A sequence-based survey of the complex structural organization of tumor genomes

Tumors and cancer cell lines were surveyed with end-sequencing profiling, yielding the largest available collection of sequence-ready tumor genome breakpoints and providing evidence that some rearrangements may be recurrent

Chemical Weathering and Riverine Carbonate System Driven by Human Activities in a Subtropical Karst Basin, South China

In the context of climate change, the input of acid substances into rivers, caused by human activities in the process of industrial and agricultural development, has significantly disrupted river systems and has had a profound impact on the carbon cycle. The hydrochemical composition and which main sources of the Lianjiang River (LR), a subtropical karst river in northern Guangdong Province, South China, were analyzed in January 2018. The objective was to explicate the influence on the deficit proportion of CO2 consumption, resulting from carbonate chemical weathering (CCW), driven by nitric acid (HNO3) and sulfuric acid (H2SO4), which is affected by exogenous acids from the industrial regions in north of the Nanling Mountains and the Pearl River Delta. The response of the riverine carbonate system to exogenous acid-related weathering was also discussed. HCO3− and Ca2+, respectively, accounted for 84.97% of the total anions and 78.71% of the total cations in the surface runoff of the LR, which was characterized as typical karst water. CCW was the most important material source of river dissolved loads in the LR, followed by human activities and silicate chemical weathering (SCW). Dissolved inorganic carbon (DIC), derived from CCW induced by carbonic acid (H2CO3), had the largest contribution to the total amount of DIC in the LR (76.79%), and those from CCW induced by anthropogenic acids (HNO3 and H2SO4) and SCW contributed 13.56% and 9.64% to the total DIC, respectively. The deficit proportion of CO2 consumption associated with CCW resulting from sulfuric acid and nitric acid (13.56%), was slightly lower than that of the Guizhou Plateau in rainy and pre-rainy seasons (15.67% and 14.17%, respectively). The deficit percentage of CO2 uptake associated with CCW induced by sulfuric acid and nitric acid, accounted for 38.44% of the total CO2 consumption related to natural CCW and 18.84% of the anthropogenic acids from external areas. DIC derived from CCW induced by human activities, had a significant positive correlation with the total alkalinity, SIc and pCO2 in river water, indicating that the carbonate system of the LR was also driven by exogenous acids, with the exception of carbonic acid. More attention should be paid to the effects of human activities on the chemical weathering and riverine carbonate system in the karst drainage basin

Chemical weathering and CO2 consumption in the Xijiang River basin, South China

Monthly samples of riverine water were collected and analyzed for the concentrations of major ions (Ca2+, Mg2+, K+, Na+, HCO3-, SO42-, Cl-, NO3-), dissolved silicon, and total dissolved solids (TDS) at Wuzhou hydrological station located between the middle and lower reaches of the Xijiang River (XJR) from March 2005 to April 2006. More frequent sampling and analysis were carried out during the catastrophic flooding in June 2005. Stoichiometric analysis was applied for tracing sources of major ions and estimating CO2 consumption from the chemical weathering of rocks. The results demonstrate that the chemical weathering of carbonate and silicate rocks within the drainage basin is the main source of the dissolved chemical substances in the XJR. Some 81.20% of the riverine cations originated from the chemical weathering processes induced by carbonic acid, 11.32% by sulfuric acid, and the other 7.48% from the dissolution of gypsum and precipitates of sea salts within the drainage basin. The CO2 flux consumed by the rock chemical weathering within the XJR basin is 2.37 x 10(11) mol y(-1), of which 0.64 x 10(11) mol y(-1) results from silicate rock chemical weathering, and 1.73 x 10(11) mol y(-1) results from carbonate rock chemical weathering. The CO2 consumption comprises 0.38 x 10(11) mol during the 9-d catastrophic flooding. The CO2 consumption from rock chemical weathering in humid subtropical zones regulates atmospheric CO2 level and constitutes a significant part of the global carbon budget. The carbon sink potential of rock chemical weathering processes in the humid subtropical zones deserves extra attention. (C) 2008 Elsevier B.V. All rights reserved

A sunflower BAC library suitable for PCR screening and physical mapping of targeted genomic regions

International audienceA sunflower BAC library consisting of 147,456 clones with an average size of 118 kb has been constructed and characterized. It represents approximately 5x sunflower haploid genome equivalents. The BAC library has been arranged in pools and superpools of DNA allowing screening with various PCR-based markers. Each of the 32 superpools contains 4,608 clones and corresponds to a 36 matrix pools. Thus, the screening of the entire library could be accomplished in less than 80 PCR reactions including positive and negative controls. As a demonstration of the feasibility of the concept, a set of 24 SSR markers covering about 36 cM in the sunflower SSR map (Tang et al. in Theor Appl Genet 105:1124-1136, 2002) have been used to screen the BAC library. About 125 BAC clones have been identified and then organized in 23 contigs by HindIII digestion. The contigs are anchored on the SSR map and thus constitutes a first-generation physical map of this region. The utility of this BAC library as a genomic resource for physical mapping and map-based cloning in sunflower is discussed

Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting

\u3cp\u3ePurpose: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images. Methods: We retrospectively assessed 363 breast cancer cases (Luminal A 151, Luminal B 96, HER2 76, and BLBC 40). Eighty-two features defined in the BI-RADS lexicon were visually described. A decision tree model with the Chi-squared automatic interaction detector (CHAID) algorithm was applied for feature selection and classification. A 10-fold cross-validation was performed to investigate the performance (i.e., accuracy, positive predictive value, sensitivity, and F1-score) of the decision tree model. Results: Seven of the 82 variables were derived from the decision tree-based feature selection and used as features for the classification of molecular subtypes including mass margin calcification on mammography, mass margin types of kinetic curves in the delayed phase, mass internal enhancement characteristics, non-mass enhancement distribution on MRI, and breastfeeding history. The decision tree model accuracy was 74.1%. For each molecular subtype group, Luminal A achieved a sensitivity, positive predictive value, and F1-score of 79.47%, 75.47%, and 77.42%, respectively; Luminal B showed a sensitivity, positive predictive value, and F1-score of 64.58%, 55.86%, and 59.90%, respectively; HER2 had a sensitivity, positive predictive value, and F1-scores of 81.58%, 95.38%, and 87.94%, respectively; BLBC showed sensitivity, positive predictive value, and F1-scores of 62.50%, 89.29%, and 73.53%, respectively. Conclusions: We applied a complete “white box” machine learning method to predict the molecular subtype of breast cancer based on the BI-RADS feature description in a multi-modal setting. By combining BI-RADS features in both mammography and MRI, the prediction accuracy is boosted and robust. The proposed method can be easily applied widely regardless of variability of imaging vendors and settings because of the applicability and acceptance of the BI-RADS.\u3c/p\u3

Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting

Purpose: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images. Methods: We retrospectively assessed 363 breast cancer cases (Luminal A 151, Luminal B 96, HER2 76, and BLBC 40). Eighty-two features defined in the BI-RADS lexicon were visually described. A decision tree model with the Chi-squared automatic interaction detector (CHAID) algorithm was applied for feature selection and classification. A 10-fold cross-validation was performed to investigate the performance (i.e., accuracy, positive predictive value, sensitivity, and F1-score) of the decision tree model. Results: Seven of the 82 variables were derived from the decision tree-based feature selection and used as features for the classification of molecular subtypes including mass margin calcification on mammography, mass margin types of kinetic curves in the delayed phase, mass internal enhancement characteristics, non-mass enhancement distribution on MRI, and breastfeeding history. The decision tree model accuracy was 74.1%. For each molecular subtype group, Luminal A achieved a sensitivity, positive predictive value, and F1-score of 79.47%, 75.47%, and 77.42%, respectively; Luminal B showed a sensitivity, positive predictive value, and F1-score of 64.58%, 55.86%, and 59.90%, respectively; HER2 had a sensitivity, positive predictive value, and F1-scores of 81.58%, 95.38%, and 87.94%, respectively; BLBC showed sensitivity, positive predictive value, and F1-scores of 62.50%, 89.29%, and 73.53%, respectively. Conclusions: We applied a complete “white box” machine learning method to predict the molecular subtype of breast cancer based on the BI-RADS feature description in a multi-modal setting. By combining BI-RADS features in both mammography and MRI, the prediction accuracy is boosted and robust. The proposed method can be easily applied widely regardless of variability of imaging vendors and settings because of the applicability and acceptance of the BI-RADS

Prediction of molecular subtypes of breast cancer using BI-RADS features based on a “white box” machine learning approach in a multi-modal imaging setting

Purpose: To develop and validate an interpretable and repeatable machine learning model approach to predict molecular subtypes of breast cancer from clinical metainformation together with mammography and MRI images. Methods: We retrospectively assessed 363 breast cancer cases (Luminal A 151, Luminal B 96, HER2 76, and BLBC 40). Eighty-two features defined in the BI-RADS lexicon were visually described. A decision tree model with the Chi-squared automatic interaction detector (CHAID) algorithm was applied for feature selection and classification. A 10-fold cross-validation was performed to investigate the performance (i.e., accuracy, positive predictive value, sensitivity, and F1-score) of the decision tree model. Results: Seven of the 82 variables were derived from the decision tree-based feature selection and used as features for the classification of molecular subtypes including mass margin calcification on mammography, mass margin types of kinetic curves in the delayed phase, mass internal enhancement characteristics, non-mass enhancement distribution on MRI, and breastfeeding history. The decision tree model accuracy was 74.1%. For each molecular subtype group, Luminal A achieved a sensitivity, positive predictive value, and F1-score of 79.47%, 75.47%, and 77.42%, respectively; Luminal B showed a sensitivity, positive predictive value, and F1-score of 64.58%, 55.86%, and 59.90%, respectively; HER2 had a sensitivity, positive predictive value, and F1-scores of 81.58%, 95.38%, and 87.94%, respectively; BLBC showed sensitivity, positive predictive value, and F1-scores of 62.50%, 89.29%, and 73.53%, respectively. Conclusions: We applied a complete “white box” machine learning method to predict the molecular subtype of breast cancer based on the BI-RADS feature description in a multi-modal setting. By combining BI-RADS features in both mammography and MRI, the prediction accuracy is boosted and robust. The proposed method can be easily applied widely regardless of variability of imaging vendors and settings because of the applicability and acceptance of the BI-RADS.</p

Inhibitory kinetics of citric acid on beta-N-acetyl-D-glucosaminidase from prawn (Litopenaeus vannamei)

NAGase (EC.3.2.1.52) from crustaceans has the important roles in immunity, molting and digestion of chitinous foods. In this paper, the effects of citric acid on the activity of NAGase from Litopenaeus vannamei for the hydrolysis of pNP-NAG have been studied. The results showed that appropriate concentrations of citric acid could lead to reversible inhibition on NAGase and IC50 was estimated to be 5.00 +/- 0.35 mM. Using the plots of Lineweaver-Burk, the inhibition of NAGase by citric acid belongs to competitive type, the inhibitory equilibrium constant for citric acid binding with free NAGase, K-[, is 3.26 +/- 0.25 mM. The inhibitory kinetics of citric acid on NAGase in the appropriate concentrations of citric acid has been studied using the kinetic method of substrate reaction. The time course of NAGase for the hydrolysis of pNP-NAG in the presence of different concentrations of citric acid showed that at each citric acid concentration, the rate decreased with increasing time until a straight line was approached. The results show that the inhibition of NAGase by citric acid is a slow, reversible reaction with fractional remaining activity. The microscopic rate constants are determined for the reaction on citric acid with NAGase. (C) 2010 Elsevier Ltd. All rights reserved.China Postdoctoral Science Foundation [20070410806]; Science and Technology Foundation of Fujian Province [2006N0067]; Master Degree Subject Foundation of Quanzhou Normal University [MDSCh-2009A