ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Tuning the Roundabout of Four-Point-Star Tiles with the Core Arm Length of Three Half-Turns for 2D DNA Arrays

By rationally adjusting the weaving modes of point-star tiles, the curvature inherent in the tiles can be changed, and various DNA nanostructures can be assembled, such as planar wireframe meshes, perforated wireframe tubes, and curved wireframe polyhedra. Based on the weaving and tiling architectures for traditional point-star tiles with the core arm length at two DNA half-turns, we improved the weaving modes of our newly reported four-point-star tiles with the core arm length at three half-turns to adjust their curvature and rigidity for assembling 2D arrays of DNA grids and tubes. Following our previous terms and methods to analyze the structural details of E-tiling tubes, we used the chiral indices (n,m) to describe the most abundant tube of typical assemblies; especially, we applied both one-locus and/or dual-locus biotin/streptavidin (SA) labelling strategies to define the configurations of two specific tubes, along with the absolute conformations of their component tiles. Such structural details of the DNA tubes composed of tiles with addressable concave and convex faces and packing directions should help us understand their physio-chemical and biological properties, and therefore promote their applications in drug delivery, biocatalysis, biomedicine, etc.
Recognition of English calling cards by using enhanced fuzzy radial basis function neural networks

In this paper, we proposed the novel method for the recognition of English calling cards by using the contour tracking algorithm and the enhanced fuzzy RBF (Radial Basis Function) neural networks. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method the feature areas are split into the areas of individual characters. We also proposed the enhanced fuzzy RBF neural network that organizes the middle layer effectively by using the enhanced fuzzy ART neural network adjusting the vigilance parameter dynamically according to the similarity between patterns. In the recognition phase, the proposed fuzzy neural network was applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the conventional RBF network based recognitions.
Computed Tomography of Chemiluminescence (CTC): Instantaneous 3D measurements and Phantom studies of a turbulent opposed jet flame

Time resolved 3D measurements are required to further the understanding of turbulent combustion and to support the development of advanced simulation techniques such as LES. The Computed Tomography of Chemiluminescence (CTC) technique reconstructs the 3D chemiluminescence field of a turbulent flame from a series of integral measurements (camera images). The resulting data can be analysed to obtain the flame surface density, wrinkling factor, flame normal direction and possibly heat release rate, and also to study transient phenomena. High resolution CTC requires measurements from many viewing angles, and the capabilities of recent machine vision cameras make this affordable. The present paper investigates CTC using such commodity cameras. CTC is implemented using five PicSight P32M cameras and mirrors to provide 10 simultaneous views of a premixed turbulent opposed jet (TOJ) flame. The reconstructions are then performed using a 3D Algebraic Reconstruction Technique (ART) algorithm. For the flame investigated, camera exposure times of only 250 mu s were found to provide more than sufficient signal-to-noise ratios for ART reconstruction with still shorter exposures times possible. All reconstructions capture the main features of the TOJ flame and were found to provide a useful spatial resolution, even with just 10 views. Detailed Phantom studies were performed to assess the resolution available from ART. The resolution was found to be object dependent but a good working estimate was obtained from a relation by Frieder and Herman (1971)[64]. Reconstructions of realistic LES Phantom data have shown that high resolution reconstructions, which resolve wavelengths of 0.035 object diameters, can be a achieved from only 20 views, with each view costing less than $1000. (C) 2010 The Combustion Institute. Published by Elsevier Inc. All rights reserved.
A paradigm shift in retinal detachment repair: The concept of integrity

The management of rhegmatogenous retinal detachment has rapidly evolved over recent decades. A range of surgical techniques exist, all of which can achieve retinal reattachment in most cases. In recent years there have also been vast technical advances in retinal imaging that have introduced novel ways of visualizing and studying the retinal macro and microstructural anatomy following retinal detachment repair. Recent clinical trial data demonstrates that functional and patient-reported outcomes of retinal reattachment differ with surgical technique, accompanied by differences in anatomic biomarkers of retinal recovery or 'integrity'. We discuss recent insights into the physiology of retinal reattachment gleaned from multimodal imaging, which shed light on the pathophysiology of various post-operative anatomic abnormalities. The ideal scenario is to achieve retinal reattachment as soon as possible, without retinal displacement, outer retinal folds or discontinuity of the external limiting membrane, ellipsoid zone and interdigitation zone, with an intact foveal bulge. To this end, we present an in-depth contemporary account of current concepts and mechanisms involved during retinal reattachment surgery, supported by clinical data and mathematical modelling, awareness of which can help the vitreoretinal surgeon achieve better post-operative outcomes. In this review we substantiate the case for a paradigm shift in rhegmatogenous retinal detachment repair; beyond the emphasis on single-operation reattachment rates, and instead striving to maximize functional outcomes using minimally invasive techniques. This can only be achieved if vitreoretinal surgeons embrace all of the available techniques, with individualized selection of surgical approach and the resolute goal of optimizing the 'integrity' of retinal reattachment.
Position-aware lightweight object detectors with depthwise separable convolutions

Recently, significant improvements have been achieved for object detection algorithm by increasing the size of convolutional neural network (CNN) models, but the resulting increase of computational complexity poses an obstacle to practical applications. And some of the lightweight methods fail to consider the characteristics of object detection into and suffer a huge loss of accuracy. In this paper, we design a multi-scale feature lightweight network structure and specific convolution module for object detection based on depthwise separable convolution, which not only reduces the computational complexity but also improves the accuracy by using the specific position information in object detection. Furthermore, in order to improve the detection accuracy for small objects, we construct a multi-channel position-aware map and propose training based on knowledge distillation for object detection to train the lightweight model effectively. Last, we propose a training strategy based on a key-layer guiding structure to balance performance with training time. The experimental results show that on the COCO dataset that takes the state-of-the-art object detection algorithm, YOLOv3, as the baseline, our model size is compressed to 1/11 while accuracy drops by 7.4 mmAP, and the computational latency on the GPU and ARM platforms are reduced to 43.7% and 0.29%, respectively. Compared with the state-of-the-art lightweight object detection model, MNet V2 + SSDLite, the accuracy of our model increases by 3.5 mmAP while the inferencing time stays nearly the same. On the PASCAL VOC2007 dataset, the accuracy of our model increases by 5.2 mAP compared to the state-of-the-art lightweight algorithm based on knowledge distillation. Therefore, in terms of accuracy, parameter count, and real-time performance, our algorithm has better performance than lightweight algorithms based on knowledge distillation or depthwise separable convolution.
Achieving mainstream anammox in biological aerated filter by regulating bacteria community structure

Although mainstream partial nitrification-anammox (PN-A) is a highly efficient and sustainable wastewater treatment process, it is difficult to achieve and stabilize due to the competition among functional bacteria. In this study, achieving one-stage mainstream anammox via regulating bacteria community structure was studied in a lab-scale biological aerated filter (BAF). The results showed that high free ammonia with 89.57 mg/L, nitrite nitrogen (NO2--N) competition between anammox bacteria (AnAOB) and nitrite oxidizing bacteria (NOB), and backwash regulated the bacteria community structure. After backwash, Candidatus Kuenenia became the dominant bacteria and the relative abundance increased to 5.56 %. In BAF, one-stage mainstream anammox with total nitrogen (TN) being lower than 15 mg/L in the effluent was achieved using lag-time of bacteria activity recovery caused by alternating operation of high and low ammonia nitrogen (NH4+-N), which have great potential applied in municipal wastewater treatment plants (MWWTPs).
Oligostilbenes from the seeds of Paeonia lactiflora as potent GLP-1 secretagogues targeting TGR5 receptor

One unusual stilbene trimer-flavonoid hybrid, paeonilactiflobenoid (1), together with six known stilbenes (2-7) were isolated from the seeds of Paeonia lactiflora. The structure of 1 was elucidated with the aid of HRESIMS, 1D and 2D NMR, [α]D spectroscopic data and ECD calculation. Compounds 2-7 showed stimulative effects on GLP-1 secretion with promoting rates of 79.8%-880.4% (25 μM) and 217.6%-1089.4% (50 μM), more potent than the positive control, oleoylethanolamide (250.2% at 50 μM). Moreover, compounds 4 and 6 exhibited agonistic activity on the G protein-coupled receptor (GPCR) TGR5 with stimulative ratios of 40.2% and 40.5% at 50 μM, and 54.2% and 49.1% at 100 μM, respectively. Docking study manifested that 6 well located in the catalytic pocket of TGR5 by hydrogen-bond and hydrophobic interactions. The GLP-1 promotion of 6 could be attenuated by IP3, Ca2+/CaMKII and MEK/ERK pathway inhibitors, suggesting that these pathways played important roles in GLP-1 secretion. Thus, stilbenes in peony seeds maybe regarded as potential GLP-1 secretagogues through TGR5-IP3-Ca2+/CaMKII-MEK/ERK pathways.
Photonic-Assisted Arbitrary Waveform Generation for Uplink Applications in Beyond 5G Taking Advantage of Low Frequency Technology

We present a photonic-assisted arbitrary waveform generation for uplink applications in beyond 5G taking advantage of low frequency technology and it has a potential to upgrade signal frequency from MHz to more than tens GHz retaining the high ENOB of conventional low frequency electrical technology competitive to the state of the arts of tens GHz class AWG. In machine-to-machine communications over uplink traffic as one of application scenario examples, massive multi-data should be accommodated and be conveyed to beyond GHz region in the era of beyond 5G. The proposed photonic assistance for temporal waveform compression has a potential to upgrade signal frequency from MHz to more than tens GHz retaining the high ENOB of the low frequency technology and surpass the state of the arts of tens GHz class AWG. We successfully demonstrate the principle of the proposed approach in experiment and the potential of achievable ENOB over 12 bit at 50 GHz in simulation.
Morphological and release characterization of nanoparticles formulated with poly (dl-lactide-co-glycolide) (PLGA) and lupeol: In vitro permeability and modulator effect on NF-κB in Caco-2 cell system stimulated with TNF-α

Lupeol exhibits anti-inflammatory effects; unfortunately it shows low water solubility. An alternative to overcome this is the development of nanomaterials. Several methods for nanomaterial production are available. One of them is emulsification/solvent-evaporation. The objective of the present work was to evaluate physical properties, transport and in vitro modulator effects on NF-κB of poly (lactide-co-glycolide) (PLGA) nanoparticles loaded with lupeol. Nanonutraceuticals were prepared with 16% (w/v) of lupeol. Size distribution and morphology were measured by particle size analyzer and TEM. In vitro release of lupeol was studied by three different models: Higuchi, Siepmann & Peppas, and Power law. Transport of nanonutraceutical was studied in a Caco-2 cell model and by GC-MS. Modulator effect on NK-κB was studied by western blot analysis. Nanonutraceuticals were 10% larger than the nanoparticles without lupeol (372 vs 337 nm) and presented a broader size distribution (0.28 vs 0.22). TEM results displayed spherical structures with a broader size distribution. Entrapment efficiency of lupeol was 64.54% and it in vitro release data fitted well to the Power law and Higuchi equation (R > 0.84-0.84). Strong regulation of NF-κB of nanonutraceutical was observed. It was not observed any transport across the Caco-2 cell model at the different experimental conditions.
Adaptive Greedy Dictionary Selection for Web Media Summarization

Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via l(2,0) norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks.
Facilely Achieved Self-Biased Black Silicon Heterojunction Photodiode with Broadband Quantum Efficiency Approaching 100

Photodiodes are fundamental components in modern optoelectronics. Heterojunction photodiodes, simply configured by two different contact materials, have been a hot research topic for many years. Currently reported self-biased heterojunction photodiodes routinely have external quantum efficiency (EQE) significantly below 100% due to optical and electrical losses. Herein, an approach that virtually overcomes this 100% EQE challenge via low-aspect-ratio nanostructures and drift-dominated photocarrier transport in a heterojunction photodiode is proposed. Broadband near-ideal EQE is achieved in nanocrystal indium tin oxide/black silicon (nc-ITO/b-Si) Schottky photodiodes. The b-Si comprises nanostalagmites which balance the antireflection effect and surface morphology. The built-in electric field is explored to match the optical generation profile, realizing enhanced photocarrier transport over a broadband of photogeneration. The devices exhibit unprecedented EQE among the reported leading-edge heterojunction photodiodes: average EQE surpasses ≈98% for wavelengths of 570-925 nm, while overall EQE is greater than ≈95% from 500 to 960 nm. Further, only elementary fabrication techniques are explored to achieve these excellent device properties. A heart rate sensor driven by nanowatt faint light is demonstrated, indicating the enormous potential of this near-ideal b-Si photodiode for low power consuming applications.
Remote sensing image super-resolution and object detection: Benchmark and state of the art

For the past two decades, there have been significant efforts to develop methods for object detection in Remote Sensing (RS) images. In most cases, the datasets for small object detection in remote sensing images are inadequate. Many researchers used scene classification datasets for object detection, which has its limitations; for example, the large-sized objects outnumber the small objects in object categories. Thus, they lack diversity; this further affects the detection performance of small object detectors in RS images. This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We also propose a largescale, publicly available benchmark Remote Sensing Super-resolution Object Detection (RSSOD) dataset. The RSSOD dataset consists of 1,759 hand-annotated images with 22,091 instances of very high-resolution (VHR) images with a spatial resolution of ~ 0.05 m. There are five classes with varying frequencies of labels per class; the images are annotated in You Only Look Once (YOLO) and Common Objects in Context (COCO) format. The image patches are extracted from satellite images, including real image distortions such as tangential scale distortion and skew distortion. The proposed RSSOD dataset will help researchers benchmark the state-of-the-art object detection methods across various classes, especially for small objects using image super-resolution. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolution-based object detection and compare with the existing state-of-the-art methods based on image super-resolution (SR). The proposed MCGR achieved state-of-the-art performance for image SR with an improvement of 1.2 dB in peak signal-to-noise ratio (PSNR) compared to the current state-of-the-art non-local sparse network (NLSN). MCGR achieved best object detection mean average precisions (mAPs) of 0.758, 0.881, 0.841, and 0.983, respectively, for five-class, four-class, two-class, and single classes, respectively surpassing the performance of the state-of-the-art object detectors YOLOv5, EfficientDet, Faster RCNN, SSD, and RetinaNet.
Fast and Accurate Craniomaxillofacial Landmark Detection via 3D Faster R-CNN

Automatic craniomaxillofacial (CMF) landmark localization from cone-beam computed tomography (CBCT) images is challenging, considering that 1) the number of landmarks in the images may change due to varying deformities and traumatic defects, and 2) the CBCT images used in clinical practice are typically large. In this paper, we propose a two-stage, coarse-to-fine deep learning method to tackle these challenges with both speed and accuracy in mind. Specifically, we first use a 3D faster R-CNN to roughly locate landmarks in down-sampled CBCT images that have varying numbers of landmarks. By converting the landmark point detection problem to a generic object detection problem, our 3D faster R-CNN is formulated to detect virtual, fixed-size objects in small boxes with centers indicating the approximate locations of the landmarks. Based on the rough landmark locations, we then crop 3D patches from the high-resolution images and send them to a multi-scale UNet for the regression of heatmaps, from which the refined landmark locations are finally derived. We evaluated the proposed approach by detecting up to 18 landmarks on a real clinical dataset of CMF CBCT images with various conditions. Experiments show that our approach achieves state-of-the-art accuracy of 0.89 +/- 0.64 mm in an average time of 26.2 seconds per volume.
[FPGA Implementation of Digital Coordinate Conversion of Intravascular Ultrasound Imaging System Based on CORDIC Algorithm]

In order to facilitate doctors to better obtain cardiovascular images by using intravascular ultrasound imaging system and make a more accurate diagnosis, a digital coordinate conversion method of intravascular ultrasound imaging system based on CORDIC algorithm is proposed, it converts polar coordinates into rectangular coordinates through angular rotation and orientation calculation. The experimental simulation test is carried out on the platform of intravascular ultrasound imaging system by FPGA. Experimental simulation shows that, CORDIC algorithm can effectively output sine and cosine values, compared with the traditional table finding method, this algorithm has faster speed, stronger real-time performance and needs less hardware resources. It is more suitable for intravascular ultrasound imaging system.
Enabling Ultrasound In-Body Communication: FIR Channel Models and QAM Experiments

Ultrasound waves pose a promising alternative to the commonly used electromagnetic waves for intra-body communication. This due to the lower ultrasound wave attenuation, the reduced health risks, and the reduced external interference. Current state-of-the-art ultrasound designs, however, are limited in their practical in-body deployment and reliability. This stems from their use of bulky, focused transducers, the use of simple modulation schemes or the absence of a realistic test environment and corresponding realistic channel models. Therefore, this paper proposes a new, ultrasound, static emulation test bed consisting of small, omnidirectional transducers, and custom gelatin phantoms with additional scattering materials. Using this test bed, we investigate different in-body communication scenarios. Multiple communication channels were extracted and mapped onto finite impulse response (FIR) channel models, which are verified and open sourced for future research. Furthermore, a basic quadrature-amplitude modulation (QAM) modem was built to assess the communication performance under various modulation schemes. A link was established using 4-QAM and 200 kbit/s resulting in a BER <1e-4 at received Eb/No of 13dB. Identical results were obtained by computer simulations on the FIR channels, which makes the extracted FIR channels suitable for the design of future ultrasound in-body communication schemes.
High efficiency self-cleaning of nanocomposites ZnO with additional chitosan for helping electron and hole transport

ZnO/chitosan coated wool fabric has been applied as a self-cleaning which obtained through sol-gel method for various pH: 5, 7, and 9. The self-cleaning test was carried out by irradiating the cloth samples using UVA-UVB lamps up to 15 h with dye for the clothing stain. ZnO/chitosan composites were characterized using X-Ray Diffraction (XRD), Fourier Transform Infra-Red (FTIR), and UV-Visible spectroscopy. The diffraction spectra from chitosan for pH 9 is 19.56°. The vibration bond at the wavenumbers 422 cm-1- 621 cm-1 identified as ZnO bond and at the wavenumber 3495 cm-1 identified for stretching -OH and -NH2 from chitosan. Optical properties were analysed using the Kamers Kronig (KK) relation which was applied to the FTIR spectra and shows the highest distance between two optical photon vibration modes (∆ (LO-TO)) is 199 cm-1 was obtained when the pH 9. The pH 9 is the best self-cleaning performance for dye stain which completely lost after being exposed for 15 h irradiation. Samples pH 9 shows the best self-cleaning due to the smallest crystallite size and highest bandgap and (∆ (LO-TO)) indicated high potential for new self-cleaning material in future.