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Cancer stem cell (CSC) is thought to be the major cause of radio-resistance and relapse post radiotherapy (RT). Recently ultra-high dose rate “FLASH-RT” evokes great interest for its decreasing normal tissue damages while maintaining tumor responses compared with conventional dose rate RT. However, the killing effect and mechanism of FLASH irradiation (FLASH-IR) on CSC and normal cancer cell are still unclear. Presently the radiation induced death profile of CSC and normal cancer cell were studied. Cells were irradiated with FLASH-IR (∼109 Gy/s) at the dose of 6–9 Gy via laser-accelerated nanosecond particles. Then the ratio of apoptosis, pyroptosis and necrosis were determined. The results showed that FLASH-IR can induce apoptosis, pyroptosis and necrosis in both CSC and normal cancer cell with different ratios. And CSC was more resistant to radiation than normal cancer cell under FLASH-IR. Further experiments tracing lysosome and autophagy showed that CSCs had higher levels of lysosome and autophagy. Taken together, our results suggested that the radio-resistance of CSC may associate with the increase of lysosome-mediated autophagy, and the decrease of apoptosis, necrosis and pyroptosis. To our limited knowledge, this is the first report shedding light on the killing effects and death pathways of CSC and normal cancer cell under FLASH-IR. By clarifying the death pathways of CSC and normal cancer cell under FLASH-IR, it may help us improve the understanding of the radio-resistance of CSC and thus help to optimize the future clinical FLASH treatment plan.

Circulating tumor cells (CTCs) are tumor cells that escaped from the primary tumor or the metastasis into the blood and they play a major role in the initiation of metastasis and tumor recurrence. Thus, it is widely accepted that CTC is the main target of liquid biopsy. In the past few decades, the separation of CTC based on the electrochemical method has attracted widespread attention due to its convenience, rapidness, low cost, high sensitivity, and no need for complex instruments and equipment. At present, CTC detection is not widely used in the clinic due to various reasons. Point-of-care CTC detection provides us with a possibility, which is sensitive, fast, cheap, and easy to operate. More importantly, the testing instrument is small and portable, and the testing does not require specialized laboratories and specialized clinical examiners. In this review, we summarized the latest developments in the electrochemical-based CTC detection and point-of-care CTC detection, and discussed the challenges and possible trends.

Background The impact of macroscopic pathologic features of primary tumor that could be obtained preoperatively on pT classification has not been reported so far. The aim of this study was to investigate the feasibility of incorporation of Borrmann type IV gastric cancer into the pT classification. Materials and methods Clinicopathologic and prognostic data of 1622 patients with advanced gastric cancer who underwent radical surgery were retrospectively studied. Results Of 1622 patients, 135 (8.32%) patients were classified as having Borrmann type IV gastric cancer. We first confirmed that Borrmann type IV gastric cancer was one of the independent prognostic factors for patients with advanced gastric cancer who underwent radical surgery. Interestingly, we found that overall survival of patients with Borrmann type IV gastric cancer could be clearly distinguished by pN classification and pathological TNM stage but not by pT classification. Importantly, further analysis demonstrated that the prognosis of Borrmann type IV gastric cancers was homogeneous with that of pT4b cancers but poorer than pT2, pT3, pT4a cancers. Therefore, we proposed a novel pT classification in which pT4b disease was defined as cancers that were Borrmann type IV or those that had invaded adjacent structures. Two-step multivariate analysis demonstrated that the novel pT classification was more suitable for prognostic assessment than the original classification. Conclusions Classifying Borrmann type IV gastric cancer as pT4b disease improves pT classification prediction of prognosis in patients with advanced gastric cancer after radical surgery.

In 2018, 88% of the crude steel manufactured in China was produced by blast furnace - basic oxygen furnace (BF-BOF) route. This leads to high energy consumption and greenhouse gas emissions in China’s iron and steel industry. In this study, the life cycle assessment of environmental impact in an iron and steel plant was evaluated by using GaBi v.8.7 software. Eight assessment categories were evaluated to determine direct/indirect contribution, and the results of impact categories were further normalized for comparison among different unit processes. The results indicated that the BF alone contributed greatly to various environmental impact categories, or 73% Abiotic Depletion Potential (ADP), 54% Eutrophication Potential (EP), and 69% Global Warming Potential (GWP). Different scenario settings were established to reduce nonrenewable fuel consumption and corresponding environmental impacts. The scenario 2 (replacement of coke and anthracite in BF with charcoal) and scenario 3 (increasing electric arc furnace (EAF) route) were able to reduce total environmental impacts by 14% and 25%, respectively, as compared to the scenario 1 (existing conditions). The normalized results of each assessment category scores indicated that the main environmental impacts of production process were Human Toxicity Potential (33%), GWP (30%) and ADP (25%). The EAF route exhibits less environmental impact than BF-BOF route, especially in GWP, ADP, Cumulative Energy Demand and EP. In the future, steel making enterprises in China should be encouraged to shift their process from BF-BOF to EAF route as was done in developed countries.

While promoting the economic development of resource-dependent cities, the industry also brings serious environmental and ecological issues. The combination of industrial symbiosis and urban symbiosis (IS/UrS) offers a new solution to environmental and ecological challenges for resource-dependent cities. To quantify the benefits of IS/UrS in resource-dependent cities, this study assessed triple benefits including environmental (raw materials saving and wastes reuse), reduced CO2 emission, and ecological (emergy saving) benefits via IS/UrS in Yongcheng (China) by combining material flow analysis and emergy analysis. The results indicate that a total of thirty-nine IS/UrS activities (including twenty-nine in-use and ten potential IS/UrS activities) are identified, which can achieve remarkable resources saving including 2.37 Mt slag substitution for feedstock mix in cement clinker production (96% from existing in-use activities), 0.43 Mt municipal solid waste reduction (100% from potential activities), 1.07 Mt coal saving (35% from potential activities), 4.88 Mt CO2 emission reduction (23% from potential activities) and 5.53E+21 sej emergy saving (17% from potential activities). Urban symbiosis activities can contribute additional CO2 reduction and emergy saving by 11% and 7% respectively. Based on the performance of CO2 reduction and emergy saving, recycling of slag/fly ash and scrap steel should be given top priority in the local IS/UrS policy making process. The case study shows that IS/UrS is a useful approach to facilitate the sustainable development of resource-dependent cities, and could be expanded to other similar cities.

This paper evaluates the level of urban land intensive use in Henan Province and analyzes the influence of different indexes on urban land intensive use in Henan Province. PSR model was adopted. The conclusions are as follows: (1) the intensive use level of urban land in Henan Province gradually increased from 2008 to 2016, but it was still in the stage of overuse, only in 2010 reached a moderate level of utilization; (2) the intensive use level of urban land in Henan Province is not high. Population is one of the main factors restricting the intensive use level of urban land in Henan Province; (3) the government of Henan Province should comprehensively consider the actual situation of Henan Province, increase the intensity of intensive use of urban land, and formulate relevant policies to improve the level of intensive use of urban land in Henan Province.

Sleep patterns have been associated with the development of cancers, although the association between sleep duration and breast cancer remains controversial. The purpose of our study was to explore the relationship between sleep duration and breast cancer risk. The PubMed and Web of Science databases were searched, and restricted cubic splines were used to explore the dose-response relationship. Data from 415,865 participants were derived from 10 studies. A J-shaped nonlinear trend was found between sleep duration and breast cancer incidence (Pnon-linear= 0.012); compared with the reference hours (6 h or 7 h), with increasing sleep hours, the risk of breast cancer increased (Ptrend= 0.028). Moreover, a nonlinear relationship was found between sleep duration and estrogen receptor-positive breast cancer (Pnon-linear= 0.013); the risk of estrogen receptor-positive breast cancer increased with increasing sleep hours compared to the reference hours (Ptrend= 0.024). However, no nonlinear relationship was found between sleep duration and estrogen receptor-negative breast cancer; the risk of estrogen receptor-negative breast cancer was 1.035 for every additional sleep hour. Compared to women with the reference number of sleep hours, women with a longer sleep duration might have a significantly increased risk of breast cancer, especially estrogen receptor-positive breast cancer.

Background: China National Center for Cardiovascular Disease launched a guideline-based 10-year risk assessment of cardiovascular disease (CVD) phone APP, and created a heart age index to visualize the CVD risk to public. This is the first time using totally Chinese local database which includes 40,000 people following up 10-15 years to assess CVD incidence risk. We assessed the self-test effect of the app in China and the feasibility of promoting risk assessment app as a primary prevention tool for CVD by government. Methods: The app was promoted through media and government projects by encouraging people spontaneously and randomly scanning two dimensional code and answering 8 questions about CVD risk factors, including age, sex, hyperlipidemia, hypertension, diabetes, weight, height, smoking. We analyzed the current status of cardiovascular risk in Chinese residents, the prevalence of hypertension, hyperlipidemia, diabetes, the distribution of heart-age and so on by SPSS 19.0. Spearman bivariate correlation analysis was used to get the correlation coefficient among different risk factors. Results: Over 5 months, 18,214 people (39.7% of men, 60.3% of women) spontaneously used the app around the whole country. The mean age was 55.9 years, the mean heart-age was 64.8 years, and the mean 10-year CVD risk was 4.1%. For hypertension, hyperlipidemia and diabetes patients, the mean 10-year CVD risks were 8.7%, 7.1%, and 9.5% respectively. Prevalence of hypertension (SBP≥140 mmHg or DBP≥90 mmHg), hyperlipidemia (total cholesterol>6.2mmol/L) and diabetes mellitus were 15.8%, 14.4% and 8.5%, respectively. There were 11.2% people smoked. Among hypertensive patients, 33.4% had hyperlipidemia at the same time, and 10.5% of diabetes patients had hyperlipidemia. There were 0.54% people who had three diseases simultaneously. The correlation coefficient between hypertension and hyperlipidemia was 0.101 (P Conclusions: The 10-years CVD risk evaluation app can be a primary prevention tool for CVD in China, given its friendly design and visual risk. For the public, heart-age is 10 years older than actual age. Hyperlipidemia is highly associated with hypertension and diabetes, so it’s necessary for Chinese government to include blood lipid management in its national policy like hypertension and hyperglycemia management, though such policy is lacking in China

Songcheng Yin,1,* Zhan Li,2,* Jinyu Huang,1 Zhifeng Miao,1 Junyan Zhang,1 Chunyang Lu,1 Hao Xu,1 Huimian Xu1 1Department of Surgical Oncology, 2Department of Breast Surgery, First Affiliated Hospital of China Medical University, Shenyang, People’s Republic of China *These authors contributed equally to this work Background: The prognostic significance of proliferating cell nuclear antigen (PCNA) expression in gastric cancer has long been assessed, yet results remain controversial. Therefore, we performed a meta-analysis to assess the prognostic value and clinicopathological significance of PCNA in gastric cancer. Methods: A systematic literature search of PubMed, EMBASE, and the Cochrane Library databases was conducted. Summary odds ratios (ORs) and hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated to investigate the correlations between PCNA expression and clinicopathological features, overall survival (OS), and disease-free survival (DFS). Results: A total of 19 studies involving 2,852 participants were included in our analysis. The pooled HR indicated that high PCNA expression was significantly associated with poor OS (HR 1.66, 95% CI 1.32–2.08) and DFS (HR 1.81, 95% CI 1.40–2.36). Subgroup analysis revealed that the association between PCNA and OS was also significant in Asian and European patients. In addition, the pooled ORs showed that high PCNA expression was significantly associated with deeper tumor invasion (OR 2.37, 95% CI 1.71–3.27), lymph node metastasis (OR 2.49, 95% CI 1.85–3.35), and advanced stage cancer (OR 1.89, 95% CI 1.36–2.63). Conclusion: Our meta-analysis indicates that high PCNA expression might be a prognosticator of poor survival and a promising therapeutic target for gastric cancer patients. Keywords: proliferating cell nuclear antigen, gastric cancer, prognosis, biomarker, meta-analysis

Objective Comprehensive studies have investigated the prognostic and clinicopathological value of tumor-associated macrophages (TAMs) in gastric cancer patients, yet results remain controversial. Therefore, we performed a meta-analysis to clarify this issue. Methods PubMed, Embase, and the Cochrane Library databases were searched to identify eligible studies. We extracted hazard ratios (HRs) and odds ratios (ORs) with their corresponding 95% confidence intervals (95% CIs) to estimate the effect sizes. In addition, subgroup analysis and sensitivity analysis were also conducted. Results A total of 19 studies involving 2242 patients were included. High generalised TAMs density was significantly associated with poor overall survival (OS) (HR 1.49, 95% CI 1.15–1.95). Subgroup analysis revealed that CD68+ TAMs had no significant effect on OS (HR 1.38, 95% CI 1.00–1.91). High M1 TAMs density was correlated with better OS (HR 0.45, 95% CI 0.32–0.65). By contrast, high density of M2 TAMs was correlated with a poor prognosis for OS (HR 1.48, 95% CI 1.25–1.75). Furthermore, high M2 TAMs density was correlated with larger tumor size, diffuse Lauren type, poor histologic differentiation, deeper tumor invasion, lymph node metastasis, and advanced TNM stage. Conclusions Overall, this meta-analysis reveal that although CD68+ TAMs infiltration has the neutral prognostic effects on OS, the M1/M2 polarization of TAMs are predicative factor of prognosis in gastric cancer patients.

The paper presents an example of an automotive handle bracket. The design parameters of burring include fillet radius R, burring diameter D and burring height H, which are all analyzed in this paper. The objective function is thinning rate of parts. By using quadratic regression, a mathematical model between objective function and design parameters can be established, which can be guidance while selecting design parameters of burring.

In order to improve the data mining and information scheduling capabilities of geo-information system (GIS), it is necessary to optimise GIS information feature estimation and perform GIS information feature extraction, so a GIS information feature estimation algorithm based on big data analysis is proposed. In this algorithm, the piecewise linear estimation method is adopted to reconstruct feature data in the GIS information database in group, and associated information fusion is performed to the GIS data in the database, and adaptive scheduling is performed to the GIS information feature database through the cascaded distributed scheduling method; according to the spatial distribution of geographic information, vector adjustment is performed to the cluster centre, and the frequent item mining method is adopted to extract features of GIS information, and then sequential processing is adopted to the extracted feature quantity of GIS information; the regularised power density spectrum estimation method is adopted to perform unbiased estimation to GIS information feature data. Simulation results show that in GIS information feature estimation, the proposed method can provide estimation with low bias and high accuracy, so it has good GIS information scheduling capability and precision.

In order to reduce the interference of redundant information during geographic information system query and improve the accuracy of information query, a geographic information system query optimisation algorithm based on redundant data deletion and filtering technology is proposed. In this method, a distributed data query structure model of geographic information system is constructed; adaptive group matching of query intervals is conducted through the segment fusion method, and then key information points after group matching are located through the association rule feature information fusion method. Finally, the high-order cumulant feature extraction method is adopted to achieve accurate information query of geographic information system. Simulation results show that in geographic information system query under redundant data infection, this method can provide accuracy 8.75% higher than that provided by the FCM method and 12.98% higher than that provided by the support vector machine algorithm, which indicates that this method can provide strong anti-interference ability of query and cost less overhead.

There are different kinds of datacenter monitor system, but most of them just monitor a few aspects of runtime data. Some of them just focused on temperature monitoring and power consumption. And others are focusing on application behaviors and system resources. So they can't reflect the running state of a datacenter precisely. In this paper, we introduce a platform which aims at monitoring multilayer runtime data from the underlying hardware to upper application layers. Based on these data, we evaluate the health of datacenter and provide a set of alarm mechanism.

Today's data centers are facing extreme challenges in providing low latency and high throughput simultaneously for their applications. However, the fair sharing congestion control algorithms, together with the hash-based load balancing mechanisms, seriously impede the satisfaction of such requirements. In this paper, we use threshold-based signaling and marking schemes to seamlessly integrate load balancing with congestion control protocols. Through adaptive per-packet load balancing, we evenly split network traffic across multiple paths. Meanwhile, we adaptively react to ECN in the case of congestion, and prioritize flows according to their latency requirements. Using realistic data center workloads, we show that, compared to the state-of-the-art load balancing and TCP, our approach improves the application throughput and mean flow completion time by 30–50% and 40–80%, respectively.

With the rapid growth of power consumption and the cooling cost in data centers, it is more and more important to improve the utilization rate of power by more sophisticate power monitoring and management strategy. VM power consumption measurement and related scheduling strategy contribute to make cloud computing center more efficient on the basis of making full use of existing power and cooling resources. In this paper, we propose methods to meter the power consumption of VM, and design scheduling strategies based on power consumption to migrate VM between hosts in order to achieve power capping. Specifically, we propose an improved VM power metering method, in which the power consumption of cooling fans is isolated. Besides, we solve the problem of the dynamic power metering where the hyper-threading of host processor is enabled. In addition, we propose VM migration and scheduling strategy on the basis of power consumption. Last but not the least, we propose a strategy to monitor data center status, including power consumption and resource utilization of all hosts and VMs. All above are demonstrated an evaluated in a prototype system.

Highly virtualized data center have placed many new and unique requirements on the networking fabric. Conventional network protocols limit the scale, latency, throughput of cloud networks. To solve these problems, the large layer 2 technology is proposed. The large layer 2 technology such as TRILL and SPB has obvious advantages in scalability, convergence and resource utilization compared with the traditional layer 2 technology. However, the disadvantages of employing the large layer 2 technology are barely addressed. To study the overhead of large layer 2 networking, we setup a data center environment with 6 core switches and conduct experiments to compare the performance of the representative large layer 2 protocol-TRILL and the typical layer 2 technology-STP. We also evaluate the intra-VLAN forwarding and inter-VLAN forwarding of TRILL. From our experiments, it is observed that the forwarding efficiency of TRILL is 30.16% lower than STP on average. It is also observed that the inter-VLAN traffic forwarding of TRILL averagely costs about 54% more time and consumes about 5% more CPU than intra-VLAN with a fixed packet generation rate. This is the first study towards unveiling the inefficiency of large layer 2 approach in data center networks as far as we know.

Recently, there has been renewed interest in latency as a primary metric for mainstream data center applications. In this paper, we propose a coordinated mechanism for congestion control and multi-path transmission to reduce flow completion time in data centers. The approach leverages ECN to adaptively react to congestion, prioritizes flows according to their timeliness requirements, and evenly distributes network traffic across multiple paths. Through a real implementation and simulations, we show that our approach can effectively reduce flow completion time under different workflow patterns. Compared to existing solutions based on TCP and flow hashing, our approach achieves an improvement of 30% in application throughput and a reduction of up to 90% in 99th percentile flow completion time for latency-sensitive flows, and a reduction of 50%-80% in average completion time for long background flows.

It is common that many enterprises tend to operate several datacenters applying virtualization technologies from different vendors. However, for virtual machine migration across different vendors, currently there is no feasible method to transfer the corresponding network policies as well. In this paper, we show a cross-vendor network policy adaptor (CVNPA), which is able to cooperate with today's cloud computing platforms and to provide seamless network policy migration across virtual switches from different vendors. We experiment CVNPA in our simulated cloud computing environment with OpenStack as their cloud computing platform. The results show that CVNPA can transfer corresponding network policy over heterogeneous virtual switches efficiently and automatically during the process of VM migration with little overhead.

While virtualization is giving great advantages in cloud computing, power metering for virtual machines (VMs) becomes one of the challenges raised. VM power can hardly be measured purely in hardware, so linear power models have been built to infer power consumption at runtime from resource usage. Linear models are simple and of low overhead in implementation. However, power consumption is not exactly linear with certain resource usage and linearity does not necessarily hold across multiple workloads. We propose a new method, VPower, which is effective for more workloads without increasing complexity. In this method, a two dimensional lookup table (LUT) is constructed for each VM, which gives an empirical power value at certain CPU utilization and last-level-cache (LLC) miss rate. Experiments are performed on VMs running SPEC CPU2006 benchmarks. It is shown that the VM power estimated reflects the variability of the two factors effectively, and the per-VM error is about 2.61W.

Encoding topology information into logical addresses is a new trend in data center network (DCN) as it can help to improve the scalability and routing efficiency. Regretfully, to configure large-scale data center manually is not only time-consuming but also error-prone. Because of this, research in auto-configuration becomes getting more attention. Previous works in auto-configuration generally neglect the properties of given topology and cause high computational complexity. In this paper, we propose a new auto-configuration algorithm called Pivot-based Fast Automatic Configuration (PFAC). Leveraging properties of the given data center network topology, PFAC can assign addresses to physical devices in the data center automatically and efficiently. The experiments show that the processing time of PFAC is reduced by about 30% compared to previous works.

Shanghai-Nanjing high-speed railway overpass is a cast-in-site 3-span PC continuous box girder bridge. In order to cross the current railway at a skew angle of 20 degree without interrupt its daily operation, the scaffold arrangement is complicated and its span varies greatly, which cause different stiffness of underlying scaffold. A reasonable calculation method for bowl-scaffold needed to ensure the construction safety and to meet alignment requirement. Based on the cast-in-situ support of PC box girder on Shanghai-Nanjing high-speed railway overpass, a FE model of the entire scaffold was established and different bowl-scaffold joint stiffness was considered to study its influence on scaffold overall stiffness and load distribution at the bottom of bowl-scaffold. The study shows that the joint stiffness has noticeable influence on load distribution. As the joint stiffness grows, the internal force of bowl-scaffold becomes non-uniform; the load concentrates to the regions where the underlying structure is stiffer, which is adverse to bowl-scaffold but good to underlying structure. Ignoring bowl joint stiffness and overall stiffness is safe for the strength checking of underlying structure, but is unsafe for the bowl-scaffold.

Based on land use and urban plan maps, we extraced urban land information of Chongqing urban area in periods of 1995,2000.2008. With the help of GIS softwares and statistical analysis model, such indexes as expansion speed, expansion intensity, expansion direction, expansion morphological, and landscape features were used. This paper analyzed the spatio-temporal characteristics of urban land expansion. The results are drawn as follows: (1) Since 1995, the expansion speed and expansion intensity of urban land accelerate obviously, but urban land expansion show significant disparities and spatial heterogeneity. (2) By topography and policy factors, Chongqing urban land expansion showed the northeast - southwest axial pattern. (3) Compact ratio of urban land is low, space form is irregular, and the heterogeneity is apparent. (4) During the urban land expansion, the trend of function centralization of land use become stronger.