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Introduction Patients with ambulatory advanced heart failure (HF) are increasingly considered for durable mechanical circulatory support (MCS) and heart transplantation and their effective triage requires careful assessment of the clinical trajectory. Methods REVIVAL, a prospective, observational study, enrolled 400 ambulatory advanced HF patients from 21 MCS/transplant centers in 2015-2016. Study design included a clinical re-assessment of Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) profile within 120 days after enrollment. The prognostic impact of a worsening INTERMACS Profile assigned by the treating physician was assessed at 1 year after the Early Relook. Results Early Relook was done in 325 of 400 patients (81%), of whom 24% had a worsened INTERMACS Profile, associated with longer HF history and worse baseline INTERMACS profile, but no difference in baseline LVEF (median 0.20), 6-minute walk, quality of life, or other baseline parameters. Early worsening predicted higher rate of the combined primary endpoint of death, urgent MCS, or urgent transplant by 1 year after Early Relook, (28% vs 15%), with hazard ratio 2.2 (95% CI 1.2- 3.8; p = .006) even after adjusting for baseline INTERMACS Profile and Seattle HF Model score. Deterioration to urgent MCS occurred in 14% vs 5% (p = .006) during the year after Early Relook. Conclusions Early Relook identifies worsening of INTERMACS Profile in a significant population of ambulatory advanced HF, who had worse outcomes over the subsequent year. Early reassessment of ambulatory advanced HF patients should be performed to better define the trajectory of illness and inform triage to advanced therapies.

Objectives This study sought to determine cardiopulmonary exercise (CPX) predictors of the combined outcome of durable mechanical circulatory support (MCS), transplantation, or death at 1 year among patients with ambulatory advanced heart failure (HF). Background Optimal CPX predictors of outcomes in contemporary ambulatory advanced HF patients are unclear. Methods REVIVAL (Registry Evaluation of Vital Information for ventricular assist devices [VADs] in Ambulatory Life) enrolled 400 systolic HF patients, INTERMACS (Interagency Registry for Mechanically Assisted Circulatory Support) profiles 4-7. CPX was performed by 273 subjects 2 ± 1 months after study enrollment. Discriminative power of maximal (peak oxygen consumption [peak V O 2]; V O 2 pulse, circulatory power [CP]; peak systolic blood pressure • peak V O 2], peak end-tidal pressure C O 2 [PEtC O 2], and peak Borg scale score) and submaximal CPX parameters (ventilatory efficiency [VE/VC O 2 slope]; V O 2 at anaerobic threshold [V O 2AT]; and oxygen uptake efficiency slope [OUES]) to predict the composite outcome were assessed by univariate and multivariate Cox regression and Harrell’s concordance statistic. Results At 1 year, there were 39 events (6 transplants, 15 deaths, 18 MCS implantations). Peak V O 2, V O 2AT, OUES, peak PEtCO2, and CP were higher in the no-event group (all p Conclusions Among patients with ambulatory advanced HF, the strongest maximal and submaximal CPX predictor of MCS implantation, transplantation, or death at 1 year were CP and VE/VCO2, respectively. The patient-reported measure of exercise effort (Borg scale score) contributed substantially to the prediction of outcomes, a surprising and novel finding that warrants further investigation. (Registry Evaluation of Vital Information for VADs in Ambulatory Life [REVIVAL]; NCT01369407 )

While pretrained language models (LMs) have driven impressive gains over morpho-syntactic and semantic tasks, their ability to model discourse and pragmatic phenomena is less clear. As a step towards a better understanding of their discourse modeling capabilities, we propose a sentence intrusion detection task. We examine the performance of a broad range of pretrained LMs on this detection task for English. Lacking a dataset for the task, we introduce INSteD, a novel intruder sentence detection dataset, containing 170,000+ documents constructed from English Wikipedia and CNN news articles. Our experiments show that pretrained LMs perform impressively in in-domain evaluation, but experience a substantial drop in the cross-domain setting, indicating limited generalization capacity. Further results over a novel linguistic probe dataset show that there is substantial room for improvement, especially in the cross- domain setting.

The perceived quality of a document is affected by various factors, including grammat- icality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text — such as images, font choices, and visual layout — we propose a joint model that combines the text content with a visual rendering of the document for document qual- ity assessment. Our joint model achieves state-of-the-art results over five datasets in two domains (Wikipedia and academic papers), which demonstrates the complementarity of textual and visual features, and the general applicability of our model. To examine what kinds of features our model has learned, we further train our model in a multi-task learning setting, where document quality assessment is the primary task and feature learning is an auxiliary task. Experimental results show that visual embeddings are better at learning structural features while textual embeddings are better at learning readability scores, which further verifies the complementarity of visual and textual features.

Background Worsening heart failure (HF) and health-related quality of life (HRQOL) have been shown to impact the decision to proceed with left ventricular assist device (LVAD) implantation, but little is known about how socioeconomic factors influence expressed patient preference for LVAD. Methods and Results Ambulatory patients with advanced systolic HF (n=353) reviewed written information about LVAD therapy and completed a brief survey to indicate whether they would want an LVAD to treat their current level of HF. Ordinal logistic regression analyses identified clinical and demographic predictors of LVAD preference. Higher New York Heart Association (NYHA) class, worse HRQOL measured by Kansas City Cardiomyopathy Questionnaire, lower education level, and lower income were significant univariable predictors of patients wanting an LVAD. In the multivariable model, higher NYHA class (OR [odds ratio]: 1.43, CI [confidence interval]: 1.08–1.90, P = .013) and lower income level (OR: 2.10, CI: 1.18 – 3.76, P = .012 for $80,000) remained significantly associated with wanting an LVAD. Conclusion Among ambulatory patients with advanced systolic HF, treatment preference for LVAD was influenced by level of income independent of HF severity. Understanding the impact of socioeconomic factors on willingness to consider LVAD therapy may help tailor counseling towards individual needs.

While automatic summarization evaluation methods developed for English are routinely applied to other languages, this is the first attempt to systematically quantify their panlinguistic efficacy. We take a summarization corpus for eight different languages, and manually annotate generated summaries for focus (precision) and coverage (recall). Based on this, we evaluate 19 summarization evaluation metrics, and find that using multilingual BERT within BERTScore performs well across all languages, at a level above that for English., Findings of ACL 2021

Objectives This study sought to explore clinical characteristics and outcomes in women and men with ambulatory advanced heart failure (HF). Background Women have been underrepresented in studies of advanced HF and have an increased mortality on the transplant waiting list and early after mechanical circulatory support (MCS). An increased understanding of the differential burden of HF between women and men is required to inform the use of mechanical circulatory support in ambulatory advanced HF patients. Methods REVIVAL (Registry Evaluation for Vital Information on Ventricular Assist Devices in Ambulatory Life) is a prospective, observational study of 400 outpatients with chronic systolic HF, New York Heart Association functional class II to IV, and 1 additional high-risk feature. Clinical characteristics, quality of life, and functional capacity were compared between women and men, as was a primary composite endpoint of death, durable MCS, or urgent heart transplantation at 1 year. Results REVIVAL enrolled 99 women (25% of the cohort) who had similar age, ejection fraction, INTERMACS (Interagency Registry for Mechanically Assisted Circulatory Support) profiles, medication use, and willingness to consider MCS as the men enrolled; however, women reported significantly greater limitations in quality of life with respect to physical limitation, reduced 6-min walk distance, and more frequent symptoms of depression. Nevertheless, 1-year combined risk of death, durable MCS, or urgent transplantation did not differ between women and men (24% vs. 22%; p = 0.94). Conclusions This study represents the largest report to date of women with ambulatory advanced HF receiving contemporary therapies. Systematic elicitation of patient-reported outcome measures uncovered an added burden of HF in women and may be an appropriate target of augmented support and intervention.

The CRISPR/Cas system could provide an efficient and reliable means of editing the human genome and has the potential to revolutionize modern medicine; however, rapid developments are raising complex ethical issues. There has been significant scientific debate regarding the acceptability of some applications of CRISPR/Cas, with leaders in the field highlighting the need for the lay public's views to shape expert discussion. As such, we sought to determine the factors that influence public opinion on gene editing. We created a 17-item online survey translated into 11 languages and advertised worldwide. Topic modeling was used to analyze textual responses to determine what factors influenced respondents' opinions toward human somatic or embryonic gene editing, and how this varied among respondents with differing attitudes and demographic backgrounds. A total of 3,988 free-text responses were analyzed. Respondents had a mean age of 32 (range, 11-90) years, and 37% were female. The most prevalent topics cited were Future Generations, Research, Human Editing, Children, and Health. Respondents who disagreed with gene editing for health-related purposes were more likely to cite the topic Better Understanding than those who agreed to both somatic and embryonic gene editing. Respondents from Western backgrounds more frequently discussed Future Generations, compared with participants from Eastern countries. Religious respondents did not cite the topic Religious Beliefs more frequently than did nonreligious respondents, whereas Christian respondents were more likely to cite the topic Future Generations. Our results suggest that public resistance to human somatic or embryonic gene editing does not stem from an inherent mistrust of genome modification, but rather a desire for greater understanding. Furthermore, we demonstrate that factors influencing public opinion vary greatly amongst demographic groups. It is crucial that the determinants of public attitudes toward CRISPR/Cas be well understood so that the technology does not suffer the negative public sentiment seen with previous genetic biotechnologies.

Existing work on probing of pretrained language models (LMs) has predominantly focused on sentence-level syntactic tasks. In this paper, we introduce document-level discourse probing to evaluate the ability of pretrained LMs to capture document-level relations. We experiment with 7 pretrained LMs, 4 languages, and 7 discourse probing tasks, and find BART to be overall the best model at capturing discourse -- but only in its encoder, with BERT performing surprisingly well as the baseline model. Across the different models, there are substantial differences in which layers best capture discourse information, and large disparities between models., Accepted at NAACL 2021

Chemical patents contain rich coreference and bridging links, which are the target of this research. Specially, we introduce a novel annotation scheme, based on which we create the ChEMU-Ref dataset from reaction description snippets in English-language chemical patents. We propose a neural approach to anaphora resolution, which we show to achieve strong results, especially when jointly trained over coreference and bridging links.

We introduce a top-down approach to discourse parsing that is conceptually simpler than its predecessors (Kobayashi et al., 2020; Zhang et al., 2020). By framing the task as a sequence labelling problem where the goal is to iteratively segment a document into individual discourse units, we are able to eliminate the decoder and reduce the search space for splitting points. We explore both traditional recurrent models and modern pre-trained transformer models for the task, and additionally introduce a novel dynamic oracle for top-down parsing. Based on the Full metric, our proposed LSTM model sets a new state-of-the-art for RST parsing., Accepted at EACL 2021

Images are core components of multi-modal learning in natural language processing (NLP), and results have varied substantially as to whether images improve NLP tasks or not. One confounding effect has been that previous NLP research has generally focused on sophisticated tasks (in varying settings), generally applied to English only. We focus on text classification, in the context of assigning named entity classes to a given Wikipedia page, where images generally complement the text and the Wikipedia page can be in one of a number of different languages. Our experiments across a range of languages show that images complement NLP models (including BERT) trained without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing.

Adversarial learning can learn fairer and less biased models of language than standard methods. However, current adversarial techniques only partially mitigate model bias, added to which their training procedures are often unstable. In this paper, we propose a novel approach to adversarial learning based on the use of multiple diverse discriminators, whereby discriminators are encouraged to learn orthogonal hidden representations from one another. Experimental results show that our method substantially improves over standard adversarial removal methods, in terms of reducing bias and the stability of training., Comment: EACL 2021 (5 pages + 1 references)

Although the Indonesian language is spoken by almost 200 million people and the 10th most spoken language in the world, it is under-represented in NLP research. Previous work on Indonesian has been hampered by a lack of annotated datasets, a sparsity of language resources, and a lack of resource standardization. In this work, we release the IndoLEM dataset comprising seven tasks for the Indonesian language, spanning morpho-syntax, semantics, and discourse. We additionally release IndoBERT, a new pre-trained language model for Indonesian, and evaluate it over IndoLEM, in addition to benchmarking it against existing resources. Our experiments show that IndoBERT achieves state-of-the-art performance over most of the tasks in IndoLEM., Accepted at COLING 2020 - The 28th International Conference on Computational Linguistics

Existing metonymy resolution approaches rely on features extracted from external resources like dictionaries and hand-crafted lexical resources. In this paper, we propose an end-to-end word-level classification approach based only on BERT, without dependencies on taggers, parsers, curated dictionaries of place names, or other external resources. We show that our approach achieves the state-of-the-art on 5 datasets, surpassing conventional BERT models and benchmarks by a large margin. We also show that our approach generalises well to unseen data., 12 pages

As part of growing NLP capabilities, coupled with an awareness of the ethical dimensions of research, questions have been raised about whether particular datasets and tasks should be deemed off-limits for NLP research. We examine this question with respect to a paper on automatic legal sentencing from EMNLP 2019 which was a source of some debate, in asking whether the paper should have been allowed to be published, who should have been charged with making such a decision, and on what basis. We focus in particular on the role of data statements in ethically assessing research, but also discuss the topic of dual use, and examine the outcomes of similar debates in other scientific disciplines., 6 pages; accepted for ACL2020

Background Cefovecin is a long-acting third-generation cephalosporin commonly used in veterinary medicine. Third-generation cephalosporins are critically important antimicrobials that should only be used after culture and susceptibility testing. The authors describe the common indications for cefovecin use in dogs and cats, and the frequency of culture and susceptibility testing. Materials and methods A cross-sectional study was performed using clinical records extracted from VetCompass Australia. A previously described method was used to identify records containing cefovecin. The reason for cefovecin use was annotated in situ in each consultation text. Results Over a six-month period (February and September 2018), 5180 (0.4 per cent) consultations involved cefovecin administration, of which 151 were excluded. Cats were administered cefovecin more frequently than dogs (1.9 per cent of cat consultations and 0.1 per cent of dog consultations). The most common reasons for cefovecin administration to cats were cat fight injuries and abscesses (28 per cent) and dermatitis (13 per cent). For dogs, the most common reasons for cefovecin administration were surgical prophylaxis (24 per cent) and dermatitis (19 per cent). Culture and susceptibility testing were reported in 16 cases (0.3 per cent). Conclusion Cefovecin is used in many scenarios in dogs and cats where antimicrobials may be either not indicated or where an antimicrobial of lower importance to human health is recommended.

The mechanical circulatory support (MCS) devices of today trace their roots to ex-vivo studies in the 1930s, the clinical application for the heart-lung machine in the 1950s, and the advent of the National Institutes of Health Artificial Heart Program in the 1960s. Since then, MCS devices have evolved from large positive-displacement pumps with limited durability and large control consoles to battery-powered continuous-flow rotary pumps with compact smart controllers that are small enough to be implanted in infants. The progress made in these devices has occurred as a result of federal and private funding leading to various technological breakthroughs by talented and dedicated interdisciplinary teams of engineers, scientists, and clinicians. While the current devices provide a therapy for advanced stage heart failure patients to routinely live five years or longer with an MCS device with a better quality of life, adverse events such as strokes and infections are still common. Further progress on the devices with specific goals to overcome these issues is needed to benefit more heart failure patients and realize the full potential of MCS devices

The application of thorough engineering principles has been key to the successful design, development, and manufacturing of mechanical circulatory support (MCS) devices for the last five decades. The engineering involved is multidisciplinary and includes mechanical, chemical, electrical, and materials engineering as well as sub-specialties such as computer, biomedical, reliability, and software engineering. Because MCS devices vary in their intended patient population, duration of use, and operation, no one set of engineering requirements can be specified for MCS devices. As a result, engineering requirements are determined and implemented for each MCS device using the methods of risk analysis and risk management.

In this paper, we apply pre-trained language models to the Semantic Textual Similarity (STS) task, with a specific focus on the clinical domain. In low-resource setting of clinical STS, these large models tend to be impractical and prone to overfitting. Building on BERT, we study the impact of a number of model design choices, namely different fine-tuning and pooling strategies. We observe that the impact of domain-specific fine-tuning on clinical STS is much less than that in the general domain, likely due to the concept richness of the domain. Based on this, we propose two data augmentation techniques. Experimental results on N2C2-STS 1 demonstrate substantial improvements, validating the utility of the proposed methods.

Domain pretraining followed by task fine-tuning has become the standard paradigm for NLP tasks, but requires in-domain labelled data for task fine-tuning. To overcome this, we propose to utilise domain unlabelled data by assigning pseudo labels from a general model. We evaluate the approach on two clinical STS datasets, and achieve r= 0.80 on N2C2-STS. Further investigation reveals that if the data distribution of unlabelled sentence pairs is closer to the test data, we can obtain better performance. By leveraging a large general-purpose STS dataset and small-scale in-domain training data, we obtain further improvements to r= 0.90, a new SOTA.

The Paris terrorist attacks occurred on November 13, 2015, prompting a massive response on social media including Twitter, with millions of posted tweets in the first few hours after the attacks. Most of the tweets were condemning the attacks and showing support to Parisians. One of the trending debates related to the attacks concerned possible association between terrorism and Islam, and Muslims in general. This created a global discussion between those attacking and those defending Islam and Muslims. In this paper, we use this incident to examine the effect of online social network interactions prior to an event to predict what attitudes will be expressed in response to the event. Specifically, we focus on how a person's online content and network dynamics can be used to predict future attitudes and stance in the aftermath of a major event. In our study, we collected a set of 8.36 million tweets related to the Paris attacks within the 50 hours following the event, of which we identified over 900k tweets mentioning Islam and Muslims. We quantitatively analyzed users' network interactions and historical tweets to predict their attitudes towards Islam and Muslim. We provide a description of the quantitative results based on the content (hashtags) and network interactions (retweets, replies, and mentions). We analyze two types of data: (1) we use post-event tweets to learn users' stated stance towards Muslims based on sampling methods and crowd-sourced annotations; and (2) we employ pre-event interactions on Twitter to build a classifier to predict post-event stance. We found that pre-event network interactions can predict attitudes towards Muslims with 82\% macro F-measure, even in the absence of prior mentions of Islam, Muslims, or related terms.

Background Expanded use of pediatric ventricular assist devices (VADs) has decreased mortality in children awaiting heart transplantation. Pediatric Interagency Registry for Mechanical Circulatory Support (Pedimacs), a National Heart, Lung, and Blood Institute–sponsored North American database, provides a platform to understand this emerging population. Methods Between September 2012 and September 2016, patients aged younger than 19 years who underwent VAD implantation were enrolled in Pedimacs. FDA approved durable devices as well as temporary support devices were included. The second annual report updates the current Pedimacs data. Patients implanted with temporary devices are included in Pedimacs and this analysis includes this group of paracorporeal continuous flow VADs. Results Over the 4 years, 42 hospitals implanted 432 devices in 364 patients less than 19 yrs of age. Diagnoses included cardiomyopathy in 223 (61%), myocarditis in 41 (11%), and congenital heart disease in 77 (21%), of which 48 had single-ventricle physiology. At implant, 87% were at Intermacs patient profile 1 or 2. The age distribution of children (59% male) supported on VAD included 69 (19%) aged younger than 1 year, 66 (18%) aged 1 to 6 years, 56 (15%) aged 6 to 10 years, and 173 (48%) aged 11 to 19 years. Median follow-up was 2.2 months (range, 1 day to 41.5 months). Median (interquartile) age at implant was 1.7 (0.3–10.0) years for paracorporeal continuous-flow pumps ( n = 60), 1.7 (0.4–5.3) years for paracorporeal pulsatile pumps ( n = 105), and 15.0 (11.3–16.9) years for implantable continuous-flow pumps ( n = 174). Support strategies included LVAD in 293 (80%), biventricular device in 55 (15%), and total artificial heart in 8 (2%). Nearly 50% of patients underwent transplantation within 6 months, with overall mortality of 19%. Adverse event burden continues to be high. Conclusions Pedimacs constitutes the largest longitudinal pediatric VAD registry. Preimplant data across centers will be helpful at creating shared protocols with which to improve outcomes. Adverse events continue to be the major challenge, especially among the young critically ill children with complex congenital disease.

Background Currently there is an incomplete understanding of antimicrobial usage patterns in veterinary clinics in Australia, but such knowledge is critical for the successful implementation and monitoring of antimicrobial stewardship programs. Methods VetCompass Australia collects medical records from 181 clinics in Australia (as of May 2018). These records contain detailed information from individual consultations regarding the medications dispensed. One unique aspect of VetCompass Australia is its focus on applying natural language processing (NLP) and machine learning techniques to analyse the records, similar to efforts conducted in other medical studies. Results The free text fields of 4,394,493 veterinary consultation records of dogs and cats between 2013 and 2018 were collated by VetCompass Australia and NLP techniques applied to enable the querying of the antimicrobial usage within these consultations. Conclusion The NLP algorithms developed matched antimicrobial in clinical records with 96.7% accuracy and an F1 Score of 0.85, as evaluated relative to expert annotations. This dataset can be readily queried to demonstrate the antimicrobial usage patterns of companion animal practices throughout Australia.

We present a new model for acquiring comprehensive multiword lexicons from large corpora based on competition among n-gram candidates. In contrast to the standard approach of simple ranking by association measure, in our model n-grams are arranged in a lattice structure based on subsumption and overlap relationships, with nodes inhibiting other nodes in their vicinity when they are selected as a lexical item. We show how the configuration of such a lattice can be optimized tractably, and demonstrate using annotations of sampled n-grams that our method consistently outperforms alternatives by at least 0.05 F-score across several corpora and languages.

The medical burden of heart failure (HF) has spurred interest in clinicians and scientists to develop therapies to restore the function of a failing heart. To advance this agenda, the National Heart, Lung, and Blood Institute (NHLBI) convened a Working Group of experts from June 2-3, 2016, in Bethesda, MD, to develop NHLBI recommendations aimed at advancing the science of cardiac recovery in the setting of mechanical circulatory support (MCS). Mechanical circulatory support devices effectively reduce volume and pressure overload that drives the cycle of progressive myocardial dysfunction, thereby triggering structural and functional reverse remodeling. Research in this field could be innovative in many ways, and the Working Group specifically discussed opportunities associated with genome-phenome systems biology approaches; genetic epidemiology; bioinformatics and precision medicine at the population level; advanced imaging modalities, including molecular and metabolic imaging; and the development of minimally invasive surgical and percutaneous bioengineering approaches. These new avenues of investigations could lead to new treatments that target phylogenetically conserved pathways involved in cardiac reparative mechanisms. A central point that emerged from the NHLBI Working Group meeting was that the lessons learned from the MCS investigational setting can be extrapolated to the broader HF population. With the precedents set by the significant effect of studies of other well-controlled and tractable subsets on larger populations, such as the genetic work in both cancer and cardiovascular disease, the work to improve our understanding of cardiac recovery and resilience in MCS patients could be transformational for the greater HF population.

Summary: The medical burden of heart failure (HF) has spurred interest in clinicians and scientists to develop therapies to restore the function of a failing heart. To advance this agenda, the National Heart, Lung, and Blood Institute (NHLBI) convened a Working Group of experts from June 2 to 3, 2016, in Bethesda, Maryland, to develop NHLBI recommendations aimed at advancing the science of cardiac recovery in the setting of mechanical circulatory support (MCS). MCS devices effectively reduce volume and pressure overload that drives the cycle of progressive myocardial dysfunction, thereby triggering structural and functional reverse remodeling. Research in this field could be innovative in many ways, and the Working Group specifically discussed opportunities associated with genome-phenome systems biology approaches; genetic epidemiology; bioinformatics and precision medicine at the population level; advanced imaging modalities, including molecular and metabolic imaging; and the development of minimally invasive surgical and percutaneous bioengineering approaches. These new avenues of investigations could lead to new treatments that target phylogenetically conserved pathways involved in cardiac reparative mechanisms. A central point that emerged from the NHLBI Working Group meeting was that the lessons learned from the MCS investigational setting can be extrapolated to the broader HF population. With the precedents set by the significant effect of studies of other well-controlled and tractable subsets on larger populations, such as the genetic work in both cancer and cardiovascular disease, the work to improve our understanding of cardiac recovery and resilience in MCS patients could be transformational for the greater HF population. Key Words: cardiac remodeling, mechanical circulatory support, myocardial recovery, ventricular assist devices

INTRODUCTION Improved understanding of the clinical course of ambulatory advanced chronic systolic heart failure may improve the provision of appropriate care and is central to the design of clinical trials in this population. METHODS Twenty-one implanting ventricular assist device (VAD) centers enrolled 400 subjects in the Registry Evaluation of Vital Information for VADs in Ambulatory Life (REVIVAL), a prospective, observational study in ambulatory, chronic, advanced systolic heart failure, designed to identify a cohort with an approximately 25% 1-year risk of the primary composite outcome of death, urgent transplant, or durable mechanical circulatory support. Inclusion criteria utilized only information collected during routine clinical care. Exclusion criteria identified patients with contraindications to VAD. Study inclusion required at least 1 of 10 high-risk criteria derived from established hospitalization and non-hospitalization markers of increased mortality risk. We evaluated the test performance characteristics of the high-risk criteria. RESULTS Data on 373 subjects evaluable for the primary composite outcome at the 1-year visit are presented. Baseline data were consistent with a less advanced cohort than Medical Arm for Mechanically Assisted Circulatory Support or Risk Assessment (MedaMACS) and Comparative Effectiveness of Left Ventricular Assist Device and Medical Management in Ambulatory Heart Failure Patients (ROADMAP). Freedom from the primary composite outcome was 75.9%. Non-hospitalization inclusion criteria identified 89% of patients with events. CONCLUSIONS Using routinely obtained clinical information for enrollment, REVIVAL successfully recruited an ambulatory chronic systolic heart failure cohort with an approximately 25% annual risk of the primary composite outcome. Information from this registry will be relevant to the planning of future trials of earlier VAD use and of other interventions in this population.

Our review in the 2008 volume of this journal detailed the use of mechanical circulatory support (MCS) for treatment of heart failure (HF). MCS initially utilized bladder-based blood pumps generating pulsatile flow; these pulsatile flow pumps have been supplanted by rotary blood pumps, in which cardiac support is generated via the high-speed rotation of computationally designed blading. Different rotary pump designs have been evaluated for their safety, performance, and efficacy in clinical trials both in the United States and internationally. The reduced size of the rotary pump designs has prompted research and development toward the design of MCS suitable for infants and children. The past decade has witnessed efforts focused on tissue engineering–based therapies for the treatment of HF. This review explores the current state and future opportunities of cardiac support therapies within our larger understanding of the treatment options for HF.

We describe SemEval-2017 Task 3 on Community Question Answering. This year, we reran the four subtasks from SemEval-2016:(A) Question-Comment Similarity,(B) Question-Question Similarity,(C) Question-External Comment Similarity, and (D) Rerank the correct answers for a new question in Arabic, providing all the data from 2015 and 2016 for training, and fresh data for testing. Additionally, we added a new subtask E in order to enable experimentation with Multi-domain Question Duplicate Detection in a larger-scale scenario, using StackExchange subforums. A total of 23 teams participated in the task, and submitted a total of 85 runs (36 primary and 49 contrastive) for subtasks A-D. Unfortunately, no teams participated in subtask E. A variety of approaches and features were used by the participating systems to address the different subtasks. The best systems achieved an official score (MAP) of 88.43, 47.22, 15.46, and 61.16 in subtasks A, B, C, and D, respectively. These scores are better than the baselines, especially for subtasks A-C., Comment: community question answering, question-question similarity, question-comment similarity, answer reranking, Multi-domain Question Duplicate Detection, StackExchange, English, Arabic

In this paper, we apply various embedding methods on multiword expressions to study how well they capture the nuances of non-compositional data. Our results from a pool of word-, character-, and document-level embbedings suggest that Word2vec performs the best, followed by FastText and Infersent. Moreover, we find that recently-proposed contextualised embedding models such as Bert and ELMo are not adept at handling non-compositionality in multiword expressions.

Argument component extraction is a challenging and complex high-level semantic extraction task. As such, it is both expensive to annotate (meaning training data is limited and low-resource by nature), and hard for current-generation deep learning methods to model. In this paper, we reevaluate the performance of state-of-the-art approaches in both single- and multi-task learning settings using combinations of character-level, GloVe, ELMo, and BERT encodings using standard BiLSTM-CRF encoders. We use evaluation metrics that are more consistent with evaluation practice in named entity recognition to understand how well current baselines address this challenge and compare their performance to lower-level semantic tasks such as CoNLL named entity recognition. We find that performance utilizing various pre-trained representations and training methodologies often leaves a lot to be desired as it currently stands, and suggest future pathways for improvement.

Accurate, automatic evaluation of machine translation is critical for system tuning, and evaluating progress in the field. We proposed a simple unsupervised metric, and additional supervised metrics which rely on contextual word embeddings to encode the translation and reference sentences. We find that these models rival or surpass all existing metrics in the WMT 2017 sentence-level and system-level tracks, and our trained model has a substantially higher correlation with human judgements than all existing metrics on the WMT 2017 to-English sentence level dataset.

Critical to natural language generation is the production of correctly inflected text. In this paper, we isolate the task of predicting a fully inflected sentence from its partially lemmatized version. Unlike traditional morphological inflection or surface realization, our task input does not provide ``gold'' tags that specify what morphological features to realize on each lemmatized word; rather, such features must be inferred from sentential context. We develop a neural hybrid graphical model that explicitly reconstructs morphological features before predicting the inflected forms, and compare this to a system that directly predicts the inflected forms without relying on any morphological annotation. We experiment on several typologically diverse languages from the Universal Dependencies treebanks, showing the utility of incorporating linguistically-motivated latent variables into NLP models., Comment: NAACL 2019

Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting. Unfortunately matching text is often not available in sufficient quantity, and moreover, within any domain of text, data is often highly heterogenous. In this paper we propose a method to distill the important domain signal as part of a multi-domain learning system, using a latent variable model in which parts of a neural model are stochastically gated based on the inferred domain. We compare the use of discrete versus continuous latent variables, operating in a domain-supervised or a domain semi-supervised setting, where the domain is known only for a subset of training inputs. We show that our model leads to substantial performance improvements over competitive benchmark domain adaptation methods, including methods using adversarial learning., ACL 2019 (9 pages + 2 references + 1 appendices)

Use of mechanical circulatory support in children has increased as more options have become available. A national account of the use of mechanical support in children and adolescents is essential to understanding outcomes, refining patient selection and improving quality of care.The Pediatric Interagency Registry for Mechanical Circulatory Support (PediMACS) is a National Heart, Lung, and Blood Institute-supported nationwide registry for temporary and durable ventricular assist device (VAD) use in patients19 years of age. Between the launch in September 2012 and June 2015, 37 hospitals in the USA have enrolled patients. This first report of data from PediMACS analyzed pre-implant patient characteristics, survival using competing outcomes, and adverse events.Two hundred pediatric patients underwent 222 durable VAD implants. Patients' characteristics and outcomes of children supported with a temporary device (n = 41) were not analyzed in this report. The etiology of heart disease included 146 (73%) patients with cardiomyopathy and 35 (18%) with congenital heart disease. Thirty patients (15%) transitioned from extracorporeal membrane oxygenation (ECMO) and 76 (38%) had previous cardiac surgery. Most patients were Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) Level 1 (27%) or Level 2 (56%) at implant, with 13% at Level 3. Of the 200 patients supported with a durable device, 91 (46%) were supported with a pulsatile-flow device and 109 (55%) with a continuous-flow (CF) device. Patient age at first implant included 30 patients (15%)1 year of age, 37 (19%) 1 to 5 years, 32 (16%) 6 to 10 years and 101 (51%) 10 to 18 years. Patients were supported with left ventricular assist device alone in 161 (81%), biventricular ventricular assist device in 29 (15%), right ventricular assist device in 4 (2.0%) and total artificial heart in 6 (3%), together comprising 783 months of follow-up. The 200 patients receiving primary durable devices had an actuarial survival of 81% at 6 months. Competing risk analysis at 6 months revealed that 58% of patients had been transplanted, 28% were alive on support, 14% had died and 0.6% recovered. In the overall cohort, there were 28 deaths. Reported serious adverse events included infection (n = 78), bleeding (n = 68), device malfunction (n = 79) and neurologic dysfunction (n = 52).PediMACS constitutes the largest single data repository with detailed information of pediatric patients implanted with VADs. The first PediMACS report reveals favorable outcomes despite the varying patient characteristics and pump types. However, the rate of adverse events remains high. With further data collection, analysis of patient risk factors critical to improving outcomes will be possible.

Ongoing breakthroughs with CRISPR/Cas-based editing could potentially revolutionize modern medicine, but there are many questions to resolve about the ethical implications for its therapeutic application. We conducted a worldwide online survey of over 12,000 people recruited via social media to gauge attitudes toward this technology and discuss our findings here.

The clinical community faces major issues in caring for the 100,000 to 300,000 advanced heart failure patients in the United States. 1 For these patients, left ventricular assist devices (LVADs) may provide life-saving options for use as destination therapy or as a bridge to heart transplantation. Emerging and changing patterns of adverse events in continuous-flow LVADs present challenges to optimizing use of LVADs in this very ill population. These patterns have only recently been uncovered following more widespread use as compared with the controlled clinical trials, in which they performed admirably. In this issue of the journal, Kirklin et al 2 and Smedira et al 3 report on parallel analyses of HeartMate II pump thrombosis data derived from the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) registry. Kirklin and colleagues report a reversal in the first half of 2014 of a trend that had been increasing over the previous several years. They hypothesize that modifications in anti-coagulation therapy, among other strategies, are working to reduce the apparent increases in pump thrombosis observed between 2010 and 2013. Smedira and colleagues report a flattening of the pump thrombosis rate, but suggest that clinicians may reconsider and recalibrate their decision thresholds for recommending device implant, transplant and medical therapies. As this collaboration evolved, the National Heart, Lung, and Blood Institute (NHLBI), along with the NHLBIappointed study chair for INTERMACS, further explored the same INTERMACS data sets to give perspective to the 2 analyses described. Modeling choices within the analyses were assessed along with the uncertainty of statistical estimates of recent trends. Different approaches to estimating the magnitude of risk in the presence of censoring events are described. Consideration of these approaches is important for interpretation of the trends described and critical to making research and clinical decisions involving patients receiving these devices. The results of this collaborative approach demonstrate the capabilities and limitations of INTERMACS for capturing important trends in ventricular assist device (VAD) utilization and outcomes. They also show how these data may be used in concert with data from other reliable sources to inform public health, regulatory and research decisions.

The seventh annual report of the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) summarizes the first 9 years of patient enrollment. The Registry includes >15,000 patients from 158 participating hospitals. Trends in device strategy, patient profile at implant and survival are presented. Risk factors for mortality with continuous-flow pumps are updated, and the major causes/modes of death are presented. The adverse event burden is compared between eras, and health-related quality of life is reviewed. A detailed analysis of outcomes after mechanical circulatory support for ambulatory heart failure is presented. Recent summary data from PediMACS and MedaMACS is included. With the current continuous-flow devices, survival at 1 and 2 years is 80% and 70%, respectively.

Antimicrobial Resistance is a global crisis that veterinarians contribute to through their use of antimicrobials in animals. Antimicrobial stewardship has been shown to be an effective means to reduce antimicrobial resistance in hospital environments. Effective monitoring of antimicrobial usage patterns is an essential part of antimicrobial stewardship and is critical in reducing the development of antimicrobial resistance. The aim of this study is to describe how frequently antimicrobials were used in veterinary consultations and identify the most frequently used antimicrobials. Using VetCompass Australia, Natural Language Processing techniques, and the Australian Strategic Technical Advisory Group's (ASTAG) Rating system to classify the importance of antimicrobials, descriptive analysis was performed on the antimicrobials prescribed in consultations from 137 companion animal veterinary clinics in Australia between 2013 and 2017 (inclusive). Of the 4,400,519 consultations downloaded there were 595,089 consultations where antimicrobials were prescribed to dogs or cats. Antimicrobials were dispensed in 145 of every 1000 canine consultations; and 38 per 1000 consultations involved high importance rated antimicrobials. Similarly with cats, 108 per 1000 consultations had antimicrobials dispensed, and in 47 per 1000 consultations an antimicrobial of high importance rating was administered. The most common antimicrobials given to cats and dogs were cefovecin and amoxycillin clavulanate, respectively. The most common topical antimicrobial and high-rated topical antimicrobial given to dogs and cats was polymyxin B. This study provides a descriptive analysis of the antimicrobial usage patterns in Australia using methods that can be automated to inform antimicrobial use surveillance programs and promote antimicrobial stewardship.

In this paper, we present the first attempt to use multitask learning for query segmentation. We use the semantic category of the words as an auxiliary task and show that segmentation improves when the model is also trained to predict the semantic category of the query terms, outperforming benchmark methods over a novel dataset from a popular job search engine. Our further experiments show that the task of modeling the query term semantics performs better as a standalone task, without adding segmentation as an auxiliary task.

While neural networks have been shown to achieve impressive results for sentence-level sentiment analysis, targeted aspect-based sentiment analysis (TABSA) --- extraction of fine-grained opinion polarity w.r.t. a pre-defined set of aspects --- remains a difficult task. Motivated by recent advances in memory-augmented models for machine reading, we propose a novel architecture, utilising external "memory chains" with a delayed memory update mechanism to track entities. On a TABSA task, the proposed model demonstrates substantial improvements over state-of-the-art approaches, including those using external knowledge bases., Accepted to NAACL 2018 (camera-ready)

Most real world language problems require learning from heterogenous corpora, raising the problem of learning robust models which generalise well to both similar (in domain) and dissimilar (out of domain) instances to those seen in training. This requires learning an underlying task, while not learning irrelevant signals and biases specific to individual domains. We propose a novel method to optimise both in- and out-of-domain accuracy based on joint learning of a structured neural model with domain-specific and domain-general components, coupled with adversarial training for domain. Evaluating on multi-domain language identification and multi-domain sentiment analysis, we show substantial improvements over standard domain adaptation techniques, and domain-adversarial training.

This paper describes a warrant classification system for SemEval 2018 Task 12, that attempts to learn semantic representations of reasons, claims and warrants. The system consists of 3 stacked LSTMs: one for the reason, one for the claim, and one shared Siamese Network for the 2 candidate warrants. Our main contribution is to force the embeddings into a shared feature space using vector operations, semantic similarity classification, Siamese networks, and multi-task learning. In doing so, we learn a form of generative implication, in encoding implication interrelationships between reasons, claims, and the associated correct and incorrect warrants. We augment the limited data in the task further by utilizing WordNet synonym “fuzzing”. When applied to SemEval 2018 Task 12, our system performs well on the development data, and officially ranked 8th among 21 teams.

Automatic geolocation of microblog posts from their text content is particularly difficult because many location-indicative terms are rare terms, notably entity names such as locations, people or local organisations. Their low frequency means that key terms observed in testing are often unseen in training, such that standard classifiers are unable to learn weights for them. We propose a method for reasoning over such terms using a knowledge base, through exploiting their relations with other entities. Our technique uses a graph embedding over the knowledge base, which we couple with a text representation to learn a geolocation classifier, trained end-to-end. We show that our method improves over purely text-based methods, which we ascribe to more robust treatment of low-count and out-of-vocabulary entities.

Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel method, tracking various semantic aspects with external neural memory chains while encouraging each to focus on a particular semantic aspect. Evaluated on the task of story ending prediction, our model demonstrates superior performance to a collection of competitive baselines, setting a new state of the art., Comment: Accepted to ACL 2018 (camera-ready)

Crowd-sourced assessments of machine translation quality allow evaluations to be carried out cheaply and on a large scale. It is essential, however, that the crowd's work be filtered to avoid contamination of results through the inclusion of false assessments. One method is to filter via agreement with experts, but even amongst experts agreement levels may not be high. In this paper, we present a new methodology for crowd-sourcing human assessments of translation quality, which allows individual workers to develop their own individual assessment strategy. Agreement with experts is no longer required, and a worker is deemed reliable if they are consistent relative to their own previous work. Individual translations are assessed in isolation from all others in the form of direct estimates of translation quality. This allows more meaningful statistics to be computed for systems and enables significance to be determined on smaller sets of assessments. We demonstrate the methodology's feasibility in large-scale human evaluation through replication of the human evaluation component of Workshop on Statistical Machine Translation shared translation task for two language pairs, Spanish-to-English and English-to-Spanish. Results for measurement based solely on crowd-sourced assessments show system rankings in line with those of the original evaluation. Comparison of results produced by the relative preference approach and the direct estimate method described here demonstrate that the direct estimate method has a substantially increased ability to identify significant differences between translation systems.