ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Sparse analysis for mesoscale convective systems tracking

In this paper, we study the tracking of deformable shapes in sequences of images. Our target application is the tracking of clouds in satellite images. We propose to use a recent state-of-the-art method for off-the-grid sparse analysis to describe clouds in image as mixtures of 2D atoms. Then, we introduce a method to handle the tracking with its specificities: apparition or disappearance of objects, merging, and splitting. Numerically, this method corroborates the magnitude of the results provided by recent state-of-the-art alternatives. Unlike its counterparts, the choice or regularization and correlation parameters allows additional flexibility regarding the interpretation of clouds' life cycles. Finally, it also provides additional information on the cloud temperature during its life cycle, which seem in accordance with the underlying physical processes.
An Efficient SVD Shrinkage for Rank Estimation

Matrix rank estimation is a classical problem with many applications in statistical signal processing. In this letter, a logistic function based thresholding of the singular values is proposed for the rank estimation purpose. Parameters of the proposed shrinkage function are tuned using Stein's unbiased risk estimator. The proposed method is shown to outperform the state-of-the-art methods in terms of rank estimation accuracy. Further, it is also noted to result in a better denoising performance.
Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation

Localization techniques are becoming key to add location context to the Internet-of-Things (IoT) data without human perception and intervention. Meanwhile, the newly emerged low-power wide-area network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources have limited localization performance by using such IoT signals. This article reviews the IoT localization system through the following sequence: IoT localization system review, localization data sources, localization algorithms, localization error sources and mitigation, and localization performance evaluation. Compared to the related surveys, this article has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.
Rhythmic auditory stimulation during gait adaptation enhances learning aftereffects and savings by reducing common neural drives to lower limb muscles

Rhythmic auditory stimulation (RAS) improves gait symmetry in neurological patients with asymmetric gait patterns. However, whether RAS can accelerate gait adaptation remains unclear. This study aimed to investigate whether RAS during gait adaptation can enhance learning aftereffects and savings of gait symmetries. Furthermore, we investigated the differences in the coherence of paired surface electromyographic (EMG) recordings during gait adaptation between with and without RAS. Nineteen healthy young adults were subjected to continuous treadmill gait with swing phase perturbation (adaptation period) with or without RAS (RAS or no-RAS condition) for 5 or 10 min (short- or long-time condition), without the perturbation for 5 min (de-adaptation period), and with the perturbation for another 5 min (re-adaptation period). Swing phase and step length symmetries were significantly greater in the RAS conditions than in the no-RAS conditions during the adaptation period. Learning aftereffects and savings of gait symmetries were significantly greater in the RAS conditions than in the no-RAS conditions in the early de-adaptation and re-adaptation periods, respectively. There were no significant differences in savings in the early re-adaptation period between the short- and long-time conditions in the RAS condition. EMG-EMG coherence in the rectus femoris muscle in the β band (15-35 Hz) on the perturbed side was significantly lower during the early adaptation period in the RAS than in the no-RAS conditions. Therefore, RAS may enhance learning efficiency by reducing common neural drives from a cortical structure during gait adaptation, which could induce high savings of a learned gait pattern, even within short-time periods.NEW & NOTEWORTHY RAS during gait adaptation against swing phase perturbation enhances learning aftereffects and savings of gait symmetries. EMG-EMG coherence in the rectus femoris muscle in the β band on the perturbed side during the swing phase was significantly lower in the RAS than in the no-RAS conditions during the early adaptation period. These results support the application of RAS as external feedback to improve gait symmetry during gait adaptation in the rehabilitation of neurological patients.
Multimodal Triplet Attention Network for Brain Disease Diagnosis

Multi-modal imaging data fusion has attracted much attention in medical data analysis because it can provide complementary information for more accurate analysis. Integrating functional and structural multi-modal imaging data has been increasingly used in the diagnosis of brain diseases, such as epilepsy. Most of the existing methods focus on the feature space fusion of different modalities but ignore the valuable high-order relationships among samples and the discriminative fused features for classification. In this paper, we propose a novel framework by fusing data from two modalities of functional MRI (fMRI) and diffusion tensor imaging (DTI) for epilepsy diagnosis, which effectively captures the complementary information and discriminative features from different modalities by high-order feature extraction with the attention mechanism. Specifically, we propose a triple network to explore the discriminative information from the high-order representation feature space learned from multi-modal data. Meanwhile, self-attention is introduced to adaptively estimate the degree of importance between brain regions, and the cross-attention mechanism is utilized to extract complementary information from fMRI and DTI. Finally, we use the triple loss function to adjust the distance between samples in the common representation space. We evaluate the proposed method on the epilepsy dataset collected from Jinling Hospital, and the experiment results demonstrate that our method is significantly superior to several state-of-the-art diagnosis approaches.
Multi-sense embeddings through a word sense disambiguation process

Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. Nevertheless, traditional models often fall short in intrinsic issues of linguistics, such as polysemy and homonymy. Any expert system that makes use of natural language in its core, can be affected by a weak semantic representation of text, resulting in inaccurate outcomes based on poor decisions. To mitigate such issues, we propose a novel approach called Most Suitable Sense Annotation (MSSA), that disambiguates and annotates each word by its specific sense, considering the semantic effects of its context. Our approach brings three main contributions to the semantic representation scenario: (i) an unsupervised technique that disambiguates and annotates words by their senses, (ii) a multi-sense embeddings model that can be extended to any traditional word embeddings algorithm, and (iii) a recurrent methodology that allows our models to be re-used and their representations refined. We test our approach on six different benchmarks for the word similarity task, showing that our approach can produce state-of-the-art results and outperforms several more complex state-of-the-art systems. Keywords: Multi-sense embeddings Natural language processing Word similarity Systems. (C) 2019 Elsevier Ltd. All rights reserved.
Data Extrapolation From Learned Prior Images for Truncation Correction in Computed Tomography

Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction from limited data. However, its robustness is still a concern for clinical applications. Although the image quality of learning-based compensation schemes may be inadequate for clinical diagnosis, they can provide prior information for more accurate extrapolation than conventional heuristic extrapolation methods. With extrapolated projection, a conventional image reconstruction algorithm can be applied to obtain a final reconstruction. In this work, a general plug-and-play (PnP) method for truncation correction is proposed based on this idea, where various deep learning methods and conventional reconstruction algorithms can be plugged in. Such a PnP method integrates data consistency for measured data and learned prior image information for truncated data. This shows to have better robustness and interpretability than deep learning only. To demonstrate the efficacy of the proposed PnP method, two state-of-the-art deep learning methods, FBPConvNet and Pix2pixGAN, are investigated for truncation correction in cone-beam CT in noise-free and noisy cases. Their robustness is evaluated by showing false negative and false positive lesion cases. With our proposed PnP method, false lesion structures are corrected for both deep learning methods. For FBPConvNet, the root-mean-square error (RMSE) inside the FOV can be improved from 92HU to around 30HU by PnP in the noisy case. Pix2pixGAN solely achieves better image quality than FBPConvNet solely for truncation correction in general. PnP further improves the RMSE inside the FOV from 42HU to around 27HU for Pix2pixGAN. The efficacy of PnP is also demonstrated on real clinical head data.
Lightweight Polymer-Carbon Composite Current Collector for Lithium-Ion Batteries

A hermetic dense polymer-carbon composite-based current collector foil (PCCF) for lithium-ion battery applications was developed and evaluated in comparison to state-of-the-art aluminum (Al) foil collector. Water-processed LiNi0.5Mn1.5O4 (LMNO) cathode and Li4Ti5O12 (LTO) anode coatings with the integration of a thin carbon primer at the interface to the collector were prepared. Despite the fact that the laboratory manufactured PCCF shows a much higher film thickness of 55 mu m compared to Al foil of 19 mu m, the electrode resistance was measured to be by a factor of 5 lower compared to the Al collector, which was attributed to the low contact resistance between PCCF, carbon primer and electrode microstructure. The PCCF-C-primer collector shows a sufficient voltage stability up to 5 V vs. Li/Li+ and a negligible Li-intercalation loss into the carbon primer. Electrochemical cell tests demonstrate the applicability of the developed PCCF for LMNO and LTO electrodes, with no disadvantage compared to state-of-the-art Al collector. Due to a 50% lower material density, the lightweight and hermetic dense PCCF polymer collector offers the possibility to significantly decrease the mass loading of the collector in battery cells, which can be of special interest for bipolar battery architectures.
RESEARCH ON THE FACTORS OF ART IN THE DEVELOPMENT OF URBAN ENVIRONMENT

In recent years, artworks have continued to appear in cities, building an art city to achieve sustainable development. The art area is a cultural and artistic area featuring "art" in urban cultural construction with social service and commercial functions. Summarising the development process of more than 20 years, in the process of building the art district, we should highlight cultural characteristics and increase the "participation" of the people; strengthen the social service function and improve the "publicity" of the art district; promote aesthetic life, raise the residents' "happiness index" to construct a new model of sustainable art district development and give play to the new role of art districts in urban cultural construction. Identify the critical success factors for urban environmental development, verify the effectiveness of the evaluation process, and analyse the interrelationship between them. The research results can help the stakeholders of the better understand the successful development of the eco-city project and provide a valuable reference for managers to adopt the development strategy of the eco-city project in the future.
Fast incremental structure from motion based on parallel bundle adjustment

Structure from motion has attracted a lot of research in recent years, with new state-of-the-art approaches coming almost every year. One of its advantages over 3D reconstruction is that it can be used for any cameras (UAVs, depth sensor, light field) and produces relatively accurate point clouds and camera parameters. One of its disadvantages compared to other approaches is that it is computationally expensive. In this paper, we design a novel structure-from-motion framework to reduce the computational cost and implement a parallel bundle adjustment on GPU device for large-scale optimization. In our framework, the local bundle adjustment is added into the architecture of the incremental structure from motion; namely, the point clouds and camera's parameters are optimized when an additional number of images was added. Then, the purpose is not only to improve the quality of the produced point clouds but also to reduce computation time via parallel bundle adjustment. We conduct extensively experiments on several challenging datasets and make comparison with the state-of-the-art methods. Experimental results show that the proposed method has the best performance in terms of accuracy and efficiency.
Estrogen Receptor Beta 1: A Potential Therapeutic Target for Female Triple Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer characterized by the absence of estrogen receptor alpha, progesterone receptor, and HER2. These receptors often serve as targets in breast cancer treatment. As a result, TNBCs are difficult to treat and have a high propensity to metastasize to distant organs. For these reasons, TNBCs are responsible for over 50% of all breast cancer mortalities while only accounting for 15% to 20% of breast cancer cases. However, estrogen receptor beta 1 (ERβ1), an isoform of the ESR2 gene, has emerged as a potential therapeutic target in the treatment of TNBCs. Using an in vivo xenograft preclinical mouse model with human TNBC, we found that expression of ERβ1 significantly reduced both primary tumor growth and metastasis. Moreover, TNBCs with elevated levels of ERβ1 showed reduction in epithelial to mesenchymal transition markers and breast cancer stem cell markers, and increases in the expression of genes associated with inhibition of cancer cell invasiveness and metastasis, suggesting possible mechanisms underlying the antitumor activity of ERβ1. Gene expression analysis by quantitative polymerase chain reaction and RNA-seq revealed that treatment with chloroindazole, an ERβ-selective agonist ligand, often enhanced the suppressive activity of ERβ1 in TNBCs in vivo or in TNBC cells in culture, suggesting the potential utility of ERβ1 and ERβ ligand in improving TNBC treatment. The findings enable understanding of the mechanisms by which ERβ1 impedes TNBC growth, invasiveness, and metastasis and consideration of ways by which treatments involving ERβ might improve TNBC patient outcome.
Dyeing and finishing wastewater treatment in China: State of the art and perspective

China has the largest textile production and export in the world, with a massive amount of wastewater and related pollutants releasing. With an increasing concern to improve water quality, dyeing and finishing wastewater (DFW) requires better disposal or reclamation. In this review, the characteristics of wastewater in different natural and synthetic fiber production processes as well as the origins of their specific pollutants such as aniline, absorbable organic halogens (AOX), sulfide, hexavalent chromium and antimony were summarized. The discharge standards for DFW in China, have undergone complex evolution processes, which were discussed including the current standard, the revision detail and the one to be implemented. Furthermore, the current status of pollutant reduction in the textile industry, covering cleaner production and wastewater treatment, was targeted, in particular the state of the art of specific pollutant removal technologies and their feasibility of application for DFW treatment. Also, reclamation systems based on wastewater discharge characteristics and reclaimed water quality requirements were reviewed. Overall, the systematic solution of source reduction of wastewater and contaminants, coupled with subsequent treatment and recycling, is crucial for the sustainable development of the textile industry.
Social and Structural Factors Shaping High Rates of Incarceration among Sex Workers in a Canadian Setting

In light of the emphasis on enforcement-based approaches towards sex work, and the well-known negative impacts of these approaches on women's health, safety and well-being, we conducted a study to investigate the prevalence and correlates of recent incarceration among a cohort of women sex workers in Vancouver, Canada. Data were obtained from an open prospective community cohort of female and transgender women sex workers, known as An Evaluation of Sex Workers' Health Access (AESHA). Bivariate and multivariable logistic regression analyses, using generalized estimating equations (GEE), were used to model the effect of social and structural factors on the likelihood of incarceration over the 44-month follow-up period (January 2010-August 2013). Among 720 sex workers, 62.5 % (n = 450) reported being incarcerated in their lifetime and 23.9 % (n = 172) being incarcerated at least once during the study period. Of the 172 participants, about one third (36.6 %) reported multiple episodes of incarceration. In multivariable GEE analyses, younger age (adjusted odds ratio [AOR] = 1.04 per year younger, 95 % confidence interval [CI] 1.02-1.06), being of a sexual/gender minority (AOR = 1.62, 95 % CI 1.13-2.34), heavy drinking (AOR = 1.99, 95 % CI 1.20-3.29), being born in Canada (AOR = 3.28, 95 % CI 1.26-8.53), living in unstable housing conditions (AOR = 4.32, 95 % CI 2.17-8.62), servicing clients in public spaces (versus formal sex work establishments) (AOR = 2.33, 95 % CI 1.05-5.17) and experiencing police harassment without arrest (AOR = 1.82, 95 % CI 1.35-2.45) remain independently correlated with incarceration. This prospective study found a very high prevalence and frequency of incarceration among women sex workers in Vancouver, Canada, with the most vulnerable and marginalized women at increased risk of incarceration. Given the well-known social and health harms associated with incarceration, and associations between police harassment and incarceration in this study, our findings further add to growing calls to move away from criminalized and enforcement-based approaches to sex work in Canada and globally.
Uniform Distribution of Ruthenium Nanoparticles on Nitrogen-Doped Carbon Nanostructure for Oxygen Reduction Reaction

Highly efficient, durable, and economically viable electrocatalysts for oxygen reduction reaction (ORR) is central for energy conversion and storage devices such as fuel cells and metal-air batteries. Despite of the enormous recent achievements, it is challenging to achieve satisfactory activity and stability at the same time due to lack of fundamental understanding on the activity governing factors. Here, we demonstrate the mechanistic insight of oxygen reduction on the uniformly dispersed Ru nanoparticles on N-doped carbon support (Ru@NC). The promotion of energy efficient inner-sphere electron transfer mechanism with reduced HO2- generation demonstrates as the crucial descriptor for appreciable activity and remarkable stability. In terms of the mass activity, the developed catalyst outperforms other previously reported state-of-the-art catalysts. The greatly suppressed HO2- generation endows remarkable stability (similar to 6 mV of negative shift in half-wave potential and only 1.9% reduction of activity at 0.9 V after 10000 potential cycles) in alkaline medium. Overall, Ru@NC exhibits remarkably superior stability which is much better than the limit fixed the U.S. of (DOE) and the state-of-the-art Pt/C
Resting-state functional connectivity in adults with 47,XXX: a 7 Tesla MRI study

Triple X syndrome is a sex chromosomal aneuploidy characterized by the presence of a supernumerary X chromosome, resulting in a karyotype of 47,XXX in affected females. It has been associated with a variable cognitive, behavioral, and psychiatric phenotype, but little is known about its effects on brain function. We therefore conducted 7 T resting-state functional magnetic resonance imaging and compared data of 19 adult individuals with 47,XXX and 21 age-matched healthy control women using independent component analysis and dual regression. Additionally, we examined potential relationships between social cognition and social functioning scores, and IQ, and mean functional connectivity values. The 47,XXX group showed significantly increased functional connectivity of the fronto-parietal resting-state network with the right postcentral gyrus. Resting-state functional connectivity (rsFC) variability was not associated with IQ and social cognition and social functioning deficits in the participants with 47,XXX. We thus observed an effect of a supernumerary X chromosome in adult women on fronto-parietal rsFC. These findings provide additional insight into the role of the X chromosome on functional connectivity of the brain. Further research is needed to understand the clinical implications of altered rsFC in 47,XXX.
Triple Antibiotic Paste: A Suitable Medicament for Intracanal Disinfection

With increasing cases of odontogenic infections, advancements in the treatment modality gain utmost importance. Complexity in the anatomy of the root canal necessitates the selection of the correct medicament and disinfectant. Furthermore, exacerbation of the problem results due to improper cleaning and disinfection of the root canal space. In such cases, manual preparation and irrigation alone will be of no help. The treatment outcome mostly depends upon the correct selection and application of the proper intracanal disinfectant along with the proper choice of medicament. One such intracanal disinfectant is triple antibiotic paste (TAP), a mix of three antibiotics. It's the combined effect of the three drugs mixed in the paste that makes the mix a potent antimicrobial agent effective against microbes. This review aims to evaluate the properties of TAP, its composition, its various application, and its property to help maintain the vitality of the diseased pulp. This review also talks about its drawbacks and its application in primary teeth.