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In this paper, we present an embedding from discrete Morrey spaces to continuous Morrey Spaces which can be seen as a refinement of the result in [1]. We obtain the result by using a different norm on discrete Morrey spaces, which is equivalent to the existing norm.

In this paper, we discuss the properties of the embedding operator$i_{\\unicode[STIX]{x1D707}}^{\\unicode[STIX]{x1D6EC}}:M_{\\unicode[STIX]{x1D6EC}}^{\\infty }{\\hookrightarrow}L^{\\infty }(\\unicode[STIX]{x1D707})$, where$\\unicode[STIX]{x1D707}$is a positive Borel measure on$[0,1]$and$M_{\\unicode[STIX]{x1D6EC}}^{\\infty }$is a Müntz space. In particular, we compute the essential norm of this embedding. As a consequence, we recover some results of the first author. We also study the compactness (resp. weak compactness) and compute the essential norm (resp. generalized essential norm) of the embedding$i_{\\unicode[STIX]{x1D707}_{1},\\unicode[STIX]{x1D707}_{2}}:L^{\\infty }(\\unicode[STIX]{x1D707}_{1}){\\hookrightarrow}L^{\\infty }(\\unicode[STIX]{x1D707}_{2})$, where$\\unicode[STIX]{x1D707}_{1}$,$\\unicode[STIX]{x1D707}_{2}$are two positive Borel measures on [0, 1] with$\\unicode[STIX]{x1D707}_{2}$absolutely continuous with respect to$\\unicode[STIX]{x1D707}_{1}$.

Aiming at the problem of asymmetric temporal relations of events, the event representation is mapped to hyperbolic space to extract temporal relations of events. The word embedding representation of the event is constructed by using the pre-trained word vector and external knowledge through simple operation. Experimental results on publicly released datasets show that the F1 value of the model is generally 2% higher than that of the baseline model, which can improve the effect of event temporal relation extraction.

Biological embedding occurs when life experience alters biological processes to affect later life health and well-being. Although extensive correlative data exist supporting the notion that epigenetic mechanisms such as DNA methylation underlie biological embedding, causal data are lacking. We describe specific epigenetic mechanisms and their potential roles in the biological embedding of experience. We also consider the nuanced relationships between the genome, the epigenome, and gene expression. Our ability to connect biological embedding to the epigenetic landscape in its complexity is challenging and complicated by the influence of multiple factors. These include cell type, age, the timing of experience, sex, and DNA sequence. Recent advances in molecular profiling and epigenome editing, combined with the use of comparative animal and human longitudinal studies, should enable this field to transition from correlative to causal analyses.

Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies, which contain richer semantic information than plain knowledge graphs, and have been widely adopted in domains such as bioinformatics. In this paper, we propose a random walk and word embedding based ontology embedding method named OWL2Vec*, which encodes the semantics of an OWL ontology by taking into account its graph structure, lexical information and logical constructors. Our empirical evaluation with three real world datasets suggests that OWL2Vec* benefits from these three different aspects of an ontology in class membership prediction and class subsumption prediction tasks. Furthermore, OWL2Vec* often significantly outperforms the state-of-the-art methods in our experiments.

A major issue facing the field of cellular imaging, immunofluorescence (IF), and immunohistochemistry (IHC) microscopy is antibody quality. One of the main methods of antibody validation is testing on positive and negative control tissues with known expression levels of a given antigen. However, this approach is reliant on availability of tissues and reliable protein expression datasets, which are not always available. In contrast, cultured cell lines often have more extensive and reproducible protein expression data available, are relatively inexpensive to maintain, and can be used to produce knockout lines for more robust and functional validation. Due to the difference in staining protocols between formalin-fixed paraffin-embedded (FFPE) tissues and cultured cell lines, an antibody that works well in cultured cells does not always produce the same results in FFPE tissues. For this reason, there is a need for methods to embed cultured cells in paraffin for antibody testing. Previous methods have been published, but many involve use of sharps, which introduces risk of cuts to the investigator, or embedded in agarose first, which results in a lower density of cells. This paper introduces a method of embedding cultured cells using custom designed silicone molds. These molds allow an easy, risk-free embedding process that results in high density cell pellet blocks which can be used for IF and IHC experiments, as well as creation of cell microarrays. Additionally, the silicone molds can be used to embed organoids for IF and IHC analysis.

Three-dimensional (3D) data of many kinds is produced at an increasing rate throughout all scientific disciplines. The Portable Document Format (PDF) is the de-facto standard for the exchange of electronic documents and allows for embedding three-dimensional models. Therefore, it is a well suited medium for the visualization and the publication of this kind of data. The generation of the appropriate files has been cumbersome so far. This article presents the first release of a software toolbox which integrates the complete workflow for generating 3D model files and ready-to-publish 3D PDF documents for scholarly publications in a consolidated working environment. It can be used out-of-the-box as a simple working tool or as a basis for specifically tailored solutions. A comprehensive documentation, an example project and a project wizard facilitate the customization. It is available royalty-free and for Windows, MacOS and Linux.

BackgroundImproving health and social care services involves engaging stakeholders in the implementation process. The literature currently reports suboptimal stakeholder engagement in implementation science. Here we draw on the international large-scale ImpleMentAll (IMA) study to illustrate the development of the Implementation-STakeholder Engagement Model (I-STEM) for improving health and social care services. The I-STEM is a sensitising tool, which defines key considerations and activities for undertaking stakeholder engagement activities across an implementation process.MethodsThe IMA project used a stepped-wedged randomised controlled trial design to evaluate the effectiveness of tailored implementation in integrating and embedding evidence-based eHealth interventions in routine care in Europe and Australia. Tailored implementation was operationalised in the ItFits-toolkit, a self-guided platform including resources to support stakeholder engagement (e.g., surveying tool). Here we draw on the qualitative process evaluation that was undertaken alongside the effectiveness trial. We conducted 55 in-depth, semi-structured interviews and observed 19 implementation related activities (e.g., technical support calls) across twelve implementation sites. The analytical process was informed by principles of first and third generation Grounded Theory, including constant comparative method. The I-STEM was derived from the analytical work undertaken in the qualitative process evaluation.ResultsOur findings are presented as the substantive, generalisable I-STEM, consisting of five interrelated concepts: engagement objectives, stakeholder mapping, engagement approaches, engagement qualities, and engagement outcomes. Engagement objectives are goals that implementers plan to achieve by working with stakeholder in the implementation process. Stakeholder mapping involves identifying a range of organisations, groups, or people who may be instrumental in achieving the engagement objectives. Engagement approaches define the type of work that is undertaken with stakeholders to achieve the engagement objectives. Engagement qualities define the logistics of the engagement approach. Lastly, every engagement activity may result in a range of engagement outcomes.DiscussionEffective stakeholder engagement can lead to a better understanding of local needs and barriers and increased research adoption. The I-STEM represents potential avenues for effective stakeholder engagement activity across key phases of an implementation process. It provides a conceptual framework for the planning, delivery, evaluation, and reporting of stakeholder engagement activities. The I-STEM is not prescriptive, but rather highlights the importance of a flexible, iterative approach to stakeholder engagement. It is developmental and will require further application and validation across a range of implementation activities.

The digital economy is an important way to relieve current pressure on resources and the environment. Based on ecological modernization theory and global value chain theory, this paper adopts panel data of 17 manufacturing industries from 2000 to 2014, uses the non-radial directional distance function and the meta-frontier method to measure China’s green total factor productivity (GTFP) and its sub-indices, and takes the embedding degree and embedding position as moderating variables and threshold variables to construct an econometric model to explore the relationship among the digital economy, global value chain, and GTFP. The results show that (1) the digital economy has a positive effect on GTFP in both time and space. And in terms of time, the digital economy mainly enhances GTFP by improving technical efficiency and narrowing the technology gap. (2) Global value chain embedding position positively moderates the relationship between the digital economy and GTFP; In terms of sub-samples, labor-intensive and capital-intensive industries show the same impact characteristics as the total sample. (3) Further, it is found that the digital economy and GTFP have nonlinear characteristics. There is a single threshold effect on the embedding position. When the embedding position is low, the digital economy is positively correlated with GTFP but not significant; when the threshold value is crossed, the digital economy can significantly promote GTFP. The conclusions of this paper are helpful to realize the digital economy to promote the green development of the manufacturing industry, and provide an effective reference for the realization mechanism of China’s green economy transformation and ecological civilization construction in the post-pandemic era.