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The dialogue discusses the role presence could be permitted to play in academic situations of co-presence with others (in comparison to the importance of presence in artistic contexts). It ponders the extent to which conventional academic forms, quite apart from seeking to guarantee objectivity in theory, actually pre-determine, or in fact limit, the communicability of theoretical content. The conversation points to the need for a new dramaturgical attentiveness to the performativity of theory and calls for an innovative approach to the formats in which the questions and results concerning various fields in the humanities could be presented and negotiated more successfully.

The dialogue discusses the role presence could be permitted to play in academic situations of co-presence with others (in comparison to the importance of presence in artistic contexts). It ponders the extent to which conventional academic forms, quite apart from seeking to guarantee objectivity in theory, actually pre-determine, or in fact limit, the communicability of theoretical content. The conversation points to the need for a new dramaturgical attentiveness to the performativity of theory and calls for an innovative approach to the formats in which the questions and results concerning various fields in the humanities could be presented and negotiated more successfully. Resumo: O diálogo discute o papel desempenhado pela presença em situações acadêmicas de copresença com outras pessoas (em comparação com a importância da presença em contextos artísticos). Questiona a medida em que as formas acadêmicas convencionais, para além de buscar garantir a objetividade teórica, na verdade pré-determinam ou, de fato, limitam a transmissibilidade do conteúdo teórico. A conversa aponta para a necessidade de uma nova atenção dramatúrgica à performatividade da teoria, e apela por uma abordagem inovadora aos formatos como as perguntas e os resultados referentes aos diversos campos nas humanidades poderiam ser apresentados e negociados com maior sucesso. Résumé: Le dialogue thématise le rôle que pourrait jouer la présence dans des situations académiques de co-présence avec d’autres (par rapport à l’importance de la présence dans des contextes artistiques). Il s’interroge sur la mesure dans laquelle, au-delà d’idéalement garantir l’objectivité théorique, les formes académiques conventionnelles ont tendance à déterminer ou même à limiter réellement la transmissibilité de contenu théorique. La conversation souligne la nécessité d’une nouvelle approche dramaturgique par rapport à la performativité de la théorie et appelle à un développement de sensibilités pour de nouveaux formats alternatifs dans lesquels les résultats et les questions de divers domaines des sciences humaines pourraient être présentés et négociés avec plus de succès.

Ever since Tolman's proposal of cognitive maps in the 1940s, the question of how spatial representations support flexible behavior has been a contentious topic. Bellmund et al. review and combine concepts from cognitive science and philosophy with findings from neurophysiology of spatial navigation in rodents to propose a framework for cognitive neuroscience. They argue that spatial-processing principles in the hippocampalentorhinal region provide a geometric code to map information domains of cognitive spaces for high-level cognition and discuss recent evidence for this proposal. Science , this issue p. eaat6766 The hippocampal formation has long been suggested to underlie both memory formation and spatial navigation. We discuss how neural mechanisms identified in spatial navigation research operate across information domains to support a wide spectrum of cognitive functions. In our framework, place and grid cell population codes provide a representational format to map variable dimensions of cognitive spaces. This highly dynamic mapping system enables rapid reorganization of codes through remapping between orthogonal representations across behavioral contexts, yielding a multitude of stable cognitive spaces at different resolutions and hierarchical levels. Action sequences result in trajectories through cognitive space, which can be simulated via sequential coding in the hippocampus. In this way, the spatial representational format of the hippocampal formation has the capacity to support flexible cognition and behavior.

Abstract Background PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for faster and scalable implementations of key functions, such as logistic regression, linkage disequilibrium estimation, and genomic distance evaluation. In addition, GWAS and population-genetic data now frequently contain genotype likelihoods, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. Findings To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, (n) -time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. We have also developed an extension to the data format which adds low-overhead support for genotype likelihoods, phase, multiallelic variants, and reference vs. alternate alleles, which is the basis of our planned second release (PLINK 2.0). Conclusions The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.

Biodiversity data derive from myriad sources stored in various formats on many distinct hardware and software platforms. An essential step towards understanding global patterns of biodiversity is to provide a standardized view of these heterogeneous data sources to improve interoperability. Fundamental to this advance are definitions of common terms. This paper describes the evolution and development of Darwin Core, a data standard for publishing and integrating biodiversity information. We focus on the categories of terms that define the standard, differences between simple and relational Darwin Core, how the standard has been implemented, and the community processes that are essential for maintenance and growth of the standard. We present case-study extensions of the Darwin Core into new research communities, including metagenomics and genetic resources. We close by showing how Darwin Core records are integrated to create new knowledge products documenting species distributions and changes due to environmental perturbations.

To make informed decisions, the general population should have access to accessible and understandable health recommendations. To compare understanding, accessibility, usability, satisfaction, intention to implement, and preference of adults provided with a digital “Plain Language Recommendation” (PLR) format vs. the original “Standard Language Version” (SLV). An allocation-concealed, blinded, controlled superiority trial and a qualitative study to understand participant preferences. An international on-line survey. 488 adults with some English proficiency. 67.8% of participants identified as female, 62.3% were from the Americas, 70.1% identified as white, 32.2% had a bachelor's degree as their highest completed education, and 42% said they were very comfortable reading health information. In collaboration with patient partners, advisors, and the Cochrane Consumer Network, we developed a plain language format of guideline recommendations (PLRs) to compare their effectiveness vs. the original standard language versions (SLVs) as published in the source guideline. We selected two recommendations about COVID-19 vaccine, similar in their content, to compare our versions, one from the World Health Organization (WHO) and one from Centers for Disease Control and Prevention (CDC). The primary outcome was understanding, measured as the proportion of correct responses to seven comprehension questions. Secondary outcomes were accessibility, usability, satisfaction, preference, and intended behavior, measured on a 1-7 scale. Participants randomized to the PLR group had a higher proportion of correct responses to the understanding questions for the WHO recommendation (mean difference [MD] of 19.8%, 95% confidence interval [CI] 14.7–24.9%; P < 0.001) but this difference was smaller and not statistically significant for the CDC recommendation (MD of 3.9%, 95% CI −0.7% to 8.3%; P = 0.096). However, regardless of the recommendation, participants found the PLRs more accessible, (MD of 1.2 on the seven-point scale, 95% CI 0.9–1.4%; P < 0.001) and more satisfying (MD of 1.2, 95% CI 0.9–1.4%; P < 0.001). They were also more likely to follow the recommendation if they had not already followed it (MD of 1.2, 95% CI 0.7–1.8%; P < 0.001) and share it with other people they know (MD of 1.9, 95% CI 0.5–1.2%; P < 0.001). There was no significant difference in the preference between the two formats (MD of −0.3, 95% CI −0.5% to 0.03%; P = 0.078). The qualitative interviews supported and contextualized these findings. Health information provided in a PLR format improved understanding, accessibility, usability, and satisfaction and thereby has the potential to shape public decision-making behavior. •Compared with the standard language versions, plain language recommendation (PLR) summaries promote understanding, accessibility, usability, and satisfaction with health information content amongst adults.•Adhering to public health guidelines, as exemplified during the COVID-19 pandemic, is crucial for reducing the transmission of diseases and the associated negative health impacts.•An effective and clear communication of health information can provide valuable insights for guideline organizations and health policymakers on a global scale to operationalize their content-making process.

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.

Background Prioritisation of gene ontology terms from differential gene expression analyses in a two-dimensional format remains a challenge with exponentially growing data volumes. Typically, gene ontology terms are represented as tree-maps that enclose all data into defined space. However, large datasets make this type of visualisation appear cluttered and busy, and often not informative as some labels are omitted due space limits, especially when published in two-dimensional (2D) figures. Results Here we present an open source CirGO ( Cir cular G ene O ntology) software that visualises non-redundant two-level hierarchically structured ontology terms from gene expression data in a 2D space. Gene ontology terms based on statistical significance were summarised with a semantic similarity algorithm and grouped by hierarchical clustering. This software visualises the most enriched gene ontology terms in an informative, comprehensive and intuitive format that is achieved by organising data from the most relevant to the least, as well as the appropriate use of colours and supporting information. Additionally, CirGO is an easy to use software that supports researchers with little computational background to present their gene ontology data in a publication ready format. Conclusions Our easy to use open source CirGO Python software package provides biologists with a succinct presentation of terms and functions that are most represented in a specific gene expression data set in a visually appealing 2D format (e.g. for reporting research results in scientific articles). CirGO is freely available at https://github.com/IrinaVKuznetsova/CirGO.git .

The weighting of sub-indicators is a relevant problem in the composite indicators literature and impacts several fields of science. None of the existing weighting approaches, Equal-Weights, Data-Driven, and Participatory, is exempt from criticism. Specifically, weights obtained by the Participatory approach are associated with two frequent problems: assessments errors and international comparisons. Mainly, the assessments errors occur when the number of sub-indicators to be assessed is high, as it requires more cognitive effort from decision-makers. The problem of international comparison occurs because the weights of the sub-indicators reflect the specific characteristics of the countries and are not necessarily the same. Selecting experts who know the countries involved increases the impact of expert assessments on the results as the number of experts qualified to carry out the assessments decreases. These are common problems in composite indicators such as the Global Innovation Index, Multidimensional Poverty Index, Sustainable Development Goals Index, and Ease of Doing Business Index. This research presents solutions to these two problems. First, experts ordered seventeen sub-indicators by importance, decreasing the cognitive effort of the experts and the assessment errors that occur when the sub-indicators are assessed directly or compared in pairs. Second, the order of importance is converted into weights through six assessment format transformation functions. The deviant assessments are identified by the Concordance Correlation Coefficient and Intraclass Correlation Coefficient and excluded. Sub-indicators are weighted with a twenty-nine percent higher consensus degree, allowing the construction of composite indicators compatible with collective opinion.

As a representative of shared mobility, bike sharing has become a green and convenient way to travel in cities in recent years. Bike usage prediction becomes more important for supporting efficient operation and management in bike share systems as the basis of inventory management and bike rebalancing. The essential of usage prediction in bike sharing systems is to model the spatial interactions of nearby stations, the temporal dependence of demands, and the impacts of environmental and societal factors. Deep learning has shown a great advantage of making a precise prediction for bike sharing usage. Recurrent neural networks capture the temporal dependence with the memory cell and gate mechanisms. Convolutional neural networks and graph neural networks learn spatial interactions of nearby stations with local convolutional operations defined for the grid-format and graph-format inputs respectively. In this survey, the latest studies about bike sharing usage prediction with deep learning are reviewed, with a classification for the prediction problems and models. Different applications based on bike usage prediction are discussed, both within and beyond bike share systems. Some research directions are pointed out to encourage future research. To the best of our knowledge, this paper is the first comprehensive survey that focuses on bike sharing usage prediction with deep learning techniques.