KoRC: Knowledge oriented Reading Comprehension Benchmark for Deep Text Understanding

Deep text understanding, which requires the connections between a given document and prior knowledge beyond its text, has been highlighted by many benchmarks in recent years. However, these benchmarks have encountered two major limitations. On the one hand, most of them require human annotation of knowledge, which leads to limited knowledge coverage. On the other hand, they usually use choices or spans in the texts as the answers, which results in narrow answer space. To overcome these limitations, we build a new challenging benchmark named KoRc in this paper. Compared with previous benchmarks, KoRC has two advantages, i.e., broad knowledge coverage and flexible answer format. Specifically, we utilize massive knowledge bases to guide annotators or large language models (LLMs) to construct knowledgable questions. Moreover, we use labels in knowledge bases rather than spans or choices as the final answers. We test state-of-the-art models on KoRC and the experimental results show that the strongest baseline only achieves 68.3% and 30.0% F1 measure in the in-distribution and out-of-distribution test set, respectively. These results indicate that deep text understanding is still an unsolved challenge. The benchmark dataset, leaderboard, and baseline methods are released in https://github.com/THU-KEG/KoRC

Reasoning over Hierarchical Question Decomposition Tree for Explainable Question Answering

Explainable question answering (XQA) aims to answer a given question and provide an explanation why the answer is selected. Existing XQA methods focus on reasoning on a single knowledge source, e.g., structured knowledge bases, unstructured corpora, etc. However, integrating information from heterogeneous knowledge sources is essential to answer complex questions. In this paper, we propose to leverage question decomposing for heterogeneous knowledge integration, by breaking down a complex question into simpler ones, and selecting the appropriate knowledge source for each sub-question. To facilitate reasoning, we propose a novel two-stage XQA framework, Reasoning over Hierarchical Question Decomposition Tree (RoHT). First, we build the Hierarchical Question Decomposition Tree (HQDT) to understand the semantics of a complex question; then, we conduct probabilistic reasoning over HQDT from root to leaves recursively, to aggregate heterogeneous knowledge at different tree levels and search for a best solution considering the decomposing and answering probabilities. The experiments on complex QA datasets KQA Pro and Musique show that our framework outperforms SOTA methods significantly, demonstrating the effectiveness of leveraging question decomposing for knowledge integration and our RoHT framework.Comment: has been accepted by ACL202

KQA Pro: A Large-Scale Dataset with Interpretable Programs and Accurate SPARQLs for Complex Question Answering over Knowledge Base

Complex question answering over knowledge base (Complex KBQA) is challenging because it requires various compositional reasoning capabilities, such as multi-hop inference, attribute comparison, set operation, and etc. Existing benchmarks have some shortcomings that limit the development of Complex KBQA: 1) they only provide QA pairs without explicit reasoning processes; 2) questions are either generated by templates, leading to poor diversity, or on a small scale. To this end, we introduce KQA Pro, a large-scale dataset for Complex KBQA. We define a compositional and highly-interpretable formal format, named Program, to represent the reasoning process of complex questions. We propose compositional strategies to generate questions, corresponding SPARQLs, and Programs with a small number of templates, and then paraphrase the generated questions to natural language questions (NLQ) by crowdsourcing, giving rise to around 120K diverse instances. SPARQL and Program depict two complementary solutions to answer complex questions, which can benefit a large spectrum of QA methods. Besides the QA task, KQA Pro can also serves for the semantic parsing task. As far as we know, it is currently the largest corpus of NLQ-to-SPARQL and NLQ-to-Program. We conduct extensive experiments to evaluate whether machines can learn to answer our complex questions in different cases, that is, with only QA supervision or with intermediate SPARQL/Program supervision. We find that state-of-the-art KBQA methods learnt from only QA pairs perform very poor on our dataset, implying our questions are more challenging than previous datasets. However, pretrained models learnt from our NLQ-to-SPARQL and NLQ-to-Program annotations surprisingly achieve about 90\% answering accuracy, which is even close to the human expert performance..

VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering

We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries. VisKoP not only provides a neural program induction module, which converts natural language questions into knowledge oriented program language (KoPL), but also maps KoPL programs into graphical elements. KoPL programs can be edited with simple graphical operators, such as dragging to add knowledge operators and slot filling to designate operator arguments. Moreover, VisKoP provides auto-completion for its knowledge base schema and users can easily debug the KoPL program by checking its intermediate results. To facilitate the practical KBQA on a million-entity-level KB, we design a highly efficient KoPL execution engine for the back-end. Experiment results show that VisKoP is highly efficient and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. The VisKoP online demo https://demoviskop.xlore.cn (Stable release of this paper) and https://viskop.xlore.cn (Beta release with new features), highly efficient KoPL engine https://pypi.org/project/kopl-engine, and screencast video https://youtu.be/zAbJtxFPTXo are now publicly available

MiR-574-5p Activates Human TLR8 to Promote Autoimmune Signaling and Lupus

Endosomal single-stranded RNA-sensing Toll-like receptor-7/8 (TLR7/8) plays a pivotal role in inflammation and immune responses and autoimmune diseases. However, the mechanisms underlying the initiation of the TLR7/8-mediated autoimmune signaling remain to be fully elucidated. Here, we demonstrate that miR-574-5p is aberrantly upregulated in tissues of lupus prone mice and in the plasma of lupus patients, with its expression levels correlating with the disease activity. miR-574-5p binds to and activates human hTLR8 or its murine ortholog mTlr7 to elicit a series of MyD88-dependent immune and inflammatory responses. These responses include the overproduction of cytokines and interferons, the activation of STAT1 signaling and B lymphocytes, and the production of autoantigens. In a transgenic mouse model, the induction of miR-574-5p overexpression is associated with increased secretion of antinuclear and anti-dsDNA antibodies, increased IgG and C3 deposit in the kidney, elevated expression of inflammatory genes in the spleen. In lupus-prone mice, lentivirus-mediated silencing of miR-574-5p significantly ameliorates major symptoms associated with lupus and lupus nephritis. Collectively, these results suggest that the miR-574-5p-hTLR8/mTlr7 signaling is an important axis of immune and inflammatory responses, contributing significantly to the development of lupus and lupus nephritis

Genome-wide association study of maize resistance to Pythium aristosporum stalk rot

Stalk rot, a severe and widespread soil-borne disease in maize, globally reduces yield and quality. Recent documentation reveals that Pythium aristosporum has emerged as one of the dominant causal agents of maize stalk rot. However, a previous study of maize stalk rot disease resistance mechanisms and breeding had mainly focused on other pathogens, neglecting P. aristosporum. To mitigate crop loss, resistance breeding is the most economical and effective strategy against this disease. This study involved characterizing resistance in 295 inbred lines using the drilling inoculation method and genotyping them via sequencing. By combining with population structure, disease resistance phenotype, and genome-wide association study (GWAS), we identified 39 significant single-nucleotide polymorphisms (SNPs) associated with P. aristosporum stalk rot resistance by utilizing six statistical methods. Bioinformatics analysis of these SNPs revealed 69 potential resistance genes, among which Zm00001d051313 was finally evaluated for its roles in host defense response to P. aristosporum infection. Through virus-induced gene silencing (VIGS) verification and physiological index determination, we found that transient silencing of Zm00001d051313 promoted P. aristosporum infection, indicating a positive regulatory role of this gene in maize’s antifungal defense mechanism. Therefore, these findings will help advance our current understanding of the underlying mechanisms of maize defense to Pythium stalk rot

Optimal Production and Biochemical Properties of a Lipase from Candida albicans

Lipases from microorganisms have multi-faceted properties and play an important role in ever-growing modern biotechnology and, consequently, it is of great significance to develop new ones. In the present work, a lipase gene from Candida albicans (CaLIP10) was cloned and two non-unusual CUG serine codons were mutated into universal codons, and its expression in Pichia pastoris performed optimally, as shown by response surface methodology. Optimal conditions were: initial pH of culture 6.86, temperature 25.53 °C, 3.48% of glucose and 1.32% of yeast extract. The corresponding maximal lipolytic activity of CaLIP10 was 8.06 U/mL. The purified CaLIP10 showed maximal activity at pH 8.0 and 25 °C, and a good resistance to non-ionic surfactants and polar organic solvent was noticed. CaLIP10 could effectively hydrolyze coconut oil, but exhibited no obvious preference to the fatty acids with different carbon length, and diacylglycerol was accumulated in the reaction products, suggesting that CaLIP10 is a potential lipase for the oil industry

Hydroxyl radical-aided thermal pretreatment of algal biomass for enhanced biodegradability

BACKGROUND: Algal biomass, known as a potential feedstock for biofuel production, has cell wall structures that differ from terrestrial biomass. The existing methods for processing algae are limited to conventional pretreatments for terrestrial biomass. RESULTS: In this study, we investigated a novel hydroxyl radical-aided approach for pretreating different types of algal biomass. In this process, hydroxyl radicals formed by a Fenton system were employed in combination with heating to alter the crystalline structure and hydrogen bonds of cellulose in the algal biomass. FeSO(4) and H(2)O(2) at low concentrations were employed to initiate the formation of hydroxyl radicals. This method releases trapped polysaccharides in algal cell walls and converts them into fermentable sugars. The effects of temperature, time, and hydroxyl radical concentration were analyzed. The optimal pretreatment condition [100 °C, 30 min, and 5.3 mM H(2)O(2) (determined FeSO(4) concentration of 11.9 mM)] was identified using a central composite design. Complete (100 %) carbohydrate recovery was achieved with some algal biomass without formation of inhibitors such as hydroxymethylfurfural and furfural as by-products. Both microalgal and macroalgal biomasses showed higher enzymatic digestibility of cellulose conversion (>80 %) after the milder pretreatment condition. CONCLUSION: Hydroxyl radical-aided thermal pretreatment was used as a novel method to convert the carbohydrates in the algal cell wall into simple sugars. Overall, this method increased the amount of glucose released from the algal biomass. Overall, enhanced algal biomass digestibility was demonstrated with the proposed pretreatment process. The new pretreatment requires low concentration of chemical solvents and milder temperature conditions, which can prevent the toxic and corrosive effects that typically result from conventional pretreatments. Our data showed that the advantages of the new pretreatment include higher carbohydrate recovery, no inhibitor production, and lower energy consumption. The new pretreatment development mimicking natural system could be useful for biochemical conversion of algal biomass to fuels and chemicals. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13068-015-0372-2) contains supplementary material, which is available to authorized users

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO