Motivation As a highly heterogeneous disease, clear cell renal cell carcinoma (ccRCC) has quite variable clinical behaviors. The prognostic biomarkers play a crucial role in stratifying patients suffering from ccRCC to avoid over- and under-treatment. Researches based on hand-crafted features and single-modal data have been widely conducted to predict the prognosis of ccRCC. However, these experience-dependent methods, neglecting the synergy among multimodal data, have limited capacity to perform accurate prediction. Inspired by complementary information among multimodal data and the successful application of convolutional neural networks (CNNs) in medical image analysis, a novel framework was proposed to improve prediction performance. Results We proposed a cross-modal feature-based integrative framework, in which deep features extracted from computed tomography/histopathological images by using CNNs were combined with eigengenes generated from functional genomic data, to construct a prognostic model for ccRCC. Results showed that our proposed model can stratify high- and low-risk subgroups with significant difference (P-value < 0.05) and outperform the predictive performance of those models based on single-modality features in the independent testing cohort [C-index, 0.808 (0.728–0.888)]. In addition, we also explored the relationship between deep image features and eigengenes, and make an attempt to explain deep image features from the view of genomic data. Notably, the integrative framework is available to the task of prognosis prediction of other cancer with matched multimodal data. Availability and implementation https://github.com/zhang-de-lab/zhang-lab? from=singlemessage Supplementary information Supplementary data are available at Bioinformatics online.
Characterizing the natural selection of complex traits is essential for understanding human evolution and biological or pathological mechanisms. To fulfill this requirement, we leveraged Genome-wide summary statistics for 870 polygenic traits and quantified the selection pressure of different forms and time scales on them in European ancestry. We found that 88% of traits underwent polygenic adaptation in the past 2000 years. At the present time and Neolithic period, selection pressure showed profound alteration. Traits related to pigmentation, impedance, and nutrition intake exhibited strong selection signals across different time scales. Our result provided an overview of selection pressure on various human polygenic traits, which served as a foundation for further populational and medical genetic studies.
Background Dynamic arterial elastance (Eadyn) has been extensively considered as a functional parameter of arterial load. However, conflicting evidence has been obtained on the ability of Eadyn to predict mean arterial pressure (MAP) changes after fluid expansion. This meta-analysis sought to assess the predictive performance of Eadyn for the MAP response to fluid expansion in mechanically ventilated hypotensive patients. Methods We systematically searched electronic databases through November 28, 2020, to retrieve studies that evaluated the association between Eadyn and fluid expansion-induced MAP increases in mechanically ventilated hypotensive adults. Given the diverse threshold value of Eadyn among the studies, we only reported the area under the hierarchical summary receiver operating characteristic curve (AUHSROC) as the primary measure of diagnostic accuracy. Results Eight observational studies that included 323 patients with 361 fluid expansions met the eligibility criteria. The results showed that Eadyn was a good predictor of MAP increases in response to fluid expansion, with an AUHSROC of 0.92 [95% confidence interval (CI) 0.89 to 0.94]. Six studies reported the cut-off value of Eadyn, which ranged from 0.65 to 0.89. The cut-off value of Eadyn was nearly conically symmetrical, most data were centred between 0.7 and 0.8, and the mean and median values were 0.77 and 0.75, respectively. The subgroup analyses indicated that the AUHSROC was slightly higher in the intensive care unit (ICU) patients (0.96; 95% CI 0.94 to 0.98) but lower in the surgical patients in the operating room (0.72; 95% CI 0.67 to 0.75). The results indicated that the fluid type and measurement technique might not affect the diagnostic accuracy of Eadyn. Moreover, the AUHSROC for the sensitivity analysis of prospective studies was comparable to that in the primary analysis. Conclusions Eadyn exhibits good performance for predicting MAP increases in response to fluid expansion in mechanically ventilated hypotensive adults, especially in the ICU setting.
This article focuses on the distributed consensus control problem for nonlinear multi‐agent systems subject to sensor uncertainty. To be specific, we study nonlinear multi‐agent systems of lower or upper triangular structure with unknown growth rate and sensor uncertainty. A new time‐varying gain approach is proposed to construct observers as well as distributed output‐feedback controllers. By selecting suitable design parameters, the leader‐follower consensus of nonlinear multi‐agent systems is achieved. Different from the existing results, a time‐varying function in a logarithmic form is introduced to deal with unknown growth rate. Moreover, a monotonically increasing time‐varying function is constructed to cope with uncertain sensor sensitivity. Two simulation examples are provided to demonstrate the effectiveness of the proposed distributed consensus control algorithms.
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