Geological Feature Modeling and Reserve Estimation of Uranium Deposits Based on Multiple Interpolation Methods

Uranium resource distribution and accurate reserve evaluation are important references for mineral investment and production. Eight kinds of interpolation methods in the Groundwater Modeling System (GMS), including ordinary kriging (OK), are used in this study to predict the spatial distribution of reserve-related parameters, such as uranium grade, ore thickness and uranium content per square meter. The present study draws the following conclusions: (1) Cross-validation found that the uranium grade value using the spherical method is the closest to the actual value. The spherical method has the best interpolation effect. (2) The relative error, which is +3.62%, between the uranium reserves that is calculated by the spherical interpolation method and that by the traditional calculation value is the smallest. (3) The setting of the number of interpolation grids is related to the actual number of boreholes. The ratio between the two will affect the accuracy of reserve estimation, and different interpolation methods have different degrees of influence on reserve estimation. This method is applicable to all in-situ leaching sandstone uranium mines. Further study needs to be carried out toward heterogeneity of three-dimensional space, which will make the estimation more accurate.

Early Prediction of Sepsis Based on Machine Learning Algorithm

Sepsis is an organ failure disease caused by an infection resulting in extremely high mortality. Machine learning algorithms XGBoost and LightGBM are applied to construct two processing methods: mean processing method and feature generation method, aiming to predict early sepsis 6 hours in advance. The feature generation methods are constructed by combining different features, including statistical strength features, window features, and medical features. Miceforest multiple interpolation method is applied to tackle large missing data problems. Results show that the feature generation method outperforms the mean processing method. XGBoost and LightGBM algorithms are both excellent in prediction performance (AUC: 0.910∼0.979), among which LightGBM boasts a faster running speed and is stronger in generalization ability especially on multidimensional data, with AUC reaching 0.979 in the feature generation method. PTT, WBC, and platelets are the key risk factors to predict early sepsis.

Exploration to Identify Radiosensitive Genes in PD-L1 Expression and PD-1 check Point Pathway in Cancer

PurposeTo identify radiosensitive genes in PD-L1 expression and PD-1 check point pathway in cancer.Methods and MaterialsGene expression datasets and information were downloaded from TCGA. Stepwise multivariate Cox regression based on AIC was performed using stacking multiple interpolation data to identify radiosensitive (RS) genes.ResultsAmong the 74 PD-1/PD-L1 pathway genes, we identified 10 RS genes in BRCA dataset, 11 RS genes in STAD dataset and 13 RS genes in HNSC dataset. These genes could be thought as independent factors and biomarkers to identify the sensitivity of cancer patients to radiotherapy. Gene CD274 was the common RS gene in the three tumor datasets. And gene ZAP70 was verified as a RS gene in the external validation. There were moderate co-expression relationships and interactions in these genes. Functional enrichment analysis showed that most of these genes were related to T cells. ConclusionsOur study identified potential radiosensitive biomarkers of several main cancer types in an important tumor immune checkpoint pathway. New types of RS genes were identified based on the expanded definition to radiosensitive genes. Different types of tumors may share same RS genes due to the common carcinogenic mechanisms.

Integrate Exploration to Identify Radiosensitive Biomarkers in Genes in PD-L1 Expression and PD-1 Check Point Pathway in Cancer

Purpose Exploration to identify radiosensitive biomarkers in genes in PD-L1 expression and PD-1 check point pathway in cancer. Methods and Materials: Gene expression datasets and information were downloaded from TCGA. Stepwise multivariate Cox regression based on AIC was performed using stacking multiple interpolation data to identify radiosensitive (RS) genes. Results Among the 74 PD-1/PD-L1 pathway genes, we identified 10 RS genes in BRCA dataset, 11 RS genes in STAD dataset and 13 RS genes in HNSC dataset. These genes can be thought as independent factors to identify the sensitivity of cancer patients to radiotherapy. Gene CD274 was the common gene in the three tumor datasets. And gene ZAP70 was verified as a RS gene in the external validation. There were moderate co-expression relationships and interactions in these genes. Functional enrichment analysis showed that most of these genes were related to T cells. Conclusions Our study identified potential radiosensitive biomarkers of several main cancer types in an important tumor immune checkpoint pathway. New types of RS genes were identified based on expanded definition to radiosensitive genes. Different types of tumors may share some common carcinogenic mechanisms and may have same RS genes.

Multiple interpolation in the Privalov classes in a disk

For all 0 < q < +? the Privalov class ?q consists of all analytic functions f in a unit disk such that sup 0?r<1 1/2? ??,-? (ln+ |f(rei?)|)q d?< +?. In this paper we solve a multiple interpolation problem in the class ?q for all 0 < q < 1. Namely, we find the sufficient conditions for the explicit construction of the function that solves the interpolation problem in the Privalov class. In addition, we discuss the necessity of these conditions.

Interpolation by sums of series of exponentials and global Cauchy problem for convolution operators

The study is made of the problem of multiple interpolation on an infinite nodes set by the sums of absolutely convergent series of exponentials whose exponents are from a given set. For entire function conditions on nodes and exponents are obtained that give solubility of the problem. A new approach is demonstrated that enable us, for the case of holomorphic function in a domain, to obtain criteria of solubility of the problem for some class of exponents set and for a special class of nodes set. Moreover the necessity of the conditions is proved in great generality namely for arbitrary nodes sets and in the setting of interpolation by functions that are represented as the Laplace transforms of the Radon measures over the exponents set. Solubility is obtained of the global Cauchy problem for convolution operator with data on the nodes set in domain, in the form of the series of exponentials whose exponents belong to a sparse subset of zero set of characteristic function of the operator. The results substantially strengthen the known results on the theme.