Bengtssonhoumann8472

These outcomes revealed that due to the ligand-protein interactions, the presence of RNA in this structure could remarkably affect the binding affinity of inhibitor compounds.

In silico approaches, such as molecular docking, could effectively address the problem of finding appropriate treatment for COVID-19. Our results showed that IDR and FNT have a significant affinity to the RdRP of SARS-CoV-2; therefore, these drugs are remarkable inhibitors of coronaviruses.

In silico approaches, such as molecular docking, could effectively address the problem of finding appropriate treatment for COVID-19. Our results showed that IDR and FNT have a significant affinity to the RdRP of SARS-CoV-2; therefore, these drugs are remarkable inhibitors of coronaviruses.Hyperbolic geometry has been successfully applied in modeling brain cortical and subcortical surfaces with general topological structures. However, such approaches, similar to other surface-based brain morphology analysis methods, usually generate high dimensional features. It limits their statistical power in cognitive decline prediction research, especially in datasets with limited subject numbers. To address the above limitation, we propose a novel framework termed as hyperbolic stochastic coding (HSC). We first compute diffeomorphic maps between general topological surfaces by mapping them to a canonical hyperbolic parameter space with consistent boundary conditions and extracts critical shape features. Secondly, in the hyperbolic parameter space, we introduce a farthest point sampling with breadth-first search method to obtain ring-shaped patches. Thirdly, stochastic coordinate coding and max-pooling algorithms are adopted for feature dimension reduction. We further validate the proposed system by comparing its classification accuracy with some other methods on two brain imaging datasets for Alzheimer's disease (AD) progression studies. Our preliminary experimental results show that our algorithm achieves superior results on various classification tasks. Our work may enrich surface-based brain imaging research tools and potentially result in a diagnostic and prognostic indicator to be useful in individualized treatment strategies.In fetal neurosonography, aligning two-dimensional (2D) ultrasound scans to their corresponding plane in the three-dimensional (3D) space remains a challenging task. In this paper, we propose a convolutional neural network that predicts the position of 2D ultrasound fetal brain scans in 3D atlas space. Instead of purely supervised learning that requires heavy annotations for each 2D scan, we train the model by sampling 2D slices from 3D fetal brain volumes, and target the model to predict the inverse of the sampling process, resembling the idea of self-supervised learning. We propose a model that takes a set of images as input, and learns to compare them in pairs. The pairwise comparison is weighted by the attention module based on its contribution to the prediction, which is learnt implicitly during training. The feature representation for each image is thus computed by incorporating the relative position information to all the other images in the set, and is later used for the final prediction. We benchmark our model on 2D slices sampled from 3D fetal brain volumes at 18-22 weeks' gestational age. Using three evaluation metrics, namely, Euclidean distance, plane angles and normalized cross correlation, which account for both the geometric and appearance discrepancy between the ground-truth and prediction, in all these metrics, our model outperforms a baseline model by as much as 23%, when the number of input images increases. We further demonstrate that our model generalizes to (i) real 2D standard transthalamic plane images, achieving comparable performance as human annotations, as well as (ii) video sequences of 2D freehand fetal brain scans.Image reconstruction from radio-frequency (RF) data is crucial for ultrafast plane wave ultrasound (PWUS) imaging. Compared with the traditional delay-and-sum (DAS) method based on relatively imprecise assumptions, sparse regularization (SR) method directly solves the inverse problem of image reconstruction and has presented significant improvement in the image quality when the frame rate remains high. However, the computational complexity of SR is too high for practical implementation, which is inherently associated with its iterative process. In this work, a deep neural network (DNN), which is trained with an incorporated loss function including sparse regularization terms, is proposed to reconstruct PWUS images from RF data with significantly reduced computational time. It is remarkable that, a self-supervised learning scheme, in which the RF data are utilized as both the inputs and the labels during the training process, is employed to overcome the lack of the "ideal" ultrasound images as the labels for DNN. In addition, it has been also verified that the trained network can be used on the RF data obtained with steered plane waves (PWs), and thus the image quality can be further improved with coherent compounding. Using simulation data, the proposed method has significantly shorter reconstruction time (∼10 ms) than the conventional SR method (∼1-5 mins), with comparable spatial resolution and 1.5-dB higher contrast-to-noise ratio (CNR). Besides, the proposed method with single PW can achieve higher CNR than DAS with 75 PWs in reconstruction of in-vivo images of human carotid arteries.In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss wittal resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.Ten undescribed anthranoids, including three anthraquinone acetals as racemic mixtures, (±)-kenganthranol G-I, and seven prenylated anthranols, (±)-kenganthranol J-M and harunganol G-I, together with thirteen known compounds, were isolated from the stem bark of Harungana madagascariensis. UAMC-3203 price The structures of (±)-kenganthranol G and (±)-kenganthranol J were confirmed by X-ray crystallography. (±)-Kenganthranol G was separated into (+)-kenganthranol G and (-)-kenganthranol G by chiral HPLC and their absolute configurations were established by electronic circular dichroism. (±)-Kenganthranol L displayed α-glucosidase inhibitory activity with an IC50 of 4.4 μM.Municipal Solid Waste Management is yet to be eco-effectively performed, especially in developing countries. In Brazil, a considerable fraction of waste has been improperly landfilled, generating environmental, social and economic problems. In 2018, the government of the state of Paraná released a revised version of its waste management plan, defining improvement strategies to be gradually implemented until 2038. However, these strategies' eco-effectiveness has not been forecasted, nor the plan was deployed to the regional level. This research aims to fill this gap, downscaling the plan to the region of Norte Pioneiro, simulating its implementation and monitoring environmental and economic benefits. The dynamics of waste generation, collection and disposal are investigated using an agent-based model, considering the four population growth scenarios addressed in the plan. Targets for strategies of waste reduction, collection, source-separation and charging of waste fees are modelled. Multiple simulation runs were performed and outputs assessed and discussed. Results show that, if the plan is thoroughly implemented since 2020, at least 650 kilotons of avoided CO2eq emissions and US$ 40 million in avoided expenditures can be achieved in the most conservative scenario by 2038. Implications from the strategies proposed in the plan are highlighted, and recommendations to improve the plan's eco-effectiveness are outlined.Although microbial inoculants are promoted as a strategy for improving compost quality, there is no consensus in the published literature about their efficacy. A quantitative meta-analysis was performed to estimate the overall effect size of microbial inoculants on nutrient content, humification and lignocellulosic degradation. A meta-regression and moderator analyses were conducted to elucidate abiotic and biotic factors controlling the efficacy of microbial inoculants. These analyses demonstrated the beneficial effects of microbial inoculants on total nitrogen (+30%), total phosphorus (+46%), compost maturity index (CN ratio (-31%), humification (+60%) and the germination index (+28%). The mean effect size was -46%, -65% and -40% for cellulose, hemicellulose, and lignin respectively. However, the effect size was marginal for bioavailable nutrient concentrations of phosphate, nitrate, and ammonium. The effectiveness of microbial inoculants depends on inoculant form, inoculation time, composting method, and experimental duration. The microbial inoculant effect size was consistent under different feedstock types and experimental scales. These findings imply that microbial inoculants are important for accelerating lignocellulose degradation. Higher mean effect sizes have tended to be published in journals with higher impact factors, thus researchers should be encouraged to publish non-significant findings in order to provide a more reliable estimation of effect size and clarify doubts about the benefits of microbial inoculants for composting.Biological tests are widely used to assess composting process status and finished material stability. Although compost stability is known to be influenced by moisture content (MC) and storage duration, there is a lack of data supporting boundary limits for standardised testing. Using the ORG0020 dynamic respiration test we assessed the stability of materials from different commercial composting sites processing only green waste or mixed green and food waste. Samples were tested at three different MC following adjustment with the 'fist' test within the range 40-60%. The results showed manipulation of MC within this range could have significant impact on measured stability for some but not all samples. Two samples reported significantly higher activity when MC was manipulated from ~50% to ~60%. For storage duration, samples showed significant decrease in measured activity over several weeks of cold storage. However, there was no significant difference in stability for samples tested up to nine days from receipt.