ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Orbital Venolymphatic Malformation Treated With Sodium Tetradecyl Sulfate: A Case Report

Orbital and periorbital venolymphatic malformations (VLMs) are benign congenital vascular lesions and constitute 1%-3% of all orbital masses. Widespread facial venous malformations have a high incidence of associated intracranial developmental venous anomalies (DVAs). In such cases, there can be a sudden increase in proptosis following upper respiratory infection or minor trauma. Numerous percutaneous intralesional sclerosing agents like sodium tetradecyl sulfate (STS), bleomycin, doxycycline, ethanol, and OK-432 (Picibanil) have been used for treating VLMs. We hereby report a rare case of retro-orbital VLM treated successfully with STS injection and an isolated dural arterio-venous (AV) fistula in the same patient.
Identification of a novel homozygous SPG7 mutation by whole exome sequencing in a Greek family with a complicated form of hereditary spastic paraplegia

We report the clinical description and genetic analyses of a Greek family with four individuals affected with a complicated form of hereditary spastic paraplegia (HSP) and a recessive pattern of inheritance. Exome sequencing of all affected individuals led to the identification of a homozygous 25 bp deletion predicted to lead to a frameshift and premature stop codon in the SPG7 gene, encoding paraplegin. This deletion, which is located in the first exon of the SPG7 gene, has not been previously reported and likely lead to the complete absence of the SPG7 protein. Interestingly, this family shows significant phenotypic heterogeneity further highlighting the clinical variability associated with SPG7 mutations. Our findings emphasize the clinical utility of whole exome sequencing for the molecular diagnosis of HSPs.
Antiplasmodial and Antimalarial Activity of 3,5-Diarylidenetetrahydro-2H-pyran-4(3H)-ones via Inhibition of Plasmodium falciparum Pyridoxal Synthase

A series of 22 different 3,5-diarylidenetetrahydro-2H-pyran-4(3H)-ones (DATPs) were synthesized, characterized, and screened for their in vitro antiplasmodial activities against chloroquine (CQ)-sensitive Pf3D7, CQ-resistant PfINDO, and artemisinin-resistant PfMRA-1240 strains of Plasmodium falciparum. DATP 19 (3,5-bis(4-hydroxy-3,5-dimethoxybenzylidene)tetrahydro-2H-pyran-4(3H)-one) was found to be the most potent (IC50 1.07 μM) against PfMRA-1240, whereas 21 (3,5-bis(3,4,5-trimethoxybenzylidene)tetrahydro-2H-pyran-4(3H)-one) showed IC50 values of 1.72 and 1.44 μM against Pf3D7 and PfINDO, respectively. Resistance indices (RI) as low as 0.2 to 0.5 for 10 (3,5-bis(4-nitrobenzylidene)tetrahydro-2H-pyran-4(3H)-one) and 20 (3,5-bis(3-nitrobenzylidene)tetrahydro-2H-pyran-4(3H)-one), and <1 for most other DATPs reveals their greater potency against resistant strains than the sensitive one. The single-crystal XRD data for DATP 21 are reported. In silico support was obtained through docking studies. Killing all three strains within 4-8 h, these DATPs showed rapid kill kinetics toward the trophozoite stage. Furthermore, DATP 18 (3,5-bis(quinolin-4-ylmethylene)tetrahydro-2H-pyran-4(3H)-one) inhibited PfPdx1 enzyme activity with IC50 20.34 μM, which is about twofold lower than that (IC50 43 μM) for an already known inhibitor 4PEHz. At an oral dose of 300 mg/kg body weight, DATPs 19 and 21 were found to be nontoxic to mice, and at 100 mg/kg body weight, DATP 19 was found to suppress parasitaemia, which led to an increase in median survival time by three days relative to untreated control mice in a malaria curative study.
Age Estimation in Short Speech Utterances Based on LSTM Recurrent Neural Networks

Age estimation from speech has recently received increased interest as it is useful for many applications such as user-profiling, targeted marketing, or personalized call-routing. This kind of applications need to quickly estimate the age of the speaker and might greatly benefit from real-time capabilities. Long short-term memory (LSTM) recurrent neural networks (RNN) have shown to outperform state-of the-art approaches in related speech-based tasks, such as language identification or voice activity detection, especially when an accurate real-time response is required. In this paper, we propose a novel age estimation system based on LSTM-RNNs. This system is able to deal with short utterances (from 3 to 10 s) and it can be easily deployed in a real-time architecture. The proposed system has been tested and compared with a state-of-the-art i-vector approach using data from NIST speaker recognition evaluation 2008 and 2010 data sets. Experiments on short duration utterances show a relative improvement up to 28% in terms of mean absolute error of this new approach over the baseline system.
Privileged Modality Distillation for Vessel Border Detection in Intracoronary Imaging

Intracoronary imaging is a crucial imaging technology in coronary disease diagnosis as it visualizes the internal tissue morphologies of coronary arteries. Vessel border detection in intracoronary images (VBDI) is desired because it can help the succeeding procedures of computer-aided disease diagnosis. However, existing VDBI methods suffer from the challenge of vessel-environment variability (i.e. high intra- and inter-subject diversity of vessels and their surrounding tissues appeared in images). This challenge leads to the ineffectiveness in the vessel region representation for hand-crafted features, in the receptive field extraction for deeply-represented features, as well as performance suppression derived from clinical data limitation. To solve this challenge, we propose a novel privileged modality distillation (PMD) framework for VBDI. PMD transforms the single-input-single-task (SIST) learning problem in the single-mode VBDI to a multiple-input-multiple-task (MIMT) problem by using the privileged image modality to help the learning model in the target modality. This learns the enriched high-level knowledge with similar semantics and generalizes PMD on diversity-increased low-level image features for improving the model adaptation to diverse vessel environments. Moreover, PMD refines MIMT to SIST by distilling the learned knowledge from multiple to one modality. This eliminates the reliance on privileged modality in the test phase, and thus enables the applicability to each of different intracoronary modalities. A structure-deformable neural network is proposed as an elaborately-designed implementation of PMD. It expands a conventional SIST network structure to the MIMT structure, and then recovers it to the final SIST structure. The PMD is validated on intravascular ultrasound imaging and optical coherence tomography imaging. One modality is the target, and the other one can be considered as the privileged modality owing to their semantic relatedness. The experiments show that our PMD is effective in VBDI (e.g. the Dice index is larger than 0.95), as well as superior to six state-of-the-art VBDI methods.
Person re-identification using selective transformation learning

Applications like video surveillance, anomaly detection, ego-motion, recognition and re-identification (Re-ID), largely depend upon the ability of the models to learn efficient representations of the input data. Applications like re-identification or similarity matching needs the representations which can handle transformations in the input data in a predictable way. Any change in perspective and viewpoint should not change the identity of the person in re-ID systems and also, it must capture the differences accurately to discriminate two different persons correctly. We propose a Selective Transformation Learning (STL) based model which very efficiently learns to transform the image to obtain the right amount of cropping required to generate feature maps which are invariant to affine transformations of the input image. The STL approach selectively trains each of the spatial transformer modules for specific transformation in an end-to-end framework. Proposed model has very low memory footprint compared to state-of-the-art models yet performs substantially. Compared to the ResNet based or other high capacity models it performs substantially better with such a low capacity. To establish the performance quantitatively it has been tested on three publicly available re-identification datasets and on all the datasets it gives an average of 6% improvement in the mean average precision score as compared to the closest sized state-of-the-art model. This approach can be easily adapted to any other model without any special requirements.
The Fuzzy ART algorithm: A categorization method for supplier evaluation and selection

For most of managers purchasing is a strategic issue. Thus. to select the suitable Suppliers has strategic importance for every company The objective Of Supplier selection is to reduce purchasing risk. maximize overall value to the purchaser and build a long term, reliable relationship between buyers and suppliers Many methods have been proposed and used for supplier evaluation and selection, most of them try to rank the Suppliers from the best to the worst and to choose the appropriate supplier(s). Supplier evaluation and selection is a complex and typical multi criteria decision-making problem. Because of human judgment needs in many area of supplier selection Such as preferences on alternatives or on the attributes Of Suppliers or the class number and borders supplier selection becomes more difficult and risky In this study, a new tool for supplier selection is proposed in this paper, we applied Fuzzy Adaptive Resonance Theory (ART)'s classification ability to the Supplier evaluation and selection area The proposed selection method. using Fuzzy ART not only selects the most appropriate supplier(s) and also clusters all of the vendors according to chosen criteria. To explain the Fuzzy ART method a real-life supplier selection problem is solved and suppliers are categorized according to their similarities The obtained results show that the proposed method is well suited as a decision-making tool for Supplier evaluation and selection problem. (C) 2009 Elsevier Ltd. All rights reserved
Evaluation of surface water quality in basins of the Chilean Altiplano-Puna and implications for water treatment and monitoring

Water quality characterization and assessment are key to protecting human health and ecosystems, especially in arid areas such as northern Chile, where water resources are scarce and rich in pollutants. The objective of this study was to review and assess available official water quality data in the Chilean Altiplano-Puna basins for a 10-year period (2008-2018), including water treatment systems. Within the 43,600 km2 of Chilean Altiplano-Puna territory, only 16 official water quality monitoring stations had up-to-date data, and the sampling frequency was less than 3 per year. Most of the water samples collected at the evaluated stations exceeded the drinking and irrigation water Chilean standards for arsenic, boron, and electrical conductivity. Moreover, the characteristics of the Altiplano-Puna affect water quality inside and beyond the area, limiting water usage throughout the Altiplano-Puna basins. Drinking water treatment plants exist in urban and rural settlements; however, the drinking water supply in rural locations is limited due to the lack of adequate treatment and continuity of service. Wastewater treatment plants operate in some urban locations but rarely exist in rural locations. Limited data impede the proper assessment of water quality and thus the evaluation of the need for treatment systems. As such, the implementation of public policies that prioritize water with appropriate quantity and quality for local communities and ecosystems is imperative.
Colour image segmentation using the self-organizing map and adaptive resonance theory

We propose a new competitive-learning neural network model for colour image segmentation. The model, which is based on the adaptive resonance theory (ART) of Carpenter and Grossberg and on the self-organizing map (SOM) of Kohonen, overcomes the limitations of (i) the stability-plasticity trade-offs in neural architectures that employ ART; and (ii) the lack of on-line learning property in the SOM. In order to explore the generation of a growing feature map using ART and to motivate the main contribution, we first present a preliminary experimental model, SOMART, based on Fuzzy ART. Then we propose the new model, SmART, that utilizes a novel lateral control of plasticity to resolve the stability-plasticity problem. SmART has been experimentally found to perform well in RGB colour space, and is believed to be more coherent than Fuzzy ART. (c) 2005 Elsevier Ltd. All rights reserved.
Anatomic and Molecular MR Image Synthesis Using Confidence Guided CNNs

Data-driven automatic approaches have demonstrated their great potential in resolving various clinical diagnostic dilemmas in neuro-oncology, especially with the help of standard anatomic and advanced molecular MR images. However, data quantity and quality remain a key determinant, and a significant limit of the potential applications. In our previous work, we explored the synthesis of anatomic and molecular MR image networks (SAMR) in patients with post-treatment malignant gliomas. In this work, we extend this through a confidence-guided SAMR (CG-SAMR) that synthesizes data from lesion contour information to multi-modal MR images, including T1-weighted (T(1)w), gadolinium enhanced T(1)w (Gd- T(1)w), T2-weighted (T(2)w), and fluid-attenuated inversion recovery (FLAIR), as well as the molecular amide proton transfer-weighted (APTw) sequence. We introduce a module that guides the synthesis based on a confidence measure of the intermediate results. Furthermore, we extend the proposed architecture to allow training using unpaired data. Extensive experiments on real clinical data demonstrate that the proposed model can perform better than current the state-of-the-art synthesis methods. Our code is available at https://github.com/guopengf/CG-SAMR.
Renewable Energy Devices and Systems - State-of-the-Art Technology, Research and Development, Challenges and Future Trends

In this paper, essential statistics demonstrating the increasing role of renewable energy generation are firstly discussed. A state of the art review section covers fundamentals of wind turbines and PV systems. Included are schematic diagrams illustrating the main components and system topologies and the fundamental and increasing role of power electronics as an enabler for renewable energy integration, and for the future power system and smart grid. Recent examples of research and development, including new devices and system installations for utility power plants, as well for as residential and commercial applications are provided. Fuel cells, solar thermal, wave generators, and energy storage systems are also briefly presented and illustrated. Challenges and future trends for 2025 are summarized in a table for on-shore and off-shore wind energy, solar power, including photovoltaic and concentering, wave energy, fuel cells, and storage with batteries and hydrogen, respectively. Recommended further readings on topics of electric power engineering for renewable energy are included in a final section. This paper also represents an editorial introduction for two special issues of the Electric Power Component and Systems Journal, 43(8-10) and 43(12), respectively.
Recent progress in flexible color reflective cholesteric displays

Highly flexible layered full-color cholesteric displays fabricated using ultra-thin substrates with encapsulation through the phase-separation approach is reported. Recent progress of the state of the art of cholesteric display technology will be discussed as well.
A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use

Two-dimensional (2-D)-to-three-dimensional (3-D) registration can improve visualization which may aid minimally invasive neurointerventions. Using clinical and phantom studies, two state-of-the-art approaches to rigid registration are compared quantitatively: an intensity-based algorithm using the gradient difference similarity measure; and an iterative closest point (ICP)-based algorithm. The gradient difference approach was found to be more accurate, with an average registration accuracy of 1.7 mm for clinical data, compared to the ICP-based algorithm with an average accuracy of 2.8 mm. In phantom studies, the ICP-based algorithm proved more reliable, but with more complicated clinical data, the gradient difference algorithm was more robust. Average computation time for the ICP-based algorithm was 20 s per registration, compared with 14 min and 50 s for the gradient difference algorithm.
Design and analysis of an ultra-low-power LC quadrature VCO

This paper presents the design of an ultra-low-power LC quadrature VCO (QVCO). It is designed in a single-poly seven-metal 65-nm CMOS process. Several aspects of state-of-the-art QVCO design are addressed, for example tank design and circuit topologies in nano-meter CMOS technology. To minimize power dissipation, an inductor with a high LQ product of 188 nH at 2.4 GHz, and a self-resonant frequency (f(SR)) of 3.8 GHz, was designed. According to post-layout simulations, the power dissipation is below 300 mu W at a 0.6 V supply. At this supply, the simulated tuning range and phase noise at 1 MHz offset are 10.3% (2.26-2.5 GHz) and -109.6 dBc/Hz respectively. The phase noise figure of merit (FoM) is better than 182.5 dB at all supply voltages of interest, which is competitive to other state-of-the-art QVCOs.
High precision post-processing framework for industrial computed tomography detection

This paper presents a high-precision parameterless post-processing framework for industrial computed tomography (ICT) detection, which does not need parameter tuning for workpieces with different slice image conditions. The proposed framework contains three main steps: (1) adaptively calculate appropriate local window size for each pixel in all slice images by using inner and outer fitting energies to reduce the negative influence of inhomogeneity in subsequent contour extraction; (2) extract contours in all slice images via genetic algorithmbased self-optimizing multi-method combination to maximumly eliminate side effects of inhomogeneity, artifacts, noise, and low contrast; (3) use multi-resolution transformation to avoid point cloud breakpoints and dense final 3D post-processing results. To demonstrate the effectiveness of the proposed framework, its 2D contour extraction effects are compared with state-of-the-art algorithms, and its 3D detection accuracy is certified by comparing with the corresponding coordinate measuring machine (CMM) point clouds or CAD models of different industrial workpieces. The experimental results show that the framework's performance of dealing with negative effects in 2D contour extraction is better than a single state-of-the-art method, and that the deviation distributions in industrial workpiece detections are <= 0.02 mm for the simple workpiece and <= 0.05 mm for the complex workpiece.
A fast approach to deformable surface 3D tracking

Deformable surface 3D tracking is a severely under-constrained problem and great efforts have been made to solve it. A recent state-of-the-art approach solves this problem by formulating it as a second order cone programming (SOCP) problem. However, one drawback of this approach is that it is time-consuming. In this paper, we propose an effective method for 3D deformable surface tracking. First, we formulate the deformable surface tracking problem as a linear programming (LP) problem. Then, we solve the LP problem with an algorithm which converges superlinearly rather than bisection algorithm whose convergence speed is linear. Our experimental studies on synthetic and real data have demonstrated the proposed method can not only reliably recover 3D structures of surfaces but also run faster than the state-of-the-art method. (C) 2011 Elsevier Ltd. All rights reserved.
Automatic detection and localization of thighbone fractures in X-ray based on improved deep learning method

Deep learning is continuously promoting the development of fracture detection in medical images. In this study, we propose a novel two-stage region-based convolutional neural network for thighbone fractures detection. In this framework, the new network structure is designed to balance the information of each feature map in the feature pyramid of ResNeXt. In experiments, the pre-trained model is implemented on the dataset reported in the previous study, which includes 3842 thighbone X-ray radiographs. To compare the proposed framework with the latest detection techniques, transfer learning is employed to test all the state-of-the-art generic object detection algorithms on the same thighbone fracture dataset. Moreover, a few ablation experiments are given to demonstrate the effects of each component employed in the proposed framework and different hyperparameter settings on fracture detection. The experimental results show that the Average Precision of the proposed detection framework reaches 88.9% in thighbone fracture detection. This result proves the effectiveness of our framework and its superiority over other state-of-the-art methods.
Thermal Interaction for Improving Tactile Artwork Depth and Color-Depth Appreciation for Visually Impaired People

Visually impaired people can take advantage of multimodal systems in which visual information is communicated through different modes of interaction and types of feedback. Among the possible interaction modes, thermal interaction in the context of assistive devices for visually impaired people lacks research in spite of its potential. In this paper, we propose a temperature-depth mapping algorithm and a thermal display system to convey depth and depth-color of artworks' features in the context of tactile exploration by visually impaired people. Tests with a total of 18 sighted users and six visually impaired users were performed both during the mapping algorithm design and after developing a tactile temperature prototype artwork model to assess the potentials of thermal interaction for recognizing depth and color-depth in tactile art appreciation. These tests showed both an existing correlation between depth and temperature and that the mapping based on that correlation is appropriate for conveying depth during artwork tactile exploration.
A New 4-D Nonlocal Transform-Domain Filter for 3-D Magnetic Resonance Images Denoising

The simultaneous removal of noise and preservation of the integrity of 3-D magnetic resonance (MR) images is a difficult and important task. In this paper, we consider characterizing MR images with 3-D operators, and present a novel 4-D transform-domain method termed 'modified nonlocal tensor-SVD (MNL-tSVD)' for MR image denoising. The proposed method is based on the grouping, hard-thresholding and aggregation paradigms, and can be viewed as a generalized nonlocal extension of tensor-SVD (t-SVD). By keeping MR images in its natural three-dimensional form, and collaboratively filtering similar patches, MNL-tSVD utilizes both the self-similarity property and 3-D structure of MR images to preserve more actual details and minimize the introduction of new artifacts. We show the adaptability of MNL-tSVD by incorporating it into a two-stage denoising strategy with a few adjustments. In addition, analysis of the relationship between MNL-tSVD and current the state-of-the-art 4-D transforms is given. Experimental comparisons over simulated and real brain data sets at different Rician noise levels show that MNL-tSVD can produce competitive performance compared with related approaches.