Experimental warming shifts coupling of carbon and nitrogen cycles in an alpine meadow

增温对高寒草甸生态系统碳氮循环耦合关系的影响 陆地生态系统碳吸收受土壤氮素可用性的调节。然而,全球变化背景下的不同生态系统组分的碳氮比及其所反映的碳氮循环耦合关系尚不十分清楚。本文运用数据同化的方法,将一个高寒草甸增温试验的14组数据同化到草地生态系统模型中,从而评估了增温如何影响陆地生态系统的碳氮循环耦合关系。研究结果表明,增温提高了土壤氮素的有效性,降低了土壤活性碳库的碳氮比,导致植物对土壤氮的吸收增加。但是由于植物叶片吸收的碳比吸收的氮增加更多,使得叶片中碳氮比增加,而根部的碳输入增加则低于氮的增加,导致根部的碳氮比减少。同时,增温降低了凋落物碳氮比,可能是在土壤高氮有效性的条件下,凋落物氮的固定得到增强;而且增温加速了凋落物的分解。同时增温还增加了慢速土壤有机质的碳氮比,使得该土壤碳库的碳固存潜力增大。由于大多数模型在不同的环境中通常使用相对固定的碳氮比,本研究所发现的气候变暖条件下碳氮比的差异变化可为模型参数化提供一个有效的参考,有利于模型对未来气候变化背景下生态系统碳氮耦合关系响应的预测。     

Outlier detection methods are still effective even using virtual species created with the probabilistic approach

Liu et al. (Journal of Biogeography, 2018, 45 :164–176) presented an approach to detect outliers in species distribution data by developing virtual species created using the threshold approach. Meynard et al. (Journal of biogeography, 2019, 46 :2141–2144) raised concerns about this approach stating that ‘using a probabilistic approach … may significantly change results’. Here we provide a new series of simulations using the two approaches and demonstrate that the outlier detection approach based on pseudo species distribution models was still effective when using the probabilistic approach, although the detection rate was lower than when using the threshold approach.     

SAXSDom: Modeling multidomain protein structures using small-angle X-ray scattering data

Many proteins are composed of several domains that pack together into a complex tertiary structure. Multidomain proteins can be challenging for protein structure modeling, particularly those for which templates can be found for individual domains but not for the entire sequence. In such cases, homology modeling can generate high quality models of the domains but not for the orientations between domains. Small-angle X-ray scattering (SAXS) reports the structural properties of entire proteins and has the potential for guiding homology modeling of multidomain proteins. In this article, we describe a novel multidomain protein assembly modeling method, SAXSDom that integrates experimental knowledge from SAXS with probabilistic Input-Output Hidden Markov model to assemble the structures of individual domains together. Four SAXS-based scoring functions were developed and tested, and the method was evaluated on multidomain proteins from two public datasets. Incorporation of SAXS information improved the accuracy of domain assembly for 40 out of 46 critical assessment of protein structure prediction multidomain protein targets and 45 out of 73 multidomain protein targets from the ab initio domain assembly dataset. The results demonstrate that SAXS data can provide useful information to improve the accuracy of domain-domain assembly. The source code and tool packages are available at https://github.com/jianlin-cheng/SAXSDom .     

A probabilistic model for secondary structure prediction from protein chemical shifts

Protein chemical shifts encode detailed structural information that is difficult and computationally costly to describe at a fundamental level. Statistical and machine learning approaches have been used to infer correlations between chemical shifts and secondary structure from experimental chemical shifts. These methods range from simple statistics such as the chemical shift index to complex methods using neural networks. Notwithstanding their higher accuracy, more complex approaches tend to obscure the relationship between secondary structure and chemical shift and often involve many parameters that need to be trained. We present hidden Markov models (HMMs) with Gaussian emission probabilities to model the dependence between protein chemical shifts and secondary structure. The continuous emission probabilities are modeled as conditional probabilities for a given amino acid and secondary structure type. Using these distributions as outputs of first‐ and second‐order HMMs, we achieve a prediction accuracy of 82.3%, which is competitive with existing methods for predicting secondary structure from protein chemical shifts. Incorporation of sequence‐based secondary structure prediction into our HMM improves the prediction accuracy to 84.0%. Our findings suggest that an HMM with correlated Gaussian distributions conditioned on the secondary structure provides an adequate generative model of chemical shifts. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Streamlining the Life Cycle Assessment of Buildings by Structured Under‐Specification and Probabilistic Triage

Life cycle thinking plays an important role in sustainable development in the building sector. However, the complexity of data collection and scope definition limits life cycle assessment (LCA) applications. Even if the inventory data have already been collected, tabulated, and indexed, the method is still time‐consuming, which may be discouraging for designers. This study demonstrates how the LCA of buildings can be robustly streamlined using structured underspecification of impact data combined with an effective and efficient triage of the data collection. Tests were conducted with a series of building typologies that were analyzed with a cradle‐to‐gate approach. The probabilistic triage approach was tested to identify selected activities requiring detailed specification because they contribute most to total impact, thereby reducing data gathering effort. Impacts such as global warming, acidification, eutrophication, and smog creation were assessed, and results showed that 40% to 46% of the bill of materials components represent 75% of total impacts of single‐family houses and multifamily buildings. By specifying only a prioritized subset of the bill of materials to the highest level of specificity, results proved to be reasonably accurate and obtainable with less effort.     

Patellofemoral cartilage stresses are most sensitive to variations in vastus medialis muscle forces

The purpose of this study was to evaluate the effects of variations in quadriceps muscle forces on patellofemoral stress. We created subject-specific finite element models for 21 individuals with chronic patellofemoral pain and 16 pain-free control subjects. We extracted three-dimensional geometries from high resolution magnetic resonance images and registered the geometries to magnetic resonance images from an upright weight bearing squat with the knees flexed at 60°. We estimated quadriceps muscle forces corresponding to 60° knee flexion during a stair climb task from motion analysis and electromyography-driven musculoskeletal modelling. We applied the quadriceps muscle forces to our finite element models and evaluated patellofemoral cartilage stress. We quantified cartilage stress using an energy-based effective stress, a scalar quantity representing the local stress intensity in the tissue. We used probabilistic methods to evaluate the effects of variations in quadriceps muscle forces from five trials of the stair climb task for each subject. Patellofemoral effective stress was most sensitive to variations in forces in the two branches of the vastus medialis muscle. Femur cartilage effective stress was most sensitive to variations in vastus medialis forces in 29/37 (78%) subjects, and patella cartilage effective stress was most sensitive to variations in vastus medialis forces in 21/37 (57%) subjects. Femur cartilage effective stress was more sensitive to variations in vastus medialis longus forces in subjects classified as maltrackers compared to normal tracking subjects (p?=?0.006). This study provides new evidence of the importance of the vastus medialis muscle in the treatment of patellofemoral pain.     

Learning generative models for protein fold families

We introduce a new approach to learning statistical models from multiple sequence alignments (MSA) of proteins. Our method, called GREMLIN (Generative REgularized ModeLs of proteINs), learns an undirected probabilistic graphical model of the amino acid composition within the MSA. The resulting model encodes both the position-specific conservation statistics and the correlated mutation statistics between sequential and long-range pairs of residues. Existing techniques for learning graphical models from MSA either make strong, and often inappropriate assumptions about the conditional independencies within the MSA (e.g., Hidden Markov Models), or else use suboptimal algorithms to learn the parameters of the model. In contrast, GREMLIN makes no a priori assumptions about the conditional independencies within the MSA. We formulate and solve a convex optimization problem, thus guaranteeing that we find a globally optimal model at convergence. The resulting model is also generative, allowing for the design of new protein sequences that have the same statistical properties as those in the MSA. We perform a detailed analysis of covariation statistics on the extensively studied WW and PDZ domains and show that our method out-performs an existing algorithm for learning undirected probabilistic graphical models from MSA. We then apply our approach to 71 additional families from the PFAM database and demonstrate that the resulting models significantly out-perform Hidden Markov Models in terms of predictive accuracy.     

Correlating SHAPE signatures with three-dimensional RNA structures

Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) is a facile technique for quantitative analysis of RNA secondary structure. In general, low SHAPE signal values indicate Watson-Crick base-pairing, and high values indicate positions that are single-stranded within the RNA structure. However, the relationship of SHAPE signals to structural properties such as non-Watson-Crick base-pairing or stacking has thus far not been thoroughly investigated. Here, we present results of SHAPE experiments performed on several RNAs with published three-dimensional structures. This strategy allows us to analyze the results in terms of correlations between chemical reactivities and structural properties of the respective nucleotide, such as different types of base-pairing, stacking, and phosphate-backbone interactions. We find that the RNA SHAPE signal is strongly correlated with cis-Watson-Crick/Watson-Crick base-pairing and is to a remarkable degree not dependent on other structural properties with the exception of stacking. We subsequently generated probabilistic models that estimate the likelihood that a residue with a given SHAPE score participates in base-pairing. We show that several models that take SHAPE scores of adjacent residues into account perform better in predicting base-pairing compared with individual SHAPE scores. This underscores the context sensitivity of SHAPE and provides a framework for an improved interpretation of the response of RNA to chemical modification.     

A structural alphabet for local protein structures: improved prediction methods

Three-dimensional protein structures can be described with a library of 3D fragments that define a structural alphabet. We have previously proposed such an alphabet, composed of 16 patterns of five consecutive amino acids, called Protein Blocks (PBs). These PBs have been used to describe protein backbones and to predict local structures from protein sequences. The Q16 prediction rate reaches 40.7% with an optimization procedure. This article examines two aspects of PBs. First, we determine the effect of the enlargement of databanks on their definition. The results show that the geometrical features of the different PBs are preserved (local RMSD value equal to 0.41 A on average) and sequence-structure specificities reinforced when databanks are enlarged. Second, we improve the methods for optimizing PB predictions from sequences, revisiting the optimization procedure and exploring different local prediction strategies. Use of a statistical optimization procedure for the sequence-local structure relation improves prediction accuracy by 8% (Q16 = 48.7%). Better recognition of repetitive structures occurs without losing the prediction efficiency of the other local folds. Adding secondary structure prediction improved the accuracy of Q16 by only 1%. An entropy index (Neq), strongly related to the RMSD value of the difference between predicted PBs and true local structures, is proposed to estimate prediction quality. The Neq is linearly correlated with the Q16 prediction rate distributions, computed for a large set of proteins. An "expected" prediction rate QE16 is deduced with a mean error of 5%.