Personal Attribute Prediction from Conversations

Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation. Conversations containing rich personal knowledge can be regarded as a main source to populate the PKB. Given a user, a user attribute, and user utterances from a conversational system, we aim to predict the personal attribute value for the user, which is helpful for the enrichment of PKBs. However, there are three issues existing in previous studies: (1) manually labeled utterances are required for model training; (2) personal attribute knowledge embedded in both utterances and external resources is underutilized; (3) the performance on predicting some difficult personal attributes is unsatisfactory. In this paper, we propose a framework DSCGN based on the pre-trained language model with a noise-robust loss function to predict personal attributes from conversations without requiring any labeled utterances. We yield two categories of supervision, i.e., document-level supervision via a distant supervision strategy and contextualized word-level supervision via a label guessing method, by mining the personal attribute knowledge embedded in both unlabeled utterances and external resources to fine-tune the language model. Extensive experiments over two real-world data sets (i.e., a profession data set and a hobby data set) show our framework obtains the best performance compared with all the twelve baselines in terms of nDCG and MRR.Comment: Accepted by WWW'22 Companio

Supervised Adversarial Contrastive Learning for Emotion Recognition in Conversations

Extracting generalized and robust representations is a major challenge in emotion recognition in conversations (ERC). To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations. The framework applies contrast-aware adversarial training to generate worst-case samples and uses a joint class-spread contrastive learning objective on both original and adversarial samples. It can effectively utilize label-level feature consistency and retain fine-grained intra-class features. To avoid the negative impact of adversarial perturbations on context-dependent data, we design a contextual adversarial training strategy to learn more diverse features from context and enhance the model's context robustness. We develop a sequence-based method SACL-LSTM under this framework, to learn label-consistent and context-robust emotional features for ERC. Experiments on three datasets demonstrate that SACL-LSTM achieves state-of-the-art performance on ERC. Extended experiments prove the effectiveness of the SACL framework.Comment: 16 pages, accepted by ACL 202

Repressive Mutations Restore Function-Loss Caused by the Disruption of Trimerization in \u3cem\u3eEscherichia coli\u3c/em\u3e Multidrug Transporter AcrB

AcrAB-TolC and their homologs are major multidrug efflux systems in Gram-negative bacteria. The inner membrane component AcrB functions as a trimer. Replacement of Pro223 by Gly in AcrB decreases the trimer stability and drastically reduces the drug efflux activity. The goal of this study is to identify suppressor mutations that restore function to mutant AcrBP223G and explore the mechanism of function recovery. Two methods were used to introduce random mutations into the plasmid of AcrBP223G. Mutants with elevated drug efflux activity were identified, purified, and characterized to examine their expression level, trimer stability, interaction with AcrA, and substrate binding. Nine single-site repressor mutations were identified, including T199M, D256N, A209V, G257V, M662I, Q737L, D788K, P800S, and E810K. Except for M662I, all other mutations located in the docking region of the periplasmic domain. While three mutations, T199M, A209V, and D256N, significantly increased the trimer stability, none of them restored the trimer affinity to the wild type level. M662, the only site of mutation that located in the porter domain, was involved in substrate binding. Our results suggest that the function loss resulted from compromised AcrB trimerization could be restored through various mechanisms involving the compensation of trimer stability and substrate binding

Data on Spectrum-Based Fluorescence Resonance Energy Transfer Measurement of \u3cem\u3eE. coli\u3c/em\u3e Multidrug Transporter AcrB

This paper presented the dataset of correction parameters used in the determination of the energy transfer efficiencies from the spectrum-based fluorescence resonance energy transfer (FRET) measurement in a trimeric membrane protein AcrB. The cyan fluorescent protein (CFP) and yellow fluorescent protein (YPet) were used as the donor and acceptor, respectively. Two AcrB fusion proteins were constructed, AcrB-CFP and AcrB-YPet. The proteins were co-expressed in Escherichia coli cells, and energy transfer efficiency were determined in live cells. To obtain reliable energy transfer data, a complete set of correction parameters need to be first determined to accommodate for factors such as background fluorescence and spectra overlap. This paper described the methodology and determination of the correction factors, which are useful data and reference points for researchers working on fluorescence measurement of membrane protein complexes in live bacteria cells. Further interpretation and discussion of these data can be found in “Comparison of in vitro and in vivo oligomeric states of a wild type and mutant trimeric inner membrane multidrug transporter” (Wang et al., in press)

Repressive Mutations Restore Function-Loss Caused by the Disruption of Trimerization in \u3cem\u3eEscherichia coli\u3c/em\u3e Multidrug Transporter AcrB

AcrAB-TolC and their homologs are major multidrug efflux systems in Gram-negative bacteria. The inner membrane component AcrB functions as a trimer. Replacement of Pro223 by Gly in AcrB decreases the trimer stability and drastically reduces the drug efflux activity. The goal of this study is to identify suppressor mutations that restore function to mutant AcrBP223G and explore the mechanism of function recovery. Two methods were used to introduce random mutations into the plasmid of AcrBP223G. Mutants with elevated drug efflux activity were identified, purified, and characterized to examine their expression level, trimer stability, interaction with AcrA, and substrate binding. Nine single-site repressor mutations were identified, including T199M, D256N, A209V, G257V, M662I, Q737L, D788K, P800S, and E810K. Except for M662I, all other mutations located in the docking region of the periplasmic domain. While three mutations, T199M, A209V, and D256N, significantly increased the trimer stability, none of them restored the trimer affinity to the wild type level. M662, the only site of mutation that located in the porter domain, was involved in substrate binding. Our results suggest that the function loss resulted from compromised AcrB trimerization could be restored through various mechanisms involving the compensation of trimer stability and substrate binding

Dual-Functional-Tag-Facilitated Protein Labeling and Immobilization

An important strategy in the construction of biomimetic membranes and devices is to use natural proteins as the functional components for incorporation in a polymeric or nanocomposite matrix. Toward this goal, an important step is to immobilize proteins with high efficiency and precision without disrupting the protein function. Here, we developed a dual-functional tag containing histidine and the non-natural amino acid azidohomoalanine (AHA). AHA is metabolically incorporated into the protein, taking advantage of the Met-tRNA and Met-tRNA synthetase. Histidine in the tag can facilitate metal-affinity purification, whereas AHA can react with an alkyne-functionalized probe or surface via well-established click chemistry. We tested the performance of the tag using two model proteins, green fluorescence protein and an enzyme pyrophosphatase. We found that the addition of the tag and the incorporation of AHA did not significantly impair the properties of these proteins, and the histidine–AHA tag can facilitate protein purification, immobilization, and labeling

Low-resource Personal Attribute Prediction from Conversation

Personal knowledge bases (PKBs) are crucial for a broad range of applications such as personalized recommendation and Web-based chatbots. A critical challenge to build PKBs is extracting personal attribute knowledge from users' conversation data. Given some users of a conversational system, a personal attribute and these users' utterances, our goal is to predict the ranking of the given personal attribute values for each user. Previous studies often rely on a relative number of resources such as labeled utterances and external data, yet the attribute knowledge embedded in unlabeled utterances is underutilized and their performance of predicting some difficult personal attributes is still unsatisfactory. In addition, it is found that some text classification methods could be employed to resolve this task directly. However, they also perform not well over those difficult personal attributes. In this paper, we propose a novel framework PEARL to predict personal attributes from conversations by leveraging the abundant personal attribute knowledge from utterances under a low-resource setting in which no labeled utterances or external data are utilized. PEARL combines the biterm semantic information with the word co-occurrence information seamlessly via employing the updated prior attribute knowledge to refine the biterm topic model's Gibbs sampling process in an iterative manner. The extensive experimental results show that PEARL outperforms all the baseline methods not only on the task of personal attribute prediction from conversations over two data sets, but also on the more general weakly supervised text classification task over one data set.Comment: Accepted by AAAI'2

Optical Fiber Harsh Environment Sensors

Various optical fiber harsh environment sensors were reported, including the miniaturized inline Fabry-Perot interferometer sensor by femtosecond laser micromachining, the long period fiber grating sensor and the inline core-cladding mode interferometer by CO2 laser irradiations

Functional Relevance of AcrB Trimerization in Pump Assembly and Substrate Binding

AcrB is a multidrug transporter in the inner membrane of Escherichia coli. It is an obligate homotrimer and forms a tripartite efflux complex with AcrA and TolC. AcrB is the engine of the efflux machinery and determines substrate specificity. Active efflux depends on several functional features including proton translocation across the inner membrane through a proton relay pathway in the transmembrane domain of AcrB; substrate binding and migration through the substrate translocation pathway; the interaction of AcrB with AcrA and TolC; and the formation of AcrB homotrimer. Here we investigated two aspects of the inter-correlation between these functional features, the dependence of AcrA-AcrB interaction on AcrB trimerization, and the reliance of substrate binding and penetration on protein-protein interaction. Interaction between AcrA and AcrB was investigated through chemical crosslinking, and a previously established in vivo fluorescent labeling method was used to probe substrate binding. Our data suggested that dissociation of the AcrB trimer drastically decreased its interaction with AcrA. In addition, while substrate binding with AcrB seemed to be irrelevant to the presence or absence of AcrA and TolC, the capability of trimerization and conduction of proton influx did affect substrate binding at selected sites along the substrate translocation pathway in AcrB