ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
ART-TV Algorithm for Diffuse Correlation Tomography Blood Flow Imaging

Near-infrared diffuse correlation imaging (DCT) is an important method of tissue blood flow imaging for the prognosis and diagnosis of various diseases. A new solution of DCT that is based on the N th-order linear (NL) algorithm, termed as NL-DCT, was proposed in our previous study to overcome the limitations of tissue geometry and heterogeneity. The NL-DCT converts the image reconstruction into linear equations, and this solution is an ill-posed problem in mathematics. To improve the accuracy and robustness of the DCT image reconstruction, a combination of algebra reconstruction technique (ART) and total variation (TV), namely ART-TV, is proposed in this study. After each ART iteration, the TV model is used as an a priori constraint to reduce noise. The validations from computer simulation and phantom experiments with different anomalies demonstrate that the proposed ART-TV algorithm is efficient in DCT blood flow image reconstruction.
Semantic Labeling of ALS Point Cloud via Learning Voxel and Pixel Representations

Semantic labeling is a fundamental task that can provide useful semantics for many other 3-D processing tasks. To tackle the challenge of airborne laser scanning (ALS) point cloud classification, current state-of-the-art methods leverage the capabilities of deep learning. However, they are limited due to the weaknesses of the isolated use of individual representations of point clouds. To address this issue, this letter presents a novel network, VPNet, which ensembles voxel and pixel representation-based networks, to predict class probabilities for each light detection and ranging (LiDAR) point. A fully connected conditional random field-based global refinement is then performed over each point in the point cloud to produce a fine-grained classification result. On the ISPRS 3-D Semantic Labeling Contest, our solution sets a new state of the art by improving the highest average F1-score and the highest average per-class accuracy from 69.3% to 73.9%, and 69.0% to 74.9%, respectively. The overall accuracy of our approach is 84.0%.
Immediate Antiretroviral Therapy: The Need for a Health Equity Approach

Immediate antiretroviral therapy (iART), defined as same-day initiation of ART or as soon as possible after diagnosis, has recently been recommended by global and national clinical care guidelines for patients newly diagnosed with human immunodeficiency virus (HIV). Based on San Francisco's Rapid ART Program Initiative for HIV Diagnoses (RAPID) model, most iART programs in the US condense ART initiation, insurance acquisition, housing assessment, and mental health and substance use evaluation into an initial visit. However, the RAPID model does not explicitly address structural racism and homophobia, HIV-related stigma, medical mistrust, and other important factors at the time of diagnosis experienced more poignantly by African American, Latinx, men who have sex with men (MSM), and transgender patient populations. These factors negatively impact initial and subsequent HIV care engagement and exacerbate significant health disparities along the HIV care continuum. While iART has improved time to viral suppression and linkage to care rates, its association with retention in care and viral suppression, particularly in vulnerable populations, remains controversial. Considering that in the US the HIV epidemic is sharply defined by healthcare disparities, we argue that incorporating an explicit health equity approach into the RAPID model is vital to ensure those who disproportionately bear the burden of HIV are not left behind.
A recent survey on image watermarking techniques and its application in e-governance

This survey presents a brief discussion of different aspects of digital image watermarking. Included in the present discussion are these general concepts: major characteristics of digital watermark, novel and recent applications of watermarking, different kinds of watermarking techniques and common watermark embedding and extraction process. In addition, recent state-of-art watermarking techniques, potential issues and available solutions are discussed in brief. Further, the performance summary of the various state-of-art watermarking techniques is presented in tabular format. This survey contribution will be useful for the researchers to implement efficient watermarking techniques for secure e-governance applications.
Graphene Magnetic Field Sensors

Graphene extraordinary magnetoresistance (EMR) devices have been fabricated and characterized in varying magnetic fields at room temperature. The atomic thickness, high carrier mobility and high current carrying capabilities of graphene are ideally suited for the detection of nanoscale sized magnetic domains. The device sensitivity can reach 10 mV/Oe, larger than state of the art InAs 2DEG devices of comparable size and can be tuned by the electric field effect via a back gate or by imposing a biasing magnetic field.
The future of targeted therapies for brain metastases

Brain metastases (BM) are an increasing challenge in the management of patients with advanced cancer. Treatment options for BM are limited and mainly focus on the application of local therapies. Systemic therapies including targeted therapies are only poorly investigated, as patients with BM were frequently excluded from clinical trials. Several targeted therapies have shown promising activity in patients with BM. In the present review we discuss existing and emerging targeted therapies for the most frequent BM primary tumor types. We focus on challenges in the conduction of clinical trials on targeted therapies in BM patients such as patient selection, combination with radiotherapy, the obstacles of the blood-brain barrier and the definition of study end points.
THE FIRST CASE IN RUSSIA OF DYNAMIC ILLUMINATION USING LIGHT EMITTING DIODES FOR A MUSEUM INSTALLATION

This article describes the dynamic illumination of the masterpieces collection of carriage art in the State Hermitage museum using light emitting diodes. Philips (the first example of such an illumination in Russia) implements this illumination.
Deep metric learning for image retrieval in smart city development

Deep metric learning (DML) aims to learn a consistent distance embedding where an anchor is closer within the same category than others. It underpins a variety of essential and significant tasks in the development of smart city including face recognition, landmark retrieval, pedestrian detection, person/vehicle re-identification, and so on. Traditional pair-based DML methods try to make full use of the data-to-data relations within a (mini-)batch, but they cannot grasp the data distribution information due to the batch size limitation. On the other hand, proxy-based DML schemes use different proxies to approximate the data distribution. However, the proxies are too sample to represent the intra-category variance. In this paper, we propose a simple but effective method, named soft-instance-label proxy, for embedding learning. It can capture the globe data distribution information while depicting the detailed intra-class data structure. The state-of-the-art empirical results on three public image retrieval benchmarks and two backbone networks demonstrate the superiority of our proposed method. Our Softinstance-label proxy method can have a Recall@1 improvement of 2.4% with Googlenet, largely surpassing the current state-of-art-methods while demonstrating great potential in the development of smart city.
Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning

Network Intrusion Detection Systems (NIDSs) are indispensable defensive tools against various cyberattacks. Lightweight, multipurpose, and anomaly-based detection NIDSs employ several methods to build profiles for normal and malicious behaviors. In this paper, we design, implement, and evaluate the performance of machine-learning-based NIDS in IoT networks. Specifically, we study six supervised learning methods that belong to three different classes: (1) ensemble methods, (2) neural network methods, and (3) kernel methods. To evaluate the developed NIDSs, we use the distilled-Kitsune-2018 and NSL-KDD datasets, both consisting of a contemporary real-world IoT network traffic subjected to different network attacks. Standard performance evaluation metrics from the machine-learning literature are used to evaluate the identification accuracy, error rates, and inference speed. Our empirical analysis indicates that ensemble methods provide better accuracy and lower error rates compared with neural network and kernel methods. On the other hand, neural network methods provide the highest inference speed which proves their suitability for high-bandwidth networks. We also provide a comparison with state-of-the-art solutions and show that our best results are better than any prior art by 1~20%.
Carbonized Chinese Art Paper-Based High-Performance Wearable Strain Sensor for Human Activity Monitoring

Because of the rapid evolution of wearable devices, great effort has been devoted to widely developing flexible strain sensors, as one of the most important components. However, realizing the low-cost, large-scale manufacturing of flexible strain sensor with a wide response range and excellent sensitivity remains difficult to date. Chinese art paper (CAP), a significant Chinese cultural heritage, is a processed product of biological materials prepared from plant fibers that can be considered as a nonwoven type of film. Herein, we show the manufacturing of the outstanding strain sensors based on carbonized commercial CAP (CCAP), which possess fascinating properties, including large response range (0-120%), excellent sensitivity (gauge factor (GF) of 68 under the strain of 100% and 248 in the strain of (100-120%)), high durability, high stability, and low detection limits (0.01% strain). Furthermore, we demonstrate their abilities in monitoring both vigorous and tiny human motions, showing promising applications in wearable devices and artificial intelligence robots.
LiveNet: Improving features generalization for face liveness detection using convolution neural networks

Performance of face liveness detection algorithms in cross-database face liveness detection tests is one of the key issues in face-biometric based systems. Recently, Convolution Neural Networks (CNN) classifiers have shown remarkable performance in intra-database face liveness detection tests. However, a little effort has been made to improve the generalization capability of CNN classifiers for cross-database and unconstrained face liveness detection tests. In this paper, we propose an efficient strategy for training deep CNN classifiers for face liveness detection task. We utilize continuous data-randomization (like bootstrapping) in the form of small mini-batches during training CNN classifiers on small scale face anti-spoofing database. Experimental results revealed that the proposed approach reduces the training time by 18.39%, while significantly lowering the HTER by 8.28% and 14.14% in cross-database tests on CASIA-FASD and Replay-Attack database respectively as compared to state-of-the-art approaches. Additionally, the proposed approach achieves satisfactory results on intra-database and cross-database face liveness detection tests, claiming a good generality over other state-of-the-art face anti-spoofing approaches. (C) 2018 Elsevier Ltd. All rights reserved.
Hepatic transcriptome profiling according to growth rate reveals acclimation in metabolic regulatory mechanisms to cyclic heat stress in broiler chickens

Climate change has numerous effects on poultry that result in welfare concerns and economic losses in agricultural industries. However, the mechanisms underlying the acclimation to heat stress in poultry have not been comprehensively defined. Therefore, identifying associated patterns of gene regulation and understanding the molecular mechanisms of acclimation to a warmer environment will provide insights into the acclimation system of broiler chickens. We profiled differentially expressed genes (DEGs) associated with differences in growth performance under heat stress conditions in the liver tissues of broilers based on RNA sequencing data. The DEGs were identified by comparison to the gene expression levels of broilers exhibiting average growth at 28 d of age (D28A) and D36A relative to those at D21A. In D36A, 507 and 312 DEGs were up- and downregulated, respectively, whereas 400 and 156 DEGs were up- and downregulated in D28A, respectively. Pathway enrichment analysis further revealed that "fatty acid degradation" and "heat shock protein expression" were upregulated in broilers exhibiting a higher growth and weight, whereas "cell cycle arrest" and "amino acid metabolism" were downregulated. Transcriptome profiling revealed that the acclimatized group supplied fat and energy from the liver to tissues through the breakdown of fatty acids. Furthermore, homeostasis was maintained via heat shock proteins and antioxidant enzymes. The characterized candidate genes and mechanisms associated with the response to heat stress might serve as a foundation for improving the ability of broilers to acclimatize under heat stress conditions.
Inverse relationship between elemental selenium nanoparticle size and inhibition of cancer cell growth in vitro and in vivo

Elemental selenium nanoparticles (SeNPs) have been demonstrated to be equivalent to selenomethionine and methylselenocysteine in upregulating selenoenzymes; however, the toxicity of SeNPs is markedly lower than these two organic selenium compounds. The objective of this study was to determine the effect of SeNP size on cancer cell growth and ascertain whether production of reactive oxygen species (ROS) is implicated as a candidate mechanism of action. Two types of SeNPs (averaging 35 nm and 91 nm) were investigated. Cell accumulation was inhibited in vitro and in vivo in a manner inversely proportional to particle size. In vitro modeling experiments showed the reduction of SeNPs to be glutathione concentration dependent and to result in ROS formation. Both SeNP biotransformation and ROS production were size dependent, with the smaller SeNPs being more active, thereby suggesting that small-sized SeNPs are more effective in inhibiting cancer cell proliferation through an ROS mediated mechanism.
Practitioners' Experiences of the Influence of Bonsai Art on Health

Bonsai art refers to the cultivation of a miniature tree. This study was motivated by the hypothesis that bonsai art may also be an ecopsychological, therapeutic practice that can have meaningful healing qualities. An international online survey elicited the meaning of bonsai art for 255 skilled bonsai practitioners. Questionnaires and interviews were used to elicit the experiences of participants. The findings supported the hypothesis that, for skilled practitioners, bonsai art was associated with meaningful healing experiences. In particular, the evidence suggests that bonsai art facilitates improved ecological, spiritual and emotional awareness, as well as various healing dimensions, including aesthetic creativity, resilience, adaptability, and social, physical, and personal health. It is viewed as an intervention technique that requires few resources, is easy to apply, and has a minimal impact on any environmental setting. The conclusions drawn point to the ethically sound health promotion value of bonsai art in various settings, such as psychiatric hospitals, retirement homes, rehabilitation centres and prisons.
Gold(I)-Catalyzed Benzylic C(sp3 )-H Functionalizations: Divergent Synthesis of Indole[a]- and [b]-Fused Polycycles

Phenyl azides substituted by an (alkylphenyl)ethynyl group facilitate benzylic sp3 (C-H) functionalization in the presence of a JohnPhosAu catalyst, resulting in indole-fused tetra- and pentacycles via divergent N- or C-cyclization. The chemoselectivity is influenced depending on the counter-anion, the electron density of the α-imino gold(I) carbene, and the alkyl groups stabilizing the benzylic carbocation originating from a 1,5-hydride shift. An isotopic labeling experiment demonstrates the involvement of an indolylgold(I) species resulting from a tautomerization that is much faster than the deauration. The formation of a benzylic sp3 (C-H) functionalization leading to an indole-fused seven-membered ring is also demonstrated.
Artificial neural networks can be used for Ambrosia pollen emission parameterization in COSMO-ART

The emission parameterization is a crucial part of numerical pollen dispersion models. This paper shows that Artificial Neural Networks (ANNs) can substantially improve the performance of the Ambrosia pollen emission in numerical pollen dispersion models such as COSMO-ART. Based on simultaneous measurement of Ambrosia pollen concentrations and meteorological variables in the source area, ANNs were trained to predict the diurnal profile of pollen emission. Six different combinations of explanatory meteorological variables were trained with five different ANN configurations resulting in 30 candidate emission models. The best network configuration for each combination of explanatory variables were used as emission parameterization in the numerical pollen dispersion model COSMO-ART. In addition, two benchmarks were implemented: an emission parameterization based on sigmoid functions and an artificial neural network using only time as an explanatory variable. The Ambrosia pollen seasons of 2015 and 2016 were simulated using the two benchmarks and the six emission parameterizations. The modelled diurnal profile of emission fluxes at 15 different sites from Serbia, Hungary and France with strong local pollen sources were compared with observed concentrations. Artificial Neural Networks based emission parameterization substantially improved the performance of the Ambrosia pollen emission in COSMO-ART compared to the emission based on the sigmoid functions in all these three countries. However, a time-related explanatory variable must be used. This suggests that the ANN-based emission parameterizations can be used at distant locations as well. On the other hand, the use of meteorological related parameters did not increase the performance compared with the time-only benchmark.
Novel Automatic Approach for Land Cover Change Detection by Using VHR Remote Sensing Images

Many land cover change detection (LCCD) approaches applied on very high resolution (VHR) remote sensing images utilize spatial information by using a regular window or strict mathematical model. However, regular shape or strict models cannot fit the various shapes and sizes of the ground targets. In this article, a novel LCCD approach without the parameter is proposed to detect land cover change with VHR remote sensing images. First, an adaptive spatial-context extraction algorithm is applied to explore contextual information around a pixel. Second, the change magnitude between pairwise pixels is quantitatively measured by computing the band-to-band distance which is defined by the pairwise adaptive regions around the corresponding pixels. Finally, after the generation of a change magnitude image (CMI), a binary threshold method called double-window flexible pace search (DFPS) is adopted to divide CMI into a binary change detection map. The performance of the proposed approach is verified by comparing it with five state-of-the-art methods with three pairs of VHR images. The comparisons demonstrated that the proposed approach achieved the improved detected results comparing with state-of-the-art LCCD methods. The code of the proposed approach is available at https://github.com/TongfeiLiu/ASEA-CD.
Techno-economic optimization of biomass-to-methanol with solid-oxide electrolyzer

The purpose of this paper is to assess techno-economically the integration of solid-oxide electrolysis in biomass-to-methanol processes: (1) The hydrogen produced by electrolysis can be used to adjust the composition of syngas from gasification to increase the conversion of carbon in biomass, (2) the oxygen as a byproduct of electrolysis can be used in the gasifier to avoid expensive air separation units, and (3) the overall process can be thermally integrated. Two integration concepts are proposed with different sizing methods of the electrolyzer: (1) the case of full conversion of carbon in biomass, in which a large electrolyzer is driven by the electricity purchased from the grid, and (2) the case of zero power exchange, in which only part of the carbon in biomass is converted reaching self-sufficiency of electricity. The three cases including the state-of-the-art biomass-to-methanol process are investigated to identify (1) possible trade-offs between efficiency and costs, and (2) under which conditions, these concepts become economically viable. With a reference methanol production of 100 kton/year, the results show that there is an optimal design for the state-of-the-art case, which offers an efficiency of 53.3% due to steam cycles and a payback time of 4.8 years. For the integrated concepts, there are sharp trade-offs between the system efficiency and methanol production cost rate. The case of full carbon conversion can reach an energy efficiency of 64.5-66.0% but results in a longer payback time of over 11 years. The case of zero-power exchange can achieve a similar efficiency as the state-of-the-art case with a slightly increased payback time of over 5.5 years. The payback time of the full carbon conversion case can be shorter than 5 years with a reduction in stack cost and electricity price, and an increase in stack lifetime.
Systemic racism alters wildlife genetic diversity

In the United States, systemic racism has had lasting effects on the structure of cities, specifically due to government-mandated redlining policies that produced racially segregated neighborhoods that persist today. However, it is not known whether varying habitat structures and natural resource availability associated with racial segregation affect the demographics and evolution of urban wildlife populations. To address this question, we repurposed and reanalyzed publicly archived nuclear genetic data from 7,698 individuals spanning 39 terrestrial vertebrate species sampled in 268 urban locations throughout the United States. We found generally consistent patterns of reduced genetic diversity and decreased connectivity in neighborhoods with fewer White residents, likely because of environmental differences across these neighborhoods. The strength of relationships between the racial composition of neighborhoods, genetic diversity, and differentiation tended to be weak relative to other factors affecting genetic diversity, possibly in part due to the recency of environmental pressures on urban wildlife populations. However, the consistency of the direction of effects across disparate taxa suggest that systemic racism alters the demography of urban wildlife populations in ways that generally limit population sizes and negatively affect their chances of persistence. Our results thus support the idea that limited capacity to support large, well-connected wildlife populations reduces access to nature and builds on existing environmental inequities shouldered by predominantly non-White neighborhoods.