Getting Nervous: An Evolutionary Overhaul for Communication.

The evolution of a nervous system as a control system of the body's functions is a key innovation of animals. Its fundamental units are neurons, highly specialized cells dedicated to fast cell-cell communication. Neurons pass signals to other neurons, muscle cells, or gland cells at specialized junctions, the synapses, where transmitters are released from vesicles in a Ca <sup>2+</sup> -dependent fashion to activate receptors in the membrane of the target cell. Reconstructing the origins of neuronal communication out of a more simple process remains a central challenge in biology. Recent genomic comparisons have revealed that all animals, including the nerveless poriferans and placozoans, share a basic set of genes for neuronal communication. This suggests that the first animal, the Urmetazoan, was already endowed with neurosecretory cells that probably started to connect into neuronal networks soon afterward. Here, we discuss scenarios for this pivotal transition in animal evolution

Shedding light on the expansion and diversification of the Cdc48 protein family during the rise of the eukaryotic cell.

A defining feature of eukaryotic cells is the presence of various distinct membrane-bound compartments with different metabolic roles. Material exchange between most compartments occurs via a sophisticated vesicle trafficking system. This intricate cellular architecture of eukaryotes appears to have emerged suddenly, about 2 billion years ago, from much less complex ancestors. How the eukaryotic cell acquired its internal complexity is poorly understood, partly because no prokaryotic precursors have been found for many key factors involved in compartmentalization. One exception is the Cdc48 protein family, which consists of several distinct classical ATPases associated with various cellular activities (AAA+) proteins with two consecutive AAA domains. Here, we have classified the Cdc48 family through iterative use of hidden Markov models and tree building. We found only one type, Cdc48, in prokaryotes, although a set of eight diverged members that function at distinct subcellular compartments were retrieved from eukaryotes and were probably present in the last eukaryotic common ancestor (LECA). Pronounced changes in sequence and domain structure during the radiation into the LECA set are delineated. Moreover, our analysis brings to light lineage-specific losses and duplications that often reflect important biological changes. Remarkably, we also found evidence for internal duplications within the LECA set that probably occurred during the rise of the eukaryotic cell. Our analysis corroborates the idea that the diversification of the Cdc48 family is closely intertwined with the development of the compartments of the eukaryotic cell

The SM protein Sly1 accelerates assembly of the ER-Golgi SNARE complex.

Soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) and Sec1/Munc18 (SM) proteins constitute the core of an ancient vesicle fusion machine that diversified into distinct sets that now function in different trafficking steps in eukaryotic cells. Deciphering their precise mode of action has proved challenging. SM proteins are thought to act primarily through one type of SNARE protein, the syntaxins. Despite high structural similarity, however, contrasting binding modes have been found for different SM proteins and syntaxins. Whereas the secretory SM protein Munc18 binds to the ‟closed conformation" of syntaxin 1, the ER-Golgi SM protein Sly1 interacts only with the N-peptide of Sed5. Recent findings, however, indicate that SM proteins might interact simultaneously with both syntaxin regions. In search for a common mechanism, we now reinvestigated the Sly1/Sed5 interaction. We found that individual Sed5 adopts a tight closed conformation. Sly1 binds to both the closed conformation and the N-peptide of Sed5, suggesting that this is the original binding mode of SM proteins and syntaxins. In contrast to Munc18, however, Sly1 facilitates SNARE complex formation by loosening the closed conformation of Sed5

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Discretion in accounting for pensions under IAS 19: using the ‘magic telescope’?

We use a panel data set of UK-listed companies over the period 2005 to 2009 to analyse the actuarial assumptions used to value pension plan liabilities under IAS 19. The valuation process requires companies to make assumptions about financial and demographic variables, notably discount rate, price inflation, salary inflation, and mortality/life expectancy of plan members/beneficiaries. We use regression analysis to analyse the relationships between these key assumptions (except mortality, where disclosures are limited) and company-specific factors such as the pension plan funding position and duration of pension liabilities. We find evidence of selective ‘management’ of the three assumptions investigated, although the nature of this appears to differ from the findings of US authors. We conclude that IAS 19 does not prevent the use of managerial discretion, particularly by companies whose pension plan funding positions are weak, thereby reducing the representational faithfulness of the reported pension figures. We also highlight that the degree of discretion used reflects the extent to which IAS 19 defines how the assumptions are to be determined. We therefore suggest that companies should be encouraged to justify more explicitly their choice of assumptions