMC proliferation and migration result from its capacity to prevent ERK phosphorylation. CONCLUSIONS: OA and PA regulate PGC-1alpha expression in VSMCs differentially. OA stimulates VSMC proliferation and migration via suppression of PGC-1alpha expression while PA reverses the effects of OA by inducing PGC-1alpha expression. Upregulation of PGC-1alpha in VSMCs provides a potential novel strategy in preventing atherosclerosis