Silicon isotope analysis traditionally uses a standard-sample bracketing （SSB） method that relies upon greater instrument stability than can be consistently expected. The following proposed method reduces the level ...Silicon isotope analysis traditionally uses a standard-sample bracketing （SSB） method that relies upon greater instrument stability than can be consistently expected. The following proposed method reduces the level of instrumental stability required for the analysis process and provides a valid solution for high-precision and accurate studies of Si isotopic compositions. Rock samples were dissolved by using alkali fusion and acidification. Silicon isotopes were purified with an ion exchange resin. Interfering peaks for isotopes were separated by using a Nu Plasma 1700 multi-collector inductively coupled plasma mass spectrometry （MS） system in high-resolution mode （M/AM 〉 8000 RP）. Two magnesium isotopes （25Mg and 26Mg） and three silicon isotopes （28Si, 29Si, and 3;Si） were analyzed in the same data collection cycle. Mg isotopes were used as an internal standard to calibrate the mass discrimination effects in MS analysis of Si isotopes in combination with the SSB method in order to reduce the effects of MS interference and instrumental mass dis- crimination on the accuracy of measurements. The conventional SSB method without the Mg internal standard and the proposed SSB method with Mg calibration delivered consistent results within two standard deviations. When Mg was used as an internal standard for calibration, the analysis precision was better than 0.05 %0 amu.展开更多
Binary logistic regression models are commonly used to assess the association between outcomes and covariates. Many covariates are inherently continuous, and have a variety of distributions, including those that are h...Binary logistic regression models are commonly used to assess the association between outcomes and covariates. Many covariates are inherently continuous, and have a variety of distributions, including those that are heavily skewed to the left or right. Existing theoretical formulas, criteria, and simulation programs cannot accurately estimate the sample size and power of non-standard distributions. Therefore, we have developed a simulation program that uses Monte Carlo methods to estimate the exact power of a binary logistic regression model. This power calculation can be used for distributions of any shape and covariates of any type (continuous, ordinal, and nominal), and can account for nonlinear relationships between covariates and outcomes. For illustrative purposes, this simulation program is applied to real data obtained from a study on the influence of smoking on 90-day outcomes after acute atherothrombotic stroke. Our program is applicable to all effect sizes and makes it possible to apply various statistical methods, logistic regression and related simulations such as Bayesian inference with some modifications.展开更多
Regular grid of permanent sample plots (PSP) of ICP-Forests monitoring system was used for forest ecosystems biodiversity assessments and inventory. The supplementary features were added to the PSP structure to conduc...Regular grid of permanent sample plots (PSP) of ICP-Forests monitoring system was used for forest ecosystems biodiversity assessments and inventory. The supplementary features were added to the PSP structure to conduct biological diversity census: eight sample plots 1 × 1 m for geo-botanical description;two sample plots of 5 × 5 m each for description of the PSP’s undergrowth;one 25 × 25 m plot for coarse woody debris estimations;four soil inventory pits. The total number of PSP amounted to 248. Total data used are as following: 1) 1984 geo-botanical descriptions of vegetation belonging to ground cover layers made on 1 × 1 m sample plots;2) 496 descriptions of undergrowth on 5 × 5 m sample plots;3) 178 descriptions of woody debris on 25 × 25 m sample plots;4) 496 descriptions of soil inventory pits. General statistical indicators characterizing forest land cover diversity were calculated. Statistic indicators of α-diversity for the Karelian Isthmus forest vegetation cover have the following values: 1) m (mean number of species per PSP) = 26 species;2) σ (standard deviation) = 9.5 species;3) v (variation coefficient) = 36.5%;4) Р (deviation amplitude) = 60 – 7 = 53 species. β – diversity of forest ecosystems as well as γ – diversity also was studied on the base of information collected on the same regular grid of sample plots. It appears that sample plots distribution by species diversity gradation is well described by the standard curve of normal distribution for the entire Karelian Isthmus forest (determination coefficient of the curve being 95.2%) as well as for each type of forest. Hence, the criterion (standard) of biodiversity for forest ecosystems can be defined as the mean value of alpha diversity for each forest type group – m;and the standard deviation – σ, as a tool for assessing deviations from the standard. PSP locations are fixed using GPS technology, this allows biodiversity assessments at the same place in the next years for biodiversity trends estimations and consist the frame for systematic biodiversity inventory.展开更多
基金funded by the National Natural Science Foundation of China(Grant Nos.41427804,41421002,41373004)Beijing SHRIMP Center Open Foundation,and Program for Changjiang Scholars and Innovative Research Team in University(Grant No.IRT1281)the MOST Research Foundation from the State Key Laboratory of Continental Dynamics(BJ08132-1)
文摘Silicon isotope analysis traditionally uses a standard-sample bracketing （SSB） method that relies upon greater instrument stability than can be consistently expected. The following proposed method reduces the level of instrumental stability required for the analysis process and provides a valid solution for high-precision and accurate studies of Si isotopic compositions. Rock samples were dissolved by using alkali fusion and acidification. Silicon isotopes were purified with an ion exchange resin. Interfering peaks for isotopes were separated by using a Nu Plasma 1700 multi-collector inductively coupled plasma mass spectrometry （MS） system in high-resolution mode （M/AM 〉 8000 RP）. Two magnesium isotopes （25Mg and 26Mg） and three silicon isotopes （28Si, 29Si, and 3;Si） were analyzed in the same data collection cycle. Mg isotopes were used as an internal standard to calibrate the mass discrimination effects in MS analysis of Si isotopes in combination with the SSB method in order to reduce the effects of MS interference and instrumental mass dis- crimination on the accuracy of measurements. The conventional SSB method without the Mg internal standard and the proposed SSB method with Mg calibration delivered consistent results within two standard deviations. When Mg was used as an internal standard for calibration, the analysis precision was better than 0.05 %0 amu.
文摘Binary logistic regression models are commonly used to assess the association between outcomes and covariates. Many covariates are inherently continuous, and have a variety of distributions, including those that are heavily skewed to the left or right. Existing theoretical formulas, criteria, and simulation programs cannot accurately estimate the sample size and power of non-standard distributions. Therefore, we have developed a simulation program that uses Monte Carlo methods to estimate the exact power of a binary logistic regression model. This power calculation can be used for distributions of any shape and covariates of any type (continuous, ordinal, and nominal), and can account for nonlinear relationships between covariates and outcomes. For illustrative purposes, this simulation program is applied to real data obtained from a study on the influence of smoking on 90-day outcomes after acute atherothrombotic stroke. Our program is applicable to all effect sizes and makes it possible to apply various statistical methods, logistic regression and related simulations such as Bayesian inference with some modifications.
文摘Regular grid of permanent sample plots (PSP) of ICP-Forests monitoring system was used for forest ecosystems biodiversity assessments and inventory. The supplementary features were added to the PSP structure to conduct biological diversity census: eight sample plots 1 × 1 m for geo-botanical description;two sample plots of 5 × 5 m each for description of the PSP’s undergrowth;one 25 × 25 m plot for coarse woody debris estimations;four soil inventory pits. The total number of PSP amounted to 248. Total data used are as following: 1) 1984 geo-botanical descriptions of vegetation belonging to ground cover layers made on 1 × 1 m sample plots;2) 496 descriptions of undergrowth on 5 × 5 m sample plots;3) 178 descriptions of woody debris on 25 × 25 m sample plots;4) 496 descriptions of soil inventory pits. General statistical indicators characterizing forest land cover diversity were calculated. Statistic indicators of α-diversity for the Karelian Isthmus forest vegetation cover have the following values: 1) m (mean number of species per PSP) = 26 species;2) σ (standard deviation) = 9.5 species;3) v (variation coefficient) = 36.5%;4) Р (deviation amplitude) = 60 – 7 = 53 species. β – diversity of forest ecosystems as well as γ – diversity also was studied on the base of information collected on the same regular grid of sample plots. It appears that sample plots distribution by species diversity gradation is well described by the standard curve of normal distribution for the entire Karelian Isthmus forest (determination coefficient of the curve being 95.2%) as well as for each type of forest. Hence, the criterion (standard) of biodiversity for forest ecosystems can be defined as the mean value of alpha diversity for each forest type group – m;and the standard deviation – σ, as a tool for assessing deviations from the standard. PSP locations are fixed using GPS technology, this allows biodiversity assessments at the same place in the next years for biodiversity trends estimations and consist the frame for systematic biodiversity inventory.
