Experimental investigations on the characteristics of snow accretion using the EMU-320 model train

This paper presents a snow accretion test conducted in a climate wind tunnel to investigate the icing process on a model train. The model used within this experiment was the cleaned-up and 2/3-scaled version of EMU-320, which is a high-speed train in Korea. The model was designed without an electronic power source or heat source so that the wheels did not rotate and snow accretion on the model did not occur due to heat sources. To investigate snow accretion, four cases with different ambient temperatures were considered in the climate wind tunnel on Rail Tec Arsenal. Before analyzing the snow accretion on the train, the snow flux and liquid water content of snow were measured so that they could be used as the input conditions for the simulation and to ensure the analysis of the icing process was based on the characteristics of the snow. Both qualitative and quantitative data were obtained, whereby photographs was used for qualitative analysis, and the density of the snow sample and the thickness of snow accreted on the model were used for quantitative analysis. Based on the visual observations, it was deduced that as the ambient temperature increased, the range of the snow accreted was broader. The thickness of snow accreted on the model nose was the largest on the upper and lower part at -3 oC, and on the middle part at -5 oC. Additionally, the cross section of snow accreted was observed to be trench-like. Similar icing processes were observed to occur on the slope of nose. Snow accreted on all components of the bogie, and for all cases, the thickness of snow at wheel was the largest at an arc angle of 40 to 70 o. These detailed data of experimental conditions can be applied as an input to simulations to improve simulations of ice conditions. Thus, they can facilitate the development of appropriate anti-icing designs for trainsComment: 31 pages, 23 Figures, 8 Table

A Workflow for the Networked Ontologies Lifecycle. A Case Study in FAO of the UN

This document shows a preliminary framework for editing networked ontologies in the context of the NeOn project. The goal is to manage, in a collaborative way, multiple networked ontologies for large-scale semantic applications. This paper shows the main concepts on the editorial workflow and several lifecycle use cases. The ontologies produced with this framework will be used by the Food and Agriculture Organization of the United Nations (FAO) in many different large applications such the Fisheries Stock Depletion Assessment System[4]. Therefore a major goal for FAO is to have a strong and reliable ontology management system for editing the networked ontologies that applications will use as a basis. This framework for editing networked ontologies is being developed in the context of the NeOn Project1. What we present here is a brief summary of the activities carried out in this project regarding user requirements and subsequent use case analysis

Expansion of cytotoxic natural killer cells in multiple myeloma patients using K562 cells expressing OX40 ligand and membrane-bound IL-18 and IL-21.

BACKGROUND: Natural killer (NK) cell-based immunotherapy is a promising treatment approach for multiple myeloma (MM), but obtaining a sufficient number of activated NK cells remains challenging. Here, we report an improved method to generate ex vivo expanded NK (eNK) cells from MM patients based on genetic engineering of K562 cells to express OX40 ligand and membrane-bound (mb) IL-18 and IL-21. METHODS: K562-OX40L-mbIL-18/-21 cells were generated by transducing K562-OX40L cells with a lentiviral vector encoding mbIL-18 and mbIL-21, and these were used as feeder cells to expand NK cells from peripheral blood mononuclear cells of healthy donors (HDs) and MM patients in the presence of IL-2/IL-15. Purity, expansion rate, receptor expression, and functions of eNK cells were determined over four weeks of culture. RESULTS: NK cell expansion was enhanced by short exposure of soluble IL-18 and IL-21 with K562-OX40L cells. Co-culture of NK cells with K562-OX40L-mbIL-18/-21 cells resulted in remarkable expansion of NK cells from HDs (9,860-fold) and MM patients (4,929-fold) over the 28-day culture period. Moreover, eNK cells showed increased expression of major activation markers and enhanced cytotoxicity towards target K562, U266, and RPMI8226 cells. CONCLUSIONS: Our data suggest that genetically engineered K562 cells expressing OX40L, mbIL-18, and mbIL-21 improve the expansion of NK cells, increase activation signals, and enhance their cytolytic activity towards MM cells

Machine learning-based evaluation of spontaneous pain and analgesics from cellular calcium signals in the mouse primary somatosensory cortex using explainable features

IntroductionPain that arises spontaneously is considered more clinically relevant than pain evoked by external stimuli. However, measuring spontaneous pain in animal models in preclinical studies is challenging due to methodological limitations. To address this issue, recently we developed a deep learning (DL) model to assess spontaneous pain using cellular calcium signals of the primary somatosensory cortex (S1) in awake head-fixed mice. However, DL operate like a “black box”, where their decision-making process is not transparent and is difficult to understand, which is especially evident when our DL model classifies different states of pain based on cellular calcium signals. In this study, we introduce a novel machine learning (ML) model that utilizes features that were manually extracted from S1 calcium signals, including the dynamic changes in calcium levels and the cell-to-cell activity correlations.MethodWe focused on observing neural activity patterns in the primary somatosensory cortex (S1) of mice using two-photon calcium imaging after injecting a calcium indicator (GCaMP6s) into the S1 cortex neurons. We extracted features related to the ratio of up and down-regulated cells in calcium activity and the correlation level of activity between cells as input data for the ML model. The ML model was validated using a Leave-One-Subject-Out Cross-Validation approach to distinguish between non-pain, pain, and drug-induced analgesic states.Results and discussionThe ML model was designed to classify data into three distinct categories: non-pain, pain, and drug-induced analgesic states. Its versatility was demonstrated by successfully classifying different states across various pain models, including inflammatory and neuropathic pain, as well as confirming its utility in identifying the analgesic effects of drugs like ketoprofen, morphine, and the efficacy of magnolin, a candidate analgesic compound. In conclusion, our ML model surpasses the limitations of previous DL approaches by leveraging manually extracted features. This not only clarifies the decision-making process of the ML model but also yields insights into neuronal activity patterns associated with pain, facilitating preclinical studies of analgesics with higher potential for clinical translation

Expansion of Human NK Cells Using K562 Cells Expressing OX40 Ligand and Short Exposure to IL-21

Background: Natural Killer (NK) cell-based immunotherapy used to treat cancer requires the adoptive transfer of a large number of activated NK cells. Here, we report a new effective method to expand human NK cells ex vivo using K562 cells genetically engineered (GE) to express OX40 ligand (K562-OX40L) in combination with a short exposure to soluble IL-21. In addition, we describe a possible mechanism of the NK cell expansion through the OX40 receptor-OX40 ligand axis which is dependent on NK cell homotypic interaction.Methods: K562-OX40L cells were generated by lentiviral transduction and were used as feeder cells to expand and activate NK cells from PBMCs in the presence of IL-2/IL-15. Soluble IL-21 was also added in various concentrations only once at the beginning of the culture. NK cells were expanded for 4–5 weeks, and the purity, expansion rate, phenotype and function (cytotoxicity, antibody-dependent cell-mediated cytotoxicity (ADCC), cytokine production, CD107a degranulation) of these expanded NK cells were compared to those generated by using K562 feeder cells.Results: The culture of NK cells with K562-OX40L cells in combination with the transient exposure to IL-21 highly enhanced NK cell expansion to approximately 2,000-fold after 4 weeks of culture, compared to a 303-fold expansion using the conventional K562 cells. Mechanistically, the OX40-OX40L axis between the feeder cells and NK cells as well as the homotypic interaction between NK cells through the OX40-OX40L axis were both necessary for NK cell expansion. The short exposure of NK cells to IL-21 had a synergistic effect with OX40 signaling for NK cell expansion. Apart from their enhanced expansion, NK cells grown with K562-OX40L feeder cells were similar to those grown with conventional K562 cells in regard to the surface expression of various receptors, cytotoxicity, ADCC, cytokine secretion, and CD107 degranulation.Conclusion: Our data suggest that OX40 ligand is a potent co-stimulant for the robust expansion of human NK cells and the homotypic NK cell interactions through the OX40-OX40L axis is a mechanism of NK cell expansion

Increasing interoperability between food and agricultural systems: CGIAR and FAO collaboration

It is crucial that data resources can talk to each other through thesaurus, ontologies and standards. Therfore, the integration of CGIAR controlled vocabularynto the AGROVOC thesaurus is key to interlink our data sets and publications in the food and agricultural domain and produce multilingual quality labeling. The Task Group and a curation team defined the added value for the CGIAR to formally contribute to AGROVOC, and how to organize CGIAR contribution in a coherent workflow. The recommendations are the following: 1. One CGIAR needs to strengthen its contribution to AGROVOC thus supporting the consolidation of the semantic landscape for labeling data in agriculture and food systems. 2.CGIAR centers should wait a bit till the affiliation process is complete so that the appropriate unit that will be responsible for AGROVOC can consume the Agreement since the timeline for the affiliation process is just some few months away. 3.OneCGIAR data managers will have to sustain the collaboration and submit terms to populate the ONECGIAR concepts schema newly created to provide direct visibility of the set of concepts (https://agrovoc.fao.org/skosmosOneCGIAR/cgiar/en/ ). Based on the collaboration concrete results, The TG recommends that the term submission effort and collaboration with FAO continues with proper allocation of data managers’ time and a training plan. Contribution to AGROVOC should be part of the data managers ToRs to concrete provide recognition of this role

Increasing interoperability between food and agricultural information systems: CGIAR and FAO collaboration

It is crucial that data resources can talk to each other through thesaurus, ontologies and standards. Therfore, the integration of CGIAR controlled vocabularynto the AGROVOC thesaurus is key to interlink our data sets and publications in the food and agricultural domain and produce multilingual quality labeling. The Task Group and a curation team defined the added value for the CGIAR to formally contribute to AGROVOC, and how to organize CGIAR contribution in a coherent workflow. The recommendations are the following: 1. One CGIAR needs to strengthen its contribution to AGROVOC thus supporting the consolidation of the semantic landscape for labeling data in agriculture and food systems. 2.CGIAR centers should wait a bit till the affiliation process is complete so that the appropriate unit that will be responsible for AGROVOC can consume the Agreement since the timeline for the affiliation process is just some few months away. 3.OneCGIAR data managers will have to sustain the collaboration and submit terms to populate the ONECGIAR concepts schema newly created to provide direct visibility of the set of concepts (https://agrovoc.fao.org/skosmosOneCGIAR/cgiar/en/ ). Based on the collaboration concrete results, The TG recommends that the term submission effort and collaboration with FAO continues with proper allocation of data managers’ time and a training plan. Contribution to AGROVOC should be part of the data managers ToRs to concrete provide recognition of this role

The ontologies community of practice: a CGIAR initiative for Big Data in agrifood systems

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams