Ace and ace-like genes of invasive redlegged earth mite: copy number variation,target-site mutations,and their associations with organophosphate insensitivity

Background

Invasive Australian populations of redlegged earth mite, Halotydeus destructor (Tucker), are evolving increasing organophosphate resistance. In addition to the canonical ace gene, the target gene of organophosphates, the H. destructor genome contains many radiated ace-like genes that vary in copy number and amino acid sequence. In this work, we characterise copy number and target-site mutation variation at the canonical ace and ace-like genes and test for potential associations with organophosphate insensitivity. This was achieved through comparisons of whole-genome pool-seq data from alive and dead mites following organophosphate exposure.

Results

A combination of increased copy number and target-site mutations at the canonical ace was associated with organophosphate insensitivity in H. destructor. Resistant populations were segregating for G119S, A201S, F331Y at the canonical ace. A subset of populations also had copy numbers of canonical ace > 2, which potentially helps overexpress proteins carrying these target-site mutations. Haplotypes possessing different copy numbers and target-site mutations of the canonical ace gene may be under selection across H. destructor populations. We also detected some evidence that increases in copy number of radiated ace-like genes are associated with organophosphate insensitivity, which might suggest potential roles in sequestration or breakdown of organophosphates.

Conclusion

Different combinations of target-site mutations and (or) copy number variation in the canonical ace and ace-like genes may provide non-convergent ways for H. destructor to respond to organophosphate selection. However, these changes may only play a partial role in organophosphate insensitivity, which appears to have a polygenic architecture. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.     

数据密集型计算环境下的离群点挖掘算法

在数据密集型计算环境中,数据的海量、高维、分布存储等特点,为数据挖掘算法的设计与实现带来了新的挑战。基于MapReduce模型提出网格技术与基于密度的方法相结合的离群点挖掘算法,该算法分为两步：Map阶段采用网格技术删除大量不可能成为离群点的正常数据,将代表点信息发送给主节点;Reduce阶段采用基于密度的聚类方法,通过改进其核心对象选取,可以挖掘任意形状的离群点。实验结果表明,在数据密集型计算环境中,该方法能有效的对离群点进行挖掘。     

Developing robust frequentist and Bayesian fish stock assessment methods

Errors in fitting models to data are usually assumed to follow a normal (or log normal) distribution in fisheries. This assumption is usually used in formulating likelihood functions often required in frequentist and Bayesian stock assessment modelling. Fisheries data are commonly subject to atypical errors, resulting in outliers in stock assessment modelling. Because most stock assessment models are nonlinear and contain multiple variables, it is difficult, if not impossible, to identify outliers by plotting fisheries data alone. Commonly used normal distribution‐based frequentist and Bayesian stock assessment methods are sensitive to outliers, resulting in biased estimates of model parameters that are vital in defining the dynamics of fish stocks and evaluating alternative strategies for fisheries management. Because of the high likelihood of having outliers in fisheries data, frequentist or Bayesian methods robust to outliers are more desirable in fisheries stock assessment. This study reviews three approaches that can be used to develop robust frequentist or Bayesian stock assessment methods. Using simulated fisheries as examples, we demonstrate how these approaches can be used to develop the frequentist and Bayesian stock assessment approaches that are robust to outliers in fisheries data and compare the robust approaches with the commonly used normal distribution‐based approach. The proposed robust approaches provide alternative ways to developing frequentist or Bayesian stock assessment methods.     

Improving minimum detectable differences in the assessment of soil organic matter change in short‐term field experiments

The demand for information on cropping system impact on soil organic matter (SOM) calls for efforts to improve the utilization of short‐term field experiments (e.g., to evaluate the parameterization of cropping systems in models). Those approaches have coped with the problem of determining small SOM changes within a large background mass. Thus, objectives of this survey are (1) the improvement of the minimum detectable difference (MDD) in SOM in the hudycrop short‐term field experiment by methods of sampling design and data treatment, (2) the verification to what extend the hudycrop short‐term field experiment allows for the determination of management induced effects on SOM, and (3) the investigation to what extent the obtained results may be suitable to evaluate the parameterization of a SOM balance model. The design of the hudycrop is suitable for excluding outliers plotwise. The estimation of plot means can be improved by the sampling design. Instead of determining a single plot mean in a mixed sampling procedure, the design provides multiple values for each plot, allowing for the identification of extreme values before calculating plot means. In consequence, minimum detectable differences decrease by a factor of 0.53 for soil organic C (SOC) and 0.63 for soil total N (STN) masses, allowing for detection of changes in the magnitude of 3.7 and 2.6% of background SOC and STN levels, respectively. Differences between treatments, however, are significant with corrected values (after outlier exclusion) for the crop production systems with the highest impact (potatoes and mulched red clover). Determining outliers based on Student's t‐test gives the lowest MDD and is therefore considered to be the most suitable method in this case. Correlations between apparent changes and SOM balances according to the HU‐MOD–2 model, used in this survey, indicate that the experimental design, in principal, is suitable for the evaluation of the parameterization of crop production systems in models. Still, an improved precision in SOM change detection is necessary. Reasonable options for that purpose are discussed in the paper.     

Genetic diversity and population structure of Striga hermonthica populations from Kenya and Nigeria

Striga hermonthica is a parasitic weed that poses a serious threat to the production of economically important cereals in sub‐Saharan Africa. The existence of genetic diversity within and between S. hermonthica populations presents a challenge to the successful development and deployment of effective control technologies against this parasitic weed. Understanding the extent of diversity between S. hermonthica populations will facilitate the design and deployment of effective control technologies against the parasite. In the present study, S. hermonthica plants collected from different locations and host crops in Kenya and Nigeria were genotyped using single nucleotide polymorphisms. Statistically significant genetic differentiation (F_ST = 0.15, P = 0.001) was uncovered between populations collected from the two countries. Also, the populations collected in Nigeria formed three distinct subgroups. Unique loci undergoing selection were observed between the Kenyan and Nigerian populations and among the three subgroups found in Nigeria. Striga hermonthica populations parasitising rice in Kenya appeared to be genetically distinct from those parasitising maize and sorghum. The presence of distinct populations in East and West Africa and in different regions in Nigeria highlights the importance of developing and testing Striga control technologies in multiple locations, including locations representing the geographic regions in Nigeria where genetically distinct subpopulations of the parasite were found. Efforts should also be made to develop relevant control technologies for areas infested with ‘rice‐specific’ Striga spp. populations in Kenya.     

New method for the estimation of nitrous oxide emission rates from an agricultural watershed

Abstract

We developed a new and improved method, the ‘high-emission-incorporation (HEI) method’, for estimating soil nitrous oxide (N₂O) emission rates at a watershed level based on nitrogen (N) input (consisting of fertilizer, manure, slurry and excreta N) and N surplus (calculated by subtracting the amount of crop yield and consumed N from the N input) of different sites in a livestock farm located in a watershed. The main characteristic of this method is the inclusion of extremely high N₂O emission rates, ‘outlier’, which are normally excluded from estimation. High N₂O emission rates were estimated using the regression model obtained from the measured N₂O values and the amounts of N surplus; normal N₂O emission rates were estimated using the regression model obtained from the measured values and the amount of N input. The probability of occurrence of a high flux was used to incorporate calculated high and normal N₂O emissions into one. The annual N₂O emission rate from the livestock farm in the watershed (467?ha), estimated using the HEI method, was 1156?±?147?kg?N?year^?1 over a 5-year period. The annual N₂O emission rates calculated using the site-specific emission factor (EF?=?0.0789) and the emission factor of the Intergovernmental Panel on Climate Change (EF?=?0.01) were 1838?±?585?kg?N?year^?1 and 673 (522–1103) kg?N?year^?1, respectively. The estimated value using the measure-and-multiply method, in which each land-use area is multiplied by the representative emission rate for each land-use type, was 964 (509–1610) kg?N?year^?1. The N₂O emission rates estimated by our newly developed method were consistent with the values calculated by the measure-and-multiply method and offered improvement over this measure because the new measure can also predict future N₂O emission rates from the watershed.     

Improved Sage-Husa algorithm and its application in industrial online measurement

An improved Sage-Husa adaptive filter algorithm is proposed based on the existing methodologies in light of the complex and changing environment of the industrial online measurement. This algorithm,after a new observed value is obtained,first judges whether the filter is in a normal state by the filtering divergence criterion,if not,it judges whether the new observed value is an outlier and takes countermeasures varying with the judgment so as to improve the performance of the Sage-Husa algorithm in industrial measurement. The result indicates that compared with traditional methodologies,this new algorithm is applicable to measure the location of the center point of laser beam in a simulated industrial environment,makes accurate judgment about the system status,features better precision,practicality and robustness and thus can satisfy the requirements of industrial online measurement.     

降雨异常值探测的改造箱形图法

经典箱形图运用到有偏降雨数据时,常常将超过触须区间的有偏数据错误地判断为异常值。利用偏态的抗差度量参数,对经典触须区间进行偏态改造,以满足有偏降雨数据异常值的探测需求。分析和比较了度量参数的3种改造形式:线性函数、二次函数、幂函数,结果显示幂函数形式的探测风险最小。将经典箱形图和改造箱形图运用于无异常值和有异常值数据中,改造箱形图的的探测风险小,且探测效果良好。