Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank

Discourse parsing is a challenging task and plays a critical role in discourse analysis. This paper focus on the macro level discourse structure analysis, which has been less studied in the previous researches. We explore a macro discourse structure presentation schema to present the macro level discourse structure, and propose a corresponding corpus, named Macro Chinese Discourse Treebank. On these bases, we concentrate on two tasks of macro discourse structure analysis, including structure identification and nuclearity recognition. In order to reduce the error transmission between the associated tasks, we adopt a joint model of the two tasks, and an Integer Linear Programming approach is proposed to achieve global optimization with various kinds of constraints.
Bridging resolution: Task definition, corpus resources and rule-based experiments

Recent work on bridging resolution has so far been based on the corpus ISNotes (Markert et al. 2012), as this was the only corpus available with unrestricted bridging annotation. Hou et al. 2014’s rule-based system currently achieves state-of-the-art performance on this corpus, as learning-based approaches suffer from the lack of available training data. Recently, a number of new corpora with bridging annotations have become available. To test the generalisability of the approach by Hou et al. 2014, we apply a slightly extended rule-based system to these corpora. Besides the expected out-of-domain effects, we also observe low performance on some of the in-domain corpora. Our analysis shows that this is the result of two very different phenomena being defined as bridging, namely referential and lexical bridging. We also report that filtering out gold or predicted coreferent anaphors before applying the bridging resolution system helps improve bridging resolution.
Magnetosomes Extracted from Magnetospirillum gryphiswaldense as Theranostic Agents in an Experimental Model of Glioblastoma

Magnetic fluid hyperthermia (MFH) with chemically synthesized nanoparticles is currently used in clinical trials as it destroys tumor cells with an extremely localized deposition of thermal energy. In this paper, we investigated an MFH protocol based on magnetic nanoparticles naturally produced by magnetotactic bacteria: magnetosomes. The efficacy of such protocol is tested in a xenograft model of glioblastoma. Mice receive a single intratumoral injection of magnetosomes, and they are exposed three times in a week to an alternating magnetic field with concurrent temperature measurements. MRI is used to visualize the nanoparticles and to monitor tumor size before and after the treatment. Statistically significant inhibition of the tumor growth is detected in subjects exposed to the alternating magnetic field compared to control groups. Moreover, thanks to magnetosomes high transversal relaxivity, their effective delivery to the tumor tissue is monitored by MRI. It is apparent that the efficacy of this protocol is limited by inhomogeneous delivery of magnetosomes to tumor tissue. These results suggest that naturally synthesized magnetosomes could be effectively considered as theranostic agent candidates for hyperthermia based on iron oxide nanoparticles.
Answerable or Not: Devising a Dataset for Extending Machine Reading Comprehension

Machine-reading comprehension (MRC) has recently attracted attention in the fields of natural language processing and machine learning. One of the problematic presumptions with current MRC technologies is that each question is assumed to be answerable by looking at a given text passage. However, to realize human-like language comprehension ability, a machine should also be able to distinguish not-answerable questions (NAQs) from answerable questions. To develop this functionality, a dataset incorporating hard-to-detect NAQs is vital; however, its manual construction would be expensive. This paper proposes a dataset creation method that alters an existing MRC dataset, the Stanford Question Answering Dataset, and describes the resulting dataset. The value of this dataset is likely to increase if each NAQ in the dataset is properly classified with the difficulty of identifying it as an NAQ. This difficulty level would allow researchers to evaluate a machine’s NAQ detection performance more precisely. Therefore, we propose a method for automatically assigning difficulty level labels, which measures the similarity between a question and the target text passage. Our NAQ detection experiments demonstrate that the resulting dataset, having difficulty level annotations, is valid and potentially useful in the development of advanced MRC models.
The impact of interprofessional education on family nurse practitioner students' and family medicine residents' knowledge and confidence in screening for breast and cervical cancer

Background and Purpose: This study determined the impact of an interprofessional education (IPE) simulation on family nurse practitioner (FNP) students' and family medicine residents' (FMRs) self-reported confidence in counseling women reluctant to engage in cancer screening or evaluation and assessed knowledge of breast and cervical cancer risk factors. Method: A multi-item knowledge survey on breast and cervical cancer risk factors was administered to 76 FNP students and FMRs followed by an IPE simulation with a pre-/postsurvey of self-reported confidence in counseling a woman reluctant to have breast and cervical cancer screening and evaluation. Discussion: Data demonstrated knowledge deficits in breast and cervical cancer risk factors in both disciplines with the average risk factor knowledge score of 8.5/12 for breast cancer and 7.8/12 for cervical cancer. Following IPE simulation, confidence in counseling women reluctant to have breast or cervical cancer screening improved across both disciplines (p < .05) and debrief feedback findings suggest improved attitudes toward collegiality, communication, and understanding of other interprofessional roles among both disciplines. Conclusion: Knowledge gaps exist among both FNP students and FMRs in breast and cervical cancer risk factors. This study suggests IPE simulation is effective in building individual provider confidence and team collegiality.
Stance Detection with Hierarchical Attention Network

Stance detection aims to assign a stance label (for or against) to a post toward a specific target. Recently, there is a growing interest in using neural models to detect stance of documents. Most of these works model the sequence of words to learn document representation. However, much linguistic information, such as polarity and arguments of the document, is correlated with the stance of the document, and can inspire us to explore the stance. Hence, we present a neural model to fully employ various linguistic information to construct the document representation. In addition, since the influences of different linguistic information are different, we propose a hierarchical attention network to weigh the importance of various linguistic information, and learn the mutual attention between the document and the linguistic information. The experimental results on two datasets demonstrate the effectiveness of the proposed hierarchical attention neural model.
The identification of cases of major hemorrhage during hospitalization in patients with acute leukemia using routinely recorded healthcare data

Introduction Electronic health care data offers the opportunity to study rare events, although detecting these events in large datasets remains difficult. We aimed to develop a model to identify leukemia patients with major hemorrhages within routinely recorded health records. Methods The model was developed using routinely recorded health records of a cohort of leukemia patients admitted to an academic hospital in the Netherlands between June 2011 and December 2015. Major hemorrhage was assessed by chart review. The model comprised CT-brain, hemoglobin drop, and transfusion need within 24 hours for which the best discriminating cut off values were taken. External validation was performed within a cohort of two other academic hospitals. Results The derivation cohort consisted of 255 patients, 10,638 hospitalization days, of which chart review was performed for 353 days. The incidence of major hemorrhage was 0.22 per 100 days in hospital. The model consisted of CT-brain (yes/no), hemoglobin drop of ≥0.8 g/dl and transfusion of ≥6 units. The C-statistic was 0.988 (CI 0.981–0.995). In the external validation cohort of 436 patients (19,188 days), the incidence of major hemorrhage was 0.46 per 100 hospitalization days and the C-statistic was 0.975 (CI 0.970–0.980). Presence of at least one indicator had a sensitivity of 100% (CI 95.8–100) and a specificity of 90.7% (CI 90.2–91.1). The number of days to screen to find one case decreased from 217.4 to 23.6. Interpretation A model based on information on CT-brain, hemoglobin drop and need of transfusions can accurately identify cases of major hemorrhage within routinely recorded health records.
Protect Your Sleep When Work is Calling: How Work-Related Smartphone Use During Non-Work Time and Sleep Quality Impact Next-Day Self-Control Processes at Work

In view of the rapid development of information and communication technologies, the present study sheds light on how work-related smartphone use during non-work time affects employees’ subsequent working day. Specifically, we examine work-related smartphone use and sleep quality as moderators of next-day self-control processes at work. Theorizing that work-related smartphone use and self-control demands deplete a common limited regulatory resource, we suggest a strengthening two-way interaction between work-related smartphone use during non-work time and next-day self-control demands at work in predicting employees’ ego depletion at work. Moreover, in a three-way interaction, we analyze whether this interaction depends on employees’ sleep quality, assuming that when intensive work-related smartphone use is followed by high-quality sleep, the taxed regulatory resource can replenish overnight. Results from our diary study covering 10 working days (n = 63) indicate that after evenings with high work-related smartphone use, employees experience disproportionate levels of ego depletion when dealing with self-control demands at work. Sleep quality, however, attenuates this interaction. In cases of high sleep quality, next-day self-control processes at work are no longer affected by work-related smartphone use. Based on these findings, we discuss implications for employees and employers regarding work-related smartphone use and the relevance of sleep in replenishing drained resources.
Human lung development: recent progress and new challenges

ABSTRACT Recent studies have revealed biologically significant differences between human and mouse lung development, and have reported new in vitro systems that allow experimental manipulation of human lung models. At the same time, emerging clinical data suggest that the origins of some adult lung diseases are found in embryonic development and childhood. The convergence of these research themes has fuelled a resurgence of interest in human lung developmental biology. In this Review, we discuss our current understanding of human lung development, which has been profoundly influenced by studies in mice and, more recently, by experiments using in vitro human lung developmental models and RNA sequencing of human foetal lung tissue. Together, these approaches are helping to shed light on the mechanisms underlying human lung development and disease, and may help pave the way for new therapies. Summary: This Review describes how recent technological advances have shed light on the mechanisms underlying human lung development and disease, and outlines the future challenges in this field.
Information behavior shift: How and why medical students use Facebook

The poster offers a unique opportunity to learn about the information behavior of a group of medical students using a social networking site (SNS) group over the period of almost three, full, consecutive, academic years without faculty supervision to facilitate their learning. Mixed methods approach to data collection and analysis allowed us to obtain a comprehensive overview of the SNS group usage and conclude that it played an important role in student learning. Patterns of the group utilization became the evidence of extending the classic principles of PBL in medical education into a digital learning environment, which, in its turn, results in gradual transformation of medical students' information behavior. By shifting from more traditional forms of communication and information technology to using content available through social media, medical students incorporate SNS in their learning as an online environment that supplements their face-to-face interactions and facilitates acquisition and/or exchange of school-related information in easier, more efficient ways.
ARAP: Arabic Author Profiling Project for Cyber-Security

In this paper we describe the current state of the ARAP project on Arabic Author Profiling for Cyber-Security funded by the Qatar National Research Fund via the Carnegie Mellon University in Qatar. The project focuses on determining whether a suspicious message is actually a potential threat and, in that case, aims at profiling its author. The contribution of the project lies in the lack of research of this type for Arabic.
On the Reproducibility of VisRuption: A Tool for Intuitive and Efficient Visualization of Airline Disruption Data

Managing the vast amount of resources and processes of large airlines with several hundred aircraft and several thousand operated flights per day is a very complex task and makes computer-aided operation irreplaceable. Moreover, there is a multitude of disruptions which can occur every day during airline operation and can result in very expensive delays or cancellations [CTA04,MHR10,Now09]. In our paper at EuroVis 2013 [RPMO13], we have presented a design study about the tool VisRuption for providing an intuitive and efficient access to airline disruption data.
Identification of key genes and pathways for esophageal squamous cell carcinoma by bioinformatics analysis

The aim of the present study was to identify the differentially expressed genes (DEGs) in esophageal squamous-cellcarcinoma (ESCC) and provide potential therapeutic targets. The microarray dataset GSE20347 was downloaded from the Gene Expression Omnibus (GEO) database, and included 17 tissue samples and 13 normal adjacent tissue samples from patients with ESCC. A total of 22,277 DEGs were identified. A heat map for the DEGs was constructed with the Morpheus online tool and the top 200 genes (100 upregulated and 100 downregulated) were selected for further bioinformatics analysis, including analysis of gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, protein-protein interaction networks and Spearman's correlation tests. The results of the GO analysis indicated that the upregulated DEGs were most significantly enriched in membrane-bounded vesicles in the cellular component (CC) category, but were not significantly enriched in any GO terms of the categories biological process (BP) or molecular function (MF); furthermore, the downregulated DEGs were most significantly enriched in regulation of DNA metabolic processes, nucleotide binding and chromosomes in the categories BP, MF and CC, respectively. The KEGG analysis indicated that the downregulated DEGs were enriched in the regulation of cell cycle pathways. The top 10 hub proteins in the protein-protein interaction network were cyclin-dependent kinase 4, budding uninhibited by benzimidazoles 1, cyclin B2, heat shock protein 90AA1, aurora kinase A, H2A histone family member Z, replication factor C subunit 4, and minichromosome maintenance complex component 2, −4 and −7. These proteins are mainly involved in regulating tumor progression. The genes in the four top modules were mainly implicated in regulating cell cycle pathways. Secreted Ly-6/uPAR-related protein (SLURP) was the hub gene, and SLURP and its interacting genes were most enriched in the chromosomal part in the CC category, organelle organization in the BP category and protein binding in the MF category, and were involved in pathways including DNA replication, cell cycle and P53 signaling. The expression of SLURP-1 in fifteen patients with esophageal carcinoma was detected using quantitative polymerase chain reaction analysis, and the results indicated that SLURP-1 expression was significantly decreased in the tumor samples relative to that in normal adjacent tissues. These results suggest that several hub proteins and the hub gene SLURP-1 may serve as potential therapeutic targets, and that gene dysfunction may be involved in the tumorigenesis of ESCC.
Authorship Attribution By Consensus Among Multiple Features

Most existing research on authorship attribution uses various lexical, syntactic and semantic features. In this paper we demonstrate an effective template-based approach for combining various syntactic features of a document for authorship analysis. The parse-tree based features that we propose are independent of the topic of a document and reflect the innate writing styles of authors. We show that the use of templates including sub-trees of parse trees in conjunction with other syntactic features result in improved author attribution rates. Another contribution is the demonstration that Dempster’s rule based combination of evidence from syntactic features performs better than other evidence-combination methods. We also demonstrate that our methodology works well for the case where actual author is not included in the candidate author set.
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant.
Merging outdoor and indoor technologies for the creation of pervasive games

Outdoor and indoor pervasive games are an emerging concept in the entertainment field that seamlessly integrate novel technologies while keeping the playability aspects of traditional games. The characteristics of the technologies involved in this kind of games are quite different, but combined they can allow the creation of more complete games for children. In this work, we present the result of integrating a technology initially aimed at the creation of outdoor games in an indoor Interactive Space. A functional game prototype is presented as a result of this integration.
Executive Summary: Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU.

Author(s): Devlin, John W; Skrobik, Yoanna; Gelinas, Celine; Needham, Dale M; Slooter, Arjen JC; Pandharipande, Pratik P; Watson, Paula L; Weinhouse, Gerald L; Nunnally, Mark E; Rochwerg, Bram; Balas, Michele C; van den Boogaard, Mark; Bosma, Karen J; Brummel, Nathaniel E; Chanques, Gerald; Denehy, Linda; Drouot, Xavier; Fraser, Gilles L; Harris, Jocelyn E; Joffe, Aaron M; Kho, Michelle E; Kress, John P; Lanphere, Julie A; McKinley, Sharon; Neufeld, Karin J; Pisani, Margaret A; Payen, Jean-Francois; Pun, Brenda T; Puntillo, Kathleen A; Riker, Richard R; Robinson, Bryce RH; Shehabi, Yahya; Szumita, Paul M; Winkelman, Chris; Centofanti, John E; Price, Carrie; Nikayin, Sina; Misak, Cheryl J; Flood, Pamela D; Kiedrowski, Ken; Alhazzani, Waleed
Comparing teacher and student perspectives on the interplay of cognitive and motivational-affective student characteristics

For students, cognitive and motivational-affective characteristics are the most powerful prerequisites for successful learning. For teachers, judgments on their students’ characteristics shape how they plan and implement instructional activities in order to offer individual learning support. On the student side, research is starting to find out more about the interplay of different characteristics within individual students. On the teacher side, studies still regard teacher judgment accuracy of only single characteristics. By taking a person-centered approach, regarding NS = 503 students and their NT = 41 mathematics and languages arts teachers, our manuscript joined teacher and student perspectives on student characteristics interplay and suggests methodology to compare them. We found that student assessments suggested ample diversity regarding this interplay–and teachers did not perceive this. In their views, “homogeneous” sets of average characteristics were dominant. Findings suggest addressing students’ views and the diagnosis of their characteristics in teacher education to enable individual support.
Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts

We proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion structures has been explored in those works but their methods required a sophisticated design to combine structural features with a classifier. Our model introduces tree learning approaches and a graph learning approach to directly capture discussion structures without structural features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5% in accuracy and 2.2 in FB1 score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches.