Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-LL37 Antibodies Are Present in Psoriatic Arthritis (PsA) Patients: New Biomarkers in PsA

Psoriatic arthritis (PsA) is a chronic inflammatory arthritis associated with psoriasis. A third of psoriatic patients develop PsA via unknown mechanisms. No reliable diagnostic markers are available for PsA, or prognostic biomarkers for PsA development in psoriasis. We previously uncovered a pro-inflammatory role for cathelicidin LL37 in lesional psoriasis skin. LL37 binds nucleic acids and stimulates plasmacytoid/myeloid dendritic cells (pDC, mDCs) to secrete type I interferon (IFN-I) and pro-inflammatory factors. LL37 becomes an autoantigen for psoriatic Th1-Th17/CD8 T cells. Anti-LL37 antibodies were detected in systemic lupus erythematosus, an autoimmune disease characterized by neutrophil-extracellular-traps release (NETosis) in target organs. LL37 can be substrate of irreversible post-translational modifications, citrullination or carbamylation, linked to neutrophil activity. Here we analyzed inflammatory factors, included LL37, in PsA and psoriasis plasma and PsA synovial fluids (SF)/biopsies. We show that LL37 (as a product of infiltrating neutrophils) and autoantibodies to LL37 are elevated in PsA, but not OA SF. Anti-LL37 antibodies correlate with clinical inflammatory markers. Anti-carbamylated/citrullinated-LL37 antibodies are present in PsA SF/plasma and, at lower extent, in psoriasis plasma, but not in controls. Plasma anti-carbamylated-LL37 antibodies correlate with PsA (DAS44) but not psoriasis (PASI) disease activity. Ectopic lymphoid structures, and deposition of immunoglobulin-(Ig)G-complexes (IC) co-localizing with infiltrating neutrophils, are observed in PsA and not OA synovial tissues (ST). Activated complement (C5a, C9), GM-CSF and IFN-I are up-regulated in PsA and not OA synovia and in PsA and psoriasis plasma but not in HD. C9 and GM-CSF levels in PsA SF correlate with clinical inflammatory markers and DAS44 (C9) and with anti-carbamylated/citrullinated-LL37 antibodies (GM-CSF and IFN-I). Thus, we uncover a role for LL37 as a novel PsA autoantibody target and correlation studies suggest participation of anti-LL37 antibodies to PsA pathogenesis. Notably, plasma antibodies to carbamylated-LL37, which correlate with DAS44, suggest their use as new disease activity markers. GM-CSF and complement C5a and C9 elevation may be responsible for autoantigens release by neutrophils and their modification, fueling inflammation and autoreactivity establishment. Finally, targeting GM-CSF, C5a, C9 can be beneficial in PsA.
Time Aggregation Operators for Multi-label Audio Event Detection

In this paper, we present a state-of-the-art system for audio event detection. The labels on the training (and evaluation) data specify the set of events occurring in each audio clip, but neither the time spans nor the order in which they occur. Speciﬁcally, our task of weakly supervised learning is the “Detection and Classiﬁcation of Acoustic Scenes and Events (DCASE) 2017” challenge [5]. We use the winning entry in this challenge given by Xu et al. [10] as our starting point and identify several important modiﬁcations that allow us to improve on their results signiﬁcantly. Our techniques pertain to aggregation and consolidation over time and frequency signals over a (temporal) sequence before decoding the labels. In general, our work is also relevant to other tasks involving learning from weak labeling of sequential data.
Empirical Analysis of Score Fusion Application to Combined Neural Networks for Open Vocabulary Spoken Term Detection

System combination, which combines the outputs of multiple systems or internal representations, is a powerful method to improve the performance of machine learning tasks and has been widely adopted in recent knowledge transfer learning. In this study, to describe how to extract effective knowledge from an ensemble of neural networks, we ﬁrst examine several score fusions from an ensemble of neural networks tasked with open vocabulary spoken term detection, where the class probability of the neural network is utilized as a similarity metric; then, we investigate the trade-off between confusion and dark knowledge. From the experimental evaluation on open vocabulary spoken term detection, we obtain 2.09% absolute gain as compared to the best result from single systems. Furthermore, the performance gains achieved via score fusion of class probabilities exactly match the mathematical inequality for sum and power means results, and that the gain achieved via summation of class probabilities is consistently better than that achieved via score fusion of power means. The experimental analysis conﬁrms that summation, which enhances the discriminative capability of the superior class probability, can implement smoothed probability distribution to yield more effective dark knowledge, while adequately suppressing undesirable effects.
Factorized Q-learning for large-scale multi-agent systems

Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to the explosion of action space resulting from the complex dynamics between the environment and the agents. In this paper, we propose to make the computation of multi-agent Q-learning tractable by treating the Q-function (w.r.t. state and joint-action) as a high-order high-dimensional tensor and then approximate it with factorized pairwise interactions. Furthermore, we utilize a composite deep neural network architecture for computing the factorized Q-function, share the model parameters among all the agents within the same group, and estimate the agents' optimal joint actions through a coordinate descent type algorithm. All these simplifications greatly reduce the model complexity and accelerate the learning process. Extensive experiments on two different multi-agent problems demonstrate the performance gain of our proposed approach in comparison with strong baselines, particularly when there are a large number of agents.
Lightly Supervised vs. Semi-supervised Training of Acoustic Model on Luxembourgish for Low-resource Automatic Speech Recognition

In this work, we focus on exploiting ‘inexpensive’ data in order to to improve the DNN acoustic model for ASR. We explore two strategies: The ﬁrst one uses untranscribed data from the target domain. The second one is related to the proper selection of excerpts from imperfectly transcribed out-of-domain public data, as parliamentary speeches. We found out that both approaches lead to similar results, making them equally ben-eﬁcial for practical use. The Luxembourgish ASR seed sys-tem had a 38 . 8 % WER and it improved by roughly 4 % absolute, leading to 34 . 6 % for untranscribed and 34 . 9 % for lightly-supervised data. Adding both databases simultaneously led to 34 . 4 % WER, which is only a small improvement. As a secondary research topic, we experiment with semi-supervised state-level minimum Bayes risk (sMBR) training. Nonetheless, for sMBR we saw no improvement from adding the automatically transcribed target data, despite that similar techniques yield good results in the case of cross-entropy (CE) training.
Sexual abuse in very young children: a psychological assessment in the Amsterdam Sexual Abuse Case study

ABSTRACT Background: Child sexual abuse (CSA) is a worldwide problem affecting children of all ages and socioeconomic backgrounds. A knowledge gap exists regarding the psychological outcomes for children, boys in particular, who are abused during their early lives. Objective: To provide a descriptive psychological profile of children who experienced sexual abuse as infants or toddlers from a male daycare worker and babysitter, and to assess the psychopathological impact on their parents. Method: Parents of children involved in the Amsterdam Sexual Abuse Case (41 parents; 44 children, age range 3–11 years, 30 boys, 14 girls) completed measures on post-traumatic stress disorder (PTSD), dissociation, sexual and non-sexual behaviour problems, and attachment insecurity in their children, as well as on parental psychological well-being, 3 years after disclosure. Sexual abuse characteristics were obtained from police records. Results: We found that 3% of confirmed child victims had PTSD, 30% sexual behaviour problems, 24% internalizing problems, 27% attachment insecurity, and 18% any psychiatric disorder (including PTSD); 39% were asymptomatic. In parents, we found feelings of guilt, shame, and anger about the abuse of their child; 19% showed PTSD symptoms and 3% showed avoidant and 8% anxious attachment problems in their intimate relationship. Parental symptomatology was related to child symptomatology, except for child sexual behaviour problems. One-quarter of confirmed child victims and 45% of parents had received psychological treatment. Conclusions: Three years after disclosure, extrafamilial CSA in very young children was associated with sexual and non-sexual behaviour problems and attachment insecurity, but rarely with PTSD or dissociation. For parents it was associated with PTSD symptoms and emotional reactions. Assessments and interventions should focus on the wide spectrum of problems that follow CSA, as well as on parental psychopathology and the parent–child relationship. Future follow-up assessments in our longitudinal study should provide insights into longer-term outcomes.
Joint Learning of J-Vector Extractor and Joint Bayesian Model for Text Dependent Speaker Verification

J-vector and joint Bayesian have been proved to be very effective in text dependent speaker veriﬁcation with short-duration speech. However current state-of-the-art framework often consider training the J-vector extractor and the joint Bayesian classiﬁer separately. Such an approach will result in information loss for j-vector learning and also fail to exploit an end-to-end framework. In this paper we present a integrated approach to text dependent speaker veriﬁcation, which consists of a siamese deep neural network that takes two variable length speech segments and maps them to the likelihood score and speaker/phrase labels, where the likelihood score as a loss guide is computed by a variant joint Bayesian model. The likelihood loss guide can constrain the j-vector extractor for improving the veriﬁcation performance. Since the strengths of j-vector and joint Bayesian analysis appear complementary the joint learning signiﬁcantly outperforms traditional separate training scheme. Our experiments on the the public RSR2015 part I data corpus demonstrate that this new training scheme can produce more discriminative j-vectors and leading to performance improvement on the speaker veriﬁcation task.
Synthesizing Photo-Realistic 3D Talking Head: Learning Lip Synchronicity and Emotion from Audio and Video

We propose a speech driven emotional 3D talking head. First, based on face prior knowledge, an individual head model is reconstructed by integrating the internal articulators from Magnetic Resonance Imaging slices with the appearance from visible images. Second, the blendshapes of each phoneme and the facial expression are synthesized by statistical learning. Third, visual coarticulation is modeled by training a deep neural network on parallel audio-visual data. Finally, the articulatory animations of continuous phonemes are fused by visual co articulation model and facial expressions to produce expressive speech synchronized animations. Experiments show that the system can not only distinguish the visual differences among phonemes in real-time, but also significantly increase the ability of human perception.
Image Fusion and Reconstruction of Compressed Data: A Joint Approach

In the context of data fusion, pansharpening refers to the combination of a panchromatic (PAN) and a multispectral (MS) image, aimed at generating an image that features both the high spatial resolution of the former and high spectral diversity of the latter. In this work we present a model to jointly solve the problem of data fusion and reconstruction of a compressed image; the latter is envisioned to be generated solely with optical on-board instruments, and stored in place of the original sources. The burden of data downlink is hence significantly reduced at the expense of a more laborious analysis done at the ground segment to estimate the missing information. The reconstruction algorithm estimates the target sharpened image directly instead of decompressing the original sources beforehand; a viable and practical novel solution is also introduced to show the effectiveness of the approach.
Locality-Based Discriminant Feature Selection with Trace Ratio

Feature selection plays an important role to select the informative and valuable features especially in high-dimensional data. However, some conventional feature selection methods select the features according to a feature subset score, which are often time-consuming, not quite robust to noise and neglecting the local data structure. To address this problem, we propose a novel feature selection approach, namely locality-based discriminant feature selection with trace ratio (LDFS), which can perform local data structure learning, and feature selection simultaneously. Furthermore, the proposed approach is robust to data noise and can pick out genuinely valuable features. In the end, experimental results on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.
Multiple Skip Connections of Dilated Convolution Network for Semantic Segmentation

Semantic segmentation is a task to estimate class for each pixel. This task also have received benefit from the deep ConvNet and it has achieves high accuracy. In the semantic segmentation from in-vehicle camera image, the object size such as a pedestrian or a vehicle fluctuates according to the distance from the camera. We propose scale aware semantic segmentation method especially small object. The contributions of the method are 1) to feed the features of small region by multiple skip connections, 2) to extract context from multiple receptive field by multiple dilated convolution blocks. The proposed method has achieved high accuracy in the Cityscapes dataset. The comparison with state-of-the-art methods, it has achieved the comparable performance at category IoU and iIoU metrics.
Comparison of non-volatile and volatile flavor compounds in six Pleurotus mushrooms.

BACKGROUND Non-volatile and volatile flavor compounds of six Pleurotus mushrooms including Pleurotus citrinopileatus, P. cornucopiae, P. djamor, P. floridanus, P. ostreatus and P. sapidus were studied. RESULTS The content of total free amino acids ranged from 21.80 to 40.60 g kg-1 and the content of monosodium glutamate (MSG)-like amino acids ranged from 3.10 to 8.64 g kg-1 . The content of total 5'-nucleotides ranged from 4.16 to 8.80 g kg-1 while the content of flavor 5'-nucleotides ranged from 2.00 to 4.51 g kg-1 . Sixty-three volatile compounds were identified in six Pleurotus mushrooms, including 17 aldehydes, 10 ketones, 14 alcohols, 2 ethers, 5 acids, 5 hydrocarbons, 10 heterocyclic and aromatic compounds. 1-Octen-3-one and 1-octen-3-ol were the key odor compounds in P. citrinopileatus, P. djamor, P. ostreatus, P. floridanus and P. sapidus, while 1-octen-3-one, 1-octen-3-ol and 2-octenal were the key odor compounds in P. cornucopiae. CONCLUSION Pleurotus citrinopileatus had highest content of total free amino acids (40.60 g kg-1 ), total 5'-nucleotides (8.80 g kg-1 ) and flavor 5'-nucleotides (4.51 g kg-1 ) than other Pleurotus mushrooms. Moreover, eight-carbon compounds were the most abundant compounds in six Pleurotus mushrooms. Our study should be helpful in promoting the cultivation and consumption of these Pleurotus mushrooms. © 2018 Society of Chemical Industry.
Mosaicing of Images with Few Textures and Strong Illumination Changes: Application to Gastroscopic Scenes

This paper introduces a robust image mosaicing method. A variational optical flow (OF) method is first proposed to deal with scenes exhibiting strong specular reflections and few texture information. Then, a general form of descriptors invariant to complex illumination variations is given from which a novel descriptor is obtained. Non-linear transformations computed with the OF fields between the images are used to construct the mosaics. Experimental results demonstrate that the proposed method leads to coherent mosaics, even for complex gastroscopic image sequences. Furthermore, it can be potentially used to build mosaics for non-medical scenes.
Identity and role of the non-conserved acid/base catalytic residue in the GH29 fucosidase from the spider Nephilingis cruentata

α-l-Fucosidases are widely occurring enzymes that remove fucose residues from N- and O-fucosylated glycoproteins. Comparison of amino acid sequences of fucosidases reveals that although the nucleophile is conserved among all α-l-fucosidases, the position of the acid/base residue is quite variable. Although several site-directed mutation studies have previously been performed on bacterial fucosidases, the only eukaryotic fucosidase so studied was the human fucosidase. Recent alignments indicate that human and Arthropoda α-l-fucosidases share at least 50% identity and the acid/base residue seems to be conserved among them suggesting a common acid/base residue in Metazoa. Here we describe the cloning and expression in Pichia pastoris of a very active α-l-fucosidase from the spider Nephilingis cruentata (NcFuc) with a Km value for pNPFuc of 0.4 mM. NcFuc hydrolyzed fucoidan, 2´fucosyllactose and also lacto-N-difucohexaose II. Mutants modified at the conserved residues D214N, E209A, E59A were expressed and characterized. The 500-fold lower kcat of D214N than the wild type was consistent with a role in catalysis, as was the 8000-fold lower kcat value of E59A. This was supported by the 57-fold increase in the kcat of E59A upon addition of azide. A complex pH/rate profile was seen for the wild-type and mutant forms of NcFuc, similar to those measured previously for the Sulfolobus fucosidase. The non-conservative catalytic structure and distinct active site organization reinforce the necessity of structural studies of new fucosidases.
Have Studies that Measure Lumbar Kinematics and Muscle Activity Concurrently during Sagittal Bending Improved Understanding of Spinal Stability and Sub-System Interactions? A Systematic Review

In order to improve understanding of the complex interactions between spinal sub-systems (i.e., the passive (ligaments, discs, fascia and bones), the active (muscles and tendons) and the neural control systems), it is necessary to take a dynamic approach that incorporates the measurement of multiple systems concurrently. There are currently no reviews of studies that have investigated dynamic sagittal bending movements using a combination of electromyography (EMG) and lumbar kinematic measurements. As such it is not clear how understanding of spinal stability concepts has advanced with regards to this functional movement of the spine. The primary aim of this review was therefore to evaluate how such studies have contributed to improved understanding of lumbar spinal stability mechanisms. PubMed and Cochrane databases were searched using combinations of the keywords related to spinal stability and sagittal bending tasks, using strict inclusion and exclusion criteria and adhering to PRISMA guidelines. Whilst examples of the interactions between the passive and active sub-systems were shown, typically small sample sizes meant that results were not generalizable. The majority of studies used regional kinematic measurements, and whilst this was appropriate in terms of individual study aims, the studies could not provide insight into sub-system interaction at the level of the spinal motion segment. In addition, the heterogeneity in methodologies made comparison between studies difficult. The review suggests that since Panjabi’s seminal spinal control papers, only limited advancement in the understanding of these theories has been provided by the studies under review, particularly at an inter-segmental level. This lack of progression indicates a requirement for new research approaches that incorporate multiple system measurements at a motion segment level.
Fused Discriminative Metric Learning for Low Resolution Pedestrian Detection

Low resolution (LR) is one of the most challenging factor in pedestrian detection. In this paper, we propose a fused discriminative metric learning (F-DML) approach for low resolution pedestrian detection without explicit super resolution. We firstly learn a discriminative high resolution (HR) feature space as target space. Then, an optimal Mahanalobis metric is learned to transform the LR feature space into a new LR classification space, which largely preserves the discriminative structure of the HR feature space. Finally, a weighted K-nearest neighbors classifier is applied in the LR classification space which inherits good discrimination from HR feature space. A new training strategy is proposed to find the fewest and most representative LR-HR exemplars. In addition, we build a new dataset for the evaluation of low resolution pedestrian detection methods. Extensive experimental results demonstrate that the proposed approach performs favorably against the state-of-the-art methods.
Tissues Classification for Pressure Ulcer Images Based on 3D Convolutional Neural Network

Pressure ulcer (PU) is a type of chronic wounds (CWs), which is remaining unhealed for a period longer than six weeks. PU results from applying pressure and friction on the skin of the patient for a long time. It has a complex structure as it has different kinds of tissues. Reliable assessment of PU is essential to the success of the treatment and care decision. In this paper, we propose a tissue classification system for PU based on 3D convolutional neural network (CNN). The main idea of the proposed system is to provide a 3D CNN network with five different models of the colored PU RGB images to accurately segment slough, granulation, and necrotic eschar tissues. Each model of the PU RGB image is provided to the CNN as an independent pathway. The first and second models are the original RGB PU images and its equivalent HSV images. The third model is the smoothed image by convolving the original image with a preselected Gaussian kernel. The last two models are the first-order models of the current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The proposed system was trained and tested on 193 color PU images. The proposed tissue segmentation system is evaluated by using three various metrics, which are the area under the ROC curve (AUC), the Dice similarity coefficient (DSC), and the percentage area distance (PAD). The system achieved an average AUC equals to 95%, DSC equals to 92%, and PAD equals to 10%, which are a promising result.
End-To-End Chromosome Karyotyping with Data Augmentation Using GAN

Classifying human chromosomes from input cell images, i.e., karyotyping, requires domain expertise and quantity of manual effort to perform. In this paper, we propose an end-to-end chromosome karyotyping method, which can automatically detect, segment and classify chromosomes from cell images. During detection, we explore Extremal Regions (ER) to obtain chromosome candidates in input images. During segmentation, we segment overlapping chromosome candidates by approximating chromosome shapes with eclipses. In classification, we first propose Multiple Distribution Generative Advertising Network (MD-GAN) to effectively cover diverse data modes and generate more labeled samples for data augmentation. Then, we finetune pre-trained convolutional neural network (CNN) to classify chromosomes with samples generated by MD-GAN. We demonstrate the accuracy of the proposed end-to-end method in detecting, segmenting and classifying by experiments on a self-collected dataset. Experiments also prove data augmentation with MD-GAN could improve classification performance of CNN.