CyberKnife beam output factor measurements: A multi-site and multi-detector study

PurposeNew promising detectors are available for measuring small field size output factors (OFs). This study focused on a multicenter evaluation of two new generation detectors for OF measurements on CyberKnife systems.MethodsPTW-60019 microDiamond and W1 plastic scintillation detector (PSD) were used to measure OFs on eight CyberKnife units of various generations for 5–60 mm fixed cones. MicroDiamond and PSD OF were compared to routinely used silicon diodes data corrected applying published Monte Carlo (MC) factors. PSD data were corrected for Čerenkov Light Ratio (CLR). The uncertainties related to CLR determination were estimated.ResultsConsidering OF values averaged over all centers, the differences between MC corrected diode and the other two detectors were within 1.5%. MicroDiamond exhibited an over-response of 1.3% at 7.5 mm and a trend inversion at 5 mm with a difference of 0.2%. This behavior was consistent among the different units. OFs measured by PSD slightly under-responded compared to MC corrected diode for the smaller cones and the differences were within 1%. The observed CLR variability was 2.5% and the related variation in OF values was 1.9%.ConclusionThis study indicates that CyberKnife microDiamond OF require corrections below 2%. The results are enhanced by the consistency observed among different units. Scintillator shows a good agreement to MC corrected diode but CLR determination remains critical requiring further investigations. The results emphasized the value of a multi-center validation over a single center approach.     

Relative efficiency of unequal versus equal cluster sizes in cluster randomized trials using generalized estimating equation models

There is growing interest in conducting cluster randomized trials (CRTs). For simplicity in sample size calculation, the cluster sizes are assumed to be identical across all clusters. However, equal cluster sizes are not guaranteed in practice. Therefore, the relative efficiency (RE) of unequal versus equal cluster sizes has been investigated when testing the treatment effect. One of the most important approaches to analyze a set of correlated data is the generalized estimating equation (GEE) proposed by Liang and Zeger, in which the “working correlation structure” is introduced and the association pattern depends on a vector of association parameters denoted by ρ. In this paper, we utilize GEE models to test the treatment effect in a two‐group comparison for continuous, binary, or count data in CRTs. The variances of the estimator of the treatment effect are derived for the different types of outcome. RE is defined as the ratio of variance of the estimator of the treatment effect for equal to unequal cluster sizes. We discuss a commonly used structure in CRTs—exchangeable, and derive the simpler formula of RE with continuous, binary, and count outcomes. Finally, REs are investigated for several scenarios of cluster size distributions through simulation studies. We propose an adjusted sample size due to efficiency loss. Additionally, we also propose an optimal sample size estimation based on the GEE models under a fixed budget for known and unknown association parameter (ρ) in the working correlation structure within the cluster.     

Why did heterospory evolve?

The primitive land plant life cycle featured the production of spores of unimodal size, a condition called homospory. The evolution of bimodal size distributions with small male spores and large female spores, known as heterospory, was an innovation that occurred repeatedly in the history of land plants. The importance of desiccation‐resistant spores for colonization of the land is well known, but the adaptive value of heterospory has never been well established. It was an addition to a sexual life cycle that already involved male and female gametes. Its role as a precursor to the evolution of seeds has received much attention, but this is an evolutionary consequence of heterospory that cannot explain the transition from homospory to heterospory (and the lack of evolutionary reversal from heterospory to homospory). Enforced outcrossing of gametophytes has often been mentioned in connection to heterospory, but we review the shortcomings of this argument as an explanation of the selective advantage of heterospory. Few alternative arguments concerning the selective forces favouring heterospory have been proposed, a paucity of attention that is surprising given the importance of this innovation in land plant evolution. In this review we highlight two ideas that may lead us to a better understanding of why heterospory evolved. First, models of optimal resource allocation – an approach that has been used for decades in evolutionary ecology to help understand parental investment and other life‐history patterns – suggest that an evolutionary increase in spore size could reach a threshold at which small spores yielding small, sperm‐producing gametophytes would return greater fitness per unit of resource investment than would large spores and bisexual gametophytes. With the advent of such microspores, megaspores would evolve under frequency‐dependent selection. This argument can account for the appearance of heterospory in the Devonian, when increasingly tall and complex vegetative communities presented competitive conditions that made large spore size advantageous. Second, heterospory is analogous in many ways to anisogamy. Indeed, heterospory is a kind of re‐invention of anisogamy within the context of a sporophyte‐dominant land plant life cycle. The evolution of anisogamy has been the subject of important theoretical and empirical investigation. Recent work in this area suggests that mate‐encounter dynamics set up selective forces that can drive the evolution of anisogamy. We suggest that similar dispersal and mating dynamics could have underlain spore size differentiation. The two approaches offer predictions that are consistent with currently available data but could be tested far more thoroughly. We hope to re‐establish attention on this neglected aspect of plant evolutionary biology and suggest some paths for empirical investigation.     

Relative growth rate correlates negatively with pathogen resistance in radish: the role of plant chemistry

Plant growth rate has frequently been associated with herbivore defence: a large investment in quantitative defence compounds occurs at the expense of growth. We tested whether such a relationship also holds for growth rate and pathogen resistance. For 15 radish (Raphanus sativus L.) cultivars, we determined the potential growth rate and the resistance to fungal wilt disease caused by Fusarium oxysporum. We subsequently aimed to explain a putative negative relationship between growth rate and resistance based on plant chemical composition. Both growth rate and resistance level varied greatly among cultivars. Moreover, there was a strong negative correlation between growth rate and resistance, i.e. there are costs associated with a high resistance level. Roots of slow-growing, resistant cultivars have a higher biomass density. Using pyrolysis mass spectrometry. we part1y explained variation in both growth rate and resistance in terms of the same change in chemical composition. Leaves of slow-growing, resistant cultivars contained more cell wall material. Surprisingly, roots of slow-growing, highly resistant cultivars contained significantly less cell wall material, and more cytoplasmic elements (proteins). We speculate that this higher protein concentration is related to high construction and turn-over costs and high metabolic activity. The latter in turn is thought to be responsible for a rapid and adequate resistance reaction, in which phenols may be involved.     

Measurement of 7-dehydrocholesterol and cholesterol in hair can be used in the diagnosis of Smith-Lemli-Opitz syndrome

7-dehydrocholesterol (7-DHC) and cholesterol (CHOL) are biomarkers of Smith-Lemli-Opitz Syndrome (SLOS), a congenital autosomal recessive disorder characterized by elevated 7-DHC level in patients. Hair samples have been shown to have great diagnostic and research value, which has long been neglected in the SLOS field. In this study, we sought to investigate the feasibility of using hair for SLOS diagnosis. In the presence of antioxidants (2,6-ditert-butyl-4-methylphenol and triphenylphosphine), hair samples were completely pulverized and extracted by micro-pulverized extraction in alkaline solution or in n-hexane. After microwave-assisted derivatization with N,O-Bis(trimethylsilyl)trifluoroacetamide, the analytes were measured by GC-MS. We found that the limits of determination for 7-DHC and CHOL were 10 ng/mg and 8 ng/mg, respectively. In addition, good linearity was obtained in the range of 50–4000 ng/mg and 30–6000 ng/mg for 7-DHC and CHOL, respectively, which fully meets the requirement for SLOS diagnosis and related research. Finally, by applying the proposed method to real hair samples collected from 14 healthy infants and two suspected SLOS patients, we confirmed the feasibility of hair analysis as a diagnostic tool for SLOS. In conclusion, we present an optimized and validated analytical method for the simultaneous determination of two SLOS biomarkers using human hair.     

Features and influencing factors of carbon emissions indicators in the perspective of residential consumption: Evidence from Beijing,China

This research establishes a residential indirect carbon emissions model through input–output structure decomposition analysis (IO-SDA) and LMDI, analyses the influencing factors affecting urban and rural residential carbon emissions indicators in Beijing through input–output tables from 2000 to 2010, and calculates the direct carbon emissions from residential consumption. As the results suggest, the total carbon emissions from residential consumption in Beijing showed volatility. Growing rural and urban differences in direct emissions, and for indirect emissions, mean that urban greatly exceeds rural in this regard. Rising per capita GDP and population, as well as intermediate demand and sectoral emissions intensity change induce growth in indirect emissions in both urban and rural settings: of which, per capita GDP contributes the most. Declining energy intensity contributes the most to emission reductions, followed by residential consumption rates, the rural to urban consumption ratio and consumption structure effects are much smaller.     

Joint multiple quantitative trait loci mapping for allometries of body compositions and metabolic traits to body weights in broiler

In order to map quantitative trait loci (QTLs) for allometries of body compositions and metabolic traits in chicken, we phenotypically characterize the allometric growths of multiple body components and metabolic traits relative to BWs using joint allometric scaling models and then establish random regression models (RRMs) to fit genetic effects of markers and minor polygenes derived from the pedigree on the allometric scalings. Prior to statistically inferring the QTLs for the allometric scalings by solving the RRMs, the LASSO technique is adopted to rapidly shrink most of marker genetic effects to zero. Computer simulation analysis confirms the reliability and adaptability of the so-called LASSO-RRM mapping method. In the F₂ population constructed by multiple families, we formulate two joint allometric scaling models of body compositions and metabolic traits, in which six of nine body compositions are tested as significant, while six of eight metabolic traits are as significant. For body compositions, a total of 14 QTLs, of which 9 dominant, were detected to be associated with the allometric scalings of drumstick, fat, heart, shank, liver and spleen to BWs; while for metabolic traits, a total of 19 QTLs also including 9 dominant be responsible for the allometries of T4, IGFI, IGFII, GLC, INS, IGR to BWs. The detectable QTLs or highly linked markers can be used to regulate relative growths of the body components and metabolic traits to BWs in marker-assisted breeding of chickens.     

Structural Decomposition Analysis of Raw Material Consumption

The aim of this article is to quantify the drivers for the changes in raw material consumption (domestic material consumption expressed in the form of all materials extracted and used in the production phase) in terms of technology, which refers to the concept of sustainable production; the product structure of final demand, which refers to the concept of sustainable consumption; and the volume of final demand, which is related to economic growth. We also aim to determine to what extent the technological development and a shift in product structure of the final demand compensate for the growth in final consumption volume. Therefore, we apply structural decomposition analysis (SDA) to the change in raw material consumption (RMC) of the Czech Republic between 2000 and 2007. To present the study in a broader context, we also show other material flow indicators for the Czech Republic for 2000 and 2007. Our findings of SDA show that final demand structure has a very limited effect on the change in material flows. The rapid change in final demand volume was not compensated for crude oil, metal ores, construction materials, food crops, and timber. For the material category of non‐iron metal ores, even the change in technology contributes to an increase in material flows. The largest relative increases are reported for non‐iron metal ores (38%) and construction materials (30%). The main changes in material flows related to the Czech Republic are driven by exports and enabled by imports, the main source of these increased material flows. This emphasizes the increasing role of international trade.