Dual Dynamic Cross-Linked Epoxy Vitrimers Used for Strong, Detachable, and Reworkable Adhesives.

Novel reprocessable thermosetting adhesives (RTAs), which combine high adhesive strength, reusability, disassembly, and recyclability features, have attracted increasing attention. However, developing RTAs with a rapidly adhesive rate while ensuring high adhesive strength and self-healing ability is still a significant challenge. Here, we prepared a novel vitrimer called DAx-DTSAy, which can be used as an RTA. First, by adjusting the ratio of rigid and flexible segments, maximum tensile strength reached 35.92 MPa. Second, the combined effect of dynamic hydroxyl ester bonds and dynamic disulfide bonds resulted in a rapid stress relaxation behavior, with a complete relaxation time 13.6 times shorter than a vitrimer only cross-linked with hydroxy ester bonds. This feature endowed its good self-healing and reprocessing capabilities. After self-healing at 180 °C, the maximum healing rate of mechanical properties was 91.8%. After three reprocesses, the maximum recovery rate of tensile strength was 120.2%. Furthermore, the combination of rigid and flexible segments and the synergistic effect of dual dynamic covalent bonds made DAx-DTSAy capable of use as a high-performance RTA. The lap shear strength of a DAx-DTSAy film on stainless steel reached 18.18 MPa after 15 min, with a recovery rate of 91.9% after 5 rebonding cycles. Additionally, DAx-DTSAy can be disassembled in chemical agents and exhibited better insulation properties compared to traditional epoxy resins. DAx-DTSAy can be employed as a novel high-performance adhesive in applications such as electronic devices and transportation, contributing to the development of thermosetting adhesives toward recyclability and sustainability.

Modulation of poly (acrylic acid) hydrogels with κ-carrageenan for high-performance quasi-solid Al-air batteries.

Primary quasi-solid Al-air batteries using hydrogels have attracted increasing research attention owing to their high energy density, good handling, safety and reliability. However, it is still difficult to develop hydrogel electrolytes with high ionic conductivity and water retention owing to limited capacity of single material hydrogels. Herein, we report a hydrogel electrolyte of poly (acrylic acid) (PAA) is modified by κ-carrageenan (KC) for solid-state Al-air batteries. The result suggests that the hydrogels not only exhibit outstanding water retention but also high ionic conductivity, which is attributed to the amorphous phase and hydrophilic group of the KC. Additionally, the lifespan of solid-state Al-air battery is extended at a current density of 5 mA cm-2 owing to adding KC. Further, the lifetime of open Al-air batteries is improved by self-corrosion inhibition of Al anode.

Annotating regulatory elements by heterogeneous network embedding.

MOTIVATION: Regulatory elements (REs), such as enhancers and promoters, are known as regulatory sequences functional in a heterogeneous regulatory network to control gene expression by recruiting transcription regulators and carrying genetic variants in a context specific way. Annotating those REs relies on costly and labor-intensive next-generation sequencing and RNA-guided editing technologies in many cellular contexts. RESULTS: We propose a systematic Gene Ontology Annotation method for Regulatory Elements (RE-GOA) by leveraging the powerful word embedding in natural language processing. We first assemble a heterogeneous network by integrating context specific regulations, protein-protein interactions and gene ontology (GO) terms. Then we perform network embedding and associate regulatory elements with GO terms by assessing their similarity in a low dimensional vector space. With three applications, we show that RE-GOA outperforms existing methods in annotating TFs' binding sites from ChIP-seq data, in functional enrichment analysis of differentially accessible peaks from ATAC-seq data, and in revealing genetic correlation among phenotypes from their GWAS summary statistics data. AVAILABILITY AND IMPLEMENTATION: The source code and the systematic RE annotation for human and mouse are available at https://github.com/AMSSwanglab/RE-GOA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Matching biomedical ontologies based on formal concept analysis.

BACKGROUND: The goal of ontology matching is to identify correspondences between entities from different yet overlapping ontologies so as to facilitate semantic integration, reuse and interoperability. As a well developed mathematical model for analyzing individuals and structuring concepts, Formal Concept Analysis (FCA) has been applied to ontology matching (OM) tasks since the beginning of OM research, whereas ontological knowledge exploited in FCA-based methods is limited. This motivates the study in this paper, i.e., to empower FCA with as much as ontological knowledge as possible for identifying mappings across ontologies. METHODS: We propose a method based on Formal Concept Analysis to identify and validate mappings across ontologies, including one-to-one mappings, complex mappings and correspondences between object properties. Our method, called FCA-Map, incrementally generates a total of five types of formal contexts and extracts mappings from the lattices derived. First, the token-based formal context describes how class names, labels and synonyms share lexical tokens, leading to lexical mappings (anchors) across ontologies. Second, the relation-based formal context describes how classes are in taxonomic, partonomic and disjoint relationships with the anchors, leading to positive and negative structural evidence for validating the lexical matching. Third, the positive relation-based context can be used to discover structural mappings. Afterwards, the property-based formal context describes how object properties are used in axioms to connect anchor classes across ontologies, leading to property mappings. Last, the restriction-based formal context describes co-occurrence of classes across ontologies in anonymous ancestors of anchors, from which extended structural mappings and complex mappings can be identified. RESULTS: Evaluation on the Anatomy, the Large Biomedical Ontologies, and the Disease and Phenotype track of the 2016 Ontology Alignment Evaluation Initiative campaign demonstrates the effectiveness of FCA-Map and its competitiveness with the top-ranked systems. FCA-Map can achieve a better balance between precision and recall for large-scale domain ontologies through constructing multiple FCA structures, whereas it performs unsatisfactorily for smaller-sized ontologies with less lexical and semantic expressions. CONCLUSIONS: Compared with other FCA-based OM systems, the study in this paper is more comprehensive as an attempt to push the envelope of the Formal Concept Analysis formalism in ontology matching tasks. Five types of formal contexts are constructed incrementally, and their derived concept lattices are used to cluster the commonalities among classes at lexical and structural level, respectively. Experiments on large, real-world domain ontologies show promising results and reveal the power of FCA.

Identifying Granularity Differences between Large Biomedical Ontologies through Rules.

The objective of this study is to identify the granularity differences as well as similarity between large biomedical ontologies through rules. Two anatomical ontologies were selected, and based on a set of concept mappings obtained through simple string matching techniques, we constructed rules to distinguish among different types of subclasses and classifications. 82% of the concept mappings have exactly the same classification in subclasses between the two ontologies. Other mappings are classified in different granularity, including additional subclasses, detailed classification, and different intermediate classification concepts. Using rules and the rule inference engine enables an automatic and scalable investigation of the structural incompatibility among biomedical ontologies.

Lessons learned from cross-validating alignments between large anatomical ontologies.

OBJECTIVES: To compare the alignments of two large anatomical ontologies (the Foundational Model of Anatomy and GALEN) produced by three ontology alignment systems (AOAS, FALCON and PRIOR) in the framework of the Ontology Alignment Evaluation Initiative during its 2006 campaign. MATERIALS: Number of mappings identified by AOAS: 3,132, FALCON: 2,518 and PRIOR: 2,589. METHODS: Three approaches to analyzing and comparing the results were utilized: computing the overlap among result files, manual review of some 2,000 mappings and structural validation. CONCLUSIONS: The generic systems FALCON and PRIOR identify many false positives and false negatives. With a stricter and domain-specific lexical similarity model, AOAS has a better precision, but is more sensitive to missing synonyms and misspellings.

Comparing two approaches for aligning representations of anatomy.

OBJECTIVE: To analyze the comparison, through their results, of two distinct approaches applied to aligning two representations of anatomy. MATERIALS: Both approaches use a combination of lexical and structural techniques. In addition, the first approach takes advantage of domain knowledge, while the second approach treats alignment as a special case of schema matching. The same versions of FMA and GALEN were aligned by each approach. Two thousand one hundred and ninety-nine concept matches were obtained by both approaches. METHODS AND RESULTS: For matches identified by one approach only (337 and 336, respectively), we analyzed the reasons that caused the other approach to fail. CONCLUSIONS: The first approach could be improved by addressing partial lexical matches and identifying matches based solely on structural similarity. The second approach may be improved by taking into account synonyms in FMA and identifying semantic mismatches. However, only 33% of the possible one-to-one matches among anatomical concepts were identified by the two approaches together. New directions need to be explored in order to handle more complex matches.

Identifying mismatches in alignments of large anatomical ontologies.

UNLABELLED: The objective of this study is to propose a model of matching errors for identifying mismatches in alignments of large anatomical ontologies. Meth-ods: Three approaches to identifying mismatches are utilized: 1) lexical, based on the presence of modifiers in the names of the concepts aligned; 2) structural, identifying conflicting relations resulting from the alignment; and 3) semantic, based on disjoint top-level categories across ontologies. RESULTS: 83% of the potential mismatches identified by the HMatch system are identified by at least one of the approaches. CONCLUSIONS: Although not a substitute for a careful validation of the matches, these approaches significantly reduce the need for manual validation by effectively characterizing most mismatches.

Experience in Aligning Anatomical Ontologies.

An ontology is a formal representation of a domain modeling the entities in the domain and their relations. When a domain is represented by multiple ontologies, there is need for creating mappings among these ontologies in order to facilitate the integration of data annotated with these ontologies and reasoning across ontologies. The objective of this paper is to recapitulate our experience in aligning large anatomical ontologies and to reflect on some of the issues and challenges encountered along the way. The four anatomical ontologies under investigation are the Foundational Model of Anatomy, GALEN, the Adult Mouse Anatomical Dictionary and the NCI Thesaurus. Their underlying representation formalisms are all different. Our approach to aligning concepts (directly) is automatic, rule-based, and operates at the schema level, generating mostly point-to-point mappings. It uses a combination of domain-specific lexical techniques and structural and semantic techniques (to validate the mappings suggested lexically). It also takes advantage of domain-specific knowledge (lexical knowledge from external resources such as the Unified Medical Language System, as well as knowledge augmentation and inference techniques). In addition to point-to-point mapping of concepts, we present the alignment of relationships and the mapping of concepts group-to-group. We have also successfully tested an indirect alignment through a domain-specific reference ontology. We present an evaluation of our techniques, both against a gold standard established manually and against a generic schema matching system. The advantages and limitations of our approach are analyzed and discussed throughout the paper.

Experience in reasoning with the foundational model of anatomy in OWL DL.

The objective of this study is to compare description logics (DLs) and frames for representing large-scale biomedical ontologies and reasoning with them. The ontology under investigation is the Foundational Model of Anatomy (FMA). We converted it from its frame-based representation in Protégé into OWL DL. The OWL reasoner Racer helped identify unsatisfiable classes in the FMA. Support for consistency checking is clearly an advantage of using DLs rather than frames. The interest of reclassification was limited, due to the difficulty of defining necessary and sufficient conditions for anatomical entities. The sheer size and complexity of the FMA was also an issue.

Law and order: assessing and enforcing compliance with ontological modeling principles in the Foundational Model of Anatomy.

The objective of this study is to provide an operational definition of principles with which well-formed ontologies should comply. We define 15 such principles, related to classification (e.g., no hierarchical cycles are allowed; concepts have a reasonable number of children), incompatible relationships (e.g., two concepts cannot stand both in a taxonomic and partitive relation), dependence among concepts, and the co-dependence of equivalent sets of relations. Implicit relations--embedded in concept names or inferred from a combination of explicit relations--are used in this process in addition to the relations explicitly represented. As a case study, we investigate the degree to which the Foundational Model of Anatomy (FMA)--a large ontology of anatomy--complies with these 15 principles. The FMA succeeds in complying with all the principles: totally with one and mostly with the others. Reasons for non-compliance are analyzed and suggestions are made for implementing effective enforcement mechanisms in ontology development environments. The limitations of this study are also discussed.

The foundational model of anatomy in OWL: Experience and perspectives.

We present the method developed for migrating the Foundational Model of Anatomy (FMA) from its representation with frames in Protégé to its logical representation in OWL and our experience in reasoning with it. Despite the extensive use of metaclasses in Protégé, it proved possible to convert the FMA from Protégé into OWL DL, while capturing most of its original features. The conversion relies on a set of translation and enrichment rules implemented with flexible options. Unsurprisingly, reasoning with the FMA in OWL proved to be a real challenge, due to its sheer size and complexity, and raised significant inference problems in terms of time and memory requirements. However, various smaller versions have been successfully handled by Racer. Some inconsistencies were identified and several classes reclassified. The results obtained so far show the advantage of OWL DL over frames and, more generally, the usefulness of DLs reasoners for building and maintaining the large-scale biomedical ontologies of the future Semantic Web.

Comparing the representation of anatomy in the FMA and SNOMED CT.

OBJECTIVE: This paper reports on the alignment between two large ontologies of anatomy: the Foundational Model of Anatomy (FMA) and the representation of anatomical structures in SNOMED CT. The objective of this study is to investigate the compatibility between a reference ontology of anatomy (the FMA, 75,019 concepts) and a representation of anatomy created for use in clinical applications (SNOMED CT, 30,933 anatomical concepts). METHODS: The alignment first identifies shared concepts lexically. The presence of shared relations across ontologies is then used to validate the mappings structurally. RESULTS: 8,228 mappings were identified by lexical methods, of which over 97% were supported by structural evidence. No evidence was found for 0.5% of the mappings and 2.5% received negative evidence. CONCLUSIONS: Despite important differences in coverage and knowledge representation between the FMA and SNOMED CT, we have not noticed any major discrepancies in their representation of anatomical entities.

Of mice and men: aligning mouse and human anatomies.

This paper reports on the alignment between mouse and human anatomies, a critical resource for comparative science as diseases in mice are used as mod-els of human disease. The two ontologies under investigation are the NCI Thesaurus (human anatomy) and the Adult Mouse Anatomical Dictionary, each comprising about 2500 anatomical concepts. This study compares two approaches to aligning ontologies. One is fully automatic, based on a combination of lexical and structural similarity; the other is manual. The resulting mappings were evaluated by an expert. 715 and 781 mappings were identified by each method respectively, of which 639 are common to both and all valid. The applications of the map-ping are discussed from the perspective of biology and from that of ontology.

Alignment of multiple ontologies of anatomy: deriving indirect mappings from direct mappings to a reference.

OBJECTIVE: To investigate the indirect alignment of two anatomical ontologies through a reference ontology and to compare it to direct alignment between these two ontologies. The ontologies under investigation are the Adult Mouse Anatomical Dictionary (MA) and the NCI Thesaurus (NCI). The Foundational Model of Anatomy serves as reference ontology. METHODS: The direct alignment employs a combination of lexical and structural similarity. The indirect alignment simply derives mappings from direct alignments to the reference ontology. RESULTS: The indirect MA-NCI alignment yielded 703 mappings and the direct alignment 715, 654 of which are common to both. The mappings specific to one approach were analyzed. CONCLUSIONS: When a reference ontology exists, indirect alignment of multiple ontologies through a reference represents a valid, cost-effective alternative to pairwise alignment.

Comparing associative relationships among equivalent concepts across ontologies.

Methods for comparing associative relationships across ontologies often rely solely on lexical similarity between the names of the relationships, which may lead to missed matches and inaccurate matches. In this paper, we propose a novel method based on the analysis of paths between equivalent concepts across ontologies. Patterns of relationships are identified for each associative relationship. The most frequent patterns indicate a correspondence between an associative relationship in one ontology and one relationship (or combination thereof) in the other. We applied this method to two ontologies of anatomy. Our method was able to identify the correspondence between relationships even in the absence of lexical similarity between relationship names. The various types of matches identified are discussed as well as the application of this method to detecting inconsistencies across the ontologies.

Aligning representations of anatomy using lexical and structural methods.

OBJECTIVE: The objective of this experiment is to develop methods for aligning two representations of anatomy (the Foundational Model of Anatomy and GALEN) at the lexical and structural level. METHODS: The alignment consists of the following four steps: 1)acquiring terms, 2) identifying anchors (i.e., shared concepts) lexically, 3) acquiring explicit and implicit semantic relations, and 4) identifying anchors structurally. RESULTS: 2,353 anchors were identified by lexical methods, of which 91% were supported by structural evidence. No evidence was found for 7.5%of the anchors and 1.5% received negative evidence. DISCUSSION: The importance of taking advantage of implicit domain knowledge acquired through complementation,augmentation, and inference is discussed.