ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
A comparative characterization of SARS-CoV-2-specific T cells induced by mRNA or inactive virus COVID-19 vaccines

Unlike mRNA vaccines based only on the spike protein, inactivated severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccines should induce a diversified T cell response recognizing distinct structural proteins. Here, we perform a comparative analysis of SARS-CoV-2-specific T cells in healthy individuals following vaccination with inactivated SARS-CoV-2 or mRNA vaccines. Relative to spike mRNA vaccination, inactivated vaccines elicit a lower magnitude of spike-specific T cells, but the combination of membrane, nucleoprotein, and spike-specific T cell response is quantitatively comparable with the sole spike T cell response induced by mRNA vaccine, and they efficiently tolerate the mutations characterizing the Omicron lineage. However, this multi-protein-specific T cell response is not mediated by a coordinated CD4 and CD8 T cell expansion but by selective priming of CD4 T cells. These findings can help in understanding the role of CD4 and CD8 T cells in the efficacy of the different vaccines to control severe COVID-19 after Omicron infection.
Adaptive Estimation of Active Contour Parameters Using Convolutional Neural Networks and Texture Analysis

In this paper, we propose a generalization of the level set segmentation approach by supplying a novel method for adaptive estimation of active contour parameters. The presented segmentation method is fully automatic once the lesion has been detected. First, the location of the level set contour relative to the lesion is estimated using a convolutional neural network (CNN). The CNN has two convolutional layers for feature extraction, which lead into dense layers for classification. Second, the output CNN probabilities are then used to adaptively calculate the parameters of the active contour functional during the segmentation process. Finally, the adaptive window size surrounding each contour point is re-estimated by an iterative process that considers lesion size and spatial texture. We demonstrate the capabilities of our method on a dataset of 164 MRI and 112CT images of liver lesions that includes low contrast and heterogeneous lesions as well as noisy images. To illustrate the strength of our method, we evaluated it against state of the art CNN-based and active contour techniques. For all cases, our method, as assessed by Dice similarity coefficients, performed significantly better than currently available methods. An average Dice improvement of 0.27 was found across the entire dataset over all comparisons. We also analyzed two challenging subsets of lesions and obtained a significant Dice improvement of 0.24 with our method (p <0.001, Wilcoxon).
Identifying transformational space for transdisciplinarity: using art to access the hidden third

A challenge for transdisciplinary sustainability science is learning how to bridge diverse worldviews among collaborators in respectful ways. A temptation in transdisciplinary work is to focus on improving scientific practices rather than engage research partners in spaces that mutually respect how we learn from each other and set the stage for change. We used the concept of Nicolescu's Hidden Third to identify and operationalize this transformative space, because it focused on bridging objective and subjective worldviews through art. Between 2014 and 2017, we explored the engagement of indigenous peoples from three inland delta regions in Canada and asa team of interdisciplinary scholars and students who worked together to better understand long-term social-ecological change in those regions. In working together, we identified five characteristics associated with respectful, transformative transdisciplinary space. These included (1) establishing an unfiltered safe place where (2) subjective and objective experiences and (3) different world views could come together through (4) interactive and (5) multiple sensory experiences. On the whole, we were more effective in achieving characteristics 2-5bringing together the subjective and objective experiences, where different worldviews could come togetherthan in achieving characteristic 1creating a truly unfiltered and safe space for expression. The novelty of this work is in how we sought to change our own engagement practices to advance sustainability rather than improving scientific techniques. Recommendations for sustainability scientists working in similar contexts are provided.
Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography

Delineation of the cardiac structures from 2D echocardiographic images is a common clinical task to establish a diagnosis. Over the past decades, the automation of this task has been the subject of intense research. In this paper, we evaluate how far the state-of-the-art encoder-decoder deep convolutional neural network methods can go at assessing 2D echocardiographic images, i.e. segmenting cardiac structures and estimating clinical indices, on a dataset, especially, designed to answer this objective. We, therefore, introduce the cardiac acquisitions for multi-structure ultrasound segmentation dataset, the largest publicly-available and fully-annotated dataset for the purpose of echocardiographic assessment. The dataset contains two and four-chamber acquisitions from 500 patients with reference measurements from one cardiologist on the full dataset and from three cardiologists on a fold of 50 patients. Results show that encoder-decoder-based architectures outperform state-of-the-art non-deep learning methods and faithfully reproduce the expert analysis for the end-diastolic and end-systolic left ventricular volumes, with a mean correlation of 0.95 and an absolute mean error of 9.5 ml. Concerning the ejection fraction of the left ventricle, results are more contrasted with a mean correlation coefficient of 0.80 and an absolute mean error of 5.6%. Although these results are below the inter-observer scores, they remain slightly worse than the intra-observer's ones. Based on this observation, areas for improvement are defined, which open the door for accurate and fully-automatic analysis of 2D echocardiographic images.
W-Band Graphene-Based Six-Port Receiver

We demonstrate a full-fledged millimeter-wave graphene-based six-port receiver frontend at 90 GHz employing graphene power detectors. Exploiting the high responsivity and wide dynamic range reported for the state-of-the-art graphene field-effect transistors (GFETs), graphene power detectors are demonstrated beyond the maximum oscillation frequency, f(max), of the graphene transistor. The proposed circuit is fabricated on thinned SiC substrate and its functionality is verified by demodulation of 10-Mbps ON-OFF keying (OOK) digitally modulated signal.
Tracking of targets of interest using labeled multi-Bernoulli filter with multi-sensor control

This paper proposes a novel multi-sensor control scheme for tracking of targets of interest in the presence of clutter and detection uncertainty, using labeled multi-Bernoulli filters. The focus is on multi-target tracking applications where multiple centrally connected and controllable sensors are used for tracking of particular targets of interest. We derive an efficient analytical approximation for a task-driven objective function for multi-sensor selective control, that can be computed immediately after the prediction step. Compared to the state-of-the-art, drastic reduction in computation is achieved. Numerical experiments, involving challenging multi-sensor control scenarios, demonstrate how the proposed method can lead to significant improvements in the tracking accuracy of the targets of interest, in comparison to the generic non-selective sensor control methods. It is also shown that while our proposed method has comparable performance to the state-of-art selective sensor control method (selective-PEECS) in terms of the meansquare-error (MSE) of tracking for targets of interest, it runs significantly faster than the state-of-art (both selective and non-selective) multi-sensor control algorithms. Indeed, the resulting reduction in computation time is shown to be in the order of tens to hundreds time, depending on the number of sensors to be controlled. (C) 2020 Elsevier B.V. All rights reserved.
Expert system based on artificial neural networks for content-based image retrieval

Clustering technique is essential for fast retrieval in large database. In this paper, new image clustering technique based on artificial neural networks is proposed for content-based image retrieval. Fuzzy-ART mechanism maps high-dimensional input features into the output neuron. Joint HSV histogram and average entropy computed from gray-level co-occurrence matrices in the localized image region is employed as input feature elements. Original Fuzzy-ART suffers unnecessary increase of the number of output neurons when the noise input is presented. Modified Fuzzy-ART mechanism resolves the problem by differently updating the committed node and uncommitted node, and checking the vigilance test again. To show the validity of the proposed algorithm, experiment results on image clustering performance and comparison with original Fuzzy-ART are presented in terms of recall rates. (c) 2005 Elsevier Ltd. All rights reserved.
Variable metabolic scaling breaks the law: from 'Newtonian' to 'Darwinian' approaches

Life's size and tempo are intimately linked. The rate of metabolism varies with body mass in remarkably regular ways that can often be described by a simple power function, where the scaling exponent (b, slope in a log-linear plot) is typically less than 1. Traditional theory based on physical constraints has assumed that b is 2/3 or 3/4, following natural law, but hundreds of studies have documented extensive, systematic variation in b. This overwhelming, law-breaking, empirical evidence is causing a paradigm shift in metabolic scaling theory and methodology from 'Newtonian' to 'Darwinian' approaches. A new wave of studies focuses on the adaptable regulation and evolution of metabolic scaling, as influenced by diverse intrinsic and extrinsic factors, according to multiple context-dependent mechanisms, and within boundary limits set by physical constraints.
Experimental studies of particle removal and probability of COVID-19 infection in passenger railcars

A series of experiments in stationary and moving passenger railcars was conducted to measure the removal rates of particles in the size ranges of SARS-CoV-2 viral aerosols, and the air changes per hour provided by the existing and modified air handling systems. The effect of ventilation and air filtration systems on removal rates and their effects on estimated probability (i.e., risk) of infection was evaluated in a range of representative conditions: (1) for two different ratios of recirculated air (RA) to outdoor air (OA) (90:10 RA:OA and 67:33 RA:OA); (2) using minimum efficiency reporting value (MERV) filters with standard (MERV-8) and increased (MERV-13) filtration ratings; and (3) in the presence and absence of a portable high-efficiency particulate-air (HEPA) room air purifier system operated at clean air delivery rate (CADR) of 150 and 550 cfm. The higher-efficiency MERV-13 filters significantly increased particle removal rates on average by 3.8 to 8.4 hr-1 across particle sizes ranging from 0.3 to 10 µm (p < 0.01) compared to MERV-8 filters. The different RA:OA ratios and the use of a portable HEPA air purifier system had little effect on particle removal rates. MERV-13 filters reduced the estimated probability of infection by 42% compared to the MERV-8 filter. The use of a HEPA-air purifier with a MERV-13 filter causes a 50% reduction in the estimated probability of infection. Upgrading the efficiency of HVAC filters from MERV-8 to MERV-13 in public transit vehicles is the most effective exposure control method resulting in a clear reduction in the removal rates of aerosol particles and the estimated probability of infection.
Cancer-related cognitive impairment in non-CNS cancer patients: Targeted review and future action plans in Europe

Cancer-related cognitive impairment (CRCI) has increasingly been identified over the last two decades in non-CNS system cancer patients. Across Europe, researchers have contributed to this effort by developing preclinical models, exploring underlying mechanisms and assessing cognitive and quality of life changes. The ultimate goal is to develop interventions to treat patients experiencing CRCI. To do so, new challenges need to be addressed requiring the implementation of multidisciplinary research groups. In this consensus paper, we summarize the state of the art in the field of CRCI combined with the future challenges and action plans in Europe. These challenges include data sharing/pooling, standardization of assessments as well as assessing additional biomarkers and neuroimaging investigations, notably through translational studies. We conclude this position paper with specific actions for Europe based on shared scientific expert opinion and stakeholders involved in the Innovative Partnership for Action Against Cancer, with a particular focus on cognitive intervention programs.
The co-occurrence network patterns and keystone species of microbial communities in cattle manure-corn straw composting

Microbes often form complex ecological networks in various habitats. Co-occurrence network analysis allows exploring the complex community interactions beyond the community diversities. This study explores the interspecific relationships within and between bacterial and fungal communities during composting of cow manure using co-occurrence network analysis. Furthermore, the keystone taxa that potentially exert a considerable impact on the microbiome were revealed by network analysis. The networks in the present study harbored more positive links. Specifically, the interactions/coupling within bacterial communities was tighter and the response to changes in external environmental conditions was more quickly during the composting process, while the fungal network had a better buffer capacity for changes in external environmental conditions. Interestingly, this result was authenticated in the bacterial-fungal (BF) network and the Mantel test of major modules and environmental variables. More than that, the Zi-Pi plot revealed that the keystone taxa including "module hubs" and "connectors" were all detected in these networks, which could prevent the dissociation of modules and networks.
Towards Detecting Building Facades with Graffiti Artwork Based on Street View Images

As a recognized type of art, graffiti is a cultural asset and an important aspect of a city's aesthetics. As such, graffiti is associated with social and commercial vibrancy and is known to attract tourists. However, positional uncertainty and incompleteness are current issues of open geo-datasets containing graffiti data. In this paper, we present an approach towards detecting building facades with graffiti artwork based on the automatic interpretation of images from Google Street View (GSV). It starts with the identification of geo-tagged photos of graffiti artwork posted on the photo sharing media Flickr. GSV images are then extracted from the surroundings of these photos and interpreted by a customized, i.e., transfer learned, convolutional neural network. The compass heading of the GSV images classified as containing graffiti artwork and the possible positions of their acquisition are considered for scoring building facades according to their potential of containing the artwork observable in the GSV images. More than 36,000 GSV images and 5000 facades from buildings represented in OpenStreetMap were processed and evaluated. Precision and recall rates were computed for different facade score thresholds. False-positive errors are caused mostly by advertisements and scribblings on the building facades as well as by movable objects containing graffiti artwork and obstructing the facades. However, considering higher scores as threshold for detecting facades containing graffiti leads to the perfect precision rate. Our approach can be applied for identifying previously unmapped graffiti artwork and for assisting map contributors interested in the topic. Furthermore, researchers interested on the spatial correlations between graffiti artwork and socio-economic factors can profit from our open-access code and results.
Present status and future perspectives of ALPPS (associating liver partition and portal vein ligation for staged hepatectomy)

First International Consensus Meeting, Hamburg, Germany, 27-28 February 2015 More than 160 participants took part in the conference for 2 days. A total of 58 world renown experts on ALPPS (associating liver partition and portal vein ligation for staged hepatectomy) were invited from all over the world. The faculty was divided into many different subgroups that were in contact during the 2-3 months before the conference analyzing all the most important aspects of this technique and summarizing it in a common structured work to be presented during the congress, giving final recommendations in the form of bulleted point statements. The aim was to gain a solid basis of preliminary agreement on many controversial aspects of ALPPS. A poster area was also organized with 35 posters reporting mostly mono-institutional experiences on single aspects of the technique from all five continents.
Enhanced self-adaptive differential evolution multi-Objective algorithm for coordination of directional overcurrent relays contemplating maximum and minimum fault points

In this study, a parameter tune free enhanced self-adaptive differential evolution multi-objective (ESA-DEMO) approach has been proposed for coordination of directional overcurrent relays. The advantages of the proposed method are: avoid the use of conventional single-objective function, which requires tuning of weighting parameters; avoid tuning of algorithm parameters; minimisation of primary, backup and coordination time interval; zero violation of coordination constraints in large interconnected network; and low computational resource consumption leading to fast algorithm execution time. The proposed method has been implemented on the highly interconnected 6-bus, IEEE 14- and 30-bus systems, where results have shown robustness and consistency of the algorithm. Moreover, two-fault point coordination criterion considering close- and far-end (maximum and minimum) faults has been performed. ESA-DEMO has been compared with popular genetic algorithms and state-of-the-art multi-objective algorithm for protection coordination study.
Fetal Congenital Heart Disease Echocardiogram Screening Based on DGACNN: Adversarial One-Class Classification Combined with Video Transfer Learning

Fetal congenital heart disease (FHD) is a common and serious congenital malformation in children. In Asia, FHD birth defect rates have reached as high as 9.3 0025. For the early detection of birth defects and mortality, echocardiography remains the most effective method for screening fetal heart malformations. However, standard echocardiograms of the fetal heart, especially four-chamber view images, are difficult to obtain. In addition, the pathophysiological changes in fetal hearts during different pregnancy periods lead to ever-changing two-dimensional fetal heart structures and hemodynamics, and it requires extensive professional knowledge to recognize and judge disease development. Thus, research on the automatic screening for FHD is necessary. In this paper, we proposed a new model named DGACNN that shows the best performance in recognizing FHD, achieving a rate of 85. The motivation for this network is to deal with the problem that there are insufficient training datasets to train a robust model. There are many unlabeled video slices, but they are tough and time-consuming to annotate. Thus, how to use these un-annotated video slices to improve the DGACNN capability for recognizing FHD, in terms of both recognition accuracy and robustness, is very meaningful for FHD screening. The architecture of DGACNN comprises two parts, that is, DANomaly and GACNN (Wgan-GP and CNN). DANomaly, similar to the ALOCC network, but incorporates cycle adversarial learning to train an end-to-end one-class classification (OCC) network that is more robust and has a higher accuracy than ALOCC in screening video slices. For the GACNN architecture, we use FCH (four chamber heart) video slices at around the end-systole, as screened by DANomaly, to train a WGAN-GP for the purpose of obtaining ideal low-level features that can robustly improve the FHD recognition accuracy. A few annotated video slices, as screened by DANomaly, can also be used for data augmentation so as to improve the FHD recognition further. The experiments show that the DGACNN outperforms other state-of-the-art networks by 10025; in recognizing FHD. A comparison experiment shows that the proposed network already outperforms the performance of expert cardiologists in recognizing FHD, reaching 84 025; in a test. Thus, the proposed architecture has high potential for helping cardiologists complete early FHD screenings.
Second-Harmonic Power Generation Limits in Harmonic Oscillators

Based on piecewise linear modeling of field-effect transistors, harmonic translations are deployed to analyze the fundamental limits for a maximum second-harmonic power generation for any given field-effect transistor. Optimum waveforms at the gate-source and drain-source terminals, which yield high second-harmonic power generation by the given transistor, are derived. Two oscillators are implemented in a TSMC 65-nm CMOS process. Transistors in these oscillators have optimum voltage waveforms at their terminals. Thus, they deliver a state-of-the-art second-harmonic output power while operating at relatively higher frequencies than related arts. One of the proposed oscillators has the maximum output power of 4.9 dBm and a peak dc-to-RF efficiency of 3% at 300 GHz. Each of the implemented oscillators occupies 0.16 mm(2) of the chip area.
Effects of Antiretroviral Therapy and HIV Exposure in Utero on Adverse Pregnancy and Infant Outcomes: A Prospective Cohort Study in Guangzhou, China

Objective This study aimed to evaluate the effects of in-utero exposure to HIV and ART on pregnancy outcome and early growth of children. Methods This cohort study enrolled 802 HIV-infected pregnant women between October 2009 and May 2018 in Guangzhou, China. The women were assigned to receive combination ART (cART) or mono/dual ART or no treatment. The primary outcomes were the combined endpoints of any adverse pregnancy outcome [including ectopic pregnancy, spontaneous abortion, stillbirth, preterm birth, small for gestational age (SGA)] and adverse early growth outcome (including infant death, HIV infection of mother-to-child transmission, and underweight, wasting and stunting of infants at 4 weeks of age). Results Adverse pregnancy outcomes occurred in 202 (35.1%) of all enrolled HIV-infected women, and 121 (31.3%) of all infants exhibited adverse effects on early growth at 4 weeks of age. The rates of adverse pregnancy outcomes, spontaneous abortion, ectopic pregnancy, stillbirth, infant death and perinatal HIV infection were higher among women not receiving ART, compared to those treated with cART or mono/dual ART (P < 0.05). However, women treated with cART had a higher rate of SGA, compared to untreated women (P < 0.05). No differences in early infant growth were observed among the different treatment regimens. Conclusion Our findings underscore the essentiality of prioritizing HIV-positive pregnant women for ART, as even mono/dual ART available in resource-limited countries could improve pregnancy outcomes and infant survival.