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How did plant sexuality come to so hauntingly resemble human sexual formations? How did plant biology come to theorize plant sexuality with binary formulations of male/female, sex/gender, sperm/egg, active males and passive females—all of which resemble western categories of sex, gender, and sexuality? Tracing the extant language of sex and sexuality in plant reproductive biology, we examine the histories of science to explore how plant reproductive biology emerged historically from formations of colonial racial and sexual politics and how evolutionary biology was premised on the imaginations of racialized heterosexual romance. Drawing on key examples, the paper aims to (un)read plant sexuality and sexual anatomy and bodies to imagine new possibilities for plant sex, sexualities, and their relationalities. In short, plant sex and sexuality are not two different objects of inquiry but are intimately related—it is their inter-relation that is the focus of this essay. One of the key impulses from the humanities that we bring to this essay is a careful consideration of how terms and terminologies are related to each other historically and culturally. In anthropomorphizing plants, if plant sexuality were modeled on human sexual formations, might a re-imagination of plant sexuality open new vistas for the biological sciences? While our definitions of plant sexuality will always be informed by contemporary society and culture, interrogating the histories of our theories and terminologies can help us reimagine a biology that allows for new and more accurate understandings of plants, plant biology, and the evolution of reproduction. [ABSTRACT FROM AUTHOR]

"Scientific Papers Made Easy" by Stuart West and Lindsay Turnbull is a comprehensive guide to writing academic papers in biology. The book covers all aspects of constructing a paper, including abstracts, introductions, methods, results, and discussions. The authors emphasize the importance of clear and concise writing, and provide practical tips and examples to help researchers improve their writing skills. While the book is primarily aimed at biologists, scientists from other disciplines can also benefit from reading it. However, the book does not cover collaborative writing or other forms of scientific writing, such as blogging or op-eds. Overall, "Scientific Papers Made Easy" is a valuable resource for researchers looking to improve their scientific writing abilities. [Extracted from the article]

Comparative biologists have typically used one or more of the following methods to assist in evaluating the proposed functional and performance significance of individual traits: comparative phylogenetic analysis, direct interspecific comparison among species, genetic modification, experimental alteration of morphology (for example by surgically modifying traits), and ecological manipulation where individual organisms are transplanted to a different environment. But comparing organisms as the endpoints of an evolutionary process involves the ceteris paribus assumption: that all traits other than the one(s) of interest are held constant. In a properly controlled experimental study, only the variable of interest changes among the groups being compared. The theme of this paper is that the use of robotic or mechanical models offers an additional tool in comparative biology that helps to minimize the effect of uncontrolled variables by allowing direct manipulation of the trait of interest against a constant background. The structure and movement pattern of mechanical devices can be altered in ways not possible in studies of living animals, facilitating testing hypotheses of the functional and performance significance of individual traits. Robotic models of organismal design are particularly useful in three arenas: (1) controlling variation to allow modification only of the trait of interest, (2) the direct measurement of energetic costs of individual traits, and (3) quantification of the performance landscape. Obtaining data in these three areas is extremely difficult through the study of living organisms alone, and the use of robotic models can reveal unexpected effects. Controlling for all variables except for the length of a swimming flexible object reveals substantial non-linear effects that vary with stiffness. Quantification of the swimming performance surface reveals that there are two peaks with comparable efficiency, greatly complicating the inference of performance from morphology alone. Organisms and their ecological interactions are complex, and dissecting this complexity to understand the effects of individual traits is a grand challenge in ecology and evolutionary biology. Robotics has great promise as a "comparative method," allowing better-controlled comparative studies to analyze the many interacting elements that make up complex behaviors, ecological interactions, and evolutionary histories. [ABSTRACT FROM AUTHOR]

Despite efforts to integrate research across different subdisciplines of biology, the scale of integration remains limited. We hypothesize that future generations of Artificial Intelligence (AI) technologies specifically adapted for biological sciences will help enable the reintegration of biology. AI technologies will allow us not only to collect, connect, and analyze data at unprecedented scales, but also to build comprehensive predictive models that span various subdisciplines. They will make possible both targeted (testing specific hypotheses) and untargeted discoveries. AI for biology will be the cross-cutting technology that will enhance our ability to do biological research at every scale. We expect AI to revolutionize biology in the 21st century much like statistics transformed biology in the 20th century. The difficulties, however, are many, including data curation and assembly, development of new science in the form of theories that connect the subdisciplines, and new predictive and interpretable AI models that are more suited to biology than existing machine learning and AI techniques. Development efforts will require strong collaborations between biological and computational scientists. This white paper provides a vision for AI for Biology and highlights some challenges. [ABSTRACT FROM AUTHOR]

The organization of the living world covers a vast range of spatiotemporal scales, from molecules to the biosphere, seconds to centuries. Biologists working within specialized subdisciplines tend to focus on different ranges of scales. Therefore, developing frameworks that enable testing questions and predictions of scaling requires sufficient understanding of complex processes across biological subdisciplines and spatiotemporal scales. Frameworks that enable scaling across subdisciplines would ideally allow us to test hypotheses about the degree to which explicit integration across spatiotemporal scales is needed for predicting the outcome of biological processes. For instance, how does genomic variation within populations allow us to explain community structure? How do the dynamics of cellular metabolism translate to our understanding of whole-ecosystem metabolism? Do patterns and processes operate seamlessly across biological scales, or are there fundamental laws of biological scaling that limit our ability to make predictions from one scale to another? Similarly, can sub-organismal structures and processes be sufficiently understood in isolation of potential feedbacks from the population, community, or ecosystem levels? And can we infer the sub-organismal processes from data on the population, community, or ecosystem scale? Concerted efforts to develop more cross-disciplinary frameworks will open doors to a more fully integrated field of biology. In this paper, we discuss how we might integrate across scales, specifically by (1) identifying scales and boundaries, (2) determining analogous units and processes across scales, (3) developing frameworks to unite multiple scales, and (4) extending frameworks to new empirical systems. [ABSTRACT FROM AUTHOR]

Two hallmarks of biological processes are complexity and time. While complexity can have many meanings, in this paper we propose an explicit link to the flow of time and how it is experienced by the organism. While the flow of time is rooted in constraints of fundamental physics, understanding the operation of biological systems in terms of processual flow and tempo is more elusive. Fortunately, the convergence of new computational and methodological perspectives will provide a means to transform complicated, nonlinear paths between related phenomena at different time scales into dynamic four-dimensional perspectives. According to the complex temporal biology approach, information flow between time scales of multiple lengths is a transformational process that acts to regulate life's complexity. Interactions between temporal intervals of differing magnitude and otherwise loosely-related mechanisms can be understood as inter-timescale information flow. We further propose that informational flow between time scales is the glue that binds the multiple vertical layers of biocomplexity, as well as yielding surprising outcomes ranging from complex behaviors to the persistence of lineages. Building a foundation of rules based on common interactions between orders of time and common experiential contexts would help to reintegrate biology. Emerging methodologies such as state-of-the-art imaging, visualization techniques, and computational data analysis can help us uncover these interactions. In conclusion, we propose educational and community-level changes that would better enable our vision. [ABSTRACT FROM AUTHOR]

Both mathematical models and biological model systems stand as tractable representations of complex biological systems or behaviors. They facilitate research and provide insights, and they can describe general rules. Models that represent biological processes or formalize general hypotheses are essential to any broad understanding. Mathematical or biological models necessarily omit details of the natural systems and thus may ultimately be "incorrect" representations. A key challenge is that tractability requires relatively simple models but simplification can result in models that are incorrect in their qualitative, broad implications if the abstracted details matter. Our paper discusses this tension, and how we can improve our inferences from models. We advocate for further efforts dedicated to model development, improvement, and acceptance by the scientific community, all of which may necessitate a more explicit discussion of the purpose and power of models. We argue that models should play a central role in reintegrating biology as a way to test our integrated understanding of how molecules, cells, organs, organisms, populations, and ecosystems function. [ABSTRACT FROM AUTHOR]

The goal of this vision paper is to investigate the possible role that advanced machine learning techniques, especially deep learning (DL), could play in the reintegration of various biological disciplines. To achieve this goal, a series of operational, but admittedly very simplistic, conceptualizations have been introduced: Life has been taken as a multidimensional phenomenon that inhabits three physical dimensions (time, space, and scale) and biological research as establishing connection between different points in the domain of life. Each of these points hence denotes a position in time, space, and scale at which a life phenomenon of interest takes place. Using these conceptualizations, fragmentation of biology can be seen as the result of too few and especially too short-ranged connections. Reintegrating biology could then be accomplished by establishing more, longer ranged connections. DL methods appear to be very well suited for addressing this particular need at this particular time. Notwithstanding the numerous unsubstantiated claims regarding the capabilities of AI, DL networks represent a major advance in the ability to find complex relationships inside large data sets that would have not been accessible with traditional data analytic methods or to a human observer. In addition, ongoing advances in the automation of taking measurements from phenomena on all levels of biological organization continue to increase the number of large quantitative data sets that are available. These increasingly common data sets could serve as anchor points for making long-range connections by virtue of DL. However, connections within the domain of life are likely to be structured in a highly nonuniform fashion and hence it is necessary to develop methods, for example, theoretical, computational, and experimental, to determine linkage of biological data sets most likely to provide useful insights on a biological problem using DL. Finally, specific DL approaches and architectures should be developed to match the needs of reintegrating biology. [ABSTRACT FROM AUTHOR]