Weighted gene co-expression network analysis identifies genes related to HG Type 0 resistance and verification of hub gene GmHg1

IntroductionThe soybean cyst nematode (SCN) is a major disease in soybean production thatseriously affects soybean yield. At present, there are no studies on weighted geneco-expression network analysis (WGCNA) related to SCN resistance.MethodsHere, transcriptome data from 36 soybean roots under SCN HG Type 0 (race 3) stresswere used in WGCNA to identify significant modules.Results and DiscussionA total of 10,000 differentially expressed genes and 21 modules were identified, of which the module most related to SCN was turquoise. In addition, the hub gene GmHg1 with high connectivity was selected, and its function was verified. GmHg1 encodes serine/threonine protein kinase (PK), and the expression of GmHg1 in SCN-resistant cultivars (‘Dongnong L-204’) and SCN-susceptible cultivars (‘Heinong 37’) increased significantly after HG Type 0 stress. Soybean plants transformed with GmHg1-OX had significantly increased SCN resistance. In contrast, the GmHg1-RNAi transgenic soybean plants significantly reduced SCN resistance. In transgenic materials, the expression patterns of 11 genes with the same expression trend as the GmHg1 gene in the ‘turquoise module’ were analyzed. Analysis showed that 11genes were co-expressed with GmHg1, which may be involved in the process of soybean resistance to SCN. Our work provides a new direction for studying the Molecular mechanism of soybean resistance to SCN

The inequality of inpatient care net benefit under integration of urban-rural medical insurance systems in China

Abstract Background China has recently made efforts to integrate urban and rural basic medical insurance systems in order to ensure both urban and rural enrollees obtain unified benefits. However, whether the distribution of government healthcare subsides has become more equitable remains unknown. The purpose of this study was to analyze determinants of and inequality in net inpatient care benefits under the integration of urban-rural medical insurance systems in China. Methods Data were obtained from a nationally representative household survey, the Fifth National Health Services Survey (2013), conducted in Anhui province. A multiple regression model and concentration index (CI) was used to estimate related factors and inequality of inpatient care net benefits. Results Findings indicated that individuals received more inpatient care benefits when urban and rural social healthcare systems were integrated. Factors associated with net benefits included gender, age, marital status, retirement, educational level, history of chronic diseases, health status, willingness to seek inpatient care and per capita income. The rich were found to disproportionately benefit from inpatient care, and the CI of net benefits for integrated insurance enrollees was the lowest among all three available health insurance schemes. These findings indicate that the recent unification of urban-rural social health insurances reduces inequality in net benefits from government subsidies. Some socioeconomic factors, such as per capita income, 60 years of age and over, history of chronic disease and high educational level positively influence inequality. Conclusion In China, accelerating the integration of urban and rural medical insurance systems is an effective way to increase equity of benefit in urban and rural areas. Strategies aimed at reducing inpatient benefit inequality must address socioeconomic factors influencing healthcare outcomes

fast fourier transform based ip traffic classification system for sipto at h(e)nb

3GPP has recently introduced LIPA(Local IP Access) and SIPTO(Selected IP Traffic Offload) to offload traffic from the core network, which brings new challenge to on-line traffic classification, because of the large amount of data and the difference of mobile network from wired network, such as high bit error rates(BER) and temporary disconnections. Therefore, other proposed schemes which aim at ether LIPA at H(e)NB or SIPTO at macro network could not get high accuracy and high speed at the same time, and traffic classification methodologies in wired IP network are not applicable. This paper proposes a fast fourier transform(FFT) based IP traffic classification system for SIPTO at H(e)NB, which focuses on classifying each packet at H(e)NB by extracting the application layer payload pattern using FFT. Pattern extraction and classification using machine learning algorithms are simulated, and results show that our system outperforms existing methods by offering about 3%-6% improvement in classification accuracy with about 7% time. Simulation of SIPTO shows good reduction of press to the core network and low false rates. © 2012 IEEE.EAI; IEEE Computer Society3GPP has recently introduced LIPA(Local IP Access) and SIPTO(Selected IP Traffic Offload) to offload traffic from the core network, which brings new challenge to on-line traffic classification, because of the large amount of data and the difference of mobile network from wired network, such as high bit error rates(BER) and temporary disconnections. Therefore, other proposed schemes which aim at ether LIPA at H(e)NB or SIPTO at macro network could not get high accuracy and high speed at the same time, and traffic classification methodologies in wired IP network are not applicable. This paper proposes a fast fourier transform(FFT) based IP traffic classification system for SIPTO at H(e)NB, which focuses on classifying each packet at H(e)NB by extracting the application layer payload pattern using FFT. Pattern extraction and classification using machine learning algorithms are simulated, and results show that our system outperforms existing methods by offering about 3%-6% improvement in classification accuracy with about 7% time. Simulation of SIPTO shows good reduction of press to the core network and low false rates. © 2012 IEEE

Threefold coordinated germanium in a GeO2 melt

Abstract The local structure around germanium is a fundamental issue in material science and geochemistry. In the prevailing viewpoint, germanium in GeO2 melt is coordinated by at least four oxygen atoms. However, the viewpoint has been debated for decades due to several unexplained bands present in the GeO2 melt Raman spectra. Using in situ Raman spectroscopy and density functional theory (DFT) computation, we have found a [GeOØ2]n (Ø = bridging oxygen) chain structure in a GeO2 melt. In this structure, the germanium atom is coordinated by three oxygen atoms and interacts weakly with two neighbouring non-bridging oxygen atoms. The bonding nature of the chain has been analyzed on the basis of the computational electronic structure. The results may settle down the longstanding debate on the GeO2 melt structure and modify our view on germanate chemistry

Influence of Surface Properties on Adhesion Forces and Attachment of Streptococcus mutans to Zirconia In Vitro

Zirconia is becoming a prevalent material in dentistry. However, any foreign bodies inserted may provide new niches for the bacteria in oral cavity. The object of this study was to explore the effect of surface properties including surface roughness and hydrophobicity on the adhesion and biofilm formation of Streptococcus mutans (S. mutans) to zirconia. Atomic force microscopy was employed to determine the zirconia surface morphology and the adhesion forces between the S. mutans and zirconia. The results showed that the surface roughness was nanoscale and significantly different among tested groups (P<0.05): Coarse (23.94±2.52 nm) > Medium (17.00±3.81 nm) > Fine (11.89±1.68 nm). The contact angles of the Coarse group were the highest, followed by the Medium and the Fine groups. Increasing the surface roughness and hydrophobicity resulted in an increase of adhesion forces and early attachment (2 h and 4 h) of S. mutans on the zirconia but no influence on the further development of biofilm (6 h~24 h). Our findings suggest that the surface roughness in nanoscale and hydrophobicity of zirconia had influence on the S. mutans initial adhesion force and early attachment instead of whole stages of biofilm formation

PRSOT: Precipitation Retrieval from Satellite Observations Based on Transformer

Precipitation with high spatial and temporal resolution can improve the defense capability of meteorological disasters and provide indispensable instruction and early warning for social public services, such as agriculture, forestry, and transportation. Therefore, a deep learning-based algorithm entitled precipitation retrieval from satellite observations based on Transformer (PRSOT) is proposed to fill the observation gap of ground rain gauges and weather radars in deserts, oceans, and other regions. In this algorithm, the multispectral infrared brightness temperatures from Himawari-8, the new-generation geostationary satellite, have been used as predictor variables and the Global Precipitation Measurement (GPM) precipitation product has been employed to train the retrieval model. We utilized two data normalization schemes, area-based and pixel-based normalization, and conducted comparative experiments. Comparing the estimated results with the GPM product on the test set, PRSOT_Pixel_based model achieved a Probability Of Detection (POD) of 0.74, a False Alarm Ratio (FAR) of 0.44 and a Critical Success Index (CSI) of 0.47 for two-class metrics, and an Accuracy (ACC) of 0.75 for multi-class metrics. Pixel-based normalization is more suitable for meteorological data, highlighting the precipitation characteristics and obtaining better comprehensive retrieval performance in visualization and evaluation metrics. In conclusion, the proposed PRSOT model has made a remarkable and essential contribution to precipitation retrieval and outperforms the benchmark machine learning model Random Forests