Miracle-dz/UniFAR-FTUs huggingface dataset:
CCBE1 is required for coronary vessel development and proper coronary artery stem formation in the mouse heart

Background: Proper coronary vasculature development is essential for late‐embryonic and adult heart function. The developmental regulation of coronary embryogenesis is complex and includes the coordinated activity of multiple signaling pathways. CCBE1 plays an important role during lymphangiogenesis, enhancing VEGF‐C signaling, which is also required for coronary vasculature formation. However, whether CCBE1 plays a similar role during coronary vasculature development is still unknown. Here, we investigate the coronary vasculature development in Ccbe1 mutant embryos. Results: We show that Ccbe1 is expressed in the epicardium, like Vegf‐c, and also in the sinus venosus (SV) at the stages of its contribution to coronary vasculature formation. We also report that absence of CCBE1 in cardiac tissue inhibited coronary growth that sprouts from the SV endocardium at the dorsal cardiac wall. This disruption of coronary formation correlates with abnormal processing of VEGF‐C propeptides, suggesting VEGF‐C–dependent signaling alteration. Moreover, Ccbe1 loss‐of‐function leads to the development of defective dorsal and ventral intramyocardial vessels. We also demonstrate that Ccbe1 mutants display delayed and mispatterned coronary artery (CA) stem formation. Conclusions: CCBE1 is essential for coronary vessel formation, independent of their embryonic origin, and is also necessary for peritruncal vessel growth and proper CA stem patterning. Developmental Dynamics 247:1135‐1145, 2018. © 2018 Wiley Periodicals, Inc.
Effectiveness of adherence to recommended clinical examinations of diabetic patients in preventing diabetes-related hospitalizations.

Objective To validate a set of indicators for quality of diabetes care through their relationship with measurable clinical outcomes. Design A retrospective cohort study was carried out from 2010 to 2015. Setting Population-based study. Data were retrieved from healthcare utilization databases of three Italian regions (Lombardy, Emilia Romagna and Lazio) on the whole covering 20 million citizens. Participants The 77 285 individuals who were newly taken in care for diabetes during 2010 entered into the cohort. Interventions Exposure to selected clinical recommendations (i.e. periodic controls for glycated hemoglobin, lipid profile, urine albumin excretion, serum creatinine and dilated eye exams) was recorded. Main outcomes measures A composite outcome was employed taking into account hospitalizations for brief-term diabetes complications, uncontrolled diabetes, long-term vascular outcomes and no traumatic lower limb amputation. A multivariable proportional hazards model was fitted to estimate hazard ratio, and 95% confidence intervals (CI), for the exposure-outcome association. Results Among the newly taken in care patients with diabetes, those who adhered to almost none (0 or 1), just some (2 or 3) or almost all (4 or 5) recommendations during the first year after diagnosis were 44%, 36% and 20%, respectively. Compared patients who adhered to almost none recommendation, significant risk reductions of 16% (95% CI, 6-24%) and 20% (7-28%) were observed for those who adhered to just some and almost all recommendations, respectively. Conclusions Tight control of patients with diabetes through regular clinical examinations must to be considered the cornerstone of national guidance, national audits and quality improvement incentives schemes.
Noncontact Strain Monitoring of Osseointegrated Prostheses

The objective of this study was to develop a noncontact, noninvasive, imaging system for monitoring the strain and deformation states of osseointegrated prostheses. The proposed sensing methodology comprised of two parts. First, a passive thin film was designed such that its electrical permittivity increases in tandem with applied tensile loading and decreases while unloading. It was found that patterning the thin films could enhance their dielectric property’s sensitivity to strain. The film can be deposited onto prosthesis surfaces as an external coating prior to implant. Second, an electrical capacitance tomography (ECT) measurement technique and reconstruction algorithm were implemented to capture strain-induced changes in the dielectric property of nanocomposite-coated prosthesis phantoms when subjected to different loading scenarios. The preliminary results showed that ECT, when coupled with strain-sensitive nanocomposites, could quantify the strain-induced changes in the dielectric property of thin film-coated prosthesis phantoms. The results suggested that ECT coupled with embedded thin films could serve as a new noncontact strain sensing method for scenarios when tethered strain sensors cannot be used or instrumented, especially in the case of osseointegrated prostheses.
Health Consciousness and Its Effect on Perceived Knowledge, and Belief in the Purchase Intent of Liquid Milk: Consumer Insights from an Emerging Market

This study is based on the influence of consumers’ health consciousness (HC), perceived knowledge (PK) and beliefs affecting the attitude and purchase intent (PI) of the consumers. The outcome of this study is obtained through an exclusive survey conducted on a randomly selected sample of 712 households who purchase liquid milk (LM) in the cities of Dhaka and Chittagong in Bangladesh. A structured questionnaire is used to interview these participants to obtain data which are analysed employing descriptive statistics, Confirmatory Factor Analysis, and Structural Equation Modelling. The results of the analyses corroborate that consumers’ health consciousness has a positive impact on perceived knowledge, belief, and attitude, but not on purchase intent. In addition, belief affects both the attitude and PI positively. Although consumers’ perceived knowledge is too low to constitute their attitude towards LM, it has a positive, significant impact on the PI. The results also reveal that more than a third of the respondents consume LM several times per month, followed by more than a quarter of the sampled respondents who consume LM several times per week, and these consumption patterns have a positive and significant influence on the PI. Moreover, the monthly income of the family, age, and labelling preference are significantly correlated with PI.
Compression of Plenoptic Point Clouds Using the Region-Adaptive Hierarchical Transform

Point clouds have recently gained interest for the represention of 3D scenes in augmented and virtual reality. In real-time applications point clouds typically assume one color per point. While this approach is suited to represent diffuse objects, it is less realistic with specular surfaces. We consider the compression of plenoptic point clouds, wherein each voxel is associated to colors as seen by different angles. We propose an efficiently compressible representation to incorporate the plenoptic information of each voxel. We have proposed three compression methods, one based on a cylindrical projection and two others based on the intersection of the line of view with the voxel's face, one using flat boundaries and the other using a spherical boundary. Extensive tests have shown that the last two have the best performance, which are much superior than independently encoding the color attributes from each of the cameras point of views.
A functional study of all 40 Caenorhabditis elegans insulin-like peptides

The human genome encodes 10 insulin-like genes, whereas the Caenorhabditis elegans genome remarkably encodes 40 insulin-like genes. Knockout strategies to determine the roles of all the insulin/insulin-like peptide ligands (INS) in C. elegans has been challenging due to functional redundancy. Here, we individually overexpressed each of the 40 ins genes pan-neuronally, and monitored multiple phenotypes including: L1 arrest life span, neuroblast divisions under L1 arrest, dauer formation, and fat accumulation, as readouts to characterize the functions of each INS in vivo. Of the 40 INS peptides, we found functions for 35 INS peptides and functionally categorized each as agonists, antagonists, or of pleiotropic function. In particular, we found that 9 of 16 agonistic INS peptides shortened L1 arrest life span and promoted neuroblast divisions during L1 arrest. Our study revealed that a subset of β-class INS peptides that contain a distinct F peptide sequence are agonists. Our work is the first to categorize the structures of INS peptides and relate these structures to the functions of all 40 INS peptides in vivo. Our findings will promote the study of insulin function on development, metabolism, and aging-related diseases.
Is ATIS Too Shallow to Go Deeper for Benchmarking Spoken Language Understanding Models?

The ATIS (Air Travel Information Service) corpus will be soon celebrating its 30th birthday. Designed originally to benchmark spoken language systems, it still represents the most well-known corpus for benchmarking Spoken Language Understanding (SLU) systems. In 2010, in a paper titled "What is left to be understood in ATIS? " [1], Tur et al. discussed the relevance of this corpus after more than 10 years of research on statistical models for performing SLU tasks. Nowadays, in the Deep Neural Network (DNN) era, ATIS is still used as the main benchmark corpus for evaluating all kinds of DNN models, leading to further improvements, although rather limited, in SLU accuracy compared to previous state-of-the-art models. We propose in this paper to investigate these results obtained on ATIS from a qualitative point of view rather than just a quantitative point of view and answer the two following questions: what kind of qualitative improvement brought DNN models to SLU on the ATIS corpus? Is there anything left, from a qualitative point of view, in the remaining 5% of errors made by current state-of-the-art models?
Robust TDOA Estimation Based on Time-Frequency Masking and Deep Neural Networks

Deep learning based time-frequency (T-F) masking has dramatically advanced monaural speech separation and enhancement. This study investigates its potential for robust time difference of arrival (TDOA) estimation in noisy and reverberant environments. Three novel algorithms are proposed to improve the robustness of conventional cross-correlation-, beam- forming-and subspace-based algorithms for speaker localization. The key idea is to leverage the power of deep neural networks (DNN) to accurately identify T-F units that are relatively clean for TDOA estimation. All of the proposed algorithms exhibit strong robustness for TDOA estimation in environments with low input SNR, high reverberation and low direc-tion-to-reverberant energy ratio.
Homens vivendo em situação de rua e a atenção primária em saúde

Objetivo: Conhecer se os homens que vivem em situacao de rua sabem a forma de acesso aos servicos de atencao primaria em saude e analisar as possiveis dificuldades que enfrentam para ter acesso a esses servicos. Metodo : Realizou-se entrevista individual com 24 homens vivendo em situacao de rua, frequentadores de uma instituicao filantropica situada em Belem – Para. Para analise utilizou-se a tecnica de analise de conteudo tematica. Resultados: Os homens em situacao de rua nao acessaram os servicos de saude da atencao primaria, principalmente pelo desconhecimento de seus direitos e dos servicos oferecidos. Conclusao: o desconhecimento, questoes burocraticas e/ou discriminacao dificultam o acesso dos homens em situacao de rua aos servicos de atencao primaria em saude.
Arabidopsis PIRL6 Is Essential for Male and Female Gametogenesis and Is Regulated by Alternative Splicing1[OPEN]

A leucine-rich repeat protein related to animal cell signaling components is important for gametogenesis, with gametophytespecific expression achieved by alternative mRNA splicing. Plant intracellular Ras-group leucine-rich repeat (LRR) proteins (PIRLs) are related to Ras-interacting animal LRR proteins that participate in developmental cell signaling. Systematic knockout analysis has implicated some members of the Arabidopsis (Arabidopsis thaliana) PIRL family in pollen development. However, for PIRL6, no bona fide knockout alleles have been recovered, suggesting that it may have an essential function in both male and female gametophytes. To test this hypothesis, we investigated PIRL6 expression and induced knockdown by RNA interference. Knockdown triggered defects in gametogenesis, resulting in abnormal pollen and early developmental arrest in the embryo sac. Consistent with this, PIRL6 was expressed in gametophytes: functional transcripts were detected in wild-type flowers but not in sporocyteless (spl) mutant flowers, which do not produce gametophytes. A genomic PIRL6-GFP fusion construct confirmed expression in both pollen and the embryo sac. Interestingly, PIRL6 is part of a convergent overlapping gene pair, a scenario associated with an increased likelihood of alternative splicing. We detected multiple alternative PIRL6 mRNAs in vegetative organs and spl mutant flowers, tissues that lacked the functionally spliced transcript. cDNA sequencing revealed that all contained intron sequences and premature termination codons. These alternative mRNAs accumulated in the nonsense-mediated decay mutant upf3, indicating that they are normally subjected to degradation. Together, these results demonstrate that PIRL6 is required in both male and female gametogenesis and suggest that sporophytic expression is negatively regulated by unproductive alternative splicing. This posttranscriptional mechanism may function to minimize PIRL6 protein expression in sporophyte tissues while allowing the overlapping adjacent gene to remain widely transcribed.
Adversarial Domain Adaptation with a Domain Similarity Discriminator for Semantic Segmentation of Urban Areas

Existing semantic segmentation models of urban areas have shown to perform well in a supervised setting. However, collecting lots of annotated images from each city to train such models is time-consuming or difficult. In addition, when transferring the segmentation model from the trained city (source domain) to an unseen city (target domain), the performance will largely degrade due to the domain shift. For this reason, we propose a domain adaptation method with a domain similarity discriminator to eliminate such domain shift in the framework of adversarial learning. Contrary to the single-input adversarial network, our domain similarity discriminator, which consists of a Siamese network, is able to measure the similarity of the pairwise-input data. In this way, we can use more information about the pairwise-input to measure the similarity between different distributions so as to address the problem of domain shift. Experimental results demonstrate that our approach outperforms the competing methods on three different cities.
A Deep Learning Method for Pathological Voice Detection Using Convolutional Deep Belief Networks

Automatically detecting pathological voice disorders such as vocal cord paralysis or Reinke’s edema is a challenging and important medical classification problem. While deep learning techniques have achieved significant progress in the speech recognition field there has been less research work in the area of pathological voice disorders detection. A novel system for pathological voice detection using convolutional neural network (CNN) as the basic architecture is presented in this work. The novel system uses spectrograms of normal and pathological speech recordings as the input to the network. Initially Convolutional deep belief network (CDBN) are used to pre-train the weights of CNN system. This acts as a generative model to explore the structure of the input data using statistical methods. Then a CNN is trained using supervised back-propagation learning algorithm to fine tune the weights. It will be shown that a small amount of data can be used to achieve good results in classification with this deep learning approach. A performance analysis of the novel method is provided using real data from the Saarbrucken Voice database
3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics

In the last decade, computer-aided early diagnostics of Alzheimer's Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research. In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics. Then we propose our own design of a 3D Inception-based Convolutional Neural Network (CNN) for Alzheimer's Disease diagnostics. The network is designed with an emphasis on the interior resource utilization and uses sMRI and DTI modalities fusion on hippocampal ROI. The comparison with the conventional AlexNet-based network using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (this http URL) demonstrates significantly better performance of the proposed 3D Inception-based CNN.
Emotion Identification from Raw Speech Signals Using DNNs

We investigate a number of Deep Neural Network (DNN) architectures for emotion identiﬁcation with the IEMOCAP database. First we compare different feature extraction front-ends: we compare high-dimensional MFCC input (equivalent to ﬁlterbanks), versus frequency-domain and time-domain approaches to learning ﬁlters as part of the network. We obtain the best results with the time-domain ﬁlter-learning approach. Next we investigated different ways to aggregate information over the duration of an utterance. We tried approaches with a single label per utterance with time aggregation inside the network; and approaches where the label is repeated for each frame. Having a separate label per frame seemed to work best, and the best architecture that we tried interleaves TDNN-LSTM with time-restricted self-attention, achieving a weighted accuracy of 70.6%, versus 61.8% for the best previously published system which used 257-dimensional Fourier log-energies as input.
The Role of Geography in Adaptive Radiation

Although the importance of biogeography in the speciation process is well recognized, the fundamental role of geographic diversification during adaptive radiations has not been studied to determine its importance during the adaptive radiation process. We examined the relationship between lineage and regional diversification patterns in the South American rodent subfamily Sigmodontinae, one of the best candidates for an adaptive radiation in mammals, to propose a conceptual framework for geographic transitions during adaptive radiations. We reconstructed a time-calibrated phylogeny from four nuclear genes and one mitochondrial gene for 77% of sigmodontine diversity. Historical biogeography was reconstructed among 14 regions, for which we applied a sliding-window approach to estimate regional transition rates through time. We compared these rate patterns and measured whether regions consisted of species that were more phylogenetically related than expected by chance. Following the initial South American colonization around 7 million years ago, multiple expansions from northern regions correlated with a burst of speciation. Subsequently, both diversification and regional transition rates decreased overall and within the majority of regions. Despite high regional transition rates, nearly all regional assemblages were phylogenetically clustered, indicating that within-region diversification was common. We conclude that biogeographic complexity and partitioning played a profound role in the adaptive radiation of the South American Sigmodontinae (Oryzomyalia), the degree to which is determined by the relative scales of spatial variation and dispersal abilities.
Neural Error Corrective Language Models for Automatic Speech Recognition

We present novel neural network based language models that can correct automatic speech recognition (ASR) errors by using speech recognizer output as a context. These models, called neural error corrective language models (NECLMs), utilizes ASR hypotheses of a target utterance as a context for estimating the generative probability of words. NECLMs are expressed as conditional generative models composed of an encoder network and a decoder network. In the models, the encoder network constructs context vectors from N-best lists and ASR con-ﬁdence scores generated in a speech recognizer. The decoder network rescores recognition hypotheses by computing a generative probability of words using the context vectors so as to correct ASR errors. We evaluate the proposed models in Japanese lecture ASR tasks. Experimental results show that NECLM achieve better ASR performance than a state-of-the-art ASR system that incorporate a convolutional neural network acoustic model and a long short-term memory recurrent neural network language model.
Data Augmentation Using Healthy Speech for Dysarthric Speech Recognition

Dysarthria refers to a speech disorder caused by trauma to the brain areas concerned with motor aspects of speech giving rise to effortful, slow, slurred or prosodically abnormal speech. Traditional Automatic Speech Recognizers (ASR) perform poorly on dysarthric speech recognition tasks, owing mostly to insuf-ﬁcient dysarthric speech data. Speaker related challenges complicates data collection process for dysarthric speech. In this paper, we explore data augmentation using temporal and speed modiﬁcations to healthy speech to simulate dysarthric speech. DNN-HMM based Automatic Speech Recognition (ASR) and Random Forest based classiﬁcation were used for evaluation of the proposed method. Dysarthric speech, generated synthetically, is classiﬁed for severity level using a Random Forest clas-siﬁer that is trained on actual dysarthric speech. ASR trained on healthy speech, augmented with simulated dysarthric speech is evaluated for dysarthric speech recognition. All evaluations were carried out using Universal Access dysarthric speech corpus. An absolute improvement of 4.24% and 2% WAS achieved using tempo based and speed based data augmentation respectively as compared to ASR performance using healthy speech alone for training.
Fully Convolutional Siamese Fusion Networks for Object Tracking

In this paper, we propose fully convolutional siamese fusion networks for object tracking. We adopt the fusion strategy of convolutional layers for object tracking to achieve good feature representation based on convolutional neural networks. Specifically, we fuse three convolutional layers of VGGNet based on normalized cross correlation (NCC). First, we use three convolutional layers of VGGNet as the basis for fusion, and reduce the size of the convolutional layers based on a convolution kernel. Then, we resize the convolutional layers to be the same size as the deconvolutional layers for layer fusion. Next, we fuse the three layers based on NCC between the target and search region, and produce the response map. Finally, we get the tracking result from the response map by the maximum response. Various experiments on large-scale data sets verify that the proposed method is robust to occlusion, deformation, motion blur, and background clutter as well as outperforms state-of-the-art trackers in terms of distance precision and overlap success.