Gene connectivity, function, and sequence conservation: predictions from modular yeast co-expression networks

BACKGROUND: Genes and proteins are organized into functional modular networks in which the network context of a gene or protein has implications for cellular function. Highly connected hub proteins, largely responsible for maintaining network connectivity, have been found to be much more likely to be essential for yeast survival. RESULTS: Here we investigate the properties of weighted gene co-expression networks formed from multiple microarray datasets. The constructed networks approximate scale-free topology, but this is not universal across all datasets. We show strong positive correlations between gene connectivity within the whole network and gene essentiality as well as gene sequence conservation. We demonstrate the preservation of a modular structure of the networks formed, and demonstrate that, within some of these modules, it is possible to observe a strong correlation between connectivity and essentiality or between connectivity and conservation within the modules particularly within modules containing larger numbers of essential genes. CONCLUSION: Application of these techniques can allow a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes

Investigation of the Cofiring Process of Raw or Torrefied Bamboo and Masson Pine by Using a Cone Calorimeter

Cofiring characteristics of raw or torrefied bamboo and masson pine blends with different blend ratios were investigated by cone calorimetry, and its ash performance from cofiring was also determined by a YX-HRD testing instrument, X-ray fluorescence, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Results showed that bamboo and masson pine had the different physicochemical properties. Torrefaction improved fuel performances, resulting in a more stable cofiring process. It also decreased the heat release rate, total heat release, and total suspended particulates of fuels, especially CO2 and CO release. Masson pine ash mainly included CaO, SiO2, Fe2O3, K2O, and Al2O3. Bamboo ash was mainly composed of K2O, SiO2, MgO, and SO3. There were different melting temperatures and trends between different samples. The synergistic reaction of ash components was found during the cofiring process. The surface morphology of blend ash changed with the variation of bamboo or masson pine content

Nitrogen Self-Doped Activated Carbons Derived from Bamboo Shoots as Adsorbent for Methylene Blue Adsorption

Bamboo shoots, a promising renewable biomass, mainly consist of carbohydrates and other nitrogen-related compounds, such as proteins, amino acids and nucleotides. In this work, nitrogen self-doped activated carbons derived from bamboo shoots were prepared via a simultaneous carbonization and activation process. The adsorption properties of the prepared samples were evaluated by removing methylene blue from waste water. The factors that affect the adsorption process were examined, including initial concentration, contact time and pH of methylene blue solution. The resulting that BSNC-800-4 performed better in methylene blue removal from waste water, due to its high specific surface area (2270.9 m2 g−1), proper pore size (2.19 nm) and relatively high nitrogen content (1.06%). Its equilibrium data were well fitted to Langmuir isotherm model with a maximum monolayer adsorption capacity of 458 mg g−1 and a removal efficiency of 91.7% at methylene blue concentration of 500 mg L−1. The pseudo-second-order kinetic model could be used to accurately estimate the carbon material’s (BSNC-800-4) adsorption process. The adsorption mechanism between methylene blue solution and BSNC-800-4 was controlled by film diffusion. This study provides an alternative way to develop nitrogen self-doped activated carbons to better meet the needs of the adsorption applications

Brain cancer mortality in the United States, 1986 to 1995: A geographic analysis1

The Atlas of Cancer Mortality in the United States, 1950–94 (Devesa et al.) published in 1999 by the National Institutes of Health suggests that there are elevated rates of brain and other nervous system cancer in the northwestern, north central, and southeastern parts of the country. Being descriptive in nature, the atlas does not evaluate whether observed patterns are simply due to random variation or if they are reflective of true geographical differences in disease risk or treatment practices. To formally test for geographical clustering of disease, we analyzed U.S. brain cancer mortality data from 1986 to 1995 with Tango’s Excess Events test, the Cuzick-Edwards k-Nearest-Neighbors test, and the spatial scan statistic. All tests revealed statistically significant geographical clustering for both adult men and women. The spatial scan statistic indicated that the most likely cluster of high mortality was in parts of Arkansas, Mississippi, and Oklahoma (relative risk [RR] = 1.22, P < 0.0001) for women and in parts of Tennessee and Kentucky (RR = 1.15, P < 0.0001) for men. Several secondary clusters were detected, but there were no statistically significant clusters of a very localized nature and a high RR. For childhood brain cancer, there were no statistically significant geographical clusters. It is reassuring that no local brain cancer mortality “hot spots” with very high RRs were found. While the causes of the large geographical clusters with modest RRs are unclear, the geographical pattern of brain cancer mortality provides valuable information that can help in formulating etiological hypotheses and in targeting high-risk populations for further epidemiological and health services research

Prediction of five types of general surgical complications based on Logistic regression model

Objective To describe the occurrence of general surgical complications and establish prediction models for different types of complications based on a multicenter cohort study. Methods Based on Modern Surgery and Anesthesia Safety Management System Construction and Promotion(MSCP), patients who underwent general surgery in 4 hospitals from January to June 2015 and from January to June 2016 were selected as participants, and perioperative data of patients were collected. Logistic regression was used to identify risk factors and predict complications. Results Among 19 223 patients, 830(4.32%) had complications. Among participants who had complications, 371(44.70%) had incision complications, 190(22.89%) had fistula complications, 310(37.35%) had infection complications, 161(19.40%) had failure complications, and 104(12.5%) died. There were significant differences in the risk factors of different types of complications. The risk factors of incision,infection and failure covered the whole perioperative period, while fistula complications mainly focused on the difficulty of surgery and postoperative treatment, and for death outcomes, postoperative risk factors were more severe than preoperative risk factors. The areas under the curves of prediction were between 0.80-0.93. Conclusions In general surgery, different types of complications have different risk factors. The targeted prediction model can avoid the rough simplification of complications and fuzzy influence of each factor, and can provide reference for the prevention of complications

Assessment of the spatial association between multiple pollutants of surface water and digestive cancer incidence in China: A novel application of spatial machine learning

Surface water pollution and digestive cancers are concerning environmental health issues. Multiple pollutants of surface water coexist within unique geospatial heterogeneous contexts of surface water flow, which can lead to complex patterns of impact on cancer incidence. This complexity has posed challenges to comprehensive assessment. In this study, we integrated nationwide surface water data of 21 pollutants collected from 3632 sections of nine large river basins and cancer incidence data for six digestive cancers covering 381.6 million people in China. Local Moran's I method was used to assess the patterns of spatial aggregation of each surface water pollutant separately to observe the local spatial nonstationarity and heterogeneity of the pollutant concentrations in different spatial areas. A geographically weighted random forest (GWRF) model, a spatial machine learning approach, was applied to address the spatial heterogeneity of the pollutants and assess their association with cancer incidence. Goodness-of-fit and overall performance of the GWRF model were assessed by the local statistics in the out-of-bag set, such as the mean local pseudo-R2, increase in mean squared error, and local residuals in each local model. Compared with the aspatial random forest model, the GWRF model had better performance (mean local pseudo-R2 ranged from 0.16 (gallbladder cancer) to 0.60 (pancreatic cancer)). Surface water pollution was associated with digestive cancer incidence mainly in the Songhua and Liaohe, Haihe, and Huaihe River Basins. By focusing on these areas, we identified key pollutants specific to different cancer types. Incidence of oesophageal, stomach, colorectal, gallbladder, and pancreatic cancer was associated with common pollutants such as fluoride and arsenic. Our assessment provides targets for the government and environmental health specialists to take tailored actions to control pollution and effectively prevent cancer incidence

Challenges and research opportunities for lung cancer screening in China

Abstract Following publication of the results of the National Lung Screening Trial in the United States, a randomized controlled trial in Italy (ITALUNG) and two simulation studies in China reported similar findings in 2017 favoring lung cancer screening with low-dose computed tomography among smokers. With such advances in lung cancer screening, worldwide interest has gradually shifted from evaluating whether refining lung cancer screening protocols is effective in preventing deaths. However, there are several practical problems to be resolved, including the balance of enrollment criteria and cost effectiveness, precise measurements to reduce false positive findings, risk-based optimization of screening frequency, challenges associated with cancer heterogeneity, strategies to combine image screening with novel biomarkers, dynamic monitoring of the natural history of cancer, accurate identification and diagnosis of cases among huge populations, and the impact of tobacco control policy and environment protection. As one in three individuals with lung cancer worldwide resides in China, these questions pose great challenges as well as research opportunities for population screening programs in China