ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Complex features extraction with deep learning model for the detection of COVID19 from CT scan images using ensemble based machine learning approach

Recently the most infectious disease is the novel Coronavirus disease (COVID 19) creates a devastating effect on public health in more than 200 countries in the world. Since the detection of COVID19 using reverse transcription-polymerase chain reaction (RT-PCR) is time-consuming and error-prone, the alternative solution of detection is Computed Tomography (CT) images. In this paper, Contrast Limited Histogram Equalization (CLAHE) was applied to CT images as a preprocessing step for enhancing the quality of the images. After that, we developed a novel Convolutional Neural Network (CNN) model that extracted 100 prominent features from a total of 2482 CT scan images. These extracted features were then deployed to various machine learning algorithms - Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Finally, we proposed an ensemble model for the COVID19 CT image classification. We also showed various performance comparisons with the state-of-art methods. Our proposed model outperforms the state-of-art models and achieved an accuracy, precision, and recall score of 99.73%, 99.46%, and 100%, respectively.
An Architecture to Accelerate Convolution in Deep Neural Networks

In the past few years, the demand for real-time hardware implementations of deep neural networks (DNNs), especially convolutional neural networks (CNNs), has dramatically increased, thanks to their excellent performance on a wide range of recognition and classification tasks. When considering real-time action recognition and video/image classification systems, latency is of paramount importance. Therefore, applications strive to maximize the accuracy while keeping the latency under a given application-specific maximum: in most cases, this threshold cannot exceed a few hundred milliseconds. Until now, the research on DNNs has mainly focused on achieving a better classification or recognition accuracy, whereas very few works in literature take in account the computational complexity of the model. In this paper, we propose an efficient computational method, which is inspired by a computational core of fully connected neural networks, to process convolutional layers of state-of-the-art deep CNNs within strict latency requirements. To this end, we implemented our method customized for VGG and VGG-based networks which have shown state-of-the-art performance on different classification/recognition data sets. The implementation results in 65-nm CMOS technology show that the proposed accelerator can process convolutional layers of VGGNet up to 9.5 times faster than state-of-the-art accelerators reported to-date while occupying 3.5 mm(2).
A Deep Learning Architecture for Meningioma Brain Tumor Detection and Segmentation

The meningioma brain tumor detection and segmentation method is a complex process due to its low intensity pixel profile. In this article, the meningioma brain tumor images were detected and tumor regions were segmented using a convolutional neural network (CNN) classification approach. The source brain MRI images were decomposed using the discrete wavelet transform and these decomposed sub bands were fused using an arithmetic fusion technique. The fused image was data augmented in order to increase the sample size. The data augmented images were classified into either healthy or malignant using a CNN classifier. Then, the tumor region in the classified meningioma brain image was segmented using an connection component analysis algorithm. The tumor region segmented meningioma brain image was compressed using a lossless compression technique. The proposed method stated in this article was experimentally tested with the sets of meningioma brain images from an open access dataset. The experimental results were compared with existing methods in terms of sensitivity, specificity and tumor segmentation accuracy.
Site-specific art and geodiversity: socio-environmental effects of a special spatial investment in four Geoparks of France and Spain

This paper highlights the intentionalities and effects of artistic production of space in several peripheral, natural and rural areas characterized by high value geodiversity. The art productions discussed in this article are located in four UNESCO global geoparks (Monts d'Ardeche and Haute-Provence in France, Sobrarbe in the Aragonese Pyrenees and Lanzarote in Canary Islands, Spain). Site-specific art installed in natural settings will oftentimes call upon this geodiversity, or its heritage component (sometimes exceptional in geomorphology, aesthetics or age), for inspiration in both forms and materials used. This article is primarily concerned with the artistic and geographical specificities of these artworks, and then sketches a panorama of artistic production of space of the four geoparks selected in France and Spain. Finally, the << services >> provided by these artistic programs to the protected areas and to all the actors who animate them will be discussed. It appears that these various artistic productions can serve as tools of territorial control that allow certain artists, institutions and even administrations or businesses to claim or reclaim spaces they can/did not directly manage. This phenomenon could be a positive form of territorial control when it leads to the improved protection of geodiversity. However, it may also be criticized when it involves forms of political instrumentalization and uses strategies of territorial marketing, thus opening the door to tourist overcrowding.
Light field reconstruction using hierarchical features fusion

Light field imagery has attracted increasing attention for its capacity of simultaneously capturing intensity values of light rays from multiple directions. Such imagery technique has become widely accessible with the emergence of consumer-grade devices, e.g. Lytro, and the Virtual Reality (VR) / Augmented Reality (AR) areas. Light field reconstruction is a critical topic to mitigate the trade-off problem between the spatial and angular resolutions. Learning-based methods have attained outstanding performance among the recently proposed methods, however, the state-of-the-art methods still suffer from heavy artifacts in the case of occlusion. This is likely to be a consequence of failure in capturing the semantic information from the limited spatial receptive field during training. It is crucial for light field reconstruction to learn semantic features and understand a wider context in both the angular and spatial dimensions. To address this issue, we introduce a novel end-to-end U-Net with SAS network (U-SAS-Net) to extract and fuse hierarchical features, both local and semantic, from a relatively large receptive field while establishing the relation of the correlated sub-aperture images. Experimental results on extensive light field datasets demonstrate that our method produces a state-of-the-art performance that exceeds the previous works by more than 0.6 dB PSNR with the fused hierarchical features, especially the semantic features for handling scenes with occlusion and the local features for recovering the rich details. Meanwhile, our method is at a substantially lower cost which takes 48% parameters and less than 10% computation of the previous state-of-the-art method. (C) 2020 Elsevier Ltd. All rights reserved.
Ni-based catalysts used in heterogeneous catalytic ozonation for organic pollutant degradation: a minireview

Among various advanced oxidation processes for wastewater treatment, heterogeneous catalytic ozonation (HCO) has a growing interest in pollutant degradation, e.g., pesticides, pharmaceuticals, cresols, detergents, polymers, dyes, and others. Direct oxidation with ozone can occur by this route or indirectly, generating reactive oxygen species through the catalytic activation of the ozone molecule. Then, many catalytic materials were evaluated, such as unsupported and supported oxides, activated carbon, nanocarbons, carbon nitride, and mesoporous materials. This review focuses on the properties and performance of Ni-based catalysts (NiO, supported NiO, Ni ferrites, and M-Ni bimetallic), emphasizing the reaction mechanisms and the importance of the reactive oxygen species in removing toxic organic compounds.
Saliency-based deep convolutional neural network for no-reference image quality assessment

In this paper, we proposed a novel method for No-Reference Image Quality Assessment (NR-IQA) by combining deep Convolutional Neural Network (CNN) with saliency map. We first investigate the effect of depth of CNNs for NR-IQA by comparing our proposed ten-layer Deep CNN (DCNN) for NR-IQA with the state-of-the-art CNN architecture proposed by Kang et al. (2014). Our results show that the DCNN architecture can deliver a higher accuracy on the LIVE dataset. To mimic human vision, we introduce saliency maps combining with CNN to propose a Saliency-based DCNN (SDCNN) framework for NR-IQA. We compute a saliency map for each image and both the map and the image are split into small patches. Each image patch is assigned with a patch importance value based on its saliency patch. A set of Salient Image Patches (SIPs) are selected according to their saliency and we only apply the model on those SIPs to predict the quality score for the whole image. Our experimental results show that the SDCNN framework is superior to other state-of-the-art approaches on the widely used LIVE dataset. The TID2008 and the CISQ image quality datasets are utilised to report cross-dataset results. The results indicate that our proposed SDCNN can generalise well on other datasets.
Innovation and Development: An Analysis of Landscape Construction Factors in Quanzhou Maritime Silkroad Art Park

From the perspective of tourists, this paper takes Quanzhou Maritime Silkroad Art Park as the research object to study the botanical landscape factors concerned with tourists in the theme park. Through a questionnaire survey, and combined with interviews, the collected results were scientifically analysed using the data. According to the statistical results, the factors of plant landscape construction in the theme park concerned with tourists were summarised, extracted, and named, which were "plant landscape healing", "plant landscape culture", "plant landscape continuity", "plant landscape spatial sense", and "plant landscape aesthetic sense". Through an in-depth analysis of the five common factors of the construction of modern theme park plant landscapes, this study creatively centred on the construction of theme park landscapes and established a scientific evaluation system, combined with the development and construction of the park, and put forward innovative and constructive suggestions based on the summary and analysis results. It provides a scientific reference for plant landscape construction in other theme parks.
Embryonic and extraembryonic tissues during mammalian development: shifting boundaries in time and space

The first few days of embryonic development in eutherian mammals are dedicated to the specification and elaboration of the extraembryonic tissues. However, where the fetus ends and its adnexa begins is not always as self-evident during the early stages of development, when the definitive body axes are still being laid down, the germ layers being specified and a discrete form or bodyplan is yet to emerge. Function, anatomy, histomorphology and molecular identities have been used through the history of embryology, to make this distinction. In this review, we explore them individually by using specific examples from the early embryo. While highlighting the challenges of drawing discrete boundaries between embryonic and extraembryonic tissues and the limitations of a binary categorization, we discuss how basing such identity on fate is the most universal and conceptually consistent. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.
Low-Dose Lung CT Image Restoration Using Adaptive Prior Features From Full-Dose Training Database

The valuable structure features in full-dose computed tomography (FdCT) scans can be exploited as prior knowledge for low-dose CT (LdCT) imaging. However, lacking the capability to represent local characteristics of interested structures of the LdCT image adaptively may result in poor preservation of details/textures in LdCT image. This paper aims to explore a novel prior knowledge retrieval and representation paradigm, called adaptive prior features assisted restoration algorithm, for the purpose of better restoration of the low-dose lung CT images by capturing local features from FdCT scans adaptively. The innovation lies in the construction of an offline training database and the online patch-search scheme integrated with the principal components analysis (PCA). Specifically, the offline training database is composed of 3-D patch samples extracted from existing full-dose lung scans. For online patch-search, 3-D patches with structure similar to the noisy target patch are first selected from the database as the training samples. Then, PCA is applied on the training samples to retrieve their local prior principal features adaptively. By employing the principal features to decompose the noisy target patch and using an adaptive coefficient shrinkage technique for inverse transformation, the noise of the target patch can be efficiently removed and the detailed texture can be well preserved. The effectiveness of the proposed algorithm was validated by CT scans of patients with lung cancer. The results show that it can achieve a noticeable gain over some state-of-the-art methods in terms of noise suppression and details/textures preservation.
Convexity constrained efficient superpixel and supervoxel extraction

This paper presents an efficient superpixel (SP) and supervoxel (SV) extraction method that aims improvements over the state-of-the-art in terms of both accuracy and computational complexity. Segmentation performance is improved through convexity constrained distance utilization, whereas computational efficiency is achieved by replacing complete region processing by a boundary adaptation technique. Starting from the uniformly distributed, rectangular (cubical) equal size (volume) superpixels (supervoxels), region boundaries are iteratively adapted towards object edges. Adaptation is performed by assigning the boundary pixels to the most similar neighboring SPs (SVs). At each iteration, SP (SV) regions are updated; hence, progressively converging to compact pixel groups. Detailed experimental comparisons against the state-of-the-art competing methods validate the performance of the proposed technique considering both accuracy and speed. (C) 2015 Elsevier B.V. All rights reserved.
A fluorescent and colorimetric sensor based on ionic liquids for the on-site monitoring trace gaseous SO2

SO2 could cause severe environmental pollution and health threat, so real-time and on-site monitoring of SO2 has attracted considerable attention. This work proposed a novel ionic liquid-based sensor, called trihexyl (tetradecyl) phosphonium fluorescein ionic liquid, which can accurately detect SO2 with its fluorescent and colorimetric dual-readout assay without seventeen gases interference (eg: NO, N2, CO2, O2, COS, HCl, CHCl3). GC-MS was also used to verify the validation of the detection method. First, this fluorescein-based IL sensor exhibited fluorescence green and colorimetric yellow signals. When the sensor was exposed to gaseous SO2, the green fluorescence quenched, and the colorimetric yellow color faded due to chemical bond interaction. Also, the proposed IL sensor exhibited good linearity in the SO2 concentration range of 5.0-95.0 ppm with a detection limit of 0.9 ppm (fluorescence) and 1.9 ppm (colorimetry), and recoveries of 97%∼103% with RSD less than 1.21%. Besides, the IL sensor could be easily assembled into a paper device by simple immersion, and the paper strip was exploited to realize a semiquantitative visual detection of SO2. These results indicated that the proposed fluorescence-colorimetric dual-signal chemosensor could be used as intelligent paper labels for real-time and on-site monitoring of SO2 in ambient air.
Facile Synthesis of Uranium Complexes with a Pendant Borane Lewis Acid and 1,2-Insertion of CO into a U-N Bond

In this contribution, we illustrate uranium complexes bearing a pendant borate (i.e. 1 and 2) or a pendant borane (i.e. 3 and 4) moiety via reaction of the highly strained uranacycle I with various 3-coordinate boranes. Complexes 3 and 4 represent the first examples of uranium complexes with a pendant borane Lewis acid. Moreover, complex 3 was capable of activation of CO, delivering a new CO activation mode, and an abnormal CO 1,2-insertion pathway into a U-N bond. The importance of the pendant borane moiety was confirmed by the controlled experiments.
Looking for Abnormalities in Mammograms With Self- and Weakly Supervised Reconstruction

Early breast cancer screening through mammography produces every year millions of images world-wide. Despite the volume of the data generated, these images are not systematically associated with standardized labels. Current protocols encourage giving a malignancy probability to each studied breast but do not require the explicit and burdensome annotation of the affected regions. In this work, we address the problem of abnormality detection in the context of such weakly annotated datasets. We combine domain knowledge about the pathology and clinically available image-wise labels to propose a mixed self- and weakly supervised learning framework for abnormalities reconstruction. We also introduce an auxiliary classification task based on the reconstructed regions to improve explainability. We work with high-resolution imaging that enables our network to capture different findings, including masses, micro-calcifications, distortions, and asymmetries, unlike most state-of-the-art works that mainly focus on masses. We use the popular INBreast dataset as well as our private multi-manufacturer dataset for validation and we challenge our method in segmentation, detection, and classification versus multiple state-of-the-art methods. Our results include image-wise AUC up to 0.86, overall region detection true positives rate of 0.93, and the pixel-wise F-1 score of 64% on malignant masses.
Modeling strategies to analyse longitudinal biomarker data: An illustration on predicting immunotherapy non-response in non-small cell lung cancer

Serum tumor markers acquired through a blood draw are known to reflect tumor activity. Their non-invasive nature allows for more frequent testing compared to traditional imaging methods used for response evaluations. Our study aims to compare nine prediction methods to accurately, and with a low false positive rate, predict progressive disease despite treatment (i.e. non-response) using longitudinal tumor biomarker data. Bi-weekly measurements of CYFRA, CA-125, CEA, NSE, and SCC were available from a cohort of 412 advanced stage non-small cell lung cancer (NSCLC) patients treated up to two years with immune checkpoint inhibitors. Serum tumor marker measurements from the first six weeks after treatment initiation were used to predict treatment response at 6 months. Nine models with varying complexity were evaluated in this study, showing how longitudinal biomarker data can be used to predict non-response to immunotherapy in NSCLC patients.
Energy harvesting: State-of-the-art

This paper presents a brief history of energy harvesting for low-power systems followed by a review of the state-of-the-art of energy harvesting techniques, power conversion, power management, and battery charging. The advances in energy harvesting from vibration, thermal, and RF sources are reviewed as well as power management techniques. Examples of discrete form implementation and integrated form implementation using microelectromechanical systems (MEMS) and CMOS microelectronic processes are also given. The comparison between the reviewed works concludes this paper. (C) 2010 Elsevier Ltd. All rights reserved.
Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks

Coronary heart disease is a common cause of death despite being preventable. To treat the underlying plaque deposits in the arterial walls, intravascular optical coherence tomography can be used by experts to detect and characterize the lesions. In clinical routine, hundreds of images are acquired for each patient, which require automatic plaque detection for fast and accurate decision support. So far, automatic approaches rely on classic machine learning methods and deep learning solutions have rarely been studied. Given the success of deep learning methods with other imaging modalities, a thorough understanding of deep learning-based plaque detection for future clinical decision support systems is required. We address this issue with a new data set consisting of in vivo patient images labeled by three trained experts. Using this data set, we employ the state-of-the-art deep learning models that directly learn plaque classification from the images. For improved performance, we study different transfer learning approaches. Furthermore, we investigate the use of Cartesian and polar image representations and employ data augmentation techniques tailored to each representation. We fuse both representations in a multi-path architecture for more effective feature exploitation. Last, we address the challenge of plaque differentiation in addition to detection. Overall, we find that our combined model performs best with an accuracy of 91.7%, a sensitivity of 90.9%, and a specificity of 92.4%. Our results indicate that building a deep learning-based clinical decision support system for plaque detection is feasible.
Chronic lithium treatment ameliorates ketamine-induced mania-like behavior via the PI3K-AKT signaling pathway

Ketamine, a rapid-acting antidepressant drug, has been used to treat major depressive disorder and bipolar disorder (BD). Recent studies have shown that ketamine may increase the potential risk of treatment-induced mania in patients. Ketamine has also been applied to establish animal models of mania. At present, however, the underlying mechanism is still unclear. In the current study, we found that chronic lithium exposure attenuated ketamine-induced mania-like behavior and c-Fos expression in the medial prefrontal cortex (mPFC) of adult male mice. Transcriptome sequencing was performed to determine the effect of lithium administration on the transcriptome of the PFC in ketamine-treated mice, showing inactivation of the phosphoinositide 3-kinase (PI3K)-protein kinase B (AKT) signaling pathway. Pharmacological inhibition of AKT signaling by MK2206 (40 mg/kg), a selective AKT inhibitor, reversed ketamine-induced mania. Furthermore, selective knockdown of AKT via AAV-AKT-shRNA-EGFP in the mPFC also reversed ketamine-induced mania-like behavior. Importantly, pharmacological activation of AKT signaling by SC79 (40 mg/kg), an AKT activator, contributed to mania in low-dose ketamine-treated mice. Inhibition of PI3K signaling by LY294002 (25 mg/kg), a specific PI3K inhibitor, reversed the mania-like behavior in ketamine-treated mice. However, pharmacological inhibition of mammalian target of rapamycin (mTOR) signaling with rapamycin (10 mg/kg), a specific mTOR inhibitor, had no effect on ketamine-induced mania-like behavior. These results suggest that chronic lithium treatment ameliorates ketamine-induced mania-like behavior via the PI3K-AKT signaling pathway, which may be a novel target for the development of BD treatment.