A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.Comment: Long paper accepted at AAAI 202

Retrieval-Augmented Meta Learning for Low-Resource Text Classification

Meta learning have achieved promising performance in low-resource text classification which aims to identify target classes with knowledge transferred from source classes with sets of small tasks named episodes. However, due to the limited training data in the meta-learning scenario and the inherent properties of parameterized neural networks, poor generalization performance has become a pressing problem that needs to be addressed. To deal with this issue, we propose a meta-learning based method called Retrieval-Augmented Meta Learning(RAML). It not only uses parameterization for inference but also retrieves non-parametric knowledge from an external corpus to make inferences, which greatly alleviates the problem of poor generalization performance caused by the lack of diverse training data in meta-learning. This method differs from previous models that solely rely on parameters, as it explicitly emphasizes the importance of non-parametric knowledge, aiming to strike a balance between parameterized neural networks and non-parametric knowledge. The model is required to determine which knowledge to access and utilize during inference. Additionally, our multi-view passages fusion network module can effectively and efficiently integrate the retrieved information into low-resource classification task. The extensive experiments demonstrate that RAML significantly outperforms current SOTA low-resource text classification models.Comment: Under Revie

Prompt Learning With Knowledge Memorizing Prototypes For Generalized Few-Shot Intent Detection

Generalized Few-Shot Intent Detection (GFSID) is challenging and realistic because it needs to categorize both seen and novel intents simultaneously. Previous GFSID methods rely on the episodic learning paradigm, which makes it hard to extend to a generalized setup as they do not explicitly learn the classification of seen categories and the knowledge of seen intents. To address the dilemma, we propose to convert the GFSID task into the class incremental learning paradigm. Specifically, we propose a two-stage learning framework, which sequentially learns the knowledge of different intents in various periods via prompt learning. And then we exploit prototypes for categorizing both seen and novel intents. Furthermore, to achieve the transfer knowledge of intents in different stages, for different scenarios we design two knowledge preservation methods which close to realistic applications. Extensive experiments and detailed analyses on two widely used datasets show that our framework based on the class incremental learning paradigm achieves promising performance.Comment: Under Revie

Surface plasmon resonance hydrogen sensor based on metallic grating with high sensitivity

Abstract: High sensitivity is obtained at larger resonant incident angle if negative diffraction order of metallic grating is used to excite the surface plasmon. A highly sensitive grating-based surface plasmon resonance (SPR) sensor is designed for the hydrogen detection. A thin palladium (Pd) film deposited on the grating surface is used as transducer. The influences of grating period and the thickness of Pd on the performance of sensor are investigated using rigorous coupled-wave analysis (RCWA) method. The sensitivity as well as the width of the SPR curves and reflective amplitude is considered simultaneously for designing the grating-based SPR hydrogen sensor, and a set of optimized structural parameters is presented. The performance of grating-based SPR sensor is also compared with that of conventional prism-based SPR sensor. 12． M. J. Jory, P. S. Vukusic, and J. R. Sambles, "Development of a prototype gas sensor using surface plasmon resonance on gratings," Sens. Actuators B 17, 203-209 (1994). 13． C. R. Lawrence, N. J. Geddes, D. N. Furlong, and J. R. Sambles, "Surface plasmon resonance studies of immunoreactions utilizing disposable diffraction gratings," Biosens. Bioelectron. 11, 389-400 (1996). 14． D. Zhang, P. Wang, X. Jiao, G. Yuan, J. Zhang, C. Chen, H. Ming, and R. Rao, "Investigation of the sensitivity of H-shaped nano-grating surface plasmon resonance biosensors using rigorous coupled wave analysis," Appl. Phys. A 89, 407-411(2007 5056-5070 (1974). 18． D. R. Shankaran, K. V. Gobi, T. Sakai, K. Matsumoto, K. Toko, and N. Miura, "Surface plasmon resonance immunosensor for highly sensitive detection of 2, 4, 6-trinitrotoluene," Biosens. Bioelectron. 20, 1750-1756 (2005). 19． B. Chadwick, J. Tann, M. Brungs, and M. Gal, "A hydrogen sensor based on the optical generation of surface plasmons in a palladium alloy," Sens. Actuators B 17, 215-220 (1994)

Decoding signaling mechanisms: unraveling the targets of guanylate cyclase agonists in cardiovascular and digestive diseases

Soluble guanylate cyclase agonists and guanylate cyclase C agonists are two popular drugs for diseases of the cardiovascular system and digestive systems. The common denominator in these conditions is the potential therapeutic target of guanylate cyclase. Thanks to in-depth explorations of their underlying signaling mechanisms, the targets of these drugs are becoming clearer. This review explains the recent research progress regarding potential drugs in this class by introducing representative drugs and current findings on them

Circulating methylation level of HTR2A is associated with inflammation and disease activity in rheumatoid arthritis

ObjectivesHTR2A is previously identified as a susceptibility gene for rheumatoid arthritis (RA). In this study, we performed the association analysis between DNA methylation of HTR2A with RA within peripheral blood samples.MethodsWe enrolled peripheral blood samples from 235 patients with RA, 30 osteoarthritis (OA) patients, and 30 healthy controls. The DNA methylation levels of about 218 bp from chr13: 46898190 to chr13: 46897973 (GRCh38/hg38) around HTR2A cg15692052 from patients were analyzed by targeted methylation sequencing.ResultsWe measured methylation status for 7 CpGs in the promoter region of HTR2A and obseved overall methylation status are signficantly increased in RA compared with normal inviduals (FDR= 9.05 x 10-5). The average cg15692052 methylation levels (methylation score) showed a positive correlation with CRP (r=0.15, P=0.023). Compared with the OA group or HC group, the proportion of haplotypes CCCCCCC (FDR=0.02 and 2.81 x 10-6) is signficantly increased while TTTTTCC (FDR =0.01) and TTTTTTT(FDR =6.92 x 10-3) are significantly decreased in RA. We find methylation haplotypes combining with RF and CCP could signficantly enhance the performance of the diagnosing RA and its comorbidities (hypertension, interstitial lung disease, and osteoporosis), especially in interstitial lung disease.ConclusionsIn our study, we found signficant hypermethylation of promoter region of HTR2A which indicates the potential clinical diagnostic role in rheumatoid arthritis

Recombinant mycobacterium tuberculosis fusion protein for diagnosis of mycobacterium tuberculosis infection: a short-term economic evaluation

ObjectivesRecombinant Mycobacterium tuberculosis fusion protein (EC) was anticipated to be used for the scale-up of clinical application for diagnosis of Mycobacterium tuberculosis infection in China, but it lacked a head-to-head economic evaluation based on the Chinese population. This study aimed to estimate the cost-utility and the cost-effectiveness of both EC and tuberculin pure protein derivative (TB-PPD) for diagnosis of Mycobacterium tuberculosis infection in the short term.MethodsFrom a Chinese societal perspective, both cost-utility analysis and cost-effectiveness analysis were performed to evaluate the economics of EC and TB-PPD for a one-year period based on clinical trials and decision tree model, with quality-adjusted life years (QALYs) as the utility-measured primary outcome and diagnostic performance (including the misdiagnosis rate, the omission diagnostic rate, the number of patients correctly classified, and the number of tuberculosis cases avoided) as the effective-measured secondary outcome. One-way and probabilistic sensitivity analyses were performed to validate the robustness of the base-case analysis, and a scenario analysis was conducted to evaluate the difference in the charging method between EC and TB-PPD.ResultsThe base-case analysis showed that, compared with TB-PPD, EC was the dominant strategy with an incremental cost-utility ratio (ICUR) of saving 192,043.60 CNY per QALY gained, and with an incremental cost-effectiveness ratio (ICER) of saving 7,263.53 CNY per misdiagnosis rate reduction. In addition, there was no statistical difference in terms of the omission diagnostic rate, the number of patients correctly classified, and the number of tuberculosis cases avoided, and EC was a similar cost-saving strategy with a lower test cost (98.00 CNY) than that of TB-PPD (136.78 CNY). The sensitivity analysis showed the robustness of cost-utility and cost-effectiveness analysis, and the scenario analysis indicated cost-utility in EC and cost-effectiveness in TB-PPD.ConclusionThis economic evaluation from a societal perspective showed that, compared to TB-PPD, EC was likely to be a cost-utility and cost-effective intervention in the short term in China

Structure of the glucosyltransferase domain of TcdA in complex with RhoA provides insights into substrate recognition.

Clostridioides difficile is one of the most common causes of antibiotic-associated diarrhea in developed countries. As key virulence factors of C. difficile, toxin A (TcdA) and toxin B (TcdB) act by glucosylating and inactivating Rho and Ras family small GTPases in host cells, which leads to actin cytoskeleton disruption, cell rounding, and ultimately cell death. Here we present the co-crystal structure of the glucosyltransferase domain (GTD) of TcdA in complex with its substrate human RhoA at 2.60-angstrom resolution. This structure reveals that TcdA GTD grips RhoA mainly through its switch I and switch II regions, which is complemented by interactions involving RhoA's pre-switch I region. Comprehensive structural comparisons between the TcdA GTD-RhoA complex and the structures of TcdB GTD in complex with Cdc42 and R-Ras reveal both the conserved and divergent features of these two toxins in terms of substrate recognition. Taken together, these findings establish the structural basis for TcdA recognition of small GTPases and advance our understanding of the substrates selectivity of large clostridial toxins

OBSERVATION AND ANALYSIS OF SERS SPECTRUM OF LYSOZYME ON SILVER COLLOID

Author Institution: Department of Chemistry, Wuhan University; Center of Analysis and Measurement, Wuhan University; Department of Chemistry, Wuhan UniversityThe SERS spectrum of lysozyme on silver colloid was obtained. The sample solution was prepared by mixing O-2 ml silver colloid with 0.2 ml lysozyme. In the solutionn, the concentration of lysozyme was 6$\times10^{-2}$TM, chloride (I) was about $1\times10^{-3}$M and PH was 2.0. In the spectrum, the bands at 356, 424, 660, 828, 928, 978, 1190 and $1592 cm^{-1}$ were assigned to the p-hydroxyphenyl ring vibrations of tyrosine. The bands at 424, 620, 928, 1190 and $1508 cm^{-1}$ were assigned to the vibrations of phenylalanine. The bands at 760, 928, 1358, $1562 cm^{-1}$ due to the vibrations of tryptophan. The band at $238 cm^{-1}$ was the interface vibration. The S-S bond peak was observed at $518 cm^{-1}$ and it was very weak. The amide I, II, III band were observed at 1646, 1528 and $1280 cm^{-1}$, respectively. The band at $928 cm^{-1}$ was the skeletal vibration ($C_{O}$-C-N stretching). These suggested that lysozyme molecules adsorbed on the colloidal silver particales with the part of $\alpha$-helix conformation. This also confined that, as same as the normal Raman spectroscopy, the SERS spectrum could be used to obtain the valuable information of the macromolecules