Response Enhanced Semi-supervised Dialogue Query Generation

Leveraging vast and continually updated knowledge from the Internet has been considered an important ability for a dialogue system. Therefore, the dialogue query generation task is proposed for generating search queries from dialogue histories, which will be submitted to a search engine for retrieving relevant websites on the Internet. In this regard, previous efforts were devoted to collecting conversations with annotated queries and training a query producer (QP) via standard supervised learning. However, these studies still face the challenges of data scarcity and domain adaptation. To address these issues, in this paper, we propose a semi-supervised learning framework -- SemiDQG, to improve model performance with unlabeled conversations. Based on the observation that the search query is typically related to the topic of dialogue response, we train a response-augmented query producer (RA) to provide rich and effective training signals for QP. We first apply a similarity-based query selection strategy to select high-quality RA-generated pseudo queries, which are used to construct pseudo instances for training QP and RA. Then, we adopt the REINFORCE algorithm to further enhance QP, with RA-provided rewards as fine-grained training signals. Experimental results and in-depth analysis of three benchmarks show the effectiveness of our framework in cross-domain and low-resource scenarios. Particularly, SemiDQG significantly surpasses ChatGPT and competitive baselines. Our code is available at \url{https://github.com/DeepLearnXMU/SemiDQG}.Comment: AAAI-24 main track pape

Structure and regulation of ZCCHC4 in m6A-methylation of 28S rRNA.

N6-methyladenosine (m6A) modification provides an important epitranscriptomic mechanism that critically regulates RNA metabolism and function. However, how m6A writers attain substrate specificities remains unclear. We report the 3.1 Å-resolution crystal structure of human CCHC zinc finger-containing protein ZCCHC4, a 28S rRNA-specific m6A methyltransferase, bound to S-adenosyl-L-homocysteine. The methyltransferase (MTase) domain of ZCCHC4 is packed against N-terminal GRF-type and C2H2 zinc finger domains and a C-terminal CCHC domain, creating an integrated RNA-binding surface. Strikingly, the MTase domain adopts an autoinhibitory conformation, with a self-occluded catalytic site and a fully-closed cofactor pocket. Mutational and enzymatic analyses further substantiate the molecular basis for ZCCHC4-RNA recognition and a role of the stem-loop structure within substrate in governing the substrate specificity. Overall, this study unveils unique structural and enzymatic characteristics of ZCCHC4, distinctive from what was seen with the METTL family of m6A writers, providing the mechanistic basis for ZCCHC4 modulation of m6A RNA methylation

Epi-illumination SPIM for volumetric imaging with high spatial-temporal resolution.

We designed an epi-illumination SPIM system that uses a single objective and has a sample interface identical to that of an inverted fluorescence microscope with no additional reflection elements. It achieves subcellular resolution and single-molecule sensitivity, and is compatible with common biological sample holders, including multi-well plates. We demonstrated multicolor fast volumetric imaging, single-molecule localization microscopy, parallel imaging of 16 cell lines and parallel recording of cellular responses to perturbations

Nucleotide bias of DCL and AGO in plant anti-virus gene silencing

Plant Dicer-like (DCL) and Argonaute (AGO) are the key enzymes involved in anti-virus post-transcriptional gene silencing (AV-PTGS). Here we show that AV-PTGS exhibited nucleotide preference by calculating a relative AV-PTGS efficiency on processing viral RNA substrates. In comparison with genome sequences of dicot-infecting Turnip mosaic virus (TuMV) and monocot-infecting Cocksfoot streak virus (CSV), viral-derived small interfering RNAs (vsiRNAs) displayed positive correlations between AV-PTGS efficiency and G+C content (GC%). Further investigations on nucleotide contents revealed that the vsiRNA populations had G-biases. This finding was further supported by our analyses of previously reported vsiRNA populations in diverse plant-virus associations, and AGO associated Arabidopsis endogenous siRNA populations, indicating that plant AGOs operated with G-preference. We further propose a hypothesis that AV-PTGS imposes selection pressure(s) on the evolution of plant viruses. This hypothesis was supported when potyvirus genomes were analysed for evidence of GC elimination, suggesting that plant virus evolution to have low GC% genomes would have a unique function, which is to reduce the host AV-PTGS attack during infections

Efficient and specific gene knockdown by small interfering RNAs produced in bacteria

Synthetic small interfering RNAs (siRNAs) are an indispensable tool to investigate gene function in eukaryotic cells1,2 and may be used for therapeutic purposes to knockdown genes implicated in disease3. Thus far, most synthetic siRNAs have been produced by chemical synthesis. Here we present a method to produce highly potent siRNAs in E. coli. This method relies on ectopic expression of p19, a siRNA-binding protein found in a plant RNA virus4, 5. When expressed in E. coli, p19 stabilizes ~21 nt siRNA-like species produced by bacterial RNase III. Transfection of mammalian cells with siRNAs, generated in bacteria expressing p19 and a hairpin RNA encoding 200 or more nucleotides of a target gene, at low nanomolar concentrations reproducibly knocks down gene expression by ~90% without immunogenicity or off-target effects. Because bacterially produced siRNAs contain multiple sequences against a target gene, they may be especially useful for suppressing polymorphic cellular or viral genes

Improvement of dielectric loss of doped Ba0.5Sr0.5TiO3 thin films for tunable microwave devices

Al2O3-Ba0.5Sr0.5TiO3 (Al2O3-BST) thin films, with different Al2O3 contents, were deposited on (100) LaAlO3 substrate by pulsed laser deposition (PLD) technique. The Al2O3-BST films was demosnstrated to be a suitable systems to fabricate ferroelectric thin films with low dielectric loss and higher figure of merit for tunable microwave devices. Pure BST thin films were also fabricated for comparison purpose. The films' structure and morphology were analyzed by X-ray diffractiopn and scanning electron microscopy, respectively; nad showed that the surface roughness for the Al2O3-BST films increased with the Al2O3 content. Apart from that, the broadening in the intensity peak in XRD result indicating the grain size of the Al2O3-BST films reduced with the increasing of Al2O3 dopant. We measured the dielctric properties of Al2O3-BST films with a home-made non-destructive dual resonator method at frequency ~ 7.7 GHZ. The effect of doped Al2O3 into BST thin films significantly reduced the dielectric constant, dielectric loss and tunability compare to pure BST thin film. Our result shows the figure of merit (K), used to compare the films with varied dielectric properties, increased with the Al2O3 content. Therefore Al2O3-BST films show the potential to be exploited in tunable microwave devices.Comment: 8 pages, 4 figures, 1 table. Accepted & tentatively for Feb 15 2004 issue, Journal of Applied Physic

A hybrid influence method based on information entropy to identify the key nodes

Identifying the key nodes in complicated networks is an essential topic. A number of methods have been developed in recent years to solve this issue more effectively. Multi-attribute ranking is a widely used and efficient method to increase the accuracy of identifying the key nodes. Using k-shell iteration information and propagation threshold differences, we thoroughly analyze the node’s position attribute and the propagation attribute to offer a hybrid influence method based on information entropy. The two attributes will be weighted using the information entropy weighting method, and then the nodes’ influence ranking will be calculated. Correlation experiments in nine different networks were carried out based on the Susceptible–Infected–Recovered (SIR) model. Among these, we use the imprecision function, Kendall’s correlation coefficient, and the complementary cumulative distribution function to validate the suggested method. The experimental results demonstrate that our suggested method outperforms previous node ranking methods in terms of monotonicity, relevance, and accuracy and performs well to achieve a more accurate ranking of nodes in the network

Early detection of pine wilt disease tree candidates using time-series of spectral signatures

Pine wilt disease (PWD), caused by pine wood nematode (PWN), poses a tremendous threat to global pine forests because it can result in rapid and widespread infestations within months, leading to large-scale tree mortality. Therefore, the implementation of preventive measures relies on early detection of PWD. Unmanned aerial vehicle (UAV)-based hyperspectral images (HSI) can detect tree-level changes and are thus an effective tool for forest change detection. However, previous studies mainly used single-date UAV-based HSI data, which could not monitor the temporal changes of disease distribution and determine the optimal detection period. To achieve these purposes, multi-temporal data is required. In this study, Pinus koraiensis stands were surveyed in the field from May to October during an outbreak of PWD. Concurrently, multi-temporal UAV-based red, green, and blue bands (RGB) and HSI data were also obtained. During the survey, 59 trees were confirmed to be infested with PWD, and 59 non-infested trees were used as control. Spectral features of each tree crown, such as spectral reflectance, first and second-order spectral derivatives, and vegetation indices (VIs), were analyzed to identify those useful for early monitoring of PWD. The Random Forest (RF) classification algorithm was used to examine the separability between the two groups of trees (control and infested trees). The results showed that: (1) the responses of the tree crown spectral features to PWD infestation could be detected before symptoms were noticeable in RGB data and field surveys; (2) the spectral derivatives were the most discriminable variables, followed by spectral reflectance and VIs; (3) based on the HSI data from July to October, the two groups of trees were successfully separated using the RF classifier, with an overall classification accuracy of 0.75–0.95. Our results illustrate the potential of UAV-based HSI for PWD early monitoring