Identifying species of moths (Lepidoptera) from Baihua Mountain,Beijing, China,using DNA barcodes

DNA barcoding has become a promising means for the identification of organisms of all life‐history stages. Currently, distance‐based and tree‐based methods are most widely used to define species boundaries and uncover cryptic species. However, there is no universal threshold of genetic distance values that can be used to distinguish taxonomic groups. Alternatively, DNA barcoding can deploy a “character‐based” method, whereby species are identified through the discrete nucleotide substitutions. Our research focuses on the delimitation of moth species using DNA‐barcoding methods. We analyzed 393 Lepidopteran specimens belonging to 80 morphologically recognized species with a standard cytochrome c oxidase subunit I (COI) sequencing approach, and deployed tree‐based, distance‐based, and diagnostic character‐based methods to identify the taxa. The tree‐based method divided the 393 specimens into 79 taxa (species), and the distance‐based method divided them into 84 taxa (species). Although the diagnostic character‐based method found only 39 so‐identifiable species in the 80 species, with a reduction in sample size the accuracy rate substantially improved. For example, in the Arctiidae subset, all 12 species had diagnostics characteristics. Compared with traditional morphological method, molecular taxonomy performed well. All three methods enable the rapid delimitation of species, although they have different characteristics and different strengths. The tree‐based and distance‐based methods can be used for accurate species identification and biodiversity studies in large data sets, while the character‐based method performs well in small data sets and can also be used as the foundation of species‐specific biochips.     

Revising traditional theory on the link between plant body size and fitness under competition: evidence from old‐field vegetation

The selection consequences of competition in plants have been traditionally interpreted based on a “size‐advantage” hypothesis – that is, under intense crowding/competition from neighbors, natural selection generally favors capacity for a relatively large plant body size. However, this conflicts with abundant data, showing that resident species body size distributions are usually strongly right‐skewed at virtually all scales within vegetation. Using surveys within sample plots and a neighbor‐removal experiment, we tested: (1) whether resident species that have a larger maximum potential body size (MAX) generally have more successful local individual recruitment, and thus greater local abundance/density (as predicted by the traditional size‐advantage hypothesis); and (2) whether there is a general between‐species trade‐off relationship between MAX and capacity to produce offspring when body size is severely suppressed by crowding/competition – that is, whether resident species with a larger MAX generally also need to reach a larger minimum reproductive threshold size (MIN) before they can reproduce at all. The results showed that MIN had a positive relationship with MAX across resident species, and local density – as well as local density of just reproductive individuals – was generally greater for species with smaller MIN (and hence smaller MAX). In addition, the cleared neighborhoods of larger target species (which had relatively large MIN) generally had – in the following growing season – a lower ratio of conspecific recruitment within these neighborhoods relative to recruitment of other (i.e., smaller) species (which had generally smaller MIN). These data are consistent with an alternative hypothesis based on a ‘reproductive‐economy‐advantage’ – that is, superior fitness under competition in plants generally requires not larger potential body size, but rather superior capacity to recruit offspring that are in turn capable of producing grand‐offspring – and hence transmitting genes to future generations – despite intense and persistent (cross‐generational) crowding/competition from near neighbors. Selection for the latter is expected to favor relatively small minimum reproductive threshold size and hence – as a tradeoff – relatively small (not large) potential body size.     

南岭国家级自然保护区常绿阔叶林主要树种的种间分离

种间分离的研究对揭示种间相互作用、群落组成与动态具有重要意义。本研究在南岭国家级自然保护区的常绿阔叶林内,设置了36个样方,以样方内株数≥10的27个物种为研究对象,在样方调查的基础上,运用ArcGIS软件、N×N最近邻体列联表、2×2列联表截表以及Pielou分离指数,研究了群落内27个物种的种间分离规律,采用Clark_Evans最近邻体法测定了27个物种的分布格局。结果显示:(1)27个物种中,有3个呈聚集分布,24个呈随机分布;(2)常绿阔叶林植物群落中正分离种对占59.83%,随机毗邻种对占40.17%,没有负分离,群落稳定;(3)正分离主要发生在群落优势种或建群种之间,优势种之间也有随机毗邻发生;(4)呈聚集分布的物种与其它物种主要表现为正分离,一般不表现为负分离;而呈随机分布的物种之间多数表现为随机毗邻,也有部分表现为正分离或负分离;(5)群落内27个物种互相交错分布,是全面不分离的。     

Evolution and functional divergence of monocarboxylate transporter genes in vertebrates

Monocarboxylate transporters (MCTs) form a gene family with an ancient past. The identification of MCTs (MCHs) from bacteria, protozoa, fungi, invertebrates, as well as vertebrates, but not from plants and virus, allowed illuminating the phylogenetic and evolutionary history of this gene family. The significant expansion of vertebrate MCT genes should have primarily occurred after the divergence of vertebrates and invertebrates, but before the divergence time between ray-finned fish and mammals. The divergence of insect MCTs should have at least occurred in the common ancestor of fruit fly, beetle, and honeybee. Fungi monocarboxylate transporter homologues (MCHs) might evolve independently from an ancient ancestor. The results of functional divergence analysis provided statistical evidences for shifted evolutionary rate and/or changes of amino acid property after gene duplication. The sliding window analysis of the d(N)/d(S) ratio values showed that strong functional constraints must impose on the N- and C-terminal domains of vertebrate MCTs. These corresponding regions may play crucial roles for functionality of MCT proteins.     

Effect of nitrogen, water and neighbor density on the growth of Hesperis matronalis and two native perennials

Disturbance is a well-recognized catalyst of exotic species invasion, depriving or releasing resources into communities and favoring the spread of some invaders. Hesperis matronalis (dame’s rocket) is widespread in North America and has potential to become a major problem in natural communities due to anthropogenic influences. We used a target-neighbor design in a greenhouse to grow H. matronalis and native seedlings (Campanula rotundifolia and Muhlenbergia montana) at various levels of neighbor density, nitrogen and water. H. matronalis clearly reduced C. rotundifolia and M. montana aboveground growth and maintained its competitive advantage across all treatments. We expected H. matronalis to maximize its growth and have the greatest negative effect on native species under high resource conditions because ruderal species are able to take advantage of excess resources. H. matronalis demonstrated a clearly negative effect on native species, but a particular ability to outcompete native species at high resource levels depended on resource and native species identity. We also expected that the native species would not exploit excess resources as well as H. matronalis, instead growing better under ambient or low resource conditions because of local adaptation. Rather, M. montana benefited from high water inputs, inconsistent with plant strategies characteristic of stress tolerators. Information on the effects of H. matronalis on native plants in a controlled setting may aid land managers to understand its potential effects in natural communities.     

吉林蛟河针阔混交林乔木幼苗组成及其密度格局影响因素

以吉林蛟河次生针阔混交林42 hm ²固定监测样地中209个幼苗监测站内的乔木幼苗为研究对象, 基于2016和2017年幼苗调查数据, 探究幼苗物种组成、数量动态等特征, 并运用广义线性混合效应模型分析了幼苗密度与生物邻体及其生境因素的相关性。结果表明: (1)该样地内所有幼苗监测站共调查到幼苗4 245株, 分属10科12属18种, 新生幼苗的数量在物种和调查年份间均有明显差异, 其中水曲柳(Fraxinus mandschurica)和紫椴(Tilia amurensis)幼苗表现出大量出生和死亡的动态特征。(2)群落水平上, 幼苗密度与局域同种成体胸高断面积之和、土壤全磷和有效钾等养分含量显著正相关, 适宜大树生长的生境同样适宜幼苗的生长。(3)水曲柳幼苗密度的影响因素与群落水平一致, 红松(Pinus koraiensis)存在明显的生境偏好, 在湿度较小、土壤全磷、有效氮和有效磷含量较低的生境中密度更高。相对于多年生幼苗, 同种成年邻体对当年生幼苗密度影响更加显著。该研究证实了扩散限制和生境过滤共同影响幼苗密度格局, 生物邻体和生境异质性的相对重要性随幼苗物种种类和年龄级变化。     

Bias in genetic variance estimates due to spatial autocorrelation

Summary A central problem in the analysis of genetic field trials is the dichotomy of genetic and environmental effects because one cannot be defined without the other. Results from 768,000 simulated family trials in complete randomized block designs demonstrated a serious upward bias in estimates of family variance components from multi-unit plot designs when the phenotypic observations were compatible with a first-order autoregressive (AR1) process. The inflation of family variances and, thus, additive genetic variance and narrow sense individual heritabilities progressed exponentially with an increase in the nearest neighbor correlation () in the AR1 process. Significant differences in inflation rates persisted among various plot configurations. At = 0.2 the inflation of family variances reached 48–73%. Inflation rates were independent of the level of heritability. Modified Papadakis nearest neighbor (NN) adjustment procedures were tested for their ability to remove the bias in family variances. A NN-adjustment based on Mead's coefficient of interplant interaction and one derived from Bartlett's simultaneous autoregressive scheme removed up to 97% of the bias introduced by the phenotypic correlations. NN-adjusted estimates had slightly (5–8%) higher relative errors than did unadjusted estimates.     

DO PHYLOGENETIC METHODS PRODUCE TREES WITH BIASED SHAPES?

We examine whether phylogenetic methods provide biased estimates of tree shape with respect to the random branching model. We investigate the performance of five commonly used phylogenetic methods using computer simulation: (1) maximum parsimony; (2) neighbor joining; (3) UPGMA with an outgroup taxon; (4) UPGMA without an outgroup taxon; and (5) maximum likelihood. All methods provide estimates of tree shape that are, on average, more asymmetrical than the true tree, especially when rates of evolution are high. We suggest a simple explanation for the bias and propose a modified test of tree shape that corrects for it.     

Using machine learning to improve ensemble docking for drug discovery

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activity, researchers have not yet come up with effective ways to use these scores to classify compounds into actives and inactives. This shortcoming has led to the decrease, rather than an increase in the performance of classifying compounds when more structures are added to the ensemble. Previously, we suggested machine learning, implemented in the form of a naïve Bayesian model could alleviate this problem. However, the naïve Bayesian model assumed that the probabilities of observing the docking scores to different structures to be independent. This approximation might prevent it from achieving even higher performance. In the work presented in this paper, we have relaxed this approximation when using several other machine learning methods—k nearest neighbor, logistic regression, support vector machine, and random forest—to improve ensemble docking. We found significant improvement.     

A Single-Cell Atlas of Tumor-Infiltrating Immune Cells in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with limited treatment options. To guide the design of more effective immunotherapy strategies, mass cytometry was employed to characterize the cellular composition of the PDAC-infiltrating immune cells. The expression of 33 protein markers was examined at the single-cell level in more than two million immune cells from four types of clinical samples, including PDAC tumors, normal pancreatic tissues, chronic pancreatitis tissues, and peripheral blood. Based on the analyses, we identified 23 distinct T-cell phenotypes, with some cell clusters exhibiting aberrant frequencies in the tumors. Programmed cell death protein 1 (PD-1) was extensively expressed in CD4⁺ and CD8⁺ T cells and coexpressed with both stimulatory and inhibitory immune markers. In addition, we observed elevated levels of functional markers, such as CD137L and CD69, in PDAC-infiltrating immune cells. Moreover, the combination of PD-1 and CD8 was used to stratify PDAC tumors from The Cancer Genome Atlas database into three immune subtypes, with S1 (PD-1⁺CD8⁺) exhibiting the best prognosis. Further analysis suggested distinct molecular mechanisms for immune exclusion in different subtypes. Taken together, the single-cell protein expression data depicted a detailed cell atlas of the PDAC-infiltrating immune cells and revealed clinically relevant information regarding useful cell phenotypes and targets for immunotherapy development.