ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Feature selection through binary brain storm optimization

Feature selection stands for the process of finding the most relevant subset of features based on some criterion, which turns out to be an optimization task. In this context, several metaheuristic techniques have been extensively studied achieving results comparable to some state-of-the-art and traditional optimization techniques. This paper introduces a variation of the Brain Storm Optimization (i.e., Binary Brain Storm Optimization) for feature selection purposes, where real-valued solutions are mapped onto a boolean hyper cube using different transfer functions. The proposed Binary Brain Storm Optimization was evaluated under different scenarios and with its results compared to some state-of-the-art techniques. Its overall performance presented suitable results that are comparable to the other techniques, thus showing to be a promising tool to the problem of feature selection. (C) 2018 Elsevier Ltd. All rights reserved.
Photon-Counting Detector CT for Musculoskeletal Imaging: A Clinical Perspective

Photon-counting detector (PCD) CT has emerged as a novel imaging modality that represents a fundamental shift in the way that CT systems detect x-rays. After pre-clinical and clinical investigations showed benefits of PCD CT for a range of imaging tasks, the U.S. FDA in 2021 approved the first commercial PCD CT system for clinical use. The technologic features of PCD CT are particularly well suited for musculo-skeletal imaging applications. Advantages of PCD CT compared with conventional energy-integrating detector (EID) CT include smaller detector pixels and excellent geometric dose efficiency that enable imaging of large joints and central skeletal anatomy at ultrahigh spatial resolution; advanced multienergy spectral postprocessing that allows quantification of gout deposits and generation of virtual noncalcium images for visualization of bone edema; improved metal artifact reduction for imaging of orthopedic implants; and higher CNR and suppression of electronic noise. Given substantially improved cortical and trabecular detail, PCD CT images more clearly depict skeletal abnormalities, including fractures, lytic lesions, and mineralized tumor matrix. The purpose of this article is to review, by use of clinical examples comparing EID CT and PCD CT, the technical features of PCD CT and their associated impact on musculoskeletal imaging applications.
Impact and Detection of GNSS Jammers on Consumer Grade Satellite Navigation Receivers

Jamming is the act of intentionally directing powerful electromagnetic waves toward a victim receiver with the ultimate goal of denying its operations. This paper describes the main types of Global Navigation Satellite System (GNSS) jammers and reviews their impact on GNSS receivers. A survey of state-of-the-art methods for jamming detection is also provided. Different detection approaches are investigated with respect to the receiver stage where they can be implemented.
Current and Emerging Therapies in Pediatric Atopic Dermatitis

Atopic dermatitis (AD) is the most common inflammatory skin disease seen in children. It is a heterogeneous disorder, with a variety of associated manifestations and symptoms. Cases may range from mild to severe. As a result, a spectrum of prescription and nonprescription therapies may be utilized when managing this condition. This article provides an extensive overview of these therapies, with equal consideration provided to current, emerging, and alternative options used in the pediatric population.
Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM

Objective: Accurate segmentation of liver tumors, which could help physicians make appropriate treatment decisions and assess the effectiveness of surgical treatment, is crucial for the clinical diagnosis of liver cancer. In this study, we propose a 4-dimensional (4D) deep learning model based on 3D convolution and convolutional long short-term memory (C-LSTM) for hepatocellular carcinoma (HCC) lesion segmentation. Methods: The proposed deep learning model utilizes 4D information on dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) images to assist liver tumor segmentation. Specifically, a shallow U-net based 3D CNN module was designed to extract 3D spatial domain features from each DCE phase, followed by a 4-layer C-LSTM network module for time domain information exploitation. The combined information of multi-phase DCE images and the manner by which tissue imaging features change on multi-contrast images allow the network to more effectively learn the characteristics of HCC, resulting in better segmentation performance. Results: The proposed model achieved a Dice score of 0.825 +/- 0.077, a Hausdorff distance of 12.84 +/- 8.14 mm, and a volume similarity of 0.891 +/- 0.080 for liver tumor segmentation, which outperformed the 3D U-net model, RA-UNet model and other models in the ablation study in both internal and external test sets. Moreover, the performance of the proposed model is comparable to the nnU-Net model, which showed state-of-the-art performance in many segmentation tasks, with significantly reduced prediction time. Conclusion: The proposed 3D convolution and C-LSTM based model can achieve accurate segmentation of HCC lesions.
Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning

Lung field segmentation in the posterior-anterior (PA) chest radiograph is important for pulmonary disease diagnosis and hemodialysis treatment. Due to high shape variation and boundary ambiguity, accurate lung field segmentation from chest radiograph is still a challenging task. To tackle these challenges, we propose a joint shape and appearance sparse learning method for robust and accurate lung field segmentation. The main contributions of this paper are: 1) a robust shape initialization method is designed to achieve an initial shape that is close to the lung boundary under segmentation; 2) a set of local sparse shape composition models are built based on local lung shape segments to overcome the high shape variations; 3) a set of local appearance models are similarly adopted by using sparse representation to capture the appearance characteristics in local lung boundary segments, thus effectively dealing with the lung boundary ambiguity; 4) a hierarchical deformable segmentation framework is proposed to integrate the scale-dependent shape and appearance information together for robust and accurate segmentation. Our method is evaluated on 247 PA chest radiographs in a public dataset. The experimental results show that the proposed local shape and appearance models outperform the conventional shape and appearance models. Compared with most of the state-of-the-art lung field segmentation methods under comparison, our method also shows a higher accuracy, which is comparable to the inter-observer annotation variation.
Retinal image enhancement using adaptive histogram equalization tuned with nonsimilar grouping curvelet

Fundus images are broadly used by medical ophthalmologists to detect and assess any customary abnormalities. Fundus imaging sensors capture the eye's rigid portion, which characteristically covers the core, tangential retina, optic disc, and macula. Existing state-of-the-art fundus sensors have the drawback of producing low contrast and noisy information, which makes scientific and algorithmic evaluation very complicated. This article proposes an Adaptive Histogram Equalization-Tuned with Nonsimilar Grouping Curvelet (HET-NOSCU), which works through a joint denoising enhancement approach. The proposed algorithm's main contribution includes (i) use of curvelet features to better preserve edges during denoising. (ii) Adaptive enhancement using the histogram to prevent halo ringing and specular artifacts, which yields superior results than the very recently established state-of -the-art methods, using similar performing parameters such as peak signal to noise ratio (PSNR), structural similarity index (SSIM), and correlation coefficient (CoC). We observe an improvement of 17.66%, 0.93%, and 0.24%, respectively, for the above parameters.
Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellular manufacturing systems

Cellular manufacturing system (CMS) is regarded as an efficient production strategy for batch type of production. Literature suggests, since the last two decades neural network has been intensively used in cell formation while production factor such as operation time is merely considered. This paper presents a new hybrid neural network approach, Fuzzy ART K-Means Clustering Technique (FAKMCT), to solve the part machine grouping problem in CMS considering operation time. The performance of the proposed technique is tested with problems from open literature and the results are compared to the existing clustering models such as simple K-means algorithm and modified ART1 algorithm as found in the recent literature. The results support the better performance of the proposed algorithm. The novelty of this study lies in the simple and efficient methodology to produce quick solutions with least computational efforts.
Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband-type devices, patch-type devices, and armband-type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure-related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.
Thiamine supplementation may be associated with improved prognosis in patients with sepsis

Sepsis is a clinical syndrome characterised by a severe disorder of pathophysiology caused by infection of pathogenic micro-organisms. The addition of antioxidant micronutrient therapies such as thiamine to sepsis treatment remains controversial. This study explored the effect of thiamine on the prognosis of patients with sepsis. This study was a retrospective study involving patients with sepsis from the Medical Information Mart for Intensive Care IV. Patients were divided into two groups, the thiamine received group (TR) and the thiamine unreceived group (TUR), according to whether they were supplemented with thiamin via intravenous while in the intensive care unit (ICU). The primary outcome was ICU mortality. The association between thiamine and outcome was analysed using the Cox proportional hazards regression model, propensity score matching (PSM), generalised boosted model-based inverse probability of treatment weighting (IPTW) and doubly robust estimation. A total of 11 553 sepsis patients were enrolled in this study. After controlling for potential confounders using Cox regression models, the TR group had a statistically significantly lower ICU mortality risk than the TUR group. The hazard ratio of ICU mortality for the TR group was 0·80 (95 % CI 0·70, 0·93). We obtained the same results after using PSM, IPTW and doubly robust estimation. Supplementation with thiamine has a beneficial effect on the prognosis of patients with sepsis. More randomised controlled trials are needed to confirm the effectiveness of thiamine supplementation in the treatment of sepsis.
Polycarbonate microplastics induce oxidative stress in anaerobic digestion of waste activated sludge by leaching bisphenol A

Polycarbonate (PC) microplastics are frequently detected in waste activated sludge. However, understanding the potential impact of PC microplastics on biological sludge treatment remains challenging. By tracking the changes in methane production under different concentrations of PC microplastics, a dose-dependent effect of PC microplastics on anaerobic digestion of sludge was observed. PC microplastics at 10-60 particles/g total solids (TS) improved methane production by up to 24.7 ± 0.1 % (at 30 particles/g TS), while 200 particles/g TS PC microplastics reduced methane production by 8.09 ± 0.1 %. Bisphenol A (BPA) leached from 30 particles/g TS PC microplastics (1.26 ± 0.18 mg/L) down-regulated intracellular reactive oxygen species (ROS) production, thereby enhancing enzyme activity, biomass viability, and abundance of methanogenic (Methanobacterium sp. and Methanosarcina sp.), ultimately boosting methane production. Conversely, BPA leached from 200 particles/g TS PC microplastics (4.02 ± 0.15 mg/L) stimulated ROS production, resulting in decreased biomass viability and even apoptosis. Modulation of oxidative stress by leaching monomeric BPA is an underappreciated transformative mechanism for improving the mastery of the potential behavior of microplastics in biological sludge treatment.
Security analysis of indistinguishable obfuscation for internet of medical things applications

As a powerful cryptographic primitive, indistinguishable obfuscation has been widely used to protect data privacy on the Internet of Medical Things (IoMT) systems. Basically, the cryptographic technique protects data privacy using a function to obfuscate medical applications to perform outputs computationally indistinguishable. The state-of-the-art obfuscation technique (GGH13) utilizes a variant of the multilinear map to enhance security. However, in such schemes, it can be observed that noise lies in each element of the matrix, which means the matrix is a full rank matrix with a probability of almost 1 and results that it is unable to establish the relationship between the matrix determinant and rank. In this paper, we propose an attack to break such obfuscator. Specifically, we use approximate eigenvalues to remove the influence of noise on the matrix eigenvalues and build a specific relationship between the determinant and matrix rank. Our analysis shows the structural weakness of the state-of-the-art indistinguishable obfuscation mechanism, and we further discuss the future direction to resolve such privacy issues for IoMT applications.
Dancing Mindfulness: A Phenomenological Investigation of the Emerging Practice

An extensive review of both quantitative and qualitative literature reveals numerous connections between mindfulness practice and psychological well-being. Dancing Mindfulness, as a holistic wellness practice, is a creative approach to mindfulness meditation that draws on dance as the vehicle for engaging in the ancient practice characterized by non-judgment, loving kindness, and present-centered awareness. Of the first participants who learned the Dancing Mindfulness practice in a community-based setting, 10 shared their lived experience with the practice, and these experiences were analyzed using A.P. Giorgi׳s descriptive phenomenological psychological method. As a collective sample, the women described positive experiences with the Dancing Mindfulness practice. Specific themes indicated improvements in emotional and spiritual well-being, increased acceptance, positive changes to the self, and increased application of mindfulness techniques and strategies to real-world living. Another thematic area suggested that dancing and music are the two major components of action within Dancing Mindfulness leading to these benefits.
Time-Ordered Recent Event (TORE) Volumes for Event Cameras

Event cameras are an exciting, new sensor modality enabling high-speed imaging with extremely low-latency and wide dynamic range. Unfortunately, most machine learning architectures are not designed to directly handle sparse data, like that generated from event cameras. Many state-of-the-art algorithms for event cameras rely on interpolated event representations-obscuring crucial timing information, increasing the data volume, and limiting overall network performance. This paper details an event representation called Time-Ordered Recent Event (TORE) volumes. TORE volumes are designed to compactly store raw spike timing information with minimal information loss. This bio-inspired design is memory efficient, computationally fast, avoids time-blocking (i.e., fixed and predefined frame rates), and contains "local memory " from past data. The design is evaluated on a wide range of challenging tasks (e.g., event denoising, image reconstruction, classification, and human pose estimation) and is shown to dramatically improve state-of-the-art performance. TORE volumes are an easy-to-implement replacement for any algorithm currently utilizing event representations.
Semantic Pyramids for Gender and Action Recognition

Person description is a challenging problem in computer vision. We investigated two major aspects of person description: 1) gender and 2) action recognition in still images. Most state-of-the-art approaches for gender and action recognition rely on the description of a single body part, such as face or full-body. However, relying on a single body part is suboptimal due to significant variations in scale, viewpoint, and pose in real-world images. This paper proposes a semantic pyramid approach for pose normalization. Our approach is fully automatic and based on combining information from full-body, upper-body, and face regions for gender and action recognition in still images. The proposed approach does not require any annotations for upper-body and face of a person. Instead, we rely on pretrained state-of-the-art upper-body and face detectors to automatically extract semantic information of a person. Given multiple bounding boxes from each body part detector, we then propose a simple method to select the best candidate bounding box, which is used for feature extraction. Finally, the extracted features from the full-body, upper-body, and face regions are combined into a single representation for classification. To validate the proposed approach for gender recognition, experiments are performed on three large data sets namely: 1) human attribute; 2) head-shoulder; and 3) proxemics. For action recognition, we perform experiments on four data sets most used for benchmarking action recognition in still images: 1) Sports; 2) Willow; 3) PASCAL VOC 2010; and 4) Stanford-40. Our experiments clearly demonstrate that the proposed approach, despite its simplicity, outperforms state-of-the-art methods for gender and action recognition.
Fuzzy ART/RRR-RSS: a two-phase neural network algorithm for part-machine grouping in cellular manufacturing

In this paper an efficient methodology adopting Fuzzy ART neural network is presented to solve the comprehensive part-machine grouping (PMG) problem in cellular manufacturing (CM). Our Fuzzy ART/RRR-RSS (Fuzzy ART/ReaRRangement- ReaSSignment) algorithm can effectively handle the real-world manufacturing factors such as the operation sequences with multiple visits to the same machine, production volumes of parts, and multiple copies of machines. Our approach is based on the non-binary production data-based part-machine incidence matrix (PMIM) where the operation sequences with multiple visits to the same machine, production volumes of parts, and multiple identical machines are incorporated simultaneously. A new measure to evaluate the goodness of the non-binary block diagonal solution is proposed and compared with conventional performance measures. The comparison result shows that our performance measure has more powerful discriminating capability than conventional ones. The Fuzzy ART/RRR-RSS algorithm adopts two phase approach to find the proper block diagonal solution in which all the parts and machines are assigned to their most preferred part families and machine cells for minimisation of inter-cell part moves and maximisation of within-cell machine utilisation. Phase 1 (clustering phase) attempts to find part families and machines cells quickly with Fuzzy ART neural network algorithm which is implemented with an ancillary procedure to enhance the block diagonal solution by rearranging the order of input presentation. Phase 2 (reassignment phase) seeks to find the best proper block diagonal solution by reassigning exceptional parts and machines and duplicating multiple identical machines to cells with the purpose of minimising inter-cell part moves and maximising within-cell machine utilisation. To show the robustness and recoverability of the Fuzzy ART/RRR-RSS algorithm to large-size data sets, a modified procedure of replicated clustering which starts with the near-best solution and rigorous qualifications on the number of cells and duplicated machines has been developed. Experimental results from the modified replicated clustering show that the proposed Fuzzy ART/RRR-RSS algorithm has robustness and recoverability to large-size ill-structured data sets by producing highly independent block diagonal solution close to the near-best one.
Using Species Distribution Modeling to contextualize Lower Magdalenian social networks visible through portable art stylistic similarities in the Cantabrian region (Spain)

This research argues for a refocus of the study of prehistoric social networks that involves contextualizing the inter-site links that are often interpreted as indicators of inter-site social interactions. It focuses on the social networks created during the Lower Magdalenian of the Cantabrian region (Spain), and visible through similarities of portable art representations. It uses Species Distribution Modeling and Maximum Classification Likelihood on faunal presence data to reconstruct prehistoric biomes, and contextualize the networks reconstructed through the art analysis. It demonstrates the potential of mapping the recreated networks onto the reconstructed biomes and of identifying the linked sites' foraging and minimal band territories to distinguish between local mobility movement and inter-group social alliances. The results show that, during the Lower Magdalenian, the majority of movements seen through artistic similarities probably represent the seasonal mobility of one or two hunter-gatherer groups, and that only a few intersite links represent social networks used to exchange mates and gather information. (C) 2015 Elsevier Ltd and INQUA. All rights reserved.
Competitive Quantization for Approximate Nearest Neighbor Search

In this study, we propose a novel vector quantization algorithm for Approximate Nearest Neighbor (ANN) search, based on a joint competitive learning strategy and hence called as competitive quantization (CompQ). CompQ is a hierarchical algorithm, which iteratively minimizes the quantization error by jointly optimizing the codebooks in each layer, using a gradient decent approach. An extensive set of experimental results and comparative evaluations show that CompQ outperforms the-state-of-the-art while retaining a comparable computational complexity.