Investigating Zero- and Few-shot Generalization in Fact Verification

In this paper, we explore zero- and few-shot generalization for fact verification (FV), which aims to generalize the FV model trained on well-resourced domains (e.g., Wikipedia) to low-resourced domains that lack human annotations. To this end, we first construct a benchmark dataset collection which contains 11 FV datasets representing 6 domains. We conduct an empirical analysis of generalization across these FV datasets, finding that current models generalize poorly. Our analysis reveals that several factors affect generalization, including dataset size, length of evidence, and the type of claims. Finally, we show that two directions of work improve generalization: 1) incorporating domain knowledge via pretraining on specialized domains, and 2) automatically generating training data via claim generation.Comment: AACL-IJCNLP 2023 (main conference, long paper

The therapy of gefitinib towards breast cancer partially through reversing breast cancer biomarker arginine

Background: Breast cancer remains the leading reason of cancer death among women worldwide, and gefitinib is the efficient drug for breast cancer. Aims: To use targeted metabolomics method to elucidate the therapeutic mechanism of gefitinib through profiling the amino acids. Methods: Healthy women (n=56) and women with breast cancer (n=60) were enrolled in Affiliated Yuhuangding hospital, medical college of Qingdao University from 2012-2014. API 3200 triple quadrupole mass spectrometer was used to analyze the serum samples. Results: The concentration of amino acids was compared between healthy women and women with breast cancers. Compared with the healthy women, the concentration of arginine in breast cancer women significantly decreased (p<0.0001). To show the representative capability of arginine towards the pathogenesis of breast cancers, the receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC) was calculated to be 0.96 \ub1 0.02, indicating the high predictive capability of arginine for breast cancer . The reversing ability of gefitinib towards the level of arginine was further determined, and 1 month treatment of gefitinib (500 mg/day) significantly reversed the arginine level of breast cancer patients (p<0.0001) Conclusion: The therapy of gefitinib towards breast cancer through reversing breast cancer biomarker arginine was demonstrated

Functionalized self-assembled monolayers on mesoporous silica nanoparticles with high surface coverage

This paper proposes three content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A “merging” fusion combined with an SVM classifier, a back-propagation fusion combined with a KNN classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the “semantic gap” between the low-level descriptors and the high-level semantics of an image. All networks were evaluated using content from the repository of the aceMedia project1 and more specifically in a beach/urban scene classification problem

Functionalized self-assembled monolayers on mesoporous silica nanoparticles with high surface coverage

Mesoporous silica nanoparticles (MSNs) containing vinyl-, propyl-, isobutyl- and phenyl functionalized monolayers were reported. These functionalized MSNs were prepared via molecular self-assembly of organosilanes on the mesoporous supports. The relative surface coverage of the organic monolayers can reach up to 100% (about 5.06 silanes/nm(2)). These monolayer functionalize MSNs were analyzed by a number of techniques including transmission electron microscope, fourier transform infrared spectroscopy, X-ray diffraction pattern, cross-polarized Si(29) MAS NMR spectroscopy, and nitrogen sorption measurement. The main elements (i.e., the number of absorbed water, the reactivity of organosilanes, and the stereochemistry of organosilane) that greatly affected the surface coverage and the quality of the organic functionalized monolayers on MSNs were fully discussed. The results show that the proper amount of physically absorbed water, the use of high active trichlorosilanes, and the functional groups with less steric hindrance are essential to generate MSNs with high surface coverage of monolayers

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..

Study on the Informatization Construction of Public Stomatological Medical Institutions in China

With the deepening of healthcare system reform in China, the competition in the oral healthcare market is becoming stronger day by day. The public hospital is the main body of the medical service system in China, its degree of informatization greatly affects rational market competition and then affects the allocation of resources and the quality of medical service. By analyzing the problems existing in the current informatization of China’s public stomatological medical institutions, this paper discusses how to strengthen the informatization of China’s public stomatological medical institutions, and puts forward targeted optimization measures, to provide a reference for the innovation and development of smart hospital construction of the stomatological industry

Production of Gadolinium-loaded Liquid Scintillator for the Daya Bay Reactor Neutrino Experiment

We report on the production and characterization of liquid scintillators for the detection of electron antineutrinos by the Daya Bay Reactor Neutrino Experiment. One hundred eighty-five tons of gadolinium-loaded (0.1% by mass) liquid scintillator (Gd-LS) and two hundred tons of unloaded liquid scintillator (LS) were successfully produced from a linear-alkylbenzene (LAB) solvent in six months. The scintillator properties, the production and purification systems, and the quality assurance and control (QA/QC) procedures are described.Comment: 15 pages, 11 figures. Submitted to Nuclear Instruments and Methods in Physics Research Section

Research e-infrastructures for open science: The national example of CSTCloud in China

ABSTRACTThis paper focuses on research e-infrastructures in the open science era. We analyze some of the challenges and opportunities of cloud-based science and introduce an example of a national solution in the China Science and Technology Cloud (CSTCloud). We selected three CSTCloud use cases in deploying open science modules, including scalable engineering in astronomical data management, integrated Earth-science resources for SDG-13 decision making, and the coupling of citizen science and artificial intelligence (AI) techniques in biodiversity. We conclude with a forecast on the future development of research e-infrastructures and introduce the idea of the Global Open Science Cloud (GOSC). We hope this analysis can provide some insights into the future development of research e-infrastructures in support of open science

Exercise ameliorates the FGF21–adiponectin axis impairment in diet-induced obese mice

Objective: The protective effects of exercise against glucose dysmetabolism have been generally reported. However, the mechanism by which exercise improves glucose homeostasis remains poorly understood. The FGF21–adiponectin axis participates in the regulation of glucose metabolism. Elevated levels of FGF21 and decreased levels of adiponectin in obesity indicate FGF21–adiponectin axis dysfunction. Hence, we investigated whether exercise could improve the FGF21–adiponectin axis impairment and ameliorate disturbed glucose metabolism in diet-induced obese mice. Methods: Eight-week-old C57BL/6J mice were randomly assigned to three groups: low-fat diet control group, high-fat diet group and high-fat diet plus exercise group. Glucose metabolic parameters, the ability of FGF21 to induce adiponectin, FGF21 receptors and co-receptor levels and adipose tissue inflammation were evaluated after 12 weeks of intervention. Results: Exercise training led to reduced levels of fasting blood glucose and insulin, improved glucose tolerance and better insulin sensitivity in high-fat diet-induced obese mice. Although serum FGF21 levels were not significantly changed, both total and high-molecular-weight adiponectin concentrations were markedly enhanced by exercise. Importantly, exercise protected against high-fat diet-induced impaired ability of FGF21 to stimulate adiponectin secretion. FGF21 co-receptor, β-klotho, as well as receptors, FGFR1 and FGFR2, were upregulated by exercise. We also found that exercise inhibited adipose tissue inflammation, which may contribute to the improvement in the FGF21–adiponectin axis impairment. Conclusions: Our data indicate exercise protects against high-fat diet-induced FGF21–adiponectin axis impairment, and may thereby exert beneficial effects on glucose metabolism

Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis

BackgroundTo identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis.MethodsThe lipid metabolism-related hub genes were screened using machine learning algorithms, and the immune cell infiltration of these hub genes were assessed by CIBERSORT and Single-sample GSEA. Next, the immune function of these hub genes at the single-cell level were validated by comparing multiregional immune landscapes between septic patients (SP) and healthy control (HC). Then, the support vector machine-recursive feature elimination (SVM-RFE) algorithm was conducted to compare the significantly altered metabolites critical to hub genes between SP and HC. Furthermore, the role of the key hub gene was verified in sepsis rats and LPS-induced cardiomyocytes, respectively.ResultsA total of 508 DE-LMRGs were identified between SP and HC, and 5 hub genes relevant to lipid metabolism (MAPK14, EPHX2, BMX, FCER1A, and PAFAH2) were screened. Then, we found an immunosuppressive microenvironment in sepsis. The role of hub genes in immune cells was further confirmed by the single-cell RNA landscape. Moreover, significantly altered metabolites were mainly enriched in lipid metabolism-related signaling pathways and were associated with MAPK14. Finally, inhibiting MAPK14 decreased the levels of inflammatory cytokines and improved the survival and myocardial injury of sepsis.ConclusionThe lipid metabolism-related hub genes may have great potential in prognosis prediction and precise treatment for sepsis patients