AIMTo investigate the clinical significance of preoperative systemic immune-inflammation index (SII) in patients with colorectal cancer (CRC).METHODSA retrospective analysis of 1383 cases with CRC was performed following radical surgery. SII was calculated with the formula SII = (P × N)/L, where P, N, and L refer to peripheral platelet, neutrophil, and lymphocyte counts, respectively. The clinicopathological features and follow-up data were evaluated to compare SII with other systemic inflammation-based prognostic indices such as the neutrophil-lymphocyte ratio (NLR) and platelet-lymphocyte ratio (PLR) in patients with CRC.RESULTSThe optimal cut-off point for SII was defined as 340. The overall survival (OS) and disease-free survival (DFS) were better in patients with low NLR, PLR, and SII (P < 0.05). The SII was an independent predictor of OS and DFS in multivariate analysis. The area under the receiver-operating characteristics (ROC) curve for SII (0.707) was larger than those for NLR (0.602) and PLR (0.566). In contrast to NLR and PLR, SII could effectively discriminate between the TNM subgroups.CONCLUSIONSII is a more powerful tool for predicting survival outcome in patients with CRC. It might assist the identification of high-risk patients among patients with the same TNM stage.

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Logistic Regression is a well-known classification method that has been used widely in many applications of data mining, machine learning, computer vision, and bioinformatics. Sparse logistic regression embeds feature selection in the classification framework using the 1-norm regularization, and is attractive in many applications involving high-dimensional data. In this paper, we propose Lassplore for solving Large-scale sparse logistic regression. Specifically, we formulate the problem as the 1-ball constrained smooth convex optimization, and propose to solve the problem using the Nesterov's method, an optimal first-order black-box method for smooth convex optimization. One of the critical issues in the use of the Nesterov's method is the estimation of the step size at each of the optimization iterations. Previous approaches either applies the constant step size which assumes that the Lipschitz gradient is known in advance, or requires a sequence of decreasing step size which leads to slow convergence in practice. In this paper, we propose an adaptive line search scheme which allows to tune the step size adaptively and meanwhile guarantees the optimal convergence rate. Empirical comparisons with several state-of-theart algorithms demonstrate the efficiency of the proposed Lassplore algorithm for large-scale problems.

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We present a pollen‐based precipitation reconstruction and multi‐proxy records from a 485‐cm‐long sequence from a sediment core from Xingyun Lake, Yunnan Plateau, south‐west China, which depicts the evolution of the Indian Summer Monsoon (ISM) during the last 8500 years. Pollen and other palaeoenvironmental records document several stages of vegetation history and climate change. The warmest and wettest climate in the Xingyun Lake catchment occurred before 5500 cal a BP, and subsequently the climate became gradually drier. After 2000 cal a BP the regional environmental conditions became unstable, and a wet Medieval Warm Period is probably recorded. Our reconstruction of the ISM is similar to that portrayed by Holocene speleothem δ18O records from southern China, but is distinctly different from the East Asian Summer Monsoon (EASM) evolution, which features a mid‐Holocene maximum. Our results support the hypothesis that the ISM and EASM evolved asynchronously during the Holocene, and imply that the Chinese speleothem δ18O records from southern China may principally reflect changes in moisture source from the Indian monsoon domain, and thus record the history of the ISM rather than the EASM.