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Neuronal specification, expansion and differentiation are tightly regulated by the concerted actions of transcription and chromatin modifying factors that are recruited to regulatory elements in the genome. Tissue-specific distal regulatory elements are typically located tens to hundreds of kilobases from the gene they regulate. Thus, to identify the distal enhancers that directly regulate a gene, information on the localisation of enhancers relative to the gene promoter in the nucleus is crucial. Cerebellar granule cell progenitors (GCps) are important transit amplifying neuronal progenitors, giving rise to the most abundant neuronal cell type in the brain. Many of the key factors that regulate fundamental developmental processes in GCps have been identified. For instance, the proneural transcription factor Atoh1 is essential for GCp specification, proliferation and differentiation and the ATP-dependent chromatin remodeller CHD7 is necessary for normal GCp proliferation and differentiation. However, both these factors are recruited to distal regulatory elements and the direct regulatory relationships between these factors, the enhancers they are recruited to, and the genes they regulate in GCps remain uncharacterised. To identify active, long-range gene regulatory interactions in GCps, we used promoter capture Hi-C (pcHi-C), and integrated pcHi-C data with ATAC-seq and ChIP-seq data. We present a rich dataset consisting of 46,428 interactions between 22,797 putative distal regulatory regions and 12,905 protein coding gene promoters in primary mouse GCps. Using VISTA-designated hindbrain enhancers as an example, we identify the genes most likely regulated directly by these enhancers and update their annotation accordingly. Motif enrichment analyses identified a significant enrichment of proneural transcription factor motifs in CHD7-regulated enhancers. Further analyses revealed co-localisation of Atoh1 and CHD7 at gene enhancers, suggesting a novel regulatory relationship between Atoh1 and CHD7 in controlling the expression of key genes in the GCp lineage. We used our data to identify >1,500 Atoh-regulated enhancers, contacting the promoters of 577 genes in GCps, and 197 enhancers of 22 genes that appear to be co-regulated by Atoh1 and CHD7. Co-immunoprecipitation experiments showed that Atoh1 and CHD7 proteins interact with each other. These findings support the emerging picture of CHD7 as an important gene regulatory co-factor for lineage-specific transcription factors. The pcHi-C data is presented as a useful resource to the community for investigating the function of long-range enhancers in the cerebellar GCp lineage.Competing Interest StatementThe authors have declared no competing interest.Footnotes* We include luciferase assays as functional validation for some of the putative enhancers identified by our work (Suppl Fig. 3). We also include a comparative analysis between our data and the Reddy et al. Hi-C dataset (Suppl. Fig. 1) and a comparison between embryonic VISTA hindbrain enhancers and our P7 GCp ATAC-seq data in Fig. 1.Funder Information DeclaredMedical Research Council, https://ror.org/03x94j517, MR/K022377/1, MR/Y008170/1Medical Research Council, https://ror.org/03x94j517, G1000902Wellcome Trust, 224619/Z/21/Z

Loss-of-function mutations in genes encoding lysine demethylases specific for trimethylated lysine 4 of histone 3 (H3K4me3) are associated with neurodevelopmental conditions, including autism spectrum disorder (ASD) and intellectual disability. To study the role of KDM5B (Lysine DeMethylase-5B)-mediated H3K4me3 demethylation, we investigated neurodevelopmental phenotypes in mice without KDM5B demethylase activity. These mice exhibited autism-like behaviours and increased brain size. H3K4me3 levels and the expression of neurodevelopmental genes were increased in the developing Kdm5b mutant neocortex. Increased H3K4me3 levels at the promoter and associated expression of the Grin2d gene was associated with increased levels of NMDAR2D protein in synaptosomes isolated from the early postnatal Kdm5b-deficient neocortex. Treating mice with the NMDAR antagonist memantine rescued deficits in ultrasonic vocalizations. These findings suggest that increased H3K4me3 levels and associated Grin2d gene upregulation disrupt brain development and function, leading to socio-communication deficits and identify a potential therapeutic target for neurodevelopmental disorders associated with KDM5B deficiency.Competing Interest StatementThe authors have declared no competing interest.Footnotes* Revrt to previous version as per journal submission rulesFunder Information DeclaredMedical Research Council, https://ror.org/03x94j517, MR/V013173/1, MR/Y008170/1, MR/X010481/1, MR/W017156/1

Rapid technological change, globalization, increasing competition and concerns about sustainability have changed the nature of technological innovation processes with the result that these activities are less often confined to a single firm. Thus, there is a growing demand for someone to foster collaboration. This prompted the question: how do these individuals foster collaborative technological innovation (CTI)? The steps of collaborative technological innovation at the firm level generally include recognition of an opportunity, idea generation, problem solving, and implementation/diffusion of the technology. Each stage and its activities require a different mix of individuals and their contributions. When the mix includes people from different organizations, getting these people to work together effectively requires someone with special skills. We call these people “champions of collaboration.” This study advances our understanding of these people by building hypotheses that explain how “champions of collaboration” foster interorganizational research and development collaboration in Japanese firms. A case study research strategy is applied to this research, and seven hypotheses are developed. Of these, four were found to be unique in the existing literature. (H 1) Champions of Collaboration (CoC) contribute to a form of advanced market awareness and innovation project initiation called “ demand articulation.” (H 2) CoC contribute to the selection of partner(s) by considering the goals for the collaboration and the fit (person-to-person, time, strategic, organizational, functional) between their own firm and the target firm or organization. (H 3) CoC help to set collaborative research and development agendas for technological innovation. (H 4) CoC motivate researcher(s) and developer(s) within their own and partner firms. The research suggests that “senkennomei” (long-term perspective), “shinnen” (conviction), along with passion, persistence, coordination, and negotiation skills are key personality characteristics that enable the champion of collaboration to foster collaboration in technological innovation. Additionally, a CoC emerges at various stages of a CTI and different individuals may play this role at different times as they work collaboratively with others within and outside their organizations. This thesis provides some directions for practitioners who wish to optimize technological innovation processes through collaboration, and makes several recommendations for further research in the area.

Data classification has many approaches in data mining and machine learning. The artificial neural network (ANN) is applied to classify the data that might belong to various domains like chemical, botanical, medical, spatial, textual, and image. In this work, an ANN technique is applied to the 7 Life sciences (botanical and medical) data sets extracted from public data repositories. Various optimization approaches like exhaustive validation, cross-validation, and multiple seeding are used to discover the most optimized networks for the given datasets. Finally, voting predicts the class where the whole dataset is used as a test set instead of folds. The results obtained by the proposed approach outperform other approaches on all the datasets. Cleveland’s heart, Statlog heart, Dermatology, Hepatitis, Seeds, Abalone and Vertebral Column data sets (all of UCI) after applying the voting showed the accuracy of 94.61%, 93.7%, 99.73%, 96.77%, 99.05%, 89.37% and 90.32% respectively. In the future deep neural network may be used to improve the results.