bluuebunny/crossref_metadata_2025 huggingface dataset:
Elevated WBP2 Expression in HER2-positive Breast Cancers Correlates with Sensitivity to Trastuzumab-based Neoadjuvant Therapy: A Retrospective and Multicentric Study
Authors: Kang Shin-Ae , Guan Jye Swei , Tan Hock Jin , Chu Tinghine , Thike Aye Aye , Bernadó Cristina , Arribas Joaquín , Wong Chow Yin , Tan Puay Hoon , Gudi Mihir , Putti Thomas Choudary , Sohn Joohyuk , Lim Swee Ho , Lee Soo Chin , Lim Yoon Pin
Published: December 2018

Purpose:

Trastuzumab-based chemotherapy has shown remarkable clinical benefits for patients with HER2-positive breast cancer. However, treatment regimens involving trastuzumab had little or no effect for a subset of patients. Preliminary studies revealed WW-binding protein 2 (WBP2), an oncogenic transcription coactivator, to be coamplified with HER2 in 36% of HER2-positive breast cancers. We hypothesize that WBP2 regulates and correlates with the response of HER2-positive breast cancer to trastuzumab.

Experimental Design:

The coexpression of WBP2 and HER2 in breast tumors was validated using IHC. The role and mechanism of WBP2 in regulating breast cancer response to trastuzumab was elucidated using in vitro, patient-derived xenograft and murine xenograft models. A multicenter retrospective study involving 143 patients given neoadjuvant trastuzumab-based chemotherapy was conducted to determine whether WBP2 expression correlates with pathologic complete response (pCR).

Results:

Elevated expression of WBP2 significantly enhanced breast cancer's response to trastuzumab by augmenting trastuzumab-induced HER2 downregulation and cell-cycle arrest via inhibition of cyclin D expression. High level of WBP2 correlated with better pCR (67.19%) compared with low WBP2 level (26.58%). The highest response was observed in subgroups of patients with high WBP2–expressing tumors also aged below 50 years (77.78%) or were premenopausal in status (73.33%). Retrospectively, WBP2 demonstrated sensitivity of 80% to 81% and specificity of 76.5% to 80% in discriminating between patients showing pCR and non-pCR.

Conclusions:

WBP2 expression correlates with the response of HER2-positive breast cancer to trastuzumab-based neoadjuvant chemotherapy.
ALMANYA’DA GÜVENCELİ-ESNEKLİK UYGULAMALARI
Authors: GİRGİN Mehmet Sencer , DOĞAN Seyhun
Published: December 2018

Bu çalışma, Avrupa’nın işgücü
piyasalarında ihtiyaç duyulan esnekliği sağlamak amacıyla geliştirilen ve Danimarka
önderliğinde hayata geçirilen, güvenceli-esneklik işgücü piyasası yapısının,
Almanya’da ulusal piyasa yapısına ve toplumsal kabullere uygun olarak
uygulamaya konulma şeklinin incelenmesini amaçlamaktadır. Bu kapsamda,
Almanya’nın güvenceli-esneklik uyarlaması sonucu ulusal işgücü piyasasında sağlanan
dönüşüm süreciyle işsizlik oranlarında elde edilen başarılı sonuçlar incelenmiştir.
Pushing the Limits of Translation Quality Estimation
Authors: Martins André F. T. , Junczys-Dowmunt Marcin , Kepler Fabio N. , Astudillo Ramón , Hokamp Chris , Grundkiewicz Roman
Published: December 2018

Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways. However, this potential is currently limited by the relatively low accuracy of existing systems. In this paper, we achieve remarkable improvements by exploiting synergies between the related tasks of word-level quality estimation and automatic post-editing. First, we stack a new, carefully engineered, neural model into a rich feature-based word-level quality estimation system. Then, we use the output of an automatic post-editing system as an extra feature, obtaining striking results on WMT16: a word-level FMULT1 score of 57.47% (an absolute gain of +7.95% over the current state of the art), and a Pearson correlation score of 65.56% for sentence-level HTER prediction (an absolute gain of +13.36%).
Learning to Understand Phrases by Embedding the Dictionary
Authors: Hill Felix , Cho Kyunghyun , Korhonen Anna , Bengio Yoshua
Published: December 2018

Distributional models that learn rich semantic word representations are a success story of recent NLP research. However, developing models that learn useful representations of phrases and sentences has proved far harder. We propose using the definitions found in everyday dictionaries as a means of bridging this gap between lexical and phrasal semantics. Neural language embedding models can be effectively trained to map dictionary definitions (phrases) to (lexical) representations of the words defined by those definitions. We present two applications of these architectures: reverse dictionaries that return the name of a concept given a definition or description and general-knowledge crossword question answerers. On both tasks, neural language embedding models trained on definitions from a handful of freely-available lexical resources perform as well or better than existing commercial systems that rely on significant task-specific engineering. The results highlight the effectiveness of both neural embedding architectures and definition-based training for developing models that understand phrases and sentences.
A Bayesian Model of Diachronic Meaning Change
Authors: Frermann Lea , Lapata Mirella
Published: December 2018

Word meanings change over time and an automated procedure for extracting this information from text would be useful for historical exploratory studies, information retrieval or question answering. We present a dynamic Bayesian model of diachronic meaning change, which infers temporal word representations as a set of senses and their prevalence. Unlike previous work, we explicitly model language change as a smooth, gradual process. We experimentally show that this modeling decision is beneficial: our model performs competitively on meaning change detection tasks whilst inducing discernible word senses and their development over time. Application of our model to the SemEval-2015 temporal classification benchmark datasets further reveals that it performs on par with highly optimized task-specific systems.
Concept Grounding to Multiple Knowledge Bases via Indirect Supervision
Authors: Tsai Chen-Tse , Roth Dan
Published: December 2018

We consider the problem of disambiguating concept mentions appearing in documents and grounding them in multiple knowledge bases, where each knowledge base addresses some aspects of the domain. This problem poses a few additional challenges beyond those addressed in the popular Wikification problem. Key among them is that most knowledge bases do not contain the rich textual and structural information Wikipedia does; consequently, the main supervision signal used to train Wikification rankers does not exist anymore. In this work we develop an algorithmic approach that, by carefully examining the relations between various related knowledge bases, generates an indirect supervision signal it uses to train a ranking model that accurately chooses knowledge base entries for a given mention; moreover, it also induces prior knowledge that can be used to support a global coherent mapping of all the concepts in a given document to the knowledge bases.

Using the biomedical domain as our application, we show that our indirectly supervised ranking model outperforms other unsupervised baselines and that the quality of this indirect supervision scheme is very close to a supervised model. We also show that considering multiple knowledge bases together has an advantage over grounding concepts to each knowledge base individually.
Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models
Authors: Stratos Karl , Collins Michael , Hsu Daniel
Published: December 2018

We tackle unsupervised part-of-speech (POS) tagging by learning hidden Markov models (HMMs) that are particularly well-suited for the problem. These HMMs, which we call anchor HMMs, assume that each tag is associated with at least one word that can have no other tag, which is a relatively benign condition for POS tagging (e.g., “the” is a word that appears only under the determiner tag). We exploit this assumption and extend the non-negative matrix factorization framework of Arora et al. (2013) to design a consistent estimator for anchor HMMs. In experiments, our algorithm is competitive with strong baselines such as the clustering method of Brown et al. (1992) and the log-linear model of Berg-Kirkpatrick et al. (2010). Furthermore, it produces an interpretable model in which hidden states are automatically lexicalized by words.
Multilingual Projection for Parsing Truly Low-Resource Languages
Authors: Agić Željko , Johannsen Anders , Plank Barbara , Alonso Héctor Martínez , Schluter Natalie , Søgaard Anders
Published: December 2018

We propose a novel approach to cross-lingual part-of-speech tagging and dependency parsing for truly low-resource languages. Our annotation projection-based approach yields tagging and parsing models for over 100 languages. All that is needed are freely available parallel texts, and taggers and parsers for resource-rich languages. The empirical evaluation across 30 test languages shows that our method consistently provides top-level accuracies, close to established upper bounds, and outperforms several competitive baselines.
Erratum: “Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment”
Authors: Belinkov Yonatan , Lei Tao , Barzilay Regina , Globerson Amir
Published: December 2018

Correction for the list of authors in the reference (Seddah et al., 2013).
Exploiting Parallel News Streams for Unsupervised Event Extraction
Authors: Zhang Congle , Soderland Stephen , Weld Daniel S.
Published: December 2018

Most approaches to relation extraction, the task of extracting ground facts from natural language text, are based on machine learning and thus starved by scarce training data. Manual annotation is too expensive to scale to a comprehensive set of relations. Distant supervision, which automatically creates training data, only works with relations that already populate a knowledge base (KB). Unfortunately, KBs such as FreeBase rarely cover event relations ( e.g. “person travels to location”). Thus, the problem of extracting a wide range of events — e.g., from news streams — is an important, open challenge.

This paper introduces NewsSpike-RE, a novel, unsupervised algorithm that discovers event relations and then learns to extract them. NewsSpike-RE uses a novel probabilistic graphical model to cluster sentences describing similar events from parallel news streams. These clusters then comprise training data for the extractor. Our evaluation shows that NewsSpike-RE generates high quality training sentences and learns extractors that perform much better than rival approaches, more than doubling the area under a precision-recall curve compared to Universal Schemas.
Entity Disambiguation with Web Links
Authors: Chisholm Andrew , Hachey Ben
Published: December 2018

Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories. We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, name, context, and coherence models from a corpus of web pages with links to Wikipedia. Experiments compare web link models to Wikipedia models on well-known conll and tac data sets.

Results show that using 34 million web links approaches Wikipedia performance. Combining web link and Wikipedia models produces the best-known disambiguation accuracy of 88.7 on standard newswire test data.
Learning Composition Models for Phrase Embeddings
Authors: Yu Mo , Dredze Mark
Published: December 2018

Lexical embeddings can serve as useful representations for words for a variety of NLP tasks, but learning embeddings for phrases can be challenging. While separate embeddings are learned for each word, this is infeasible for every phrase. We construct phrase embeddings by learning how to compose word embeddings using features that capture phrase structure and context. We propose efficient unsupervised and task-specific learning objectives that scale our model to large datasets. We demonstrate improvements on both language modeling and several phrase semantic similarity tasks with various phrase lengths. We make the implementation of our model and the datasets available for general use.
Combining Minimally-supervised Methods for Arabic Named Entity Recognition
Authors: Althobaiti Maha , Kruschwitz Udo , Poesio Massimo
Published: December 2018

Supervised methods can achieve high performance on NLP tasks, such as Named Entity Recognition (NER), but new annotations are required for every new domain and/or genre change. This has motivated research in minimally supervised methods such as semi-supervised learning and distant learning, but neither technique has yet achieved performance levels comparable to those of supervised methods. Semi-supervised methods tend to have very high precision but comparatively low recall, whereas distant learning tends to achieve higher recall but lower precision. This complementarity suggests that better results may be obtained by combining the two types of minimally supervised methods. In this paper we present a novel approach to Arabic NER using a combination of semi-supervised and distant learning techniques. We trained a semi-supervised NER classifier and another one using distant learning techniques, and then combined them using a variety of classifier combination schemes, including the Bayesian Classifier Combination (BCC) procedure recently proposed for sentiment analysis. According to our results, the BCC model leads to an increase in performance of 8 percentage points over the best base classifiers.
One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations
Authors: Ji Yangfeng , Eisenstein Jacob
Published: December 2018

Discourse relations bind smaller linguistic units into coherent texts. Automatically identifying discourse relations is difficult, because it requires understanding the semantics of the linked arguments. A more subtle challenge is that it is not enough to represent the meaning of each argument of a discourse relation, because the relation may depend on links between lowerlevel components, such as entity mentions. Our solution computes distributed meaning representations for each discourse argument by composition up the syntactic parse tree. We also perform a downward compositional pass to capture the meaning of coreferent entity mentions. Implicit discourse relations are then predicted from these two representations, obtaining substantial improvements on the Penn Discourse Treebank.
Plato: A Selective Context Model for Entity Resolution
Authors: Lazic Nevena , Subramanya Amarnag , Ringgaard Michael , Pereira Fernando
Published: December 2018

We present Plato, a probabilistic model for entity resolution that includes a novel approach for handling noisy or uninformative features, and supplements labeled training data derived from Wikipedia with a very large unlabeled text corpus. Training and inference in the proposed model can easily be distributed across many servers, allowing it to scale to over 107 entities. We evaluate Plato on three standard datasets for entity resolution. Our approach achieves the best results to-date on TAC KBP 2011 and is highly competitive on both the CoNLL 2003 and TAC KBP 2012 datasets.
Parallel Algorithms for Unsupervised Tagging
Authors: Ravi Sujith , Vassilivitskii Sergei , Rastogi Vibhor
Published: December 2018

We propose a new method for unsupervised tagging that finds minimal models which are then further improved by Expectation Maximization training. In contrast to previous approaches that rely on manually specified and multi-step heuristics for model minimization, our approach is a simple greedy approximation algorithm DMLC (Distributed-Minimum-Label-Cover) that solves this objective in a single step.

We extend the method and show how to efficiently parallelize the algorithm on modern parallel computing platforms while preserving approximation guarantees. The new method easily scales to large data and grammar sizes, overcoming the memory bottleneck in previous approaches. We demonstrate the power of the new algorithm by evaluating on various sequence labeling tasks: Part-of-Speech tagging for multiple languages (including low-resource languages), with complete and incomplete dictionaries, and supertagging, a complex sequence labeling task, where the grammar size alone can grow to millions of entries. Our results show that for all of these settings, our method achieves state-of-the-art scalable performance that yields high quality tagging outputs.
A Tabular Method for Dynamic Oracles in Transition-Based Parsing
Authors: Goldberg Yoav , Sartorio Francesco , Satta Giorgio
Published: December 2018

We develop parsing oracles for two transition-based dependency parsers, including the arc-standard parser, solving a problem that was left open in (Goldberg and Nivre, 2013). We experimentally show that using these oracles during training yields superior parsing accuracies on many languages.
Joint Modeling of Opinion Expression Extraction and Attribute Classification
Authors: Yang Bishan , Cardie Claire
Published: December 2018

In this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification. Traditional fine-grained opinion analysis systems address these problems in isolation and thus cannot capture interactions among the textual spans of opinion expressions and their opinion-related properties. We present two types of joint approaches that can account for such interactions during 1) both learning and inference or 2) only during inference. Extensive experiments on a standard dataset demonstrate that our approaches provide substantial improvements over previously published results. By analyzing the results, we gain some insight into the advantages of different joint models.
Predicting the Difficulty of Language Proficiency Tests
Authors: Beinborn Lisa , Zesch Torsten , Gurevych Iryna
Published: December 2018

Language proficiency tests are used to evaluate and compare the progress of language learners. We present an approach for automatic difficulty prediction of C-tests that performs on par with human experts. On the basis of detailed analysis of newly collected data, we develop a model for C-test difficulty introducing four dimensions: solution difficulty, candidate ambiguity, inter-gap dependency, and paragraph difficulty. We show that cues from all four dimensions contribute to C-test difficulty.
An HDP Model for Inducing Combinatory Categorial Grammars
Authors: Bisk Yonatan , Hockenmaier Julia
Published: December 2018

We introduce a novel nonparametric Bayesian model for the induction of Combinatory Categorial Grammars from POS-tagged text. It achieves state of the art performance on a number of languages, and induces linguistically plausible lexicons.
Learning to translate with products of novices: a suite of open-ended challenge problems for teaching MT
Authors: Lopez Adam , Post Matt , Callison-Burch Chris , Weese Jonathan , Ganitkevitch Juri , Ahmidi Narges , Buzek Olivia , Hanson Leah , Jamil Beenish , Lee Matthias , Lin Ya-Ting , Pao Henry , Rivera Fatima , Shahriyari Leili , Sinha Debu , Teichert Adam , Wampler Stephen , Weinberger Michael , Xu Daguang , Yang Lin , Zhao Shang
Published: December 2018

Machine translation (MT) draws from several different disciplines, making it a complex subject to teach. There are excellent pedagogical texts, but problems in MT and current algorithms for solving them are best learned by doing. As a centerpiece of our MT course, we devised a series of open-ended challenges for students in which the goal was to improve performance on carefully constrained instances of four key MT tasks: alignment, decoding, evaluation, and reranking. Students brought a diverse set of techniques to the problems, including some novel solutions which performed remarkably well. A surprising and exciting outcome was that student solutions or their combinations fared competitively on some tasks, demonstrating that even newcomers to the field can help improve the state-of-the-art on hard NLP problems while simultaneously learning a great deal. The problems, baseline code, and results are freely available.
Parsing entire discourses as very long strings: Capturing topic continuity in grounded language learning
Authors: Luong Minh-Thang , Frank Michael C. , Johnson Mark
Published: December 2018

Grounded language learning, the task of mapping from natural language to a representation of meaning, has attracted more and more interest in recent years. In most work on this topic, however, utterances in a conversation are treated independently and discourse structure information is largely ignored. In the context of language acquisition, this independence assumption discards cues that are important to the learner, e.g., the fact that consecutive utterances are likely to share the same referent (Frank et al., 2013). The current paper describes an approach to the problem of simultaneously modeling grounded language at the sentence and discourse levels. We combine ideas from parsing and grammar induction to produce a parser that can handle long input strings with thousands of tokens, creating parse trees that represent full discourses. By casting grounded language learning as a grammatical inference task, we use our parser to extend the work of Johnson et al. (2012), investigating the importance of discourse continuity in children’s language acquisition and its interaction with social cues. Our model boosts performance in a language acquisition task and yields good discourse segmentations compared with human annotators.
Erratum: “An Empirical Analysis of Formality in Online Communication”
Authors: Pavlick Ellie , Tetreault Joel
Published: December 2018

Corrected citation to (Oraby et al., 2015).
Erratum: “Improving Topic Models with Latent Feature Word Representations”
Authors: Nguyen Dat Quoc , Billingsley Richard , Du Lan , Johnson Mark
Published: December 2018

Change in clustering and classification results due to the dmm and lf-dmm bugs.
Synopsis on Passalidae family (Coleoptera: Scarabaeoidea) of Brazil with description of a new species of Veturius Kaup, 1871
Authors: FONSECA CLÁUDIO RUY VASCONCELOS DA , REYES-CASTILLO PEDRO
Published: December 2018

Even though a huge area of the country remains untouched, two tribes, eight genera and ninetyeight species of Passalidae are found in Brazil until now. Specialists described many taxa and gave both immature and adult taxonomic characteristics, showing bionomic aspects, which are shown in this paper. A checklist of Brazilian species is presented in this study with geographic distribution data. Veturius criniferous sp. nov. from Rond nia, Brazil is described and illustrated.
An account of the Pacific mealybug genus Laminicoccus Williams (Hemiptera: Coccoidea: Pseudococcidae)
Authors: WILLIAMS D.J. , MARTIN J.H.
Published: December 2018

The Pacific mealybug genus Laminicoccus Williams is reviewed. At present six species are assigned to the genus including a new species, Laminicoccus samoanus sp. nov., described from Western Samoa. Most of the host-plant records of these mealybugs are from monocotyledons and many of the species seem to prefer plants belonging to the families Arecaceae, Pandanaceae, and Poaceae. One or two host-plant records, nevertheless, are from dicotyledons. A key to species of Laminicoccus is provided. The mealybug Pseudococcus pandanicola Takahashi, described originally from Palau, had been included in Laminicoccus but is here reassigned to Pseudococcus and redescribed.
Inpatient Mortality After Endoscopic Sinus Surgery for Invasive Fungal Rhinosinusitis
Authors: Burton Brittany N. , Jafari Aria , Asmerom Betial , Swisher Matthew W. , Gabriel Rodney A. , DeConde Adam
Published: December 2018

Objectives:

Invasive fungal rhinosinusitis is a rare, life-threatening condition that affects the paranasal sinuses. The standard of care after diagnosis includes surgical debridement and aggressive medical management. Despite treatment, mortality remains unacceptably high. Most data are derived from small cohort experiences, with limited identification of mortality risk factors in the acute setting. The authors used a large national database to better understand clinical factors associated with inpatient mortality for this challenging condition.

Methods:

Using the 2000-2014 National (Nationwide) Inpatient Sample database, the authors identified 979 adult patients with an International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis code of mucormycosis or aspergillosis and a procedure code of sinus surgery. Multivariate imputation by chained equation was performed to account for missing data, followed by multivariate logistic regression to identify predictors of inpatient mortality.

Results:

In total, 979 adult patients were identified, with a median age of 57 years. The inpatient mortality rate was 15.8%. The most prevalent comorbidity was hematologic disorders (42.9%). Mucormycosis versus aspergillosis was associated with increased odds of inpatient mortality (odds ratio, 2.95; 95% confidence interval, 2.00-4.34; P < .001). The odds of inpatient mortality were significantly increased between patients with hematologic disorders and those without (odds ratio, 1.92; 95% confidence interval, 1.08-3.39; P = .024). Diabetes (odds ratio, 0.53; 95% confidence interval, 0.34 − 0.80; P = .003) was associated with the lowest odds of inpatient mortality.

Conclusions:

This represents the first population-based study evaluating the factors associated with inpatient mortality. These findings support prior observations demonstrating that the underlying immune dysfunction and type of fungal infection are important predictors of early mortality.
The Function and Regulatory Network of Pax9 Gene in Palate Development
Authors: Li R. , Chen Z. , Yu Q. , Weng M. , Chen Z.
Published: December 2018

Cleft palate, a common congenital deformity, can arise from disruptions in any stage of palatogenesis, including palatal shelf growth, elevation, adhesion, and fusion. Paired box gene 9 (Pax9) is recognized as a vital regulator of palatogenesis with great relevance to cleft palate in humans and mice. Pax9-deficient murine palatal shelves displayed deficient elongation, postponed elevation, failed contact, and fusion. Pax9 is expressed in epithelium and mesenchyme, exhibiting a dynamic expression pattern that changes according to the proceeding of palatogenesis. Recent studies highlighted the Pax9-related genetic interactions and their critical roles during palatogenesis. During palate growth, PAX9 interacts with numerous molecules and members of pathways (e.g., OSR2, FGF10, SHOS2, MSX1, BARX1, TGFβ3, LDB1, BMP, WNT β-catenin dependent, and EDA) in the mesenchyme and functions as a key mediator in epithelial-mesenchymal communications with FGF8, TBX1, and the SHH pathway. During palate elevation, PAX9 is hypothesized to mediate the time point of the elevation event in the anterior and posterior parts of the palatal shelves. The delayed elevation of Pax9 mutant palatal shelves probably results from abnormal expressions of a series of genes ( Osr2 and Bmpr1a) leading to deficient palate growth, abnormal tongue morphology, and altered hyaluronic acid distribution. The interactions between PAX9 and genes encoding the OSR2, TGFβ3, and WNT β-catenin-dependent pathways provide evidence that PAX9 might participate in the regulation of palate fusion. This review summarizes the current understanding of PAX9’s functions and emphasizes the interactions between PAX9 and vital genes during palatogenesis. We hope to provide some clues for further exploration of the function and mechanism of PAX9, especially during palate elevation and fusion events.
Do I Belong in the Profession? The Cost of Fitting In as a Preservice Teacher With a Passion for Social Justice
Authors: McKay Loraine , Manning Heather
Published: December 2018

Preservice teachers enter university with a range of personally held beliefs related to inclusive education and themselves as educators. This article reports on one case study from a larger qualitative research project. The study examined a preservice teacher’s perceptions of herself as an inclusive educator as she approached the final year of her undergraduate degree. Data included a metaphorical representation of being an inclusive educator, and two semi-structured interviews held 6 months apart. The use of collage and a structured written response completed the data set. Evelein and Korthagen’s model of core reflection and Voice-Centred Relational Method were used to analyze the data. Analysis exposed the dissonance between the layers that separate the preservice teacher’s core qualities and the environment. Data are presented using I-poems and discussed using the emerging themes. The consequences for her emerging identity as an inclusive educator and sense of belonging in the profession are discussed.
Spatial Forms of Ethnic Coexistence in Ottoman Cyprus: The Role of Urban Form in Patterns of Everyday Life
Authors: Charalambous Nadia
Published: December 2018

The rationale behind this article is an identified need for enhancing research on the relationship between the physical and the social aspects of the city of Nicosia through the lens of spatial history. Building on Baker’s categorizations of approaches to space in historical research and a syntactical, morphological approach to historical subjects, this article proposes an integrated methodology for researching historical space that may prove important to inform forms of urban segregation patterns. The article maintains that the analysis of the relationship between the physical, material form of a city and social processes at distinct historical periods may be key to understanding patterns of socio-spatial phenomena as well as for interpreting historical data of how people were distributed in cities and what they actually did in urban spaces at particular points in time.
The Brokenness of Caesar's Things
Authors: Kim Sharon
Published: December 2018

Caesar's Things is a semi-autobiographical novel combining modernist literary experimentation with narrative structures derived from the Bible. This unfinished work is seldom analyzed by literary scholars, in part because Fitzgerald's Christian conversion in the 1930s coincided with a mental breakdown, which made her faith and writing both suspect. Criticized as “incoherent,” the novel nonetheless becomes legible when Fitzgerald's religion is disentangled from madness and its contributions examined. The novel confesses the spiritual impoverishment of the Jazz Age protagonist, then seeks her redemption, healing the divide between the self and her soul, between the material world and the kingdom of God.
A 24-hour perioperative case study on argatroban use for left ventricle assist device insertion during cardiopulmonary bypass and veno-arterial extracorporeal membrane oxygenation
Authors: Fernandes Philip , O’Neil Michael , Del Valle Samantha , Cave Anita , Nagpal Dave
Published: December 2018

A 44-year-old male with ongoing chest pain and left ventricular ejection fraction <20% was transferred from a peripheral hospital with intra-aortic balloon pump placement following a non-ST-elevation myocardial infarction (STEMI). The patient underwent emergent multi-vessel coronary artery bypass grafting requiring veno-arterial (VA) extracorporeal membrane oxygenation (ECMO) on post-operative day (POD)#9 secondary to cardiogenic shock with biventricular failure. Due to clot formation, an oxygenator change-out was necessary shortly after initiation. Following a positive heparin-induced thrombocytopenia (HIT) assay, a total circuit exchange was required to eliminate all heparin coating and argatroban was deemed the anticoagulant of choice due to acute kidney injury. On POD#24, the decision was made to implant a left ventricle assist device (LVAD) as a bridge to heart transplantation. There was difficulty achieving an activated clotting time (ACT) >400 s: multiple argatroban bolus doses were required, along with accelerated up-titration of infusion dosing. Despite maintaining an ACT >484 s, clot formation was observed in the cardiotomy reservoir prior to separation. Subsequently, the patient developed severe disseminated intravascular coagulopathy, with both intra-cardiac and intravascular thrombi, requiring massive transfusion and continuous cell saving due to severe hemorrhage post cardiopulmonary bypass (CPB). The patient received a total of 105 units of plasma, 74 units of packed red cells, 19 units of platelets, 13 bottles of 5% albumin, 6 units of cryoprecipitate and 2 doses of factor VIIa intraoperatively over the course of 24 hours. A total of 19.7 L of washed red blood cells were returned to the patient from the cell saver. With the LVAD in place, the patient developed transfusion-related acute lung injury and acute respiratory distress syndrome with right ventricular dysfunction requiring VA ECMO once again. On POD#30, ECMO was discontinued and the patient was discharged from the intensive care unit (ICU) on POD 66. After a very complex post-operative stay with numerous surgeries and extensive rehabilitation, the patient was discharged home with the LVAD on POD#112.
Embracing Digitalization: Student Learning and New Technologies
Authors: Crittenden William F. , Biel Isabella K. , Lovely William A.
Published: December 2018

Digital change is like a fast-moving tsunami, with the digitalization of many business practices creating new relationships between businesses and customers and altering the marketing landscape. We provide an overview of several of these emerging digital innovations and their evolving impact on marketing activities. It is imperative that college students gain exposure to such cutting-edge technologies and ingrain the conceptual, inquiry, critical thinking, creativity, and integrative learning skills needed to add value in a world where machines will work alongside human professionals. We suggest that by embracing rather than banning technology, faculty enfranchise students through increased sensory experiences and enhanced digital activities, which will lead to greater learning. We contend that vested stakeholders—institutions of higher education, professional associations, publishing companies, and technology companies—need to support and strengthen faculty efforts in embracing technology to continuously enhance learning.
Development of a LIDAR array to study and classify wakes at the U.S. Department of Energy (DOE)/Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility
Authors: Pereira Tassia Penha , Pol Suhas , Ruiz-Columbie Arquimedes , Westergaard Carsten
Published: December 2018

Wind turbine wake has the wind speed deficit and the increased turbulent flow to the downstream turbines as signatures. Various experiments and simulations have been performed over the years to investigate the wake parameters; however, a statistical characterization of wake states is still to be uncovered. An innovative wake measurement approach that uses five ground-based Spidar Light Detection and Ranging (LIDAR) has been developed in partnership with Texas Tech University (TTU), Sandia National Laboratories, and Pentalum Technologies to develop, test, and validate a system and methodology that enables the capture of statistically significant wake dynamics in real atmospheric conditions. This article will discuss the potential of this new direct detection remote sensing equipment for studying the wake states as well as report the validation process of the LIDAR and the feasibility of continuing to pursue the primary purpose of the initiative.
Graduate and undergraduate university programs in wind energy in the United States
Authors: Swift Andrew , Tegen Suzanne , Acker Tom , Manwell James , Pattison Chris , McGowan Jon
Published: December 2018

Advances in wind energy technology and continued expansion of wind energy into the United States and global electricity grids will depend upon an educated and skilled workforce. While wind energy technician programs at community colleges or vocational schools have prospered in the United States over the past decade, partially due to the high demand for wind technicians, university programs that prepare graduates pursuing baccalaureate and advanced graduate degrees have lagged behind. At the same time, European university programs in wind energy have flourished, providing experts with advanced degrees who are then employed worldwide by the global wind energy industry. According to a projection of wind industry jobs needed in the U.S. Department of Energy’s Wind Vision report1 and estimates of education level requirements for jobs, as provided in this article, the United States may need more than 50,000 university-educated professionals with advanced degrees to support wind energy development by 2030. To provide these professionals, the number of wind energy academic programs must increase significantly beyond those available today—a task that will require collaboration among universities and external support from both industry and government. This article provides a review of the growing need for a university graduate-level-educated wind energy workforce, an overview of the current domestic wind energy workforce picture, existing global and domestic university wind energy programs, and recommendations for university-level wind energy education programs in the United States.
Study on wake-induced fatigue on wind turbine blade based on elastic actuator line model and two-dimensional finite element model
Authors: Meng Hang , Lien Fue-Sang , Glinka Gregory , Li Li , Zhang Jinhua
Published: December 2018

Atmospheric and wake turbulence have a great and immediate impact on the fatigue life of wind turbine blades. Generally speaking, wake-induced fatigue accounts for 5%–15% increase of fatigue load on the wind turbine rotor, which definitely threats the safety and economy of the whole wind farm. However, this effect is difficult to simulate which involves multi-wake interaction and fluid structure interaction. To better simulate the wake-induced fatigue on wind turbine blades, a novel elastic actuator line model is employed in this study. The elastic actuator line is a two-way coupling model, consisting of traditional actuator line model and one-dimensional implicit or explicit finite difference method beam structural model, among which the beam model takes gravitational force, aerodynamic force and centrifugal force into consideration. Large eddy simulation method in the NREL SOWFA code is employed to model the turbulence effect, including wake-induced turbulence and atmospheric turbulence. For the fatigue analysis part, the fatigue life of an NREL 5MW turbine blade subjected to upstream wind turbine wake effects is studied using the elastic actuator line model and laminate data available from Sandia Laboratory in the United States. First, the strain and stress on different composite materials, such as uniaxial carbon fibre and biaxial composite material, are recovered by using the sectional force and moment obtained with the one-dimensional beam model and two-dimensional finite element method model, namely BECAS. Second, the stress-life method, rain-flow counting method, shifted Goodman diagram (constant life diagram) and Miners rule are employed to estimate the fatigue life for different composite materials. Noticeably, elastic actuator line largely reduces the computational efforts compared with a high-resolution computational fluid dynamics model, in which each wind turbine blade is fully resolved. Both the characteristics of different composite materials and airfoil geometries will be considered during fatigue analysis. As a result, the above procedure makes the fatigue life estimation more reliable and feasible. In the case studies, the moment time series predicted by elastic actuator line and FAST are compared. The fatigue damage of NREL 5MW wind turbine under turbulent neutral atmospheric boundary layer is calculated, and the fatigue critical section is determined to be at 10.25 m section from root. Finally, in the study of two in-line turbines, the fatigue damage increase by wake flow is 16%, which is close to the results from previous studies.
The Importance of Religiosity to the Well-Being of African American Older Adults Living With Dementia
Authors: Epps Fayron , Williams Ishan C.
Published: December 2018

This study was a post hoc analysis of a larger qualitative descriptive study exploring family involvement in health promotion activities for African Americans living with dementia where participants identified religious practices as meaningful health promotion activities. The purpose of this study was to explore ways in which religiosity may influence the well-being of older adults living with dementia. Semi-structured interviews were conducted among a sample of 22 family caregivers and 15 older adults living with dementia ( N = 37). Three themes emerged: Engagement, Promotion of Faith and Spiritual Connectedness, and Maintenance of Religious Practices. It is imperative for family caregivers to understand the important contributions of religious activities and beliefs to the well-being of their family member. This information might be of use for faith communities, policy makers, and health care providers in the provision of optimal person-centered care and the promotion of quality of life for persons living with dementia.
Contemporary College Students’ Reflections on Their High School Peer Crowds
Authors: Crabbe Rowena , Pivnick Lilla K. , Bates Julia , Gordon Rachel A. , Crosnoe Robert
Published: December 2018

High school peer crowds are fundamental components of adolescent development with influences on short- and long-term life trajectories. This study provides the perspectives of contemporary college students regarding their recent high school social landscapes, contributing to current research and theory on the social contexts of high school. This study also highlights the experiences of college-bound students who represent a growing segment of the adolescent population. Sixty-one undergraduates attending universities in two states participated in 10 focus groups to reflect on their experiences with high school peer crowds during the late 2010s. Similar to seminal research on peer crowds, we examined crowds and individuals along several focal domains: popularity, extracurricular involvement, academic orientation, fringe media, illicit risk-taking, and race ethnicity. We find that names and characteristics of crowds reflect the current demographic and cultural moment (i.e., growing importance of having a college education, racial-ethnic diversity) and identify peer crowds that appear to be particularly salient for college-bound youth. Overall, this study illuminates how the retrospective accounts of college-bound students offer insight into high school social hierarchies during a time of rapid social change.
Climate Change, Individual Obligations and the Virtue of Justice
Authors: Darr Ryan
Published: December 2018

Over the last decade, a number of climate ethicists have turned their attention to the question of individual moral obligations to reduce greenhouse gas emissions. Important problems face their efforts, especially what is called the problem of inconsequentialism. The problems, I argue, arise largely from the failure to treat individual obligations as a matter of justice, a failure that stems from the common modern assumption that justice primarily concerns social institutions. I develop an alternative approach by appealing to the account of justice as a virtue in Thomas Aquinas. This approach allows us to talk about individual obligations to reduce emissions as obligations of justice, even in the current context of institutional failure. At the same time, I argue that approaching climate change in Thomistic terms requires an important modification of Aquinas’s understanding of justice.
An optimization method for brake instability reduction with fuzzy-boundary interval variables
Authors: Lü Hui , Feng Qianlang , Cai Zicheng , Shangguan Wen-Bin
Published: December 2018

In some special engineering circumstances, it is likely that all parameters of an uncertain automotive structure can only be treated as interval variables due to limited knowledge, but meanwhile their lower and upper bounds can just be modeled as fuzzy variables rather than as deterministic values due to ambiguous information. To handle this dual uncertainties case, a reliability-based optimization method with fuzzy-boundary interval variables is developed in this study, and it is further extended to carry out squeal instability analysis and reduction of brake involving both limited and vague information. In the proposed method, fuzzy-boundary interval variables are utilized to cope with the above dual uncertainties of structure parameters and help to build up the structure response analysis model. First, the structure responses are derived on the basis of α-cut strategy, Taylor series expansion, subinterval analysis, and central difference method. Then, with the aid of fuzzy possibility theory, a reliability analysis model of structure response is developed, which can make use of extra reliability information and thus quantify the reliability more accurately. Next, a reliability-based optimization model involving fuzzy-boundary interval variables is established by integrating the uncertain response analysis model and the reliability analysis model. Finally, the proposed method is extended to carry out automotive brake squeal instability analysis and optimization. The numerical investigations demonstrate the applicability and effectiveness of the proposed method.
Content, evaluations and influences in newspaper coverage of predictive genetic testing: A comparative media content analysis from the United Kingdom and Switzerland
Authors: Zimmermann Bettina M. , Aebi Noah , Kolb Steffen , Shaw David , Elger Bernice S.
Published: December 2018

Predictive genetic testing often entails challenging decisions about preventive measures and uncertain health-related risk predictions. Because of its increasing availability, it is important to assess how to debate it publicly. Newspaper content analysis represents a common and reliable way to investigate public discourse retrospectively. We thus quantitatively compare broadsheet newspaper coverage about predictive genetic testing in the United Kingdom and Switzerland during the period of 2011–2016 regarding content, evaluations, stakeholder influence, and trigger events. British coverage was more extensive and positive and included more personal stories. Swiss coverage had more focus on political issues. Angelina Jolie’s announcement about her double mastectomy was the most important coverage trigger. Researchers were the most frequently cited stakeholder group, but stakeholders from government and civil society were also represented. Our results thus reflect a movement toward a more active public engagement with predictive genetic testing. The findings help to improve and enrich public engagement regarding predictive genetic testing.