ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Hibiscus sabdariffa L. polyphenolic-rich extract promotes muscle glucose uptake and inhibits intestinal glucose absorption with concomitant amelioration of Fe2+ -induced hepatic oxidative injury

In this current study, the antidiabetic effectiveness of Hibiscus sabdariffa and its protective function against Fe2+ -induced oxidative hepatic injury were elucidated using in vitro, in silico, and ex vivo studies. The oxidative damage was induced in hepatic tissue by incubation with 0.1 mMolar ferrous sulfate (FeSO4) and then treated with different concentrations of crude extracts (ethyl acetate, ethanol, and aqueous) of H. sabdariffa flowers for 30 min at 37°C. When compared to ethyl acetate and aqueous extracts, the ethanolic extract displayed the most potent scavenging activity in ferric-reducing antioxidant power (FRAP), 1,1-diphenyl-2-picrylhydrazyl (DPPH), and nitric oxide (NO) assays, with IC50 values of 2.8 μl/ml, 3.3 μl/ml, and 9.2 μl/ml, respectively. The extracts significantly suppressed α-glucosidase and α-amylase activities (p < .05), with the ethanolic extract demonstrating the highest activity. H. sabdariffa significantly (p < .05) raised reduced glutathione (GSH) levels while simultaneously decreasing malondihaldehyde (MDA) and NO levels and increasing superoxide dismutase (SOD) and catalase activity in Fe2+ induced oxidative hepatic injury. The extract of the plant inhibited intestinal glucose absorption and increased muscular glucose uptake. The extract revealed the presence of several phenolic compounds when submitted to gas chromatography-mass Spectroscopy (GC-MS) screening, which was docked with α-glucosidase and α- amylase. The molecular docking displayed the compound 4-(3,5-Di-tert-butyl-4-hydroxyphenyl)butyl acrylate strongly interacted with α-glucosidase and α-amylase and had the lowest free binding energy compared to other compounds and acarbose. These results suggest that H. sabdariffa has promising antioxidant and antidiabetic activity. PRACTICAL APPLICATIONS: In recent years, there has been increased concern about the side effects of synthetic anti-diabetic drugs, as well as their expensive cost, especially in impoverished nations. This has instigated a radical shift towards the use of traditional plants, which are rich in phytochemicals many years ago. Among these plants, H. sabdariffa has been used to treat diabetes in traditional medicine. In this present study, H. sabdariffa extracts demonstrated the ability to inhibit carbohydrate digesting enzymes, facilitate muscle glucose uptake and attenuate oxidative stress in oxidative hepatic injury. Hence, demonstrating H. sabdariffa's potential to protect against oxidative damage and the complications associated with diabetes. Consumption of Hibiscus tea or juice may be a potential source for developing an anti-diabetic drug.
Modified Visibility Restoration-Based Contrast Enhancement Algorithm for Colour Foggy Images

The visibility enhancement of colour foggy images is a very challenging task for many real-time applications. In this paper, we have proposed an efficient and robust algorithm for visibility enhancement of colour foggy images. The proposed algorithm works in two steps: in the first step, modified visibility restoration algorithm is applied for visibility enhancement, and in the second step, sigmoid-function-based contrast enhancement technique is applied for colour contrast enhancement of images. The quantitative and qualitative results of proposed and other state-of-the-art algorithms for colour foggy images are obtained in terms of fog aware density evaluator, fog reduction factor (FRF), measure of enhancement (EME), and measure of enhancement factor (EMF) on different colour foggy image databases. Results reveal the strength of proposed algorithm mathematically on the basis of fog thickness estimation from original image and output-enhanced image, FRF, EME, and EMF for colour foggy images. Experimental results shows that the proposed algorithm provides better quantitative and qualitative results as compared to other state-of-the-art algorithms for colour foggy images. Finally, the proposed algorithm is highly efficient for visibility enhancement of colour foggy images.
Multi-Sensory Color Expression with Sound and Temperature in Visual Arts Appreciation for People with Visual Impairment

For years the HCI community's research has been focused on the hearing and sight senses. However, in recent times, there has been an increased interest in using other types of senses, such as smell or touch. Moreover, this has been accompanied with growing research related to sensory substitution techniques and multi-sensory systems. Similarly, contemporary art has also been influenced by this trend and the number of artists interested in creating novel multi-sensory works of art has increased substantially. As a result, the opportunities for visually impaired people to experience artworks in different ways are also expanding. In spite of all this, the research focusing on multimodal systems for experiencing visual arts is not large and user tests comparing different modalities and senses, particularly in the field of art, are insufficient. This paper attempts to design a multi-sensory mapping to convey color to visually impaired people employing musical sounds and temperature cues. Through user tests and surveys with a total of 18 participants, we show that this multi-sensory system is properly designed to allow the user to distinguish and experience a total of 24 colors. The tests consist of several semantic correlational adjective-based surveys for comparing the different modalities to find out the best way to express colors through musical sounds and temperature cues based on previously well-established sound-color and temperature-color coding algorithms. In addition, the resulting final algorithm is also tested with 12 more users.
Intensity Filtering and Group Fusion for Accurate Mobile Place Recognition

Mobile place recognition targets at matching query images captured by mobile devices with database images collected from vehicle-mounted cameras, such as Google street view panoramas, which plays an important role in many applications. However, current solutions deriving from image retrieval suffer from the problem of low precision on top results, which significantly challenges their usability. By investigating the state-of-the-art approaches, we find that the bad illumination significantly affects initial results, and these initial results are correlative in both spatial location and visual content, which can be utilized for further improvement. In this paper, we propose an effective approach to rerank initial top-ranked results to improve the recognition recall. First, initial retrieval results with low intensity are filtered as they usually depict irrelevant places with dark background. Second, the correlation between top-ranked results is modeled as a reciprocal neighborhood graph by jointly considering spatial location and visual similarity. With the graph, the initial results are reranked based on voting similarity from the query and reciprocal neighbors. In this way, the underlying structure of initial retrieval results is exploited for refining. Experimental results on the public Tokyo 24/7 and San Francisco landmark datasets demonstrate that the proposed approach can achieve persisting improvement of recognition recall over the state-of-the-art approach.
Safety of β-hydroxybutyrate salts as a novel food pursuant to Regulation (EU) 2015/2283

Following a request from the European Commission, the EFSA Panel on Nutrition, Novel Foods and Food Allergens (NDA) was asked to deliver an opinion on β-hydroxybutyrate (BHB) salts as a novel food (NF) pursuant to Regulation (EU) 2015/2283. The NF consists of sodium, magnesium and calcium BHB salts, and is proposed to be used by adults as a food ingredient in a number of food categories and as food supplement. The data provided by the applicant about the identity, the production process and the compositional data of the NF over the course of the risk assessment period were overall considered unsatisfactory. The Panel noted inconsistencies in the reporting of the test item used in the subchronic toxicity study and human studies provided by the applicant. Owing to these deficiencies, the Panel cannot establish a safe intake level of the NF. The Panel concludes that the safety of the NF has not been established.
Methanol synthesis revisited: reaction mechanisms in CO/CO2 hydrogenation over Cu/ZnO and DFT analysis

One of the very long-standing controversies in CO/CO2 hydrogenation chemistry has been the following: What is the principal source of C in the methanol product, CO or CO2? In other words, do CO hydrogenation and CO2 hydrogenation proceed independently, in parallel, over the industrial Cu/ZnO/Al2O3 catalyst? Using state of the art experimental data and our analysis of prior studies and experimental techniques including isotopic labeling studies, including radioactive tracers, FTIR, XPS, and E-TEM, we discuss the experimental findings of the prior art and analyze the experimental and theoretical first-principles calculational data, to come up with a rational and cogent explanation for all controversial areas in methanol synthesis chemistry.
All-Aqueous, Surfactant-Free, and pH-Driven Nanoformulation Methods of Dual-Responsive Polymer Nanoparticles and their Potential use as Nanocarriers of pH-Sensitive Drugs

All-aqueous, surfactant-free, and pH-driven nanoformulation methods to generate pH- and temperature-responsive polymer nanoparticles (NPs) are described. Copolymers comprising a poly(methyl methacrylate) (PMMA) backbone with a few units of 2-(dimethylamino)ethyl methacrylate (DMAEMA) are solubilized in acidic buffer (pH 2.0) to produce pH-sensitive NPs. Copolymers of different molar mass (2.3-11.5 kg mol-1 ) and DMAEMA composition (7.3-14.2 mol%) are evaluated using a "conventional" pH-driven nanoformulation method (i.e., adding an aqueous polymer solution (acidic buffer) into an aqueous non-solvent (basic buffer)) and a robotized method for pH adjustment of polymer dispersions. Dynamic light scattering, zeta-potential (ζ), and sedimentation-diffusion analyses suggest the formation of dual-responsive NPs of tunable size (from 20 to 110 nm) being stable for at least 28 days in the pH and temperature intervals from 2.0 to 6.0 and 25 to 50 °C, respectively. Ultraviolet-visible spectroscopic experiments show that these NPs can act as nanocarriers for the pH-sensitive dipyridamole drug, expanding its bioavailability and potential controlled release as a function of pH and temperature. These approaches offer alternative strategies to prepare stimuli-responsive NPs, avoiding the use of harmful solvents and complex purification steps, and improving the availability of biocompatible polymer nanoformulations for specific controlled release of pH-sensitive cargos.
Saliency modeling via outlier detection

Based on the fact that human attention is more likely to be attracted by different objects or statistical outliers of a scene, a bottom-up saliency detection model is proposed. Our model regards the saliency patterns of an image as the outliers in a dataset. For an input image, first, each image element is described as a feature vector. The whole image is considered as a dataset and an image element is classified as a saliency pattern if its corresponding feature vector is an outlier among the dataset. Then, a binary label map can be built to indicate the salient and the nonsalient elements in the image. According to the Boolean map theory, we compute multiple binary maps as a set of Boolean maps which indicate the outliers in multilevels. Finally, we linearly fused them into the final saliency map. This saliency model is used to predict the human eye fixation, and has been tested on the most widely used three benchmark datasets and compared with eight state-of-the-art saliency models. In our experiments, we adopt the shuffled the area under curve metric to evaluate the accuracy of our model. The experimental results show that our model outperforms the state-of-the-art models on all three datasets. (C) 2014 SPIE and IS&T
Pathways to depression: Dynamic associations between neural responses to appetitive cues in the environment, stress, and the development of illness

This review focuses on research my colleagues and I have conducted on etiological pathways to depression. Much of this work has focused on the measurement of neural responses to appetitive cues, using two event-related brain potential (ERP) components, the Late Positive Potential (LPP) and the Reward Positivity (RewP). Reductions in each of these components have been associated with current symptoms of depression, and in some cases have been shown to differentiate anxious from depressive phenotypes. In this review, I will describe three broad and related approaches we have taken in our research to address a series of interdependent issuess. The first attempts to understand different sources of variation in the LPP and RewP, and how these sources interact with one another. The second tries to identify whether variation in the processes measured by these ERP components might reflect a latent vulnerability to depression and its symptoms, that is evident prior to illness onset. And the third examines the possibility that the processes reflected in the LPP and RewP might play a mechanistic role in the development of depression.
Fetal electrocardiogram estimation using polynomial eigenvalue decomposition

In this paper, we propose the application of polynomial matrix eigenvalue decomposition (PEVD) to the problem of fetal electrocardiogram (ECG) extraction from real ECG recordings obtained from abdominal leads. We model the fetal ECG extraction problem as a broadband sensor array signal processing problem in order to account for the broadband nature of the ECG noise present in the recordings. An algorithm for providing an approximate PEVD is used in order to estimate the broadband noise subspace. Suppression of the broadband noise and maternal ECG is achieved by carrying out an orthonormal projection of the recordings onto the estimated fetal subspace. The proposed scheme was evaluated with multichannel synthetic ECG signals and real ECG data from the PhysioNet Challenge database and is shown to perform favorably as compared to prior-art methods. Results indicate that our method is more robust than the prior-art ones for the task of fetal ECG estimation.
Sexual Risk Behaviors of Patients with HIV/AIDS over the Course of Antiretroviral Treatment in Northern Vietnam

Antiretroviral therapy (ART) improves the health and well-being of people living with the human immunodeficiency virus (HIV, PLWH), and reduces their risk of transmitting the virus to sexual partners. However, patterns of sexual risk behavior among HIV-positive patients taking ART in Vietnam remain largely unknown. In this study, we sought to examine sexual risk behaviors and their associated factors among HIV-positive patients receiving ART in northern Vietnam. The socio-demographic characteristics, ART use, health status, and sexual behaviors of 1133 patients taking ART in the Hanoi and Nam Dinh provinces were explored through face-to-face interviews. There were 63.5% of patients who had one sex partner, while 3.6% and 5.6% of patients had sexual intercourse with casual partners or sex workers, respectively, in the previous 12 months. Most participants tended to use condoms more often with commercial sex partners (90.2%) and intimate partners (79.7%), and less often with casual partners (60.9%). Higher age (odds ratio, OR = 1.0; 95% CIs = 1.0, 1.1) or suffering pain/discomfort (OR = 1.7; 95% CIs = 1.2, 2.4) were factors more likely to be associated with multiple sex partners. Patients who were self-employed were more likely to have sexual intercourse with casual partners/sex workers (OR = 2.1; 95% CIs = 1.1, 4.0). Meanwhile, a higher score on the EuroQol visual analog scale (EQ-VAS), an unknown HIV stage, and a longer duration of ART were adversely associated with not using condoms with casual partners/sex workers. Patients with longer durations of ART had a lower likelihood of not using a condom with casual partners/sex workers (OR = 0.5; 95% CIs = 0.3, 0.8). Our study underscored a relatively high rate of unsafe sexual behaviors among HIV-positive patients. Continuing to improve the physical and psychological well-being of HIV-positive patients in Vietnam is important in reducing the spread of HIV via risky sexual behaviors. In addition, safe-sex education should be provided to older people, and to those who are self-employed.
Efficient finite impulse response filters in massively-parallel recursive systems

This paper presents strategies to massively parallelize complete recursive systems. Each algorithm handles systems with feedforward and feedback coefficients allowing to compute high-complexity filtering operators. The final algorithm is linear in time and memory, exposes a high number of parallel tasks, and it is implemented on graphics processing units, i.e. GPUs. The key to the final algorithm is the derivation of closed-form formulas to combine both non-recursive and recursive linear filters, based on an efficient state-of-the-art block-based strategy. Applications to early vision are considered in this work, hence the GPU implementation runs on images computing an approximation of the Gaussian filter and its first and second derivatives. Finally, comparison results are given showing that this work outperforms prior state-of-the-art algorithms, enabling it to achieve real-time image filtering on ultra-high-definition videos.
Cross-Corpus Speech Emotion Recognition Based on Domain-Adaptive Least-Squares Regression

In this letter, a novel cross-corpus speech emotion recognition (SER) method using domain-adaptive least-squares regression (DaLSR) model is proposed. In this method, an additional unlabeled data set from target speech corpus is used to serve as an auxiliary data set and combined with the labeled training data set from source speech corpus for jointly training the DaLSR model. In contrast to the traditional least-squares regression (LSR) method, the major novelty of DaLSR is that it is able to handle the mismatch problem between source and target speech corpora. Hence, the proposed DaLSR method is very suitable for coping with cross-corpus SER problem. For evaluating the performance of the proposed method in dealing with the cross-corpus SER problem, we conduct extensive experiments on three emotional speech corpora and compare the results with several state-of-the-art transfer learning methods that are widely used for cross-corpus SER problem. The experimental results show that the proposed method achieves better recognition accuracies than the state-of-the-art methods.
Pattern analysis of neuroimaging data reveals novel insights on threat learning and extinction in humans

Several decades of rodent neurobiology research have identified a network of brain regions that support Pavlovian threat conditioning and extinction, focused predominately on the amygdala, hippocampus, and medial prefrontal cortex (mPFC). Surprisingly, functional magnetic resonance imaging (fMRI) studies have shown inconsistent evidence for these regions while humans undergo threat conditioning and extinction. In this review, we suggest that translational neuroimaging efforts have been hindered by reliance on traditional univariate analysis of fMRI. Whereas univariate analyses average activity across voxels in a given region, multivariate pattern analyses (MVPA) leverage the information present in spatial patterns of activity. MVPA therefore provides a more sensitive analysis tool to translate rodent neurobiology to human neuroimaging. We review human fMRI studies using MVPA that successfully bridge rodent models of amygdala, hippocampus, and mPFC function during Pavlovian learning. We also highlight clinical applications of these information-sensitive multivariate analyses. In sum, we advocate that the field should consider adopting a variety of multivariate approaches to help bridge cutting-edge research on the neuroscience of threat and anxiety.
Integrated energy systems of data centers and smart grids: State-of-the-art and future opportunities

Cloud computing platforms are critical cyber infrastructures in modern society. As the backbone of cloud systems, data centers act as large energy consumers in today's power grids. The integration of on-site renewable energy sources and energy storage systems further transforms data centers to be energy prosumers (producersand-consumers). As a result, optimizing data centers' energy production and consumption, and exploiting their potential of actively engaging in the external grid's planning, operation, and control has been drawing increasing attention in the last few years. This paper conducts a comprehensive review of the state-of-the-art research efforts on integrated energy systems of data centers and smart grids. A taxonomy of such integration scenarios is provided. Consequently, this paper identifies several future application scenarios of integrating data centers and smart grids, which serves as a roadmap towards future research. This article is expected to provide a useful reference for researchers and engineers in the areas of energy systems and cloud computing.
Data-driven health deficit assessment improves a frailty index's prediction of current cognitive status and future conversion to dementia: results from ADNI

Frailty is a dementia risk factor commonly measured by a frailty index (FI). The standard procedure for creating an FI requires manually selecting health deficit items and lacks criteria for selection optimization. We hypothesized that refining the item selection using data-driven assessment improves sensitivity to cognitive status and future dementia conversion, and compared the predictive value of three FIs: a standard 93-item FI was created after selecting health deficit items according to standard criteria (FIs) from the ADNI database. A refined FI (FIr) was calculated by using a subset of items, identified using factor analysis of mixed data (FAMD)-based cluster analysis. We developed both FIs for the ADNI1 cohort (n = 819). We also calculated another standard FI (FIc) developed by Canevelli and coworkers. Results were validated in an external sample by pooling ADNI2 and ADNI-GO cohorts (n = 815). Cluster analysis yielded two clusters of subjects, which significantly (pFDR < .05) differed on 26 health items, which were used to compute FIr. The data-driven subset of items included in FIr covered a range of systems and included well-known frailty components, e.g., gait alterations and low energy. In prediction analyses, FIr outperformed FIs and FIc in terms of baseline cognition and future dementia conversion in the training and validation cohorts. In conclusion, the data show that data-driven health deficit assessment improves an FI's prediction of current cognitive status and future dementia, and suggest that the standard FI procedure needs to be refined when used for dementia risk assessment purposes.
Current strategies in biomaterial-based periosteum scaffolds to promote bone regeneration: A review

The role of periosteum rich in a variety of bone cells and growth factors in the treatment of bone defects has gradually been discovered. However, due to the limited number of healthy transplantable periosteum, there are still major challenges in the clinical treatment of critical-size bone defects. Various techniques for preparing biomimetic periosteal scaffolds that are similar in composition and structure to natural periosteal scaffold have gradually emerged. This article reviews the current preparation methods of biomimetic periosteal scaffolds based on various biomaterials, which are mainly divided into natural periosteal materials and various polymer biomaterials. Several preparation methods of biomimetic periosteal scaffolds with different principles are listed, their strengths and weaknesses are also discussed. It aims to provide a more systematic perspective for the preparation of biomimetic periosteal scaffolds in the future.