Miracle-dz/UniFAR-FTUs huggingface dataset:
The human gut microbiome – a new and exciting avenue in cardiovascular drug discovery

ABSTRACT Introduction: Over the past decade, numerous research efforts have identified the gut microbiota as a novel regulator of human metabolic syndrome and cardiovascular disease (CVD). With the elucidation of underlying molecular mechanisms of the gut microbiota and its metabolites, the drug-discovery process of CVD therapeutics might be expedited. Areas covered: The authors describe the evidence concerning the impact of gut microbiota on metabolic disorders and CVD and summarize the current knowledge of the gut microbial mechanisms that underlie CVD with a focus on microbial metabolites. In addition, they discuss the potential impact of the gut microbiota on the drug efficacy of available cardiometabolic therapeutic agents. Most importantly, the authors review the role of the gut microbiome as a promising source of potential drug targets and novel therapeutics for the development of new treatment modalities for CVD. This review also presents the various effective strategies to investigate the gut microbiome for CVD drug-discovery approaches. Expert opinion: With the elucidation of its causative role in cardiometabolic disease and atherosclerosis, the human gut microbiome holds promises as a reservoir of novel potential therapeutic targets as well as novel therapeutic agents, paving a new and exciting avenue in cardiovascular drug discovery.
Student's Perception of the Impact of E-learning on Dental Education.

AIM The aim of this study is to assess the influence of e-learning on dental education as perceived by predoctoral dental students. MATERIALS AND METHODS In an institutional review board (IRB) approved protocol, a 14-question survey was created and electronically distributed to second-, third-, and fourth-year dental students. The participation was considered voluntary and all responses were anonymous. RESULTS The survey targeted 1,130 predoctoral students, of which 255 (22.6%) responded. Of the respondents, 124 students (48.6%) preferred traditional lecture mixed with online learning, while 46 students (18%) preferred only the traditional lecture style. The top three electronic resources/applications, which students perceived as having the greatest impact on their learning, were: YouTube, Bone Box, and Google. The responses also indicated that 76.5% of the students gave high credibility (scores of 4 and 5) to electronic resources recommended by faculties. Sixty percent of students spent 1 to more than 4 hours per day on electronic resources for academic performance. The most important factor for online applications influencing academic performance was "organization and logic of content" (54%). E-learning had a significant perceived effect (scores of 4/5) on didactic understanding (65.1%) and on clinical understanding (71.4%). Students observed that faculties estimated to be under 50 years of age were more likely to incorporate e-learning into courses (52.6%) and more likely to use social media for communication (41.6%). CONCLUSION The results indicate that e-learning may successfully be used in a dental school's curriculum to enhance students' perceptions of fundamental concepts and to enable students to apply this knowledge to clinical cases. CLINICAL SIGNIFICANCE E-learning has recently been proposed as a basic supplementary tool to enhance medical and dental education. It is crucial to determine dental students' preferences regarding social media, online applications, and databases in order to incorporate e-learning into dental school courses.
Coffee Abundant in Chlorogenic Acids Reduces Abdominal Fat in Overweight Adults: A Randomized, Double-Blind, Controlled Trial

The components of roasted or green coffee beans that promote abdominal fat reduction are not clear. We investigated the effects of daily consumption of coffee enriched in chlorogenic acids (CGA) on abdominal fat area in a randomized, double-blind, parallel controlled trial. Healthy, overweight men and women (n = 150, body mass index (BMI) ≥25 to <30 kg/m2) were randomly allocated to high-CGA (369 mg CGA/serving) or control (35 mg CGA/serving) coffee groups. Instant coffee was consumed once daily for 12 weeks, with four-week pre- and post-observation periods. Abdominal fat area and anthropometric measurements were analyzed at baseline and at four, eight, and 12 weeks, and 142 subjects completed the trial. Visceral fat area (VFA), total abdominal fat area (TFA), body weight, and waist circumference significantly decreased in the CGA group compared with the control group, with a group × time interaction (p < 0.001, p = 0.001, p = 0.025, and p = 0.001, respectively). Changes in VFA and TFA from baseline to 12 weeks were significantly greater in the CGA group than in the control group (−9.0 ± 13.9 cm2 vs. −1.0 ± 14.3 cm2, p < 0.001; −13.8 ± 22.9 cm2 vs. −2.0 ± 16.2 cm2, p < 0.001). No severe adverse events occurred. Consumption of high-CGA coffee for 12 weeks by overweight adults might lower VFA, TFA, BMI, and waist circumference.
MAP1B related syndrome: Case presentation and review of literature

The microtubule‐associated protein 1B (MAP1B) gene serves an important role in axonal growth and brain development. Its expression is known to be elevated in regions that retain high brain plasticity and is regulated by the fragile X mental retardation protein. MAP1B mutations have recently been associated with a phenotype including periventricular nodular heterotopia (PVNH), intellectual disability (ID), seizures, and dysmorphic features. We describe a child presenting with global developmental delays, ID, microcephaly, short stature, seizures, dysmorphic features, and prenatal alcohol exposure with a de novo nonsense MAP1B mutation (c.2035G>T, p.Glu679X) detected on whole exome sequencing (WES). His brain MRI showed PVNH and dysgenesis of the corpus callosum. While significant prenatal alcohol exposure could have modified his phenotype, we believe that this patient presents with features that cannot be explained by fetal alcohol exposure alone. This is the first case report that describes dysmorphic features associated with MAP1B mutations in detail along with supporting pictures and review of previous reported phenotypes. This case not only highlights the value of WES as a screening tool for unrecognized syndromes, but also supports the need for a better description of the phenotype associated with newly detected genetic syndromes by molecular screening.
Assessing the impact of dementia inclusive environmental adjustment in the emergency department

Objectives Acute hospitals, in particular the emergency department, can be disorienting for people living with dementia. As part of a larger project to improve care for people living with dementia, dementia-inclusive modifications were made to two emergency department bays in a large acute care hospital in Ireland. Modifications to spatial configuration included noise reduction, altered lighting and the addition of an orientation aid and fixed seating for relatives. Method A mixed methods approach was employed with both service user and service provider perspectives explored (survey of service providers (n = 16) and interviews with family carers (n = 10) at one time point and interviews with service providers (n = 8 and n = 5) and key stakeholders (n = 3) as well as audit data (at two time points) to evaluate the impact of the modifications made to the emergency department. Results Orientation and navigation within the modified bays were improved though technical issues with the orientation aid were highlighted. Further user information on the functionality of the adjustable lighting would be required to maximise its benefits. This lighting and use of calming colours, together with the addition of noise-reduction bay screens, served to reduce sensory stimulation. The provision of adequate space and seating for family carers was extremely beneficial. The removal of unnecessary equipment and use of new structures to store relevant clinical equipment were other positive changes implemented. A number of challenges in the design development of the modified bays were highlighted, as well as ongoing broader environmental challenges within the emergency department environment. Conclusion The findings suggest that the modified bays contributed positively to the experience of people living with dementia and their families in the emergency department.
Long-Lasting Mucosal and Systemic Immunity against Influenza A Virus Is Significantly Prolonged and Protective by Nasal Whole Influenza Immunization with Mucosal Adjuvant N3 and DNA-Plasmid Expressing Flagellin in Aging In- and Outbred Mice

Background: Vaccination is commonly used to prevent and control influenza infection in humans. However, improvements in the ease of delivery and strength of immunogenicity could markedly improve herd immunity. The aim of this pre-clinical study is to test the potential improvements to existing intranasal delivery of formalin-inactivated whole Influenza A vaccines (WIV) by formulation with a cationic lipid-based adjuvant (N3). Additionally, we combined WIV and N3 with a DNA-encoded TLR5 agonist secreted flagellin (pFliC(-gly)) as an adjuvant, as this adjuvant has previously been shown to improve the effectiveness of plasmid-encoded DNA antigens. Methods: Outbred and inbred mouse strains were intranasally immunized with unadjuvanted WIV A/H1N1/SI 2006 or WIV that was formulated with N3 alone. Additional groups were immunized with WIV and N3 adjuvant combined with pFliC(-gly). Homo and heterotypic humoral anti-WIV immune responses were assayed from serum and lung by ELISA and hemagglutination inhibition assay. Homo and heterotypic cellular immune responses to WIV and Influenza A NP were also determined. Results: WIV combined with N3 lipid adjuvant the pFliC(-gly) significantly increased homotypic influenza specific serum antibody responses (>200-fold), increased the IgG2 responses, indicating a mixed Th1/Th2-type immunity, and increased the HAI-titer (>100-fold). Enhanced cell-mediated IFNγ secreting influenza directed CD4+ and CD8+ T cell responses (>40-fold) to homotypic and heterosubtypic influenza A virus and peptides. Long-term and protective immunity was obtained. Conclusions: These results indicate that inactivated influenza virus that was formulated with N3 cationic adjuvant significantly enhanced broad systemic and mucosal influenza specific immune responses. These responses were broadened and further increased by incorporating DNA plasmids encoding FliC from S. typhimurum as an adjuvant providing long lasting protection against heterologous Influenza A/H1N1/CA09pdm virus challenge.
Development and validation of the QUECOMIICAT questionnaire: a tool to assess disease-related knowledge in patients with inflammatory bowel disease.

INTRODUCTION adequate knowledge of inflammatory bowel disease (IBD) is essential for a successful patient-centered management of IBD. OBJECTIVE due to the scarcity of up-to-date tools for measuring IBD literacy, this single-center, prospective study aimed to develop and validate a new questionnaire to assess IBD-related knowledge. MATERIAL AND METHODS the study included patients followed up at the Crohn-Colitis Care Unit (UACC) at the Hospital Vall d'Hebron (Barcelona, Spain). Patients admitted to the UACC for the first time were subsequently enrolled into a standard IBD educational program. A pilot questionnaire was developed and validated in 92 IBD patients by determining the internal consistency reliability (Cronbach's α test), feasibility, construct validity (correlation with the Crohn's and Colitis Knowledge [CCKNOW] questionnaire and a knowledge visual analog scale [VAS]) and sensitivity (score change before and after a standard IBD educational program). The questionnaire, named "Qüestionari Coneixements Malaltia Inflamatòria Intestinal Catalunya" (IBD-knowledge questionnaire Catalonia) (QUECOMIICAT) was written in Spanish and had 25 items addressing six dimensions: general concepts, clinic, treatment, surgery, habits and social context. RESULTS the median (interquartile range) completion time was 15 (10-20) minutes and the floor and ceiling effects were 1.1% and 2.1%, respectively. The Cronbach's α coefficient was α = 0.75. QUECOMIICAT significantly correlated with the VAS (rho = 0.34, p < 0.01) and CCKNOW questionnaires (rho = 0.74, p < 0.01). Patient knowledge significantly increased 24 hours after attending a standard IBD educational program and remained statistically significant one month later (Pearson's test-retest correlation coefficient r = 0.81, p < 0.001). CONCLUSION in conclusion, the QUECOMIICAT questionnaire is a new up-to-date tool to assess IBD-related knowledge with good feasibility and validation results for use in the routine clinical practice.
A method of ontology-aided expertise matching for facilitating knowledge exchange

The paper proposes a new method for facilitating knowledge exchange by seeking relevant university experts for commenting actual information events arising in the open environment of a modern economical cluster. This method is based on a new mathematical model of ontology concepts matching. We propose to use in the formal core of our method a new modification of Latent Dirichlet allocation. The method and the mathematical model of ontology matching were validated in the form of a software-based solution: the newly designed decision support system titled EXPERTIZE. The system regularly monitors different text sources in the Internet, performs document analysis and provides university employees with critical information about relevant events according a developed matching algorithm. In the proposed solution we made several contributions to the advances of knowledge processing, including: new modifications of topic modeling method suitable for application in expert finding tasks, integration of new algorithms and existing ontology services to show feasibility of the solution.
To err again is human: exploring a bidirectional relationship between pressure and performance failure feedback

ABSTRACT Background and Objectives: While the potentially negative effects of pressure on skilled performance have been well studied in laboratory-based research, theoretically driven questions based on real-world performance data are lacking. Design: We aimed to test the predictions of the newly developed Attentional Control Theory: Sport (ACTS), using archived play-by-play data from the past seven seasons of the National Football League (American Football). Methods: An additive scoring system was developed to characterize the degree of pressure on 212,356 individual offensive plays and a Bayesian regression model was used to test the relationship between performance, pressure and preceding negative outcomes, as outlined in ACTS. Results: There was found to be a clear increase in the incidence of failures on high pressure plays (odds ratio = 1.20), and on plays immediately following a previous play failure (odds ratio = 1.09). Additionally, a combined interactive effect of previous failure and pressure indicated that the feedback effect of negative outcomes was greater when pressure was already high (odds ratio = 1.10), in line with the predictions of ACTS. Conclusions: These findings reveal the importance of exploring momentary changes in pressure in real-world sport settings, and the role of failure feedback in influencing the pressure-performance relationship.
Improving an open-source commercial system to reliably perform activity-dependent stimulation

Objective. Activity-dependent stimulation (ADS) is designed to strengthen the connections between neuronal circuits and therefore may be a promising tool for promoting neurophysiological reorganization following a brain injury. To successfully perform this technique, two criteria must be met: (1) spikes in the extracellular electrical field potential must be detected accurately at one site of interest, and (2) stimulation pulses generated at fixed (<1 ms jitter), low-latency (<10 ms) intervals relative to each detected spike must be delivered reliably to a second site of interest. Here, we aimed to improve noise rejection in a low-cost commercial system to reliably perform ADS in awake, behaving rats, while maintaining latency requirements. Approach. We implemented a spike detection state machine on a field-programmable gate array (FPGA). Because the accuracy of spike detection can be heavily reduced in awake and behaving animals due to biological artifacts such as movement and chewing, the state machine tracks candidate spike waveforms, checking them against multiple programmable thresholds and rejecting any spikes that fail to meet a programmed threshold criterion. Main Results. A series of offline analyses showed that our implementation was able to appropriately trigger stimulation during epochs of biological artifacts with an overall accuracy between 72% and 97%, fixed computational latency of 167 µs, and an algorithmic latency of 300 µs to 800 µs. Significance. Our improvements have been made open-source and are freely available to all scientists working on closed-loop neuroprosthetic devices. Importantly, the improvements are easily incorporated into existing workflows that utilize the Intan Stimulation and Recording Controller.
Properties Of Concrete

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Survey on Statistical Machine Translation

The paper gives a survey on three approaches of statistical machine translation and the evaluation methods used in SMT. The basic idea of parallel grammar based approach is to build parallel grammars for source and target languages, which conform the same probabilistic distribution. In the source-channel approach, the translation probability is expressed as a language model and a translation model. In the maximum entropy approach, the optimal translation is searched according to a linear combination of a series of real-valued feature functions. The source-channel approach can be regard as a special case of maximum entropy approach.
Un outil de visualisation de classifications et d'intégration de données phénotypiques et génétiques pour faciliter le codage des maladies rares Coding rare diseases in health information systems: a tool for visualizing classifications and integrating phenotypic and genetic data

Establishing the diagnosis and coding for a rare disease (RD) needs to characterize multiple information including patient's phenotype and genotype. A major barrier to coding is a lack of consolidation of such information, scattered in several resources such as Orphanet, OMIM or HPO. We developed a web portal, Linking Open data for RD (LORD), offering an integrated view of 8,336 RDs linked to more than 12,500 signs and 3,000 genes. It allows navigating through the relationships between diseases, signs and genes, and provides Application Programming Interfaces for its integration in information systems (IS). LORD is dedicated to the 131 French RD reference centers and 501 competence centers, for coding RD diagnoses in the health IS. Articles courts des 15es Journees francophones d’informatique medicale, JFIM 2014, pages 198–203 Fes, Maroc, 12–13 juin 2014 Mots-cles : Ontologies des maladies rares ; Big Data ; Codage diagnostique ; Web semantique ; Informatique medicale ; Services web
Comparing source inversion techniques for GPS‐based local tsunami forecasting: A case study for the April 2014 M8.1 Iquique, Chile, earthquake

Real‐time GPS is nowadays considered as a valuable component of next generation near‐field tsunami early warning systems able to provide fast and reliable source parameters. Looking for optimal methodologies and assessing corresponding uncertainties becomes an important task. We take the opportunity and consider the 2014 Pisagua event as a case study to explore tsunami forecast uncertainty related to the GPS‐based source inversion. We intentionally neglect all other sources of uncertainty (observation set, signal processing, wave simulation, etc.) and exclusively assess the effect of inversion technique. In particular, we compare three end‐member methods: (1) point‐source fastCMT (centroid moment tensor), (2) distributed slip along predefined plate interface, and (3) unconstrained inversion into a single uniform slip finite fault. The three methods provide significantly different far‐field tsunami forecast but show surprisingly similar tsunami predictions in the near field.
Learning sentiment and semantic relatedness in user generated content using neural models

Online review platforms and discussion forums are filled with insights that are critical to unlocking the value in user-generated content. In this thesis, we investigate two major Natural Language Processing (NLP) research areas: Aspect-Based Sentiment Analysis (ABSA) and Community Question Answering (cQA) ranking problems, for the purposes of harnessing and understanding the sentiment and semantics expressed in review platforms and discussion forums. Riding on the recent trends of deep learning, this work applies neural networks to solve these tasks. We design neuralbased models including Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs) to capture the semantic and sentiment information. Aspect Based Sentiment Analysis is concerned with predicting the aspect categories mentioned in a sentence and the sentiments associated with each aspect category. We refer to these tasks as Aspect Category Detection and Aspect category Sentiment Prediction, respectively. We present a neural-based model with convolutional layers and Multi-Layer Perceptron (MLP) to address these tasks. The model uses the word vector representations generated using word2vec and computes the convolutional vectors of the user-generated reviews. These vectors are then employed to predict the aspect categories and their corresponding sentiments. We evaluate the performance of our ABSA models on a restaurant review dataset and show that our results on the aspect category detection task and aspect category sentiment prediction task outperform the baselines. The Community Question Answering system is concerned with automatically finding the related questions in an existing set of questions, and finding the relevant answers to a new question. We address these ranking problems, which we respectively refer to as similar-Question Retrieval and Answer Selection. We present a neural-based model with stacked bidirectional LSTMs and MLP to address these tasks. The model generates the vector representations of the question-question or question-answer pairs and computes their semantic similarity scores. These scores are then used to rank and predict relevancies. Extensive experiments demonstrate that our cQA models for the question retrieval and answer selection tasks outperform