Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Detection of Obstructive Sleep Apnea Using Wavelet Transform and Entropy-Based Features From Single-Lead ECG Signal

Obstructive sleep apnea (OSA) is a prevalent sleep disorder and highly affects the quality of human life. Currently, gold standard for OSA detection is polysomnogram. Since this method is time consuming and cost inefficient, practical systems focus on the usage of electrocardiogram (ECG) signals for OSA detection. In this paper, a novel automatic OSA detection method using a single-lead ECG signal has been proposed. A nonlinear feature extraction using wavelet transform (WT) coefficients obtained by an ECG signal decomposition is employed. In addition, different classification methods are investigated. ECG signals are decomposed into eight levels using a Symlet function as a mother Wavelet function with third order. Then, the entropy-based features including fuzzy/approximate/sample/correct conditional entropy as well as other nonlinear features including interquartile range, mean absolute deviation, variance, Poincare plot, and recurrence plot are extracted from WT coefficients. The best features are chosen using the automatic sequential forward feature selection algorithm. In order to assess the introduced method, 95 single-lead ECG recordings are used. The support vector machine classifier having a radial basis function kernel leads to an accuracy of 94.63% (sensitivity: 94.43% and specificity: 94.77%) and 95.71% (sensitivity: 95.83% and specificity: 95.66%) for minute-by-minute and subject-by-subject classifications, respectively. The results show that applying entropy-based features for extracting hidden information of the ECG signals outperforms other available automatic OSA detection methods. The results indicate that a highly accurate OSA detection is attained by just exploiting the single-lead ECG signals. Furthermore, due to the low computational load in the proposed method, it can easily be applied to the home monitoring systems.
Estimating Energy Parameters for RNA Secondary Structure Predictions Using Both Experimental and Computational Data

Computational RNA secondary structure prediction depends on a large number of nearest-neighbor free-energy parameters, including 10 parameters for Watson–Crick stacked base pairs that were estimated from experimental measurements of the free energies of 90 RNA duplexes. These experimental data are provided by time-consuming and cost-intensive experiments. In contrast, various modified nucleotides in RNAs, which would affect not only their structures but also functions, have been found, and rapid determination of energy parameters for a such modified nucleotides is needed. To reduce the high cost of determining energy parameters, we propose a novel method to estimate energy parameters from both experimental and computational data, where the computational data are provided by a recently developed molecular dynamics simulation protocol. We evaluate our method for Watson–Crick stacked base pairs, and show that parameters estimated from 10 experimental data items and 10 computational data items can predict RNA secondary structures with accuracy comparable to that using conventional parameters. The results indicate that the combination of experimental free-energy measurements and molecular dynamics simulations is capable of estimating the thermodynamic properties of RNA secondary structures at lower cost.
A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play critical roles in many important biological processes. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and aid in finding biomarkers for disease diagnosis, treatment, and prevention. In this paper, we constructed a bipartite network based on known lncRNA-disease associations; based on this work, we proposed a novel model for inferring potential lncRNA-disease associations. Specifically, we analyzed the properties of the bipartite network and found that it closely followed a power-law distribution. Moreover, to evaluate the performance of our model, a leave-one-out cross-validation (LOOCV) framework was implemented, and the simulation results showed that our computational model significantly outperformed previous state-of-the-art models, with AUCs of 0.8825, 0.9004, and 0.9292 for known lncRNA-disease associations obtained from the LncRNADisease database, Lnc2Cancer database, and MNDR database, respectively. Thus, our approach may be an excellent addition to the biomedical research field in the future.
HapPro: A Wearable Haptic Device for Proprioceptive Feedback

Objective: Myoelectric hand prostheses have reached a considerable technological level and gained an increasing attention in assistive robotics. However, their abandonment rate remains high, with unintuitive control and lack of sensory feedback being major causes. Among the different types of sensory information, proprioception, e.g., information on hand aperture, is crucial to successfully perform everyday actions. Despite the many attempts in literature to restore and convey this type of feedback, much remains to be done to close the action-perception loop in prosthetic devices. Methods: With this as motivation, in this paper we introduce HapPro, a wearable, noninvasive haptic device that can convey proprioceptive information for a prosthetic hand. The device was used with an under-actuated, simple to control anthropomorphic robotic hand, providing information about hand aperture by mapping it to the position of a wheel that can run on the user's forearm. Tests with 43 able bodied subjects and one amputee subject were conducted in order to quantify the effectiveness of HapPro as a feedback device. Results: HapPro provided a good level of accuracy for item discrimination. Participants also reported the device to be intuitive and effective in conveying proprioceptive cues. Similar results were obtained in the proof-of-concept experiment with an amputee subject. Conclusions: Results show that HapPro is able to convey information on the opening of a prosthetic hand in a noninvasive way. Significance: Using this device for proprioceptive feedback could improve usability of myoelectric prostheses, potentially reducing abandonment and increasing quality of life for their users.
Symmetric Predictive Estimator for Biologically Plausible Neural Learning

In a real brain, the act of perception is a bidirectional process, depending on both feedforward sensory pathways and feedback pathways that carry expectations. We are interested in how such a neural network might emerge from a biologically plausible learning rule. Other neural network learning methods either only apply to feedforward networks, or employ assumptions (such as weight copying) that render them unlikely in a real brain. Predictive estimators (PEs) offer a better solution to this bidirectional learning scenario. However, PEs also depend on weight copying. In this paper, we propose the symmetric PE (SPE), an architecture that can learn both feedforward and feedback connection weights individually using only locally available information. We demonstrate that the SPE can learn complicated mappings without the use of weight copying. The SPE networks also show promise in deeper architectures.
ASAP: Super-Contrast Vasculature Imaging Using Coherence Analysis and High Frame-Rate Contrast Enhanced Ultrasound

The very high frame rate afforded by ultrafast ultrasound, combined with microbubble contrast agents, opens new opportunities for imaging tissue microvasculature. However, new imaging paradigms are required to obtain superior image quality from the large amount of acquired data while allowing real-time implementation. In this paper, we report a technique–acoustic sub-aperture processing (ASAP)–capable of generating very high contrast/signal-to-noise ratio (SNR) images of macro-and microvessels, with similar computational complexity to classical power Doppler (PD) imaging. In ASAP, the received data are split into subgroups. The reconstructed data from each subgroup are temporally correlated over frames to generate the final image. As signals in subgroups are correlated but the noise is not, this substantially reduces the noise floor compared to PD. Using a clinical imaging probe, the method is shown to visualize vessels down to $200~\mu \text{m}$ with a SNR of 10 dB higher than PD and to resolve microvascular flow/perfusion information in rabbit kidneys noninvasively in vivo at multiple centimeter depths. With careful filter design, the technique also allows the estimation of flow direction and the separation of fast flow from tissue perfusion. ASAP can readily be implemented into hardware/firmware for real-time imaging and can be applied to contrast enhanced and potentially noncontrast imaging and 3-D imaging.
Adaptive Neural Tracking Control of Switched Stochastic Pure-Feedback Nonlinear Systems With Unknown Bouc–Wen Hysteresis Input

This paper aims to analyze the problem of adaptive neural network (NN) tracking control for a class of switched stochastic nonlinear pure-feedback systems with unknown direction hysteresis. In the light of recent studies on the hysteresis phenomenon in the field of nonlinear switched systems, this paper focuses on Bouc–Wen hysteresis model with unknown parameters and direction conditions. To simplify the control design, the following procedure is applied. Prior to tackling the unknown direction hysteresis problem based on the Nussbaum function and the backstepping techniques, the pure-feedback structure difficulty is governed by the mean value theorem. Furthermore, an optimized adaptation method is utilized to cope with computational burden. Universal approximation capability of radial basis function NNs and Lyapunov function method is synthesized to develop an adaptive NN tracking control scheme. It is demonstrated that under arbitrary deterministic switching, the presented controller can guarantee that all signals in the closed-loop system are semiglobally uniformly ultimately bounded in probability and the tracking error converges to a neighborhood of the origin. Finally, two simulation examples are given to illustrate the advantages of the proposed control design approach.
Clinical and ultrasonographic predictors for achieving minimal disease activity in patients with psoriatic arthritis: the UPSTREAM (Ultrasound in PSoriatic arthritis TREAtMent) prospective observational study protocol

Introduction Psoriatic arthritis (PsA) occurs in 10%–15% of people with psoriasis and accounts for 10%–20% of early arthritis clinics referral. Only a few prognostic factors of therapeutic response in patients with PsA have been identified. In the last years, the role of imaging has grown up and the European League Against Rheumatism recognised that ultrasound (US) has higher sensitivity than clinical examination to detect inflammatory disease activity. The aims of the Ultrasound in PSoriatic arthritis TREAtMent (UPSTREAM) study are to integrate clinic and US in order to inform whether US has provide an added prognostic value in PsA. Methods and analysis UPSTREAM is an observational prospective cohort study enrolling patients with PsA having clinically active joint disease and starting a new course of therapy. The primary objective is to evaluate the additional value of US over clinical examination in detecting patients achieving minimal disease activity after 6 months. Data will be obtained at baseline and at standard clinical follow-up visits. Patient’s clinical assessment will be performed according to the core set proposed by the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis-Outcome Measures in Rheumatology. Sonographic evaluations will be performed by expert sonographers in 42 joints, 36 tendons, 12 entheses and 2 bursae, according to a score that will be purposely developed for PsA by the US Study Group of the Italian Society for Rheumatology. The UPSTREAM study will identify clinical and US predictors of response to treatment in patients with PsA and active peripheral arthritis starting a new course of therapy. Ethics and dissemination Ethic approval for this study has been obtained from the institutional review board (IRB)/independent ethics committee (IEC) Comitato Etico Lazio 1 (Prot. N 198 02-02-2017) and then locally from the IRB/IEC of each participating centre. Results will be published in relevant scientific journals and be disseminated in international conferences. Fully anonymised data will be accessible from authors upon request. Trial registration number NCT03330769; Pre-results.
Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction

Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance the development of biomedical research. KBs contain huge amounts of structured information about entities and relationships, therefore plays a pivotal role in chemical-disease relation (CDR) extraction. However, previous researches pay less attention to the prior knowledge existing in KBs. This paper proposes a neural network-based attention model (NAM) for CDR extraction, which makes full use of context information in documents and prior knowledge in KBs. For a pair of entities in a document, an attention mechanism is employed to select important context words with respect to the relation representations learned from KBs. Experiments on the BioCreative V CDR dataset show that combining context and knowledge representations through the attention mechanism, could significantly improve the CDR extraction performance while achieve comparable results with state-of-the-art systems.
Multiscale Frame-Based Kernels for Large Deformation Diffeomorphic Metric Mapping

We present a set of multiscale frame-based kernels that can be used to construct diffeomorphic transformation in the large deformation diffeomorphic metric mapping (LDDMM) framework. We construct multiscale kernels via compact wavelet frames that are equipped with the hierarchical multiresolution analysis. We show that these kernels under certain conditions can form reproducing kernel Hilbert spaces of smooth velocity fields and hence can be used to generate multiscale diffeomorphic transformation for LDDMM. As a proof of concept, we incorporate these kernels in the LDDMM framework. We show the improvement of whole brain mapping accuracy using the LDDMM with frame-based kernels in comparison to that obtained using the LDDMM with Gaussian kernels. Moreover, we evaluate whole brain mapping accuracy of the LDDMM with frame-based kernels against that obtained from the 14 brain mapping methods given by Klein et al. Our results suggest that the LDDMM with frame-based kernels has the potential to outperform the 14 brain mapping methods for whole brain mapping.
Urban Weathering: Interactive Rendering of Polluted Cities

Weathering effects are ubiquitous phenomena in cities. Buildings age and deteriorate over time as they interact with the environment. Pollution accumulating on facades is a particularly visible consequence of this. Even though relevant work has been done to produce impressive images of virtual urban environments including weathering effects, so far, no technique using a global approach has been proposed to deal with weathering effects. Here, we propose a technique based on a fast physically-inspired approach, that focuses on modeling the changes in appearance due to pollution soiling on an urban scale. We consider pollution effects to depend on three main factors: wind, rain and sun exposure, and we take into account three intervening steps: deposition, reaction and washing. Using a low-cost pre-computation, we evaluate the pollution distribution throughout the city. Based on this and the use of screen-space operators, our method results in an efficient approach able to generate realistic images of urban scenes by combining the intervening factors at interactive rates. In addition, the pre-computation demands a reduced amount of memory to store the resulting pollution map and, as it is independent from scene complexity, it can suit large and complex models by adapting the map resolution.
Measuring bilingual corpus comparability

Abstract Comparable corpora serve as an important substitute for parallel resources in cases of under-resourced language pairs. Previous work mostly aims to find a better strategy to exploit existing comparable corpora, while ignoring the variety in corpus quality. The quality of comparable corpora affects a lot its usability in practice, a fact that has been justified by several studies. However, researchers have not been able to establish a widely accepted and fully validated framework to measure corpus quality. We will thus investigate in this paper a comprehensive methodology to deal with the quality of comparable corpora. To be exact, we will propose several comparability measures and a quantitative strategy to test those measures. Our experiments show that the proposed comparability measure can capture gold-standard comparability levels very well and is robust to the bilingual dictionary used. Moreover, we will show in the task of bilingual lexicon extraction that the proposed measure correlates well with the performance of the real world application.
Robust fuzzy sliding mode control for air supply on PEM fuel cell system

In this paper, an adaptive fuzzy sliding mode controller is employed for air supply on proton exchange membrane fuel cell (PEMFC) systems. The control objective is to adjust the oxygen excess ratio at a given set point in order to prevent oxygen starvation and damage to the fuel-cell stack. The proposed control scheme consists of two parts: a sliding mode controller (SMC) and fuzzy logic controller (FLC) with an adjustable gain factor. The SMC is used to calculate the equivalent control law and the FLC is used to approximate the control hitting law. The performance of the proposed control strategy is analysed through simulations for different load variations. The results indicated that the adaptive fuzzy sliding mode controller (AFSMC) is excellent in terms of stability and several key performance indices such as the integral squared error (ISE), the integral absolute error (IAE) and the integral time-weighted absolute error (ITAE), as well as the settling and rise times for the closed-loop control system.
Local Motion Intensity Clustering (LMIC) Model for Segmentation of Right Ventricle in Cardiac MRI Images

Analysis of the morphology and function of the right ventricle (RV) can be used for the prediction and diagnosis of cardiovascular disease. Accurate description of the structure and function of heart can be provided by analyzing cardiac magnetic resonance imaging (MRI) images. Noise interference and intensity inhomogeneity of MRI images can be addressed by using a local intensity clustering (LIC) model. However, the segmentation of the RV in MRI images still remains a challenge mainly due to its ill-defined borders. To address such a challenge, an algorithm for segmenting the RV based on a local motion intensity clustering (LMIC) model is proposed in this paper. The LMIC model combines the LIC model with the motion intensity information, due to cardiac motion and blood flow. The motion intensity is calculated by using the Lucas Kanade optical flow method and utilized in the LMIC model as an energy parameter. Because the motion intensity of the RV region is stronger than other areas, the RV can be accurately segmented by this approach. Experimental results demonstrate that the LMIC model is able to address the challenge of the ill-defined RV borders in cardiac MRI images and improved RV segmentation accuracy over existing methods.
The metabolic axis of macrophage and immune cell polarization

ABSTRACT The extracellular space of solid tumors ranges from being well-nurtured to being completely ischemic and can serve as a source of intratumoral heterogeneity, determining the behavior and molecular profiles of malignant and stromal cells. Here, we discuss how the metabolic tumor microenvironment modulates the phenotypes of the immune cells that infiltrate tumors, with an emphasis on tumor-associated macrophages. These cells constitute a diverse population that has pro-tumoral and anti-inflammatory properties, and are likened to anti-inflammatory ‘M2’ macrophages. Recent findings show how different metabolic microenvironments specify an array of phenotypic changes in macrophages. In tumors, extracellular metabolite levels vary predictably according to proximity to the vasculature, and phenotypic changes in tumor-associated macrophages and in other immune cells are also predictable. We speculate that this ‘metabolic axis’ of macrophage polarization modulates – and is modulated by – the response to inflammatory cues, creating a wide variety of possible phenotypic states. Understanding how extracellular metabolites influence cell phenotypes allows us to predict how tumor-associated macrophages and other tumor cells might change, with the aim of harnessing this predictability for therapy. Overall, we describe an emerging picture in which chemokines, growth factors and the metabolic tumor microenvironment act together to determine the phenotypes of tumor-infiltrating immune cells. Summary: Extracellular metabolites can mediate cell communication and change transcriptional and functional aspects of tumor and normal cells. This Review focuses on how these metabolic cues affect the immune system with a special focus on tumor-associated macrophages.
Empirical study on developer factors affecting tossing path length of bug reports

Bug reassignment (also known bug tossing) is a common activity in the life cycle of bug reports, and it increases the cost of time and labour to fix bugs in software projects. In large-scale projects, about 6–10% of bug reports are tossed at least three times. However, the nature of repeatedly-tossed bug reports was usually overlooked in previous works. This study focuses on developer features from four aspects, namely network centrality, developer workspace, developer expertise, and transmissibility of developers, to investigate which factors affect the tossing path length (TPL). By using statistical methods, this study finds that working theme, product, component, and degree centrality are key impact factors affecting the change of TPL. The four key features are then simplified to three core features, namely working theme, product, and component, which contribute about 90% of the variance of TPL. Finally, the two feature groups mentioned above are applied in six machine learning algorithms to predict potential developers for bug reports from Eclipse and Mozilla, and the results validate the effectiveness of the feature groups for developer recommendation. Hence, this study provides an easy-to-use feature selection method to train quality developer recommenders for automatic bug triage in an efficient way.