Tractable approximate deduction for OWL

Acknowledgements This work has been partially supported by the European project Marrying Ontologies and Software Technologies (EU ICT2008-216691), the European project Knowledge Driven Data Exploitation (EU FP7/IAPP2011-286348), the UK EPSRC project WhatIf (EP/J014354/1). The authors thank Prof. Ian Horrocks and Dr. Giorgos Stoilos for their helpful discussion on role subsumptions. The authors thank Rafael S. Gonçalves et al. for providing their hotspots ontologies. The authors also thank BoC-group for providing their ADOxx Metamodelling ontologies.Peer reviewedPostprin

Boundary Hamiltonian theory for gapped topological phases on an open surface

In this paper we propose a Hamiltonian approach to gapped topological phases on an open surface with boundary. Our setting is an extension of the Levin-Wen model to a 2d graph on the open surface, whose boundary is part of the graph. We systematically construct a series of boundary Hamiltonians such that each of them, when combined with the usual Levin-Wen bulk Hamiltonian, gives rise to a gapped energy spectrum which is topologically protected; and the corresponding wave functions are robust under changes of the underlying graph that maintain the spatial topology of the system. We derive explicit ground-state wavefunctions of the system and show that the boundary types are classified by Morita-equivalent Frobenius algebras. We also construct boundary quasiparticle creation, measuring and hopping operators. These operators allow us to characterize the boundary quasiparticles by bimodules of Frobenius algebras. Our approach also offers a concrete set of tools for computations. We illustrate our approach by a few examples.Comment: 21 pages;references correcte

Structures of human gastrin-releasing peptide receptors bound to antagonist and agonist for cancer and itch therapy

Gastrin releasing peptide receptor (GRPR), a member of the bombesin (BBN) G protein-coupled receptors, is aberrantly overexpressed in several malignant tumors, including those of the breast, prostate, pancreas, lung, and central nervous system. Additionally, it also mediates non-histaminergic itch and pathological itch conditions in mice. Thus, GRPR could be an attractive target for cancer and itch therapy. Here, we report the inactive state crystal structure of human GRPR in complex with the non-peptide antagonist PD176252, as well as two active state cryo-electron microscopy (cryo-EM) structures of GRPR bound to the endogenous peptide agonist gastrin-releasing peptide and the synthetic BBN analog [D-Ph

In Search of the Long-Tail: Systematic Generation of Long-Tail Knowledge via Logical Rule Guided Search

Since large language models have approached human-level performance on many tasks, it has become increasingly harder for researchers to find tasks that are still challenging to the models. Failure cases usually come from the long-tail distribution - data that an oracle language model could assign a probability on the lower end of its distribution. Current methodology such as prompt engineering or crowdsourcing are insufficient for creating long-tail examples because humans are constrained by cognitive bias. We propose a Logic-Induced-Knowledge-Search (LINK) framework for systematically generating long-tail knowledge statements. Grounded by a symbolic rule, we search for long-tail values for each variable of the rule by first prompting a LLM, then verifying the correctness of the values with a critic, and lastly pushing for the long-tail distribution with a reranker. With this framework we construct a dataset, Logic-Induced-Long-Tail (LINT), consisting of 200 symbolic rules and 50K knowledge statements spanning across four domains. Human annotations find that 84% of the statements in LINT are factually correct. In contrast, ChatGPT and GPT4 struggle with directly generating long-tail statements under the guidance of logic rules, each only getting 56% and 78% of their statements correct. Moreover, their "long-tail" generations in fact fall into the higher likelihood range, and thus are not really long-tail. Our findings suggest that LINK is effective for generating data in the long-tail distribution while enforcing quality. LINT can be useful for systematically evaluating LLMs' capabilities in the long-tail distribution. We challenge the models with a simple entailment classification task using samples from LINT. We find that ChatGPT and GPT4's capability in identifying incorrect knowledge drop by ~3% in the long-tail distribution compared to head distribution

Two Strains of Lactobacilli Effectively Decrease the Colonization of VRE in a Mouse Model

Vancomycin-resistant Enterococcus (VRE) infection is a serious challenge for clinical management and there is no effective treatment at present. Fecal microbiota transplantation (FMT) and probiotic intervention have been shown to be promising approaches for reducing the colonization of certain pathogenic bacteria in the gastrointestinal tract, however, no such studies have been done on VRE. In this study, we evaluated the effect of FMT and two Lactobacillus strains (Y74 and HT121) on the colonization of VRE in a VRE-infection mouse model. We found that both Lactobacilli strains reduced VRE colonization rapidly. Fecal microbiota and colon mRNA expression analyses further showed that mice in FMT and the two Lactobacilli treatment groups restored their intestinal microbiota diversity faster than those in the phosphate buffer saline (PBS) treated group. Administration of Lactobacilli restored Firmicutes more quickly to the normal level, compared to FMT or PBS treatment, but restored Bacteroides to their normal level less quickly than FMT did. Furthermore, these treatments also had an impact on the relative abundance of intestinal microbiota composition from phylum to species level. RNA-seq showed that FMT treatment induced the expression of more genes in the colon, compared to the Lactobacilli treatment. Defense-related genes such as defensin α, Apoa1, and RegIII were down-regulated in both FMT and the two Lactobacilli treatment groups. Taken together, our findings indicate that both FMT and Lactobacilli treatments were effective in decreasing the colonization of VRE in the gut

A residual network with geographical and meteorological attention for multi-year ENSO forecasts

IntroductionAs global temperatures continue to rise, extreme weather phenomena such as El Niño and the Southern Oscillation (ENSO) near the equatorial Pacific Ocean are occurring more frequently and leading to tropical cyclones, droughts, and a series of extreme weather disasters. Accurately predicting ENSO in advance can greatly reduce the serious damage to human society, economy, and ecological environment. However, existing methods often neglect the data relation between geographical regions and meteorological factors, hindering the accuracy of ENSO prediction.MethodsTo overcome this problem, we propose a residual network with geographical and meteorological attention to capture important geographical information and explore the spatio-temporal correlation of different meteorological factors. Specifically, we propose two main attention modules: (1) the Geographical Semantic Information Enhancement Module (GSIEM), which selectively attends to important geographical regions and filters out irrelevant noise through a spatial-axis attention map, and (2) the Meteorological Factors Discriminating Enhancement Module (MFDEM), which aims to learn the spatio-temporal dependency of different meteorological factors using a learnable channel-axis weight map. We then integrate our proposed two attention modules into the backbone using residual connection, enhancing the model's prediction ability.ResultsWe conducted extensive experimental comparisons and ablation studies to evaluate the performance of our proposed method. The results show that our method outperforms existing state-of-the-art methods in ENSO prediction, with a significant improvement in prediction accuracy.DiscussionOur proposed method effectively captures geographical and meteorological information, facilitating accurate ENSO prediction. The attention modules we proposed can effectively filter out irrelevant noise and learn the spatio-temporal dependency of different meteorological factors, contributing to the superior performance of our model. Overall, our study provides a novel approach for ENSO prediction and has great potential for practical applications

Cryogenic in-memory computing using tunable chiral edge states

Energy-efficient hardware implementation of machine learning algorithms for quantum computation requires nonvolatile and electrically-programmable devices, memristors, working at cryogenic temperatures that enable in-memory computing. Magnetic topological insulators are promising candidates due to their tunable magnetic order by electrical currents with high energy efficiency. Here, we utilize magnetic topological insulators as memristors (termed magnetic topological memristors) and introduce a chiral edge state-based cryogenic in-memory computing scheme. On the one hand, the chiral edge state can be tuned from left-handed to right-handed chirality through spin-momentum locked topological surface current injection. On the other hand, the chiral edge state exhibits giant and bipolar anomalous Hall resistance, which facilitates the electrical readout. The memristive switching and reading of the chiral edge state exhibit high energy efficiency, high stability, and low stochasticity. We achieve high accuracy in a proof-of-concept classification task using four magnetic topological memristors. Furthermore, our algorithm-level and circuit-level simulations of large-scale neural networks based on magnetic topological memristors demonstrate a software-level accuracy and lower energy consumption for image recognition and quantum state preparation compared with existing memristor technologies. Our results may inspire further topological quantum physics-based novel computing schemes.Comment: 33 pages, 12 figure

Regulation of serotonin production by specific microbes from piglet gut

Abstract Background Serotonin is an important signaling molecule that regulates secretory and sensory functions in the gut. Gut microbiota has been demonstrated to affect serotonin synthesis in rodent models. However, how gut microbes regulate intestinal serotonin production in piglets remains vague. To investigate the relationship between microbiota and serotonin specifically in the colon, microbial composition and serotonin concentration were analyzed in ileum-cannulated piglets subjected to antibiotic infusion from the ileum when comparing with saline infusion. Microbes that correlated positively with serotonin production were isolated from piglet colon and were further used to investigate the regulation mechanisms on serotonin production in IPEC-J2 and a putative enterochromaffin cell line RIN-14B cells. Results Antibiotic infusion increased quantities of Lactobacillus amylovorus (LA) that positively correlated with increased serotonin concentrations in the colon, while no effects observed for Limosilactobacillus reuteri (LR). To understand how microbes regulate serotonin, representative strains of LA, LR, and Streptococcus alactolyticus (SA, enriched in feces from prior observation) were selected for cell culture studies. Compared to the control group, LA, LR and SA supernatants significantly up-regulated tryptophan hydroxylase 1 (TPH1) expression and promoted serotonin production in IPEC-J2 cells, while in RIN-14B cells only LA exerted similar action. To investigate potential mechanisms mediated by microbe-derived molecules, microbial metabolites including lactate, acetate, glutamine, and γ-aminobutyric acid were selected for cell treatment based on computational and metabolite profiling in bacterial supernatant. Among these metabolites, acetate upregulated the expression of free fatty acid receptor 3 and TPH1 while downregulated indoleamine 2,3-dioxygenase 1. Similar effects were also recapitulated when treating the cells with AR420626, an agonist targeting free fatty acid receptor 3. Conclusions Overall, these results suggest that Lactobacillus amylovorus showed a positive correlation with serotonin production in the pig gut and exhibited a remarkable ability to regulate serotonin production in cell cultures. These findings provide evidence that microbial metabolites mediate the dialogue between microbes and host, which reveals a potential approach using microbial manipulation to regulate intestinal serotonin biosynthesis