ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Generating Adversarial Examples in One Shot With Image-to-Image Translation GAN

Deep Neural Networks (DNNs) provide state-of-the-art results for most machine learning and computer vision tasks. However, they have been found susceptible to adversarial examples. In the recent literature, many ways of generating adversarial examples have been discovered. In this work, we propose a novel method to generate adversarial examples with generative adversarial networks (GANs). Compared to traditional optimisation-based methods, our method provides a fast yet powerful alternative for adversary generation. Unlike other GAN-based approaches in the literature which learn to generate an intermediate perturbation vector, our method generates adversarial examples from benign input images in a straightforward manner. By directly generating adversarial examples from given input images, our method produces perturbations that better align with the underlying edge and shape contained in the inputs, hence more natural-looking and imperceptible to human eyes. We evaluate our method on the MNIST and the CIFAR-10 dataset and demonstrate that it outperforms the state-of-the-art GAN-based attack AdvGAN with similar attack capability in terms of distortion. We show that our method produces competitive results to notable optimisation-based attacks in the literature including the strongest Carlini Wagner (CW) attack.
Hydrodynamic performance optimization and experimental verification of underwater glider based on parametric method

In this paper, the wing body fusion method is used to complete the design of underwater glider. On this basis, the traditional optimization algorithm of underwater gliding wing shape is improved. Based on the improved Hicks Henne algorithm and genetic algorithm, the shape optimization of underwater glider is completed. Through the further optimization of the overall performance, the overall shape of the glider is improved and the maximum lift drag ratio is increased. Finally, the physical experiment of the optimized shape is carried out according to the experimental water area of the circulating water tank. Through the comparative analysis of the data, the accuracy of the numerical calculation is verified.
Preference dynamics with multimodal user-item interactions in social media recommendation

Recommender systems elicit the interests and preferences of individuals and make recommendations accordingly, a main challenge for expert and intelligent systems. An essential problem in recommender systems is to learn users' preference dynamics, that is, the constant evolution of the explicit or the implicit information, which is diversified throughout time according to the user actions. Also, in real settings data sparsity degrades the recommendation accuracy. Hence, state-of-the-art methods exploit multimodal information of users-item interactions to reduce sparsity, but they ignore preference dynamics and do not capture users' most recent preferences. In this article, we present a Temporal Collective Matrix Factorization (TCMF) model, making the following contributions: (i) we capture preference dynamics through a joint decomposition model that extracts the user temporal patterns, and (ii) co-factorize the temporal patterns with multimodal user-item interactions by minimizing a joint objective function to generate the recommendations. We evaluate the performance of TCMF in terms of accuracy and root mean square error, and show that the proposed model significantly outperforms state-of-the-art strategies. (C) 2017 Elsevier Ltd. All rights reserved.
Adolescent Mental Health Resilience and Combinations of Caregiver Monitoring and Warmth: A Person-centred Perspective

Caregiver monitoring and warmth have protective mental health effects for adolescents, including vulnerable adolescents. However, combinations of the aforesaid parenting behaviours and their relationship with adolescent mental health are underexplored, especially among younger and older South African (SA) adolescents challenged by structural disadvantage. Hence, the purpose of this study was to investigate unique profiles of caregiver monitoring and warmth and their associations with depression and conduct problems as reported by younger and older adolescents from disadvantaged SA communities. Latent profile and linear regression analyses were used to examine cross-sectional survey data generated by 891 adolescents from two disadvantaged SA communities (62.2% aged 13-17 [average age: 16.13]; 37.5% aged 18-24 [average age: 20.62]). Two profiles emerged. The first, i.e. substantial caregiver warmth and some monitoring, was associated with younger and older adolescent reports of statistically significantly fewer symptoms of depression and conduct problems. The second, i.e. caregiver monitoring without much warmth, was associated with significantly more symptoms of depression or conduct problems among younger and older adolescents. Traditional gender effects (i.e. higher depression symptoms among girls; higher conduct problem symptoms among boys) were amplified when caregiver monitoring was combined with low warmth. In short, protecting the mental health of younger and older adolescents from disadvantaged communities requires higher levels of caregiver warmth combined with moderate levels of caregiver supervision. Because stressors associated with disadvantaged communities jeopardise warm parenting, supporting caregiver resilience to those stressors is integral to supporting adolescent mental health.
Hyperspectral Video Tracker Based on Spectral Deviation Reduction and a Double Siamese Network

The advent of hyperspectral cameras has popularized the study of hyperspectral video trackers. Although hyperspectral images can better distinguish the targets compared to their RGB counterparts, the occlusion and rotation of the target affect the effectiveness of the target. For instance, occlusion obscures the target, reducing the tracking accuracy and even causing tracking failure. In this regard, this paper proposes a novel hyperspectral video tracker where the double Siamese network (D-Siam) forms the basis of the framework. Moreover, AlexNet serves as the backbone of D-Siam. The current study also adopts a novel spectral-deviation-based dimensionality reduction approach on the learned features to match the input requirements of the AlexNet. It should be noted that the proposed dimensionality reduction method increases the distinction between the target and background. The two response maps, namely the initial response map and the adjacent response map, obtained using the D-Siam network, were fused using an adaptive weight estimation strategy. Finally, a confidence judgment module is proposed to regulate the update for the whole framework. A comparative analysis of the proposed approach with state-of-the-art trackers and an extensive ablation study were conducted on a publicly available benchmark hyperspectral dataset. The results show that the proposed tracker outperforms the existing state-of-the-art approaches against most of the challenges.
Long-Term Person Tracking for Unmanned Aerial Vehicle Based on Human-Machine Collaboration

Unmanned Aerial Vehicle (UAV) has been widely used in military reconnaissance, smart transportation, public security and other fields. UAV-based person tracking is attracting incremental attention for its wide application requirements. Currently, some state-of-the-art visual tracking methods have achieved promising performance in common scenarios. However, in the scene of UAV-based person tracking, there will be long-term target disappearance and unpredictable dramatic target appearance changes, which still pose a huge challenge to UAV-based person tracking. In this work, a human-machine hybrid augmented tracking system based on eye tracking is proposed to cope with the challenge. During tracking, through the interaction between humans and machines, humans can provide real-time guidance and corrections to the tracker, and the tracker can also learn interesting targets from humans to enhance itself. The experimental results show that human-in-the-loop can remarkable improve the success rate and robustness of the tracking and our tracking system outperforms the state-of-the-art tracker in complex environments.
MMSE-Optimal Spectral Amplitude Estimation Given the STFT-Phase

In this letter, we derive a minimum mean squared error (MMSE) optimal estimator for clean speech spectral amplitudes, which we apply in single channel speech enhancement. As opposed to state-of-the-art estimators, the optimal estimator is derived for a given clean speech spectral phase. We show that the phase contains additional information that can be exploited to distinguish outliers in the noise from the target signal. With the proposed technique, incorporating the phase can potentially improve the PESQ-MOS by 0.5 in babble noise as compared to state-of-the-art amplitude estimators. In a blind setup we achieve a PESQ improvement of around 0.25 in voiced speech.
An Adaptive Mean-Shift Framework for MRI Brain Segmentation

An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. An adaptive mean-shift methodology is utilized in order to classify brain voxels into one of three main tissue types: gray matter, white matter, and Cerebro-spinal fluid. The MRI image space is represented by a high-dimensional feature space that includes multimodal intensity features as well as spatial features. An adaptive mean-shift algorithm clusters the joint spatial-intensity feature space, thus extracting a representative set of high-density points within the feature space, otherwise known as modes. Tissue segmentation is obtained by a follow-up phase of intensity-based mode clustering into the three tissue categories. By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial voxels. The proposed method is validated on 3-D single and multimodal datasets, for both simulated and real MRI data. It is shown to perform well in comparison to other state-of-the-art methods without the use of a preregistered statistical brain atlas.
NEDD8 Ultimate Buster 1 Long (NUB1L) Protein Suppresses Atypical Neddylation and Promotes the Proteasomal Degradation of Misfolded Proteins

Neddylation is a posttranslational modification that controls diverse biological processes by covalently conjugating the ubiquitin-like protein NEDD8 to specific targets. Neddylation is commonly mediated by NEDD8-specific enzymes (typical neddylation) and, sometimes, by ubiquitin enzymes (atypical neddylation). Although typical neddylation is known to regulate protein function in many ways, the regulatory mechanisms and biological consequence of atypical neddylation remain largely unexplored. Here we report that NEDD8 conjugates were accumulated in the diseased hearts from mouse models and human patients. Proteotoxic stresses induced typical and atypical neddylation in cardiomyocytes. Loss of NUB1L exaggerated atypical neddylation, whereas NUB1L overexpression repressed atypical neddylation through promoting the degradation of NEDD8. Activation of atypical neddylation accumulated a surrogate misfolded protein, GFPu. In contrast, suppression of atypical neddylation by NUB1L overexpression enhanced GFPu degradation. Moreover, NUB1L depletion accumulated a cardiomyopathy-linked misfolded protein, CryAB(R120G), whereas NUB1L overexpression promoted its degradation through suppressing neddylation of ubiquitinated proteins in cardiomyocytes. Consequently, NUB1L protected cells from proteotoxic stress-induced cell injury. In summary, these data indicate that NUB1L suppresses atypical neddylation and promotes the degradation of misfolded proteins by the proteasome. Our findings also suggest that induction of NUB1L could potentially become a novel therapeutic strategy for diseases with increased proteotoxic stress.
An Implicit Contour Morphing Framework Applied to Computer-Aided Severe Weather Forecasting

We propose a contour morphing framework that allows large deformations and topological changes while guaranteeing non self-intersecting contours. Instead of explicitly matching contour points, the proposed algorithm combines an implicit contour representation with state-of-the-art deformable image registration. Synthetic and real-life examples from a meteorological application are presented to demonstrate the efficacy of the framework.
Cost-Effectiveness Analysis in Performance Assessments: A Case Study of the Objective Structured Clinical Examination

Medical education assessments are becoming more complex, resulting in the inappropriateness of traditional methods primarily consisting of direct observations, oral examinations, and multiple-choice tests. Advancements in research methods have led to the formation of new modalities, namely performance assessments, which are, on the other hand, always costly in development and implementation. Proposing using the Program Effectiveness and Cost Generalization flow within an assessment context (PRECOG-A), this brief report explores the real financial cost drivers associated with an assessment case in the context of medical education, presents the steps in bridging the effectiveness with its psychometric properties via cost-effectiveness analysis, and evaluates the two-side outcomes for further evaluation decision-making. Referentially providing a framework to investigators and researchers, the illustration of PRECOG-A in this study outlines instructional guidelines for conducting cost-effectiveness analysis in a performance assessment.
Analyzing Lymphoma Development and Progression Using HDACi in Mouse Models

Besides the physiological role of histone deacetalylases in maintaining normal cellular integrity, the acetylation landscape is changed in cancer cells, which has been implicated as a potential target in cancer therapy. The overexpression of certain HDACs correlates with specific cancer types. Therefore, the development of specific HDAC inhibitors may extend the therapeutic strategy for cancer therapy. Here, we describe how to investigate the therapeutic potential of specific HDACi by treatment in a mouse model for B-cell lymphoma, exemplified by the HDAC6 inhibitor Marbostat-100.
EgNRT2.3 and EgNAR2 expression are controlled by nitrogen deprivation and encode proteins that function as a two-component nitrate uptake system in oil palm

Oil palm (Elaeis guineensis Jacq.) is an important crop for oil and biodiesel production. Oil palm plantations require extensive fertilizer additions to achieve a high yield. Fertilizer application decisions and management for oil palm farming rely on leaf tissue and soil nutrient analyses with little information available to describe the key players for nutrient uptake. A molecular understanding of how nutrients, especially nitrogen (N), are taken up in oil palm is very important to improve fertilizer use and formulation practice in oil palm plantations. In this work, two nitrate uptake genes in oil palm, EgNRT2.3 and EgNAR2, were cloned and characterized. Spatial expression analysis showed high expression of these two genes was mainly found in un-lignified young roots. Interestingly, EgNRT2.3 and EgNAR2 were up-regulated by N deprivation, but their expression pattern depended on the form of N source. Promoter analysis of these two genes confirmed the presence of regulatory elements that support these expression patterns. The Xenopus oocyte assay showed that EgNRT2.3 and EgNAR2 had to act together to take up nitrate. The results suggest that EgNRT2.3 and EgNAR2 act as a two-component nitrate uptake system in oil palm.
A 0.6V 785-nW Multimodal Sensor Interface IC for Ozone Pollutant Sensing and Correlated Cardiovascular Disease Monitoring

In this article, we present the design and analysis of a 785-nW multimodal sensor interface IC for ozone pollutant sensing and correlated cardiovascular disease monitoring based on electrocardiography (ECG) and photoplethysmography (PPG). The proposed hybrid dc offset current cancellation (DCOC) along with a 4-M Omega gain-regulated cascode transimpedance amplifier (RGC-TIA) enable PPG readout power reduction by 37x, compared with the state-of-the-art PPG sensor interfaces. The ozone sensing channel proposes an adaptive architecture to enable low V-DD operation, achieving a 300x power reduction, compared with the state-of-the-art gas sensing readouts. The ozone sensing channel's performance was also verified using custom resistive metal-oxide sensors for concentrations from 50 to 900 ppb. The sensor interface IC is fabricated in a 65-nm CMOS, integrating a 165-nW voltage-mode ECG channel, a 532-nW current- mode PPG channel, 76-nW resistive-mode ozone channel, and 12.6-nW peripheral circuits, all at 0.6 V. The total system power consumption including the LED and a custom digital readout IC is 10.98-15.51 mu W, which is 41x-57x less than prior ozone/CVD joint monitoring sensor interface systems.
Fertility and contraception: The experience of Spanish women born in the first half of the twentieth century

New data based on retrospective interviews with older informants enable us to review the history of contraceptive use among Spanish women over much of the twentieth century. This source is unique because it includes cohorts of women whose reproductive lives took place before, during, and after the baby boom. Traditional contraceptive methods (withdrawal and periodic abstinence) were central to the experience of the first set of women, while the last set made full use of modern as well as some traditional methods. For the first cohorts, traditional methods spearheaded the historic decline in fertility, while among the last set of women modern methods led to a precipitous decline towards the below-replacement fertility that continues in Spain today. There is no evidence that the modest increases in fertility during the baby boom in Spain were the result of a decline in the use of contraception among married women.
Real-time tracking-with-detection for coping with viewpoint change

We consider real-time visual tracking with targets undergoing viewpoint changes. The problem is evaluated on a new and extensive dataset of vehicles undergoing large viewpoint changes. We propose an evaluation method in which tracking accuracy is measured under real-time computational complexity constraints and find that state-of-the-art agnostic trackers, as well as class detectors, are still struggling with this task. We study tracking schemes fusing real-time agnostic trackers with a non-real-time class detector used for template update, with two dominating update strategies emerging. We rigorously analyze the template update latency and demonstrate that such methods significantly outperform stand-alone trackers and class detectors. Results are demonstrated using two different trackers and a state-of-the-art classifier, and at several operating points of algorithm/hardware computational speed.
Total Synthesis of Natural Products using Gold Catalysis

Gold catalysis is an extremely enthusiastic field of investigation in the catalysis area. The development of alternative, highly inventive, precompetitive techniques based on gold catalysis has paved the way for executing a broad spectrum of chemical transformations from uncomplicated starting materials. The total synthesis of natural products is a complex and more complicated task. An amalgamation of natural product synthesis through gold-catalysis has been a thought-provoking job. The protocol has solved several problems related to the synthesis of numerous complicated natural products. Thus, this review has outlined some of the most notable benchmarks from the last seven years (2015-2021) on gold catalysis and their application in the total synthesis of numerous natural products. The strategy acquired by the authors to accomplish the total synthesis will be elaborately discussed by emphasizing the role of the gold-catalyzed reactions.
The connections between art and science in Antarctica: Activating Science*Art

Art may be made as a guide to understanding sense of place, and also as a pathway to understanding and valuing scientific ideas. Here we consider this connection in the context of a selected history of artists working in Antarctica, from early explorers to the modern era. This provides a parallel trajectory for the nature, realisation and purpose of the art. We then consider the interaction between art and science and the nature of interdisciplinary work by looking at work produced in a sea ice-based science field camp by an artist collecting data - both scientific and art focused. The artist participated in two field campaigns a year apart, allowing comparison of the evolution of both the artistic practice and the science data collection. Furthermore, the collection of data that served both needs provides a unique point of connection between two fields of endeavour, which are typically considered as separate.
INSPECTION OF THE STATE (GENERAL AND INSTRUMENTAL) OF HISTORICAL TRANSLUCENT STRUCTURES OF THE PUSHKIN STATE MUSEUM OF FINE ARTS

The article is focused on the general and instrumental survey of historical translucent structures in the Pushkin State Museum of Fine Arts in 2018. It is shown that they don't complying with the current requirements, neither in heat transfer resistance nor in air permeability. The improvement recommendations have been developed. It is noted that in case of preservation of metal window frames (according to the requirements of the law on protection of cultural heritage sites) the large-scale computer calculations should be performed to determine the best ways of window restoration.
Electrocoagulation of Corrugated Box Industrial Effluents and Optimization by Response Surface Methodology

The electrocoagulation method using stainless steel anodes was applied to a corrugated cardboard box manufacturing plant's wastewater with high COD content. The effects of current density, processing time and stirring speed on response functions were studied using the Response Surface Methodology (RSM). The removal efficiency of chemical oxygen demand (COD) and energy consumption were selected as response functions. The Central Composite Design (CCD) was chosen to explain the single and combined effects of independent variables on response functions. The COD concentration of the real industrial wastewater used in the experiments was 9130 mg L-1. The maximum COD removal efficiency of 91.6% is obtained with 19.78 Wh g-1 energy consumption. Current density and treatment time were effective parameters for both COD removal and energy consumption. Optimization for maximum COD removal with minimum energy consumption showed 80.9% of COD removal with 6.7 Wh g-1 of energy consumption at 15 mA cm-2, 700 rpm, and 28 min treatment time. The variables are optimized with a few experiments using the response surface method.