Miracle-dz/UniFAR-FTUs huggingface dataset:
Downregulation of N-Acetylglucosaminyltransferase GCNT3 by miR-302b-3p Decreases Non-Small Cell Lung Cancer (NSCLC) Cell Proliferation, Migration and Invasion

Background/Aims: GCNT3 is a member of N-acetylglucosaminyltransferase family involved with mucin biosynthesis. GCNT3 aberrant expression is known to promote the progression of several human cancers. However, its role in tumorigenesis and the progression of non-small cell lung cancer (NSCLC) has not been well-characterized. Our study investigated the functional mechanisms of GCNT3 regulated by microRNAs (miRNAs) in NSCLC. Methods: The differential expression of mRNAs in NSCLC tissues and matched adjacent non-cancerous lung tissues from patients in Xuanwei, Yunnan province, China, was screened via mRNA microarray. The expression of GCNT3 and its correlation with NSCLC progression was measured in 92 paired tumor tissues and adjacent normal tissues. The functions of GCNT3 in NSCLC cells and its underlying mechanisms were measured using siRNA and GCNT3-expression vectors. The miRNA immunoprecipitation (miRIP) method was used to identify the miRNAs targeting GCNT3. The protein were measured using western blot assay, and the mRNAs were measured by quantitative real-time PCR (qRT-PCR) assay. Cell proliferation was measured using Cell Counting Kit-8 (CCK-8) and a colony forming assays; cell migration and invasion assays were performed using 24-well Transwell chambers with 8-μm pores filter, and analyses of the cell cycle and apoptosis were performed via flow cytometric analysis. The dual luciferase reporter assay was performed to confirm whether GCNT3 gene was a direct target of miR-302b-3p. Results: GCNT3 was found to be highly expressed in both NSCLC tissues and cell lines, and higher expression correlated significantly with advanced tumor-node-metastasis (TNM) stage, positive lymph node metastasis, and poor overall survival. Knockdown of GCNT3 inhibited the proliferation, migration and invasion ability of NSCLC cells, while overexpression facilitated these activities. Further mechanistic experiments using miRIP and dual luciferase reporter assays revealed that GCNT3 was a direct target of miR-302b-3p. Low expression of miR-302b-3p was found in NSCLC cells and negatively correlated with GCNT3 levels, while miR-302b-3p overexpression inhibited the proliferation, migration and invasion of NSCLC cells. Co-transfection with miR-302b-3p and the expression vector of GCNT3 abrogated the effects of mir-302b-3p, confirming that miR-302b-3p inhibited NSCLC progression by targeting GCNT3. Western blotting revealed that E-cadherin, N-cadherin, vimentin, p-Erk and cyclin D1 were downstream molecules of miR-302b-3p/GCNT3 pathway. Conclusion: miR-302b-3p/GCNT3 axis regulated cell proliferation, migration, and invasion by activating the Erk signaling pathway and epithelial-mesenchymal transition (EMT), which was identified as a potential therapeutic target for NSCLC.
Lingonberries and their two separated fractions differently alter the gut microbiota, improve metabolic functions, reduce gut inflammatory properties, and improve brain function in ApoE−/− mice fed high-fat diet

Lingonberries (LB) have been shown to have beneficial metabolic effects, which is associated with an altered gut microbiota. This study investigated whether the LB-induced improvements were associated with altered gut- and neuroinflammatory markers, as well as cognitive performance in ApoE−/− mice fed high-fat (HF) diets. Whole LB, as well as two separated fractions of LB were investigated. Eight-week-old male ApoE−/− mice were fed HF diets (38% kcal) containing whole LB (wLB), or the insoluble (insLB) and soluble fractions (solLB) of LB for 8 weeks. Inclusion of wLB and insLB fraction reduced weight gain, reduced fat deposition and improved glucose response. Both wLB and insLB fraction also changed the caecal microbiota composition and reduced intestinal S100B protein levels. The solLB fraction mainly induced weight loss in the mice. There were no significant changes in spatial memory, but significant increases in synaptic density in the hippocampus were observed in the brain of mice-fed wLB and insLB. Thus, this study shows that all lingonberry fractions counteracted negative effects of HF feedings on metabolic parameters. Also, wLB and insLB fraction showed to potentially improve brain function in the mice.
Structure Learning of Deep Networks via DNA Computing Algorithm

Convolutional Neural Network (CNN) has gained state-of-the-art results in many pattern recognition and computer vision tasks. However, most of the CNN structures are manually designed by experienced researchers. Therefore, auto- matically building high performance networks becomes an important problem. In this paper, we introduce the idea of using DNA computing algorithm to automatically learn high-performance architectures. In DNA computing algorithm, we use short DNA strands to represent layers and long DNA strands to represent overall networks. We found that most of the learned models perform similarly, and only those performing worse during the first runs of training will perform worse finally than others. The indicates that: 1) Using DNA computing algorithm to learn deep architectures is feasible; 2) Local minima should not be a problem of deep networks; 3) We can use early stop to kill the models with the bad performance just after several runs of training. In our experiments, an accuracy 99.73% was obtained on the MNIST data set and an accuracy 95.10% was obtained on the CIFAR-10 data set.
Annotated Dendrograms for Neurons From the Larval Fruit Fly Brain

Recent advances in neuroscience have made it possible to reconstruct all neurons of an entire neural circuit in the larval fruit ﬂy brain from serial electron microscopy image stacks. The reconstructed neurons are morphologically complex 3D graphs whose nodes are annotated with labels representing different types of synapses. Here, we propose a method to draw simpli-ﬁed, yet realistic 2D neuron sketches of insect neurons in order to help biologists formulate hypotheses on neural function at the microcircuit level. The sketches are dendrograms that capture a neuron’s branching structure and that preserve branch lengths, providing realistic estimates for distances and signal travel times between synapses. To improve readability of the often densely clustered synapse annotations, synapses are automatically summarized in local clusters of synapses of the same type and arranged to minimize label overlap. We show that two major neuron classes of an olfactory circuit in the larval fruit ﬂy brain can be discriminated visually based on the dendrograms. Unsupervised and supervised data analysis reveals that class discrimination can be performed using morphological features derived from the dendrograms.
A Simple Weight Recall for Semantic Segmentation: Application to Urban Scenes

In many learning tasks, including semantic image segmentation, performance can be effectively improved through the fine-tuning of a pre-trained convolutional network, instead of training from scratch. With fine-tuning, the underlying assumption is that the pre-trained model extracts generic features, which are at least partially relevant for solving a segmentation task, but that would be difficult to extract from the smaller amount of data that is available for training in urban driving scenes segmentation. However, besides the initialization with the pre-trained model and the early stopping, there is no mechanism in classical fine-tuning approaches for keeping the generic features. Even worse, the standard weight decay drives the parameters towards the origin and affects the learned features. In this paper, we show that a simple regularization that uses the pre-trained model as a reference consistently improves the performance when applied to semantic urban driving scene segmentation. Experiments are done on the Cityscapes dataset, with four different architectures of convolutional networks.
Context-Dependent Risk Aversion: A Model-Based Approach

Most research on risk aversion in behavioral science with human subjects has focused on a component of risk aversion that does not adapt itself to context. More recently, studies have explored risk aversion adaptation to changing circumstances in sequential decision-making tasks. It is an open question whether one can identify evidence, at the single subject level, for such risk aversion adaptation. We conducted a behavioral experiment on human subjects, using a sequential decision making task. We developed a model-based approach for estimating the adaptation of risk-taking behavior with single-trial resolution by modeling a subject's goals and internal representation of task contingencies. Using this model-based approach, we estimated the subject-specific adaptation of risk aversion depending on the current task context. We found striking inter-subject variations in the adaptation of risk-taking behavior. We show that these differences can be explained by differences in subjects' internal representations of task contingencies and goals. We discuss that the proposed approach can be adapted to a wide range of experimental paradigms and be used to analyze behavioral measures other than risk aversion.
Effect of a Mobile Phone Intervention on Quitting Smoking in a Young Adult Population of Smokers: Randomized Controlled Trial

Background Digital mobile technology presents a promising medium for reaching young adults with smoking cessation interventions because they are the heaviest users of this technology. Objective The aim of this study was to determine the efficacy of an evidence-informed smartphone app for smoking cessation, Crush the Crave (CTC), on reducing smoking prevalence among young adult smokers in comparison with an evidence-informed self-help guide, On the Road to Quitting (OnRQ). Methods A parallel, double-blind, randomized controlled trial with 2 arms was conducted in Canada to evaluate CTC. In total, 1599 young adult smokers (aged 19 to 29 years) intending to quit smoking in the next 30 days were recruited online and randomized to receive CTC or the control condition OnRQ for a period of 6 months. The primary outcome measure was self-reported continuous abstinence at the 6-month follow-up. Results Overall follow-up rates were 57.41% (918/1599) and 60.48% (967/1599) at 3 and 6 months, respectively. Moreover, 45.34% (725/1599) of participants completed baseline, 3-, and 6-month follow-up. Intention-to-treat analysis (last observation carried forward) showed that continuous abstinence (N=1599) at 6 months was not significantly different at 7.8% (64/820) for CTC versus 9.2% (72/779) for OnRQ (odds ratio; OR 0.83, 95% CI 0.59-1.18). Similarly, 30-day point prevalence abstinence at 6 months was not significantly different at 14.4% (118/820) and 16.9% (132/779) for CTC and OnRQ, respectively (OR 0.82, 95% CI 0.63-1.08). However, these rates of abstinence were favorable compared with unassisted 30-day quit rates of 11.5% among young adults. Secondary measures of quit attempts and the number of cigarettes smoked per day at 6-month follow-up did not reveal any significant differences between groups. For those who completed the 6-month follow-up, 85.1% (359/422) of young adult smokers downloaded CTC as compared with 81.8% (346/423) of OnRQ, χ21(N=845)=1.6, P=. 23. Furthermore, OnRQ participants reported significantly higher levels of overall satisfaction (mean 3.3 [SD 1.1] vs mean 2.6 [SD 1.3]; t644=6.87, P<.001), perceived helpfulness (mean 5.8 [SD 2.4] vs mean 4.3 [SD 2.6], t657=8.0, P<.001), and frequency of use (mean 3.6 [SD 1.2] vs mean 3.2 [SD 1.1], t683=5.7, P<.001) compared with CTC participants. Conclusions CTC was feasible for delivering cessation support but was not superior to a self-help guide in helping motivated young adults to quit smoking. CTC will benefit from further formative research to address satisfaction and usage. As smartphone apps may not serve as useful alternatives to printed self-help guides, there is a need to conduct further research to understand how digital mobile technology smoking cessation interventions for smoking cessation can be improved. Trial Registration ClinicalTrials.gov NCT01983150; http://clinicaltrials.gov/ct2/show/NCT01983150 (Archived by WebCite at http://www.webcitation.org/6VGyc0W0i)
Cofilin-actin rod formation in neuronal processes after brain ischemia

Functional impairment after brain ischemia results in part from loss of neuronal spines and dendrites, independent of neuronal death. Cofilin-actin rods are covalently linked aggregates of colfilin-1 and actin that form in neuronal processes (neurites) under conditions of ATP depletion and oxidative stress, and which cause neurite degeneration if not disassembled. ATP depletion and oxidative stress occur with differing severity, duration, and time course in different ischemic conditions. Here we evaluated four mouse models of brain ischemia to define the conditions that drive formation of cofilin-actin rods. Three of the models provide early reperfusion: transient middle cerebral artery occlusion (MCAo), transient bilateral common carotid occlusion (CCAo), and cardiac arrest / cardiopulmonary resuscitation (CA/CPR). Early reperfusion restores ATP generating capacity, but also induces oxidative stress. The fourth model, photothrombotic cortical infarction, does not provide reperfusion. Cofilin-actin rods were formed in each of these models, but with differing patterns. Where acute reperfusion occurred, rod formation was maximal within 4 hours after reperfusion. Where infarction occurred, rods continued to form for at least 24 hours after ischemic onset, and extended into the adjacent non-ischemic tissue. Interventions that limit cofilin-actin rod formation may help to preserve integrity of neuronal processes in permanent ischemia.
Interactive Visual Exploration of Line Clusters

We propose a visualization approach to interactively explore the structure of clusters of lines in 3D space. We introduce cluster consistency ﬁelds to indicate the local consistency of the lines in a cluster depending on line density and dispersion of line directions. Via brushing the user can select a focus region where lines are shown, and the consistency ﬁelds are used to automatically control the density of displayed lines according to information content. The brush is automatically continued along the gradient of the consistency ﬁeld towards high information regions, or along a derived mean direction ﬁeld to reveal major pathways
An Automated Approach for Classifying Reverse-Engineered and Forward-Engineered UML Class Diagrams

UML Class diagrams are commonly used to describe the designs of systems. Such designs can be used to guide the construction of software. In practice, we have identified two main types of using UML: i) FwCD refers to diagrams are hand-made as part of the forward-looking development process; ii) RECD refers to those diagrams that are reverse engineered from the source code; Recently, empirical studies in Software Engineering have started looking at open source projects. This enables the automated extraction and analysis of large sets of project-data. For researching the effects of UML modeling in open source projects, we need a way to automatically determine the way in which UML used in such projects. For this, we propose an automated classifier for deciding whether a diagram is an FwCD or an RECD. We present the construction of such a classifier by means of (supervised) machine learning algorithms. As part of its construction, we analyse which features are useful in classifying FwCD and RECD. By comparing different machine learning algorithms, we find that the Random Forest algorithm is the most suitable algorithm for our purpose. We evaluate the performance of the classifier on a test set of 999 class diagrams obtained from open source projects.
Loop corrections in spin models through density consistency

Computing marginal distributions of discrete or semidiscrete Markov random fields (MRFs) is a fundamental, generally intractable problem with a vast number of applications in virtually all fields of science. We present a new family of computational schemes to approximately calculate the marginals of discrete MRFs. This method shares some desirable properties with belief propagation, in particular, providing exact marginals on acyclic graphs, but it differs with the latter in that it includes some loop corrections; i.e., it takes into account correlations coming from all cycles in the factor graph. It is also similar to the adaptive Thouless-Anderson-Palmer method, but it differs with the latter in that the consistency is not on the first two moments of the distribution but rather on the value of its density on a subset of values. The results on finite-dimensional Isinglike models show a significant improvement with respect to the Bethe-Peierls (tree) approximation in all cases and with respect to the plaquette cluster variational method approximation in many cases. In particular, for the critical inverse temperature β_{c} of the homogeneous hypercubic lattice, the expansion of (dβ_{c})^{-1} around d=∞ of the proposed scheme is exact up to d^{-4} order, whereas the latter two are exact only up to d^{-2} order.
Classifying and Visualizing Emotions with Emotional DAN

Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN) with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%. Furthermore, we visualize image regions analyzed by the network when making a decision and the results indicate that our EmotionalDAN model is able to correctly identify facial landmarks responsible for expressing the emotions.
Changes in the expression of worries, anxiety, and generalized anxiety disorder with increasing age: A population study of 70 to 85‐year‐olds

The prevalence of generalized anxiety disorder (GAD) is supposed to decrease with age. Reasons suggested include that emotional control increases and that anxiety and worry are expressed differently in older adults. The aim of this study was to examine how the expression of anxiety and worry changes with age and how this influences diagnoses in current classification systems.
Computing of Low Shear Stress-Driven Endothelial Gene Network Involved in Early Stages of Atherosclerotic Process

Background In the pathogenesis of atherosclerosis, a central role is represented by endothelial inflammation with influx of chemokine-mediated leukocytes in the vascular wall. Aim of this study was to analyze the effect of different shear stresses on endothelial gene expression and compute gene network involved in atherosclerotic disease, in particular to homeostasis, inflammatory cell migration, and apoptotic processes. Methods HUVECs were subjected to shear stress of 1, 5, and 10 dyne/cm2 in a Flow Bioreactor for 24 hours to compare gene expression modulation. Total RNA was analyzed by Affymetrix technology and the expression of two specific genes (CXCR4 and ICAM-1) was validated by RT-PCR. To highlight possible regulations between genes and as further validation, a bioinformatics analysis was performed. Results At low shear stress (1 dyne/cm2) we observed the following: (a) strong upregulation of CXCR4; (b) mild upregulation of Caspase-8; (c) mild downregulation of ICAM-1; (d) marked downexpression of TNFAIP3. Bioinformatics analysis showed the presence of network composed by 59 new interactors (14 transcription factors and 45 microRNAs) appearing strongly related to shear stress. Conclusions The significant modulation of these genes at low shear stress and their close relationships through transcription factors and microRNAs suggest that all may promote an initial inflamed endothelial cell phenotype, favoring the atherosclerotic disease.
Audit and feedback in mental healthcare: staff experiences

Purpose Audit and feedback (A&F) often underlie implementation projects, described as a circular process; i.e. an A&F cycle. They are widely used, but effect varies with no apparent explanation. We need to understand how A&F work in real-life situations. The purpose of this paper, therefore, is to describe and explore mental healthcare full A&F cycle experiences. Design/methodology/approach This is a naturalistic qualitative study that uses four focus groups and qualitative content analysis. Findings Staff accepted the initial A&F stages, perceiving it to enhance awareness and reassure them about good practice. They were willing to participate in the full cycle and implement changes, but experienced poor follow-up and prioritization, not giving them a chance to own to the process. An important finding is the need for an A&F cycle facilitator. Practical implications Research teams cannot be expected to be involved in implementing clinical care. Guidelines will keep being produced to improve service quality and will be expected to be practiced. This study gives insights into planning and tailoring A&F cycles. Originality/value Tools to ease implementation are not enough, and the key seems to lie with facilitating a process using A&F. This study underscores leadership, designated responsibility and facilitation throughout a full audit cycle.
Genotype‐phenotype relations for the Parkinson's disease genes SNCA, LRRK2, VPS35: MDSGene systematic review

This comprehensive MDSGene review is devoted to the three autosomal‐dominant PD forms: PARK‐SNCA, PARK‐LRRK2, and PARK‐VPS35. It follows MDSGene's standardized data extraction protocol, screened a total of 2,972 citations, and is based on fully curated phenotypic and genotypic data on 937 patients with dominantly inherited PD attributed to 44 different mutations in SNCA, LRRK2, or VPS35. All of these data are also available in an easily searchable online database (www.mdsgene.org), which additionally provides descriptive summary statistics on phenotypic and genetic data. Despite the high degree of missingness of phenotypic features and unsystematic reporting of genotype data in the original literature, the present review recapitulates many of the previously described findings including later onset of disease (median age at onset: ∼49 years) compared to recessive forms of PD of an overall excellent treatment response. Our systematic review validates previous reports showing that SNCA mutation carriers have a younger age at onset compared to LRRK2 and VPS35 (P < 0.001). SNCA mutation carriers often have additional psychiatric symptoms, and although not exclusive to only LRRK2 or VPS35 mutation carriers, LRRK2 mutation carriers have a typical form of PD, and, lastly, VPS35 mutation carriers have good response to l‐dopa. © 2018 International Parkinson and Movement Disorder Society