Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel dermo-cosmetic product containing thermal spring water, sucralfate, copper sulfate, and zinc sulfate in the management of hand eczema

Background The regular use of cosmetic products plays a role in the management of hand eczema (HE) and aids in improving barrier function reducing dryness, roughness, pruritus and improving quality of life (QoL). The aim of this open-label study was to assess the efficacy and the reparative effect of a dermo-cosmetic product on subjects suffering from HE after 7 and 21 days of daily application. Methods The product was a water-in-oil (W/O) emulsion containing the active ingredients Avène thermal spring water, sucralfate, and copper and zinc sulfates. In total, 32 subjects suffering from either contact dermatitis or climatic dermatitis participated in the trial. The modified total lesion symptom score and physician global assessment scores were used to describe the severity of HE. The safety of the product was assessed through clinical scoring. The subjective tolerance, and acceptance, were documented using a self-assessment questionnaire completed by the subjects. The impact of the dermatosis on QoL was evaluated using the Dermatology Life Quality Index. Results After 7 days of application, both the physician and subjects noticed a significant improvement in HE. The formula was very well tolerated and accepted. These benefits were correlated with a significant improvement in QoL. Conclusion The W/O emulsion used in this study demonstrated real benefits for the subjects suffering from contact dermatitis and climatic dermatitis.
The Neuronal Basis of Predictive Coding Along the Auditory Pathway: From the Subcortical Roots to Cortical Deviance Detection

In this review, we attempt to integrate the empirical evidence regarding stimulus-specific adaptation (SSA) and mismatch negativity (MMN) under a predictive coding perspective (also known as Bayesian or hierarchical-inference model). We propose a renewed methodology for SSA study, which enables a further decomposition of deviance detection into repetition suppression and prediction error, thanks to the use of two controls previously introduced in MMN research: the many-standards and the cascade sequences. Focusing on data obtained with cellular recordings, we explain how deviance detection and prediction error are generated throughout hierarchical levels of processing, following two vectors of increasing computational complexity and abstraction along the auditory neuraxis: from subcortical toward cortical stations and from lemniscal toward nonlemniscal divisions. Then, we delve into the particular characteristics and contributions of subcortical and cortical structures to this generative mechanism of hierarchical inference, analyzing what is known about the role of neuromodulation and local microcircuitry in the emergence of mismatch signals. Finally, we describe how SSA and MMN are occurring at similar time frame and cortical locations, and both are affected by the manipulation of N-methyl-D-aspartate receptors. We conclude that there is enough empirical evidence to consider SSA and MMN, respectively, as the microscopic and macroscopic manifestations of the same physiological mechanism of deviance detection in the auditory cortex. Hence, the development of a common theoretical framework for SSA and MMN is all the more recommendable for future studies. In this regard, we suggest a shared nomenclature based on the predictive coding interpretation of deviance detection.
Towards Distributed Coevolutionary GANs

Generative Adversarial Networks (GANs) have become one of the dominant methods for deep generative modeling. Despite their demonstrated success on multiple vision tasks, GANs are difficult to train and much research has been dedicated towards understanding and improving their gradient-based learning dynamics. Here, we investigate the use of coevolution, a class of black-box (gradient-free) co-optimization techniques and a powerful tool in evolutionary computing, as a supplement to gradient-based GAN training techniques. Experiments on a simple model that exhibits several of the GAN gradient-based dynamics (e.g., mode collapse, oscillatory behavior, and vanishing gradients) show that coevolution is a promising framework for escaping degenerate GAN training behaviors.
Platelet-Rich Plasma, Hydroxyapatite, and Chitosan in the Bone and Cartilaginous Regeneration of Femoral Trochlea in Rabbits: Clinical, Radiographic, and Histomorphometric Evaluations

The aim of this study was to evaluate the trochlear bone and cartilaginous regeneration of rabbits using the association of PRP, chitosan, and hydroxyapatite. Hole was made in rabbit troches, one hole in each animal remained empty (group C), and one was filled by a combination of PRP, chitosan, and hydroxyapatite (group T). Clinical-orthopedic, radiographic, and histomorphometric evaluations were performed. Clinical-orthopedic evaluation showed lameness of two members of the T group and one member of group C. The radiographic evaluation showed that the T group showed absence of subchondral bone reaction (33%). The presence of moderate subchondral bone reaction was more frequently reported in group C with 67%. Microscopic evaluation revealed a presence of tissue neoformation, composed of connective tissue. Microscopic findings were similar in both groups, with a difference in the amount of neoformed tissue being perceptible, which was confirmed after the morphometric analysis, which revealed a significant difference in the quantity of newly formed tissue at the bone/cartilage/implant interface. The composite base of the association of chitosan, hydroxyapatite, and platelet-rich plasma favored bone and cartilage healing.
The Understanding of Visual Metaphors by the Congenitally Blind

Results from a narrow set of empirical studies suggest that blind individuals’ comprehension of metaphorical expressions does not differ from that of sighted participants. However, prominent accounts of metaphor comprehension yield different predictions about the blind’s ability to comprehend visual metaphors. While conceptual metaphor theory leads to predicting that blind individuals should lag behind their sighted peers in making sense of this particular kind of utterances, from traditional accounts of analogical reasoning it follows that blind individuals’ ability to comprehend the literal meaning of visual concepts might be sufficient to support their metaphorical application. In Experiment 1, 20 sighted and 20 congenitally blind participants were asked to select the most appropriate meaning for visual, grasping and filler metaphorical expressions. Results failed to reveal group differences for any type of metaphorical expressions. In order to implement a more stringent test of blind individuals’ ability to understand visual metaphors, in Experiment 2 blind and sighted participants were presented with very novel figurative expressions, as indicated by low or no occurrence in the “Google” corpus. In line with the results of Experiment 1, blind participants’ comprehension of visual metaphors was both high in absolute terms and comparable to that of sighted participants. We advance some speculations about the mechanisms by which blind individuals comprehend visual metaphors and we discuss the implications of these results for current theories of metaphor.
Exosomes: mediators of bone diseases, protection, and therapeutics potential

Bone remodeling is a continuous lifelong process in the repair of micro-damage to bone architecture and replacement of aging tissue in bone. A failure to such process leads to pathological destructive bone diseases such as osteoporosis, rheumatoid arthritis, and osteoarthritis. However, this active process is regulated by; osteoclasts, which are involved in the bone resorption process; osteoblasts, with involvement in the bone formation process and bone-derived endothelial cells, which promote angiogenesis. In the bone micro-environment, these cellular interactions are mediated by a complex interplay between cell types via direct interaction of cell secreted growth factors, such as cytokines. Recently, the discovery of exosomes (∼ 40–100 nm in size), has attracted more attention in the field of the bone remodeling process. Exosomes and microvesicles are derived from different types of bone cells such as mesenchymal stem cells, osteoblasts, osteoclasts and their precursors. They are also recognized to play pivotal roles in bone remodeling processes including osteogenesis, osteoclastogenesis, and angiogenesis. In this review, we especially emphasize the origin and biogenesis of exosomes and bone cell derived exosomes in the regulatory process of bone remodeling. Moreover, this review article also focuses on exosomal secreted proteins and microRNAs and their involvement in the regulation of bone remodeling.
An Intelligent Parkinson's Disease Diagnostic System Based on a Chaotic Bacterial Foraging Optimization Enhanced Fuzzy KNN Approach

Parkinson's disease (PD) is a common neurodegenerative disease, which has attracted more and more attention. Many artificial intelligence methods have been used for the diagnosis of PD. In this study, an enhanced fuzzy k-nearest neighbor (FKNN) method for the early detection of PD based upon vocal measurements was developed. The proposed method, an evolutionary instance-based learning approach termed CBFO-FKNN, was developed by coupling the chaotic bacterial foraging optimization with Gauss mutation (CBFO) approach with FKNN. The integration of the CBFO technique efficiently resolved the parameter tuning issues of the FKNN. The effectiveness of the proposed CBFO-FKNN was rigorously compared to those of the PD datasets in terms of classification accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic curve). The simulation results indicated the proposed approach outperformed the other five FKNN models based on BFO, particle swarm optimization, Genetic algorithms, fruit fly optimization, and firefly algorithm, as well as three advanced machine learning methods including support vector machine (SVM), SVM with local learning-based feature selection, and kernel extreme learning machine in a 10-fold cross-validation scheme. The method presented in this paper has a very good prospect, which will bring great convenience to the clinicians to make a better decision in the clinical diagnosis.
Cardiac Stem Cells in the Postnatal Heart: Lessons from Development

Heart development in mammals is followed by a postnatal decline in cell proliferation and cell renewal from stem cell populations. A better understanding of the developmental changes in cardiac microenvironments occurring during heart maturation will be informative regarding the loss of adult regenerative potential. We reevaluate the adult heart's mitotic potential and the reported adult cardiac stem cell populations, as these are two topics of ongoing debate. The heart's early capacity for cell proliferation driven by progenitors and reciprocal signalling is demonstrated throughout development. The mature heart architecture and environment may be more restrictive on niches that can host progenitor cells. The engraftment issues observed in cardiac stem cell therapy trials using exogenous stem cells may indicate a lack of supporting stem cell niches, while tissue injury adds to a hostile microenvironment for transplanted cells. Engraftment may be improved by preconditioning the cultured stem cells and modulating the microenvironment to host these cells. These prospective areas of further research would benefit from a better understanding of cardiac progenitor interactions with their microenvironment throughout development and may lead to enhanced cardiac niche support for stem cell therapy engraftment.
Lactobacillus rhamnosus GR-1 Ameliorates Escherichia coli-Induced Activation of NLRP3 and NLRC4 Inflammasomes With Differential Requirement for ASC

Escherichia coli is a common cause of mastitis in dairy cows. The adaptor protein apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC) synergizes with caspase-1 to regulate inflammasome activation during pathogen infection. Here, the ASC gene was knocked out in bovine mammary epithelial (MAC-T) cells using clustered, regularly interspaced, short palindromic repeat (CRISPR)/CRISPR-associated (Cas)-9 technology. MAC-T cells were pre-incubated with and without Lactobacillus rhamnosus GR-1 and then exposed to E. coli. Western blot analysis demonstrated increased expression of NLRP3 and NLRC4 following E. coli infection, but this increase was attenuated by pre-incubation with L. rhamnosus GR-1, regardless of ASC knockout. Western blot and immunofluorescence analyses revealed that pre-incubation with L. rhamnosus GR-1 decreased E. coli-induced caspase-1 activation at 6 h after E. coli infection, as also observed in ASC-knockout MAC-T cells. The E. coli-induced increase in caspase-4 mRNA expression was inhibited by pre-incubation with L. rhamnosus GR-1. ASC knockout diminished, but did not completely prevent, increased production of IL-1β and IL-18 and cell pyroptosis associated with E. coli infection, whereas pre-incubation with L. rhamnosus GR-1 inhibited this increase. Our data indicate that L. rhamnosus GR-1 suppresses activation of ASC-dependent NLRP3 and NLRC4 inflammasomes and production of downstream IL-lβ and IL-18 during E. coli infection. L. rhamnosus GR-1 also inhibited E. coli-induced cell pyroptosis, in part through attenuation of NLRC4 and non-canonical caspase-4 activation independently of ASC.
Rhizosphere Bacterial Communities Differ According to Fertilizer Regimes and Cabbage (Brassica oleracea var. capitata L.) Harvest Time, but Not Aphid Herbivory

Rhizosphere microbial communities are known to be highly diverse and strongly dependent on various attributes of the host plant, such as species, nutritional status, and growth stage. High-throughput 16S rRNA gene amplicon sequencing has been used to characterize the rhizosphere bacterial community of many important crop species, but this is the first study to date to characterize the bacterial and archaeal community of Brassica oleracea var. capitata. The study also tested the response of the bacterial community to fertilizer type (organic or synthetic) and N dosage (high or low), in addition to plant age (9 or 12 weeks) and aphid (Myzus persicae) herbivory (present/absent). The impact of aboveground herbivory on belowground microbial communities has received little attention in the literature, and since the type (organic or mineral) and amount of fertilizer applications are known to affect M. percicae populations, these treatments were applied at agricultural rates to test for synergistic effects on the soil bacterial community. Fertilizer type and plant growth were found to result in significantly different rhizosphere bacterial communities, while there was no effect of aphid herbivory. Several operational taxonomic units were identified as varying significantly in abundance between the treatment groups and age cohorts. These included members of the S-oxidizing genus Thiobacillus, which was significantly more abundant in organically fertilized 12-week-old cabbages, and the N-fixing cyanobacteria Phormidium, which appeared to decline in synthetically fertilized soils relative to controls. These responses may be an effect of accumulating root-derived glucosinolates in the B. oleracea rhizosphere and increased N-availability, respectively.
Multi-Scale Alignment and Contextual History for Attention Mechanism in Sequence-to-Sequence Model

A sequence-to-sequence model is a neural network module for mapping two sequences of different lengths. The sequence-to-sequence model has three core modules: encoder, decoder, and attention. Attention is the bridge that connects the encoder and decoder modules and improves model performance in many tasks. In this paper, we propose two ideas to improve sequence-to-sequence model performance by enhancing the attention module. First, we maintain the history of the location and the expected context from several previous time-steps. Second, we apply multiscale convolution from several previous attention vectors to the current decoder state. We utilized our proposed framework for sequence-to-sequence speech recognition and text-to-speech systems. The results reveal that our proposed extension can improve performance significantly compared to a standard attention baseline.
Loneliness, Depressive Symptoms, and Cognitive Functioning Among U.S. Chinese Older Adults

Objective: Loneliness has been associated with cognitive functioning in the general older adult population. Previous studies further indicate that loneliness has a strong association with depressive symptoms and the two constructs can reinforce each other to diminish well-being. However, such relationships have not been examined in U.S. Chinese older adults. This study attempts to bridge this knowledge gap. Method: Data were drawn from a population-based study of 3,159 U.S. Chinese older adults in the Greater Chicago area. Stepwise multivariate regression analyses were conducted to examine the relationship between loneliness, depressive symptoms, and global cognitive functioning. Results: Loneliness was associated with poor global cognitive functioning in U.S. Chinese older adults, though the relationship became nonsignificant after adjustment for depressive symptoms. The interaction term between loneliness and cognitive functioning was statistically significant (p < .01). The findings further highlight the importance of age, education, number of children, number of people in household, and length of residence in the U.S. in cognitive functioning among U.S. Chinese older adults. Discussion: The study findings indicate that loneliness and depressive symptoms act together to influence cognitive functioning in U.S. Chinese older adults. Research and clinical implications of the findings are discussed.
Genome-Wide Identification and Functional Analysis of NADPH Oxidase Family Genes in Wheat During Development and Environmental Stress Responses

As the key producers of reactive oxygen species (ROS), NADPH oxidases (NOXs), also known as respiratory burst oxidase homologs (RBOHs), play crucial roles in various biological processes in plants with considerable evolutionary selection and functional diversity in the entire terrestrial plant kingdom. However, only limited resources are available on the phylogenesis and functions of this gene family in wheat. Here, a total of 46 NOX family genes were identified in the wheat genome, and these NOXs could be classified into three subgroups: typical TaNOXs, TaNOX-likes, and ferric reduction oxidases (TaFROs). Phylogenetic analysis indicated that the typical TaNOXs might originate from TaFROs during evolution, and the TaFROs located on Chr 2 might be the most ancient forms of TaNOXs. TaNOXs are highly expressed in wheat with distinct tissue or organ-specificity and stress-inducible diversity. A large-scale expression and/or coexpression analysis demonstrated that TaNOXs can be divided into four functional groups with different expression patterns under a broad range of environmental stresses. Different TaNOXs are coexpressed with different sets of other genes, which widely participate in several important intracellular processes such as cell wall biosynthesis, defence response, and signal transduction, suggesting their vital but diversity of roles in plant growth regulation and stress responses of wheat.
Question Relevance in Visual Question Answering

Free-form and open-ended Visual Question Answering systems solve the problem of providing an accurate natural language answer to a question pertaining to an image. Current VQA systems do not evaluate if the posed question is relevant to the input image and hence provide nonsensical answers when posed with irrelevant questions to an image. In this paper, we solve the problem of identifying the relevance of the posed question to an image. We address the problem as two sub-problems. We first identify if the question is visual or not. If the question is visual, we then determine if it's relevant to the image or not. For the second problem, we generate a large dataset from existing visual question answering datasets in order to enable the training of complex architectures and model the relevance of a visual question to an image. We also compare the results of our Long Short-Term Memory Recurrent Neural Network based models to Logistic Regression, XGBoost and multi-layer perceptron based approaches to the problem.
From Prediction to Planning: Improving Software Quality with BELLTREE

The current generation of software analytics tools are mostly prediction algorithms (e.g. support vector machines, naive bayes, logistic regression, etc). While prediction is useful, after prediction comes planning about what actions to take in order to improve quality. This research seeks methods that generate demonstrably useful guidance on ''what to do'' within the context of a specific software project. Specifically, we propose XTREE (for within-project planning) and BELLTREE (for cross-project planning) to generating plans that can improve software quality. Each such plan has the property that, if followed, it reduces the probability of future defect reports. When compared to other planning algorithms from the SE literature, we find that this new approach is most effective at learning plans from one project, then applying those plans to another. In 10 open-source JAVA systems, several hundreds of defects were reduced in sections of the code that followed the plans generated by our planners. Further, we show that planning is possible across projects, which is particularly useful when there are no historical logs available for a particular project to generate plans from.
RGBiD-SLAM for Accurate Real-time Localisation and 3D Mapping

In this paper we present a complete SLAM system for RGB-D cameras, namely RGB-iD SLAM. The presented approach is a dense direct SLAM method with the main characteristic of working with the depth maps in inverse depth parametrisation for the routines of dense alignment or keyframe fusion. The system consists in 2 CPU threads working in parallel, which share the use of the GPU for dense alignment and keyframe fusion routines. The first thread is a front-end operating at frame rate, which processes every incoming frame from the RGB-D sensor to compute the incremental odometry and integrate it in a keyframe which is changed periodically following a covisibility-based strategy. The second thread is a back-end which receives keyframes from the front-end. This thread is in charge of segmenting the keyframes based on their structure, describing them using Bags of Words, trying to find potential loop closures with previous keyframes, and in such case perform pose-graph optimisation for trajectory correction. In addition, our system allows is able to compute the odometry both with unregistered and registered depth maps, allowing to use customised calibrations of the RGB-D sensor. As a consequence in the paper we also propose a detailed calibration pipeline to compute customised calibrations for particular RGB-D cameras. The experiments with our approach in the TUM RGB-D benchmark datasets show results superior in accuracy to the state-of-the-art in many of the sequences. The code has been made available on-line for research purposes this https URL.