Understanding behaviour patterns of multi-agents in digital business ecosystems: an organisational semiotics inspired framework

Digital business ecosystem (DBE) is a collaborative network of organisations, processes and technologies that collectively create value. Thus, value creation in DBEs is jointly undertaken by multiple human and digital agents. To aid appropriate apportionment of work and design of information systems, it is essential to understand behaviour of both human and digital agents. However limited attention has been paid to agents’ behaviour in the extant DBEs literature. Moreover, multi-agent research has also largely focused on technical issues while limited research exists on agents’ behaviour. As such, in this paper, we develop a framework to understand behaviour patterns of multi-agent in DBEs. This framework builds its foundation on the theoretical lens of Organisational Semiotics, a sociotechnical theory towards contribution to DBE research

Preliminary studies on optimizing protoplast culture of ryegrasses

The suspension growth, protoplast isolation and culture of Lolium mulliflorum Lam and L.perenne-L were studied Ryegrass suspension cultures were in higher growth volume subcultured in the volume ratio of 1 ml cells to 20 ml liquid medium.The enzyme solution produccd maximum yield of the protoplasts when the proportion of cells in enzyme solution was 20%～30%. Glucose was more suitable than sucrose and mannitol in protoplast medium as carbon source or osmoticum and led to higher plating efficiency of ryegrass protoplasts

Multi-source knowledge graph reasoning for ocean oil spill detection from satellite SAR images

Marine oil spills can cause severe damage to the marine environment and biological resources. Using satellite remote sensing technology is one of the best ways to monitor the sea surface in near real-time to obtain oil spill information. The existing methods in the literature either use deep convolutional neural networks in synthetic aperture radar (SAR) images to directly identify oil spills or use traditional methods based on artificial features sequentially to distinguish oil spills from sea surface. However, both approaches currently only use image information and ignore some valuable auxiliary information, such as marine weather conditions, distances from oil spill candidates to oil spill sources, etc. In this study, we proposed a novel method to help detect marine oil spills by constructing a multi-source knowledge graph, which was the first one specifically designed for oil spill detection in the remote sensing field. Our method can rationally organize and utilize various oil spill-related information obtained from multiple data sources, such as remote sensing images, vectors, texts, and atmosphere-ocean model data, which can be stored in a graph database for user-friendly query and management. In order to identify oil spills more effectively, we also proposed 13 new dark spot features and then used a feature selection technique to create a feature subset that was favorable to oil spill detection. Furthermore, we proposed a knowledge graph-based oil spill reasoning method that combines rule inference and graph neural network technology, which pre-inferred and eliminated most non-oil spills using statistical rules to alleviate the problem of imbalanced data categories (oil slick and non-oil slick). Entity recognition is ultimately performed on the remaining oil spill candidates using a graph neural network algorithm. To verify the effectiveness of our knowledge graph approach, we collected 35 large SAR images to construct a new dataset, for which the training set contained 110 oil slicks and 66264 non-oil slicks from 18 SAR images, the validation set contained 35 oil slicks and 69005 non-oil slicks from 10 SAR images, and the testing set contained 36 oil slicks and 36281 non-oil slicks from the remaining 7 SAR images. The results showed that some traditional oil spill detection methods and deep learning models failed when the dataset suffered a severe imbalance, while our proposed method identified oil spills with a sensitivity of 0.8428, specificity of 0.9985, and precision of 0.2781 under those same conditions. The knowledge graph method we proposed using multi-source data can not only help solve the problem of information island in oil spill detection, but serve as a guide for construction of remote sensing knowledge graphs in many other applications as well. The dataset gathered has been made freely available online (https://pan.baidu.com/s/1DDaqIljhjSMEUHyaATDIYA?pwd=qmt6)

GeneSegNet: a deep learning framework for cell segmentation by integrating gene expression and imaging

When analyzing data from in situ RNA detection technologies, cell segmentation is an essential step in identifying cell boundaries, assigning RNA reads to cells, and studying the gene expression and morphological features of cells. We developed a deep-learning-based method, GeneSegNet, that integrates both gene expression and imaging information to perform cell segmentation. GeneSegNet also employs a recursive training strategy to deal with noisy training labels. We show that GeneSegNet significantly improves cell segmentation performances over existing methods that either ignore gene expression information or underutilize imaging information.ISSN:1474-760

Immune‐check blocking combination multiple cytokines shown curative potential in mice tumor model

Abstract Objective In order to ensure the stable transcription of target genes, we constructed a eukaryotic high expression vector carrying an immune‐check inhibitor PD‐1v and a variety of cytokines, and studied their effects on activating immune response to inhibit tumor growth. Methods A novel eukaryotic expression plasmid vector named pT7AMPCE containing T7RNA polymerase, T7 promoter, internal ribosome entry site (IRES), and poly A tailing signal was constructed by T4 DNA ligase, on which homologous recombination was used to clone and construct the vector carrying PD‐1v, IL‐2/15, IL‐12, GM‐CSF, and GFP. In vitro transfection of CT26 cells was performed, and the protein expression of PD‐1v, IL‐12 and GM‐CSF was detected by Western blot and ELISA after 48 h. Mice were subcutaneously inoculated with CT26‐IRFP tumor cells in the rib abdomen, and the tumor tissues were injected with PD‐1v, IL‐2/15, IL‐12, and GM‐CSF recombinant plasmids for treatment during the experimental period. The efficacy of the treatment was evaluated by assay tumor size and survival time of tumor‐bearing mice during the experiment. Expression levels of IFN‐γ, TNF, IL‐4, IL‐2, and IL‐5 in mouse blood were measured using the CBA method. Tumor tissues were extracted and immune cell infiltration in tumor tissues was detected by HE staining and the IHC method. Results The recombinant plasmids carrying PD‐1v, IL‐2/15, IL‐12, and GM‐CSF were successfully constructed, and the Western blot and ELISA results showed that PD‐1v, IL‐12, and GM‐CSF were expressed in the supernatant of CT26 cells 48 h after in vitro cell transfection. The combined application of PD‐1v, IL‐2/15, IL‐12, and GM‐CSF recombinant plasmids significantly inhibited tumor growth in mice, and the tumor growth rate was significantly lower than that in the blank control group and GFP plasmid control group (p < 0.05). Cytometric bead array data suggested that the combination of PD‐1v and various cytokines can effectively activate immune cells. HE and IHC analysis revealed plenty of immune cell infiltrates in the tumor tissue, and a large proportion of tumor cells showed the necrotic phenotype in the combination treatment group. Conclusion The combination of immune check blockade and multiple cytokine therapy can significantly activate the body's immune response and inhibit tumor growth

Full-band, multi-angle, multi-scale, and temporal dynamic field spectral measurements in China

Abstract Field-measured spectra are critical for remote sensing physical modelling, retrieval of structural, biophysical, and biochemical parameters, and other practical applications. We present a library of field spectra, which includes (1) portable field spectroradiometer measurements of vegetation, soil, and snow in the full-wave band, (2) multi-angle spectra measurements of desert vegetation, chernozems, and snow with consideration of the anisotropic reflectance of land surface, (3) multi-scale spectra measurements of leaf and canopy of different vegetation cover surfaces, and (4) continuous reflectance spectra time-series data revealing vegetation growth dynamics of maize, rice, wheat, rape, grassland, and so on. To the best of our knowledge, this library is unique in simultaneously providing full-band, multi-angle, multi-scale spectral measurements of the main surface elements of China covering a large spatial extent over a 10-year period. Furthermore, the 101 by 101 satellite pixels of Landsat ETM/OLI and MODIS surface reflectance centered around the field site were extracted, providing a vital linkage between ground measurements and satellite observations. The code language used for this work is Matlab 2016a