Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams

Conventional text-to-SQL studies are limited to a single task with a fixed-size training and test set. When confronted with a stream of tasks common in real-world applications, existing methods struggle with the problems of insufficient supervised data and high retraining costs. The former tends to cause overfitting on unseen databases for the new task, while the latter makes a full review of instances from past tasks impractical for the model, resulting in forgetting of learned SQL structures and database schemas. To address the problems, this paper proposes integrating semi-supervised learning (SSL) and continual learning (CL) in a stream of text-to-SQL tasks and offers two promising solutions in turn. The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks. The improved solution SFNet takes advantage of the intrinsic connection between CL and SSL. It uses in-memory past information to help current SSL, while adding high-quality pseudo instances in memory to improve future replay. The experiments on two datasets shows that SFNet outperforms the widely-used SSL-only and CL-only baselines on multiple metrics.Comment: Accepted by AAAI-202

Urinary biomarkers associated with podocyte injury in lupus nephritis

The most prevalent and devastating form of organ damage in systemic lupus erythematosus (SLE) is lupus nephritis (LN). LN is characterized by glomerular injury, inflammation, cell proliferation, and necrosis, leading to podocyte injury and tubular epithelial cell damage. Assays for urine biomarkers have demonstrated significant promise in the early detection of LN, evaluation of disease activity, and tracking of reaction to therapy. This is because they are non-invasive, allow for frequent monitoring and easy self-collection, transport and storage. Podocyte injury is believed to be a essential factor in LN. The extent and type of podocyte injury could be connected to the severity of proteinuria, making podocyte-derived cellular debris and injury-related urinary proteins potential markers for the diagnosis and monitoring of LN. This article focuses on studies examining urinary biomarkers associated with podocyte injury in LN, offering fresh perspectives on the application of biomarkers in the early detection and management of LN

Association of Body Mass Index with Insulin-like Growth Factor-1 Levels among 3227 Chinese Children Aged 2–18 Years

Objectives: Insulin-like growth factor-1 (IGF-1) levels are affected by nutritional status, yet there is limited research exploring the association between body mass index (BMI) and IGF-1 levels among children. Methods: This cross-sectional study included 3227 children aged 2–18 years without specific diseases, whose height, weight, and pubertal stages were measured and assessed by pediatricians. BMI standard deviation scores (BMISDS) were used to categorize children as underweight (BMISDS 2). Children were divided into low-level (β = 0.174, 95% CI: 0.141 to 0.208, p β = −0.358, 95% CI: −0.474 to −0.241, p < 0.01) when BMISDS was greater than 1.71 SD. Conclusions: The relationship between BMI and IGF-1 levels was found to depend on the type of variable, and extremely low or high BMI values could result in a tendency toward low IGF-1 levels, emphasizing the importance of maintaining a normal BMI range for normal IGF-1 levels

Biosynthesis of ansamitocin P-3 incurs stress on the producing strain Actinosynnema pretiosum at multiple targets

Abstract Microbial bioactive natural products mediate ecologically beneficial functions to the producing strains, and have been widely used in clinic and agriculture with clearly defined targets and underlying mechanisms. However, the physiological effects of their biosynthesis on the producing strains remain largely unknown. The antitumor ansamitocin P-3 (AP-3), produced by Actinosynnema pretiosum ATCC 31280, was found to repress the growth of the producing strain at high concentration and target the FtsZ protein involved in cell division. Previous work suggested the presence of additional cryptic targets of AP-3 in ATCC 31280. Herein we use chemoproteomic approach with an AP-3-derived photoaffinity probe to profile the proteome-wide interactions of AP-3. AP-3 exhibits specific bindings to the seemingly unrelated deoxythymidine diphosphate glucose-4,6-dehydratase, aldehyde dehydrogenase, and flavin-dependent thymidylate synthase, which are involved in cell wall assembly, central carbon metabolism and nucleotide biosynthesis, respectively. AP-3 functions as a non-competitive inhibitor of all three above target proteins, generating physiological stress on the producing strain through interfering diverse metabolic pathways. Overexpression of these target proteins increases strain biomass and markedly boosts AP-3 titers. This finding demonstrates that identification and engineering of cryptic targets of bioactive natural products can lead to in-depth understanding of microbial physiology and improved product titers

Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning

Single-table text-to-SQL aims to transform a natural language question into a SQL query according to one single table. Recent work has made promising progress on this task by pre-trained language models and a multi-submodule framework. However, zero-shot table, that is, the invisible table in the training set, is currently the most critical bottleneck restricting the application of existing approaches to real-world scenarios. Although some work has utilized auxiliary tasks to help handle zero-shot tables, expensive extra manual annotation limits their practicality. In this paper, we propose a new approach for the zero-shot text-to-SQL task which does not rely on any additional manual annotations. Our approach consists of two parts. First, we propose a new model that leverages the abundant information of table content to help establish the mapping between questions and zero-shot tables. Further, we propose a simple but efficient meta-learning strategy to train our model. The strategy utilizes the two-step gradient update to force the model to learn a generalization ability towards zero-shot tables. We conduct extensive experiments on a public open-domain text-to-SQL dataset WikiSQL and a domain-specific dataset ESQL. Compared to existing approaches using the same pre-trained model, our approach achieves significant improvements on both datasets. Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive. More importantly, on the zero-shot subsets of both the datasets, our approach further increases the improvements

Microstructure evolution and mechanical property of a new multi-component β titanium alloy with ultrahigh strength above 1350 MPa

The microstructure evolution and precipitation behavior of a multi-component β titanium alloy (namely TB17) were investigated through various characterization methods. The results show that with the increase of the solution temperature, the coarse lamellar α phase (αl) and fine secondary α phase (αs) existed in the original as-forged TB17 alloy decrease. At the same time, the molybdenum equivalent value of the β matrix also decreases gradually, leading to the increase of αs phase during the following aging process. For the aged samples, the micro-strain accumulated in the β matrix resulted from phase transformation strain exhibits an increasing trend as the solution treatment temperature rises, highly depending on the volume fraction of αs phase. When the alloy is subjected to a solution treatment at temperature of 805 °C plus aging, it can achieve a good combination of high strength of 1375 MPa and considerable ductility due to mixed microstructure of suitable amount of micro-scale αl and nano-scale αs precipitates. The strength is further improved by increasing the solution temperature (from α+β to β field), which is attributed to higher volume fraction of fine αs precipitates formed during aging that can effectively hinder dislocation slip and induce micro-strain. Morphological features of the fracture surfaces are also discussed against the different microstructural morphologies, revealing the fracture mechanism of TB17 alloy under different heat treatment conditions. The current work could contribute to a better understanding of phase transformation behavior and strengthening mechanism in TB17 alloy

Tunable Plasmons in Shallow Silver Nanowell Arrays for Directional Surface-Enhanced Raman Scattering

The purpose of this article is to improve the collection efficiency of surface-enhanced Raman scattering (SERS) further to increase SERS detection sensitivity in trace detection. To achieve this, a silver nanowell array substrate was designed based on its tunable propagating surface plasmons. This substrate supported directional surface plasmon coupling emission and could guide SERS to the vertical direction of the substrate. Silver nanoparticles were assembled on the shallow silver nanowell array to contribute localized surface plasmons for higher electromagnetic enhancement. Spatial SERS radiation patterns on the silver nanoparticle assembled nanowell array substrate were simulated by the finite-difference time-domain method and recorded by a self-made 3D angle-resolved Raman spectrometer. The results showed that SERS signals were strong and unidirectional in space. The half divergence angle of the SERS pattern was about 10°, which would facilitate SERS collection by using a conventional backscattering Raman spectrometer. This silver nanowell array is supposed to be an applicable configuration to many systems that require high collection efficiency like single-molecule SERS detection and tip-enhanced Raman spectroscopy

Soil protist in cucumber continuous cropping

Dynamic changes of soil protist in cucumber continuous cropping

Soil protist in cucumber continuous cropping

Dynamic changes of soil protist in cucumber continuous cropping

Protists as main indicators and determinants of plant performance

BACKGROUND: Microbiomes play vital roles in plant health and performance, and the development of plant beneficial microbiomes can be steered by organic fertilizer inputs. Especially well-studied are fertilizer-induced changes on bacteria and fungi and how changes in these groups alter plant performance. However, impacts on protist communities, including their trophic interactions within the microbiome and consequences on plant performance remain largely unknown. Here, we tracked the entire microbiome, including bacteria, fungi, and protists, over six growing seasons of cucumber under different fertilization regimes (conventional, organic, and Trichoderma bio-organic fertilization) and linked microbial data to plant yield to identify plant growth-promoting microbes. RESULTS: Yields were higher in the (bio-)organic fertilization treatments. Soil abiotic conditions were altered by the fertilization regime, with the prominent effects coming from the (bio-)organic fertilization treatments. Those treatments also led to the pronounced shifts in protistan communities, especially microbivorous cercozoan protists. We found positive correlations of these protists with plant yield and the density of potentially plant-beneficial microorganisms. We further explored the mechanistic ramifications of these relationships via greenhouse experiments, showing that cercozoan protists can positively impact plant growth, potentially via interactions with plant-beneficial microorganisms including Trichoderma, the biological agent delivered by the bio-fertilizer. CONCLUSIONS: We show that protists may play central roles in stimulating plant performance through microbiome interactions. Future agricultural practices might aim to specifically enhance plant beneficial protists or apply those protists as novel, sustainable biofertilizers. Video abstract