Kin interactions and changing social structure during a population outbreak of feral house mice

Populations of feral house mice (Mus domesticus L.) in Australia undergo multiannual fluctuations in density, and these outbreaks may be partly driven by some change in behavioural self-regulation. In other vertebrate populations with multiannual fluctuations, changes in kin structure have been proposed as a causal mechanism for changes in spacing behaviour, which consequently result in density fluctuations. We tested the predictions of two alternative conceptual models based on kin selection in a population of house mice during such an outbreak. Both published models (Charnov & Finerty 1980; Lambin & Krebs 1991) propose that the level of relatedness between interacting individuals affects their behavioural response and that this changes with population density, though the nature of this relationship differs between the two models. Neither of the models was consistent with all observed changes in relatedness between interacting female mice; however, our results suggested that changes in kin structure still have potential for explaining why mouse outbreaks begin. Therefore, we have developed a variant of one of these conceptual models suggesting that the maintenance of female kin groups through the preceding winter significantly improves recruitment during the subsequent breeding season, and is therefore necessary for mouse outbreaks. We provide six testable predictions to falsify this hypothesis.     

Identification of microRNA precursors with support vector machine and string kernel

MicroRNAs （miRNAs） are one family of short （21-23 nt） regulatory non-coding RNAs processed from long （70-110 nt） miRNA precursors （pre-miRNAs）. Identifying true and false precursors plays an important role in computational identification of miRNAs. Some numerical features have been extracted from precursor sequences and their secondary structures to suit some classification methods; however, they may lose some usefully discriminative information hidden in sequences and structures. In this study, pre-miRNA sequences and their secondary structures are directly used to construct an exponential kernel based on weighted Levenshtein distance between two sequences. This string kernel is then combined with support vector machine （SVM） for detecting true and false pre-miRNAs. Based on 331 training samples of true and false human pre-miRNAs, 2 key parameters in SVM are selected by 5-fold cross validation and grid search, and 5 realizations with different 5-fold partitions are executed. Among 16 independent test sets from 3 human, 8 animal, 2 plant, 1 virus, and 2 artificially false human pre-miRNAs, our method statistically outperforms the previous SVM-based technique on 11 sets, including 3 human, 7 animal, and 1 false human pre-miRNAs. In particular, premiRNAs with multiple loops that were usually excluded in the previous work are correctly identified in this study with an accuracy of 92.66%.     

Optimizing selection for function-valued traits

We consider a function-valued trait z(t) whose pre-selection distribution is Gaussian, and a fitness function W that models optimizing selection, subject to certain natural assumptions. We show that the post-selection distribution of z(t) is also Gaussian, compute the selection differential, and derive an equation that expresses the selection gradient in terms of the parameters of W and of the pre-selection distribution. We make no assumptions on the nature of the “time” parameter t.      

Predicting the impact of increasing carbon dioxide concentration and temperature on seed germination and seedling establishment of African grasses in Brazilian Cerrado

Global climate changes and biological invasions are environmental disturbances that may interact synergistically, causing loss of biodiversity. As the early stages of development are the most sensitive and easily affected by these constraints, this study investigated the effects of increased carbon dioxide (CO₂) and temperature, as forecasted for 2100, on seed germination and early development of three species of invasive African grasses that have gradually replaced landscapes of the Brazilian Cerrado biome. It was observed that these parameters affected percentage and rate of germination in Urochloa brizantha, rate of germination and mean germination time in Urochloa decumbens and accelerated autotrophy acquisition in U. brizantha, U. decumbens and Megathyrsus maximus. Regarding root elongation, all species showed changes in total length, absolute and relative growth rate, but at different stages of development or time intervals, with increased temperature being more significant than increased CO₂, probably due to seed reserves still being the main carbon sources at this stage. Taken together, the results indicate that the effects of CO₂ and increased temperature are species specific and highlight the greatest potential of U. brizantha to germinate, and of U. decumbens for seedling establishment under these environmental changes.     

Molecular basis for dimer formation of TRbeta variant D355R

Protein quality and stability are critical during protein purification for X-ray crystallography. A target protein that is easy to manipulate and crystallize becomes a valuable product useful for high-throughput crystallography for drug design and discovery. In this work, a single surface mutation, D355R, was shown to be crucial for converting the modestly stable monomeric ligand binding domain of the human thyroid hormone receptor (TR LBD) into a stable dimer. The structure of D335R TR LBD mutant was solved using X-ray crystallography and refined to 2.2 A resolution with R(free)/R values of 24.5/21.7. The crystal asymmetric unit reveals the TR dimer with two molecules of the hormone-bound LBD related by twofold symmetry. The ionic interface between the two LBDs comprises residues within loop H10-H11 and loop H6-H7 as well as the C-terminal halves of helices 8 of both protomers. Direct intermolecular contacts formed between the introduced residue Arg 355 of one TR molecule and Glu 324 of the second molecule become a part of the extended dimerization interface of 1330 A(2) characteristic for a strong complex assembly that is additionally strengthened by buffer solutes.     

帽儿山天然次生林10种主要阔叶树冠径与胸径关系研究

根据帽儿山天然次生林30块标准地每木检尺的实测数据,建立帽儿山天然次生林10种主要阔叶树冠径与胸径以及胸径和冠径与胸径比值（cd/D）之间的关系,并且简要论述冠径与胸径比值在树木空间、断面积和林分密度和间伐强度的应用。研究表明：胸径在10~30 cm之间,相关系数（R²）在0.8以上时,冠径与胸径关系近似一条直线。通过对不同树种的不同大小树木(cd/D)比值的分析,得出随着胸径的增加的比值是逐渐减小的,并且减小的趋势随着胸径的增加趋于平稳,最后趋于一个常数。     

奇异数据筛选法在玉米籽粒蛋白质近红外光谱检测中的应用

相对于传统生化测定方法,基于近红外光谱(Near infrared spectroscopy,NIRS)玉米籽粒蛋白质含量检测是一种快速、非破坏、且适用于多组分同时检测的新方法。但在建模过程中,由于奇异数据(异常值)的存在会影响近红外光谱模型的预测精度和稳定性,我们采用奇异数据筛选法剔除了玉米籽粒近红外光谱中的奇异数据并建立了玉米籽粒蛋白质含量的偏最小二乘支持向量机(Least squares support vector machine,LS-SVM)模型。本文分别采用杠杆值法(Leverage)、半数重采样法(Resampling by Half-Mean,RHM)和蒙特卡洛采样法(Monte-Carlo Sampling,MCS)剔除了玉米籽粒蛋白质光谱数据中的奇异数据并对模型结果进行比较。在剔除奇异数据的基础上,采用偏最小二乘回归法(Partial least squares regression,PLSR)提取主成分,并基于小生境蚁群算法(Niche ant colony algorithm,NACA)优化偏最小二乘支持向量机(LS-SVM)模型参数(γ和σ2),建立基于LS-SVM的玉米籽粒蛋白质定量分析模型。结果表明,采用3种奇异数据筛选法剔除奇异数据后所建LS-SVM模型的预测结果都优于采用原光谱数据所建模型,相比较而言,蒙特卡洛采样法为基于近红外光谱检测玉米籽粒蛋白质的最佳奇异数据筛选法。