ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Impact of cognition-related single nucleotide polymorphisms on brain imaging phenotype in Parkinson's disease

Multiple single nucleotide polymorphisms may contribute to cognitive decline in Parkinson's disease. However, the mechanism by which these single nucleotide polymorphisms modify brain imaging phenotype remains unclear. The aim of this study was to investigate the potential effects of multiple single nucleotide polymorphisms on brain imaging phenotype in Parkinson's disease. Forty-eight Parkinson's disease patients and 39 matched healthy controls underwent genotyping and 7T magnetic resonance imaging. A cognitive-weighted polygenic risk score model was designed, in which the effect sizes were determined individually for 36 single nucleotide polymorphisms. The correlations between polygenic risk score, neuroimaging features, and clinical data were analyzed. Furthermore, individual single nucleotide polymorphism analysis was performed to explore the main effects of genotypes and their interactive effects with Parkinson's disease diagnosis. We found that, in Parkinson's disease, the polygenic risk score was correlated with the neural activity of the hippocampus, parahippocampus, and fusiform gyrus, and with hippocampal-prefrontal and fusiform-temporal connectivity, as well as with gray matter alterations in the orbitofrontal cortex. In addition, we found that single nucleotide polymorphisms in α-synuclein (SNCA) were associated with white matter microstructural changes in the superior corona radiata, corpus callosum, and external capsule. A single nucleotide polymorphism in catechol-O-methyltransferase was associated with the neural activities of the lingual, fusiform, and occipital gyri, which are involved in visual cognitive dysfunction. Furthermore, DRD3 was associated with frontal and temporal lobe function and structure. In conclusion, imaging genetics is useful for providing a better understanding of the genetic pathways involved in the pathophysiologic processes underlying Parkinson's disease. This study provides evidence of an association between genetic factors, cognitive functions, and multi-modality neuroimaging biomarkers in Parkinson's disease.
Life cycle assessment of a pulverized coal power plant with post-combustion capture, transport and storage of CO2

In this study the methodology of life cycle assessment has been used to assess the environmental impacts of three pulverized coal fired electricity supply chains with and without carbon capture and storage (CCS) on a cradle to grave basis. The chain with CCS comprises post-combustion CO2 capture with monoethanolamine, compression, transport by pipeline and storage in a geological reservoir. The two reference chains represent sub-critical and state-of-the-art ultra supercritical pulverized coal fired electricity generation. For the three chains we have constructed a detailed greenhouse gas (GHG) balance, and disclosed environmental trade-offs and co-benefits due to CO2 capture, transport and storage. Results show that, due to CCS, the GHG emissions per kWh are reduced substantially to 243 g/kWh. This is a reduction of 78 and 71% compared to the sub-critical and state-of-the-art power plant, respectively. The removal Of CO2 is partially offset by increased GHG emissions in up- and downstream processes, to a small extent (0.7 g/kWh) caused by the CCS infrastructure. An environmental co-benefit is expected following from the deeper reduction of hydrogen fluoride and hydrogen chloride emissions. Most notable environmental trade-offs are the increase in human toxicity, ozone layer depletion and fresh water ecotoxicity potential for which the CCS chain is outperformed by both other chains. The state-of-the-art power plant without CCS also shows a better score for the eutrophication, acidification and photochemical oxidation potential despite the deeper reduction of SO, and NO, in the CCS power plant. These reductions are offset by increased emissions in the life cycle due to the energy penalty and a factor five increase in NH3 emissions. (C) 2008 Elsevier Ltd. All rights reserved.
Soft-Change Detection in Optical Satellite Images

In this letter, we propose a novel approach for unsupervised change detection in multitemporal optical satellite images. Unlike the traditional methods, the proposed method, called the soft-change detection, models the change detection as a transparency computation problem and assigns to each pixel a set of soft labels. In order to extract the pixel opacity, we optimize an objective function by exploiting the Bayesian matting method. Comparisons between the proposed method and the state-of-the-art methods are reported. Experimental results demonstrate the effectiveness of the proposed method.
Superpixel-Based Segmentation for 3D Prostate MR Images

This paper proposes a method for segmenting the prostate on magnetic resonance (MR) images. A superpixel-based 3D graph cut algorithm is proposed to obtain the prostate surface. Instead of pixels, superpixels are considered as the basic processing units to construct a 3D superpixel-based graph. The superpixels are labeled as the prostate or background by minimizing an energy function using graph cut based on the 3D superpixel-based graph. To construct the energy function, we proposed a superpixel-based shape data term, an appearance data term, and two superpixel-based smoothness terms. The proposed superpixel-based terms provide the effectiveness and robustness for the segmentation of the prostate. The segmentation result of graph cuts is used as an initialization of a 3D active contour model to overcome the drawback of the graph cut. The result of 3D active contour model is then used to update the shape model and appearance model of the graph cut. Iterations of the 3D graph cut and 3D active contour model have the ability to jump out of local minima and obtain a smooth prostate surface. On our 43 MR volumes, the proposed method yields a mean Dice ratio of 89.3 +/- 1.9%. On PROMISE12 test data set, our method was ranked at the second place; the mean Dice ratio and standard deviation is 87.0 +/- 3.2%. The experimental results show that the proposed method outperforms several state-of-the-art prostate MRI segmentation methods.
Novel post-acquisition image processing to attenuate red blood cell autofluorescence for quantitative image analysis

Quantitative analysis of microscopy images from samples stained with fluorescent probes necessitates a very low fluorescence background signal. In tissues prepared by immersion in a chemical fixative, followed by conventional processing for paraffin embedding, red blood cell autofluorescence across several imaging channels can be a nuisance. Although many protocols have been proposed to suppress red blood cell autofluorescence prior to microscopy imaging, in many instances they may not prove totally effective. Moreover, in environments such as core facilities where control over tissue processing and staining may not be feasible, methods to address autofluorescence via post-image acquisition processing may be of some advantage. To this end, we have developed an image analysis algorithm using a commercially based software platform to remove contaminating red blood cell autofluorescence during quantitative evaluation of the fluorescence signal from an immunostaining protocol. The method is based upon the low autofluorescence signal of red blood cells exhibited in the blue channel (used to detect DAPI nuclear signal of all cells), which can be subtracted from the total channel signal by increasing the threshold for DAPI signal in the nuclear detection settings during nuclear segmentation. With the contributing signal from the red blood cells eliminated, the specific immunostained signal for the antigen of interest could be determined. We believe that this simple algorithm performed on post-acquisition microscopy images will be of use for quantitative fluorescence analyses whenever red blood cell autofluorescence is present, especially in amounts where creating regions of interest for evaluation is not possible.
Phytocytokine SCREWs increase plant immunity through actively reopening stomata

Plants secreted phytocytokine SMALL PHYTOCYTOKINES REGULATING DEFENSE AND WATER LOSS (SCREWs) and its receptor PLANT SCREW UNRESPONSIVE RECEPTOR (NUT) to counter abscisic acid (ABA)- and pathogen-induced stomatal closure (Liu et al.). This novel signaling process provides plants with a new strategy to increase immunity through disrupting an aqueous habitat for pathogen colonization.
Competitive interaction between smoking and chronic obstructive pulmonary disease for explaining renal function reduction in hypertensive patients

Chronic kidney disease is a risk factor for cardiovascular events. Smoking and chronic obstructive pulmonary disease (COPD) are risk factors for renal impairment. The aim of this study was to test the combined effect of smoking and COPD on renal function decline in hypertensives. We enrolled 1728 hypertensives stratified by smoking status and presence/absence of COPD. To test the mutual effect modification by both smoking and COPD and e-GFR, we performed crude and adjusted linear regression analyses, these latter taking into account potential confounders. Smokers displayed significantly lower e-GFR values than non-smokers (90 ± 24 vs. 121 ± 35 ml/min/1.73 m2); this difference was confirmed when comparing e-GFR values between patients with/without COPD (81 ± 17 vs. 109 ± 32 ml/min/1.73 m2). Smoking and COPD were directly and significantly interrelated (Cramer's V coefficient = 0.200; P = < 0.001). At interaction analyses, smoking significantly modified the effect of COPD on e-GFR and COPD significantly modified the effect of smoking on e-GFR, indicating a competitive interaction between smoking and COPD in the appearance of renal damage. e-GFR was 35 ml/min/1.73 m2 lower in patients with COPD than in those without; this reduction was of higher magnitude than that found between COPD and COPD-free patients among smokers (19 ml/min/1.73 m2). Smoking and COPD competitively interact in the appearance of renal function decline. These results suggest to screen for kidney damageboth smokers and COPD patients, especially those with both conditions.
Moderate resolution LAI prediction using Sentinel-2 satellite data and indirect field measurements in Sikkim Himalaya

The leaf area index (LAI) has been traditionally used as a photosynthetic variable. LAI plays an essential role in forest cover monitoring and has been identified as one of the important climate variables. However, due to challenges in field sampling, complex topography, and availability of cloud-free optical satellite data, LAI assessment on larger scale is still unexplored in the Sikkim Himalayan area. We used two optical instruments, digital hemispherical photography (DHP) and LAI-2200C, to assess the LAI across four different forests following 20 × 20 m2 elementary sampling units (ESUs) in the Himalayan state of Sikkim, India. The use of Sentinel-2 derived vegetation indices (VIs) demonstrated a better correlation with the DHP based LAI estimates than using LAI-2200C. Further, the combination of both reflectance bands and VIs were integrated to predict the LAI maps using random forest model. The temperate evergreen forests demonstrated the highest LAI value, while the predicted maps exhibited LAI maxima of 3.4. The estimated vs predicted LAI for DHP and LAI-2200C based estimation demonstrated reasonably good (R2 = 0.63 and R2 = 0.68, respectively) agreement. Further, improvements on the LAI prediction can be attempted by minimizing errors from the inherent field protocols, optimizing the density of field measurements, and representing heterogeneity. The recent rise of frequent forest fires in Sikkim Himalaya prompts for better understanding of fuel load in terms of surface fuel or canopy fuel that can be linked to LAI. The high-resolution LAI map could serve as input to forest fuel bed characterization, especially in seasonal forests with significant variations in green leaves and litter, thereby offering inputs for forest management in changing climate.
A practical guideline for performing a comprehensive transthoracic echocardiogram in the congenital heart disease patient: consensus recommendations from the British Society of Echocardiography

Transthoracic echocardiography is an essential tool in the diagnosis, assessment, and management of paediatric and adult populations with suspected or confirmed congenital heart disease. Congenital echocardiography is highly operator-dependent, requiring advanced technical acquisition and interpretative skill levels. This document is designed to complement previous congenital echocardiography literature by providing detailed practical echocardiography imaging guidance on sequential segmental analysis, and is intended for implementation predominantly, but not exclusively, within adult congenital heart disease settings. It encompasses the recommended dataset to be performed and is structured in the preferred order for a complete anatomical and functional sequential segmental congenital echocardiogram. It is recommended that this level of study be performed at least once on all patients being assessed by a specialist congenital cardiology service. This document will be supplemented by a series of practical pathology specific congenital echocardiography guidelines. Collectively, these will provide structure and standardisation to image acquisition and reporting, to ensure that all important information is collected and interpreted appropriately.
A comparison of temporomandibular disorder headache in young adults in relation to perceived stress

Chronic pain in the head and face region has a predicted prevalence of 20% in Europe, and is more common in women than men. The etiology of temporomandibular disorder (TMD) is multifactorial, and high levels of psychological stress amplify its symptoms. We were interested in how headache reported in RDC/TMD is associated with stress. Individuals with an average age of 18 years were included in the study. They were all volunteer participants in a research project. Clinical evaluation of each subject was performed using the RDC/TMD dual-axis diagnostic system. All participants filled out the PSS-10 questionnaire. A total of 138 individuals participated in the study, of which 107 were female. Headache was reported by 83 participants (59.4%), with females presenting higher scores on the PSS-10; this was statistically significant. A comparison between PSS-10 questionnaire results and headache level shows insignificant differences. However, the higher the stress level in the participant, the higher the headache score. Females are more susceptible to perceived stress, which can have an effect on TMD.
Breast tumor classification in ultrasound images using texture analysis and super-resolution methods

Ultrasound images can be used to detect tumors that do not appear in the mammograms of dense breasts. Several computer-aided diagnosis (CAD) systems based on this type of images have been proposed to detect tumors and discriminate between benign and malignant ones. To characterize those lesions, many of the aforementioned systems rely on texture analysis methods. However, speckle noise and artifacts that appear in ultrasound images may degrade their performance. To tackle this problem, and contrary to the state-of-the-art methods that utilize a single image of the breast, this paper proposes the use of a super-resolution approach that exploits the complementary information provided by multiple images of the same target. The proposed CAD system consists of four stages: super-resolution computation, extraction of the region of interest, feature extraction and classification. We have evaluated the performance of five texture methods with the proposed CAD system: gray level co-occurrence matrix features, local binary patterns, phase congruency-based local binary pattern, histogram of oriented gradients and pattern lacunarity spectrum. We show that our super-resolution based approach improves the performance of the evaluated texture methods and thus outperforms the state of the art in benign/malignant tumor classification.
Thermal-Aware Design for Approximate DNN Accelerators

Recent breakthroughs in Neural Networks (NNs) have made DNN accelerators ubiquitous and led to an ever-increasing quest on adopting them from Cloud to edge computing. However, state-of-the-art DNN accelerators pack immense computational power in a relatively confined area, inducing significant on-chip power densities that lead to intolerable thermal bottlenecks. Existing state of the art focuses on using approximate multipliers only to trade-off efficiency with inference accuracy. In this work, we present a thermal-aware approximate DNN accelerator design in which we additionally trade-off approximation with temperature effects towards designing DNN accelerators that satisfy tight temperature constraints. Using commercial multi-physics tool flows for heat simulations, we demonstrate how our thermal-aware approximate design reduces the temperature from 139 degrees C, in an accurate circuit, down to 79 degrees C. This enables DNN accelerators to fulfill tight thermal constraints, while still maximizing the performance and reducing the energy by around 75% with a negligible accuracy loss of merely 0.44% on average for a wide range of NN models. Furthermore, using physics-based transistor aging models, we demonstrate how reductions in voltage and temperature obtained by our approximate design considerably improve the circuit's reliability. Our approximate design exhibits around 40% less aging-induced degradation compared to the baseline design.
Multiple idiopathic cervical root resorption: A systematic review

The current literature on multiple idiopathic cervical root resorption (MICRR), a rare and aggressive form of external root resorption, is limited to case reports and series. Therefore, we performed a systematic review of this condition. A comprehensive search of PubMed, Embase, Web of science, Cochrane Library, CNKI, and WANFANG was conducted using key terms relevant to MICRR, supplemented by a grey literature search. Risk of bias was assessed using Cochrane's and Joanna Briggs Institute's tools. A total of 36 studies with 47 cases were included. MICRR is more common among younger females and may be related to hormonal changes and denosumab use. Initially, the premolars are usually affected but all permanent teeth may eventually be involved. Cone-beam computed tomography is recommended for diagnosis and assessment of resorptive lesions. The management is focused on complete removal and restoration of the resorptive tissue to maintain the tooth's structural integrity. However, MICRR usually has a poor prognosis. Due to its invasive and aggressive behavior, MICRR requires greater attention.
Drone Detection and Pose Estimation Using Relational Graph Networks

With the upsurge in use of Unmanned Aerial Vehicles (UAVs), drone detection and pose estimation by using optical sensors becomes an important research subject in cooperative flight and low-altitude security. The existing technology only obtains the position of the target UAV based on object detection methods. To achieve better adaptability and enhanced cooperative performance, the attitude information of the target drone becomes a key message to understand its state and intention, e.g., the acceleration of quadrotors. At present, most of the object 6D pose estimation algorithms depend on accurate pose annotation or a 3D target model, which costs a lot of human resource and is difficult to apply to non-cooperative targets. To overcome these problems, a quadrotor 6D pose estimation algorithm was proposed in this paper. It was based on keypoints detection (only need keypoints annotation), relational graph network and perspective-n-point (PnP) algorithm, which achieves state-of-the-art performance both in simulation and real scenario. In addition, the inference ability of our relational graph network to the keypoints of four motors was also evaluated. The accuracy and speed were improved significantly compared with the state-of-the-art keypoints detection algorithm.
An Overview of the State of the Art in Aircraft Prognostic and Health Management Strategies

Aircraft are complex engineering systems composed of many interconnected subsystems with possible uncertainties in their structure. They often function for a long number of flight hours under varying or harsh environments. Hence, prognostic and health management (PHM) of critical subsystems or components within the overall system is crucial for maintaining the safety and reliability of the aircraft. This article reviews the state of the art in aircraft failure prognostic. The main definitions and concepts are presented and discussed. In addition, a selected important failure in the representative aircraft components is outlined, and various categories of prognostic strategies are reviewed. Finally, some recommendations and directions for the most promising research to address the PHM problem in aircraft are outlined.
Robust optical flow estimation via edge preserving filtering

It is known that optical flow estimation techniques suffer from the issues of ill-defined edges and boundaries of the moving objects. Traditional variational methods for optical flow estimation are not robust to handle these issues since the local filters in these methods do not hold the robustness near the edges. In this paper, we propose a non-local total variation NLTV-L-1 optical flow estimation method based on robust weighted guided filtering. Specifically, first, the robust weighted guided filtering objective function is proposed to preserve motion edges. The proposed objective function is based on the linear model which is computationally efficient and edge-preserving in complex natural scenarios. Second, the proposed weighted guided filtering objective function is incorporated into the non-local total variation NLTV-L-1 energy function. Finally, the novel NLTV-L-1 optical flow method is performed using the coarse-to-fine process. Additionally, we modify some state-of-the-art variational optical flow estimation methods by the robust weighted guided filtering objective function to verify the performance on Middlebury, MPI-Sintel, and Foggy Zurich sequences. Experimental results show that the proposed method can preserve edges and improve the accuracy of optical flow estimation compared with several state-of-the-art methods.
Clinical, biochemical, neuroimaging and molecular findings of X-linked Adrenoleukodystrophy patients in South China

X-linked adrenoleukodystrophy is a common X-linked recessive peroxisomal disorder caused by the mutations in the ABCD1 gene. In this study, we analyzed 19 male patients and 9 female carriers with X-linked adrenoleukodystrophy in South China. By sequencing the ABCD1 gene, 13 different mutations were identified, including 7 novel mutations, and 6 known mutations, and 1 reported polymorphism. Mutation c.1180delG was demonstrated to be de novo mutation. 26.3 % (5/19) patients carried the deletion c.1415_16delAG, which may be the mutational hot spot in South China population. In addition, 73.7 % (14/19) patients were type of childhood cerebral adrenoleukodystrophy, 26.3 %(5/19) were in Addison only. Half of the childhood cerebral adrenoleukodystrophy patients had the adrenocortical insufficiency preceded the onset of neurological symptoms. Furthermore, 5 of 19 cases underwent hematopoietic stem cell transplantation. Our data showed that hematopoietic stem cell transplantation performed at an advanced stage of the cerebral X- linked adrenoleukodystrophy would accelerate the progression of the disease. Good clinical outcome achieved when hematopoietic stem cell transplantation performed at the very early stage of the disease.