Genomic variations define divergence of water/wildlife-associated Campylobacter jejuni niche specialists from common clonal complexes

Although the major food-borne pathogen Campylobacter jejuni has been isolated from diverse animal, human and environmental sources, our knowledge of genomic diversity in C. jejuni is based exclusively on human or human food-chain-associated isolates. Studies employing multilocus sequence typing have indicated that some clonal complexes are more commonly associated with particular sources. Using comparative genomic hybridization on a collection of 80 isolates representing diverse sources and clonal complexes, we identified a separate clade comprising a group of water/wildlife isolates of C. jejuni with multilocus sequence types uncharacteristic of human food-chain-associated isolates. By genome sequencing one representative of this diverse group (C. jejuni 1336), and a representative of the bank-vole niche specialist ST-3704 (C. jejuni 414), we identified deletions of genomic regions normally carried by human food-chain-associated C. jejuni. Several of the deleted regions included genes implicated in chicken colonization or in virulence. Novel genomic insertions contributing to the accessory genomes of strains 1336 and 414 were identified. Comparative analysis using PCR assays indicated that novel regions were common but not ubiquitous among the water/wildlife group of isolates, indicating further genomic diversity among this group, whereas all ST-3704 isolates carried the same novel accessory regions. While strain 1336 was able to colonize chicks, strain 414 was not, suggesting that regions specifically absent from the genome of strain 414 may play an important role in this common route of Campylobacter infection of humans. We suggest that the genomic divergence observed constitutes evidence of adaptation leading to niche specialization

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

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

Integrated Genomic and Transcriptomic Analysis of the Peridinin Dinoflagellate Amphidinium carterae Plastid.

The plastid genomes of peridinin-containing dinoflagellates are highly unusual, possessing very few genes, which are located on small chromosomal elements termed "minicircles". These minicircles may contain genes, or no recognisable coding information. Transcripts produced from minicircles may undergo unusual processing events, such as the addition of a 3' poly(U) tail. To date, little is known about the genetic or transcriptional diversity of non-coding sequences in peridinin dinoflagellate plastids. These sequences include empty minicircles, and regions of non-coding DNA in coding minicircles. Here, we present an integrated plastid genome and transcriptome for the model peridinin dinoflagellate Amphidinium carterae, identifying a previously undescribed minicircle. We also profile transcripts covering non-coding regions of the psbA and petB/atpA minicircles. We present evidence that antisense transcripts are produced within the A. carterae plastid, but show that these transcripts undergo different end cleavage events from sense transcripts, and do not receive 3' poly(U) tails. The difference in processing events between sense and antisense transcripts may enable the removal of non-coding transcripts from peridinin dinoflagellate plastid transcript pools.CNRS Investissements de l'avenir programme Gordon and Betty Moore Foundatio

The Campylobacter jejuni MarR-like transcriptional regulators RrpA and RrpB both influence bacterial responses to oxidative and aerobic stresses.

The ability of the human intestinal pathogen Campylobacter jejuni to respond to oxidative stress is central to bacterial survival both in vivo during infection and in the environment. Re-annotation of the C. jejuni NCTC11168 genome revealed the presence of two MarR-type transcriptional regulators Cj1546 and Cj1556, originally annotated as hypothetical proteins, which we have designated RrpA and RrpB (regulator of response to peroxide) respectively. Previously we demonstrated a role for RrpB in both oxidative and aerobic (O2) stress and that RrpB was a DNA binding protein with auto-regulatory activity, typical of MarR-type transcriptional regulators. In this study, we show that RrpA is also a DNA binding protein and that a rrpA mutant in strain 11168H exhibits increased sensitivity to hydrogen peroxide oxidative stress. Mutation of either rrpA or rrpB reduces catalase (KatA) expression. However, a rrpAB double mutant exhibits higher levels of resistance to hydrogen peroxide oxidative stress, with levels of KatA expression similar to the wild-type strain. Mutation of either rrpA or rrpB also results in a reduction in the level of katA expression, but this reduction was not observed in the rrpAB double mutant. Neither the rrpA nor rrpB mutant exhibits any significant difference in sensitivity to either cumene hydroperoxide or menadione oxidative stresses, but both mutants exhibit a reduced ability to survive aerobic (O2) stress, enhanced biofilm formation and reduced virulence in the Galleria mellonella infection model. The rrpAB double mutant exhibits wild-type levels of biofilm formation and wild-type levels of virulence in the G mellonella infection model. Together these data indicate a role for both RrpA and RrpB in the C. jejuni peroxide oxidative and aerobic (O2) stress responses, enhancing bacterial survival in vivo and in the environment