ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Segmentation of Vasculature From Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images

Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods; however, particularly, in the study of tumors, it is of particular interest to be able to visualize both the perfused and the non-perfused vasculature. Imaging vasculature by highlighting the endothelium provides a way to separate the morphology of vasculature from the potentially confounding factor of perfusion. Here, we present a method for the segmentation of tumor vasculature in 3D fluorescence microscopic images using signals from the endothelial and surrounding cells. We show that our method can provide complete and semantically meaningful segmentations of complex vasculature using a supervoxel-Markov random field approach. We show that in terms of extracting meaningful segmentations of the vasculature, our method outperforms both state-of-the-art method, specific to these data, as well as more classical vasculature segmentation methods.
Neuroplasticity and MRI: A perfect match

Numerous studies have illustrated the benefits of physical workout and cognitive exercise on brain function and structure and, more importantly, on decelerating cognitive decline in old age and promoting functional rehabilitation following injury. Despite these behavioral observations, the exact mechanisms underlying these neuroplastic phenomena remain obscure. This gap illustrates the need for carefully designed in-depth studies using valid models and translational tools which allow to uncover the observed events up to the molecular level. We promote the use of in vivo magnetic resonance imaging (MRI) because it is a powerful translational imaging technique able to extract functional, structural, and biochemical information from the entire brain. Advanced processing techniques allow performing voxel-based analyses which are capable of detecting novel loci implicated in specific neuroplastic events beyond traditional regions-of-interest analyses. In addition, its non-invasive character sets it as currently the best global imaging tool for performing dynamic longitudinal studies on the same living subject, allowing thus exploring the effects of experience, training, treatment etc. in parallel to additional measures such as age, cognitive performance scores, hormone levels, and many others. The aim of this review is (i) to introduce how different animal models contributed to extend the knowledge on neuroplasticity in both health and disease, over different life stages and upon various experiences, and (ii) to illustrate how specific MRI techniques can be applied successfully to inform on the fundamental mechanisms underlying experience-dependent or activity-induced neuroplasticity including cognitive processes.
Automatic Resonance Tuning Technique for an Ultra-Broadband Piezoelectric Energy Harvester

The main drawback of energy harvesting using the piezoelectric direct effect is that the maximum electric power is generated at the fundamental resonance frequency. This can clearly be observed in the size and dimensions of the components of any particular energy harvester. In this paper, we are investigating a new proposed energy harvesting device that employs the Automatic Resonance Tuning (ART) technique to enhance the energy harvesting mechanism. The proposed harvester is composed of a cantilever beam and sliding masse with varying locations. ART automatically adjusts the energy harvester's natural frequency according to the ambient vibration natural frequency. The ART energy harvester modifies the natural frequency of the harvester using the motion of the mobile (sliding) mass. An analytical model of the proposed model is presented. The investigation is conducted using the Finite Element Method (FEM). THE FEM COMSOL model is successfully validated using previously published experimental results. The results of the FEM were compared with the experimental and analytical results. The validated model is then used to demonstrate the displacement profile, the output voltage response, and the natural frequency for the harvester at different mass positions. The bandwidth of the ART harvester (17 Hz) is found to be 1130% larger compared to the fixed resonance energy harvester. It is observed that the proposed broadband design provides a high-power density of 0.05 mW mm(-3). The piezoelectric dimensions and load resistance are also optimized to maximize the output voltage output power.
Indicators of Targeted Physical Fitness in Judo and Jujutsu-Preliminary Results of Research

(1) Study aim: This is a comparative study for judo and jujutsu practitioners. It has an intrinsic value. The aim of this study was to showcase a comparison of practitioners of judo and a similar martial art jujutsu with regard to manual abilities. The study applied the measurement of simple reaction time in response to a visual stimulus and handgrip measurement. (2) Materials and Methods: The group comprising N = 69 black belts from Poland and Germany (including 30 from judo and 39 from jujutsu) applied two trials: "grasping of Ditrich rod" and dynamometric handgrip measurement. The analysis of the results involved the calculations of arithmetic means, standard deviations, and Pearson correlations. Analysis of the differences (Mann-Whitney U test) and Student's t-test were also applied to establish statistical differences. (3) Results: In the test involving handgrip measurement, the subjects from Poland (both those practicing judo and jujutsu) gained better results compared to their German counterparts. In the test involving grasping of Ditrich rod, a positive correlation was demonstrated in the group of German judokas between the age and reaction time of the subjects (r(xy) = 0.66, p < 0.05), as well as in the group of jujutsu subjects between body weight and the reaction time (r(xy) = 0.49, p < 0.05). A significant and strong correlation between handgrip and weight was also established for the group of German judokas (r(xy) = 0.75, p < 0.05). In Polish competitors, the correlations were only established between the age and handgrip measurements (r(xy) = 0.49, p < 0.05). (4) Conclusions: Simple reaction times in response to visual stimulation were shorter in the subjects practicing the martial art jujutsu. However, the statement regarding the advantage of the judokas in terms of handgrip force was not confirmed by the results.
AUTS2 Controls Neuronal Lineage Choice Through a Novel PRC1-Independent Complex and BMP Inhibition

Despite a prominent risk factor for Neurodevelopmental disorders (NDD), it remains unclear how Autism Susceptibility Candidate 2 (AUTS2) controls the neurodevelopmental program. Our studies investigated the role of AUTS2 in neuronal differentiation and discovered that AUTS2, together with WDR68 and SKI, forms a novel protein complex (AWS) specifically in neuronal progenitors and promotes neuronal differentiation through inhibiting BMP signaling. Genomic and biochemical analyses demonstrated that the AWS complex achieves this effect by recruiting the CUL4 E3 ubiquitin ligase complex to mediate poly-ubiquitination and subsequent proteasomal degradation of phosphorylated SMAD1/5/9. Furthermore, using primary cortical neurons, we observed aberrant BMP signaling and dysregulated expression of neuronal genes upon manipulating the AWS complex, indicating that the AWS-CUL4-BMP axis plays a role in regulating neuronal lineage specification in vivo. Thus, our findings uncover a sophisticated cellular signaling network mobilized by a prominent NDD risk factor, presenting multiple potential therapeutic targets for NDD.
Efficient Modeling of Distributed Dynamic Self-Heating and Thermal Coupling in Multifinger SiGe HBTs

In this paper, we propose an efficient model for dynamic self-heating and thermal coupling in a multifinger transistor system. Essentially, the proposed model is an improvement over a state-of-the-art existing model from the viewpoint of simulation time. Verilog-A implementation of the proposed model does not require to use any voltage controlled voltage source. In a multifinger transistor system, with n emitter fingers, our model uses 3n extra nodes in Verilog-A implementation whereas it is 2n(2) - n for the state-of-the-art model. Note that our model requires no extra nodes for implementing the thermal coupling effects. We present that the transient simulation results of our model are identical with those of the state-of-the-art model. Electrothermal simulation using the proposed thermal model shows good agreement with the measured data. It is found that the proposed model simulates more than 40% faster compared with the existing model for a ring oscillator circuit.
Sparse graphs with smoothness constraints: Application to dimensionality reduction and semi-supervised classification

Sparse representation is a useful tool in machine learning and pattern recognition area. Sparse graphs (graphs constructed using sparse representation of data) proved to be very informative graphs for many learning tasks such as label propagation, embedding, and clustering. It has been shown that constructing an informative graph is one of the most important steps since it significantly affects the final performance of the post graph-based learning algorithm. In this paper, we introduce a new sparse graph construction method that integrates manifold constraints on the unknown sparse codes as a graph regularizer. These constraints seem to be a natural regularizer that was discarded in existing state-of-the art graph construction methods, This regularizer imposes constraints on the graph coefficients in the same way a locality preserving constraint imposes on data projection in non-linear manifold learning. The proposed method is termed Sparse Graph with Laplacian Smoothness (SGLS). We also propose akernelized version of the SGLS method. A series of experimental results on several public image datasets show that the proposed methods can out-perform many state-of-the-art methods for the tasks of label propagation, nonlinear and linear embedding. (C) 2019 Elsevier Ltd. All rights reserved.
Astrocyte-derived sEVs alleviate fibrosis and promote functional recovery after spinal cord injury in rats

After spinal cord injury (SCI), there are complex pathological states in which the formation of scar tissues is a great obstacle to nerve repair. There are currently many potential treatments that can help to reduce the formation of glial scars. However, little attention has been paid to fibrous scarring. Astrocytes have neuroprotective effects on the central nervous system. Similar to other cells, they release small extracellular vesicles (sEVs). Astrocytes, pericytes, endothelial cells, and the basement membrane constitute the blood-spinal cord barrier. It can be seen that astrocytes are structurally closely related to pericytes that form fibrous scars. In this study, astrocyte-derived sEVs were injected into rats with SCI to observe the formation of fibrosis at the site of spinal cord injury. We found that astrocyte-derived sEVs can be ingested by pericytes in vitro and inhibit the proliferation and migration of pericytes. In vivo, astrocyte-derived sEVs could converge around the injury, promote tissue repair, and reduce fibrosis formation, thus promoting the recovery of limb function and improving walking ability. In conclusion, sEVs derived from astrocytes can reduce fibrosis and improve functional recovery after SCI, which provides a new possibility for the study of SCI.
Bilateral motor cortex functional differences in left-handed approaching-avoiding behavior

Automatic action tendencies occur at behavioral and neurophysiological levels during task performance with the dominant right hand, with shorter reaction times (RTs) and higher excitability of the contralateral primary motor cortex (M1) during automatic vs. regulated behavior. However, effects associated with the non-dominant left-hand in approaching-avoiding behavior remain unclear. Here, we used transcranial magnetic stimulation during the performance by 18 participants of an approaching-avoiding task using the non-dominant left hand. Single-pulse transcranial magnetic stimulation was applied over left or right M1 at 150 and 300 ms after the onset of an emotional stimulus. RTs and motor-evoked potentials (MEPs) were recorded. Significant automatic action tendencies were observed at the behavioral level. Higher MEP amplitudes were detected 150 ms after stimulus onset from the right hand (non-task hand, corresponding to left M1) during regulated behavior compared with during automatic behavior. However, no significant modulation was found for MEP amplitudes from the left hand (task hand, corresponding to right M1). These findings suggested that left M1 may play a principal role in the early phase of mediating left-handed movement toward an emotional stimulus.
Miniature directive antennas

This paper presents the work carried out to assess the feasibility of miniature directive antennas. It is based on an analysis of the physical limits of antenna directivity in general and in particular as a function of their compact dimensions. A state of the art is done to identify and classify techniques to increase the directivity of compact antennas.
Bangla Speech Emotion Recognition and Cross-Lingual Study Using Deep CNN and BLSTM Networks

In this study, we have presented a deep learning-based implementation for speech emotion recognition (SER). The system combines a deep convolutional neural network (DCNN) and a bidirectional long-short term memory (BLSTM) network with a time-distributed flatten (TDF) layer. The proposed model has been applied for the recently built audio-only Bangla emotional speech corpus SUBESCO. A series of experiments were carried out to analyze all the models discussed in this paper for baseline, cross-lingual, and multilingual training-testing setups. The experimental results reveal that the model with a TDF layer achieves better performance compared with other state-of-the-art CNN-based SER models which can work on both temporal and sequential representation of emotions. For the cross-lingual experiments, cross-corpus training, multi-corpus training, and transfer learning were employed for the Bangla and English languages using the SUBESCO and RAVDESS datasets. The proposed model has attained a state-of-the-art perceptual efficiency achieving weighted accuracies (WAs) of 86.9%, and 82.7% for the SUBESCO and RAVDESS datasets, respectively.
HMiner: Efficiently mining high utility itemsets

High utility itemset mining problem uses the notion of utilities to discover interesting and actionable patterns. Several data structures and heuristic methods have been proposed in the literature to efficiently mine high utility itemsets. This paper advances the state-of-the-art and presents HMiner, a high utility itemset mining method. HMiner utilizes a few novel ideas and presents a compact utility list and virtual hyperlink data structure for storing itemset information, It also makes use of several pruning strategies for efficiently mining high utility itemsets. The proposed ideas were evaluated on a set of benchmark sparse and dense datasets. The execution time improvements ranged from a modest thirty percent to three orders of magnitude across several benchmark datasets. The memory consumption requirements also showed up to an order of magnitude improvement over the state-of-the-art methods. In general, HMiner was found to work well in the dense regions of both sparse and dense benchmark datasets. (C) 2017 Elsevier Ltd. All rights reserved.
The role of landscape installations in climate change communication

Engaging the public in the issue of climate change is critical in fostering the support required for climate change adaptation. Designers and artists can contribute to public engagement using the landscape as a setting and medium to visualize climate change futures. This research note presents the case example of High Tide, a temporary landscape installation in Boston, MA, designed to bring attention to projected flooding in the area due to sea level rise. Our study sought to pilot the use of social science methods to gain initial insight on whether a landscape installation, through its accessible and site-specific qualities, could engage local audiences in the subject of climate change. Our findings provide an initial proof-of-concept for the role of public art in contributing to public engagement by bringing attention to and visualizing local effects of climate change using the landscape as a publicly accessible setting. Future research using robust social science methods would further illuminate these issues.
Channel spatial attention based single-shot object detector for autonomous vehicles

Real-time object detection with high accuracy is the measure concern for the autonomous vehicle to provide safety. Recently many state-of-the-art methods used Convolutional Neural Network (CNN) for object detection. Although these methods provide better results but still provide a trade-off between accuracy and real-time detection becomes challenging tasks. High accuracy ensures the vehicle for avoiding collisions and abide the traffic rules while the faster speed helps to make the decision quickly. In this paper, the single-shot object detection is provided faster results and the attention module helps to provide more accurate detection. The channel attention mechanism provides more grained refine features and emphasizes 'what' is a semantic part from a given input. Apart from the channel attention mechanisms, spatial attention emphasizes 'where' is meaningful information which is working as a booster for the attention block. The proposed model incorporates these two attention mechanisms sequentially such as channel (RGB-wise) as well spatial attention for single-shot object detection (CSA-SS). The proposed model is trained and tested using challenging datasets such as KITTI and Berkeley Deep Drive (BDD). The experimental result shows that the proposed model surpasses the state-of-the-art techniques by 1.66 and 1.13 mAP for the KITTI and BDD datasets.
Uncovering the impact of income inequality and population aging on carbon emission efficiency: An empirical analysis of 139 countries

Income inequality and carbon emission efficiency are the primary issues that need to be addressed to achieve UN sustainable development goals. However, research on the relationship between income inequality and carbon emission efficiency has not received enough attention. To more comprehensively understand how income inequality affects carbon emission efficiency, and how aging and economic growth affect the relationship between income inequality and carbon emissions efficiency, fixed effect regression estimation and threshold effect regression estimation approaches are developed based on panel data of 139 countries from 1998 to 2018. The results show that: (i) there is an inhibitory effect of income inequality on the improvement of carbon emission efficiency; (ii) under the influence of aging, there is a U-shaped relationship between income inequality and carbon emission efficiency, that is, income inequality has an inhibitory effect on the improvement of carbon emission efficiency before promoting it; (iii) along with the rapid economic growth, the inhibitory effect of income inequality on carbon emission efficiency increases, that is, there is an inverted U-shaped relationship between income inequality and carbon emission efficiency. Finally, we combine the changes in spatial and temporal distributions to propose corresponding policy recommendations.
Knowledge of identity reduces variability in trait judgements across face images

Faces vary from image to image, eliciting different judgements of traits and often different judgements of identity. Knowledge that two face images belong to the same person facilitates the processing of identity information across images, but it is unclear if this also applies to trait judgements. In this preregistered study, participants (N = 100) rated the same 340 face images on perceived trustworthiness, dominance, or attractiveness presented in randomised order and again later presented in sets consisting of the same identity. We also explored the role of implicit person theory beliefs in the variability of social judgements across images. We found that judgements of trustworthiness varied less when images were presented in sets consisting of the same identity than in randomised order and were more consistent for images presented later in a set than those presented earlier. However, knowledge of identity had little effect on perceptions of dominance and attractiveness. Finally, implicit person theory beliefs were not associated with variability in social judgements and did not account for effects of knowledge of identity. Our findings suggest that knowledge of identity and perceptual familiarity stabilises judgements of trustworthiness, but not perceptions of dominance and attractiveness.
Per- and polyfluoroalkyl substances activate UPR pathway, induce steatosis and fibrosis in liver cells

Per- and polyfluoroalkyl substances (PFAS), which include perfluorooctanoic acid (PFOA), heptafluorobutyric acid (HFBA), and perfluorotetradecanoic acid (PFTA), are commonly occurring organic pollutants. Exposure to PFAS affects the immune system, thyroid and kidney function, lipid metabolism, and insulin signaling and is also involved in the development of fatty liver disease and cancer. The molecular mechanisms by which PFAS cause fatty liver disease are not understood in detail. In the current study, we investigated the effect of low physiologically relevant concentrations of PFOA, HFBA, and PFTA on cell survival, steatosis, and fibrogenic signaling in liver cell models. Exposure of PFOA and HFBA (10 to 1000 nM) specifically promoted cell survival in HepaRG and HepG2 cells. PFAS increased the expression of TNFα and IL6 inflammatory markers, increased endogenous reactive oxygen species (ROS) production, and activated unfolded protein response (UPR). Furthermore, PFAS enhanced cell steatosis and fibrosis in HepaRG and HepG2 cells which were accompanied by upregulation of steatosis (SCD1, ACC, SRBP1, and FASN), and fibrosis (TIMP2, p21, TGFβ) biomarkers expression, respectively. RNA-seq data suggested that chronic exposures to PFOA modulated the expression of fatty acid/lipid metabolic genes that are involved in the development of NFALD and fatty liver disease. Collectively our data suggest that acute/chronic physiologically relevant concentrations of PFAS enhance liver cell steatosis and fibrosis by the activation of the UPR pathway and by modulation of NFALD-related gene expression.
Performance and mechanism of azo dyes degradation and greenhouse gases reduction in single-chamber electroactive constructed wetland system

A single-chamber microbial fuel cell-microbial electrolytic cell with a novel constructed wetland system was proposed for synergistic degradation of congo red and reduction in emissions of greenhouse gases. The closed-circuit system showed higher chemical oxygen demand and congo red removal efficiencies by 98 % and 96 % on average, respectively, than traditional constructed wetland. It could also significantly reduce the emissions of CH4 and N2O (about 52 % CO2-equivalents) by increasing the electron transfer. Microbial community analysis demonstrated that the progressive enrichment of dye-degrading microorganisms (Comamonas), electroactive bacteria (Tolumonas, Trichococcus) and denitrifying microorganisms (Dechloromonas) promoted pollutant removal and electron transfer. Based on gene abundance of xenobiotics biodegradation, the congo red biodegradation pathway was described as congo red → naphthalene and alcohols → CO2 and H2O. In summary, the single-chamber closed-circuit system could significantly improve the degradation of congo red and reduce the emissions of greenhouse gases by influencing electron transfer and microbial activity.