The timing of the ca-660 BCE Miyake solar-proton event constrained to between 664 and 663 BCE.

Extreme solar energetic particle events, known as Miyake events, are rare phenomena observed by cosmogenic isotopes, with only six documented. The timing of the ca. 660 BCE Miyake event remains undefined until now.

Impact of the COVID-19 pandemic on levels of deep-ocean acoustic noise.

The extraordinary circumstances of the COVID-19 pandemic led to measures to mitigate the spread of the disease, with lockdowns and mobility restrictions at national and international levels. These measures led to sudden and sometimes dramatic reductions in human activity, including significant reductions in ship traffic in the maritime sector. We report on a reduction of deep-ocean acoustic noise in three ocean basins in 2020, based on data acquired by hydroacoustic stations in the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty.

What Drives Symbiotic Calcium Signalling in Legumes? Insights and Challenges of Imaging.

We review the contribution of bioimaging in building a coherent understanding of Ca 2 + signalling during legume-bacteria symbiosis. Currently, two different calcium signals are believed to control key steps of the symbiosis: a Ca 2 + gradient at the tip of the legume root hair is involved in the development of an infection thread, while nuclear Ca 2 + oscillations, the hallmark signal of this symbiosis, control the formation of the root nodule, where bacteria fix nitrogen. Additionally, different Ca 2 + spiking signatures have been associated with specific infection stages.

Analysis of Primary Care Computerised Medical Records with Deep Learning.

The analysis of primary care data plays an important role in understanding health at an individual and population level. Currently the utilization of computerized medical records is low due to the complexities, heterogeneities and veracity associated with these data. We present a deep learning methodology that clusters 11,000 records in an unsupervised manner identifying non-linear patterns in the data.

A statistical method for the evaluation of long term trends in underwater noise measurements.

Deep ocean ambient sound levels have been the subject of recent studies, with particular interest in the identification of long term trends. This paper describes a statistical method for performing long term trend analysis and uncertainty evaluation of the estimated trends. Uncertainties are needed if the quality of the estimates are to be assessed and if the results from different studies or different methods are to be compared.

Automated Differentiation of Incident and Prevalent Cases in Primary Care Computerised Medical Records (CMR).

Identifying incident (first or new) episodes of illness is critical in sentinel networks to inform about the seasonal onset of diseases and to give early warning of epidemics, as well as differentiating change in health service utilization from change in pattern of disease. The most reliable way of differentiating incident from prevalent cases is through the clinician assigning episode type to the patient's computerized medical record (CMR). However, episode type assignment is often made inconsistently.

The human physiological impact of global deoxygenation.

There has been a clear decline in the volume of oxygen in Earth's atmosphere over the past 20 years. Although the magnitude of this decrease appears small compared to the amount of oxygen in the atmosphere, it is difficult to predict how this process may evolve, due to the brevity of the collected records. A recently proposed model predicts a non-linear decay, which would result in an increasingly rapid fall-off in atmospheric oxygen concentration, with potentially devastating consequences for human health.

Early warning signals of ecological transitions: methods for spatial patterns.

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions.

Spontaneous abrupt climate change due to an atmospheric blocking-sea-ice-ocean feedback in an unforced climate model simulation.

Abrupt climate change is abundant in geological records, but climate models rarely have been able to simulate such events in response to realistic forcing. Here we report on a spontaneous abrupt cooling event, lasting for more than a century, with a temperature anomaly similar to that of the Little Ice Age. The event was simulated in the preindustrial control run of a high-resolution climate model, without imposing external perturbations.

No increase in global temperature variability despite changing regional patterns.

Evidence from Greenland ice cores shows that year-to-year temperature variability was probably higher in some past cold periods, but there is considerable interest in determining whether global warming is increasing climate variability at present. This interest is motivated by an understanding that increased variability and resulting extreme weather conditions may be more difficult for society to adapt to than altered mean conditions. So far, however, in spite of suggestions of increased variability, there is considerable uncertainty as to whether it is occurring.

Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data.

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called 'early warning signals', and successful empirical examples suggest a potential for practical applicability.

Using GENIE to study a tipping point in the climate system.

We have used the Grid ENabled Integrated Earth system modelling framework to study the archetypal example of a tipping point in the climate system; a threshold for the collapse of the Atlantic thermohaline circulation (THC). eScience has been invaluable in this work and we explain how we have made it work for us. Two stable states of the THC have been found to coexist, under the same boundary conditions, in a hierarchy of models.

Memory in the occurrence of earthquakes.

We study the statistics of the recurrence times tau between earthquakes above a certain magnitude M in six (one global and five regional) earthquake catalogs. We find that the distribution of the recurrence times strongly depends on the previous recurrence time tau0, such that small and large recurrence times tend to cluster in time. This dependence on the past is reflected in both the conditional mean recurrence time and the conditional mean residual time until the next earthquake, which increase monotonically with tau0.

Nonlinear volatility of river flux fluctuations.

We study the spectral properties of the magnitudes of daily river flux increments, the volatility. The volatility series exhibits (i) strong seasonal periodicity and (ii) power-law correlations for time scales less than 1 yr. We test the nonlinear properties of the river flux increment series by randomizing its Fourier phases and find that the surrogate volatility series (i) has almost no seasonal periodicity and (ii) is weakly correlated for time scales less than 1 yr.