ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Simultaneous Truth and Performance Level Estimation Through Fusion of Probabilistic Segmentations

Recent research has demonstrated that improved image segmentation can be achieved by multiple template fusion utilizing both label and intensity information. However, intensity weighted fusion approaches use local intensity similarity as a surrogate measure of local template quality for predicting target segmentation and do not seek to characterize template performance. This limits both the usefulness and accuracy of these techniques. Our work here was motivated by the observation that the local intensity similarity is a poor surrogate measure for direct comparison of the template image with the true image target segmentation. Although the true image target segmentation is not available, a high quality estimate can be inferred, and this in turn allows a principled estimate to be made of the local quality of each template at contributing to the target segmentation. We developed a fusion algorithm that uses probabilistic segmentations of the target image to simultaneously infer a reference standard segmentation of the target image and the local quality of each probabilistic segmentation. The concept of comparing templates to a hidden reference standard segmentation enables accurate assessments of the contribution of each template to inferring the target image segmentation to be made, and in practice leads to excellent target image segmentation. We have used the new algorithm for the multiple-template-based segmentation and parcellation of magnetic resonance images of the brain. Intensity and label map images of each one of the aligned templates are used to train a local Gaussian mixture model based classifier. Then, each classifier is used to compute the probabilistic segmentations of the target image. Finally, the generated probabilistic segmentations are fused together using the new fusion algorithm to obtain the segmentation of the target image. We evaluated our method in comparison to other state-of-the-art segmentation methods. We demonstrated that our new fusion algorithm has higher segmentation performance than these methods.
FHENet: Lightweight Feature Hierarchical Exploration Network for Real-Time Rail Surface Defect Inspection in RGB-D Images

In recent years, computer vision systems have been increasingly applied to rail defect inspection. Rail defects should be identified quickly and accurately to ensure safe, stable, and fast train operations and thereby reduce the incidence of accidents and economic losses. As most existing methods focus on accuracy, they cannot be deployed on mobile devices with limited computational resources. To solve this problem, we propose a lightweight feature hierarchical exploration network (FHENet) for real-time rail surface defect inspection. First, boundary textures of rail defects are acquired based on the maximum function and maximum pooling in a novel boundary extraction module (BEM), which improves boundary prediction while avoiding heavy computations. Second, a novel cross-modality exploration module (CEM) complements prominent regions through basic operations to avoid complex inference while providing high detection performance. Third, a novel multifeature integration module (MIM) optimizes representative feature regions by using simple operations to avoid complex computations. Results from extensive experiments demonstrate the superiority of the proposed FHENet to 14 state-of-the-art methods. Regarding efficiency, FHENet outperforms the state-of-the-art methods with only 5.26 M parameters and a processing speed of 60.33 frames/s. The FHENet code and results are available at https://github.com/hjklearn/Rail-Defect-Detection.
Rock art imagery as a proxy for Holocene environmental change: A view from Shuwaymis, NW Saudi Arabia

The animal species depicted in the rock art of Shuwaymis, Saudi Arabia, provide a record of Holocene climatic changes, as seen by the engravers. Of 1903 animal engravings, 1514 contained sufficient detail to allow identification with confidence. In addition, the stratigraphy of the engravings and the depiction of domesticates provide a broad chronological framework that allows a division into images created during the Holocene humid phase and animals represented after the onset of desert conditions. Despite the large sample size, only 16 animal species could be identified, which represents an extraordinarily narrow species spectrum. Comparison with the scarce faunal record of the Arabian Peninsula shows that all larger animals that are thought to have been present in the area were also depicted in the rock art. The contemporaneous presence of at least four large carnivores during the Holocene humid phase suggests that prey animals were abundant, and that the landscape consisted of a mosaic of habitats, potentially with thicker vegetation along the water courses of the wadis and more open vegetation in the landscape around them. Community Earth System Models (COSMOS) climate simulations show that Shuwaymis was at the northern edge of the African Summer Monsoon rainfall regime. It is therefore possible that Shuwaymis was ecologically connected with southwestern Arabia, and that an arid barrier remained in place to the north, restricting the dispersal of Levantine species into Arabia.
Skin dose contamination conversion coefficients. Benchmark with three simulation codes

Handling of radioactive material by operators can lead to contamination at the surface of the skin in case of an accident. The quantification of the dose received by the skin due to a contamination scenario is performed by means of dedicated dose coefficients as it is the case for other radiation protection dose quantities described in the literature. However, most available coefficients do not match realistic scenarios according to state-of-the-art of science and technology. Therefore, this work deals with dedicated dose conversion factors for skin contamination. Since there is an increasing demand on dose coefficients in general, these specific coefficients can be used for various calculations in radiation protection. In this work a method to evaluate such coefficients for the skin contamination dose related to photons, electrons, positrons, alpha and neutron particles is proposed. The coefficients are generated using Monte-Carlo simulations with three well established calculation codes (FLUKA, MCNP, and GEANT4). The results of the various codes are compared against each other for benchmarking purposes. The new dose coefficients allow the computation of the skin received dose, in the case of skin contamination scenario of an individual, taking into account the decay radiation of the radionuclides of interest. To benchmark the quantity derived here, comparisons of radionuclide contamination doses to the skin using the VARSKIN code available in the literature are performed with the results of this work.
Can economic growth and carbon emissions reduction be owned: evidence from the convergence of digital services and manufacturing in China

Under the background of the deep convergence of China's digital services and manufacturing, it is of great significance to investigate the effects of the convergence of digital services and manufacturing on economic growth and carbon emissions reduction to the application of digital technology in the whole world. This paper constructs a simultaneous equation model and uses three-stage least squares to estimate the effect and mechanism of industrial convergence on economic growth and carbon emissions. The results show that (i) industrial convergence improves the change of total factor productivity (TFP) and the change of technical efficiency, and the reduction of carbon emissions is the main factor driving the growth of TFP and technical efficiency; (ii) industrial convergence and carbon emissions show a significant U-shaped relationship; (iii) the heterogeneity analysis shows that the convergence of capital-intensive, technology-intensive and labor-intensive manufacturing with digital services will help to improve the growth of TFP, it can inhibit carbon emissions first and then promote it. Therefore, the government should take targeted measures to promote industrial convergence of digital services and manufacturing according to the economic development and industry characteristics, so as to give full play to its positive role in economic growth and emissions reduction.
Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short-term memory units

Fatigue driving has become one of the major causes of traffic accidents. The authors propose an effective method capable of detecting fatigue state via the spatial-temporal feature of driver's eyes. In this work, the authors consider fatigue detection as image-based sequence recognition and an end-to-end trainable convolutional neural network with long short-term memory (LSTM) units is designed. First, the authors apply a deep cascaded multi-task framework to extract eye region from infrared videos. Then the spatial features are learned by deep convolutional layers and the relationships between adjacent frames are analysed via LSTM units. Finally, through authors' model, a sequence-level prediction for driving state is produced. The proposed method achieves superior accuracy over the state-of-the-art techniques on authors' own dataset. Experimental results demonstrate the feasibility of authors' method.
A new focus evaluation operator based on max-min filter and its application in high quality multi-focus image fusion

Multi-focus image fusion plays an important role in the field of image recognition and analysis. However, current focus evaluation operators are complex and inefficiency. In this paper, a new focus evaluation operator based on max-min filter is proposed. In new focus measure, we use max-min filter with the help of average filter and median filter (MMAM) to evaluate the focus degree of source images. This evaluation algorithm can well measure the sharpness of different regions of the image, and the selected clear region will be more useful for human visual or machine perception. The experiment proved that MMAM can perform better than sum-of-modified-Laplacian in most of cases. Later, MMAM is used to fused multi-focus image by combined structure-driven fused regions and depth information of blurred images. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art fusion algorithms on image quality and objective fusion criteria. This paper firstly proposes the concept and computational processing of MMAM, which provides a new research direction and innovative idea for multi-focus image fusion base on filter, and MMAM can embedded into state-of-the-art fusion algorithm to achieve high quality multi-focus image fusion.
Generalized Quadrature Data Weighted Averaging

This brief focuses on quadrature multibit digital-to-analog conversion with data weighted averaging (DWA) as a linearization method. Previous study on generalized DWA is expanded here, into the complex domain, and is compared with prior art on complex DWA. It will be shown that the generalized DWA in the complex domain has several advantages such as reduced complexity, reduced spurious behavior, and inherent quadrature error suppression.
A thermoresponsive cationic block copolymer brush-grafted silica bead interface for temperature-modulated separation of adipose-derived stem cells

Adipose-derived mesenchymal stem cells (ADSCs) have beneficial effects in cell transplantation therapy; these cells are collected from adipose tissue using low-invasive methods. However, to prepare ADSCs for cell therapy, a cell separation method that neither involves modification of the cell surface nor causes loss of cell activity is needed. Here, we aimed to develop ADSC separation columns using thermoresponsive cationic block copolymer brush-grafted beads as packing materials. The block copolymer brush was formed by a bottom cationic segment, poly(N,N-dimethylaminopropylacrylamide) (PDMAPAAm), and an upper thermoresponsive segment, poly(N-isopropylacrylamide) (PNIPAAm), and was grafted in two atom transfer radical polymerization reactions. The copolymer brush-grafted silica beads were packed into a column. An ADSC suspension was introduced into the columns at 37 °C and adsorbed on the copolymer brush-modified beads through electrostatic and hydrophobic interactions with the PDMAPAAm and PNIPAAm segments, respectively. The adsorbed ADSCs eluted from the column by lowering the temperature to 4 °C. In contrast, most Jurkat and vascular endothelial cells eluted at 37 °C, because of the relatively weaker electrostatic interactions with the block copolymer brush compared to ADSCs. Using the prepared column, a mixture of ADSCs and Jurkat cells was separated by changing the column temperature. The recovered ADSCs exhibited cell activity. The developed cell separation column may be useful for isolating ADSCs without cell surface modification, while maintaining cell activity.
Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI

Susceptibility induced distortion is a major artifact that affects the diffusion MRI (dMRI) data analysis. In the Human Connectome Project (HCP), the state-of-the-art method adopted to correct this kind of distortion is to exploit the displacement field from the B0 image in the reversed phase encoding images. However, both the traditional and learning-based approaches have limitations in achieving high correction accuracy in certain brain regions, such as brainstem. By utilizing the fiber orientation distribution (FOD) computed from the dMRI, we propose a novel deep learning framework named DistoRtion Correction Net (DrC-Net), which consists of the U-Net to capture the latent information from the 4D FOD images and the spatial transformer network to propagate the displacement field and back propagate the losses between the deformed FOD images. The experiments are performed on two datasets acquired with different phase encoding (PE) directions including the HCP and the Human Connectome Low Vision (HCLV) dataset. Compared to two traditional methods topup and FODReg and two deep learning methods S-Net and flow-net, the proposed method achieves significant improvements in terms of the mean squared difference (MSD) of fractional anisotropy (FA) images and minimum angular difference between two PEs in white matter and also brainstem regions. In the meantime, the proposed DrC-Net takes only several seconds to predict a displacement field, which is much faster than the FODReg method.
Verification of road databases using multiple road models

In this paper a new approach for automatic road database verification based on remote sensing images is presented. In contrast to existing methods, the applicability of the new approach is not restricted to specific road types, context areas or geographic regions. This is achieved by combining several stateof-the-art road detection and road verification approaches that work well under different circumstances. Each one serves as an independent module representing a unique road model and a specific processing strategy. All modules provide independent solutions for the verification problem of each road object stored in the database in form of two probability distributions, the first one for the state of a database object (correct or incorrect), and a second one for the state of the underlying road model (applicable or not applicable). In accordance with the Dempster-Shafer Theory, both distributions are mapped to a new state space comprising the classes correct, incorrect and unknown. Statistical reasoning is applied to obtain the optimal state of a road object. A comparison with state-of-the-art road detection approaches using benchmark datasets shows that in general the proposed approach provides results with larger completeness. Additional experiments reveal that based on the proposed method a highly reliable semiautomatic approach for road data base verification can be designed. (C) 2017 Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS).
Art Painting Diagnostic Before Restoration with Terahertz and Millimeter Waves

Art painting diagnostic is commonly performed using electromagnetic waves at wavelengths from terahertz to X-ray. These former techniques are essential in conservation and art history research, but they could be also very useful for restoring artwork. While most studies use time domain imaging technique, in this study, a painting has been investigated using both time domain imaging (TDI) and frequency-modulated continuous wave (FMCW) system in the millimeter frequency range. By applying these systems to a painting of the eighteenth century, we detect and analyze the structure of some defects. This study underlines the differences between FMCW and TDI. We present the advantages and disadvantages of each technique on a real artwork.
Saliency Based Ulcer Detection for Wireless Capsule Endoscopy Diagnosis

Ulcer is one of the most common symptoms of many serious diseases in the human digestive tract. Especially for the ulcers in the small bowel where other procedures cannot adequately visualize, wireless capsule endoscopy (WCE) is increasingly being used in the diagnosis and clinical management. Because WCE generates large amount of images from the whole process of inspection, computer-aided detection of ulcer is considered an indispensable relief to clinicians. In this paper, a two-staged fully automated computer-aided detection system is proposed to detect ulcer from WCE images. In the first stage, we propose an effective saliency detection method based on multi-level superpixel representation to outline the ulcer candidates. To find the perceptually and semantically meaningful salient regions, we first segment the image into multi-level superpixel segmentations. Each level corresponds to different initial region sizes of the superpixels. Then we evaluate the corresponding saliency according to the color and texture features in superpixel region of each level. In the end, we fuse the saliency maps from all levels together to obtain the final saliency map. In the second stage, we apply the obtained saliency map to better encode the image features for the ulcer image recognition tasks. Because the ulcer mainly corresponds to the saliency region, we propose a saliency max-pooling method integrated with the Locality-constrained Linear Coding (LLC) method to characterize the images. Experiment results achieve promising 92.65% accuracy and 94.12% sensitivity, validating the effectiveness of the proposed method. Moreover, the comparison results show that our detection system outperforms the state-of-the-art methods on the ulcer classification task.
Hydrological impacts of future climate and land use/cover changes in the Lower Mekong Basin: a case study of the Srepok River Basin, Vietnam

This study presents hydrological impacts of future climate change (CC) and land use/cover change (LUCC) for the Srepok River Basin (SRB) in the Vietnam's Central Highlands. The hydrology cycle of this basin was reproduced using Soil and Water Assessment Tool (SWAT) allowing an evaluation of hydrological responses to CC and LUCC. Future climate scenarios of the 2015-2100 period under Representative Concentration Pathways (RCP) 4.5 simulated by five General Circulation Models (GCMs) and LUCC scenario in 2050 were developed. Compared to the reference scenario (1980-2005), future LUCC increases the streamflow (0.25%) and surface runoff (1.2%) and reduces the groundwater discharge (2.1%). Climate change may cause upward trends in streamflow (0.1 to 2.7%), surface runoff (0.4 to 4.3%), and evapotranspiration (0.8 to 3%), and a change in the groundwater discharge (- 1.7 to 0.1%). The combination of CC and LUCC increases the streamflow (0.2 to 2.8%), surface runoff (1.6 to 5.6%), and evapotranspiration (1.0 to 3.1%), and reduces the groundwater discharge (1.5 to 2.7%) with respect to the reference scenario. Moreover, the results noted that the water scarcity may happen in the dry-seasonal months.
Exploring the intersection of structural racism and ageism in healthcare

The American Geriatrics Society (AGS) has consistently advocated for a healthcare system that meets the needs of older adults, including addressing impacts of ageism in healthcare. The intersection of structural racism and ageism compounds the disadvantage experienced by historically marginalized communities. Structural racism and ageism have long been ingrained in all aspects of US society, including healthcare. This intersection exacerbates disparities in social determinants of health, including poor access to healthcare and poor outcomes. These deeply rooted societal injustices have been brought to the forefront of the collective public consciousness at different points throughout history. The COVID-19 pandemic laid bare and exacerbated existing inequities inflicted on historically marginalized communities. Ageist rhetoric and policies during the COVID-19 pandemic further marginalized older adults. Although the detrimental impact of structural racism on health has been well-documented in the literature, generative research on the intersection of structural racism and ageism is limited. The AGS is working to identify and dismantle the healthcare structures that create and perpetuate these combined injustices and, in so doing, create a more just US healthcare system. This paper is intended to provide an overview of important frameworks and guide future efforts to both identify and eliminate bias within healthcare delivery systems and health professions training with a particular focus on the intersection of structural racism and ageism.
Necrotizing Hepatitis Associated with Clostridium perfringens in Broiler Chicks

In this retrospective study we describe unusual cases of clostridial hepatitis associated with high mortality in young broiler chicks. Eleven cases of necrotizing hepatitis in broiler chicks from four companies were submitted to the Poultry Diagnostic and Research Center or the Georgia Poultry Laboratory Network between 2017 and 2020. In most flocks, increased 3-day mortality was followed by an elevated 7-day mortality. Gross lesions included green to dark brown discoloration of the liver, congested lungs, serosanguineous fluid in the caudoventral aspect of the abdomen, and emphysema in the yolk sacs. In birds older than a week of age, disease with neurologic signs became evident and consisted of tremors, stargazing, and incoordination. Histopathologic evaluation revealed multifocal to coalescing fibrinoheterophilic and necrotizing hepatitis associated with gram-positive, long, rod-shaped bacteria. Formalin-fixed liver samples from six cases out of eight cases tested were positive for Clostridium perfringens by immunohistochemistry. Liver samples from two cases were culture positive for Clostridium spp., and C. perfringens was isolated from one sample. Toxinotyping by PCR performed in seven samples revealed the presence of the genes that code for alpha toxin phospholipase C (cpa or plc) and necrotic enteritis toxin B-like (netB) in six samples and as well as C. perfringens large cytotoxin (tpeL) in one sample. Broiler breeders are the suspected source of the infection, and testing revealed C. perfringens in hatchery samples and among broiler breeder flocks. Antimicrobial therapy was coupled with enhanced sanitation at the farm and hatchery in that company, markedly decreasing the mortality and clinical signs. This is the first comprehensive evaluation of clostridial necrotizing hepatitis in newly hatched chicks, and the second ever reported in the literature.
Brucellosis unusually presented as septic knee arthritis: A case report

Brucellosis is one of the world's most prevalent zoonotic illnesses. The most often afflicted joints are the sacroiliac joints, although spondylitis and peripheral arthritis are becoming increasingly prevalent. We described a case of a 40-year-old male patient with Brucellosis presented as septic knee arthritis.