Discovery of Ubonodin,an Antimicrobial Lasso Peptide Active against Members of the Burkholderia cepacia Complex

We report the heterologous expression, structure, and antimicrobial activity of a lasso peptide, ubonodin, encoded in the genome of Burkholderia ubonensis. The topology of ubonodin is unprecedented amongst lasso peptides, with 18 of its 28 amino acids found in the mechanically bonded loop segment. Ubonodin inhibits RNA polymerase in vitro and has potent antimicrobial activity against several pathogenic members of the Burkholderia genus, most notably B. cepacia and B. multivorans, causative agents of lung infections in cystic fibrosis patients.     

Virtual metrology modeling of time-dependent spectroscopic signals by a fused lasso algorithm

This paper proposes a fused lasso model to identify significant features in the spectroscopic signals obtained from a semiconductor manufacturing process, and to construct a reliable virtual metrology (VM) model. Analysis of spectroscopic signals involves combinations of multiple samples collected over time, each with a vast number of highly correlated features. This leads to enormous amounts of data, which is a challenge even for modern-day computers to handle. To simplify such complex spectroscopic signals, dimension reduction is critical. The fused lasso is a regularized regression method that performs automatic variable selection for the predictive modeling of highly correlated datasets such as those of spectroscopic signals. Furthermore, the fused lasso is especially useful for analyzing high-dimensional data in which the features exhibit a natural order, as is the case in spectroscopic signals. In this paper, we conducted an experimental study to demonstrate the usefulness of a fused lasso-based VM model and compared it with other VM models based on the lasso and elastic-net models. The results showed that the VM model constructed with features selected by the fused lasso algorithm yields more accurate and robust predictions than the lasso- and elastic net-based VM models. To the best of our knowledge, ours is the first attempt to apply a fused lasso to VM modeling.     

基于先验知识的肝脏轮廓线提取算法研究

准确有效地提取肝脏CT序列的轮廓线是腹部软组织三维模型重建与可视化的关键问题之一。针对肝脏轮廓线提取准确性不高的问题, 提出了一种基于先验知识的肝脏轮廓线提取算法。首先利用拉普拉斯算法进行CT图像增强, 再利用基于边缘先验知识的套索模型对感兴趣区域进行半自动的初始化, 最后通过改进的Snake算法准确地提取肝脏CT图像的边缘。针对序列CT肝脏的边缘提取, 提出根据CT图像序列之间的相关性, 将上一幅图像的轮廓线提取结果作为下一幅CT图像边缘提取的初始化点, 接着批处理地提取CT序列的肝脏边缘。实验结果表明：该算法大大减少了手动初始化结果对目标边缘轮廓准确提取的依赖性, 并有效地解决了肝脏轮廓线的提取问题。     

Initial Molecular Recognition Steps of McjA Precursor during Microcin J25 Lasso Peptide Maturation

Microcin J25 (MccJ25) has emerged as an excellent model to understand the maturation of ribosomal precursor peptides into the entangled lasso fold. MccJ25 biosynthesis relies on the post‐translational modification of the precursor McjA by the ATP‐dependent protease McjB and the lactam synthetase McjC. Here, using NMR spectroscopy, we showed that McjA is an intrinsically disordered protein without detectable conformational preference, which emphasizes the active role of the maturation machinery on the three‐dimensional folding of MccJ25. We further showed that the N‐terminal region of the leader peptide is involved in interaction with both maturation enzymes and identified a predominant interaction of V43–S55 in the core McjA sequence with McjC. Moreover, we demonstrated that residues K23–Q34 in the N‐terminal McjA leader peptide tend to adopt a helical conformation in the presence of membrane mimics, implying a role in directing McjA to the membrane in the vicinity of the lasso synthetase/export machinery. These data provide valuable insights into the initial molecular recognition steps in the MccJ25 maturation process.     

浅谈数字图像抠图技术

本文对Photoshop中一些常用的抠图方法进行了分析和研究，希望能为Photoshop爱好者提供一定的帮助。     

Absolute penalty and shrinkage estimation in partially linear models

In the context of a partially linear regression model, shrinkage semiparametric estimation is considered based on the Stein-rule. In this framework, the coefficient vector is partitioned into two sub-vectors: the first sub-vector gives the coefficients of interest, i.e., main effects (for example, treatment effects), and the second sub-vector is for variables that may or may not need to be controlled. When estimating the first sub-vector, the best estimate may be obtained using either the full model that includes both sub-vectors, or the reduced model which leaves out the second sub-vector. It is demonstrated that shrinkage estimators which combine two semiparametric estimators computed for the full model and the reduced model outperform the semiparametric estimator for the full model. Using the semiparametric estimate for the reduced model is best when the second sub-vector is the null vector, but this estimator suffers seriously from bias otherwise. The relative dominance picture of suggested estimators is investigated. In particular, suitability of estimating the nonparametric component based on the B-spline basis function is explored. Further, the performance of the proposed estimators is compared with an absolute penalty estimator through Monte Carlo simulation. Lasso and adaptive lasso were implemented for simultaneous model selection and parameter estimation. A real data example is given to compare the proposed estimators with lasso and adaptive lasso estimators.     

Sparse multivariate regression with missing values and its application to the prediction of material properties

In the field of materials science and engineering, statistical analysis and machine learning techniques have recently been used to predict multiple material properties from an experimental design. These material properties correspond to response variables in the multivariate regression model. In this study, we conduct a penalized maximum likelihood procedure to estimate model parameters, including the regression coefficients and covariance matrix of response variables. In particular, we employ $l_1$-regularization to achieve a sparse estimation of The regression coefficients and inverse covariance matrix of response variables. In some cases, there may be a relatively large number of missing values in the response variables, owing to the difficulty of collecting data on material properties. We therefore propose a method that incorporates a correlation structure among the response variables into a statistical model to improve the prediction accuracy under the situation with missing values. The expectation maximization algorithm is also constructed, which enables application to a dataset with missing values in the responses. We apply our proposed procedure to real data consisting of 22 material properties.     

Factors Governing the Thermal Stability of Lasso Peptides

Lasso peptides belong to the natural product superfamily of ribosomally synthesized and post-translationally modified peptides (RiPPs). They are defined by an N-terminal macrolactam ring that is threaded by the C-terminal tail. In class II lasso peptides, this fold is maintained only through steric hindrance. Nonetheless, this fold can often withstand prolonged incubation at highly elevated temperatures. However, some lasso peptides will irreversibly unthread into their branched-cyclic counterparts upon heating. In recent years, an increasing number of research studies have focused on studying the factors that govern the thermal stability (or the lack thereof) of lasso peptides by using in vitro stability assays, mutational analysis, and molecular dynamics simulations. In this review, the current state of understanding the physicochemical parameters deciding the fate of a lasso peptide at elevated temperatures is discussed, and an overview is given of the techniques developed to streamline the separation and discrimination of lasso peptides from their branched-cyclic topoisomers.     

Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms

In this paper, we survey and compare different algorithms that, given an overcomplete dictionary of elementary functions, solve the problem of simultaneous sparse signal approximation, with common sparsity profile induced by a ?_p−?_q mixed-norm. Such a problem is also known in the statistical learning community as the group lasso problem. We have gathered and detailed different algorithmic results concerning these two equivalent approximation problems. We have also enriched the discussion by providing relations between several algorithms. Experimental comparisons of the detailed algorithms have also been carried out. The main lesson learned from these experiments is that depending on the performance measure, greedy approaches and iterative reweighted algorithms are the most efficient algorithms either in term of computational complexities, sparsity recovery or mean-square error.