Multigroup Data Research Articles

Attribute-Based Encryption Scheme for Secure Multi-Group Data Sharing in Cloud

Most of the organizations using the cloud-based data sharing platforms are multi-group in nature. The existing directly revocable attribute-based encryption (ABE) schemes though seem to be a good fit, but they fail to provide any effective solution for secure multi-group data sharing scenarios. To bridge this gap, we first propose <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Re</b> vocable <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ABE</b> with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</b> erifiable <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> utsourced decryption (ReVO-ABE)- a directly revocable collusion-resistant ABE scheme that allows any number of user revocation and joining without affecting the secret membership keys of the nonrevoked users. Based on ReVO-ABE, we build a <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ynamic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> ulti- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> roup <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> ecure <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> ata <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> haring scheme called DMG-SDS. For operations that are exclusive to multi-groups like group merge and split can be performed without affecting the attribute secret keys or membership keys of the nonrevoked users, which is not possible with any of the existing schemes. Our proposed scheme meets the necessary security requirements, and the performance assessment shows that it has much better performance benefits when compared with most the recent competitive schemes.

CytokineExplore: An Online Tool for Statistical Analysis of Cytokine Concentration Datasets.

PurposeCytokine data sets are increasing both in the number of different cytokines measured and the number of samples assayed. Further, typically data from different groups may be contrasted, eg, normal vs complication subjects. Many univariate and multivariate statistical techniques exist to study such cytokine datasets, but the ability to implement these techniques may be lacking for some practitioners, or may not be available quickly and conveniently. Here, we introduce CytokineExplore, an online tool for multi-cytokine and multi-group data analysis of user-provided Microsoft Excel data files.Materials and MethodsIn order to illustrate the tool features, we use data from intrauterine growth retardation (IUGR), a pregnancy complication, and normal healthy subjects as a control. The dataset contains levels for 10 cytokines, namely: IL-4, IL-6, IL-8, IL-10, IL-12, IL-13, IL-18, IL-23, interferon-gamma (IFN-γ) and tumour necrosis-alpha (TNF-α), obtained from 34 women with IUGR (further divided into 17 symmetric and 17 asymmetric cases) and 24 gestationally age-matched normal controls.ResultsThe online tool automatically generates box-plots, histograms, PCA and PLSDA plots, t-tests and Mann–Whitney statistical tests, cytokine importance values for separating two groups, heatmaps for comparing multiple groups, and other functionalities. Figures generated can be directly downloaded for use in presentations or journal articles.ConclusionThe tool facilitates quick and easy numerical exploration and multivariate analysis of cytokine datasets, to aid basic understanding and hypothesis generation.

The relevance of transdiagnostic shared networks to the severity of symptoms and cognitive deficits in schizophrenia: a multimodal brain imaging fusion study

Schizophrenia (SZ) is frequently concurrent with substance use, depressive symptoms, social communication and attention deficits. However, the relationship between common brain networks (e.g., SZ vs. substance use, SZ vs. depression, SZ vs. developmental disorders) with SZ on specific symptoms and cognition is unclear. Symptom scores were used as a reference to guide fMRI-sMRI fusion for SZ (n = 94), substance use with drinking (n = 313), smoking (n = 104), major depressive disorder (MDD, n = 260), developmental disorders with autism spectrum disorder (ASD, n = 421) and attention-deficit/hyperactivity disorder (ADHD, n = 244) respectively. Common brain regions were determined by overlapping the symptom-related components between SZ and these other groups. Correlation between the identified common brain regions and cognition/symptoms in an independent SZ dataset (n = 144) was also performed. Results show that (1): substance use was related with cognitive deficits in schizophrenia through gray matter volume (GMV) in anterior cingulate cortex and thalamus; (2) depression was linked to PANSS negative dimensions and reasoning in SZ through a network involving caudate-thalamus-middle/inferior temporal gyrus in GMV; (3) developmental disorders pattern was correlated with poor attention, speed of processing and reasoning in SZ through inferior temporal gyrus in GMV. This study reveals symptom driven transdiagnostic shared networks between SZ and other mental disorders via multi-group data mining, indicating that some potential common underlying brain networks associated with schizophrenia differently with respect to symptoms and cognition. These results have heuristic value and advocate specific approaches to refine available treatment strategies for comorbid conditions in schizophrenia.

Bayesian reduced rank multigroup regression analysis: a new model for multigroup data with hybrid parameter sharing

In this paper, we develop a new multiple regression analysis method for multigroup data. The model enforces some rank conditions on the matrix with regression coefficient vectors, and thus enables a parsimonious representation of group characteristics. The technique used can be considered as a hybrid approach to soft and hard parameter sharing in multi-task learning.

Resonance parameter adjustment in the resolved region based upon an Asymptotic Generalized Linear Least-Squares methodology in conjunction with the Monte Carlo method

In order to reduce methodological effects, a stochastic technique is proposed for adjusting basic ENDF parameters instead of preprocessed multi-group data as it is normally the case; the required sensitivity coefficients of the integral parameters to these parameters are recomputed by means of NJOY/Serpent within an iterative scheme based upon the Asymptotic Progressing Incremental nuclear data Adjustment (APIA) methodology. It is anticipated that the provided illustrative examples including selected benchmarks along with ENDF parameters to adjust are certainly limited, making the study too preliminary in order to reach meaningful conclusions on the quality of the posterior nuclear data. However, they were conceived for showing how the proposed novel methodology along with its main principles could be generalized and equally used in the framework of a future exhaustive adjustment, largely justifying this paper.More specifically dealt with in this study is the assimilation of the effective multiplication factor for low-enriched, U-solution, thermal systems by adjusting resonance widths for U-235 and U-238. It is shown that for the experiments part of the assimilation, the posterior ratios of computed to experimental central values are exactly equal to one, reflecting the general situation. This outcome may be expected from the theory, provided that convergence can be reached, which is always the case if the chosen experiments are not too strongly correlated among each other from the analytical viewpoint. Otherwise, differently from non-iterative methods, the procedure, since diverging, cannot provide useful data: in this case, some experiments of less relevance should be withdrawn.A similar approach could thus be used by considering a wide database in conjunction with a broader selection of basic parameters, to provide adjusted ENDF format files ensuring, by means of a reference stochastic tool, equality between computed and experimental values for a large variety of experimental data e.g. those available in the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP).

Detecting outlying variables in multigroup data: A comparison of different loading similarity coefficients

AbstractMultivariate multigroup data are collected in many fields of science, where the so‐called groups pertain to, for instance, experimental groups or countries the participants are nested in. To summarize the main information in such data, principal component analysis (PCA) is highly popular. PCA reduces the variables to a few components that are linear combinations of the original variables. Researchers usually assume those components to be the same across the groups and aim to apply a simultaneous component analysis. To investigate whether this assumption is reasonable, one often analyzes the groups separately and computes a similarity index between the group‐specific component loadings of the variables. In many cases, however, most variables have highly similar loadings across the groups, but a few variables, which we will call “outlying variables,” behave differently, indicating that a simultaneous analysis is not warranted. In such cases, the outlying variables should be removed before proceeding with the simultaneous analysis. To do so, the variables are ranked according to their relative outlyingness. Although some procedures have been proposed that yield such an outlyingness ranking, they might not be optimal, because they all rely on the same choice of similarity coefficient without evaluating other alternatives. In this paper, we give an overview of other options and report extensive simulations in which we investigate how this choice affects the correctness of the outlyingness ranking. We also illustrate the added value of the outlying variable approach by means of sensometric data on different bread samples.

Current status of the verification and processing system GALILÉE-1 for evaluated data

This paper describes the current status of GALILÉE-1 that is the new verification and processing system for evaluated data, developed at CEA. It consists of various components respectively dedicated to read/write the evaluated data whatever the format is, to diagnose inconsistencies in the evaluated data and to provide continuous-energy and multigroup data as well as probability tables for transport and depletion codes. All these components are written in C++ language and share the same objects. Cross-comparisons with other processing systems (NJOY, CALENDF or PREPRO) are systematically carried out at each step in order to fully master possible discrepancies. Some results of such comparisons are provided.

Optimization of energy-group structure for LWR high-fidelity neutronics calculation based on the contributon theory

ABSTRACTDeterministic high-fidelity neutronics calculation is to solve the neutron transport equation using the multi-group (MG) nuclear data libraries. The energy-group structure (ES) in MG nuclear data libraries has a significant impact on the precision and efficiency of neutronics calculation. Therefore, to meet the requirement of high precision and efficiency for high-fidelity neutronics calculation, the contributon theory is adopted to select the optimal ESs for the high-fidelity neutronics code NECP-X which is developed by Nuclear Engineering Computational Physics (NECP) lab. at Xi’an Jiaotong University (XJTU). By combining the contributon theory with the exhaustive searching method, the optimal ESs can be selected effectively. Two optimal ESs named NECP-69 and NECP-47 are obtained and the nuclear data processing code NECP-Atlas developed by NECP lab. is utilized to generate the corresponding MG nuclear data libraries for NECP-X. The neutronics parameters of the MOX pin cell problems and the VERA benchmarks are evaluated. The numerical results show that more precise neutronics parameters are obtained based on the optimal ESs compared with those based on the conventional WIMS-69 and HELIOS-47 for NECP-X.

Uncertainty and sensitivity analysis of PWR mini-core transients in the presence of nuclear data uncertainty using non-parametric tolerance limits

The impact of nuclear data uncertainties is studied for the reactor power and the reactivity during control rod withdrawal transients with reactivity insertions of 0.5$ and 0.97$, respectively, for a PWR mini-core model. Multi-group cross sections, the multiplicities of both prompt and delayed neutrons, and fission spectra are varied by the application of the random sampling-based method XSUSA with covariance data of SCALE 6.1 supplemented by JENDL-4.0. The varied multi-group data are used by TRITON/NEWT to generate varied 2-group cross sections, which are then applied in neutron-kinetic/thermal-hydraulic calculations with DYN3D-ATHLET. A significant impact on both the reactivity uncertainty and the power uncertainty is observed. Since the distributional properties of the output time series vary across the problem time, the distribution-free Wilks tolerance limit is applied as a robust uncertainty measure to complex time series patterns. The most contributing nuclide reactions to the power uncertainty are identified via sensitivity analysis.

Lactic Acid Treatment of Cereals and Dietary Phytase Modified Fecal Microbiome Composition Without Affecting Expression of Virulence Factor Genes in Growing Pigs.

Besides the major nutrients, phosphorus (P) is an essential mineral for both the host animal and the porcine gut microbiota. Different strategies including phytase supplementation and more recently lactic acid (LA) are used to enhance the P availability from cereals in pig diets; however, their impact on the gut microbiota has been rarely related to fecal shedding of opportunistic pathogens. The present study investigated the effect of phytase supplementation and the treatment of dietary cereals with 2.5% LA on the fecal microbiome composition of metabolic active bacteria and expression of virulence factor genes of enterotoxigenic Escherichia coli and Clostridium perfringens in growing pigs. Phytase supplementation reduced the fecal abundance of the most abundant Lactobacillaceae family, whereas the LA-treatment of cereals had a stronger impact on the bacterial community, reducing amylolytic, pullulanolytic and hemicellulolytic Lactobacillaceae, Lachnospiraceae and Ruminococcaceae as well as the fecal bacterial species richness (Chao1) and diversity (Shannon index). Mainly the family Clostridiaceae benefited from the decline in the aforementioned families, being enriched by both dietary treatments. Multigroup data integration using sparse partial least squares-discriminant analysis showed that among the most discriminative operational taxonomic units (OTU) especially two unclassified Clostridiaceae-OTUs, one Prevotella copri-like OTU and one OTU within the vadinCA11 group were associated with calcium and P levels but were negatively linked with complex carbohydrates in feces. Heat-stable toxin A (Sta) of enterotoxigenic E. coli and Stx2e of Shiga-toxin producing E. coli were expressed in feces but were similar among feeding groups. Without modifying the total bacterial gene copies and virulence factor expression of E. coli, both dietary phytase supplementation and LA-treatment of cereals drastically altered the bacterial community composition in pig feces. Results thereby allowed for the characterization of bacterial nutrient dependencies, indicating a link between fecal P availability, complex carbohydrate composition and alterations in the predominant genera.

Fecal Microbiota Transplant From Highly Feed Efficient Donors Affects Cecal Physiology and Microbiota in Low- and High-Feed Efficient Chickens.

Fecal microbiota transplants (FMT) may be used to improve chicken’s feed efficiency (FE) via modulation of the intestinal microbiota and microbe-host signaling. This study investigated the effect of the administration of FMT from highly feed efficient donors early in life on the jejunal and cecal microbiota, visceral organ size, intestinal morphology, permeability, and expression of genes for nutrient transporters, barrier function and innate immune response in chickens of diverging residual feed intake (RFI; a metric for FE). Chicks (n = 110) were inoculated with the FMT or control transplant (CT) on 1, 6, and 9 days posthatch (dph), from which 56 chickens were selected on 30 dph as the extremes in RFI, resulting in 15 low and 13 high RFI chickens receiving the FMT and 14 low and 14 high RFI chickens receiving the CT. RFI rank and FMT only caused tendencies for alterations in the jejunal microbiota and only one unclassified Lachnospiraceae genus in cecal digesta was indicative of high RFI. By contrast, the FMT caused clear differences in the short-chain fatty acid (SCFA) profile in the crop and cecal microbiota composition compared to the CT, which indicated alterations in amylolytic, pullulanolytic and hemicellulolytic bacteria such as Lactobacillus, Dorea, and Ruminococcus. Moreover, the FMT caused alterations in intestinal development as indicated by the longer duodenum and shallower crypts in the ceca. From the observed RFI-associated variation, energy-saving mechanisms and moderation of the mucosal immune response were indicated by higher jejunal permeability, shorter villi in the ileum, and enhanced cecal expression of the anti-inflammatory cytokine IL10 in low RFI chickens. Relationships obtained from supervised multigroup data integration support that certain bacteria, including Ruminococcocaceae-, Lactobacillus-, and unclassified Clostridiales-phylotypes, and SCFA in jejunal and cecal digesta modulated expression levels of cytokines, tight-junction protein OCLN and nutrient transporters for glucose and SCFA uptake. In conclusion, results suggest that the intestine only played a moderate role for the RFI-associated variation of the present low and high RFI phenotypes, whereas modulating the early microbial colonization resulted in long-lasting changes in bacterial taxonomic and metabolite composition as well as in host intestinal development.

Analyzing Visitors’ Preferences and Evaluation of Satisfaction Based on Different Attributes, with Forest Trails in the Akasawa National Recreational Forest, Central Japan

For forest park managers and designers, it is very important to provide forest trails and high–quality forest landscapes that meet visitor’s needs. In this study, based on demographic characteristics, our main purpose is to analyze whether the preferences of visitors for different trails differ, and to discuss whether forest park visitors’ satisfaction is affected when preferences for choosing trails change, mainly in order to improve knowledge about visitors’ needs for forest walking spaces. Through this analysis, we can aid in the planning and management of forest parks to improve visitors’ experiences. We mainly consider five attributes, namely, sex, age, number of visitors, number of visits and duration of stay in the park, in a questionnaire, and use the Kruskal–Wallis Test and Mann–Whitney U Test to analyze multigroup data in “SPSS for Windows”. The results are as follows: (1) Visitors with different attributes exhibit significant differences in terms of their choices of forest trails and the evaluation of visitor satisfaction in the duration of stay. (2) The correlation analysis showed that trail condition factors, such as the degree of difficulty and facility status, affect the satisfaction evaluation of tourists on many levels. In addition, the number of visits by visitors is negatively correlated with the preference for a forest trail based on recreational indicators. (3) Compared to visitors who stay in the forest for a short amount of time, visitors who remain in the forest for longer have a higher satisfaction level. Based on these results, we recommend that parks consider the number of visits (visiting experience) and the duration of stays in the forest when planning walking routes to more effectively plan forest park trails.

A deterministic-stochastic energy-hybrid method for neutron-transport calculation

To solve the neutron-transport equation, the deterministic method discretizes the space-angle-energy phase-space to obtain the corresponding algebraic equations. It is superior in computing speed, but short at resonance energy range self-shielding treatment due to the multi-group approximation. In contrast, the stochastic or Monte Carlo method transforms the differential-integral equation into an integral form to construct the sampling space for continuous phase-space simulation. It can guarantee the computing accuracy with sufficient samples, but requires large computing time to simulate these samples. To combine the advantages of these two methods, a hybrid deterministic-stochastic method in incident-neutron energy is investigated in this paper. Considering the geometry handling ability, Method Of Characteristics (MOC) was employed to treat the fast and thermal energy ranges in which cross sections slowly vary along with the incident-neutron energy. Meanwhile, Monte Carlo method was employed to deal with the epithermal energy range where severe resonance effect appears. Different energy ranges are coupled through the neutron scattering and fission contributions between each other. Encouraging conclusions can be demonstrated by the numerical results. (1) The multi-group data library for deterministic calculation in fast and thermal energy ranges and the ACE format data library for stochastic calculation in resonance energy range are consistent if they are made from the same evaluated nuclear data library. (2) The transport correction employed by the deterministic calculation in fast energy range can handle the strong anisotropic scattering effect in the investigated hybrid method in this paper. (3) The hybrid method can combine the advantages of the deterministic and stochastic methods.

Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data.

Empirical Bayes is a choice framework for differential expression (DE) analysis for multi-group RNA-seq count data. Its characteristic ability to compute posterior probabilities for predefined expression patterns allows users to assign the pattern with the highest value to the gene under consideration. However, current Bayesian methods such as baySeq and EBSeq can be improved, especially with respect to normalization. Two R packages (baySeq and EBSeq) with their default normalization settings and with other normalization methods (MRN and TCC) were compared using three-group simulation data and real count data. Our findings were as follows: (1) the Bayesian methods coupled with TCC normalization performed comparably or better than those with the default normalization settings under various simulation scenarios, (2) default DE pipelines provided in TCC that implements a generalized linear model framework was still superior to the Bayesian methods with TCC normalization when overall degree of DE was evaluated, and (3) baySeq with TCC was robust against different choices of possible expression patterns. In practice, we recommend using the default DE pipeline provided in TCC for obtaining overall gene ranking and then using the baySeq with TCC normalization for assigning the most plausible expression patterns to individual genes.

Current status of the verification and processing system GALILÉE-1 for Evaluated Data

This paper describes the current status of GALILÉE-1 that is the new verification and processing system for evaluated data, developed at CEA. It consists of various components respectively dedicated to read/write the evaluated data whatever the format is, to diagnose inconsistencies in the evaluated data and to provide continuous-energy and multigroup data as well as probability tables for transport and depletion codes. All these components are written in C++ language and share the same objects. Cross-comparisons with other processing systems (NJOY, CALENDF or PREPRO) are systematically carried out at each step in order to fully master possible discrepancies. Some results of such comparisons are provided.

Effects of bone marrow mesenchymal stem cells on the expressions of inflammatory factors in rats with multiple organ dysfunction syndrome

Objective To investigate the effects of bone marrow mesenchymal stem cells (BMSC) on the expression of inflammatory factors in rats with multiple organ dysfunction syndrome (MODS). Methods BMSC extracted from the 4-week-old Sprague-Dawley (SD) rats was cultivated and identified in vitro, then the 4th passage of which was used in the experimental study. Sixty SD rats were randomly(random number) divided into three groups (n=20 in each group): Sham group (SG), MODS group (MG) and BMSC group (BG). Rats in the MG was injected by 1 mg/kg lipopolysaccaride (LPS) via great saphenous vein, rats in the SG injected with the same volume sterile phosphate buffer saline and rats in the BG infused by 1×106/cells BMSCs through the tail vein at 2 h after LPS injection. The survival rate, tissue pathological changes of the lung, liver and heart by hematoxylin and eosin (HE) staining, organ dysfunction measurement by blood gas analysis and biochemical indicators as well as the related inflammatory factors by protein microarray and enzyme linked immunosorbent assay (ELISA), were detected 72 h post operation. Multi-group quantitative data was analyzed by one way ANOVA, paired-comparisons by LSD-t test and the comparisons of survival curves in the three groups by Log-rank test. The value of P<0.05 was considered statistically significant. Results The survival rate in SG, MG and BG was 100%, 60% and 80%, respectively. The survival curves showed that the survival rate of SG was higher than the MG and BG (SG vs. MG, χ2=9.798, P=0.001 7; SG vs. BG, χ2=4.333, P=0.037 4), but there was no significant difference comparing the BG to the MG (χ2=2.408, P=0.120 7). The tissue congestion and edema, and inflammatory cells infiltration in the lung, liver, and heart of the MG were observed by HE staining, while these changes reduced in the BG. Compared with the SG, the levels of pH and PaCO2 and lactic acid (Lac) increased significantly (all P<0.01), the level of total bilirubin (TB) significantly increased [(0.801±0.501)U/L vs. (2.533±0.382)U/L, P=0.003], while the albumin(ALB) level decreased significantly[(35.471±4.015)U/L vs. (23.202±4.872)U/L, P<0.01], and creatine kinase (CK) level increased significantly in MG [(315.670±41.402) vs. (708.250±219.201), P=0.042]. After BMSC treatment, the organ function improved significantly (all P<0.05). Gamma interferon (IFN-γ) and monocyte chemotactic protein 1 (MCP-1) were the differential expression factors in protein chips. The results of ELISA were similar to the protein chips: compared with the SG, IFN-γ and MCP-1 expressions in the MG increased significantly (P<0.01). Compared with MG, the expressions of IFN-γ and MCP-1 decreased significantly in the BG (P<0.01). Conclusion BMSC administration could modulate the inflammatory response of MODS rats by inhibiting the levels of IFN-γ and MCP-1, and improve the organ function and the survival rate. Key words: Multiple organ dysfunction syndrome; Bone marrow mesenchymal stem cells; Interleukin -γ; Monocyte chemoattractant protein-1; Inflammatory factor

Propagation of uncertainties in basic nuclear reaction data to uncertainties in the parameters of primary radiation damage by neutrons

The primary radiation damage formation in materials subjected to neutron radiation is very sensitive to the energy spectra of primary knock-on atoms (PKAs) and their collision cascade in the crystal lattice. The estimated values of the primary radiation damage metrics, such as displacements per atom (dpa) cross sections, integrated dpa, PKA spectrum and spectrum averaged PKA energy are uncertain because of uncertainties present in both the basic nuclear reaction data and the displacement damage model parameters. The Total Monte Carlo (TMC) methodology, which uses the random evaluated nuclear data files for nuclides, is well recognized to propagate the uncertainties in basic nuclear data to the derived quantities in nuclear reactor physics. The uncertainties in the above mentioned primary radiation damage parameters are obtained in this study by propagating the uncertainties in basic evaluated nuclear data, following the TMC methodology. The knowledge of uncertainties in the spectrum averaged PKA energies also enables us to ascertain the uncertainties in the production of vacancies through self-ion simulations using SRIM-2013 code. The present study is an outcome of our systematic approach towards establishing the procedure. This includes pre-processing a large number of TENDL nuclear data random files using NJOY-2016, developing an indigenous code CRaD for handling these point and multigroup data libraries and computing primary radiation damage metrics and their associated uncertainties including energy-energy and nuclear reaction channel-channel correlation matrices. The uncertainties in dpa cross sections and PKA spectra due to uncertainties in basic nuclear data are found to be comparatively larger than the uncertainties in respective integrated parameters; viz. dpa and average PKA energy. This study also confirms the non-Gaussian distributions of derived primary radiation damage parameters.

Correlation between FibroScan, APRI Detecting the degree of esophageal and gastric varices of liver cirrhosis

Objective: To investigate the predictive value of transient elastography (FibroScan), aspartate aminotransferase-to-platelet ratio index (APRI) in the detection of esophagogastric varices in patients with liver cirrhosis. Methods: 236 patients with liver cirrhosis who met the criteria were selected. All patients underwent gastroscopy. According to the degree of esophagogastric varices, patients were divided into four groups: none, mild, moderate, and severe. The patient's liver stiffness (LSM) and aspartate aminotransferase- to-platelet ratio index (APRI) were measured within 3 days of gastroscopy. One-way analysis of variance was used to compare multi-group data. The ROC curves of LSM, APRI, LSM+APRI in patients with liver cirrhosis with esophageal varices were plotted and their area under the ROC curve (AUC) were compared. Results: The area under the ROC curve of LSM, APRI, LSM + APRI in patients with mild esophagogastric varices were 0.856, 0.900, and 0.906, respectively; moderate esophagogastric varices were 0.857, 0.924, and 0.923 respectively; and severe esophagogastric varices were 0.801, 0.903, and 0.901, respectively. Conclusion: APRI and LSM+APRI have better predictive value for patients with cirrhosis who have esophagogastric varices.

Adaptive multigroup confidence intervals with constant coverage

SUMMARYCommonly used interval procedures for multigroup data attain their nominal coverage rates across a population of groups on average, but their actual coverage rate for a given group will be above or below the nominal rate, depending on the group mean. While correct coverage for a given group can be achieved with a standard $t$-interval, this approach is not adaptive to the available information about the distribution of group-specific means. In this article we construct confidence intervals that have a constant frequentist coverage rate and that make use of information about across-group heterogeneity, resulting in constant-coverage intervals that are narrower than standard $t$-intervals on average across groups. Such intervals are constructed by inverting biased Bayes-optimal tests for the mean of each group, where the prior distribution for a given group is estimated with data from the other groups.

Generalized equivalence methods for 3D multi-group neutron transport

The process of generating multi-group cross section data to be used in full core 3D transport models requires not only accurate resonance self-shielding methods, but also some form of equivalence method in order to precisely preserve reaction rates of spectral geometry calculations. This paper presents extensions of the traditional concepts of local reaction rate preservation (common in discontinuity factor, SPH, and BBH methods), to derive a new state-of-the-art transport equivalence method that incorporates angular flux jump conditions that provide polar angle neutron current preservation. This method is tested on numerous fixed-source pin-cell problems by condensing fine energy resonance fluxes and cross sections. The method is demonstrated to precisely preserve all spectral geometry multi-group reaction rates as well as polar angle neutron currents for a wide range of cross section resonance heights, fuel pin diameters, coolant densities, and group energy widths.