Human epidermal growth factor receptor 1 or 2 (HER1/2), and fibroblast growth factor receptor 1 (FGFR1) signaling serve critical roles in the progression of breast cancer; however, cross-talk between HER1/2 and FGFR1 signaling has not been extensively studied. In the present study, the copy number variation status of FGFR1 and HER1/2, and the clinical implications and prognostic relevance of this, were evaluated in invasive ductal breast cancer (IDC) tissue samples. Quantitative polymerase chain reaction and fluorescence in situ hybridization were used to assess gene copy number variation in IDC samples, and the clinical characteristics and survival curves of patients with IDC were analyzed. The amplification of FGFR1 was identified in 16.0% of the samples (12 of 75), of HER1 in 26.7% (20 of 75), of HER2 in 37.3% (28 of 75), and of FGFR1 and HER1/2 simultaneously in 8.0% (6 of 75). FGFR1 and HER1/2 co-amplification were significantly correlated with distant metastasis (P=0.035), recurrence (P=0.026) and decreased disease-free survival time (P=0.042). This was the case for patients undergoing endocrine therapy (P=0.002) and chemotherapy (P=0.044). Taken together, the results indicate that patients with FGFR1 and HER1/2 co-amplification may exhibit a less favorable prognosis compared with patients with either FGFR1, HER1/2 amplification or without amplification.

ErbB signaling serves essential roles in invasive ductal carcinoma (IDC). The aim of the present study was to assess gene amplification in ErbB family members in IDC with clinical implications. Quantitative polymerase chain reaction and fluorescence in situ hybridization were performed on formalin-fixed paraffin-embedded tumor samples for gene amplification detection. The clinical and histopathological characteristics, as well as the prognostic significance, were analyzed. Among the 119 IDC patients evaluated, epidermal growth factor receptor [EGFR; also known as human epidermal growth factor receptor (HER)1], HER2, HER3 and HER4 gene amplification was observed in 30 (25.2%), 44 (36.9%), 0 (0.0%) and 1 (0.8%) patients, respectively. EGFR amplification was associated with estrogen receptor status (P=0.028) and higher possibilities of recurrence (P=0.015) and distant metastasis (following initial surgery) (P=0.011). In survival analysis, EGFR amplification was also associated with disease-free survival (DFS) (P=0.001) and overall survival (OS) (P=0.003). HER2 amplification was associated with larger tumor size (P=0.006), later clinical stage (P=0.003) and distant metastasis (following initial surgery) (P=0.006). In survival analysis, HER2 amplification was also associated with DFS (P=0.011). Notably, the present study identified a group of patients in whom EGFR and HER2 were co-amplified. This group of patients appeared to have a higher possibility of metastasis (when diagnosed) (P=0.014) and distant metastasis (following initial surgery) (P<0.001). In survival analysis, these patients were noticed to be associated with DFS (P<0.001) and OS (P=0.002). With respect to treatment regimen, this was also true for the DFS association with chemotherapy (P<0.001), radiotherapy (P<0.001) and hormonal therapy (P=0.001). The present results suggest that EGFR and HER2 amplification favor distant metastasis following initial surgery and are significantly associated with poor clinical outcome in breast cancer patients.

Unlike traditional industrial robots, indoor service robots are usually required to possess high intelligence, such as the skills of flexible moving, precise spacial perceiving. And high intelligence is always accompanied by consuming complicated and expensive computation resources. One solution for indoor service robots is centralization of expensive computation resource so that it is possible to design a low cost client with a high-intelligence brain. However, as a fundamental intelligence function for mobile indoor robots, if a real-time visual Simultaneously Localization and Mapping (vSLAM) system is split into client and brain, it will be confronted with new challenges, such as the barrier of instant data sharing and performance degradation brought by network delay inbetween. To solve the problem, we focus on a framework and approach of cloud-based visual SLAM in this paper, and provide an efficient solution to offload the expensive computation and reduce the cost of robot clients. The integrated system is distributed in a 3-level Cloud with lightweight tracking, high precision dense mapping, and map sharing. Based on recent excellent algorithms, our system is able to run a real-time sparse tracking on the client, and a real-time dense mapping on the cloud server, which outputs an explicit 3D dense map. Only keyframes are sent to the local cloud center to reduce the network bandwidth requirement. Dense geometric pose estimation besides feature-based methods is computed to make the system resistant to featureless indoor scenes. The camera poses associated with keyframes are optimized on the local computing cloud center, and are sent back to the client to decrease the trajectory drift. We evaluate the system on the Technical University of Munich (TUM) datasets, Imperial College London and National University of Ireland Maynooth (ICL-NUIM) datasets, and the real data captured by our robot in terms of visual odometry on the client side and dense maps generated on the server cloud. Qualitative and quantitative experiments show our cloud visual SLAM system is able to bear the network delay in Local Area Network (LAN), and it is an efficient vSLAM solution for indoor service robots with high intelligence from a centric brain.

Large-scale synthesis of graphene-based nanomaterials in stirred tank reactor (STR) often results in serious agglomeration because of the poor control during micromixing process. In this work, reactive impingement mixing is conducted in a two-stage impinging jet microreactor (TS-IJMR) for the controllable and scale-up synthesis of nickel-cobalt boride@borate core-shell nanostructures on RGO flakes (NCBO/RGO). Benefiting from the good process control and improved micromixing efficiency of TS-IJMR, NCBO/RGO nanosheet provides a large BET surface area, abundant of suitable mesopores (2–5 nm), fast ion diffusion, and facile electron transfer within the whole electrode. Therefore, NCBO/RGO electrode exhibits a high specific capacitance of 2383 F g−1 at 1 A g−1, and still retains 1650 F g−1 when the current density is increased to 20 A g−1, much higher than those of nickel boride@borate/RGO (NBO/RGO) and cobalt boride@borate/RGO (CBO/RGO) synthesized in TS-IJMR, as well as NCBO/RGO-S synthesized in STR. In addition, an asymmetric supercapacitor (NCBO/RGO//AC) is constructed with NCBO/RGO and activated carbon (AC), which displays a high energy density of 53.3 W h kg−1 and long cyclic lifespan with 91.8% capacitance retention after 5000 charge-discharge cycles. Finally, NCBO/RGO is used as OER electrocatalyst to possess a low overpotential of 309 mV at a current density of 10 mA cm−2 and delivers a good long-term durability for 10 h. This study opens up the potential of controllable and scale-up synthesis of NCBO/RGO nanosheets for high-performance supercapacitor electrode materials and OER catalysts.

The water level fluctuation forecast of Yangtze River plays an important role in the navigation planning and other areas. This study used LSTM neural network to make short term forecast of the water level of Nanjing navigable river, focusing on the 2 days, 3 days and 5 days-forecast from the past 14 days. The error of mean square reached 0.064, 0.121 and 0.195, showing a rather accurate prediction. The model was also suitable for longer time series prediction. This study optimized the model by adjusting the hyper-parameters using qualitative and quantitative analysis and further decided that the batch size equals to 90 and the epoch equals to 250 in the case of 5 days-forecast from the past 14 days. The prediction accuracy increased by 21% with a good prediction performance, and the error of mean square was reduced to 0.153. It provided a reference for the selection of hyper-parameters for the prediction of water level in Nanjing station by LSTM neural network.