ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Motor networks in children with autism spectrum disorder: a systematic review on EEG studies

Motor disturbance and altered motor networks are commonly reported in individuals with autism spectrum disorder (ASD). It has been suggested that electroencephalogram (EEG) can be used to provide exquisite temporal resolution for understanding motor control processes in ASD. However, the variability of study design and EEG approaches can impact our interpretation. Here, we conducted a systematic review on recent 11 EEG studies that involve motor observation and/or execution tasks and evaluated how these findings help us understand motor difficulties in ASD. Three behavior paradigms with different EEG analytic methods were demonstrated. The main findings were quite mixed: children with ASD did not always show disrupted neuronal activity during motor observation. Additionally, they might have intact ability for movement execution but have more difficulties in neuronal modulation during movement preparation. We would like to promote discussions on how methodological selections of behavioral tasks and data analytic approaches impact our interpretation of motor deficits in ASD. Future EEG research addressing the inconsistency across methodological approaches is necessary to help us understand neurophysiological mechanism of motor abnormalities in ASD.
A pyramid multi-level face descriptor: application to kinship verification

Texture descriptors such as Local Binary Pattern (LBP), Local Phase Quantization (LPQ), and Histogram of Oriented Gradients (HOG) have been widely used for face image analysis. This work introduces a novel framework for image-based kinship verification able to efficiently combine local and global facial information extracted from diverse descriptors. The proposed scheme relies on two main points: (1) we model the face images using a Pyramid Multi-level (PML) representation where local descriptors are extracted from several blocks at different resolution scales; (2) we compute the covariance (second-order statistics) between diverse local features characterizing each individual block in the PML representation. This gives rise to a face descriptor with two interesting properties: (i) thanks to the PML representation, scales and face parts are explicitly encoded in the final descriptor without having to detect the facial landmarks; (ii) the covariance descriptor encodes spatial features of any type allowing the integration of several state-of-the-art texture and color features. Experiments conducted on three public kinship databases show that the proposed descriptor can outperform many state-of-the-art kinship verification algorithms and descriptors including those that are based on deep Convolutional Neural Nets.
3-D Consistent and Robust Segmentation of Cardiac Images by Deep Learning With Spatial Propagation

We propose a method based on deep learning to perform cardiac segmentation on short axis Magnetic resonance imaging stacks iteratively from the top slice (around the base) to the bottom slice (around the apex). At each iteration, a novel variant of the U-net is applied to propagate the segmentation of a slice to the adjacent slice below it. In other words, the prediction of a segmentation of a slice is dependent upon the already existing segmentation of an adjacent slice. The 3-D consistency is hence explicitly enforced. The method is trained on a large database of 3078 cases from the U.K. Biobank. It is then tested on the 756 different cases from the U.K. Biobank and three other state-of-the-art cohorts (ACDC with 100 cases, Sunnybrook with 30 cases, and RVSC with 16 cases). Results comparable or even better than the state of the art in terms of distance measures are achieved. They also emphasize the assets of our method, namely, enhanced spatial consistency (currently neither considered nor achieved by the state of the art), and the generalization ability to unseen cases even from other databases.
MRI Upsampling Using Feature-Based Nonlocal Means Approach

In magnetic resonance imaging (MRI), spatial resolution is limited by several factors such as acquisition time, short physiological phenomena, and organ motion. The acquired image usually has higher resolution in two dimensions (the acquisition plane) in comparison with the third dimension, resulting in highly anisotropic voxel size. Interpolation of these low resolution (LR) images using standard techniques, such as linear or spline interpolation, results in distorted edges in the planes perpendicular to the acquisition plane. This poses limitation on conducting quantitative analyses of LR images, particularly on their voxel-wise analysis and registration. We have proposed a new non-local means feature- based technique that uses structural information of a high resolution (HR) image with a different contrast and interpolates the LR image. In this approach, the similarity between voxels is estimated using a feature vector that characterizes the laminar pattern of the brain structures, resulting in a more accurate similarity measure in comparison with conventional patch-based approach. This technique can be applied to LR images with both anisotropic and isotropic voxel sizes. Experimental results conducted on brain MRI scans of patients with brain tumors, multiple sclerosis, epilepsy, as well as schizophrenic patients and normal controls show that the proposed method is more accurate, requires fewer computations, and thus is significantly faster than a previous state-of-the-art patch-based technique. We also show how the proposed method may be used to upsample regions of interest drawn on LR images.
List-Mode PET Motion Correction Using Markerless Head Tracking: Proof-of-Concept With Scans of Human Subject

A custom designed markerless tracking system was demonstrated to be applicable for positron emission tomography (PET) brain imaging. Precise head motion registration is crucial for accurate motion correction (MC) in PET imaging. State-of-the-art tracking systems applied with PET brain imaging rely on markers attached to the patient's head. The marker attachment is the main weakness of these systems. A healthy volunteer participating in a cigarette smoking study to image dopamine release was scanned twice for 2 h with C-11-racolopride on the high resolution research tomograph (HRRT) PET scanner. Head motion was independently measured, with a commercial marker-based device and the proposed vision-based system. A list-mode event-by-event reconstruction algorithm using the detected motion was applied. A phantom study with hand-controlled continuous random motion was obtained. Motion was time-varying with long drift motions of up to 18 mm and regular step-wise motion of 1-6 mm. The evaluated measures were significantly better for motion-corrected images compared to no MC. The demonstrated system agreed with a commercial integrated system. Motion-corrected images were improved in contrast recovery of small structures.
3-D fabrication using electrical discharge-milling: an overview

The electrical discharge machining (EDM) process has predominantly gained considerable impetus in the varieties of high-tech areas for impeccable machining of complex microfeatures in ultra-hard and exotic materials. The development of computer numerical control (CNC) of machine tools has permitted to explore the capabilities of the EDM process for the fabrication of three-dimensional (3-D) micro/macro features employing the controlled movement of the simpler tools in all three axes (x-, y- and z-axes) in addition to the high-speed rotation about its axis. This variant of the EDM process, which entails a simple cylindrical/rectangular/square cross-section tool to accomplish the intended shape by following the programmed instructions is termed as electrical discharge-milling (ED-Milling) operation. In this review article, an endeavor has been made to present the state-of-the-art research that has been accomplished in ED-Milling and the allied EDM processes for 3-D machining. The article reviews the comparative study of majorly available micro-fabrication techniques, different means of complex 3-D fabrication using spark erosion phenomena, machining performance enhancement methods, material aspects, tool wear analysis and sustainable dielectrics in the ED-Milling. Critical assessment and prospects in the respective areas of research in ED-Milling are recommended.
Protection of farmed camels (Camelus Dromedarius): Welfare problems and legislative perspective

In the last years animal welfare has assumed an increasing interest in our society, influencing legislation to enact many provisions aimed at the protection of animals. Along with increased consumer awareness of the need to maintain ethically acceptable conditions of raised animals, scientists too have begun to investigate the conditions of animal welfare, the tools for its evaluation and for its improvement. Although there are many advances in knowledge, much remains to be investigated concerning many species considered "minor", that is, camels and dromedaries. Dromedaries, recently, have attracted the interest of some breeders following the results of studies concerning the nutritional and therapeutic properties of their products - milk in particular - that make them ideal for some particular categories of consumers, such as diabetics, obesity sufferers, lactose-intolerant subjects, menopausal women and so on. Considering their use in dairy husbandry, dromedaries are reared under intensive and/or semi-intensive systems with the resulting emergence of specific needs, which should be fulfilled in order to have appropriate welfare. This paper's purpose is to give practical elements in order to find out dromedary welfare standards, promoting a comprehensive set of regulations on welfare, care and protection of this animal.
CRISPR Gene Editing of Hematopoietic Stem and Progenitor Cells

Genetic editing of hematopoietic stem and progenitor cells can be employed to understand gene-function relationships underlying hematopoietic cell biology, leading to new therapeutic approaches to treat disease. The ability to collect, purify, and manipulate primary cells outside the body permits testing of many different gene editing approaches. RNA-guided nucleases, such as CRISPR, have revolutionized gene editing based simply on Watson-Crick base-pairing, employed to direct activity to specific genomic loci. Given the ease and affordability of synthetic, custom RNA guides, testing of precision edits or large random pools in high-throughput screening studies is now widely available. With the ever-growing number of CRISPR nucleases being discovered or engineered, researchers now have a plethora of options for directed genomic change, including single base edits, nicks or double-stranded DNA cuts with blunt or staggered ends, as well as the ability to target CRISPR to other cellular oligonucleotides such as RNA or mitochondrial DNA. Except for single base editing strategies, precise rewriting of larger segments of the genetic code requires delivery of an additional component, templated DNA oligonucleotide(s) encoding the desired changes flanked by homologous sequences that permit recombination at or near the site of CRISPR activity. Altogether, the ever-growing CRISPR gene editing toolkit is an invaluable resource. This chapter outlines available technologies and the strategies for applying CRISPR-based editing in hematopoietic stem and progenitor cells.
Sn Dopants with Synergistic Oxygen Vacancies Boost CO2 Electroreduction on CuO Nanosheets to CO at Low Overpotential

Using the electrochemical CO2 reduction reaction (CO2RR) with Cu-based electrocatalysts to achieve carbon-neutral cycles remains a significant challenge because of its low selectivity and poor stability. Modulating the surface electron distribution by defects engineering or doping can effectively improve CO2RR performance. Herein, we synthesize the electrocatalyst of Vo-CuO(Sn) nanosheets containing oxygen vacancies and Sn dopants for application in CO2RR-to-CO. Density functional theory calculations confirm that the incorporation of oxygen vacancies and Sn atoms substantially reduces the energy barrier for *COOH and *CO intermediate formation, which results in the high efficiency, low overpotential, and superior stability of the CO2RR to CO conversion. This electrocatalyst possesses a high Faraday efficiency (FE) of 99.9% for CO at a low overpotential of 420 mV and a partial current density of up to 35.22 mA cm-2 at -1.03 V versus reversible hydrogen electrode (RHE). The FECO of Vo-CuO(Sn) could retain over 95% within a wide potential area from -0.48 to -0.93 V versus RHE. Moreover, we obtain long-term stability for more than 180 h with only a slight decay in its activity. Therefore, this work provides an effective route for designing environmentally friendly electrocatalysts to improve the selectivity and stability of the CO2RR to CO conversion.
Adaptive algorithm for spreading factor selection in LoRaWAN networks with multiple gateways

Recently, LoRaWAN has been considered a promising technology for large-scale IoT applications owing to its ability to achieve low power and long range communications. However, LoRaWAN is limited using Aloha random access scheme. When in dense scenarios, such scheme leads to a high number of collisions, thus severely impacts the reliability and scalability of LoRaWAN. In this paper, we investigate the impact of scalability and densification of nodes and gateways on the system reliability taking into account the capture effect. We propose an optimization problem to derive the node distribution at different spreading factors (SF) in LoRaWAN networks with multiple gateways. We then introduce an adaptive algorithm that enables to easily implement SF optimization by adjusting the signal-to-noise ratio thresholds. Moreover, the performance of the proposed algorithm is compared with the performance of legacy LoRaWAN and relevant algorithms from the state-of-the-art. Simulation results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms, and improves the throughput and packet delivery ratio of the network.
In vivo EPR tooth dosimetry for triage after a radiation event involving large populations

The management of radiation injuries following a catastrophic event where large numbers of people may have been exposed to life-threatening doses of ionizing radiation will rely critically on the availability and use of suitable biodosimetry methods. In vivo electron paramagnetic resonance (EPR) tooth dosimetry has a number of valuable and unique characteristics and capabilities that may help enable effective triage. We have produced a prototype of a deployable EPR tooth dosimeter and tested it in several in vitro and in vivo studies to characterize the performance and utility at the state of the art. This report focuses on recent advances in the technology, which strengthen the evidence that in vivo EPR tooth dosimetry can provide practical, accurate, and rapid measurements in the context of its intended use to help triage victims in the event of an improvised nuclear device. These advances provide evidence that the signal is stable, accurate to within 0.5 Gy, and can be successfully carried out in vivo. The stability over time of the radiation-induced EPR signal from whole teeth was measured to confirm its long-term stability and better characterize signal behavior in the hours following irradiation. Dosimetry measurements were taken for five pairs of natural human upper central incisors mounted within a simple anatomic mouth model that demonstrates the ability to achieve 0.5 Gy standard error of inverse dose prediction. An assessment of the use of intact upper incisors for dose estimation and screening was performed with volunteer subjects who have not been exposed to significant levels of ionizing radiation and patients who have undergone total body irradiation as part of bone marrow transplant procedures. Based on these and previous evaluations of the performance and use of the in vivo tooth dosimetry system, it is concluded that this system could be a very valuable resource to aid in the management of a massive radiological event.
Rendezvous Cost-Aware Opportunistic Routing in Heterogeneous Duty-Cycled Wireless Sensor Networks

This paper analyzes the packet transmission cost in asynchronous heterogeneous duty-cycled wireless sensor network systems (WSNs). We discover limitations of conventional opportunistic routing protocols when they are applied to asynchronous heterogeneous duty-cycled WSNs. The key reason is that the conventional opportunistic routing protocols overlook the rendezvous cost in calculating the packet transmission cost. To solve the above issue, we introduce a novel routing metric, expected transmission cost (ETC), which is designed with the rendezvous cost-aware. The proposed metric, ETC, appropriately captures the packet transmitting cost in heterogeneous duty-cycled WSN environments by incorporating the estimation for both expected communication as well as rendezvous cost. We then design an efficient ETC-based opportunistic routing protocol (EoR) which selects the best forwarding candidates with the least packet transmission cost to reduce the actual energy consumption of sensors in packet transmission. We conduct comprehensive testbed experiments and simulations with Telosb motes for the performance evaluation of EoR in comparison with the state-of-the-art routing protocols. Obtained experimental results indicate that EoR achieves significant improvements in terms of packet latency, delivery ratio, and energy efficiency, in comparison with the state-of-the-art protocols.
Impact of wildfire on granite outcrops in archaeological sites surrounded by different types of vegetation

The lack of scientific information about the effects of wildfire on prehistoric structures and rock art, such as dolmens and petroglyphs, impedes the development of conservation guidelines. In this study, the impact of a recent wildfire (late 2017) on granite outcrops in the San Salvador de Coruxo archaeological site (Vigo, SW Galicia) was evaluated. Samples of the same type of granite were obtained from three sites characterised by different types of vegetation ( natural scrub, native deciduous oak and non- native pine-eucalypt forest) in order to determine how the vegetation influences the fire-caused damage to the rock. Three subsamples were taken from each of the granite samples at depths of 1 cm(-3) cm to study how fire affects the rock at depth. In all sites, the temperature reached at the granite surface was below 380 degrees C. No mineralogical changes due to fire exposure were detected, and no physical changes that could be attributed to the effect of the fire on the fissure system of the granite were identified. However, aesthetic colour changes due to the deposition of organic and charred matter, which even penetrated the fissures, were detected. The existence of lignin-derived compounds, lipids and carbohydrates in the samples from the oakwood site indicates greater resistance to fire effects in this type of vegetation than in the other two types. Although preliminary, these findings suggest that oakwoods could act as protective belts around archaeological sites by reducing the wildfire severity, because of their greater resistance to being burnt, and that they could buffer the damaging effects of fire in natural areas where parietal art is found. (C) 2020 Elsevier B.V. All rights reserved.
Learning semantic ambiguities for zero-shot learning

Zero-shot learning (ZSL) aims at recognizing classes for which no visual sample is available at training time. To address this issue, one can rely on a semantic description of each class. A typical ZSL model learns a mapping between the visual samples of seen classes and the corresponding semantic descriptions, in order to do the same on unseen classes at test time. State of the art approaches rely on generative models that synthesize visual features from the prototype of a class, such that a classifier can then be learned in a supervised manner. However, these approaches are usually biased towards seen classes whose visual instances are the only one that can be matched to a given class prototype. We propose a regularization method that can be applied to any conditional generative-based ZSL method, by leveraging only the semantic class prototypes. It learns to synthesize discriminative features for possible semantic description that are not available at training time, that is the unseen ones. The approach is evaluated for ZSL and GZSL on four datasets commonly used in the literature, either in inductive or transductive settings, with results on-par or above state of the art approaches. The code is available at https://github.com/hanouticelina/lsa-zsl.
Intranasal immunization with HRSV prefusion F protein and CpG adjuvant elicits robust protective effects in mice

Human respiratory syncytial virus (HRSV) is a leading cause of lower respiratory tract infections in elderly individuals and young children/infants and can cause bronchiolitis and even death. There is no licensed HRSV vaccine. An ideal vaccine should induce high titers of neutralizing antibodies and a Th1-biased immune response. In this study, we used EXPI293 cells to express the fusion (F) protein with a prefusion conformation (PrF) and compared the safety and efficacy of intranasal immunization with PrF in combination with two mucosal adjuvants (CpG ODN and liposomes) in mice. After two intranasal administrations, mice in the PrF + CpG group produced high titers of neutralizing antibodies (4961) and a Th1-biased immune response compared with the PrF + Lipo group. The lung viral load of mice in the PrF + CpG group was significantly reduced (3.5 log) compared with that in the adjuvant control group, and the survival rate was 100 %, while the survival rate of mice in the PrF + Lipo group was only 67 %. At the same time, this immunization strategy reduced the pathological damage to the lungs in mice. In conclusion, the combination of PrF and CpG adjuvant is immunogenic, elicits a Th1 type immune response, and completely protects mice from a lethal HRSV challenge. It is worthy of further evaluation as an HRSV vaccine in clinical trials. Clinical trial registration. This study was not related to human participation or experimentation.
Antiretroviral therapy adherence strategies used by patients of a large HIV clinic in Lesotho

A high degree of adherence to antiretroviral therapy (ART) in patients infected with human immunodeficiency virus (HIV) is necessary for long term treatment effects. This study explores the role of timing of ART intake, the information patients received from health workers, local adherence patterns, barriers to and facilitators of ART among 28 HIV-positive adults at the Senkatana HIV Clinic in Maseru, Lesotho. This qualitative, semi-structured interview study was carried out during February and March of 2011 and responses were analyzed inspired by the Grounded Theory method. Results were then compared and discussed between the authors and the main themes that emerged were categorized. The majority of the respondents reported having missed one or more doses of medicine in the past and it was a widespread belief among patients that they were required to skip the dose of ART if they were "late". The main barriers to adherence were interruptions of daily routines or leaving the house without sufficient medicine. The use of mobile phone alarms, phone clocks and support from family and friends were major facilitators of adherence. None of the patients reported to have been counseled on family support or the use of mobile phones as helpful methods in maintaining or improving adherence to ART. Being on-time with ART was emphasized during counseling by health workers. In conclusion, patients should be advised to take the dose as soon as they remember instead of skipping the dose completely when they are late. Mobile phones and family support could be subjects to focus on during future counseling particularly with the growing numbers of mobile phones in Africa and the current focus on telemedicine.