Miracle-dz/UniFAR-FTUs huggingface dataset:
Trunk deformity evaluation based on 3D measurements of front body surface landmarks in people with severe physical disabilities

ABSTRACT Purpose: To assess reliability and validity of a trunk deformity evaluation method expressed as rotational and lateral lean angles between the upper and the lower trunk and between the lower trunk and the pelvis using 3D positions of six front body surface landmarks. Methods: Inter- and intra-rater reliabilities of the proposed method in adults with typical development (n = 22) were assessed, and its validity was also assessed through correlations between the Cobb angle and the analyzed trunk deformity parameters in adults with severe physical disabilities (n = 22). Results: The mean differences between two raters and between the initial and second measures were within 2°. Moderate correlations were found between the Cobb angle and both the upper and the lower trunk lateral lean angle and the upper trunk rotation angle. Conclusions: The proposed trunk deformity evaluation appears to be a reliable and valid approach for bedridden people with physical disabilities.
Tissue engineering; strategies, tissues, and biomaterials

Abstract Current tissue regenerative strategies rely mainly on tissue repair by transplantation of the synthetic/natural implants. However, limitations of the existing strategies have increased the demand for tissue engineering approaches. Appropriate cell source, effective cell modification, and proper supportive matrices are three bases of tissue engineering. Selection of appropriate methods for cell stimulation, scaffold synthesis, and tissue transplantation play a definitive role in successful tissue engineering. Although the variety of the players are available, but proper combination and functional synergism determine the practical efficacy. Hence, in this review, a comprehensive view of tissue engineering and its different aspects are investigated.
Research and Implementation of ECG-Based Biological Recognition Parallelization

Nowadays, biological recognition technologies attract more attention in electrocardiograph (ECG) signals, which vary among different people and are difficult to counterfeit. However, the robustness of recognition cannot be well sustained in the case of diversified application scenarios and huge human crowds. In order to tackle this problem, this paper puts forward a fiducial and non-fiducial mixed feature extraction method, which can effectively complete the multidimensional feature modeling of ECG signal. In addition, this paper proposes a linear discriminant analysis (LDA) based on multiple features (LOMF) algorithm based on ECG mixed feature to solve time-overhead problem of big data training. LOMF includes ECG signal preprocessing, sub-block division, and block training. By combining the MapReduce distributed computing framework and the secondary retrieval method based on the multi-dimensional feature space, LOMF is parallelized to improve recognition rate and computing efficiency at the same time. The experiment results show that, in the diversified scenarios, utilizing ECG mixed feature can return a higher recognition rate than the traditional ECG 1-D feature. Moreover, compared with the traditional LDA and support vector machine algorithms, the precision of LOMF increases by 7%–8%, which depends on the most competitive advantage of using LOMF. LOMF fits MapReduce parallel framework well so it is more effective than traditional algorithms, especially on diversified application scenarios (such as Internet) where the amount of data grows rapidly.
Adverse Consequences of Unmet Needs for Care in High-Need/High-Cost Older Adults.

Objectives We explore adverse consequences of unmet needs for care among high-need/high-cost (HNHC) older adults. Method Interviews with 4,024 community-dwelling older adults with ADL/IADL/mobility disabilities from the 2011 National Health and Aging Trends Study (NHATS). Reports of socio-demographics, disability compensatory strategies, and adverse consequences of unmet needs in the past month were obtained from older adults with multiple chronic conditions (MCC), probable dementia (DEM), and/or near end-of-life (EOL) and compared older adults not meeting these criteria. Results Older adults with MCC (31.6%), DEM (39.6%), and EOL (48.7%) reported significantly more adverse consequences than low-need older adults (21.4%). Persons with MCC & DEM (53.4%), MCC & EOL (53.2%), and all three (MCC, DEM, EOL, 65.6%) reported the highest levels of adverse consequences. HNHC participants reported more environmental modifications, assistive device, and larger helper networks. HNHC status independently predicted greater adverse consequences after controlling for disability compensatory strategies in multivariate models. Discussion Adverse consequences of unmet needs for care are prevalent among HNHC older adults, especially those with multiple indicators, despite more disability-related compensatory efforts and larger helper networks. Helping caregivers provide better informal care has potential to contain healthcare costs by reducing hospitalization and unplanned readmissions.
Carbon Nanotube-Based Chemiresistive Sensors

The development of simple and low-cost chemical sensors is critically important for improving human life. Many types of chemical sensors have been developed. Among them, the chemiresistive sensors receive particular attention because of their simple structure, the ease of high precise measurement and the low cost. This review mainly focuses on carbon nanotube (CNT)-based chemiresistive sensors. We first describe the properties of CNTs and the structure of CNT chemiresistive sensors. Next, the sensing mechanism and the performance parameters of the sensors are discussed. Then, we detail the status of the CNT chemiresistive sensors for detection of different analytes. Lastly, we put forward the remaining challenges for CNT chemiresistive sensors and outlook the possible opportunity for CNT chemiresistive sensors in the future.
Domain named entity recognition method based on skip-gram model

Traditional domain named entity recognition (NER) methods mainly depended on manual features and were implemented by machine learning methods. These features have no capability to express semantic meaning and these methods are very sensitive for artificial features. To resolve these problems, a method based on Skip-gram model is proposed in this paper. In this method, using word embedding with semantic meaning as features, named entity recognition problem is straightly modeled as Skip-gram model, so it achieves end-to-end solution. Domain characteristics are integrated into this model for further improvement in result. The experiment is carried on Sogou and domain corpus. It shows that the proposed method can improve Recall, Precision and F measure of domain named entity recognition.
Auxiliary Truncated Unscented Kalman Filtering for Bearings-Only Maneuvering Target Tracking

Novel auxiliary truncated unscented Kalman filtering (ATUKF) is proposed for bearings-only maneuvering target tracking in this paper. In the proposed algorithm, to deal with arbitrary changes in motion models, a modified prior probability density function (PDF) is derived based on some auxiliary target characteristics and current measurements. Then, the modified prior PDF is approximated as a Gaussian density by using the statistical linear regression (SLR) to estimate the mean and covariance. In order to track bearings-only maneuvering target, the posterior PDF is jointly estimated based on the prior probability density function and the modified prior probability density function, and a practical algorithm is developed. Finally, compared with other nonlinear filtering approaches, the experimental results of the proposed algorithm show a significant improvement for both the univariate nonstationary growth model (UNGM) case and bearings-only target tracking case.
Allergy detection with statistical modelling of HRV-based non-reaction baseline features

This paper investigates the automated classification of oral food challenges ('allergy tests'). The electrocardiograms (ECG) of the subjects being tested for allergies were recorded via a wireless mote, and the QRS complexes were manually annotated and 18 features were extracted from the signals. Principal component analysis was used for feature decorelation and dimensionality reduction and diagonal covariance Gaussian mixture models were used to model non-reaction baseline patient condition. The generated subject independent log likelihood plots were used to separate allergic reaction by means of subject adaptive thresholding. The platform resulted in 87% accuracy of classification with 100% specificity. The algorithm presented can detect allergy up to 30 minutes sooner than the current state of the clinical art allergy detection (7minutes ± 9).
Virtual data mules for data collection in road-side sensor networks

It has been proven that the use of mobile nodes as data mules in wireless sensor networks can improve the information collection process, increase the sensors' lifetime, and reduce the probability that sensors experience buffer overflow. To that end, data mules are typically required to follow precise trajectories. However, controlling the movement of mobile nodes is not always possible. In this paper, we introduce the concept of virtual data mule (VDM), which, by leveraging opportunistic cooperation between mobile nodes, abstracts the data collection function from specific physical mules. We apply the VDM concept to the case of data retrieval from road-side sensors through vehicular nodes. We propose two VDM algorithms targeting such a scenario and, by means of simulation in realistic mobility settings, we show that VDMs can fulfill data collection requirements that are simply unfeasible when traditional data mules are employed.
Smart Geographic object: Toward a new understanding of GIS Technology in Ubiquitous Computing

One of the fundamental aspects of ubiquitous computing is the instrumentation of the real world by smart devices. This instrumentation constitutes an opportunity to rethink the interactions between human beings and their environment on the one hand, and between the components of this environment on the other. In this paper we discuss what this understanding of ubiquitous computing can bring to geographic science and particularly to GIS technology. Our main idea is the instrumentation of the geographic environment through the instrumentation of geographic objects composing it. And then investigate how this instrumentation can meet the current limitations of GIS technology, and offers a new stage of rapprochement between the earth and its abstraction. As result, the current research work proposes a new concept we named Smart Geographic Object SGO. The latter is a convergence point between the smart objects and geographic objects, two concepts appertaining respectively to t
A Novel Role of Listeria monocytogenes Membrane Vesicles in Inhibition of Autophagy and Cell Death

Bacterial membrane vesicle (MV) production has been mainly studied in Gram-negative species. In this study, we show that Listeria monocytogenes, a Gram-positive pathogen that causes the food-borne illness listeriosis, produces MVs both in vitro and in vivo. We found that a major virulence factor, the pore-forming hemolysin listeriolysin O (LLO), is tightly associated with the MVs, where it resides in an oxidized, inactive state. Previous studies have shown that LLO may induce cell death and autophagy. To monitor possible effects of LLO and MVs on autophagy, we performed assays for LC3 lipidation and LDH sequestration as well as analysis by confocal microscopy of HEK293 cells expressing GFP-LC3. The results revealed that MVs alone did not affect autophagy whereas they effectively abrogated autophagy induced by pure LLO or by another pore-forming toxin from Vibrio cholerae, VCC. Moreover, Listeria monocytogenes MVs significantly decreased Torin1-stimulated macroautophagy. In addition, MVs protected against necrosis of HEK293 cells caused by the lytic action of LLO. We explored the mechanisms of LLO-induced autophagy and cell death and demonstrated that the protective effect of MVs involves an inhibition of LLO-induced pore formation resulting in inhibition of autophagy and the lytic action on eukaryotic cells. Further, we determined that these MVs help bacteria to survive inside eukaryotic cells (mouse embryonic fibroblasts). Taken together, these findings suggest that intracellular release of MVs from L. monocytogenes may represent a bacterial strategy to survive inside host cells, by its control of LLO activity and by avoidance of destruction from the autophagy system during infection.
Feature extraction using gaze of participants for classifying gender of pedestrians in images

Human participants look at informative regions when attempting to identify the gender of a pedestrian in images. In our preliminary experiment, participants mainly looked at the head and chest regions when classifying gender in these images. Thus, we hypothesized that the regions in which participants gaze locations were clustered would contain discriminative features for a gender classifier. In this paper, we discuss how to reveal and use gaze locations for the gender classification of pedestrian images. Our method acquired the distribution of gaze locations from various participants while they manually classified gender. We termed this distribution a gaze map. To extract discriminative features, we assigned large weights to regions with clusters of gaze locations in the gaze map. Our experiments show that this gaze-based feature extraction method significantly improved the performance of gender classification when combined with either a deep learning or a metric learning classifier.
Application of Hyperspectral Imaging to Detect Sclerotinia sclerotiorum on Oilseed Rape Stems

Hyperspectral imaging covering the spectral range of 384–1034 nm combined with chemometric methods was used to detect Sclerotinia sclerotiorum (SS) on oilseed rape stems by two sample sets (60 healthy and 60 infected stems for each set). Second derivative spectra and PCA loadings were used to select the optimal wavelengths. Discriminant models were built and compared to detect SS on oilseed rape stems, including partial least squares-discriminant analysis, radial basis function neural network, support vector machine and extreme learning machine. The discriminant models using full spectra and optimal wavelengths showed good performance with classification accuracies of over 80% for the calibration and prediction set. Comparing all developed models, the optimal classification accuracies of the calibration and prediction set were over 90%. The similarity of selected optimal wavelengths also indicated the feasibility of using hyperspectral imaging to detect SS on oilseed rape stems. The results indicated that hyperspectral imaging could be used as a fast, non-destructive and reliable technique to detect plant diseases on stems.
Integrative analysis of DNA copy number, DNA methylation and gene expression in multiple myeloma reveals alterations related to relapse

Multiple myeloma (MM) remains incurable despite the introduction of novel agents, and a relapsing course is observed in most patients. Although the development of genomic technologies has greatly improved our understanding of MM pathogenesis, the mechanisms underlying relapse have been less thoroughly investigated. In this study, an integrative analysis of DNA copy number, DNA methylation and gene expression was conducted in matched diagnosis and relapse samples from MM patients. Overall, the acquisition of abnormalities at relapse was much more frequent than the loss of lesions present at diagnosis, and DNA losses were significantly more frequent in relapse than in diagnosis samples. Interestingly, copy number abnormalities involving more than 100 Mb of DNA at relapse significantly affect the gene expression of these samples, provoking a particular deregulation of the IL-8 pathway. On the other hand, no significant modifications of gene expression were observed in those samples with less than 100 Mb affected by chromosomal changes. Although several statistical approaches were used to identify genes whose abnormal expression at relapse was regulated by methylation, only two genes that were significantly deregulated in relapse samples (SORL1 and GLT1D1) showed a negative correlation between methylation and expression. Further analysis revealed that DNA methylation was involved in regulating SORL1 expression in MM. Finally, relevant changes in gene expression observed in relapse samples, such us downregulation of CD27 and P2RY8, were most likely not preceded by alterations in the corresponding DNA. Taken together, these results suggest that the genomic heterogeneity described at diagnosis remains at relapse.
4D effect classification by encoding CNN features

4D effects are physical effects simulated in sync with videos, movies, and games to augment the events occurring in a story or a virtual world. Types of 4D effects commonly used for the immersive media may include seat motion, vibration, flash, wind, water, scent, thunderstorm, snow, and fog. Currently, the recognition of physical effects from a video is mainly conducted by human experts. Although 4D effects are promising in giving immersive experience and entertainment, this manual production has been the main obstacle to faster and wider application of 4D effects. In this paper, we utilize pretrained models of Convolutional Neural Networks (CNNs) to extract local visual features and propose a new representation method that combines extracted features into video level features. Classification tasks are conducted by employing Support Vector Machine (SVM). Comprehensive experiments are performed to investigate different architecture of CNNs and different type of features for 4D effect classification task and compare baseline average pooling method with our proposed video level representation. Our framework outperforms the baseline up to 2–3% in terms of mean average precision (mAP).
Space-planning: placement of modules with controlled empty area by single-sequence

The single-sequence (simply SS ) is a permutation of integers 1,2,3,. , n each represents an object placed on a plane without mutual overlapping. An SS represents a system of ABLR-relations (above, below, left-of, right-of) between every pair of integers by SS-to-ABLR rule: if (x, y) is in SS in this order and xy, then x is left-of or below y, respectively. If objects are rooms of a T-junction floorplan of n rooms and integers denote their Abe-orders, the ABLR-relations between rooms are coded to an SS by just reading the rooms in a dual way. We enhance the definition and the fact to the case when the floorplan consists of m(/spl ges/n) rooms and the ABLR-relations are defined on the focused n of these rooms. Coding, i.e. getting the minimum SS from a given floorplan and decoding, i.e. getting the minimum floorplan from a given SS such that focused n rooms satisfy the ABLR-relations defined through the SS-to-ABLR rule are fixed by linear time algorithms. The first application is in a core technique space-planning in physical design of large systems. It is to suggest global routes to insert empty-rooms to relieve spot congestion, critical-path, crosstalk, unroutable nets, etc. without changing the ABLR-relations among the focused rooms.
Vibro-Shock Dynamics Analysis of a Tandem Low Frequency Resonator—High Frequency Piezoelectric Energy Harvester

Frequency up-conversion is a promising technique for energy harvesting in low frequency environments. In this approach, abundantly available environmental motion energy is absorbed by a Low Frequency Resonator (LFR) which transfers it to a high frequency Piezoelectric Vibration Energy Harvester (PVEH) via impact or magnetic coupling. As a result, a decaying alternating output signal is produced, that can later be collected using a battery or be transferred directly to the electric load. The paper reports an impact-coupled frequency up-converting tandem setup with different LFR to PVEH natural frequency ratios and varying contact point location along the length of the harvester. RMS power output of different frequency up-converting tandems with optimal resistive values was found from the transient analysis revealing a strong relation between power output and LFR-PVEH natural frequency ratio as well as impact point location. Simulations revealed that higher power output is obtained from a higher natural frequency ratio between LFR and PVEH, an increase of power output by one order of magnitude for a doubled natural frequency ratio and up to 150% difference in power output from different impact point locations. The theoretical results were experimentally verified.
Sensor selection for source extraction in heterogeneous wireless sensor networks

A sensor selection scheme for source extraction in heterogeneous wireless sensor networks is proposed. The proposed scheme selects the sensors corresponding to the reconstructed mixtures with the largest signal‐to‐noise‐ratios to transmit their data to a fusion center. It exploits the heterogeneity of the sensor observations and reduces the amount of transmissions. The results demonstrate that the proposed scheme outperforms the full utilization scheme and the random selection scheme. Copyright © 2009 John Wiley & Sons, Ltd.
Light-field flow: A subpixel-accuracy depth flow estimation with geometric occlusion model from a single light-field image

Light-field cameras capture not only 2D images, but also the angles of the incoming light. These additional light angles bring the benefit of getting a sub-aperture image array from a single light-field image. Inspired by the traditional optical flow with occlusion detection, this paper focuses on the correlation analysis and the occlusion modeling for the sub-aperture array, and unifies them into a light-field flow framework. The main challenges faced are subpixel displacements and occlusion handling among the sub-aperture images. We build a light-field flow for joint depth estimation and occlusion detection, and develop a geometric occlusion model. More specifically, we firstly estimate subpixel-accuracy optical flows from each two sub-aperture images by the phase shift theorem, then a forward-backward consistency checking is adopted to detect the occluded regions. According to the geometric complementary character of occlusion in a light-field image, an occlusion filling strategy is proposed to refine depth estimation in the occluded regions. Experimental results on the synthetic scenes and Lytro Illum camera data both demonstrate the effectiveness and robustness of our method which has excellent performance in handling occlusions.
Generalized Chirp Scaling Combined with Baseband Azimuth Scaling Algorithm for Large Bandwidth Sliding Spotlight SAR Imaging

This paper presents an efficient and precise imaging algorithm for the large bandwidth sliding spotlight synthetic aperture radar (SAR). The existing sub-aperture processing method based on the baseband azimuth scaling (BAS) algorithm cannot cope with the high order phase coupling along the range and azimuth dimensions. This coupling problem causes defocusing along the range and azimuth dimensions. This paper proposes a generalized chirp scaling (GCS)-BAS processing algorithm, which is based on the GCS algorithm. It successfully mitigates the deep focus along the range dimension of a sub-aperture of the large bandwidth sliding spotlight SAR, as well as high order phase coupling along the range and azimuth dimensions. Additionally, the azimuth focusing can be achieved by this azimuth scaling method. Simulation results demonstrate the ability of the GCS-BAS algorithm to process the large bandwidth sliding spotlight SAR data. It is proven that great improvements of the focus depth and imaging accuracy are obtained via the GCS-BAS algorithm.