Online health communities (OHCs) have increasingly gained traction with patients, caregivers, and supporters globally. Chinese OHCs are no exception. However, user-generated content (UGC) and the ...associated user behaviors in Chinese OHCs are largely underexplored and rarely analyzed systematically, forfeiting valuable opportunities for optimizing treatment design and care delivery with insights gained from OHCs.
This study aimed to reveal both the shared and distinct characteristics of 2 popular OHCs in China by systematically and comprehensively analyzing their UGC and the associated user behaviors.
We concentrated on studying the lung cancer forum (LCF) and breast cancer forum (BCF) on Mijian, and the diabetes consultation forum (DCF) on Sweet Home, because of the importance of the 3 diseases among Chinese patients and their prevalence on Chinese OHCs in general. Our analysis explored the key user activities, small-world effect, and scale-free characteristics of each social network. We examined the UGC of these forums comprehensively and adopted the weighted knowledge network technique to discover salient topics and latent relations among these topics on each forum. Finally, we discussed the public health implications of our analysis findings.
Our analysis showed that the number of reads per thread on each forum followed gamma distribution (H
=0, H
=0, and H
=0); the number of replies on each forum followed exponential distribution (adjusted R
=0.946, adjusted R
=0.958, and adjusted R
=0.971); and the number of threads a user is involved with (adjusted R
=0.978, adjusted R
=0.964, and adjusted R
=0.970), the number of followers of a user (adjusted R
=0.989, adjusted R
=0.962, and adjusted R
=0.990), and a user's degrees (adjusted R
=0.997, adjusted R
=0.994, and adjusted R
=0.968) all followed power-law distribution. The study further revealed that users are generally more active during weekdays, as commonly witnessed in all 3 forums. In particular, the LCF and DCF exhibited high temporal similarity (ρ=0.927; P<.001) in terms of the relative thread posting frequencies during each hour of the day. Besides, the study showed that all 3 forums exhibited the small-world effect (mean σ
=517.15, mean σ
=275.23, and mean σ
=525.18) and scale-free characteristics, while the global clustering coefficients were lower than those of counterpart international OHCs. The study also discovered several hot topics commonly shared among the 3 disease forums, such as disease treatment, disease examination, and diagnosis. In particular, the study found that after the outbreak of COVID-19, users on the LCF and BCF were much more likely to bring up COVID-19-related issues while discussing their medical issues.
UGC and related online user behaviors in Chinese OHCs can be leveraged as important sources of information to gain insights regarding individual and population health conditions. Effective and timely mining and utilization of such content can continuously provide valuable firsthand clues for enhancing the situational awareness of health providers and policymakers.
The coronavirus disease 2019 (COVID-19) pandemic, which emerged in late 2019, has caused millions of infections and fatalities globally, disrupting various aspects of human society, including ...socioeconomic, political, and educational systems. One of the key challenges during the COVID-19 pandemic is accurately predicting the clinical development and outcome of the infected patients. In response, scientists and medical professionals globally have mobilized to develop prognostic strategies such as risk scores, biomarkers, and machine learning models to predict the clinical course and outcomes of COVID-19 patients. In this contribution, we deployed a mathematical approach called matrix factorization feature selection to select the most relevant features from the anonymized laboratory biomarkers and demographic data of COVID-19 patients. Based on these features, developed a model that leverages the deep stacking neural network (DSNN) to aid in clinical care by predicting patients' mortality risk. To gauge the performance of our suggested model, performed a comparative analysis with principal component analysis plus support vector machine, deep learning, and random forest, achieving outstanding performances. The DSNN model outperformed all the other models in terms of area under the curve (96.0%), F
-score (98.1%), recall (98.5%), accuracy (99.0%), precision (97.7%), specificity (97.0%), and maximum probability of correction decision (93.4%). Our model outperforms the clinical predictive models regarding patient mortality risk and classification in the literature. Therefore, we conclude that our robust model can help healthcare professionals to manage COVID-19 patients more effectively. We expect that early prediction of COVID-19 patients and preventive interventions can reduce the mortality risk of patients.
Due to the urgency caused by the COVID-19 pandemic worldwide, vaccine manufacturers have to shorten and parallel the development steps to accelerate COVID-19 vaccine production. Although all usual ...safety and efficacy monitoring mechanisms remain in place, varied attitudes toward the new vaccines have arisen among different population groups.
This study aimed to discern the evolution and disparities of attitudes toward COVID-19 vaccines among various population groups through the study of large-scale tweets spanning over a whole year.
We collected over 1.4 billion tweets from June 2020 to July 2021, which cover some critical phases concerning the development and inoculation of COVID-19 vaccines worldwide. We first developed a data mining model that incorporates a series of deep learning algorithms for inferring a range of individual characteristics, both in reality and in cyberspace, as well as sentiments and emotions expressed in tweets. We further conducted an observational study, including an overall analysis, a longitudinal study, and a cross-sectional study, to collectively explore the attitudes of major population groups.
Our study derived 3 main findings. First, the whole population's attentiveness toward vaccines was strongly correlated (Pearson r=0.9512) with official COVID-19 statistics, including confirmed cases and deaths. Such attentiveness was also noticeably influenced by major vaccine-related events. Second, after the beginning of large-scale vaccine inoculation, the sentiments of all population groups stabilized, followed by a considerably pessimistic trend after June 2021. Third, attitude disparities toward vaccines existed among population groups defined by 8 different demographic characteristics. By crossing the 2 dimensions of attitude, we found that among population groups carrying low sentiments, some had high attentiveness ratios, such as males and individuals aged ≥40 years, while some had low attentiveness ratios, such as individuals aged ≤18 years, those with occupations of the 3rd category, those with account age <5 years, and those with follower number <500. These findings can be used as a guide in deciding who should be given more attention and what kinds of help to give to alleviate the concerns about vaccines.
This study tracked the year-long evolution of attitudes toward COVID-19 vaccines among various population groups defined by 8 demographic characteristics, through which significant disparities in attitudes along multiple dimensions were revealed. According to these findings, it is suggested that governments and public health organizations should provide targeted interventions to address different concerns, especially among males, older people, and other individuals with low levels of education, low awareness of news, low income, and light use of social media. Moreover, public health authorities may consider cooperating with Twitter users having high levels of social influence to promote the acceptance of COVID-19 vaccines among all population groups.
In this paper, the control of robotic manipulators (RMs) is studied. The RMs are widely used in industry. The RMs are multi-input-multi-output systems, and their dynamics are highly nonlinear. To ...improve the accuracy in practice, it is impossible to ignore the influence of nonlinear dynamics and the interaction of inputs–outputs. Non-structural uncertainties such as friction, disturbance, and unmodeled dynamics are other challenges of these systems. Recently, type-3 (T3) fuzzy logic systems (FLSs) have been suggested that result in better accuracy in a noisy environment. In this paper, a new control idea on the basis of T3-FLSs is suggested. T3-FLSs are used to estimate the dynamics of RMs and the symmetrical perturbations. The T3-FLSs are learned using online laws to enhance the stability. To eliminate the effect of the interconnection of inputs and estimation errors, a compensator is developed. By several simulations, the superiority of the suggested controller is demonstrated.
Yale Image Finder (YIF) is a publicly accessible search engine featuring a new way of retrieving biomedical images and associated papers based on the text carried inside the images. Image queries can ...also be issued against the image caption, as well as words in the associated paper abstract and title. A typical search scenario using YIF is as follows: a user provides few search keywords and the most relevant images are returned and presented in the form of thumbnails. Users can click on the image of interest to retrieve the high resolution image. In addition, the search engine will provide two types of related images: those that appear in the same paper, and those from other papers with similar image content. Retrieved images link back to their source papers, allowing users to find related papers starting with an image of interest. Currently, YIF has indexed over 140 000 images from over 34 000 open access biomedical journal papers. Availability: http://krauthammerlab.med.yale.edu/imagefinder/ Contact: michael.krauthammer@yale.edu
The Convolutional Neural Network (CNN) has poor performance in non-uniform and edge regions due to the limitation of fixed receptive field. At the same time, feature stacking of input data can bring ...burden and overfitting to the network. To solve these problems, this paper proposes a reg-superpixel guided convolutional neural network based on feature selection and receptive field reconstruction. Firstly, a feature selection method is designed, which uses polarimetric SAR statistical distribution features to calculate distance and similarity, and selects features that are easier to identify to avoid the negative impact of low distinguishing features on classification. Secondly, the reg-superpixel, which means regular superpixel, is used to reconstruct the receptive field and represent the features of the central pixel. The classification result of the central pixel is extended to the whole superpixel during the test. This method can extend the pixel-level CNN network to superpixel-level. Finally, by using edge information of the small-scale superpixel and spatial information of the large-scale superpixel to adjust receptive field of the central pixel, the classification results with uniform smooth region and high edge fitting can be generated. Experimental results with four state-of-the-art methods on four datasets show that FS-MSRSnet is effective for polarimetric SAR classification problems.
Uranium dioxide (UO2) is widely used in nuclear reactors. This fuel has a low thermal conductivity (TC). Increasing its TC can effectively enhance the safety of reactors and fuel efficiencies. A ...prevalent approach to increasing the TC of UO2 is to inject a second phase material with a high TC into a UO2 matrix. Due to operational difficulties in the fabrication, deployment, and testing of such composite fuels, measurement data regarding effective thermal conductivity (ETC) of these composite fuels are rarely available, which hinders the development of these composites. To overcome such a barrier, finite element method is utilized to generate massive simulated measurements over the concerned composites. Subsequently, a novel algorithmic method is developed that automatically learns from gathered simulation results to accurately and reliably: 1) predict the ETC of a composite fuel according to its given structural characteristics, and 2) reversely infer the structural characteristics of a composite fuel from its expected ETC. The relative error of forward prediction and inverse design is <5% by the new algorithm. The new computational solution provides a novel and effective approach to developing new composite fuels with significant design acceleration and cost reduction.
Display omitted
•A bi-directional prediction model trained using massive data from finite element method for UO2 composites is developed.•The model can predict thermal conductivity of a UO2-SiC composite from its structural features with relative error <5%.•The model can infer structural features of a UO2-SiC composite from target thermal conductivity with relative error <5%.
Different countries have adopted various control measures for the COVID-19 pandemic in different periods, and as the virus continues to mutate, the progression of the pandemic and preventive measures ...adopted have varied dynamically over time. Thus, quantitative analysis of the dynamic impact of different factors such as vaccination, mutant virus, social isolation, etc., on transmission and predicting pandemic progress has become a difficult task. To overcome the challenges above and enable governments to formulate reasonable countermeasures against the ongoing COVID-19 pandemic, we integrate several mathematical methods and propose a new adaptive multifactorial and geographically diverse epidemiological model based on a modified version of the classical susceptible-exposed-infectious-recovered (SEIR) model. Based on public datasets, a multi-center study was carried out considering 21 regions. First, a retrospective study was conducted to predict the number of infections over the next 30 days in 13 representative pandemic areas worldwide with an accuracy of 87.53%, confirming the robustness of the proposed model. Second, the impact of three scenarios on COVID-19 was quantified based on the scalability of the model: two different vaccination regimens were analyzed, and it was found that the number of infections would progressively decrease over time after vaccination; variant virus caused a 301.55% increase in infections in the United Kingdom; and 3-tier social lockdown in the United Kingdom reduced the infections by 47.01%. Third, we made short-term prospective predictions for the next 15 and 30 days for six countries with severe COVID-19 transmission and the predicted trend is accurate. This study is expected to inform public health responses. Code and data are publicly available at https://github.com/yuanyuanpei7/covid-19.
We introduce a novel method for synthesizing dance motions that follow the emotions and contents of a piece of music. Our method employs a learning-based approach to model the music to motion mapping ...relationship embodied in example dance motions along with those motions' accompanying background music. A key step in our method is to train a music to motion matching quality rating function through learning the music to motion mapping relationship exhibited in synchronized music and dance motion data, which were captured from professional human dance performance. To generate an optimal sequence of dance motion segments to match with a piece of music, we introduce a constraint-based dynamic programming procedure. This procedure considers both music to motion matching quality and visual smoothness of a resultant dance motion sequence. We also introduce a two-way evaluation strategy, coupled with a GPU-based implementation, through which we can execute the dynamic programming process in parallel, resulting in significant speedup. To evaluate the effectiveness of our method, we quantitatively compare the dance motions synthesized by our method with motion synthesis results by several peer methods using the motions captured from professional human dancers' performance as the gold standard. We also conducted several medium-scale user studies to explore how perceptually our dance motion synthesis method can outperform existing methods in synthesizing dance motions to match with a piece of music. These user studies produced very positive results on our music-driven dance motion synthesis experiments for several Asian dance genres, confirming the advantages of our method.
Preimplantation embryo development is characterized by drastic nuclear reprogramming and dynamic stage-specific gene expression. Key regulators of this earliest developmental stage have not been ...revealed. In the present study, a “non-classical” nuclear-localization pattern of eIF1A was observed during early developmental stages of mouse preimplantation embryo before late-morula. In particular, eIF1A is most highly expressed in the nuclear of 2-cell embryo. Knockdown eIF1A by siRNA microinjection affected the development of mouse preimplantation embryo, resulted in decreased blastocyst formation rate. CDX2 protein expression level significantly down-regulated after eIF1A knockdown in morula stage. In addition, the mRNA expression level of Hsp70.1 was also decreased in 2-cell embryo. The results indicate an indispensable role of eIF1A in mouse preimplantation embryos.
PDF
