ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
An Efficient High-Throughput Generic QAM Transmitter with Scalable Spiral FIR Filter

The need for efficient Finite Impulse Response (FIR) filters in high-speed applications such as telecommunications targets Field Programmable Gate Arrays (FPGAs) as an effective and flexible platform for digital implementation. Although FIR filter offers many advantages, its convolution nature poises a challenge in parallelization due to data dependency and computational complexity. To resolve this, we propose a novel FPGA-based reconfigurable filter architecture, which processes several data samples in parallel and breaks down data interdependency in a spiral fashion. Experimental results show a throughput of 7.2 GSPS with an operating frequency of only 450MHz for a filter length of 11 with 16 parallel inputs. With parallelization of 4, it is 4.44 times faster than the state-of-the-art solution for a filter length of 16 and a promising 1.04 GSPS throughput is achieved for a higher order of length 61. Incorporated into a generic Quadrature Amplitude Modulation (QAM) transmitter fitted with Forward Error Correction technique, a maximum throughput of 23 Gb/s is achieved by the system for processing 16 input samples in parallel. In comparison to the state-of-the-art mixed domain approach, a threefold performance gain, while utilizing comparatively less Look-up Tables (LUTs), registers and DSP48 slices with an average gain factor of 43.3x, 4.7x and 3.9x, respectively, is accomplished.
Interactive Few-Shot Learning: Limited Supervision, Better Medical Image Segmentation

Many known supervised deep learning methods for medical image segmentation suffer an expensive burden of data annotation for model training. Recently, few-shot segmentation methods were proposed to alleviate this burden, but such methods often showed poor adaptability to the target tasks. By prudently introducing interactive learning into the few-shot learning strategy, we develop a novel few-shot segmentation approach called Interactive Few-shot Learning (IFSL), which not only addresses the annotation burden of medical image segmentation models but also tackles the common issues of the known few-shot segmentation methods. First, we design a new few-shot segmentation structure, called Medical Prior-based Few-shot Learning Network (MPrNet), which uses only a few annotated samples (e.g., 10 samples) as support images to guide the segmentation of query images without any pretraining. Then, we propose an Interactive Learning-based Test Time Optimization Algorithm (IL-TTOA) to strengthen our MPrNet on the fly for the target task in an interactive fashion. To our best knowledge, our IFSL approach is the first to allow few-shot segmentation models to be optimized and strengthened on the target tasks in an interactive and controllable manner. Experiments on four few-shot segmentation tasks show that our IFSL approach outperforms the state-of-the-art methods by more than 20% in the DSC metric. Specifically, the interactive optimization algorithm (IL-TTOA) further contributes similar to 10% DSC improvement for the few-shot segmentation models.
Hace1 overexpression mitigates myocardial hypoxia/reoxygenation injury via the effects on Keap1/Nrf2 pathway

HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (Hace1) is a crucial mediator of multiple pathological disorders. However, there are few studies regarding the role of Hace1 in myocardial ischemia/reperfusion injury. Here, we studied the functional role of Hace1 on myocardial ischemia/reperfusion injury using hypoxia/reoxygenation (H/R)-injured cardiac cells in vitro. Reduced levels of Hace1 were observed in H/R-exposed cardiac cells. Hace1-overexpressed cardiac cells were resistant to H/R injuries with reduced apoptosis, lowered oxidative stress, and a suppressed inflammatory response. Subsequent analysis revealed that Hace1 overexpression enhanced the activation of nuclear translocation of nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and increased the transcriptional activity of Nrf2 in H/R-exposed cardiac cells. The knockout of kelch-like ECH-associated protein 1 (Keap1) diminished the regulatory role of Hace1 on Nrf2 activation. Additionally, inhibiting Nrf2 reversed Hace1-elicited cardioprotective effects in H/R-injured cardiac cells. In short, these data demonstrated that Hace1 overexpression mitigated myocardial H/R injury by enhancing the Nrf2 pathway via Keap1. This work underlines a possible role of Hace1 in myocardial ischemia/reperfusion injury and suggests Hace1 as a candidate target for exploiting cardioprotective therapy.
Spallation reactions and accelerator-driven systems

The state-of-the-art in the field of neutron and nuclear-fragment emissions from spallation reactions initiated by protons in the GeV energy range with extended heavy targets is discussed. Such reactions are essential for the operation of accelerator-driven systems. (C) 2003 Elsevier Science Ltd. All rights reserved.
Object detection on remote sensing images using deep learning: an improved single shot multibox detector method

Remote sensing images recognition technology has great significance in many aspects, such as military navigation and environmental monitoring. We propose an improved single shot multibox detector approach by combining some strategies, including upsampling, focal loss, and proper calibration of key parameters. Comprehensive experiments on three remote sensing images datasets have demonstrated the effectiveness of the proposed approach in benchmarking with several state-of-the-art object detection methods. (C) 2019 SPIE and IS&T
First-Principles-Based Quantum Transport Simulations of High-Performance and Low-Power MOSFETs Based on Monolayer Ga2O3

The electronic properties of monolayer (ML) Ga2O3 and transport properties of ML Ga2O3-based n-type metal-oxide-semiconductor field-effect transistors (MOSFETs) are investigated by first-principles calculations under the framework of density functional theory (DFT) coupled with the nonequilibrium Green's function (NEGF) formalism. The results show that ML Ga2O3 has a quasi-direct band gap of 4.92 eV, and the x- and y-directed electron mobilities are 1210 and 816 cm2 V-1 s-1 at 300 K, respectively, under the full consideration of phonon scattering. The electron-phonon scattering mechanism shows a temperature-dependent behavior, with the acoustic modes dominating below 300 K and optical modes dominating above 300 K. At a gate length of Lg = 5 nm, the on-current of ML Ga2O3 n-MOSFET for high-performance (HP) application is 2890 μA/μm, which is more than those of the most reported two-dimensional (2D) materials. The delay time as well as the power delay product of ML Ga2O3 MOSFETs can meet the demands of the latest International Technology Roadmap for Semiconductors (ITRS) for HP and low-power (LP) applications until Lg is less than 4 and 5 nm, respectively. Through underlap structure and doping optimization strategies, ML Ga2O3 n-MOSFET can further fulfill the ITRS requirements for 1 nm. At last, we compare the performance of the 32-bit arithmetic logic unit (ALU) built on ML Ga2O3 MOSFETs with the recently reported beyond-CMOS devices. Our results indicate that ML Ga2O3 can serve as a promising channel material in the post-silicon era.
A recent epidemiological cluster of acute hepatitis B genotype F1b infection in a restricted geographical area of Italy

In this study, by phylogenetic analysis, we identified an epidemiological cluster involving eight individuals diagnosed with acute hepatitis B virus (HBV) infection related to unprotected sexual intercourse in a restricted area of central Italy (time period: 2011-2014). Notably, these patients (six of eight Italians) were infected by subgenotype F1b, which is not commonly found in western countries. Ultra-deep pyrosequencing confirmed a superimposable composition of HBV quasi-species in these patients. Despite the availability of effective vaccination, this study highlights the importance of not underestimating the risk of HBV infection, of continuing to set up surveillance programmes for HBV infection, and of investigating the pathogenetic potential of these atypical genotypes.
An ART-based construction of RBF networks

Radial basis function (RBF) networks are widely used for modeling a function from given input-output patterns. However, two difficulties are involved with traditional RBF (TRBF) networks: The initial configuration of an RBF network needs to be determined by a trial-and-error method, and the performance suffers when the desired output has abrupt changes or constant values in certain intervals. We propose a novel approach to overcome these difficulties. New kernel functions are used for hidden nodes, and the number of nodes is determined automatically by an adaptive resonance theory (ART)-like algorithm. Parameters and weights are initialized appropriately, and then tuned and adjusted by the gradient-descent method to improve the performance of the network. Experimental results have shown that the RBF networks constructed by our method have a smaller number of nodes, a faster learning speed, and a smaller approximation error than the networks produced by other methods.
Adaptation of the classical end-point ITS-PCR for the diagnosis of avian trichomonosis to a real-time PCR reveals Bonelli's eagle as a new host for Trichomonas gypaetinii

Avian trichomonosis is a parasitic disease caused mainly by Trichomonas gallinae and other Trichomonas species. It can be asymptomatic, or it can produce a necrotic lesion in the upper digestive tract and spread to other organs, causing the death of the infected birds. In this study, we aimed to evaluate an adapted real-time PCR method for the diagnosis of different genotypes and species of avian oropharyngeal trichomonads. Fifty-six samples from the oropharynx of Bonelli's eagles (Aquila fasciata) obtained between 2018 and 2019 were analyzed using the real-time PCR and the end-point PCR, both targeting trichomonads ITS, and the results were compared by a coefficient of agreement. All positive samples were sequenced. The analysis showed a higher percentage of detection of real-time PCR ITS compared with end-point PCR ITS (64.3 vs 55.4%), and good agreement value (Kappa = 0.816). Melting temperature value for resulting amplicons of real-time PCR for avian trichomonads was 83.45 ± 0.72 °C. Genotypes A, D, and III were found among the sequences. Moreover, Trichomonas gypaetinii, a common species in scavenger birds, is reported for the first time in Bonelli's eagles.
Robust PCA Unrolling Network for Super-Resolution Vessel Extraction in X-Ray Coronary Angiography

Although robust PCA has been increasingly adopted to extract vessels from X-ray coronary angiography (XCA) images, challenging problems such as inefficient vessel-sparsity modelling, noisy and dynamic background artefacts, and high computational cost still remain unsolved. Therefore, we propose a novel robust PCA unrolling network with sparse feature selection for super-resolution XCA vessel imaging. Being embedded within a patch-wise spatiotemporal super-resolution framework that is built upon a pooling layer and a convolutional long short-term memory network, the proposed network can not only gradually prune complex vessel-like artefacts and noisy backgrounds in XCA during network training but also iteratively learn and select the high-level spatiotemporal semantic information of moving contrast agents flowing in the XCA-imaged vessels. The experimental results show that the proposed method significantly outperforms state-of-the-art methods, especially in the imaging of the vessel network and its distal vessels, by restoring the intensity and geometry profiles of heterogeneous vessels against complex and dynamic backgrounds. The source code is available at https://github.com/Binjie-Qin/RPCA-UNet
A Resource-Optimized VLSI Implementation of a Patient-Specific Seizure Detection Algorithm on a Custom-Made Wireless Device for Ambulatory Epilepsy Diagnostics

A patient-specific epilepsy diagnostic solution in the form of a wireless wearable ambulatory device is presented. First, the design, VLSI implementation, and experimental validation of a resource-optimized machine learning algorithm for epilepsy seizure detection are described. Next, the development of a mini-PCB that integrates a low-power wireless data transceiver and a programmable processor for hosting the seizure detection algorithm is discussed. The algorithm uses only EEG signals from the frontal lobe electrodes while yielding a seizure detection sensitivity and specificity competitive to the standard full EEG systems. The experimental validation of the algorithm VLSI implementation proves the possibility of conducting accurate seizure detection using quickly-mountable dry-electrode headsets without the need for uncomfortable/painful through-hair electrodes or adhesive gels. Details of design and optimization of the algorithm, the VLSI implementation, and the mini-PCB development are presented and resource optimization techniques are discussed. The optimized implementation is uploaded on a low-power Microsemi Igloo FPGA, requires 1237 logic elements, consumes 110 $\mu$W dynamic power, and yields a minimum detection latency of 10.2 $\mu$s. The measurement results from the FPGA implementation on data from 23 patients (198 seizures in total) shows a seizure detection sensitivity and specificity of 92.5 and 80.1, respectively. Comparison to the state of the art is presented from system integration, the VLSI implementation, and the wireless communication perspectives.
Perceived no reference image quality measurement for chromatic aberration

Today there is need for no reference (NR) objective perceived image quality measurement techniques as conducting subjective experiments and making reference image available is a very difficult task. Very few NR perceived image quality measurement algorithms are available for color distortions like chromatic aberration (CA), color quantization with dither, and color saturation. We proposed NR image quality assessment (NR-IQA) algorithms for images distorted with CA. CA is mostly observed in images taken with digital cameras, having higher sensor resolution with inexpensive lenses. We compared our metric performance with two state-of-the-art NR blur techniques, one full reference IQA technique and three general-purpose NR-IQA techniques, although they are not tailored for CA. We used a CA dataset in the TID-2013 color image database to evaluate performance. Proposed algorithms give comparable performance with state-of-the-art techniques in terms of performance parameters and outperform them in terms of monotonicity and computational complexity. We have also discovered that the proposed CA algorithm best predicts perceived image quality of images distorted with realistic CA. (C) 2016 SPIE and IS&T
Building Resilience: An Art-Food Hub to Connect Local Communities

Resilience thinking is an appropriate framework when assessing the transitional potential of complex urban systems. The transformation of abandoned spaces into local hubs attracting new and innovative activities and events promotes a socioeconomic renaissance in urban communities, by stimulating adaptation to change, enhancing local resilience and strengthening urban-rural links. Under the conceptual umbrella of resilience thinking, the present study illustrates the outcomes of an integrated program of research-action aimed at urban regeneration in a medium-sized, economically disadvantaged city in Southern Italy (Battipaglia, Campania). The transformation of an abandoned building into an 'Art-Food Hub'-a multi-purpose and creative cultural space-based on resilience thinking was the specific case analyzed in our study. Appropriate stakeholders were identified and involved in a series of field activities and workshops, with the final objective of informing a comprehensive strategy strengthening awareness to change and capacity building. More specifically, stakeholder involvement was carried out with two aims: first, to make stakeholders active participants in co-designing a Strategic Urban Planning Document for Battipaglia and, second, to evaluate to what extent the proposed initiative contributes to building local resilience. By explicitly considering cross-scale drivers of community resilience, the results of this study show how the concept of resilience can be practically applied to policy formulation and implementation.
Foot/sandal prints and ovaliods in the rock art assemblage Ramat Matred, the Negev Desert, Israel

Studying the prints from Ramat Matred, their place within the different engraving phases and their relation to other motifs, abstracts, zoomorphs and inscriptions, several observations can be made referring to their date and possible meaning. Foot and sandal prints are not one of the most common motifs found in the Negev rock art. When examining the data globally, foot/sandal prints account for around 1-2% of images displayed. These prints present a rich variety of forms with many found clustered on a single panel. At Ramat Matred, prints are presented roughly ten times more often than at any other rock art site in the Negev with some 208 examples. These engravings are stylized, some represent feet, others sandals, though most are rather simplified elongated ovaloids with little other detail. Prints appear either as a single print or as a pair, with the feet/sandals set side-by-side or 'stepping' forward. Looking at the 'Islamic' rock art phase, the relationship between the foot/sandal prints and the formalized Arabic inscriptions, clarifies the cultural distinction and change in religious concepts and traditions that have occurred during the early historic period. (C) 2016 Elsevier Ltd. All rights reserved.
Understanding the influence of fluctuated low-temperature combined with high-humidity thawing on gelling properties of pork myofibrillar proteins

The present study further investigated the effects of fluctuated low-temperature combined with high-humidity thawing (FLHT) on the gelling properties of pork myofibrillar proteins (MPs). Results showed that compared with refrigerator thawing (RT) and low-temperature combined with high-humidity thawing (LHT), FLHT effectively reduced the protein aggregation and maintained the relative stability by decreasing the variation in the turbidity and absolute ζ-potential value. The rheological results confirmed its improved elastic gel network. Meanwhile, FLHT samples exhibited markedly higher WHC with lower cooking loss (P < 0.05). The whiteness and strength of MPs gel were significantly higher in the FLHT group (P < 0.05). Moreover, there was no difference in textural properties between FLHT samples and fresh meat (FS) (P > 0.05), due to its homogeneous and compact microstructure. Therefore, FLHT plays an essential role in holding a superior gel quality and a compact structure, thereby evidencing its potential application in meat thawing.
Molecular regulators of human blastocyst development and hatching: Their significance in implantation and pregnancy outcome

In humans, blastocyst hatching and implantation events are two sequential, critically linked and rate-limiting events for a prospective pregnancy. These events are regulated by embryo-endometrium derived molecular factors which include hormones, growth factors, cytokines, immune-modulators, cell adhesion molecules and proteases. Due to poor viability of blastocysts, they fail to hatch and implant, leading to a low 'Live Birth Rates', majorly contributing to infertility. Here, embryo-derived biomarkers analysis plays a key role to assess potential biological viability of blastocysts which are capable of implantation and prospective pregnancy. Thus far, embryo-derived biomarkers examined are mostly immune-modulators which are thought to be associated with blastocyst development-implantation and progression of pregnancy, leading to live births. There is an urgent need to develop a quantitative and a reliable non-invasive approach aiding embryo selection for elective single embryo transfer and to minimize recurrent pregnancy loss and multiple pregnancies. In this article, we provide a comprehensive review on our current knowledge and understanding of potential embryo-derived molecular regulators, that is, biomarkers, of development of human blastocysts, their hatching and implantation. We discuss their potential implications in the assessment of blastocyst implantation potential and pregnancy outcome in terms of live births in humans.
Advanced nano biosensors for rapid detection of zoonotic bacteria

An infectious disease that is transmitted from animals to humans and vice-versa is called zoonosis. Bacterial zoonotic diseases can re-emerge after they have been eradicated or controlled and are among the world's major health problems which inflict tremendous burden on healthcare systems. The first step to encounter such illnesses can be early and precise detection of bacterial pathogens to further prevent the following losses due to their infections. Although conventional methods for diagnosing pathogens, including culture-based, polymerase chain reaction-based, and immunological-based techniques, benefit from their advantages, they also have their own drawbacks, for example, taking long time to provide results, and requiring laborious work, expensive materials, and special equipment in certain conditions. Consequently, there is a greater tendency to introduce simple, innovative, quicker, accurate, and low-cost detection methods to effectively characterize the causative agents of infectious diseases. Biosensors, therefore, seem to practically be one of those novel promising diagnostic tools on this aim. These are effective and reliable elements with high sensitivity and specificity, that their usability can even be improved in medical diagnostic systems when empowered by nanoparticles. In the present review, recent advances in the development of several bio and nano biosensors, for rapid detection of zoonotic bacteria, have been discussed in details.
Linearized proximal alternating minimization algorithm for motion deblurring by nonlocal regularization

Non-blind motion deblurring problems are highly ill-posed and so it is quite difficult to find the original sharp and clean image. To handle ill-posedness of the motion deblurring problem, we use nonlocal total variation (abbreviated as TV) regularization approaches. Nonlocal TV can restore periodic textures and local geometric information better than local TV. But, since nonlocal TV requires weighted difference between pixels in the whole image, it demands much more computational resources than local TV. By using the linearization of the fidelity term and the proximal function, our proposed algorithm does not require any inversion of blurring operator and nonlocal operator. Therefore, the proposed algorithm is very efficient for motion deblurring problems. We compare the numerical performance of our proposed algorithm with that of several state-of-the-art algorithms for deblurring problems. Our numerical results show that the proposed method is faster and more robust than state-of-the-art algorithms on motion deblurring problems. (C) 2010 Elsevier Ltd. All rights reserved.