Ferrate(VI) enhanced photocatalytic oxidation of pollutants in aqueous TiO?suspensions

Author name used in this publication: Nigel J. D. Graham2009-2010 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Ferrate(VI) oxidation of endocrine disruptors and antimicrobials in water

Author name used in this publication: X. Z. LiAccepted ManuscriptPublishe

Monocyte Subset Recruitment Marker Profile Is Inversely Associated With Blood ApoA1 Levels.

Dyslipidemia promotes development of the atherosclerotic plaques that characterise cardiovascular disease. Plaque progression requires the influx of monocytes into the vessel wall, but whether dyslipidemia is associated with an increased potential of monocytes to extravasate is largely unknown. Here (using flow cytometry) we examined recruitment marker expression on monocytes from generally healthy individuals who differed in lipid profile. Comparisons were made between monocyte subsets, participants and relative to participants' lipid levels. Monocyte subsets differed significantly in their expression of recruitment markers, with highest expression being on either the classical or non-classical subsets. However, these inter-subset differences were largely overshadowed by considerable inter-participant differences with some participants having higher levels of recruitment markers on all three monocyte subsets. Furthermore, when the expression of one recruitment marker was high, so too was that of most of the other markers, with substantial correlations evident between the markers. The inter-participant differences were explained by lipid levels. Most notably, there was a significant inverse correlation for most markers with ApoA1 levels. Our results indicate that dyslipidemia, in particular low levels of ApoA1, is associated with an increased potential of all monocyte subsets to extravasate, and to do so using a wider repertoire of recruitment markers than currently appreciated

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201

Giant Nonlinear Optical Activity from Planar Metasurfaces

Second harmonic generation circular dichroism (CD) is more sensitive to the handedness of chiral materials than its linear optical counterpart. In this work, we show that 3D chiral structures are not necessary for introducing strong CD for harmonic generations. Specifically, we demonstrate giant CD for both second harmonic generation and third harmonic generation on suitably designed ultrathin plasmonic metasurfaces. It is experimentally and theoretically verified that the overwhelming contribution to this nonlinear CD is of achiral origin. The results shed new light on the origin of the nonlinear CD effect in achiral planar surfaces

Identifying hazardousness of sewer pipeline gas mixture using classification methods: a comparative study

In this work, we formulated a real-world problem related to sewer pipeline gas detection using the classification-based approaches. The primary goal of this work was to identify the hazardousness of sewer pipeline to offer safe and non-hazardous access to sewer pipeline workers so that the human fatalities, which occurs due to the toxic exposure of sewer gas components, can be avoided. The dataset acquired through laboratory tests, experiments, and various literature sources was organized to design a predictive model that was able to identify/classify hazardous and non-hazardous situation of sewer pipeline. To design such prediction model, several classification algorithms were used and their performances were evaluated and compared, both empirically and statistically, over the collected dataset. In addition, the performances of several ensemble methods were analyzed to understand the extent of improvement offered by these methods. The result of this comprehensive study showed that the instance-based learning algorithm performed better than many other algorithms such as multilayer perceptron, radial basis function network, support vector machine, reduced pruning tree. Similarly, it was observed that multi-scheme ensemble approach enhanced the performance of base predictors

Coupling to short linear motifs creates versatile PME-1 activities in PP2A holoenzyme demethylation and inhibition

Protein phosphatase 2A (PP2A) holoenzymes target broad substrates by recognizing short motifs via regulatory subunits. PP2A methylesterase 1 (PME-1) is a cancer-promoting enzyme and undergoes methylesterase activation upon binding to the PP2A core enzyme. Here, we showed that PME-1 readily demethylates different families of PP2A holoenzymes and blocks substrate recognition in vitro. The high-resolution cryoelectron microscopy structure of a PP2A-B56 holoenzyme–PME-1 complex reveals that PME-1 disordered regions, including a substrate-mimicking motif, tether to the B56 regulatory subunit at remote sites. They occupy the holoenzyme substrate-binding groove and allow large structural shifts in both holoenzyme and PME-1 to enable multipartite contacts at structured cores to activate the methylesterase. B56 interface mutations selectively block PME-1 activity toward PP2A-B56 holoenzymes and affect the methylation of a fraction of total cellular PP2A. The B56 interface mutations allow us to uncover B56-specific PME-1 functions in p53 signaling. Our studies reveal multiple mechanisms of PME-1 in suppressing holoenzyme functions and versatile PME-1 activities derived from coupling substrate-mimicking motifs to dynamic structured cores

Neonatal umbilical cord blood transplantation halts skeletal disease progression in the murine model of MPS-I

Umbilical cord blood (UCB) is a promising source of stem cells to use in early haematopoietic stem cell transplantation (HSCT) approaches for several genetic diseases that can be diagnosed at birth. Mucopolysaccharidosis type I (MPS-I) is a progressive multi-system disorder caused by deficiency of lysosomal enzyme α-L-iduronidase, and patients treated with allogeneic HSCT at the onset have improved outcome, suggesting to administer such therapy as early as possible. Given that the best characterized MPS-I murine model is an immunocompetent mouse, we here developed a transplantation system based on murine UCB. With the final aim of testing the therapeutic efficacy of UCB in MPS-I mice transplanted at birth, we first defined the features of murine UCB cells and demonstrated that they are capable of multi-lineage haematopoietic repopulation of myeloablated adult mice similarly to bone marrow cells. We then assessed the effectiveness of murine UCB cells transplantation in busulfan-conditioned newborn MPS-I mice. Twenty weeks after treatment, iduronidase activity was increased in visceral organs of MPS-I animals, glycosaminoglycans storage was reduced, and skeletal phenotype was ameliorated. This study explores a potential therapy for MPS-I at a very early stage in life and represents a novel model to test UCB-based transplantation approaches for various diseases

NODIS: Neural Ordinary Differential Scene Understanding

Semantic image understanding is a challenging topic in computer vision. It requires to detect all objects in an image, but also to identify all the relations between them. Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image. In previous works, relations were identified by solving an assignment problem formulated as Mixed-Integer Linear Programs. In this work, we interpret that formulation as Ordinary Differential Equation (ODE). The proposed architecture performs scene graph inference by solving a neural variant of an ODE by end-to-end learning. It achieves state-of-the-art results on all three benchmark tasks: scene graph generation (SGGen), classification (SGCls) and visual relationship detection (PredCls) on Visual Genome benchmark