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Effect of exercising in bone tissue spring denseness amid individuals using osteoporosis as well as osteopenia: An organized evaluation and system meta-analysis.
tribute to more effective public communication and efficient public health research responses. The development of protocols and the standardization of epidemic message templates-as well as the use of uniform operating procedures to provide regular information updates-should be prioritized to ensure a coordinated national response.
Innovative laboratory testing approaches for SARS-CoV-2 infection and immune response are needed to conduct research to establish estimates of prevalence and incidence. Self-specimen collection methods have been successfully used in HIV and sexually transmitted infection research and can provide a feasible opportunity to scale up SARS-CoV-2 testing for research purposes.

The aim of this study was to assess the willingness of adults to use different specimen collection modalities for themselves and children as part of a COVID-19 research study.

Between March 27 and April 1, 2020, we recruited 1435 adults aged 18 years or older though social media advertisements. Participants completed a survey that included 5-point Likert scale items stating how willing they were to use the following specimen collection testing modalities as part of a research study home collection of a saliva sample, home collection of a throat swab, home finger-prick blood collection, drive-through site throat swab, clinic throat swab,ing; however, there was a strong preference for home specimen collection procedures over drive-through or clinic-based testing. To increase participation and minimize bias, epidemiologic research studies of SARS-CoV-2 infection and immune response should consider home specimen collection methods.The COVID-19 pandemic has swept across the world, altering nearly every facet of contemporary life and causing behavioral and socioeconomic changes that seemed unthinkable a few months ago. The increased risks for human health include not just the dangers posed by the virus itself, but also the upheaval to the broader health care and societal landscapes, which has threatened access to critical sexual and reproductive health services. In this viewpoint, we describe how the pandemic has already posed challenges to reproductive autonomy in both the United States and globally, and then offer insights on how it may do so in the future. We conclude with a call not only to resist a rollback of access to reproductive health care during this pandemic, but to center a broad conception of reproductive autonomy in sexual and reproductive health research, policies and programs moving forward.In this article, the output synchronization problem of passive multiagent systems (MASs) with transmission delays and switching graphs is addressed by a novel logic-based distributed switching mechanism. Our result shows that synchronization is reached for arbitrarily large and bounded constant, time varying, or distributed delays, which, compared with the existing results for passive MASs, has an obvious advantage. This delay robustness holds under the very weak connectivity assumptions on the underlying graph, that is, as long as the graph is uniformly jointly strongly connected and switches with a dwell time. SRI-011381 datasheet The proposed algorithm is applied to the position synchronization problem of multiple robotic manipulators to show its applicability.The context-based convolutional neural network (CNN) is one of the most well-known CNNs to improve the performance of semantic segmentation. It has achieved remarkable success in various medical image segmentation tasks. However, extracting rich and useful context information from complex and changeable medical images is a challenge for medical image segmentation. In this study, a novel Multi-Receptive-Field CNN (MRFNet) is proposed to tackle this challenge. MRFNet offers the optimal receptive field for each subnet in the encoder-decoder module (EDM) and generates multi-receptive-field context information at the feature map level. Moreover, MRFNet fuses these multi-feature maps by the concatenation operation. MRFNet is evaluated on 3 public medical image data sets, including SISS, 3DIRCADb, and SPES. Experimental results show that MRFNet achieves the outstanding performance on all 3 data sets, and outperforms other segmentation methods on 3DIRCADb test set without pre-training the model.This article investigates the problem of neural network (NN)-based adaptive backstepping control design for stochastic nonlinear systems with unmodeled dynamics in finite-time prescribed performance. NNs are used to study the uncertain control plants, and the problem of unmodeled dynamics is tackled by the combination of the changing supply function and the dynamical signal function methods. The outstanding contribution of this article is that based on the finite-time performance function (FTPF), a modified finite-time adaptive NN control design strategy is proposed, which makes the controller design simpler. Eventually, by using the Itô's differential lemma, the backstepping recursive design technique, and the FTPFs, a novel adaptive prescribed performance tracking control scheme is presented, which can guarantee that all the variables in the control system are bounded in probability, and the tracking error can converge to a specified performance range in the finite time. Finally, both numerical simulation and applied simulation examples are provided to verify the effectiveness and applicability of the proposed method.Accurate Medical Subject Headings (MeSH) annotation is an important issue for researchers in terms of effective information retrieval and knowledge discovery in the biomedical literature. We have developed a powerful dual triggered correspondence topic(DTCT) model for MeSH annotated articles. In our model, two types of data are assumed to be generated by the same latent topic factors and words in abstracts and titles serve as descriptions of the other type, MeSH terms. Our model allows the generation of MeSHs in abstracts to be triggered either by general document topics or by document-specific "special" word distributions in a probabilistic manner, allowing for a trade-off between the benefits of topic-based abstraction and specific word matching. In order to relax the topic influences of non-topical words or domain-frequent words in text description, we integrated the discriminative feature of Okapi BM25 into word sampling probability. This allows the model to choose keywords, which stand out from others, in order to generate MeSH terms. We further incorporate prior knowledge about relations between word and MeSH in DTCT with phi-coefficient to improve topic coherence. We demonstrated the model's usefulness in automatic MeSH annotation. Our model obtained 0.62 F-score 150,00 MEDLINE test set and showed a strength in recall.The development of hardware for neural interfacing remains a technical challenge. We introduce a flexible, transversal intraneural tungstentitanium electrode array for acute studies. We characterize the electrochemical properties of this new combination of tungsten and titanium using cyclic voltammetry and electrochemical impedance spectroscopy. With an in-vivo rodent study, we show that the stimulation of peripheral nerves with this electrode array is possible and that more than half of the electrode contacts can yield a stimulation selectivity index of 0.75 or higher at low stimulation currents. This feasibility study paves the way for the development of future cost-effective and easy-to-fabricate neural interfacing electrodes for acute settings, which ultimately can inform the development of technologies that enable bi-directional communication with the human nervous system.The analysis of vector fields is crucial for the understanding of several physical phenomena, such as natural events (e.g., analysis of waves), diffusive processes, electric and electromagnetic fields. While previous work has been focused mainly on the analysis of 2D or 3D vector fields on volumes or surfaces, we address the meshless analysis of a vector field defined on an arbitrary domain, without assumptions on its dimension and discretisation. The meshless approximation of the Helmholtz-Hodge decomposition of a vector field is achieved by expressing the potential of its components as a linear combination of radial basis functions and by computing the corresponding conservative, irrotational, and harmonic components as solution to a least-squares or to a differential problem. To this end, we identify the conditions on the kernel of the radial basis functions that guarantee the existence of their derivatives. Finally, we demonstrate our approach on 2D and 3D vector fields measured by sensors or generated through simulation.We propose an algorithm to compute global conformal parameterizations of high-genus meshes, which is based on an implementation of holomorphic quadratic differentials. First, we design a novel diffusion method which is capable of computing a pole-free discrete harmonic measured foliation. Second, we propose a definition for discrete holomorphic quadratic differential which consists of a horizontal and a vertical harmonic measured foliation. Third, we present a practical algorithm to approximate the discrete natural coordinates for a holomorphic quadratic differential, which represents a flat metric with cones conformal to the original metric, i.e., a parameterization. Finally, we apply the discrete natural coordinates for parameterization of high genus meshes. Our parameterization method is global conformal and simple to implement. The advantage of our method over the approach based on holomorphic differential one-forms is that ours has a larger space of parameterizations. We demonstrate our approach with hundreds of configurations on dozens of meshes to show its robustness on conformal parameterization.This article presents a novel framework that can generate a high-fidelity isosurface model of X-ray computed tomography (CT) data. CT surfaces with subvoxel precision and smoothness can be simply modeled via isosurfacing, where a single CT value represents an isosurface. However, this inevitably results in geometric distortion of the CT data containing CT artifacts. An alternative is to treat this challenge as a segmentation problem. However, in general, segmentation techniques are not robust against noisy data and require heavy computation to handle the artifacts that occur in three-dimensional CT data. Furthermore, the surfaces generated from segmentation results may contain jagged, overly smooth, or distorted geometries. We present a novel local isosurfacing framework that can address these issues simultaneously. The proposed framework exploits two primary techniques 1) Canny edge approach for obtaining surface candidate boundary points and evaluating their confidence and 2) screened Poisson optimization for fitting a surface to the boundary points in which the confidence term is incorporated. This combination facilitates local isosurfacing that can produce high-fidelity surface models. We also implement an intuitive user interface to alleviate the burden of selecting the appropriate confidence computing parameters. Our experimental results demonstrate the effectiveness of the proposed framework.In recent years, deep-based models have achieved great success in the field of single image super-resolution (SISR), where tremendous parameters are always needed to obtain a satisfying performance. However, the high computational complexity extremely limits its applications to some mobile devices that possess less computing and storage resources. To address this problem, in this paper, we propose a flexibly adjustable super lightweight SR network s-LWSR. Firstly, in order to efficiently abstract features from the low resolution image, we design a high-efficient U-shape based block, where an information pool is constructed to mix multi-level information from the first half part of the pipeline. Secondly, a compression mechanism based on depth-wise separable convolution is employed to further reduce the numbers of parameters with negligible performance degradation. Thirdly, by revealing the specific role of activation in deep models, we remove several activation layers in our SR model to retain more information, thus leading to the final performance improvement.
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