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Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

In eukaryotes, up to one-third of cellular proteins are targeted to the endoplasmic reticulum, where they undergo folding, processing, sorting and trafficking to subsequent endomembrane compartments(1). Targeting to the endoplasmic reticulum has been shown to occur co-translationally by the signal recognition particle (SRP) pathway(2) or post-translationally by the mammalian transmembrane recognition complex of 40 kDa (TRC40)(3,4) and homologous yeast guided entry of tail-anchored proteins (GET)(5,6) pathways. Despite the range of proteins that can be catered for by these two pathways, many proteins are still known to be independent of both SRP and GET, so there seems to be a critical need for an additional dedicated pathway for endoplasmic reticulum relay(7,8). We set out to uncover additional targeting proteins using unbiased high-content screening approaches. To this end, we performed a systematic visual screen using the yeast Saccharomyces cerevisiae(9,10), and uncovered three uncharacterized proteins whose loss affected targeting. We suggest that these proteins work together and demonstrate that they function in parallel with SRP and GET to target a broad range of substrates to the endoplasmic reticulum. The three proteins, which we name Snd1, Snd2 and Snd3 (for SRP-independent targeting), can synthetically compensate for the loss of both the SRP and GET pathways, and act as a backup targeting system. This explains why it has previously been difficult to demonstrate complete loss of targeting for some substrates. Our discovery thus puts in place an essential piece of the endoplasmic reticulum targeting puzzle, highlighting how the targeting apparatus of the eukaryotic cell is robust, interlinked and flexible.

Anthelmintic resistance in human and animal pathogenic helminths has been spreading in prevalence and severity to a point where multidrug resistance against the three major classes of anthelmintics--the benzimidazoles, imidazothiazoles and macrocyclic lactones--has become a global phenomenon in gastrointestinal nematodes of farm animals. Hence, there is an urgent need for an anthelmintic with a new mode of action. Here we report the discovery of the amino-acetonitrile derivatives (AADs) as a new chemical class of synthetic anthelmintics and describe the development of drug candidates that are efficacious against various species of livestock-pathogenic nematodes. These drug candidates seem to have a novel mode of action involving a unique, nematode-specific clade of acetylcholine receptor subunits. The AADs are well tolerated and of low toxicity to mammals, and overcome existing resistances to the currently available anthelmintics.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO

© 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. The response of the terrestrial carbon cycle to climate change is among the largest uncertainties affecting future climate change projections. The feedback between the terrestrial carbon cycle and climate is partly determined by changes in the turnover time of carbon in land ecosystems, which in turn is an ecosystem property that emerges from the interplay between climate, soil and vegetation type. Here we present a global, spatially explicit and observation-based assessment of whole-ecosystem carbon turnover times that combines new estimates of vegetation and soil organic carbon stocks and fluxes. We find that the overall mean global carbon turnover time is years (95 per cent confidence interval). On average, carbon resides in the vegetation and soil near the Equator for a shorter time than at latitudes north of 75° north (mean turnover times of 15 and 255 years, respectively). We identify a clear dependence of the turnover time on temperature, as expected from our present understanding of temperature controls on ecosystem dynamics. Surprisingly, our analysis also reveals a similarly strong association between turnover time and precipitation. Moreover, we find that the ecosystem carbon turnover times simulated by state-of-the-art coupled climate/carbon-cycle models vary widely and that numerical simulations, on average, tend to underestimate the global carbon turnover time by 36 per cent. The models show stronger spatial relationships with temperature than do observation-based estimates, but generally do not reproduce the strong relationships with precipitation and predict faster carbon turnover in many semi-arid regions. Our findings suggest that future climate/carbon-cycle feedbacks may depend more strongly on changes in the hydrological cycle than is expected at present and is considered in Earth system models.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems1–3. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales3–14. Here we use empirical models based on eddy covariance data15 and process-based models16,17 to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal waterdriven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the yearto- year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance3–6,9,11,12,14. Our study indicates that spatial climate covariation drives the global carbon cycle response.