Nomogram Based on Body Composition and Prognostic Nutritional Index Predicts Survival After Curative Resection of Gastric Cancer.

Rationale And Objectives: This study aimed to identify independent prognostic factors for gastric cancer (GC) patients after curative resection using quantitative computed tomography (QCT) combined with prognostic nutritional index (PNI), and to develop a nomogram prediction model for individualized prognosis.

Materials And Methods: This study retrospectively analyzed 119 patients with GC who underwent curative resection from January 2016 to March 2018. The patients' preoperative clinical pathological data were recorded, and all patients underwent QCT scans before and after curative resection to obtain QCT parameters: bone mineral density (BMD), skeletal muscle area (SMA), visceral fat area (VFA), subcutaneous fat area (SFA) and CT fat fraction (CTFF), then relative rate of change in each parameter (ΔBMD, ΔSMA, ΔVFA, ΔSFA, ΔCTFF) was calculated after time normalization.

Transfer learning for bearing performance degradation assessment based on deep hierarchical features.

Bearing design, production and complicated operating condition can lead to the scattered life cycle degradation distribution, which will bring a challenge of generalization for performance degradation assessment models. And it is costly and time-consuming to collect a large amount of labeled data for supervised diagnosis, especially when the task comes from a new operating condition. Thus in this paper, a novel bearing degradation assessment model is proposed based on transfer learning and deep hierarchical features extraction.