Hybrid question answering (HQA) aims to answer questions over heterogeneous data, including tables and passages linked to table cells. The heterogeneous data can provide different granularity evidence to HQA models, e.t., column, row, cell, and link. Conventional HQA models usually retrieve coarse- or fine-grained evidence to reason the answer. Through comparison, we find that coarse-grained evidence is easier to retrieve but contributes less to the reasoner, while fine-grained evidence is the opposite. To preserve the advantage and eliminate the disadvantage of different granularity evidence, we propose MuGER2, a Multi-Granularity Evidence Retrieval and Reasoning approach. In evidence retrieval, a unified retriever is designed to learn the multi-granularity evidence from the heterogeneous data. In answer reasoning, an evidence selector is proposed to navigate the fine-grained evidence for the answer reader based on the learned multi-granularity evidence. Experiment results on the HybridQA dataset show that MuGER2 significantly boosts the HQA performance. Further ablation analysis verifies the effectiveness of both the retrieval and reasoning designs.
This umbrella review aims to provide a systematic and comprehensive overview of current evidence from prospective studies on the diverse health effects of cheese consumption. We searched PubMed, Embase, and Cochrane Library to identify meta-analyses/pooled analyses of prospective studies examining the association between cheese consumption and major health outcomes from inception to August 31, 2022. We reanalyzed and updated previous meta-analyses and performed de novo meta-analyses with recently published prospective studies, where appropriate. We calculated the summary effect size, 95% prediction confidence intervals, between-study heterogeneity, small-study effects, and excess significance bias for each health outcome. We identified 54 eligible articles of meta-analyses/pooled analyses. After adding newly published original articles, we performed 35 updated meta-analyses and 4 de novo meta-analyses. Together with 8 previous meta-analyses, we finally included 47 unique health outcomes. Cheese consumption was inversely associated with all-cause mortality (highest compared with lowest category: RR = 0.95; 95% CI: 0.92, 0.99), cardiovascular mortality (RR = 0.93; 95% CI: 0.88, 0.99), incident cardiovascular disease (CVD) (RR = 0.92; 95% CI: 0.89, 0.96), coronary heart disease (CHD) (RR = 0.92; 95% CI: 0.86, 0.98), stroke (RR = 0.93; 95% CI: 0.89, 0.98), estrogen receptor-negative (ER-) breast cancer (RR = 0.89; 95% CI: 0.82, 0.97), type 2 diabetes (RR = 0.93; 95% CI: 0.88, 0.98), total fracture (RR = 0.90; 95% CI: 0.86, 0.95), and dementia (RR = 0.81; 95% CI: 0.66, 0.99). Null associations were found for other outcomes. According to the NutriGrade scoring system, moderate quality of evidence was observed for inverse associations of cheese consumption with all-cause and cardiovascular mortality, incident CVD, CHD, and stroke, and for null associations with cancer mortality, incident hypertension, and prostate cancer. Our findings suggest that cheese consumption has neutral to moderate benefits for human health.
Journal Article The rise of the 'shareholding state': financialization of economic management in China Get access Yingyao Wang Yingyao Wang * Yale University, New Haven, CT, USA *Correspondence: yingyao.wang@yale.edu Search for other works by this author on: Oxford Academic Google Scholar Socio-Economic Review, Volume 13, Issue 3, July 2015, Pages 603–625, https://doi.org/10.1093/ser/mwv016 Published: 02 September 2015
Clinical evidence indicates that tumor-colonizing bacteria can be closely related to the tumor development and therapeutic responses. Selectively eliminating bacteria within tumors may be an attractive approach to enhance cancer treatment without additional side effects. Herein, it is found that, owing to the high affinity between the membrane protein Fap-2 on Fusobacterium nucleatum and d-galactose-β (1-3)-N-acetyl-d-galactosamine (Gal-GalNAc) overexpressed on colorectal tumor cells, F. nucleatum can colonize in colorectal tumors, as evidenced by both clinical samples and animal tumor models. Notably, F. nucleatum colonized in colorectal tumors can lead to an immunosuppressive tumor microenvironment, greatly reducing their responses to immune checkpoint blockade (ICB) therapy. Inspired by this finding, an F. nucleatum-mimetic nanomedicine is designed by fusing F. nucleatum cytoplasmic membrane (FM) with Colistin-loaded liposomes to achieve selective killing of tumor-colonizing F. nucleatum without affecting gut microbes. As a result, the therapeutic responses of F. nucleatum-colonized tumors to ICB therapies can be successfully restored, as demonstrated in an F. nucleatum-infected subcutaneous CT-26 tumor model, chemically induced spontaneous colorectal cancer models, and MC-38 tumor model. In summary, this work presents an F. nucleatum-mimicking nanomedicine that can selectively eliminate tumor-colonized bacteria, which is promising for enhancing the responses of cancer immunotherapy against F. nucleatum-colonized colorectal cancer.
Based on generally modified soybeans as materials,used the qualitative research methods of PCR to study the effects to Lectin gene and exogenous gene of Cp4 epsps and promoter and terminator by crushing and sticky-heating and extrusion processing.Researches showed that endogenous gene degraded below to 407 bp through processing at 100℃ sticky-heating for 15 min.Exogenous gene degraded below to 408 bp after 100℃ sticky-heating for 15 min.Promoter and terminator turn to degrade by 100℃ sticky-heating for 3 min.The results also indicated that gene sections had no changes through mechanical disintegration and liquid nitrogen processings.While promoter and terminator showd no degrades through extrusion processing,on the contrary,endogenous gene degraded below to 836 bp and exogenous gene degraded below to 1 512 bp.
Reaction products obtained from enzymatic acidolysis preparation of MLM-type(M,medium-chain fatty acids;L,long-chain fatty acids) structured lipids(SLs)were studied.The effects of KOH-hydroalcoholic solution to remove free fatty acids on recovery rate and composition of SLs were discussed,such as the volume of the KOH-hydroalcoholic solution,the ethanol-water ratio of the solution,the volume of the hexane and the volume of the hexane in second extraction step.The results indicated that the highest recovery rate was attained with the proportion of KOH-hydroalcoholic solution volume(mL) and reaction products mass(g) 8∶ 1,ethanol and water volume ratio 30∶ 70,hexane volume(mL) and reaction products mass(g) ratio 12∶ 1,in second extraction step hexane volume(mL) and reaction products mass(g) ratio 3∶ 1.Under these conditions the recovery rate of SLs was 94.35%,and triacylglycerol content and the acid value of SLs were 95.27% and 0.11 mg(KOH)/g,respectively.
Abstract Human papillomavirus (HPV) infection is related to the occurrence of cervical cancer. We enrolled 21,282 individuals, including 634 males and 20,648 females, in Jilin Province, China, from October 2017 to September 2019. Significant variations were observed in the prevalence of HPV types 16, 18, 31, 33, 52, 56, 58, 59, 66, 53, 6, and 11 across different age groups (P < 0.05). The occurrence of HPV infection was considerably greater among females than among males. Additionally, a noteworthy association was found between HPV16, 18, 33, and 58 infections and the presence of high-grade squamous intraepithelial lesions or cervical cancer in females. This relationship exhibited a U-shaped relationship with age. Our study might play a role in guiding women of different ages to get tested for HPV infections and determine the right HPV vaccine in relation to a woman's age.
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations. Due to the fact that some relations usually keep high co-occurrence in the same context, previous few-shot relation classifiers struggle to distinguish them with few annotated instances. To alleviate the above relation confusion problem, we propose CTEG, a model equipped with two mechanisms to learn to decouple these easily-confused relations. On the one hand, an Entity-Guided Attention (EGA) mechanism, which leverages the syntactic relations and relative positions between each word and the specified entity pair, is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations by playing a pushing-away game between classifying a sentence into a true relation and its confusing relation. Extensive experiments are conducted on the FewRel dataset, and the results show that our proposed model achieves comparable and even much better results to strong baselines in terms of accuracy. Furthermore, the ablation test and case study verify the effectiveness of our proposed EGA and CAT, especially in addressing the relation confusion problem.
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