ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Kinematic Analysis of Mae-Geri Kicks in Beginner and Advanced Kyokushin Karate Athletes

Background: Each of the techniques used in sport is a complex technique requiring a combination of neuromuscular conduction, motor anticipation, and extremely developed proprioception. This is especially the case in martial arts when we deal with a kick or a blow to a specific target. Methods: The main purpose of this study was to determine the kinematic differences in the tested movement pattern among athletes with different levels of advancement in the conditions of kicking: in the air, at a target (a shield), and in direct contact with a competitor. Comparative analysis was performed among 26 players: 13 advanced (group G1) and 13 beginners (group G2). Kinematic data was recorded using an optical motion capture system. The examination consisted of performing three tests of mae-geri kick in sequences of three kicks in three different conditions (without a target, with a static target, and with an opponent). The examination was performed with the back leg and only the moment of kick was analyzed. Results: The most significant differences were observed in the movement of head, torso, hip, knee, and ankle segments, especially during a kick at a shield. Based on the conducted analysis, we can assume that karate training changes the strategy of neuromuscular control, promoting improvement of mobility pattern efficiency. Conclusion: Acquiring this type of knowledge can lead to better results, elimination of errors in training, especially in the initial period of training, and the prevention of possible injuries that occur during exercise or competition.
A Hybrid Approach for Segmentation and Tracking of Myxococcus Xanthus Swarms

Cell segmentation and motion tracking in time-lapse images are fundamental problems in computer vision, and are also crucial for various biomedical studies. Myxococcus xanthus is a type of rod-like cells with highly coordinated motion. The segmentation and tracking of M. xanthus are challenging, because cells may touch tightly and form dense swarms that are difficult to identify individually in an accurate manner. The known cell tracking approaches mainly fall into two frameworks, detection association and model evolution, each having its own advantages and disadvantages. In this paper, we propose a new hybrid framework combining these two frameworks into one and leveraging their complementary advantages. Also, we propose an active contour model based on the Ribbon Snake, which is seamlessly integrated with our hybrid framework. Evaluated by 10 different datasets, our approach achieves considerable improvement over the state-of-the-art cell tracking algorithms on identifying complete cell trajectories, and higher segmentation accuracy than performing segmentation in individual 2D images.
Mechano-regulation of gap junction communications between tendon cells is dependent on the magnitude of tensile strain

Large magnitudes of mechanical strain applied to tendon cells induce catabolic and inflammatory responses, whereas a moderate level of strain promotes anabolism. Gap junction intercellular communication (GJIC) plays an essential role in these responses, however direct regulation of GJIC by mechanical loading has not been characterised in detail. Here, we show that the GJIC between tenocytes are enhanced or inhibited depending on the magnitude of the tensile strain. The GJIC was analysed using fluorescence loss in photobleaching (FLIP), combined with a molecular diffusion model. Intercellular and intracellular transport of fluorescence tracer molecules, calcein, across multiple cells through the gap junctions was evaluated by determining the intercellular and intracellular diffusion coefficients of calcein. It was demonstrated that the intercellular diffusion coefficient was significantly higher when the cells were subjected to a physiological static tensile strain (4%) for 1 h, but significantly lower when subjected to a strain with non-physiological amplitude (8%). The intracellular diffusion coefficient was not altered by the application of static strain at any level. Connexin 43 proteins were localised within cytoplasm and at cell-cell boundaries in no strained state and were also localised near cell nuclei by the 4% strain, but the localisation was reduced by the 8% strain. The findings suggest that the increase in GJIC in response to 4% strain involves opening of gap junction pores via mechanotransduction events of tenocytes, whereas the inhibition in response to 8% strain involves mechanical disruption of the junctions.
On maximization of the modularity index in network psychometrics

The modularity index (Q) is an important criterion for many community detection heuristics used in network psychometrics and its subareas (e.g., exploratory graph analysis). Some heuristics seek to directly maximize Q, whereas others, such as the walktrap algorithm, only use the modularity index post hoc to determine the number of communities. Researchers in network psychometrics have typically not employed methods that are guaranteed to find a partition that maximizes Q, perhaps because of the complexity of the underlying mathematical programming problem. In this paper, for networks of the size commonly encountered in network psychometrics, we explore the utility of finding the partition that maximizes Q via formulation and solution of a clique partitioning problem (CPP). A key benefit of the CPP is that the number of communities is naturally determined by its solution and, therefore, need not be prespecified in advance. The results of two simulation studies comparing maximization of Q to two other methods that seek to maximize modularity (fast greedy and Louvain), as well as one popular method that does not (walktrap algorithm), provide interesting insights as to the relative performances of the methods with respect to identification of the correct number of communities and the recovery of underlying community structure.
Benthic studies adjacent to Sakhalin Island, Russia, 2015 I: benthic biomass and community structure in the nearshore gray whale feeding area

Okhotsk or western gray whales feed in summer along the northeastern coast of Sakhalin Island, Russia, a region with oil and gas extraction facilities. Seismic surveys increased sound levels in the nearshore feeding area in 2015 for part of the summer, potentially displacing whales from preferred foraging habitat or reducing foraging efficiency. Since lost foraging opportunities might lead to vital rate effects on this endangered species, detailed benthic surveys were conducted to characterize benthic community biomass patterns and spatial and temporal differences. Benthic biomass demonstrated strong spatial-temporal interactions indicating that prey biomass differences among locations were dependent on sampling period. Of greatest interest, Amphipoda biomass declined from June to October in the northern and southern portions of the nearshore study area but increased in the middle and Actinopterygii biomass increased in the northern area in mid-summer. Water depth and sediment type were significant covariates with community structure, and water depth strongly covaried with bivalve biomass. Total average prey biomass was ~ 100 g/m2 within the nearshore feeding area with no evidence of reduced biomass among sampling periods or locations, although there were fewer amphipods in the south. Multi-prey investigations provide a stronger basis for inferences than single-prey studies of amphipods when gray whales feed on diverse prey. Benthic community-level variability was moderate to high as would be expected for a shallow-water nearshore area. Overall, spatial and temporal changes in dominant macrofauna biomass reflected small to medium-sized effects that were well within the natural boundaries expected for benthic communities.
Semi-Supervised Capsule cGAN for Speckle Noise Reduction in Retinal OCT Images

Speckle noise is the main cause of poor optical coherence tomography (OCT) image quality. Convolutional neural networks (CNNs) have shown remarkable performances for speckle noise reduction. However, speckle noise denoising still meets great challenges because the deep learning-based methods need a large amount of labeled data whose acquisition is time-consuming or expensive. Besides, many CNNs-based methods design complex structure based networks with lots of parameters to improve the denoising performance, which consume hardware resources severely and are prone to overfitting. To solve these problems, we propose a novel semi-supervised learning based method for speckle noise denoising in retinal OCT images. First, to improve the model's ability to capture complex and sparse features in OCT images, and avoid the problem of a great increase of parameters, a novel capsule conditional generative adversarial network (Caps-cGAN) with small number of parameters is proposed to construct the semi-supervised learning system. Then, to tackle the problem of retinal structure information loss in OCT images caused by lack of detailed guidance during unsupervised learning, a novel joint semi-supervised loss function composed of unsupervised loss and supervised loss is proposed to train the model. Compared with other state-of-the-art methods, the proposed semi-supervised method is suitable for retinal OCT images collected from different OCT devices and can achieve better performance even only using half of the training data.
Freestanding 3D-interconnected carbon nanofibers as high-performance transducers in miniaturized electrochemical sensors

3D-carbon nanomaterials have proven to be high-performance transducers in electrochemical sensors but their integration into miniaturized devices is challenging. Herein, we develop printable freestanding laser-induced carbon nanofibers (f-LCNFs) with outstanding analytical performance that furthermore can easily allow such miniaturization through a paper-based microfluidic strategy. The f-LCNF electrodes were generated from electrospun polyimide nanofibers and one-step laser carbonization. A three-electrode system made of f-LCNFs exhibited a limit of detection (LOD) as low as 1 nM (S/N = 8) for anodic stripping analysis of silver ions, exhibiting the peak at ca. 100 mV vs f-LCNFs RE, without the need of stirring. The as-described system was implemented in miniaturized devices via wax-based printing, in which their electroanalytical performance was characterized for both outer- and inner-sphere redox markers and then applied to the detection of dopamine (the peak appeared at ca. 200 mV vs f-LCNFs RE) with a remarkable LOD of 55 pM. When modified with Nafion, the f-LCNFs were highly selective to dopamine even against high concentrations of uric and ascorbic acids. Especially the integration into closed microfluidic systems highlights the strength 3D porous structures provides excellent analytical performance paving the way for their translation to affordable lab-on-a-chip devices where mass-production capability, unsophisticated fabrication techniques, transfer-free, and customized electrode designs can be realized.
Realistic Lung Nodule Synthesis With Multi-Target Co-Guided Adversarial Mechanism

The important cues for a realistic lung nodule synthesis include the diversity in shape and background, controllability of semantic feature levels, and overall CT image quality. To incorporate these cues as the multiple learning targets, we introduce the Multi-Target Co-Guided Adversarial Mechanism, which utilizes the foreground and background mask to guide nodule shape and lung tissues, takes advantage of the CT lung and mediastinal window as the guidance of spiculation and texture control, respectively. Further, we propose a Multi-Target Co-Guided Synthesizing Network with a joint loss function to realize the co-guidance of image generation and semantic feature learning. The proposed network contains a Mask-Guided Generative Adversarial Sub-Network (MGGAN) and a Window-Guided Semantic Learning Sub-Network (WGSLN). The MGGAN generates the initial synthesis using the mask combined with the foreground and background masks, guiding the generation of nodule shape and background tissues. Meanwhile, the WGSLN controls the semantic features and refines the synthesis quality by transforming the initial synthesis into the CT lung and mediastinal window, and performing the spiculation and texture learning simultaneously. We validated our method using the quantitative analysis of authenticity under the Frechet Inception Score, and the results show its state-of-the-art performance. We also evaluated our method as a data augmentation method to predict malignancy level on the LIDC-IDRI database, and the results show that the accuracy of VGG-16 is improved by 5.6%. The experimental results confirm the effectiveness of the proposed method.
Similar in vitro response of rat brain nerve terminals, colon preparations and COLO 205 cells to smoke particulate matter from different types of wood

Major source of carbon-containing air born particular matter that significantly pollutes environment and provokes development of neuropathology is forest fires and wood combustion. Here, water-suspended smoke particulate matter preparations (SPs) were synthesized from birch, pine, poplar wood, and also birch bark and pine needles. Taking into account importance of the gut-brain communication system, SP properties were compared regarding their capability to modulate functioning of nerve terminals and gut cells/preparations. In cortex nerve terminals, poplar wood SP was more effective in decreasing uptake and increasing the extracellular levels of excitatory and inhibitory neurotransmitters L-[14C]glutamate and [3H]GABA, respectively. Spontaneous and H2O2-stimulated ROS generation in nerve terminals decreased by SPs, the most efficient one was from poplar wood. SPs from birch, pine and poplar wood caused membrane depolarization, poplar wood SP effect was 5-fold higher vs. birch and pine wood ones. Functional characteristics of gut cells/preparations, which tightly related to nerve terminal experiments, were assessed. SPs increased paracellular permeability of proximal colon mucosal-submucosal preparations monitored in Ussing chamber system (FITC-dextran, 4 kDa), where the most prominent effect had poplar wood SP. The latter demonstrated more considerable influence on COLO 205 cell causing 30 % loss of cell viability. PM emitted to the environment during combustion of various wood caused similar unidirectional harmful effects on brain and gut cell functioning, thereby triggering development of pathologies in gut and brain and gut-brain communication system.
A multi-approach to community question answering

In this paper we face the problem of Community Question Answering for the Arabic language. In this setting, a member of the community posts an initial query expressed in Natural Language. Other participants post their own interventions: answers, comments, additional questions, etc. contributing to building a rather tangled thread of nodes containing Natural Language short texts. The task consists in answering the initial query using the thread as the space of possible answers. The task can be approached as a classification, a regression or a ranking problem. In our case we select the set of possible candidates, we assign a relevance score to each candidate and we rank them accordingly. We propose a bunch of unsupervised models and show that a model based on Latent Semantic Indexing approach outperforms state of the art models for this task. We also use transfer learning to power the embeddings layer of various deep learning models and prove that the pairwise approaches outperform their pointwise counterparts. All the proposed models have been evaluated on Semeval 2017 Task 3 Subtask D: Arabic Community Question Answering and achieve state of the art or near performance. (C) 2019 Elsevier Ltd. All rights reserved.
Bone Marrow Grafts From Pediatric Donors May Contain A Considerable Number of Hematogones

During CD34 + stem cell count to determine the number of stem cells in the allografts from pediatric donors, we noticed a considerable amount of early hematogones (eHGs) within the stem cell gate in flow cytometry. Since the number of hematogones causes a decrease in the total number of stem cells counted within the graft, we planned a retrospective study to analyze the effect of eHGs on transplant outcomes. We also wanted to show how allografts containing high amounts of early HGs affect transplant outcomes. Quantification of CD34 numbers and the number of eHGs were determined by flow cytometry. Patients were divided into 2 groups according to the number of CD 34+ cells calculated after subtracting the number of hematogones within the allograft. Those who received < 2 × 106/kg CD34+ cells and ≥ 2 × 106/kg were defined as group 1 and 2, respectively. Twenty-six patients and their 26 donors were included in the study. The median age of patients was 6.5 years and 5.4 years in Group 1 and 2, respectively. The median donor age was 9 years in Group 1 and 7 years in Group 2. The ages and genders were similar in the two groups (p > 0.05). The number of nucleated cells given to both groups was not different. The number of early hematogones given to both groups was similar (p = 0.93). The mean times to myeloid and platelet engraftments were also similar in the two groups. In this study, we provided trilineage engraftment to all patients in two groups. We could not find a considerable effect of these eHGs in myeloid and platelet engraftments. However, the number of patients included in our study is low, therefore we suggest a study including a large number of donors in order to confirm our findings.
Automated and Interactive Lesion Detection and Segmentation in Uterine Cervix Images

This paper presents a procedure for automatic extraction and segmentation of a class-specific object (or region) by learning class-specific boundaries. We describe and evaluate the method with a specific focus on the detection of lesion regions in uterine cervix images. The watershed segmentation map of the input image is modeled using a Markov random field (MRF) in which watershed regions correspond to binary random variables indicating whether the region is part of the lesion tissue or not. The local pairwise factors on the arcs of the watershed map indicate whether the arc is part of the object boundary. The factors are based on supervised learning of a visual word distribution. The final lesion region segmentation is obtained using a loopy belief propagation applied to the watershed arc-level MRF. Experimental results on real data show state-of-the-art segmentation results on this very challenging task that, if necessary, can be interactively enhanced.
2D Orthogonal Locality Preserving Projection for Image Denoising

Sparse representations using transform-domain techniques are widely used for better interpretation of the raw data. Orthogonal locality preserving projection (OLPP) is a linear technique that tries to preserve local structure of data in the transform domain as well. Vectorized nature of OLPP requires high-dimensional data to be converted to vector format, hence may lose spatial neighborhood information of raw data. On the other hand, processing 2D data directly, not only preserves spatial information, but also improves the computational efficiency considerably. The 2D OLPP is expected to learn the transformation from 2D data itself. This paper derives mathematical foundation for 2D OLPP. The proposed technique is used for image denoising task. Recent state-of-the-art approaches for image denoising work on two major hypotheses, i.e., non-local self-similarity and sparse linear approximations of the data. Locality preserving nature of the proposed approach automatically takes care of self-similarity present in the image while inferring sparse basis. A global basis is adequate for the entire image. The proposed approach outperforms several state-of-the-art image denoising approaches for gray-scale, color, and texture images.
Integrity Monitoring of GNSS/INS Based Positioning Systems for Autonomous Vehicles: State-of-the-Art and Open Challenges

Positioning and navigation are critical functions of automated driving functions, which help autonomous vehicles determine their absolute and relative positions in the environment that they operate. Integrity Monitoring (IM) systems, which are intended to assess the reliability and trustworthiness of the information provided by the navigation systems, are crucial for ensuring the safety of automated driving functions. This paper provides a comprehensive review of the existing IM frameworks for safety-critical navigation applications and expands on the state-of-the-art of the most recent development of such systems for connected automated vehicles. We mainly focus on IM methods for Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, we also cover IM for map assisted and wireless signal augmented navigation systems, which are promising for high-performance navigation applications, such as automated driving functions. For each main category of solutions, key aspects such as the characteristics of measurement errors and faults related to various data sources are discussed to provide deeper insights into designing of reliable IM systems. Also, some of the major open research challenges to the best knowledge of the authors have been identified and discussed.
Dating the Paleolithic: Trapped charge methods and amino acid geochronology

Despite the vast array of different geochronological tools available, dating the Paleolithic remains one of the discipline's greatest challenges. This review focuses on two different dating approaches: trapped charge and amino acid geochronology. While differing in their fundamental principles, both exploit time-dependent changes in signals found within crystals to generate a chronology for the material dated and hence, the associated deposits. Within each method, there is a diverse range of signals that can be analyzed, each covering different time ranges, applicable to different materials and suitable for different paleoenvironmental and archaeological contexts. This multiplicity of signals can at first sight appear confusing, but it is a fundamental strength of the techniques, allowing internal checks for consistency and providing more information than simply a chronology. For each technique, we present an overview of the basis for the time-dependent signals and the types of material that can be analyzed, with examples of their archaeological application, as well as their future potential.
Urban climate and resiliency: A synthesis report of state of the art and future research directions

The Urban Climate and Resiliency-Science Working Group (i.e., The WG) was convened in the summer of 2018 to explore the scientific grand challenges related to climate resiliency of cities. The WG leveraged the presentations at the 10th International Conference on Urban Climate (ICUC10) held in New York City (NYC) on 6-10 August 2018 as input forum. ICUC10 was a collaboration between the International Association of Urban Climate, American Meteorological Society, and World Meteorological Organization. It attracted more than 600 participants from more than 50 countries, resulting in close to 700 oral and poster presentations under the common theme of "Sustainable & Resilient Urban Environments". ICUC10 covered topics related to urban climate and weather processes with far-reaching implications to weather forecasting, climate change adaptation, air quality, health, energy, urban planning, and governance. This article provides a synthesis of the analysis of the current state of the art and of the recommendations of the WG for future research along each of the four Grand Challenges in the context of urban climate and weather resiliency; Modeling, Observations, Cyber-Informatics, and Knowledge Transfer & Applications.