Time and biosequences

In this paper we discuss and demonstrate the importance of several factors relative to the relationship between time and evolution of biosequences. In both quantitative and qualitative measurements of the genetic distances, the compositional constraints of the nucleotide sequences play a very important role. We demonstrate that when homologous sequences significantly differ in base composition we get erratic branching order and/or wrong evaluation of the evolutionary rates. We must consider that every gene may have a different evolutionary dynamic along its sequence, generally linked to its functional constraints; this too can seriously affect its clocklike behavior. We report some cases showing how these factors can affect the quantitative measurements of the genetic distances of biosequences. Presented at the NATO Advanced Research Workshop onGenome Organization and Evolution, Spetsai, Greece, 16–22 September 1992     

Early response of herbaceous vegetation to Rhododendron ponticum subsp. baeticum invasion in European Atlantic forests

Questions

Rhododendron ponticum subsp. baeticum is an invasive shrub of growing concern in continental Europe, but little is known about its impact on native plant communities. Here we ask: do environmental conditions differ between forest stands invaded by it and uninvaded stands? Do these differences correlate with R. ponticum's cover? Are these differences associated with differences in taxonomic and functional diversity of vascular plant species of the herb layer? Can these vegetation changes be explained by the sorting of certain life-history traits by R. ponticum-induced environmental changes?

Location

Several forests invaded by R. ponticum in the French Atlantic domain.

Methods

We recorded vegetation composition and a number of environmental variables in 400-m² plots that were established in 64 paired forest stands (32 invaded vs 32 uninvaded). We compiled traits from existing databases. We computed several metrics of taxonomic and functional diversity. We compared environmental variables and diversity metrics between invaded and uninvaded stands. We used correlation and regression analyses to relate them with R. ponticum's cover. We ran RLQ and fourth-corner analyses to explore the relationships between R. ponticum invasion, environmental variables, species traits, and vegetation composition.

Results

Independent of its abundance, R. ponticum invasion was associated with lower light arrival at the forest floor and increased litter thickness. Concomitantly, species richness and diversity and trait diversity were reduced. The major driver of species assemblages was soil pH, which strongly interacted with the invasion gradient. R. ponticum did not sort species according to traits associated with shade tolerance and thick-litter tolerance. However, tree and shrub saplings were more abundant in invaded than uninvaded stands, at the expense of graminoid and fern species.

Conclusions

As R. ponticum becomes the dominant shrub, it exerts new selection forces on life-history traits of extant species, mostly via reduced light availability, increased litter thickness, and physical competition, thereby reducing taxonomic and functional diversity of the herb layer, without impeding tree and shrub self-regeneration, at least in the short term.     

Defining null expectations for animal site fidelity

Site fidelity—the tendency to return to previously visited locations—is widespread across taxa. Returns may be driven by several mechanisms, including memory, habitat selection, or chance; however, pattern-based definitions group different generating mechanisms under the same label of ‘site fidelity’, often assuming memory as the main driver. We propose an operational definition of site fidelity as patterns of return that deviate from a null expectation derived from a memory-free movement model. First, using agent-based simulations, we show that without memory, intrinsic movement characteristics and extrinsic landscape characteristics are key determinants of return patterns and that even random movements may generate substantial probabilities of return. Second, we illustrate how to implement our framework empirically to establish ecologically meaningful, system-specific null expectations for site fidelity. Our approach provides a conceptual and operational framework to test hypotheses on site fidelity across systems and scales.     

Maximal ecological diversity exceeds evolutionary diversity in model ecosystems

Understanding community saturation is fundamental to ecological theory. While investigations of the diversity of evolutionary stable states (ESSs) are widespread, the diversity of communities that have yet to reach an evolutionary endpoint is poorly understood. We use Lotka–Volterra dynamics and trait-based competition to compare the diversity of randomly assembled communities to the diversity of the ESS. We show that, with a large enough founding diversity (whether assembled at once or through sequential invasions), the number of long-time surviving species exceeds that of the ESS. However, the excessive founding diversity required to assemble a saturated community increases rapidly with the dimension of phenotype space. Additionally, traits present in communities resulting from random assembly are more clustered in phenotype space compared to random, although still markedly less ordered than the ESS. By combining theories of random assembly and ESSs we bring a new viewpoint to both the saturation and random assembly literature.     

Integrating sample similarities into latent class analysis: a tree-structured shrinkage approach

This paper is concerned with using multivariate binary observations to estimate the probabilities of unobserved classes with scientific meanings. We focus on the setting where additional information about sample similarities is available and represented by a rooted weighted tree. Every leaf in the given tree contains multiple samples. Shorter distances over the tree between the leaves indicate a priori higher similarity in class probability vectors. We propose a novel data integrative extension to classical latent class models with tree-structured shrinkage. The proposed approach enables (1) borrowing of information across leaves, (2) estimating data-driven leaf groups with distinct vectors of class probabilities, and (3) individual-level probabilistic class assignment given the observed multivariate binary measurements. We derive and implement a scalable posterior inference algorithm in a variational Bayes framework. Extensive simulations show more accurate estimation of class probabilities than alternatives that suboptimally use the additional sample similarity information. A zoonotic infectious disease application is used to illustrate the proposed approach. The paper concludes by a brief discussion on model limitations and extensions.     

Recognition of errors in three-dimensional structures of proteins

A major problem in the determination of the three-dimensional structure of proteins concerns the quality of the structural models obtained from the interpretation of experimental data. New developments in X-ray crystallography and nuclear magnetic resonance spectroscopy have acceleratedd the process of structure determination and the biological community is confronted with a steadily increasing number of experimentally determined protein folds. However, in the recent past several experimentally determined protein structures have been proven to contain major errors, indicating that in some cases the interpretation of experimental data is difficult and may yield incorrect models. Such problems can be avoided when computational methods are employed which complement experimental structure determinations. A prerequisite of such computational tools is that they are independent of the parameters obtained from a particular experiment. In addition such techniques are able to support and accelerate experimental structure determinations. Here we present techniques based on knowledge based mean fields which can be used to judge the quality of protein folds. The methods can be used to identify misfolded structures as well as faulty parts of structural models. The techniques are even applicable in cases where only the C_α trace of a protein conformation is available. The capabilities of the technique are demonstrated using correct and incorrect protein folds. © 1993 Wiley-Liss, Inc.     

Individual risk prediction: Comparing random forests with Cox proportional-hazards model by a simulation study

With big data becoming widely available in healthcare, machine learning algorithms such as random forest (RF) that ignores time-to-event information and random survival forest (RSF) that handles right-censored data are used for individual risk prediction alternatively to the Cox proportional hazards (Cox-PH) model. We aimed to systematically compare RF and RSF with Cox-PH. RSF with three split criteria [log-rank (RSF-LR), log-rank score (RSF-LRS), maximally selected rank statistics (RSF-MSR)]; RF, Cox-PH, and Cox-PH with splines (Cox-S) were evaluated through a simulation study based on real data. One hundred eighty scenarios were investigated assuming different associations between the predictors and the outcome (linear/linear and interactions/nonlinear/nonlinear and interactions), training sample sizes (500/1000/5000), censoring rates (50%/75%/93%), hazard functions (increasing/decreasing/constant), and number of predictors (seven, 15 including noise variables). Methods' performance was evaluated with time-dependent area under curve and integrated Brier score. In all scenarios, RF had the worst performance. In scenarios with a low number of events (⩽70), Cox-PH was at least noninferior to RSF, whereas under linearity assumption it outperformed RSF. Under the presence of interactions, RSF performed better than Cox-PH as the number of events increased whereas Cox-S reached at least similar performance with RSF under nonlinear effects. RSF-LRS performed slightly worse than RSF-LR and RSF-MSR when including noise variables and interaction effects. When applied to real data, models incorporating survival time performed better. Although RSF algorithms are a promising alternative to conventional Cox-PH as data complexity increases, they require a higher number of events for training. In time-to-event analysis, algorithms that consider survival time should be used.     

ELF磁场对3T3细胞生长的影响及其机理的探讨

用细胞分析成像光盘记录系统测量了3T3细胞在某些ELF磁场和温度条件下生长周期的变化.实验结果表明,只有某些频率的ELF磁场才对细胞生长产生影响,磁场对细胞生长的影响还与培养细胞的生化环境有关.温度和ELF磁场都能影响细胞的生长.但二者的机理是不一样的,当撤除ELF磁场后,细胞在短时间内(2天以上)继续保持着ELF场对其生长的影响.而细胞生长周期能在短时间内(2天以内)随着温度的变化而变化.温度引起的细胞生长的变化可能与细胞内的各种生长因子、生物离子的活性有关.ELF磁场引起的细胞生长的变化可能与ELF磁场对细胞膜的影响有关,与细胞内细胞生长必不可少的生物离子(如Ca~(2+))的浓度有关.