Metabolic peculiarities of Aspergillus niger disclosed by comparative metabolic genomics

A genome-scale metabolic network and an in-depth genomic comparison of Aspergillus niger with seven other fungi is presented, revealing more than 1,100 enzyme-coding genes that are unique to A. niger

Neighbor Regularized Bayesian Optimization for Hyperparameter Optimization

Bayesian Optimization (BO) is a common solution to search optimal hyperparameters based on sample observations of a machine learning model. Existing BO algorithms could converge slowly even collapse when the potential observation noise misdirects the optimization. In this paper, we propose a novel BO algorithm called Neighbor Regularized Bayesian Optimization (NRBO) to solve the problem. We first propose a neighbor-based regularization to smooth each sample observation, which could reduce the observation noise efficiently without any extra training cost. Since the neighbor regularization highly depends on the sample density of a neighbor area, we further design a density-based acquisition function to adjust the acquisition reward and obtain more stable statistics. In addition, we design a adjustment mechanism to ensure the framework maintains a reasonable regularization strength and density reward conditioned on remaining computation resources. We conduct experiments on the bayesmark benchmark and important computer vision benchmarks such as ImageNet and COCO. Extensive experiments demonstrate the effectiveness of NRBO and it consistently outperforms other state-of-the-art methods.Comment: Accepted by BMVC 202

The Relation between Initial Returns and Audits by the Big Four Accounting Firms

This paper mainly explores the relation between initial returns and audits by the big four accounting firms (the Big Four) in China. The sample period is from January 2007 to December 2012 (the new accounting standards in China is implemented after January 2007 for integrating with the international standards), and selected 1,069 IPO firms listed in the Shanghai Stock Exchange and Shenzhen Stock Exchange in this paper.Many previous studies have proposed the Informational Hypothesis, which states that the initial returns of IPOs being audited by the Big Four are lower than those IPOs being audited by other accounting firms. Oppositely, this paper proposes the Snap-up Hypothesis due to consider the IPOs in mainland China are characterized by “three lows,”: the low reliability of audits being performed by non-Big Four, low proportion of IPOs audits being performed by the Big Four, and low balling ratio. These “three lows” features indicate that the Snap-up Hypothesis applies in the IPOs market of mainland China. In other words, the initial returns of the IPOs being audited by the Big Four are higher than those IPOs being audited by other accounting firms due to the Big Four have the superior reputations.This paper further collects the trading volumes and the turnover ratio on the first day, and selects the Big Four audited IPOs by snap-up tide. As above mentioned, because the snap-up tide and raised stock prices on the first-day listing, investors may purchase the shares when offering and sell them on the first-day listing to obtain considerable profits

The Direct Assignment Option as a Modular Design Component: An Example for the Setting of Two Predefined Subgroups

Background. A phase II design with an option for direct assignment (stop randomization and assign all patients to experimental treatment based on interim analysis, IA) for a predefined subgroup was previously proposed. Here, we illustrate the modularity of the direct assignment option by applying it to the setting of two predefined subgroups and testing for separate subgroup main effects. Methods. We power the 2-subgroup direct assignment option design with 1 IA (DAD-1) to test for separate subgroup main effects, with assessment of power to detect an interaction in a post-hoc test. Simulations assessed the statistical properties of this design compared to the 2-subgroup balanced randomized design with 1 IA, BRD-1. Different response rates for treatment/control in subgroup 1 (0.4/0.2) and in subgroup 2 (0.1/0.2, 0.4/0.2) were considered. Results. The 2-subgroup DAD-1 preserves power and type I error rate compared to the 2-subgroup BRD-1, while exhibiting reasonable power in a post-hoc test for interaction. Conclusion. The direct assignment option is a flexible design component that can be incorporated into broader design frameworks, while maintaining desirable statistical properties, clinical appeal, and logistical simplicity

The intra- and extracellular proteome of Aspergillus niger growing on defined medium with xylose or maltose as carbon substrate

The filamentous fungus Aspergillus niger is well-known as a producer of primary metabolites and extracellular proteins. For example, glucoamylase is the most efficiently secreted protein of Aspergillus niger, thus the homologous glucoamylase (glaA) promoter as well as the glaA signal sequence are widely used for heterologous protein production. Xylose is known to strongly repress glaA expression while maltose is a potent inducer of glaA promoter controlled genes. For a more profound understanding of A. niger physiology, a comprehensive analysis of the intra- and extracellular proteome of Aspergillus niger AB1.13 growing on defined medium with xylose or maltose as carbon substrate was carried out using 2-D gel electrophoresis/Maldi-ToF and nano-HPLC MS/MS

Probabilistic models for topic learning from images and captions in online biomedical literatures

Biomedical images and captions are one of the major sources of information in online biomedical publications. They often contain the most important results to be reported, and provide rich information about the main themes in published papers. In the data mining and information retrieval community, there are a lot of research works on using text mining and language modeling algorithms to extract knowledge from the text content of online biomedical publications; however, the problem of knowledge extraction from biomedical images and captions has not been fully studied yet. In this paper, a hierarchical probabilistic topic model with background distribution (HPB) is introduced to uncover the latent semantic topics from the co-occurrence patterns of caption words, visual words and biomedical concepts. With downloaded biomedical figures, restricted captions ar