Object-oriented Modular Expert System Shell

Computer Scienc

Comparison of Altmetrics across Multiple Disciplines: Psychology, History, and Linguistics

Since their emergence in 2010, altmetrics as new indicators of research impact receive considerable attention from scientometricians and various other parties including librarians, who have long been providing citation analysis and bibliometric services in academic and research institutions. Despite their rapid popularity, the validity of altmetrics in research evaluation is not yet clear. One way of assessing a new metric for its suitability in research evaluation is to find out its correlation with an established source of evidence. This study investigates the correlation between altmetrics and citation count in Psychology, History and Linguistics disciplines. In addition, the study also explores the coverage of altmetric measures in each of these disciplines to identify the presence and potential usefulness of different altmetric measures in different disciplines. Altmetrics data from altmetrics.com and citation data from Web of Science database are used for articles published between 2010 and 2012 in top 70 journals of Psychology and Linguistics disciplines and 53 journals of History discipline. Out of 10, 5 metrics (Twitter, Facebook, Blogs, Mendeley and CiteULike) are found to have significant correlation with citation in Psychology while only 2 metrics (Mendeley and Twitter) are in Linguistic and History. In terms of coverage, discipline with higher citation rate (Psychology) attracts higher altmetric coverage while disciplines with lower citation rate (Linguistics and History) attract lower altmetric coverage. Of the three disciplines studied, Psychology is found to be the most likely candidate where altmetrics could be meaningful measures.Accepted versio

Literature review writing: a study of information selection from cited papers / Kokil Jaidka, Christopher Khoo and Jin-Cheon Na

This paper reports the results of a small study of how researchers select and edit research information from cited papers to include in a literature review. This is part of a bigger content analysis and linguistic analysis of literature reviews. This study aims to answer the following questions: where do authors select information from the cited papers (e.g., Abstract, Introduction, Conclusion section, etc.)? What types of information do they select (e.g., research objectives, results, etc.), and How do they transform that information (e.g., paraphrasing, cut-pasting, etc.)? In order to answer these questions, we analyzed the literature review section of 20 articles from the Journal of the American Society for Information Science & Technology, 2001-2008, to answer these questions. Referencing sentences were mapped to source papers to determine their origin. Other features of the source information were also annotated, such as the type of information selected and the types of editing changes made to it before including into the literature review. Preliminary results indicate that authors prefer to select information from the Abstract, Introduction and Conclusion sections of the cited papers. This information is transformed through cut-paste, paraphrase or higher-level semantic transformations to describe the research objective, methodology and results of the referenced study. The choices made in selecting and transforming the source information appeared to be related to the two styles of literature review finally constructed – integrative and descriptive literature reviews. Keywords: Literature reviews; Multi-document summarization; Information science; Information extraction; Information selection

Ontology Learning for Medical Digital Libraries

Abstract. Ontologies play an important role in the Semantic Web as well as in digital library and knowledge portal applications. This project seeks to develop an automatic method to enrich existing ontologies, especially in the identifica-tion of semantic relations between concepts in the ontology. The initial study investigates an approach of identifying pairs of related concepts in a medical domain using association rule induction and inferring the type of semantic rela-tion using the UMLS (Unified Medical Language System) semantic net. This is evaluated by comparing the result with manually assigned semantic relations based on an analysis of medical abstracts containing each pair of concepts. Our initial finding shows that the automatic process is promising, achieving a 68% coverage compared to manually tagging. However, natural language processing of medical abstracts is likely to improve the identification of semantic relations.

Evaluation of an immunochromatographic assay for the detection of anti-hepatitis A virus IgM

<p>Abstract</p> <p>Background</p> <p>Hepatitis A virus (HAV) is a causative agent of acute hepatitis, which is transmitted by person-to-person contact and via the faecal-oral route. Acute HAV infection is usually confirmed by anti-HAV IgM detection. In order to detect anti-HAV IgM in the serum of patients infected with HAV, we developed a rapid assay based on immunochromatography (ICA) and evaluated the sensitivity of this assay by comparing it with a commercial microparticle enzyme immunoassay (MEIA) that is widely used for serological diagnosis.</p> <p>Results</p> <p>The newly developed ICA showed 100% sensitivity and specificity when used to test 150 anti-HAV IgM-positive sera collected from infected patients and 75 negative sera from healthy subjects. Also, the sensitivity of ICA is about 10 times higher than MEIA used in this study by determining end point to detect independent on infected genotype of HAV. In addition, the ICA was able to detect 1 positive sample from among 50 sera from acute hepatitis patients that had tested negative for anti-HAV IgM using the MEIA.</p> <p>Conclusion</p> <p>Conclusively, ICA for the detection of anti-HAV IgM will be very effective for rapid assay to apply clinical diagnosis and epidemiological investigation on epidemics due to the simplicity, rapidity and specificity.</p

Tumor immune profiles noninvasively estimated by FDG PET with deep learning correlate with immunotherapy response in lung adenocarcinoma

Rationale: The clinical application of biomarkers reflecting tumor immune microenvironment is hurdled by the invasiveness of obtaining tissues despite its importance in immunotherapy. We developed a deep learning-based biomarker which noninvasively estimates a tumor immune profile with fluorodeoxyglucose positron emission tomography (FDG-PET) in lung adenocarcinoma (LUAD). Methods: A deep learning model to predict cytolytic activity score (CytAct) using semi-automatically segmented tumors on FDG-PET trained by a publicly available dataset paired with tissue RNA sequencing (n = 93). This model was validated in two independent cohorts of LUAD: SNUH (n = 43) and The Cancer Genome Atlas (TCGA) cohort (n = 16). The model was applied to the immune checkpoint blockade (ICB) cohort, which consists of patients with metastatic LUAD who underwent ICB treatment (n = 29). Results: The predicted CytAct showed a positive correlation with CytAct of RNA sequencing in validation cohorts (Spearman rho = 0.32, p = 0.04 in SNUH cohort; spearman rho = 0.47, p = 0.07 in TCGA cohort). In ICB cohort, the higher predicted CytAct of individual lesion was associated with more decrement in tumor size after ICB treatment (Spearman rho = -0.54, p < 0.001). Higher minimum predicted CytAct in each patient associated with significantly prolonged progression free survival and overall survival (Hazard ratio 0.25, p = 0.001 and 0.18, p = 0.004, respectively). In patients with multiple lesions, ICB responders had significantly lower variance of predicted CytActs (p = 0.005). Conclusion: The deep learning model that predicts CytAct using FDG-PET of LUAD was validated in independent cohorts. Our approach may be used to noninvasively assess an immune profile and predict outcomes of LUAD patients treated with ICB.

Independent effect of body mass index variation on amyloid-β positivity

ObjectivesThe relationship of body mass index (BMI) changes and variability with amyloid-β (Aβ) deposition remained unclear, although there were growing evidence that BMI is associated with the risk of developing cognitive impairment or AD dementia. To determine whether BMI changes and BMI variability affected Aβ positivity, we investigated the association of BMI changes and BMI variability with Aβ positivity, as assessed by PET in a non-demented population.MethodsWe retrospectively recruited 1,035 non-demented participants ≥50 years of age who underwent Aβ PET and had at least three BMI measurements in the memory clinic at Samsung Medical Center. To investigate the association between BMI change and variability with Aβ deposition, we performed multivariable logistic regression. Further distinctive underlying features of BMI subgroups were examined by employing a cluster analysis model.ResultsDecreased (odds ratio [OR] = 1.68, 95% confidence interval [CI] 1.16–2.42) or increased BMI (OR = 1.60, 95% CI 1.11–2.32) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI variability. A greater BMI variability (OR = 1.73, 95% CI 1.07–2.80) was associated with a greater risk of Aβ positivity after controlling for age, sex, APOE e4 genotype, years of education, hypertension, diabetes, baseline BMI, and BMI change. We also identified BMI subgroups showing a greater risk of Aβ positivity.ConclusionOur findings suggest that participants with BMI change, especially those with greater BMI variability, are more vulnerable to Aβ deposition regardless of baseline BMI. Furthermore, our results may contribute to the design of strategies to prevent Aβ deposition with respect to weight control

TonEBP recognizes R-loops and initiates m6A RNA methylation for R-loop resolution

R-loops are three-stranded, RNA???DNA hybrid, nucleic acid structures produced due to inappropriate processing of newly transcribed RNA or transcription-replication collision (TRC). Although R-loops are important for many cellular processes, their accumulation causes genomic instability and malignant diseases, so these structures are tightly regulated. It was recently reported that R-loop accumulation is resolved by methyltransferase-like 3 (METTL3)-mediated m6A RNA methylation under physiological conditions. However, it remains unclear how R-loops in the genome are recognized and induce resolution signals. Here, we demonstrate that tonicity-responsive enhancer binding protein (TonEBP) recognizes R-loops generated by DNA damaging agents such as ultraviolet (UV) or camptothecin (CPT). Single-molecule imaging and biochemical assays reveal that TonEBP preferentially binds a R-loop via both 3D collision and 1D diffusion along DNA in vitro. In addition, we find that TonEBP recruits METTL3 to R-loops through the Rel homology domain (RHD) for m6A RNA methylation. We also show that TonEBP recruits RNaseH1 to R-loops through a METTL3 interaction. Consistent with this, TonEBP or METTL3 depletion increases R-loops and reduces cell survival in the presence of UV or CPT. Collectively, our results reveal an R-loop resolution pathway by TonEBP and m6A RNA methylation by METTL3 and provide new insights into R-loop resolution processes