Building green innovation networks for people, planet, and profit: A multi-level, multi-value approach

In this conceptual paper we explore the problem of how firms balance profit considerations against their contribution to society and the environmental. We theorize how firms build networks that support green transition, enabling them to reconfigure processes that match sustainability goals and maintain profitable. We explore how building networks for green transition supports firms\u27 transition to more sustainable approaches that support the adoption of, and transition to, green strategies. We extend current theorization of how firms build multi-level B2B networks that support green transition that benefits society and the environment. We suggest three propositions that support the development of a multi-level, multi-value model for building green innovation networks. We identify four critical success factors - embedding technological diversity, developing knowledge sharing mechanisms, embracing open innovation strategies, overcoming resistance to change, − that support this process and help firms overcome value creation frictions and deliver multi-value benefits to society (people) and the environment (planet), whilst enabling firms to make a profit. Our conclusion outlines our contribution and highlights areas for future research

RVM-based multi-class classification of remotely sensed data

The relevance vector machine (RVM), a Bayesian extension of the support vector machine (SVM), has considerable potential for the analysis of remotely sensed data. Here, the RVM is introduced and used to derive a multi-class classification of land cover with an accuracy of 91.25%, a level comparable to that achieved by a suite of popular image classifiers including the SVM. Critically, however, the output of the RVM includes an estimate of the posterior probability of class membership. This output may be used to illustrate the uncertainty of the class allocations on a per-case basis and help to identify possible routes to further enhance classification accuracy

Novel Study of Model-Based Clustering Time Series Gene Expression in Different Tissues: Applications to Aging Process

BACKGROUND: Aging is an organized biological process that is regulated by highly interconnected pathways between different cells and tissues in the living organism. Identification of similar genes between tissues in different ages may also help to discover the general mechanism of aging or to discover more effective therapeutic decisions. OBJECTIVE: According to the wide application of model-based clustering techniques, the aim is to evaluate the performance of the Mixture of Multivariate Normal Distributions (MMNDs) as a valid method for clustering time series gene expression data with the Mixture of Matrix-Variate Normal Distributions (MMVNDs). METHODS: In this study, the expression of aging data from NCBI's Gene Expression Omnibus was elaborated to utilize proper data. A set of common genes which were differentially expressed between different tissues were selected and then clustered together through two methods. Finally, the biological significance of clusters was evaluated, using their ability to find genes in the cell using Enricher. RESULTS: The MMVNDs is more efficient to find co-express genes. Six clusters of genes were observed using the MMVNDs. According to the functional analysis, most genes in clusters 1-6 are related to the B-cell receptors and IgG immunoglobulin complex, proliferating cell nuclear antigen complex, the metabolic pathways of iron, fat, and body mass control, the defense against bacteria, the cancer development incidence, and the chronic kidney failure, respectively. CONCLUSION: Results showed that most biological changes of aging between tissues are related to the specific components of immune cells. Also, the application of MMVNDs can increase the ability to find similar genes. Copyright© Bentham Science Publishers; For any queries, please email at [email protected]

WDR81 Gene Silencing Can Reduce Exosome Levels in Human U87-MG Glioblastoma Cells

Glioblastoma is a very invasive and prevalent brain tumor that affects 15 in 100,000 persons over the age of 70 years. Studies have shown that the expression of the WD repeat domain 81 (WDR81) gene, which is effective in vesicular transport and inhibition of autophagy, is increased in glioblastoma. The decreased autophagy was found to be related to the increased production of exosomes, which is a major factor in the pathogenesis of glioblastoma. The PI-3kinase complex is a pre-autophagic complex that is highly active in the absence of WDR81. The WDR81 gene, as a negative regulator of PI3K activity, prevents autophagy and increases exosome secretion by preventing the formation of the class III PI3K complex. Therefore, targeted reduction of exosomes can be considered an effective strategy for reducing the pathogenesis of glioblastoma. This study aimed to assess the effect of WDR81 gene silencing with siRNA on exosome levels in a U87-MG cell line. Culturing of U87-MG cells was carried out in Dulbecco�s modified Eagle medium (DMEM) containing 5 FBS and 1 penicillin/streptomycin. Thereafter, silencing of WDR81 was performed using WDR81 siRNA, whose gene expression level was determined via real-time qRT-PCR. Cell viability was evaluated using the MTT assay. The exosomes were extracted from a cell culture using the Exocib kit. The size accuracy of the exosomes was confirmed by dynamic light scattering (DLS). Finally, the protein content and RNA of the exosomes were assessed. WDR81 gene expression of siRNA-transfected cells was decreased to 82 after 24 h compared to the non-transfected control cells. The analysis of the exosomes showed that the concentration of exosomes and their RNA and protein content in the siRNA-transfected cells decreased significantly compared to the non-transfected control cells. No considerable difference was observed in cell viability after transfection with either WDR81-specific siRNAs or scrambled control siRNAs. Our findings showed that silencing the WDR81 gene could reduce the level of exosomes in human U87-MG glioblastoma cells. Therefore, the reduced exosome content may be suggested as a new gene therapy strategy for targeted therapy of glioblastoma by increasing autophagy via activation of PI3KIII. However, more studies are needed in this regard. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

In silico drug repurposing for the treatment of heart diseases using gene expression data and molecular docking techniques

Heart diseases are known as the most primary causes of mortality worldwide. Although many therapeutic approaches and medications are proposed for these diseases, the identification of novel therapeutics in fatal heart conditions is promptly demanded. Besides, the interplay between gene expression data and molecular docking provides several novel insights to discover more effective and specific drugs for the treatment of the diseases. This study aimed to discover potent therapeutic drugs in the heart diseases based on the expression profile of heart-specific genes exclusively. Initially, the heart-specific and highly expressed genes were identified by comparing the gene expression profile of different body tissues. Subsequently, the druggable-genes were identified using in silico techniques. The interaction between these druggable genes with more than 1600 FDA approved drugs was then investigated using the molecular docking simulation. By comprehensively analyzing RNA-sequencing data obtained from 949 normal tissue samples, 48 heart-specific genes were identified in both the heart development and function. Notably, of these, 24 heart-specific genes were capable to be considered as druggable genes, among which only MYBPC3, MYLK3, and SCN5A genes entered the molecular docking process due to their functions. Afterward, the pharmacokinetics properties of top 10 ligands with the highest binding affinity for these proteins were studied. Accordingly, methylergonovine, fosaprepitant, pralatrexate, daunorubicin, glecaprevir, digoxin, and venetoclax drugs were competent, in order to interact with the target proteins perfectly. It was shown that these medications can be used as specific drugs for the treatment of heart diseases after fulfilling further experiments in this regard. © 2021 Elsevier Inc