ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Measurements and model for the satellite-to-aircraft channel in L-band

Wireless radio transmission from a satellite-based emitter to a receiver located on an aircraft is of interest for many applications such as internet access for passengers, air traffic management or positioning by global navigation satellite systems especially when a worldwide service shall be granted. In particular, the last two mentioned applications are related to the safety of life requiring realistic and accurate channel models for software-based system testing. State-of-the-art channel models for the satellite-to-aircraft case lack of accuracy in terms of modelling all propagation impairments. In this contribution, the authors describe airborne propagation experiments using Global Positioning System signals for channel sounding, the data evaluation and the derived channel model.
Comparative study of effective antibiofilm activity of beneficial microbes-mediated zirconia nanoparticles

In the present study, beneficial microbes-mediated zirconia nanoparticles were prepared using endophytic bacteria isolated from the seed of Terminalia chebula which were evaluated on inhibition of bacterial adherence and promotion to exhibit antibiofilm properties. The structure and distribution of the zirconia nanoparticles were examined through SEM (Scanning Electron Microscopy), EDS (Energy-Dispersive X-Ray spectroscopy), and XRD (X-ray diffraction analysis), which reveal the distribution of the particles. The morphology of biogenic zirconia nanoparticles was monoclinic and cubic. The formation of zirconia particle was confirmed using UV spectrum and the functional groups were intensified in FTIR (Fourier-transform infrared spectroscopy). The antibiofilm activity of the synthesized nanoparticles was tested in oral pathogens that cause biofilm by membrane integrity and leads to periodontal associated disease. The results showed that the particles had a significant effect on biofilm removal caused by oral pathogens. For determined concentration, the cytotoxicity of the endophytic bacterial facilitated zirconia nanoparticle (Zr NPs) was examined in HGF (Human gingival fibroblast cell line).
Exploiting objective text description of images for visual sentiment analysis

This paper addresses the problem of Visual Sentiment Analysis focusing on the estimation of the polarity of the sentiment evoked by an image. Starting from an embedding approach which exploits both visual and textual features, we attempt to boost the contribution of each input view. We propose to extract and employ anObjective Textdescription of images rather than the classicSubjective Textprovided by the users (i.e., title, tags and image description) which is extensively exploited in the state of the art to infer the sentiment associated to social images.Objective Textis obtained from the visual content of the images through recent deep learning architectures which are used to classify object, scene and to perform image captioning.Objective Textfeatures are then combined with visual features in an embedding space obtained with Canonical Correlation Analysis. The sentiment polarity is then inferred by a supervised Support Vector Machine. During the evaluation, we compared an extensive number of text and visual features combinations and baselines obtained by considering the state of the art methods. Experiments performed on a representative dataset of 47235 labelled samples demonstrate that the exploitation ofObjective Texthelps to outperform state-of-the-art for sentiment polarity estimation.
Dioxane-Linked Novel Bacterial Topoisomerase Inhibitors Exhibit Bactericidal Activity against Planktonic and Biofilm Staphylococcus aureus In Vitro

The development of novel treatments for Staphylococcus aureus infections remains a high priority worldwide. We previously reported compounds 0147 and 0186, novel bacterial topoisomerase inhibitors (NBTIs) with potent antibacterial activity against S. aureus, including methicillin-resistant S. aureus. Here, we further investigated the in vitro activity of 0147 and 0186 against S. aureus ATCC 29213. Both compounds demonstrated bactericidal activity against planktonic and biofilm S. aureus, which then translated into significant inhibition of biofilm formation. Combinations of NBTIs and glycopeptides yielded indifferent interactions against planktonic S. aureus, but several had synergistic effects against S. aureus biofilms. This work reinforces the potential of NBTIs as future therapeutics for S. aureus infections. IMPORTANCE The pathogen Staphylococcus aureus contributes substantially to infection-related mortality. Biofilms render bacteria more recalcitrant to antibacterial therapy. The manuscript describes the potent activity of a new class of antibacterial agents against both planktonic and biofilm populations of Staphylococcus aureus.
Sparse Regression Driven Mixture Importance Sampling for Memory Design

In this paper, we present a sparse regression (SpaRe) model-based yield analysis methodology and apply it to memory designs with state-of-the-art write-assist circuitry. At the core of its engine is a mixture importance sampling technique which consists of a uniform sampling stage and an importance sampling stage. The proposed methodology allows for fast and accurate statistical analysis of rare fail events. In our approach, a SpaRe model is built using the uniform sampling stage data points obtained via circuit simulation (CktSim). Along with the model, an optimal threshold value is determined for proper pass/fail predict capability. The model and the threshold value are then used to predict the response in the importance sampling stage. This alleviates the need for CktSims in the latter stage and introduces significant speedup compared to fully CktSim-based approaches. The SpaRe model-based yield analysis is tested on a 14-nm FinFET SRAM design, and the results corroborate well with that of full CktSim-based yield analysis. The methodology is used to compare multiple state-of-the-art SRAM designs including selective boost and write-assist designs. The operating Vmin ranges and trends corroborate well with hardware measurements.
Deep Prior Approach for Room Impulse Response Reconstruction

In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural network (CNN) is employed prior, in order to obtain a regularized solution to the RIR reconstruction problem for uniform linear arrays. This approach allows us to avoid assumptions on sound wave propagation, acoustic environment, or measuring setting made in state-of-the-art RIR reconstruction algorithms. Moreover, differently from classical deep learning solutions in the literature, the deep prior approach employs a per-element training. Therefore, the proposed method does not require training data sets, and it can be applied to RIRs independently from available data or environments. Results on simulated data demonstrate that the proposed technique is able to provide accurate results in a wide range of scenarios, including variable direction of arrival of the source, room T-60, and SNR at the sensors. The devised technique is also applied to real measurements, resulting in accurate RIR reconstruction and robustness to noise compared to state-of-the-art solutions.
Saccharomyces boulardii and bismuth subsalicylate as low-cost interventions to reduce the duration and severity of cholera

We conducted a randomised single-blinded clinical trial of 100 cholera patients in Port-au-Prince, Haiti to determine if the probiotic Saccharomyces cerevisiae var. boulardii and the anti-diarrhoeal drug bismuth subsalicylate (BS) were able to reduce the duration and severity of cholera. Subjects received either: S. boulardii 250 mg, S. boulardii 250 mg capsule plus BS 524 mg tablet, BS 524 mg, or two placebo capsules every 6 hours alongside standard treatment for cholera. The length of hospitalisation plus the number and volume of emesis, stool and urine were recorded every 6 hours until the study subject was discharged (n = 83), left against medical advice (n = 11), or requested removal from the study (n = 6). There were no reported deaths or adverse study-related events. There were no statistically significant differences between the study arms and the outcomes of interest.
Graph-based people segmentation using a genetically optimized combination of classifiers

Many approaches for background subtraction and people detection have been developed so far. However, the best state-of-the-art methods do not yet give satisfactory results in real transportation environments. Indeed, these latter configurations imply several difficulties such as fast brightness changes, noise, shadows, scrolling background, etc., and a single approach cannot deal with all these. We propose an approach for people segmentation and tracking in videos that is suited for real-world conditions. Our strategy combines several state-of-the-art methods for people detection, silhouette appearance modeling and tracking. Each process also uses its own frame preprocessing pipeline. The optimal combination of the people classifiers used, as well as the optimal parameters of each of the combined methods, being too difficult to be determined altogether, a genetic algorithm is used to determine the optimal classifier parameters and their combination weights. The output of the latter is used as an initialization for a multiframe graph-cut operating on superpixel graphs. Our proposed approach is evaluated on the BOSS European project database that was acquired in moving trains and contains typical scientific locks encountered in real transportation systems. (C) 2018 SPIE and IS&T
Modeling Type-1 Iodothyronine Deiodinase with Peptide-Based Aliphatic Diselenides: Potential Role of Highly Conserved His and Cys Residues as a General Acid Catalyst

Type-1 iodothyronine deiodinase (ID-1) catalyzes the reductive elimination of 5'-I and 5-I on the phenolic and tyrosyl rings of thyroxine (T4), respectively. Chemically verifying whether I atoms with different chemical properties undergo deiodination through a common mechanism is challenging. Herein, we report the modeling of ID-1 using aliphatic diselenide (Se-Se) and selenenylsulfide (Se-S) compounds. Mechanistic investigations of deiodination using the ID-1-like reagents suggested that the 5'-I and 5-I deiodinations proceed via the same mechanism through an unstable intermediate containing a Se⋅⋅⋅I halogen bond between a selenolate anion, reductively produced from Se-Se (or Se-S) in the compound, and an I atom in T4. Moreover, imidazolium and thiol groups, which may act as general acid catalysts, promoted the heterolytic cleavage of the C-I bond in the Se⋅⋅⋅I intermediate, which is the rate-determining step, by donating a proton to the C atom.
De novo design of anti-tuberculosis agents using a structure-based deep learning method

Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.
Convolutional Sparse Coding for Compressed Sensing CT Reconstruction

Over the past few years, dictionary learning (DL)-based methods have been successfully used in various image reconstruction problems. However, the traditional DL-based computed tomography (CT) reconstruction methods are patch-based and ignore the consistency of pixels in overlapped patches. In addition, the features learned by these methods always contain shifted versions of the same features. In recent years, convolutional sparse coding (CSC) has been developed to address these problems. In this paper, inspired by several successful applications of CSC in the field of signal processing, we explore the potential of CSC in sparse-view CT reconstruction. By directly working on the whole image, without the necessity of dividing the image into overlapped patches in DL-based methods, the proposed methods can maintain more details and avoid artifacts caused by patch aggregation. With predetermined filters, an alternating scheme is developed to optimize the objective function. Extensive experiments with simulated and real CT data were performed to validate the effectiveness of the proposed methods. The qualitative and quantitative results demonstrate that the proposed methods achieve better performance than the several existing state-of-the-art methods.
Comprehensive study of aerosols properties over various terrain types

Aerosols are a crucial part of the climate system. Numerous factors, including aerosols, govern Earth's radiation balance. Different aerosols have distinct radiational effects on the earth system, and thus the slight change in their composition may lead to a drastic change in their radiative effects. Aerosols' chemical and physical properties also depend on generation processes, generation source, and geographical location. Significant spatio-temporal inconsistency is noticed in the distribution of aerosols. It makes it much difficult task to assess their radiative properties. We attempted to explore aerosol's optical properties and wavelength dependence over different locations. We have used AERONET (Aerosol Robotic Network) data over various stations (Kanpur, Jaipur, Gandhi College, Pune) with varying terrain properties in the Indian continent. We have studied the variation of different optical parameters: aerosol optical depth (AOD), single scattering albedo (SSA), and Angstrom exponent (α), and their wavelength dependence. This study indicated that Jaipur is the cleanest site, with dust aerosols as a primary aerosol. Though over Pune also aerosol concentration was relatively low but the anthropogenic aerosols contributed primarily over this site. Over the Indo-Gangetic Plain (IGP) sites, dust aerosols dominated the pre-monsoon season, while anthropogenic aerosols dominated the post-monsoon and winter seasons. The scatter plot of AOD with α gives the details of different aerosols (desert dust, continental aerosols, mixed aerosol, biomass burning aerosols, and sulfate aerosols) in the different seasons and places. This study provides an overview of aerosol properties, dominant aerosols in the aerosol system, and their seasonal and spectral variation.
Optimal Cooperative Strategies for PHY Security Maximization Subject to SNR Constraints

The cooperative jamming (CJ) and decode-and-forward (DF) protocols for physical layer security are studied in this paper. We propose a design that aims at maximizing the security gap, which is defined as the signal-to-noise ratio (SNR) difference between the destination and an eavesdropper, subject to security and reliability constraints defining the thresholds on the received signals' SNR values at the destination and the eavesdropper. A fractional quadratically constrained quadratic program (QCQP) is formulated, which is solved analytically and closed-form expressions are determined for both protocols. Numerical results demonstrate that the proposed designs achieve the same performance for the secrecy rate under both strategies compared with state-of-the-art approaches, for a proper choice of thresholds on SNR values. Additionally, for relaxed thresholds and at the cost of a slight decrease on the optimal secrecy rate value, the received SNR values at the eavesdropper are greatly decreased for the CJ protocol and even more for the DF protocol, while guaranteeing target SNR values at the destination, compared with previous state-of-the-art approaches.
Local Learning With Deep and Handcrafted Features for Facial Expression Recognition

We present an approach that combines automatic features learned by convolutional neural networks (CNN) and handcrafted features computed by the bag-of-visual-words (BOVW) model in order to achieve the state-of-the-art results in facial expression recognition (FER). To obtain automatic features, we experiment with multiple CNN architectures, pre-trained models, and training procedures, e.g., Dense-Sparse-Dense. After fusing the two types of features, we employ a local learning framework to predict the class label for each test image. The local learning framework is based on three steps. First, a k-nearest neighbors model is applied in order to select the nearest training samples for an input test image. Second, a one-versus-all support vector machines (SVM) classifier is trained on the selected training samples. Finally, the SVM classifier is used to predict the class label only for the test image it was trained for. Although we have used local learning in combination with handcrafted features in our previous work, to the best of our knowledge, local learning has never been employed in combination with deep features. The experiments on the 2013 FER Challenge data set, the FER+ data set, and the AffectNet data set demonstrate that our approach achieves the state-of-the-art results. With a top accuracy of 75.42% on the FER 2013, 87.76% on the FER+, 59.58% on the AffectNet eight-way classification, and 63.31% on the AffectNet seven-way classification, we surpass the state-of-the-art methods by more than 1% on all data sets.
The "Weather Intelligence for Renewable Energies" Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies (WIRE) with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.
Coexistent Routing and Flooding Using WiFi Packets in Heterogeneous IoT Network

Routing and flooding are important functions in wireless networks. However, until now routing and flooding protocols are investigated separately within the same network (i.e., a WiFi network or a ZigBee network). Moreover, further performance improvement has been hampered by the assumption of the harmful cross technology interference. In this paper, we present coexistent routing and flooding (CRF), which leverages the unique feature of physical layer cross-technology communication technique for concurrently conducting routing within the WiFi network and flooding among ZigBee nodes using a single stream of WiFi packets. We extensively evaluate our design under different network settings and scenarios. The evaluation results show that CRF i) improves the throughput of WiFi network by 1.12 times than the state-of-the-art routing protocols; and ii) significantly reduces the flooding delay in ZigBee network (i.e., 31 times faster than the state-of-the-art flooding protocol).
[Study on Test Method of Radiation Emission of Proton Therapy Equipment]

Electromagnetic compatibility testing of proton therapy system is different from that of traditional products in an anechoic chamber. It has high requirements on the division of sample composition, the understanding of applicable standards, the formulation of operation mode, the selection of test location, and the test of ambient noise. According to the requirements of GB 4824-2019 standard, the test method of radiation emission of proton therapy equipment was developed to provide reference advice for the industry, and the problems encountered in the actual test were studied.
Polymersomes with Red/Near-Infrared Emission and Reactive Oxygen Species Generation

In photodynamic therapy (PDT), the uses of nanoparticles bearing photosensitizers (PSs) can overcome some of the drawbacks of using a PS alone (e.g., poor water solubility and low tumor selectivity). However, numerous nano-formulations are developed by physical encapsulation of PSs through Van der Waals interactions, which have not only a limited load efficiency but also some in vivo biodistribution problems caused by leakage or burst release. Herein, polymersomes made from an amphiphilic block copolymer, in which a PS with aggregation-induced emission (AIE-PS) is covalently attached to its hydrophobic poly(amino acid) block, are reported. These AIE-PS polymersomes dispersed in aqueous solution have a high AIE-PS load efficiency (up to 46% as a mass fraction), a hydrodynamic diameter of 86 nm that is suitable for in vivo applications, and an excellent colloidal stability for at least 1 month. They exhibit a red/near-infrared photoluminescence and ability to generate reactive oxygen species (ROS) under visible light. They are non-cytotoxic in the dark as tested on Hela cells up to concentration of 100 µm. Benefiting from colloidal stability, AIE property and ROS generation capability, such a family of polymersomes can be great candidates for image-guided PDT.