Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosting a Bridge Artificial Intelligence

Bridge is an incomplete information game which is complex both for humans and for Computer-Bridge programs. The purpose of this paper is to present our work related to the adaptation to Bridge of a recent methodology used for boosting game Artificial Intelligence (AI) by seeking a random seed, or a probability distribution on random seeds, better than the others on a particular game. The Bridge AI Wbridge5 developed by Yves Costel has been boosted with the best seed found on the outcome of these experiments and has won the World Computer-Bridge Championship in September 2016.
Oscilometría de impulso (IOS) en niños

Este articulo tiene como proposito revisar aspectos fisiologicos, tecnicos y clinicos de la oscilometria de impulso aplicada en pediatria; ya que en la actualidad tiene un rol importante en el estudio de la mecanica respiratoria. Es una interesante aproximacion al diagnostico funcional del sistema respiratorio por su facil realizacion; en cuanto no necesita de la cooperacion activa del paciente, se obtienen resultados comprensibles del sitio y magnitud de la alteracion, puede complementar las pruebas de provocacion, pesquizar precozmente la respuesta broncodilatadora y participar en el seguimiento de enfermedades cronicas.
Management of diabetic macular edema with intravitreal dexamethasone implants: Expert recommendations using a Delphi-based approach

Objectives: Despite being approved and effective, steroids, and especially dexamethasone intravitreal implants, still have a poorly-defined role in management of diabetic macular edema. In order to overcome some of the limitations in current recommendations, a group of experts met to define consensus on some of the most controversial issues on the use of dexamethasone intravitreal implants in daily management of diabetic macular edema. Methods: A Delphi-based approach was utilized to develop clinically relevant statements applicable to routine treatment settings. A Steering Committee composed of four experts formulated 30 relevant statements, which were voted upon by a panel of 40 ophthalmologists/retinal specialists from across Italy. Results: Dexamethasone intravitreal implants were considered to be a valid first-line alternative to treatment with an anti–vascular endothelial growth factor agent and should be the first choice in pseudophakic and vitrectomized patients. A Pro Re Nata regimen was felt to be appropriate for retreatment with dexamethasone intravitreal implants while a 6-month waiting period was not considered suitable. Among steroid treatments, dexamethasone intravitreal implants were considered to have the best ocular tolerability. In patients with persistent macular edema after the loading-phase treatment with an anti–vascular endothelial growth factor, consensus was reached that clinicians should consider switching therapy to dexamethasone intravitreal implants. Moreover, dexamethasone intravitreal implants can reduce the treatment burden for individuals who are not able to cope with the more intensive treatment regimen required by anti–vascular endothelial growth factor therapy. Conclusions: While further studies are needed, this survey provides some key recommendations for clinicians treating diabetic macular edema that may be useful when choosing dexamethasone intravitreal implants in daily practice.
Sequencing and Analysis of Chrysanthemum carinatum Schousb and Kalimeris indica. The Complete Chloroplast Genomes Reveal Two Inversions and rbcL as Barcoding of the Vegetable

Chrysanthemum carinatum Schousb and Kalimeris indica are widely distributed edible vegetables and the sources of the Chinese medicine Asteraceae. The complete chloroplast (cp) genome of Asteraceae usually occurs in the inversions of two regions. Hence, the cp genome sequences and structures of Asteraceae species are crucial for the cp genome genetic diversity and evolutionary studies. Hence, in this paper, we have sequenced and analyzed for the first time the cp genome size of C. carinatum Schousb and K. indica, which are 149,752 bp and 152,885 bp, with a pair of inverted repeats (IRs) (24,523 bp and 25,003) separated by a large single copy (LSC) region (82,290 bp and 84,610) and a small single copy (SSC) region (18,416 bp and 18,269), respectively. In total, 79 protein-coding genes, 30 distinct transfer RNA (tRNA) genes, four distinct rRNA genes and two pseudogenes were found not only in C. carinatum Schousb but also in the K. indica cp genome. Fifty-two (52) and fifty-nine (59) repeats, and seventy (70) and ninety (90) simple sequence repeats (SSRs) were found in the C. carinatum Schousb and K. indica cp genomes, respectively. Codon usage analysis showed that leucine, isoleucine, and serine are the most frequent amino acids and that the UAA stop codon was the significantly favorite stop codon in both cp genomes. The two inversions, the LSC region ranging from trnC-GCA to trnG-UCC and the whole SSC region were found in both of them. The complete cp genome comparison with other Asteraceae species showed that the coding area is more conservative than the non-coding area. The phylogenetic analysis revealed that the rbcL gene is a good barcoding marker for identifying different vegetables. These results give an insight into the identification, the barcoding, and the understanding of the evolutionary model of the Asteraceae cp genome.
Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis

We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns.
Reply to Lehrer, Shechtman and Leasure

L ehrer, Shechtman, and Leasure (hereafter LSL) give us an opportunity to amplify our theory of the value of children. Regrettably, much of their criticism flows from a misunderstanding of our theory-particularly, of its scope conditions. Some of the criticism, however, appears to derive from epistemological roots. LSL's comment concludes by claiming that we attempted to complement existing theories of fertility with a new theory. Yet there are no existing theories that can account for women's and couples' decisions to have children when their net instrumental value is negative, as it is in all advanced industrial societies. Ours is the first and only theory that explains this empirical puzzle. Becker's (1981) economic theory of fertility, which serves as the acknowledged foundation for LSL's comment, explains why women who earn lower wages in the labor market are likely to have more children than those who can command higher wages. Why anyone in an advanced industrial society would have any children at all remains a mystery, however. Despite its apparent complexity, the logic of Becker's fertility theory is actually quite simple. It purposely says nothing whatsoever about the value of children to parents; people can desire children for any number of reasons. Whatever these reasons may be, the greater the opportunity cost of having a child, the fewer children will be demanded and therefore the fewer will be born. This logic is unimpeachable, and we certainly have no quarrel with it as far as it goes. Becker's theory assumes precisely what our theory sets out to explain-namely, why people value children in the first place. His theory concerns the fertility of people who already have decided to have children. Before deciding whether to have a child, rational people must compare the expected value of a child with that of other goods, such as expensive cars, houses and alternative uses of time. Such decisions necessarily involve trade-offs between valued goods.
Frequency-Diverse Transmission Metamaterial Aperture With a Bunching Random Beam

In this letter, a frequency-diverse transmission metamaterial aperture (FDTMA) with a bunching random beam (BRB) is proposed for coincidence imaging. The FDTMA consists of a random modulation metamaterial lens and a broadband microstrip patch antenna array, which is used as a feed source. First, an equivalent medium containing both dielectric layer and metamaterial structure is modeled and analyzed using the electromagnetic theory. The zoom coefficients of the metamaterial elements are analyzed to simplify the design of the broadband aperture. Then, the basic metamaterial elements are designed based on the proposed model. Moreover, the FDTMA is designed using the basic metamaterial elements considering the requirements of the coincidence imaging system. Finally, the waved metal plate leveraged in the FDTMA with a BRB is optimized using the High-Frequency Structure Simulator software. The radiation patterns of the proposed FDTMA with a BRB are randomly distributed in a limited angle range in the X-band. The proposed equivalent model and the FDTMA with a BRB are validated through simulations and experiments.
Epidemiology and risk factors of peri‐implantitis: A systematic review

The purpose of this systematic review and meta-analysis was to assess the prevalence, incidence and risk factors of peri-implantitis in the current literature. An electronic search was performed to identify publications from January 1980 until March 2016 on 9 databases. The prevalence and incidence of peri-implantitis were assessed in different subgroups of patients and the prevalences were adjusted for sample size (SSA) of studies. For 12 of 111 identified putative risk factors and risk indicators, forest plots were created. Heterogeneity analysis and random effect meta-analysis were performed for selected potential risk factors of peri-implantitis. The search retrieved 8357 potentially relevant studies. Fifty-seven studies were included in the systematic review. Overall, the prevalence of peri-implantitis on implant level ranged from 1.1% to 85.0% and the incidence from 0.4% within 3 years, to 43.9% within 5 years, respectively. The median prevalence of peri-implantitis was 9.0% (SSA 10.9%) for regular participants of a prophylaxis program, 18.8% (SSA 8.8%) for patients without regular preventive maintenance, 11.0% (SSA 7.4%) for non-smokers, 7.0% (SSA 7.0%) among patients representing the general population, 9.6% (SSA 9.6%) for patients provided with fixed partial dentures, 14.3% (SSA 9.8%) for subjects with a history of periodontitis, 26.0% (SSA 28.8%) for patients with implant function time ≥5 years and 21.2% (SSA 38.4%) for ≥10 years. On a medium and medium-high level of evidence, smoking (effect summary OR 1.7, 95% CI 1.25-2.3), diabetes mellitus (effect summary OR 2.5; 95% CI 1.4-4.5), lack of prophylaxis and history or presence of periodontitis were identified as risk factors of peri-implantitis. There is medium-high evidence that patient's age (effect summary OR 1.0, 95% CI 0.87-1.16), gender and maxillary implants are not related to peri-implantitis. Currently, there is no convincing or low evidence available that identifies osteoporosis, absence of keratinized mucosa, implant surface characteristics or edentulism as risk factors for peri-implantitis. Based on the data analyzed in this systematic review, insufficient high-quality evidence is available to the research question. Future studies of prospective, randomized and controlled type including sufficient sample sizes are needed. The application of consistent diagnostic criteria (eg, according to the latest definition by the European Workshop on Periodontology) is particularly important. Very few studies evaluated the incidence of peri-implantitis; however, this study design may contribute to examine further the potential risk factors.
Identifying Person-Fit Latent Classes, and Explanation of Categorical and Continuous Person Misfit

Latent class (LC) cluster analysis of a set of subscale lz person-fit statistics was proposed to explain person misfit on multiscale measures. The proposed explanatory LC person-fit analysis was used to analyze data of students (N = 91,648) on the nine-subscale School Attitude Questionnaire Internet (SAQI). Inspection of the class-specific lz mean and variance structure combined with explanatory analysis of class membership showed that the data included a poor-fit class, a class showing good fit combined with social desirability bias, a good-fit class, and two classes that were more difficult to interpret. A comparison of multinomial logistic regression predicting class membership and multiple regression predicting continuous person fit showed that LC cluster analysis provided information about aberrant responding unattainable by means of linear multiple regression. It was concluded that LC person-fit analysis has added value to common approaches to explaining aberrant responding to multiscale measures.
Personalized EntityRank-Based Entity Linking with DBpedia

Entity linking is the task of linking the textual mention in a document to the referent entity in the existing knowledge base. This step is important for many tasks, such as text understanding, semantic search, and question answering over knowledge base. This task is challenging due to name ambiguity and word polysemy. In this paper, we propose an approach to link the entity mention in the document to the DBpedia knowledge base entity. We measure the semantic similarity between a document and a candidate entity by comparing their personalized EntityRank vectors, and then score the candidate entities for each mention combined with other local features. Evaluation on three diï¬€erent datasets shows that our approach outperforms state-of-the-art methods.
Lessons learned from HIV antiretroviral treatment interruption trials

Purpose of review Clinical trials with an antiretroviral therapy (ART) interruption remains indispensable for assessing strategies for ART-free HIV remission. This review highlights the lessons learned from ART interruption studies so far, including the risks to the participants and implications for HIV remission. Recent findings Historically, analytic HIV treatment interruption (ATI) studies were commonly designed with a prolonged duration of ART interruption and with viral load set point as the primary outcome. For a variety of reasons, including participant risk, recent treatment interruption trials have frequently used time to viral rebound as the primary endpoint and have restarted ART once a predetermined viral load threshold is reached. Through treatment interruption trials, investigators have tested the efficacy of therapeutic and curative strategies that showed promise in preclinical trials, including therapeutic vaccines, latency-reversing agents, and broadly neutralizing antibodies. In most populations, ATI trials have been well tolerated, with few adverse clinical events and no significant changes to the reservoir. Several reservoir predictors of HIV-rebound timing have been reported, with a subset of trials uncovering posttreatment controllers who can maintain HIV remission despite ART discontinuation. Summary Treatment interruption trials are a vital tool, but their optimal design remain uncertain and must balance participant risks with scientific rigor. The ability to predict the timing or extent of HIV rebound and identify mechanisms of posttreatment control may accelerate the development of novel therapeutics for sustained HIV remission.
Real-Time Measurements of Cell Proliferation Using a Lab-on-CMOS Capacitance Sensor Array

We describe a capacitance sensor array that has been incorporated into a lab-on-CMOS system for applications in monitoring cell viability. This paper presents analytical models, calibration results, and measured experimental results of the biosensor. The sensor has been characterized and exhibits a sensitivity of 590 kHz/fF. We report results from benchtop tests and in vitro experiments demonstrating on-chip tracking of cell adhesion as well as monitoring of cell viability. Human ovarian cancer cells were cultured on chip, and measured capacitance responses were validated by comparison with images from photomicrographs of the chip surface. Analysis was performed to quantify cell proliferation and adhesion, and responses to live cells were estimated to be 100 aF/cell.
Renin-Angiotensin System Activation and Extracellular Signal-Regulated Kinases in Glomerulonephritis

Abnormal extracellular matrix (ECM) remodeling is a prominent biological feature of progressive glomerulonephritis (GN), and leads to glomerular dysfunction and sclerosis (Kagami et al., 2001; Prols et al., 1999). This condition is pathologically manifested in all the three major cells types of the glomerulus, and is characterized by the accumulation of fibronectin, laminin, and collagen in the diseased glomeruli (Kagami et al., 2004; Schnaper et al., 2003). Published genetic data (Boute et al., 2000; Kaplan et al., 2000; Patrakka et al., 2000) and the finding of podocyte abnormalities in transgenic mouse models of glomerulosclerosis (Shih et al., 1999) and patients with focal segmental glomerulosclerosis (Srivastava et al., 2001) suggest that the visceral epithelial cell plays a significant role. This assertion is supported by earlier data implicating potential epithelial cell stressors, such as glomerular hypertension, hyperfiltration, or hypertrophy in sclerosis (Brenner, 1985). Some models implicate the endothelial cells in the sclerotic process (Akaoka et al., 1995; Lee et al., 1995b). Still others suggest a role for the mesangial cells (MC) (Habib, 1973). As seen in human GN and experimental rat GN models, activated MCs acquire increased mitogenicity and migratory activity, and exhibit de novo synthesis of -smooth muscle actin (-SMA) and interstitial collagen (Hugo et al., 1996; Johnson, 1994). In addition, filtered macromolecules may be trapped in the mesangium, initiating an inflammatory response that could play a role in stimulating ECM synthesis. A unifying hypothesis that includes participation of all the cellular elements of the glomerulus can be constructed. Glomerular capillary hypertension, or a genetic or acquired abnormality in podocyte adhesion or structure, permits hyperfiltration of macromolecules. Paracrine signals from the injured podocyte stimulate endothelial cell expression of leukocyte adhesion molecules and impair endothelial cell fibrinolytic activity. Signals from epithelial or endothelial cells to the mesangium, or direct delivery of proinflammatory substances through the glomerular filtrate, initiates a process that culminates in the accumulation of ECM (Schnaper and Kopp, 2003). Mesangial expansion infringes on the capillary spaces, decreasing filtration surface area in the glomerular tuft. A major concept emerging from molecular cell biological studies is that pathological mesangial remodeling in progressive kidney disease is caused by
Duration of residence and prospective migration: Further evidence

A basic assumption of the Cornell Mobility Model insofar as it is relevant to inter-community migration is that an individual’s propensity to move is a function of, among other things, his length of residence in the community. Parameters of the functional relationship of migration probabilities to duration status are estimated from migration histories for a sample of residents in Monterrey, Mexico, and compared with estimates reported by P. A. Morrison using data from Amsterdam, The Netherlands. In both data sets a negative nonlinear relation of the probability of migrating to duration status, as well as an interaction between age and duration status, is found. Values of parameters describing the relation within age groups differ sufficiently between the two data sets, however, to suggest that further specification of conditions under which a particular form of functional relation will obtain is necessary if the model is to be useful in migration research.
The effects of feeding on cell morphology and proliferation of the gastrointestinal tract of juvenile Burmese pythons (Python molurus)

The gastrointestinal tract of Burmese pythons (Python molurus (L., 1758)) exhibits large morphological and physiological changes in response to feeding and extended periods of fasting. In this study the mucosa of the stomach, small intestine, and colon were examined for changes in structure and cellular proliferation. The mucosa of fasting pythons exhibited low levels of cellular replication, but after feeding, cellular replication was evident as early as 12 h in the small intestine and colon and 24 h in the stomach. Replication peaked 3 days postfeeding for the small intestine and colon, but was still increasing at 6 days postfeeding in the stomach. Interestingly, cell proliferation was still evident after 45 days in the colon. In these tissues, a stock of “ready-to-use” primary lysosomes is found in the mucosal cells of fasting animals, whereas profound intracellular recycling is typical of animals that have been fed. These findings indicate that during the postprandial period, the intestinal mucosa und.
Deep Learned Cumulative Attribute Regression

Learning regression-based machine learning models for computer vision problems is a challenging task due to noisy features, variation in pose and illumination, occlusion, etc. Typically the problem is compounded by the non-uniform distribution of labels in the training data, resulting in parts of the label space that suffer from data sparsity and a problem of label imbalance in general. Deep Convolutional Neural Networks (CNN) have shown remarkable success on a number of computer vision tasks such as object classification and face recognition. However, they too suffer from sparse and imbalanced training datasets for regression problems, even when those datasets are very large. Cumulative Attributes have previously been proposed to address the issue of label imbalance, but to date this concept has not been integrated with Deep Learning. In this work, we propose a CNN-based framework for learning regression models by using Cumulative Attributes as intermediate features. We evaluate our method on a number of tasks which includes pain intensity estimation, Facial Action Unit intensity estimation and age estimation. Our results show that the proposed method is robust to imbalance and sparsity present in the training datasets, and performs significantly better than the current methods where CNNs are learnt directly for regression.