Miracle-dz/UniFAR-FTUs huggingface dataset:
Wavelength and Fibrosis Affect Phase Singularity Locations During Atrial Fibrillation

The mechanisms underlying atrial fibrillation (AF), the most common sustained cardiac rhythm disturbance, remain elusive. Atrial fibrosis plays an important role in the development of AF and rotor dynamics. Both electrical wavelength (WL) and the degree of atrial fibrosis change as AF progresses. However, their combined effect on rotor core location remains unknown. The aim of this study was to analyze the effects of WL change on rotor core location in both fibrotic and non-fibrotic atria. Three patient specific fibrosis distributions (total fibrosis content: 16.6, 22.8, and 19.2%) obtained from clinical imaging data of persistent AF patients were incorporated in a bilayer atrial computational model. Fibrotic effects were modeled as myocyte-fibroblast coupling + conductivity remodeling; structural remodeling; ionic current changes + conductivity remodeling; and combinations of these methods. To change WL, action potential duration (APD) was varied from 120 to 240ms, representing the range of clinically observed AF cycle length, by modifying the inward rectifier potassium current (IK1) conductance between 80 and 140% of the original value. Phase singularities (PSs) were computed to identify rotor core locations. Our results show that IK1 conductance variation resulted in a decrease of APD and WL across the atria. For large WL in the absence of fibrosis, PSs anchored to regions with high APD gradient at the center of the left atrium (LA) anterior wall and near the junctions of the inferior pulmonary veins (PVs) with the LA. Decreasing the WL induced more PSs, whose distribution became less clustered. With fibrosis, PS locations depended on the fibrosis distribution and the fibrosis implementation method. The proportion of PSs in fibrotic areas and along the borders varied with both WL and fibrosis modeling method: for patient one, this was 4.2–14.9% as IK1 varied for the structural remodeling representation, but 12.3–88.4% using the combination of structural remodeling with myocyte-fibroblast coupling. The degree and distribution of fibrosis and the choice of implementation technique had a larger effect on PS locations than the WL variation. Thus, distinguishing the fibrotic mechanisms present in a patient is important for interpreting clinical fibrosis maps to create personalized models.
Efficacy of adjunctive low-level laser therapy in the treatment of aggressive periodontitis: A systematic review.

The aim of the present study was to systematically review the efficacy of low-level laser therapy (LLLT) as an adjunct to scaling and root planing (SRP) vs SRP alone in the treatment of aggressive periodontitis (AgP). The addressed PICO (Population, Interventions, Comparisons and Outcomes) question was: Is LLLT as an adjunct to SRP effective in the treatment of AgP? Electronic databases, including MEDLINE via PubMed, Cochrane Central Register of Controlled Trials and Cochrane Oral Health Group Trials, and EMBASE, were searched until March 2018. Four clinical studies were included. Three studies showed significant improvement in periodontal outcomes among LLLT group compared to SRP alone, whereas only one study showed comparable periodontal outcomes between the adjunctive LLLT and SRP groups at follow up. The overall mean difference for clinical attachment level gain (weighted mean difference [WMD] = -1.69, 95% confidence interval [CI] = -3.46 to 0.07, P < 0.061) was not significant. However, significant difference for probing depth reduction (WMD = -0.95, 95% CI = -1.66 to 0.23, P = 0.009) was noticed between groups at follow up. Whether LLLT as an adjunct to SRP is more effective than SRP alone in the treatment of AgP remains debatable. Further randomized, clinical trials are required with long follow-up periods and standard laser parameters to reach a strong conclusion.
Transforming Growth Factor-β-Induced Cell Plasticity in Liver Fibrosis and Hepatocarcinogenesis

The Transforming Growth Factor-beta (TGF-β) family plays relevant roles in the regulation of different cellular processes that are essential for tissue and organ homeostasis. In the case of the liver, TGF-β signaling participates in different stages of disease progression, from initial liver injury toward fibrosis, cirrhosis and cancer. When a chronic injury takes place, mobilization of lymphocytes and other inflammatory cells occur, thus setting the stage for persistence of an inflammatory response. Macrophages produce profibrotic mediators, among them, TGF-β, which is responsible for activation -transdifferentiation- of quiescent hepatic stellate cells (HSC) to a myofibroblast (MFB) phenotype. MFBs are the principal source of extracellular matrix protein (ECM) accumulation and prominent mediators of fibrogenesis. TGF-β also mediates an epithelial-mesenchymal transition (EMT) process in hepatocytes that may contribute, directly or indirectly, to increase the MFB population. In hepatocarcinogenesis, TGF-β plays a dual role, behaving as a suppressor factor at early stages, but contributing to later tumor progression, once cells escape from its cytostatic effects. As part of its potential pro-tumorigenic actions, TGF-β induces EMT in liver tumor cells, which increases its pro-migratory and invasive potential. In parallel, TGF-β also induces changes in tumor cell plasticity, conferring properties of a migratory tumor initiating cell (TIC). The main aim of this review is to shed light about the pleiotropic actions of TGF-β that explain its effects on the different liver cell populations. The cross-talk with other signaling pathways that contribute to TGF-β effects, in particular the Epidermal Growth Factor Receptor (EGFR), will be presented. Finally, we will discuss the rationale for targeting the TGF-β pathway in liver pathologies.
Logical Rule Induction and Theory Learning Using Neural Theorem Proving

A hallmark of human cognition is the ability to continually acquire and distill observations of the world into meaningful, predictive theories. In this paper we present a new mechanism for logical theory acquisition which takes a set of observed facts and learns to extract from them a set of logical rules and a small set of core facts which together entail the observations. Our approach is neuro-symbolic in the sense that the rule pred- icates and core facts are given dense vector representations. The rules are applied to the core facts using a soft unification procedure to infer additional facts. After k steps of forward inference, the consequences are compared to the initial observations and the rules and core facts are then encouraged towards representations that more faithfully generate the observations through inference. Our approach is based on a novel neural forward-chaining differentiable rule induction network. The rules are interpretable and learned compositionally from their predicates, which may be invented. We demonstrate the efficacy of our approach on a variety of ILP rule induction and domain theory learning datasets.
Protection through participation: Involving children in child-safe organisations

Enabling children and young people to understand, identify and raise their safety concerns with a trusted adult and within safe organisations is vital. However, there is little advice provided to adults and institutions about how this might be done in child-centred and child-informed ways. Drawing from the Children’s Safety Study, a study that considered children’s perceptions of safety, their safety needs and how well they believed adults and institutions protected them from harm, this paper provides practical guidance based on what children and young people have said themselves, as well as observations about how this might be put into practice. Also included in this paper is a description of some tools successfully used to talk to children and young people about their safety, and some ways that adults and institutions can respond.
Memory for Faces in Old Age: A Meta-Analysis

The present meta-analysis investigated the influence of age on face recognition. A total of 19 studies with 79 comparisons of younger and older participants were included. Analyses revealed small to moderate effects for hits, and large effects for false alarms and signal detection theory (SDT) measures. Younger participants outperformed older participants on most face recognition measures. Younger participants made more hits (gu = 0.31) and fewer false alarms (gu = 0.95) and thus had better SDT recognition performance (gu = 1.01) than older participants. These effects were largest for young faces, smaller for mixed-age faces, and smallest for older faces. Furthermore, older participants used a more liberal response criterion, that is, they were more likely to choose a face than younger participants (gu = 0.54). Meta-regression analyses revealed that young faces (vs. mixed-age faces) and longer retention intervals were associated with greater differences between the age groups for hits but not for false alarms. Funnel plot and trim-and-fill analyses indicated the presence of a publication bias. Theoretical implications for future research and for older people as eyewitnesses are outlined.
Role of age, Rho‐kinase 2 expression, and G protein‐mediated signaling in the myogenic response in mouse small mesenteric arteries

The myogenic response (MR) and myogenic tone (MT) in resistance vessels is crucial for maintaining peripheral vascular resistance and blood flow autoregulation. Development of MT involves G protein‐coupled receptors, and may be affected by aging. Aims: (1) to estimate the mesenteric blood flow in myogenically active small mesenteric arteries; (2) to investigate the signaling from Gαq/11 and/or Gα12 activation to MT development; (3) to investigate the role of Rho‐kinase 2 and aging on MT in mesenteric resistance arteries. Methods: we used pressure myography, quantitative real‐time PCR, and immunolocalization to study small (<200 μm) mesenteric arteries (SMA) from young, mature adult, and middle aged mice. Results: Poiseuille flow calculations indicated autoregulation of blood flow at 60−120 mm Hg arterial pressure. Gαq/11 and Gα12 were abundantly expressed at the mRNA and protein levels in SMA. The Gαq/11 inhibitor YM‐254890 suppressed MT development, and the Phosholipase C inhibitors U73122 and ET‐18‐OCH3 robustly inhibited it. We found an age‐dependent increase in ROCK2 mRNA expression, and in basal MT. The specific ROCK2 inhibitor KD025 robustly inhibited MT in SMAs in all mice with an age‐dependent variation in KD025 sensitivity. The inhibitory effect of KD025 was not prevented by the L‐type Ca2+ channel activator BayK 8644. KD025 reversibly inhibited MT and endothelin‐1 vasoconstriction in small pial arteries from Göttingen minipigs. Conclusions: MT development in SMAs occurs through a Gαq/11/PLC/Ca2+‐dependent pathway, and is maintained via ROCK2‐mediated Ca2+ sensitization. Increased MT at mature adulthood can be explained by increased ROCK2 expression/activity.
MOOC : les conditions de la réussite

« MOOCs are a lousy product », ou dans la langue de Moliere, « Les MOOC sont un mauvais produit ». C’est en ces termes peu flatteurs qu’en parlait il y a un an Sebastian Thrun (Schuman, 2013). Une affirmation qui en a surpris beaucoup, quand l’on sait qu’il est l’un des fondateurs du mouvement MOOC, et que son cours d’intelligence artificielle, premier du genre, a attire pres de 160.000 personnes (Cisel & Bruillard 2012). Affirmation d’autant plus surprenante qu’il a lance en 2012 Udacity, un.
Accelerating Deep Neural Networks with Spatial Bottleneck Modules

This paper presents an efficient module named spatial bottleneck for accelerating the convolutional layers in deep neural networks. The core idea is to decompose convolution into two stages, which first reduce the spatial resolution of the feature map, and then restore it to the desired size. This operation decreases the sampling density in the spatial domain, which is independent yet complementary to network acceleration approaches in the channel domain. Using different sampling rates, we can tradeoff between recognition accuracy and model complexity. As a basic building block, spatial bottleneck can be used to replace any single convolutional layer, or the combination of two convolutional layers. We empirically verify the effectiveness of spatial bottleneck by applying it to the deep residual networks. Spatial bottleneck achieves 2x and 1.4x speedup on the regular and channel-bottlenecked residual blocks, respectively, with the accuracies retained in recognizing low-resolution images, and even improved in recognizing high-resolution images.
Oral Health Status of Male Swedish Citizens at Admission to Prison

The aim of the study was to describe oral health status as well as risk factors related to the oral health in a prison inmate population at admission. A sample of 186 men admitted to one of Sweden’s prisons were examined and interviewed to collect information. The results indicate a high prevalence of untreated oral disease and oral health risk factors in the sample. Seventy-one percent of the study population indicated drug abuse. Only 4.9% were assessed with completely healthy periodontal tissues, and 66% showed at least one tooth with untreated caries. According to the current regulations on dental care for inmates, only 11% of the study population was entitled to basic dental treatment (except emergency).
Bayesian Nonparametric Spectral Estimation

Spectral estimation (SE) aims to identify how the energy of a signal (e.g., a time series) is distributed across different frequencies. This can become particularly challenging when only partial and noisy observations of the signal are available, where current methods fail to handle uncertainty appropriately. In this context, we propose a joint probabilistic model for signals, observations and spectra, where SE is addressed as an inference problem. Assuming a Gaussian process prior over the signal, we apply Bayes' rule to find the analytic posterior distribution of the spectrum given a set of observations. Besides its expressiveness and natural account of spectral uncertainty, the proposed model also provides a functional-form representation of the power spectral density, which can be optimised efficiently. Comparison with previous approaches is addressed theoretically, showing that the proposed method is an infinite-dimensional variant of the Lomb-Scargle approach, and also empirically through three experiments.
Recent Advances on the Multiplex Molecular Detection of Plant Viruses and Viroids

Plant viruses are still one of the main contributors to economic losses in agriculture. It has been estimated that plant viruses can cause as much as 50 billion euros loss worldwide, per year. This situation may be worsened by recent climate change events and the associated changes in disease epidemiology. Reliable and early detection methods are still one of the main and most effective actions to develop control strategies for plant viral diseases. During the last years, considerable progress has been made to develop tools with high specificity and low detection limits for use in the detection of these plant pathogens. Time and cost reductions have been some of the main objectives pursued during the last few years as these increase their feasibility for routine use. Among other strategies, these objectives can be achieved by the simultaneous detection and (or) identification of several viruses in a single assay. Nucleic acid-based detection techniques are especially suitable for this purpose. Polyvalent detection has allowed the detection of multiple plant viruses at the genus level. Multiplexing RT polymerase chain reaction (PCR) has been optimized for the simultaneous detection of more than 10 plant viruses/viroids. In this short review, we provide an update on the progress made during the last decade on techniques such as multiplex PCR, polyvalent PCR, non-isotopic molecular hybridization techniques, real-time PCR, and array technologies to allow simultaneous detection of multiple plant viruses. Also, the potential and benefits of the powerful new technique of deep sequencing/next-generation sequencing are described.
Intrinsic cooperativity potentiates parallel cis-regulatory evolution

Convergent evolutionary events in independent lineages provide an opportunity to understand why evolution favors certain outcomes over others. We studied such a case, where a large set of genes—those coding for the ribosomal proteins—gained cis-regulatory sequences for a particular transcription regulator (Mcm1) in independent fungal lineages. We present evidence that these gains occurred because Mcm1 shares a mechanism of transcriptional activation with an ancestral regulator of the ribosomal protein genes, Rap1. Specifically, we show that Mcm1 and Rap1 have the inherent ability to cooperatively activate transcription through contacts with the general transcription factor TFIID. Because the two regulatory proteins share a common interaction partner, the presence of one ancestral cis-regulatory sequence can “channel” random mutations into functional sites for the second regulator. At a genomic scale, this type of intrinsic cooperativity can account for a pattern of parallel evolution involving the fixation of hundreds of substitutions.
Microbiome Interaction Networks and Community Structure From Laboratory-Reared and Field-Collected Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus Mosquito Vectors

Microbial interactions are an underappreciated force in shaping insect microbiome communities. Although pairwise patterns of symbiont interactions have been identified, we have a poor understanding regarding the scale and the nature of co-occurrence and co-exclusion interactions within the microbiome. To characterize these patterns in mosquitoes, we sequenced the bacterial microbiome of Aedes aegypti, Ae. albopictus, and Culex quinquefasciatus caught in the field or reared in the laboratory and used these data to generate interaction networks. For collections, we used traps that attracted host-seeking or ovipositing female mosquitoes to determine how physiological state affects the microbiome under field conditions. Interestingly, we saw few differences in species richness or microbiome community structure in mosquitoes caught in either trap. Co-occurrence and co-exclusion analysis identified 116 pairwise interactions substantially increasing the list of bacterial interactions observed in mosquitoes. Networks generated from the microbiome of Ae. aegypti often included highly interconnected hub bacteria. There were several instances where co-occurring bacteria co-excluded a third taxa, suggesting the existence of tripartite relationships. Several associations were observed in multiple species or in field and laboratory-reared mosquitoes indicating these associations are robust and not influenced by environmental or host factors. To demonstrate that microbial interactions can influence colonization of the host, we administered symbionts to Ae. aegypti larvae that either possessed or lacked their resident microbiota. We found that the presence of resident microbiota can inhibit colonization of particular bacterial taxa. Our results highlight that microbial interactions in mosquitoes are complex and influence microbiome composition.
Associations between gait speed and well‐known fall risk factors among community‐dwelling older adults

BACKGROUND AND PURPOSE Exercise interventions are effective at preventing falls in community-dwelling older adults, especially before disability is present. Gait speed below 1.0 m/s is a strong predictor for falls in the elderly. However, evidence is sparse for gait speed alone being sufficient to identify individuals at a high risk of falling. This study aimed to describe the prevalence of fall risk factors among community-dwelling older adults in their late 70s and to investigate the associations between these risk factors and low gait speed in this population. METHODS This cross-sectional cohort study comprised 108 elderly living in a small Norwegian municipality, born between 1936 and 1938. Exclusion criteria were living in residential care, inability to walk 4 m, and severe cognitive impairment. Measurements included gait speed, depressive symptoms, executive functions, fear of falling, vision function, fall history, body mass index, medications, and comorbidity. Gait speed was dichotomized using a cut-off of 1 m/s, and associations between different risk factors and low gait speed was explored using logistic regression analysis. RESULTS Mean gait speed was 1.0 ± 0.3 m/s. In 44.4% of the participants, gait speed was below 1.0 m/s, indicating increased fall risk. Low gait speed was significantly associated with a history of multiple falls (odds ratio [OR] = 3.70, 95% CI [1.18, 11.65]), low educational level (OR = 3.58, 95% CI [1.10, 11.66]), higher number of medications (OR = 4.28, 95% CI [1.63, 11.2]), and higher number of depressive symptoms (OR = 1.31, 95% CI [1.09, 1.58]). We found no significant associations between gait speed and comorbidity, sex, vision, executive functions, or fear of falling. CONCLUSION Our results indicate that gait speed with cut-off 1.0 m/s could represent a useful tool for identifying individuals who are vulnerable but not yet disabled and could benefit from fall-preventive exercise. However, extended assessment is probably needed to personalize interventions.
Clinical Predictors of Excessive Daytime Sleepiness in Patients with Parkinson's Disease

Background and Purpose Excessive daytime sleepiness (EDS) is a common complaint among patients with Parkinson's disease (PD). Several factors have been associated with EDS in PD, especially neuropsychiatric symptoms. This study aimed to determine the relationships between neuropsychiatric symptoms, sociodemographic and clinical parameters, and EDS in PD. Methods This cross-sectional study analyzed 85 patients with PD. All patients underwent socioeconomic and clinical data evaluations followed by a psychiatric interview and a neurological examination, including the assessment of sleep features. Patients were divided into two groups according to the presence or absence of EDS, which was defined as a score higher than 10 on the Epworth Sleepiness Scale. Binary logistic regression was performed in order to describe the predictors of EDS. Results We found that EDS affects 40% of PD patients and is associated with older age, restless legs syndrome, depressive and anxious symptoms, and worse sleep quality. In the multivariate analysis, older age, levodopa use, and worse sleep quality remained as significant predictors of EDS in PD. Conclusions Nighttime sleep problems, older age, and levodopa use are significantly associated with EDS in PD. A careful assessment and the management of sleep problems in PD patients might help to improve their quality of life.