Secreted Proteins from the Helminth Fasciola hepatica Inhibit the Initiation of Autoreactive T Cell Responses and Prevent Diabetes in the NOD Mouse

Infections with helminth parasites prevent/attenuate auto-inflammatory disease. Here we show that molecules secreted by a helminth parasite could prevent Type 1 Diabetes (T1D) in nonobese diabetic (NOD) mice. When delivered at 4 weeks of age (coincident with the initiation of autoimmunity), the excretory/secretory products of Fasciola hepatica (FhES) prevented the onset of T1D, with 84% of mice remaining normoglycaemic and insulitis-free at 30 weeks of age. Disease protection was associated with suppression of IFN-γ secretion from autoreactive T cells and a switch to the production of a regulatory isotype (from IgG2a to IgG1) of autoantibody. Following FhES injection, peritoneal macrophages converted to a regulatory M2 phenotype, characterised by increased expression levels of Ym1, Arg-1, TGFβ and PD-L1. Expression of these M2 genetic markers increased in the pancreatic lymph nodes and the pancreas of FhES-treated mice. In vitro, FhES-stimulated M2 macrophages induced the differentiation of Tregs from splenocytes isolated from naïve NOD mice. Collectively, our data shows that FhES contains immune-modulatory molecules that mediate protection from autoimmune diabetes via the induction and maintenance of a regulatory immune environment.

Self-assembly of hybrid organic–inorganic polyoxovanadates: functionalised mixed-valent clusters and molecular cages’

Herein we report the intra- and inter-molecular assembly of a {V5O9} subunit. This mixed-valent structural motif can be stabilised as [V5O9(L1–3)4]5−/9− (1–3) by a range of organoarsonate ligands (L1–L3) whose secondary functionalities influence its packing arrangement within the crystal structures. Variation of the reaction conditions results in the dodecanuclear cage structure [V12O14(OH)4(L1)10]4− (4) where two modified convex building units are linked via two dimeric {O4VIV(OH)2VIVO4} moieties. Bi-functional phosphonate ligands, L4–L6 allow the intramolecular connectivity of the {V5O9} subunit to give hybrid capsules [V10O18(L4–6)4]10− (5–7). The dimensions of the electrophilic cavities of the capsular entities are determined by the incorporated ligand type. Mass spectrometry experiments confirm the stability of the complexes in solution. We investigate and model the temperature-dependent magnetic properties of representative complexes 1, 4, 6 and 7 and provide preliminary cell-viability studies of three different cancer cell lines with respect to Na8H2[6]·36H2O and Na8H2[7]·2DMF·29H2O.

New insights into dihydrogenphosphate recognition with dirhenium(I) tricarbonyl complexes bridged by a thiourea moiety

Three thiourea bridged 2,2’-bipyridine ligands bearing either a single thiourea group (L1), or two units separated by either a para (L2) or meta-substituted (L3) aromatic spacer, along with the corresponding bis(fac-tricarbonylrhenium(I)) complexes are reported. The three ligands all show the anticipated binding to acetate. However 1H NMR titrations reveal an unusual cooperative binding to, and selectivity for, two dihydrogenphosphate ions. The rhenium(I) complexes similarly demonstrate unusual sigmoidal titration curves, and in the case of {Re(CO)3Br}2(-L1) a surprisingly strong interaction to two anions. These were further exemplified in the emissive behaviour leading to the conclusion that there is an unusual interaction with dihydrogenphosphate, giving an initial increase in the emission, followed by a decrease and a blue shift in wavelength possibly as a result of partial deprotonation. It appears that dihydrogenphosphate binds cooperatively, with the addition of a second anion enhancing the interaction of the first, probably by proton transfer; this could explain the remarkable selectivity for phosphate seen with many reported anion receptors.

Improved diffusive gradients in thin films (DGT) measurement of total dissolved inorganic arsenic in waters and soils using a hydrous zirconium oxide binding layer

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A high-capacity diffusive gradients in thin films (DGT) technique has been developed for measurement of total dissolved inorganic arsenic (As) using a long shelf life binding gel layer containing hydrous zirconium oxide (Zr-oxide). Both As(III) and As(V) were rapidly accumulated in the Zr-oxide gel and could be quantitatively recovered by elution using 1.0 M NaOH for freshwater or a mixture of 1.0 M NaOH and 1.0 M H2O2 for seawater. DGT uptake of As(III) and As(V) increased linearly with deployment time and was independent of pH (2.0–9.1), ionic strength (0.01–750 mM), the coexistence of phosphate (0.25–10 mg P L–1), and the aging of the Zr-oxide gel up to 24 months after production. The capacities of the Zr-oxide DGT were 159 μg As(III) and 434 μg As(V) per device for freshwater and 94 μg As(III) and 152 μg As(V) per device for seawater. These values were 5–29 times and 3–19 times more than those reported for the commonly used ferrihydrite and Metsorb DGTs, respectively. Deployments of the Zr-oxide DGT in As-spiked synthetic seawater provided accurate measurements of total dissolved inorganic As over the 96 h deployment, whereas ferrihydrite and Metsorb DGTs only measured the concentrations accurately up to 24 and 48 h, respectively. Deployments in soils showed that the Zr-oxide DGT was a reliable and robust tool, even for soil samples heavily polluted with As. In contrast, As in these soils was underestimated by ferrihydrite and Metsorb DGTs due to insufficient effective capacities, which were likely suppressed by the competing effects of phosphate.

A Novel Bradykinin-Related Dodecapeptide (RVALPPGFTPLR) from the Skin Secretion of the Fujian Large-Headed Frog (Limnonectes fujianensis) Exhibiting Unusual Structural and Functional Features

Bradykinin-related peptides (BRPs) are significant components of the defensive skin secretions of many anuran amphibians, and these secretions represent the source of the most diverse spectrum of such peptides so far encountered in nature. Of the many families of bioactive peptides that have been identified from this source, the BRPs uniquely appear to represent homologues of counterparts that have specific distributions and receptor targets within discrete vertebrate taxa, ranging from fishes through mammals. Their broad spectra of actions, including pain and inflammation induction and smooth muscle effects, make these peptides ideal weapons in predator deterrence. Here, we describe a novel 12-mer BRP (RVALPPGFTPLR-RVAL-(L1, T6, L8)-bradykinin) from the skin secretion of the Fujian large-headed frog (Limnonectes fujianensis). The C-terminal 9 residues of this BRP (-LPPGFTPLR) exhibit three amino acid substitutions (L/R at Position 1, T/S at Position 6 and L/F at Position 8) when compared to canonical mammalian bradykinin (BK), but are identical to the kinin sequence present within the cloned kininogen-2 from the Chinese soft-shelled turtle (Pelodiscus sinensis) and differ from that encoded by kininogen-2 of the Tibetan ground tit (Pseudopodoces humilis) at just a single site (F/L at Position 8). These data would imply that the novel BRP is an amphibian defensive agent against predation by sympatric turtles and also that the primary structure of the avian BK, ornithokinin (RPPGFTPLR), is not invariant within this taxon. Synthetic RVAL-(L1, T6, L8)-bradykinin was found to be an antagonist of BK-induced rat tail artery smooth muscle relaxation acting via the B2-receptor.

Roche tomography of cataclysmic variables - VI. Differential rotation of AE Aqr - not tidally locked!

We present Roche tomograms of the K4V secondary star in the cataclysmic variable AE Aqr, reconstructed from two data sets taken 9 d apart, and measure the differential rotation of the stellar surface. The tomograms show many large, cool starspots, including a large high-latitude spot and a prominent appendage down the trailing hemisphere. We find two distinct bands of spots around 22° and 43° latitude, and estimate a spot coverage of 15.4-17 per cent on the Northern hemisphere. Assuming a solar-like differential rotation law, the differential rotation of AE Aqr was measured using two different techniques. The first method yields an equator-pole lap time of 269 d and the second yields a lap time of 262 d. This shows that the star is not fully tidally locked, as was previously assumed for CVs, but has a co-rotation latitude of ˜40°. We discuss the implications that these observations have on stellar dynamo theory, as well as the impact that spot traversal across the L1 point may have on accretion rates in CVs as well as some of their other observed properties. The entropy landscape technique was applied to determine the system parameters of AE Aqr. For the two independent data sets, we find M1 = 1.20 and 1.17 M⊙, M2 = 0.81 and 0.78 M⊙, and orbital inclinations of 50° to 51° at optimal systemic velocities of γ = -64.7 and -62.9 km s-1.

Type-I interferons induce lung protease responses following respiratory syncytial virus infection via RIG-I-like receptors

The role of proteases in viral infection of the lung is poorly understood. Thus, we examined matrix metalloproteinases (MMPs) and cathepsin proteases in respiratory syncytial virus (RSV)-infected mouse lungs. RSV-induced gene expression for MMPs -2, -3, -7, -8, -9, -10, -12, -13, -14, -16, -17, -19, -20, -25, -27, and -28 and cathepsins B, C, E, G, H, K, L1, S, W, and Z in the airways of Friend leukemia virus B sensitive strain mice. Increased proteases were present in the bronchoalveolar lavage fluid (BALF) and lung tissue during infection. Mitochondrial antiviral-signaling protein (MAVS) and TIR-domain-containing adapter-inducing interferon-β-deficient mice were exposed to RSV. Mavs-deficient mice had significantly lower expression of airway MMP-2, -3, -7, -8, -9, -10, -12, -13, and -28 and cathepsins C, G, K, S, W, and Z. In lung epithelial cells, retinoic acid-inducible gene-1 (RIG-I) was identified as the major RIG-I-like receptor required for RSV-induced protease expression via MAVS. Overexpression of RIG-I or treatment with interferon-β in these cells induced MMP and cathepsin gene and protein expression. The significance of RIG-1 protease induction was demonstrated by the fact that inhibiting proteases with batimastat, E64 or ribavirin prevented airway hyperresponsiveness and enhanced viral clearance in RSV-infected mice.

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

Robust visual tracking using structurally random projection and weighted least squares

Sparse representation based visual tracking approaches have attracted increasing interests in the community in recent years. The main idea is to linearly represent each target candidate using a set of target and trivial templates while imposing a sparsity constraint onto the representation coefficients. After we obtain the coefficients using L1-norm minimization methods, the candidate with the lowest error, when it is reconstructed using only the target templates and the associated coefficients, is considered as the tracking result. In spite of promising system performance widely reported, it is unclear if the performance of these trackers can be maximised. In addition, computational complexity caused by the dimensionality of the feature space limits these algorithms in real-time applications. In this paper, we propose a real-time visual tracking method based on structurally random projection and weighted least squares techniques. In particular, to enhance the discriminative capability of the tracker, we introduce background templates to the linear representation framework. To handle appearance variations over time, we relax the sparsity constraint using a weighed least squares (WLS) method to obtain the representation coefficients. To further reduce the computational complexity, structurally random projection is used to reduce the dimensionality of the feature space while preserving the pairwise distances between the data points in the feature space. Experimental results show that the proposed approach outperforms several state-of-the-art tracking methods.

An Adaptive Machine Learning Decision System for Flexible Predictive Maintenance

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs and downtime. This is especially true in industries that are data intensive such as semiconductor manufacturing. In this paper an adaptive PdM based flexible maintenance scheduling decision support system, which pays particular attention to associated opportunity and risk costs, is presented. The proposed system, which employs Machine Learning and regularized regression methods, exploits new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks. The system has been validated on a real industrial dataset related to an Ion Beam Etching process for semiconductor manufacturing.

Neural basis of language switching in the brain: fMRI evidence from Korean-Chinese early bilinguals

Using fMRI, we conducted two types of property generation task that involved language switching, with early bilingual speakers of Korean and Chinese. The first is a more conventional task in which a single language (L1 or L2) was used within each trial, but switched randomly from trial to trial. The other consists of a novel experimental design where language switching happens within each trial, alternating in the direction of the L1/L2 translation required. Our findings support a recently introduced cognitive model, the 'hodological' view of language switching proposed by Moritz-Gasser and Duffau. The nodes of a distributed neural network that this model proposes are consistent with the informative regions that we extracted in this study, using both GLM methods and Multivariate Pattern Analyses: the supplementary motor area, caudate, supramarginal gyrus and fusiform gyrus and other cortical areas. 

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

Enhancing Linear Programming with Motion Modeling for Multi-target Tracking

In this paper we extend the minimum-cost network flow approach to multi-target tracking, by incorporating a motion model, allowing the tracker to better cope with longterm occlusions and missed detections. In our new method, the tracking problem is solved iteratively: Firstly, an initial tracking solution is found without the help of motion information. Given this initial set of tracklets, the motion at each detection is estimated, and used to refine the tracking solution.
Finally, special edges are added to the tracking graph, allowing a further revised tracking solution to be found, where distant tracklets may be linked based on motion similarity. Our system has been tested on the PETS S2.L1 and Oxford town-center sequences, outperforming the baseline system, and achieving results comparable with the current state of the art.

Indications for an influence of hot Jupiters on the rotation and activity of their host stars

Context. The magnetic activity of planet-hosting stars is an importantfactor for estimating the atmospheric stability of close-in exoplanetsand the age of their host stars. It has long been speculated thatclose-in exoplanets can influence the stellar activity level. However,testing for tidal or magnetic interaction effects in samples ofplanet-hosting stars is difficult because stellar activity hindersexoplanet detection, so that stellar samples with detected exoplanetsshow a bias toward low activity for small exoplanets.

Aims: Weaim to test whether exoplanets in close orbits influence the stellarrotation and magnetic activity of their host stars.

Methods: Wedeveloped a novel approach to test for systematic activity-enhancementsin planet-hosting stars. We use wide (several 100 AU) binary systems inwhich one of the stellar components is known to have an exoplanet, whilethe second stellar component does not have a detected planet andtherefore acts as a negative control. We use the stellar coronal X-rayemission as an observational proxy for magnetic activity and analyzeobservations performed with Chandra and XMM-Newton.

Results: Wefind that in two systems for which strong tidal interaction can beexpected the planet-hosting primary displays a much higher magneticactivity level than the planet-free secondary. In three systems forwhich weaker tidal interaction can be expected the activity levels ofthe two stellar components agree with each other.

Conclusions:Our observations indicate that the presence of Hot Jupiters may inhibitthe spin-down of host stars with thick outer convective layers. Possiblecauses for this effect include a transfer of angular momentum from theplanetary orbit to the stellar rotation through tidal interaction, ordifferences during the early evolution of the system, where the hoststar may decouple from the protoplanetary disk early because of a gapopened by the forming Hot Jupiter.

Associations between intensive diabetes therapy and NMR-determined lipoprotein subclass profiles in Type 1 diabetes

Our objective is to define differences in circulating lipoprotein subclasses between intensive vs. conventional management of Type 1 diabetes during the randomization phase of the Diabetes Control and Complications Trial (DCCT). Nuclear magnetic resonance-determined lipoprotein subclass profiles (NMR-LSP), which estimate molar subclass concentrations and mean particle diameters, were determined in 1,294 DCCT subjects after a median of five (interquartile range: four, six) years following randomization to intensive or conventional diabetes management. In cross-sectional analyses, we compared standard lipids and NMR-LSP between treatment groups. Standard total-, LDL- and HDL-cholesterol levels were similar between randomization groups, while triglyceride levels were lower in the intensively treated group. NMR-LSP showed that intensive therapy was associated with larger LDL diameter (20.7 vs. 20.6 nm, p=0.01) and lower levels of small LDL (median: 465 vs. 552 nmol/l, p=0.007), total IDL/LDL (mean: 1000 vs. 1053 nmol/l, p=0.01), and small HDL (mean: 17.3 vs. 18.6 μmol/l, p<0.0001), the latter accounting for reduced total HDL (mean: 33.8 vs. 34.8 μmol/l, p=0.01). In conclusion, intensive diabetes therapy was associated with potentially favorable changes in LDL and HDL subclasses in sera. Further research will determine whether these changes contribute to the beneficial effects of intensive diabetes management on vascular complications.