Miracle-dz/UniFAR-FTUs huggingface dataset:
CIC-GIL Approach to Cross-domain Authorship Attribution: Notebook for PAN at CLEF 2018

We present the CIC-GIL approach to the cross-domain authorship attribution task at PAN 2018. This year’s evaluation lab focuses on the closed-set attribution task applied to a Fanfiction corpus in five languages: English, French, Italian, Polish, and Spanish. We followed a traditional machine learning approach and selected different feature sets depending on the language. We evaluated document features such as typed and untyped character n-grams, word n-grams, and function word n-grams. Our final system uses the log-entropy weighting scheme and SVM as classifier.
Strong biomechanical relationships bias the tempo and mode of morphological evolution

The influence of biomechanics on the tempo and mode of morphological evolution is unresolved, yet is fundamental to organismal diversification. Across multiple four-bar linkage systems in animals, we discovered that rapid morphological evolution (tempo) is associated with mechanical sensitivity (strong correlation between a mechanical system’s output and one or more of its components). Mechanical sensitivity is explained by size: the smallest link(s) are disproportionately affected by length changes and most strongly influence mechanical output. Rate of evolutionary change (tempo) is greatest in the smallest links and trait shifts across phylogeny (mode) occur exclusively via the influential, small links. Our findings illuminate the paradigms of many-to-one mapping, mechanical sensitivity, and constraints: tempo and mode are dominated by strong correlations that exemplify mechanical sensitivity, even in linkage systems known for exhibiting many-to-one mapping. Amidst myriad influences, mechanical sensitivity imparts distinct, predictable footprints on morphological diversity.
Association between a New Sugar Index and Caries Experience: Results of a Cross-Sectional Field Study

Objectives: The aim of this study was to test the relationship between a newly developed sugar index and caries experience in 4th- and 6th-graders. Materials and Methods: A total of 1,019 fourth-graders and 925 sixth-graders from 2 regions in Northern Germany participated in the study. Caries experience was recorded by applying the ICDAS criteria. Tooth-brushing habits and other independent variables were examined psychometrically. Structured questions were formulated that were related to specific everyday eating situations, especially between main meals, which proved to be relevant for a higher caries risk. To compare mean caries scores, nonparametric tests were applied. The Pearson coefficient was calculated in order to verify the correlation between the sugar index and the caries experience. The influence of various independent variables on caries experience was assessed by stepwise backward logistic regression analysis. Results: With regard to the primary teeth of the 4th-graders, significant correlations with values > 0.1 were found for all ICDAS outcome variables. With regard to the permanent teeth of the 6th-graders, the only significant correlations with the sugar index were for the ICDAS outcome variables D3–6MFT and D3–6FS. Conclusion: The results of our study show that not only the correlation between the sugar index and caries experience was stronger for 4th-graders than for 6th-graders, but also more distinctly visible differences were observed when comparing extreme groups. This indicates that protective factors, such as fluoridation and the preventive sealing of fissures, help level out the differences in caries occurrence in older children when compared to younger children.
A Simple Approach to Abbreviation Resolution at BARR2, IberEval 2018

Acronyms and abbreviations are widely used in clinical and other specialized texts. Understanding their meaning constitutes an important problem in the automatic extraction and mining of information from text. Moreover, an even harder problem is its sense disambiguation; that is, where a single acronym refers to many different meanings in different texts, a common occurrence in the clinical texts. In such cases, it is necessary to identify the correct corresponding sense for the acronym or abbreviation, which is often not directly specified in the text. Here we present an approach to identify acronyms and abbreviations for the BARR2 competition. We use cTAKES [7] as a framework to develop an approach to identify abbreviations and acronyms as part of a lookup entity recognition system and a word sense disambiguation classifier. The results of the BARR2 test set have shown a 79.13 F measure.
Increased environmental sample area and recovery of Clostridium difficile spores from hospital surfaces by quantitative PCR and enrichment culture

Abstract Objective Clostridium difficile spores play an important role in transmission and can survive in the environment for several months. Optimal methods for measuring environmental C. difficile are unknown. We sought to determine whether increased sample surface area improved detection of C. difficile from environmental samples. Setting Samples were collected from 12 patient rooms in a tertiary-care hospital in Toronto, Canada. Methods Samples represented small surface-area and large surface-area floor and bedrail pairs from single-bed rooms of patients with low (without prior antibiotics), medium (with prior antibiotics), and high (C. difficile infected) shedding risk. Presence of C. difficile in samples was measured using quantitative polymerase chain reaction (qPCR) with targets on the 16S rRNA and toxin B genes and using enrichment culture. Results Of the 48 samples, 64·6% were positive by 16S qPCR (geometric mean, 13·8 spores); 39·6% were positive by toxin B qPCR (geometric mean, 1·9 spores); and 43·8% were positive by enrichment culture. By 16S qPCR, each 10-fold increase in sample surface area yielded 6·6 times (95% CI, 3·2–13) more spores. Floor surfaces yielded 27 times (95% CI, 4·9–181) more spores than bedrails, and rooms of C. difficile–positive patients yielded 11 times (95% CI, 0·55–164) more spores than those of patients without prior antibiotics. Toxin B qPCR and enrichment culture returned analogous findings. Conclusions Clostridium difficile spores were identified in most floor and bedrail samples, and increased surface area improved detection. Future research aiming to understand the role of environmental C. difficile in transmission should prefer samples with large surface areas.
The Retrosplenial-Parietal Network and Reference Frame Coordination for Spatial Navigation

The retrosplenial cortex is anatomically positioned to integrate sensory, motor, and visual information and is thought to have an important role in processing spatial information and guiding behavior through complex environments. Anatomical and theoretical work has argued that the retrosplenial cortex participates in spatial behavior in concert with input from the parietal cortex. Although the nature of these interactions is unknown, a central position is that the functional connectivity is hierarchical with egocentric spatial information processed in the parietal cortex and higher-level allocentric mappings generated in the retrosplenial cortex. Here, we review the evidence supporting this proposal. We begin by summarizing the key anatomical features of the retrosplenial-parietal network, and then review studies investigating the neural correlates of these regions during spatial behavior. Our summary of this literature suggests that the retrosplenial-parietal circuitry does not represent a strict hierarchical parcellation of function between the two regions but instead a heterogeneous mixture of egocentric-allocentric coding and integration across frames of reference. We also suggest that this circuitry should be represented as a gradient of egocentric-to-allocentric information processing from parietal to retrosplenial cortices, with more specialized encoding of global allocentric frameworks within the retrosplenial cortex and more specialized egocentric and local allocentric representations in parietal cortex. We conclude by identifying the major gaps in this literature and suggest new avenues of research.
Pre-post effectiveness evaluation of Chronic Disease Self-Management Program (CDSMP) participation on health, well-being and health service utilization

Objectives The Chronic Disease Self-Management Program (CDSMP) is a standardized self-management intervention for patients with various chronic diseases. CDSMP provides self-management skills to enhance patient health, well-being, and coping skills. The present study evaluates the effectiveness of CDSMP delivered in routine clinical services on health, health behaviors and healthcare utilization in patients with various chronic illnesses. Methods A pragmatic single group pre-post design evaluated the effectiveness of the CDSMP in an Irish cohort using self-report data collected by service providers in hospital, community health and patient organizations. Data on health, health behavior and healthcare utilization were collected at baseline (n = 263), immediately post-program (n = 102), and six months (n = 81) after enrollment. Results CDSMP participants reported statistically significant increases in activity levels, self-efficacy, energy and quality of life, and a significant decrease in depression scores at six months follow-up. There was a significant decrease in self-reported visits to the GP and in total nights spent in hospital. Discussion This national pre–post study provides preliminary evidence for the potential effectiveness of CDSMP delivered during routine care in improving important health outcomes and reducing health care utilization among a heterogeneous sample of chronic disease patients.
Combining Textual and Visual Representations for Multimodal Author Profiling: Notebook for PAN at CLEF 2018

Social media data allows researchers to establish relationships between everyday language and people’s sociodemographic variables, such as gender, age, language variety or personality. Author Proﬁling studies the common use of language inside those demographic groups. This work describes our proposed method for the PAN 2018 Author Proﬁling shared task. This year’s task consisted of evaluating gender using multimodal information (text and images) which was extracted from Twitter users. We trained separate models for text, image and multimodal approaches. In multimodal approaches we explored early, late and hybrid approaches. We found experimentally that early approaches obtained the best performance. We obtained 0 . 80 , 0 . 74 and 0 . 81 of accuracy in the multimodal scenario for the test partition for English, Spanish and Arabic respectively.
Osmotic and hypoxic induction of the complement factor C9 in cultured human retinal pigment epithelial cells: Regulation of VEGF and NLRP3 expression

Purpose Variants of complement factor genes, hypoxia and oxidative stress of the outer retina, and systemic hypertension affect the risk of age-related macular degeneration. Hypertension often results from the high intake of dietary salt that increases extracellular osmolarity. We determined the effects of extracellular hyperosmolarity, hypoxia, and oxidative stress on the expression of complement genes in cultured (dedifferentiated) human RPE cells and investigated the effects of C9 siRNA and C9 protein on RPE cells. Methods Hyperosmolarity was induced by adding 100 mM NaCl or sucrose to the culture medium. Hypoxia was induced by culturing cells in 1% O2 or by adding the hypoxia mimetic CoCl2. Oxidative stress was induced by adding H2O2. Gene and protein expression levels were determined with real-time RT–PCR, western blot, and ELISA analyses. The expression of the nuclear factor of activated T cell 5 (NFAT5) and complement factor (C9) was knocked down with siRNA. Results Extracellular hyperosmolarity, hypoxia, and oxidative stress strongly increased the transcription of the C9 gene, while the expression of the C3, C5, CFH, and CFB genes was moderately altered or not altered at all. Hyperosmolarity also induced a moderate increase in the cytosolic C9 protein level. The hyperosmotic C9 gene expression was reduced by inhibitors of the p38 MAPK, ERK1/2, JNK, and PI3K signal transduction pathways and of the transcription factors STAT3 and NFAT5. The hypoxic C9 gene expression was reduced by a STAT3 inhibitor. The knockdown of C9 with siRNA decreased the hypoxic vascular endothelial growth factor (VEGF) and NLRP3 gene expression, the hypoxic secretion of VEGF, and the hyperosmotic expression of the NLRP3 gene. Exogenous C9 protein inhibited the hyperosmotic expression of the C9 gene, the hypoxic and hyperosmotic VEGF gene expression, and the hyperosmotic expression of the NLRP3 gene. Both C9 siRNA and C9 protein inhibited inflammasome activation under hyperosmotic conditions, as indicated by the decrease in the cytosolic level of mature IL-1β. Conclusions The expression of the C9 gene in cultured RPE cells is highly induced by extracellular hyperosmolarity, hypoxia, and oxidative stress. The data may support the assumption that C9 gene expression may stimulate the expression of inflammatory (NLRP3) and angiogenic growth factors (VEGF) in RPE cells. Extracellular C9 protein may attenuate this effect, in part via negative regulation of the C9 mRNA level.
Omnidirectional DSO: Direct Sparse Odometry With Fisheye Cameras

We propose a novel real-time direct monocular visual odometry for omnidirectional cameras. Our method extends direct sparse odometry by using the unified omnidirectional model as a projection function, which can be applied to fisheye cameras with a field-of-view (FoV) well above 180$^\circ$ . This formulation allows for using the full area of the input image even with strong distortion, while most existing visual odometry methods can only use a rectified and cropped part of it. Model parameters within an active keyframe window are jointly optimized, including the intrinsic/extrinsic camera parameters, three-dimensional position of points, and affine brightness parameters. Thanks to the wide FoV, image overlap between frames becomes bigger and points are more spatially distributed. Our results demonstrate that our method provides increased accuracy and robustness over state-of-the-art visual odometry algorithms.
Bird Identification from Timestamped, Geotagged Audio Recordings

Large-scale biodiversity monitoring would profit from automated solutions supporting or complementing human experts and citizen scientists. To foster their development, the yearly BirdCLEF scientific challenge compares approaches for identifying bird species in recorded vocalizations. The solution described in this work is based on an ensemble of Convolutional Neural Networks (CNNs) processing a mel spectrogram combined with Multi-Layer Perceptrons (MLPs) processing the recording date, time and geographic location. In BirdCLEF 2018, it achieved a mean average precision of 0.705 in detecting 1,500 South American bird species (0.785 for the foreground species), the second best entry to the challenge.
UPF's Participation at the CLEF eRisk 2018: Early Risk Prediction on the Internet

This paper describes the participation of the Web Science and Social Computing Research Group from the Universitat Pompeu Fabra, Barcelona (UPF) at CLEF 2018 eRisk Lab. Its main goal, divided in two different tasks, is to detect, with enough anticipation, cases of depression (T1) and anorexia (T2) given a labeled dataset with texts written by social media users. Identifying depressed and anorexic individuals by using automatic early detection methods, can provide experts a tool to do further research regarding these conditions, and help people living with them. Our proposal presents several machine learning models that rely on features based on linguistic information, domain-specific vocabulary and psychological processes. The results, regarding the F-Score, place our best models among the top 5 approaches for both tasks.
Bird Sound Classification using a Bidirectional LSTM

While RNN’s are widely applied to many similar tasks, such as automatic speech recognition and transcription, nobody succeeded yet using them for classiﬁcation in the BirdCLEF challenge. Recent work at ZHAW on the topic of speaker clustering has yielded very promising results using a Bidirectional LSTM. We have set out to apply the LSTM-Network mentioned above to the BirdCLEF challenge. By doing so, we hope to make a valuable contribution to the ongoing research in this ﬁeld. Unfortunately, our approach did not perform as well as we hoped for. In the following, we detail the approach taken and some issues a bach-elor student with little experience in the ﬁeld of machine learning will encounter when participating in such a challenging competition.
Performance evaluation of the PET component of a hybrid PET/CT-ultrafast ultrasound imaging instrument

We recently introduced a hybrid imaging instrument, PETRUS, based on a combination of positron emission tomography (PET) for molecular imaging, x-ray computed tomography (CT) for anatomical imaging, co-registration and attenuation correction, and ultrafast ultrasound imaging (UUI) for motion-correction, hemodynamic and biomechanical imaging. In order to ensure a precise co-registration of simultaneous PET-UUI acquisitions, ultrasound probes attached to an ultrafast ultrasound scanner are operated in the field of view (FOV) of a small animal PET/CT scanner using a remote-controlled micro-positioner. Here we explore the effect of the presence of ultrasound probes on PET image quality. We compare the performance of PET and image quality with and without the presence of probes in the PET field of view, both in vitro following the NEMA-NU-4-2008 standard protocol, and in vivo in small animals. Overall, deviations in the quality of images acquired with and without the ultrasound probes were under 10% and under 7% for the NEMA protocol and in vivo tests, respectively. Our results demonstrate the capability of the PETRUS device to acquire multimodal images in vivo without significant degradation of image quality.
ImageCLEF 2018 Tuberculosis Task: Ensemble of 3D CNNs with Multiple Inputs for Tuberculosis Type Classification

Convolutional neural networks have achieved state-of-theart results in general image classification tasks and have shown success in several applications within the medical imaging domain. In this paper, we apply a 3D convolutional neural network (CNN) to a dataset of tuberculosis-positive computed tomography (CT) scans to solve the task of automatically categorizing each tuberculosis (TB) case into one of five possible TB types in the context of the ImageCLEFtuberculosis 2018 challenge. The size of the volumetric scans poses unique constraints on the network and the training process. The CT volumes are segmented with the provided masks, which are further pre-processed prior to training our model. Our best run ranked 2 with an unweighted Cohen’s Kappa of 0.1736 and an accuracy of 35.33%.
Semi-automated creation of regulation rule bases using generic template-driven rule extraction

Formal approaches to checking compliance manually encode individual obligations from the regulation text as rules. Automated extraction approaches identify key elements in regulatory text, and create annotated, in some cases structured, representations of regulation text. It is desirable to combine the two approaches to automate creation of a regulation rule base that can be used for inferencing and reasoning about compliance. In this paper we present a semi-automated approach that uses a generic semantic model of regulations to guide automated extraction of rule suggestions. The suggestions help domain experts author rules in Structured English using the generic model as template. Rules are translated automatically into a Semantics of Business Vocabulary and Rules (SBVR) model and defeasible logic rules, creating a hierarchical knowledge base that reflects the regulation structure and enables querying and reasoning about compliance.
A 3D-CNN and SVM for Multi-Drug Resistance Detection

Recently, deep Convolutional neural networks (CNN) have obtained significant results in volumetric medical image classification task. However, a number of studies have claimed that replacing the trainable classifier (the conventional softmax function) of a CNN model with an SVM classifier can improve the classification performance. In this paper, we investigate the use of a hybrid approach which combines 3D CNN and a linear SVM to tackle the task of Multi-Drug Resistant detection.
Distinct Microbial Community Performing Dissimilatory Nitrate Reduction to Ammonium (DNRA) in a High C/NO3− Reactor

A dissimilatory nitrate reduction to ammonium (DNRA) microbial community was developed under a high organic carbon to nitrate (C/NO3−) ratio in an anoxic semi-continuous sequencing batch reactor (SBR) fed with glucose as the source of carbon and NO3− as the electron acceptor. Activated sludge collected from a municipal wastewater treatment plant with good denitrification efficiency was used as the inoculum to start the system. The aim of this study was to examine the microbial populations in a high C/NO3− ecosystem for potential DNRA microorganisms, which are the microbial group with the ability to reduce NO3− to ammonium (NH4+). A low C/NO3− reactor was operated in parallel for direct comparisons of the microbial communities that developed under different C/NO3− values. The occurrence of DNRA in the high C/NO3− SBR was evidenced by stable isotope-labeled nitrate and nitrite (15NO3− and 15NO2−), which proved the formation of NH4+ from dissimilatory NO3−/NO2− reduction, in which both nitrogen oxides induced DNRA activity in a similar manner. An analysis of sludge samples with Illumina MiSeq 16S rRNA sequencing showed that the predominant microorganisms in the high C/NO3− SBR were related to Sulfurospirillum and the family Lachnospiraceae, which were barely present in the low C/NO3− system. A comparison of the populations and activities of the two reactors indicated that these major taxa play important roles as DNRA microorganisms under the high C/NO3− condition. Additionally, a beta-diversity analysis revealed distinct microbial compositions between the low and high C/NO3− SBRs, which reflected the activities observed in the two systems.
Imaging modalities for temporomandibular joint disorders: an update

The diagnosis and management of temporomandibular disorders (TMD) require both clinical and imaging examinations of the temporomandibular joint (TMJ). A variety of modalities can be used to image the TMJ, including magnetic resonance imaging (MRI), computed tomography (CT), cone beam CT, ultrasonography, conventional radiography. The present review outlines the indications of the most frequently used imaging techniques in TMD diagnosis. Because of the anatomic complexity of the TMJ, imaging can be difficult. Choosing the proper imaging technique is essential. Conventional radiography, nowadays, is of limited interest. The use of flat plane films for TMJ pathology is not sufficient, because this joint requires three dimensional imaging views. Osseous changes are better visualized with CT and cone beam CT. Cone beam CT provides high-resolution multiplanar reconstruction of the TMJ, with a low radiation dose, without superimposition of the bony structures. MRI is a noninvasive technique, considered to be the gold standard in imaging the soft tissue components of the TMJ. MRI is used to evaluate the articular disc in terms of location and morphology. Moreover, the early signs of TMD and the presence of joint effusion can be determined. High-resolution ultrasonography is a noninvasive, dynamic, inexpensive imaging technique, which can be useful in diagnosing TMJ disc displacements. The diagnostic value of high-resolution ultrasonography is strictly dependent on the examiner’s skills and on the equipment used.