Projected climate impacts to South African maize and wheat production in 2055: a comparison of empirical and mechanistic modeling approaches

Crop model‐specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic (MMs) and empirical models (EMs) are rare despite both being used widely in this field. We combined MMs and EMs to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in South Africa under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EMs projected larger yield losses or smaller gains than MMs. The EMs’ median‐projected maize and wheat yield changes were ?3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM. The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region (EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM) substantially overestimated the extent of current suitability. The substantial water‐use efficiency gains simulated by the MMs under elevated CO₂ accounted for much of the EM?MM difference, but EMs may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EMs may show larger climate change losses than MMs. Crop forecasting efforts should expand to include EM?MM comparisons to provide a fuller picture of crop–climate response uncertainties.     

小儿喘息性疾病喘息反复发作的病原学及危险因素分析

摘要目的：分析小儿喘息性疾病病原学及与喘息发作有关的因素。方法：227例患儿来我院儿科住院的患儿,在有喘息发作及无喘息发作时均取分泌物进行细菌和病毒检测,并对可能与喘息发作有关的因素做统计分析。结果：喘息发作时细菌感染91例（40．1％）,病毒感染110例（48．5％）,无喘息时72例（31．7％）检出细菌感染,59例（26％）检出病毒感染,喘息发作时细菌和病毒感染检出率均显著高于无喘息时（P〈0．05）。单因素分析过敏史、细菌感染、病毒感染、被动吸烟史、家族史和季节等暴露因素在喘息次数超过和低于4次患儿之间存在差异（P〈O．05）。多因素非条件Logistic回归分析显示病毒感染（OR=2．839）、细菌感染（OR=2．434）、过敏史（OR=4．412）和家族史（OR=2．158）为喘息性疾病患儿喘息发作次数增多的主要危险因素。结论：病毒和细菌感染为小儿喘息性疾病的主要致病原,病毒和细菌感染、有过敏史与家族史是喘息反复发作的危险因素。     

Physical maturity in common bottlenose dolphins (Tursiops truncatus) from Sarasota Bay,Florida

Cetacean physical maturity is defined by growth cessation and complete fusion of epiphyses to vertebral bodies indicated by invisible sutures. Many studies have shown epiphyseal fusion is highly variable among individuals. In-depth examinations into fusion variability are lacking. We analyzed vertebrae of 37 (n = 21 female, n = 16 male) stranded common bottlenose dolphins (Tursiops truncatus) from the well-studied Gulf of Mexico, Sarasota Bay community. For each specimen, vertebrae were examined by vertebral region for degree of fusion anteriorly and posteriorly of each centrum and categorized from unfused to fused in five degrees. An ordinal logistic regression was used to estimate degree of fusion probability for each epiphysis. The model had fixed effects for age, number of offspring, sex, sexual maturity, and a random effect for epiphysis. Results show that age/reproductive status significantly explains an individual's degree of fusion. Adult females with fewer calves had more fusion than those with more reproductive experience across multiple ages. Access to long-term observational and sample data on the dolphins residing in the area served by Mote Marine Laboratory's Stranding Investigations Program offers a unique opportunity to examine the relationship between energetic demands of reproduction (calcium production/reproductive output) versus preconceived definitions of physical maturity (skeletal fusion) more closely.     

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active-learning methods found in the machine-learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.     

A Bayesian extension of phylogenetic generalized least squares: Incorporating uncertainty in the comparative study of trait relationships and evolutionary rates

Phylogenetic comparative methods use tree topology, branch lengths, and models of phenotypic change to take into account nonindependence in statistical analysis. However, these methods normally assume that trees and models are known without error. Approaches relying on evolutionary regimes also assume specific distributions of character states across a tree, which often result from ancestral state reconstructions that are subject to uncertainty. Several methods have been proposed to deal with some of these sources of uncertainty, but approaches accounting for all of them are less common. Here, we show how Bayesian statistics facilitates this task while relaxing the homogeneous rate assumption of the well-known phylogenetic generalized least squares (PGLS) framework. This Bayesian formulation allows uncertainty about phylogeny, evolutionary regimes, or other statistical parameters to be taken into account for studies as simple as testing for coevolution in two traits or as complex as testing whether bursts of phenotypic change are associated with evolutionary shifts in intertrait correlations. A mixture of validation approaches indicates that the approach has good inferential properties and predictive performance. We provide suggestions for implementation and show its usefulness by exploring the coevolution of ankle posture and forefoot proportions in Carnivora.     

Tagging SNPs in the ERCC4 gene are associated with gastric cancer risk

ERCC4 plays an essential role in the nucleotide excision repair (NER) pathway, which is involved in the removal of a wide variety of DNA lesions. To determine whether the ERCC4 tagging SNPs (tSNPs) are associated with risk of gastric cancer, we conducted a hospital-based case-control study of 350 cases and 468 cancer-free controls. In the logistic regression (LR) analysis, we found a significantly decreased risk of gastric cancer associated with the rs744154 GC/CC genotypes [adjusted odds ratio (OR) = 0.56, 95% confidence interval (CI) = 0.42–0.75, false discovery rate (FDR) P = 0.003] compared with the wild-type GG genotype. Haplotype-based association study revealed that the CGC haplotype that containing the rs744154 C allele can decrease the risk of gastric cancer compared with the most common haplotype GGT (adjusted OR = 0.61, 95% CI = 0.46–0.81). Using the multifactor dimensionality reduction (MDR) analysis, we identified that the SNP rs744154 and smoking status were the best two predictive factors for gastric cancer with a testing accuracy of 55.76% and a perfect cross-validation consistency (CVC) of 10 (P = 0.001). Furthermore, the smokers with the rs744154 GC/CC genotypes showed a decreased risk of gastric cancer (adjusted OR = 0.55, 95% CI = 0.35–0.85) compared with the smokers with the GG genotype using multivariate LR analysis. The above findings consistently suggested that genetic variants in the ERCC4 gene may play a protective role in the etiology of gastric cancer, even in the smokers.     

pH‐selective mutagenesis of protein–protein interfaces: In silico design of therapeutic antibodies with prolonged half‐life

Understanding the effects of mutation on pH‐dependent protein binding affinity is important in protein design, especially in the area of protein therapeutics. We propose a novel method for fast in silico mutagenesis of protein–protein complexes to calculate the effect of mutation as a function of pH. The free energy differences between the wild type and mutants are evaluated from a molecular mechanics model, combined with calculations of the equilibria of proton binding. The predicted pH‐dependent energy profiles demonstrate excellent agreement with experimentally measured pH‐dependency of the effect of mutations on the dissociation constants for the complex of turkey ovomucoid third domain (OMTKY3) and proteinase B. The virtual scanning mutagenesis identifies all hotspots responsible for pH‐dependent binding of immunoglobulin G (IgG) to neonatal Fc receptor (FcRn) and the results support the current understanding of the salvage mechanism of the antibody by FcRn based on pH‐selective binding. The method can be used to select mutations that change the pH‐dependent binding profiles of proteins and guide the time consuming and expensive protein engineering experiments. As an application of this method, we propose a computational strategy to search for mutations that can alter the pH‐dependent binding behavior of IgG to FcRn with the aim of improving the half‐life of therapeutic antibodies in the target organism. © Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Impact of flow velocity on the dynamic behaviour of biofilm bacteria

Abstract

The impact of flow velocity (FV) on the growth dynamics of biofilms and bulk water heterotrophic plate count (HPC) bacteria in drinking water distribution systems was quantified and modeled by combining a logistic growth model with mass balance equations. The dynamic variations in the specific growth and release rates of biofilm bacteria were also quantified. The experimental results showed that the maximum biofilm biomass did not change when flow velocity was increased from 20 to 40 cm s^?1, but was significantly affected when flow velocity was further increased to 60 cm s^?1. Although the concentration of biofilm bacteria was substantially reduced by the higher shear stress, the concentration of bacteria in the bulk fluid was slightly increased. From this it is estimated that the specific growth rate and specific release rate of biofilm bacteria had doubled. The specific release (detachment) rate was dependent on the specific growth rate of the biofilm bacteria.     

A Leap-frog Algorithm for Stochastic Dynamics

Abstract

A third-order algorithm for stochastic dynamics (SD) simulations is proposed, identical to the powerful molecular dynamics leap-frog algorithm in the limit of infinitely small friction coefficient γ. It belongs to the class of SD algorithms, in which the integration time step Δt is not limited by the condition Δt ≤ γ^?1, but only by the properties of the systematic force. It is shown how constraints, such as bond length or bond angle constraints, can be incorporated in the computational scheme. It is argued that the third-order Verlet-type SD algorithm proposed earlier may be simplified without loosing its third-order accuracy. The leap-frog SD algorithm is proven to be equivalent to the verlet-type SD algorithm. Both these SD algorithms are slightly more economical on computer storage than the Beeman-type SD algorithm.