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1. Sexual selection and the ascent of women: Mate choice research since Darwin

   https://doi.org/10.1126/science.abi6308  

   Rosenthal, Gil G. ; Ryan, Michael J. ( January 2022 , Science)

   BACKGROUND Charles Darwin’s  Descent of Man, and Selection in Relation to Sex  tackled the two main controversies arising from the Origin of Species:  the evolution of humans from animal ancestors and the evolution of sexual ornaments. Most of the book focuses on the latter, Darwin’s theory of sexual selection. Research since supports his conjecture that songs, perfumes, and intricate dances evolve because they help secure mating partners. Evidence is overwhelming for a primary role of both male and female mate choice in sexual selection—not only through premating courtship but also through intimate interactions during and long after mating. But what makes one prospective mate more enticing than another? Darwin, shaped by misogyny and sexual prudery, invoked a “taste for the beautiful” without speculating on the origin of the “taste.” How to explain when the “final marriage ceremony” is between two rams? What of oral sex in bats, cloacal rubbing in bonobos, or the sexual spectrum in humans, all observable in Darwin’s time? By explaining desire through the lens of those male traits that caught his eyes and those of his gender and culture, Darwin elided these data in his theory of sexual evolution. Work since Darwin has focused on how traits and preferences coevolve. Preferences can evolve even if attractive signals only predict offspring attractiveness, but most attention has gone to the intuitive but tenuous premise that mating with gorgeous partners yields vigorous offspring. By focusing on those aspects of mating preferences that coevolve with male traits, many of Darwin’s influential followers have followed the same narrow path. The sexual selection debate in the 1980s was framed as “good genes versus runaway”: Do preferences coevolve with traits because traits predict genetic benefits, or simply because they are beautiful? To the broader world this is still the conversation. ADVANCES Even as they evolve toward ever-more-beautiful signals and healthier offspring, mate-choice mechanisms and courter traits are locked in an arms race of coercion and resistance, persuasion and skepticism. Traits favored by sexual selection often do so at the expense of chooser fitness, creating sexual conflict. Choosers then evolve preferences in response to the costs imposed by courters. Often, though, the current traits of courters tell us little about how preferences arise. Sensory systems are often tuned to nonsexual cues like food, favoring mating signals resembling those cues. And preferences can emerge simply from selection on choosing conspecifics. Sexual selection can therefore arise from chooser biases that have nothing to do with ornaments. Choice may occur before mating, as Darwin emphasized, but individuals mate multiple times and bias fertilization and offspring care toward favored partners. Mate choice can thus occur in myriad ways after mating, through behavioral, morphological, and physiological mechanisms. Like other biological traits, mating preferences vary among individuals and species along multiple dimensions. Some of this is likely adaptive, as different individuals will have different optimal mates. Indeed, mate choice may be more about choosing compatible partners than picking the “best” mate in the absolute sense. Compatibility-based choice can drive or reinforce genetic divergence and lead to speciation. The mechanisms underlying the “taste for the beautiful” determine whether mate choice accelerates or inhibits reproductive isolation. If preferences are learned from parents, or covary with ecological differences like the sensory environment, then choice can promote genetic divergence. If everyone shares preferences for attractive ornaments, then choice promotes gene flow between lineages. OUTLOOK Two major trends continue to shift the emphasis away from male “beauty” and toward how and why individuals make sexual choices. The first integrates neuroscience, genomics, and physiology. We need not limit ourselves to the feathers and dances that dazzled Darwin, which gives us a vastly richer picture of mate choice. The second is that despite persistent structural inequities in academia, a broader range of people study a broader range of questions. This new focus confirms Darwin’s insight that mate choice makes a primary contribution to sexual selection, but suggests that sexual selection is often tangential to mate choice. This conclusion challenges a persistent belief with sinister roots, whereby mate choice is all about male ornaments. Under this view, females evolve to prefer handsome males who provide healthy offspring, or alternatively, to express flighty whims for arbitrary traits. But mate-choice mechanisms also evolve for a host of other reasons Understanding mate choice mechanisms is key to understanding how sexual decisions underlie speciation and adaptation to environmental change. New theory and technology allow us to explicitly connect decision-making mechanisms with their evolutionary consequences. A century and a half after Darwin, we can shift our focus to females and males as choosers, rather than the gaudy by-products of mate choice. Mate choice mechanisms across domains of life. Sensory periphery for stimulus detection (yellow), brain for perceptual integration and evaluation (orange), and reproductive structures for postmating choice among pollen or sperm (teal). ILLUSTRATION: KELLIE HOLOSKI/ SCIENCE 

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2. Engineered Microsystems for Spheroid and Organoid Studies

   https://doi.org/10.1002/adhm.202001284  

   Kang, Sung‐Min ; Kim, Daehan ; Lee, Ji‐Hoon ; Takayama, Shuichi ; Park, Joong Yull ( November 2020 , Advanced Healthcare Materials)

   3D in vitro model systems such as spheroids and organoids provide an opportunity to extend the physiological understanding using recapitulated tissues that mimic physiological characteristics of in vivo microenvironments. Unlike 2D systems, 3D in vitro systems can bridge the gap between inadequate 2D cultures and the in vivo environments, providing novel insights on complex physiological mechanisms at various scales of organization, ranging from the cellular, tissue‐, to organ‐levels. To satisfy the ever‐increasing need for highly complex and sophisticated systems, many 3D in vitro models with advanced microengineering techniques have been developed to answer diverse physiological questions. This review summarizes recent advances in engineered microsystems for the development of 3D in vitro model systems. The relationship between the underlying physics behind the microengineering techniques, and their ability to recapitulate distinct 3D cellular structures and functions of diverse types of tissues and organs are highlighted and discussed in detail. A number of 3D in vitro models and their engineering principles are also introduced. Finally, current limitations are summarized, and perspectives for future directions in guiding the development of 3D in vitro model systems using microengineering techniques are provided.

    

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3. Nomenclature over 5 years in TaxonWorks: Approach, implementation, limitations and outcomes

   https://doi.org/10.3897/biss.5.75441  

   Yoder, Matthew ; Dmitriev, Dmitry ( September 2021 , Biodiversity Information Science and Standards)

   We are now over four decades into digitally managing the names of Earth's species. As the number of federating (i.e., software that brings together previously disparate projects under a common infrastructure, for example TaxonWorks) and aggregating (e.g., International Plant Name Index, Catalog of Life (CoL)) efforts increase, there remains an unmet need for both the migration forward of old data, and for the production of new, precise and comprehensive nomenclatural catalogs. Given this context, we provide an overview of how TaxonWorks seeks to contribute to this effort, and where it might evolve in the future. In TaxonWorks, when we talk about governed names and relationships, we mean it in the sense of existing international codes of nomenclature (e.g., the International Code of Zoological Nomenclature (ICZN)). More technically, nomenclature is defined as a set of objective assertions that describe the relationships between the names given to biological taxa and the rules that determine how those names are governed. It is critical to note that this is not the same thing as the relationship between a name and a biological entity, but rather nomenclature in TaxonWorks represents the details of the (governed) relationships between names. Rather than thinking of nomenclature as changing (a verb commonly used to express frustration with biological nomenclature), it is useful to think of nomenclature as a set of data points, which grows over time. For example, when synonymy happens, we do not erase the past, but rather record a new context for the name(s) in question. The biological concept changes, but the nomenclature (names) simply keeps adding up. Behind the scenes, nomenclature in TaxonWorks is represented by a set of nodes and edges, i.e., a mathematical graph, or network (e.g., Fig. 1). Most names (i.e., nodes in the network) are what TaxonWorks calls "protonyms," monomial epithets that are used to construct, for example, bionomial names (not to be confused with "protonym" sensu the ICZN). Protonyms are linked to other protonyms via relationships defined in NOMEN, an ontology that encodes governed rules of nomenclature. Within the system, all data, nodes and edges, can be cited, i.e., linked to a source and therefore anchored in time and tied to authorship, and annotated with a variety of annotation types (e.g., notes, confidence levels, tags). The actual building of the graphs is greatly simplified by multiple user-interfaces that allow scientists to review (e.g. Fig. 2), create, filter, and add to (again, not "change") the nomenclatural history. As in any complex knowledge-representation model, there are outlying scenarios, or edge cases that emerge, making certain human tasks more complex than others. TaxonWorks is no exception, it has limitations in terms of what and how some things can be represented. While many complex representations are hidden by simplified user-interfaces, some, for example, the handling of the ICZN's Family-group name, batch-loading of invalid relationships, and comparative syncing against external resources need more work to simplify the processes presently required to meet catalogers' needs. The depth at which TaxonWorks can capture nomenclature is only really valuable if it can be used by others. This is facilitated by the application programming interface (API) serving its data (https://api.taxonworks.org), serving text files, and by exports to standards like the emerging Catalog of Life Data Package. With reference to real-world problems, we illustrate different ways in which the API can be used, for example, as integrated into spreadsheets, through the use of command line scripts, and serve in the generation of public-facing websites. Behind all this effort are an increasing number of people recording help videos, developing documentation, and troubleshooting software and technical issues. Major contributions have come from developers at many skill levels, from high school to senior software engineers, illustrating that TaxonWorks leads in enabling both technical and domain-based contributions. The health and growth of this community is a key factor in TaxonWork's potential long-term impact in the effort to unify the names of Earth's species. 

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4. The Deep Learning Epilepsy Detection Challenge: Design, Implementation, and Test of a New Crowd-Sourced AI Challenge Ecosystem

   Kiral, Isabell ; Roy, Subhrajit ; Mummert, Todd ; Braz, Alan ; Tsay, Jason ; Tang, Jianbin ; Asif, Umar ; Schaffter, Thomas ; Mehmet, Eren ; Picone, Joseph ; et al ( April 2019 , Challenges in Machine Learning Competitions for All (CiML))

   null (Ed.)

   The DeepLearningEpilepsyDetectionChallenge: design, implementation, andtestofanewcrowd-sourced AIchallengeecosystem Isabell Kiral*, Subhrajit Roy*, Todd Mummert*, Alan Braz*, Jason Tsay, Jianbin Tang, Umar Asif, Thomas Schaffter, Eren Mehmet, The IBM Epilepsy Consortium◊ , Joseph Picone, Iyad Obeid, Bruno De Assis Marques, Stefan Maetschke, Rania Khalaf†, Michal Rosen-Zvi† , Gustavo Stolovitzky† , Mahtab Mirmomeni† , Stefan Harrer† * These authors contributed equally to this work † Corresponding authors: rkhalaf@us.ibm.com, rosen@il.ibm.com, gustavo@us.ibm.com, mahtabm@au1.ibm.com, sharrer@au.ibm.com ◊ Members of the IBM Epilepsy Consortium are listed in the Acknowledgements section J. Picone and I. Obeid are with Temple University, USA. T. Schaffter is with Sage Bionetworks, USA. E. Mehmet is with the University of Illinois at Urbana-Champaign, USA. All other authors are with IBM Research in USA, Israel and Australia. Introduction This decade has seen an ever-growing number of scientific fields benefitting from the advances in machine learning technology and tooling. More recently, this trend reached the medical domain, with applications reaching from cancer diagnosis [1] to the development of brain-machine-interfaces [2]. While Kaggle has pioneered the crowd-sourcing of machine learning challenges to incentivise data scientists from around the world to advance algorithm and model design, the increasing complexity of problem statements demands of participants to be expert data scientists, deeply knowledgeable in at least one other scientific domain, and competent software engineers with access to large compute resources. People who match this description are few and far between, unfortunately leading to a shrinking pool of possible participants and a loss of experts dedicating their time to solving important problems. Participation is even further restricted in the context of any challenge run on confidential use cases or with sensitive data. Recently, we designed and ran a deep learning challenge to crowd-source the development of an automated labelling system for brain recordings, aiming to advance epilepsy research. A focus of this challenge, run internally in IBM, was the development of a platform that lowers the barrier of entry and therefore mitigates the risk of excluding interested parties from participating. The challenge: enabling wide participation With the goal to run a challenge that mobilises the largest possible pool of participants from IBM (global), we designed a use case around previous work in epileptic seizure prediction [3]. In this “Deep Learning Epilepsy Detection Challenge”, participants were asked to develop an automatic labelling system to reduce the time a clinician would need to diagnose patients with epilepsy. Labelled training and blind validation data for the challenge were generously provided by Temple University Hospital (TUH) [4]. TUH also devised a novel scoring metric for the detection of seizures that was used as basis for algorithm evaluation [5]. In order to provide an experience with a low barrier of entry, we designed a generalisable challenge platform under the following principles: 1. No participant should need to have in-depth knowledge of the specific domain. (i.e. no participant should need to be a neuroscientist or epileptologist.) 2. No participant should need to be an expert data scientist. 3. No participant should need more than basic programming knowledge. (i.e. no participant should need to learn how to process fringe data formats and stream data efficiently.) 4. No participant should need to provide their own computing resources. In addition to the above, our platform should further • guide participants through the entire process from sign-up to model submission, • facilitate collaboration, and • provide instant feedback to the participants through data visualisation and intermediate online leaderboards. The platform The architecture of the platform that was designed and developed is shown in Figure 1. The entire system consists of a number of interacting components. (1) A web portal serves as the entry point to challenge participation, providing challenge information, such as timelines and challenge rules, and scientific background. The portal also facilitated the formation of teams and provided participants with an intermediate leaderboard of submitted results and a final leaderboard at the end of the challenge. (2) IBM Watson Studio [6] is the umbrella term for a number of services offered by IBM. Upon creation of a user account through the web portal, an IBM Watson Studio account was automatically created for each participant that allowed users access to IBM's Data Science Experience (DSX), the analytics engine Watson Machine Learning (WML), and IBM's Cloud Object Storage (COS) [7], all of which will be described in more detail in further sections. (3) The user interface and starter kit were hosted on IBM's Data Science Experience platform (DSX) and formed the main component for designing and testing models during the challenge. DSX allows for real-time collaboration on shared notebooks between team members. A starter kit in the form of a Python notebook, supporting the popular deep learning libraries TensorFLow [8] and PyTorch [9], was provided to all teams to guide them through the challenge process. Upon instantiation, the starter kit loaded necessary python libraries and custom functions for the invisible integration with COS and WML. In dedicated spots in the notebook, participants could write custom pre-processing code, machine learning models, and post-processing algorithms. The starter kit provided instant feedback about participants' custom routines through data visualisations. Using the notebook only, teams were able to run the code on WML, making use of a compute cluster of IBM's resources. The starter kit also enabled submission of the final code to a data storage to which only the challenge team had access. (4) Watson Machine Learning provided access to shared compute resources (GPUs). Code was bundled up automatically in the starter kit and deployed to and run on WML. WML in turn had access to shared storage from which it requested recorded data and to which it stored the participant's code and trained models. (5) IBM's Cloud Object Storage held the data for this challenge. Using the starter kit, participants could investigate their results as well as data samples in order to better design custom algorithms. (6) Utility Functions were loaded into the starter kit at instantiation. This set of functions included code to pre-process data into a more common format, to optimise streaming through the use of the NutsFlow and NutsML libraries [10], and to provide seamless access to the all IBM services used. Not captured in the diagram is the final code evaluation, which was conducted in an automated way as soon as code was submitted though the starter kit, minimising the burden on the challenge organising team. Figure 1: High-level architecture of the challenge platform Measuring success The competitive phase of the "Deep Learning Epilepsy Detection Challenge" ran for 6 months. Twenty-five teams, with a total number of 87 scientists and software engineers from 14 global locations participated. All participants made use of the starter kit we provided and ran algorithms on IBM's infrastructure WML. Seven teams persisted until the end of the challenge and submitted final solutions. The best performing solutions reached seizure detection performances which allow to reduce hundred-fold the time eliptologists need to annotate continuous EEG recordings. Thus, we expect the developed algorithms to aid in the diagnosis of epilepsy by significantly shortening manual labelling time. Detailed results are currently in preparation for publication. Equally important to solving the scientific challenge, however, was to understand whether we managed to encourage participation from non-expert data scientists. Figure 2: Primary occupation as reported by challenge participants Out of the 40 participants for whom we have occupational information, 23 reported Data Science or AI as their main job description, 11 reported being a Software Engineer, and 2 people had expertise in Neuroscience. Figure 2 shows that participants had a variety of specialisations, including some that are in no way related to data science, software engineering, or neuroscience. No participant had deep knowledge and experience in data science, software engineering and neuroscience. Conclusion Given the growing complexity of data science problems and increasing dataset sizes, in order to solve these problems, it is imperative to enable collaboration between people with differences in expertise with a focus on inclusiveness and having a low barrier of entry. We designed, implemented, and tested a challenge platform to address exactly this. Using our platform, we ran a deep-learning challenge for epileptic seizure detection. 87 IBM employees from several business units including but not limited to IBM Research with a variety of skills, including sales and design, participated in this highly technical challenge. 

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5. The Power of Environmental Observatories for Advancing Multidisciplinary Research, Outreach, and Decision Support: The Case of the Minnesota River Basin

   https://doi.org/10.1029/2018WR024211  

   Gran, K. B. ; Dolph, C. ; Baker, A. ; Bevis, M. ; Cho, S. J. ; Czuba, J. A. ; Dalzell, B. ; Danesh‐Yazdi, M. ; Hansen, A. T. ; Kelly, S. ; et al ( April 2019 , Water Resources Research)

   Observatory‐scale data collection efforts allow unprecedented opportunities for integrative, multidisciplinary investigations in large, complex watersheds, which can affect management decisions and policy. Through the National Science Foundation‐funded REACH (REsilience under Accelerated CHange) project, in collaboration with the Intensively Managed Landscapes‐Critical Zone Observatory, we have collected a series of multidisciplinary data sets throughout the Minnesota River Basin in south‐central Minnesota, USA, a 43,400‐km²tributary to the Upper Mississippi River. Postglacial incision within the Minnesota River valley created an erosional landscape highly responsive to hydrologic change, allowing for transdisciplinary research into the complex cascade of environmental changes that occur due to hydrology and land use alterations from intensive agricultural management and climate change. Data sets collected include water chemistry and biogeochemical data, geochemical fingerprinting of major sediment sources, high‐resolution monitoring of river bluff erosion, and repeat channel cross‐sectional and bathymetry data following major floods. The data collection efforts led to development of a series of integrative reduced complexity models that provide deeper insight into how water, sediment, and nutrients route and transform through a large channel network and respond to change. These models represent the culmination of efforts to integrate interdisciplinary data sets and science to gain new insights into watershed‐scale processes in order to advance management and decision making. The purpose of this paper is to present a synthesis of the data sets and models, disseminate them to the community for further research, and identify mechanisms used to expand the temporal and spatial extent of short‐term observatory‐scale data collection efforts.

    

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