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The two-stage Cox-nnet complex model combining histopathology image and transcriptomic RNA-seq data achieves much better prognosis prediction, with a median C-index of 0.75 and log-rank P-value of 6e-7 in the testing datasets, compared to PAGE-Net (median C-index of 0.68 and log-rank P-value of 0.03). Imaging features present additional predictive information to gene expression features, as the combined model is more accurate than the model with gene expression alone (median C-index 0.70). Pathological image features are correlated with gene expression, as genes correlated to top imaging features present known associations with HCC patient survival and morphogenesis of liver tissue. This work proposes two-stage Cox-nnet, a new class of biologically relevant and interpretable models, to integrate multiple types of heterogenous data for survival prediction.The integration of personal protective equipment (PPE) and procedures into breast imaging and intervention practices will mitigate the risk of transmission of COVID-19 during the pandemic. this website Although supply chain shortages have improved, understanding the proper use of PPE and protocols to mitigate overconsumption are important to ensure efficacious utilization of PPE. Protocols and best practices are reviewed, and guidelines and resource materials are referenced in order to support breast imaging healthcare professionals.Accurate and individualized breast cancer risk assessment can be used to guide personalized screening and prevention recommendations. Existing risk prediction models use genetic and nongenetic risk factors to provide an estimate of a woman's breast cancer risk and/or the likelihood that she has a BRCA1 or BRCA2 mutation. Each model is best suited for specific clinical scenarios and may have limited applicability in certain types of patients. For example, the Breast Cancer Risk Assessment Tool, which identifies women who would benefit from chemoprevention, is readily accessible and user-friendly but cannot be used in women under 35 years of age or those with prior breast cancer or lobular carcinoma in situ. Emerging research on deep learning-based artificial intelligence (AI) models suggests that mammographic images contain risk indicators that could be used to strengthen existing risk prediction models. This article reviews breast cancer risk factors, describes the appropriate use, strengths, and limitations of each risk prediction model, and discusses the emerging role of AI for risk assessment.Freeride skiing is an activity that is, or at least can be, quite dangerous. Risk-taking in high-risk sports has usually been understood within a psychological framework. Building on Pierre Bourdieu's sociology, this article highlights the social dimension of risk-taking in freeride skiing by scrutinizing values within a freeride culture. A central question in this article is what kind of actions are given recognition and credibility in freeride skiing? The findings show that there is a clear link between risk-taking and credibility and that risk-taking might be seen as a form of capital. However, risk-taking's link to recognition is not straightforward-it is limited by the skiers' skill level. To further develop our understanding of the social dimension of risk-taking we use Michelle Lamont's theory of symbolic boundaries. By expanding the Bourdieusian understanding of social practice with Lamont's work, we gain insight into how risk-taking is socially regulated by social conventions within a subculture. This means that we in this article describe three social dimensions of risk-taking (1) The link between risk-taking and recognition, (2) The limits of the risk-recognition nexus, and (3) The moral boundaries of risk-taking.Worldwide, 1.3 billion people live in Poverty, a socio-economic status that has been identified as a key determinant of a lack of sports participation. Still, numerous athletes around the world have grown up in underprivileged socio-economic conditions. This is the case in Brazil, a country with around 13.5 million impoverished citizens, yet, over decades, many of its best professional footballers have emerged from its favelas. In this article, we explore the role of the socio-cultural-economic constraints in shaping the development of skill and expertise of Brazilian professional football players. The methodological and epistemological assumptions of the "Contextualized Skill Acquisition Research" (CSAR) approach are used as an underpinning framework for organizing and analyzing data. Results suggested that, at the exosystemic level of Brazilian society, Poverty emerges as an influential constraint that can potentially enrich football development experiences of Brazilian players. Poverty, however, is not the direct causation of outstanding football skill development. Rather, from the perspective of ecological dynamics, Poverty creates specific contexts that can lead to the emergence of physical as well as socio-cultural environment constraints (e.g., Pelada, Malandragem) that can shape affordances (opportunities) for skill acquisition. These ideas suggest the need to ensure that environmental constraints can support people to amuse themselves cheaply, gain access to employment opportunities and maintain health and well-being through (unstructured and more structured) sport and physical activities in dense urban environments such as favelas, inner city areas, and banlieues. For this purpose, design of open play areas and even parkour installations can provide affordances landscapes for physical activity and sports participation in urban settings.This study aimed to quantify the influence of an increase in power output (PO) on joint kinematics and electromyographic (EMG) activity during an incremental test to exhaustion for a population of professional cyclists. The hip flexion/extension and internal/external rotation as well as knee abduction/adduction ranges of motion were significantly decreased at 100% of the maximal aerobic power (MAP). EMG analysis revealed a significant increase in the root mean square (RMS) for all muscles from 70% of the MAP. Gastrocnemius muscles [lateralis gastrocnemius (GasL) and medialis gastrocnemius (GasM)] were the less affected by the increase of PO. Cross-correlation method showed a significant increase in the lag angle values for VM in the last stage compared to the first stage, meaning that the onset of the activation started earlier during the pedaling cycle. Statistical Parametric Mapping (SPM) demonstrated that from 70% MAP, biceps femoris (BF), tibialis anterior (TA), gluteus maximus (GM), and rectus femoris (RF) yielded larger ranges of the crank cycle on which the level of recruitment was significantly increased. This study revealed specific muscular and kinematic coordination for professional cyclists in response to PO increase.Music preferences are strongly shaped by the cultural and socio-economic background of the listener, which is reflected, to a considerable extent, in country-specific music listening profiles. Previous work has already identified several country-specific differences in the popularity distribution of music artists listened to. In particular, what constitutes the "music mainstream" strongly varies between countries. To complement and extend these results, the article at hand delivers the following major contributions First, using state-of-the-art unsupervized learning techniques, we identify and thoroughly investigate (1) country profiles of music preferences on the fine-grained level of music tracks (in contrast to earlier work that relied on music preferences on the artist level) and (2) country archetypes that subsume countries sharing similar patterns of listening preferences. Second, we formulate four user models that leverage the user's country information on music preferences. Among others, we propose a user modeling approach to describe a music listener as a vector of similarities over the identified country clusters or archetypes. Third, we propose a context-aware music recommendation system that leverages implicit user feedback, where context is defined via the four user models. More precisely, it is a multi-layer generative model based on a variational autoencoder, in which contextual features can influence recommendations through a gating mechanism. Fourth, we thoroughly evaluate the proposed recommendation system and user models on a real-world corpus of more than one billion listening records of users around the world (out of which we use 369 million in our experiments) and show its merits vis-à-vis state-of-the-art algorithms that do not exploit this type of context information.Due to the global response to the COVID-19 pandemic, there have been a variety of policy responses that have produced a range of expected and unexpected effects on society and our surrounding environment. One widely reported result of the pandemic response is that travel restrictions have resulted in improvements in regional air quality. This study aims to determine the effect of COVID-19 related Stay at Home precautions on air quality in a metropolitan area. We specifically focus on CO, NO2, and PM10 in Maricopa County (Phoenix), Arizona, as these all contribute to local air quality concerns. The role of meteorological parameters on ambient concentrations for these pollutants was investigated by using the local planetary boundary layer height (PBH) to account for vertical mixing. Across all three sites studied, there was no uniform decrease in either CO or NO2, even when freeway traffic volume was down by ~35%. For PM10, there was a significant decrease of ~45% seen at all the sites for the period most directly impacted by local Stay at Home restrictions compared to the past two years. This indicates that different pollutants have fundamentally different behavior in the local environment and suggests that these pollutants originate from different sources.Pseudomonas aeruginosa (P. aeruginosa) is the common infection-causing bacterial pathogen. Conventional methods for the detection of P. aeruginosa are time-consuming, and therefore, a more rapid analytical method is required. Here, monoclonal antibodies (Mabs) against P. aeruginosa (CICC 10419) were prepared and based on paired Mabs, an immunochromatographic assay (ICA) was developed. The ICA strip showed a limit of detection of 2.41 × 104 CFU/mL and the linear range of detection was 3.13 × 104-1.0 × 106 CFU/mL. No cross-reactivity was observed when other common Gram-negative and Gram-positive bacteria were used. The analytical performance of the ICA strip indicated that the developed ICA had good specificity and stability. Moreover, the feasibility of the ICA strip was verified by detecting P. aeruginosa (CICC 10419) in spiked water and food samples. The ICA strip could detect samples contaminated with a low-level of P. aeruginosa (CICC 10419) after 8 h enrichment.The COVID-19 pandemic has been ongoing for close to a year, with second waves occurring presently and many viewing vaccine uptake as the most likely way to curb successive waves and promote herd immunity. Reaching herd immunity status likely necessitates that children, as well as their parents, receive a vaccine targeting SARS-CoV-2. In this exploratory study, we investigated the demographic, experiential, and psychological factors associated with the anticipated likelihood and speed of having children receive a SARS-CoV-2 vaccine in a sample of 455 Canadian families (858 children; parents' mean age = 38.2 ± 6.82 years). Using linear mixed-effects and proportional odds logistic regression models, we demonstrated that older parental age, living in the Prairies (relative to Central Canada), more complete child vaccination history, and a greater tendency to prioritise the risks of the disease relative to the risks of side effects (i.e. lower omission bias) were associated with higher likelihoods of intention to vaccinate participants' children, with trend-level associations with lower perceived danger of the vaccine and higher psychological avoidance of the pandemic.