Are They "American" Enough to Teach Social Studies? Korean American Teachers' Social Studies Teaching Experiences in American Public Schools

This study explores three Korean American social studies teachers' experiences of teaching social studies, focusing on their curricular and pedagogical perceptions and practices. Framed by sociocultural theory, this study aims to shed light on the heterogeneous stories and socially and culturally contextualized teaching experiences of Korean American social studies teachers, which have been largely undocumented in the social studies scholarship. The major research question for this study is: How do three Korean American social studies teachers perceive social studies curriculum and implement pedagogy in the realities of their classrooms? Subsidiary questions are: (a) What are these Korean American social studies teachers' perceptions and experiences of teaching profession in American public schools?; (b) How do these Korean American social studies teachers perceive social studies curriculum and implement pedagogy in the realities of classrooms?; and (c) How do sociocultural experiences of these teachers influence their curricular and pedagogical practices? This qualitative multicase study investigates three Korean American teachers who taught global history in urban public high schools in the Northeast. Data sources include a semester-long classroom observation, interviews, and artifacts. Findings indicate these three Korean American teachers exhibited diverse, complex, and contextualized experiences of teaching profession, and particularly teaching social studies. The racial minority teachers' experiences of racism and academic struggles during K-12 schooling, cross-cultural/international experiences, and familial immigration backgrounds served as a springboard for them to have better understandings of their culturally and linguistically diverse students and to teach for global/multicultural perspectives. Powerful teacher education infused with social justice perspectives and supportive, autonomous, and cooperative school atmosphere aided them to implement student-centered, inquiry-based pedagogies which improved academic engagement of their students. Meanwhile, misguided curricular beliefs and philosophical stance, rigid school culture, bureaucratic school personnel, pressure from high-stakes tests, test-driven contexts, and racism pervasive in school culture became barriers for the teachers to practice culturally relevant and meaningful pedagogy in their classrooms

Information extraction of +/-effect events to support opinion inference

Recently, work in NLP was initiated on a type of opinion inference that arises when opinions are expressed toward events which have positive or negative effects on entities, called +/-effect events. The ultimate goal is to develop a fully automatic system capable of recognizing inferred attitudes. To achieve its results, the inference system requires all instances of +/-effect events. Therefore, this dissertation focuses on +/-effect events to support opinion inference. To extract +/-effect events, we first need the list of +/-effect events. Due to significant sense ambiguity, our goal is to develop a sense-level rather than word-level lexicon. To handle sense-level information, WordNet is adopted. We adopt a graph-based method which is seeded by entries culled from FrameNet and then expanded by exploiting semantic relations in WordNet. We show that WordNet relations are useful for the polarity propagation in the graph model. In addition, to maximize the effectiveness of different types of information, we combine a graph-based method using WordNet relations and a standard classifier using gloss information. Further, we provide evidence that the model is an effective way to guide manual annotation to find +/-effect senses that are not in the seed set. To exploit the sense-level lexicons, we have to carry out word sense disambiguation. We present a knowledge-based +/-effect coarse-grained word sense disambiguation method based on selectional preferences via topic models. For more information, we first group senses, and then utilize topic models to model selectional preferences. Our experiments show that selectional preferences are helpful in our work. To support opinion inferences, we need to identify not only +/-effect events but also their affected entities automatically. Thus, we address both +/-effect event detection and affected entity identification. Since +/-effect events and their affected entities are closely related, instead of a pipeline system, we present a joint model to extract +/-effect events and their affected entities simultaneously. We demonstrate that our joint model is promising to extract +/-effect events and their affected entities jointly

Damped Euler system with attractive Riesz interaction forces

We consider the barotropic Euler equations with pairwise attractive Riesz interactions and linear velocity damping in the periodic domain. We establish the global-in-time well-posedness theory for the system near an equilibrium state. We also analyze the large-time behavior of solutions showing the exponential rate of convergence toward the equilibrium state as time goes to infinity.Comment: 24 page

Breakpoint mapping by whole genome sequencing identifies PTH2R gene disruption in a patient with midline craniosynostosis and a de novo balanced chromosomal rearrangement

Background: Craniosynostosis (CRS) is a premature closure of calvarial sutures caused by gene mutation or environmental factors or interaction between the two. Only a small proportion of non-syndromic CRS (NSC) patients have a known genetic cause, and thus, it would be meaningful to search for a causative gene disruption for the development NSC. We applied a whole genome sequencing approach on a 15-month-old boy with sagittal and metopic synostosis to identify a gene responsible for the development of the disease. Methods and results Conventional chromosome study revealed a complex paracentric inversion involving 2q14.3 and 2q34. Array comparative genomic hybridisation did not show any copy number variation. Multicolour banding analysis was carried out and the breakpoints were refined to 2q14 and 2q34. An intronic break of the PTH2R gene was detected by whole genome sequencing and fluorescence in situ hybridisation analysis confirmed disruption of PTH2R. Conclusions: We report PTH2R as a gene that is disrupted in NSC. The disruption of the PTH2R gene may cause uncontrolled proliferation and differentiation of chondrocytes, which in turn results in premature closure of sutures. This addition of PTH2R to the list of genes associated with NSC expands our understanding of the development of NSC

Tiotropium Is Predicted to Be a Promising Drug for COVID-19 Through Transcriptome-Based Comprehensive Molecular Pathway Analysis

The coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects almost everyone in the world in many ways. We previously predicted antivirals (atazanavir, remdesivir and lopinavir/ritonavir) and non-antiviral drugs (tiotropium and rapamycin) that may inhibit the replication complex of SARS-CoV-2 using our molecular transformer–drug target interaction (MT–DTI) deep-learning-based drug–target affinity prediction model. In this study, we dissected molecular pathways upregulated in SARS-CoV-2-infected normal human bronchial epithelial (NHBE) cells by analyzing an RNA-seq data set with various bioinformatics approaches, such as gene ontology, protein–protein interaction-based network and gene set enrichment analyses. The results indicated that the SARS-CoV-2 infection strongly activates TNF and NFκB-signaling pathways through significant upregulation of the TNF, IL1B, IL6, IL8, NFKB1, NFKB2 and RELB genes. In addition to these pathways, lung fibrosis, keratinization/cornification, rheumatoid arthritis, and negative regulation of interferon-gamma production pathways were also significantly upregulated. We observed that these pathologic features of SARS-CoV-2 are similar to those observed in patients with chronic obstructive pulmonary disease (COPD). Intriguingly, tiotropium, as predicted by MT–DTI, is currently used as a therapeutic intervention in COPD patients. Treatment with tiotropium has been shown to improve pulmonary function by alleviating airway inflammation. Accordingly, a literature search summarized that tiotropium reduced expressions of IL1B, IL6, IL8, RELA, NFKB1 and TNF in vitro or in vivo, and many of them have been known to be deregulated in COPD patients. These results suggest that COVID-19 is similar to an acute mode of COPD caused by the SARS-CoV-2 infection, and therefore tiotropium may be effective for COVID-19 patients

Generating Domain-Specific Clues Using News Corpus for Sentiment Classification

This paper addresses the problem of automatically generating domain-specific sentiment clues. The main idea is to bootstrap from a small seed set and generate new clues by using dependencies and collocation information between sentiment clues and sentence-level topics that would be a primary subject of sentiment expression (e.g., event, company, and person). The experiments show that the aggregated clues are effective for sentiment classification

Determining Mood for a Blog by Combining Multiple Sources of Evidence

Mood classification for blogs is useful in helping user-to-agent interaction for a variety of applications involving the web, such as user modeling, recommendation systems, and user interface fields. It is challenging at the same time because of the diversity of the characteristics of bloggers, their experiences, and the way moods are expressed. As an attempt to handle the diversity, we combine multiple sources of evidence for a mood type. Support Vector Machine based Mood Classifier (SVMMC) is integrated with Mood Flow Analyzer (MFA) that incorporates commonsense knowledge obtained from the general public (i.e. ConceptNet), the Affective Norms English Words (ANEW) list, and mood transitions. In combining the two different approaches, we employ a statistically weighted voting scheme based on the Support Vector Machine (SVM). For evaluation, we have built a mood corpus consisting of manually annotated blogs, which amounts to over 4000 blogs. Our proposed method outperforms SVMMC by 5.68% in precision. The improvement is attributed to the strategy of choosing more trustable classification results in an interleaving fashion between the SVMMC and our MFA

Domain-specific sentiment analysis using contextual feature generation

This paper presents a novel framework for sentiment analysis, which exploits sentiment topic information for generating context-driven features. Since the domain-specific nature of sentiment classification led the task more problematic, considering more contextual-information such as topic or domain is essential. In our system, we first automatically extract sentiment clues in different domains by our observation. We identified that a sentiment clue is often syntactically related to a sentiment topic in a sentence, which is defined as a primary subject of sentiment expression, such as event, company, and person. We bootstrap from a small set of seed clues and generate new clues by utilizing linguistic dependencies and collocation information between sentiment clues and sentiment topics. Next, we learn a domain-specific sentiment classifier for each domain with the newly aggregated clues. We ran experiments to see how the bootstrapping algorithm to converge and aggregate new clues and verified that the extracted domain-context features are more effective than generally-used features in sentiment analysis by running them on the same sentiment classifier