Modeling evolution of the bacterial regulatory signals involving secondary structure

An algorithm for modeling the evolution of the regulatory signals involving the interaction with RNA secondary structure is proposed. The algorithm implies that the species phylogenetic tree is known and is based on the assumption that the considered signals have a conserved secondary structure. The input data are the extant primary structure of a signal for all leaves of the phylogenetic tree; the algorithm computes the signal primary and secondary structures at all the nodes. Concurrently, the algorithm constructs a multiple alignment of the extant (in leaves) sites of a regulatory signal taking into account its secondary structure. The results of successful testing of the algorithm for three main types of attenuation regulation in bacteria—classic attenuation (threonine and leucine biosyntheses in Gammaproteobacteria), T-box (in Actinobacteria), and RFN-mediated (in Eubacteria) regulations—are described.     

Gauging the effects of sampling failure in biogeographical analysis

Aim Various methods are employed to recover patterns of area relationships in extinct and extant clades. The fidelity of these patterns can be adversely affected by sampling error in the form of missing data. Here we use simulation studies to evaluate the sensitivity of an analytical biogeographical method, namely tree reconciliation analysis (TRA), to this form of sampling failure. Location Simulation study. Methods To approximate varying degrees of taxonomic sampling failure within phylogenies varying in size and in redundancy of biogeographical signal, we applied sequential pruning protocols to artificial taxon–area cladograms displaying congruent patterns of area relationships. Initial trials assumed equal probability of sampling failure among all areas. Additional trials assigned weighted probabilities to each of the areas in order to explore the effects of uneven geographical sampling. Pruned taxon–area cladograms were then analysed with TRA to determine if the optimal area cladograms recovered match the original biogeographical signal, or if they represent false, ambiguous or uninformative signals. Results The results indicate a period of consistently accurate recovery of the true biogeographical signal, followed by a nonlinear decrease in signal recovery as more taxa are pruned. At high levels of sampling failure, false biogeographical signals are more likely to be recovered than the true signal. However, randomization testing for statistical significance greatly decreases the chance of accepting false signals. The primary inflection of the signal recovery curve, and its steepness and slope depend upon taxon–area cladogram size and area redundancy, as well as on the evenness of sampling. Uneven sampling across geographical areas is found to have serious deleterious effects on TRA, with the accuracy of recovery of biogeographical signal varying by an order of magnitude or more across different sampling regimes. Main conclusions These simulations reiterate the importance of taxon sampling in biogeographical analysis, and attest to the importance of considering geographical, as well as overall, sampling failure when interpreting the robustness of biogeographical signals. In addition to randomization testing for significance, we suggest the use of randomized sequential taxon deletions and the construction of signal decay curves as a means to assess the robustness of biogeographical signals for empirical data sets.     

互信息理论结合决策树算法的土壤质量预测

在充分利用土壤类型、土地利用方式、岩性类型、地形、道路、工业类型等影响土壤质量主要因素,准确获取区域土壤质量的空间分布特征的基础上,采用互信息理论对13个辅助变量（岩性类型、土地利用方式、土壤类型、到城镇的距离、到道路的距离、到工业用地的距离、到河流的距离、相对高程、坡度、坡向、平向曲率、纵向曲率和切线曲率）进行筛选,然后通过决策树See5.0预测研究区土壤质量.结果表明： 影响研究区土壤质量的主要因素包括土壤类型、土地利用方式、岩性类型、到城镇的距离、到水域的距离、相对高程、到道路的距离和到工业用地的距离;以互信息理论选取的因子为预测变量的决策树模型精度明显优于以全部因子为预测变量的决策树模型,在前者的决策树模型中,无论是决策树还是决策规则,分类预测精度均达到80%以上.互信息理论结合决策树的方法在充分利用连续型和字符型数据的基础上,不仅精简了一般决策树算法的输入参数,而且能有效地预测和评价区域土壤质量等级.     

A phase transition for a random cluster model on phylogenetic trees

We investigate a simple model that generates random partitions of the leaf set of a tree. Of particular interest is the reconstruction question: what number k of independent samples (partitions) are required to correctly reconstruct the underlying tree (with high probability)? We demonstrate a phase transition for k as a function of the mutation rate, from logarithmic to polynomial dependence on the size of the tree. We also describe a simple polynomial-time tree reconstruction algorithm that applies in the logarithmic region. This model and the associated reconstruction questions are motivated by a Markov model for genomic evolution in molecular biology.     

不同去趋势方法对树轮气候信号识别的影响

树木生长受到气候因子、随年龄增长的内在生长趋势、环境干扰和其他扰动信号的影响。目前存在不同的去趋势方法对树木年轮进行去趋势以识别树木生长中的气候信号。以往的研究多基于单个方法识别树轮气候信号,而不同去趋势方法识别的树轮气候信号可能会有一定的差别。为了对比不同去趋势方法对树轮气候信号识别的影响,我们基于国际年轮数据库网站获取中国西部地区68个点的树轮宽度数据,采用最常用的"signal-free"方法（SsfCrn）、线性和负指数函数法（std）、67%样条函数法（spline）、firedman方法、以及基于经验模式分解去趋势方法（EEMD）5种去趋势方法分别建立树轮年表,并对比分析同一地点的不同年表对气候响应的异同。结果表明：不同去趋势方法得到的年表对温度、降水以及相对湿度等气候因素的响应具有明显差异。其中,SsfCrn去趋势方法建立的年表对温度（月平均温、月最低温、月平均最低温）响应中相关最高的样点在所有样点中占比最高;EEMD去趋势方法建立的年表对降水量、相对湿度和月最高温响应中相关最高的样点在所有样点中占比最高;firedman去趋势方法建立的年表对月平均最高温响应中相关最高的样点在所有样点中占比最高。研究结果表明SsfCrn,EEMD和firedman方法在识别树轮气候信号方法具有一定的优势。在不同研究区域中,不同去趋势方法建立的年表对不同气候条件响应有差异,因此选择不同的去趋势方法识别树木生长趋势,分析哪种方法可以更好的反应气候变化对树木生长的影响显得尤为重要。     

Fast tree search for a triangular lattice model of protein folding

Using a triangular lattice model to study the designability of protein folding, we overcame the parity problem of previous cubic lattice model and enumerated all the sequences and compact structures on a simple two-dimensional triangular lattice model of size 4 5 6 5 4. We used two types of amino acids, hydrophobic and polar, to make up the sequences, and achieved 223W212 different sequences excluding the reverse symmetry sequences. The total string number of distinct compact structures was 219,093, excluding reflection symmetry in the self-avoiding path of length 24 triangular lattice model. Based on this model, we applied a fast search algorithm by constructing a cluster tree. The algorithm decreased the computation by computing the objective energy of non-leaf nodes. The parallel experiments proved that the fast tree search algorithm yielded an exponential speed-up in the model of size 4 5 6 5 4. Designability analysis was performed to understand the search result.     

Stomatal regulation by microclimate and tree water relations: interpreting ecophysiological field data with a hydraulic plant model

Dynamics in microclimate and physiological plant traits were studied for Pubescent oak and Scots pine in a dry inner-alpine valley in Switzerland, at a 10 min resolution for three consecutive years (2001-2003). As expected, stomata tended to close with increasing drought in air and soil. However, stomatal aperture in oak was smaller than in pine under relatively wet conditions, but larger under dry conditions. To explore underlying mechanisms, a model was applied that (i) quantifies water relations within trees from physical principles (mechanistic part) and (ii) assumes that signals from light, stomatal aperture, crown water potential, and tree water deficit in storage pools control stomata (systemic part). The stomata of pine showed a more sensitive response to increasing drought because both factors, the slowly changing tree water deficit and the rapidly changing crown water potential, closed the stomata. By contrast, the stomata of oak became less drought-sensitive as the closing signal of crown water potential was opposed by the opening signal of tree water deficit. Moreover, parameter optimization suggests that oak withdrew more water from the storage pools and reduced leaf water potentials to lower levels, without risking serious damage by cavitation. The new model thus suggests how the hydraulic water flow and storage system determines the responses in stomatal aperture and transpiration to drought at time scales ranging from hours to multiple years, and why pine and oak might differ in such responses. These differences explain why oaks are more efficient competitors during drought periods, although this was not the case in the extremely dry year 2003, which provoked massive leaf loss and, from July onwards, physiological activity almost ceased.     

Estimating species trees using multiple-allele DNA sequence data

Several techniques, such as concatenation and consensus methods, are available for combining data from multiple loci to produce a single statement of phylogenetic relationships. However, when multiple alleles are sampled from individual species, it becomes more challenging to estimate relationships at the level of species, either because concatenation becomes inappropriate due to conflicts among individual gene trees, or because the species from which multiple alleles have been sampled may not form monophyletic groups in the estimated tree. We propose a Bayesian hierarchical model to reconstruct species trees from multiple-allele, multilocus sequence data, building on a recently proposed method for estimating species trees from single allele multilocus data. A two-step Markov Chain Monte Carlo (MCMC) algorithm is adopted to estimate the posterior distribution of the species tree. The model is applied to estimate the posterior distribution of species trees for two multiple-allele datasets--yeast (Saccharomyces) and birds (Manacus-manakins). The estimates of the species trees using our method are consistent with those inferred from other methods and genetic markers, but in contrast to other species tree methods, it provides credible regions for the species tree. The Bayesian approach described here provides a powerful framework for statistical testing and integration of population genetics and phylogenetics.     

黄土高原南北样带尺度人工刺槐林对气候响应的敏感性分析

根据黄土高原南北样带尺度的人工刺槐林(Robinia pseudoacacia)的年轮宽度资料,分析了该地区刺槐树木生长趋势,以及刺槐年表对气候响应随降雨梯度变化规律。研究结果表明延安以北的刺槐样点(绥德、神木)年轮指数近期趋于下降,树木有生长衰退现象;而延安以南刺槐样点(延安、富县、宜君、永寿)年轮指数近期趋于上升,树木无生长衰退现象。气候响应结果表明,刺槐年表对气候响应均以延安样点最为敏感,表现年表与温度的负相关关系,以及年表与降雨和干旱指数的正相关关系,而延安以北和以南刺槐样点对气候响应敏感性均较低。黄土高原中部延安地区地处森林草原过渡带,刺槐生长对外界环境变化最为敏感,年表中气候信号也较强;延安以南地区地处森林植被带,气候条件较为适宜刺槐林生长,因而年表中气候信号较弱;延安以北地区地处草原植被带,气候条件比较恶劣,刺槐生长对干旱气候已有一定适应性特征,因而年表中气候信号也较弱。     

树种及多样性组配对南亚热带人工林早期树木生长的影响

为科学筛选提升南亚热带人工林生产力的树种配置模式,选择南亚热带8个乡土树种,采用随机区组的试验设计,建立了树种多样性梯度(1、2、4、6个树种)人工新造林试验平台,研究树种多样性及不同功能特性树种混交对人工林早期树木生长的影响。结果表明: 在树木生长第5年,树木生长并没有随树种多样性增加而增加;速生树种马尾松和米老排纯林生长量是珍贵树种红椎和格木纯林的2.5～4.5倍;2个树种混交和4个树种混交情况下,针阔树种混交、速生树种与固氮树种混交显著提高树木生长量51.5%～132.8%,而当6个树种混交时,不同树种组配对生长量没有显著影响。不同树种配置模式下土壤氮、磷养分是影响树木早期生长的主要因素。针阔树种混交、速生和固氮树种混交能显著提高南亚热带人工林树木的早期生长。     

基于不同决策树的面向对象林区遥感影像分类比较

面向地理对象影像分析技术(GEOBIA)是影像分辨率越来越高的背景下的产物.如何提高高分辨率影像分类精度和分类效率是影像处理的重要议题之一.本研究对QuickBird影像多尺度分割后的对象进行分类,分析了C5.0、C4.5、CART决策树算法在林区面向对象分类中的效率,并与kNN算法的分类精度进行比较.利用eCognition软件对遥感影像进行多尺度分割,分析得到最佳尺度为90和40.在90尺度下分离出植被和非植被后,在40尺度下提取不同类别植被的光谱、纹理、形状等共21个特征,并利用C5.0、C4.5、CART决策树算法分别对其进行知识挖掘,自动建立分类规则.最后利用建立的分类规则分别对植被区域进行分类,并比较分析其精度.结果表明: 基于决策树的分类精度均高于传统的kNN法.其中,C5.0方法的精度最高,其总体分类精度为90.0%,Kappa系数0.87.决策树算法能有效提高林区树种分类精度,且C5.0决策树的Boosting算法对该分类效果具有最明显的提升.     

诱发电位的多通道时频相干提取算法

多通道时频域相干成分提取算法是针对低信噪比的宽频带信号提取问题提出的。它采用多通道同步观测,在各通道的观测数据中信号成分具有较高的相干性,而噪声的相干性较低,因此根据其相干性的高低差别即可将信号与噪声分离,提取有效信号。为实现信号与噪声的分离,首先应用小波包分解将信号在时频域展开,然后通过计算相干系数确定信号的时频分布,最终通过小波包重构将信号从噪声中分离出来。这一算法不需要信号的任何先验知识,收敛快,可以有效地提取宽频带信号,极大地提高信号的信噪比,对非重复性信号具有良好的捕捉能力．应用此算法成功地实现了视觉诱发电位的单次提取。     

A community model of mutually learning neuronal nets

A model of a community is suggested whose members are formal neuron nets interacting by signals exchange. As a signal each net can emit an image formed by it when recognising the preceding signal. The emitted signal comes to the inputs of other nets and is used as their initial state for the recognition process. The collective dynamics of such model is discussed for the case of non-learning nets. Possible algorithm of mutual learning of the nets in them course of signals exchange is considered.     

Monitoring individual tree‐based change with airborne lidar

Understanding the carbon flux of forests is critical for constraining the global carbon cycle and managing forests to mitigate climate change. Monitoring forest growth and mortality rates is critical to this effort, but has been limited in the past, with estimates relying primarily on field surveys. Advances in remote sensing enable the potential to monitor tree growth and mortality across landscapes. This work presents an approach to measure tree growth and loss using multidate lidar campaigns in a high‐biomass forest in California, USA. Individual tree crowns were delineated in 2008 and again in 2013 using a 3D crown segmentation algorithm, with derived heights and crown radii extracted and used to estimate individual tree aboveground biomass. Tree growth, loss, and aboveground biomass were analyzed with respect to tree height and crown radius. Both tree growth and loss rates decrease with increasing tree height, following the expectation that trees slow in growth rate as they age. Additionally, our aboveground biomass analysis suggests that, while the system is a net source of aboveground carbon, these carbon dynamics are governed by size class with the largest sources coming from the loss of a relatively small number of large individuals. This study demonstrates that monitoring individual tree‐based growth and loss can be conducted with multidate airborne lidar, but these methods remain relatively immature. Disparities between lidar acquisitions were particularly difficult to overcome and decreased the sample of trees analyzed for growth rate in this study to 21% of the full number of delineated crowns. However, this study illuminates the potential of airborne remote sensing for ecologically meaningful forest monitoring at an individual tree level. As methods continue to improve, airborne multidate lidar will enable a richer understanding of the drivers of tree growth, loss, and aboveground carbon flux.     

Finding signals for plant promoters

The strongest signal of plant promoter is searched with the model of single motif with two types. It turns out that the dominant type is the TATA-box. The other type may be called TATA-less signal, and may be used in gene finders for promoter recognition. While the TATA signals are very close for the monocot and the dicot, their TATA-less signals are significantly different. A general and flexible multi-motif model is also proposed for promoter analysis based on dynamic programming. By extending the Gibbs sampler to the dynamic programming and introducing temperature, an efficient algorithm is developed for searching signals in plant promoters.     

Tracing the decay of the historical signal in biological sequence data

Alignments of nucleotide or amino acid sequences may contain a variety of different signals, one of which is the historical signal that we often try to recover by phylogenetic analysis. Other signals, such as those arising due to compositional heterogeneities, among-lineage and among-site rate heterogeneities, invariant sites, and covariotides, may interfere adversely with the recovery of the historical signal. The effect of the interaction of these signals on phylogenetic inference is not well understood and may, in many cases, even be underappreciated. In this study, we investigate this matter and present results based on Monte Carlo simulations. We explored the success of four phylogenetic methods in recovering the true tree from data that had evolved under conditions where the equilibrium base frequencies and substitution rates were allowed to vary among lineages. Seven scenarios with increasingly complex conditions were investigated. All of the methods tested, with the exception of neighbor-joining using LogDet distances, were sensitive to compositional convergence in nonsister lineages. Maximum parsimony was also susceptible to attraction between long edges. In many cases, however, phylogenetic inference methods can still recover the true tree when misleading signals are present, in some instances even when the historical signal is no longer dominant. These results highlight the growing need for simple methods to detect violation of the phylogenetic assumptions.     

Coalescent-based species tree inference from gene tree topologies under incomplete lineage sorting by maximum likelihood

Incomplete lineage sorting can cause incongruence between the phylogenetic history of genes (the gene tree) and that of the species (the species tree), which can complicate the inference of phylogenies. In this article, I present a new coalescent-based algorithm for species tree inference with maximum likelihood. I first describe an improved method for computing the probability of a gene tree topology given a species tree, which is much faster than an existing algorithm by Degnan and Salter (2005). Based on this method, I develop a practical algorithm that takes a set of gene tree topologies and infers species trees with maximum likelihood. This algorithm searches for the best species tree by starting from initial species trees and performing heuristic search to obtain better trees with higher likelihood. This algorithm, called STELLS (which stands for Species Tree InfErence with Likelihood for Lineage Sorting), has been implemented in a program that is downloadable from the author's web page. The simulation results show that the STELLS algorithm is more accurate than an existing maximum likelihood method for many datasets, especially when there is noise in gene trees. I also show that the STELLS algorithm is efficient and can be applied to real biological datasets.     

Finding Signals for Plant Promoters

The strongest signal of plant promoter is searched with the model of single motif with two types. It turns out that the dominant type is the TATA-box. The other type may be called TATA-less signal, and may be used in gene finders for promoter recognition. While the TATA signals are very close for the monocot and the dicot, their TATA-less signals are significantly different. A general and flexible multi-motif model is also proposed for promoter analysis based on dynamic programming. By extending the Gibbs sampler to the dynamic programming and introducing temperature, an efficient algorithm is developed for searching signals in plant promoters.     

The primary divisions of life: a phylogenomic approach employing composition-heterogeneous methods

The three-domains tree, which depicts eukaryotes and archaebacteria as monophyletic sister groups, is the dominant model for early eukaryotic evolution. By contrast, the ‘eocyte hypothesis’, where eukaryotes are proposed to have originated from within the archaebacteria as sister to the Crenarchaeota (also called the eocytes), has been largely neglected in the literature. We have investigated support for these two competing hypotheses from molecular sequence data using methods that attempt to accommodate the across-site compositional heterogeneity and across-tree compositional and rate matrix heterogeneity that are manifest features of these data. When ribosomal RNA genes were analysed using standard methods that do not adequately model these kinds of heterogeneity, the three-domains tree was supported. However, this support was eroded or lost when composition-heterogeneous models were used, with concomitant increase in support for the eocyte tree for eukaryotic origins. Analysis of combined amino acid sequences from 41 protein-coding genes supported the eocyte tree, whether or not composition-heterogeneous models were used. The possible effects of substitutional saturation of our data were examined using simulation; these results suggested that saturation is delayed by among-site rate variation in the sequences, and that phylogenetic signal for ancient relationships is plausibly present in these data.     

Earlywood vessels of Castanea sativa record temperature before their formation

The aim of this study was to identify the climatic signal contained in the earlywood vessel size of the ring-porous chestnut (Castanea sativa) and the physiological processes involved in the underlying mechanisms. In order to assign the encoded signal to a specific physiological process, bud phenology and vessel formation were monitored along an elevation transect and chronologies of the size of the first row of earlywood vessels were retrospectively correlated with 40 yr of early spring temperatures. The first vessels appeared in late April to early May, after encoding both a negative temperature signal in February-March (during tree quiescence) and a positive temperature signal in early April (at the time of resumption of shoot growth). We hypothesize that February and March temperatures affect cambial sensitivity to auxin, preconditioning tree responses later in the season. Furthermore, April temperature is related to tree activation whereby new hormone production fosters vessel expansion.