Feasibility study of the Fricke chemical dosimeter as an independent dosimetric system for the small animal radiation research platform (SARRP)

For the small animal radiation research platform (SARRP) with X-ray beams in the medium energy range (tube operating voltage at 220 kVp), reference dosimetry is based on the AAPM TG-61 recommendations following the in-phantom method. The objective of this study was to evaluate the feasibility of the Fricke solution as a dosimeter to determine the absorbed dose to water. Feasibility studies at this X-ray energy range are not widely available. We evaluated the accuracy, dose linearity and dose rate dependence in a comparison with an NE 2571 Farmer ionization chamber (IC) and measurements in water. The G(Fe³⁺) factor was calculated from the curve fitting of the chemical yields for two radioactive sources (¹⁹²Ir and ⁶⁰Co) and one X-ray system with a tube operating at 150 and 250 kVp. The same methodology was followed for the dependence of the G(Fe³⁺) value on the energy and the dose agreement assessment for 180 and 200 kVp in the SARRP. The Fricke system exhibits a good linear response over the range of 5–70 Gy and an accuracy better than 2% for a 2 Gy/min dose rate. The dose rate dependence is smaller than 1% for dose rates greater than 1 Gy/min. The dependence of the G(Fe³⁺) value on the energy is smaller than 0.41%, with dose agreements better than 2%. The feasibility of the dosimeter for measurements at high doses and high dose rates makes it a suitable tool for dosimetric verifications in several preclinical irradiation configurations.     

Gap‐filling approaches for eddy covariance methane fluxes: A comparison of three machine learning algorithms and a traditional method with principal component analysis

Methane flux (FCH₄) measurements using the eddy covariance technique have increased over the past decade. FCH₄ measurements commonly include data gaps, as is the case with CO₂ and energy fluxes. However, gap‐filling FCH₄ data are more challenging than other fluxes due to its unique characteristics including multidriver dependency, variabilities across multiple timescales, nonstationarity, spatial heterogeneity of flux footprints, and lagged influence of biophysical drivers. Some researchers have applied a marginal distribution sampling (MDS) algorithm, a standard gap‐filling method for other fluxes, to FCH₄ datasets, and others have applied artificial neural networks (ANN) to resolve the challenging characteristics of FCH₄. However, there is still no consensus regarding FCH₄ gap‐filling methods due to limited comparative research. We are not aware of the applications of machine learning (ML) algorithms beyond ANN to FCH₄ datasets. Here, we compare the performance of MDS and three ML algorithms (ANN, random forest [RF], and support vector machine [SVM]) using multiple combinations of ancillary variables. In addition, we applied principal component analysis (PCA) as an input to the algorithms to address multidriver dependency of FCH₄ and reduce the internal complexity of the algorithmic structures. We applied this approach to five benchmark FCH₄ datasets from both natural and managed systems located in temperate and tropical wetlands and rice paddies. Results indicate that PCA improved the performance of MDS compared to traditional inputs. ML algorithms performed better when using all available biophysical variables compared to using PCA‐derived inputs. Overall, RF was found to outperform other techniques for all sites. We found gap‐filling uncertainty is much larger than measurement uncertainty in accumulated CH₄ budget. Therefore, the approach used for FCH₄ gap filling can have important implications for characterizing annual ecosystem‐scale methane budgets, the accuracy of which is important for evaluating natural and managed systems and their interactions with global change processes.     

Comparison of different methods for determination of the duodenal and ileal flows of endogenous nitrogen and amino acids in growing goats

The objective of the present study was to compare the isotope dilution, the difference and the amino acid profile (AAP) methods for the quantification of duodenal and ileal flows of endogenous nitrogen (N) and amino acids (AA) in growing goats. Nine growing goats were fed the same diet containing maize stover, ground corn and soybean meal. The duodenal flow of endogenous N determined by the isotope dilution method was significantly (p < 0.01) higher than that determined by the difference and AAP methods, while there was no difference between the difference and the AAP methods. The duodenal flows of individual endogenous AA determined by the isotope dilution method exceed those determined by the difference and the AAP method by 10 to 106%. The endogenous flow at the ileum determined by the isotope dilution method was not significantly different to those determined by the water-soluble method, but tended to be lower for N and all amino acids. It is concluded that the difference method and AAP method underestimate the duodenal flow of endogenous N and AA compared to the isotope dilution method.     

Interspecies differences in the empty body chemical composition of domestic animals

Domestication of animals has resulted in phenotypic changes by means of natural and human-directed selection. Body composition is important for farm animals because it reflects the status of energy reserves. Thus, there is the possibility that farm animals as providers of food have been more affected by human-directed selection for body composition than laboratory animals. In this study, an analysis was conducted to determine what similarities and differences in body composition occur between farm and laboratory animals using literature data obtained from seven comparative slaughter studies (n = 136 observations). Farm animals from four species (cattle, goats, pigs and sheep) were all castrated males, whereas laboratory animals from three species (dogs, mice and rats) comprised males and/or females. All animals were fed ad libitum. The allometric equation, Y = aX^b, was used to determine the influence of species on the accretion rates of chemical components (Y, kg) relative to the growth of the empty body, fat-free empty body or protein weights (X, kg). There were differences between farm and laboratory animals in terms of the allometric growth coefficients for chemical components relative to the empty BW and fat-free empty BW (P < 0.01); farm animals had more rapid accretion rates of fat (P < 0.01) but laboratory animals had more rapid accretion rates of protein, water and ash (P < 0.01). In contrast, there was no difference in terms of the allometric growth coefficients for protein and water within farm animals (P > 0.2). The allometric growth coefficients for ash weight relative to protein weight for six species except sheep were not different from a value of 1 (P > 0.1), whereas that of sheep was smaller than 1 (P < 0.01). When compared at the same fat content of the empty body, the rate of change in water content (%) per unit change in fat content (%) was not different (P > 0.05) across farm animal species and similar ash-to-protein ratios were obtained except for dogs. The fraction of empty body energy gain retained as fat increased in a curvilinear manner, and there was little variation among farm animals at the same fat content of the empty body. These findings may provide the opportunity to develop a general model to predict empty body composition across farm animal species. In contrast, there were considerable differences of chemical body composition between farm and laboratory animals.     

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.     

Interactive learning and action: realizing the promise of synthetic biology for global health

The emerging field of synthetic biology has the potential to improve global health. For example, synthetic biology could contribute to efforts at vaccine development in a context in which vaccines and immunization have been identified by the international community as being crucial to international development efforts and, in particular, the millennium development goals. However, past experience with innovations shows that realizing a technology’s potential can be difficult and complex. To achieve better societal embedding of synthetic biology and to make sure it reaches its potential, science and technology development should be made more inclusive and interactive. Responsible research and innovation is based on the premise that a broad range of stakeholders with different views, needs and ideas should have a voice in the technological development and deployment process. The interactive learning and action (ILA) approach has been developed as a methodology to bring societal stakeholders into a science and technology development process. This paper proposes an ILA in five phases for an international effort, with national case studies, to develop socially robust applications of synthetic biology for global health, based on the example of vaccine development. The design is based on results of a recently initiated ILA project on synthetic biology; results from other interactive initiatives described in the literature; and examples of possible applications of synthetic biology for global health that are currently being developed.     

Exhaustive comparison and classification of ligand‐binding surfaces in proteins

Many proteins function by interacting with other small molecules (ligands). Identification of ligand‐binding sites (LBS) in proteins can therefore help to infer their molecular functions. A comprehensive comparison among local structures of LBSs was previously performed, in order to understand their relationships and to classify their structural motifs. However, similar exhaustive comparison among local surfaces of LBSs (patches) has never been performed, due to computational complexity. To enhance our understanding of LBSs, it is worth performing such comparisons among patches and classifying them based on similarities of their surface configurations and electrostatic potentials. In this study, we first developed a rapid method to compare two patches. We then clustered patches corresponding to the same PDB chemical component identifier for a ligand, and selected a representative patch from each cluster. We subsequently exhaustively as compared the representative patches and clustered them using similarity score, PatSim. Finally, the resultant PatSim scores were compared with similarities of atomic structures of the LBSs and those of the ligand‐binding protein sequences and functions. Consequently, we classified the patches into ～2000 well‐characterized clusters. We found that about 63% of these clusters are used in identical protein folds, although about 25% of the clusters are conserved in distantly related proteins and even in proteins with cross‐fold similarity. Furthermore, we showed that patches with higher PatSim score have potential to be involved in similar biological processes.     

Complex trait divergence contributes to environmental niche differentiation in ecological speciation of Boechera stricta

Ecological factors may contribute to reproductive isolation if differential local adaptation causes immigrant or hybrid fitness reduction. Because local adaptation results from the interaction between natural selection and adaptive traits, it is crucial to investigate both to understand ecological speciation. Previously, we used niche modelling to identify local water availability as an environmental correlate of incipient ecological speciation between two subspecies in Boechera stricta, a close relative of Arabidopsis. Here, we performed several large‐scale glasshouse experiments to investigate the divergence of various physiological, phenological and morphological traits. Although we found no significant difference in physiological traits, the Western subspecies has significantly faster growth rate, larger leaf area, less succulent leaves, delayed reproductive time and longer flowering duration. These trait differences are concordant with previous results that habitats of the Western genotypes have more consistent water availability, while Eastern genotypes inhabit locations with more ephemeral water supplies. In addition, by comparing univariate and multivariate divergence of complex traits (Q_ST) to the genomewide distribution of SNP F_ST, we conclude that the aspects of phenology and morphology (but not physiology) are under divergent selection. In addition, we also identified several highly diverged traits without obvious water‐related functions.