What is food poverty? A conceptual framework

Purpose – Recently, food poverty has been subject to much academic, political and media attention following the recent reduction in consumer purchasing power as a result of food and energy price volatility. Yet the lack of consensus related to food poverty terminology acts as an inhibitor in both identifying and addressing the issue in the UK, specifically as a separate problem to that of food insecurity. Misunderstanding of terminology is an impediment to identifying similarities and differentials with both developed and developing countries. The purpose of this paper is to address these issues and enhance political and academic discourse. Design/methodology/approach – An exploratory approach utilising secondary research was conducted to assemble sufficient information to ensure an extensive examination, consisting of several sources inclusive of academia, government and non-governmental organisations. The literature was screened for relevance following a broad search which primarily focused upon UK publications, with the exception of national data relevant to specified countries of USA, Canada, Yemen and United Republic of Tanzania (Tanzania). Findings – Economic access, quality, quantity, duration and social dimensions were the common features identified in the majority of the literature. Based upon these elements the proposed concise definition was constructed as; food poverty is the insufficient economic access to an adequate quantity and quality of food to maintain a nutritionally satisfactory and socially acceptable diet. Originality/value - – This study provides a conceptual approach in defining food poverty. Comparative to the countries examined, the UK has significant gaps in understanding and providing strategies in relation to individuals experiencing food poverty, causes and symptoms, methods of alleviation and coping strategies. There is no peer reviewed paper clearly discussing the definition of food poverty, hence, this review paper is original in three areas: establishing a definition for food poverty; clarifying the relationship between food poverty and food security; and discuss food poverty in UK with international comparison

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Automatic organ segmentation is an important yet challenging problem for medical image analysis. The pancreas is an abdominal organ with very high anatomical variability. This inhibits previous segmentation methods from achieving high accuracies, especially compared to other organs such as the liver, heart or kidneys. In this paper, we present a probabilistic bottom-up approach for pancreas segmentation in abdominal computed tomography (CT) scans, using multi-level deep convolutional networks (ConvNets). We propose and evaluate several variations of deep ConvNets in the context of hierarchical, coarse-to-fine classification on image patches and regions, i.e. superpixels. We first present a dense labeling of local image patches via $P{-}\mathrm{ConvNet}$ and nearest neighbor fusion. Then we describe a regional ConvNet ($R_1{-}\mathrm{ConvNet}$) that samples a set of bounding boxes around each image superpixel at different scales of contexts in a "zoom-out" fashion. Our ConvNets learn to assign class probabilities for each superpixel region of being pancreas. Last, we study a stacked $R_2{-}\mathrm{ConvNet}$ leveraging the joint space of CT intensities and the $P{-}\mathrm{ConvNet}$ dense probability maps. Both 3D Gaussian smoothing and 2D conditional random fields are exploited as structured predictions for post-processing. We evaluate on CT images of 82 patients in 4-fold cross-validation. We achieve a Dice Similarity Coefficient of 83.6$\pm$6.3% in training and 71.8$\pm$10.7% in testing.Comment: To be presented at MICCAI 2015 - 18th International Conference on Medical Computing and Computer Assisted Interventions, Munich, German

Chemical screening identifies the β-Carboline alkaloid harmine to be synergistically lethal with doxorubicin.

Despite being an invaluable chemotherapeutic agent for several types of cancer, the clinical utility of doxorubicin is hampered by its age-related and dose-dependent cardiotoxicity. Co-administration of dexrazoxane as a cardioprotective agent has been proposed, however recent studies suggest that it attenuates doxorubicin-induced antitumor activity. Since compounds of natural origin present a rich territory for drug discovery, we set out to identify putative natural compounds with the view to mitigate or minimize doxorubicin cardiotoxicity. We identify the DYRK1A kinase inhibitor harmine, which phosphorylates Tau that is deregulated in Alzheimer's disease, as a potentiator of cell death induced by non-toxic doses of doxorubicin. These observations suggest that harmine or other compounds that target the DYRK1A kinase my offer a new therapeutic opportunity to suppress doxorubicin age-related and dose-dependent cardiotoxicity

Polyunsaturated fatty acids inhibit k_v1.4 by interacting with positively charged extracellular pore residues

Polyunsaturated fatty acids (PUFAs) modulate voltage-gated K(+) channel inactivation by an unknown site and mechanism. The effects of ω-6 and ω-3 PUFAs were investigated on the heterologously expressed K(v)1.4 channel. PUFAs inhibited wild-type K(v)1.4 during repetitive pulsing as a result of slowing of recovery from inactivation. In a mutant K(v)1.4 channel lacking N-type inactivation, PUFAs reversibly enhanced C-type inactivation (K(d), 15–43 μM). C-type inactivation was affected by extracellular H(+) and K(+) as well as PUFAs and there was an interaction among the three: the effect of PUFAs was reversed during acidosis and abolished on raising K(+). Replacement of two positively charged residues in the extracellular pore (H508 and K532) abolished the effects of the PUFAs (and extracellular H(+) and K(+)) on C-type inactivation but had no effect on the lipoelectric modulation of voltage sensor activation, suggesting two separable interaction sites/mechanisms of action of PUFAs. Charge calculations suggest that the acidic head group of the PUFAs raises the pK(a) of H508 and this reduces the K(+) occupancy of the selectivity filter, stabilizing the C-type inactivated state

Condensates formed by prion-like low-complexity domains have small-world network structures and interfaces defined by expanded conformations

Biomolecular condensates form via coupled associative and segregative phase transitions of multivalent associative macromolecules. Phase separation coupled to percolation is one example of such transitions. Here, we characterize molecular and mesoscale structural descriptions of condensates formed by intrinsically disordered prion-like low complexity domains (PLCDs). These systems conform to sticker-and-spacers architectures. Stickers are cohesive motifs that drive associative interactions through reversible crosslinking and spacers affect the cooperativity of crosslinking and overall macromolecular solubility. Our computations reproduce experimentally measured sequence-specific phase behaviors of PLCDs. Within simulated condensates, networks of reversible inter-sticker crosslinks organize PLCDs into small-world topologies. The overall dimensions of PLCDs vary with spatial location, being most expanded at and preferring to be oriented perpendicular to the interface. Our results demonstrate that even simple condensates with one type of macromolecule feature inhomogeneous spatial organizations of molecules and interfacial features that likely prime them for biochemical activity