ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Thymoquinone ameliorates testicular tissue inflammation induced by chronic administration of oral sodium nitrite

Although sodium nitrite has been widely used as food preservative, building bases of scientific evidence about nitrite continues to oppose the general safety in human health. Moreover, thymoquinone (TQ) has therapeutic potential as antioxidant, anti-inflammatory, antibacterial and anticancer. Therefore, we investigated the effects of both sodium nitrite and TQ on testicular tissues of rats. Forty adult male Sprague Dawley rats were used. They received either 80 mg kg(-1) sodium nitrite or 50 mg kg(-1) TQ daily for twelve weeks. Serum testosterone was measured. Testis were weighed and the testicular tissue homogenates were used for measurements of tumour necrosis factor (TNF)-α, interleukin (IL)-1β, IL-4, IL-6, IL10, caspase-3, caspase-8 and caspase-9. Sodium nitrite resulted in significant reduction in serum testosterone concentration and elevation in testis weight and Gonado-Somatic Index. We found significant reduction in testicular tissues levels of IL-4 and IL-10 associated with elevated levels of TNF-α, IL-1β, IL-6, caspase-3, caspase-8 and caspase-9. In conclusion, chronic oral sodium nitrite induced changes in the weight of rat testis accompanied by elevation in the testicular tissue level of oxidative stress markers and inflammatory cytokines. TQ attenuated sodium nitrite-induced testicular tissue damage through blocking oxidative stress, restoration of normal inflammatory cytokines balance and blocking of apoptosis.
P2R2: Parallel Pseudo-Round-Robin arbiter for high performance NoCs

Networks-on-Chip (NoCs) play an important role in the performance of Chip Multi-Processors (CMPs). Providing the desired performance under heavy traffics imposed by some applications necessitates NoC routers to have a large number of Virtual Channels (VCs). Increasing the number of VCs, however, will add to the delay of the critical path of the arbitration logic, and hence restricts the clock frequency of the router. In order to make it possible to enjoy the benefits of having many VCs and keep the clock frequency as high as that of a low-VC router, we propose Parallel Pseudo-Round-Robin (P2R2) arbiter. Our proposal is based on processing multiple groups of requests in parallel. Our experimental results show that the proposed scheme can beat the state-of-the-art arbiter design by up to 12.5% and 6.8% in terms of saturation rate and zero-load latency, respectively, under synthetic traffic patterns. These results also demonstrate a 29.5% improvement in average packet latency in Splash-2 applications in favor of P2R2 with respect to the state-of-the-art arbiter. (C) 2014 Elsevier B.V. All rights reserved.
Fast Compression of Large Semantic Web Data Using X10

The Semantic Web comprises enormous volumes of semi-structured data elements. For interoperability, these elements are represented by long strings. Such representations are not efficient for the purposes of applications that perform computations over large volumes of such information. A common approach to alleviate this problem is through the use of compression methods that produce more compact representations of the data. The use of dictionary encoding is particularly prevalent in Semantic Web database systems for this purpose. However, centralized implementations present performance bottlenecks, giving rise to the need for scalable, efficient distributed encoding schemes. In this paper, we propose an efficient algorithm for fast encoding large Semantic Web data. Specially, we present the detailed implementation of our approach based on the state-of-art asynchronous partitioned global address space (APGAS) parallel programming model. We evaluate performance on a cluster of up to 384 cores and datasets of up to 11 billion triples (1.9 TB). Compared to the state-of-art approach, we demonstrate a speed-up of 2.6 - 7.4x and excellent scalability. In the meantime, these results also illustrate the significant potential of the APGAS model for efficient implementation of dictionary encoding and contributes to the engineering of more efficient, larger scale Semantic Web applications.
Highly Robust Observer Sliding Mode Based Frequency Control for Multi Area Power Systems with Renewable Power Plants

This paper centers on the design of highly robust observer sliding mode (HROSM)-based load frequency and tie-power control to compensate for primary frequency control of multi-area interconnected power systems integrated with renewable power generation. At first, the power system with external disturbance is model in the state space form. Then the state observer is used to estimate the system states which are difficult or expensive to measure. Secondly, the sliding mode control (SMC) is designed with a new single phase sliding surface (SPSS). In addition, the whole system asymptotic stability is proven with Lyapunov stability theory based on the linear matrix inequality (LMI) technique. The new SPSS without reaching time guarantees rapid convergence of high transient frequency, tie-power change as well as reduces chattering without loss of accuracies. Therefore, the superiority of modern state-of-the-art SMC-based frequency controllers relies on good practical application. The experimental simulation results on large interconnected power systems show good performance and high robustness against external disturbances when compared with some modern state of art controllers in terms of overshoots and settling time.
Hypertension and Dyslipidaemia in Argentina: Patient Journey Stages

Cardiovascular disease (CVD) leads to one-third of all deaths in Argentina. To implement patient-centric strategies for reducing CVD burden, available data on hypertension and hypercholesterolemia patients at different stages of their journey: awareness, screening, diagnosis, treatment, adherence, and control were analysed. A semi-systematic review in peer-reviewed databases (EMBASE and MEDLINE) and unstructured sources such as Google Scholar, Argentine Ministry of Health, and World Health Organization websites was conducted till 06.07.2021 for hypertension and dyslipidemia. English articles published in 2010-2021, depicting patient journey data for hypertension or hypercholesterolemia of the nationally representative adult population of Argentina were included. Thesis abstracts, letters to the editor, editorials, and case studies were excluded. No limits were used for unstructured sources. Weighted or simple means were estimated for patient journey stages. Out of 296 and 1257 articles retrieved for hypertension and hypercholesterolemia, respectively, five articles were retained for each of the conditions. The estimates for hypertension and hypercholesterolemia, respectively, were 46.6% and 30.7% for prevalence, 61.6% and 37.3% for awareness, 97.5% and ≥80% for screening, 64.1% and 28.9% for diagnosis, and 49.7% and 36.6% for treatment, and 19.9% and 20% for overall control. Adherence data were not available for hypercholesterolemia, while the same for hypertension was 50.4%. Various determinants are responsible for low adherence such as patient-level barriers, physician-related barriers, and health system-related issues. The review reveals that hypertension and hypercholesterolemia are poorly controlled in Argentina. Although further studies with more accurate data are needed to confirm these results, they should alert the medical community and the public health institutions to take urgent corrective actions.
Interfacing Power System and ICT Simulators: Challenges, State-of-the-Art, and Case Studies

With the transition toward a smart grid, the power system has become strongly intertwined with the information and communication technology (ICT) infrastructure. The interdependency of both domains requires a combined analysis of physical and ICT processes, but simulating these together is a major challenge due to the fundamentally different modeling and simulation concepts. After outlining these challenges, such as time synchronization and event handling, this paper presents an overview of state-of-the-art solutions to interface power system and ICT simulators. Due to their prominence in recent research, a special focus is set on co-simulation approaches and their challenges and potentials. Further, two case studies analyzing the impact of ICT on applications in power system operation illustrate the necessity of a holistic approach and show the capabilities of state-of-the-art co-simulation platforms.
Reduced search space mechanism for solving constrained optimization problems

Over the last few decades, a considerable number of evolutionary algorithms (EAs) have been proposed for solving constrained optimization problems (COPs). As for most of these problems, the optimal solution exists on the boundary of the feasible space, we aim to focus the search process around the boundary. In this paper a new concept, called reduced search space (R2S), is introduced. In the process, we first identify active constraints, based on the current solutions, and then define R2S around those constraint's boundaries. However, the search may be conducted either in the entire R2S or in some portions of it. To judge the impact of this concept, we have incorporated it with a number of state-of-the-art algorithms, and we have comprehensively tested it on three sets of benchmark test functions, namely, 24 test functions taken from IEEE CEC2006, 18 test functions with 10D and 301) taken from IEEE CEC2010 and 10 test functions taken from IEEE CEC2011. The results show that our proposed mechanism significantly improves the performances of state-of-the-art algorithms. (C) 2017 Elsevier Ltd. All rights reserved.
Compressive Sensing and Recovery for Binary Images

We propose a method for compressive sensing and recovery of binary images. To achieve this, we combine two ideas: in the sensing step, ordered aperture patterns are employed instead of random aperture patterns, and in the recovery step, a dense reconstruction scheme replaces sparse reconstruction. We demonstrate that this approach is more effective for binary images than the state-of-the-art algorithms relying on random sensing and sparse reconstruction.
Facial reanimation using free partial latissimus dorsi muscle transfer: Single versus dual innervation method

The aim of the present study was to analyze the consequences of partial free latissimus dorsi muscle flap with nerve splitting technique (Partial LD transfer) for facial reanimation and compare outcomes according to innervation method (singer versus dual innervation). Patients with complete unilateral facial paralysis underwent either the single (ipsilateral masseteric nerve only) or dual (ipsilateral masseteric nerve plus contralateral buccal branch of the facial nerve) nerve innervation method for facial reanimation. An assessment was carried out to compare the outcomes between the single and dual innervation. Total of 21 patients were involved in this study. In the single innervation group, 7 out of 8 patients developed a voluntary smile. However, none were able to achieve a spontaneous smile. On the other hand, 9 out of 13 patients developed a voluntary smile and 3 out of 13 patients achieved a spontaneous smile. The mean increases of smile excursion assessed by Emotrics software and Terzis grades showed no significant differences between two groups. Within the limitations of the study it seems that partial LD transfer approach utilizing the dual innervation method has a positive effect on achieving a spontaneous smile and could be a valuable option for facial reanimation.
Transcriptome Analysis of Protein Kinase MoCK2, which Affects Acetyl-CoA Metabolism and Import of CK2-Interacting Mitochondrial Proteins into Mitochondria in the Rice Blast Fungus Magnaporthe oryzae

The rice pathogen Magnaporthe oryzae causes severe losses to rice production. Previous studies have shown that the protein kinase MoCK2 is essential for pathogenesis, and this ubiquitous eukaryotic protein kinase might affect several processes in the fungus that are needed for infection. To better understand which cellular processes are affected by MoCK2 activity, we performed a detailed transcriptome sequencing analysis of deletions of the MoCK2 b1 and b2 components in relation to the background strain Ku80 and connected this analysis with the abundance of substrates for proteins in a previous pulldown of the essential CKa subunit of CK2 to estimate the effects on proteins directly interacting with CK2. The results showed that MoCK2 seriously affected carbohydrate metabolism, fatty acid metabolism, amino acid metabolism, and the related transporters and reduced acetyl-CoA production. CK2 phosphorylation can affect the folding of proteins and especially the effective formation of protein complexes by intrinsically disordered or mitochondrial import by destabilizing soluble alpha helices. The upregulated genes found in the pulldown of the b1 and b2 mutants indicate that proteins directly interacting with CK2 are compensatorily upregulated depending on their pulldown. A similar correlation was found for mitochondrial proteins. Taken together, the classes of proteins and the changes in regulation in the b1 and b2 mutants suggest that CK2 has a central role in mitochondrial metabolism, secondary metabolism, and reactive oxygen species (ROS) resistance, in addition to its previously suggested role in the formation of new ribosomes, all of which are processes central to efficient nonself responses as innate immunity. IMPORTANCE The protein kinase CK2 is highly expressed and essential for plants, animals, and fungi, affecting fatty acid-related metabolism. In addition, it directly affects the import of essential mitochondrial proteins into mitochondria. These effects mean that CK2 is essential for lipid metabolism and mitochondrial function and, as shown previously, is crucial for making new translation machinery proteins. Taken together, our new results combined with previously reported results indicate that CK2 is an essential protein necessary for the capacities to launch efficient innate immunity responses and withstand the negative effects of such responses necessary for general resistance against invading bacteria and viruses as well as to interact with plants, withstand plant immunity responses, and kill plant cells.
Multimodal Target Detection by Sparse Coding: Application to Paint Loss Detection in Paintings

Sparse representation based methods have demonstrated their superior performance in target detection tasks compared to more traditional approaches such as matched subspace detectors and adaptive subspace detectors. However, the existing sparsity-based target detection methods were mostly formulated for and validated on a single imaging modality (sometimes with multiple spectral bands). In many application domains, including art investigation, multimodal data, acquired by different sensors are readily available, and yet, efficient processing techniques for such data are still scarce. In this paper, we propose a sparsity-based multimodal target detection method that processes jointly the information from multiple imaging modalities in a kernel feature space, and making use of the spatial context. We develop our target detector such to be robust to errors in labelled data, which is especially important in applications like digital painting analysis, where pixel-wise manual annotations are unreliable. We apply the proposed method to a challenging application of paint loss detection in master paintings and we demonstrate its effectiveness on a case study with multimodal acquisitions of the Ghent Altarpiece.
Highly polarized single-chip ELED sources using oppositely strained MQW emitters and absorbers

Integrated polarizer components with polarization extinctions >40 dB are desirable for state-of-the-art photonic integrated circuits. We demonstrate >60-dB polarization extinction from a single-chip InGaAsP-InP broadband source by combining an edge light-emitting diode consisting of compressively strained quantum wells (QWs) with an absorber consisting of tensile strained QWs. A 600-pm polarizer exhibits only 5 dB of insertion loss.
How has the state-of-the-art for quantification of landscape pattern advanced in the twenty-first century?

Context Landscape ecology was founded on the idea that there is a reciprocal relationship between spatial pattern and ecological processes. I provide a retrospective look at how the state-of-the-art of landscape pattern analysis has changed since 1998. Objectives My objective is to show how pattern analysis techniques have evolved and identify some of the key lessons learned. Results The state-of-the-art in 1998 was derived from information theory, fractal geometry, percolation theory, hierarchy theory and graph theory, relying heavily on the island-patch conceptual model using categorical maps, although point-data analysis methods were actively being explored. We have gradually winnowed down the list of fundamental components of spatial pattern, and have clarified the appropriate and inappropriate use of landscape metrics for research and application. We have learned to let the objectives choose the metric, guided by the scale and nature of the ecological process of interest. The use of alternatives to the binary patch model (such as gradient analysis) shows great promise to advance landscape ecological knowledge. Conclusions The patch paradigm is often of limited usefulness, and other ways to represent the pattern of landscape properties may reveal deeper insights. The field continues to advance as illustrated by papers in this special issue.
Radial Basis Functions With Adaptive Input and Composite Trend Representation for Portfolio Selection

We propose a set of novel radial basis functions with adaptive input and composite trend representation (AICTR) for portfolio selection (PS). Trend representation of asset price is one of the main information to be exploited in PS. However, most state-of-the-art trend representation-based systems exploit only one kind of trend information and lack effective mechanisms to construct a composite trend representation. The proposed system exploits a set of RBFs with multiple trend representations, which improves the effectiveness and robustness in price prediction. Moreover, the input of the RBFs automatically switches to the best trend representation according to the recent investing performance of different price predictions. We also propose a novel objective to combine these RBFs and select the portfolio. Extensive experiments on six benchmark data sets (including a new challenging data set that we propose) from different real-world stock markets indicate that the proposed RBFs effectively combine different trend representations and AICTR achieves state-of-the-art investing performance and risk control. Besides, AICTR withstands the reasonable transaction costs and runs fast; hence, it is applicable to real-world financial environments.
Deep learning-regularized, single-step quantitative susceptibility mapping quantification

The purpose of the current study was to develop deep learning-regularized, single-step quantitative susceptibility mapping (QSM) quantification, directly generating QSM from the total phase map. A deep learning-regularized, single-step QSM quantification model, named SS-POCSnet, was trained with datasets created using the QSM synthesis approach in QSM reconstruction challenge 2.0. In SS-POCSnet, a data fidelity term based on a single-step model was iteratively applied that combined the spherical mean value kernel and dipole model. Meanwhile, SS-POCSnet regularized susceptibility maps, avoiding underestimating susceptibility values. We evaluated the SS-POCSnet on 10 synthetic datasets, 24 clinical datasets with lesions of cerebral microbleed (CMB) and calcification, and 10 datasets with multiple sclerosis (MS).On synthetic datasets, SS-POCSnet showed the best performance among the methods evaluated, with a normalized root mean squared error of 37.3% ± 4.2%, susceptibility-tuned structured similarity index measure of 0.823 ± 0.02, high-frequency error norm of 37.0 ± 5.7, and peak signal-to-noise ratio of 42.8 ± 1.1. SS-POCSnet also reduced the underestimations of susceptibility values in deep brain nuclei compared with those from the other models evaluated. Furthermore, SS-POCSnet was sensitive to CMB/calcification and MS lesions, demonstrating its clinical applicability. Our method also supported variable imaging parameters, including matrix size and resolution. It was concluded that deep learning-regularized, single-step QSM quantification can mitigate underestimating susceptibility values in deep brain nuclei.
Facial image super-resolution guided by adaptive geometric features

This paper addresses the traditional issue of restoring a high-resolution (HR) facial image from a low-resolution (LR) counterpart. Current state-of-the-art super-resolution (SR) methods commonly adopt the convolutional neural networks to learn a non-linear complex mapping between paired LR and HR images. They discriminate local patterns expressed by the neighboring pixels along the planar directions but ignore the intrinsic 3D proximity including the depth map. As a special case of general images, the face has limited geometric variations, which we believe that the relevant depth map can be learned and used to guide the face SR task. Motivated by it, we design a network including two branches: one for auxiliary depth map estimation and the other for the main SR task. Adaptive geometric features are further learned from the depth map and used to modulate the mid-level features of the SR branch. The whole network is implemented in an end-to-end trainable manner under the extra supervision of depth map. The supervisory depth map is either a paired one from RGB-D scans or a reconstructed one by a 3D prior model of faces. The experiments demonstrate the effectiveness of the proposed method and achieve improved performance over the state of the arts.
Methodology focused on the selection of construction operations for the standardization of work with an emphasis on the occupational safety criterion

This article indicates that work standardization is an effective tool for the improvement of occupational safety in any process performed by people. Work standardization has an impact on improving productivity, quality and engaging employees in improving current working methods. The article shows that in the construction industry there are problems concerning the selection of operations for work standardization due to the specificity of the operations performed there. As a result, work standardization is not a common methodology used in the construction industry, which may be one of the reasons for the greater number of accidents and near misses when compared to the manufacturing industry. The article presents the author's safety-complication-frequency (SCF) model for the selection of operations for work standardization, which is dedicated to the construction industry. The SCF model enables operations with the highest priority in terms of implementation for work standardization to be selected.