LncRNA HOTAIR promotes DNA damage repair and radioresistance by targeting ATR in colorectal cancer

Long non-coding RNAs(lncRNAs)have been implicated in cancer progression and drug resistance development.Moreover,there is evidence that lncRNA HOX transcript antisense intergenic RNA(HOTAIR)is involved in colorectal cancer(CRC)progression.The present study aimed to examine the functional role of lncRNA HOTAIR in conferring radiotherapy resistance in CRC cells,as well as the underlying mechanism.The relative expression levels of HOTAIR were examined in 70 pairs of CRC tumor and para-cancerous tissues,as well as in radiosensitive and radioresistant samples.The correlations between HOTAIR expression levels and clinical features of patients with CRC were assessed using the Chi-square test.Functional assays such as cell proliferation,colony formation and apoptosis assays were conducted to determine the radiosensitivity in CRC cells with HOTAIR silencing after treatment with different doses of radiation.RNA pull-down assay andfluorescence in situ hybridization(FISH)were used to determine the interaction between HOTAIR and DNA damage response mediator ataxia-telangiectasia mutated-and Rad3-related(ATR).HOTAIR was significantly upregulated in CRC tumor tissues,especially in radioresistant tumor samples.The elevated expression of HOTAIR was correlated with more advanced histological grades,distance metastasis and the poor prognosis in patients with CRC.Silencing HOTAIR suppressed the proliferation and promoted apoptosis and radiosensitivity in CRC cells.HOTAIR knockdown also inhibited the tumorigenesis of CRC cells and enhanced the sensitivity to radiotherapy in a mouse xenograft model.Moreover,the data showed that HOTAIR could interact with ATR to regulate the DNA damage repair signaling pathway.Silencing HOTAIR impaired the ATR-ATR interacting protein(ATRIP)complex and signaling in cell cycle progression.Collectively,the present results indicate that lncRNA HOTAIR facilitates the DNA damage response pathway and promotes radioresistance in CRC cells by targeting ATR.

The effect of fire disturbance on short-term soil respiration in typical forest of Greater Xing'an Range, China

We investigated the effect of fire disturbance on short-term soil respiration in birch （Betula platyphylla Suk.） and larch （Larix gmelinii Rupr.） forests in Greater Xing’an range, northeastern China for further understanding of its effect on the carbon cycle in ecosystems. Our study show that post-fire soil respiration rates in B. platyphylla and L. gmelinii forests were reduced by 14%and 10%, respectively. In contrast, the soil heterotrophic respiration rates in the two types of forest were similar in post-fire and control plots. After fire, the contribution of root respiration to total soil respiration was dramatically reduced. Variation in soil respiration rates was explained by soil moisture （W） and soil tem-perature （T） at a depth of 5 cm. Exponential regression fitted T and W models explained Rs rates in B. platyphylla control and post-fire plots （83.1% and 86.2%） and L. gmelinii control and post-fire plots （83.7%and 88.7%）. In addition, the short-term temperature coefficients in B.

Spatial distribution of heavy metals(Cu, Pb, Zn, and Cd)in sediments of a coastal wetlands in eastern Fujian, China

We investigated the spatial distribution （horizontal and vertical concentrations） of copper （Cu）, lead （Pb）, zinc （Zn）, and cadmium （Cd） in five wetland types （mudflat, aquaculture wetland, water area, farmland wetland and mangrove） from three areas （Ningde, Fuding, and Xiapu）, China. Cu concentrations in five wetland types descended in the order： farm wetland, mudflat, aquaculture, water area and mangrove. Pb concentrations decreased in the order： aquaculture, mangrove, farm wetland, mudflat, and water area. Zn content decreased in the order： farm wetland, water area, aquaculture, mudflat and mangrove, and Cd content decreased as follows： mangrove, aquacul- ture, water area, rnudflat, and farm wetland. Comparison of the concentrations of the same heavy metals in different areas showed that the highest Cu （63.75 mg kg-1） and Zn （152.32mgkg-1） concentrations occurred in Ningdecoastal wetlands; Pb （110.58 mg kg-1） and Cd （2.81 mg kg-1） contents were highest in Fuding wetlands, and the average contents of all heavy metals were very low in Xiapu wetlands. Examination of the vertical distribution showed that the Cu content was high in all mudflat layers; Pb and Cd concentrations were highest in aquaculture and mangrove wetlands, respectively, and Zn content was highest in farm wetlands. The spatial distribution of Cu and Zn contents for different areas decreased as follows： Ningde 〉 Fuding 〉 Xiapu, for Pb and Cd were most concentrated in Fuding coastal wetlands. Concentrations of Zn and Cu were highly correlated, while Zn and Cu were not significantly correlated with Pb.

A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance

Background:Supply chain finance(SCF)is a series of financial solutions provided by financial institutions to suppliers and customers facing demands on their working capital.As a systematic arrangement,SCF utilizes the authenticity of the trade between(SMEs)and their“counterparties”,which are usually the leading enterprises in their supply chains.Because in these arrangements the leading enterprises are the guarantors for the SMEs,the credit levels of such counterparties are becoming important factors of concern to financial institutions’risk management(i.e.,commercial banks offering SCF services).Thus,these institutions need to assess the credit risks of the SMEs from a view of the supply chain,rather than only assessing an SME’s repayment ability.The aim of this paper is to research credit risk assessment models for SCF.Methods:We establish an index system for credit risk assessment,adopting a view of the supply chain that considers the leading enterprise’s credit status and the relationships developed in the supply chain.Furthermore,We conducted two credit risk assessment models based on support vector machine(SVM)technique and BP neural network respectly.Results:(1)The SCF credit risk assessment index system designed in this paper,which contained supply chain leading enterprise’s credit status and cooperative relationships between SMEs and leading enterprises,can help banks to raise their accuracy on predicting a small and medium enterprise whether default or not.Therefore,more SMEs can obtain loans from banks through SCF.(2)The SCF credit risk assessment model based on SVM is of good generalization ability and robustness,which is more effective than BP neural network assessment model.Hence,Banks can raise the accuracy of credit risk assessment on SMEs by applying the SVM model,which can alleviate credit rationing on SMEs.Conclusions:(1)The SCF credit risk assessment index system can solve the problem of banks incorrectly labeling a creditworthy enterprise as a default enterprise,and thereby improve the credit rating status in the process of SME financing.(2)By analyzing and comparing the empirical results,we find that the SVM assessment model,on evaluating the SME credit risk,is more effective than the BP neural network assessment model.This new assessment model based on SVM can raise the accuracy of classification between good credit and bad credit SMEs.(3)Therefore,the SCF credit risk assessment index system and the assessment model based on SVM,is the optimal combination for commercial banks to use to evaluate SMEs’credit risk.

Spatial heterogeneity of soil respiration in a Larix gmelinii forest and the response to prescribed fire in the Greater Xing0an Mountains,China

This study was conducted in a fire-prone region in the Greater Xing＇an Mountains, the primary forested area of northeastern China. We measured soil respiration and the affecting soil factors, i.e., soil microbial biomass and soil moisture, within an experimental plot of Larix gmelinii Rupr. A low-intensity, prescribed fire was applied as the treatment. Traditional descriptive statistics and geostatistics were used to analyze the spatial heterogeneity of soil respiration and the response of respiration to fire disturbance. Coefficients of variation （CVs） for pre-fire and post-fire soil respiration were 23.4 and 32.0 %, respec- tively. CVs for post-fire soil respiration increased signifi- cantly, with a moderate variation of all CVs. Soil respiration pre-fire was significantly correlated with soil microbial biomass carbon, biomass nitrogen, and soil moisture （W）; post-fire soil respiration was not correlated with these factors. From the geostatistical analyses, the Co ＋ C （sill） for post-fire soil respiration increased sig- nificantly, indicating that the post-fire spatial heterogeneity of soil respiration increased significantly. The nugget effect （nc） of soil respiration and the affecting factors pre-fire and post-fire disturbance were in the range of 12.5-50 %, with strong spatial autocorrelation. Fire disturbance changed the components of spatial heterogeneity, and the proportion of functional heterogeneity increased significantly post-fire. The ranges （a） for pre-fire and post-fire soil respiration were 81.0 and 68.2 m, respectively. The homogeneity of the distribution of post-fire soil respiration decreased and the spatial heterogeneity increased, thus the range for post- fire soil respiration decreased significantly. The fractal dimension （D） for soil respiration increased post-fire, the spatial heterogeneity of soil respiration affected by random components increased, indicating that the change in spatial heterogeneity of post-fire soil respiration should be con- sidered within the scale of the forest stand. Following Kriging interpolation, the increase in the patchiness of post-fire soil respiration was illustrated using a contour map. Based on these preliminary results, the change in the spatial heterogeneity of post-fire soil respiration was likely caused by changes in the distribution of soil moisture and microbial activity within the experimental plot at the scale of the forest stand.

Effect of fire intensity on active organic and total soil carbon in a Larix gmelinii forest in the Daxing'anling Mountains,Northeastern China

Studying contents and seasonal dynamics of active organic carbon in the soil is an important method for revealing the turnover and regulation mechanism of soil carbon pool. Through 3 years of field sampling and lab analysis, we studied the seasonal variations, content differences, and interrelationships of total organic carbon （TOC）, light fraction organic carbon （LFOC）, and particulate organic carbon （POC） of the soil in the forest areas burned with different fire intensities in the Daxing＇anling Mountains. The mean TOC content in the low-intensity burned area was greater than that in the unburned area, moderate-intensity, and high-intensity burned areas in June and November （P 〈 0.05）. LFOC and POC in the low-intensity burned area were greater than that in either moderate-intensity or high-intensity burned areas, with significant differences in LFOC in September and November （P 〈 0.05）. A significant difference in LFOC between the unburned and burned areas was only found in July （P 〈 0.05）. However, the differences in POC between the unburned and burned areas were not significant in all the whole seasons （P 〉 0.05）. Soil LFOC and POC varied significantly with the seasons （P 〈 0.05） in the Daxing＇anling Mountains. Significant linear relationships were observed between soil TOC, LFOC, and POC, which were positively correlated with soil nitrogen and negatively correlated with soil temperature in the Daxing＇anling Mountains.

Effect of fire disturbance on active organic carbon of Larix gmelinii forest soil in Northeastern China

Active organic carbon in soil has high biological activity and plays an important role in forest soil ecosystem structure and function. Fire is an important disturbance factor in many forest ecosystems and occurs frequently over forested soils. However, little is known about its impact on soil active organic carbon (SAOC), which is important to the global carbon cycle. To investigate this issue, we studied the active organic carbon in soils in the Larix gmelinii forests of the Da Xing'an Mountains (Greater Xing'an Mountains) in Northeastern China, which had been burned by high-intensity wildfire in two different years (2002 and 2008). Soil samples were collected monthly during the 2011 growing season from over 12 sample plots in burned and unburned soils and then analyzed to examine the dynamics of SAOC. Our results showed that active organic carbon content changed greatly after fire disturbance in relation to the amount of time elapsed since the fire. There were significant differences in microbial biomass carbon, dissolved organic carbon, light fraction organic carbon, particulate organic carbon between burned and unburned sample plots in 2002 and 2008 (p < 0.05). The correlations between active organic carbon and environmental factors such as water content, pH value and temperature of soils, and correlations between each carbon component changed after fire disturbance, also in relation to time since the fire. The seasonal dynamics of SAOC in all of the sample plots changed after fire disturbance; peak values appeared during the growing season. In plots burned in 2002 and 2008, the magnitude and occurrence time of peak values differed. Our findings provide basic data regarding the impact of fire disturbance on boreal forest soil-carbon cycling, carbon-balance mechanisms, and carbon contributions of forest ecosystem after wildfire disturbance.

Diurnal variation models for fine fuel moisture content in boreal forests in China

Studying diurnal variation in the moisture content of fine forest fuel(FFMC)is key to understanding forest fire prevention.This study established models for predicting the diurnal mean,maximum,and minimum FFMC in a boreal forest in China using the relationship between FFMC and meteorological variables.A spline interpolation function is proposed for describing diurnal variations in FFMC.After 1 day with a 1 h field measurement data testing,the results indicate that the accuracy of the sunny slope model was 100%and 84%when the absolute error was<3%and<10%,respectively,whereas the accuracy of the shady slope model was 72%and 76%when the absolute error was<3%and<10%,respectively.The results show that sunny slope and shady slope models can predict and describe diurnal variations in fine fuel moisture content,and provide a basis for forest fire danger prediction in boreal forest ecosystems in China.

Gamma generalized linear model to investigate the effects of climate variables on the area burned by forest fire in northeast China

The purpose of this study was to determine a suitable model for investigating the effects of climate factors on the area burned by forest fire in the Tahe forest region, Daxing＇an Mountains, in northeast China. The response variables were the area burned by lightning- caused fire, human-caused fire, and total burned area. The predictor variables were nine climate variables collected from the local weather station. Three regression models were utilized, including multiple linear regression, log- linear model （log-transformation on both response and predictor variables）, and gamma-generalized linear model. The goodness-of-fit of the models were compared based on model fitting statistics such as R2, AIC, and RMSE. The results revealed that the gamma-generalized linear model was generally superior to both multiple linear regressionmodel and log-linear model for fitting the fire data. Further, the best models were selected based on the criteria that the climate variables were statistically significant at at = 0.05. The gamma best models indicated that maximum wind speed, precipitation, and days that rainfall greater than 0.1 mm had significant impacts on the area burned by the lightning-caused fire, while the mean temperature and minimum relative humidity were the .main drivers of the burned area caused by human activities. Overall, the total burned area by forest fire was significantly influenced by days that rainfall greater than 0.1 mm and minimum rela- tive humidity, indicating that the moisture condition of forest stands determine the burned area by forest fire.

Estimation of gases emitted by forest fires based on remote sensing data

Forest fire, an important agent for change in many forest ecosystems, plays an important role in atmo- spheric chemical cycles and the carbon cycle. The primary emissions from forest fire, CO2, CO, CH4, long-chained hydrocarbons and volatile organic oxides, however, have not been well quantified. Quantifying the carbonaceous gas emissions of forest fires is a critical part to better under- stand the significance of forest fire in calculating carbon balance and forecasting climate change. This study uses images from Enhanced Thematic Mapper Plus （ETM＋） on the Earth-observing satellite LANDSAT-7 for the year 2005 to estimate the total gases emitted by the 2006 Kanduhe forest fire in the Daxing＇an Mountains. Our results suggest that the fire emitted approximately 149,187.66 t CO2, 21,187.70 t CO, 1925.41 t CxHy, 470.76 t NO and 658.77 t SO2. In addition, the gases emitted from larch forests were significantly higher than from both broadleaf-needle leaf mixed forests and broadleaf mixed forests.

Historic distribution and driving factors of human-caused fires in the Chinese boreal forest between 1972 and 2005

Aims The pattern and driving factors of forest fires are of interest for fire occurrence prediction and forest fire management.The aims of the study were:(i)to describe the history of human-caused fires by season and size of burned area over time;(ii)to identify the spatial patterns of human-caused fires and test for the existence of‘hotspots’to determine their exact locations in the Daxing’an mountains;(iii)to determine the driving factors that determine the spatial distribution and the possibility of human-caused fire occurrence.Methods In this study,K-function and Kernel density estimation were used to analyze the spatial pattern of human-caused fires.The analysis was conducted in s-plus and arcgIs environments,respectively.The analysis of driving factors was performed in SPSS 19.0 based on a logistic regression model.The variables used to identify factors that influence fire occurrence included vegetation types,meteorological conditions,socioeconomic factors,topography and infrastructure factors,which were extracted and collected through the spatial analysis mode of arcgIs and from official statistics,respectively.Important Findings The annual number of human-caused fires and the area burnt have declined since 1987 due to the implementation of a forest fire protection act.There were significant spatial heterogeneity and seasonal variations in the distribution of human-caused fires in the Daxing’an mountains.The heterogeneity was caused by elevation,distance to the nearest railway,forest type and temperature.a logistic regression model was developed to predict the likelihood of human-caused fire occurrence in the Daxing’an mountains;its global accuracy attained 64.8%.The model was thus comparable to other relevant studies.

A possible endoscopic therapy for large hiatal hernia complicated with refractory gastroesophageal reflux disease

A hiatal hernia(HH)is usually associated with gastroesophageal reflux disease(GERD).An HH can increase the incidence of GERD.[1]The coexistence of these diseases increases the difficulty of treatment and can be challenging for endoscopic treatment.Therapeutic methods for small HHs(≤2 cm)combined with refractory GERD have recently been emerging.However,there are still knowledge gaps in the endoscopic treatment of large HHs(≥3cm)combined with refractory GERD.We developed a new endoscopic method called hiatal hernia-endoscopic submucosal dissection(HH-ESD)and performed the present study to clarify the efficacy and safety of HH-ESD.

Intelligent reasoning and management decision making with grey rough influence diagrams

Purpose-Influence diagrams(IDs)have been widely applied as a form of knowledge expression and a decision analysis tool in the management and engineering fields.Relationship measurements and expectation values are computed depending on probability distributions in traditional IDs,however,most information systems in the real world are nondeterministic,and data in information tables can be interval valued,multiple valued and even incomplete.Consequently,conventional numeric models of IDs are not suitable for information processing with respect to imprecise data whose boundaries are uncertain.The paper aims to discuss these issues.Design/methodology/approach-The grey system theory and rough sets have proved to be effective tools in the data processing of uncertain information systems,approximate knowledge acquisition and representation are also the objectives in intelligent reasoning and decision analysis.Hence,this study proposes a new mathematical model by combining grey rough sets with IDs,and approximate measurements are used instead of probability distribution,an implicational relationship is utilized instead of an indiscernible relationship,and all of the features of the proposed approach contribute to deal with uncertain problems.Findings-The focus of this paper is to provide a more comprehensive framework for approximate knowledge representation and intelligent decision analysis in uncertain information systems and an example of decision support in product management systems with the new approach is illustrated.Originality/value-Collaboration of IDs and grey rough sets is first proposed,which provides a new mathematical and graphical tool for approximate reasoning and intelligent decision analysis within interval-valued information systems.