Analysing Magnetism Using Scanning SQUID Microscopy

Scanning superconducting quantum interference device microscopy (SSM) is a scanning probe technique that images local magnetic flux, which allows for mapping of magnetic fields with high field and spatial accuracy. Many studies involving SSM have been published in the last decades, using SSM to make qualitative statements about magnetism. However, quantitative analysis using SSM has received less attention. In this work, we discuss several aspects of interpreting SSM images and methods to improve quantitative analysis. First, we analyse the spatial resolution and how it depends on several factors. Second, we discuss the analysis of SSM scans and the information obtained from the SSM data. Using simulations, we show how signals evolve as a function of changing scan height, SQUID loop size, magnetization strength and orientation. We also investigated 2-dimensional autocorrelation analysis to extract information about the size, shape and symmetry of magnetic features. Finally, we provide an outlook on possible future applications and improvements.Comment: 16 pages, 10 figure

Merging fragments of classical logic

We investigate the possibility of extending the non-functionally complete logic of a collection of Boolean connectives by the addition of further Boolean connectives that make the resulting set of connectives functionally complete. More precisely, we will be interested in checking whether an axiomatization for Classical Propositional Logic may be produced by merging Hilbert-style calculi for two disjoint incomplete fragments of it. We will prove that the answer to that problem is a negative one, unless one of the components includes only top-like connectives.Comment: submitted to FroCoS 201

A whole-cell biosensor for the detection of gold

Geochemical exploration for gold (Au) is becoming increasingly important to the mining industry. Current processes for Au analyses require sampling materials to be taken from often remote localities. Samples are then transported to a laboratory equipped with suitable analytical facilities, such as Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) or Instrumental Neutron Activation Analysis (INAA). Determining the concentration of Au in samples may take several weeks, leading to long delays in exploration campaigns. Hence, a method for the on-site analysis of Au, such as a biosensor, will greatly benefit the exploration industry. The golTSB genes from Salmonella enterica serovar typhimurium are selectively induced by Au(I/III)-complexes. In the present study, the golTSB operon with a reporter gene, lacZ, was introduced into Escherichia coli. The induction of golTSB::lacZ with Au(I/III)-complexes was tested using a colorimetric β-galactosidase and an electrochemical assay. Measurements of the β-galactosidase activity for concentrations of both Au(I)- and Au(III)-complexes ranging from 0.1 to 5 µM (equivalent to 20 to 1000 ng g⁻¹ or parts-per-billion (ppb)) were accurately quantified. When testing the ability of the biosensor to detect Au(I/III)-complexes(aq) in the presence of other metal ions (Ag(I), Cu(II), Fe(III), Ni(II), Co(II), Zn, As(III), Pb(II), Sb(III) or Bi(III)), cross-reactivity was observed, i.e. the amount of Au measured was either under- or over-estimated. To assess if the biosensor would work with natural samples, soils with different physiochemical properties were spiked with Au-complexes. Subsequently, a selective extraction using 1 M thiosulfate was applied to extract the Au. The results showed that Au could be measured in these extracts with the same accuracy as ICP-MS (P<0.05). This demonstrates that by combining selective extraction with the biosensor system the concentration of Au can be accurately measured, down to a quantification limit of 20 ppb (0.1 µM) and a detection limit of 2 ppb (0.01 µM).Carla M. Zammit, Davide Quaranta, Shane Gibson, Anita J. Zaitouna, Christine Ta, Joël Brugger, Rebecca Y. Lai, Gregor Grass, Frank Reit

Using force covariance to derive effective stochastic interactions in dissipative particle dynamics

There exist methods for determining effective conservative interactions in coarse grained particle based mesoscopic simulations. The resulting models can be used to capture thermal equilibrium behavior, but in the model system we study do not correctly represent transport properties. In this article we suggest the use of force covariance to determine the full functional form of dissipative and stochastic interactions. We show that a combination of the radial distribution function and a force covariance function can be used to determine all interactions in dissipative particle dynamics. Furthermore we use the method to test if the effective interactions in dissipative particle dynamics (DPD) can be adjusted to produce a force covariance consistent with a projection of a microscopic Lennard-Jones simulation. The results indicate that the DPD ansatz may not be consistent with the underlying microscopic dynamics. We discuss how this result relates to theoretical studies reported in the literature.Comment: 10 pages, 10 figure

Gambling Marketing from 2014 to 2018: a Literature Review

Purpose of Review: Legislation and technology have led to unprecedented changes in the frequency and content of gambling marketing in many countries. We build upon previous reviews by exploring research on gambling marketing from between 2014 and 2018. Recent Findings: Most literature reviewed was from the UK or Australia, with three key findings identified. First, gambling marketing is highly targeted and ubiquitous around sport, with the most popular strategies being increasing brand awareness, advertising complex financial incentives for participation and advertising complex betting odds. Second, perceptions of gambling advertising, particularly among vulnerable groups (e.g. children, problem gamblers) appear to be influenced by this targeted content. Third, emerging research suggests that awareness of gambling marketing is associated with more frequent and riskier gambling behaviour. Summary: The reviewed literature suggests that gambling marketing is targeted and influences how gambling is perceived, and that it may affect gambling-related behaviours

In Situ Detection of Iron in Oxidation States ≥ IV in Cobalt‐Iron Oxyhydroxide Reconstructed during Oxygen Evolution Reaction

Cobalt‐iron oxyhydroxides (CoFeOOHx) are among the most active catalysts for the oxygen evolution reaction (OER). However, their redox behavior and the electronic and chemical structure of their active sites are still ambiguous. To shed more light on this, the complete and rapid reconstruction of four helical cobalt‐iron borophosphates with different Co:Fe ratios into disordered cobalt‐iron oxyhydroxides can be achieved, which are electrolyte‐penetrable and thus most transition metal sites can potentially participate in the OER. To track the redox behavior and to identify the active structure, quasi in situ X‐ray absorption spectroscopy is applied. Iron in high oxidation states ≥ IV (Fe4+) and its substantial redox behavior with an average oxidation state of around 2.8 to above 3.2 is detected. Furthermore, a 6% contraction of the Fe‐O bond length compared to Fe3+OOH references is observed during OER and a strong distortion of the [MO6] octahedra is identified. It is hypothesized that this bond contraction is caused by the presence of oxyl radicals and that di‐µ‐oxyl radical bridged cobalt‐iron centers are the active sites. It is anticipated that the detailed electronic and structural description can substantially contribute to the debate on the nature of the active site in bimetallic iron‐containing OER catalysts

T Cell Priming by Activated Nlrc5-Deficient Dendritic Cells Is Unaffected despite Partially Reduced MHC Class I Levels.

NLRC5, a member of the NOD-like receptor (NLR) protein family, has recently been characterized as the master transcriptional regulator of MHCI molecules in lymphocytes, in which it is highly expressed. However, its role in activated dendritic cells (DCs), which are instrumental to initiate T cell responses, remained elusive. We show in this study that, following stimulation of DCs with inflammatory stimuli, not only did NLRC5 level increase, but also its importance in directing MHCI transcription. Despite markedly reduced mRNA and intracellular H2-K levels, we unexpectedly observed nearly normal H2-K surface display in Nlrc5(-/-) DCs. Importantly, this discrepancy between a strong intracellular and a mild surface defect in H2-K levels was observed also in DCs with H2-K transcription defects independent of Nlrc5. Hence, alongside with demonstrating the importance of NLRC5 in MHCI transcription in activated DCs, we uncover a general mechanism counteracting low MHCI surface expression. In agreement with the decreased amount of neosynthesized MHCI, Nlrc5(-/-) DCs exhibited a defective capacity to display endogenous Ags. However, neither T cell priming by endogenous Ags nor cross-priming ability was substantially affected in activated Nlrc5(-/-) DCs. Altogether, these data show that Nlrc5 deficiency, despite significantly affecting MHCI transcription and Ag display, is not sufficient to hinder T cell activation, underlining the robustness of the T cell priming process by activated DCs

Loss of Tsc2 in radial glia models the brain pathology of tuberous sclerosis complex in the mouse

Tuberous sclerosis complex (TSC) is an autosomal dominant, tumor predisposition disorder characterized by significant neurodevelopmental brain lesions, such as tubers and subependymal nodules. The neuropathology of TSC is often associated with seizures and intellectual disability. To learn about the developmental perturbations that lead to these brain lesions, we created a mouse model that selectively deletes the Tsc2 gene from radial glial progenitor cells in the developing cerebral cortex and hippocampus. These Tsc2 mutant mice were severely runted, developed post-natal megalencephaly and died between 3 and 4 weeks of age. Analysis of brain pathology demonstrated cortical and hippocampal lamination defects, hippocampal heterotopias, enlarged dysplastic neurons and glia, abnormal myelination and an astrocytosis. These histologic abnormalities were accompanied by activation of the mTORC1 pathway as assessed by increased phosphorylated S6 in brain lysates and tissue sections. Developmental analysis demonstrated that loss of Tsc2 increased the subventricular Tbr2-positive basal cell progenitor pool at the expense of early born Tbr1-positive post-mitotic neurons. These results establish the novel concept that loss of function of Tsc2 in radial glial progenitors is one initiating event in the development of TSC brain lesions as well as underscore the importance of Tsc2 in the regulation of neural progenitor pools. Given the similarities between the mouse and the human TSC lesions, this model will be useful in further understanding TSC brain pathophysiology, testing potential therapies and identifying other genetic pathways that are altered in TSC

Scalable and flexible inference framework for stochastic dynamic single-cell models

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability