ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Sensitive colorimetric assay of hydrogen peroxide and glucose in humoral samples based on the enhanced peroxidase-mimetic activity of NH2-MIL-88-derived FeS2@CN nanocomposites compared to its precursors

By employing NH2-MIL-88 as a template, we synthesized the intermediate Fe@CN under high-temperature calcination and further fabricated the FeS2@CN nanocomposites in the presence of sulfur powder. Under varying temperatures (300-600 °C) and Fe@CN-to-S ratios (1:3-6), FeS2@CN500-5 nanocomposites had the highest peroxidase-mimetic activity. Under optimized conditions (incubation temperature 40 °C; solution pH 4.0 and nanocomposite concentration 10 μg/mL; 652-nm absorption), the Michaelis-Menten constant (Km) of FeS2@CN was much lower than that of horseradish peroxidase (HRP), therefore demonstrating that it had a higher affinity for both chromogenic substrates than conventional HRP. The limits of detection for H2O2 and glucose were 0.15 and 0.30 μmol/L, respectively, and the recoveries for glucose were 91.8-103% with RSDs <5.2%. The novelty of this study lies in (1) the FeS2@CN was confirmed to possess stronger enzyme-mimetic activity than its precursors (NH2-MIL-88 and Fe@CN); (2) the enhanced activity resulted from the unsaturated sites of N and S doping and the plentiful defects on the porous carbon surface; and (3) free radical trapping experiments evidenced that •OH played a major role in the catalytic reaction, while h+ and •O2- simultaneously participated in the catalytic process. These convincing performance metrics lead us to postulate that the FeS2@CN-based colorimetric biosensor provides a promising approach for several real-world applications, such as point-of-care diagnosis and workplace health evaluations.
Online Learning and Classification of EMG-Based Gestures on a Parallel Ultra-Low Power Platform Using Hyperdimensional Computing

This paper presents a wearable electromyographic gesture recognition system based on the hyperdimensional computing paradigm, running on a programmable parallel ultra-low-power (PULP) platform. The processing chain includes efficient on-chip training, which leads to a fully embedded implementation with no need to perform any offline training on a personal computer. The proposed solution has been tested on 10 subjects in a typical gesture recognition scenario achieving 85% average accuracy on 11 gestures recognition, which is aligned with the state-of-the-art, with the unique capability of performing online learning. Furthermore, by virtue of the hardware friendly algorithm and of the efficient PULP system-on-chip (Mr. Wolf) used for prototyping and evaluation, the energy budget required to run the learning part with 11 gestures is 10.04 mJ, and 83.2 mu J per classification. The system works with a average power consumption of 10.4 mW in classification, ensuring around 29 h of autonomy with a 100 mAh battery. Finally, the scalability of the system is explored by increasing the number of channels (up to 256 electrodes), demonstrating the suitability of our approach as universal, energy-efficient biopotential wearable recognition framework.
E-LOAM: LiDAR Odometry and Mapping With Expanded Local Structural Information

This paper investigates the real time LiDAR odometry and mapping (LOAM) problem in unstructured environments. We propose E-LOAM (LOAM with Expanded Local Structural Information), a paradigm which expands the pre-extracted geometric information with local point cloud information around the geometric feature points. State-of-the-art approaches usually extract pointed geometric features as the only correspondence primitives for point cloud scan-to-scan and scan-to-map registration. We argue that, in unstructured environments, sometimes, the extracted geometric features are too sparse for adequate point cloud registration. Therefore, E-LOAM expands the 'pointed' geometric correspondence primitives with the point clouds around them, i.e., the local point clouds in the voxel around the feature point. The local point clouds, approximated by a multivariate normal distribution, offer additional local structural information, on top of the pointed geometric information. Additionally, to enrich the sparse geometric features, we make use of the intensity information of point clouds, and extract the places with high intensity variations as additional feature points. Experimental results with the KITTI dataset show the efficacy of E-LOAM, when compared with state of the arts. We further implement E-LOAM on a real robot platform, and evaluate E-LOAM with in-field tests.
Wide Band-Gap Polymer Donors Functionalized with Unconventional Carbamate Side Chains for Polymer Solar Cells

Side-chain engineering with heteroatoms is not only effective in tuning frontier molecular orbitals, but also possible for forming secondary bonds which can be utilized to planarize the molecular backbone, hence, improving the photon absorption as well as charge-transport abilities of polymer solar-cell (PSC) materials. Herein, two types of unconventional side chains, namely carboxylate and carbamate, containing various heteroatoms are introduced to the thiophene bridges in high performance benzodithiophene (BDT) based donor polymers to from the novel polymers PTzTz-C and PTzTz-N, respectively. In these polymers, non-covalent O⋅⋅⋅S and N⋅⋅⋅H interactions induce a high tendency to aggregation. In a ternary-blend PSC with PTzTz-N added to the high-performance D18 : BTP-eC9 blend, complimentary absorption and improved thin-film morphology were observed with a top power conversion efficiency of 18.76 %, which is an improvement of almost 5 % over the D18 : BTP-eC9 binary blends.
An efficient and light weight polynomial multiplication for ideal lattice-based cryptography

Ring-Learning With Errors (Ring-LWE) based cryptographic schemes such as signature, key exchange, and encryption require polynomial multiplication. This multiplication operation is the most time consuming and computationally rigorous process in Ring-LWE. In order to improve the efficiency of the Ring-LWE based schemes, most of the existing schemes use Fast Fourier Transform (FFT) based polynomial multiplication algorithm. It is known that Discrete Sine Transformation (DST) and Discrete Cosine Transformation (DCT) are faster than the FFT. The combination of DCT and DST is Discrete Trigonometric Transform (DTT). When we generalize DTT in terms of FFT form, it becomes Generalized Discrete Fourier Transform (GDFT). In this paper, we propose two new polynomial multiplication techniques using DTT and GDFT. When we applycircular convolutionandskew-circular convolutionon DTT or GDFT for the polynomial multiplication, it gives us wrong results. To overcome this issue, we usesymmetric convolutionoperation on DTT and GDFT. We implemented and compared the proposed polynomial multiplication schemes with the current state-of-the-art schemes in terms of computation and communication costs. The implementation results show that the proposed schemes DTT and GDFT perform more efficiently as compared to current state-of-the-art schemes in terms of computation and communication costs.
Worms and gills, plates and spines: the evolutionary origins and incredible disparity of deuterostomes revealed by fossils, genes, and development

Deuterostomes are the major division of animal life which includes sea stars, acorn worms, and humans, among a wide variety of ecologically and morphologically disparate taxa. However, their early evolution is poorly understood, due in part to their disparity, which makes identifying commonalities difficult, as well as their relatively poor early fossil record. Here, we review the available morphological, palaeontological, developmental, and molecular data to establish a framework for exploring the origins of this important and enigmatic group. Recent fossil discoveries strongly support a vermiform ancestor to the group Hemichordata, and a fusiform active swimmer as ancestor to Chordata. The diverse and anatomically bewildering variety of forms among the early echinoderms show evidence of both bilateral and radial symmetry. We consider four characteristics most critical for understanding the form and function of the last common ancestor to Deuterostomia: Hox gene expression patterns, larval morphology, the capacity for biomineralization, and the morphology of the pharyngeal region. We posit a deuterostome last common ancestor with a similar antero-posterior gene regulatory system to that found in modern acorn worms and cephalochordates, a simple planktonic larval form, which was later elaborated in the ambulacrarian lineage, the ability to secrete calcium minerals in a limited fashion, and a pharyngeal respiratory region composed of simple pores. This animal was likely to be motile in adult form, as opposed to the sessile origins that have been historically suggested. Recent debates regarding deuterostome monophyly as well as the wide array of deuterostome-affiliated problematica further suggest the possibility that those features were not only present in the last common ancestor of Deuterostomia, but potentially in the ur-bilaterian. The morphology and development of the early deuterostomes, therefore, underpin some of the most significant questions in the study of metazoan evolution.
Going Deeper With Contextual CNN for Hyperspectral Image Classification

In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatiospectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets.
Accelerating Edit-Distance Sequence Alignment on GPU Using the Wavefront Algorithm

Sequence alignment remains a fundamental problem with practical applications ranging from pattern recognition to computational biology. Traditional algorithms based on dynamic programming are hard to parallelize, require significant amounts of memory, and fail to scale for large inputs. This work presents eWFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute the exact edit-distance sequence alignment based on the wavefront alignment algorithm (WFA). This approach exploits the similarities between the input sequences to accelerate the alignment process while requiring less memory than other algorithms. Our implementation takes full advantage of the massive parallel capabilities of modern GPUs to accelerate the alignment process. In addition, we propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast shared memory of a GPU. Our results show that our GPU implementation outperforms by 3-9 x the baseline edit-distance WFA implementation running on a 20 core machine. As a result, eWFA-GPU is up to 265 times faster than state-of-the-art CPU implementation, and up to 56 times faster than state-of-the-art GPU implementations.
Student Team Achievement Division as a tool for peer assisted co-operative learning in neuroanatomy

Student Team Achievement Division (STAD) is a co-operative learning approach premised on a group learning activity that emphazises learning as a social exchange of knowledge between students, in which each student is accountable for his or her own learning and is also encouraged to assist others in achieving their goals. It promotes the cognitive, psychomotor, and emotional growth of students involved in the team. By random sequencing, 60 participants were allocated to interventional group (n=30) and control group (n=30). The participants of the interventional group were subjected to STAD strategy and participants of the control group were instructed to do a conventional self learning on the ventricles of brain. The outcomes were statistically analysed. It was found that the performance of the students is far better with STAD approach than conventional self learning. Our study has shown that Students team Achievement Division can be used as an effective tool for Peer assisted Co-operative Learning in Anatomy. Further studies can be done to investigate the contribution of STAD to teaching other disciplines of Anatomy and other basic medical sciences.
Impact of internal structural design on quality and nutritional properties of 3D printed food products during post-printing: a critical review

3D food printing (3DFP) provides an excellent opportunity to deposit layers of multiple food materials to create unique complex structures of products with more engaging visuals, specific textures, and customized nutritional properties. Many printed products require post-printing processing which can result in sensory variance, texture changes, and even nutritional modification. Hence it is necessary to implement the design of the complex internal structure to ensure the desired quality of the printed products following post-printing. 3-D printing of various types of food products, for example, chocolate, cheese, meat, vegetables, fruits, fish, eggs, cereal-based products, and so on, has been examined with regard to post-printing requirements. This review aims to summarize the current work on the latest developments in 3DFP technology concerning the internal structure design of 3D printed products and its effect on quality during post-printing. The quality parameters include: textural, physical, morphological, and dimensional characteristics as well as nutritional properties. Furthermore, post-printing modifications such as 4D are also analyzed.
Fun with food - A parent-child community cooking intervention reduces parental fear and increases children's perceived competence

Cooking is being promoted as a preventative strategy for numerous health outcomes. However, there has been a reported decline in opportunities for children to learn in the home environment due to parental barriers such as time and concerns around children conducting certain skills. Therefore, this study aimed to understand the impact of a parent-child community cooking intervention on children's perceived cooking competence and interest in cooking and parental perceptions around including children in cooking. 'Fun with Food' was a four-week parent-child cooking intervention based on Experiential Learning Theory and designed by Home Economists. A mixed-method approach was undertaken to understand the effectiveness of the community-based intervention. Parents completed pre and post focus group discussions that were analyzed using Thematic Analysis. Pre and post surveys were used to investigate children's perceived cooking competence and analyzed with paired-samples t-tests and Cohen's d. Children's perceived cooking competence significantly increased after the intervention (P < 0.001, effect size -0.92). Parental fears around children performing certain skills, such as chopping and cutting, were reduced. Additionally, both children and parents found it an enjoyable experience, and appreciated the time spent together, which may be an indicator for positive wellbeing. Parents reported that children have been more actively involved in cooking since the intervention. Further, parents felt strongly that children should be cooking from as young as possible and that Home Economics should be introduced in primary school and made compulsory for older students in secondary school. The parent-child format for cooking has shown to be effective for increasing children's perceived cooking competence and reducing parental fears, highlighting it as a promising method for future interventions.
Prehistoric landmarks in contrasted territories: Rock art of the Libyan Desert massifs, Egypt

In the Libyan Desert, the Gilf el-Kebir and Jebel el-'Uweinat are two large rock formations located in the extreme South-West of Egypt, at the edge of the Libyan and Sudanese borders. A hundred and twenty kilometers from each other, they are surrounded by plains and sandy formations, punctuated by a few smaller massifs. Although they are of different ages and geological formations, the two great massifs both offered interesting and complementary refuges for prehistoric groups who used rock shelters, cliffs and boulders for engraving and painting. The existence of a multitude of styles and techniques allow to detect striking parallels between the rock art record of the two regions, providing a dynamic view of the regionalization of rock art and of how these territories were conceived and occupied by semi-nomadic groups during the Holocene optimum period (8000-3500 BCE). Paintings from the Gilf el-Kebir show very close stylistic affinities with representations identified in the Jebel el-'Uweinat. But the fact that they remain a minority in the overall rock art record from both areas tends to evidence that, contrary to what has been hypothesized before, migrations between the Gilf el-Kebir and the Jebel el-'Uweinat were not systematic. This paper also highlights a possible increase in the contacts and migrations between the two massifs after the adoption of pastoralist lifestyles. The repartition of rock art and the evolution through times of the parallels offers interesting insights into land use strategies of both hunter-gatherers and herd keepers in such contrasted environments, and into what can be called symbolic territories. (c) 2017 Published by Elsevier Ltd.
Study on the desulfurization performance of calcium-based desulfurizer and NaHCO3 desulfurizer

The commonly used calcium desulfurizers have low desulfurization efficiency. NaHCO3 desulfurizers can meet the requirements of desulfurization efficiency, but the high price and the difficulty in handling desulfurization products make dry flue desulfurization technology quite difficult to realize the large-scale application. Preliminary research found a new calcium desulfurizer, to understand its performance, comparing investigation into the desulfurization performance of different calcium desulfurizer and NaHCO3 desulfurizer. The results showed that with the high-performance calcium desulfurizer, conventional NaHCO3 desulfurizer, and ultrafine NaHCO3 desulfurizer, the operating time with 100% desulfurization efficiency is 25,200, 21,600, and 6000 s, when the flue temperature of 373.15-573.15 K, the "break-through" temperature is 533.15, 473.15, and 373.15 K, expand the use range of desulfurizer flue gas temperature. Regarding the desulfurizer per unit mass, the production costs of ultrafine NaHCO3 desulfurizer are 5.36 times higher than calcium desulfurizer. Compared with NaHCO3 desulfurizer, high-performance calcium desulfurizer is characterized by several advantages, including high desulfurization efficiency, wider applicable temperatures, and low preparation cost, allowing for significant development potential in flue gas desulfurization.
Circadian preference in young adults: Associations with sleep and mental health outcomes from a national survey of Norwegian university students

Individual preferred timing of sleep and activity patterns, known as circadian preference, ranges from definitely morning types to definitely evening types. Being an evening type has been linked to adverse sleep and mental health outcomes. This study aimed to explore the associations between circadian preference and self-reported sleep, depression, anxiety, quality of life, loneliness, and self-harm/suicidal thoughts. Data stem from a national survey of students in higher education in Norway (the SHoT-study). All 169,572 students in Norway were invited to participate, and 59,554 students (66.5% women) accepted (response rate = 35.1%). Circadian preference was associated with sleep and mental health outcomes in a dose-response manner. For both genders, being an evening type (either definitely evening or more evening than morning) was associated with an increase in age-adjusted relative risk (RR-adjusted; range = 1.44 to 2.52 vs. 1.15 to 1.90, respectively) across all outcomes compared with definitely morning types. Overall, the present study provides further evidence that evening circadian preference is associated with adverse sleep and mental health outcomes in young adults. As such, future efforts to improve sleep and mental health in young adults should consider their circadian preferences.
Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks

Recently, functional brain network has been used for the classification of brain disorders, such as Autism Spectrum Disorder (ASD) and Alzheimer's disease (AD). Existing methods either ignore the non-imaging information associated with the subjects and the relationship between the subjects, or cannot identify and analyze disease-related local brain regions and biomarkers, leading to inaccurate classification results. This paper proposes a local-to-global graph neural network (LG-GNN) to address this issue. A local ROI-GNN is designed to learn feature embeddings of local brain regions and identify biomarkers, and a global Subject-GNN is then established to learn the relationship between the subjects with the embeddings generated by the local ROI-GNN and the non-imaging information. The local ROI-GNN contains a self-attention based pooling module to preserve the embeddings most important for the classification. The global Subject-GNN contains an adaptive weight aggregation block to generate the multi-scale feature embedding corresponding to each subject. The proposed LG-GNN is thoroughly validated using two public datasets for ASD and AD classification. The experimental results demonstrated that it achieves the state-of-the-art performance in terms of various evaluation metrics.