西北干旱区泡泡刺灌丛的降雨再分配特征及影响因素分析

定量分析植被冠层对降雨再分配过程的影响,是认识陆地生态系统水文循环的重要环节。然而,由于干旱区天然植被分布稀疏、形态结构特殊,其降雨再分配过程的测算较为困难,相关研究较少,特别是关于荒漠低矮灌丛的降雨再分配研究鲜有报道。本文以河西走廊中段临泽绿洲—荒漠过渡带的天然建群种泡泡刺灌丛(Nitraria sphaerocarpa)为研究对象,基于3年逐个单次降雨事件的观测数据分析了生长季泡泡刺灌丛的降雨再分配特征及主要影响因素,量化了泡泡刺灌丛覆盖下实际进入土壤的有效降雨量及其空间分布特征。结果表明:(1)生长季泡泡刺灌丛的平均穿透率、树干茎流率和冠层截留损失率分别为87.89%、1.61%和10.50%;(2)降雨量是影响泡泡刺灌丛降雨再分配特征的关键气象因素,其与穿透雨量、树干茎流量、冠层截留损失量之间具有显著的统计关系(P<0.001);(3)与干旱区其他稀疏植被相比,泡泡刺灌丛的穿透率和集流率较高,冠层截留损失率较低,与其特殊的植被形态特征有关,相关分析的结果表明,泡泡刺灌丛的穿透雨量与植被面积指数和株高呈显著的负相关关系(P<0.001),树干茎流量与树干倾角呈显著的正...     

A Deep Learning Architecture for P300 Detection with Brain-Computer Interface Application

In this paper, a brain-computer interface (BCI) system for character recognition is proposed based on the P300 signal. A P300 speller is used to spell the word or character without any muscle movement. P300 detection is the first step to detect the character from the electroencephalogram (EEG) signal. The character is recognized from the detected P300 signal. In this paper, sparse autoencoder (SAE) and stacked sparse autoencoder (SSAE) based feature extraction methods are proposed for P300 detection. This work also proposes a fusion of deep-features with the temporal features for P300 detection. A SSAE technique extracts high-level information about input data. The combination of SSAE features with the temporal features provides abstract and temporal information about the signal. An ensemble of weighted artificial neural network (EWANN) is proposed for P300 detection to minimize the variation among different classifiers. To provide more importance to the good classifier for final classification, a higher weightage is assigned to the better performing classifier. These weights are calculated from the cross-validation test. The model is tested on two different publicly available datasets, and the proposed method provides better or comparable character recognition performance than the state-of-the-art methods.     

Structural heterogeneity in microcrystalline ubiquitin studied by solid-state NMR

By applying [1-¹³C]- and [2-¹³C]-glucose labeling schemes to the folded globular protein ubiquitin, a strong reduction of spectral crowding and increase in resolution in solid-state NMR (ssNMR) spectra could be achieved. This allowed spectral resonance assignment in a straightforward manner and the collection of a wealth of long-range distance information. A high precision solid-state NMR structure of microcrystalline ubiquitin was calculated with a backbone rmsd of 1.57 to the X-ray structure and 1.32 Å to the solution NMR structure. Interestingly, we can resolve structural heterogeneity as the presence of three slightly different conformations. Structural heterogeneity is most significant for the loop region β1-β2 but also for β-strands β1, β2, β3, and β5 as well as for the loop connecting α1 and β3. This structural polymorphism observed in the solid-state NMR spectra coincides with regions that showed dynamics in solution NMR experiments on different timescales.     

Imputing missing data in plant traits: A guide to improve gap-filling

Aim

Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, ‘gap-filling’ approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage.

Innovation

We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi- and multivariate) and (3) taxonomic and functional clustering (valuewise, uni- and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set–external trait data had little effect on induced taxonomic clustering and stabilized trait–trait correlations.

Main Conclusions

Our study extends the criteria for the evaluation of gap-filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation.     

西藏珠峰－卓奥友峰普士拉地区高山稀疏植被的群落特征及小地形的影响

在西藏定日县珠穆朗玛峰-卓奥友峰附近的普士拉地区的-山坡上(海拔5176—5390m),设置13个样方。进行群落调查、小地形测定和土壤剖面调查,分析该地区高寒植被的群落特征及其与小地形的关系。13个样方(面积25—200m^2不等)中,共记载物种80个(含变种和亚种),分别隶属于47个属。出现频度较高的种有高山嵩草(Kobresia pygmaea)、矮兔耳草(Lagottes humilis)、楔叶委陵菜(Potentilla funeata)、华马先蒿(Pedicularis oederi var.sinensis)、高山委陵菜(Potentilla polyjchista)、密生雪灵芝(Arenaria densissima)等;含物种数较多的属有：虎耳草属(Saxifraga)、风毛菊属(Saussurea)、嵩草属(Kobresia)、委陵菜属(Potentilla)、龙胆属(Gentiana)、葶苈属(Draba)等。随着海拔降低,物种和属的丰富度呈现出上升的趋势在该地区,植被盖度能较好地指示生境条件。等级聚类的结果也支持了这一观点：随着植被盖度的增加,转换物种丰富度(TSR)和转换属丰富度(TGR)增加,而物种多样性指数(Shannon-Wiener指数)减小。利用物种矩阵以及海拔、坡度、坡向、土壤深度、植被盖度等环境变量进行CCA排序,结果分出4类生境类型,它们较好地反映了群落特征与地形的关系。     

Inferential backbone assignment for sparse data

This paper develops an approach to protein backbone NMR assignment that effectively assigns large proteins while using limited sets of triple-resonance experiments. Our approach handles proteins with large fractions of missing data and many ambiguous pairs of pseudoresidues, and provides a statistical assessment of confidence in global and position-specific assignments. The approach is tested on an extensive set of experimental and synthetic data of up to 723 residues, with match tolerances of up to 0.5 ppm for and resonance types. The tests show that the approach is particularly helpful when data contain experimental noise and require large match tolerances. The keys to the approach are an empirical Bayesian probability model that rigorously accounts for uncertainty in the data at all stages in the analysis, and a hybrid stochastic tree-based search algorithm that effectively explores the large space of possible assignments.     

Decoding Temporal Information Through Slow Lateral Excitation in the Olfactory System of Insects

Sensory information is represented in a spatio-temporal code in the antennal lobe, the first processing stage of the olfactory system of insects. We propose a novel mechanism for decoding this information in the next processing stage, the mushroom body. The Kenyon cells in the mushroom body of insects exhibit lateral excitatory connections at their axons. We demonstrate that slow lateral excitation between Kenyon cells allows one to decode sequences of activity in the antennal lobe. We are thus able to clarify the role of the existing connections as well as to demonstrate a novel mechanism for decoding temporal information in neuronal systems. This mechanism complements the variety of existing temporal decoding schemes. It seems that neuronal systems not only have a rich variety of code types but also quite a diversity of algorithms for transforming different codes into each other.     

MOTOR: Model assisted software for NMR structure determination

Eukaryotic proteins with important biological function can be partially unstructured, conformational flexible, or heterogenic. Crystallization trials often fail for such proteins. In NMR spectroscopy, parts of the polypeptide chain undergoing dynamics in unfavorable time regimes cannot be observed. De novo NMR structure determination is seriously hampered when missing signals lead to an incomplete chemical shift assignment resulting in an information content of the NOE data insufficient to determine the structure ab initio. We developed a new protein structure determination strategy for such cases based on a novel NOE assignment strategy utilizing a number of model structures but no explicit reference structure as it is used for bootstrapping like algorithms. The software distinguishes in detail between consistent and mutually exclusive pairs of possible NOE assignments on the basis of different precision levels of measured chemical shifts searching for a set of maximum number of consistent NOE assignments in agreement with 3D space. Validation of the method using the structure of the low molecular‐weight‐protein tyrosine phosphatase A (MptpA) showed robust results utilizing protein structures with 30–45% sequence identity and 70% of the chemical shift assignments. About 60% of the resonance assignments are sufficient to identify those structural models with highest conformational similarity to the real structure. The software was benchmarked by de novo solution structures of fibroblast growth factor 21 (FGF21) and the extracellular fibroblast growth factor receptor domain FGFR4 D2, which both failed in crystallization trials and in classical NMR structure determination. Proteins 2013; 81:2007–2022. © 2013 Wiley Periodicals, Inc.     

人类活动对榆树疏林土壤环境和植物多样性的影响

人类活动已成为地球上生物多样性的主要影响因素,而生物多样性的变化是目前主要的全球变化问题之一。榆树疏林是分布于温带草原地带的沙地生境顶级群落,其生态环境脆弱,极易遭到人类活动的破坏。研究结果表明,各种人类活动（包括人工林的建造）对原生榆树疏林土壤环境和植物多样性有很大的影响,其中人工樟子松林和重牧干扰影响最大,其次是人工杨树林。在沙地环境治理中应充分地考虑自然规律和系统的短期与长期生态过程的统一。     

Systematic evaluation of CS‐Rosetta for membrane protein structure prediction with sparse NOE restraints

We critically test and validate the CS‐Rosetta methodology for de novo structure prediction of ‐helical membrane proteins (MPs) from NMR data, such as chemical shifts and NOE distance restraints. By systematically reducing the number and types of NOE restraints, we focus on determining the regime in which MP structures can be reliably predicted and pinpoint the boundaries of the approach. Five MPs of known structure were used as test systems, phototaxis sensory rhodopsin II (pSRII), a subdomain of pSRII, disulfide binding protein B (DsbB), microsomal prostaglandin E2 synthase‐1 (mPGES‐1), and translocator protein (TSPO). For pSRII and DsbB, where NMR and X‐ray structures are available, resolution‐adapted structural recombination (RASREC) CS‐Rosetta yields structures that are as close to the X‐ray structure as the published NMR structures if all available NMR data are used to guide structure prediction. For mPGES‐1 and Bacillus cereus TSPO, where only X‐ray crystal structures are available, highly accurate structures are obtained using simulated NMR data. One main advantage of RASREC CS‐Rosetta is its robustness with respect to even a drastic reduction of the number of NOEs. Close‐to‐native structures were obtained with one randomly picked long‐range NOEs for every 14, 31, 38, and 8 residues for full‐length pSRII, the pSRII subdomain, TSPO, and DsbB, respectively, in addition to using chemical shifts. For mPGES‐1, atomically accurate structures could be predicted even from chemical shifts alone. Our results show that atomic level accuracy for helical membrane proteins is achievable with CS‐Rosetta using very sparse NOE restraint sets to guide structure prediction. Proteins 2017; 85:812–826. © 2016 Wiley Periodicals, Inc.