Spiders in random environment

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)A spider consists of several, say N, particles. Particles can jump independently according to a random walk if the movement does not violate some given restriction rules. If the movement violates a rule it is not carried out. We consider random walk in random environment (RWRE) on Z as underlying random walk. We suppose the environment omega = (omega(x))(x is an element of Z) to be elliptic, with positive drift and nestling, so that there exists a unique positive constant kappa such that E[((1 - omega(0))/omega(0))(kappa)] = 1. The restriction rules are kept very general; we only assume transitivity and irreducibility of the spider. The main result is that the speed of a spider is positive if kappa/N > 1 and null if kappa/N < 1. In particular, if kappa/N < 1 a spider has null speed but the speed of a (single) RWRE is positive.A spider consists of several, say N, particles. Particles can jump independently according to a random walk if the movement does not violate some given restriction rules. If the movement violates a rule it is not carried out. We consider random walk in ra8129147CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)FAPESP [2009/51139-3, 2009/08665-6]DFG [MU 2868/1-1]CNPq [300886/2008-0, 472431/2009-9, 304561/2006-1]SEM INFORMAÇÃO300886/2008–0; 472431/2009–9; 304561/2006–12009/51139-3; 2009/08665-

The FLC-like gene BvFL1 is not a major regulator of vernalization response in biennial beets

Many plant species in temperate climate regions require vernalization over winter to initiate flowering. Flowering Locus C (FLC) and FLC-like genes are key regulators of vernalization requirement and growth habit in winter-annual and perennial Brassicaceae. In the biennial crop species Beta vulgaris ssp. vulgaris in the evolutionarily distant Caryophyllales clade of core eudicots growth habit and bolting time are controlled by the vernalization and photoperiod response gene BTC1 and the downstream BvFT1-BvFT2 module. B. vulgaris also contains a vernalization-responsive FLC homolog (BvFL1). Here, to further elucidate the regulation of vernalization response and growth habit in beet, we functionally characterized BvFL1 by RNAi and over-expression in transgenic plants. BvFL1 RNAi neither eliminated the requirement for vernalization of biennial beets nor had a major effect on bolting time after vernalization. Over-expression of BvFL1 resulted in a moderate late-bolting phenotype, with bolting after vernalization being delayed by approximately 1 week. By contrast, RNAi-induced down-regulation of the BvFT1-BvFT2 module led to a strong delay in bolting after vernalization by several weeks. The data demonstrate for the first time that an FLC homolog does not play a major role in the control of vernalization response in a dicot species outside the Brassicaceae

Learning Interpretable Microscopic Features of Tumor by Multi-task Adversarial CNNs Improves Generalization

Adopting Convolutional Neural Networks (CNNs) in the daily routine of primary diagnosis requires not only near-perfect precision, but also a sufficient degree of generalization to data acquisition shifts and transparency. Existing CNN models act as black boxes, not ensuring to the physicians that important diagnostic features are used by the model. Building on top of successfully existing techniques such as multi-task learning, domain adversarial training and concept-based interpretability, this paper addresses the challenge of introducing diagnostic factors in the training objectives. Here we show that our architecture, by learning end-to-end an uncertainty-based weighting combination of multi-task and adversarial losses, is encouraged to focus on pathology features such as density and pleomorphism of nuclei, e.g. variations in size and appearance, while discarding misleading features such as staining differences. Our results on breast lymph node tissue show significantly improved generalization in the detection of tumorous tissue, with best average AUC 0.89 (0.01) against the baseline AUC 0.86 (0.005). By applying the interpretability technique of linearly probing intermediate representations, we also demonstrate that interpretable pathology features such as nuclei density are learned by the proposed CNN architecture, confirming the increased transparency of this model. This result is a starting point towards building interpretable multi-task architectures that are robust to data heterogeneity. Our code is available at https://bit.ly/356yQ2u.Comment: 21 pages, 4 figure

Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale System

Emulating spiking neural networks on analog neuromorphic hardware offers several advantages over simulating them on conventional computers, particularly in terms of speed and energy consumption. However, this usually comes at the cost of reduced control over the dynamics of the emulated networks. In this paper, we demonstrate how iterative training of a hardware-emulated network can compensate for anomalies induced by the analog substrate. We first convert a deep neural network trained in software to a spiking network on the BrainScaleS wafer-scale neuromorphic system, thereby enabling an acceleration factor of 10 000 compared to the biological time domain. This mapping is followed by the in-the-loop training, where in each training step, the network activity is first recorded in hardware and then used to compute the parameter updates in software via backpropagation. An essential finding is that the parameter updates do not have to be precise, but only need to approximately follow the correct gradient, which simplifies the computation of updates. Using this approach, after only several tens of iterations, the spiking network shows an accuracy close to the ideal software-emulated prototype. The presented techniques show that deep spiking networks emulated on analog neuromorphic devices can attain good computational performance despite the inherent variations of the analog substrate.Comment: 8 pages, 10 figures, submitted to IJCNN 201

A High-Resolution Digital Terrain Model Mosaic of the Mars 2020 Perseverance Rover Landing Site at Jezero Crater

We demonstrate the capabilities of a published MADNet monocular height estimation network in producing a refined digital terrain model (DTM) mosaic at 50 cm/pixel resolution for the Mars 2020 Perseverance rover landing site in Jezero crater on Mars. Our approach utilizes the publicly available Mars 2020 Terrain Relative Navigation (TRN) High-Resolution Imaging Science Experiment (HiRISE) Digital Terrain Model (DTM) mosaic, which was originally created by the United States Geological Survey (USGS) Astrogeology Science Centre. Our resultant HiRISE MADNet DTM mosaic is strictly matched with the original HiRISE TRN DTM and orthoimage mosaics. These mosaics are themselves co-aligned with the USGS TRN Context Camera (CTX) based DTM and orthoimage mosaics, as well as the ESA/DLR/FUB (European Space Agency/German Aerospace Center/Free University Berlin) High Resolution Stereo Camera (HRSC) level 5 DTM and orthoimage mosaics. In this paper, we provide a brief description of the technical details, and present both visual and quantitative assessments of the refined MADNet HiRISE Jezero DTM mosaic product. This DTM product is now publicly available at http://dx.doi.org/10.17169/refubium-38359

Emergence of daptomycin resistance in daptomycin-naïve rabbits with methicillin-resistant Staphylococcus aureus prosthetic joint infection is associated with resistance to host defense cationic peptides and mprF polymorphisms.

BackgroundPrevious studies of both clinically-derived and in vitro passage-derived daptomycin-resistant (DAP-R) Staphylococcus aureus strains demonstrated the coincident emergence of increased DAP MICs and resistance to host defense cationic peptides (HDP-R).MethodsIn the present investigation, we studied a parental DAP-susceptible (DAP-S) methicillin-resistant Staphylococcus aureus (MRSA) strain and three isogenic variants with increased DAP MICs which were isolated from both DAP-treated and DAP-untreated rabbits with prosthetic joint infections. These strains were compared for: in vitro susceptibility to distinct HDPs differing in size, structure, and origin; i.e.; thrombin-induced platelet microbicidal proteins [tPMPs] and human neutrophil peptide-1 [hNP-1]; cell membrane (CM) phospholipid and fatty acid content; CM order; envelope surface charge; cell wall thickness; and mprF single nucleotide polymorphisms (SNPs) and expression profiles.ResultsIn comparison with the parental strain, both DAP-exposed and DAP-naive strains exhibited: (i) significantly reduced susceptibility to each HDP (P&lt;0.05); (ii) thicker cell walls (P&lt;0.05); (iii) increased synthesis of CM lysyl-phosphatidylglycerol (L-PG); (iv) reduced content of CM phosphatidylglycerol (PG); and (v) SNPs within the mprF locus No significant differences were observed between parental or variant strains in outer CM content of L-PG, CM fluidity, CM fatty acid contents, surface charge, mprF expression profiles or MprF protein content. An isolate which underwent identical in vivo passage, but without evolving increased DAP MICs, retained parental phenotypes and genotype.ConclusionsTHESE RESULTS SUGGEST: i) DAP MIC increases may occur in the absence of DAP exposures in vivo and may be triggered by organism exposure to endogenous HDPs: and ii) gain-in-function SNPs in mprF may contribute to such HDP-DAP cross-resistance phenotypes, although the mechanism of this relationship remains to be defined

Forebrain CRF₁ modulates early-life stress-programmed cognitive deficits

Childhood traumatic events hamper the development of the hippocampus and impair declarative memory in susceptible individuals. Persistent elevations of hippocampal corticotropin-releasing factor (CRF), acting through CRF receptor 1 (CRF1), in experimental models of early-life stress have suggested a role for this endogenous stress hormone in the resulting structural modifications and cognitive dysfunction. However, direct testing of this possibility has been difficult. In the current study, we subjected conditional forebrain CRF1 knock-out (CRF1-CKO) mice to an impoverished postnatal environment and examined the role of forebrain CRF1 in the long-lasting effects of early-life stress on learning and memory. Early-life stress impaired spatial learning and memory in wild-type mice, and postnatal forebrain CRF overexpression reproduced these deleterious effects. Cognitive deficits in stressed wild-type mice were associated with disrupted long-term potentiation (LTP) and a reduced number of dendritic spines in area CA3 but not in CA1. Forebrain CRF1 deficiency restored cognitive function, LTP and spine density in area CA3, and augmented CA1 LTP and spine density in stressed mice. In addition, early-life stress differentially regulated the amount of hippocampal excitatory and inhibitory synapses in wild-type and CRF1-CKO mice, accompanied by alterations in the neurexin-neuroligin complex. These data suggest that the functional, structural and molecular changes evoked by early-life stress are at least partly dependent on persistent forebrain CRF1 signaling, providing a molecular target for the prevention of cognitive deficits in adults with a history of early-life adversity

Large Area High-Resolution 3D Mapping of the Von Kármán Crater: Landing Site for the Chang’E-4 Lander and Yutu-2 Rover

We demonstrate the creation of a large area of high-resolution (260 × 209 km2 at 1 m/pixel) DTM mosaic from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images over the Chang’E-4 landing site at Von Kármán crater using an in-house deep learning-based 3D modelling system developed at University College London, called MADNet, trained with lunar orthorectified images and digital terrain models (DTMs). The resultant 1 m DTM mosaic is co-aligned with the Chang’E-2 (CE-2) and the Lunar Orbiter Laser Altimeter (LOLA)—SELenological and Engineering Explorer (SELENE) blended DTM product (SLDEM), providing high spatial and vertical congruence. In this paper, technical details are briefly discussed, along with visual and quantitative assessments of the resultant DTM mosaic product. The LROC NAC MADNet DTM mosaic was compared with three independent DTM datasets, and the mean differences and standard deviations are as follows: PDS photogrammetric DTM at 5 m grid-spacing had a mean difference of −0.019 ± 1.09 m, CE-2 DTM at 20 m had a mean difference of −0.048 ± 1.791 m, and SLDEM at 69 m had a mean difference of 0.577 ± 94.940 m. The resultant LROC NAC MADNet DTM mosaic, alongside a blended LROC NAC and CE-2 MADNet DTM mosaic and a separate LROC NAC, orthorectified image mosaic, are made publicly available via the ESA planetary science archive’s guest storage facility