Thermoluminescence studies of zinc borate (ZnB2O4) phosphor doped with rare earths for dosimetric aspects

A series of ZnB₂O₄ phosphors doped with different concentrations of Eu and Dy (0.05 0.1, 0.2, 0.5, 1.0 mol%) and co-doped with Ce (1, 2, 5, 7, 10 mol%) respectively was prepared via the solid-state reaction technique and the thermoluminescence (TL) behaviour of gamma ray-irradiated samples was studied. The synthesized samples were irradiated with γ-rays for the dose range 0.03–1.20 kGy. The TL intensity variations with dose, dopant concentration, and the effect of co-doping were studied. The TL response curves for ZnB₂O₄:Eu³⁺ and ZnB₂O₄:Dy³⁺, ZnB₂O₄:Eu³,Ce³⁺ and ZnB₂O₄:Dy³⁺,Ce³⁺ phosphor were observed. It was revealed that ZnB₂O₄:Eu³⁺ showed a linear TL behaviour for the dose 0.03–1.20 kGy and ZnB₂O₄:Dy³⁺ showed linearity for the gamma dose range 0.03–0.10 kGy. Furthermore, fading for all the samples was observed to be less than 10% for a storage period of 30 days. In addition to this, the trapping parameters, especially activation energies were evaluated using the Ilich method and the initial rise method. The activation energy values obtained from both methods were in complete agreement with each other.     

Dominant vs. codominant genetic markers in the estimation of male mating success

Although a major component of fitness, male reproductive success is generally extremely difficult to estimate. As a result, genetic methods and maximum likelihood models have been developed to estimate male parentage, but all are limited in practice by the degree of genetic variation observable. Scoring individuals phenotypically at a large number of random loci exhibiting dominance (e.g. RAPD markers) may provide a means of detecting sufficient genetic variation. Dominance, however, represents a loss of information and therefore greater variation in the estimate of paternity. A mixture model describing mating in a population is presented to quantify the trade-off between marker types when estimates of male fertility are sought. A sample size 1.5-2.0 times greater is required for dominant markers under some conditions to obtain the same confidence in fertility estimates as for codominant markers, although with large sample sizes the fertility estimates are similar for either marker type. Since the number of dominant DN A markers is not limited in the same manner as is the number of codominant protein markers, one's confidence in the estimates can be increased above that possible from proteins by surveying additional loci. However, for a fixed sample size a trade-off exists between the number of progeny assayed per female and the number of loci surveyed. In many cases more progeny per female provide better estimates of fertility than more loci.     

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.     

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.     

Coherent modeling of longitudinal causal effects on binary outcomes

Analyses of biomedical studies often necessitate modeling longitudinal causal effects. The current focus on personalized medicine and effect heterogeneity makes this task even more challenging. Toward this end, structural nested mean models (SNMMs) are fundamental tools for studying heterogeneous treatment effects in longitudinal studies. However, when outcomes are binary, current methods for estimating multiplicative and additive SNMM parameters suffer from variation dependence between the causal parameters and the noncausal nuisance parameters. This leads to a series of difficulties in interpretation, estimation, and computation. These difficulties have hindered the uptake of SNMMs in biomedical practice, where binary outcomes are very common. We solve the variation dependence problem for the binary multiplicative SNMM via a reparameterization of the noncausal nuisance parameters. Our novel nuisance parameters are variation independent of the causal parameters, and hence allow for coherent modeling of heterogeneous effects from longitudinal studies with binary outcomes. Our parameterization also provides a key building block for flexible doubly robust estimation of the causal parameters. Along the way, we prove that an additive SNMM with binary outcomes does not admit a variation independent parameterization, thereby justifying the restriction to multiplicative SNMMs.     

Examination of the method for measuring soil respiration in cultivated land: Effect of carbon dioxide concentration on soil respiration

An acceleration of soil respiration with decreasing CO₂ concentration was suggested in the field measurements. The result supporrs that obtained in laboratory experiments in our previous study. The CO₂ concentrations in a chamber of the alkali absorption method (the AA-method) were about 150–250 parts/10⁶ lower than that in the atmosphere (about 350 parts/10⁶), while those observed in the open-flow IRGA method (the OF-method) were nearly equal to the soil surface CO₂ levels. The AA-method at such low CO₂ levels in the chamber appears to overestimate the soil respiration. Our results showed that the rates obtained by the AA-method were about twice as large as those by the OF-method in field and laboratory measurements. This finding has important consequences with respect to the validity of the existing data obtained by the AA-method and the estimation of changes in the terrestrial carbon flow with elevated CO₂     

信号分子在生物脱氮中的作用及检测方法

生物脱氮是由微生物主导的地球氮循环中的重要环节之一,主要包括硝化、反硝化和厌氧氨氧化(anaerobic ammonium oxidation,anammox)等过程。在微生物联合作用下,污水中的有机氮及氨氮经一系列作用转化为氮气,这种经济高效、环境友好的处理工艺在世界范围内得到广泛应用。群体感应(quorum sensing,QS)以信号分子为媒介通过改变菌群密度和周围环境变化来调节微生物的各种行为。大量的研究已证实调控QS信号分子在生物脱氮中具有应用潜力。本文介绍了各种信号分子类型,从基因组学、实际应用等方面综述了各类信号分子以及检测方法,同时针对酰基高丝氨酸内酯(acyl homoserine lactones,AHLs)类信号分子在生物脱氮中的作用进行详细介绍。然而不足之处在于信号分子研究只是停留在实验室阶段,仅仅研究了单一信号分子对生物脱氮的影响。未来可将信号分子应用于实际污水,研究多种信号分子共同作用以及多种微生物之间的QS现象。     

基于微波热声层析成像定量重建生物组织电导率的改进方法

目的 生物电磁学参数中的电导率与组织的功能性信息直接相关,精准重建生物组织电导率在医学成像技术和医学诊断领域中有着重要意义。本文改进定量微波热声层析成像（microwave-induced thermoacoustic tomography,MTAT）算法,使组织电导率的重建精度提高。方法 本文在利用有限元离散法求解热声波动方程和亥姆霍兹方程的基础之上,提出了一种基于正则化牛顿迭代法（regularized Newton iteration method,RNIM）定量重建组织电导率的改进方法。结果 通过数值模拟实验和含不同浓度NaCl溶液的仿体实验,验证了算法改进的有效性。组织仿体实验结果表明,目标在不同位置、不同大小、不同对比度情况下,相比于定量微波热声层析成像采用拟合（fitting）的方法,采用正则化牛顿法定量重建的仿体电导率相对误差明显降低,重建目标精度提高。在仿体实验中采用RNIM方法重建相同浓度的单目标在不同位置的电导率变化幅度更小,以及重建多目标电导率的相对比值与实际更接近,实验结果验证了改进方法的稳定性。结论 研究结果表明优化算法能更加准确地定量重建组织仿体的电导率,...     

iBench: A ground truth approach for advanced validation of mass spectrometry identification method

The discovery of many noncanonical peptides detectable with sensitive mass spectrometry inside, outside, and on cells shepherded the development of novel methods for their identification, often not supported by a systematic benchmarking with other methods. We here propose iBench, a bioinformatic tool that can construct ground truth proteomics datasets and cognate databases, thereby generating a training court wherein methods, search engines, and proteomics strategies can be tested, and their performances estimated by the same tool. iBench can be coupled to the main database search engines, allows the selection of customized features of mass spectrometry spectra and peptides, provides standard benchmarking outputs, and is open source. The proof-of-concept application to tryptic proteome digestions, immunopeptidomes, and synthetic peptide libraries dissected the impact that noncanonical peptides could have on the identification of canonical peptides by Mascot search with rescoring via Percolator (Mascot+Percolator).     

Milk metabolites can characterise individual differences in animal resilience to a nutritional challenge in lactating dairy goats

The aim of this study is built in two phases: to quantify the ability of novel milk metabolites to measure between-animal variability in response and recovery profiles to a short-term nutritional challenge, then to derive a resilience index from the relationship between these individual variations. At two different stages of lactation, sixteen lactating dairy goats were exposed to a 2-d underfeeding challenge. The first challenge was in late lactation, and the second was carried out on the same goats early in the following lactation. During the entire experiment period, samples were taken at each milking for milk metabolite measures. For each metabolite, the response profile of each goat was characterised using a piecewise model for describing the dynamic pattern of response and recovery profiles after the challenge relative to the start of the nutritional challenge. Cluster Analysis identified three types of response/recovery profiles per metabolite. Using cluster membership, multiple correspondence analyses (MCAs) were performed to further characterise response profile types across animals and metabolites. This MCA analysis identified three groups of animals. Further, discriminant path analysis was able to separate these groups of multivariate response/recovery profile type based on threshold levels of three milk metabolites: β-hydroxybutyrate, free glucose and uric acid. Further analyses were done to explore the possibility of developing an index of resilience from milk metabolite measures. Different types of performance response to short-term nutritional challenge can be distinguished using multivariate analyses of a panel of milk metabolites.