Miracle-dz/UniFAR-FTUs huggingface dataset:
Copy Number Variation of Human Satellite III (1q12) With Aging

Introduction: Human satellite DNA is organized in long arrays in peri/centromeric heterochromatin. There is little information about satellite copy number variants (CNVs) in aging and replicative cell senescence (RS). Materials and Methods: Biotinylated pUC1.77 probe was used for the satellite III (f-SatIII) quantitation in leukocyte DNA by the non-radioactive quantitative hybridization for 557 subjects between 2 and 91 years old. The effect of RS and genotoxic stress (GS, 4 or 6 µM of K2CrO4) on the f-SatIII CNV was studied on the cultured human skin fibroblast (HSF) lines of five subjects. Results: f-SatIII in leukocyte and HSFs varies between 5.7 and 40 pg/ng of DNA. During RS, the f-SatIII content in HSFs increased. During GS, HSFs may increase or decrease f-SatIII content. Cells with low f-SatIII content have the greatest proliferative potential. F-SatIII CNVs in different individuals belonging to the different generations depend on year of their birth. Children (born in 2005–2015 years) differed significantly from the other age groups by low content and low coefficient of variation of f-SatIII. In the individuals born in 1912–1925 and living in unfavorable social conditions (FWW, the Revolution and the Russian Civil War, SWW), there is a significant disproportion in the content of f-SatIII. The coefficient of variation reaches the maximum values than in individuals born in the period from 1926 to 1975. In the group of people born in 1990–2000 (Chernobyl disaster, the collapse of the Soviet Union, and a sharp decline in the population living standard), again, there is a significant disproportion of individuals in the content of f-SatIII. A similar disproportion was observed in the analysis of a group of individuals born in 1926–1975 who in their youth worked for a long time in high-radioactive environment. Conclusion: In generations that were born and who lived in childhood in a period of severe social perturbations or in conditions of environmental pollution, we found a significant increase in leukocyte DNA f-SatIII variability. It is hypothesized that the change of the f-SatIII content in the blood cells reflects the body response to stress of different nature and intensity.
Non-linear Amplification of Variability Through Interaction Across Scales Supports Greater Accuracy in Manual Aiming: Evidence From a Multifractal Analysis With Comparisons to Linear Surrogates in the Fitts Task

Movement coordination depends on directing our limbs to the right place and in the right time. Movement science can study this central requirement in the Fitts task that asks participants to touch each of two targets in alternation, as accurately and as fast as they can. The Fitts task is an experimental attempt to focus on how the movement system balances its attention to speed and to accuracy. This balance in the Fitts task exhibits a hierarchical organization according to which finer details (e.g., kinematics of single sweeps from one target to the other) change with relatively broader constraints of task parameters (e.g., distance between targets and width of targets). The present work seeks to test the hypothesis that this hierarchical organization of movement coordination reflects a multifractal tensegrity in which non-linear interactions across scale support stability. We collected movement series data during a easy variant of the Fitts task to apply just such a multifractal analysis with surrogate comparison to allow clearer test of non-linear interactions across scale. Furthermore, we test the role of visual feedback both in potential and in fact, i.e., by manipulating both whether experimenters instructed participants that they might potentially have to close their eyes during the task and whether participants actually closed their eyes halfway through the task. We predict that (1) non-linear interactions across scales in hand movement series will produce variability that will actually stabilize aiming in the Fitts task, reducing standard deviation of target contacts; (2) non-linear interactions across scales in head sway will stabilize aiming following the actual closing eyes; and (3) non-linear interactions across scales in head sway and in hand movements will interact to support stabilizing effects of expectation about closing eyes. In sum, this work attempts to make the case that the multifractal-tensegrity hypothesis supports more accurate aiming behavior in the Fitts task.
Taurine Attenuates Streptococcus uberis-Induced Bovine Mammary Epithelial Cells Inflammation via Phosphoinositides/Ca2+ Signaling

Taurine may alleviate the inflammatory injury induced by Streptococcus uberis (S. uberis) infection by regulating intracellular Ca2+ levels. However, the underlying mechanisms remain unclear. Infection leads to subversion of phosphoinositides (PIs) which are closely related to Ca2+ signaling. In order to investigate whether taurine regulates inflammation by means of PIs/ Ca2+ systems, competitive inhibitors of taurine (β-alanine) siTauT, siPAT1, siPLC, siCaN, siPKC, and inhibitors of PLC (U73122), PKC (RO31-8220), and CaN (FK 506) were used. The results indicate that taurine transfers the extracellular nutrient signal for intercellular innate immunity to phosphoinositides without a need to enter the cytoplasm while regulating intracellular Ca2+ levels during inflammation. Both the Ca2+-PKCα-NF-κB, and Ca2+-CaM-CaN-NFAT signaling pathways of S. uberis infection and the regulatory roles of taurine follow activation of PIs/Ca2+ systems. These data increase our understanding on the mechanisms of multifunctional nutrient, taurine attenuated inflammatory responses caused by S. uberis infection, and provide theoretical support for the prevention of this disease.
Comparison of five international indices of adherence to the Mediterranean diet among healthy adults: similarities and differences

BACKGROUND/OBJECTIVES To compare five indices of adherence to the Mediterranean Diet (MD) among adults living in the Mediterranean region. SUBJECTS/METHODS A total of 100 healthy Lebanese adults aged between 18 and 65 years. Face-to-face interviews to collect sociodemographic and medical information, to take anthropometric measurements, and to fill a validated, culturally adapted, food frequency questionnaire (FFQ). The score for each item was calculated following the recommendations for each corresponding index. The five MD indices were Mediterranean Diet Scale (MDScale), Mediterranean Food Pattern (MFP), MD Score (MDS), Short Mediterranean Diet Questionnaire (SMDQ), and the MedDiet score. RESULTS Significant correlations were detected between items with P-values < 0.001. Minimal agreement was seen between MDScale and MedDiet score and maximal agreement between MDS and MedDiet score. Univariate and multivariate analyses showed that MDS and MedDiet scores had significant correlations with fiber and olive oil intake, main components of the MD. MDScale showed a significant correlation with waist-to-hip ratio and with total energy intake but none of the five indices was correlated to body mass index (BMI). CONCLUSIONS The indices that showed the highest correlation with variables related to the MD are the MDScale and the MedDiet score; therefore, they can be used to assess our future study populations. Based on the current results, more than half of the study population was non-adherent to the MD and adherence to this diet did not appear to protect against being overweight (BMI ≥ 30).
Egg phospholipids exert an inhibitory effect on intestinal cholesterol absorption in mice

BACKGROUND/OBJECTIVES Consumption of cholesterol-rich foods, such as eggs, has a minimal effect on circulating cholesterol levels in healthy humans. To gain insight, we investigated whether phospholipids rich in eggs (EPL) interfere with intestinal cholesterol absorption in vivo. MATERIALS/METHODS To investigate the acute effect of EPL on intestinal cholesterol absorption, male C57BL/6J mice were orally administered with 6, 11, or 19 mg of EPL for three days. We also tested the effect of chronic EPL consumption on cholesterol metabolism in the small intestine and the liver in mice with diet-induced hypercholesterolemia. Male C57BL/6J mice were fed a high fat/high cholesterol (HF/HC; 35% fat, 0.25% cholesterol, w/w) diet for 4 weeks to induce hypercholesterolemia, and subsequently the mice were either fed 0, 0.4 or 0.8% (w/w) of EPL for 6 weeks. RESULTS Intestinal cholesterol absorption was significantly decreased by the highest dose of acute EPL administration compared to control. Chronic EPL supplementation did not significantly alter intestinal cholesterol absorption nor plasma levels of total cholesterol and low-density lipoprotein cholesterol. In the small intestine and the liver, EPL supplementation minimally altered the expression of genes which regulate cellular cholesterol levels. CONCLUSION Although chronic EPL consumption was not able to counteract hypercholesterolemia in HF/HC-fed mice, acute EPL administration decreased intestinal cholesterol absorption. This study provides in vivo evidence that acute administration of PLs in eggs prevent cholesterol absorption in the intestine, suggesting a mechanism for a minimal effect of egg consumption on circulating cholesterol levels.
Identifying and Analyzing Different Aspects of English-Hindi Code-Switching in Twitter

Code-switching or the juxtaposition of linguistic units from two or more languages in a single utterance, has, in recent times, become very common in text, thanks to social media and other computer mediated forms of communication. In this exploratory study of English-Hindi code-switching on Twitter, we automatically create a large corpus of code-switched tweets and devise techniques to identify the relationship between successive components in a code-switched tweet. More specifically, we identify pragmatic functions such as narrative-evaluative, negative reinforcement, translation or semantically equivalent statements, and so on characterizing the relation between successive components. We analyze the difference/similarity between switching patterns in code-switched and monolingual multi-component tweets. We observe strong dominance of narrative-evaluative (non-opinion to opinion or vice versa) switching in case of both code-switched and monolingual multi-component tweets in around 40% of cases. Polarity switching appears to be a prevalent switching phenomenon (10%) specifically in code-switched tweets (three to four times higher than monolingual multi-component tweets) where preference of expressing negative sentiment in Hindi is approximately twice compared to English. Positive reinforcement appears to be an important pragmatic function for English multi-component tweets, whereas negative reinforcement plays a key role for Devanagari multi-component tweets. Our results also indicate that the extent and nature of code-switching also strongly depend on the topic (sports, politics, etc.) of discussion.
Detection of Negation Cues in Spanish: The CLiC-Neg System

This paper describes the system proposed by the CLiC team at the University of Barcelona for detecting negation cues in Spanish. It applies the Conditional Random Field (CRF), a supervised machine learning method, to the identiﬁcation of negative expressions. After carrying out an error analysis, we tried to improve on the results by the CRF adding vocabulary lists and rules. The results obtained show that, contrary to our expectations, neither adding rules nor creating a lexicon of multiword expressions signiﬁcantly improved the performance of the model.
The Role of Culture in Business Process Management Adoption

BPM adoption is defined as “the use and deployment of BPM concepts in organizations” [1]. However, many organizations struggle when adopting BPM because it requires extensive effort, time, resources and discipline [2-4]. Several studies have attempted to identify the factors that influence BPM adoption [3]. While many of these studies have culture among the key factors that influence BPM adoption, Syed, Bandara, French and Stewart [5] note that culture is not discussed extensively in the body of BPM literature and few studies have explored in depth or empirically the influence of culture on BPM adoption.
UH-MAJA-KD at eHealth-KD Challenge 2019: Deep Learning Models for Knowledge Discovery in Spanish eHealth Documents

This paper describes the solution presented by the UH-MAJAKD team in IberLEF eHealth-KD 2019: eHealth Knowledge Discovery challenge. Separate strategies were developed to solve substasks A and B, both based on deep learning models using domain-specific word embeddings, and architectures using Bidirectional Long-Short Term Memory (BiLSTM) cells. In the case of Subtask A, Conditional Random Field was used to produce an output in BMEWO-V tag system to extract keyphrases. For Subtask B, two stacked BiLSTM layers are used along with Shortest Dependency Path in-between a pair of keyphrases to determine possible relationships between them.
LaSTUS/TALN at IroSvA: Irony Detection in Spanish Variants

The Irony Detection in Spanish Variants (IroSvA’19) shared task aims at investigating the recognition of irony in Twitter messages in three different Spanish variants: from Spain , Mexico, and Cuba. This paper describes our approach to the shared task: a Neural Network system based on a simple bidirectional LSTM (biLSTM) model. Since we have developed the system in the context of other related IberLEF 2019 shared tasks, we train multiple models simultaneously sharing some of the layers of the neural architecture between them. Furthermore, we also have applied a method for data augmentation given the scarcity of resources which were available.
Protected Health Information Recognition by BiLSTM-CRF

Medical records contain relevant information about patients, which can be beneficial to improving healthcare and research in the clinical domain. Due to this, there is a growing interest in developing automatic methods to extract and exploit the information from medical records. However, medical records also contain protected health information about patients. To protect the confidentiality and privacy of patients, this sensitive information should be removed prior to any processing of these documents. In this paper, we describe an architecture for the detection and identification of protected health information from medical records. The architecture is composed of two bidirectional Long ShortTerm Memory layers and a final layer based on Conditional Random Fields. Our system participated in the Meddocan shared task, obtaining a micro-F1 of 93.22%.
Resource-Based Anonymization for Spanish Clinical Cases

This implementation notes describe a system for anonymization of clinical case texts written in Spanish based on careful collection of gazetteers for several entity types. The entities gathered are improved converting their strings to regular expressions covering commonest text variations. A simple grammar formalism is implemented to sugar the creation of context-dependent regular expressions. These expressions are applied in order (organized according to reliability) and context specified by rules may refer to the character/word/regular expressions and/or annotations previously introduced in the running text by already applied rules. With this simple approach, the system obtained an F1-score of 0.9595 in the NER offset and entity type classification (subtask 1) and 0.96409 in the sensitive token detection (subtask 2).
On the Susceptibility of Deep Neural Networks to Natural Perturbations

Deep learning systems have achieved great success in various types of applications over recent years. They are increasingly being adopted for safety critical tasks, such as face recognition, surveillance systems, speech recognition, and autonomous driving. On the other hand, it has been found that deep neural networks (DNNs) can be easily fooled by adversarial input samples. These imperceptible perturbations on images can lead any machine learning system to misclassify the objects with high confidence. Furthermore, they can be almost indistinguishable to a human observer. These systems can also be exposed to adverse weather conditions such as fog, rain, and snow. This vulnerability raises major concerns in security-sensitive environments. Therefore, vulnerability of deep learning systems to synthetic adversarial attacks has been extensively studied and demonstrated, but the impact of natural weather conditions on these systems has not been studied in detail.
Amrita_CEN@FACT: Factuality Identification in Spanish Text

This paper presents the description of the system used by the team Amrita CEN for the shared task on FACT (Factuality Analysis and Classiﬁcation Task) at IberLEF2019 (Iberian Languages Evaluation Forum) workshop. The goal of the task was to automatically annotate an event with its factuality status. Factuality status is categorized into three as Fact, Counter Fact and Undeﬁned. Our proposed system predicts the factuality of an event with a prediction accuracy of 72.1%. The classiﬁcation model for this task was trained using Random Forest classiﬁer which uses word embedding of the events as input features. The word embedding of an event was generated by using Word2vec algorithm. Random Forest was implemented by giving higher weights to minority classes and lesser weights to majority classes so that more number of elements in the minority class will be predicted precisely.
Stream Recommendation using Individual Hyper-Parameters

Nowadays, with the widely usage of on-line stream video platforms, the number of media resources available and the volume of crowd-sourced feedback volunteered by viewers is increasing exponentially. In this scenario, the adoption of recommendation systems allows platforms to match viewers with resources. However, due to the sheer size of the data and the pace of the arriving data, there is the need to adopt stream mining algorithms to build and maintain models of the viewer preferences as well as to make timely personalised recommendations. In this paper, we propose the adoption of optimal individual hyper-parameters to build more accurate dynamic viewer models. First, we use a grid search algorithm to identify the optimal individual hyper-parameters (IHP) and, then, use these hyper-parameters to update incrementally the user model. This technique is based on an incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations.
A Roadmap to Congestion Management in Museums from a Socio-Technical Perspective

Cultural heritage spaces, and in particular museums, constitute a special type of socio-physical systems, where the decision making burden with respect to visitor touring choices, mainly lies on visitor himself. Due to the inherent characteristics of such a system, that embodies both human behaviors as well as physical or technical constraints and requirements, it becomes apparent that a socio-technical perspective should be considered to treat the key problem of congestion management, while improving visitor experience and satisfaction. In this paper, we provide a roadmap to dealing with this issue, by suggesting a realistic visitor behavior modeling approach and promoting alternative and feasible congestion management solutions. Our considerations and mechanisms are founded on and powered by the principles of Prospect Theory and the Tragedy of the Commons, with respect to modeling and capturing visitor behaviors and decision making under potential risks and uncertainties, typically encountered by visitors during their visiting experience. Based on this framework, initially a risk-based visitor time management mechanism is provided, and then pricing is suggested as a valid measure to reduce overcrowding in cultural heritage spaces. Finally, the consequences of people’s tendency to overweight small probabilities and underweight large ones, as well as the potential of adopting framing effects by museum curators, on visitor’s decision making process are
Towards a Pragmatic Open Information Extraction for Portuguese Text - ICEIS17, InferPortOIE and PragmaticOIE on IberLEF

This paper describes the participation of the FORMAS research group with the systems ICEIS17, InferPortOIE, and PragmaticOIE in the Iberian Languages Evaluation Forum 2019. Our activities have focused on the “General Open Relation Extraction” task of relation extraction for Portuguese texts. We present our choices on this challenge, as well as the performance of our systems and their results.
Anonymization of Clinical Reports in Spanish: a Hybrid Method Based on Machine Learning and Rules

Biomedicine is an ideal environment for the use of Natural Language Processing, due to the huge amount of information processed and stored in electronic format. This information cannot be shared with confidential patient data. In order to achieve this task, the Medical Document Anonymization workshop has been created. In this paper, we present an automated anonymization system for clinical reports written in Spanish. Three different methods are evaluated and compared. The first method is rule-based, the second method uses machine learning and the third is a hybrid method between the first two. The evaluation showed that the use of the hybrid method obtained the best results. The results are as expected, we obtained 90% in measure F1 in sub-task 1 and 95% in sub-task 2.
Spanish Medical Document Anonymization with Three-channel Convolutional Neural Networks

This paper describes the system presented at the MEDDOCAN (Medical Document Anonymization) task. The system consists of a candidate generator which uses a PoS-tagger, and a candidate classifier based on a convolutional neural network which uses three channels and pretrained word embeddings to represent the sequence of words to be classified and its left and right context. On the MEDDOCAN Test Set, the systems achieved an F1 score of 0.9184 for subtask 1 (NER offset and entity type classification) and 0.9345 for subtask 2 (sensitive token detection).
From Recurrency to Attention in Opinion Analysis: Comparing RNN vs Transformer Models

This paper describes the participation of ITAINNOVA at the Sentiment Analysis in Twitter task (TASS) framed within the new evaluation forum IberLEF (Iberian Languages Evaluation Forum). This work explores two different Deep Learning approaches, validating their performance on both subtasks (Monolingual and Crosslingual Sentiment Analysis). The first one is an embedding-based strategy combined with bidirectional recurrent neural networks, which receives the name Char Bi-LSTM network, and the second one, a recent language representation model, called BERT (Bidirectional Encoder Representations from Transformers). Although the performance of the second approach is not recognized in the official results of the task, we also present this approach, which performance has been reasonably remarkable and greater than the first approach.
Hulat-TaskAB at eHealth-KD Challenge 2019: Knowledge Recognition from Health Documents by BiLSTM-CRF

Currently, the number of electronic health documents is increasing exponentially. Due to this, there is a growing interest in developing automatic systems to extract interesting information from these texts. In this paper, we describe a deep learning architecture for the identification and classification of named entities of interest in health documents. The architecture consists of two bidirectional Long ShortTerm Memory layers and a final layer based on Conditional Random Fields. Our system (Hulat-TaskAB) participated in the ehealthkd-2019 sub-task A and obtained a micro-F1 of 76.63%.