A nonautonomous predator–prey system with stage structure and double time delays

AbstractIn the present paper we study a nonautonomous predator–prey model with stage structure and double time delays due to maturation time for both prey and predator. We assume that the immature and mature individuals of each species are divided by a fixed age, and the mature predator only attacks the immature prey. Based on some comparison arguments we discuss the permanence of the species. By virtue of the continuation theorem of coincidence degree theory, we prove the existence of positive periodic solution. By means of constructing an appropriate Lyapunov functional, we obtain sufficient conditions for the uniqueness and the global stability of positive periodic solution. Two examples are given to illustrate the feasibility of our main results

Tailoring inputs to achieve maximal neuronal firing

We consider the constrained optimization of excitatory synaptic input patterns to maximize spike generation in leaky integrate-and-fire (LIF) and theta model neurons. In the case of discrete input kicks with a fixed total magnitude, optimal input timings and strengths are identified for each model using phase plane arguments. In both cases, optimal features relate to finding an input level at which the drop in input between successive spikes is minimized. A bounded minimizing level always exists in the theta model and may or may not exist in the LIF model, depending on parameter tuning. We also provide analytical formulas to estimate the number of spikes resulting from a given input train. In a second case of continuous inputs of fixed total magnitude, we analyze the tuning of an input shape parameter to maximize the number of spikes occurring in a fixed time interval. Results are obtained using numerical solution of a variational boundary value problem that we derive, as well as analysis, for the theta model and using a combination of simulation and analysis for the LIF model. In particular, consistent with the discrete case, the number of spikes in the theta model rises and then falls again as the input becomes more tightly peaked. Under a similar variation in the LIF case, we numerically show that the number of spikes increases monotonically up to some bound and we analytically constrain the times at which spikes can occur and estimate the bound on the number of spikes fired

Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning

Reasoning is essential for the development of large knowledge graphs, especially for completion, which aims to infer new triples based on existing ones. Both rules and embeddings can be used for knowledge graph reasoning and they have their own advantages and difficulties. Rule-based reasoning is accurate and explainable but rule learning with searching over the graph always suffers from efficiency due to huge search space. Embedding-based reasoning is more scalable and efficient as the reasoning is conducted via computation between embeddings, but it has difficulty learning good representations for sparse entities because a good embedding relies heavily on data richness. Based on this observation, in this paper we explore how embedding and rule learning can be combined together and complement each other's difficulties with their advantages. We propose a novel framework IterE iteratively learning embeddings and rules, in which rules are learned from embeddings with proper pruning strategy and embeddings are learned from existing triples and new triples inferred by rules. Evaluations on embedding qualities of IterE show that rules help improve the quality of sparse entity embeddings and their link prediction results. We also evaluate the efficiency of rule learning and quality of rules from IterE compared with AMIE+, showing that IterE is capable of generating high quality rules more efficiently. Experiments show that iteratively learning embeddings and rules benefit each other during learning and prediction.Comment: This paper is accepted by WWW'1

Spektrofotometrijska metoda određivanja triptofana iz hidrolizata proteina

A novel spectrophotometric method for determination of tryptophan content in protein hydrolysates has been developed. The reagent used is diphenylamine sulphonate which is oxidised to diphenylbenzidine sulphonic acid after reacting with sodium nitrite in the sulphuric acid medium. The unstable oxidation product reacts quickly with sodium nitrite to produce a diazotized intermediate. When the diazotized intermediate is coupled with tryptophan, a pink colour product is developed, which is stable for at least 1 h at the ambient temperature. This coloured product has the absorption maximum at 522 nm and the molar absorptivity is 8900 L/(mol·cm). Beer’s law is obeyed in the range of 0.30–12 mg/mL. The method is applied for the analysis of tryptophan content in grass carp protein hydrolysates. Moreover, it is compared with the reversed-phase high-performance liquid chromatography analysis. There is no significant (p<0.05) difference between the two results. The method is simple, rapid and accurate compared to the previous methods.Razvijena je nova metoda određivanja udjela triptofana u hidrolizatima proteina. Kao reagens upotrijebljen je difenilamin-sulfonat, koji reakcijom s natrijevim nitritom u mediju sa sulfatnom kiselinom oksidira u difenilbenzidin-sulfonsku kiselinu. Nestabilan proizvod oksidacije brzo reagira s natrijevim nitritom te nastaje diazotizirani intermedijer. Spajanjem diazotiziranog intermedijera s triptofanom nastaje produkt ružičaste boje koji je barem 1 sat stabilan na sobnoj temperaturi. Taj obojeni produkt ima apsorpcijski maksimum pri 522 nm i molarni apsorpcijski koeficijent od 8900 L/(mol·cm), te se ponaša u skladu s Beerovim zakonom u rasponu od 0,30 do 12 µg/mL. Ova se metoda primjenjuje za analizu udjela triptofana u hidrolizatima proteina amura, te je uspoređena s visokotlačnom tekućinskom kromatografijom obrnutih faza (RP-HPLC). Nije primijećena značajna razlika (p<0,05) između rezultata tih dviju metoda. Spektrofotometrijska metoda određivanja triptofana je jednostavnija, brža i preciznija od ranije korištenih metoda

Ontology-driven and weakly supervised rare disease identification from clinical notes

BACKGROUND: Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. METHODS: We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-driven framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets, MIMIC-III discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging reports from two institutions in the US and the UK, with annotations. RESULTS: The improvements in the precision were pronounced (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. The overall pipeline processing clinical notes can extract rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). CONCLUSION: The study provides empirical evidence for the task by applying a weakly supervised NLP pipeline on clinical notes. The proposed weak supervised deep learning approach requires no human annotation except for validation and testing, by leveraging ontologies, NER+L tools, and contextual representations. The study also demonstrates that Natural Language Processing (NLP) can complement traditional ICD-based approaches to better estimate rare diseases in clinical notes. We discuss the usefulness and limitations of the weak supervision approach and propose directions for future studies

The Gut-Microglia Connection: Implications for Central Nervous System Diseases

The importance of the gut microbiome in central nervous system (CNS) diseases has long been recognized; however, research into this connection is limited, in part, owing to a lack of convincing mechanisms because the brain is a distant target of the gut. Previous studies on the brain revealed that most of the CNS diseases affected by the gut microbiome are closely associated with microglial dysfunction. Microglia, the major CNS-resident macrophages, are crucial for the immune response of the CNS against infection and injury, as well as for brain development and function. However, the current understanding of the mechanisms controlling the maturation and function of microglia is obscure, especially regarding the extrinsic factors affecting microglial function during the developmental process. The gut microflora has been shown to significantly influence microglia from before birth until adulthood, and the metabolites generated by the microbiota regulate the inflammation response mediated by microglia in the CNS; this inspired our hypothesis that microglia act as a critical mediator between the gut microbiome and CNS diseases. Herein, we highlight and discuss current findings that show the influence of host microbiome, as a crucial extrinsic factor, on microglia within the CNS. In addition, we summarize the CNS diseases associated with both the host microbiome and microglia and explore the potential pathways by which the gut bacteria influence the pathogenesis of CNS diseases. Our work is thus a comprehensive theoretical foundation for studies on the gut-microglia connection in the development of CNS diseases; and provides great potential for researchers to target pathways associated with the gut-microglia connection and overcome CNS diseases

Construction and Applications of Billion-Scale Pre-trained Multimodal Business Knowledge Graph

Business Knowledge Graphs (KGs) are important to many enterprises today, providing factual knowledge and structured data that steer many products and make them more intelligent. Despite their promising benefits, building business KG necessitates solving prohibitive issues of deficient structure and multiple modalities. In this paper, we advance the understanding of the practical challenges related to building KG in non-trivial real-world systems. We introduce the process of building an open business knowledge graph (OpenBG) derived from a well-known enterprise, Alibaba Group. Specifically, we define a core ontology to cover various abstract products and consumption demands, with fine-grained taxonomy and multimodal facts in deployed applications. OpenBG is an open business KG of unprecedented scale: 2.6 billion triples with more than 88 million entities covering over 1 million core classes/concepts and 2,681 types of relations. We release all the open resources (OpenBG benchmarks) derived from it for the community and report experimental results of KG-centric tasks. We also run up an online competition based on OpenBG benchmarks, and has attracted thousands of teams. We further pre-train OpenBG and apply it to many KG- enhanced downstream tasks in business scenarios, demonstrating the effectiveness of billion-scale multimodal knowledge for e-commerce. All the resources with codes have been released at \url{https://github.com/OpenBGBenchmark/OpenBG}.Comment: OpenBG. Work in Progres

Ontology-driven and weakly supervised rare disease identification from clinical notes

Background: Computational text phenotyping is the practice of identifying patients with certain disorders and traits from clinical notes. Rare diseases are challenging to be identified due to few cases available for machine learning and the need for data annotation from domain experts. Methods: We propose a method using ontologies and weak supervision, with recent pre-trained contextual representations from Bi-directional Transformers (e.g. BERT). The ontology-driven framework includes two steps: (i) Text-to-UMLS, extracting phenotypes by contextually linking mentions to concepts in Unified Medical Language System (UMLS), with a Named Entity Recognition and Linking (NER+L) tool, SemEHR, and weak supervision with customised rules and contextual mention representation; (ii) UMLS-to-ORDO, matching UMLS concepts to rare diseases in Orphanet Rare Disease Ontology (ORDO). The weakly supervised approach is proposed to learn a phenotype confirmation model to improve Text-to-UMLS linking, without annotated data from domain experts. We evaluated the approach on three clinical datasets, MIMIC-III discharge summaries, MIMIC-III radiology reports, and NHS Tayside brain imaging reports from two institutions in the US and the UK, with annotations. Results: The improvements in the precision were pronounced (by over 30% to 50% absolute score for Text-to-UMLS linking), with almost no loss of recall compared to the existing NER+L tool, SemEHR. Results on radiology reports from MIMIC-III and NHS Tayside were consistent with the discharge summaries. The overall pipeline processing clinical notes can extract rare disease cases, mostly uncaptured in structured data (manually assigned ICD codes). Conclusion: The study provides empirical evidence for the task by applying a weakly supervised NLP pipeline on clinical notes. The proposed weak supervised deep learning approach requires no human annotation except for validation and testing, by leveraging ontologies, NER+L tools, and contextual representations. The study also demonstrates that Natural Language Processing (NLP) can complement traditional ICD-based approaches to better estimate rare diseases in clinical notes. We discuss the usefulness and limitations of the weak supervision approach and propose directions for future studies

First integral method for an oscillator system

In this article, we consider the nonlinear Duffing-van der Pol-type oscillator system by means of the first integral method. This system has physical relevance as a model in certain flow-induced structural vibration problems, which includes the van der Pol oscillator and the damped Duffing oscillator etc as particular cases. Firstly, we apply the Division Theorem for two variables in the complex domain, which is based on the ring theory of commutative algebra, to explore a quasi-polynomial first integral to an equivalent autonomous system. Then, through solving an algebraic system we derive the first integral of the Duffing-van der Pol-type oscillator system under certain parametric condition

Work conjugate strain of virial stress

Certain stress tensor and strain tensor form a conjugate pair if there exists a scalar valued strain energy function such that the stress tensor is equal to the derivative of strain energy function with respect to the strain tensor. Virial stress is widely accepted as the stress measurement in molecular dynamics (MD). However, its conjugate strain is not yet identified. An atomic logarithmic strain is proposed and numerically verified as the conjugate strain of virial stress at $$0\ {\rm{K}}$$ temperature. The strain energy is calculated by virial stress and the proposed atomic logarithmic strain equals to the interatomic potential energy density. This conclusion is numerically verified with (1) Coulomb-Buckingham potential, Lenard-Jones potential, or arbitrary nonlinear pair potential and (2) randomly generated atomic configurations and deformation gradients. Examples are given in determining the stress–strain relation for magnesium oxide with MD simulation. The result shows that the atomic logarithmic strain is identical to engineering strain when deformation is small