Retrospective analysis of 119 Chinese noninflammatory locally advanced breast cancer cases treated with intravenous combination of vinorelbine and epirubicin as a neoadjuvant chemotherapy: a median follow-up of 63.4 months

<p>Abstract</p> <p>Background</p> <p>This study is a retrospective evaluation of the efficacy of neoadjuvant chemotherapy (NC) with a vinorelbine (V) and epirubicin (E) intravenous combination regimen and is aimed at identification of predictive markers for the long-term outcome in noninflammatory locally advanced breast cancer (NLABC).</p> <p>Methods</p> <p>One-hundred-and-nineteen patients with NLABC were identified from September 2001 to May 2006. Analysis was performed in March 2008, with a median follow-up of 63.4 months (range, 9-76 months). All patients were diagnosed with invasive breast cancer using 14 G core needle biopsy and treated with three cycles of VE before surgery. Local-regional radiotherapy was offered to all patients after the completion of chemotherapy followed by hormonal therapy according to hormone receptor status. Tissue sections cut from formalin-fixed paraffin-embedded blocks from biopsy specimens and postoperative tumor tissues were stained for the presence of estrogen receptor (ER), progesterone receptor (PgR), HER-2 (human epidermal growth factor receptor-2), and MIB-1(Ki-67).</p> <p>Results</p> <p>Patients characteristics were median age 52 years (range: 25-70 years); clinical TNM stage, stage IIB (n = 32), stage IIIA (n = 56), stage IIIB (n = 22) and stage IIIC (n = 9). All patients were evaluable for response: clinically complete response was documented in 27 patients (22.7%); 78 (65.6%) obtained partial response; stable disease was observed in 13 (10.9%); 1 patient (0.8%) had progressive disease. Pathological complete response was found in 22 cases (18.5%). Seventy-five patients were alive with no recurrence after a median follow-up of 63.4 months, the 5-year rates for disease-free survival and overall survival were 58.7% and 71.3%, respectively, after the start of NC. On multivariate analysis, the independent variables associated with increased risk of relapse and death were high pre-Ki-67(p = 0.012, p = 0.017, respectively), high post-Ki-67 expression (p = 0.045, p = 0.001, respectively), and non-pCR (p = 0.034, p = 0.027, respectively). A significantly increased risk of death was associated with lack of pre-ER expression (p = 0.002). Among patients with non-pCR, those with a pathological response at the tumor site with special involvement (i.e. skin, vessel and more than one quadrant) were at a higher risk of disease relapse and death (p < 0.001, p = 0.001, respectively).</p> <p>Conclusion</p> <p>This study suggests the promising use of a VE regimen as NC for Chinese NLABC after a median follow-up of 63.4 months. Pathological response in the tumor site, pre-Ki-67 and post-Ki-67 expression, and pre-ER expression were the important variables that predicted long-term outcome. Patients with pathological special involvement at the primary site after NC had the lowest survival rates.</p

Multi-view Inference for Relation Extraction with Uncertain Knowledge

Knowledge graphs (KGs) are widely used to facilitate relation extraction (RE) tasks. While most previous RE methods focus on leveraging deterministic KGs, uncertain KGs, which assign a confidence score for each relation instance, can provide prior probability distributions of relational facts as valuable external knowledge for RE models. This paper proposes to exploit uncertain knowledge to improve relation extraction. Specifically, we introduce ProBase, an uncertain KG that indicates to what extent a target entity belongs to a concept, into our RE architecture. We then design a novel multi-view inference framework to systematically integrate local context and global knowledge across three views: mention-, entity- and concept-view. The experiment results show that our model achieves competitive performances on both sentence- and document-level relation extraction, which verifies the effectiveness of introducing uncertain knowledge and the multi-view inference framework that we design

Adaptively Scheduling Parallel Loops in Distributed Shared-Memory Systems

Using runtime information of load distributions and processor affinity, we propose an adaptive scheduling algorithm and its variations from different control mechanisms. The proposed algorithm applies different degrees of aggressiveness to adjust loop scheduling granularities, aiming at improving the execution performance of parallel loops by making scheduling decisions that match the real workload distributions at runtime. We experimentally compared the performance of our algorithm and its variations with several existing scheduling algorithms on two parallel machines: the KSR-1 and the Convex Exemplar. The kernel application programs we used for performance evaluation were carefully selected for different classes of parallel loops. Our results show that using runtime information to adaptively adjust scheduling granularity is an effective way to handle loops with a wide range of load distributions when no prior knowledge of the execution can be used. The overhead caused by coll..

ATSD: Anchor-Free Two-Stage Ship Detection Based on Feature Enhancement in SAR Images

Syntheticap erture radar (SAR) ship detection in harbors is challenging due to the similar backscattering of ship targets to surrounding background interference. Prevalent two-stage ship detectors usually use an anchor-based region proposal network (RPN) to search for the possible regions of interest on the whole image. However, most pre-defined anchor boxes are redundantly and randomly tiled on the image, manifested as low-quality object proposals. To address these issues, this paper proposes a novel detection method combined with two feature enhancement modules to improve ship detection capability. First, we propose a flexible anchor-free detector (AFD) to generate fewer but higher-quality proposals around the object centers in a keypoint prediction manner, which completely avoids the complicated computation in RPN, such as calculating overlapping related to anchor boxes. Second, we leverage the proposed spatial insertion attention (SIA) module to enhance the feature discrimination between ship targets and background interference. It accordingly encourages the detector to pay attention to the localization accuracy of ship targets. Third, a novel weighted cascade feature fusion (WCFF) module is proposed to adaptively aggregate multi-scale semantic features and thus help the detector boost the detection performance of multi-scale ships in complex scenes. Finally, combining the newly-designed AFD and SIA/WCFF modules, we present a new detector, named anchor-free two-stage ship detector (ATSD), for SAR ship detection under complex background interference. Extensive experiments on two public datasets, i.e., SSDD and HRSID, verify that our ATSD delivers state-of-the-art detection performance over conventional detectors

Germination of Deyeuxia angustifolia as affected by soil type, burial depth, water depth and oxygen level

Deyeuxia angustifolia , Sanjiang plain, Seed, Emergence, Germination,

Comparative Study of Carbon Storage and Allocation Characteristics of Mature Evergreen Broad-leaved Forest

Evergreen broad-leaved forest is an important forest type in China. This paper analyzes the allocation characteristics of vegetation and soil carbon pool of evergreen broad-leaved forest, to understand the current status of research on the carbon storage of evergreen broad-leaved forest as well as shortcomings. In the context of global climate change, it is necessary to carry out the long-term research of evergreen broad-leaved forest, in order to grasp the formation mechanism of evergreen broad-leaved forest productivity, and the impact of climate change on the carbon sequestration function of evergreen broad-leaved forest ecosystem

Study on the Water Quality and Protection Measures of Dongting Lake

From nitrogen and phosphorus, chemical oxygen demand, phytoplankton, and eutrophication, this article analyzes the current situation of water quality in the Dongting Lake area, and discusses the factors influencing the water quality of Dongting Lake. Based on the actual production, geographic characteristics and outstanding problem of grim water pollution situation in the Dongting Lake area, some specific proposed measures are put forward in order to provide the basis and reference for the future pollution control in the Dongting Lake area, such as strengthening the industrial pollution control, developing ecological agriculture, and enhancing ecological restoration and water quality early warning

Carbon Stock and Carbon Cycle of Wetland Ecosystem

Wetland ecosystem is an essential ecosystem in the world. Its organic carbon stock and carbon cycle are important basis of global carbon cycle researches and also major contents of global climate change researches. Researches have shown that wetland protection and restoration can promote carbon accumulation and reduce emission of greenhouse gases. This paper discussed influence of carbon stock and carbon balance of wetland ecosystem and emission of greenhouse gases, as well as the relationship between wetland and global climate changes. Finally, it made prospect on researches about carbon cycle of Dongting Lake

Hyperspectral Inversion Model of Relative Heavy Metal Content in <i>Pennisetum sinese Roxb</i> via EEMD-db3 Algorithm

Detection rapidity and model accuracy are the keys to hyperspectral nondestructive testing technology, especially for Pennisetum sinese Roxb (PsR) due to its extremely high adsorptive heavy metal content. The study of the resolution of PsR is conducive to the analysis of the accumulated heavy metal content in its different parts. In this paper, the contents of Cd, Cu and Zn accumulated in the old leaves, young leaves, upper stem, middle stem and lower stem, as well as the hyperspectral data of the corresponding parts, were measured simultaneously in both fresh and dry states. To begin, the spectral data of PsR were preprocessed by using Ensemble Empirical Mode Decomposition-Daubechies3 (EEMD-db3), Savitzky–Golay (SG), Symlet3 (sym3), Symlet5 (sym5), and multiplicative scatter correction (MSC). The 40 samples were divided into 32 training sets and 8 validation sets. The preprocessed spectral data were transformed by the first derivative (FD) and reciprocal logarithm (log(1/R)) to highlight the singularities using binary wavelet decomposition. After screening the significant bands from the correlation curve, the competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were applied to extract the spectral characteristic variables, which were used to establish the partial least-squares (PLS) regression and multiple stepwise linear regression (MSLR) inversion models of Cd, Cu, and Zn contents. Based on EEMD-db3 pretreatment, the inversion model of Zn in the dry (fresh) state had R2 values of 0.884 (0.880), NRMSE values of 0.179 (0.253) and RPD values of 3.191 (3.221), indicating excellent stability and predictive performance. The findings of this study can not only aid in the rapid nondestructive detection of heavy metal adsorption in various parts of PsR, but can also be applied to guide the development and use of animal feed