ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Switchable RAFT Polymerization Employing Photoresponsive HABI as a Mediator

Recently, considerable interest has been devoted to developing switchable reversible addition fragmentation chain transfer (RAFT) polymerizations via photoactivation methods. Herein, a photo-deactivation strategy is introduced to regulate RAFT polymerization using photoresponsive hexaarylbiimidozole (HABI) as a mediator, which leads to switchable RAFT polymerization by repeated ON/OFF experiments. In comparison with well-known PET-RAFT polymerization, photo-deactivation RAFT (PD-RAFT) polymerization can be temporally stopped with UV light ON, where photoresponsive HABI can reversibly quench propagating radicals, resulting in switchable RAFT polymerization. The proposed mechanism of PD-RAFT polymerization in the presence of HABI involving radical quenching is based on ESR, NMR, GPC, MALDI-TOF-MS, and kinetics studies.
Gaussian Process Regression for Estimating Chlorophyll Concentration in Subsurface Waters From Remote Sensing Data

In this letter, we explore the effectiveness of a novel regression method in the context of the estimation of biophysical parameters from remotely sensed imagery as an alternative to state-of-the-art regression methods like those based on artificial neural networks and support vector machines. This method, called Gaussian process (GP) regression, formulates the learning of the regressor within a Bayesian framework, where the regression model is derived by assuming the model variables follow a Gaussian prior distribution encoding the prior knowledge about the output function. One of its interesting properties, which gives it a key advantage over state-of-the-art regression methods, is the possibility to tune the free parameters of the model in an automatic way. Experiments were focused on the problem of estimating chlorophyll concentration in subsurface waters. The achieved results suggest that the GP regression method is very promising from both viewpoints of estimation accuracy and free parameter tuning. Moreover, it handles particularly well the problem of limited availability of training samples, typically encountered in biophysical parameter estimation applications.
Calcium-permeable AMPA receptors trigger vesicular glutamate release from Bergmann gliosomes

The Bergmann glia is equipped with Ca2+-permeable AMPA receptors for glutamate, indispensable for structural and functional relations between the Bergmann glia and parallel/climbing fibers-Purkinje cell synapses. To better understand roles for the Bergmann AMPA receptors, herein we investigate on gliotransmitter release and Ca2+ signals in isolated Bergmann glia processes obtained from adult rat cerebellum. We found that: 1) the rat cerebellar purified astrocyte processes (gliosomes) expressed astrocytic and Bergmann markers and exhibited negligible contamination by nerve terminals, microglia, or oligodendrocytes; 2) activation of Ca2+-permeable AMPA receptors caused Ca2+ signals in the processes, and the release of glutamate from the processes; 3) effectiveness of rose bengal, trypan blue or bafilomycin A1, indicated that activation of the AMPA receptors evoked vesicular glutamate release. Cerebellar purified nerve terminals appeared devoid of glutamate-releasing Ca2+-permeable AMPA receptors, indicating that neuronal contamination may not be the source of the signals detected. Ultrastructural analysis indicated the presence of vesicles in the cytoplasm of the processes; confocal imaging confirmed the presence of vesicular glutamate transporters in Bergmann glia processes. We conclude that: a vesicular mechanism for release of the gliotransmitter glutamate is present in mature Bergmann processes; entry of Ca2+ through the AMPA receptors located on Bergmann processes is coupled with vesicular glutamate release. The findings would add a new role for a well-known Bergmann target for glutamate (the Ca2+-permeable AMPA receptors) and a new actor (the gliotransmitter glutamate) at the cerebellar excitatory synapses onto Purkinje cells.
Using percentiles in the interpretation of Patient-Reported Outcomes Measurement Information System scores: Guidelines for autism

The objectives of this study were to (1) demonstrate the application of percentiles to advance the interpretation of patient-reported outcomes and (2) establish autism-specific percentiles for four Patient-Reported Outcomes Measurement Information System (PROMIS) measures. PROMIS measures were completed by parents of autistic children and adolescents ages 5-17 years as part of two studies (n = 939 parents in the first study and n = 406 parents in the second study). Data from the first study were used to develop autism-specific percentiles for PROMIS parent-proxy sleep disturbance, sleep-related impairment, fatigue, and anxiety. Previously established United States general population percentiles were applied to interpret PROMIS scores in both studies. Results of logistic regression models showed that parent-reported material hardship was associated with scoring in the moderate-severe range (defined as ≥75th percentile in the general population) on all four PROMIS measures (odds ratios 1.7-2.2). In the second study, the percentage of children with severe scores (defined as ≥95th percentile in the general population) was 30% for anxiety, 25% for sleep disturbance, and 17% for sleep-related impairment, indicating a high burden of these problems among autistic children. Few children had scores at or above the autism-specific 95th percentile on these measures (3%-4%), indicating that their scores were similar to other autistic children. The general population and condition-specific percentiles provide two complementary reference points to aid interpretation of PROMIS scores, including corresponding severity categories that are comparable across different PROMIS measures.
Activated Single-Phase Ti4 O7 Nanosheets with Efficient Use of Precious Metal for Inspired Oxygen Reduction Reaction

The oxygen reduction reaction (ORR) is central to modern energy storage and conversion technologies for grids such as fuel cells and electrolyzers, but challenges remain due to the lack of reliable, economic, and durable electrocatalysts. Here, we develop single-crystal conductive black titanium (Ti4 O7 ) nanosheets (NSs) as a new precious metal carrier based on sacrificial hard templates and ultrasonic-assisted peeling, and deposit Pt clusters on Ti4 O7 NSs induced by wetness impregnation under the irradiation of visible light (VI; 650 nm). Pt/Ti4 O7 NSs provide Ti3+ , Pt2+ , and Pt0+ continuous active sites for the ORR multielectron process, achieving synergy among them. The assistance of visible light not only makes a more uniform and smaller distribution of Pt nanoclusters, but also strengthens the charge transfer, thereby constructing a strong metal-support interaction interface. VI-Pt/Ti4 O7 NSs show superior initial oxidation potential and a mass activity of 1.61 A mg-1 Pt at a E1/2 =0.91 V, which is nine times higher than that of commercial Pt/C. This work provides an effective strategy for achieving high-value applications of titanium sub-oxides and further explores the enhanced interface in metals Tin O2n-1 by light radiation.
Multi-Oriented Object Detection in High-Resolution Remote Sensing Imagery Based on Convolutional Neural Networks with Adaptive Object Orientation Features

In high-resolution earth observation systems, object detection in high spatial resolution remote sensing images (HSRIs) is the key technology for automatic extraction, analysis and understanding of image information. With respect to the multi-angle features of object orientation in HSRIs object detection, this paper presents a novel HSRIs object detection method based on convolutional neural networks (CNN) with adaptive object orientation features. First, an adaptive object orientation regression method is proposed to obtain object regions in any direction. In the adaptive object orientation regression method, five coordinate parameters are used to regress the object region with any direction. Then, a CNN framework for object detection of HSRIs is designed using the adaptive object orientation regression method. Using multiple object detection datasets, the proposed method is compared with some state-of-the-art object detection methods. The experimental results show that the proposed method can more accurately detect objects with large aspect ratios and densely distributed objects than some state-of-the-art object detection methods using a horizontal bounding box, and obtain better object detection results for HSRIs.
Visual Grouping by Neural Oscillator Networks

Distributed synchronization is known to occur at several scales in the brain, and has been suggested as playing a key functional role in perceptual grouping. State-of-the-art visual grouping algorithms, however, seem to give comparatively little attention to neural synchronization analogies. Based on the framework of concurrent synchronization of dynamical systems, simple networks of neural oscillators coupled with diffusive connections are proposed to solve visual grouping problems. The key idea is to embed the desired grouping properties in the choice of the diffusive couplings, so that synchronization of oscillators within each group indicates perceptual grouping of the underlying stimulative atoms, while desynchronization between groups corresponds to group segregation. Compared with state-of-the-art approaches, the same algorithm is shown to achieve promising results on several classical visual grouping problems, including point clustering, contour integration, and image segmentation.
Reweighted sparse subspace clustering

Motion segmentation and human face clustering are two fundamental problems in computer vision. The state-of-the-art algorithms employ the subspace clustering scheme when processing the two problems. Among these algorithms, sparse subspace clustering (SSC) achieves the state-of-the-art clustering performance via solving a l(1) minimization problem and employing the spectral clustering technique for clustering data points into different subspaces. In this paper, we propose an iterative weighting (reweighted) l(1) minimization framework which largely improves the performance of the traditional l(1) minimization framework. The reweighted l(1) minimization framework makes a better approximation to the to minimization than tradition l(1) minimization framework. Following the reweighted l(1) minimization framework, we propose a new subspace clustering algorithm, namely, reweighted sparse subspace clustering (RSSC). Through an extensive evaluation on three benchmark datasets, we demonstrate that the proposed RSSC algorithm significantly reduces the clustering errors over the SSC algorithm while the additional reweighted step has a moderate impact on the computational cost. The proposed RSSC also achieves lowest clustering errors among recently proposed algorithms. On the other hand, as majority of the algorithms were evaluated on the Hopkins155 dataset, which is insufficient of non-rigid motion sequences, the dataset can hardly reflect the ability of the existing algorithms on processing non-rigid motion segmentation. Therefore, we evaluate the performance of the proposed RSSC and state-of-the-art algorithms on the Freiburg-Berkeley Motion Segmentation Dataset, which mainly contains non-rigid motion sequences. The performance of these state-of-the-art algorithms, as well as RSSC, will drop dramatically on this dataset with mostly non-rigid motion sequences. Though the proposed RSSC achieves the better performance than other algorithms, the results suggest that novel algorithms that focus on segmentation of non-rigid motions are still in need. (C) 2015 Elsevier Inc. All rights reserved.
Life cycle assessment comparison of emerging and traditional Titanium dioxide manufacturing processes

Titanium dioxide (TiO2) is used as pigment in a wide variety of domestic and industrial applications, and is becoming an increasingly valuable nanomaterial. TiO2 is manufactured by the traditional sulfate process or high temperature chloride process. Several hydrometallurgical processes for manufacturing TiO2 have recently emerged to reduce the environmental impact of TiO2 production. A new process is reported that features alkaline roasting of titania slag (ARTS), with subsequent washing, leaching, solvent extraction, hydrolysis, and calcination stages, and implements the recycling and regeneration of alkaline and acid process streams to minimize waste generation. A virtual ARTS processing plant is described in detail and is used to conduct an LCA comparison with the sulfate, chloride, and Altairnano processes. The cumulative energy demand (CED) and total CO2 emissions for the ARTS process are 92.6 MJ/kg TiO2 and 7.47 kg CO2/kg TiO2, respectively, which compares favorably with the traditional and Altairnano processes. (C) 2014 Elsevier Ltd. All rights reserved.
Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut

We propose an automatic multiorgan segmentation method for 3-D radiological images of different anatomical contents and modalities. The approach is based on a simultaneous multilabel graph cut optimization of location, appearance, and spatial configuration criteria of target structures. Organ location is defined by target-specific probabilistic atlases (PA) constructed from a training dataset using a fast (2+1)D SURF-based multiscale registration method involving a simple four-parameter transformation. PAs are also used to derive target-specific organ appearance models represented as intensity histograms. The spatial configuration prior is derived from shortest-path constraints defined on the adjacency graph of structures. Thorough evaluations on Visceral project benchmarks and training dataset, as well as comparisons with the stateof-the-art confirm that our approach is comparable to and often outperforms similar approaches in multiorgan segmentation, thus proving that the combination of multiple suboptimal but complementary information sources can yield very good performance.
Preprocedural Multimodality Imaging in Atrial Fibrillation

Atrial fibrillation (AF) is the most common arrhythmia worldwide and is associated with increased risk of heart failure, stroke, and death. In current medical practice, multimodality imaging is routinely used in the management of AF. Twenty-one years ago, the ACUTE trial (Assessment of Cardioversion Using Transesophageal Echocardiography) results were published, and the management of AF changed forever by incorporating transesophageal echocardiography guided cardioversion of patients in AF for the first time. Current applications of multimodality imaging in AF in 2022 include the use of transesophageal echocardiography and computed tomography before cardioversion to exclude left atrial thrombus and in left atrial appendage occlusion device implantation. Transesophageal echocardiography, cardiac computed tomography, and cardiac magnetic resonance are clinically used for AF ablation planning. The decision to use a particular imaging modality in AF is based on patient's characteristics, guideline recommendation, institutional preferences, expertise, and cost. In this first of 2-part review series, we discuss the preprocedural role of echocardiography, computed tomography, and cardiac magnetic resonance in the AF, with regard to their clinical applications, relevant outcomes data and unmet needs, and highlights future directions in this rapidly evolving field.
Quantum circuit-based modeling of continuous-variable quantum key distribution system

The second generation Continuous-Variable Quantum Key Distribution is one of the most promising application of quantum mechanics in communications. This paper investigates a state-of-the-art Continuous-Variable Quantum Key Distribution solution from circuit modeling point of view in order to build an appropriate simulation framework. This framework allows detailed evaluation of the system's performance and adjusting the system parameters. Copyright (c) 2017 John Wiley & Sons, Ltd.
Efficient Shapelet Discovery for Time Series Classification

Time-series shapelets are discriminative subsequences, recently found effective for time series classification (TSC). It is evident that the quality of shapelets is crucial to the accuracy of TSC. However, major research has focused on building accurate models from some shapelet candidates. To determine such candidates, existing studies are surprisingly simple, e.g., enumerating subsequences of some fixed lengths, or randomly selecting some subsequences as shapelet candidates. The major bulk of computation is then on building the model from the candidates. In this paper, we propose a novel efficient shapelet discovery method, called BSPCOVER, to discover a set of high-quality shapelet candidates for model building. Specifically, BSPCOVER generates abundant candidates via Symbolic Aggregate approXimation with sliding window, then prunes identical and highly similar candidates via Bloom filters, and similarity matching, respectively. We next propose a p-Cover algorithm to efficiently determine discriminative shapelet candidates that maximally represent each time-series class. Finally, any existing shapelet learning method can be adopted to build a classification model. We have conducted extensive experiments with well-known time-series datasets and representative state-of-the-art methods. Results show that BSPCOVER speeds up the state-of-the-art methods by more than 70 times, and the accuracy is often comparable to or higher than existing works.
A Unified Approach to Diffusion Direction Sensitive Slice Registration and 3-D DTI Reconstruction From Moving Fetal Brain Anatomy

This paper presents an approach to 3-D diffusion tensor image (DTI) reconstruction from multi-slice diffusion weighted (DW) magnetic resonance imaging acquisitions of the moving fetal brain. Motion scatters the slice measurements in the spatial and spherical diffusion domain with respect to the underlying anatomy. Previous image registration techniques have been described to estimate the between slice fetal head motion, allowing the reconstruction of 3D a diffusion estimate on a regular grid using interpolation. We propose Approach to Unified Diffusion Sensitive Slice Alignment and Reconstruction (AUDiSSAR) that explicitly formulates a process for diffusion direction sensitive DW-slice-to-DTI-volume alignment. This also incorporates image resolution modeling to iteratively deconvolve the effects of the imaging point spread function using the multiple views provided by thick slices acquired in different anatomical planes. The algorithm is implemented using a multi-resolution iterative scheme and multiple real and synthetic data are used to evaluate the performance of the technique. An accuracy experiment using synthetically created motion data of an adult head and an experiment using synthetic motion added to sedated fetal monkey dataset show a significant improvement in motion-trajectory estimation compared to current state-of-the-art approaches. The performance of the method is then evaluated on challenging but clinically typical in utero fetal scans of four different human cases, showing improved rendition of cortical anatomy and extraction of white matter tracts. While the experimental work focuses on DTI reconstruction (second-order tensor model), the proposed reconstruction framework can employ any 5-D diffusion volume model that can be represented by the spatial parameterizations of an orientation distribution function.
Estimation of Age Using Alveolar Bone Loss: Forensic and Anthropological Applications

The objective of this study was to utilize a new odontological methodological approach based on radiographic for age estimation. The study was comprised of 397 participants aged between 9 and 87 years. A clinical examination and a radiographic assessment of alveolar bone loss were performed. Direct measures of alveolar bone level were recorded using CT scans. A medical examination report was attached to the investigation file. Because of the link between alveolar bone loss and age, a model was proposed to enable simple, reliable, and quick age estimation. This work added new arguments for age estimation. This study aimed to develop a simple, standardized, and reproducible technique for age estimation of adults of actual populations in forensic medicine and ancient populations in funeral anthropology.
Dominant negative Ras attenuates pathological ventricular remodeling in pressure overload cardiac hypertrophy

The importance of the oncogene Ras in cardiac hypertrophy is well appreciated. The hypertrophic effects of the constitutively active mutant Ras-Val12 are revealed by clinical syndromes due to the Ras mutations and experimental studies. We examined the possible anti-hypertrophic effect of Ras inhibition in vitro using rat neonatal cardiomyocytes (NRCM) and in vivo in the setting of pressure-overload left ventricular (LV) hypertrophy (POH) in rats. Ras functions were modulated via adenovirus directed gene transfer of active mutant Ras-Val12 or dominant negative mutant N17-DN-Ras (DN-Ras). Ras-Val12 expression in vitro activates NFAT resulting in pro-hypertrophic and cardio-toxic effects on NRCM beating and Z-line organization. In contrast, the DN-Ras was antihypertrophic on NRCM, inhibited NFAT and exerted cardio-protective effects attested by preserved NRCM beating and Z line structure. Additional experiments with silencing H-Ras gene strategy corroborated the antihypertrophic effects of siRNA-H-Ras on NRCM. In vivo, with the POH model, both Ras mutants were associated with similar hypertrophy two weeks after simultaneous induction of POH and Ras-mutant gene transfer. However, LV diameters were higher and LV fractional shortening lower in the Ras-Val12 group compared to control and DN-Ras. Moreover, DN-Ras reduced the cross-sectional area of cardiomyocytes in vivo, and decreased the expression of markers of pathologic cardiac hypertrophy. In isolated adult cardiomyocytes after 2 weeks of POH and Ras-mutant gene transfer, DN-Ras improved sarcomere shortening and calcium transients compared to Ras-Val12. Overall, DN-Ras promotes a more physiological form of hypertrophy, suggesting an interesting therapeutic target for pathological cardiac hypertrophy.
The neuroprotective and antidiabetic effects of trigonelline: A review of signaling pathways and molecular mechanisms

The global epidemic of diabetes has brought heavy pressure on public health. New effective anti-diabetes strategies are urgently needed. Trigonelline is the main component of fenugreek, which has been proved to have a good therapeutic effect on diabetes and diabetic complications. Trigonelline achieves amelioration of diabetes, the mechanisms of which include the modulation of insulin secretion, a reduction in oxidative stress, and the improvement of glucose tolerance and insulin resistance. Besides, trigonelline has been reported to be a neuroprotective agent against many neurologic diseases including Alzheimer's disease, Parkinson's disease, stroke, and depression. Concerning the potential therapeutic effects of trigonelline, comprehensive clinical trials are warranted to evaluate this valuable molecule.