Morphological and Molecular Characterization of Orchid Fruit Development

Efficient seed dispersal in flowering plants is enabled by the development of fruits, which can be either dehiscent or indehiscent. Dehiscent fruits open at maturity to shatter the seeds, while indehiscent fruits do not open and the seeds are dispersed in various ways. The diversity in fruit morphology and seed shattering mechanisms is enormous within the flowering plants. How these different fruit types develop and which molecular networks are driving fruit diversification is still largely unknown, despite progress in eudicot model species. The orchid family, known for its astonishing floral diversity, displays a huge variation in fruit dehiscence types, which have been poorly investigated. We undertook a combined approach to understand fruit morphology and dehiscence in different orchid species to get more insight into the molecular network that underlies orchid fruit development. We describe fruit development in detail for the epiphytic orchid species Erycina pusilla and compare it to two terrestrial orchid species: Cynorkis fastigiata and Epipactis helleborine. Our anatomical analysis provides further evidence for the split carpel model, which explains the presence of three fertile and three sterile valves in most orchid species. Interesting differences were observed in the lignification patterns of the dehiscence zones. While C. fastigiata and E. helleborine develop a lignified layer at the valve boundaries, E. pusilla fruits did not lignify at these boundaries, but formed a cuticle-like layer instead. We characterized orthologs of fruit-associated MADS-domain transcription factors and of the Arabidopsis dehiscence-related genes INDEHISCENT (IND)/HECATE 3 (HEC3), REPLUMLESS (RPL) and SPATULA (SPT)/ALCATRAZ (ALC) in E. pusilla, and found that the key players of the eudicot fruit regulatory network appear well-conserved in monocots. Protein-protein interaction studies revealed that MADS-domain complexes comprised of FRUITFULL (FUL), SEPALLATA (SEP) and AGAMOUS (AG) /SHATTERPROOF (SHP) orthologs can also be formed in E. pusilla, and that the expression of HEC3, RPL, and SPT can be associated with dehiscence zone development similar to Arabidopsis. Our expression analysis also indicates differences, however, which may underlie fruit divergence

A HLTF (Helicase like transcription factor) és az SHPRH (SNF2 histone linker PHD RING helicase) fehérjék szerepe a károsodott DNS replikációjában

Abstract — Game environments tend to be highly responsive and demanding and thus provoke active learner involvement. Surprisingly, gaming and in the same line also serious gaming, still make little use of one of people’s most common type of interactions i.e. natural language. Despite the presumed positive effect in e-learning of interactive online characters, the use of virtual characters or so-called Non Player Characters still seems in its infancy. In this work, therefore, we started to look at the use of relatively simple chatbots for serious games. We describe the first step of our exploration i.e. to extend EMERGO, an existing serious game environment, with a chatbot to enhance the interaction with the student. EMERGO is a toolkit and methodology that enables to develop new cases with relatively ease and limited time. We will introduce EMERGO and give an overview of chatbot technology fitting our case. Next, we will explain the EMERGO case under development, and how it makes use of the chatbot selected and the technical architecture enabling the chatbot – EMERGO integration. We will conclude with a description of the evaluation planned

Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond

Objective: The COVID-19 pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDM) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500,000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. Materials and methods: We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHR) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data. Results: We identified 502,505 participants (3,086 with COVID-19) and transformed 690 fields (1,373,239,555 rows) to the OMOP CDM using eight different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported non-cancer illnesses 946,053 (83.91% of all source entries), cancers 37,802 (70.81%), medications 1,218,935 (88.25%), and prescriptions 864,788 (86.96%). In EHR, we transformed 1,3028,182 (99.95%) hospital diagnoses, 6,465,399 (89.2%) procedures, 337,896,333 primary care diagnoses (CTV3, SNOMED-CT), 139,966,587 (98.74%) prescriptions (dm+d) and 77,127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. Discussion and conclusion: Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies