Semantic wikis as flexible database interfaces for biomedical applications

Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it

Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.

Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generative model REINVENT and compare model performance and behaviour to a ligand-based scoring function. Additionally, we modify the previously published MOSES benchmarking dataset to remove any induced bias towards non-protonatable groups. We also propose a new metric to measure dataset diversity, which is less confounded by the distribution of heavy atom count than the commonly used internal diversity metric. With respect to the main findings, we found that when optimizing the docking score against DRD2, the model improves predicted ligand affinity beyond that of known DRD2 active molecules. In addition, generated molecules occupy complementary chemical and physicochemical space compared to the ligand-based approach, and novel physicochemical space compared to known DRD2 active molecules. Furthermore, the structure-based approach learns to generate molecules that satisfy crucial residue interactions, which is information only available when taking protein structure into account. Overall, this work demonstrates the advantage of using molecular docking to guide de novo molecule generation over ligand-based predictors with respect to predicted affinity, novelty, and the ability to identify key interactions between ligand and protein target. Practically, this approach has applications in early hit generation campaigns to enrich a virtual library towards a particular target, and also in novelty-focused projects, where de novo molecule generation either has no prior ligand knowledge available or should not be biased by it

Southern Adventist University Catalog 2002-2003

Southern Adventist University\u27s undergraduate catalog for the academic year 2002-2003.https://knowledge.e.southern.edu/undergrad_catalog/1071/thumbnail.jp

Networking Architecture and Key Technologies for Human Digital Twin in Personalized Healthcare: A Comprehensive Survey

Digital twin (DT), refers to a promising technique to digitally and accurately represent actual physical entities. One typical advantage of DT is that it can be used to not only virtually replicate a system's detailed operations but also analyze the current condition, predict future behaviour, and refine the control optimization. Although DT has been widely implemented in various fields, such as smart manufacturing and transportation, its conventional paradigm is limited to embody non-living entities, e.g., robots and vehicles. When adopted in human-centric systems, a novel concept, called human digital twin (HDT) has thus been proposed. Particularly, HDT allows in silico representation of individual human body with the ability to dynamically reflect molecular status, physiological status, emotional and psychological status, as well as lifestyle evolutions. These prompt the expected application of HDT in personalized healthcare (PH), which can facilitate remote monitoring, diagnosis, prescription, surgery and rehabilitation. However, despite the large potential, HDT faces substantial research challenges in different aspects, and becomes an increasingly popular topic recently. In this survey, with a specific focus on the networking architecture and key technologies for HDT in PH applications, we first discuss the differences between HDT and conventional DTs, followed by the universal framework and essential functions of HDT. We then analyze its design requirements and challenges in PH applications. After that, we provide an overview of the networking architecture of HDT, including data acquisition layer, data communication layer, computation layer, data management layer and data analysis and decision making layer. Besides reviewing the key technologies for implementing such networking architecture in detail, we conclude this survey by presenting future research directions of HDT

Diversity and patterns of marine non‐native species in the archipelagos of Macaronesia

Aims The present study is the first attempt to grasp the scale and richness of marine biological invasions in Macaronesia. We pioneered a comprehensive non-native species (NNS), inventory in the region to determine their diversity patterns and native distribution origins. NNS were defined here as the result of both introductions and range expansions. We also used statistical modelling to examine relationships among NNS richness, anthropogenic activities, demographic and geographical variables across Macaronesia. Location Macaronesia. Methods A comprehensive literature search was conducted for marine NNS records in Macaronesia, registering the first record's location and year from 1884 to 2020. We used univariate and multivariate analyses to evaluate differences and similarities in community composition. By applying a Generalized Linear Model (GLM), we tested hypotheses regarding NNS richness as a function of anthropogenic activities, demographic and geographical variables. Results A total of 144 marine non-native species (NNS) were recorded for the whole of Macaronesia. The highest NNS richness was registered in the Canary Islands (76 NNS), followed by the Azores (66 NNS), Madeira (59 NNS) and finally Cabo Verde (18 NNS). Some differences amongst archipelagos were observed, such as the high number of non-native macroalgae in the Azores, fishes in the Canary Islands and tunicates in Cabo Verde. Overall, macroalgae, tunicates and bryozoans were the predominant taxonomic groups in the Macaronesian archipelagos. Madeira and Canary Islands were the archipelagos with more similarity in marine NNS, and Cabo Verde the most divergent. Finally, GLM suggested that non-native richness patterns across Macaronesia were dependent on the considered archipelago and strongly affected by (1) minimum distance to the mainland, (2) the total number of ports and marinas and (3) total marinas area (km2). Conclusions The model results and NNS listing in the present study will likely raise the awareness and response regarding marine NNS in the whole Macaronesia region, serving as a baseline for future research as well as implementing and enforcing regulations related to the introduction of marine NNS in oceanic islands

Diversity and patterns of marine non-native species in the archipelagos of Macaronesia

Tiago Marques and Carolina Marques thank partial support by CEAUL (funded by FCT through the project UIDB/00006/2020).Aims The present study is the first attempt to grasp the scale and richness of marine biological invasions in Macaronesia. We pioneered a comprehensive non-native species (NNS), inventory in the region to determine their diversity patterns and native distribution origins. NNS were defined here as the result of both introductions and range expansions. We also used statistical modelling to examine relationships among NNS richness, anthropogenic activities, demographic and geographical variables across Macaronesia. Location Macaronesia. Methods A comprehensive literature search was conducted for marine NNS records in Macaronesia, registering the first record's location and year from 1884 to 2020. We used univariate and multivariate analyses to evaluate differences and similarities in community composition. By applying a Generalized Linear Model (GLM), we tested hypotheses regarding NNS richness as a function of anthropogenic activities, demographic and geographical variables. Results A total of 144 marine non-native species (NNS) were recorded for the whole of Macaronesia. The highest NNS richness was registered in the Canary Islands (76 NNS), followed by the Azores (66 NNS), Madeira (59 NNS) and finally Cabo Verde (18 NNS). Some differences amongst archipelagos were observed, such as the high number of non-native macroalgae in the Azores, fishes in the Canary Islands and tunicates in Cabo Verde. Overall, macroalgae, tunicates and bryozoans were the predominant taxonomic groups in the Macaronesian archipelagos. Madeira and Canary Islands were the archipelagos with more similarity in marine NNS, and Cabo Verde the most divergent. Finally, GLM suggested that non-native richness patterns across Macaronesia were dependent on the considered archipelago and strongly affected by (1) minimum distance to the mainland, (2) the total number of ports and marinas and (3) total marinas area (km2). Conclusions The model results and NNS listing in the present study will likely raise the awareness and response regarding marine NNS in the whole Macaronesia region, serving as a baseline for future research as well as implementing and enforcing regulations related to the introduction of marine NNS in oceanic islands.Publisher PDFPeer reviewe

Plant Science Decadal Vision 2020–2030: Reimagining the Potential of Plants for a Healthy and Sustainable Future

Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and green infrastructure. Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a parts store that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people\u27s natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems

Art Informing Science Education: The Potential Contributions of Ornithological Illustration to Ecology Education

Birds serve as an excellent group of organisms from which to introduce the study of ecology, being of inherent aesthetic interest to many otherwise uninterested in science, and are also ubiquitous in the immediate environment of many students. By extension, images of birds might serve as a valuable resource for the ecology educator, and bird artists – as a subset of ecologists - might provide useful models for expertise in ecology. This study examines the potential contributions of bird artists and bird art to education in ecology at the high school and college level. Eight contemporary bird artists were interviewed in depth to provide a multiple case study for the development of expertise in field ornithology as a subspecialty of ecology. Forty narrative bird paintings and forty sets of plates from popular field guides to birds were analyzed for their potential classroom use. Twenty-five ecological concepts were identified within the sample of bird art examined, all of which are recommended for inclusion in the ecology curriculum. Additionally, images of birds were found to have considerable potential for illustrating and teaching the history and nature of science

evALLution: making basic evolution concepts accessible to people with visual impairment through a multisensory tree of life

Background:- People with visual impairment have benefitted from recent developments of assistive technology that aim to decrease socio-economic inequality. However, access to post-secondary education is still extremelly challenging, especially for scientific areas. The under representation of people with visual impairment in the evolution research community is connected with the vision-based communication of evolutionary biology knowledge and the accompanying lack of multisensory alternatives for learning. Results:- Here, we describe the development of an inclusive outreach activity based on a multisensory phylogeny representing 20 taxonomic groups. We provide a tool kit of materials and ideas that allow both the replication of this activity and the adaptation of others, to include people with visual impairment. Furthermore, we provide activity evaluation data, a discussion of the lessons learned and an inclusive description of all figures and visual data presented. The presented baseline data show that people with visual impairment indeed have lack of access to education but are interested in and apt to understand evolutionary biology concepts and predict evolutionary change when education is inclusive. Conclusions:- We show that, with creative investment, basic evolutionary knowledge is perfectly possible to be transmitted through multisensory activities, which everyone can benefit from. Ultimately, we hope this case study will provide a baseline for future initiatives and a more inclusive outreach community