ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Core-Shell Structured Porous Calcium Phosphate Bioceramic Spheres for Enhanced Bone Regeneration

Adequate new bone regeneration in bone defects has always been a challenge as it requires excellent and efficient osteogenesis. Calcium phosphate (CaP) bioceramics, including hydroxyapatite (HA) and biphasic calcium phosphates (BCPs), have been extensively used in clinical bone defect filling due to their good osteoinductivity and biodegradability. Here, for the first time, we designed and fabricated two porous CaP bioceramic granules with core-shell structures, named in accordance with their composition as BCP@HA and HA@BCP (core@shell). The spherical shape and the porous structure of these granules were achieved by the calcium alginate gel molding technology combined with a H2O2 foaming process. These granules could be stacked to build a porous structure with a porosity of 65-70% and a micropore size distribution between 150 and 450 μm, which is reported to be good for new bone ingrowth. In vitro experiments confirmed that HA@BCP bioceramic granules could promote the proliferation and osteogenic ability when cocultured with bone marrow mesenchymal stem cells, while inhibiting the differentiation of RAW264.7 cells into osteoclasts. In vivo, 12 weeks of implantation in a critical-sized femoral bone defect animal model showed a higher bone volume fraction and bone mineral density in the HA@BCP group than in the BCP@HA or pure HA or BCP groups. From histological analysis, we discovered that the new bone tissue in the HA@BCP group was invading from the surface to the inside of the granules, and most of the bioceramic phase was replaced by the new bone. A higher degree of vascularization at the defect region repaired by HA@BCP was revealed by 3D microvascular perfusion angiography in terms of a higher vessel volume fraction. The current study demonstrated that the core-shell structured HA@BCP bioceramic granules could be a promising candidate for bone defect repair.
Overview of Battery Impedance Modeling Including Detailed State-of-the-Art Cylindrical 18650 Lithium-Ion Battery Cell Comparisons

Electrical models of battery cells are used in simulations to represent batteries' behavior in various fields of research and development involving battery cells and systems. Electrical equivalent circuit models, either linear or nonlinear, are commonly used for this purpose and are presented in this article. Various commercially available cylindrical, state-of-the-art lithium-ion battery cells, both protected and unprotected, are considered. Their impedance properties, according to four different equivalent circuit models, are measured using electrochemical impedance spectroscopies. Furthermore, the pricing, impedance, specific energy, and C-rate of the chosen battery cells are compared. For example, it is shown that the energy density of modern 18650 cells can vary from a typical value of 200 to about 260 Wh kg(-1), whereas the cell price can deviate by a factor of about 3 to 5. Therefore, as a result, this study presents a concise but comprehensive battery parameter library that should aid battery system designers or power electronic engineers in conducting battery simulations and in selecting appropriate battery cells based on application-specific requirements. In addition, the accuracies and computational efforts of the four equivalent circuit models are compared.
Novel Multi-IMU Tight Coupling Pedestrian Localization Exploiting Biomechanical Motion Constraints

In this article, we present a novel tight coupling inertial localization system which simultaneously processes the measurements of two inertial measurement units (IMUs) mounted on the leg, namely the upper thigh and the front part of the foot. Moreover, the proposed system exploits motion constraints of each leg link; that is, the thigh and the foot. To derive these constraints, we carry out a motion tracking experiment to collect both ground truth data and inertial measurements from IMUs mounted on the leg. The performance of the tight coupling system is assessed with a data set of approximately 10 h. The evaluation shows that the average 2D-position error of the proposed tight coupling system is at least 50% better than the average 2D-position error of two state-of-the-art systems, whereas the average height error of the tight coupling system is at least 75% better than the average height error of the two state-of-the-art systems. In this work, we improve the accuracy of the position estimation by introducing biomechanical constraints in an inertial localization system. This article allows to observe, for the first time, heading errors of an inertial localization system by using only inertial measurements and without the need for using maps or repeating totally or partially the walked trajectory.
A Hybrid Network for Large-Scale Action Recognition from RGB and Depth Modalities

The paper presents a novel hybrid network for large-scale action recognition from multiple modalities. The network is built upon the proposed weighted dynamic images. It effectively leverages the strengths of the emerging Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) based approaches to specifically address the challenges that occur in large-scale action recognition and are not fully dealt with by the state-of-the-art methods. Specifically, the proposed hybrid network consists of a CNN based component and an RNN based component. Features extracted by the two components are fused through canonical correlation analysis and then fed to a linear Support Vector Machine (SVM) for classification. The proposed network achieved state-of-the-art results on the ChaLearn LAP IsoGD, NTU RGB+D and Multi-modal & Multi-view & Interactive ((MI)-I-2) datasets and outperformed existing methods by a large margin (over 10 percentage points in some cases).
Graph-Based Rate Control in Pathology Imaging With Lossless Region of Interest Coding

The increasing availability of digital pathology images has motivated the design of tools to foster multi-disciplinary collaboration among researchers, pathologists, and computer scientists. Telepathology plays an important role in the development of collaborative tools, as it facilitates the transmission and access to pathology images by multiple users. However, the huge file size associated with pathology images usually prevents full exploitation of the collaborative telepathology system potential. Within this context, rate control (RC) is an important tool that allows meeting storage and bandwidth requirements by controlling the bit rate of the coded image. In this paper, we propose a novel graph-based RC algorithm with lossless region of interest (RoI) coding for pathology images. The algorithm, which is designed for block-based predictive transform coding methods, compresses the non-RoI in a lossy manner according to a target bit rate and the RoI in a lossless manner. It employs a graph where each node represents a constituent block of the image to be coded. By incorporating information about the coding cost similarities of blocks into the graph, a graph kernel is used to distribute a target bit budget among the non-RoI blocks. In order to increase RC accuracy, the algorithm uses a rate-lambda (R-lambda) model to approximate the slope of the rate-distortion curve of the non-RoI, and a graph-based approach to guarantee that the target bit rate is accurately attained. The algorithm is implemented in the High-Efficiency Video Coding standard and tested over a wide range of pathology images with multiple RoIs. Evaluation results show that it outperforms the other state-of-the-art-methods designed for single images by very accurately attaining the target bit rate of the non-RoI.
Triggered Self-Sorting of Peptides to Form Higher-Order Assemblies in a Living System

Biological components (protein, DNA, lipid rafts, etc.) self-sort to form higher-order structures with elegant modulation by endogenous stimuli for maintaining cellular functions in living cells. However, the challenge of producing self-sorted higher-order assemblies of peptides in living systems (cells and tissues) spatiotemporally has yet to be achieved. This work reports the using of a biocompatible strategy to construct self-sorted assemblies of peptides in living cells and tumor-bearing mice. The results show that the designed peptides self-sort to form distinct nanostructures in living cancer cells using an endogenous trigger, as evidenced by confocal laser scanning microscopy and Bio-EM. Wound-healing experiments indicate that the in situ generation of self-sorted nanostructures exhibits a synergistic effect that significantly decreases the migration of cancer cells. In vivo experiments demonstrate that the designed peptides could self-sort in tumor-bearing mice and improve the tumor penetrating ability of the impenetrable component in tumor tissue. We can further program the formation of self-sorted materials through orthogonal triggers by introducing an exogenous trigger (light) and an endogenous trigger independently. Thus, this work provides a strategy to control multiple self-assembling processes in the context of the living system and provides a general strategy to construct self-sorted structures for the emergent properties of materials science.
CHANCE: Capacitor Charging Management Scheme in Energy Harvesting Systems

The energy efficiency of emerging nonvolatile processors equipped with FRAM-SRAM memory makes them a promising solution for energy harvesting systems. To enable correct functionality and forward progress with an unreliable power supply, the system must accumulate sufficient energy in the capacitor to execute tasks atomically, even in the worst case scenario. Due to the large gap between the average and worst case energy consumption of tasks, state-of-the-art approaches like eM-map require a large capacitor to execute tasks on the SRAM. However, the size, cost, and charging time of the capacitor are major concerns in the energy harvesting systems. In this article, we proposed CHANCE, a capacitor charging management scheme that improves the capacitor size and average response time of an energy harvesting system. CHANCE analyses the energy consumption of tasks to set an appropriate capacitor size to make a balance between capacitor charging time and failure rate for each task. The results show that CHANCE improves the response time of state-of-the-art approaches up to 68% with a five times smaller capacitor.
Divergent responses of inflammatory mediators within the amygdala and medial prefrontal cortex to acute psychological stress

There is now a growing body of literature that indicates that stress can initiate inflammatory processes, both in the periphery and brain; however, the spatiotemporal nature of this response is not well characterized. The aim of this study was to examine the effects of an acute psychological stress on changes in mRNA and protein levels of a wide range of inflammatory mediators across a broad temporal range, in key corticolimbic brain regions involved in the regulation of the stress response (amygdala, hippocampus, hypothalamus, medial prefrontal cortex). mRNA levels of inflammatory mediators were analyzed immediately following 30min or 120min of acute restraint stress and protein levels were examined 0h through 24h post-termination of 120min of acute restraint stress using both multiplex and ELISA methods. Our data demonstrate, for the first time, that exposure to acute psychological stress results in an increase in the protein level of several inflammatory mediators in the amygdala while concomitantly producing a decrease in the protein level of multiple inflammatory mediators within the medial prefrontal cortex. This pattern of changes seemed largely restricted to the amygdala and medial prefrontal cortex, with stress producing few changes in the mRNA or protein levels of inflammatory mediators within the hippocampus or hypothalamus. Consistent with previous research, stress resulted in a general elevation in multiple inflammatory mediators within the circulation. These data indicate that neuroinflammatory responses to stress do not appear to be generalized across brain structures and exhibit a high degree of spatiotemporal specificity. Given the impact of inflammatory signaling on neural excitability and emotional behavior, these data may provide a platform with which to explore the importance of inflammatory signaling within the prefrontocortical-amygdala circuit in the regulation of the neurobehavioral responses to stress.
Extraordinary Creatures: The Role of Birds in Early Iron Age Slovenia

Depictions of birds are overrepresented in the Dolenjska Hallstatt culture, and appear on over a quarter of artefacts depicting animals. A wide variety of artefacts with birds have been found primarily in graves, and crosscut gender, status, and age. However, poor preservation of zooarchaeological remains has made reconstructions of lived human-bird interactions difficult. This study uses ecological and ethological data, combined with local imagery, to provide insight into prehistoric human-bird interfaces in this area, and the cultural conceptions surrounding these interactions. Birds would have been a constant presence in the lives of Dolenjska Hallstatt people; however, human relationships with them were based more on observation than direct interaction. Birds were ubiquitous in imagery, and it is proposed that this stemmed from Dolenjska Hallstatt conceptions of birds as important observers of human actions, ritual mediators, and possibly guides or guardians. Their differences from humans and other animals distinguished them - they were set apart, and depictions highlighted non-normative behaviours. Birds in the Dolenjska Hallstatt worldview were more than animals, ascribed extraordinary capabilities that made them ritually potent and richly symbolic creatures.
Unsupervised cross-domain person re-identification with self-attention and joint-flexible optimization

Unsupervised domain adaptation (UDA) for person re-identication (ReID) remains a challenging task, as the trained ReID system often fails to adapting to a new dataset. Due to the lack of supervision of real labels, the performance of the UDA models suffers from inefficient feature learning and inevitable pseudo label noise. In this work, we tackle the problems by designing an effective dual-path mutual-learning framework which can capture effective information for better feature learning and mitigate the impact of label noise. Firstly, to reduce the impact of occlusion and viewpoints, we introduce the self-attention mechanism in a two-stage strategy making the models focus on the key areas of identifying people. Secondly, considering that UDA is an open-set task, we leverage density-based spatial clustering of applications with noise (DBSCAN) to avoid manually setting the number of classes of the target domain. Thirdly, for realizing joint and flexible optimization under the supervision of soft pseudo labels and hard pseudo labels, a joint and flexible loss (JFL) is proposed to train the network. Experiments on three large-scale datasets show that our model outperforms the state-of-the-art UDA methods in both mAP and top-1 evaluation protocols by large margins. Especially on task of Duke-to-Market, our method outperforms the state-of-the-art by 6.9% mAP. (c) 2021 Elsevier B.V. All rights reserved.
EAD-GAN: A Generative Adversarial Network for Disentangling Affine Transforms in Images

This article proposes a generative adversarial network called explicit affine disentangled generative adversarial network (EAD-GAN), which explicitly disentangles affine transform in a self-supervised manner. We propose an affine transform regularizer to force the InfoGAN to have explicit properties of affine transform. To facilitate training an affine transform encoder, we decompose the affine matrix into two separate matrices and infer the explicit transform parameters by the least-squares method. Unlike the existing approaches, representations learned by the proposed EAD-GAN have clear physical meaning, where transforms, such as rotation, horizontal and vertical zooms, skews, and translations, are explicitly learned from training data. Thus, we set different values of each transform parameter individually to generate specifically affine transformed data by the learned network. We show that the proposed EAD-GAN successfully disentangles these attributes on the MNIST, CelebA, and dSprites datasets. EAD-GAN achieves higher disentanglement scores with a large margin compared to the state-of-the-art methods on the dSprites dataset. For example, on the dSprites dataset, EAD-GAN achieves the MIG and DCI score of 0.59 and 0.96 respectively, compared to 0.37 and 0.71, respectively, for the state-of-the-art methods.
Practical Implementation and Challenges of Artificial Intelligence-Driven Electronic Health Record Evaluation: Protected Health Information

Detecting protected health information in electronic health record systems is often an early step in health care analytics, and it is a nontrivial problem. Specific challenges include finding clinician names and diseases, which lack a fixed format and are often context-dependent. The general problem of finding entities, termed named-entity recognition, has received a substantial amount of attention in the natural language processing and deep learning communities. This paper begins by outlining recent methods for finding protected health information, and it then introduces a hybrid system which combines regular expressions with a natural language processing framework called FLAIR. FLAIR is open-source, it includes state-of-the-art deep learning models, and it supports straightforward development of new models for language tasks including named-entity recognition. Finally, there is a discussion of how to apply the system to structured text in a database table as well as unstructured text in clinical notes.
Robust visual tracking by embedding combination and weighted-gradient optimization

Existing tracking-by-detection approaches build trackers on binary classifiers. Despite achieving state-of-the-art performance on tracking benchmarks, these trackers pay limited attention to data imbalance issue, e.g, positive and negative, easy and hard. In this paper, we demonstrate that separately learning feature embeddings corresponding to negative samples with different semantic characteristics is effective in reducing the background diversity to handle the imbalance between positive and negative samples, which facilitates background awareness of classifiers. Specifically, we propose a negative sample embedding combination network, which helps to learn several sub-embeddings and combine them to build a robust classifier. In addition, we propose a weighted-gradient loss to handle the imbalance between easy and hard samples. The gradient contribution of each sample to model training is dynamically weighted according to the gradient distribution, which prevents easy samples from overwhelming model training. Extensive experiments on benchmarks demonstrate that our tracker performs favorably against state-of-the-art algorithms. (C) 2020 Elsevier Ltd. All rights reserved.
Image recognition of martial arts movements based on FPGA and image processing

The work is to perceive the intricate karate activity groupings consequently. It is to give a proportion of the nature of the activity performed. This is because there is a problem that requires essentially model. This work can be removed from the two motion capture hardware and consumer derived from the input sequence. The solution for the skeleton's movement proposed a depth-sensing system. The speed of the body's action because there is a karate sport classification. On the other hand, because the practice with even skilled martial arts for years, behavior patterns are repetitive. Similarly, there is no interest in the commercial market for solutions used in today's computer entertainment and tutoring system. The future of collecting analysis of elements present in the correct posture, accelerated by a punch and kick, the different aspects of performance, such as synchronizing the limb's movement and evaluation. This work aims to provide a survey to evaluate the quality of the whole body movement technology. It proposes a data set and its plan to implement the first reference file of the work's future. Generally speaking, FPGA is programmable with a group of programmable interconnect resources through digital circuits or programmable logic blocks surrounded by input/output blocks. Any system silicon chip is put together. The processor and the FPGA are essentially indeed parallel.
Spin-Coating-Based Facile Annular Photodynamic Intraocular Lens Fabrication for Efficient and Safer Posterior Capsular Opacification Prevention

Posterior capsular opacification (PCO) is the most common complication after cataract surgery, which is primarily caused by the proliferation of the residual lens epithelial cells (LECs) in the lens capsule. Previous studies have demonstrated that a drug-eluting intraocular lens (IOL), aimed to in situ eliminate LECs, are an effective and promising way to prevent PCO. However, because of the potential toxicities of the antiproliferative drugs to the adjacent tissues, the safety of such drug-eluting IOLs is still a highly important issue to be solved. In this investigation, a facile photodynamic coating-modified IOL was developed for effective and safer PCO prevention. An annular poly(lactide-co-glycolic acid) (PLGA) coating loaded with photosensitizer chlorin e6 (Ce6) was prepared by a spin-coating technique. The optical property investigations showed that the Ce6@PLGA coating was particularly suitable for the IOL surface modification. The in vitro cell culture investigation showed that Ce6@PLGA coating-modified IOLs effectively eliminated LECs when treated with light illumination, whereas it appeared to have good cytocompatibility without irradiation. The investigation of the cell elimination mechanism showed that the apoptosis of HLECs may be associated with the cytomembrane disruption induced by ROS, which is generated by the photodynamic coating during light illumination. The in vivo implantation experiments confirmed the desired PCO prevention effect, as well as the safety to and biocompatibility with the surrounding tissues. Thus, the facile Ce6@PLGA coating will provide an effective yet safe alternative of IOL surface modification for PCO prevention.
Knowledge Adaptation with Partially Shared Features for Event Detection Using Few Exemplars

Multimedia event detection (MED) is an emerging area of research. Previous work mainly focuses on simple event detection in sports and news videos, or abnormality detection in surveillance videos. In contrast, we focus on detecting more complicated and generic events that gain more users' interest, and we explore an effective solution for MED. Moreover, our solution only uses few positive examples since precisely labeled multimedia content is scarce in the real world. As the information from these few positive examples is limited, we propose using knowledge adaptation to facilitate event detection. Different from the state of the art, our algorithm is able to adapt knowledge from another source for MED even if the features of the source and the target are partially different, but overlapping. Avoiding the requirement that the two domains are consistent in feature types is desirable as data collection platforms change or augment their capabilities and we should be able to respond to this with little or no effort. We perform extensive experiments on real-world multimedia archives consisting of several challenging events. The results show that our approach outperforms several other state-of-the-art detection algorithms.