Miracle-dz/UniFAR-FTUs huggingface dataset:
Gene Variants Associated With Venous Thrombosis: A Replication Study in a Brazilian Multicentre Study

Single nucleotide polymorphisms (SNP) associated with Venous Thromboembolism (VTE) risk have been identified in European and American populations. Replicate SNPs associated with VTE in a Brazilian multicenter case-control study of the Southeast region. Patients with previous VTE assisted at the Outpatient Clinics of 3 centers of the Southeast Brazilian region were compared to normal controls of the same geographic region. We evaluated 29 SNPs associated with VTE risk in other populations, and 90 SNPs for stratification analysis of the population. Due to high admixture of Brazilian population and lack of previous studies, the calculation of the sample power was performed after genotyping. Sample size, allelic frequency and Hardy-Weinberg equilibrium were estimated. The association and odds ratio analyses were estimated by logistic regression and the results were adjusted for multiple tests using Bonferroni correction. The evaluation of the genetic structure similarity in the cases and controls was performed by AMOVA. 436 cases and 430 controls were included. It was demonstrated that this sample has a statistical power to detect a genetic association of 79.4%. AMOVA showed that the genetic variability between groups was 0.0% and 100% within each group. None of the SNPs showed association with VTE in our population. A Brazilian multicenter case-control study with adequate sample power, high genetic variability though no stratification between groups, showed no replication of SNPs associated with VTE. The high admixture of Brazilian population may be responsible for these results, emphasizing the influence of the population genetic structure in association studies.
Phospho-Mimetic Mutation at Ser602 Inactivates Human TRPA1 Channel

The Transient Receptor Potential Ankyrin 1 (TRPA1) channel is an integrative molecular sensor for detecting environmental irritant compounds, endogenous proalgesic and inflammatory agents, pressure, and temperature. Different post-translational modifications participate in the discrimination of the essential functions of TRPA1 in its physiological environment, but the underlying structural bases are poorly understood. Here, we explored the role of the cytosolic N-terminal residue Ser602 located near a functionally important allosteric coupling domain as a potential target of phosphorylation. The phosphomimetic mutation S602D completely abrogated channel activation, whereas the phosphonull mutations S602G and S602N produced a fully functional channel. Using mutagenesis, electrophysiology, and molecular simulations, we investigated the possible structural impact of a modification (mutation or phosphorylation) of Ser602 and found that this residue represents an important regulatory site through which the intracellular signaling cascades may act to reversibly restrict or “dampen” the conformational space of the TRPA1 channel and promote its transitions to the closed state.
Osteoporosis and osteoblasts cocultured with adipocytes inhibit osteoblast differentiation by downregulating histone acetylation

Osteoporosis is characterized by decreased bone mass and adipocyte accumulation within the bone marrow that inhibits osteoblast maturation, leading to a high risk of fractures. Thus, we hypothesized that osteoblasts, besides being negatively affected by interacting with adipocytes, reduce the differentiation of neighboring osteoblasts through the same mechanisms that affect osteoblasts under osteoporotic conditions. We investigated the effect of osteoporosis on osteoblast differentiation and the effect of the conditioned medium of osteoblasts cocultured with adipocytes on the differentiation of other osteoblasts. Osteoporosis was induced by orchiectomy in rats and bone marrow mesenchymal stromal cells (MSCs) were differentiated into osteoblasts. Also, the bone marrow and adipose tissue MSCs were obtained from healthy rats and differentiated into osteoblasts and adipocytes, respectively. Messenger RNA expression, in situ alkaline phosphatase activity, and mineralization confirmed the inhibitory effect of osteoporosis on osteoblast differentiation. This harmful effect was mimicked by the in vitro model using the conditioned medium and it was demonstrated that osteoblasts keep the memory of the negative impact of interacting with adipocytes, revealing an unknown mechanism relevant to the osteoporotic bone loss. Finally, we showed the involvement of acetyl‐histone 3 (AcH3) in bone homeostasis as its reduction induced by osteoporosis and conditioned medium impaired osteoblast differentiation. The AcH3 involvement was proved by treating osteoblasts with Trichostatin A that recovered the AcH3 expression and osteoblast differentiation capacity in both situations. Together, our findings indicated that AcH3 might be a target for future studies focused on epigenetic‐based therapies to treat bone diseases.
Mammary gland health of Santa Inês ewes at the drying and puerperium and evaluation of a dry-off terapy with gentamicin

ABSTRACT: Mastitis represents an important health problem for Santa Inês breed, causing losses to the producer, due to loss of ewes or the decrease in weight gain of lambs. The aim of this work was to assess the health of the mammary gland of Santa Inês ewes at the drying and puerperium and to investigate the efficacy of a dry-off therapy with gentamicin. In this study, 64 ewes were divided in a control group (GC) and treatment group (GT), and the health of the mammary gland was assessed at the drying and puerperium. The GT ewes received 250mg of gentamicin (Gentocin® DryCow/Schering-Plough, product indicated for use in dairy cows) in each mammary half. For diagnosis, clinical examination, California Mastitis Test, somatic cell count and milk culture was performed. In the GC, of the 45 (70.3%) healthy mammary halves at the drying, 12 developed subclinical mastitis and nine clinical mastitis at the puerperium. In the GT, among 51 (79.7%) healthy mammary halves at the drying, six developed subclinical mastitis and 11 clinical mastitis at the puerperium. No association was observed between treatment and the occurrence of mastitis at puerperium. Of the 19 (29.7%) mammary halves of the GC that presented subclinical mastitis at the drying, three remained with subclinical mastitis and five developed clinical mastitis at the puerperium. In the GT, of the 13 (20.3%) mammary halves that had subclinical mastitis at the drying, four remained with subclinical mastitis and four developed clinical mastitis. No association was observed between treatment and cure or persistence of mastitis at the puerperium. The main microorganisms isolated, at the drying and puerperium, from animals with subclinical or clinical mastitis were Staphylococcus spp. , predominantly coagulase negative Staphylococcus (CSN). At the puerperium, 29 cases of clinical mastitis occurred, 19 with isolation, where 10 were CNS and six S. aureus. Mannheimia haemolytica was isolated in one case of subclinical mastitis and other of clinical mastitis. News protocols and different ways of handling at drying and at puerperium must be investigated.
Leishmania-infected macrophages release extracellular vesicles that can promote lesion development

Macrophages infected with Leishmania donovani release extracellular vesicles that are composed of parasite and host-derived molecules that have the potential to induce vascular changes in tissues. Leishmania donovani infection of macrophages results in quantitative and qualitative changes in the protein profile of extracellular vesicles (EVs) released by the infected host cells. We confirmed mass spectrometry results orthogonally by performing Western blots for several Leishmania-infected macrophage-enriched EVs (LieEVs) molecules. Several host cell proteins in LieEVs have been implicated in promoting vascular changes in other systems. We also identified 59 parasite-derived proteins in LieEVs, including a putative L. donovani homolog of mammalian vasohibins (LdVash), which in mammals promotes angiogenesis. We developed a transgenic parasite that expressed an endogenously tagged LdVash/mNeonGreen (mNG) and confirmed that LdVash/mNG is indeed expressed in infected macrophages and in LieEVs. We further observed that LieEVs induce endothelial cells to release angiogenesis promoting mediators including IL-8, G-CSF/CSF-3, and VEGF-A. In addition, LieEVs induce epithelial cell migration and tube formation by endothelial cells in surrogate angiogenesis assays. Taken together, these studies show that Leishmania infection alters the composition of EVs from infected cells and suggest that LieEVs may play a role in the promotion of vascularization of Leishmania infections.
Countertransference in Forensic Inpatient Settings: An Empirical Examination of Therapist Responses to Patients With Psychotic Disorders.

OBJECTIVE Countertransference in forensic inpatient settings has received little empirical attention despite frequent emotional reactions in staff members, such as anger, disgust, or fear. In this exploratory study, we investigated countertransference in two forensic medium-secure units for patients with psychotic disorders. METHOD We measured countertransference using the Therapist Response Questionnaire and measured staff personality using the Ten-Item Personality Inventory. Our design allowed all staff members to participate anonymously. RESULTS One hundred thirty-four Therapist Response Questionnaire forms, along with data on patient and staff characteristics, were collected. Staff characteristics such as profession, experience, and personality were associated with different countertransference reactions. Psychologists and psychiatrists tended to report more countertransference feelings than nursing staff. Patient and staff variables (such as patient having committed violent offenses or a diagnosis of personality disorder and staff experience or gender) were associated with more negative countertransference feelings and subscale scores as well as less positive countertransference feelings such as parental, protective, and satisfying countertransference feelings. Some patient and staff variables (such as patient cooperativeness, staff personality trait agreeableness) had the inverse effect on countertransference feelings. CONCLUSION We discussed several conceptual problems inherent to measuring countertransference (in forensic inpatient settings) and the clinical implications of our findings.
Adaptive Speaker Normalization for CTC-Based Speech Recognition

In this paper, we propose a new speaker normalization technique for acoustic model adaptation in connectionist temporal classiﬁcation (CTC)-based automatic speech recognition. In the proposed method, for the inputs of a hidden layer, the mean and variance of each activation are ﬁrst estimated at the speaker level. Then, we normalize each speaker representation independently by making them follow a standard normal distribution. Furthermore, we propose using an auxiliary network to dynamically generate the scaling and shifting parameters of speaker normalization, and an attention mechanism is introduced to improve performance. The experiments are conducted on the public Chinese dataset AISHELL-1. Our proposed methods present high effectiveness in adapting the CTC model, achieving up to 17.5% character error rate improvement over the speaker-independent (SI) model.
Hierarchical Multi-Stage Word-to-Grapheme Named Entity Corrector for Automatic Speech Recognition

In this paper, we propose a hierarchical multi-stage word-to-grapheme Named Entity Correction (NEC) algorithm. Conventional NEC algorithms use a single-stage grapheme or phoneme level edit distance to search and replace Named Entities (NEs) misrecognized by a speech recognizer. However, longer named entities like song titles cannot be easily handled by such a single stage correction. We propose a three-stage NEC, starting with a word-level matching, followed by a phonetic double meta-phone based matching, and a ﬁnal grapheme level candidate selection. We also propose a novel NE Rejection mechanism which is important to ensure that the NEC does not replace correctly recognized NEs with unintended but similar named entities. We evaluate our solution on two different test sets from the call and music domains, for both server as well as on-device speech recognition conﬁgurations. For the on-device model, our NEC outperforms an n-gram fusion when employed standalone. Our NEC reduces the word error rate by 14% and 63% relatively for music and call , respectively, when used after an n-gram based biasing language model. The average latency of our NEC is under 3 ms per input sentence while using only ∼ 1 MB for an input NE list of 20,000 entries.
Perception of Privacy Measured in the Crowd - Paired Comparison on the Effect of Background Noises

Voice based devices and virtual assistants are widely integrated into our daily life, but the growing popularity has also raised concerns about data privacy in processing and storage. While improvements in technology and data protection regulations have been made to provide users a more secure experience, the concept of privacy continues to be subject to enormous challenges. We can observe that people intuitively adjust their way of talking in a human-to-human conversation, an intuition that devices could beneﬁt from to increase their level of privacy. In order to enable devices to quantify privacy in an acoustic scenario, this paper focuses on how people perceive privacy with respect to environmental noise. We measured privacy scores on a crowdsourcing platform with a paired comparison listening test and obtained reliable and consistent results. Our measurements show that the experience of privacy varies depending on the acoustic features of the ambient noise. Furthermore, multiple probabilistic choice models were ﬁtted to the data to obtain a meaningful ordering of noise scenarios conveying listeners’ preferences. A preference tree model was found to ﬁt best, indicating that subjects change their decision strategy depending on the scenarios under test.
Robust Raw Waveform Speech Recognition Using Relevance Weighted Representations

Speech recognition in noisy and channel distorted scenarios is often challenging as the current acoustic modeling schemes are not adaptive to the changes in the signal distribution in the presence of noise. In this work, we develop a novel acoustic modeling framework for noise robust speech recognition based on relevance weighting mechanism. The relevance weighting is achieved using a sub-network approach that performs feature selection. A relevance sub-network is applied on the output of first layer of a convolutional network model operating on raw speech signals while a second relevance sub-network is applied on the second convolutional layer output. The relevance weights for the first layer correspond to an acoustic filterbank selection while the relevance weights in the second layer perform modulation filter selection. The model is trained for a speech recognition task on noisy and reverberant speech. The speech recognition experiments on multiple datasets (Aurora-4, CHiME-3, VOiCES) reveal that the incorporation of relevance weighting in the neural network architecture improves the speech recognition word error rates significantly (average relative improvements of 10% over the baseline systems)
Perceptual Autoencoder for Compressive Sensing Image Reconstruction

This paper presents a non-iterative deep learning approach to compressive sensing (CS) image reconstruction using a convolutional autoencoder and a residual learning network. An efficient measurement design is proposed in order to enable training of the compressive sensing models on normalized and mean-centred measurements, along with a practical network initialization method based on principal component analysis (PCA). Finally, perceptual residual learning is proposed in order to obtain semantically informative image reconstructions along with high pixel-wise reconstruction accuracy at low measurement rates.
Improving the Performance of Acoustic-to-Articulatory Inversion by Removing the Training Loss of Noncritical Portions of Articulatory Channels Dynamically

For decades, average Root Mean Square Error (RMSE) over all the articulatory channels is one of the most prevalent cost functions for training statistical models for the task of acoustic-to-articulatory inversion (AAI). One of the underlying assumptions is that the samples of all the articulatory channels used for training are balanced and play the same role in AAI. However, this is not true from speech production point view. In this study, at each time instant, each articulatory channel is classified to be critical or noncritical according to their roles in the formation of constrictions along the vocal tract when producing speech sound. It is found that the training set is dominated by the samples of noncritical articulatory channels. To deal with the unbalanced dataset problem, several Bi-LSTM networks are trained by removing the of noncritical portions of each articulatory channels if the training errors are less than some dynamic threshold. The results indicate that the average RMSE over all the articulatory channels, the average RMSE over the critical articulators, and the average RMSE over the noncritical articulators can be reduced significantly by the proposed method.
Knowledge Distillation from Offline to Streaming RNN Transducer for End-to-End Speech Recognition

End-to-end training of recurrent neural network transducers (RNN-Ts) does not require frame-level alignments between audio and output symbols. Because of that, the posterior lattices defined by the predictive distributions from different RNN-Ts trained on the same data can differ a lot, which poses a new set of challenges in knowledge distillation between such models. These discrepancies are especially prominent in the posterior lattices between an offline model and a streaming model, which can be expected from the fact that the streaming RNN-T emits symbols later than the offline RNN-T. We propose a method to train an RNN-T so that the posterior peaks at each node in the posterior lattice are aligned with the ones from a pretrained model for the same utterance. By utilizing this method, we can train an offline RNN-T that can serve as a good teacher to train a student streaming RNN-T. Experimental results on the standard Switchboard conversational telephone speech corpus demonstrate accuracy improvements for a streaming unidirectional RNN-T by knowledge distillation from an offline bidirectional counterpart.
Leveraging Unlabeled Speech for Sequence Discriminative Training of Acoustic Models

State-of-the-art Acoustic Modeling (AM) techniques use long short term memory (LSTM) networks, and apply multiple phases of training on large amount of labeled acoustic data - initial cross-entropy (CE) training or connectionist temporal classiﬁcation (CTC) training followed by sequence discriminative training, such as state-level Minimum Bayes Risk (sMBR). Recently, there is considerable interest in applying Semi-Supervised Learning (SSL) methods that leverage substantial amount of unlabeled speech for improving AM. This paper proposes a novel Teacher-Student based knowledge distillation (KD) approach for sequence discriminative training, where reference state sequence of unlabeled data are estimated using a strong Bi-directional LSTM Teacher model which is then used to guide the sMBR training of a LSTM Student model. We build a strong supervised LSTM AM baseline by using 45000 hours of labeled multi-dialect English data for initial CE or CTC training stage, and 11000 hours of its British English subset for sMBR training phase. To demonstrate the efﬁcacy of the proposed approach, we leverage an additional 38000 hours of unlabeled British English data at only sMBR stage, which yields a relative Word Error Rate (WER) improvement in the range of 6% − 11% over supervised baselines in clean and noisy test conditions.
Bidirectional LSTM Network with Ordered Neurons for Speech Enhancement

Speech enhancement aims to reduce the noise and improve the quality and intelligibility of noisy speech. Long short-term memory (LSTM) network frameworks have achieved great success on many speech enhancement applications. In this paper, the ordered neurons long short-term memory (ON-LSTM) network with a new inductive bias to differen-tial the long/short-term information in each neuron is pro-posed for speech enhancement. Comparing the low-ranking neurons with short-term or local information, the high-ranking neurons which contain the long-term or global in-formation always update less frequently for a wide range of inﬂuence. Thus, the ON-LSTM can automatically learn the clean speech information from noisy input and show better expressive ability. We also propose a re-arrangement concat-enation rule to connect the ON-LSTM outputs of forward and backward layers to construct the bidirectional ON-LSTM (Bi-ONLSTM) for further performance improvement. The experimental results reveal that the proposed ON-LSTM schemes produce better enhancement performance than the vanilla LSTM baseline. And visualization result shows that our proposed model can effectively capture clean speech components from noisy inputs.
Frame-Wise Online Unsupervised Adaptation of DNN-HMM Acoustic Model from Perspective of Robust Adaptive Filtering

We present a new frame-wise online unsupervised adaptation method for an acoustic model based on a deep neural network (DNN). This is in contrast to many existing methods that assume ofﬂine and supervised processing. We use a likelihood cost function conditioned by past observations, which mathe-matically integrate the unsupervised adaptation and decoding process for automatic speech recognition (ASR). The issue is that the parameter update of the DNN should be less affected by outliers (model mismatch) and ASR recognition errors. Inspired by the robust adaptive ﬁter techniques, we propose 1) parameter update control to remove the inﬂuence of the out-liers and 2) regularization using L2-norm of DNN’s posterior probabilities of speciﬁc phonemes that are prone to recognition errors. Experiments showed that the phoneme recognition accuracies were improved by a maximum of 6.3 points, with an average error reduction rate of 10%, for various speakers.
Exploiting Cross-Domain Visual Feature Generation for Disordered Speech Recognition

Audio-visual speech recognition (AVSR) technologies have been successfully applied to a wide range of tasks. When developing AVSR systems for disordered speech characterized by severe degradation of voice quality and large mismatch against normal, it is difficult to record large amounts of high quality audio-visual data. In order to address this issue, a cross-domain visual feature generation approach is proposed in this paper. Audio-visual inversion DNN system constructed using widely available out-of-domain audio-visual data was used to generate visual features for disordered speakers for whom video data is either very limited or unavailable. Experiments conducted on the UASpeech corpus suggest that the proposed cross-domain visual feature generation based AVSR system consistently out-performed the baseline ASR system and AVSR system using original visual features. An overall word error rate reduction of 3.6% absolute (14% relative) was obtained over the previously published best system on the 8 UASpeech dysarthric speakers with audio-visual data of the same task.