Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Approach For Automatic Disability Annotation

The aim of this paper is to present the work pursued by the IXA group in the DIANN-Ibereval 2018 task. The task consists of identifying disabilities within a collection of several abstracts from Elsevier journal papers related to the biomedical domain. These corpora include the annotation of negation when it applies to a disability. The evaluation of the task is divided in two sub-tasks; one corresponding to the detection of English entities and the other to Spanish entities. In order to carry out the task, we have created a pipeline combining a Recurrent Neural Network (RNN) used for sequence to sequence tagging with simple rules to boost the coverage. Our system achieves the best task F-score for both English and Spanish disability identification, showing the suitability of our approach even with quite small training corpus. Our F-score is 0.821 for English and of 0.786 for Spanish.
Analysing TB Severity Levels with an Enhanced Deep re-sidual Learning - Depth-Resnet

This work responds to the Competition of Tuberculosis Task organised by imageCLEF 2018. While Task #3 appears to be challenging, the experience was very enjoyable. If time had been permitted, it was certain that more accurate results could have been achieved. The authors submitted 2 runs. Based on the given training datasets with severity levels of 1 to 5, an enhanced deep residual learning architecture, depthResNet, is developed and applied to train the datasets to classify 5 categories. The datasets are pre-processed with each volume being segmented into twenty- 128×128×depth blocks with ~64 pixel overlaps. While each block has been predicted with a severity level, assembling all constituent block scores together to give an overall label for the concerned volume tends to be more challenging. Since the probability of high severity is not provided from the training datasets, which bears little resemblance to the classification probability, the submission of probability for the first run was manually assigned as 0.9, 0.7, 0.5, 0.3, and 0.1 to severity levels of 1 to 5 respectively. After the deadline was extended, the model was re-trained with frame numbers increased from 1 to 8, which takes much longer to train. In addition, a new measure was introduced to calculate the overall probability of high severity based on the block scores. As a result, with regard to classification accuracy, the 2nd submitted run achieved place 14 over a total of 36 submissions, a significant improvement from position of 35 from the first run.
Continence care education: views of students and registered nurses.

BACKGROUND this article reports on a study of continence education and training for students and qualified nurses. AIMS to understand how nurses and nursing students gain their knowledge in continence education and training, to examine nurses' understanding of bladder and bowel care and to discover to what degree nurses are confident in their knowledge of bladder and bowel care. METHODS this was a qualitative enquiry and a case study methodology was used to frame the research. The data were captured through a questionnaire and group discussions. FINDINGS continence education comprised support through self-learning, classroom instruction, skills laboratory sessions and on the wards. Feedback from participants indicated that, although continence education enhanced learning, they thought continence education sessions were either too short or did not go into enough detail. Two key factors that limited learning in the workplace were time and work priorities. CONCLUSION it is recommended that a combination of learning strategies is useful and valuable in developing the nurse's skills and knowledge of continence care, if time and funding are available.
The 1-minute sit-to-stand test to detect exercise-induced oxygen desaturation in patients with interstitial lung disease

Background: Although the 6-min walk test (6MWT) is the gold standard for assessing exercise-induced impairment of gas exchange, it cannot easily be performed in a clinical office environment. The aim of this study was to compare the 1-min sit-to-stand test (1STST) with the 6MWT for the ability to assess exercise-induced oxygen desaturation in patients with interstitial lung diseases (ILDs). Methods: A total of 107 patients were enrolled and classified into three groups: sarcoidosis, fibrotic idiopathic interstitial pneumonia (f-IIP), and other forms of ILD. The 6MWT and 1STST were performed on the same day, and pulmonary function tests, pulse oxygen saturation (SpO2), and dyspnea and fatigue (modified Borg scale) were assessed. SpO2 desaturation was evaluated by intraclass correlation coefficient (ICC), Bland–Altman analysis, and kappa (κ) coefficient in the whole population and the patient subgroups. Results: The SpO2 nadir during the 1STST and 6MWT showed good consistency [mean ± standard deviation: 92.5% ± 5% and 90% ± 7%, respectively; ICC 0.77, 95% confidence interval (CI) 0.71–0.83] and correlated strongly (r = 0.9, p < 0.0001). The frequency of patients with oxygen desaturation ⩾4% was also consistent for the two exercise tests (κ = 0.68, 95% CI 0.54–0.82). The number of repetitions in the 1STST correlated with the 6MWT distance (r = 0.5, p < 0.0001), but the dyspnea scores were higher during the 1STST than the 6MWT (p < 0.0001). These findings did not differ for the three patient subgroups. Conclusion: The 1STST can measure exercise-induced desaturation in ILD patients and could be used as an alternative test to the 6MWT in office practice.
A Big Data approach to gender classification in Twitter: Notebook for PAN at CLEF 2018

This paper describes a statistical approach to the task of gender classification in tweets, with a Big Data perspective in mind. Our task started developing our own implementation of Low Dimension Representation method, with the idea to add some other statistics which had not been used in the original implementation, such as skewness, kurtosis and central moments. Exploratory analysis of the new characteristics showed the importance of skewness due to the problem presents only 2 classes. Our approach will only use skewness for describing the difference in use of the language between men and women and skewness, as well, will be used to predict gender for the test dataset.
Measuring lupus arthritis activity using contrasted high-field MRI. Associations with clinical measures of disease activity and novel patterns of disease

Objective Arthritis in SLE is poorly described, and there is no objective measure for quantification of arthritis. In this pilot study, we aim to assess the utility of the Rheumatoid Arthritis MRI Scoring System (RAMRIS) for the quantification of lupus arthritis. Methods Patients were eligible for entry into the study if they were evaluated at the Medical University of South Carolina Lupus Center and determined by their treating rheumatologist to have active hand arthritis due to SLE. Standard of care lupus activity measures were collected, along with a detailed physical exam. MRIs were obtained using standard musculoskeletal sequences with gadolinium contrast. Semiquantitative scoring of the images used the Outcome Measures in Rheumatology Clinical Trials RAMRIS system. Results RAMRIS demonstrates large amounts of synovitis, tenosynovitis, bone marrow oedema and erosive disease in only a minority of patients. Some patients were not scored as having any synovitis or tenosynovitis. We describe potential features of lupus arthritis that are not captured in the RAMRIS scores and may be contributing to symptoms. Conclusion Lupus arthritis is an entity separate from rheumatoid arthritis and requires the development of new quantitative methods to describe and quantitate it. MRI findings suggest the inadequacy of a typical lupus musculoskeletal measure including swollen/tender joint counts to assess the level of disease activity.
Opinion Argumentation based on Combined Information Retrieval and Topic Modeling

Argumentation mining is a text mining task, which aims at automatically detecting the argumentative structure concealed in a huge amount of text data. Previous researches in this field have focused on the classification of text sentences as arguments and the detection of relations between them. Different corpora have been used, such as newspaper articles and online debates. Due to the explosion of social networks, microblogging platforms like Twitter and Facebook have become interesting tools to evaluate public opinion on different domains. In this work, we propose a new pipeline process to achieve the goal of argumentation mining, based on 70 millions of twitter-microblogs released from MC2 CLEF-2018 lab dealing with cultural events. Our approach is based on Information Retrieval protocol combined with sentiment analysis and topic modeling using Latent Dirichlet Allocation (LDA).
A Prospective Study to Evaluate the Effect of CYP2D6 Polymorphism on Plasma level of Risperidone and its Metabolite in North Indian Patients with Schizophrenia

Background: Risperidone is one of commonly utilized antipsychotic in clinical practice. Various metabolizing enzymes effect the plasma levels of risperidone and its active metabolite and thus its clinical efficacy. So, we attempted to evaluate the relationship between CYP2D6*10 (rs1065852) and CYP2D6*4 (rs3892097) gene polymorphism and the plasma concentration of risperidone and its metabolite in patients with schizophrenia. Methodology: It was a 12-week prospective study carried out in patients diagnosed with schizophrenia. The dose of risperidone was increased weekly by 1 mg and rating of psychopathology was done using Positive and Negative Syndrome Scale (PANSS). Quantification of plasma level of risperidone and 9-hydroxyrisperidone was carried out at week 6 and 12 of treatment. The *4 and *10 alleles of CYP2D6 were genotyped and their effect on metabolism of risperidone was assessed. Results: The number of CYP2D6*4 alleles affected the plasma levels of risperidone, 9-hydroxyrisperidone at 6 weeks of treatment but not at 12 weeks. On the other hand, the number of mutated alleles for CYP2D6*10 influenced the dose corrected plasma concentration of risperidone and active moiety at 12 weeks of treatment. The ratio of plasma concentration of risperidone and 9-hydroxyrisperidone was more than one in all study participants, thus, suggesting that they were poor metabolizers of risperidone. Conclusion: The polymorphism of CYP2D6*10 affects the steady state plasma concentration of risperidone in Indian patients with schizophrenia.
Solving Bar Exam Questions with Deep Neural Networks

In this paper, we present a system which solves a Bar Examination written in Natural Language. The proposed system exploits the recent techniques in Deep Neural Networks which have shown promise in many Natural Language Processing (NLP) applications. We evaluate our system on a real Legal Bar Examination, the United States MultiState Bar Examination (MBE), which is a multi-choice 200questions exam for aspiring lawyers. We show that our system achieves good performance without relying on any external knowledge. Our work comes with an added effort of curating a small corpus, following similar question answering datasets from the well-known MBE examination. The proposed system beats a TFIDF-based baseline, while showing a strong performance when modified for a legal Textual Entailment evaluation.
How early can we diagnose Alzheimer disease (and is it sufficient)?

A seismic shift in our understanding of the ability to diagnose Alzheimer disease (AD) is occurring. For the last several decades, AD has been a clinical–pathologic diagnosis, and this conceptualization of the disease has served the field well. Typically, the clinician would identify a syndrome such as mild cognitive impairment or dementia, and label the condition as “probable AD” since the diagnosis of definite AD could not be made until an autopsy revealed the presence of amyloid plaques and tau-based neurofibrillary tangles. However, with the advent of biomarkers for AD including neuroimaging and CSF, the identification of AD pathology can be made in life, which greatly enhances the ability of clinicians to be precise about the underlying etiology of a clinical syndrome. Hypothetical models of the temporal relation among the pathologic elements and the clinical symptoms have been proposed and have influenced the field enormously. This has enabled clinicians to be specific about the underlying cause of a given clinical syndrome. As such, the diagnostic capability of the clinician is evolving. However, AD pathology is only a component of the puzzle describing the causes of cognitive changes in aging. Most often, there is a multitude of pathologic entities contributing to the neuropathologic explanation of cognitive changes in aging. AD changes contribute important elements to the diagnosis, but the final answer is more complex. The field of aging and dementia will have to incorporate these additional elements.
A Straightforward Multimodal Approach for Author Profiling: Notebook for PAN at CLEF 2018

In this paper we evaluate different strategies from the literature for text and image classification at PAN 2018. The main objective of this shared task is the identification of the gender of different users by using tweets and images posted. We evaluate four popular strategies for the text representation: 1) Bag of Terms (BoT), 2) Second Order Attributes (SOA) representation, 3) Convolutional Neural Network (CNN) models and 4) an Ensemble of n-grams at word and character level. For the image representation we used a Convolutional Neural Network (CNN) based on [6]. We observed that the n-grams Ensemble presented the highest performance. For our participation we chose the Ensemble and perform an early fusion with the image representation to create a multimodal representation.
Mechanisms of rapid morphogenetic movements in the metamorphosis of the bryozoan Bugula neritina (cheilostomata, cellularioidea). I. Attachment to the substratum

The larval morphology, settlement behavior, and the rapid morphogenetic movements that occur during the first 60 sec of metamorphosis of the cellularioid cheilostome bryozoan Bugula neritina have been examined and analyzed by light and electron microscopy. The larva attaches to the substratum at the onset of metamorphosis by the eversion of the internal sac. At the same time, the coronal cilia reverse their direction of beat, spreading an adhesive secreted by the neck region of the everting sac over the metamorphosing larva. During attachment, the larva goes through several configurations that coincide with the sequential contraction and relaxation of certain larval muscles. Histological and ultrastructural evidence indicates that the neck and wall regions of the internal sac are everted by the contraction of the muscles in the equatorial plane of the larva at the same time that the roof region in pulled toward the larval equator by the contraction of the axial muscles. The subsequent relaxation of the axial muscles allows the roof region to be everted by the antagonistic force generated by the sustained contraction of the equatorial musculature. After the roof region attaches to the substratum, the apical disc is temporarily retracted by a second contraction of the axial muscles. The apical disc subsequently reextends as the axial muscles relax just before coronal involution. A comparison of the ontogenetic sequence of rapid morphogenetic movements in the metamorphoses of cheilostome and ctenostome bryozoans indicates that cellularioid cheilostomes have undergone peramorphosis in the aspect of development.
CLEF eHealth 2018 Multilingual Information Extraction Task Overview: ICD10 Coding of Death Certificates in French, Hungarian and Italian

This paper reports on Task 1 of the 2018 CLEF eHealth evaluation lab which extended the previous information extraction tasks of ShARe/CLEF eHealth evaluation labs. The task continued with coding of death certificates, as introduced in CLEF eHealth 2016. This largescale classification task consisted of extracting causes of death as coded in the International Classification of Diseases, tenth revision (ICD10). The languages offered for the task this year were French, Hungarian and Italian. Participant systems were evaluated against a blind reference standard of 11,932 death certificates in the French dataset 21,176 certificates in the Hungarian dataset and 3,618 certificates in the Italian dataset using Precision, Recall and F-measure. In total, fourteen teams participated: 14 teams submitted runs for the French dataset, 5 submitted runs for the Hungarian dataset and 6 for the Italian dataset. For death certificate coding, the highest performance was 0.838 F-measure for French, 0.9627 for Hungarian and 0.9524 for Italian.
Review of methods and study designs of evaluations related to clinical pathways.

Objective The HealthPathways program is an online information portal that helps clinicians provide consistent and integrated patient care within a local health system through localised pathways for diagnosis, treatment and management of various health conditions. These pathways are consistent with the definition of clinical pathways. Evaluations of HealthPathways programs have thus far focused primarily on website utilisation and clinical users' experience and satisfaction, with limited evidence on changes to patient outcomes. This lack motivated a literature review of the effects of clinical pathways on patient and economic outcomes to inform a subsequent HealthPathways evaluation.Methods A systematic review was performed to summarise the analytical methods, study designs and results of studies evaluating clinical pathways with an economic outcome component published between 1 January 2000 and 31 August 2017 in four academic literature databases.Results Fifty-five relevant articles were identified for inclusion in this review. The practical pre-post study design with retrospective baseline data extraction and prospective intervention data collection was most commonly used in the evaluations identified. Straightforward statistical methods for comparing outcomes, such as the t-test or χ2 test, were frequently used. Only four of the 55 articles performed a cost-effectiveness analysis. Clinical pathways were generally associated with improved patient outcomes and positive economic outcomes in hospital settings.Conclusions Clinical pathways evaluations commonly use pragmatic study designs, straightforward statistical tests and cost-consequence analyses. More HealthPathways program evaluations focused on patient and economic outcomes, clinical pathway evaluations in a primary care setting and cost-effectiveness analyses of clinical pathways are needed.What is known about the topic? HealthPathways is a web-based program that originated from Canterbury, New Zealand, and has seen uptake elsewhere in New Zealand, Australia and the UK. The HealthPathways program aims to assist the provision of consistent and integrated health services through dedicated, localised pathways for various health conditions specific to the health region. Evaluations of HealthPathways program focused on patient and economic outcomes have been limited.What does this paper add? This review synthesises the academic literature of clinical pathways evaluations in order to inform a subsequent HealthPathways evaluation. The focus of the synthesis was on the analytical methods and study designs used in the previous evaluations. The previous clinical pathway evaluations have been pragmatic in nature with relatively straightforward study designs and analysis.What are the implications for practitioners? There is a need for more economic and patient outcome evaluations for HealthPathways programs. More sophisticated statistical analyses and economic evaluations could add value to these evaluations, where appropriate and taking into consideration the data limitations.
Knockdown of p66Shc Alters Lineage-Associated Transcription Factor Expression in Mouse Blastocysts

The p66Shc adaptor protein regulates apoptosis and senescence during early mammalian development. However, p66Shc expression during mouse preimplantation development is upregulated at the blastocyst stage. Our objective was to determine the biological function of p66Shc during mouse blastocyst development. In this study, we demonstrate that a reduced p66Shc transcript abundance following its short interfering RNA (siRNA)-mediated knockdown alters the spatiotemporal expression of cell lineage-associated transcription factors in the inner cell mass (ICM) of the mouse blastocyst. P66Shc knockdown blastocysts restrict OCT3/4 earlier to the inner cells of the early blastocyst and have ICMs containing significantly higher OCT3/4 levels, more GATA4-positive cells, and fewer NANOG-positive cells. P66Shc knockdown blastocysts also show a significantly reduced ability to form ICM-derived outgrowths when explanted in vitro. The increase in cells expressing primitive endoderm markers may be due to increased ERK1/2 activity, as it is reversed by ERK1/2 inhibition. These results suggest that p66Shc may regulate the relative abundance and timing of lineage-associated transcription factor expression in the blastocyst ICM.
Debunking Fake News One Feature at a Time

Identifying the stance of a news article body with respect to a certain headline is the first step to automated fake news detection. In this paper, we introduce a 2-stage ensemble model to solve the stance detection task. By using only hand-crafted features as input to a gradient boosting classifier, we are able to achieve a score of 9161.5 out of 11651.25 (78.63%) on the official Fake News Challenge (Stage 1) dataset. We identify the most useful features for detecting fake news and discuss how sampling techniques can be used to improve recall accuracy on a highly imbalanced dataset.
Sprintz

Thanks to the rapid proliferation of connected devices, sensor-generated time series constitute a large and growing portion of the world's data. Often, this data is collected from distributed, resource-constrained devices and centralized at one or more servers. A key challenge in this setup is reducing the size of the transmitted data without sacrificing its quality. Lower quality reduces the data's utility, but smaller size enables both reduced network and storage costs at the servers and reduced power consumption in sensing devices. A natural solution is to compress the data at the sensing devices. Unfortunately, existing compression algorithms either violate the memory and latency constraints common for these devices or, as we show experimentally, perform poorly on sensor-generated time series. We introduce a time series compression algorithm that achieves state-of-the-art compression ratios while requiring less than 1KB of memory and adding virtually no latency. This method is suitable not only for low-power devices collecting data, but also for servers storing and querying data; in the latter context, it can decompress at over 3GB/s in a single thread, even faster than many algorithms with much lower compression ratios. A key component of our method is a high-speed forecasting algorithm that can be trained online and significantly outperforms alternatives such as delta coding. Extensive experiments on datasets from many domains show that these results hold not only for sensor data but also across a wide array of other time series.