ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Flavonol-Aluminum Complex Formation: Enhancing Aluminum Accumulation in Tea Plants

Polyphenol-rich tea plants are aluminum (Al) accumulators. Whether an association exists between polyphenols and Al accumulation in tea plants remains unclear. This study revealed that the accumulation of the total Al and bound Al contents were both higher in tea samples with high flavonol content than in low, and Al accumulation in tea plants was significantly and positively correlated with their flavonol content. Furthermore, the capability of flavonols combined with Al was higher than that of epigallocatechin gallate (EGCG) and root proanthocyanidins (PAs) under identical conditions. Flavonol-Al complexes signals (94 ppm) were detected in the tender roots and old leaves of tea plants through solid-state 27Al nuclear magnetic resonance (NMR) imaging, and the strength of the signals in the high flavonol content tea samples was considerably stronger than that in the low flavonol content tea samples. This study provides a new perspective for studying Al accumulation in different tea varieties.
Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification

Dynamic functional connectivity (dFC) analysis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) is currently an advanced technique for capturing the dynamic changes of neural activities in brain disease identification. Most existing dFC modeling methods extract dynamic interaction information by using the sliding window-based correlation, whose performance is very sensitive to window parameters. Because few studies can convincingly identify the optimal combination of window parameters, sliding window-based correlation may not be the optimal way to capture the temporal variability of brain activity. In this paper, we propose a novel adaptive dFC model, aided by a deep spatial-temporal feature fusion method, for mild cognitive impairment (MCI) identification. Specifically, we adopt an adaptive Ultra-weighted-lasso recursive least squares algorithm to estimate the adaptive dFC, which effectively alleviates the problem of parameter optimization. Then, we extract temporal and spatial features from the adaptive dFC. In order to generate coarser multi-domain representations for subsequent classification, the temporal and spatial features are further mapped into comprehensive fused features with a deep feature fusion method. Experimental results show that the classification accuracy of our proposed method is reached to 87.7%, which is at least 5.5% improvement than the state-of-the-art methods. These results elucidate the superiority of the proposed method for MCI classification, indicating its effectiveness in the early identification of brain abnormalities.
Tunable lasers in optical networks

Tunable lasers have been the subject of considerable interest ever since the start of the wavelength division multiplexing (WDM) revolution. In this paper, we bring together views on tunable lasers from different types of companies in the value chain, from operators to laser manufacturers. The purpose of the paper is to give an overview of the state of the art in both deployment and development, as well as to try to predict trends over the coming years.
Transfer Learning for Automatic Image Orientation Detection Using Deep Learning and Logistic Regression

The number of images produced each day increased significantly. The ability to detect and correct an image's orientation can provide several advantages in computer vision. This paper presents a new framework based on a transfer learning technique for automatically detecting image orientation. To implement the power of deep neural networks, we applied a convolutional neural network model pre-trained on the ImageNet database for feature extraction. Then, we built a multi-class logistic regression classifier to detect the four image orientation probabilities corresponding to the following orientations (0 for no orientation, 90, 180, and 270). We tested our model on the SUN-397 dataset, one of the most extensive data sets currently used for image-orientation detection tasks. We conducted a cross-dataset evaluation for in-depth testing and analysis. We also examined our model using different old and recent state-of-the-art convolutional neural network (CNN) baselines. We demonstrate that our model yields promising results based on transfer learning for feature extraction combined with a one-vs-rest logistic regression classifier. Our proposed model surpassed the state-of-the-art results in terms of accuracy and performance.
Hybrid global gridded snow products and conceptual simulations of distributed snow budget: evaluation of different scenarios in a mountainous watershed

Considering snowmelt in mountainous areas as the important source of streamflow, the snow accumulation/melting processes are vital for accurate simulation of the hydrological regimes. The lack of snow-related data and its uncertainties/conceptual ambiguity in snowpack modeling are the different challenges of developing hydroclimatological models. To tackle these challenges, Global Gridded Snow Products (GGSPs) are introduced, which effectively simplify the identification of the spatial characteristics of snow hydrological variables. This research aims to investigate the performance of multi-source GGSPs using multi-stage calibration strategies in hydrological modeling. The used GGSPs were Snow-Covered Area (SCA) and Snow Water Equivalent (SWE), implemented individually or jointly to calibrate an appropriate water balance model. The study area was a mountainous watershed located in Western Iran with a considerable contribution of snowmelt to the generated streamflow. The results showed that using GGSPs as complementary information in the calibration process, besides streamflow time series, could improve the modeling accuracy compared to the conventional calibration, which is only based on streamflow data. The SCA with NSE, KGE, and RMSE values varying within the ranges of 0.47-0.57, 0.54-0.65, and 4-6.88, respectively, outperformed the SWE with the corresponding metrics of 0.36-0.59, 0.47-0.60, and 5.22-7.46, respectively, in simulating the total streamflow of the watershed. In addition to the superiority of the SCA over SWE, the two-stage calibration strategy reduced the number of optimized parameters in each stage and the dependency of internal processes on the streamflow and improved the accuracy of the results compared with the conventional calibration strategy. On the other hand, the consistent contribution of snowmelt to the total generated streamflow (ranging from 0.9 to 1.47) and the ratio of snow melting to snowfall (ranging from 0.925 to 1.041) in different calibration strategies and models resulted in a reliable simulation of the model.
Integrated Circuits for Medical Ultrasound Applications: Imaging and Beyond

Medical ultrasound has become a crucial part of modern society and continues to play a vital role in the diagnosis and treatment of illnesses. Over the past decades, the development of medical ultrasound has seen extraordinary progress as a result of the tremendous research advances in microelectronics, transducer technology and signal processing algorithms. However, medical ultrasound still faces many challenges including power-efficient driving of transducers, low-noise recording of ultrasound echoes, effective beamforming in a non-linear, high-attenuation medium (human tissues) and reduced overall form factor. This paper provides a comprehensive review of the design of integrated circuits for medical ultrasound applications. The most important and ubiquitous modules in a medical ultrasound system are addressed, i) transducer driving circuit, ii) low-noise amplifier, iii) beamforming circuit and iv) analog-digital converter. Within each ultrasound module, some representative research highlights are described followed by a comparison of the state-of-the-art. This paper concludes with a discussion and recommendations for future research directions.
Deep learning for rare disease: A scoping review

Although individually rare, collectively more than 7,000 rare diseases affect about 10% of patients. Each of the rare diseases impacts the quality of life for patients and their families, and incurs significant societal costs. The low prevalence of each rare disease causes formidable challenges in accurately diagnosing and caring for these patients and engaging participants in research to advance treatments. Deep learning has advanced many scientific fields and has been applied to many healthcare tasks. This study reviewed the current uses of deep learning to advance rare disease research. Among the 332 reviewed articles, we found that deep learning has been actively used for rare neoplastic diseases (250/332), followed by rare genetic diseases (170/332) and rare neurological diseases (127/332). Convolutional neural networks (307/332) were the most frequently used deep learning architecture, presumably because image data were the most commonly available data type in rare disease research. Diagnosis is the main focus of rare disease research using deep learning (263/332). We summarized the challenges and future research directions for leveraging deep learning to advance rare disease research.
The Affective Domain-A Program to Foster Social-Emotional Orientation in Novice Physical Education Teachers

The present study aimed to assess the influence of an emotional-based program for novice physical education teachers on their perception of the affective domain in teaching, and the influence of the program on their social-emotional orientation. Thirty-two physical educators in their induction year participated. Instrumentations included reflective assignments: individual tasks, a group artwork task, short videos containing student-teacher scenarios, and summary reflections. The study covered tasks that contained a variety of emotional expressions-verbalizing, acting, and art creation. Content analysis was conducted for each of the assignments. The results indicate that the participants felt that they gradually developed an awareness of the role of emotions in their practice. In addition to personal gain, they felt that their empathy for others-especially their students-was enhanced. These results highlight the important influence that an emotional-based program has on physical educators' social-emotional orientation.
Exploring Advertising Effectiveness of Tourist Hotels' Marketing Images Containing Nature and Performing Arts: An Eye-Tracking Analysis

The beautiful, natural environment in a tourist hotel's marketing images can evoke relaxing and soothing emotions. However, can tourist hotels use nature as a servicescape to make their performing arts services more attractive? Based on attention restoration and servicescape theory, this study explores and compares the influence of tourist hotels' performing arts images with nature- or built-based servicescapes on the advertising effectiveness (i.e., customer visual attention and behavioral intention). To analyze visual attention on the marketing images, this study uses eye-tracking technology to record customer visual trajectories. This experiment used a total of 113 participants. The sample size of the nature-based servicescape group was 59 (age with mean = 39.04), and that of the built-based servicescape group was 54 (age with mean = 40.17). A tourist hotel's (Volando Urai Spring Spa & Resort) marketing images were chosen as stimuli. All participants were randomly assigned to the nature-based or the built-based servicescape group. In each experimental group, all the images were randomly presented to reduce any order effects of the images. By using eye-tracking analysis, the experimental findings were as follows: (1) A nature-based servicescape can arouse more visual attention of customers than a built-based servicescape can; (2) Marketing images with performing arts activities in nature-based servicescapes attract the visual attention of customers; (3) Nature-based servicescapes stimulate higher behavioral intentions of consumers than built-based servicescape.
Reversible Immunosensor for the Continuous Monitoring of Cortisol in Blood Plasma Sampled with Microdialysis

Cortisol is a steroid hormone involved in a wide range of medical conditions. The level of the hormone fluctuates over time, but with traditional laboratory-based assays, such dynamics cannot be monitored in real time. Here, a reversible cortisol sensor is reported that allows continuous monitoring of cortisol in blood plasma using sampling by microdialysis. The sensor is based on measuring single-molecule binding and unbinding events of tethered particles. The particles are functionalized with antibodies and the substrate with cortisol-analogues, causing binding and unbinding events to occur between particles and substrate. The frequency of binding events is reduced when cortisol is present in the solution as it blocks the binding sites of the antibodies. The sensor responds to cortisol in the high nanomolar to low micromolar range and can monitor cortisol concentrations over multiple hours. Results are shown for cortisol monitoring in filtered and in microdialysis-sampled human blood plasma.
NFκB- and AP-1-mediated DNA looping regulates matrix metalloproteinase-9 transcription in TNF-α-treated human leukemia U937 cells

The aim of this study is to explore the spatial association of critical genomic elements in the effect of TNF-α on matrix metalloproteinase-9 (MMP-9) expression in human leukemia U937 cells. TNF-α up-regulated MMP-9 protein expression and mRNA level in U937 cells, and Akt-mediated-NFκB/p65 activation and JNK-mediated c-Jun activation were proven to be involved in TNF-α-induced MMP-9 up-regulation. Promoter luciferase activity assay revealed that NFκB (nt-600) and AP-1 (nt-79) binding sites were crucial for TNF-α-induced transcription of MMP-9 gene. The results of a chromatin immunoprecipitation assay indicated that TNF-α reduced histone deacetylase-1 (HDAC-1) recruitment but increased p300 (a histone acetyltransferase) recruitment to MMP-9 promoter regions surrounding NFκB and AP-1 binding sites. Consistently, TNF-α increased enrichment of the acetylated histone H3 mark on MMP-9 promoter regions. DNA affinity purification assay revealed that p300 and HDAC1 could bind oligonucleotides containing AP-1/c-Jun and NFκB/p65 binding sites. Chromosome conformation capture assay showed that TNF-α stimulated chromosomal loops in the MMP-9 promoter via NFκB/p65 and AP-1/c-Jun. The p300-associated acetyltransferase activity was crucial for p65/c-Jun-mediated DNA looping, and inhibition of HDAC activity increased the level of DNA looping. Reduction in the level of DNA looping eliminated all TNF-α-stimulated MMP-9 up-regulation. Taken together, our data suggest that p65/c-Jun-mediated DNA looping is involved in TNF-α-induced MMP-9 up-regulation and that the recruitment of p300 or HDAC1 to NFκB and AP-1 binding sites modifies the level of DNA looping.
T-2 Toxin Caused Mice Testicular Inflammation Injury via ROS-Mediated NLRP3 Inflammasome Activation

T-2 toxin treatment causes male reproduction system dysfunction, although the exact mechanism remains unclear. In this research, male Kunming mice and TM4 cells were treated with varying concentrations of the T-2 toxin for evaluating the adverse effect of T-2 toxin on male reproductive function. MCC950 or NAC was used to block NLRP3 inflammasome activation and eliminate reactive oxygen species (ROS) accumulation in the TM4 cell, respectively. The results showed that: (1) T-2 toxin caused testicular atrophy, destroyed the microstructure and ultrastructure of the testis, and caused sperm deformities; (2) T-2 toxin increased the content and gene expressions of TNF-α and IL-6 and decreased the IL-10 content and gene expression, causing testis and TM4 cell inflammatory injury; (3) T-2 toxin activated NLRP3 inflammasome in the testis and TM4 cells and caused ROS accumulation in the testis; (4) suppressing NLRP3 inflammasome activation using 20 nM MCC950 alleviated the TM4 cell inflammatory damage caused via the T-2 toxin; nevertheless, 20 nM MCC950 did not reduce ROS accumulation in TM4 cells; and (5) NAC relieved the inflammatory damage in TM4 cells by inhibiting NLRP3 inflammasome activation. Taken together, T-2 toxin caused testicular inflammation injury through ROS-mediated NLRP3 inflammasome activation, resulting in male reproductive dysfunction.
Multidisciplinary Approach Towards Hypertensive and Chronic Alcoholic Patient With Intracerebral Bleed

Intracerebral haemorrhage, the most lethal form of stroke, accounts for almost a third of all strokes. The brain receives and expels blood through blood arteries. Veins or arteries may rupture due to trauma, improper development, or excessive pressure. Blood itself has the potential to harm brain tissue. Here, we discuss the case of a 36-year-old individual who experienced giddiness, two to three seizure episodes, and left extremity weakness. Investigation revealed an intracerebral bleed. Physiotherapy was necessary to enable the patient to carry out his everyday activities comfortably in addition to medical management. The patient's condition was improved with the help of a physiotherapy protocol.
An Improved Linear Model for High-Frequency Class-DE Resonant Converter Using the Generalized Averaging Modeling Technique

As the operating frequency of power converters increases, the passive component values likewise decrease. This results in the effect of the parasitic components becoming more prominent, leading to significant modeling errors if not considered. For resonant converters, this especially becomes a problem at high frequencies. This article presents a reduced model for a class-DE series resonant converter based on generalized averaging that incorporates the relevant parasitics and uses multiple harmonics to obtain an accurate linear model. Comparison between the proposed model, a prior art, and a prototype converter running at 1 MHz is conducted, and a PI controller is designed based on each model and tested. The results show that the parasitics have a significant impact on the dc gain and dynamics of the converter and that the proposed model improves on the prior art by reducing the dc-gain error by more than 7 dB, and the error in the low-frequency pole from 168% to 16.9%. Furthermore, the PI controller designed on the prior art was found to have more than 40 times larger overshoot in the control signal when measured compared with the model prediction, while the controller based on the proposed model showed correct performance when simulated and measured.
We're Not Gonna Break It! Consistency-Preserving Operators for Efficient Product Line Configuration

When configuring a software product line, finding a good trade-off between multiple orthogonal quality concerns is a challenging multi-objective optimisation problem. State-of-the-art solutions based on search-based techniques create invalid configurations in intermediate steps, requiring additional repair actions that reduce the efficiency of the search. In this work, we introduce consistency-preserving configuration operators (CPCOs)-genetic operators that maintain valid configurations throughout the entire search. CPCOs bundle coherent sets of changes: the activation or deactivation of a particular feature together with other (de)activations that are needed to preserve validity. In our evaluation, our instantiation of the IBEA algorithm with CPCOs outperforms two state-of-the-art tools for optimal product line configuration in terms of both speed and solution quality. The improvements are especially pronounced in large product lines with thousands of features.
Electrostatic Adsorption Enables Layer Stacking Thickness-Dependent Hollow Ti3 C2 Tx MXene Bowls for Superior Electromagnetic Wave Absorption

Although transition metal carbides/carbonitrides (MXenes) exhibit immense potential for electromagnetic wave (EMW) absorption, their absorbing ability is hindered by facile stacking and high permittivity. Layer stacking and geometric structures are expected to significantly affect the conductivity and permittivity of MXenes. However, it is still a formidable task to simultaneously regulate layer stacking and microstructure of MXenes to realize high-performance EMW absorption. Herein, a simple and viable strategy using electrostatic adsorption is developed to integrate 2D Ti3 C2 Tx MXene nanosheets into 3D hollow bowl-like structures with tunable layer stacking thickness. Density functional theory calculations indicate an increase in the density of states of the d orbital from the Ti atom near the Fermi level and the generation of additional electrical dipoles in the MXene nanosheets constituting the bowl walls upon reducing the layer stacking thickness. The hollow MXene bowls exhibit a minimum reflection loss (RLmin ) of -53.8 dB at 1.8 mm. The specific absorbing performance, defined as RLmin (dB)/thickness (mm)/filler loading (wt%), exceeds 598 dB mm-1 , far surpassing that of the most current MXene and bowl-like materials reported in the literature. This work can guide future exploration on designing high-performance MXenes with "lightweight" and "thinness" characteristics for superior EMW absorption.
Role of Inorganic Amorphous Constituents in Highly Mineralized Biomaterials and Their Imitations

A highly mineralized biomaterial is one kind of biomaterial that usually possesses a high content of crystal minerals and hierarchical microstructure, exhibiting excellent mechanical properties to support the living body. Recent studies have revealed the presence of inorganic amorphous constituents (IAC) either during the biomineralization process or in some mature bodies, which heavily affects the formation and performance of highly mineralized biomaterials. These results are surprising given the preceding intensive research into the microstructure design of these materials. Herein, we highlight the role of IAC in highly mineralized biomaterials. We focused on summarizing works demonstrating the presence or phase transformation of IAC and discussed in detail how IAC affects the formation and performance of highly mineralized biomaterials. Furthermore, we described some imitations of highly mineralized biomaterials that use IAC as the synthetic precursor or final strengthening phase. Finally, we briefly summarized the role of IAC in biomaterials and provided an outlook on the challenges and opportunities for future IAC and IAC-containing bioinspired materials researches.
In-Hardware Training Chip Based on CMOS Invertible Logic for Machine Learning

Deep Neural Networks (DNNs) have recently shown state-of-the-art results on various applications, such as computer vision and recognition tasks. DNN inference engines can be implemented in hardware with high energy efficiency as the computation can be realized using a low-precision fixed point or even binary precision with sufficient cognition accuracies. On the other hand, training DNNs using the well-known back-propagation algorithm requires high-precision floating-point computations on a CPU and/or GPU causing significant power dissipation (more than hundreds of kW) and long training time (several days or more). In this paper, we demonstrate a training chip fabricated using a commercial 65-nm CMOS technology for machine learning. The chip performs training without back propagation by using invertible logic with stochastic computing that can directly obtain weight values using input/output training data with low precision suitable for inference. When training neurons that compute the weighted sum of all inputs and then apply a non-linear activation function, our chip demonstrates a reduction of power dissipation and latency by 99.98% and 99.95%, respectively, in comparison with a state-of-the-art software implementation.