Miracle-dz/UniFAR-FTUs huggingface dataset:
IL-6 Receptor Inhibition by Tocilizumab Attenuated Expression of C5a Receptor 1 and 2 in Non-ST-Elevation Myocardial Infarction

Background: Elevated interleukin-6 (IL-6) and complement activation are associated with detrimental effects of inflammation in coronary artery disease (CAD). The complement anaphylatoxins C5a and C3a interact with their receptors; the highly inflammatory C5aR1, and the C5aR2 and C3aR. We evaluated the effect of the IL-6 receptor (IL-6R)-antagonist tocilizumab on the expression of the anaphylatoxin receptors in whole blood from non-ST-elevation myocardial infarction (NSTEMI) patients. Separately, anaphylatoxin receptor expression in peripheral blood mononuclear cells (PBMC) from patients with different entities of CAD was investigated. Materials and Methods: NSTEMI patients were randomized to one dose of tocilizumab (n = 28) or placebo (n = 32) and observed for 6 months. Whole blood samples drawn at inclusion, at day 2, 3 and after 6 months were used for mRNA isolation. Plasma was prepared for analysis of complement activation measured as sC5b-9 by ELISA. Furthermore, patients with different CAD entities comprising stable angina pectoris (SAP, n = 22), non-ST-elevation acute coronary syndrome (NSTE-ACS, n = 21) and ST-elevation myocardial infarction (STEMI, n = 20) were included. PBMC was isolated from blood samples obtained at admission to hospital and mRNA isolated. Anaphylatoxin-receptor-expression was analyzed with qPCR using mRNA from whole blood and PBMC, respectively. Results: Our main findings were (i) Tocilizumab decreased C5aR1 and C5aR2 mRNA expression significantly (p < 0.001) and substantially (>50%) at day 2 and 3, whereas C3aR expression was unaffected. (ii) Tocilizumab did not affect complement activation. (iii) In analyzes of different CAD entities, C5aR1 expression was significantly increased in all CAD subgroups compared to controls with the highest level in the STEMI patients (p < 0.001). For C5aR2 and C3aR the expression compared to controls were more moderate with increased expression of C5aR2 in the STEMI group (p < 0.05) and C3aR in the NSTE-ACS group (p < 0.05). Conclusion: Expression of C5aR1 and C5aR2 in whole blood was significantly attenuated by IL-6R-inhibition in NSTEMI patients. These receptors were significantly upregulated in PBMC CAD patients with particularly high levels of C5aR1 in STEMI patients.
Prediction of Turn-taking Using Multitask Learning with Prediction of Backchannels and Fillers

We address prediction of turn-taking considering related behaviors such as backchannels and ﬁllers. Backchannels are used by the listeners to acknowledge that the current speaker can hold the turn. On the other hand, ﬁllers are used by the prospective speakers to indicate a will to take a turn. We propose a turn-taking model based on multitask learning in conjunction with prediction of backchannels and ﬁllers. The multitask learning of LSTM neural networks shared by these tasks allows for ef-ﬁcient and generalized learning, and thus improves prediction accuracy. Evaluations with two kinds of dialogue corpora of human-robot interaction demonstrate that the proposed multi-task learning scheme outperforms the conventional single-task learning.
Human Early Life Exposome (HELIX) study: a European population-based exposome cohort

Purpose Essential to exposome research is the collection of data on many environmental exposures from different domains in the same subjects. The aim of the Human Early Life Exposome (HELIX) study was to measure and describe multiple environmental exposures during early life (pregnancy and childhood) in a prospective cohort and associate these exposures with molecular omics signatures and child health outcomes. Here, we describe recruitment, measurements available and baseline data of the HELIX study populations. Participants The HELIX study represents a collaborative project across six established and ongoing longitudinal population-based birth cohort studies in six European countries (France, Greece, Lithuania, Norway, Spain and the UK). HELIX used a multilevel study design with the entire study population totalling 31 472 mother-child pairs, recruited during pregnancy, in the six existing cohorts (first level); a subcohort of 1301 mother-child pairs where biomarkers, omics signatures and child health outcomes were measured at age 6–11 years (second level) and repeat-sampling panel studies with around 150 children and 150 pregnant women aimed at collecting personal exposure data (third level). Findings to date Cohort data include urban environment, hazardous substances and lifestyle-related exposures for women during pregnancy and their offspring from birth until 6–11 years. Common, standardised protocols were used to collect biological samples, measure exposure biomarkers and omics signatures and assess child health across the six cohorts. Baseline data of the cohort show substantial variation in health outcomes and determinants between the six countries, for example, in family affluence levels, tobacco smoking, physical activity, dietary habits and prevalence of childhood obesity, asthma, allergies and attention deficit hyperactivity disorder. Future plans HELIX study results will inform on the early life exposome and its association with molecular omics signatures and child health outcomes. Cohort data are accessible for future research involving researchers external to the project.
Prediction of Perceived Speech Quality Using Deep Machine Listening

Subjective ratings of speech quality (SQ) are essential for evaluating algorithms for speech transmission and enhancement. In this paper we explore a non-intrusive model for SQ prediction based on the output of a deep neural net (DNN) from a regular automatic speech recognizer. The degradation of phoneme probabilities obtained from the net is quantiﬁed with the mean temporal distance proposed earlier for multi-stream ASR. The SQ predicted with this method is compared with average subject ratings from the TCD-VoIP speech quality database that covers several effects of SQ degradation that can occur in VoIP applications such as clipping, packet loss, echo effects, background noise, and competing speakers. Our approach is tailored to speech and therefore not applicable when quality is degraded by a competing speaker, which is reﬂected by an in-signiﬁcant correlation between model output and subjective SQ. In all other conditions mentioned above, the model reaches an average correlation of r = 0 . 87 , which is higher than the correlation achieved with the baseline ITU-T P.563 ( r = 0 . 71 ) and the American National Standard ANIQUE+ ( r = 0 . 75 ). Since the most robust ASR system is not necessarily the best model to predict SQ, we investigate the effect of the amount of training data on quality prediction.
The PPARγ Agonist Pioglitazone Fails to Alter the Abuse Potential of Heroin, But Does Reduce Heroin Craving and Anxiety

ABSTRACT Possibly through its effects on glia, the peroxisome proliferator-activated gamma receptor (PPARγ) agonist pioglitazone (PIO) has been shown to alter the effects of heroin in preclinical models. Until now, these results have not been assessed in humans. Heroin-dependent participants were randomized to either active (45 mg, n = 14) or placebo (0 mg, n = 16) PIO maintenance for the duration of the three-week study. After stabilization on buprenorphine (8 mg), participants began a two-week testing period. On the first to fourth test days, participants could self-administer drug or money by making verbal choices for either option. On the fifth day, active heroin and money were administered and participants could work to receive heroin or money using a progressive ratio choice procedure. Test days 6–10 were identical to test days 1–5 with the exception that, during one of the test weeks, placebo was available on the first four days, and during the other week heroin was available. PIO failed to alter the reinforcing or positive subjective effects of heroin, but it did reduce heroin craving and overall anxiety. Although we were unable to replicate the robust effects found in preclinical models, these data provide an indication of drug effects that deserves further exploration.
Non-Uniform Spectral Smoothing for Robust Children's Speech Recognition

Insufﬁcient spectral smoothing during front-end speech parametrization results in pitch-induced distortions in the short-time magnitude spectra. This, in turn, degrades the performance of an automatic speech recognition (ASR) system for high-pitched speakers. Motivated by this fact, a non-uniform spectral smoothing algorithm is proposed in this paper in order to mitigate the acoustic mismatch resulting from pitch differences. In the proposed technique, the speech utterance is ﬁrst segmented into vowel and non-vowel regions. The short-time magnitude spectrum obtained by discrete Fourier transform is then processed through a single-pole low-pass ﬁlter with different pole values for vowel and non-vowel regions. Sufﬁciently smoothed spectra is obtained by keeping higher values for the pole in the case of vowels while lower values are chosen for non-vowel regions. The Mel-frequency cepstral coefﬁcients computed using the derived smoothed spectra are observed to be less affected by pitch variations. In order to validate this claim, an ASR system is developed on speech from adult speakers and evaluated on a test set which consists of children’s speech to simulate large pitch differences. The experimental evaluations as well as signal domain analyses presented in this paper support the claim.
Investigations on Data Augmentation and Loss Functions for Deep Learning Based Speech-Background Separation

A successful deep learning-based method for separation of a speech signal from an interfering background audio signal is based on neural network prediction of time-frequency masks which multiply noisy signal’s short-time Fourier transform (STFT) to yield the STFT of an enhanced signal. In this paper, we investigate training strategies for mask-prediction based speech-background separation systems. First, we examine the impact of mixing speech and noise ﬁles on the ﬂy during training, which enables models to be trained on virtually inﬁnite amount of data. We also investigate the effect of using a novel signal-to-noise ratio related loss function, instead of mean-squared error which is prone to scaling differences among utterances. We evaluate bi-directional long-short term memory (BLSTM) networks as well as a combination of convolutional and BLSTM (CNN+BLSTM) networks for mask prediction and compare performances of real and complex-valued mask prediction. Data-augmented training combined with a novel loss function yields signiﬁcant improvements in signal to distortion ratio (SDR)and perceptual evaluation of speech quality (PESQ) as compared to the best published result on CHiME-2 medium vocabulary data set when using a CNN+BLSTM network.
The socket-shield technique and immediate implant placement

Healing of extraction socket leads to the dimensional changes which will affect the placement of the implant and its emergence profile. Atraumatic extraction, socket preservation technique, and immediate implant placement decrease the alveolar bone resorption by maintaining the postextraction socket. Hürzeler et al. introduced socket-shield technique by keeping a buccal fragment of the tooth to prevent the buccal cortical bone from resorption. This case report represents a 40-year-old male patient with grossly decayed maxillary left lateral incisor indicated for extraction replaced with an immediate implant. While examining cone-beam computed tomography, thin buccal cortical plate was noticed which may get fracture during extraction. Socket-shield technique was designed for implant placement to protect buccal bone and to get the proper esthetic form. Two millimeters buccal fragment of the tooth was kept attached and the implant was placed in contact with tooth fragment. Three-month follow-up shows proper healing, and healthy peri-implant tissue shows that socket-shield technique with immediate implant placement will be a good alternative to preserve buccal cortical plate and implant placement, especially in the esthetic area.
Video Summarization via Weighted Neighborhood Based Representation

The recent explosive growth of multimedia data has posed a new set of challenges in computer vision, and video summarization (VS) techniques are increasingly important to automatically summarize a large amount of multimedia data in an effective and efficient manner. Recent years have witnessed the rise and developments of sparse representation based approaches for VS. While the existing methods select keyframes according to the information contained in the single frame, and such a selection based solely on single-frame information may not be robust. Therefore, in this paper, the information of the single frame's neighborhood is taken into consideration, and different weights are assigned to these neighbouring frames. We formulate the VS problem as a weighted neighborhood based representation model, and design a greedy pursuit algorithm to extract keyframes. Experimental results on a benchmark dataset demonstrate that the proposed method can outperform the state of the arts.
Spatiotemporal Pyramid Pooling in 3D Convolutional Neural Networks for Action Recognition

Deep 3-dimensional convolutional networks (3D ConvNets) trained on large scale video datasets have achieved promising results on action recognition. This paper improves their performance by taking into account the spatiotemporal pyramid pooling. Specifically, we propose the spatiotemporal pyramid pooling layer to tackle the temporal variations of video sequences. Based on this layer, we develop a new network architecture, called STPP-net, by incorporating it with 3D ConvNets. The proposed network is robust to spatial and temporal variation of human actions and can generate a fixed-dimensional representation regardless of video size/scale. We show that our new network architecture outperforms the original 3D ConvNets by a large margin on three large-scale video classification/action recognition benchmarks including HMDB51, UCF101, and Kinetics.
Factorized Deep Neural Network Adaptation for Automatic Scoring of L2 Speech in English Speaking Tests

Speaker adaptation has been shown to be effective on speech recognition and evaluation of L2 speech. However, other factors, such as environments and foreign accents, can affect the speech signal in addition to speakers. Factorizing the speaker, environment and other acoustic factors is crucial in evaluating L2 speech to effectively reduce acoustic mismatch between train and test conditions. In this study, we investigate the effects of deep neural network factorized adaptation techniques on L2 speech assessment in real speaking tests. Through recognition and automatic scoring experiments on L2 speech, we demonstrate that factorized fMLLR and iVector based DNN adaptation can better utilize adaptation data to efficiently adapt to complex speaker and environment conditions. Combining the factored components of iVectors and fMLLR transforms can further improve robustness of DNN models in speech recognition and automatic scoring of L2 speech in dynamic environments.
Low Light Image Enhancement Using Grover'S Algorithm on Superposed Luminance Levels

Usually, limited information is available for enhancing low light images. We propose that presence of interactions between superposed luminance levels be exploited to draw reliable information for the enhancement. Grover's algorithm implements the interaction resulting in information passing between luminance levels, following which a noise-resilient transformation is employed. Qualitative and quantitative results depict that our proposal is capable of enhancing low light images better than the state-of-the-art.
Cross-Lingual Multi-Task Neural Architecture for Spoken Language Understanding

Cross-lingual spoken language understanding (SLU) systems traditionally require machine translation services for language portability and liberation from human supervision. However, restriction exists in parallel corpora and model architectures. Assuming reliable data are provided with human-supervision, which encourages non-parallel corpora and alleviate translation errors, this paper aims to explore cross-lingual knowledge transfer from multiple levels by taking advantage of neural architectures. We ﬁrst investigate a joint model of slot ﬁlling and intent determination for SLU, which alleviates the out-of-vocabulary problem and explicitly models dependencies be-tween output labels by combining character and word representations, bidirectional Long Short-Term Memory and conditional random ﬁelds together, while attention-based classiﬁer is introduced for intent determination. Knowledge transfer is further operated on character-level and sequence-level, aiming to share morphological and phonological information between languages with similar alphabets by sharing character representations, and characterize the sequence with language-general and language-speciﬁc knowledge adaptively acquired by separate encoders. Experimental results on the MIT-Restaurant-Corpus and the ATIS corpora in different languages demonstrate the effectiveness of the proposed methods.
Recent Selection on a Class I ADH Locus Distinguishes Southwest Asian Populations Including Ashkenazi Jews

The derived human alcohol dehydrogenase (ADH)1B*48His allele of the ADH1B Arg48His polymorphism (rs1229984) has been identified as one component of an East Asian specific core haplotype that underwent recent positive selection. Our study has been extended to Southwest Asia and additional markers in East Asia. Fst values (Sewall Wright’s fixation index) and long-range haplotype analyses identify a strong signature of selection not only in East Asian but also in Southwest Asian populations. However, except for the ADH2B*48His allele, different core haplotypes occur in Southwest Asia compared to East Asia and the extended haplotypes also differ. Thus, the ADH1B*48His allele, as part of a core haplotype of 10 kb, has undergone recent rapid increases in frequency independently in the two regions after divergence of the respective populations. Emergence of agriculture may be the common factor underlying the evident selection.
Vocalic, Lexical and Prosodic Cues for the INTERSPEECH 2018 Self-Assessed Affect Challenge

The INTERSPEECH 2018 Self-Assessed Affect Challenge consists in the prediction of the affective state of mind from speech. Experiments were conducted on the Ulm State-of-Mind in Speech database (USoMS) where subjects self-report their affective state. Dimensional representation of emotion (valence) is used for labeling. We have investigated cues related to the perception of the emotional valence according to three main relevant linguistic levels: phonetics, lexical and prosodic. For this purpose we studied: the degree-of-articulation, the voice quality, an affect lexicon and the expressive prosodic contours. For the phonetics level, a set of gender-dependent audio-features was computed on vowel analysis (voice quality and speech articulation measurements). At the lexical level, an affect lexicon was extracted from the automatic transcription of the USoMS database. This lexicon has been assessed for the Challenge task comparatively to a reference polarity lexicon. In order to detect expressive prosody, N-gram models of the prosodic contours were computed from an intonation labeling system. At last, an emotional valence classifier was designed combining ComParE and eGeMAPS feature sets with other phonetic, prosodic and lexical features. Experiments have shown an improvement of 2.4% on the Test set, compared to the baseline performance of the Challenge.
Learning Named Entity Tagger using Domain-Specific Dictionary

Recent advances in deep neural models allow us to build reliable named entity recognition (NER) systems without handcrafting features. However, such methods require large amounts of manually-labeled training data. There have been efforts on replacing human annotations with distant supervision (in conjunction with external dictionaries), but the generated noisy labels pose significant challenges on learning effective neural models. Here we propose two neural models to suit noisy distant supervision from the dictionary. First, under the traditional sequence labeling framework, we propose a revised fuzzy CRF layer to handle tokens with multiple possible labels. After identifying the nature of noisy labels in distant supervision, we go beyond the traditional framework and propose a novel, more effective neural model AutoNER with a new Tie or Break scheme. In addition, we discuss how to refine distant supervision for better NER performance. Extensive experiments on three benchmark datasets demonstrate that AutoNER achieves the best performance when only using dictionaries with no additional human effort, and delivers competitive results with state-of-the-art supervised benchmarks.
Compression of End-to-End Models

End-to-end models, which directly output text given speech using a single neural network, have been shown to be competitive with conventional speech recognition models containing separate acoustic, pronunciation, and language model components. Such models do not require additional resources for decoding and are typically much smaller than conventional models. This makes them particularly attractive in the context of on-device speech recognition where both small memory footprint and low power consumption are critical. This work explores the problem of compressing end-to-end models with the goal of satisfying device constraints without sacriﬁcing model accuracy. We evaluate matrix factorization, knowledge distillation, and parameter sparsity to determine the most effective methods given constraints such as a ﬁxed parameter budget.