无比对样本的STR混合分型分析策略

目的充分利用数据库的比对功能,指导无比对样本混合分型的分析、数据库比对和比对结果的筛选。方法使用联合包含概率描述混合分型的识别力。利用联合被包含概率来估计被混合分型包含的样本的可靠性。结果混合分型的联合包含概率小于10^(-7)时具备入库比对的价值。混合分型单个基因座上能确定的等位基因大于等于2个时,增加一个额外等位基因,不会过多降低识别力。数据库检索获得多个无容差比中结果后,可以根据联合被包含概率来进行排序,快速找到最有价值样本。

混合STR分型分析方法研究进展

混合STR分型的解释及分析一直是国际上法医物证领域研究的热点和难点。随着DNA检验技术的发展,案件中检出的混合STR分型呈现出模板量降低以及混合组分数增加的趋势,其解释变得愈加复杂,传统的人工分析方法已难以满足现实要求。近几年国外基于统计算法模型的自动化软件解析方法渐趋成为混合STR分型分析的主要方法。本文综述了混合STR分型分析方法的相关研究进展,包括人工为主的定性分析方法以及基于统计模型的基因型概率分析方法;讨论了混合STR分型分析方法的未来发展趋势以及人工智能技术的应用前景。

两种混合STR分型分析软件的比较与应用

随着DNA技术的发展和应用拓广,案件现场生物检材及其提取检验愈受重视、愈显重要性,近年来混合检材的DNA有效检出比例越来越高,混合STR分型的拆分与合理解释也益发成为DNA鉴定技术领域的研究热点与难点。本文主要针对当下国际主流的两款混合分型分析软件——STRmix和云算GPM进行比较研究,就两者的基本信息、理论、分析过程作比较,并结合实战应用和典型案例综合分析两者的异同,希冀为法医DNA工作者提供选用参考和使用依据,也希望能够推动国内混合STR分型分析的发展及与国际交流互鉴。

DNA混合基因分型与图像侦查的结合应用

目的通过对案件所获混合基因分型与图像侦查相结合,探讨类似案件侦破的经验和借鉴价值。方法对案件中9份检材进行DNA常染色体STR检验,选取有效混合基因型直接入库比对,结合图像侦查进行嫌疑人排查。结果案件中8号检材所得混合基因分型比中嫌疑人,根据现场监控视频图像将其锁定。结论无法拆分的混合基因分型,可直接入库比对,再结合案件其他线索作嫌疑人排查,可以为案件侦破提供线索。

母胎混合STR分型流产胎儿的亲权指数计算方法探讨

目的 在流产胎儿获得母胎混合STR分型的案、事件中,对疑似生父进行亲权指数计算提出两种计算方法,为包含混合分型的亲权鉴定提供理论参考。方法 依据是否先拆分出胎儿基因型,提出拆分胎儿基因型亲权指数计算法和推断生父基因亲权指数计算法。结果 拆分胎儿基因型亲权指数计算法,是先将胎儿基因型拆分出来,再按照标准三联体进行亲权指数计算。推断生父基因亲权指数计算法,是不考虑混合分型中峰高、峰面积的比例,推断出所有符合孟德尔遗传定律的生父基因(推断),再分别计算亲权指数,取其中的最小值作为该基因座的亲权指数。结论 当流产组织混合分型中,胎儿混合比率M_(R)≥0.43时,拆分准确性极高,使用拆分胎儿基因型亲权指数计算法计算结果准确性也极高,建议使用拆分胎儿基因型亲权指数计算法。当0.05≤M_(R)<0.43时,建议使用推断生父基因亲权指数计算法,可以最大程度的避免对无关人员的误判。当M_(R)<0.05时,胎儿等位基因丢失的潜在风险极高,不建议对混合斑进行亲权鉴定。由于推断生父基因亲权指数计算法得出的累计亲权指数通常低于拆分胎儿基因型亲权指数计算法,在检出的基因座较少的情况下,可能出现累计亲权指数低于10 000的情况,此时应增加检测遗传标记。

不完整STR分型的价值挖掘

清晰的DNA图谱对于认定犯罪嫌疑人具有决定性的证据作用。然而在实际检验中,经常得到不完整DNA图谱或混合图谱,相关案件因无法查询比对而长期搁置成为"冷案"。本文从这一难题出发,结合混合样品人工拆分和"全国公安机关DNA数据库快速协查平台",对2011年的未破积案进行梳理,为21起未破案件中的19起查拆分出有价值的DNA分型,并成功告破两起命案。通过深度挖掘不完整STR分型的潜在利用价值,大大提高了"休眠"DNA信息的使用效率,为形成破案模式的新常态提供了思路和借鉴。

基于新一代测序技术进行孕早期流产组织胎儿STR分型及亲子鉴定

流产组织胎儿STR(short tandem repeats)分型检测长久以来始终是法医DNA检验面临的一大难题,对于孕早期流产组织无法通过组织形态学检验获取绒毛组织时,母体与胎儿混合STR分型结果可以为强奸致孕案件嫌疑人认定提供关键依据。本研究应用新一代测序技术,在4份强奸致孕早期流产组织中成功检出母体与疑似胎儿混合STR分型或疑似胎儿单一STR分型,与Y-STR、侧翼序列信息联合应用,为嫌疑人认定提供了更为全面可靠的遗传学依据。

DNA证据及其审查判断若干问题研究

DNA证据作为新科学证据的代表,给刑事司法领域带来了深远的、变革性的影响。在强调DNA证据特殊证明价值的同时,也要关注其自身出现错误以及对其进行错误解读的风险。在司法实践中,DNA证据的应用已经暴露出诸多技术、管理和程序等方面的疑难问题,有必要加强对DNA数据库比对、混合DNA分型、DNA检材污染以及鉴定文书等的审查,确保DNA证据在诉讼过程中发挥预期的正向功能,避免司法错误发生。

Characterizing heterogeneity in vehicular traffic speed using two-step cluster analysis

In order to analyze the heterogeneity in vehicular traffic speed, a new method that integrates cluster analysis and probability distribution function fitting is presented. First, for identifying the optimal number of clusters, the two-step cluster method is applied to analyze actual speed data, which suggests that dividing speed data into two clusters can best reflect the intrinsic patterns of traffic flows. Such information is then taken as guidance in probability distribution function fitting. The normal, skew-normal and skew-t distribution functions are used to fit the probability distribution of each cluster respectively, which suggests that the skew-t distribution has the highest fitting accuracy; the second is skew-normal distribution; the worst is normal distribution. Model analysis results demonstrate that the proposed mixture model has a better fitting and generalization capability than the conventional single model. In addition, the new method is more flexible in terms of data fitting and can provide a more accurate model of speed distribution.

Adaptive moving target detection algorithm based on Gaussian mixture model

In order to enhance the reliability of the moving target detection, an adaptive moving target detection algorithm based on the Gaussian mixture model is proposed. This algorithm employs Gaussian mixture distributions in modeling the background of each pixel. As a result, the number of Gaussian distributions is not fixed but adaptively changes with the change of the pixel value frequency. The pixels of the difference image are divided into two parts according to their values. Then the two parts are separately segmented by the adaptive threshold, and finally the foreground image is obtained. The shadow elimination method based on morphological reconstruction is introduced to improve the performance of foreground image＇s segmentation. Experimental results show that the proposed algorithm can quickly and accurately build the background model and it is more robust in different real scenes.

Reliability Analysis of a Mixed Redundant Model in C 3I System

Aim To define a mixed redundant model(MRM), improving the reliability of C 3I system. Methods The model combined the technology characters of two? unit system with one warm stand by unit and function substitute system. The reliability and availability equations of MRM were deduced. Results and Conclusion Compared with several other reliability models, it has obvious effect upon improving the system reliability. The effect? cost rate is very high among these models. The model can be used in reliability design, evaluation and check of C 3I system. Only a little attached cost is needed to improve C 3I system reliability effectively.

EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION

A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.

Gaussian mixture model clustering with completed likelihood minimum message length criterion

An improved Gaussian mixture model （GMM）- based clustering method is proposed for the difficult case where the true distribution of data is against the assumed GMM. First, an improved model selection criterion, the completed likelihood minimum message length criterion, is derived. It can measure both the goodness-of-fit of the candidate GMM to the data and the goodness-of-partition of the data. Secondly, by utilizing the proposed criterion as the clustering objective function, an improved expectation- maximization （EM） algorithm is developed, which can avoid poor local optimal solutions compared to the standard EM algorithm for estimating the model parameters. The experimental results demonstrate that the proposed method can rectify the over-fitting tendency of representative GMM-based clustering approaches and can robustly provide more accurate clustering results.

Bayesian analysis for mixed-effects model defined by stochastic differential equations

The nonlinear mixed-effects model with stochastic differential equations （SDEs） is used to model the population pharmacokinetic （PPK） data that are extended from ordinary differential equations （ODEs） by adding a stochastic term to the state equation. Compared with the ODEs, the SDEs can model correlated residuals which are ubiquitous in actual pharmacokinetic problems. The Bayesian estimation is provided for nonlinear mixed-effects models based on stochastic differential equations. Combining the Gibbs and the Metropolis-Hastings algorithms, the population and individual parameter values are given through the parameter posterior predictive distributions. The analysis and simulation results show that the performance of the Bayesian estimation for mixed-effects SDEs model and analysis of population pharmacokinetic data is reliable. The results suggest that the proposed method is feasible for population pharmacokinetic data.

Bi-level programming model and algorithm for optimizing headway of public transit line

Due to the fact that headway is a key factor to be considered in bus scheduling, this paper proposes a bi-level programming model for optimizing bus headway in public transit lines. In this model, with the interests of bus companies and passengers in mind, the upper-level model＇s objective is to minimize the total cost, which is affected by frequency settings, both in time and economy in the transit system. The lower-level model is a transit assignment model used to describe the assignment of passengers＇ trips to the network based on the optimal bus headway. In order to solve the proposed model, a hybrid genetic algorithm, namely the genetic algorithm and the simulated annealing algorithm （GA-SA）, is designed. Finally, the model and the algorithm are tested against the transit data, by taking some of the bus lines of Changzhou city as an example. Results indicate that the proposed model allows supply and demand to be linked, which is reasonable, and the solving algorithm is effective.

WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML)

WOMBAT is a software package for quantitative genetic analyses of continuous traits, fitting a linear, mixed model; estimates of covariance components and the resulting genetic parameters are obtained by restricted maximum likelihood. A wide range of models, comprising numerous traits, multiple fixed and random effects, selected genetic covariance structures, random regression models and reduced rank estimation are accommodated. WOMBAT employs up-to-date numerical and computational methods. Together with the use of efficient compilers, this generates fast executable programs, suitable for large scale analyses. Use of WOMBAT is illustrated for a bivariate analysis. The package consists of the executable program, available for LINUX and WINDOWS environments, manual and a set of worked example, and can be downloaded free of charge from http：//agbu. une.edu.au/-kmeyer/wombat.html

A Hybrid Inexact Optimization Model for Land-use Allocation of China

Land scarcity has become the prominent obstacle on the way to sustainable development for China. Under the constraints of land shortage, how to allocate the finite land resources to the multiple land users in China considering various political, environmental, ecological and economic conditions have become research topics with great significance. In this study, an interval fuzzy national-scale land-use model(IFNLM) was developed for optimizing land systems of China. IFNLM is based on an integration of existing interval linear programming(ILP), and fuzzy flexible programming(FFP) techniques. IFNLM allows uncertainties expressed as discrete interval values and fuzzy sets to be incorporated within a general optimization framework. It can also facilitate national-scale land-use planning under various environmental, ecological, social conditions within a multi-period and multi-option context. Then, IFNLM was applied to a real case study of land-use planning in China. The satisfaction degree of environmental constraints is between 0.69 and 0.97, the system benefit will between 198.25 × 1012 USD and 229.67 × 1012 USD. The results indicated that the hybrid model can help generate desired policies for land-use allocation with a maximized economic benefit and minimized environmental violation risk. Optimized land-use allocation patterns can be generated from the proposed IFNLM.

A hybrid decomposition-boosting model for short-term multi-step solar radiation forecasting with NARX neural network

Due to global energy depletion,solar energy technology has been widely used in the world.The output power of the solar energy systems is affected by solar radiation.Accurate short-term forecasting of solar radiation can ensure the safety of photovoltaic grids and improve the utilization efficiency of the solar energy systems.In the study,a new decomposition-boosting model using artificial intelligence is proposed to realize the solar radiation multi-step prediction.The proposed model includes four parts:signal decomposition(EWT),neural network(NARX),Adaboost and ARIMA.Three real solar radiation datasets from Changde,China were used to validate the efficiency of the proposed model.To verify the robustness of the multi-step prediction model,this experiment compared nine models and made 1,3,and 5 steps ahead predictions for the time series.It is verified that the proposed model has the best performance among all models.

Minimum distance constrained nonnegative matrix factorization for hyperspectral data unmixing

This paper considers a problem of unsupervised spectral unmixing of hyperspectral data. Based on the Linear Mixing Model （ LMM）, a new method under the framework of nonnegative matrix fac- torization （NMF） is proposed, namely minimum distance constrained nonnegative matrix factoriza- tion （MDC-NMF）. In this paper, firstly, a new regularization term, called endmember distance （ED） is considered, which is defined as the sum of the squared Euclidean distances from each end- member to their geometric center. Compared with the simplex volume, ED has better optimization properties and is conceptually intuitive. Secondly, a projected gradient （PG） scheme is adopted, and by the virtue of ED, in this scheme the optimal step size along the feasible descent direction can be calculated easily at each iteration. Thirdly, a finite step （ no more than the number of endmem- bers） terminated algorithm is used to project a point on the canonical simplex, by which the abun- dance nonnegative constraint and abundance sum-to-one constraint can be accurately satisfied in a light amount of computation. The experimental results, based on a set of synthetic data and real da- ta, demonstrate that, in the same running time, MDC-NMF outperforms several other similar meth- ods proposed recently.

Improvement of Urban Impervious Surface Estimation in Shanghai Using Landsat7 ETM+ Data

This paper explores the potential to improve the impervious surface estimation accuracy using a multi-stage approach on the basis of vegetation-impervious surface-soil (V-I-S) model. In the first stage of Spectral Mixture Analysis (SMA) process, pixel purity index, a quantitative index for defining endmember quality, and a 3-dimensional endmember selection method were applied to refining endmembers. In the second stage, instead of obtaining impervious surface fraction by adding high and low albedo fractions directly, a linear regression model was built between impervious surface and high/low albedo using a random sampling method. The urban impervious surface distribution in the urban central area of Shanghai was predicted by the linear regression model. Estimation accuracy of spectral mixture analysis and impervious surface fraction were assessed using root mean square (RMS) and color aerial photography respectively. In comparison with three different research methods, this improved estimation method has a higher overall accuracy than traditional Linear Spectral Mixture Analysis (LSMA) method and the normalized SMA model both in root mean square error (RMSE) and standard error (SE). However, the model has a tendency to overestimate the impervious surface distribution.