Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL

Recently, Text-to-SQL for multi-turn dialogue has attracted great interest. Here, the user input of the current turn is parsed into the corresponding SQL query of the appropriate database, given all previous dialogue history. Current approaches mostly employ end-to-end models and consequently face two challenges. First, dialogue history modeling and Text-to-SQL parsing are implicitly combined, hence it is hard to carry out interpretable analysis and obtain targeted improvement. Second, SQL annotation of multi-turn dialogue is very expensive, leading to training data sparsity. In this paper, we propose a novel decoupled multi-turn Text-to-SQL framework, where an utterance rewrite model first explicitly solves completion of dialogue context, and then a single-turn Text-to-SQL parser follows. A dual learning approach is also proposed for the utterance rewrite model to address the data sparsity problem. Compared with end-to-end approaches, the proposed decoupled method can achieve excellent performance without any annotated in-domain data. With just a few annotated rewrite cases, the decoupled method outperforms the released state-of-the-art end-to-end models on both SParC and CoSQL datasets.
Bag of Tricks for Node Classification with Graph Neural Networks

Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs. However, in addition to their reliance on elaborate architectures and algorithms, there are several key technical details that are frequently overlooked, and yet nonetheless can play a vital role in achieving satisfactory performance. In this paper, we first summarize a series of existing tricks-of-the-trade, and then propose several new ones related to label usage, loss function formulation, and model design that can significantly improve various GNN architectures. We empirically evaluate their impact on final node classification accuracy by conducting ablation studies and demonstrate consistently-improved performance, often to an extent that outweighs the gains from more dramatic changes in the underlying GNN architecture. Notably, many of the top-ranked models on the Open Graph Benchmark (OGB) leaderboard and KDDCUP 2021 Large-Scale Challenge MAG240M-LSC benefit from these techniques we initiated.
Incomplete Graph Representation and Learning via Partial Graph Neural Networks

Graph Neural Networks (GNNs) are gaining increasing attention on graph data learning tasks in recent years. However, in many applications, graph may be coming in an incomplete form where attributes of graph nodes are partially unknown/missing. Existing GNNs are generally designed on complete graphs which can not deal with attribute-incomplete graph data directly. To address this problem, we develop a novel partial aggregation based GNNs, named Partial Graph Neural Networks (PaGNNs), for attribute-incomplete graph representation and learning. Our work is motivated by the observation that the neighborhood aggregation function in standard GNNs can be equivalently viewed as the neighborhood reconstruction formulation. Based on it, we define two novel partial aggregation (reconstruction) functions on incomplete graph and derive PaGNNs for incomplete graph data learning. Extensive experiments on several datasets demonstrate the effectiveness and efficiency of the proposed PaGNNs.
Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation

Question answering (QA) models for reading comprehension have achieved human-level accuracy on in-distribution test sets. However, they have been demonstrated to lack robustness to challenge sets, whose distribution is different from that of training sets. Existing data augmentation methods mitigate this problem by simply augmenting training sets with synthetic examples sampled from the same distribution as the challenge sets. However, these methods assume that the distribution of a challenge set is known a priori, making them less applicable to unseen challenge sets. In this study, we focus on question-answer pair generation (QAG) to mitigate this problem. While most existing QAG methods aim to improve the quality of synthetic examples, we conjecture that diversity-promoting QAG can mitigate the sparsity of training sets and lead to better robustness. We present a variational QAG model that generates multiple diverse QA pairs from a paragraph. Our experiments show that our method can improve the accuracy of 12 challenge sets, as well as the in-distribution accuracy.
Sustained low‐dose interleukin‐2 therapy alleviates pathogenic humoral immunity via elevating the Tfr/Tfh ratio in lupus

Low‐dose interleukin‐2 (IL‐2) has shown promising clinical benefits in the treatment of systemic lupus erythematosus (SLE), but how this therapy alleviates pathogenic humoral immunity remains not well understood. The dilemma is that IL‐2 can suppress both follicular helper and regulatory T (Tfh and Tfr) cells, which counteract each other in regulating autoantibody production.
Patterns of Concomitant Prescription, Over-the-Counter and Natural Sleep Aid Use over a 12-Month Period: A Population Based Study.

STUDY OBJECTIVES Concomitant patterns of sleep aid use may provide insight for understanding the transition to chronic sleep medication use. Therefore, we sought to characterize the trajectories of concomitant natural product (NP), over-the-counter (OTC), and prescribed (Rx) sleep aid use in a population-based sample over 12-months. METHODS Self-reported data on the use of NP, OTC, and Rx sleep aids were extracted from a Canadian longitudinal study on the natural history of insomnia (N=3416, M age = 49.7±14.7 years old; 62% women) at baseline, 6 months and 12 months. Latent class growth modeling was used to identify latent class trajectories using MPlus 7. Participants completed a battery of clinical measures: Ford Insomnia Response to Stress Test, abbreviated Dysfunctional Beliefs and Attitudes about Sleep Scale, Beck Depression Inventory, Insomnia Severity Index and, the Pittsburgh Sleep Quality Index. Associations between class membership and baseline covariates were evaluated. RESULTS Concurrent sleep aid use fell into six distinct latent class trajectories over a 12-month period: Minimal Use (74.5%), Rx-Dominant (11.3%), NP-Dominant (6.3%), OTC-Dominant (4.3%), Rx-NP-Dominant (2.4%), and Rx-OTC-Dominant (1.1%). The three latent classes with prominent prescribed agent use predicted greater incidence of healthcare professional consultations for their sleep (p< 0.05), poorer sleep quality (p< 0.001), elevated dysfunctional sleep beliefs (p< 0.001), and sleep reactivity (p< 0.001). Compared to the other four latent classes, clinical profiles of Rx-NP-dominant and Rx-OTC-dominant groups endorsed greater severity across measures. CONCLUSIONS Patterns of sleep aid use may provide insight for identifying individuals who may be vulnerable to inappropriate self-medicating practices.
PtBRI1.2 promotes shoot growth and wood formation through a brassinosteroid-mediated PtBZR1-PtWNDs module in poplar.

Brassinosteroid-insensitive-1 (BRI1) plays important roles in various signaling pathways controlling plant growth and development. However, the regulatory mechanism of BRI1 in brassinosteroid (BR)-mediated signaling for shoot growth and wood formation in woody plant is largely unknown. In this study, PtBRI1.2, a brassinosteroid-insensitive-1 gene, was overexpressed in poplar. Shoot growth and wood formation of transgenic plants were examined and the regulatory genes involved were verified. PtBRI1.2 was localized to plasma membrane, with a predominant expression in leaves. Ectopic expression of PtBRI1.2 in Arabidopsis bri1-201 and bri1-5 mutants rescued their retarded-growth phenotype. Overexpression of PtBRI1.2 in poplar promoted shoot growth and wood formation of transgenic plants. Further studies revealed that overexpression of PtBRI1.2 impelled the accumulation of PtBZR1 into the nucleus, which subsequently activated PtWNDs to up-regulate the expression of secondary cell wall biosynthesis genes involved in wood formation. Our results suggest that PtBRI1.2 plays a crucial role in regulating shoot growth and wood formation by activating BR signaling.
Electric demand response and bounded rationality: mean-field control for large populations of heterogeneous bounded-rational agents

The increased penetration of renewable energy sources into existing power systems induces challenges in supply–demand balancing. Demand-side flexibility is seen as an option to accommodate variability and limited predictability from renewable energy generation. Heat pumps at residential level, if well coordinated, can be one of those flexibility sources. The complexity involved is high though, since their coordinated operation combines control, population effects and the fact agents may actually not behave as rational decision-makers. We describe here a coordinated control framework that accounts for those aspects altogether. Decentralized model predictive control for large populations of heterogeneous agents is employed. As the cost to be minimized is affected by the population behaviour as a whole through the electricity price, the decentralized control is re-thought as a mean-field game. Existence and uniqueness of a Nash equilibrium are discussed while the Picard–Banach algorithm is used as a solution approach. It is extended to the case of bounded-rational agents. The impact on system dynamics of modelling agents as bounded rational is illustrated through numerical simulations. This article is part of the theme issue ‘The mathematics of energy systems’.
PVA coating of ferrite nanoparticles triggers pH-responsive release of 5-fluorouracil in cancer cells

Abstract The use of magnetic nanoparticles (MNPs) has transformed both diagnostics and therapeutic approaches in cancer treatment. Along with developing novel anti-cancer drugs with high therapeutic potential, researchers are exploring innovative strategies for more targeted delivery in order to alleviate the associated potent side effects. In this study, we describe the synthesis of Mg0.5Co0. 5Fe2O4 ferrite nanoparticles, their functionalisation with polyvinyl alcohol (PVA), and encapsulation of the anti-cancer drug 5-fluorouracil (5-FU). Functionalised nanoparticles viz. PVA-Mg0.5Co0. 5Fe2O4 -5-FU displayed desirable physiochemical properties with regards to the spherical shape, hydrodynamic sizes of <120 nm and relative colloidal stability of up to <−33 mV. The drug encapsulating efficiency was found to be 68%. In vitro cytotoxicity profiles were determined using the MTT and SRB assays, with >65% cell death recorded in MCF-7 and HeLa cancer cell lines. Overall, the nanocomposites exhibited excellent physiochemical elements, high specificity towards cancerous cells and displayed pH-sensitive drug release in a simulated acidic tumour micro-environment. The encapsulation of 5-FU improved bioavailability of the drug in cancer cell lines for a prolonged duration, with the promise to enhance its therapeutic effect, biocompatibility and safety. These MNPs present as promising in vitro delivery systems that can be further developed for therapeutic applications.
A Novel Ensemble Reinforcement Learning Gated Recursive Network for Traffic Speed Forecasting

Traffic speed forecasting is one of the important issues in the intelligent transportation system, which is related to traffic management planning. The existing studies tend to use single models to forecast the traffic speed, and cannot completely extract the complex information of the traffic speed sequence. This research proposes a new hybrid model based on reinforcement learning for the accurate forecasting of traffic speed. The model contains the LSTM network and the GRU network as predictors for in-depth mining of the characteristics of traffic speed data and uses reinforcement learning to integrate the results of the two predictors, combining the advantages of multiple predictors to achieve stable and accurate forecasting results of traffic speed. This paper uses two sets of measured traffic data from Guangzhou to test the effectiveness, and five other traffic speed forecasting models are also established for comparison. Experimental results show that the hybrid model applied in the article has the best performance on both data sets, and the MAPEs are 5.02% and 3.25%.
A Lightweight and Effective Semantic Segmentation Network for Ethnic Clothing Images Based on DeepLab

At present, with the continuous development of deep learning technology, the network applied in the field of image segmentation has continuously improved in accuracy. But then the problem is that there are more and more network parameters. It is difficult to meet the needs for scenarios with poor hardware or high real-time requirements. Therefore, based on the classic DeepLab V3+ model, this paper proposes a new lightweight network structure with the goal of optimizing the size and running speed of the convolutional neural network model while ensuring the segmentation accuracy. This paper uses the MobileNet V2 structure, which is currently popular in the field of mobile terminals, to replace the original ResNet backbone network of DeepLab V3+. Then apply asymmetric convolution to improve the ASPP structure in the DeepLab V3+ network to further reduce the amount of network parameters. Finally, the Dice function and the cross-entropy function are combined to redefine the loss function of the entire network. The experimental results show that, compared with the DeepLab V3+ network, the improved network model is only 10.5% of the latter. It takes 45.8ms to process a 1220×2440 image, which is lower than traditional semantic segmentation algorithms. After testing on our self-built national costume data set, the mPA reached 86.12 and the mIoU index reached 72.83. It not only guarantees the segmentation precision, but also reduces the running time, which can better adapt to the fast segmentation network with higher requirements on segmentation performance.
A Compact Dual-Band Circularly Polarized RFID Reader/WLAN Antenna

In this paper, a compact two-port dual-band circularly polarized antenna for Radio Frequency Identification (RFID) and Wireless Local Area Network (WLAN) is presented. The novelty of this research lies in the combination of four inverted-F meandered monopoles and stacked patch antenna. The antenna is designed to cover the European RFID Ultra-high frequency (UHF) band (865-868 MHz) and WLAN band (5.725-5.85GHz). The proposed antenna operating at 865MHz consists of four inverted-F meandered monopoles printed on substrate. In order to transmit the data collected by the reader to the management data center through WLAN, a stacked antenna operating at WLAN band is added to the available space between the two substrates of the antenna operating at the lower band. The antenna is fabricated. And the performance is validated. The measured results show an impedance bandwidth of 1.97% (860- 877 MHz) and 7.2% (5.5-5.92 GHz). The peak gain is 1.82 dBic in the lower band and 6.02 dBic in the upper band. The circularly polarized radiation of the lower band and the upper band are excited by two ports respectively. Port isolation exhibiting an acceptable result better than 18 dB is achieved. The proposed antenna is a good candidate for ship-borne RFID reader.
Methanosarcina acetivorans simultaneously produces molybdenum, vanadium, and iron-only nitrogenases in response to fixed nitrogen and molybdenum depletion

All nitrogen-fixing bacteria and archaea (diazotrophs) use molybdenum (Mo) nitrogenase to reduce dinitrogen (N2) to ammonia. Some diazotrophs also contain alternative nitrogenases that lack Mo: vanadium (V) and iron-only (Fe) nitrogenases. Among diazotrophs, the regulation and usage of the alternative nitrogenases in methanogens is largely unknown. Methanosarcina acetivorans contains nif, vnf, and anf gene clusters encoding putative Mo-, V-, and Fe-nitrogenases, respectively. This study investigated the effect of fixed nitrogen and Mo/V availability on nitrogenase expression and growth by M. acetivorans. The availability of Mo and V did not affect growth of M. acetivorans with fixed nitrogen but significantly affected growth with N2. M. acetivorans exhibited the fastest growth rate and highest cell yield during growth with N2 in medium containing Mo. Depletion of Mo (Fe-only condition) resulted in a significant decrease in growth rate and cell yield. The addition of V to Mo-depleted medium stimulated diazotrophic growth but was still less than growth in Mo-replete medium. qPCR analysis revealed transcription of the nif operon is only moderately affected by depletion of fixed nitrogen and Mo. However, vnf and anf transcription increased significantly when fixed nitrogen and Mo were depleted, with removal of Mo being the key factor. Immunoblot analysis revealed Mo-nitrogenase is produced when fixed nitrogen is depleted regardless of Mo availability, while V- and Fe-nitrogenases are produced only in the absence of fixed nitrogen and Mo. These results reveal that alternative nitrogenase production in M. acetivorans is tightly controlled and that all three nitrogenases can be simultaneously produced. IMPORTANCE Methanogens and closely related methanotrophs are the only archaea known or predicted to possess nitrogenase. As such, methanogens play critical roles in both the global biological nitrogen and carbon cycles. Moreover, methanogens are an ancient microbial lineage and nitrogenase likely originated in methanogens. An understanding of the usage and properties of nitrogenases in methanogens can provide new insight into the evolution of nitrogen fixation and aid in the development nitrogenase-based biotechnology. This study provides the first evidence that a methanogen can produce all three forms of nitrogenases, even simultaneously. Surprisingly, Mo-nitrogenase was produced in cells grown in the absence of Mo, indicating components of Mo-nitrogenase regulate or are needed to produce V- and Fe-nitrogenases in methanogens. The results provide a foundation to understanding the assembly, regulation, and activity of the alternative nitrogenases in methanogens.
Reg4 defenses the intestine against Salmonella infection via binding the flagellin

Salmonella Typhimurium is gram-negative flagellated bacteria that can cause food-borne gastroenteritis and diarrhea in humans and animals. The regenerating islet-derived family member 4 (Reg4) is overexpressed in the gastrointestinal tract during intestinal inflammation. However, the role of Reg4 in the intestinal inflammation induced by Salmonella Typhimurium is largely unknown. In this study, we reported for the first time that Reg4 has bactericidal activity against intestinal infection caused by Salmonella Typhimurium. In vivo, Reg4 could reduce the colonization of Salmonella Typhimurium and attenuate intestinal inflammation in the Salmonella Typhimurium-infected model. Additionally, the mice with the epithelial cell specific deletion of Reg4 (Reg4ΔIEC) exhibited more severe intestinal inflammation and more colonization of Salmonella Typhimurium. However, the administration of Reg4 could reverse these negative impacts. In vitro, Reg4 protein was showed to inhibit the growth of Salmonella Typhimurium. We further investigate the function motif of Reg4 and find that the “HDPQK” motif in Reg4 is essential to its bactericidal activity. Reg4 exerted the bactericidal effect by binding to the flagellin of Salmonella Typhimurium and suppressing its motility, adhesion, and invasion to the intestinal epithelia. In conclusion, our findings identify Reg4 as a novel antimicrobial peptide against infection by Salmonella Typhimurium and explore its possible mechanism, which may be of great significance for developing novel agents against flagellated micro pathogens.
Sequence analysis paradigm shift reveals unsuspected semantic properties of species proteomes

Alignment-based methods dominate molecular biology. However, by primarily allowing one-to-one comparisons, they are focused on gene-centered viewpoint and lack the broad aperture needed for complex biological systems analysis. We hypothesized existence of contextual information related to gene’s inclusion in a molecular network of the cell being distributed among more than one sequence. The need for conservation of established interactions, which is arguably more important to the evolutionary success of species than conservation of individual function was the rationale behind this. To test whether this information exists, we applied distributional semantics method - Latent Semantic Analysis (LSA) to thousands of species proteomes. Using natural language processing we identified Latent Taxonomic Signatures (LTSs), a novel proteome distributed feature supporting the argument that protein-coding genes do not evolve as taxonomy independent variables. LTSs reflect constraint imposed to individual gene/protein evolution by their genome/proteome context. In summary, discovery of LTSs indicates that genes had to trade some of their “selfishness” by becoming parts of genome conglomerates.
Systems-wide analysis of glycoprotein conformational changes by limited deglycosylation assay

A new method to probe the conformational changes of glycoproteins on a systems-wide scale, termed limited deglycosylation assay (LDA), is described. The method measures the differential rate of deglycosylation of N-glycans on natively folded proteins by the common peptide:N-glycosidase F (PNGase F) enzyme which in turn informs on their spatial presentation and solvent exposure on the protein surface hence ultimately the glycoprotein conformation. LDA involves 1) protein-level N-deglycosylation under native conditions, 2) trypsin digestion under denaturing conditions, 3) glycopeptide enrichment, 4) peptide-level N-deglycosylation and 5) quantitative MS-based analysis of the formerly N-glycosylated peptides. LDA was initially developed and the experimental conditions optimized using bovine RNase B and fetuin. The method was then applied to glycoprotein extracts from LLC-MK2 epithelial cells upon treatment with dithiothreitol to induce endoplasmic reticulum stress and promote protein misfolding. Data from the LDA and 3D structure analysis showed that glycoproteins predominantly undergo structural changes in loops/turns upon ER stress as exemplified with detailed analysis of ephrin-A5, GALNT10, PVR and BCAM. These results show that LDA accurately reports on systems-wide conformational changes of glycoproteins induced under controlled treatment regimes. Thus, LDA opens avenues to study glycoprotein structural changes in a range of other physiological and pathophysiological conditions relevant to acute and chronic diseases.