ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Lipid bands of approx. 1740 cm-1 as spectral biomarkers and image of tissue oxidative stress

Studies with the use of FTIR and FTR methods to find spectroscopic biomarkers within the 1740 cm-1 band of pathological tissues found that oxidative stress, including damage to epidermis and structural changes in pathological amnion and placenta tissue, are associated with the occurrence of products of lipid peroxidation and have impact on fluidity and transport function of membranes. In particular, the findings show that the absence of a marker lipid band of approx. 1743 cm-1 and the occurrence of a minimum of 1764 cm-1 (FTIR) and 1734 cm-1 (FTR) testify to the integrity and absence of damage in the allogeneic dermis, while the presence the 1743 or 1747 cm-1 bands indicates denaturation of the thermally or electrically burned epidermis. The absence of a marker lipid band of approx. 1736-1740 cm-1 for a healthy placental and amniotic tissue and the presence of a band of 1740 cm-1 indicate placental gestosis, while the presence of a band of 1742 cm-1 indicates hypotrophy. The 1738 cm-1 bands indicate amniotic macrosomia. Structural changes caused by tissue modification with antioxidants, which were observed on individual samples: the L-ascorbic acid (presence of a lipid band marker at a frequency of 1755 cm-1), sodium ascorbate (disappearance of the marker band), orthosilicic acid (disappearance or decrease in the intensity of the marker band with a decrease in the concentration of the modifier), as well as graphene oxide (separation of the marker lipid band of 1755 cm-1), inform us about the effect of modifiers on the tissue repair process. The studies also tracked spectral changes identified in serum. Withing the range of the lipid band and the amide I and II bands (α → β conversion), there are clear differences between normal and pathological serum lyophilisates and a sample analyzed from the solution.
Video cloning for paintings via artistic style transfer

In the past, visual arts usually represented the static art like paintings, photography and sculptures. In recent years, many museums, artwork galleries, and even art exhibitions demonstrated dynamic artworks for visitors to relish. The most famous dynamic artwork is "The moving painting of Along the River During the Qingming Festival". Nevertheless, it took 2 years to complete this work. They had to plan each action for every character at first, then drew each video frame by animators. Finally, it could achieve seamless stitching by using lots of projectors to render scene on the screen. In our research, we develop a method for generating animated paintings. It only needs a number of videos on a network of existing databases and requires users to perform some simple auxiliary operations to achieve the effect of animation synthesis. First, our system lets users select an object with the same class from the first video frame. We then employ random forests as learning algorithm to retrieve from a video the object which users want to insert into an artwork. Second, we utilize style transferring, which enables the video frames to be consistent with the style of painting. At last, we use the seamless image cloning algorithm to yield seamless synthesizing result. Our approach allows different users to synthesize animating paintings up to their selected styled video frames. The resulting work not only maintains the original author's painting style, but also generates a variety of artistic conception for people to enjoy.
Dielectric Resonator Antennas: A Historical Review and the Current State of the Art

This article presents a historical review of the research carried out on dielectric resonator antennas (DRAs) over the last three decades. Major research activities in each decade are highlighted. The current state of the art of dielectric-resonator-antenna technology is then reviewed. The achievable performance of dielectric resonator antennas designed for compactness, wide impedance bandwidth, low profiles, circular polarization, or high gain are illustrated. The latest developments in dielectric-resonator-antenna arrays and fabrication techniques are also examined.
Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD)

Defects are the primary problem affecting steel product quality in the steel industry. The specific challenges in developing detect defectors involve the vagueness and tiny size of defects. To solve these problems, we propose incorporating super-resolution technique, sequential feature pyramid network, and boundary localization. Initially, the ensemble of enhanced super-resolution generative adversarial networks (ESRGAN) was proposed for the preprocessing stage to generate a more detailed contour of the original steel image. Next, in the detector section, the latest state-of-the-art feature pyramid network, known as De-tectoRS, utilized the recursive feature pyramid network technique to extract deeper multi-scale steel features by learning the feedback from the sequential feature pyramid network. Finally, Side-Aware Boundary Localization was used to precisely generate the output prediction of the defect detectors. We named our approach EnsGAN-SDD. Extensive experimental studies showed that the proposed methods improved the defect detector's performance, which also surpassed the accuracy of state-of-the-art methods. Moreover, the proposed EnsGAN achieved better performance and effectiveness in processing time compared with the original ESRGAN. We believe our innovation could significantly contribute to improved production quality in the steel industry.
Evolution of polyphenolic, anthocyanin, and organic acid components during coinoculation fermentation (simultaneous inoculation of LAB and yeast) and sequential fermentation of blueberry wine

This research aims to investigate the effects of both sequential fermentation and coinoculation fermentation with yeast and lactic acid bacterial (LAB) on the dynamics of changes in basic quality parameters and organic acid, anthocyanin, and phenolic components as well as antioxidant activity during the fermentation of blueberry. The coculture-fermented blueberry wine showed significant decreases in total phenolics, flavonoids, and anthocyanins,by 23.9%, 15.9%, and 13.7%, respectively, as compared with those before fermentation Fermentation changed the contents of organic acids in each group, with a more than 7-fold increase in lactic acid contents as well as a more than 4-fold reduction in quinic acid and malic acid contents. The content of all investigated anthocyanins first increased and then decreased. Moreover, different fermentation strategies exerted a profound influence on the dynamic change in phenolic components during fermentation; specifically, most of the phenolic acids showed a trend of increasing first, then decreasing, and finally increasing. Gallic acid, p-coumaric acid, quercetin, and myricetin were increased by 116.9%, 130.1%, 127.2% and 177.6%, respectively, while syringic acid, ferulic acid, cinnamic acid, and vanillic acid were decreased by 49.5%, 68.5%, and 37.1% in sequentially fermented blueberry wine. Coinoculation fermentation with yeast and LAB produces faster dynamic variations and higher organic acid, anthocyanin, and phenolic profiles than sequential inoculation fermentation. PRACTICAL APPLICATION: In this work, brewing technology of sequential fermentation and coinoculation fermentation with yeast and LAB (Lactobacillus plantarum SGJ-24 and Oenococcus oeni SD-2a) was adopted to ferment blueberry wine. This is an innovative technology of fruit wine brewing technology to produce wine products. Compared with traditional sequential brewing, simultaneous inoculation brewing can significantly accelerate the brewing process of fruit wine and slightly improve the quality of fruit wine in terms of active ingredients.
Tertiary lymphoid tissues in kidney diseases: a perspective for the pediatric nephrologist

Chronic kidney disease (CKD) is a major public health problem worldwide. In the pediatric population, CKD is also an important health issue because it causes several comorbid conditions that can have long-term consequences beyond the pediatric age. Chronic inflammation is a common pathological feature of CKD, irrespective of etiology, and leads to maladaptive repair and kidney dysfunction. Tertiary lymphoid tissues (TLTs) are ectopic lymphoid structures that develop in non-lymphoid organs under chronic inflammation caused by pathological conditions, including infections, autoimmune diseases, and cancers. TLTs in the kidneys have been poorly researched due to the lack of an animal model. We have recently found that, in aged but not young mice, TLTs develop in multiple kidney injury models, and the analysis of age-dependent TLTs has brought about several novel insights into the development and pathogenic impacts of TLTs in the kidney. Age-dependent TLT formation is also observed in human kidneys. In addition to aged kidneys, TLT development is also reported in several human kidney diseases including kidney allografts, lupus nephritis, and IgA nephropathy in both adults and children. In this review, we describe the novel findings on TLTs in the kidney obtained mainly from the analysis of age-dependent TLTs and discuss the clinical relevance of TLTs in kidney diseases.
Printed circuit board recycling: A state-of-the-art survey

This survey is done with an intention of providing a clear and comprehensive review of current practices and recent developments in the area of printed circuit boards (PCB) recycling, The aim of this paper is to be a reference for research and implementation for PCB recycling process. Original information is collected from the companies engaged in PCB recycling industry and articles published after 1990. The paper gives an overview of PCB structure, material composition and different recycling processes.
Enhanced molecular mobility of ordinarily structured regions drives polyglutamine disease

Polyglutamine expansion is a hallmark of nine neurodegenerative diseases, with protein aggregation intrinsically linked to disease progression. Although polyglutamine expansion accelerates protein aggregation, the misfolding process is frequently instigated by flanking domains. For example, polyglutamine expansion in ataxin-3 allosterically triggers the aggregation of the catalytic Josephin domain. The molecular mechanism that underpins this allosteric aggregation trigger remains to be determined. Here, we establish that polyglutamine expansion increases the molecular mobility of two juxtaposed helices critical to ataxin-3 deubiquitinase activity. Within one of these helices, we identified a highly amyloidogenic sequence motif that instigates aggregation and forms the core of the growing fibril. Critically, by mutating residues within this key region, we decrease local structural fluctuations to slow ataxin-3 aggregation. This provides significant insight, down to the molecular level, into how polyglutamine expansion drives aggregation and explains the positive correlation between polyglutamine tract length, protein aggregation, and disease severity.
Shadow detection and removal for moving objects using Daubechies complex wavelet transform

Shadow detection and removal is a challenging problem for several computer vision applications because shadow always makes object misclassified. A number of shadow detection and removal algorithms have been reported, and some of these algorithms require manual calibration in terms of some hypothesis and predefined specific parameters whereas others do not require manual intervention, but fail to give accurate result in various lighting and environmental conditions. This paper introduces a novel method for shadow detection and removal with Daubechies complex wavelet domain. Daubechies complex wavelet transform has been used in the proposed algorithm due to its strong edge detection, approximate shift-invariance as well as approximate rotation invariance properties. For shadow detection, we have proposed a new threshold in the form of coefficient of variation of wavelet coefficients. This threshold is automatically determined and does not require any manual calibration and training. Results of shadow detection and removal from moving objects after applying the proposed method are compared with the those of other state-of-the-art methods in terms of visual performance and number of quantitative performance evaluation parameters. The proposed method is found to perform better than other state-of-the-art methods.
Navigation With Time Limits in Transportation Networks: A Fourth Moment Approach

This paper investigates the stochastic on-time arrival (SOTA) problem in transportation networks. We propose a fourth moment approach (FMA), which calculates the tight lower bound of a given routing policy's on-time-arrival probability, through estimating the first four moments of the policy's travel time. Then, we employ the generalized policy iteration (GPI) scheme to gradually improve the policy towards the optimal one. Different from state-of-the-art algorithms for the SOTA problem, which require the full travel time distribution and usually incur high computational cost due to the convolution integration operation, FMA only requires the moments of travel-time statistics, which are easily estimated from the statistics perspective. Moreover, the algorithm's computational complexity analysis indicates the relatively light computational load requirement of FMA. Experimental results in a range of transportation networks show FMA's superior performance over state of the arts.
Compressive sensing reconstruction via decomposition

When recovering images from a small number of Compressive Sensing (CS) measurements, a problem arises whereby image features (e.g., smoothness, edges, textures) cannot be preserved well in reconstruction, especially textures at small-scale. Since the missing information still remains in the residual measurement, we propose a novel Decomposition-based CS-recovery framework (DCR) which utilizes residual reconstruction and state-of-the-art filters. The proposed method iteratively refines residual measurement which is closely related to the denoise-boosting techniques. DCR is further incorporated with a weighted total variation and nonlocal structures in the gradient domain as priors to form the proposed Decomposition based Texture preserving Reconstruction (DETER). We subsequently demonstrate robustness of the proposed framework to noise and its superiority over the other state-of-the-art methods, especially at low subrates. Its fast implementation based on the split Bregman technique is also presented.
Learn to Threshold: ThresholdNet With Confidence-Guided Manifold Mixup for Polyp Segmentation

The automatic segmentation of polyp in endoscopy images is crucial for early diagnosis and cure of colorectal cancer. Existing deep learning-based methods for polyp segmentation, however, are inadequate due to the limited annotated dataset and the class imbalance problems. Moreover, these methods obtained the final polyp segmentation results by simply thresholding the likelihood maps at an eclectic and equivalent value (often set to 0.5). In this paper, we propose a novel ThresholdNet with a confidence-guided manifold mixup (CGMMix) data augmentation method, mainly for addressing the aforementioned issues in polyp segmentation. The CGMMix conducts manifold mixup at the image and feature levels, and adaptively lures the decision boundary away from the under-represented polyp class with the confidence guidance to alleviate the limited training dataset and the class imbalance problems. Two consistency regularizations, mixup feature map consistency (MFMC) loss and mixup confidence map consistency (MCMC) loss, are devised to exploit the consistent constraints in the training of the augmented mixup data. We then propose a two-branch approach, termed ThresholdNet, to collaborate the segmentation and threshold learning in an alternative training strategy. The threshold map supervision generator (TMSG) is embedded to provide supervision for the threshold map, thereby inducing better optimization of the threshold branch. As a consequence, ThresholdNet is able to calibrate the segmentation result with the learned threshold map. We illustrate the effectiveness of the proposed method on two polyp segmentation datasets, and our methods achieved the state-of-the-art result with 87.307% and 87.879% dice score on the EndoScene dataset and the WCE polyp dataset. The source code is available at https://github.com/Guo-Xiaoqing/ThresholdNet.
A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.
A toolkit for wide-screen dynamic area of interest measurements using the Pupil Labs Core Eye Tracker

Eye tracking measurements taken while watching a wide field screen are challenging to perform. Commercially available remote eye trackers typically do not measure more than 35 degrees in eccentricity. Analysis software was developed using the Pupil Core Eye Tracking data to analyze viewing behavior under circumstances as natural as possible, on a 1.55-m-wide screen allowing free head movements. Additionally, dynamic area of interest (AOI) analyses were performed on data of participants viewing traffic scenes. A toolkit was created including software for simple allocation of dynamic AOIs (semi-automatically and manually), measurement of parameters such as dwell times and time to first entry, and overlaying gaze and AOIs on video. Participants (n =11) were asked to look at 13 dynamic AOIs in traffic scenes from appearance to disappearance in order to validate the setup and software. Different AOI margins were explored for the included objects. The median ratio between total appearance time and dwell time was about 90% for most objects when appropriate margins were chosen. This validated open-source toolkit is readily available for researchers who want to perform dynamic AOI analyses with the Pupil Core eye tracker, especially when measurements are desired on a wide screen, in various fields such as psychology, transportation, and low vision research.
Begomovirus populations in single plants are complex and may include both well-adapted and poorly-adapted viruses

Begomoviruses (single-stranded DNA plant viruses transmitted by whiteflies) are economically important pathogens causing epidemics worldwide. Tomato-infecting begomoviruses emerged in Brazil in the 1990's following the introduction of Bemisia tabaci Middle East-Asia Minor 1. It is believed that these viruses evolved from indigenous viruses infecting non-cultivated hosts. However, tomato-infecting viruses are rarely found in non-cultivated hosts, and vice-versa. It is possible that viral populations in a given host are composed primarily of viruses which are well adapted to this host, but also include a small proportion of poorly adapted viruses. Following transfer to a new host, the composition of the viral population would shift rapidly, with the viruses which are better adapted to the new host becoming predominant. To test this hypothesis, we collected tomato and Sida plants growing next to each other at two locations in 2014 and 2018. Total DNA was extracted from tomato and Sida samples from each location and year and used as a template for high-throughput sequencing. Reads were mapped following a highly stringent set of criteria. For the 2014 samples, >98% of the Sida reads mapped to Sida micrantha mosaic virus (SiMMV), but 0.1% of the reads mapped to tomato severe rugose virus (ToSRV). Conversely, >99% of the tomato reads mapped to ToSRV, with 0.18% mapping to SiMMV. For the 2018 samples, 41% of the Sida reads mapped to three Sida-adapted viruses and 0.1% of the reads mapped to ToSRV, while 99.9% of the tomato reads mapped to ToSRV. These results are consistent with the hypothesis that viral populations in a single plant are composed primarily of the virus that is better adapted to the host but also include a small proportion of viruses that are poorly adapted.
Refining deep convolutional features for improving fine-grained image recognition

Fine-grained image recognition, a computer vision task filled with challenges due to its imperceptible inter-class variance and large intra-class variance, has been drawing increasing attention. While manual annotation can be utilized to effectively enhance performance in this task, it is extremely time-consuming and expensive. Recently, Convolutional Neural Networks (CNN) achieved state-of-the-art performance in image classification. We propose a fine-grained image recognition framework by exploiting CNN as the raw feature extractor along with several effective methods including a feature encoding method, a feature weighting method, and a strategy to better incorporate information from multi-scale images to further improve recognition ability. Besides, we investigate two dimension reduction methods and successfully merge them to our framework to compact the final image representation. Based on the discriminative and compact framework, we achieved the state-of-the-art performance in terms of classification accuracy on several fine-grained image recognition benchmarks based on weekly supervision.