Inflammatory Adipokines, High Molecular Weight Adiponectin, and Insulin Resistance: A Population-Based Survey in Prepubertal Schoolchildren

BackgroundThe aim of this study was to investigate sex differences and associations of high molecular weight (HMW) adiponectin, leptin and proinflammatory adipokines, individually or in combinations, with adiposity and insulin resistance (IR) measures in prepubertal childhood.MethodologyWe studied 305 prepubertal children (boys/girls: 144/161; Tanner stage 1; age: 5-13 yr), included in a cohort of 44,231 adolescents who participated in an extensive Italian school-based survey. According to Cole's criteria, 105 individuals were lean (L; boys/girls: 59/46), 60 overweight (OW; boys/girls: 32/28) and 140 obese (OB; boys/girls: 70/70). Measurements comprised total and HMW adiponectin, leptin, as well as a panel of proinflammatory adipokines/chemokines associated with diabetes risk.Principal findingsLeptin-, and the leptin-to-HMW adiponectin ratio (L/HMW)-, increased progressively (pConclusionsIn prepubertal children, leptin emerges as a sex-independent discrimination marker of adiposity degree and as a useful, sex-associated predictor of the systemic insulin resistance

Research needs in allergy: an EAACI position paper, in collaboration with EFA

Abstract In less than half a century, allergy, originally perceived as a rare disease, has become a major public health threat, today affecting the lives of more than 60 million people in Europe, and probably close to one billion worldwide, thereby heavily impacting the budgets of public health systems. More disturbingly, its prevalence and impact are on the rise, a development that has been associated with environmental and lifestyle changes accompanying the continuous process of urbanization and globalization. Therefore, there is an urgent need to prioritize and concert research efforts in the field of allergy, in order to achieve sustainable results on prevention, diagnosis and treatment of this most prevalent chronic disease of the 21 st century. The European Academy of Allergy and Clinical Immunology (EAACI) is the leading professional organization in the field of allergy, promoting excellence in clinical care, education, training and basic and translational research, all with the ultimate goal of improving the health of allergic patients. The European Federation of Allergy and Airways Diseases Patients' Associations (EFA) is a non-profit network of allergy, asthma and Chronic Obstructive Pulmonary Disorder (COPD) patients' organizations. In support of their missions, the present EAACI Position Paper, in collaboration with EFA, highlights the most important research needs in the field of allergy to serve as key recommendations for future research funding at the national and European levels. Although allergies may involve almost every organ of the body and an array of diverse external factors act as triggers, there are several common themes that need to be prioritized in research efforts. As in many other chronic diseases, effective prevention, curative treatment and accurate, rapid diagnosis represent major unmet needs. Detailed phenotyping/endotyping stands out as widely required in order to arrange or re-categorize clinical syndromes into more coherent, uniform and treatment-responsive groups. Research efforts to unveil the basic pathophysiologic pathways and mechanisms, thus leading to the comprehension and resolution of the pathophysiologic complexity of allergies will allow for the design of novel patient-oriented diagnostic and treatment protocols. Several allergic diseases require well-controlled epidemiological description and surveillance, using disease registries, pharmacoeconomic evaluation, as well as large biobanks. Additionally, there is a need for extensive studies to bring promising new biotechnological innovations, such as biological agents, vaccines of modified allergen molecules and engineered components for allergy diagnosis, closer to clinical practice. Finally, particular attention should be paid to the difficult-to-manage, precarious and costly severe disease forms and/or exacerbations. Nonetheless, currently arising treatments, mainly in the fields of immunotherapy and biologicals, hold great promise for targeted and causal management of allergic conditions. Active involvement of all stakeholders, including Patient Organizations and policy makers are necessary to achieve the aims emphasized herein

Blind cluster structured sparse signal recovery: A nonconvex approach

open3noWe consider the problem of recovering a sparse signal when its nonzero coefficients tend to cluster into blocks, whose number, dimension and position are unknown. We refer to this problem as {it blind cluster structured sparse recovery}. For its solution, differently from the existing methods that consider the problem in a statistical context, we propose a deterministic neighborhood based approach characterized by the use both of a nonconvex, nonseparable sparsity inducing function and of a penalized version of the iterative $ell_1$ reweighted method. Despite the high nonconvexity of the approach, a suitable integration of these building elements led to the development of MB-NFCS ({it Model Based Nonlinear Filtering for Compressed Sensing}), an iterative fast, self-adaptive, and efficient algorithm that, without requiring any information on the sparsity pattern, adjusts at each iteration the action of the sparsity inducing function in order to strongly encourage the emerging cluster structure. The effectiveness of the proposed approach is demonstrated by a large set of numerical experiments that show the superior performance of MB-NFCS to the state-of-the-art algorithms.This work was supported by Miur, R.F.O. projects.openDamiana Lazzaro;Laura B. Montefusco;Serena PapiDamiana Lazzaro;Laura B. Montefusco;Serena Pap

Image denoising using principal component analysis in the wavelet domain

AbstractIn this work we describe a method for removing Gaussian noise from digital images, based on the combination of the wavelet packet transform and the principal component analysis. In particular, since the aim of denoising is to retain the energy of the signal while discarding the energy of the noise, our basic idea is to construct powerful tailored filters by applying the Karhunen–Loéve transform in the wavelet packet domain, thus obtaining a compaction of the signal energy into a few principal components, while the noise is spread over all the transformed coefficients. This allows us to act with a suitable shrinkage function on these new coefficients, removing the noise without blurring the edges and the important characteristics of the images. The results of a large numerical experimentation encourage us to keep going in this direction with our studies

Filtered Wavelet Thresholding Methods

When working with nonlinear filtering algorithms for image denoising problems, there are two crucial aspects, namely, the choice of the thresholding parameter λ and the use of a proper filter function. Both greatly influence the quality of the resulting denoised image. In this paper we propose two new filters, which are a piecewise quadratic and an exponential function of λ, respectively, arid we show how they can be successfully used instead of the classical Donoho and Johnstone's Soft thresholding filter. We exploit the increased regularity and flexibility of the new filters to improve the quality of the final results. Moreover, we prove that our filtered approximation is a near-minimizer of the functional which has to be minimized to solve the denoising problem. We also show that the quadratic filter, due to its shape, yields good results if we choose λ as the Donoho and Johnstone universal threshold, while the exponential one is more suitable if we use the recently proposed H-curve criterion. Encouraging results in extensive numerical experiments on several test images confirm the effectiveness of our proposal

Grocery product detection and recognition

Object detection and recognition are challenging computer vision tasks receiving great attention due to the large number of applications. This work focuses on the detection/recognition of products in supermarket shelves; this framework has a number of practical applications such as providing additional product/price information to the user or guiding visually impaired customers during shopping. The automatic creation of planograms (i.e., actual layout of products on shelves) is also useful for commercial analysis and management of large stores. Although in many object detection/recognition contexts it can be assumed that training images are representative of the real operational conditions, in our scenario such assumption is not realistic because the only training images available are acquired in well-controlled conditions. This gap between the training and test data makes the object detection and recognition tasks far more complex and requires very robust techniques. In this paper we prove that good results can be obtained by exploiting color and texture information in a multi-stage process: pre-selection, fine-selection and post processing. For fine-selection we compared a classical Bag of Words technique with a more recent Deep Neural Networks approach and found interesting outcomes. Extensive experiments on datasets of varying complexity are discussed to highlight the main issues characterizing this problem, and to guide toward the practical development of a real application

A fast algorithm for nonconvex approaches to sparse recovery problems

This paper addresses the problem of sparse signal recovery from a lower number of measurements than those requested by the classical compressed sensing theory. This problem is formalized as a constrained minimization problem, where the objective function is nonconvex and singular at the origin. Several algorithms have been recently proposed, which rely on iterative reweighting schemes, that produce better estimates at each new minimization step. Two such methods are iterative reweighted l2 and l1 minimization that have been shown to be effective and general, but very computationally demanding. The main contribution of this paper is the proposal of the algorithm WNFCS, where the reweighted schemes represent the core of a penalized approach to the solution of the constrained nonconvex minimization problem. The algorithm is fast, and succeeds in exactly recovering a sparse signal from a smaller number of measurements than the l1 minimization and in a shorter time. WNFCS is very general, since it represents an algorithmic framework that can easily be adapted to different reweighting strategies and nonconvex objective functions. Several numerical experiments and comparisons with some of the most recent nonconvex minimization algorithms confirm the capabilities of the proposed algorithm

Saliency-based keypoint selection for fast object detection and matching

In this paper we present a new approach to rank and select keypoints based on their saliency for object detection and matching under moderate viewpoint and lighting changes. Saliency is defined in terms of detectability, repeatability and distinctiveness by considering both the keypoint strength (as returned by the detector algorithm) and the associated local descriptor discriminating power. Our experiments prove that selecting a small amount of available keypoints (e.g., 10%) not only boosts efficiency but can also lead to better detection/matching accuracy thus making the proposed method attractive for real-time applications (e.g., augmented reality)

Saliency-based Keypoint Reduction for Augmented-Reality Applications in Smart Cities

In this paper we show that Saliency-based keypoint selection makes natural landmark detection and object recognition quite effective and efficient, thus enabling augmented reality techniques in a plethora of applications in smart city contexts. As a case study we address a tour of a museum where a modern smart device like a tablet or smartphone can be used to recognize paintings, retrieve their pose and graphically overlay useful information

On the Generation of Synthetic Fingerprint Alterations

In this paper we propose some techniques to generate synthetic altered fingerprints and prove the utility of the generated datasets for developing, tuning and evaluating algorithms for altered fingerprint detection/matching. Due to the lack of public databases of altered fingerprints the generation tool proposed (and made freely available) can be a valid instrument to boost research on these challenging problems