ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Sentence-Based Sentiment Analysis for Expressive Text-to-Speech

Current research to improve state of the art Text-To-Speech (TTS) synthesis studies both the processing of input text and the ability to render natural expressive speech. Focusing on the former as a front-end task in the production of synthetic speech, this article investigates the proper adaptation of a Sentiment Analysis procedure (positive/neutral/negative) that can then be used as an input feature for expressive speech synthesis. To this end, we evaluate different combinations of textual features and classifiers to determine the most appropriate adaptation procedure. The effectiveness of this scheme for Sentiment Analysis is evaluated using the Semeval 2007 dataset and a Twitter corpus, for their affective nature and their granularity at the sentence level, which is appropriate for an expressive TTS scenario. The experiments conducted validate the proposed procedure with respect to the state of the art for Sentiment Analysis.
Leaves of Persimmon (Diospyros kaki Thunb.) Ameliorate N-Methyl-N-nitrosourea (MNU)-Induced Retinal Degeneration in Mice

The purpose of the study was to investigate the protective effects of the ethanol extract of Diospyros kaki (EEDK) persimmon leaves to study N-methyl-N-nitrosourea (MNU)-induced retinal degeneration in mice. EEDK was orally administered after MNU injection. Retinal layer thicknesses were significantly increased in the EEDK-treated group compared with the MNU-treated group. The outer nuclear layer was preserved in the retinas of EEDK-treated mice. Moreover, EEDK treatment reduced the MNU-dependent up-regulation of glial fibrillary acidic protein (GFAP) and nestin expression in Müller and astrocyte cells. EEDK treatment also inhibited MNU-dependent down-regulation of rhodopsin expression. Quercetin exposure significantly attenuated the negative effects of H2O2 in R28 cells, suggesting that quercetin can act in an antioxidative capacity. Thus, EEDK may be considered as an agent for treating or preventing degenerative retinal diseases, such as retinitis pigmentosa and age-related macular degeneration.
Enhanced recovery after surgery in transurethral surgery for benign prostatic hyperplasia

Enhanced recovery after surgery (ERAS) measures have not been systematically applied in transurethral surgery for benign prostatic hyperplasia (BPH). This study was performed on patients with BPH who required surgical intervention. From July 2019 to June 2020, the ERAS program was applied to 248 patients, and the conventional program was applied to 238 patients. After 1 year of follow-up, the differences between the ERAS group and the conventional group were evaluated. The ERAS group had a shorter time of urinary catheterization compared with the conventional group (mean ± standard deviation [s.d.]: 1.0 ± 0.4 days vs 2.7 ± 0.8 days, P < 0.01), and the pain (mean ± s.d.) was significantly reduced through postoperative hospitalization days (PODs) 0-2 (POD 0: 1.7 ± 0.8 vs 2.4 ± 1.0, P < 0.01; POD 1: 1.6 ± 0.9 vs 3.5 ± 1.3, P < 0.01; POD 2: 1.2 ± 0.7 vs 3.0 ± 1.3, P < 0.01). No statistically significant difference was found in the rate of postoperative complications, such as postoperative bleeding (P = 0.79), urinary retention (P = 0.40), fever (P = 0.55), and readmission (P = 0.71). The hospitalization cost of the ERAS group was similar to that of the conventional group (mean ± s.d.: 16 927.8 ± 5808.1 Chinese Yuan [CNY] vs 17 044.1 ± 5830.7 CNY, P =0.85). The International Prostate Symptom Scores (IPSS) and quality of life (QoL) scores in the two groups were also similar when compared at 1 month, 3 months, 6 months, and 12 months after discharge. The ERAS program we conducted was safe, repeatable, and efficient. In conclusion, patients undergoing the ERAS program experienced less postoperative stress than those undergoing the conventional program.
Dynamic Interactions Between Brilliant Green and MscL Investigated by Solid-State NMR Spectroscopy and Molecular Dynamics Simulations

The mechanosensitive ion channel of large conductance (MscL) is a promising template for the development of new antibiotics due to its high conservation and uniqueness to microbes. Brilliant green (BG), a triarylmethane dye, has been identified as a new antibiotic targeted MscL. However, the detailed binding sites to MscL and the dynamic pathway of BG through the MscL channel remain unknown. Here, the dynamic interactions between BG and MscL were investigated using solid-state NMR spectroscopy and molecule dynamics (MD) simulations. Residue site-specific binding sites of BG to the MscL channel were identified by solid-state NMR. In addition, MD simulations revealed that BG conducts through the MscL channel via residues along the inner surface of the pore sequentially, in which the strong hydrophobic interactions between BG and hydrophobic residues F23 and I27 in the hydrophobic gate region of the MscL channel are major restrictions. Particularly, it was demonstrated that BG activates the MscL channel by reducing the hydrophobicity of the F23 in the gate region by water molecules that are bound to BG. Taken together, these simulations and experimental data provide novel insights into the dynamic interactions between BG and MscL, based on which new hydrophobic antibiotics and adjuvants targeting MscL can be developed.
A green and environmental sustainable approach to synthesis the Mn oxide nanomaterial from Punica granatum leaf extracts and its in vitro biological applications

Pathogenic fungal infections in fruit cause economic losses and have deleterious effects on human health globally. Despite the low pH and high water contents of vegetables and fresh, ripened fruits, they are prone to fungal and bacterial diseases. The ever-increasing resistance of phytopathogens toward pesticides, fungicides and bactericides has resulted in substantial threats to plant growth and production in recent years. However, plant-mediated nanoparticles are useful tools for combating parasitic fungi and bacteria. Herein, we synthesized biogenic manganese oxide nanoparticles (MnONPs) from an extract of Punica granatum (P. granatum), and these nanoparticles showed significant antifungal and antibacterial activities. The production of MnONPs from plant extracts was confirmed by infrared spectroscopy (FTIR), X-ray diffraction (XRD) and UV visible spectroscopy (UV). The surface morphology and shape of the nanoparticles were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Using a detached fruit method, the MnONPs were shown to exhibit significant antimicrobial activities against two bacterial strains, E. coli and S. aureus, and against the fungal species P. digitatum. The results revealed that the MnONPs had a minimum antimicrobial activity at 25 µg/mL and a maximum antimicrobial activity at 100 µg/mL against bacterial strains in lemon (citrus). Furthermore, the MnONPs exhibited significant ROS scavenging activity. Finally, inconclusive results from the green-synthesized MnONPs magnified their significant synergetic effects on the shelf life of tomatoes (Lycopercicum esculantum) and indicated that they could be used to counteract the phytopathological effects of postharvest fungal diseases in fruits and vegetables. Overall, this method of MnONPs synthesis is inexpensive, rapid and ecofriendly. MnONPs can be used as potential antimicrobial agents against different microbial species.
CANet: Context Aware Network for Brain Glioma Segmentation

Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or competitive performance against several State-of-The-Art approaches under different segmentation metrics on the training and validation sets.
Electrochemical Response of Redox Amino Acid Encoded Fluorescence Protein for Hydroxychloroquine Sensing

The sudden rise in the demand has led to large-scale production of hydroxychloroquine (HCQ) in the global market for various diseases such as malaria, rheumatic arthritis, and systemic lupus erythematous and prophylactic treatment of early SARS-CoV-2 outbreak. Thorough monitoring of HCQ intake patients is in high demand; hence, we have developed a redox amino acid encoded fluorescent protein-based electrochemical biosensor for sensitive and selective detection of HCQ. This electrochemical biosensor is generated based on the two-electron transfer process between redox amino acid (3,4-dihydroxy-L-phenylalanine, DOPA) encoded bio-redox protein and the HCQ forms the conjugate. The DOPA residue in the bio-redox protein specifically binds with HCQ, thereby producing a remarkable electrochemical response on the glassy carbon electrode. Experimental results show that the developed biosensor selectively and sensitively detects the HCQ in spiked urine samples. The reagent-free bio-redox capacitor detects HCQ in the range of 90 nM to 4.4 µM in a solution with a detection limit of 58 nM, signal to noise ratio of 3:1, and strong anti-interference ability. Real-time screening, quantification, and relative mean recoveries of HCQ on spiked urine samples were monitored through electron shuttling using bio-redox protein and were found to be 97 to 101%. Overall, the developed bio-redox protein-based sensor has specificity, selectivity, reproducibility, and sensitivity making it potentially attractive for the sensing of HCQ and also applicable to clinical research.
A comparison between state-of-the-art and neural network modelling of solar collectors

The state-of-the-art modelling of solar collectors as described in the European Standard EN 12975-2 is based on equations describing the thermal behaviour of the collectors by characterising the physical phenomena, e.g. transmission of irradiance through transparent covers, absorption of irradiance by the absorber, temperature dependent heat losses and others. This approach leads to so called collector parameters that describe these phenomena, e.g. the zero-loss collector efficiency eta(0) or the heat loss coefficients a(1) and a(2). Although the state-of-the-art approach in collector modelling and testing fits most of the collector types very well there are some collector designs (e.g. "Sydney" tubes using heat pipes and "water-in-glass" collectors) which cannot be modelled with the same accuracy than conventional collectors like flat plate or standard evacuated tubular collectors. The artificial neural network (ANN) approach could be an appropriate alternative to overcome this drawback. To compare the different approaches of modelling investigations for a conventional flat plate collector and an evacuated "Sydney" tubular collector have been carried out based on performance measurements according to the European Standard EN 12975-2. The investigations include the parameter identification (training), the comparisons between measured and modelled collector output and the simulated yearly collector yield for a solar domestic hot water system for both models. The obtained results show better agreement between measured and calculated collector output for the artificial neural network approach compared with the state-of-the-art modelling. The investigations also show that for the ANN approach special test sequences have to be designed and that the determination of the ANN that fits the thermal performance of the collector in the best way depends significantly on the expertise of the user. Nevertheless artificial neural networks have the potential to become an interesting alternative to the state-of-the-art collector models used today. (C) 2012 Elsevier Ltd. All rights reserved.
Automating design with intelligent human-machine integration

This paper reviews the state-of-the-art methodologies for automating design with intelligent human-machine integration from the perspectives of ontology and epistemology. The human-machine integrated automating design paradigm is reviewed systematically based on a proposed prototype of human-machine integrated design, from the aspects of ontology-based knowledge management with local-to-global ontology transitions, and epistemology-based upward-spiral cognitive process of coupled design ideation. Particularly, imaginal thinking frame is proposed as the foundation of intelligent human-machine interaction that puts human and machine on an equal platform. Further, this paper presents implementations and applications of the automating design paradigm and concludes with the identification of future trend. (C) 2015 CIRP.
Gradients of Orientation, Composition, and Hydration of Proteins for Efficient Light Collection by the Cornea of the Horseshoe Crab

The lateral eyes of the horseshoe crab, Limulus polyphemus, are the largest compound eyes within recent Arthropoda. The cornea of these eyes contains hundreds of inward projecting elongated cuticular cones and concentrate light onto proximal photoreceptor cells. Although this visual system has been extensively studied before, the precise mechanism allowing vision has remained controversial. Correlating high-resolution quantitative refractive index (RI) mapping and structural analysis, it is demonstrated how gradients of RI in the cornea stem from structural and compositional gradients in the cornea. In particular, these RI variations result from the chitin-protein fibers architecture, heterogeneity in protein composition, and bromine doping, as well as spatial variation in water content resulting from matrix cross-linking on the one hand and cuticle porosity on the other hand. Combining the realistic cornea structure and measured RI gradients with full-wave optical modeling and ray tracing, it is revealed that the light collection mechanism switches from refraction-based graded index (GRIN) optics at normal light incidence to combined GRIN and total internal reflection mechanism at high incident angles. The optical properties of the cornea are governed by different mechanisms at different hierarchical levels, demonstrating the remarkable versatility of arthropod cuticle.
An Efficient Eulerian Video Magnification Technique for Micro-biology Applications

The micro-biology videos often contain motions of particles which are not visible to naked eye. Therefore an efficient motion magnification technique is required to magnify these motions. A time efficient Eulerian video magnification technique for micro-biological applications is proposed. The proposed technique utilizes the concept of time and spatial uniformity to reduce the computational complexity. Simulation results reveal that the proposed scheme is almost four times efficient and more accurate as compared to state of art video magnification technique.
A chemical timer approach to delayed chemiluminescence

Although the onset time of chemical reactions can be manipulated by mechanical, electrical, and optical methods, its chemical control remains highly challenging. Herein, we report a chemical timer approach for manipulating the emission onset time of chemiluminescence (CL) reactions. A mixture of Mn2+, NaHCO3, and a luminol analog with H2O2 produced reactive oxygen species (ROS) radicals and other superoxo species (superoxide containing complex) with high efficiency, accompanied by strong and immediate CL emission. Surprisingly, the addition of thiourea postponed CL emission in a concentration-dependent manner. The delay was attributed to a slow-generation-scavenging mechanism, which was found to be generally applicable not only to various types of CL reagents and ROS radical scavengers but also to popular chromogenic reactions. The precise regulation of CL kinetics was further utilized in dynamic chemical coding with improved coding density and security. This approach provides a powerful platform for engineering chemical reaction kinetics using chemical timers, which is of application potential in bioassays, biosensors, CL microscopic imaging, microchips, array chips, and informatics.
How Context or Knowledge Can Benefit Healthcare Question Answering?

Healthcare question answering (HQA) is a challenging task as questions are generally non-factoid in nature. Traditional information retrieval techniques do not perform well on non-factoid questions. Recent neural question answering systems are reported to have performance gains over traditional methods. However, little attention has been given to HQA as datasets are generally too small to train a neural model from scratch. Recently, several systems have been proposed to learn context representations for HQA. Despite moderate progress, these systems have not been thoroughly compared with state-of-the-art neural models, and these neural models were tested only on self-created datasets. This makes it difficult for practitioners to decide which models should be used for various scenarios. To address the above challenges, we develop a new joint model to incorporate both context and knowledge embeddings into neural ranking architectures. First, we adapt context embedding pre-trained from large open-domain corpus to small healthcare datasets. Second, we learn knowledge embedding from knowledge graphs to provide external information for understanding non-factoid questions. To evaluate how context or knowledge embedding can benefit HQA, we adapt many state-of-the-art methods for general QA to HQA, by injecting the context or knowledge information only, or both of them. Extensive experiments are conducted to compare our approach with those adapted methods and current HQA systems. The results show that our approach achieves the state-of-the-art performance on both HealthQA and NFCorpus datasets.
SfSNet: Learning Shape, Reflectance and Illuminance of Faces in the Wild

We present SfSNet, an end-to-end learning framework for producing an accurate decomposition of an unconstrained human face image into shape, reflectance and illuminance. SfSNet is designed to reflect a physical lambertian rendering model. SfSNet learns from a mixture of labeled synthetic and unlabeled real-world images. This allows the network to capture low-frequency variations from synthetic and high-frequency details from real images through the photometric reconstruction loss. SfSNet consists of a new decomposition architecture with residual blocks that learns a complete separation of albedo and normal. This is used along with the original image to predict lighting. SfSNet produces significantly better quantitative and qualitative results than state-of-the-art methods for inverse rendering and independent normal and illumination estimation. We also introduce a companion network, SfSMesh, that utilizes normals estimated by SfSNet to reconstruct a 3D face mesh. We demonstrate that SfSMesh produces face meshes with greater accuracy than state-of-the-art methods on real-world images.
Exploiting All Programmable SoCs in Neural Signal Analysis: A Closed-Loop Control for Large-Scale CMOS Multielectrode Arrays

Microelectrode array (MEA) systems with up to several thousands of recording electrodes and electrical or optical stimulation capabilities are commercially available or described in the literature. By exploiting their submillisecond and micrometric temporal and spatial resolutions to record bioelectrical signals, such emerging MEA systems are increasingly used in neuroscience to study the complex dynamics of neuronal networks and brain circuits. However, they typically lack the capability of implementing real-time feedback between the detection of neuronal spiking events and stimulation, thus restricting large-scale neural interfacing to open-loop conditions. In order to exploit the potential of such large-scale recording systems and stimulation, we designed and validated a fully reconfigurable FPGA-based processing system for closed-loop multichannel control. By adopting a Xilinx Zynq-all-programmable system on chip that integrates reconfigurable logic and a dual-core ARM-based processor on the same device, the proposed platform permits low-latency preprocessing (filtering and detection) of spikes acquired simultaneously from several thousands of electrode sites. To demonstrate the proposed platform, we tested its performances through ex vivo experiments on the mice retina using a state-of-the-art planar high-density MEA that samples 4096 electrodes at 18 kHz and record light-evoked spikes from several thousands of retinal ganglion cells simultaneously. Results demonstrate that the platform is able to provide a total latency from whole-array data acquisition to stimulus generation below 2 ms. This opens the opportunity to design closed-loop experiments on neural systems and biomedical applications using emerging generations of planar or implantable large-scale MEA systems.
Simultaneous Registration of Location and Orientation in Intravascular Ultrasound Pullbacks Pairs Via 3D Graph-Based Optimization

A novel method is reported for simultaneous registration of location (axial direction) and orientation (circumferential direction) of two intravascular ultrasound (IVUS) pullbacks of the same vessel taken at different times. Monitoring plaque progression or regression (e.g., during lipid treatment) is of high clinical relevance. Our method uses a 3D graph optimization approach, in which the cost function jointly reflects similarity of plaque morphology and plaque/perivascular image appearance. Graph arcs incorporate prior information about temporal correspondence of the two IVUS sequences and limited angular twisting between consecutive IVUS images. Additionally, our approach automatically identifies starting and ending frame pairs in the two IVUS pullbacks. Validation of our method was performed in 29 pairs of IVUS baseline/follow-up pullback sequences consisting of 8 622 IVUS image frames in total. In comparison to manual registration by three experts, the average location and orientation registration errors ranged from 0.72 mm to 0.79 mm and from 7.3 degrees to 9.3 degrees, respectively, all close to the inter-observer variability with no difference being statistically significant (p = NS) . Rotation angles determined by our automated approach and expert observers showed high correlation (r(2) of 0.97 to 0.98) and agreed closely (mutual bias between the automated method and expert observers ranged from -1.57 degrees to 0.15 degrees). Compared with state-of-the-art approaches, the new method offers lower errors in both location and orientation registration. Our method offers highly automated and accurate IVUS pullback registration and can be employed in IVUS-based studies of coronary disease progression, enabling more focal studies of coronary plaque development and transition of vulnerability.