高斯混合模型的上采样分析

在机器学习问题中,类别不平衡问题严重影响一些标准分类器的性能。因此,解决类别不平衡问题尤为重要。上采样是解决类不平衡问题的常用方法,其通过合成新的少数类样本来平衡类的分布。在文中,使用一种基于高斯混合模型的上采样方法来解决不平衡学习问题。通过高斯混合模型来模拟少数类的分布,在此基础上使用高斯模型来生成新的少数类样本。在UCI类别不平衡数据集上的实验结果表明,所提出的方法能够缓解类不平衡所带来的负面影响并帮助提升分类性能。     

Implications of a statistical occurrence model for mixture toxicity estimation

This study provides a method for characterizing the effects of concentration variability and correlation among co-acting compounds on mixture toxicity, considering the implications of missing chemical data. The method is explored by developing a set of multiple occurrence scenarios for mixtures of related chemicals. The calculations are performed for hypothetical mixtures of a group of ten synthetic antibiotics that have been tested on marine bacterium to fit dose-response relationships for long-term bioluminescence inhibition of Vibrio fischeri. Mixture toxicities are computed and compared for the assumptions of independent joint action theory and concentration/dose addition theory. The study results show that higher variability in concentrations is associated with higher effective (average) mixture toxicity, in this application by as much as a factor of ten for mixtures with highly variable component concentrations. Moreover, omitting the most toxic compounds caused mixture toxicities to be underestimated by a factor of approximately two. We recommend a pre-assessment of the effect of different chemical occurrence patterns and variability on mixture toxicity to help prioritize needs for further co-occurrence data and toxicity studies.     

Effect of Shuangjinlian mixture on oral ulcer model in rat

ObjectiveTo observe the effect of common clinical drug Shuangjin Lian mixture on rats with oral ulcer and discuss its mechanism.MethodsSodium carboxymethyl cellulose caused leukocyte aggregation in rats, observed the anti-inflammatory effect of Shuangjin mixture. 3 mm * 3 mm size ulcer surface in the oral cavity of rats was caused by 90% phenol solution, to observe the therapeutic effect and anti-inflammatory effect of Shuangjinlian mixture on rats with oral ulcers.ResultThe low, middle and high dose Shuangjinlian mixture can inhibits the accumulation of white blood cells caused by sodium carboxymethyl cellulose in rats significantly (P < 0.01). And reduce the degree of edema and hyperaemia around the ulcer tissue significantly (P < 0.01), improve the ulcer healing probability, and reduce the level of TNF-α, VEGF levels and increase IL-2 level in the serum of rats with oral ulcers significantly (P < 0.01). Pathological examination showed that the lesion of ulcer tissue in each treatment group was obviously alleviated.ConclusionShuangjinlian mixture had anti-inflammatory effect and was effective for the prevention and treatment of oral ulcer in rats.     

高寒草甸生态系统牲畜种群的线性规划模型和最优化利用策略

本文研究了高寒草甸生态系统牲畜种群的线性规划模型和最优利用策略。以门源马场牧场的实际数据作为模型的一个例子,分别提出了藏羊、改良羊和牦牛的最优种群结构和最优出栏方案。在改良羊、藏羊、牦牛和马为主的牧场上,改良羊是牧场上的主要牲畜,牦牛和马保持其数量下限,藏羊全部淘汰。在藏羊、牦牛和马为主的牧场上,藏羊是牧场上的主要牲畜,牦牛和马保持其数量下限。按照线性规划模型方案经营,可提高经济收益,并减轻冬舂草场上牲畜的过多采食。价格分析说明青海省现行的畜产品价格体系需要调整。     

The one‐inflated positive Poisson mixture model for use in population size estimation

The one‐inflated positive Poisson mixture model (OIPPMM) is presented, for use as the truncated count model in Horvitz–Thompson estimation of an unknown population size. The OIPPMM offers a way to address two important features of some capture–recapture data: one‐inflation and unobserved heterogeneity. The OIPPMM provides markedly different results than some other popular estimators, and these other estimators can appear to be quite biased, or utterly fail due to the boundary problem, when the OIPPMM is the true data‐generating process. In addition, the OIPPMM provides a solution to the boundary problem, by labelling any mixture components on the boundary instead as one‐inflation.     

Methods for identifying and averaging variable molecular conformations in tomograms of actively contracting insect flight muscle

During active muscle contraction, tension is generated through many simultaneous, independent interactions between the molecular motor myosin and the actin filaments. The ensemble of myosin motors displays heterogeneous conformations reflecting different mechanochemical steps of the ATPase pathway. We used electron tomography of actively contracting insect flight muscle fast-frozen, freeze substituted, Araldite embedded, thin-sectioned and stained, to obtain 3D snapshots of the multiplicity of actin-attached myosin structures. We describe procedures for alignment of the repeating lattice of sub-volumes (38.7 nm cross-bridge repeats bounded by troponin) and multivariate data analysis to identify self-similar repeats for computing class averages. Improvements in alignment and classification of repeat sub-volumes reveals (for the first time in active muscle images) the helix of actin subunits in the thin filament and the troponin density with sufficient clarity that a quasiatomic model of the thin filament can be built into the class averages independent of the myosin cross-bridges. We show how quasiatomic model building can identify both strong and weak myosin attachments to actin. We evaluate the accuracy of image classification to enumerate the different types of actin–myosin attachments.     

Inclusion of water temperature in a fuzzy logic Atlantic salmon (Salmo salar) parr habitat model

As water temperature is projected to increase in the next decades and its rise is clearly identified as a threat for cold water fish species, it is necessary to adapt and optimize the tools allowing to assess the quantity and quality of habitats with the inclusion of temperature. In this paper, a fuzzy logic habitat model was improved by adding water temperature as a key determinant of juvenile Atlantic salmon parr habitat quality. First, salmon experts were consulted to gather their knowledge of salmon parr habitat, then the model was validated with juvenile salmon electrofishing data collected on the Sainte-Marguerite, Matapedia and Petite-Cascapedia rivers (Québec, Canada). The model indicates that when thermal contrasts exist at a site, cooler temperature offered better quality of habitat. Our field data show that when offered the choice, salmon parr significantly preferred to avoid both cold areas (<15 °C) and warm areas (>20.5 °C). Because such thermal contrasts were not consistently present among the sites sampled, the model was only validated for less than 60% of the sites. The results nevertheless indicate a significant correlation between median Habitat Quality Index and parr density for the Sainte-Marguerite River (R² = 0.38). A less important, albeit significant (F-test; p = 0.036) relationship was observed for the Petite-Cascapedia river (R² = 0.14). In all instances, the four-variable (depth, velocity, substrate size and temperature) model provided a better explanation of parr density than a similar model excluding water temperature.     

A bivariate joint frailty model with mixture framework for survival analysis of recurrent events with dependent censoring and cure fraction

In the study of multiple failure time data with recurrent clinical endpoints, the classical independent censoring assumption in survival analysis can be violated when the evolution of the recurrent events is correlated with a censoring mechanism such as death. Moreover, in some situations, a cure fraction appears in the data because a tangible proportion of the study population benefits from treatment and becomes recurrence free and insusceptible to death related to the disease. A bivariate joint frailty mixture cure model is proposed to allow for dependent censoring and cure fraction in recurrent event data. The latency part of the model consists of two intensity functions for the hazard rates of recurrent events and death, wherein a bivariate frailty is introduced by means of the generalized linear mixed model methodology to adjust for dependent censoring. The model allows covariates and frailties in both the incidence and the latency parts, and it further accounts for the possibility of cure after each recurrence. It includes the joint frailty model and other related models as special cases. An expectation-maximization (EM)-type algorithm is developed to provide residual maximum likelihood estimation of model parameters. Through simulation studies, the performance of the model is investigated under different magnitudes of dependent censoring and cure rate. The model is applied to data sets from two colorectal cancer studies to illustrate its practical value.     

Semiparametric maximum likelihood for measurement error model regression

This paper presents an EM algorithm for semiparametric likelihood analysis of linear, generalized linear, and nonlinear regression models with measurement errors in explanatory variables. A structural model is used in which probability distributions are specified for (a) the response and (b) the measurement error. A distribution is also assumed for the true explanatory variable but is left unspecified and is estimated by nonparametric maximum likelihood. For various types of extra information about the measurement error distribution, the proposed algorithm makes use of available routines that would be appropriate for likelihood analysis of (a) and (b) if the true x were available. Simulations suggest that the semiparametric maximum likelihood estimator retains a high degree of efficiency relative to the structural maximum likelihood estimator based on correct distributional assumptions and can outperform maximum likelihood based on an incorrect distributional assumption. The approach is illustrated on three examples with a variety of structures and types of extra information about the measurement error distribution.     

AMB-Wnet: Embedding attention model in multi-bridge Wnet for exploring the mechanics of disease

In recent years, progressive application of convolutional neural networks in image processing has successfully filtered into medical diagnosis. As a prerequisite for images detection and classification, object segmentation in medical images has attracted a great deal of attention. This study is based on the fact that most of the analysis of pathological diagnoses requires nuclei detection as the starting phase for obtaining an insight into the underlying biological process and further diagnosis. In this paper, we introduce an embedded attention model in multi-bridge Wnet (AMB-Wnet) to achieve suppression of irrelevant background areas and obtain good features for learning image semantics and modality to automatically segment nuclei, inspired by the 2018 Data Science Bowl. The proposed architecture, consisting of the redesigned down sample group, up-sample group, and middle block (a new multiple-scale convolutional layers block), is designed to extract different level features. In addition, a connection group is proposed instead of skip-connection to transfer semantic information among different levels. In addition, the attention model is well embedded in the connection group, and the performance of the model is improved without increasing the amount of calculation. To validate the model's performance, we evaluated it using the BBBC038V1 data sets for nuclei segmentation. Our proposed model achieves 85.83% F1-score, 97.81% accuracy, 86.12% recall, and 83.52% intersection over union. The proposed AMB-Wnet exhibits superior results compared to the original U-Net, MultiResUNet, and recent Attention U-Net architecture.     

Accounting for multiplicities in assessing drug safety: a three-level hierarchical mixture model

Multiple comparisons and other multiplicities are among the most difficult of problems that face statisticians, frequentists, and Bayesians alike. An example is the analysis of the many types of adverse events (AEs) that are recorded in drug clinical trials. We propose a three-level hierarchical mixed model. The most basic level is type of AE. The second level is body system, each of which contains a number of types of possibly related AEs. The highest level is the collection of all body systems. Our analysis allows for borrowing across body systems, but there is greater potential-depending on the actual data-for borrowing within each body system. The probability that a drug has caused a type of AE is greater if its rate is elevated for several types of AEs within the same body system than if the AEs with elevated rates were in different body systems. We give examples to illustrate our method and we describe its application to other types of problems.     

A Laplace mixture model for identification of differential expression in microarray experiments

Microarrays have become an important tool for studying the molecular basis of complex disease traits and fundamental biological processes. A common purpose of microarray experiments is the detection of genes that are differentially expressed under two conditions, such as treatment versus control or wild type versus knockout. We introduce a Laplace mixture model as a long-tailed alternative to the normal distribution when identifying differentially expressed genes in microarray experiments, and provide an extension to asymmetric over- or underexpression. This model permits greater flexibility than models in current use as it has the potential, at least with sufficient data, to accommodate both whole genome and restricted coverage arrays. We also propose likelihood approaches to hyperparameter estimation which are equally applicable in the Normal mixture case. The Laplace model appears to give some improvement in fit to data, though simulation studies show that our method performs similarly to several other statistical approaches to the problem of identification of differential expression.     

The <Emphasis Type="Italic">t</Emphasis>-mixture model approach for detecting differentially expressed genes in microarrays

The finite mixture model approach has attracted much attention in analyzing microarray data due to its robustness to the excessive variability which is common in the microarray data. Pan (2003) proposed to use the normal mixture model method (MMM) to estimate the distribution of a test statistic and its null distribution. However, considering the fact that the test statistic is often of t-type, our studies find that the rejection region from MMM is often significantly larger than the correct rejection region, resulting an inflated type I error. This motivates us to propose the t-mixture model (TMM) approach. In this paper, we demonstrate that TMM provides significantly more accurate control of the probability of making type I errors (hence of the familywise error rate) than MMM. Finally, TMM is applied to the well-known leukemia data of Golub et al. (1999). The results are compared with those obtained from MMM.     

Joint Variable Selection for Fixed and Random Effects in Linear Mixed‐Effects Models

Summary It is of great practical interest to simultaneously identify the important predictors that correspond to both the fixed and random effects components in a linear mixed‐effects (LME) model. Typical approaches perform selection separately on each of the fixed and random effect components. However, changing the structure of one set of effects can lead to different choices of variables for the other set of effects. We propose simultaneous selection of the fixed and random factors in an LME model using a modified Cholesky decomposition. Our method is based on a penalized joint log likelihood with an adaptive penalty for the selection and estimation of both the fixed and random effects. It performs model selection by allowing fixed effects or standard deviations of random effects to be exactly zero. A constrained expectation–maximization algorithm is then used to obtain the final estimates. It is further shown that the proposed penalized estimator enjoys the Oracle property, in that, asymptotically it performs as well as if the true model was known beforehand. We demonstrate the performance of our method based on a simulation study and a real data example.     

Theoretical results for chemotactic response and drift of E. coli in a weak attractant gradient

The bacterium Escherichia coli (E. coli) moves in its natural environment in a series of straight runs, interrupted by tumbles which cause change of direction. It performs chemotaxis towards chemo-attractants by extending the duration of runs in the direction of the source. When there is a spatial gradient in the attractant concentration, this bias produces a drift velocity directed towards its source, whereas in a uniform concentration, E. coli adapts, almost perfectly in case of methyl aspartate. Recently, microfluidic experiments have measured the drift velocity of E. coli in precisely controlled attractant gradients, but no general theoretical expression for the same exists. With this motivation, we study an analytically soluble model here, based on the Barkai-Leibler model, originally introduced to explain the perfect adaptation. Rigorous mathematical expressions are obtained for the chemotactic response function and the drift velocity in the limit of weak gradients and under the assumption of completely random tumbles. The theoretical predictions compare favorably with experimental results, especially at high concentrations. We further show that the signal transduction network weakens the dependence of the drift on concentration, thus enhancing the range of sensitivity.     

A quasi-physical algorithm for the structure optimization in an off-lattice protein model

Introduction Predicting the native structure of a protein from its amino acid sequence is one of the most challeng- ing problems in biophysics and bioinformatics. The difficulty of the problem comes from two aspects. One is the determination of the potential energy func- tion. The effective energy function can generally dis- tinguish the native states from non-native states of protein molecules. The other is that the potential en- ergy landscape of the system can be characterized by a multitu…     

SPR and NMR characterization of the molecular interaction between A9 peptide and a model system of HER2 receptor: A fragment approach for selecting peptide structures specific for their target

The binding process of A9 peptide toward HER2‐DIVMP, a synthetic model of the receptor domain IV, was studied by using the surface plasmon resonance (SPR) technique, with the aim of validating it as a fast and reliable screening method for selecting peptide ligands specifically targeting a domain of their target. To investigate the structural basis of A9 binding to the model of HER2‐DIVMP, multiple ligand‐based nuclear magnetic resonance (NMR) methods were applied. The use of saturation transfer difference (STD) and WaterLOGSY NMR experiments identified key residues in the peptide for the receptor binding. Moreover, the bound conformation of the A9 peptide was obtained using transferred nuclear Overhauser effect spectroscopy (trNOESY) experiments. The NMR data revealed an extended binding surface that confirms an in silico model previously reported. These structural findings could provide good starting points for future lead structures optimization specific for the receptor target.     

Strategies for rare-event detection: an approach for automated fetal cell detection in maternal blood.

This article explores the feasibility of the use of automated microscopy and image analysis to detect the presence of rare fetal nucleated red blood cells (NRBCs) circulating in maternal blood. The rationales for enrichment and for automated image analysis for "rare-event" detection are reviewed. We also describe the application of automated image analysis to 42 maternal blood samples, using a protocol consisting of one-step enrichment followed by immunocytochemical staining for fetal hemoglobin (HbF) and FISH for X- and Y-chromosomal sequences. Automated image analysis consisted of multimode microscopy and subsequent visual evaluation of image memories containing the selected objects. The FISH results were compared with the results of conventional karyotyping of the chorionic villi. By use of manual screening, 43% of the slides were found to be positive (>=1 NRBC), with a mean number of 11 NRBCs (range 1-40). By automated microscopy, 52% were positive, with on average 17 NRBCs (range 1-111). There was a good correlation between both manual and automated screening, but the NRBC yield from automated image analysis was found to be superior to that from manual screening (P=.0443), particularly when the NRBC count was >15. Seven (64%) of 11 XY fetuses were correctly diagnosed by FISH analysis of automatically detected cells, and all discrepancies were restricted to the lower cell-count range. We believe that automated microscopy and image analysis reduce the screening workload, are more sensitive than manual evaluation, and can be used to detect rare HbF-containing NRBCs in maternal blood.