Object-oriented Neural Programming (OONP) for Document Understanding

We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a predesigned object-oriented data structure (referred to as ontology in this paper) that reflects the domain-specific semantics of the document. An OONP parser models semantic parsing as a decision process: a neural net-based Reader sequentially goes through the document, and during the process it builds and updates an intermediate ontology to summarize its partial understanding of the text it covers. OONP supports a rich family of operations (both symbolic and differentiable) for composing the ontology, and a big variety of forms (both symbolic and differentiable) for representing the state and the document. An OONP parser can be trained with supervision of different forms and strength, including supervised learning (SL) , reinforcement learning (RL) and hybrid of the two. Our experiments on both synthetic and real-world document parsing tasks have shown that OONP can learn to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201

Syntenin-1 is a promoter and prognostic marker of head and neck squamous cell carcinoma invasion and metastasis.

Metastasis represents a key factor associated with poor prognosis of head and neck squamous cell carcinoma (HNSC). However, the underlying molecular mechanisms remain largely unknown. In this study, our liquid chromatography with tandem mass spectrometry analysis revealed a number of significantly differentially expressed membrane/membrane-associated proteins between high invasive UM1 and low invasive UM2 cells. One of the identified membrane proteins, Syntenin-1, was remarkably up-regulated in HNSC tissues and cell lines when compared to the controls, and also over-expressed in recurrent HNSC and high invasive UM1 cells. Syntenin-1 over-expression was found to be significantly associated with lymph node metastasis and disease recurrence. HNSC patients with higher syntenin-1 expression had significantly poorer long term overall survival and similar results were found in many other types of cancers based on analysis of The Cancer Genome Atlas data. Finally, knockdown of syntenin-1 inhibited the proliferation, migration and invasion of HNSC cells, and opposite findings were observed when syntenin-1 was over-expressed. Collectively, our studies indicate that syntenin-1 promotes invasion and progression of HNSC. It may serve as a valuable biomarker for lymph node metastasis or a potential target for therapeutic intervention in HNSC

The effect and molecular mechanism of hypoxia on proliferation and apoptosis of CD133+ renal stem cells

Congenital hydronephrosis caused by ureteropelvic junction obstruction (UPJO) eventually leads to renal interstitial fibrosis and atrophy, after a series of pathophysiological problems. Renal repair after injury depends on renal stem cells. This study aimed to determine the expression of renal stem cell marker CD133 in children of different ages and the regulatory effect of stem cell microenvironment. Renal stem cells from children of different ages were identified and screened out by flow cytometry in the study. Children with hydronephrosis were divided into neonates, infants, preschool age, school age, and adolescents groups. A hypoxic cell model prepared with CoCl2 was developed to detect the effect of hypoxia on the proliferation and apoptosis of renal stem cells. The effect and molecular mechanism of hypoxia-inducible factor 1-alpha (HIF-1α) on the proliferation and apoptosis of renal stem cells were also explored. Both hypoxia and HIF-1α significantly promoted the proliferation of renal stem cells and inhibited cell apoptosis. HIF-1α could bind to the promoter region of proliferating cell nuclear antigen (PCNA) and PROM1 (CD133) to mediate their transcription and expression. The content of CD133+ renal stem cells was the highest in the neonatal group and it decreased with the increase of age. Taken together, this study clarified the effect of age on the content of human renal stem cells and determined the regulatory mechanism of hypoxia on renal stem cells. We expect our results to provide a research basis for the treatment and clinical application of renal stem cells

Interpretable Detection of Out-of-Context Misinformation with Neural-Symbolic-Enhanced Large Multimodal Model

Recent years have witnessed the sustained evolution of misinformation that aims at manipulating public opinions. Unlike traditional rumors or fake news editors who mainly rely on generated and/or counterfeited images, text and videos, current misinformation creators now more tend to use out-of-context multimedia contents (e.g. mismatched images and captions) to deceive the public and fake news detection systems. This new type of misinformation increases the difficulty of not only detection but also clarification, because every individual modality is close enough to true information. To address this challenge, in this paper we explore how to achieve interpretable cross-modal de-contextualization detection that simultaneously identifies the mismatched pairs and the cross-modal contradictions, which is helpful for fact-check websites to document clarifications. The proposed model first symbolically disassembles the text-modality information to a set of fact queries based on the Abstract Meaning Representation of the caption and then forwards the query-image pairs into a pre-trained large vision-language model select the ``evidences" that are helpful for us to detect misinformation. Extensive experiments indicate that the proposed methodology can provide us with much more interpretable predictions while maintaining the accuracy same as the state-of-the-art model on this task.Comment: 9 Pages, 3 Figure

Effect and safety of combined Gegen Qinlian decoction/metformin in the treatment of diabetes mellitus in patients, and its influence on serum C peptide and glycosylated hemoglobin

Purpose: To investigate the clinical efficacy and safety of combined Gegen Qinlian decoction /metformin in the treatment of diabetes mellitus (DM), and its influence on serum C peptide and glycosylated hemoglobin (HbAlc).Methods: One hundred and eighty-six DM patients who received treatment in Tangshan Gongren Hospital, Tangshan City, China from July 2018 to November 2019 were randomly assigned to group X (n = 93) and group Y (n = 93). Group Y was given metformin, while X received a combination of Gegen Qinlian decoction and metformin. Total effectiveness, incidence of adverse reactions, blood glucose, TCM syndrome scores, as well as serum C peptide and HbAlc were determined and compared between the two groups.Results: Compared with group Y, group X had significantly higher treatment effectiveness (p < 0.05), lower incidence of adverse reactions (p < 0.05), significantly lower levels of blood glucose and TCM syndrome score after treatment (p < 0.001), but significantly higher serum C-peptide levels (p < 0.001) and lower levels of HbAlc.Conclusion: the combination of Gegen Qinlian decoction and metformin produces a good anti-diabetic efficacy, with a lower incidence of adverse reactions in patients. Therefore, the combined therapy has potentials for application in clinical practice, but further clinical trials are required

Trinity: Syncretizing Multi-/Long-tail/Long-term Interests All in One

Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this paper, we argue that these tasks suffer from a common "interest amnesia" problem, and a solution exists to mitigate it simultaneously. We figure that long-term cues can be the cornerstone since they reveal multi-interest and clarify long-tail interest. Inspired by the observation, we propose a novel and unified framework in the retrieval stage, "Trinity", to solve interest amnesia problem and improve multiple interest modeling tasks. We construct a real-time clustering system that enables us to project items into enumerable clusters, and calculate statistical interest histograms over these clusters. Based on these histograms, Trinity recognizes underdelivered themes and remains stable when facing emerging hot topics. Trinity is more appropriate for large-scale industry scenarios because of its modest computational overheads. Its derived retrievers have been deployed on the recommender system of Douyin, significantly improving user experience and retention. We believe that such practical experience can be well generalized to other scenarios

Polymer-stabilized blue phase liquid crystal with a negative Kerr constant

A polymer-stabilized blue-phase liquid crystal (BPLC) with a negative Kerr constant is reported. In a voltage-on state, the double-twist BPLC molecules within the lattice cylinders are reoriented perpendicular to the applied electric field because of their negative dielectric anisotropy. As a result, the induced birefringence has a negative value, which leads to a negative Kerr constant. The negative sign of Kerr constant is experimentally validated by using a quarter-wave plate and a vertical field switching cell. Such a BPLC shows a negligible (similar to 1%) hysteresis and fast response time (similar to 1ms) at the room temperature, although its Kerr constant is relatively small because the employed host has a small Delta epsilon