ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Transcriptional and post-transcriptional mechanisms that regulate the genetic program in Zika virus-infected macrophages

Besides our understanding of the effects of ZIKA virus (ZIKV) infection on neural progenitors' cells the pathogenesis of this RNA virus also involves antigen-presenting cells, including macrophages. However, the molecular mechanisms that control gene activation and repression associated with the macrophage response to acute ZIKV infection are not fully understood. We approached the issue by RNA-seq and miRNA-seq datasets to understand the genetic program of ZIKV-infected macrophages. Results indicate that macrophage activates a regulatory program, involving 1067 differentially expressed genes. These genetic programs induced an inflammatory response mediated by chemokines as well as an interferon-independent anti-viral response, presumptively activated by IL-27. Additionally, the pathogenetic process involves changes in other signaling pathways such as cellular stress, cell signaling, metabolism, and cell differentiation. Furthermore, transcriptional control analysis revealed regulatory functions of key transcription factors principally, NFκB and STAT1, as well as HIF1A, ETV7, and PRMD1 that are associated with metabolic reprogramming during viral infection. We also noted six long-noncoding RNAs (lncRNAs) that may act in the regulation of gene expression, including MROCKI and ZC2HC1A-2, that are involved in the inflammatory response and expression of the cytokines, respectively. On the other hand, post-transcriptional control by miRNAs, including miR-155-5p and miR-146a-5p, are associated with modulation of genes related to inflammatory and antiviral responses. Relevant to the post-transcriptional control, our data unveiled the role of RNA binding proteins that have diverse functions such as ribonucleases (PNPT1, ZC3H12A, and ZC3HAV1), splicing factors (SSB, RBM11, and RAVER2), and RNA modifiers (PARP10 and PARP14). Overall, the results establish an unbiased approach to discerning the wiring of a regulatory mechanism controlling the genetic program in ZIKV-infected macrophages.
Respiratory neuromodulation in patients with neurological pathologies: for whom and how?

Implanted phrenic nerve stimulation is a technique restoring spontaneous breathing in patients with respiratory control failure, leading to being dependent on mechanical ventilation. This is the case for quadriplegic patients with a high spinal cord injury level and for patients with congenital central hypoventilation syndrome. The electrophysiological diaphragm explorations permits better patient selection, confirming on the one hand a definite issue with central respiratory command and on the other hand the integrity of diaphragmatic phrenic nerves. Today there are two different phrenic stimulation techniques: the quadripolar intrathoracic stimulation and the bipolar intradiaphragmatic stimulation. Both techniques allow patients to be weaned off their mechanical ventilator, improving dramatically their quality of life. In fact, one of the systems (phrenic intradiaphragmatic stimulation) was granted social security reimbursement in 2009, and now both are reimbursed. In the future, phrenic intradiaphragmatic stimulation may find its place in the intensive care unit, for patients needing it temporarily, for example, after certain surgeries with respiratory complications as well as diaphragmatic atrophies induced by prolonged mechanical ventilation.
Multi-Band Brain Network Analysis for Functional Neuroimaging Biomarker Identification

The functional connectomic profile is one of the non-invasive imaging biomarkers in the computer-assisted diagnostic system for many neuro-diseases. However, the diagnostic power of functional connectivity is challenged by mixed frequency-specific neuronal oscillations in the brain, which makes the single Functional Connectivity Network (FCN) often underpowered to capture the disease-related functional patterns. To address this challenge, we propose a novel functional connectivity analysis framework to conduct joint feature learning and personalized disease diagnosis, in a semi-supervised manner, aiming at focusing on putative multi-band functional connectivity biomarkers from functional neuroimaging data. Specifically, we first decompose the Blood Oxygenation Level Dependent (BOLD) signals into multiple frequency bands by the discrete wavelet transform, and then cast the alignment of all fully-connected FCNs derived from multiple frequency bands into a parameter-free multi-band fusion model. The proposed fusion model fuses all fully-connected FCNs to obtain a sparsely-connected FCN (sparse FCN for short) for each individual subject, as well as lets each sparse FCN be close to its neighbored sparse FCNs and be far away from its furthest sparse FCNs. Furthermore, we employ the l(1)-SVM to conduct joint brain region selection and disease diagnosis. Finally, we evaluate the effectiveness of our proposed framework on various neuro-diseases, i.e., Fronto-Temporal Dementia (FTD), Obsessive-Compulsive Disorder (OCD), and Alzheimer's Disease (AD), and the experimental results demonstrate that our framework shows more reasonable results, compared to state-of-the-art methods, in terms of classification performance and the selected brain regions.
Scene-Graph Augmented Data-Driven Risk Assessment of Autonomous Vehicle Decisions

There is considerable evidence that evaluating the subjective risk level of driving decisions can improve the safety of Autonomous Driving Systems (ADS) in both typical and complex driving scenarios. In this paper, we propose a novel data-driven approach that uses scene-graphs as intermediate representations for modeling the subjective risk of driving maneuvers. Our approach includes a Multi-Relation Graph Convolution Network, a Long-Short Term Memory Network, and attention layers. To train our model, we formulate subjective risk assessment as a supervised scene classification problem. We evaluate our model on both synthetic lane-changing datasets and real-driving datasets with various driving maneuvers. We show that our approach achieves a higher classification accuracy than the stateof-the-art approach on both large (96.4% vs. 91.2%) and small (91.8% vs. 71.2%) lane-changing synthesized datasets, illustrating that our approach can learn effectively even from small datasets. We also show that our model trained on a lane-changing synthesized dataset achieves an average accuracy of 87.8% when tested on a real-driving lane-changing dataset. In comparison, the state-of-the-art model trained on the same synthesized dataset only achieved 70.3% accuracy when tested on the real-driving dataset, showing that our approach can transfer knowledge more effectively. Moreover, we demonstrate that the addition of spatial and temporal attention layers improves our model's performance and explainability. Finally, our results illustrate that our model can assess the risk of various driving maneuvers more accurately than the state-of-the-art model (86.5% vs. 58.4%, respectively).
DeepDemosaicking: Adaptive Image Demosaicking via Multiple Deep Fully Convolutional Networks

Convolutional neural networks are currently the state-of-the-art solution for a wide range of image processing tasks. Their deep architecture extracts low-and high-level features from images, thus improving the model's performance. In this paper, we propose a method for image demosaicking based on deep convolutional neural networks. Demosaicking is the task of reproducing full color images from incomplete images formed from overlaid color filter arrays on image sensors found in digital cameras. Instead of producing the output image directly, the proposed method divides the demosaicking task into an initial demosaicking step and a refinement step. The initial step produces a rough demosaicked image containing unwanted color artifacts. The refinement step then reduces these color artifacts using deep residual estimation and multi-model fusion producing a higher quality image. Experimental results show that the proposed method outperforms several existing and state-of-the-art methods in terms of both the subjective and objective evaluations.
An Improved Flicker Noise Model for Circuit Simulations

Compact flicker noise models used in SPICE circuit simulators are derived from the seminal BSIM unified noise model. In this paper, we show that use of this model can give anomalous bias dependence of input referred noise. In addition, we find that the state-of-the art flicker noise models are not adequate to capture the drain bias dependence of flicker noise in short channel devices. In this paper, we address both the issues with a new compact model.
The Significance of Hypothalamic Inflammation and Gliosis for the Pathogenesis of Obesity in Humans

Accumulated preclinical literature demonstrates that hypothalamic inflammation and gliosis are underlying causal components of diet-induced obesity in rodent models. This review summarizes and synthesizes available translational data to better understand the applicability of preclinical findings to human obesity and its comorbidities. The published literature in humans includes histopathologic analyses performed postmortem and in vivo neuroimaging studies measuring indirect markers of hypothalamic tissue microstructure. Both support the presence of hypothalamic inflammation and gliosis in children and adults with obesity. Findings predominantly point to tissue changes in the region of the arcuate nucleus of the hypothalamus, although findings of altered tissue characteristics in whole hypothalamus or other hypothalamic regions also emerged. Moreover, the severity of hypothalamic inflammation and gliosis has been related to comorbid conditions, including glucose intolerance, insulin resistance, type 2 diabetes, and low testosterone levels in men, independent of elevated body adiposity. Cross-sectional findings are augmented by a small number of prospective studies suggesting that a greater degree of hypothalamic inflammation and gliosis may predict adiposity gain and worsening insulin sensitivity in susceptible individuals. In conclusion, existing human studies corroborate a large preclinical literature demonstrating that hypothalamic neuroinflammatory responses play a role in obesity pathogenesis. Extensive or permanent hypothalamic tissue remodeling may negatively affect the function of neuroendocrine regulatory circuits and promote the development and maintenance of elevated body weight in obesity and/or comorbid endocrine disorders.
MSB-FCN: Multi-Scale Bidirectional FCN for Object Skeleton Extraction

The performance of state-of-the-art object skeleton detection (OSD) methods have been greatly boosted by Convolutional Neural Networks (CNNs). However, the most existing CNN-based OSD methods rely on a 'skip-layer' structure where low-level and high-level features are combined to gather multi-level contextual information. Unfortunately, as shallow features tend to be noisy and lack semantic knowledge, they will cause errors and inaccuracy. Therefore, in order to improve the accuracy of object skeleton detection, we propose a novel network architecture, the Multi-Scale Bidirectional Fully Convolutional Network (MSB-FCN), to better gather and enhance multi-scale high-level contextual information. The advantage is that only deep features are used to construct multi-scale feature representations along with a bidirectional structure for better capturing contextual knowledge. This enables the proposed MSB-FCN to learn semantic-level information from different sub-regions. Moreover, we introduce dense connections into the bidirectional structure to ensure that the learning process at each scale can directly encode information from all other scales. An attention pyramid is also integrated into our MSB-FCN to dynamically control information propagation and reduce unreliable features. Extensive experiments on various benchmarks demonstrate that the proposed MSB-FCN achieves significant improvements over the state-of-the-art algorithms.
A Smartphone Step Counter Using IMU and Magnetometer for Navigation and Health Monitoring Applications

The growing market of smart devices make them appealing for various applications. Motion tracking can be achieved using such devices, and is important for various applications such as navigation, search and rescue, health monitoring, and quality of life-style assessment. Step detection is a crucial task that affects the accuracy and quality of such applications. In this paper, a new step detection technique is proposed, which can be used for step counting and activity monitoring for health applications as well as part of a Pedestrian Dead Reckoning (PDR) system. Inertial and Magnetic sensors measurements are analyzed and fused for detecting steps under varying step modes and device pose combinations using a free-moving handheld device (smartphone). Unlike most of the state of the art research in the field, the proposed technique does not require a classifier, and adaptively tunes the filters and thresholds used without the need for presets while accomplishing the task in a real-time operation manner. Testing shows that the proposed technique successfully detects steps under varying motion speeds and device use cases with an average performance of 99.6%, and outperforms some of the state of the art techniques that rely on classifiers and commercial wristband products.
Solving 3-D PDEs by Tensor B-Spline Methodology: A High Performance Approach Applied to Optical Diffusion Tomography

Solutions of 3-D elliptic PDEs form the basis of many mathematical models in medicine and engineering. Solving elliptic PDEs numerically in 3-D with fine discretization and high precision is challenging for several reasons, including the cost of 3-D meshing, the massive increase in operation count, and memory consumption when a high-order basis is used, and the need to overcome the "curse of dimensionality." This paper describes how these challenges can be either overcome or relaxed by a Tensor B-spline methodology with the following key properties: 1) the tensor structure of the variational formulation leads to regularity, separability, and sparsity, 2) a method for integration over the complex domain boundaries eliminates meshing, and 3) the formulation induces high-performance and memory-efficient computational algorithms. The methodology was evaluated by application to the forward problem of Optical Diffusion Tomography (ODT), comparing it with the solver from a state-of-the-art Finite-Element Method (FEM)-based ODT reconstruction framework. We found that the Tensor B-spline methodology allows one to solve the 3-D elliptic PDEs accurately and efficiently. It does not require 3-D meshing even on complex and non-convex boundary geometries. The Tensor B-spline approach outperforms and is more accurate than the FEM when the order of the basis function is > 1, requiring fewer operations and lower memory consumption. Thus, the Tensor B-spline methodology is feasible and attractive for solving large elliptic 3-D PDEs encountered in real-world problems.
Analysis of the solution structure parameter α in the relationship between the molar fraction and the freezing points, and hydration parameter h determined from viscosity and density measurements, for sugar alcohols and related sugars in water

The parameter α was obtained from the molar fraction of solute and the freezing points of sugar alcohols and their related sugars in water. For comparison with this parameter, simple measurement of the hydration parameter h was performed using a capillary viscometer and a density meter. This parameter was calculated from the viscosity B coefficient and the partial molar volume of solute. The viscosity B coefficient was more suitable than the partial molar volume for h calculation, as indicated by the determination coefficients of the linear regression lines. h correlated well with α for various compounds, including sugar alcohols in water, supporting the parameters' theoretical correspondence (α =-h). In addition, the activation energy required for hydration implies that the thermal stability increases with the saccharide molecular weight.
Accurate image super-resolution using dense connections and dimension reduction network

Recently, the convolution neural network (CNN) has achieved significant performance improvements toward the image Super-Resolution (SR) problems. Yet, the existing benchmark arts exist multiple limitations, including make use of the feature information deficiently, accompany with the gradient disappearance phenomenon and have serious time consumption. The paper utilizes a newly designed fully convolutional neural network named Accurate Image Super-resolution Using Dense Connections and Dimension Reduction Network (DCDRN) to fully exploit the image features. Contextual information of image regions utilizes efficiently and accurately through uniting dense connections and cascading small filters multiple times. And such implementation can be regarded as feature extractors to fuse local and global image features. We newly introduce 1 x 1 CNNs parallelization structure in the image reconstruction section to reduce data dimensions of the previous layers, which alleviates the computational burden effectively while avoiding the context info losing. The calculation becomes more complex and the convergence becomes slower during training because of the pre-processed images. The proposed DCDRN invents a simple and effective method which processes the original image directly and the optimization of layers and filters of CNNs shorten the cost of training significantly. Experiments on benchmark datasets with different methods show that DCDRN achieves gratifying performance against state-of-the-art methods. Code is available at https://github.com/doctorwgd/DCDRN.
Motion robust magnetic resonance imaging via efficient Fourier aggregation

We present a method for suppressing motion artifacts in anatomical magnetic resonance acquisitions. Our proposed technique, termed MOTOR-MRI, can recover and salvage images which are otherwise heavily corrupted by motion induced artifacts and blur which renders them unusable. Contrary to other techniques, MOTOR-MRI operates on the reconstructed images and not on k-space data. It relies on breaking the standard acquisition protocol into several shorter ones (while maintaining the same total acquisition time) and subsequent efficient aggregation in Fourier space of locally sharp and consistent information among them, producing a sharp and motion mitigated image. We demonstrate the efficacy of the technique on T2-weighted turbo spin echo magnetic resonance brain scans with severe motion corruption from both 3 T and 7 T scanners and show significant qualitative and quantitative improvement in image quality. MOTOR-MRI can operate independently, or in conjunction with additional motion correction methods.
Multifunctional Platform Based on a Copper(I) Complex and NaYF4:Tm3+,Yb3+ Upconverting Nanoparticles Immobilized into a Polystyrene Matrix: Downshifting and Upconversion Oxygen Sensing

This work presents an innovative approach to obtain a multifunctional hybrid material operating via combined anti-Stokes (upconversion) and Stokes (downshifting) emissions for oxygen gas sensing and related functionalities. The material is based on a Cu(I) complex exhibiting thermally activated delayed fluorescence emission (TADF) and infrared-to-visible upconverting Tm3+/Yb3+-doped NaYF4 nanoparticles supported in a polystyrene (PS) matrix. Excitation of the hybrid material at 980 nm leads to efficient transfer of Tm3+ emission in the ultraviolet/blue region to the Cu(I) complex and consequently intense green emission (560 nm) of the latter. Additionally, the green emission of the complex can also be directly generated with excitation at 360 nm. Independently of the excitation wavelength, the emission intensity is efficiently suppressed by the presence of molecular oxygen and the quenching rate is properly characterized by the Stern-Volmer plots. The results indicate that the biocompatible hybrid material can be applied as an efficient O2 sensor operating via near-infrared or ultraviolet excitation, unlike most optical oxygen sensors currently available which only work in downshifting mode.
Superhydrophobic Polyurethane Membrane with a Biomimetically Hierarchical Structure for Self-Cleaning

In this study, a stable and durable hexadecyltrimethoxysilane (HDTMS)/thermoplastic polyurethane (TPU) superhydrophobic film is successfully prepared by a simple and low-cost two-step method, namely, carrying out biomimetically hierarchical structures and low surface energy material modification concurrently. Meanwhile, effective parameters affecting the water contact angle (WCA) are studied and optimized. More importantly, under optimum parameters, the maximum WCA is 165°, the minimum slide angle (SA) is 3°, and the adhesion force is 13 μN, showing good self-cleaning performance. Besides, considerable mechanical stability to withstand 4000 tension or 5000 compression cycles, breathability, and moisture penetrability, as well as chemical resistance with sustained superhydrophobic properties in various harsh environments, are presented.
Effect of critical care pharmacist's intervention on medication errors: A systematic review and meta-analysis of observational studies

Pharmacists are integral members of the multidisciplinary team for critically ill patients. Multiple nonrandomized controlled studies have evaluated the outcomes of pharmacist interventions in the intensive care unit (ICU). This systematic review focuses on controlled clinical trials evaluating the effect of pharmacist intervention on medication errors (MEs) in ICU settings. Two independent reviewers searched Medline, Embase, and Cochrane databases. The inclusion criteria were nonrandomized controlled studies that evaluated the effect of pharmacist services vs no intervention on ME rates in ICU settings. Four studies were included in the meta-analysis. Results suggest that pharmacist intervention has no significant contribution to reducing general MEs, although pharmacist intervention may significantly reduce preventable adverse drug events and prescribing errors. This meta-analysis highlights the need for high-quality studies to examine the effect of the critical care pharmacist.
Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network

In contrast to images taken on land scenes, images taken over water are more prone to degradation due to the influence of the haze. However, existing image dehazing methods are mainly developed for land-scene images and perform poorly when applied to overwater images. To address this problem, we collect the first overwater image dehazing dataset and propose a Generative Adversial Network (GAN)-based method called OverWater Image Dehazing GAN (OWI-DehazeGAN). Due to the difficulties of collecting paired hazy and clean images, the dataset contains unpaired hazy and clean images taken over water. The proposed OWI-DehazeGAN is composed of an encoder-decoder framework, supervised by a forward-backward translation consistency loss for self-supervision and a perceptual loss for content preservation. In addition to qualitative evaluation, we design an image quality assessment neural network to rank the dehazed images. Experimental results on both real and synthetic test data demonstrate that the proposed method performs superiorly against several state-of-the-art land dehazing methods. Compared with the state-of-the-art, our method gains a significant improvement by 1.94% for SSIM, 7.13% for PSNR and 4.00% for CIEDE2000 on the synthetic test dataset.