Heterologous expression and characterization of a malathion-hydrolyzing carboxylesterase from a thermophilic bacterium, Alicyclobacillus tengchongensis

A carboxylesterase gene from thermophilic bacterium, Alicyclobacillus tengchongensis, was cloned and expressed in Escherichia coli BL21 (DE3). The gene coded for a 513 amino acid protein with a calculated molecular mass of 57.82 kDa. The deduced amino acid sequence had structural features highly conserved among serine hydrolases, including Ser204, Glu325, and His415 as a catalytic triad, as well as type-B carboxylesterase serine active site (FGGDPENITIGGQSAG) and type-B carboxylesterase signature 2 (EDCLYLNIWTP). The purified enzyme exhibited optimum activity with β-naphthyl acetate at 60 °C and pH 7 as well as stability at 25 °C and pH 7. One unit of the enzyme hydrolyzed 5 mg malathion l(−1) by 50 % within 25 min and 89 % within 100 min. The enzyme strongly degraded malathion and has a potential use for the detoxification of malathion residues

Identify latent group structures in panel data: The classifylasso command

In this article, we introduce a new command, classifylasso, that implements the classifier-lasso method (Su, Shi, and Phillips, 2016, Econometrica 84: 2215–2264) to simultaneously identify and estimate unobserved parameter heterogeneity in panel-data models using penalized techniques. We document the functionality of this command, including 1) penalized least-squares estimation of group-specific coefficients and classification of unknown group membership under a certain number of groups; 2) two lasso-type estimators with robust standard errors, namely, classifier-lasso and postlasso; and 3) determination of the number of groups based on an information criterion. We further develop some postestimation commands to display and visualize the estimation results

Unlocking the mystery of the hard-to-sequence phage genome: PaP1 methylome and bacterial immunity

BACKGROUND: Whole-genome sequencing is an important method to understand the genetic information, gene function, biological characteristics and survival mechanisms of organisms. Sequencing large genomes is very simple at present. However, we encountered a hard-to-sequence genome of Pseudomonas aeruginosa phage PaP1. Shotgun sequencing method failed to complete the sequence of this genome. RESULTS: After persevering for 10 years and going over three generations of sequencing techniques, we successfully completed the sequence of the PaP1 genome with a length of 91,715 bp. Single-molecule real-time sequencing results revealed that this genome contains 51 N-6-methyladenines and 152 N-4-methylcytosines. Three significant modified sequence motifs were predicted, but not all of the sites found in the genome were methylated in these motifs. Further investigations revealed a novel immune mechanism of bacteria, in which host bacteria can recognise and repel modified bases containing inserts in a large scale. This mechanism could be accounted for the failure of the shotgun method in PaP1 genome sequencing. This problem was resolved using the nfi(-) mutant of Escherichia coli DH5α as a host bacterium to construct a shotgun library. CONCLUSIONS: This work provided insights into the hard-to-sequence phage PaP1 genome and discovered a new mechanism of bacterial immunity. The methylome of phage PaP1 is responsible for the failure of shotgun sequencing and for bacterial immunity mediated by enzyme Endo V activity; this methylome also provides a valuable resource for future studies on PaP1 genome replication and modification, as well as on gene regulation and host interaction. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2164-15-803) contains supplementary material, which is available to authorized users

Regional innovation and spillover effects of foreign direct investment in China: a threshold approach

Using a data set on twenty-nine Chinese provinces for the period 1985–2008, this paper establishes a threshold model to analyse the relationship between spillover effects of foreign direct investment (FDI) and regional innovation in China. There is clear evidence of double-threshold effects of regional innovation on productivity spillovers from FDI. Specifically, only when the level of regional innovation reaches the minimum innovation threshold will FDI in the region begin to produce positive productivity spillovers. Furthermore, positive productivity spillovers from FDI will be substantial only when the level of regional innovation attains a higher threshold. The double threshold divides Chinese provinces into three super-regions in terms of innovation, with most provinces positioned within the middle-level innovation super-region. Policy implications are discussed

Rethinking Alignment and Uniformity in Unsupervised Image Semantic Segmentation

Unsupervised image semantic segmentation(UISS) aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the critical properties from the view of feature alignments and feature uniformity for UISS models. We also make a comparison between UISS and image-wise representation learning. Based on the analysis, we argue that the existing MI-based methods in UISS suffer from representation collapse. By this, we proposed a robust network called Semantic Attention Network(SAN), in which a new module Semantic Attention(SEAT) is proposed to generate pixel-wise and semantic features dynamically. Experimental results on multiple semantic segmentation benchmarks show that our unsupervised segmentation framework specializes in catching semantic representations, which outperforms all the unpretrained and even several pretrained methods.Comment: AAAI2

Aggregation of Disentanglement: Reconsidering Domain Variations in Domain Generalization

Domain Generalization (DG) is a fundamental challenge for machine learning models, which aims to improve model generalization on various domains. Previous methods focus on generating domain invariant features from various source domains. However, we argue that the domain variantions also contain useful information, ie, classification-aware information, for downstream tasks, which has been largely ignored. Different from learning domain invariant features from source domains, we decouple the input images into Domain Expert Features and noise. The proposed domain expert features lie in a learned latent space where the images in each domain can be classified independently, enabling the implicit use of classification-aware domain variations. Based on the analysis, we proposed a novel paradigm called Domain Disentanglement Network (DDN) to disentangle the domain expert features from the source domain images and aggregate the source domain expert features for representing the target test domain. We also propound a new contrastive learning method to guide the domain expert features to form a more balanced and separable feature space. Experiments on the widely-used benchmarks of PACS, VLCS, OfficeHome, DomainNet, and TerraIncognita demonstrate the competitive performance of our method compared to the recently proposed alternatives