Search

The sub-Alpine chains of south-east France have been the subject of geological inquiry for over a century. While some salt structures have previously been identified in the area, our understanding of salt tectonics has since matured significantly, driven by observations from seismic data in offshore basins worldwide and by analogue modelling. Armed with this new understanding, this study identifies new salt structures in the sub-Alpine chains, and reinterprets structures previously attributed to Alpine shortening as pre-Alpine salt-related structures. The area near the Dôme de Barrot highlights the influence of Permian to Triassic pre-salt faults on the deposition of the Triassic salt section, and how the reactivation of the Permian structural fabric locally controlled the evolution of Jurassic to Cretaceous diapirs. Along the Rouaine-Daluis strike-slip fault system, multiple periods of salt tectonic rise have now been identified, including the extrusion of a salt sheet to surface in the Aptian-Albian, and the development of a Roho-like listric normal fault associated with salt evacuation into the active fault zone during early Alpine shortening. The evolution of the salt wall and diapir along the Rouaine-Daluis fault system is analogous to that of the Svend structure of the South Arne field in the Danish Central Graben, and the two areas represent different endmembers for salt wall-diapir pairs affected by varying magnitudes and orientations of shortening. Salt structures in the sub-Alpine chains are strongly associated with Tethyan (Early-Mid Jurassic) rifting and with the palaeotopography of the Dauphinois (Jurassic) and Vocontian (Cretaceous) basins. They strongly influenced the regional structural fabric with the pre-existing salt structures utilized as thrusts and strike-slip faults as Alpine shortening emplaced the basinal units over the carbonate platform. The importance of salt in the structural evolution of the area may therefore necessitate reassessment of previous regional shortening estimates. Open Access

In this work we propose Neuro-Nav, an open-source library for neurally plausible reinforcement learning (RL). RL is among the most common modeling frameworks for studying decision making, learning, and navigation in biological organisms. In utilizing RL, cognitive scientists often handcraft environments and agents to meet the needs of their particular studies. On the other hand, artificial intelligence researchers often struggle to find benchmarks for neurally and biologically plausible representation and behavior (e.g., in decision making or navigation). In order to streamline this process across both fields with transparency and reproducibility, Neuro-Nav offers a set of standardized environments and RL algorithms drawn from canonical behavioral and neural studies in rodents and humans. We demonstrate that the toolkit replicates relevant findings from a number of studies across both cognitive science and RL literatures. We furthermore describe ways in which the library can be extended with novel algorithms (including deep RL) and environments to address future research needs of the field.

Additional file 1: Supplementary Materials and Methods. Table S1. MetaSIPSim dependencies. Table S2. Parameters used for the MetaSIPSim validation and case study simulations. * Values may differ between simulations as described in the materials and methods. Table S3. Processing times for all simulations from the cases study. All times are in seconds. Table S4. Summary statistics for recovery of reads, contigs, and MAGs for labeled genomes in initial simulations. Figure S1. Visual description of variables defined in the manuscript and Table 2. Thick blue line indicates the position of a single DNA fragment during and after ultracentrifugation. Centrifugation setup shown here is based on a fixed angled rotor. Figure S2. Diagram of experimental design for case study simulations. Figure S3. RefSeq genomes G + C distribution with G + C bins rounded to nearest whole number (downloaded January 25, 2019). Figure S4. G + C distributions for the 500 genomes in each reference set. G + C bins are rounded to the nearest whole number. Vertical line indicates G + C skew of the set. Figure S5. Rank-abundance plots for each simulated sample or replicate community including the 12C-control (Con) and all five 13C-trials (T1-T5). Random genomes are only labeled in the 13C-labeled samples. Vertical grey lines indicate ranks of the 50 labeled genomes per treatment. Labeled genomes were selected such that mean ranks of the labeled genomes averaged across all samples were similar across the reference sets (lowGC, medGC, and highGC). Figure S6. Fold difference in raw read coverage for each labeled genome between the metagenomic-SIP and shotgun metagenomic libraries from the original simulations. Values above one indicate greater coverage in the metagenomic-SIP compared to the shotgun metagenomic libraries. Figure S7. Fold difference in proportion of each labeled genome recovered by reads between the metagenomic-SIP and shotgun metagenomic libraries from the original simulations. Values above one indicate greater recovery in the metagenomic-SIP compared to the shotgun metagenomic libraries. Figure S8. Fold difference in raw read coverage for each labeled genome between the metagenomic-SIP and shotgun metagenomic libraries from the follow-up simulations. Values above one indicate greater coverage in the metagenomic-SIP compared to the shotgun metagenomic libraries. Simulation with the lowGC reference set with varying number of labeled genomes per sample is in the top row while the simulation with the highGC reference set with different sequencing window BD ranges is in the bottom row. Figure S9. Fold difference in proportion of each labeled genome recovered by reads between the metagenomic-SIP and shotgun metagenomic libraries from the follow-up simulations. Values above one indicate greater recovery in the metagenomic-SIP compared to the shotgun metagenomic libraries. Simulation with the lowGC reference set with varying number of labeled genomes per sample is in the top row while the simulation with the highGC reference set with different sequencing window BD ranges is in the bottom row. Figure S10. Difference in proportion of each labeled genome recovered in co-assembled contigs between the metagenomic-SIP and shotgun metagenomic libraries from the original simulations. Values above zero indicate greater recovery in the metagenomic-SIP compared to the shotgun metagenomic contigs. Figure S11. Difference in NGA50 of each labeled genome covered by co-assembled contigs between the metagenomic-SIP and shotgun metagenomic libraries from the original simulations. Values above zero indicate greater NGA50 in the metagenomic-SIP compared to the shotgun metagenomic contigs. Only genomes with over 50% recovery in both SIP and shotgun metagenomes were used in this analysis. Figure S12. Difference in proportion of each labeled genome recovered in co-assembled contigs between the metagenomic-SIP and shotgun metagenomic libraries from the follow-up simulations. Values above zero indicate greater recovery in the metagenomic-SIP compared to the shotgun metagenomic contigs. Simulation with the lowGC reference set with varying number of labeled genomes per sample is in the top row while the simulation with the highGC reference set with different sequencing window BD ranges is in the bottom row. Figure S13. Difference in NGA50 of each labeled genome covered by co-assembled contigs between the metagenomic-SIP and shotgun metagenomic libraries from the original simulations. Values above zero indicate greater NGA50 in the metagenomic-SIP compared to the shotgun metagenomic contigs. Only genomes with over 50% recovery in both SIP and shotgun metagenomes were used in this analysis. Simulation with the lowGC reference set with varying number of labeled genomes per sample is in the top row while the simulation with the highGC reference set with different sequencing window BD ranges is in the bottom row. Figure S14. Difference in binning quality between the SIP-metagenomes and shotgun metagenomes with the initial simulations using multiple bins per labeled genome. A) Difference in proportion of each labeled genome recovered in bins. B) Difference in the cumulative contamination for each labeled genome bin set. Figure S15. Difference in proportion of each labeled genome recovered in a single most complete bin between the metagenomic-SIP and shotgun metagenomic libraries from the follow-up simulations. Values above zero indicate greater recovery in the metagenomic-SIP compared to the shotgun metagenomic bins. Simulation with the lowGC reference set with varying number of labeled genomes per sample is in the top row while the simulation with the highGC reference set with different sequencing window BD ranges is in the bottom row.

Techniques such as Stochastic Optical Reconstruction Microscopy (STORM) and Structured Illumination Microscopy (SIM) have increased the achievable resolution of optical imaging, but few fluorescent proteins are suitable for super-resolution microscopy, particularly in the far-red and near-infrared emission range. Here we demonstrate the applicability of CpcA, a subunit of the photosynthetic antenna complex in cyanobacteria, for STORM and SIM imaging. The periodicity and width of fabricated nanoarrays of CpcA, with a covalently attached phycoerythrobilin (PEB) or phycocyanobilin (PCB) chromophore, matched the lines in reconstructed STORM images. SIM and STORM reconstructions of Escherichia coli cells harbouring CpcA-labelled cytochrome bd 1 ubiquinol oxidase in the cytoplasmic membrane show that CpcA-PEB and CpcA-PCB are suitable for super-resolution imaging in vivo. The stability, ease of production, small size and brightness of CpcA-PEB and CpcA-PCB demonstrate the potential of this largely unexplored protein family as novel probes for super-resolution microscopy.

Generative adversarial networks (GANs) are a novel approach to generative modelling, a task whose goal it is to learn a distribution of real data points. They have often proved difficult to train: GANs are unlike many techniques in machine learning, in that they are best described as a two-player game between a discriminator and generator. This has yielded both unreliability in the training process, and a general lack of understanding as to how GANs converge, and if so, to what. The purpose of this dissertation is to provide an account of the theory of GANs suitable for the mathematician, highlighting both positive and negative results. This involves identifying the problems when training GANs, and how topological and game-theoretic perspectives of GANs have contributed to our understanding and improved our techniques in recent years., Comment: 47 pages, 4 figures

Skull base inverted papilloma (IP) is an unusual entity for many neurosurgeons. IP is renowned for its high rate of recurrence, its ability to cause local destruction, and its association with malignancy. This paper is a comprehensive review of the reports, studies, and reviews published in the current biomedical literature from 1947 to September 2010 and synthesize this information to focus on its potential invasion to the base of the skull and possible intradural extension. The objective is to familiarize the clinician with the different aspects of this unusual disease. The role of modern diagnostic tools in medical imaging in order to assess clearly the limits of the tumors and to enhance the efficiency and the safety in the choice of a surgical approach is pointed out. The treatment guidelines for IP have undergone a complex evolution that continues today. Radical excision of the tumour is technically difficult and often incomplete. Successful management of IP requires resection of the affected mucosa which could be achieved with open surgery, endoscopic, or combined approach. Radio and chemotherapy were used for certain indications. More optimally research would be a multicenter randomized trials with large size cohorts.

The Correlation Between Dynamic Trunk Stability and Lower Extremity and Trunk Biomechanics During a Cutting Task Purpose: Examine the relationship between trunk neuromuscular control (NMC) during unstable-sitting (UST) and trunk and knee biomechanics during an athletic cutting task (ACT). Design: Descriptive Laboratory Study. Methods: Participants performed UST and ACT. Center of pressure (CoP) sway data were collected during UST. Peak triplanar trunk and knee biomechanics were collected during ACT. Pearson product-moment correlation coefficients were calculated among CoP data, and trunk and knee biomechanics. Results: Greater CoP sway area (r = -0.548, p = 0.006) and path (r = - 0.407, p = 0.032) measures were associated with greater knee extension moment magnitudes. Transverse plane trunk angle was positively associated with both knee varus moment (r = 0.371, p = 0.043), and transverse plane trunk angle (r = 0.587, p = 0.001). Conclusion: There is evidence of a relationship between trunk NMC and ACL injury risk factors. Key Words: ACL Injury, Trunk Stability, Core, ACL Injury Risk Factors

n/a

n/a