ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Fluorescent melamine-formaldehyde/polyamine coatings for microcapsules enabling their tracking in composites

This study aimed the development of fluorescent melamine-formaldehyde (MF)/polyamine coatings for labelling of prefabricated microcapsules and their tracking in composites. The composition of the fluorescent MF coatings was studied by FTIR spectroscopy, thermogravimetric analysis, and elemental analysis. The characteristics of the coatings and its deposition on different surfaces were investigated using optical and fluorescence microscopy and fluorescence spectroscopy. MF prepolymers were polymerised with tri- and polyamines yielding in fluorescent coatings without addition of fluorescent dyes. Both, MF/poly(ethylene imine) and MF/poly(vinyl amine) (PVAm) coated glass beads showed maximum fluorescence at an excitation wavelength of λmax = 360 nm with the emission maxima at λmax = 490 nm and λmax = 410 nm, correspondingly. The MF/PVAm polymer was coated on diuron-poly(methyl methacrylate) microcapsules and tracked in highly filled composites (water-based plaster/paint) to show its applicability. MF/polyamine coatings were identified as promising materials for the fluorescent labelling of prefabricated microcapsules.
Role of Vitamin D Deficiency in Increased Susceptibility to Respiratory Infections Among Children: A Systematic Review

Vitamin D has several roles in the immune system besides its effects on bone metabolism. Acute respiratory infections are common infections in children. Severe lower respiratory tract infections (LRTIs) even cause death in children, especially in those less than five years of age. Our study aims to examine whether children with vitamin D deficiency are susceptible to respiratory infections and to study the association between vitamin D deficiency and the severity of respiratory infections. We comprehensively searched research articles in PubMed, ScienceDirect, and Cochrane library databases. The main keywords were vitamin D deficiency, respiratory infections, and children. We used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to conduct this systematic review. The initial search showed 16,120 papers. A meticulous screening of research articles using the eligibility criteria and quality appraisal tools was done. Finally, 10 research articles qualified for this systematic review, including eight case-control studies, one randomized controlled trial (RCT), and one cohort study. Seven of 10 research studies reviewed found that children with low vitamin D levels are susceptible to respiratory infections. Five studies discussed the severity of respiratory infections and low vitamin D levels. This systematic review concluded that children with low vitamin D levels are prone to developing respiratory infections. But we could not find a conclusive association between the severity of respiratory infections and low vitamin D levels.
High-Performance 1-D and 2-D Inverse DWT 5/3 Filter Architectures for Efficient Hardware Implementation

This paper presents high-performance and memory-efficient hardware architectures for one-dimensional (1-D) and two-dimensional (2-D) inverse discrete wavelet transform (DWT) 5/3 filters. The proposed 1-D filter architecture requires 33% less memory resources and 17% less logic resources than the best state-of-the-art solutions. The proposed 1-D filter architecture has 100% hardware utilization, which is defined as the ratio of the actual computation time to the total processing time, both expressed in numbers of clock cycles. It allows a 7% higher operational frequency and simultaneously has the lowest total power dissipation in comparison with the best state-of-the-art solutions. The proposed 2-D inverse DWT 5/3 architecture, based on the proposed 1-D inverse DWT filter design, provides medium total computing time and output latency, but outperforms the best state-of-the-art solutions for at least 20% in terms of required memory capacity.
Simultaneously Tolerate Thermal and Process Variations Through Indirect Feedback Tuning for Silicon Photonic Networks

Silicon photonics is the leading candidate technology for high-speed and low-energy-consumption networks. Thermal and process variations are the two main challenges of achieving high-reliability photonic networks. Thermal variation is due to the heat issues created by application, floorplan, and environment, while process variation is caused by fabrication variability in the deposition, masking, exposition, etching, and doping. Tuning techniques are then required to overcome the impact of the variations and efficiently stabilize the performance of silicon photonic networks. We extend our previous optical switch integration model, BOSIM, to support the variation and thermal analyses. Based on device properties, we propose indirect feedback tuning (IFT) to simultaneously alleviate thermal and process variations. IFT can improve the BER of silicon photonic networks to 10(-9) under different variation situations. Compared to state-of-the-art techniques, IFT can achieve an up to 1.52 x 10(8) times bit-error-rate improvement and 4.11X better heater energy efficiency. Indirect feedback does not require high-speed optical signal detection, and thus, the circuit design of IFT saves up to 61.4% of the power and 51.2% of the area compared to state-of-the-art designs.
4D Printing: dawn of an emerging technology cycle

Purpose - The purpose of this article is to reviews state-of-the-art developments in four-dimensional (4D) printing, discuss what it is, investigate new applications that have been discovered and suggest its future impact. Design/methodology/approach - The article clarifies the definition of 4D printing and describes notable examples covering material science, equipment and applications. Findings - This article highlights an emerging technology cycle where 4D printing research has gained traction within additive manufacturing. The use of stimuli-responsive materials can be programmed and printed to enable pre-determined reactions when subject to external stimuli. Originality/value - This article reviews state-of-the-art developments in 4D printing, discusses what it is, investigates new applications that have been discovered and suggests its future impact.
Reversible Transformation between Isolated Skyrmions and Bimerons

Skyrmions and bimerons are versatile topological spin textures that can be used as information bits for both classical and quantum computing. The transformation between isolated skyrmions and bimerons is an essential operation for computing architecture based on multiple different topological bits. Here we report the creation of isolated skyrmions and their subsequent transformation to bimerons by harnessing the electric current-induced Oersted field and temperature-induced perpendicular magnetic anisotropy variation. The transformation between skyrmions and bimerons is reversible, which is controlled by the current amplitude and scanning direction. Both skyrmions and bimerons can be created in the same system through the skyrmion-bimeron transformation and magnetization switching. Deformed skyrmion bubbles and chiral labyrinth domains are found as nontrivial intermediate transition states. Our results may provide a unique way for building advanced information-processing devices using different types of topological spin textures in the same system.
A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning

Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating intervention outcomes. Reliable, fully automated, and accurate segmentation of pulmonary airway trees is critical to such exploration. We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evaluated and compared with results from several state-of-the-art methods including an industry-standard one, where segmentation results were manually reviewed and corrected. Both new algorithms show a reproducibility of 95% or higher for total lung capacity (TLC) repeat CT scans. Experiments on TLC CT scans from different imaging sites at standard and low radiation dosages show that both new algorithms outperform the other methods in terms of leakages and branch-level accuracy. Considering the performance and execution times, the deep learning-based FG algorithm is a fully automated option for large multi-site studies.
Sketched Subspace Clustering

The immense amount of daily generated and communicated data presents unique challenges in their processing. Clustering, the grouping of data without the presence of ground-truth labels, is an important tool for drawing inferences from data. Subspace clustering (SC) is a relatively recent method that is able to successfully classify nonlinearly separable data in a multitude of settings. In spite of their high clustering accuracy, SC methods incur prohibitively high computational complexity when processing large volumes of high-dimensional data. Inspired by random sketching approaches for dimensionality reduction, the present paper introduces a randomized scheme for SC, termed Sketch-SC, tailored for large volumes of high-dimensional data. Sketch-SC accelerates the computationally heavy parts of state-of-the-art SC approaches by compressing the data matrix across both dimensions using random projections, thus enabling fast and accurate large-scale SC. Performance analysis as well as extensive numerical tests on real data corroborate the potential of Sketch-SC and its competitive performance relative to state-of-the-art scalable SC approaches.
3D Object Detection for Autonomous Driving: A Survey

Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc. . Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth information from images, representation learning from partially occluded unstructured point clouds, and semantic alignments over heterogeneous features from cross modalities. Despite existing efforts, 3D ob-ject detection for autonomous driving is still in its infancy. Recently, a large body of literature have been investigated to address this 3D vision task. Nevertheless, few investigations have looked into collecting and structuring this growing knowledge. We therefore aim to fill this gap in a comprehensive survey, encompassing all the main concerns including sensors, datasets, performance metrics and the recent state-of-the-art detection methods, together with their pros and cons. Furthermore, we provide quan-titative comparisons with the state of the art. A case study on fifteen selected representative methods is presented, involved with runtime analysis, error analysis, and robustness analysis. Finally, we provide concluding remarks after an in-depth analysis of the surveyed works and identify promising directions for future work.(c) 2022 Elsevier Ltd. All rights reserved.
A multi-strategy approach to biological named entity recognition

Recognizing and disambiguating bio-entities (genes, proteins, cells, etc.) names are very challenging tasks as some biologica databases can be outdated, names may not be normalized, abbreviations are used, syntactic and word order is modified, etc. Thus, the same bio-entity might be written into different ways making searching tasks a key obstacle as many candidate relevant literature containing those entities might not be found. As consequence, the same protein mention but using different names should be looked for or the same discovered protein name is being used to name a new protein using completely different features hence named-entity recognition methods are required. In this paper, we developed a bio-entity recognition model which combines different classification methods and incorporates simple pre-processing tasks for bio-entities (genes and proteins) recognition is presented. Linguistic pre-processing and feature representation for training and testing is observed to positively affect the overall performance of the method, showing promising results. Unlike some state-of-the-art methods, the approach does not require additional knowledge bases or specific-purpose tasks for post processing which make it more appealing. Experiments showing the promise of the model compared to other state-of-the-art methods are discussed. (c) 2012 Elsevier Ltd. All rights reserved.
Non-Local Low-Rank Cube-Based Tensor Factorization for Spectral CT Reconstruction

Spectral computed tomography (CT) reconstructs material-dependent attenuation images from the projections of multiple narrow energy windows, which is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection datasets usually have lower signal-noise ratios (SNR). Very recently, a spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectral similarities for spectral CT. This method constructs a group by clustering up a seriesof non-local spatial-spectralcubes. Thesmall size of spatial patches for such a group makes the SSCMF fail to fully encode the sparsity and low-rank properties. The hard-thresholding and collaboration filtering in the SSCMF also cause difficulty in recovering the image features and spatial edges. While all the steps are operated on 4-D group, the huge computational cost andmemory loadmight not be affordable in practice. To avoid the above limitations and further improve the image quality, we first formulate a non-local cube-based tensor instead of group to encode the sparsity and low-rank properties. Then, as a newregularizer, the Kronecker-basis-representation tensor factorization is employed into a basic spectral CT reconstruction model to enhance the capability of image feature extraction and spatial edge preservation, generating a non-local low-rank cube-based tensor factorization (NLCTF) method. Finally, the split-Bregman method is adopted to solve the NLCTF model. Both numerical simulations and preclinical mouse studies are performed to validate and evaluate the NLCTF algorithm. The results show that the NLCTF method outperforms the other state-of-the-art competing algorithms.
DCBT-Net: Training Deep Convolutional Neural Networks With Extremely Noisy Labels

Obtaining data with correct labels is crucial to attain the state-of-the-art performance of Convolutional Neural Network (CNN) models. However, labeling datasets is significantly time-consuming and expensive process because it requires expert knowledge in a particular domain. Therefore, real-life datasets often exhibit incorrect labels due to the involvement of nonexperts in the data-labeling process. Consequently, there are many cases of incorrectly labeled data in the wild. Although the issue of poorly labeled datasets has been studied, the existing methods are complex and difficult to reproduce. Thus, in this study, we proposed a simpler algorithm called "Deep Clean Before Training Net" (DCBT-Net) that is based on cleaning wrongly labeled data points using the information from eigenvalues of the Laplacian matrix obtained from similarities between the data samples. The cleaned data were trained using deep CNN (DCNN) to attain the state-of-the-art results. This system achieved better performance than the existing approaches. In conducted experiments, the performance of the DCBT-Net was tested on three commercially available datasets, namely, Modified National Institute of Standards and Technology (MNIST) database of handwritten digits, Canadian Institute for Advanced Research (CIFAR) and WebVision1000 datasets. The proposed method achieved better results when assessed using several evaluation metrics compared with the existing state-of-the-art methods. Specifically, the DCBT-Net attained an average 15%, 20%, and 3% increase in accuracy score using MNIST database, CIFAR-10 dataset, and WebVision dataset, respectively. Also, the proposed approach demonstrated better results in specificity, sensitivity, positive predictive value, and negative predictive value evaluation metrics.
Impact of Government Support on Performing Artists' Job and Life Satisfaction: Findings from The National Survey in Korea

In this study, we aim to propose motives that can help increase the creative activities of Korean performing artists and discuss the policy implications for the sustainable management of Korean performing arts. First, we investigate the characteristics of Korean artists that receive subsidies as a form of government support for undertaking artistic activities. Second, we examine whether receipt of such grants influences the artists' job and life satisfaction. Through a logistics model, we reconstructed the "2015 Survey Report on Artists & Activities" and validated the research hypothesis. We first considered subsidies that could directly impact artists' income and activities and then verified whether subsidies influence artists' job and life satisfaction. As a result of the research, first, art grants should be supported in order to help artists produce creative and experimental works. Second, we showed that artists' subsidies should be expanded in order to enhance artists' quality of life and the sustainability of artistic activities. Above all, subsidy support for artists showed that art can be legitimate as a public good, which is a common asset in society.
Augmented reality application assessment for disseminating rock art

Currently, marker-based tracking is the most used method to develop augmented reality (AR) applications (apps). However, this method cannot be applied in some complex and outdoor settings such as prehistoric rock art sites owing to the fact that the usage of markers is restricted on site. Thus, natural feature tracking methods have to be used. There is a wide range of libraries to develop AR apps based on natural feature tracking. In this paper, a comparative study of Vuforia and ARToolKit libraries is carried out, analysing factors such as distance, occlusion and lighting conditions that affect user experience in both indoor and outdoor environments, and eventually the app developer. Our analysis confirms that Vuforia's user experience indoor is better, faster and flicker-free whether the images are properly enhanced, but it does not work properly on site. Therefore, the development of AR apps for complex outdoor environments such as rock art sites should be performed with ARToolKit.
A novel C-type lectin from Trichinella spiralis mediates larval invasion of host intestinal epithelial cells

The aim of this study was to investigate the characteristics of a novel type C lectin from Trichinella spiralis (TsCTL) and its role in larval invasion of intestinal epithelial cells (IECs). TsCTL has a carbohydrate recognition domain (CRD) of C-type lectin. The full-length TsCTL cDNA sequence was cloned and expressed in Escherichia coli BL21. The results of qPCR, Western blotting and immunofluorescence assays (IFAs) showed that TsCTL was a surface and secretory protein that was highly expressed at the T. spiralis intestinal infective larva (IIL) stages and primarily located at the cuticle, stichosome and embryos of the parasite. rTsCTL could specifically bind with IECs, and the binding site was localized in the IEC nucleus and cytoplasm. The IFA results showed that natural TsCTL was secreted and bound to the enteral epithelium at the intestinal stage of T. spiralis infection. The rTsCTL had a haemagglutinating effect on murine erythrocytes, while mannose was able to inhibit the rTsCTL agglutinating effect for mouse erythrocytes. rTsCTL accelerated larval intrusion into the IECs, whereas anti-rTsCTL antibodies and mannose significantly impeded larval intrusion in a dose-dependent manner. The results indicated that TsCTL specifically binds to IECs and promotes larval invasion of intestinal epithelium, and it might be a potential target of vaccines against T. spiralis enteral stages.
A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces

Currently, state-of-the-art motor intention decoding algorithms in brain-machine interfaces are mostly implemented on a PC and consume significant amount of power. A machine learning coprocessor in 0.35-mu m CMOS for the motor intention decoding in the brain-machine interfaces is presented in this paper. Using Extreme Learning Machine algorithm and low-power analog processing, it achieves an energy efficiency of 3.45 pJ/MAC at a classification rate of 50 Hz. The learning in second stage and corresponding digitally stored coefficients are used to increase robustness of the core analog processor. The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99.3% for movement type. The same coprocessor is also used to decode time of movement from asynchronous neural spikes. With time-delayed feature dimension enhancement, the classification accuracy can be increased by 5% with limited number of input channels. Further, a sparsity promoting training scheme enables reduction of number of programmable weights by approximate to 2X.
Precise A∙T to G∙C base editing in the allotetraploid rapeseed (Brassica napus L.) genome

Rapeseed is an important source of oilseed crop in the world. Achieving genetic improvement has always been the major goal in rapeseed production. Single nucleotide substitution is the basis of most genetic variation underpinning important agronomic traits. Nowadays, Cas-base editing acts as an efficient tool to mediate single-base substitution at the target site. In this study, four adenine base editors (ABE) were modified to achieve adenosine base editing at different genome sites in allotetraploid Brassica napus. We designed 18 small guide RNAs to target phytoene desaturase (PDS), acetolactate synthase (ALS), CLAVATA3 (CLV3), CLV2, TRANSPARENT TESTA12 (TT12), carotenoid isomerase (CRTISO), designated de-etiolated-2 (DET2), BRANCHED1 (BRC1), zeaxanthin epoxidase (ZEP) genes, respectively. Among the four ABE systems, pBGE17 had the highest base-editing efficiency, with an average editing efficiency of 3.51%. Target sequencing results revealed that the editing window ranged from A5 to A8 of the protospacer-adjacent motif (PAM) sequence. Moreover, the ABEmax-nCas9NG system with NG PAM was developed, with a base-editing efficiency of 1.22%. These results revealed that ABE system developed in this study could efficiently induce A to G substitution and the ABE-nCas9NG system could broaden editing window in oilseed rape.