ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
High-Density 2-mu m-Pitch pH Image Sensor With High-Speed Operation up to 1933 fps

Various biosensing platforms for real-time monitoring and mapping of chemical signals in neural networks have been developed based on CMOS process technology. Despite their achievements, however, there remains a demand for an advanced method that can offer detailed insights into cellular functions with higher spatiotemporal resolution. Here, we present a pH image sensor that employs a high-density array of 256 x 256 pixels and readout circuitry designed for fast operation. The sensor's characteristics, such as the pH sensitivity of 55.1 mV/pH and higher frame speed of 1933 fps, are experimentally demonstrated and compared to those of state-of-the-art pH image sensors. Among them, our sensor presents the smallest pitch of 2 mu m with a significantly high operation speed. This sensor can successfully detect a pH change, but also transform the measured data to a two-dimensional image series in real time. The practical spatial resolution of images is investigated by an evaluation method that we first propose in this paper. By this method, we confirm that our sensor can discriminate objects distanced over 4 mu m apart, which is twice bigger than the pixel pitch. In order to analyze the degraded resolution and image blur, a capacitive coupling effect at an ion-sensitive membrane is suggested as the main factor and demonstrated by simulation.
Architecting Ultrathin Graphitic C3N4 Nanosheets Incorporated PVA/Gelatin Bionanocomposite for Potential Biomedical Application: Effect on Drug Delivery, Release Kinetics, and Antibacterial Activity

Planar (2D) nanomaterials are garnering broad recognition in diverse scientific areas because of their intrinsic features. Herein, bulk graphitic carbon nitride (g-C3N4) was prepared from melamine, which was exfoliated to produce g-C3N4 nanosheets. The prepared g-C3N4 nanosheets were characterized by transmission electron microscopy (TEM), atomic force microscopy (AFM), photo luminescence (PL) spectroscopy, and dynamic light scattering (DLS). The stable dispersion of a g-C3N4 nanosheet was incorporated into a PVA/Gelatin matrix to explore its efficacy as a promising drug carrier. A remarkable 42% increase in tensile strength for 1% g-C3N4/PVA/Gelatin was attained compared with that of the PVA/Gelatin film. Thermal stability increased due to addition of g-C3N4 nanosheet in the PVA/Gelatin film, where the maximum thermal degradation temperature increased by 9.5 °C when the 1% nanosheet was added to the PVA/Gelatin film. Moreover, the g-C3N4 nanosheets and g-C3N4/PVA/Gelatin showed no cytotoxicity against HeLa and BHK-21 cells. To investigate the in vitro drug releasing efficacy, ciprofloxacin was incorporated into g-C3N4/PVA/Gelatin. Experimental results showed a 62% drug release within 120 min at physiological pH 7.4. The data was curve fitted by different kinetic models of drug release to understand the drug release mechanism. The experimental data was found to fit best with the Higuchi model and revealed the diffusion control mechanism of drug release. Additionally, antibacterial study confirmed the drug release potency from g-C3N4/PVA/Gelatin film on both Gram-positive and Gram-negative bacteria. The above-mentioned promising findings might lead to an opportunity of using g-C3N4 as a potential drug carrier.
Real-time embedded system for valve detection in water pipelines

Condition assessment is an essential process to comprehend the condition of the water pipelines and facilitate the maintenance as well as the renewal plans. Nowadays, varied in-pipe inspection platforms equipped with closed-circuit cameras are employed to capture the internal condition of the water pipelines. However, the automated platform often faces the challenge to negotiate with the installed valves during the inspection. To ensure continuous inspection, the platform needs identify the valve automatically and activate the control mechanism to pass through it. Thus, the valves need to be detected to facilitate the negotiation and ensure that the control mechanism can take an action in time. This paper focuses on real-time valve detection using Jetson TX2 (TM) and a lightweight algorithm, namely YOLOv3-tiny. The performance of the implementation is compared with state-of-the-art real-time detection models. The experimental results demonstrate that YOLOv3-tiny has a high detection speed in frame per second for valve detection and outperforms the state-of-the-art real-time algorithms. Hence, the deployment YOLOv3-tiny into the embedded system will aid the automated platform to accomplish the uninterrupted inspection and enhance the capability for the condition assessment of the water pipelines.
Nonlinear Energy-Maximizing Optimal Control of Wave Energy Systems: A Moment-Based Approach

Linear dynamics are virtually always assumed when designing optimal controllers for wave energy converters (WECs), motivated by both their simplicity and computational convenience. Nevertheless, unlike traditional tracking control applications, the assumptions under which the linearization of WEC models is performed are challenged by the energy-maximizing controller itself, which intrinsically enhances device motion to maximize power extraction from incoming ocean waves. In this article, we present a moment-based energy-maximizing control strategy for WECs subject to nonlinear dynamics. We develop a framework under which the objective function (and system variables) can be mapped to a finite-dimensional tractable nonlinear program, which can be efficiently solved using state-of-the-art nonlinear programming solvers. Moreover, we show that the objective function belongs to a class of generalized convex functions when mapped to the moment domain, guaranteeing the existence of a global energy-maximizing solution and giving explicit conditions for when a local solution is, effectively, a global maximizer. The performance of the strategy is demonstrated through a case study, where we consider (state and input-constrained) energy maximization for a state-of-the-art CorPower-like WEC, subject to different hydrodynamic nonlinearities.
Food inauthenticity: Authority activities, guidance for food operators, and mitigation tools

Historically, food fraud was a major public health concern which helped drive the development of early food regulations in many markets including the US and EU market. In the past 10 years, the integrity of food chains with respect to food fraud has again been questioned due to high profile food fraud cases. We provide an overview of the resulting numerous authoritative activities underway within different regions to counter food fraud, and we describe the guidance available to the industry to understand how to assess the vulnerability of their businesses and implement appropriate mitigation. We describe how such controls should be an extension of those already in place to manage wider aspects of food authenticity, and we provide an overview of relevant analytical tools available to food operators and authorities to protect supply chains. Practical Application: Practical Application of the provided information by the food industry in selecting resources (guidance document, analytical methods etc.).
Indoor air quality assessment in painting and printmaking department of a fine arts faculty building

Measurements for indoor air quality assessment were carried out in Painting and Printmaking Department of Anadolu University Faculty of Fine Arts in Turkey. Concentrations of nitrogen dioxide (NO2), ozone (O-3) and 29 Volatile Organic Compounds (VOCs) were measured simultaneously by using diffusive samplers. Simultaneous outdoor measurements were also performed at some sampling points. Analyses of NO2 and ozone samples were performed by using ion chromatography and VOCs were analyzed by using gas chromatography-mass spectrometry. Indoor NO2 and ozone concentrations varied between 13.47-89.77 mu g m(-3) and 3.89-51.82 mu g m(-3), respectively. Average indoor NO2 concentration was obtained as 35.37 +/- 10.9 mu g m(-3). Indoor/outdoor NO2 ratio (I/O) was found as 1.44 +/- 0.4 which indicated the presence of some indoor sources. Average indoor ozone concentration was 9.97 +/- 4.4 mu g m(-3) and I/O ratio was obtained lower than 1 (0.46 +/- 0.4). The highest VOC concentrations were observed at workshops where oil painting and stained glass studies were performed. Especially, the concentrations obtained from the stained glass workshop (benzene: 3.98 +/- 1.3 mu g m(-3), toluene: 999.33 +/- 104.2 mu g m(-3), ethly benzene: 66.06 +/- 16.1 mu g m(-3), m,p xylene: 129.44 +/- 33.1 mu g m(-3), o-xylene: 76.14 +/- 23.1 mu g m(-3)) were much higher than the other sampling points. Toluene concentrations exceeded the WHO (World Health Organization) limit value (260 mu g m(-3) weekly average) at 40% of the sampling points. Cancer risks were estimated by using the personal exposure concentrations. Lifetime cancer risks for the people working in the department such as faculty members and technicians were obtained higher than USEPA acceptable risk value (1 x 10(-6)) while the risks for the students were below this value. Copyright (c) 2015 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V. All rights reserved.
Substitution of carbonate by non-physiological synergistic anion modulates the stability and iron release kinetics of serum transferrin

Serum transferrin (sTf) is a bi-lobal protein. Each lobe of sTf binds one Fe3+ ion in the presence of a synergistic anion. Physiologically, carbonate is the main synergistic anion but other anions such as oxalate, malonate, glycolate, maleate, glycine, etc. can substitute for carbonate in vitro. The present work provides the possible pathways by which the substitution of carbonate with oxalate affects the structural, kinetic, thermodynamic, and functional properties of blood plasma sTf. Analysis of equilibrium experiments measuring iron release and structural unfolding of carbonate and oxalate bound diferric-sTf (Fe2sTf) as a function of pH, urea concentration, and temperature reveal that the structural and iron-centers stability of Fe2sTf increase by substitution of carbonate with oxalate. Analysis of isothermal titration calorimetry (ITC) scans showed that the affinity of Fe3+ with apo-sTf is enhanced by substituting carbonate with oxalate. Analysis of kinetic and thermodynamic parameters measured for the iron release from the carbonate and oxalate bound monoferric-N-lobe of sTf (FeNsTf) and Fe2sTf at pH 7.4 and pH 5.6 reveals that the substitution of carbonate with oxalate inhibits/retards the iron release via increasing the enthalpic barriers.
Pathophysiological Association Between Diabetes Mellitus and Alzheimer's Disease

Worldwide elderly people are being affected by diabetes mellitus (DM) and dementia. The risk for the development of dementia is higher in people with DM. DM causes a marked cognitive reduction and increases the risk of dementia, most commonly vascular dementia and Alzheimer's disease. People affected by DM and dementia seem to be at higher risk for intense hypoglycemia. Hypoglycemia, the complication of DM treatment, is believed as an independent risk factor for dementia in people with DM. Both Alzheimer's disease and DM are linked with decreased insulin secretion, reduced uptake of glucose, raised oxidative stress, angiopathy, activation of the apoptotic pathway, aging, abnormal peroxidation of lipids, increased production of advanced glycation end products and tau phosphorylation, brain atrophy, and decreased fat metabolism. In this paper, we will review the association between Alzheimer's disease and DM. In addition, we will discuss the agents that enhance the risk for dementia in elderly people with DM and how to prevent the development of cognitive dysfunction in DM.
Objective Detection of Eloquent Axonal Pathways to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery Using Diffusion Tractography and Convolutional Neural Networks

Convolutional neural networks (CNNs) have recently been used in biomedical imaging applications with great success. In this paper, we investigated the classification performance of CNN models on diffusion weighted imaging (DWI) streamlines defined by functional MRI (fMRI) and electrical stimulation mapping (ESM). To learn a set of discriminative and interpretable features from the extremely unbalanced dataset, we evaluated different CNN architectures with multiple loss functions (e.g., focal loss and center loss) and a soft attention mechanism and compared our models with current state-of-the-art methods. Through extensive experiments on streamlines collected from 70 healthy children and 70 children with focal epilepsy, we demonstrated that our deep CNN model with focal and central losses and soft attention outperforms all existing models in the literature and provides clinically acceptable accuracy (73%-100%) for the objective detection of functionally important white matter pathways, including ESM determined eloquent areas such as primary motors, aphasia, speech arrest, auditory, and visual functions. The findings of this paper encourage further investigations to determine if DWI-CNN analysis can serve as a noninvasive diagnostic tool during pediatric presurgical planning by estimating not only the location of essential cortices at the gyral level but also the underlying fibers connecting these cortical areas to minimize or predict postsurgical functional deficits. This paper translates an advanced CNN model to clinical practice in the pediatric population where currently available approaches (e.g., ESM and fMRI) are suboptimal. The implementation will be released at https://github.com/HaotianMXu/Brain-fiber-classification-using-CNNs.
Fast and Compact Image Segmentation Using Instance Stixels

State-of-the-art stixel methods fuse dense stereo disparity and semantic class information, e.g., from a Convolutional Neural Network (CNN), into a compact representation of driveable space, obstacles and background. However, they do not explicitly differentiate instances within the same semantic class. We investigate several ways to augment single-frame stixels with instance information, which can be extracted by a CNN from the RGB image input. As a result, our novel Instance Stixels method efficiently computes stixels that account for boundaries of individual objects, and represents instances as grouped stixels that express connectivity. Experiments on the Cityscapes dataset demonstrate that including instance information into the stixel computation itself, rather than as a post-processing step, increases the segmentation performance (i.e., Intersection over Union and Average Precision). This holds especially for overlapping objects of the same class. Furthermore, we show the superiority of our approach in terms of segmentation performance and computational efficiency compared to combining the separate outputs of Semantic Stixels and a state-of-the-art pixel-level CNN. We achieve processing throughput of 28 frames per second on average for 8 pixel wide stixels on images from the Cityscapes dataset at 1792 x 784 pixels. Our Instance Stixels software is made freely available for non-commercial research purposes.
Infection, pathology and interferon treatment of the SARS-CoV-2 Omicron BA.1 variant in juvenile, adult and aged Syrian hamsters

The new predominant circulating SARS-CoV-2 variant, Omicron, can robustly escape current vaccines and neutralizing antibodies. Although Omicron has been reported to have milder replication and disease manifestations than some earlier variants, its pathogenicity in different age groups has not been well elucidated. Here, we report that the SARS-CoV-2 Omicron BA.1 sublineage causes elevated infection and lung pathogenesis in juvenile and aged hamsters, with more body weight loss, respiratory tract viral burden, and lung injury in these hamsters than in adult hamsters. Juvenile hamsters show a reduced interferon response against Omicron BA.1 infection, whereas aged hamsters show excessive proinflammatory cytokine expression, delayed viral clearance, and aggravated lung injury. Early inhaled IFN-α2b treatment suppresses Omicron BA.1 infection and lung pathogenesis in juvenile and adult hamsters. Overall, the data suggest that the diverse patterns of the innate immune response affect the disease outcomes of Omicron BA.1 infection in different age groups.
FRESH-FRI-Based Single-Image Super-Resolution Algorithm

In this paper, we consider the problem of single image super-resolution and propose a novel algorithm that outperforms state-of-the-art methods without the need of learning patches pairs from external data sets. We achieve this by modeling images and, more precisely, lines of images as piecewise smooth functions and propose a resolution enhancement method for this type of functions. The method makes use of the theory of sampling signals with finite rate of innovation (FRI) and combines it with traditional linear reconstruction methods. We combine the two reconstructions by leveraging from the multi-resolution analysis in wavelet theory and show how an FRI reconstruction and a linear reconstruction can be fused using filter banks. We then apply this method along vertical, horizontal, and diagonal directions in an image to obtain a single-image super-resolution algorithm. We also propose a further improvement of the method based on learning from the errors of our super-resolution result at lower resolution levels. Simulation results show that our method outperforms state-of-the-art algorithms under different blurring kernels.
Reducing over-smoothness in HMM-based speech synthesis using exemplar-based voice conversion

Speech synthesis has been applied in many kinds of practical applications. Currently, state-of-the-art speech synthesis uses statistical methods based on hidden Markov model (HMM). Speech synthesized by statistical methods can be considered over-smooth caused by the averaging in statistical processing. In the literature, there have been many studies attempting to solve over-smoothness in speech synthesized by an HMM. However, they are still limited. In this paper, a hybrid synthesis between HMM and exemplar-based voice conversion has been proposed. The experimental results show that the proposed method outperforms state-of-the-art HMM synthesis using global variance.
The complete mitochondrial genome of the Poecilia formosa (Amazon molly)

The Amazon molly, Poecilia formosa, a member of the Poeciliidae family, is a freshwater fish reproducing through gynogenesis. The complete mitochondrial genome of the P. formosa is determined for the first time in this study. It is a circular molecule of 16 542 bp in length, including 13 protein-coding genes, 22 transfer RNA genes, 2 ribosomal RNA genes and 1 putative control region. The overall base composition of the genome is A (29.59%), T (27.57%), C (28.27%), and G (14.57%) with 42.84% GC content, which is lower than the content of AT. Most protein-coding genes started with a traditional ATG codon except for COX2, ND5 and ND6, which initiated with ATA, GTG and TTA, respectively. The stop codon was a single T- - base in most of the protein-coding genes, but COX2 and ATP8 both employed TAA and ND2 terminated with AGG codon. Phylogenetic tree was constructed based on the complete mitogenome of P. formosa and closely related 11 chondrichthian species to assess their phylogenic relationship and evolution. The complete mitochondrial genome of the amazon molly would help to study the evolution of Poeciliidae family.
Deep Learning-Based ECG-Free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging

For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, time-continous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of > 98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstruction.
Weakly Supported Plane Surface Reconstruction via Plane Segmentation Guided Point Cloud Enhancement

Most of the widely used multi-view 3D reconstruction algorithms assume that object appearance is predominantly diffuse and full of good texture. For the objects that violate this restriction, the surface can hardly be reconstructed because such area lacks sufficient support from dense point clouds. To tackle this problem, we introduce a novel two-stage prior-guided method based on point clouds enhancement to enable the application of multi-view reconstruction approaches in such scenes. In the first stage, we optimize the original PlaneNet plane segmentation priors by taking advantage of the estimated depth map and confidence map from multi-view stereo. In the second stage, we correct and supply 3D point clouds for the weakly supported plane surface on the basis of the upgraded priors. Furthermore, we utilize a slight disturbance of the enhanced point clouds to facilitate the subsequent mesh reconstruction. The proposed point cloud enhancement approach is evaluated on the large-scale DTU dataset. Our method significantly outperforms previous multi-view stereo state-of-the-arts. We also demonstrate weakly supported plane surface reconstruction results from real-world photos that are unachievable with either the methods aiming at preserving weakly supported surfaces or the traditional state-of-the-art 3D reconstruction systems.