Integrated functional anlaysis of quorum-sensing in the rice pathogenic bacterium Burkholderia glumae

Quorum sensing (QS) is a cell-to-cell communication mechanism that allows bacterial cells to collectively behave like a multicellular organism. It regulates the expression of toxoflavin, one of the major virulence factors of the rice pathogen, Burkholderia glumae. The QS system of B. glumae is mediated by the core genes, tofI and tofR. N-octanoyl-L-homoserine lactone, the primary QS signal molecule of B. glumae, is synthesized by tofI and binds to the cognate receptor tofR at the quorum point. However, tofI and tofR null mutants produce toxoflavin in certain growth conditions, indicating the presence of tofI- and tofR-independent pathways for toxoflavin production. The present study identified regulators required for the tofI- and tofR-independent pathways, including flagella transcriptional activator, diguanylate cyclase, O-antigen polymerase family protein, QsmR QS-dependent master regulator and one hypothetical protein with its encoding gene located upstream of toxJ (encoding toxoflavin production activator). A novel QS regulatory element, tofM, was identified as a positive regulator of pathogenicity and a putative modulator of tofR in B. glumae. RNA-sequencing was also performed to investigate the QS regulon and medium condition-dependent gene expression in B. glumae. A large collection of target genes and noncoding RNAs was detected by comparative transcriptome analysis. From a comparison of the transcriptional profile of the wild type (336gr-1) and quorum sensing mutants grown on solid and liquid media, it is postulated that an alternative global regulator is activated to compensate for the dysfunction of AHL QS on solid medium

A molecular genetic study on the Tofl/TofR quorum-sensing system of Burkholderia glumae: the major pathogen that causes bacterial panicle blight of rice

Burkholderia glumae is the major causal agent of an economically important rice disease, bacterial panicle blight (BPB). The known virulence factors of B. glumae share the TofI/TofR quorum sensing system as their regulator. tofI and tofR genes encode the N-acyl homoserine lactone (AHL) synthase for the B. glumae quorum sensing signals, N-octanoyl homoserine lactone (C8-HSL) and N-hexanoyl homoserine lactone (C6-HSL), and the receptor for AHL, respectively. To better understand the relationship between quorum sensing and known virulence factors (toxoflavin, flagella and lipase), as well as, putative virulence factors (i.e. extracellular polysaccharide), mutagenetic and phenotypic analyses were applied to this study. A technical breakthrough is the creation of a novel deletion mutation system-pBBSacB vector, which can effectively delete target genes from the genome and gives more reliable results. Quorum sensing gene deletion mutants were successfully created by using pBBSacB with a sucrose-sensitive counter selective marker, SacB. The parental strain 336gr-1 and its mutants have undergone a series of phenotypic observations and quantification tests for virulence changes. Toxoflavin and swarming motility were confirmed as the major virulence factors in 336gr-1, whereas lipase and EPS were not determined as critical for causing symptoms. The results confirmed the importance of quorum sensing system in expressing virulence, but also indicated that other regulators may be implicated in pathogenicity. Additionally, orf1, which is located between tofI and tofR, was postulated as a functional regulatory component

The Relationship Between Twitter Sentiment and Stock Performance: A Decision Tree Approach

Social media has become a communication tool, but also a valuable database for researchers and practitioners to gather information, share knowledge, as well as express opinions about stock performance. The sentiment embedded in social media content can be analyzed to predict stock performance. Although numerous past studies have attempted to predict stock price movement using social media sentiment, some emerging analytical tools, like existing lexicons, may require further testing and validation in a financial decision making context. In this study, we develop and test predictive models for stock price and trend forecasting. By using a large-scale sample of tweets collected from Twitter, related to four companies, Apple, Google, Microsoft, and Netflix, we propose a novel decision tree approach to stock performance prediction. Based on our findings, we then provide theoretical and practical implications and discuss the directions for future work

Learning to Rank for Active Learning via Multi-Task Bilevel Optimization

Active learning is a promising paradigm to reduce the labeling cost by strategically requesting labels to improve model performance. However, existing active learning methods often rely on expensive acquisition function to compute, extensive modeling retraining and multiple rounds of interaction with annotators. To address these limitations, we propose a novel approach for active learning, which aims to select batches of unlabeled instances through a learned surrogate model for data acquisition. A key challenge in this approach is developing an acquisition function that generalizes well, as the history of data, which forms part of the utility function's input, grows over time. Our novel algorithmic contribution is a bilevel multi-task bilevel optimization framework that predicts the relative utility -- measured by the validation accuracy -- of different training sets, and ensures the learned acquisition function generalizes effectively. For cases where validation accuracy is expensive to evaluate, we introduce efficient interpolation-based surrogate models to estimate the utility function, reducing the evaluation cost. We demonstrate the performance of our approach through extensive experiments on standard active classification benchmarks. By employing our learned utility function, we show significant improvements over traditional techniques, paving the way for more efficient and effective utility maximization in active learning applications

AdvCheck: Characterizing Adversarial Examples via Local Gradient Checking

Deep neural networks (DNNs) are vulnerable to adversarial examples, which may lead to catastrophe in security-critical domains. Numerous detection methods are proposed to characterize the feature uniqueness of adversarial examples, or to distinguish DNN's behavior activated by the adversarial examples. Detections based on features cannot handle adversarial examples with large perturbations. Besides, they require a large amount of specific adversarial examples. Another mainstream, model-based detections, which characterize input properties by model behaviors, suffer from heavy computation cost. To address the issues, we introduce the concept of local gradient, and reveal that adversarial examples have a quite larger bound of local gradient than the benign ones. Inspired by the observation, we leverage local gradient for detecting adversarial examples, and propose a general framework AdvCheck. Specifically, by calculating the local gradient from a few benign examples and noise-added misclassified examples to train a detector, adversarial examples and even misclassified natural inputs can be precisely distinguished from benign ones. Through extensive experiments, we have validated the AdvCheck's superior performance to the state-of-the-art (SOTA) baselines, with detection rate ($\sim \times 1.2$) on general adversarial attacks and ($\sim \times 1.4$) on misclassified natural inputs on average, with average 1/500 time cost. We also provide interpretable results for successful detection.Comment: 26 page

The Evaluation of Asset Pricing Models in Hong Kong Stock Market

Asset pricing models play an important role in financial markets. Different asset pricing models take diverse factors into account. The study in this paper focus on the performance of different asset pricing models including the CAPM, the Fama and French (1993) three-factor model (FF3), the Fama and French (1993) five-factor model (FF5), the Kim (2006) two-factor model (K2), the Chen, Novy-Marx and Zhang (2010) three-factor model (C3), the CCAPM, the Campbell (1996) five-factor model (C5), and the Vassalou (2003) two-factor model (V2) in Hong Kong stock market in the period from 1992 to 2011. The time series regression, cross sectional regression, GRS F-tests, Hansen and Jagannathan (1997) distance, the Fama-MacBeth (1973) t-test and the Shanken (1992) errors in variables (EIV) corrected t-test are used in this paper. The result of this paper shows that the model FF5 and C3 work better than other models in Hong Kong stock market. Key Words: asset pricing models, performance, CAPM, APT-motivated models, CCAPM, Intertemporal CCAPM, Hong Kong stock marke

Just Fine-tune Twice: Selective Differential Privacy for Large Language Models

With the increasing adoption of NLP models in real-world products, it becomes more and more important to protect these models from privacy leakage. Because private information in language data is sparse, previous research formalized a Selective-Differential-Privacy (SDP) notion to provide protection for sensitive tokens detected by policy functions, and prove its effectiveness on RNN-based models. But the previous mechanism requires separating the private and public model parameters and thus cannot be applied on large attention-based models. In this paper, we propose a simple yet effective just-fine-tune-twice privacy mechanism to first fine-tune on in-domain redacted data and then on in-domain private data, to achieve SDP for large Transformer-based language models. We also design explicit and contextual policy functions to provide protections at different levels. Experiments show that our models achieve strong performance while staying robust to the canary insertion attack. We further show that even under low-resource settings with a small amount of in-domain data, SDP can still improve the model utility. We will release the code, data and models to facilitate future research