ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Cost-effective color filter array demosaicing using spatial correlation

In this paper, we propose a cost-effective color filter array (CFA) demosaicing method for digital still cameras in which a single CCD or CMOS image sensor is used. Since a CFA is adopted, we must interpolate missing color values in the red, green and blue channels at each pixel location. While most state-of-the-art algorithms invest a great deal of computational effort in the enhancement of the reconstructed image to overcome the color artifacts, we focus on eliminating the color artifacts with low computational complexity. Using spatial correlation of the adjacent pixels, the edge-directional information of the neighbor pixels is used for determining the edge direction of the current pixel. We apply our method to the state-of-the-art algorithms which use edge-directed methods to interpolate the missing color channels. The experiment results show that the proposed method enhances the demosaiced image quality, especially visually, by removing most of the color artifacts.
Performance of AlGaN/GaN High-Electron Mobility Transistors With AlSiN Passivation

The performance of AlGaN/GaN high-electron mobility transistors that were passivated with AlSiN and SiN and fabricated side by side has been studied. It is found that the AlSiN passivation produced state-of-the-art devices, improving both small- and large-signal performance over the SiN passivation, particularly at higher drain bias. With large-signal excitation at 10 GHz, the effects of second-harmonic termination on the load pull were also studied. Significant improvements in the power-added efficiency were demonstrated.
State-of-the-Art Review of Technologies for Pipe Structural Health Monitoring

Advances in electronics, sensor technology, information science, electrical and computer engineering give rise to emerging technologies, some of which could be applied to the inspection, monitoring, and condition assessment of buried water mains. This paper presents a state of the review of sensor technologies used for monitoring indicators pointing to pipe structural deterioration. The potential for multi-sensor system and sensor data fusion for condition-based maintenance are also discussed.
N-Heterocyclic Carbene Catalyzed Synthesis of δ-Sultones via α,β-Unsaturated Sulfonyl Azolium Intermediates

A limited array of reactive intermediates have enabled a wealth of discoveries in N-heterocyclic carbene organocatalysis. In this study, the viability of α,β-unsaturated sulfonyl azoliums as double electrophiles in new reactions is examined. Specifically, the (3+3) annulation of such species with the trimethylsilyl enol ethers of various 1,3-dicarbonyl compounds has been developed. This reaction provides access to a range of novel unsaturated δ-sultones (18 examples) in good yields (40-88 %) under mild reaction conditions. Mechanistic studies and the development of an enantioselective variant (55 % yield, 73:27 e.r.) support the intermediacy of an α,β-unsaturated sulfonyl azolium species.
Pearson syndrome: a multisystem mitochondrial disease with bone marrow failure

Pearson syndrome (PS) is a rare fatal mitochondrial disorder caused by single large-scale mitochondrial DNA deletions (SLSMDs). Most patients present with anemia in infancy. Bone marrow cytology with vacuolization in erythroid and myeloid precursors and ring-sideroblasts guides to the correct diagnosis, which is established by detection of SLSMDs. Non hematological symptoms suggesting a mitochondrial disease are often lacking at initial presentation, thus PS is an important differential diagnosis in isolated hypogenerative anemia in infancy. Spontaneous resolution of anemia occurs in two-third of patients at the age of 1-3 years, while multisystem non-hematological complications such as failure to thrive, muscle hypotonia, exocrine pancreas insufficiency, renal tubulopathy and cardiac dysfunction develop during the clinical course. Some patients with PS experience a phenotypical change to Kearns-Sayre syndrome. In the absence of curative therapy, the prognosis of patients with PS is dismal. Most patients die of acute lactic acidosis and multi-organ failure in early childhood. There is a great need for the development of novel therapies to alter the natural history of patients with PS.
User credit-based collaborative filtering

Memory-based collaborative filtering is the state-of-the-art method in recommender systems and has proven to be successful in various applications. In this paper we develop novel memory-based methods that incorporate the level of a user credit instead of using similarity between users. The user credit is the degree of one's rating reliability that measures how adherently the user rates items as others do. Preliminary simulation results show that the proposed methods outperform the conventional memory-based ones. The methods are effective in a cold-starting problem. (C) 2008 Elsevier Ltd. All rights reserved.
Rhetorics-based multi-document summarization

In this paper, a new multi-document summarization framework which combines rhetorical roles and corpus-based semantic analysis is proposed. The approach is able to capture the semantic and rhetorical relationships between sentences so as to combine them to produce coherent summaries. Experiments were conducted on datasets extracted from web-based news using standard evaluation methods. Results show the promise of our proposed model as compared to state-of-the-art approaches. (C) 2013 Elsevier Ltd. All rights reserved.
FAST Pre-Filtering-Based Real Time Road Sign Detection for Low-Cost Vehicle Localization

In order to overcome the limitations of GNSS/INS and to keep the cost affordable for mass-produced vehicles, a precise localization system fusing the estimated vehicle positions from low-cost GNSS/INS and low-cost perception sensors is being developed. For vehicle position estimation, a perception sensor detects a road facility and uses it as a landmark. For this localization system, this paper proposes a method to detect a road sign as a landmark using a monocular camera whose cost is relatively low compared to other perception sensors. Since the inside pattern and aspect ratio of a road sign are various, the proposed method is based on the part-based approach that detects corners and combines them to detect a road sign. While the recall, precision, and processing time of the state of the art detector based on a convolutional neural network are 99.63%, 98.16%, and 4802 ms respectively, the recall, precision, and processing time of the proposed method are 97.48%, 98.78%, and 66.7 ms, respectively. The detection performance of the proposed method is as good as that of the state of the art detector and its processing time is drastically reduced to be applicable for an embedded system.
Effects of liming on oxic and anoxic N2O and CO2 production in different horizons of boreal acid sulfate soil and non-acid soil under controlled conditions

In acid sulfate (AS) soils, organic rich topsoil and subsoil horizons with highly variable acidity and moisture conditions and interconnected reactions of sulfur and nitrogen make them potential sources of greenhouse gases (GHGs). Subsoil liming can reduce the acidification of sulfidic subsoils in the field. However, the mitigation of GHG production in AS subsoils by liming, and the mechanisms involved, are still poorly known. We limed samples from different horizons of AS and non-AS soils to study the effects of liming on the N2O and CO2 production during a 56-day oxic and subsequent 72-h anoxic incubation. Liming to pH ≥ 7 decreased oxic N2O production by 97-98 % in the Ap1 horizon, 38-50 % in the Bg1 horizon, and 34-36 % in the BC horizon, but increased it by 136-208 % in the C horizon, respectively. Liming decreased anoxic N2O production by 86-94 % and 78-91 % in Ap1 and Bg1 horizons, but increased it by 100-500 % and 50-162 % in BC and C horizons, respectively. Liming decreased N2O/(N2O + N2) in anoxic denitrification in most horizons of both AS and non-AS soils. Liming significantly increased the cumulative oxic and anoxic CO2 production in AS soil, but less so in non-AS soil due to the initial high soil pH. Higher carbon and nitrogen contents in AS soil compared to non-AS soil agreed with the respectively higher cumulative oxic N2O production in all horizons, and the higher CO2 production in the subsoil horizons of all lime treatments. Overall, liming reduced the proportion of N2O in the GHGs produced in most soil horizons under oxic and anoxic conditions but reduced the total GHG production (as CO2 equivalents) only in the Ap1 horizon of both soils. The results suggest that liming of subsoils may not always effectively mitigate GHG emissions due to concurrently increased CO2 production and denitrification.
Nonrigid Registration of Volumetric Images Using Ranked Order Statistics

Nonrigid image registration techniques using intensity based similarity measures are widely used in medical imaging applications. Due to high computational complexities of these techniques, particularly for volumetric images, finding appropriate registration methods to both reduce the computation burden and increase the registration accuracy has become an intensive area of research. In this paper, we propose a fast and accurate nonrigid registration method for intra-modality volumetric images. Our approach exploits the information provided by an order statistics based segmentation method, to find the important regions for registration and use an appropriate sampling scheme to target those areas and reduce the registration computation time. A unique advantage of the proposed method is its ability to identify the point of diminishing returns and stop the registration process. Our experiments on registration of end-inhale to end-exhale lung CT scan pairs, with expert annotated landmarks, show that the new method is both faster and more accurate than the state of the art sampling based techniques, particularly for registration of images with large deformations.
Effective dynamic dispatching rule and constructive heuristic for solving single-machine scheduling problems with a common due window

This study addresses the single-machine scheduling problem with a common due window (CDW) that has a constant size and position. The objective is to minimise the total weighted earliness-tardiness penalties for jobs completed out of the CDW. To determine a schedule as close to optimum as possible, this study develops a dynamic dispatching rule and an effective constructive heuristic. The better performance of the proposed heuristic is demonstrated by comparing the results of it with those of a state-of-the-art greedy heuristic on a well-known benchmark problem set. In addition, we incorporate the constructive heuristic into a best-so-far meta-heuristic to examine the benefit of the proposed heuristic. The results show that the best known solutions in 144 out of the 250 benchmark instances are improved.
Cyclododecane as a Contrast Improving Substance for the Terahertz Imaging of Artworks

This paper presents measurements of the terahertz properties of the art conservation substance cyclododecane, demonstrating that it can act as a contrast improving agent in the terahertz imaging of concealed wall paintings. Results are presented which show that the terahertz optical properties of cyclododecane are dependent on the rate at which it has cooled from the melt. Based on the results, a theoretical explanation of the contrast enhancement mechanism is postulated. The findings presented here may lead to the development of novel coating materials that could improve the quality of terahertz images in a variety of fields and not just in art conservation.
Optical coherence tomography for longitudinal monitoring of vasculature in scars treated with laser fractionation

This study presents the first in vivo longitudinal assessment of scar vasculature in ablative fractional laser treatment using optical coherence tomography (OCT). A method based on OCT speckle decorrelation was developed to visualize and quantify the scar vasculature over the treatment period. Through reliable co-location of the imaging field of view across multiple imaging sessions, and compensation for motion artifact, the study was able to track the same scar tissue over a period of several months, and quantify changes in the vasculature area density. The results show incidences of occlusion of individual vessels 3 days after the first treatment. The subsequent responses ˜20 weeks after the initial treatment show differences between immature and mature scars. Image analysis showed a distinct decrease (25 ± 13%, mean ± standard deviation) and increase (19 ± 5%) of vasculature area density for the immature and mature scars, respectively. This study establishes the feasibility of OCT imaging for quantitative longitudinal monitoring of vasculature in scar treatment. En face optical coherence tomography vasculature images pre-treatment (top) and ˜20 weeks after the first laser treatment (bottom) of a mature burn scar. Arrows mark the same vessel pattern.
A microbiome abundant environment remodels the intestinal microbiota and improves resistance to obesity induced by chlorpyrifos in mice

There is a growing consensus that the appropriate microbiome abundant environment actuates microbiota changes to influence human health. Whether living environment reacts on the threat of contaminants and the underlying mechanism remain largely unknown. Therefore, we constructed microbiome abundant environment models, focusing on their regulatory effects on the obesity induced by the exogenous chemical chlorpyrifos (CPF) and the related mechanisms. The results uncovered that the constructed farm and woodland microbiome abundant environment could protect mice against CPF-induced obesity effectively. The microbiome abundant environment regulated CPF-induced microbiota imbalance, characterized by an increase in Lactobacillus abundance. These altered microbiotas modified the intestinal immune system by increasing the expression of Foxp3 and IL-10, and mitigated intestinal barrier injury by upregulating the expression of IL-22 and intestinal tight junction proteins. Fecal microbiota transplantation could receive similar phenotypes on alleviating CPF-induced obesity development. Our results demonstrate that the microbiome abundant environment attenuates exogenous chemical-induced health risks by remodeling the intestinal microbiota, improving the intestinal ecosystem, and preventing intestinal epithelial leakage.
Ecotoxicological assessment of estuarine surface waters receiving treated and untreated sanitary wastewater

Pollution from sewage discharge is one of the most critical environmental problems worldwide, e.g., in Brazil, where basic sanitation is still scarce. As pollution can affect biomes, especially estuaries where intensive ecological and human activities occur, has caused widespread concern. This work aimed to study the water quality of the Jundiaí/Potengi Estuary (JPE) in an area close to the discharge of treated and untreated wastewater for 18 months. Physicochemical and microbiological parameters were measured and integrated using the Water Quality Index of the Canadian Council of Ministers of the Environment. Ecotoxicological tests were performed with Brazilian endemic organisms to assess the impact of water pollution on biota. A generalized linear regression model was applied to understand the effects of water quality on ecotoxicological responses. Concentrations of metals, dissolved oxygen, total ammonia nitrogen, nitrate, and thermotolerant coliforms did not comply with Brazilian environmental regulations. A significant increase in the mortality rate of Mysidopsis juniae and Nitocra sp. and a significant decrease in the reproductive rate of Nitocra sp. indicated the most affected areas related to the discharge of treated and untreated wastewater. Only 10% of the samples from sites without direct wastewater impact showed a toxic response in at least one organism. Both water quality and sampling sites were statistical predictors of ecotoxicological response, describing not only the pollutant load but also the type of effluent. This study demonstrated the degradation of the environmental quality of the JPE, particularly due to the discharge of sanitary wastewater, and highlights the importance of protection and remediation measures to preserve this protected area.
Neural Network Based Kalman Filters for the Spatio-Temporal Interpolation of Satellite-Derived Sea Surface Temperature

The forecasting and reconstruction of oceanic dynamics is a crucial challenge. While model driven strategies are still the state-of-the-art approaches in the reconstruction of spatio-temporal dynamics. The ever increasing availability of data collections in oceanography raised the relevance of data-driven approaches as computationally efficient representations of spatio-temporal fields reconstruction. This tools proved to outperform classical state-of-the-art interpolation techniques such as optimal interpolation and DINEOF in the retrievement of fine scale structures while still been computationally efficient comparing to model based data assimilation schemes. However, coupling this data-driven priors to classical filtering schemes limits their potential representativity. From this point of view, the recent advances in machine learning and especially neural networks and deep learning can provide a new infrastructure for dynamical modeling and interpolation within a data-driven framework. In this work we adress this challenge and develop a novel Neural-Network-based (NN-based) Kalman filter for spatio-temporal interpolation of sea surface dynamics. Based on a data-driven probabilistic representation of spatio-temporal fields, our approach can be regarded as an alternative to classical filtering schemes such as the ensemble Kalman filters (EnKF) in data assimilation. Overall, the key features of the proposed approach are two-fold: (i) we propose a novel architecture for the stochastic representation of two dimensional (2D) geophysical dynamics based on a neural networks, (ii) we derive the associated parametric Kalman-like filtering scheme for a computationally-efficient spatio-temporal interpolation of Sea Surface Temperature (SST) fields. We illustrate the relevance of our contribution for an OSSE (Observing System Simulation Experiment) in a case-study region off South Africa. Our numerical experiments report significant improvements in terms of reconstruction performance compared with operational and state-of-the-art schemes (e.g., optimal interpolation, Empirical Orthogonal Function (EOF) based interpolation and analog data assimilation).
Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation

Recent advances in deep learning for medical image segmentation demonstrate expert-level accuracy. However, application of these models in clinically realistic environments can result in poor generalization and decreased accuracy, mainly due to the domain shift across different hospitals, scanner vendors, imaging protocols, and patient populations etc. Common transfer learning and domain adaptation techniques are proposed to address this bottleneck. However, these solutions require data (and annotations) from the target domain to retrain the model, and is therefore restrictive in practice for widespread model deployment. Ideally, we wish to have a trained (locked) model that can work uniformly well across unseen domains without further training. In this paper, we propose a deep stacked transformation approach for domain generalization. Specifically, a series of n stacked transformations are applied to each image during network training. The underlying assumption is that the "expected" domain shift for a specific medical imaging modality could be simulated by applying extensive data augmentation on a single source domain, and consequently, a deep model trained on the augmented "big" data (BigAug) could generalize well on unseen domains. We exploit four surprisingly effective, but previously understudied, image-based characteristics for data augmentation to overcome the domain generalization problem. We train and evaluate the BigAug model (with n = 9 transformations) on three different 3D segmentation tasks (prostate gland, left atrial, left ventricle) covering two medical imaging modalities (MRI and ultrasound) involving eight publicly available challenge datasets. The results show that when training on relatively small dataset (n = 10 similar to 32 volumes, depending on the size of the avail- able datasets) from a single source domain: (i) BigAug models degrade an average of 11% (Dice score change)from source to unseen domain, substantially better than conventional augmentation (degrading 39%) and CycleGAN-based domain adaptation method (degrading 25%), (ii) BigAug is better than "shallower" stacked transforms (i.e. those with fewer transforms) on unseen domains and demonstrates modest improvement to conventional augmentation on the source domain, (iii) after training with BigAug on one source domain, performance on an unseen domain is similar to training a model from scratch on that domain when using the same number of training samples. When training on large datasets (n = 465 volumes) with BigAug, (iv) application to unseen domains reaches the performance of state-ofthe-art fully supervised models that are trained and tested on their source domains. These findings establish a strong benchmark for the study of domain generalization in medical imaging, and can be generalized to the design of highly robust deep segmentation models for clinical deployment.
Robust Central Difference Kalman Filter With Mixture Correntropy: A Case Study for Integrated Navigation

In this paper, a robust central difference Kalman filter is proposed to address the process uncertainty and non-Gaussian measurement noise induced by the vehicle's severe maneuver and abnormal measurements in MEMS-SINS/GNSS integrated navigation system. Compared with the state-of-the-art noise distribution based robust filter, in the proposed filter, the process uncertainty and measurement uncertainty are simultaneously suppressed based on a new constructed cost function, which is independent of noise distribution and more insensitive to the non-Gaussian noise. To be specific, the statistical linearization approach is first presented to derive a linear-like regression model. Then, by resorting to the innovation orthogonal theory and Cholesky triangular decomposition, the fading factor of cost function is adaptively and robustly determined in the process of iteration, where the filtering performance and the stability of the algorithm under the condition of process uncertainty are extremely enhanced. Furthermore, the correntropy using the mixture of two Gaussian functions as the kernel function is incorporated into the cost function to prevent the non-Gaussian measurement noise. Our extensive simulation and car-mounted experiment demonstrate that the proposed filter can achieve higher estimation accuracy and better robustness as compared with the related state-of-the-art methods.