ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Random sampling for fast face sketch synthesis

Exemplar-based face sketch synthesis plays an important role in both digital entertainment and law enforcement. It generally consists of two parts: neighbor selection and recognition weight representation. In this paper, we proposed a simple but effective method which employs offline random sampling instead of K-NN used in state-of-the-art methods. The proposed random sampling strategy reduces the time consuming for synthesis and has stronger scalability than state-of-the-art methods. In addition, we introduced locality constraint to model the distinct correlations between the test patch and random sampled patches. Extensive experiments on public face sketch databases demonstrate the superiority of the proposed method in comparison to state-of-the-art methods, in terms of both synthesis quality and time consumption. The proposed method could be extended to other heterogeneous face image transformation problems such as face hallucination. We release the source codes of our proposed methods and the evaluation metrics for future study online: http://www.ihitworld.com/RSLCR.html. (C) 2017 Elsevier Ltd. All rights reserved.
Multi-source information art painting fusion interactive 3D dynamic scene virtual reality technology application research

Art painting is a very important major in China's education system. Under the background of the new era, the country vigorously promotes the modernization of education and the development of intelligence. Many subjects of teaching have been more mature and carried out intelligent teaching based on virtual reality technology. But throughout the art painting profession, the research on this aspect is almost blank. The main reason is that there are deep academic barriers between art painting and computer science. Therefore, this paper puts forward the application research of multi-source information art painting fusion interactive 3D dynamic scene virtual reality technology. This paper deeply analyzes the needs of art painting and its teaching reform and believes that the existing virtual reality technology cannot adapt to the teaching environment of art painting. In view of the possible inaccurate calculation and other problems, this paper puts forward the optimization scheme. The core of the scheme is to use the information integration advantages of multi-source information fusion to provide more comprehensive and complete object information for 3D modeling of virtual reality technology, so as to improve the imaging quality of virtual reality technology. In order to meet the requirement of high precision, the calculation method is optimized, the calculation steps are simplified, and the sensor selection is optimized again. This paper introduces the design principle and operation steps of the optimization method in detail. In order to further verify the actual effect of this method, comparative analysis method is used to test the comprehensive performance. In order to ensure the test quality, the median filtering algorithm and fuzzy algorithm are compared. Through the analysis and comparison of the data, the optimization method in this paper greatly improves the accuracy of calculation and the comprehensive performance is improved.
Cochlear Implantation: The Variation in Cochlear Height

This study aimed to identify the association between different cochlear metrics, including the basal turn diameter (A-value), the basal turn width (B-value), and the height of the cochlea (H-value). We also reported an association between H-value and hearing outcomes with cochlear implants (CI). This is a retrospective study that included all patients who underwent CI procedures between 2012 and 2018 at a tertiary center and have; preoperative high-resolution computed tomography (CT), normal cochlea, postoperative follow-up duration of at least 2 years, scores of the category auditory performance II (CAP-II), and speech intelligibility rating (SIR) scales. A total of 65 ears implanted with CI in 46 patients (24 boys and 20 girls; mean age of 7 (±10) years) fulfilled the inclusion criteria. We found significant positive correlations between A vs B, A vs H, and B vs H (P-value = 0.008, 0.018, and 0.0039, respectively). We also found a significant positive relationship between A, B, and H values and cochlear duct length (CDL) (P-value < 0.0001, 0.008, and 0.018, respectively). Finally, the H-value was significantly correlated with the SIR (P-value = 0.027). However, its correlation with the CAP score was not statistically significant (P-value = 0.62). Cochlear height significantly correlated with CDL and the other cochlear parameters. The variation in cochlear height can also affect speech outcomes in patients undergoing CI. Therefore, the H-value together with the other cochlear metrics should be adequately assessed preoperatively in CI patients.
Eikonal-based region growing for efficient clustering

We describe an Eikonal-based algorithm for computing dense oversegmentation of an image, often called supeipixels. This oversegmentation respects local image boundaries while limiting undersegmentation. The proposed algorithm relies on a region growing scheme, where the potential map used is not fixed and evolves during the diffusion. Refinement steps are also proposed to enhance at low cost the first oversegmentation. Quantitative comparisons on the Berkeley dataset show good performance on traditional metrics over current state-of-the art superpixel methods. (C) 2014 Elsevier B.V. All rights reserved.
ImageGCN: Multi-Relational Image Graph Convolutional Networks for Disease Identification With Chest X-Rays

Image representation is a fundamental task in computer vision. However, most of the existing approaches for image representation ignore the relations between images and consider each input image independently. Intuitively, relations between images can help to understand the images and maintain model consistency over related images, leading to better explainability. In this paper, we consider modeling the image-level relations to generate more informative image representations, and propose ImageGCN, an end-to-end graph convolutional network framework for inductive multi-relational image modeling. We apply ImageGCN to chest X-ray images where rich relational information is available for disease identification. Unlike previous image representation models, ImageGCN learns the representation of an image using both its original pixel features and its relationship with other images. Besides learning informative representations for images, ImageGCN can also be used for object detection in a weakly supervised manner. The experimental results on 3 open-source x-ray datasets, ChestX-ray14, CheXpert and MIMIC-CXR demonstrate that ImageGCN can outperform respective baselines in both disease identification and localization tasks and can achieve comparable and often better results than the state-of-the-art methods.
Future orientation and perceived employability of chinese undergraduates: a moderated mediation model

Although scholars and practitioners have highlighted the significance of students' attitudes for their future employment, few empirical examinations have attempted to determine the potential association between students' future orientation and their perceived employability. Thus, drawing on career construction theory, we test the positive effect of students' future orientation on their perceived employability by exploring the mediator of problem-based learning and the moderators of job market knowledge and proactive personality. Collecting our data via a time-lagged design (N = 368), we have found that the positive association between future orientation and employability is mediated by problem-based learning. Our moderation analyses further revealed that job market knowledge positively moderates the relationship between future orientation and problem-based learning and that students' proactive personality positively moderates the relationship between problem-based learning and perceived employability.
Improving Restore Performance of Packed Datasets in Deduplication Systems via Reducing Persistent Fragmented Chunks

Data deduplication, though being efficient for redundancy elimination in storage systems, introduces chunk fragmentation which severely decreases restore performance. Rewriting algorithms are proposed to reduce the chunk fragmentation. Typically, the backup software aggregates files into larger "tar" type files for storage. We observe that, in tar type datasets, a large number of Persistent Fragmented Chunks (PFCs) are repeatedly rewritten by state-of-the-art rewriting algorithms in every backup, which severely impacts restore performance. We found that the existence of PFCs is due to the traditional strategy of storing PFCs along with other chunks in the containers to preserve the stream locality, rendering them always stored in the containers with low utilization. We propose DePFC to reduce PFCs. DePFC identifies and removes PFCs from the containers preserving the stream locality, and groups them together, to increase the utilization of containers holding them for the subsequent backup, thus preventing them from being rewritten again. We further propose an FC Buffer to avoid mistaken rewrites of PFCs and grouping PFCs that cause restore cache thrashing together. Experimental results demonstrate that DePFC improves restore performance of state-of-the-art rewriting algorithms by 44.24-89.42 percent, while attaining comparable deduplication efficiency, and FC Buffer further improves restore performance.
Aggregation-Induced-Emission Photosensitizer-Loaded Nano-Superartificial Dendritic Cells with Directly Presenting Tumor Antigens and Reversed Immunosuppression for Photodynamically Boosted Immunotherapy

The success of tumor immunotherapy highlights the potential of harnessing immune system to fight cancer. Activating both native T cells and exhausted T cells is a critical step for generating effective antitumor immunity, which is determined based on the efficient presentation of tumor antigens and co-stimulatory signals by antigen-presenting cells, as well as immunosuppressive reversal. However, strategies for achieving an efficient antigen presentation process and improving the immunosuppressive microenvironment remain unresolved. Here, aggregation-induced-emission (AIE) photosensitizer-loaded nano-superartificial dendritic cells (saDC@Fs-NPs) are developed by coating superartificial dendritic cells membranes from genetically engineered 4T1 tumor cells onto nanoaggregates of AIE photosensitizers. The outer cell membranes of saDC@Fs-NPs are derived from recombinant lentivirus-infected 4T1 tumor cells in which peptide-major histocompatibility complex class I, CD86, and anti-LAG3 antibody are simultaneously anchored. These saDC@Fs-NPs could directly stimulate T-cell activation and reverse T-cell exhaustion for cancer immunotherapy. The inner AIE-active photosensitizers induce immunogenic cell death to activate dendritic cells and enhance T lymphocyte infiltration by photodynamic therapy, promoting the transformation of "cold tumors" into "hot tumors," which further boosts immunotherapy efficiency. This work presents a powerful photoactive and artificial antigen-presenting platform for activating both native T cells and exhausted T cells, as well as facilitating tumor photodynamic immunotherapy.
PLAG1 and NCAPG-LCORL in livestock

A recent progress on stature genetics has revealed simple genetic architecture in livestock animals in contrast to that in humans. PLAG1 and/or NCAPG-LCORL, both of which are known as a locus for adult human height, have been detected for association with body weight/height in cattle and horses, and for selective sweep in dogs and pigs. The findings indicate a significant impact of these loci on mammalian growth or body size and usefulness of the natural variants for selective breeding. However, association with an unfavorable trait, such as late puberty or risk for a neuropathic disease, was also reported for the respective loci, indicating an importance to discriminate between causality and association. Here I review the recent findings on quantitative trait loci (QTL) for stature in livestock animals, mainly focusing on the PLAG1 and NCAPG-LCORL loci. I also describe our recent efforts to identify the causative variation for the third major locus for carcass weight in Japanese Black cattle.
Fabrication of highly porous platinum electrodes for micro-scale applications by pulsed electrodeposition and dealloying

We present the implementation and optimization of a novel electrodeposition method for the fabrication of highly porous platinum electrodes. It is based on the co-deposition of platinum and copper and the selective dealloying of copper in a pulsed manner. The new process yields mechanically stable platinum electrodes with roughness factors of up to RF = 6500 +/- 700, compared to the state-of-the-art cyclic electrodeposition method this corresponds to an improvement in RF by 111%. Furthermore the time demand for fabrication is reduced by 59%, whereas the platinum utilization is increased by 53%. The method is particularly advantageous for applications such as micro fuel cells since it enables the precise deposition of catalytically active electrodes on micro-structured conductive areas. In this context the novel platinum electrodes show higher current densities for the oxidation of formic acid and glucose than state-of-the-art electrodes. In terms of methanol oxidation their catalytic activity is comparable to commercial direct methanol fuel cell (DMFC) electrodes, fabricated from Pt-Ru nanoparticles dispersed on carbon black. (C) 2013 Elsevier B.V. All rights reserved.
Biomedical production of implants by additive electro-chemical and physical processes

Biomanufacturing integrates life science and engineering fundamentals to produce biocompatible products enhancing the quality of life. The state-of-the-art of this rapidly evolving manufacturing sector is presented and discussed, in particular the additive electrical, chemical and physical processes currently being applied to produce synthetic and biological parts. This fabrication strategy is strongly material-dependent, so the main classes of biomaterials are detailed. It is explained the potential to process composite materials combining synthetic and biological materials, such as cells, proteins and growth factors, as well the interdependences between materials and processes. The techniques commonly used to increase the bioactivity of clinical implants and improve the interface characteristics between biological tissues and implants are also presented. (C) 2012 CIRP.
Insulator polymers achieve efficient catalysis under visible light due to contact electrification

Under the limitation of the carrier yield and mobility of semiconductor photocatalysts and the reaction domain, it seems that the photocatalytic efficiency cannot be greatly improved. Here, an efficient contact-electro-catalysis (CEC) system based on droplet triboelectric nanogenerator (TENG) is developed. Instead of using traditional semiconductor catalysts, the electric charge transferred during the electrification process of the contact between water droplets and polytetrafluoroethylene (PTFE) is used to participate in catalysis, and the output electrical signal can also monitor the degree of catalysis. The important role of light in the circulation of this CEC system is studied and discussed for the first time. It is proved that the contact electrification at the liquid-solid interface is accompanied by the generation of a large number of strong oxidizing radicals. The efficient transport of charge carriers driven by mechanical force and the active oxygen species distributed in the whole domain greatly improve the degradation rate of dyes. The experimental data show that the degradation efficiency of crystal violet (CV) reaches 90% within 38 s, and the rate constant k is as high as 3.7 min-1. This is a breakthrough in the field of catalysis.
Automated Classification of Apoptosis in Phase Contrast Microscopy Using Capsule Network

Automatic and accurate classification of apoptosis, or programmed cell death, will facilitate cell biology research. The state-of-the-art approaches in apoptosis classification use deep convolutional neural networks (CNNs). However, these networks are not efficient in encoding the part-whole relationships, thus requiring a large number of training samples to achieve robust generalization. This paper proposes an efficient variant of capsule networks (CapsNets) as an alternative to CNNs. Extensive experimental results demonstrate that the proposed CapsNets achieve competitive performances in target cell apoptosis classification, while significantly outperforming CNNs when the number of training samples is small. To utilize temporal information within microscopy videos, we propose a recurrent CapsNet constructed by stacking a CapsNet and a bi-directional long short-term recurrent structure. Our experiments show that when considering temporal constraints, the recurrent CapsNet achieves 93.8% accuracy and makes significantly more consistent prediction than NNs.
On the toxicity of gold nanoparticles: Histological, histochemical and ultrastructural alterations

Gold nanoparticles (Au NPs) are used in diagnostic and therapeutic applications together with a variety of industrial purposes and in many biomedical sectors with potential risks to human health. The present study aimed to the histological, histochemical, and ultrastructural alterations induced by Au NPS in vital organs. Healthy male Wistar Albino rats (Rattus norvegicus) were subjected to 20 injections of 10-nm Au NPs at a daily dose of 2 mg/kg. Liver, kidney, heart, and lung biopsies from control and Au NPs-treated rats under study were subjected to histological and histochemical examinations. In comparison with the control rats, the renal tissue of Au NPs-treated rats demonstrated glomerular congestion, interstitial inflammatory cell infiltration, renal tubular hydropic degeneration, cloudy swelling, necrosis, and hyaline cast precipitation. In addition, Au NPs induced the following hepatic alterations: hepatocyte cytolysis, cytoplasmic vacuolation, hydropic degeneration, and nuclear alterations together with sinusoidal dilatation. Moreover, the hearts of the treated rats demonstrated myocarditis, cardiac congestion, hyalinosis, cardiomyocyte hydropic degeneration, myofiber disarray and cardiac congestion. The lungs of Au NPs-treated rats also exhibited the following pulmonary alterations: alectasis, emphysema, inflammatory cell inflammation, thickened alveolar walls, pulmonary interstitial edema, congestion, hypersensitivity, fibrocyte proliferation, and honeycombing. In conclusion, exposure to Au NPs induced histological, histochemical and ultrastructural alterations in the vital organs that may alter the function of these organs. Additional efforts are needed for better understanding the potential risks of Au NPs to human health.
Spontaneous Pulmonary Embolism Leading to Sudden Cardiac Arrest and Perimortem C-Section in a 39-Week Parturient During Induction of Labor: A Case Report

We report the successful salvage of mother and baby after a perimortem cesarean delivery (PMCD) complicated by a 21-minute asystolic maternal cardiac arrest (MCA) that was precipitated by a pulmonary embolism during the early stages of induction of labor. With rapid PMCD, recovery of maternal quality of life is possible even after prolonged resuscitation.
The power of art to foster systems thinking, one of the key competencies of education for sustainable development

Systems thinking is one of the most difficult competencies for students to acquire. Nevertheless it is one of the key competencies of education for sustainable development. Although there is relatively little research available on the link between didactic approaches and sustainability competencies, it is clear that alternative ways of teaching, such as project based learning and multi-perspective and interdisciplinary thinking and working are more effective for acquiring this competency. In this paper we explore the power of art as a way to foster systems thinking and to distinguish between different ways of thinking about sustainability. We selected two paintings, Les fours Gigantesques by Rene Magritte and Sky and Water II by Maurits Escher, to explain a top-down and bottom-up approach to sustainability. The paper discusses how business students respond to art in a course on corporate social responsibility and how it helps them to understand the meaning of systems thinking. The findings reveal that these paintings are relevant for improving the comprehension of concepts related to sustainability. Moreover, they add an extra dimension to the cognitive understanding of systems thinking, i.e. enriching the whole person, reinforcing critical and creative thinking skills. (C) 2018 Elsevier Ltd. All rights reserved.
Environmental Sound Classification on the Edge: A Pipeline for Deep Acoustic Networks on Extremely Resource-Constrained Devices

Significant effort s are being invested to bring state-of-the-art classification and recognition to edge de-vices with extreme resource constraints (memory, speed, and lack of GPU support). Here, we demon-strate the first deep network for acoustic recognition that is small, flexible and compression-friendly yet achieves state-of-the-art performance for raw audio classification. Rather than handcrafting a once-off solution, we present a generic pipeline that automatically converts a large deep convolutional network via compression and quantization into a network for resource-impoverished edge devices. After introduc-ing ACDNet, which produces above state-of-the-art accuracy on ESC-10 (96.65%), ESC-50 (87.10%), Urban-Sound8K (84.45%) and AudioEvent (92.57%), we describe the compression pipeline and show that it allows us to achieve 97.22% size reduction and 97.28% FLOP reduction while maintaining close to state-of-the-art accuracy 96.25%, 83.65%, 78.27% and 89.69% on these datasets. We describe a successful implementation on a standard off-the-shelf microcontroller and, beyond laboratory benchmarks, report successful tests on real-world datasets.(c) 2022 Elsevier Ltd. All rights reserved.
Do the progression of experimentally induced gingivitis and peri-implant mucositis present common features? A systematic review of clinical human studies

This systematic review evaluated the features of the progression of experimentally induced gingivitis and peri-implant mucositis in humans. Included were studies that evaluated clinical, immunological, or microbiological responses between experimentally induced gingivitis and peri-implant mucositis in periodontally healthy patients. A total of 887 articles were initially identified, but only 12 were included in the final analysis. Implants accumulate less biofilm and suffer the most heterogeneous alterations in the microbiota, in the abstinence of oral hygiene, compared with the tooth. Interestingly, although dental implants presented less biofilm accumulation, the peri-implant mucosa showed a more exacerbated clinical response than the gingival tissue. The risk of bias of the selected studies was moderate to low, with one study presenting serious risk. The progression events of peri-implant mucositis were similar to those of experimental gingivitis but led to a different host response. This review was registered in the PROSPERO database CRD420201 123360.