The outcomes of patients treated with surgery for early stage pancreatic ductal adenocarcinoma (PDAC) are variable with median survival ranging from 6 months to more than 5 years. This challenge underscores an unmet need for developing personalized medicine strategies to refine the current treatment decision-making process. To derive a prognostic gene signature for patients with early stage PDAC, a PDAC cohort from Moffitt Cancer Center (n = 63) was used with overall survival (OS) as the primary endpoint. This was further evaluated using an independent microarray cohort dataset (Stratford et al: n = 102). Technical validation was performed by NanoString platform. A prognostic 15-gene signature was developed and showed a statistically significant association with OS in the Moffitt cohort (hazard ratio [HR] = 3.26; p<0.001) and Stratford et al cohort (HR = 2.07; p = 0.02), and was independent of other prognostic variables. Moreover, integration of the signature with the TNM staging system improved risk prediction (p<0.01 in both cohorts). In addition, NanoString validation showed that the signature was robust with a high degree of reproducibility and the association with OS remained significant in the two cohorts. The gene signature could be a potential prognostic tool to allow risk-adapted stratification of PDAC patients into personalized treatment protocols; possibly improving the currently poor clinical outcomes of these patients.

Covalent organic frameworks (COFs) are crystalline organic materials of interest for a wide range of applications due to their porosity, tunable architecture, and precise chemistry. However, COFs are typically produced in powder form and are difficult to process. Herein, we report a simple and versatile approach to fabricate macroscopic, crystalline COF gels and aerogels. Our method involves the use of dimethyl sulfoxide as a solvent and acetic acid as a catalyst to first produce a COF gel. The COF gel is then washed, dried, and reactivated to produce a pure macroscopic, crystalline, and porous COF aerogel that does not contain any binders or additives. We tested this approach for six different imine COFs and found that the crystallinities and porosities of the COF aerogels matched those of COF powders. Electron microscopy revealed a robust hierarchical pore structure, and we found that the COF aerogels could be used as absorbents in oil–water separations, for the removal of organic and inorganic micropollutants, and for the capture and retention of iodine. This study provides a versatile and simple approach for the fabrication of COF aerogels and will provide novel routes for incorporating COFs in applications that require macroscopic, porous materials.

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Uncertainty estimation in deep learning becomes more important recently. A deep learning model can't be applied in real applications if we don't know whether the model is certain about the decision or not. Some literature proposes the Bayesian neural network which can estimate the uncertainty by Monte Carlo Dropout (MC dropout). However, MC dropout needs to forward the model N times which results in N times slower. For real-time applications such as a self-driving car system, which needs to obtain the prediction and the uncertainty as fast as possible, so that MC dropout becomes impractical. In this work, we propose the region-based temporal aggregation (RTA) method which leverages the temporal information in videos to simulate the sampling procedure. Our RTA method with Tiramisu backbone is 10x faster than the MC dropout with Tiramisu backbone (N = 5). Furthermore, the uncertainty estimation obtained by our RTA method is comparable to MC dropout's uncertainty estimation on pixel-level and frame-level metrics.