Miracle-dz/UniFAR-FTUs huggingface dataset:
The utility of 3D-printed airway stents to improve treatment strategies for central airway obstructions

Abstract Airway stents are commonly used in the management of patients suffering from central airway obstruction (CAO). CAO may occur directly from airway strictures, obstructing airway cancers, airway fistulas or tracheobronchomalacia, resulting from the weakening and dynamic collapse of the airway wall. Current airway stents are constructed from biocompatible medical-grade silicone or from a nickel–titanium (nitinol) alloy with fixed geometry. The stents are inserted via the mouth during a bronchoscopic procedure. Existing stents have many shortcomings including the development of obstructing granulation tissue in the weeks and months following placement, mucous build up within the stent, and cough. Furthermore, airway stents are expensive and, if improperly sized for a given airway, may be easily dislodged (stent migration). Currently, in Australia, it is estimated that approximately 12,000 patients will develop CAO annually, many of whom will require airway stenting intervention. Of all stenting procedures, the rate of failure is currently reported to be at 22%. With a growing incidence of lung cancer prevalence globally, the need for updating airway stent technology is now greater than ever and personalizing stents using 3D-printing technology may offer the best chance of addressing many of the current limitations in stent design. This review article will assess what represents the gold standard in stent manufacture with regards to treatment of tracheobronchial CAO, the challenges of current airway stents, and outlines the necessity and challenges of incorporating 3D-printing technology into personalizing airway stents today.
The Effects of Multiple Dimensions of Risk and Protective Factors on Depressive Symptoms Among Nonresident African American Fathers

Compared to other groups, African American men experience proportionately greater adverse social and economic circumstances, which have been linked to poor mental health. A growing body of literature has begun to examine depressive symptoms among African American men; however, limited literature has examined the concurrent contributions of risk and protective factors among nonresident African American fathers. This study examined the relative contribution of perceived financial strain, perceived neighborhood characteristics, and interpersonal stress on depressive symptoms among 347 nonresident African American fathers. Social support was examined as a protective factor for depressive symptoms. Results from hierarchical regression analyses indicated interpersonal stress was associated with depressive symptoms even after controlling for perceived financial strain, perceived neighborhood characteristics, and specific sociodemographic factors. Additionally, among fathers with high interpersonal stress, having more social support buffered the negative effect of interpersonal stress on depressive symptoms. Findings suggest experiencing strain from multiple dimensions can increase the risk of depressive symptoms among nonresident African American fathers. We also found that interpersonal stress was especially harmful for mental health. Family service providers and mental health professionals should incorporate stress management techniques to reduce stressful interpersonal relationships as a way to lower depressive symptoms among nonresident African American fathers.
Dysarthric Speech Classification Using Glottal Features Computed from Non-words, Words and Sentences

Dysarthria is a neuro-motor disorder resulting from the disruption of normal activity in speech production leading to slow, slurred and imprecise (low intelligible) speech. Automatic clas-siﬁcation of dysarthria from speech can be used as a potential clinical tool in medical treatment. This paper examines the effectiveness of glottal source parameters in dysarthric speech classiﬁcation from three categories of speech signals, namely non-words, words and sentences. In addition to the glottal parameters, two sets of acoustic parameters extracted by the openSMILE toolkit are used as baseline features. A dysarthric speech classiﬁcation system is proposed by training support vector machines (SVMs) using features extracted from speech utterances and their labels indicating dysarthria/healthy. Clas-siﬁcation accuracy results indicate that the glottal parameters contain discriminating information required for the identiﬁcation of dysarthria. Additionally, the complementary nature of the glottal parameters is demonstrated when these parameters, in combination with the openSMILE-based acoustic features, result in improved classiﬁcation accuracy. Analysis of classi-ﬁcation accuracies of the glottal and openSMILE features for non-words, words and sentences is carried out. Results indicate that in terms of classiﬁcation accuracy the word level is best suited in identifying the presence of dysarthria.
Gas Leak Location Detection Based on Data Fusion with Time Difference of Arrival and Energy Decay Using an Ultrasonic Sensor Array

Ultrasonic gas leak location technology is based on the detection of ultrasonic waves generated by the ejection of pressured gas from leak holes in sealed containers or pipes. To obtain more accurate leak location information and determine the locations of leak holes in three-dimensional space, this paper proposes an ultrasonic leak location approach based on multi-algorithm data fusion. With the help of a planar ultrasonic sensor array, the eigenvectors of two individual algorithms, i.e., the arrival distance difference, as determined from the time difference of arrival (TDOA) location algorithm, and the ratio of arrival distances from the energy decay (ED) location algorithm, are extracted and fused to calculate the three-dimensional coordinates of leak holes. The fusion is based on an extended Kalman filter, in which the results of the individual algorithms are seen as observation values. The final system state matrix is composed of distances between the measured leak hole and the sensors. Our experiments show that, under the condition in which the pressure in the measured container is 100 kPa, and the leak hole–sensor distance is 800 mm, the maximum error of the calculated results based on the data fusion location algorithm is less than 20 mm, and the combined accuracy is better than those of the individual location algorithms.
Candida albicans Hyphal Expansion Causes Phagosomal Membrane Damage and Luminal Alkalinization

C. albicans is the most common cause of nosocomial fungal infection, and over 3 million people acquire life-threatening invasive fungal infections every year. Even if antifungal drugs exist, almost half of these patients will die. Despite this, fungi remain underestimated as pathogens. Our study uses quantitative biophysical approaches to demonstrate that yeast-to-hypha transition occurs within the nutrient-deprived, acidic phagosome and that alkalinization is a consequence, as opposed to the cause, of hyphal growth. ABSTRACT Macrophages rely on phagosomal acidity to destroy engulfed microorganisms. To survive this hostile response, opportunistic fungi such as Candida albicans developed strategies to evade the acidic environment. C. albicans is polymorphic and able to convert from yeast to hyphae, and this transition is required to subvert the microbicidal activity of the phagosome. However, the phagosomal lumen, which is acidic and nutrient deprived, is believed to inhibit the yeast-to-hypha transition. To account for this apparent paradox, it was recently proposed that C. albicans produces ammonia that alkalinizes the phagosome, thus facilitating yeast-to-hypha transition. We reexamined the mechanism underlying phagosomal alkalinization by applying dual-wavelength ratiometric pH measurements. The phagosomal membrane was found to be highly permeable to ammonia, which is therefore unlikely to account for the pH elevation. Instead, we find that yeast-to-hypha transition begins within acidic phagosomes and that alkalinization is a consequence of proton leakage induced by excessive membrane distension caused by the expanding hypha. IMPORTANCE C. albicans is the most common cause of nosocomial fungal infection, and over 3 million people acquire life-threatening invasive fungal infections every year. Even if antifungal drugs exist, almost half of these patients will die. Despite this, fungi remain underestimated as pathogens. Our study uses quantitative biophysical approaches to demonstrate that yeast-to-hypha transition occurs within the nutrient-deprived, acidic phagosome and that alkalinization is a consequence, as opposed to the cause, of hyphal growth.
State of Mind: Classification through Self-reported Affect and Word Use in Speech

Human state–of-mind (SOM; e.g.: perception, cognition, attention) constantly shifts due to internal and external demands. Mental health is influenced by the habitual use of either adaptive or maladaptive SOM. Therefore, the training of conscious regulation of SOM could be promising in self-help (e- and m-health), blended care and psychotherapy. The presented study indicates that SOM can be influenced by telling personal narratives. Furthermore, SOM and narrative sentiment (positive vs. negative) can be predicted through word use. Such results lay the groundwork for the development of applications that analyse text and speech for: i) the early detection of mental health; ii) the early detection of maladaptive changes in emotion dynamics; (iii) the use of personal narratives to improve emotion regulation skills; iv) the distribution of tailored interventions; and finally, v) the evaluation of therapy outcome.
A Fusion Algorithm: Fully Convolutional Networks and Student'S-$T$ Mixture Model for Brain Magnetic Resonance Imaging Segmentation

Deep convolutional neural networks (DCNN) are applied widely in image recognition and segmentation. In this paper, a novel algorithm (U-SMM) which incorporates the convolutional neural network U-net and modified Student's-$t$ mixture model (MSMM) is provided. The proposed framework considers the spatial relationships in segmenting medical images with MSMM and then uses U-net to correct the mistake labels made by unsupervised method. Because a few error-segmented regions may be caused by MSMM, the U-net is then applied to learn the features of these regions. In our method, the purpose of U-net is to assist the MSMM in improving the accuracy of segmentation and acquiring rich details in image segmentation tasks. Finally, the proposed framework is evaluated on real MR images with several related supervised and unsupervised methods, and the experimental results confirm the effectiveness of our approach.
Fusing Text-dependent Word-level i-Vector Models to Screen 'at Risk' Child Speech

Speech sound disorders (SSDs) are the most prevalent type of communication disorder among preschoolers. The earlier an SSD is identiﬁed, the earlier an intervention can be provided to potentially reduce the social/academic impact of the disorder. The challenge, lies in early identiﬁcation of such disorders. In this study 29 carefully selected words were produced by 165 children from 3-6 years of age. The audio recordings, were collected by parents using a mobile application /platform. ”Ground truth” child status as ’typically developing’ vs ’at risk’ was based on a percentage of consonants correct-revised growth curve model. State-of-the-art speech processing/speaker recognition models were employed along with our clinical group veriﬁcation framework. Results showed that text-dependent i-Vector models were superior to both text dependent and text-independent Gaussian Mixture Models (GMMs) for correct classiﬁcation of children. Fusing individual word, i-Vector models provides insight into word and consonant groupings that are more indicative of ’at risk’ child speech.
User Information Augmented Semantic Frame Parsing Using Progressive Neural Networks

Semantic frame parsing is a crucial component in spoken language understanding (SLU) to build spoken dialog systems. It consists of two main tasks: intent detection and slot ﬁlling. State-of-the-art deep learning models have demonstrated good results on these tasks. However, these models require not only a large scale annotated training set but also a long training procedure. In this paper, we aim to alleviate these drawbacks for semantic frame parsing by utilizing the ubiquitous user information. We design a novel progressive deep neural network model to incorporate prior knowledge of user information intermediately to better and quickly train a semantic frame parser. Due to the lack of benchmark dataset with real user information, we synthesize the simplest type of user information (location and time) on ATIS benchmark data. The results show that our approach leverages such simple user information to outperform state-of-the-art approaches by 0.25% for intent detection and 0.31% for slot ﬁlling using standard training data. When using smaller training data, the performance improvement on intent detection and slot ﬁlling reaches up to 1.35% and 1.20% respectively. We also show that our approach can achieve similar performance as state-of-the-art approaches by using less than 80% annotated training data. Moreover, the training time to achieve the similar performance is also reduced by over 60%.
Novel Empirical Mode Decomposition Cepstral Features for Replay Spoof Detection

The advances in Automatic Speaker Veriﬁcation (ASV) system for voice biometric purpose comes with the danger of spoofing attacks. The replay attack is the most accessible attack, where the attacker imitates speaker’s identity by replaying the pre-recorded speech samples of the target speaker. Most of the conventional features, such as Mel Frequency Cepstral Coefﬁ-cients (MFCC), Instantaneous Frequency Cepstral Coefﬁcients (IFCC), etc. uses ﬁlterbank structure for feature extraction purpose. In this paper, we propose a novel Empirical Mode De-composition Cepstral Coefﬁcient (EMDCC) feature set, where the ﬁlterbank in MFCC is replaced with the Empirical Mode Decomposition (EMD) to obtain the subband signals. The proposed feature set takes an advantage of using EMD that acts as a dyadic ﬁlterbank and handles the nonlinear and non-stationary nature of the speech signal. The stand-alone EMDCC feature set gives the Equal Error Rate (EER) of 28.06 % compared to the baseline CQCC and MFCC system with EER of 29.18 % and 31.3 %, respectively on the evaluation set of ASV Spoof 2017 Challenge database. Furthermore, the proposed feature set is fused with the Linear Frequency Modiﬁed Group Delay Cepstral Coefﬁcient (LFMGDCC) at score-level and we obtain a reduced EER of 18.36 % on evaluation set.
Effects of Isolation and Social Subchronic Stresses on Food Intake and Levels of Leptin, Ghrelin, and Glucose in Male Rats

Background: Exposure to psychological stresses can be a reason for obesity. Therefore, identifying the effective nutritional mechanisms such as feeding markers is of high necessity for the psychological stress conditions. Hence, the present study investigates the effects of subchronic isolation and social stresses on food intake, body weight differences (BWD), and levels of leptin, ghrelin, and glucose in rats. Materials and Methods: Eighteen male rats were randomly allocated into three groups: control (Co), isolation stress (IS), and social stress (SS) groups. Rats were under stresses for 7 days. The food intake (for three continuous hours after 16–18 h of food deprivation), BWD, levels of ghrelin, leptin, and glucose were measured. Results: The results showed that the food intake significantly (P < 0.05) reduced during the 1st h in the SS group compared to the Co group. At the 2nd h, the food intake significantly (P < 0.001 and P < 0.01, respectively) decreased in the IS group compared to the Co and SS groups. The cumulative food intake and body weight were significantly (P < 0.05) reduced in the IS group compared to the Co group. The serum ghrelin level significantly reduced in the IS group compared to the Co group. Conclusions: The subchronic psychological stresses led to a reduction in food intake by the reduction of serum ghrelin levels. It seems that ghrelin might have a more fundamental role in the food intake with respect to the leptin and glucose levels in subchronic stress condition. Furthermore, the decreased body weight justified the reduction of food intake, particularly in subchronic isolation stress.
Crosstalk of the Brassinosteroid Signalosome with Phytohormonal and Stress Signaling Components Maintains a Balance between the Processes of Growth and Stress Tolerance

Brassinosteroids (BRs) are a class of phytohormones, which regulate various processes during plant life cycle. Intensive studies conducted with genetic, physiological and molecular approaches allowed identification of various components participating in the BR signaling—from the ligand perception, through cytoplasmic signal transduction, up to the BR-dependent gene expression, which is regulated by transcription factors and chromatin modifying enzymes. The identification of new components of the BR signaling is an ongoing process, however an emerging view of the BR signalosome indicates that this process is interconnected at various stages with other metabolic pathways. The signaling crosstalk is mediated by the BR signaling proteins, which function as components of the transmembrane BR receptor, by a cytoplasmic kinase playing a role of the major negative regulator of the BR signaling, and by the transcription factors, which regulate the BR-dependent gene expression and form a complicated regulatory system. This molecular network of interdependencies allows a balance in homeostasis of various phytohormones to be maintained. Moreover, the components of the BR signalosome interact with factors regulating plant reactions to environmental cues and stress conditions. This intricate network of interactions enables a rapid adaptation of plant metabolism to constantly changing environmental conditions.
Integrated Presentation Attack Detection and Automatic Speaker Verification: Common Features and Gaussian Back-end Fusion

The vulnerability of automatic speaker verification (ASV) systems to spoofing is widely acknowledged. Recent years have seen an intensification in research efforts to develop spoofing countermeasures, also known as presentation attack detection (PAD) systems. Much of this work has involved the exploration of features that discriminate reliably between bona fide and spoofed speech. While there are grounds to use different front-ends for ASV and PAD systems (they are different tasks) the use of a single front-end has obvious benefits, not least convenience and computational efficiency, especially when ASV and PAD are combined. This paper investigates the performance of a variety of different features used previously for both ASV and PAD and assesses their performance when combined for both tasks. The paper also presents a Gaussian back-end fusion approach to system combination. In contrast to cascaded architec-tures, it relies upon the modelling of the two-dimensional score distribution stemming from the combination of ASV and PAD in parallel. This approach to combination is shown to gener-alise particularly well across independent ASVspoof 2017 v2.0 development and evaluation datasets.
Improved Prediction Via Thresholding Transform Coefficients

This paper presents a thresholding method for processing the predicted samples in the state-of-the-art High Efficiency Video Coding (HEVC) standard. The method applies an integer-based approximation of the discrete cosine transform to an extended prediction block and sets transform coefficients beneath a certain threshold to zero. Transforming back into the sample domain yields the improved prediction signal. The method is incorporated into a software implementation that is conforming to the HEVC standard and applies to both intra and inter predictions. Consequently, bit-rate savings ranging from 2.3% to 8.0% have been measured in terms of the Bjϕntegaard-Delta bit rate (BD-rate).
PhaseNet: Discretized Phase Modeling with Deep Neural Networks for Audio Source Separation

Previous research on audio source separation based on deep neural networks (DNNs) mainly focuses on estimating the magnitude spectrum of target sources and typically, phase of the mixture signal is combined with the estimated magnitude spectra in an ad-hoc way. Although recovering target phase is assumed to be important for the improvement of separation quality, it can be difﬁcult to handle the periodic nature of the phase with the regression approach. Unwrapping phase is one way to eliminate the phase discontinuity, however, it increases the range of value along with the times of unwrapping, making it difﬁcult for DNNs to model. To overcome this difﬁculty, we propose to treat the phase estimation problem as a classiﬁcation problem by discretizing phase values and assigning class indices to them. Experimental results show that our classiﬁcation-based approach 1) successfully recovers the phase of the target source in the discretized domain, 2) improves signal-to-distortion ratio (SDR) over the regression-based approach in both speech enhancement task and music source separation (MSS) task, and 3) outperforms state-of-the-art MSS.
Detection of Dementia from Responses to Atypical Questions Asked by Embodied Conversational Agents

Detection of dementia requires examinations, such as blood tests and functional magnetic resonance imaging (fMRI), that can be very stressful for the patient. Previous studies proposed screenings for easy detection of dementia that utilized acoustic and language information derived from conversations between patients and medical staff. Although these studies demonstrated effectiveness in automatically detecting dementia, the tasks used were created based on neuropsychological tests. The effect of habituation on this limited variety of tasks might have a negative impact on routine dementia screening. We propose a method to detect dementia using responses to more atypical questions asked by embodied conversational agents. Through consultations with neuropsychologists, we created a total of 13 questions. The embodied conversational agent obtained answers to these questions from 24 participants (12 dementia and 12 non-dementia). We recorded their responses and extracted speech and language features. We classiﬁed the two groups (dementia/non-dementia) by a machine learning al-gorithm (support vector machines and logistic regression) using the extracted features. The results showed a 0.95 detection performance in the area under the curve of the receiver operating characteristic (AUROC). This result demonstrates that our sys-tem using atypical questions can detect dementia.
Convolutional Neural Network-Based Arithmetic Coding of DC Coefficients for HEVC Intra Coding

In the state-of-the-art video coding standard-High Efficiency Video Coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding tool. In CABAC, the binarization processes are manually designed, and the context models are empirically crafted, both of which incur that the probability distribution of the syntax elements may not be estimated accurately, and restrict the coding efficiency. In this paper, we adopt a convolutional neural network-based arithmetic coding (CNNAC) strategy, and conduct studies on the coding of the DC coefficients for HEVC intra coding. Instead of manually designing binarization process and context model, we propose to directly estimate the probability distribution of the value of the DC coefficient using densely connected convolutional networks. The estimated probability together with the real DC coefficient are then input into a multi-level arithmetic codec to fulfill entropy coding. Simulation results show that our proposed CNNAC leads to on average 22.47% bits saving compared with CABAC for the bits of DC coefficients, which corresponds to 1.6% BD-rate reduction than the HEVC anchor.