Deciphering Temperature Seasonality in Earth's Ancient Oceans

2021 ◽  
Vol 50 (1) ◽  
Author(s):  
Linda C. Ivany ◽  
Emily J. Judd
Keyword(s):  
Climate Model ◽  
Temperature Data ◽  
Temperature Cycle ◽  
Annual Review ◽  
Publication Date ◽  
Mean Annual Temperature ◽  
Data Sets ◽  
Proxy Data ◽  
Seamless Integration ◽  
Seasonal Data

Ongoing global warming due to anthropogenic climate change has long been recognized, yet uncertainties regarding how seasonal extremes will change in the future persist. Paleoseasonal proxy data from intervals when global climate differed from today can help constrain how and why the annual temperature cycle has varied through space and time. Records of past seasonal variation in marine temperatures are available in the oxygen isotope values of serially sampled accretionary organisms. The most useful data sets come from carefully designed and computationally robust studies that enable characterization of paleoseasonal parameters and seamless integration with mean annual temperature data sets and climate models. Seasonal data sharpen interpretations of—and quantify overlooked or unconstrained seasonal biases in—the more voluminous mean temperature data and aid in the evaluation of climate model performance. Methodologies to rigorously analyze seasonal data are now available, and the promise of paleoseasonal proxy data for the next generation of paleoclimate research is significant. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 50 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Biotic and Abiotic Controls on the Phanerozoic History of Marine Animal Biodiversity

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Andrew M. Bush ◽  
Jonathan L. Payne
Keyword(s):  
Biotic Interactions ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Marine Animal ◽  
Marine Animals ◽  
Marine Habitat ◽  
Positive Feedbacks ◽  
Abiotic Controls ◽  
Time Changes

During the past 541 million years, marine animals underwent three intervals of diversification (early Cambrian, Ordovician, Cretaceous–Cenozoic) separated by nondirectional fluctuation, suggesting diversity-dependent dynamics with the equilibrium diversity shifting through time. Changes in factors such as shallow-marine habitat area and climate appear to have modulated the nondirectional fluctuations. Directional increases in diversity are best explained by evolutionary innovations in marine animals and primary producers coupled with stepwise increases in the availability of food and oxygen. Increasing intensity of biotic interactions such as predation and disturbance may have led to positive feedbacks on diversification as ecosystems became more complex. Important areas for further research include improving the geographic coverage and temporal resolution of paleontological data sets, as well as deepening our understanding of Earth system evolution and the physiological and ecological traits that modulated organismal responses to environmental change. Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 52 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Smartphones and the Neuroscience of Mental Health

2021 ◽  
Vol 44 (1) ◽  
Author(s):  
Claire M. Gillan ◽  
Robb B. Rutledge
Keyword(s):  
Mental Health ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Daily Lives ◽  
Neuroscience Research ◽  
Health And Illness ◽  
Passive Data ◽  
Smartphone Sensors ◽  
Rich Data

Improvements in understanding the neurobiological basis of mental illness have unfortunately not translated into major advances in treatment. At this point, it is clear that psychiatric disorders are exceedingly complex and that, in order to account for and leverage this complexity, we need to collect longitudinal datasets from much larger and more diverse samples than is practical using traditional methods. We discuss how smartphone-based research methods have the potential to dramatically advance our understanding of the neuroscience of mental health. This, we expect, will take the form of complementing lab-based hard neuroscience research with dense sampling of cognitive tests, clinical questionnaires, passive data from smartphone sensors, and experience-sampling data as people go about their daily lives. Theory- and data-driven approaches can help make sense of these rich data sets, and the combination of computational tools and the big data that smartphones make possible has great potential value for researchers wishing to understand how aspects of brain function give rise to, or emerge from, states of mental health and illness. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Synthetic Data

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Trivellore E. Raghunathan
Keyword(s):  
Synthetic Data ◽  
Future Research ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Sensitive Information ◽  
Inferential Justification ◽  
Widespread Access ◽  
Access To Data ◽  
Privacy And Confidentiality

Demand for access to data, especially data collected using public funds, is ever growing. At the same time, concerns about the disclosure of the identities of and sensitive information about the respondents providing the data are making the data collectors limit the access to data. Synthetic data sets, generated to emulate certain key information found in the actual data and provide the ability to draw valid statistical inferences, are an attractive framework to afford widespread access to data for analysis while mitigating privacy and confidentiality concerns. The goal of this article is to provide a review of various approaches for generating and analyzing synthetic data sets, inferential justification, limitations of the approaches, and directions for future research. Expected final online publication date for the Annual Review of Statistics, Volume 8 is March 8, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals The Society of Algorithms

2021 ◽  
Vol 47 (1) ◽  
Author(s):  
Jenna Burrell ◽  
Marion Fourcade
Keyword(s):  
Social Reproduction ◽  
Mathematical Optimization ◽  
Computer Code ◽  
Social Institution ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Occupational Class ◽  
Tight Coupling ◽  
Technical Control

The pairing of massive data sets with processes—or algorithms—written in computer code to sort through, organize, extract, or mine them has made inroads in almost every major social institution. This article proposes a reading of the scholarly literature concerned with the social implications of this transformation. First, we discuss the rise of a new occupational class, which we call the coding elite. This group has consolidated power through their technical control over the digital means of production and by extracting labor from a newly marginalized or unpaid workforce, the cybertariat. Second, we show that the implementation of techniques of mathematical optimization across domains as varied as education, medicine, credit and finance, and criminal justice has intensified the dominance of actuarial logics of decision-making, potentially transforming pathways to social reproduction and mobility but also generating a pushback by those so governed. Third, we explore how the same pervasive algorithmic intermediation in digital communication is transforming the way people interact, associate, and think. We conclude by cautioning against the wildest promises of artificial intelligence but acknowledging the increasingly tight coupling between algorithmic processes, social structures, and subjectivities. Expected final online publication date for the Annual Review of Sociology, Volume 47 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Neurocomputational Models of Language Processing

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
John T. Hale ◽  
Luca Campanelli ◽  
Jixing Li ◽  
Shohini Bhattasali ◽  
Christophe Pallier ◽  
...  
Keyword(s):  
Language Processing ◽  
Computational Models ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Linguistic Features ◽  
Neural Signals ◽  
Data Set ◽  
Interpretable Models ◽  
Compare And Contrast

Efforts to understand the brain bases of language face the Mapping Problem: At what level do linguistic computations and representations connect to human neurobiology? We review one approach to this problem that relies on rigorously defined computational models to specify the links between linguistic features and neural signals. Such tools can be used to estimate linguistic predictions, model linguistic features, and specify a sequence of processing steps that may be quantitatively fit to neural signals collected while participants use language. Progress has been helped by advances in machine learning, attention to linguistically interpretable models, and openly shared data sets that allow researchers to compare and contrast a variety of models. We describe one such data set in detail in the Supplementary Appendix. Expected final online publication date for the Annual Review of Linguistics, Volume 8 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals Toward an Integrative Geological and Geophysical View of Cascadia Subduction Zone Earthquakes

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Maureen A.L. Walton ◽  
Lydia M. Staisch ◽  
Tina Dura ◽  
Jessie K. Pearl ◽  
Brian Sherrod ◽  
...  
Keyword(s):  
Subduction Zone ◽  
Pacific Northwest ◽  
Current Knowledge ◽  
Cascadia Subduction Zone ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Megathrust Earthquake ◽  
Megathrust Earthquakes ◽  
National Boundaries

The Cascadia subduction zone (CSZ) is an exceptional geologic environment for recording evidence of land-level changes, tsunamis, and ground motion that reveals at least 19 great megathrust earthquakes over the past 10 kyr. Such earthquakes are among the most impactful natural hazards on Earth, transcend national boundaries, and can have global impact. Reducing the societal impacts of future events in the US Pacific Northwest and coastal British Columbia, Canada, requires improved scientific understanding of megathrust earthquake rupture, recurrence, and corresponding hazards. Despite substantial knowledge gained from decades of research, large uncertainties remain about the characteristics and frequencies of past CSZ earthquakes. In this review, we summarize geological, geophysical, and instrumental evidence relevant to understanding megathrust earthquakes along the CSZ and associated uncertainties. We discuss how the evidence constrains various models of great megathrust earthquake recurrence in Cascadia and identify potential paths forward for the earthquake science community. ▪ Despite outstanding geologic records of past megathrust events, large uncertainty of the magnitude and frequency of CSZ earthquakes remains. ▪ This review outlines current knowledge and promising future directions to address outstanding questions on CSZ rupture characteristics and recurrence. ▪ Integration of diverse data sets with attention to the geologic processes that create different records has potential to lead to major progress. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 49 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Human Sleep Paradox: The Unexpected Sleeping Habits of Homo sapiens

2021 ◽  
Vol 50 (1) ◽  
Author(s):  
David R. Samson
Keyword(s):  
Homo Sapiens ◽  
Paradoxical Sleep ◽  
Small Scale ◽  
Ecological Niches ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Human Sleep ◽  
The Social ◽  
Sleeping Habits

The human sleep pattern is paradoxical. Sleep is vital for optimal physical and cognitive performance, yet humans sleep the least of all primates. In addition, consolidated and continuous monophasic sleep is evidently advantageous, yet emerging comparative data sets from small-scale societies show that the phasing of the human pattern of sleep–wake activity is highly variable and characterized by significant nighttime activity. To reconcile these phenomena, the social sleep hypothesis proposes that extant traits of human sleep emerged because of social and technological niche construction. Specifically, sleep sites function as a type of social shelter by way of an extended structure of social groups that increases fitness. Short, high-quality, and flexibly timed sleep likely originated as a response to predation risks while sleeping terrestrially. This practice may have been a necessary preadaptation for migration out of Africa and for survival in ecological niches that penetrate latitudes with the greatest seasonal variation in light and temperature on the planet. Expected final online publication date for the Annual Review of Anthropology, Volume 50 is October 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

A 2020 Observational Perspective of Io

2021 ◽  
Vol 49 (1) ◽  
Author(s):  
Imke de Pater ◽  
James T. Keane ◽  
Katherine de Kleer ◽  
Ashley Gerard Davies
Keyword(s):  
Adaptive Optics ◽  
Near Infrared ◽  
Heating Surface ◽  
Annual Review ◽  
Heating Process ◽  
Publication Date ◽  
Data Sets ◽  
Volcanic Plumes ◽  
Orbital Resonance ◽  
Tidal Heating

Jupiter's Galilean satellite Io is one of the most remarkable objects in our Solar System. The tidal heating Io undergoes through its orbital resonance with Europa and Ganymede has resulted in a body rich in active silicate volcanism. Over the past decades, Io has been observed from ground-based and Earth-orbiting telescopes and by several spacecraft. In this review we summarize the progress made toward our understanding of the physical and chemical processes related to Io and its environment since the Galileo era. Io science has been revolutionized by the use of adaptive optics techniques on large, 8- to 10-m telescopes. The resultant ever-increasing database, mapping the size, style, and spatial distribution of Io's diverse volcanoes, has improved our understanding of Io's interior structure, its likely composition, and the tidal heating process. Additionally, new observations of Io's atmosphere obtained with these large optical/infrared telescopes and the Atacama Large Millimeter/submillimeter Array reveal the presence of volcanic plumes, the (at times) near-collapse of Io's atmosphere during eclipse, and the interactions of plumes with the sublimation atmosphere. ▪ Extensive new data sets of Io at ultraviolet, mid- to near-infrared, and radio wavelengths have been gathered since the Galileo era. ▪ New data and models inform us about tidal heating, surface properties, and magma composition across Io—although key questions remain. ▪ Atmospheric observations indicate a dominant sublimation-supported component and reinforce the presence of stealth volcanism. ▪ Observations of volcanic plumes show high gas velocities (up to ∼1 km/s) and their effect on Io's atmosphere. Expected final online publication date for the Annual Review of Earth and Planetary Sciences, Volume 49 is May 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

The Role of Community Science in Entomology

2021 ◽  
Vol 67 (1) ◽  
Author(s):  
Mary M. Gardiner ◽  
Helen E. Roy
Keyword(s):  
Large Scale ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Pest Monitoring ◽  
The Status ◽  
Control Community ◽  
Patterns And Processes ◽  
Community Science

Community (or citizen) science, the involvement of volunteers in scientific endeavors, has a long history. Over the past few centuries, the contributions of volunteers to our understanding of patterns and processes in entomology has been inspiring. From the collation of large-scale and long-term data sets, which have been instrumental in underpinning our knowledge of the status and trends of many insect groups, to action, including species management, whether for conservation or control, community scientists have played pivotal roles. Contributions, such as pest monitoring by farmers and species discoveries by amateur naturalists, set foundations for the research engaging entomologists today. The next decades will undoubtedly bring new approaches, tools, and technologies to underpin community science. The potential to increase inclusion within community science is providing exciting opportunities within entomology. An increase in the diversity of community scientists, alongside increasing taxonomic and geographic breadth of initiatives, will bring enormous benefits globally for people and nature. Expected final online publication date for the Annual Review of Entomology, Volume 67 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Emerging Applications of Machine Learning in Food Safety

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xiangyu Deng ◽  
Shuhao Cao ◽  
Abigail L. Horn
Keyword(s):  
Machine Learning ◽  
Food Safety ◽  
Foodborne Pathogen ◽  
Large Data ◽  
Outbreak Detection ◽  
Annual Review ◽  
Publication Date ◽  
Data Sets ◽  
Source Attribution ◽  
The Impact

Food safety continues to threaten public health. Machine learning holds potential in leveraging large, emerging data sets to improve the safety of the food supply and mitigate the impact of food safety incidents. Foodborne pathogen genomes and novel data streams, including text, transactional, and trade data, have seen emerging applications enabled by a machine learning approach, such as prediction of antibiotic resistance, source attribution of pathogens, and foodborne outbreak detection and risk assessment. In this article, we provide a gentle introduction to machine learning in the context of food safety and an overview of recent developments and applications. With many of these applications still in their nascence, general and domain-specific pitfalls and challenges associated with machine learning have begun to be recognized and addressed, which are critical to prospective use and future deployment of large data sets and their associated machine learning models for food safety applications. Expected final online publication date for the Annual Review of Food Science and Technology, Volume 12 is March 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Sign in / Sign up

Export Citation Format

Share Document