A PLC Variable Identification Method by Manual Declaration of Time-Stamped Events

International audienc

The RNA-binding protein ATX-2 regulates cytokinesis through PAR-5 and ZEN-4

The spindle midzone harbors both microtubules and proteins necessary for furrow formation and the completion of cytokinesis. However, the mechanisms that mediate the temporal and spatial recruitment of cell division factors to the spindle midzone and midbody remain unclear. Here we describe a mechanism governed by the conserved RNA-binding protein ATX-2/Ataxin-2, which targets and maintains ZEN-4 at the spindle midzone. ATX-2 does this by regulating the amount of PAR-5 at mitotic structures, particularly the spindle, centrosomes, and midbody. Preventing ATX-2 function leads to elevated levels of PAR-5, enhanced chromatin and centrosome localization of PAR-5-GFP, and ultimately a reduction of ZEN-4-GFP at the spindle midzone. Codepletion of ATX-2 and PAR-5 rescued the localization of ZEN-4 at the spindle midzone, indicating that ATX-2 mediates the localization of ZEN-4 upstream of PAR-5. We provide the first direct evidence that ATX-2 is necessary for cytokinesis and suggest a model in which ATX-2 facilitates the targeting of ZEN-4 to the spindle midzone by mediating the posttranscriptional regulation of PAR-5

Production Process Modelling Architecture to Support Improved Cyber-Physical Production Systems

With the proliferation of intelligent networks in industrial environments, manufacturing SME’s have been in a continuous search for integrating and retrofitting existing assets with modern technologies that could provide low-cost solutions for optimizations in their production processes. Their willingness to support a technological evolution is firmly based on the perception that, in the future, better tools will guarantee process control, surveillance and maintenance. For this to happen, the digitalization of valuable and extractable information must be held in a cost-effective manner, through contemporary approaches such as IoT, creating the required fluidity between hardware and software, for implementing Cyber-Physical modules in the manufacturing process. The goal of this work is to develop an architecture that will support companies to digitize their machines and processes through an MDA approach, by modeling their production processes and physical resources, and transforming into an implementation model, using contemporary CPS and IoT concepts, to be continuously improved using forecasting/predictive algorithms and analytics.authorsversionpublishe

Moisture monitoring in clay embankments using electrical resistivity tomography

Systems and methods are described for monitoring temporal and spatial moisture content changes in clay embankments using electrical resistivity tomography (ERT) imaging. The methodology is based upon development of a robust relationship between fill resistivity and moisture content and its use in the transformation of resistivity image differences in terms of relative moisture content changes. Moisture level and moisture content movement applications are exemplified using two case histories from the UK. The first is the BIONICS embankment, near Newcastle (NE England), which was constructed in 2005 using varying degrees of compaction of a medium plasticity sandy, silty clay derived from the Durham Till. The second is a Victorian embankment south of Nottingham (Central England), constructed in 1897 using end tipping of Late Triassic siltstone and mudstone taken from local cuttings. Climate change forecasts for the UK suggest that transportation earthworks will be subjected to more sustained, higher temperatures and increased intensity of rainfall. Within the context of preventative geotechnical asset maintenance, ERT imaging can provide a monitoring framework to manage moisture movement and identify failure trigger conditions within embankments, thus supporting on demand inspection scheduling and low cost early interventions

On key technologies for realising digital twins for structural dynamics applications

The term digital twin has gained increasing popularity over the last few years. The concept, loosely based on a virtual model framework that can replicate a particular system for contexts of interest over time, will require the development and integration of several key technologies in order to be fully realised. This paper, focusing on vibration-related problems in mechanical systems, discusses these key technologies as the building blocks of a digital twin. The example of a simulation digital twin that can be used for asset management is then considered. After briefly discussing the building blocks required, the process of data-augmented modelling is selected for detailed investigation. This concept is one of the defining characteristics of the digital twin idea, and using a simple numerical example, it is shown how augmenting a model with data can be used to compensate for the inherent model discrepancy. Finally the implications of this type of data augmentation for future digital twin technology is discussed

Dark Force Detection in Low Energy e-p Collisions

We study the prospects for detecting a light boson X with mass m_X < 100 MeV at a low energy electron-proton collider. We focus on the case where X dominantly decays to e+ e- as motivated by recent "dark force" models. In order to evade direct and indirect constraints, X must have small couplings to the standard model (alpha_X 10 MeV). By comparing the signal and background cross sections for the e- p e+ e- final state, we conclude that dark force detection requires an integrated luminosity of around 1 inverse attobarn, achievable with a forthcoming JLab proposal.Comment: 38 pages, 19 figures; v2, references adde

The Schro¨\ddot{o}dinger-Poisson equations as the large-N limit of the Newtonian N-body system: applications to the large scale dark matter dynamics

In this paper it is argued how the dynamics of the classical Newtonian N-body system can be described in terms of the Schr$\ddot{o}$dinger-Poisson equations in the large $N$ limit. This result is based on the stochastic quantization introduced by Nelson, and on the Calogero conjecture. According to the Calogero conjecture, the emerging effective Planck constant is computed in terms of the parameters of the N-body system as $\hbar \sim M^{5/3} G^{1/2} (N/)^{1/6}$, where is $G$ the gravitational constant, $N$ and $M$ are the number and the mass of the bodies, and $$ is their average density. The relevance of this result in the context of large scale structure formation is discussed. In particular, this finding gives a further argument in support of the validity of the Schr$\ddot{o}$dinger method as numerical double of the N-body simulations of dark matter dynamics at large cosmological scales.Comment: Accepted for publication in the Euro. Phys. J.

Systematic Review of Gut Microbiota and Major Depression

Background: Recently discovered relationships between the gastrointestinal microbiome and the brain have implications for psychiatric disorders, including major depressive disorder (MDD). Bacterial transplantation from MDD patients to rodents produces depression-like behaviors. In humans, case-control studies have examined the gut microbiome in healthy and affected individuals. We systematically reviewed existing studies comparing gut microbial composition in MDD and healthy volunteers.Methods: A PubMed literature search combined the terms “depression,” “depressive disorder,” “stool,” “fecal,” “gut,” and “microbiome” to identify human case-control studies that investigated relationships between MDD and microbiota quantified from stool. We evaluated the resulting studies, focusing on bacterial taxa that were different between MDD and healthy controls.Results: Six eligible studies were found in which 50 taxa exhibited differences (p &lt; 0.05) between patients with MDD and controls. Patient characteristics and methodologies varied widely between studies. Five phyla—Bacteroidetes, Firmicutes, Actinobacteria, Fusobacteria, and Protobacteria—were represented; however, divergent results occurred across studies for all phyla. The largest number of differentiating taxa were within phylum Firmicutes, in which nine families and 12 genera differentiated the diagnostic groups. The majority of these families and genera were found to be statistically different between the two groups in two identified studies. Family Lachnospiraceae differentiated the diagnostic groups in four studies (with an even split in directionality). Across all five phyla, nine genera were higher in MDD (Anaerostipes, Blautia, Clostridium, Klebsiella, Lachnospiraceae incertae sedis, Parabacteroides, Parasutterella, Phascolarctobacterium, and Streptococcus), six were lower (Bifidobacterium, Dialister, Escherichia/Shigella, Faecalibacterium, and Ruminococcus), and six were divergent (Alistipes, Bacteroides, Megamonas, Oscillibacter, Prevotella, and Roseburia). We highlight mechanisms and products of bacterial metabolism as they may relate to the etiology of depression.Conclusions: No consensus has emerged from existing human studies of depression and gut microbiome concerning which bacterial taxa are most relevant to depression. This may in part be due to differences in study design. Given that bacterial functions are conserved across taxonomic groups, we propose that studying microbial functioning may be more productive than a purely taxonomic approach to understanding the gut microbiome in depression