Progressive and biased divergent evolution underpins the origin and diversification of peridinin dinoflagellate plastids

Dinoflagellates are algae of tremendous importance to ecosystems and to public health. The cell biology and genome organization of dinoflagellate species is highly unusual. For example, the plastid genomes of peridinin-containing dinoflagellates encode only a minimal number of genes arranged on small elements termed "minicircles". Previous studies of peridinin plastid genes have found evidence for divergent sequence evolution, including extensive substitutions, novel insertions and deletions, and use of alternative translation initiation codons. Understanding the extent of this divergent evolution has been hampered by the lack of characterized peridinin plastid sequences. We have identified over 300 previously unannotated peridinin plastid mRNAs from published transcriptome projects, vastly increasing the number of sequences available. Using these data, we have produced a well-resolved phylogeny of peridinin plastid lineages, which uncovers several novel relationships within the dinoflagellates. This enables us to define changes to plastid sequences that occurred early in dinoflagellate evolution, and that have contributed to the subsequent diversification of individual dinoflagellate clades. We find that the origin of the peridinin dinoflagellates was specifically accompanied by elevations both in the overall number of substitutions that occurred on plastid sequences, and in the Ka/Ks ratio associated with plastid sequences, consistent with changes in selective pressure. These substitutions, alongside other changes, have accumulated progressively in individual peridinin plastid lineages. Throughout our entire dataset, we identify a persistent bias toward non-synonymous substitutions occurring on sequences encoding photosystem I subunits and stromal regions of peridinin plastid proteins, which may have underpinned the evolution of this unusual organelle.Wellcome Trus

Evolution of Chloroplast Transcript Processing in Plasmodium and Its Chromerid Algal Relatives

Chloroplasts contain their own genomes, containing two broad functional types of gene: genes encoding proteins directly involved in photosynthesis, and genes with a non-photosynthesis function, such as cofactor biosynthesis, assembly of protein complexes, or expression of the chloroplast genome. Thus far, to our knowledge, no chloroplast gene expression pathways in any lineage have been found to target one functional category of gene specifically. Here, we show that a chloroplast RNA processing pathway – the addition of a 3′ poly(U) tail – is specifically associated with photosynthesis genes in two species of algae, the ‘chromerids’ Chromera and Vitrella. The addition of the poly(U) tail enables the precise processing of mature photosynthesis gene transcripts from precursor RNA, and is likely to be essential for expression of the chromerid photosynthesis machinery. The chromerid algae are the closest photosynthetic relatives of a parasitic group of eukaryotes, the apicomplexans, which include the malaria pathogen Plasmodium. Apicomplexans are descended from algae, and retain a reduced chloroplast, which contains genes only of non-photosynthesis function. We have confirmed that 3′ poly(U) tails are not added to Plasmodium chloroplast transcripts. The expression pathways associated with photosynthesis genes have therefore been lost in the evolution of the apicomplexan chloroplast, and this loss could potentially have driven the transition from photosynthesis to parasitism

Effect of hydro-climate variation on biofilm dynamics and its impact in intertidal environments

Shallow tidal environments are very productive ecosystems but are sensitive to environmental changes and sea level rise. Bio-morphodynamic control of these environments is therefore a crucial consideration; however, the effect of small-scale biological activity on large-scale cohesive sediment dynamics like tidal basins and estuaries is still largely unquantified. This study advances our understanding by assessing the influence of biotic and abiotic factors on biologically cohesive sediment transport and morphology. An idealised benthic biofilm model is incorporated in a 1D morphodynamic model of tide-dominated channels. This study investigates the effect of a range of environmental and biological conditions on biofilm growth and their feedback on the morphological evolution of the entire intertidal channel. By carrying out a sensitivity analysis of the bio-morphodynamic model, parameters like (i) hydrodynamic disturbances, (ii) seasonality, (iii) biofilm growth rate, (iv) temperature variation and (v) bio-cohesivity of the sediment are systematically changed. Results reveal that key parameters such as growth rate and temperature strongly influence the development of biofilm and are key determinants of equilibrium biofilm configuration and development under a range of disturbance periodicities and intensities. Long-term simulations of intertidal channel development demonstrate that the hydrodynamic disturbances induced by tides play a key role in shaping the morphology of the bed and that the presence of surface biofilm increases the time to reach morphological equilibrium. In locations characterised by low hydrodynamic forces, the biofilm grows and stabilises the bed, inhibiting the transport of coarse sediment (medium and fine sand). These findings suggest biofilm presence in channel beds results in intertidal channels that have significantly different characteristics in terms of morphology and stratigraphy compared abiotic sediments. It is concluded that inclusion of bio-cohesion in morphodynamic models is essential to predict estuary development and mitigate coastal erosion.</p

Disentangling with Biological Constraints: A Theory of Functional Cell Types

Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons, namely nonnegativity and energy efficiency in both activity and weights, promote such sought after disentangled representations by enforcing neurons to become selective for single factors of task variation. We demonstrate these constraints lead to disentangling in a variety of tasks and architectures, including variational autoencoders. We also use this theory to explain why the brain partitions its cells into distinct cell types such as grid and object-vector cells, and also explain when the brain instead entangles representations in response to entangled task factors. Overall, this work provides a mathematical understanding of why, when, and how neurons represent factors in both brains and machines, and is a first step towards understanding of how task demands structure neural representations

Disentanglement via Latent Quantization

In disentangled representation learning, a model is asked to tease apart a dataset's underlying sources of variation and represent them independently of one another. Since the model is provided with no ground truth information about these sources, inductive biases take a paramount role in enabling disentanglement. In this work, we construct an inductive bias towards compositionally encoding and decoding data by enforcing a harsh communication bottleneck. Concretely, we do this by (i) quantizing the latent space into learnable discrete codes with a separate scalar codebook per dimension and (ii) applying strong model regularization via an unusually high weight decay. Intuitively, the quantization forces the encoder to use a small number of latent values across many datapoints, which in turn enables the decoder to assign a consistent meaning to each value. Regularization then serves to drive the model towards this parsimonious strategy. We demonstrate the broad applicability of this approach by adding it to both basic data-reconstructing (vanilla autoencoder) and latent-reconstructing (InfoGAN) generative models. In order to reliably assess these models, we also propose InfoMEC, new metrics for disentanglement that are cohesively grounded in information theory and fix well-established shortcomings in previous metrics. Together with regularization, latent quantization dramatically improves the modularity and explicitness of learned representations on a representative suite of benchmark datasets. In particular, our quantized-latent autoencoder (QLAE) consistently outperforms strong methods from prior work in these key disentanglement properties without compromising data reconstruction.Comment: 20 pages, 8 figures, code available at https://github.com/kylehkhsu/disentangl

Pulse propagation in gravity currents

Real world gravity current flows rarely exist as a single discrete event, but are instead made up of multiple surges. This paper examines the propagation of surges as pulses in gravity currents. Using theoretical shallow-water modeling, we analyze the structure of pulsed flows created by the sequential release of two lock-boxes. The first release creates a gravity current, while the second creates a pulse that eventually propagates to the head of the first current. Two parameters determine the flow structure: the densimetric Froude number at the head of the current, Fr, and a dimensionless time between releases, tre. The shallow-water model enables the flow behavior to be mapped in (Fr, tre) space. Pulse speed depends on three critical characteristic curves: two that derive from the first release and correspond to a wavelike disturbance which reflects between the head of the current and the back of the lock-box and a third that originates from the second release and represents the region of the flow affected by the finite supply of source material. Pulses have non-negative acceleration until they intersect the third characteristic, after which they decelerate. Variations in pulse speed affect energy transfer and dissipation. Critically for lahars, landslides, and avalanches, pulsed flows may change from erosional to depositional, further affecting their dynamics. Gravity current hazard prediction models for such surge-prone flows may underpredict risk if they neglect internal flow dynamics

Mixing in density- and viscosity-stratified flows

The lock-exchange problem is used extensively to study the flow dynamics of density-driven flows, such as gravity currents, and as a canonical problem to mixing in stratified flows. Opposite halves of a domain are filled with two fluids of different densities and held in place by a lock-gate. Upon release, the density difference drives the flow causing the fluids to slosh back and forth. In many scenarios, density stratification will also impose a viscosity stratification (e.g., if there are suspended sediments or the two fluids are distinct). However, numerical models often neglect variable viscosity. This paper characterizes the effect of both density and viscosity stratification in the lock-exchange configuration. The governing Navier-Stokes equations are solved using direct numerical simulation. Three regimes are identified in terms of the viscosity ratio μ 2 / μ 1 = (1 + γ) between the dense and less dense fluids: when γ ≪ 1, the flow dynamics are similar to the equal-viscosity case; for intermediate values (γ ∼ 1), viscosity inhibits interface-scale mixing leading to a global reduction in mixing and enhanced transfer between potential and kinetic energy. Increasing the excess viscosity ratio further (γ ≫ 1) results in significant viscous dissipation. Although many gravity or turbidity current models assume constant viscosity, our results demonstrate that viscosity stratification can only be neglected when γ ≪ 1. The initial turbidity current composition could enhance its ability to become self-sustaining or accelerating at intermediate excess viscosity ratios. Currents with initially high excess viscosity ratio may be unable to dilute and propagate long distances because of the decreased mixing rates and increased dissipation

Actionable Neural Representations: Grid Cells from Minimal Constraints

To afford flexible behaviour, the brain must build internal representations that mirror the structure of variables in the external world. For example, 2D space obeys rules: the same set of actions combine in the same way everywhere (step north, then south, and you won't have moved, wherever you start). We suggest the brain must represent this consistent meaning of actions across space, as it allows you to find new short-cuts and navigate in unfamiliar settings. We term this representation an `actionable representation'. We formulate actionable representations using group and representation theory, and show that, when combined with biological and functional constraints - non-negative firing, bounded neural activity, and precise coding - multiple modules of hexagonal grid cells are the optimal representation of 2D space. We support this claim with intuition, analytic justification, and simulations. Our analytic results normatively explain a set of surprising grid cell phenomena, and make testable predictions for future experiments. Lastly, we highlight the generality of our approach beyond just understanding 2D space. Our work characterises a new principle for understanding and designing flexible internal representations: they should be actionable, allowing animals and machines to predict the consequences of their actions, rather than just encode

The effect of Schmidt number on gravity current flows: The formation of large-scale three-dimensional structures

The Schmidt number, defined as the ratio of scalar to momentum diffusivity, varies by multiple orders of magnitude in real-world flows, with large differences in scalar diffusivity between temperature, solute, and sediment driven flows. This is especially crucial in gravity currents, where the flow dynamics may be driven by differences in temperature, solute, or sediment, and yet the effect of Schmidt number on the structure and dynamics of gravity currents is poorly understood. Existing numerical work has typically assumed a Schmidt number near unity, despite the impact of Schmidt number on the development of fine-scale flow structure. The few numerical investigations considering high Schmidt number gravity currents have relied heavily on two-dimensional simulations when discussing Schmidt number effects, leaving the effect of high Schmidt number on three-dimensional flow features unknown. In this paper, three-dimensional direct numerical simulations of constant-influx solute-based gravity currents with Reynolds numbers 100 ≤ R e ≤ 3000 and Schmidt number 1 are presented, with the effect of Schmidt number considered in cases with (R e, S c) = (100, 10), (100, 100), and (500, 10). These data are used to establish the effect of Schmidt number on different properties of gravity currents, such as density distribution and interface stability. It is shown that increasing Schmidt number from 1 leads to substantial structural changes not seen with increased Reynolds number in the range considered here. Recommendations are made regarding lower Schmidt number assumptions, usually made to reduce computational cost