Cramernorwood8415

r in-patient setting and should be implemented widely.

The exposure and consumption of information during epidemic outbreaks may alter people's risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited.

The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries Italy, the United Kingdom, the United States, and Canada.

We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19-related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users' collectes toward behavioral change.

Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people's collective awareness and risk perception and thus on their tendencies toward behavioral change.[This corrects the article DOI 10.2196/20285.].

Antimicrobial resistance is an important global health issue. In Germany, the national agenda supports various interventions to convert habits of antibiotic use. In the CHANGE-3 (Converting Habits of Antibiotic Use for Respiratory Tract Infections in German Primary Care) study, digital tools were applied for information delivery tablet computers in primary care practices, e-learning platforms for medical professionals, and a public website to promote awareness and health literacy among primary care physicians, their teams, and their patients.

This study is embedded in the process evaluation of the CHANGE-3 study. The aim of this study was to evaluate the acceptance and uptake of digital devices for the delivery of health-related information to enhance awareness and change habits of antibiotic use in primary care in Germany.

This study used a convergent-parallel mixed-methods design. Audio-recorded semistructured telephone interviews were conducted with physicians, nonphysician health professionals, and lutions into existing routines in primary care and to align them with their professional values. Low technology affinity was a major barrier to the use of digital information in primary care. Patients welcomed the general idea of introducing health-related information in digital formats; however, they expressed concerns about device-related hygiene and the appropriateness of the digital tools for older patients.

Patients and medical professionals in German primary care are reluctant to use digital devices for information and education. Using a Diffusion of Innovations approach can support assessment of existing barriers and provide information about setting-specific preconditions that are necessary for future tailoring of implementation strategies.

International Standard Randomized Controlled Trial Number (ISRCTN) 15061174; http//www.isrctn.com/ISRCTN15061174.

International Standard Randomized Controlled Trial Number (ISRCTN) 15061174; http//www.isrctn.com/ISRCTN15061174.Radiomics has shown remarkable potential for predicting the survival outcome for various types of cancers such as pancreatic ductal adenocarcinoma (PDAC). However, to date, there has been limited research using convolutional neural networks (CNN) with radiomic methods for this task, due to their requirement for large training sets. To overcome this issue, we propose a new type of radiomic descriptor modeling the distribution of learned features with a Gaussian mixture model (GMM). These parametric features (GMM-CNN) are computed from gross tumor volumes of PDAC patients defined semiautomatically in pre-operative computed tomography (CT) scans. We use the proposed GMM-CNN features as input to a robust classifier based on random forests (RF) to predict the survival outcome of patients with PDAC. Our experiments assess the advantage of GMM-CNN compared to employing the same 3D CNN model directly, standard radiomic (i.e., histogram, texture and shape), conditional entropy (CENT) based on 3DCNN, and clinical features (i.e., serum carbohydrate antigen 19-9 and chemotherapy neoadjuvant). Using the RF model (100 samples for training; 59 samples for validation), GMM-CNN features provided the highest area under the ROC curve (AUC) of 72.0% (p = 6.4105) compared to 64.0% (p = 0.01) for the 3D CNN model output, 66.8% (p = 0.01) for standard radiomic features, 64.2% (p = 0.003) for CENT, and 57.6% (p = 0.3) for clinical variables. Our results suggest that the proposed GMM-CNN features used with a RF classifier can significantly improve the capacity to prognosticate PDAC patients prior to surgery via routinely-acquired imaging data.Recognizing the movements during sleep is crucial for the monitoring of patients with sleep disorders. However, the utilization of Ultra-Wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of the off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View Learning, entitled SleepPoseNet or SPN, with time series data augmentation aims to classify four standard SPTs. SPN exhibits an ability to capture both time and frequency features, including the movement and direction of sleeping positions. The data recorded from 38 volunteers displayed that SPN with a mean accuracy of 73.7 ±0.8 % significantly outperformed the mean accuracy of 59.9 ±0.7 % obtained from deep convolution neural network (DCNN) in recent state-of-the-art work on human activity recognition using UWB. Apart from UWB system, SPN with the data augmentation can ultimately be adopted to learn and classify time series data in various applications.Recently, the advances in passive brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have shed light on real-world neuromonitoring technologies. However, human variability in the EEG activities hinders the development of practical applications of EEG-based BCI. To tackle this problem, many transfer-learning techniques perform supervised calibration. This kind of calibration approach requires task-relevant data, which is impractical in real-life scenarios such as drowsiness during driving. Smad2 signaling This study presents a transfer-learning framework for EEG decoding based on the low-dimensional representations of subjects learned from the pre-trial EEG. Tensor decomposition was applied to the pre-trial EEG of subjects to extract the underlying characteristics in subject, spatial and spectral domains. Then, the proposed framework assessed the characteristics to obtain the low-dimensional subject representations such that the subjects with similar brain dynamics can be identified. This method can leverage the existing data from other users and a small number of data from a rapid, non-task, unsupervised calibration from a new user to build an accurate BCI. Our results demonstrated that, in terms of prediction accuracy, the proposed low-dimensional subject representation-based transfer learning (LDSR-TL) framework outperformed the random selection and the Riemannian manifold approach in cognitive-state tracking, while requiring fewer training data. The results can greatly improve the practicability and usability of EEG-based BCI in the real world.An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insights into learning from nonstationary and imbalanced data in online learning, a largely unexplored area. We propose the novel Adaptive REBAlancing (AREBA) algorithm that selectively includes in the training set a subset of the majority and minority examples that appeared so far, while at its heart lies an adaptive mechanism to continually maintain the class balance between the selected examples. We compare AREBA with strong baselines and other state-of-the-art algorithms and perform extensive experimental work in scenarios with various class imbalance rates and different concept drift types on both synthetic and real-world data. AREBA significantly outperforms the rest with respect to both learning speed and learning quality. Our code is made publicly available to the scientific community.In this study, we investigated whether it is possible to change the absolute detection threshold and intensity difference threshold of electrovibration at fingertip of index finger via remote masking, i.e. by applying a (mechanical) vibrotactile stimulus on the proximal phalanx of the same finger. The masking stimuli were generated by a voice coil (Haptuator). For 16 participants, we first measured the detection thresholds for electrovibration at the fingertip and for vibrotactile stimuli at the proximal phalanx. Then, the vibrations on the skin were measured at four different locations on the index finger of subjects to investigate how the mechanical masking stimulus propagated as the masking level was varied. Later, masked absolute thresholds of 8 participants were measured. Finally, for another group of 8 participants, intensity difference thresholds were measured in the presence/absence of vibrotactile masking stimuli. We proposed two models based on hypothetical neural signals for prediction of masking effect on intensity difference threshold for electrovibration amplitude and energy models. The energy model was able to predict the effect of masking more accurately, especially at high intensity masking levels.With the successful application of single-cell sequencing technology, a large number of single-cell multi-omics sequencing (scMO-seq) data have been generated, which enables researchers to study heterogeneity between individual cells. One prominent problem in single-cell data analysis is the prevalence of dropouts, caused by failures in amplication during the experiments. It is necessary to develop effective approaches for imputing the missing values. Different with general methods imputing single type of single-cell data, we propose a imputation method called scLRTD, using low-rank tensor decomposition method based on nuclear norm to impute scMO-seq data and single-cell RNA-sequencing (scRNA-seq) data with different stages, tissues or conditions. Furthermore, three sets of simulated and two sets of real scRNA-seq data from mouse embryonic stem cells and hepatocellular carcinoma, respectively, are used to carry out numerical experiments and compared with other six published methods. Error accuracy and clustering results demonstrate the effectiveness of proposed method. Moreover, we clearly identify two cell subpopulations after imputing the real scMO-seq data from hepatocellular carcinoma. Further, Gene Ontology identifies 7 genes in Bile secretion pathway, which is related to metabolism in hepatocellular carcinoma. The survival analysis using the database TCGA also show that two cell subpopulations after imputing have distinguished survival rates.