Londa Schiebinger.

Dr. Londa Schiebinger is an international leader on the intersection of sex, gender, and science. In this interview with Cell, she discusses the Gendered Innovations project, the persistent STEM gender gap, the importance of considering sex- and gender-related variables and intersectionality in research, and the future of sex and gender research.

Predicting Splicing from Primary Sequence with Deep Learning.

The splicing of pre-mRNAs into mature transcripts is remarkable for its precision, but the mechanisms by which the cellular machinery achieves such specificity are incompletely understood. Here, we describe a deep neural network that accurately predicts splice junctions from an arbitrary pre-mRNA transcript sequence, enabling precise prediction of noncoding genetic variants that cause cryptic splicing. Synonymous and intronic mutations with predicted splice-altering consequence validate at a high rate on RNA-seq and are strongly deleterious in the human population. De novo mutations with predicted splice-altering consequence are significantly enriched in patients with autism and intellectual disability compared to healthy controls and validate against RNA-seq in 21 out of 28 of these patients. We estimate that 9%-11% of pathogenic mutations in patients with rare genetic disorders are caused by this previously underappreciated class of disease variation.

Global prediction of extreme floods in ungauged watersheds.

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.

Predator mass mortality events restructure food webs through trophic decoupling.

Predators have a key role in structuring ecosystems1-4. However, predator loss is accelerating globally4-6, and predator mass-mortality events7 (MMEs)-rapid large-scale die-offs-are now emblematic of the Anthropocene epoch6. Owing to their rare and unpredictable nature7, we lack an understanding of how MMEs immediately impact ecosystems. Past predator-removal studies2,3 may be insufficient to understand the ecological consequences of MMEs because, in nature, dead predators decompose in situ and generate a resource pulse8, which could alter ensuing ecosystem dynamics by temporarily enhancing productivity. Here we experimentally induce MMEs in tritrophic, freshwater lake food webs and report ecological dynamics that are distinct from predator losses2,3 or resource pulses9 alone, but that can be predicted from theory8. MMEs led to the proliferation of diverse consumer and producer communities resulting from weakened top-down predator control1-3 and stronger bottom-up effects through predator decomposition8. In contrast to predator removals alone, enhanced primary production after MMEs dampened the consumer community response. As a consequence, MMEs generated biomass dynamics that were most similar to those of undisturbed systems, indicating that they may be cryptic disturbances in nature. These biomass dynamics led to trophic decoupling, whereby the indirect beneficial effects of predators on primary producers are lost and later materialize as direct bottom-up effects that stimulate primary production amid intensified herbivory. These results reveal ecological signatures of MMEs and demonstrate the feasibility of forecasting novel ecological dynamics arising with intensifying global change.

Oceanic climate changes threaten the sustainability of Asia's water tower.

Water resources sustainability in High Mountain Asia (HMA) surrounding the Tibetan Plateau (TP)-known as Asia's water tower-has triggered widespread concerns because HMA protects millions of people against water stress1,2. However, the mechanisms behind the heterogeneous trends observed in terrestrial water storage (TWS) over the TP remain poorly understood. Here we use a Lagrangian particle dispersion model and satellite observations to attribute about 1 Gt of monthly TWS decline in the southern TP during 2003-2016 to westerlies-carried deficit in precipitation minus evaporation (PME) from the southeast North Atlantic. We further show that HMA blocks the propagation of PME deficit into the central TP, causing a monthly TWS increase by about 0.5 Gt. Furthermore, warming-induced snow and glacial melt as well as drying-induced TWS depletion in HMA weaken the blocking of HMA's mountains, causing persistent northward expansion of the TP's TWS deficit since 2009. Future projections under two emissions scenarios verified by satellite observations during 2020-2021 indicate that, by the end of the twenty-first century, up to 84% (for scenario SSP245) and 97% (for scenario SSP585) of the TP could be afflicted by TWS deficits. Our findings indicate a trajectory towards unsustainable water systems in HMA that could exacerbate downstream water stress.

Trait-based sensitivity of large mammals to a catastrophic tropical cyclone.

Extreme weather events perturb ecosystems and increasingly threaten biodiversity1. Ecologists emphasize the need to forecast and mitigate the impacts of these events, which requires knowledge of how risk is distributed among species and environments. However, the scale and unpredictability of extreme events complicate risk assessment1-4-especially for large animals (megafauna), which are ecologically important and disproportionately threatened but are wide-ranging and difficult to monitor5. Traits such as body size, dispersal ability and habitat affiliation are hypothesized to determine the vulnerability of animals to natural hazards1,6,7. Yet it has rarely been possible to test these hypotheses or, more generally, to link the short-term and long-term ecological effects of weather-related disturbance8,9. Here we show how large herbivores and carnivores in Mozambique responded to Intense Tropical Cyclone Idai, the deadliest storm on record in Africa, across scales ranging from individual decisions in the hours after landfall to changes in community composition nearly 2 years later. Animals responded behaviourally to rising floodwaters by moving upslope and shifting their diets. Body size and habitat association independently predicted population-level impacts: five of the smallest and most lowland-affiliated herbivore species declined by an average of 28% in the 20 months after landfall, while four of the largest and most upland-affiliated species increased by an average of 26%. We attribute the sensitivity of small-bodied species to their limited mobility and physiological constraints, which restricted their ability to avoid the flood and endure subsequent reductions in the quantity and quality of food. Our results identify general traits that govern animal responses to severe weather, which may help to inform wildlife conservation in a volatile climate.

Learning from prepandemic data to forecast viral escape.

Effective pandemic preparedness relies on anticipating viral mutations that are able to evade host immune responses to facilitate vaccine and therapeutic design. However, current strategies for viral evolution prediction are not available early in a pandemic-experimental approaches require host polyclonal antibodies to test against1-16, and existing computational methods draw heavily from current strain prevalence to make reliable predictions of variants of concern17-19. To address this, we developed EVEscape, a generalizable modular framework that combines fitness predictions from a deep learning model of historical sequences with biophysical and structural information. EVEscape quantifies the viral escape potential of mutations at scale and has the advantage of being applicable before surveillance sequencing, experimental scans or three-dimensional structures of antibody complexes are available. We demonstrate that EVEscape, trained on sequences available before 2020, is as accurate as high-throughput experimental scans at anticipating pandemic variation for SARS-CoV-2 and is generalizable to other viruses including influenza, HIV and understudied viruses with pandemic potential such as Lassa and Nipah. We provide continually revised escape scores for all current strains of SARS-CoV-2 and predict probable further mutations to forecast emerging strains as a tool for continuing vaccine development ( evescape.org ).

Targeting natural killer cells in cancer immunotherapy.

Alteration in the expression of cell-surface proteins is a common consequence of malignant transformation. Natural killer (NK) cells use an array of germline-encoded activating and inhibitory receptors that scan for altered protein-expression patterns, but tumor evasion of detection by the immune system is now recognized as one of the hallmarks of cancer. NK cells display rapid and potent immunity to metastasis or hematological cancers, and major efforts are now being undertaken to fully exploit NK cell anti-tumor properties in the clinic. Diverse approaches encompass the development of large-scale NK cell-expansion protocols for adoptive transfer, the establishment of a microenvironment favorable to NK cell activity, the redirection of NK cell activity against tumor cells and the release of inhibitory signals that limit NK cell function. In this Review we detail recent advances in NK cell-based immunotherapies and discuss the advantages and limitations of these strategies.

The Next Quarter Century.

Immunity celebrates its 25th anniversary at an exciting time in immunology, marked by the advent of new, door-opening approaches and a deeper understanding of the centrality of the immune system to both health and disease. We asked 25 investigators to look forward and share a vision of the next quarter century of immunology research.

Response of the East Antarctic Ice Sheet to past and future climate change.

The East Antarctic Ice Sheet contains the vast majority of Earth's glacier ice (about 52 metres sea-level equivalent), but is often viewed as less vulnerable to global warming than the West Antarctic or Greenland ice sheets. However, some regions of the East Antarctic Ice Sheet have lost mass over recent decades, prompting the need to re-evaluate its sensitivity to climate change. Here we review the response of the East Antarctic Ice Sheet to past warm periods, synthesize current observations of change and evaluate future projections. Some marine-based catchments that underwent notable mass loss during past warm periods are losing mass at present but most projections indicate increased accumulation across the East Antarctic Ice Sheet over the twenty-first century, keeping the ice sheet broadly in balance. Beyond 2100, high-emissions scenarios generate increased ice discharge and potentially several metres of sea-level rise within just a few centuries, but substantial mass loss could be averted if the Paris Agreement to limit warming below 2 degrees Celsius is satisfied.

The Emerging Nature of Astrocyte Diversity.

Astrocytes are morphologically complex, ubiquitous cells that are viewed as a homogeneous population tiling the entire central nervous system (CNS). However, this view has been challenged in the last few years with the availability of RNA sequencing, immunohistochemistry, electron microscopy, morphological reconstruction, and imaging data. These studies suggest that astrocytes represent a diverse population of cells and that they display brain area- and disease-specific properties and functions. In this review, we summarize these observations, emphasize areas where clear conclusions can be made, and discuss potential unifying themes. We also identify knowledge gaps that need to be addressed in order to exploit astrocyte diversity as a biological phenomenon of physiological relevance in the CNS. We thus provide a summary and a perspective on astrocyte diversity in the vertebrate CNS.

Acoustic Pattern Recognition and Courtship Songs: Insights from Insects.

Across the animal kingdom, social interactions rely on sound production and perception. From simple cricket chirps to more elaborate bird songs, animals go to great lengths to communicate information critical for reproduction and survival via acoustic signals. Insects produce a wide array of songs to attract a mate, and the intended receivers must differentiate these calls from competing sounds, analyze the quality of the sender from spectrotemporal signal properties, and then determine how to react. Insects use numerically simple nervous systems to analyze and respond to courtship songs, making them ideal model systems for uncovering the neural mechanisms underlying acoustic pattern recognition. We highlight here how the combination of behavioral studies and neural recordings in three groups of insects-crickets, grasshoppers, and fruit flies-reveals common strategies for extracting ethologically relevant information from acoustic patterns and how these findings might translate to other systems.

Sodium Channels in Human Pain Disorders: Genetics and Pharmacogenomics.

Acute pain is adaptive, but chronic pain is a global challenge. Many chronic pain syndromes are peripheral in origin and reflect hyperactivity of peripheral pain-signaling neurons. Current treatments are ineffective or only partially effective and in some cases can be addictive, underscoring the need for better therapies. Molecular genetic studies have now linked multiple human pain disorders to voltage-gated sodium channels, including disorders characterized by insensitivity or reduced sensitivity to pain and others characterized by exaggerated pain in response to normally innocuous stimuli. Here, we review recent developments that have enhanced our understanding of pathophysiological mechanisms in human pain and advances in targeting sodium channels in peripheral neurons for the treatment of pain using novel and existing sodium channel blockers.

Including environmental and climatic considerations for sustainable coral reef restoration.

Coral reefs provide ecosystem benefits to millions of people but are threatened by rapid environmental change and ever-increasing human pressures. Restoration is becoming a priority strategy for coral reef conservation, yet implementation remains challenging and it is becoming increasingly apparent that indirect conservation and restoration approaches will not ensure the long-term sustainability of coral reefs. The important role of environmental conditions in restoration practice are currently undervalued, carrying substantial implications for restoration success. Giving paramount importance to environmental conditions, particularly during the pre-restoration planning phase, has the potential to bring about considerable improvements in coral reef restoration and innovation. This Essay argues that restoration risk may be reduced by adopting an environmentally aware perspective that gives historical, contemporary, and future context to restoration decisions. Such an approach will open up new restoration opportunities with improved sustainability that have the capacity to dynamically respond to environmental trajectories.

Climate-driven flyway changes and memory-based long-distance migration.

Millions of migratory birds occupy seasonally favourable breeding grounds in the Arctic1, but we know little about the formation, maintenance and future of the migration routes of Arctic birds and the genetic determinants of migratory distance. Here we established a continental-scale migration system that used satellite tracking to follow 56 peregrine falcons (Falco peregrinus) from 6 populations that breed in the Eurasian Arctic, and resequenced 35 genomes from 4 of these populations. The breeding populations used five migration routes across Eurasia, which were probably formed by longitudinal and latitudinal shifts in their breeding grounds during the transition from the Last Glacial Maximum to the Holocene epoch. Contemporary environmental divergence between the routes appears to maintain their distinctiveness. We found that the gene ADCY8 is associated with population-level differences in migratory distance. We investigated the regulatory mechanism of this gene, and found that long-term memory was the most likely selective agent for divergence in ADCY8 among the peregrine populations. Global warming is predicted to influence migration strategies and diminish the breeding ranges of peregrine populations of the Eurasian Arctic. Harnessing ecological interactions and evolutionary processes to study climate-driven changes in migration can facilitate the conservation of migratory birds.

Integrating explanation and prediction in computational social science.

Computational social science is more than just large repositories of digital data and the computational methods needed to construct and analyse them. It also represents a convergence of different fields with different ways of thinking about and doing science. The goal of this Perspective is to provide some clarity around how these approaches differ from one another and to propose how they might be productively integrated. Towards this end we make two contributions. The first is a schema for thinking about research activities along two dimensions-the extent to which work is explanatory, focusing on identifying and estimating causal effects, and the degree of consideration given to testing predictions of outcomes-and how these two priorities can complement, rather than compete with, one another. Our second contribution is to advocate that computational social scientists devote more attention to combining prediction and explanation, which we call integrative modelling, and to outline some practical suggestions for realizing this goal.

Developing a predictive science of the biosphere requires the integration of scientific cultures.

Increasing the speed of scientific progress is urgently needed to address the many challenges associated with the biosphere in the Anthropocene. Consequently, the critical question becomes: How can science most rapidly progress to address large, complex global problems? We suggest that the lag in the development of a more predictive science of the biosphere is not only because the biosphere is so much more complex, or because we do not have enough data, or are not doing enough experiments, but, in large part, because of unresolved tension between the three dominant scientific cultures that pervade the research community. We introduce and explain the concept of the three scientific cultures and present a novel analysis of their characteristics, supported by examples and a formal mathematical definition/representation of what this means and implies. The three cultures operate, to varying degrees, across all of science. However, within the biosciences, and in contrast to some of the other sciences, they remain relatively more separated, and their lack of integration has hindered their potential power and insight. Our solution to accelerating a broader, predictive science of the biosphere is to enhance integration of scientific cultures. The process of integration-Scientific Transculturalism-recognizes that the push for interdisciplinary research, in general, is just not enough. Unless these cultures of science are formally appreciated and their thinking iteratively integrated into scientific discovery and advancement, there will continue to be numerous significant challenges that will increasingly limit forecasting and prediction efforts.

Academics are more specific, and practitioners more sensitive, in forecasting interventions to strengthen democratic attitudes.

Concern over democratic erosion has led to a proliferation of proposed interventions to strengthen democratic attitudes in the United States. Resource constraints, however, prevent implementing all proposed interventions. One approach to identify promising interventions entails leveraging domain experts, who have knowledge regarding a given field, to forecast the effectiveness of candidate interventions. We recruit experts who develop general knowledge about a social problem (academics), experts who directly intervene on the problem (practitioners), and nonexperts from the public to forecast the effectiveness of interventions to reduce partisan animosity, support for undemocratic practices, and support for partisan violence. Comparing 14,076 forecasts submitted by 1,181 forecasters against the results of a megaexperiment (n = 32,059) that tested 75 hypothesized effects of interventions, we find that both types of experts outperformed members of the public, though experts differed in how they were accurate. While academics' predictions were more specific (i.e., they identified a larger proportion of ineffective interventions and had fewer false-positive forecasts), practitioners' predictions were more sensitive (i.e., they identified a larger proportion of effective interventions and had fewer false-negative forecasts). Consistent with this, practitioners were better at predicting best-performing interventions, while academics were superior in predicting which interventions performed worst. Our paper highlights the importance of differentiating types of experts and types of accuracy. We conclude by discussing factors that affect whether sensitive or specific forecasters are preferable, such as the relative cost of false positives and negatives and the expected rate of intervention success.

Brain activity of professional investors signals future stock performance.

A major aspiration of investors is to better forecast stock performance. Interestingly, emerging "neuroforecasting" research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study, we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional MRI, we sampled investors' neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We found that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors' predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance.