João Mendes Moreira

Immune response in the adipose tissue of lean mice infected with the protozoan parasite Neospora caninum

The adipose tissue can make important contributions to immune function. Nevertheless, only a limited number of reports have investigated in lean hosts the immune response elicited in this tissue upon infection. Previous studies suggested that the intracellular protozoan Neospora caninum might affect adipose tissue physiology. Therefore, we investigated in mice challenged with this protozoan if immune cell populations within adipose tissue of different anatomical locations could be differently affected. Early in infection, parasites were detected in the adipose tissue and by 7 days of infection increased numbers of macrophages, regulatory T (Treg) cells and T‐bet+ cells were observed in gonadal, mesenteric, omental and subcutaneous adipose tissue. Increased expression of interferon‐γ was also detected in gonadal adipose tissue of infected mice. Two months after infection, parasite DNA was no longer detected in these tissues, but T helper type 1 (Th1) cell numbers remained above control levels in the infected mice. Moreover, the Th1/Treg cell ratio was higher than that of controls in the mesenteric and subcutaneous adipose tissue. Interestingly, chronically infected mice presented a marked increase of serum leptin, a molecule that plays a role in energy balance regulation as well as in promoting Th1‐type immune responses. Altogether, we show that an apicomplexa parasitic infection influences immune cellular composition of adipose tissue throughout the body as well as adipokine production, still noticed at a chronic phase of infection when parasites were already cleared from that particular tissue. This strengthens the emerging view that infections can have long‐term consequences for the physiology of adipose tissue.

Improving SVM-Linear predictions using CART for example selection

This paper describes the study on example selection in regression problems using μ-SVM (Support Vector Machine) linear as prediction algorithm. The motivation case is a study done on real data for a problem of bus trip time prediction. In this study we use three different training sets: all the examples, examples from past days similar to the day where prediction is needed, and examples selected by a CART regression tree. Then, we verify if the CART based example selection approach is appropriate on different regression data sets. The experimental results obtained are promising.

The Effect of Varying Parameters and Focusing on Bus Travel Time Prediction

Travel time prediction is an important tool for the planning tasks of mass transit and logistics companies. In this paper we investigate the use of regression methods for the problem of predicting the travel time of buses in a Portuguese public transportation company. More specifically, we empirically evaluate the impact of varying parameters on the performance of different regression algorithms, such as support vector machines (SVM), random forests (RF) and projection pursuit regression (PPR). We also evaluate the impact of the focusing tasks (example selection, domain value definition and feature selection) in the accuracy of those algorithms. Concerning the algorithms, we observe that 1) RF is quite robust to the choice of parameters and focusing methods; 2) the choice of parameters for SVM can be made independently of focusing methods while 3) for PPR they should be selected simultaneously. For the focusing methods, we observe that a stronger effect is obtained using example selection, particularly in combination with SVM.

Differential expression of Cathepsin E in transthyretin amyloidosis: from neuropathology to the immune system

BackgroundIncreasing evidence supports a key role for inflammation in the neurodegenerative process of familial amyloidotic polyneuropathy (FAP). While there seems to be an overactivation of the neuronal interleukin-1 signaling pathway, the immune response is apparently compromised in FAP. Accordingly, little immune cell infiltration is observed around pre-fibrillar or fibrillar amyloid deposits, with the underlying mechanism for this phenomenon remaining poorly understood. Cathepsin E (CtsE) is an important intermediate for antigen presentation and chemotaxis, but its role in the pathogenesis of FAP disease remains unknown.MethodsIn this study, we used both mouse primary macrophages and in vivo studies based on transgenic models of FAP and human samples to characterize CtsE expression in different physiological systems.ResultsWe show that CtsE is critically decreased in bone marrow-derived macrophages from a FAP mouse model, possibly contributing for cell function impairment. Compromised levels of CtsE were also found in injured nerves of transgenic mice and, most importantly, in naïve peripheral nerves, sensory ganglia, murine stomach, and sural nerve biopsies derived from FAP patients. Expression of CtsE in tissues was associated with transthyretin (TTR) deposition and differentially regulated accordingly with the physiological system under study. Preventing deposition with a TTR small interfering RNA rescued CtsE in the peripheral nervous system (PNS). In contrast, the expression of CtsE increased in splenic cells (mainly monocytes) or peritoneal macrophages, indicating a differential macrophage phenotype.ConclusionAltogether, our data highlights the potential of CtsE as a novel FAP biomarker and a possible modulator for innate immune cell chemotaxis to the disease most affected tissues—the peripheral nerve and the gastrointestinal tract.

MMP-14 overexpression correlates with the neurodegenerative process in familial amyloidotic polyneuropathy

ABSTRACT Levels of matrix metalloproteases (MMPs) can be differentially regulated in response to injury or neurological diseases. For instance, it is known that selective and short-term inhibition of MMP-14, a membrane-type 1 MMP, accelerates axon regeneration. Because axon growth and regeneration is impaired in familial amyloidotic polyneuropathy (FAP), a neurodegenerative disorder characterized by misfolding and deposition of mutant transthyretin (TTR) in the peripheral nervous system (PNS), we presently investigated the expression levels and the potential role for MMP-14 in this condition. By using cell culture studies, a mouse model of disease and human clinical samples, we observed that MMP-14: (i) is overexpressed in FAP nerves, correlating with TTR deposition; (ii) is upregulated in sciatic nerves from a preclinical transgenic mouse model, increasing with TTR deposition; (iii) levels in the PNS and plasma are rescued upon treatment of mice with anakinra or TTR siRNA, drugs acting over the IL-1 signaling pathway or TTR liver synthesis, respectively; (iv) increases in Schwann cells upon incubation with amyloid-like aggregates; and, finally, (v) is increased in plasma of FAP patients, correlating with disease progression. These results highlight the relevance of MMP-14 in the pathophysiology of FAP, suggesting not only a potential role for this molecule as a novel biomarker for therapy follow up, but also as a new potential therapeutic target. Summary: MMP-14 overexpression correlates with familial amyloidotic polyneuropathy disease and progression, as determined using mouse models and human samples. TTR gene silencing in the preclinical model decreases MMP-14 plasma levels.

Human Activity Recognition by Means of Online Semi-supervised Learning

Human activity recognition (HAR) is a reasonably recent field of study for the computer science community. It aims at automatically analysing ongoing events and extract their context from the captured data. The detection of human activities, such as walking, running, falling, or even cycling, allows for several heterogeneous applications, from surveillance systems to patient monitoring systems. Despite being a particularly active field of study in the past years, HAR still leaves many strategies left to explore and key aspects left to address. There are two main approaches in terms of data extraction: Video and sensors. The sensor approach is, however, the most promising, due to its extreme portability and unobtrusiveness. Most sensor-based HAR systems are trained in a static dataset with Supervised Learning techniques, generating a classification model with a relatively low error rate. However, these systems commonly ignore one of HARs challenges, the difference of input signals produced by different people when doing the same activities. Consequently, as a users movements drift from the generic, the system error increases. The activity classification method should therefore be able to generate adapted results for each different user. This article exhibits and discloses an under explored approach to this problem: By means of Online Semi-supervised Learning, An incremental technique capable of adapting the classification model to the user of the application by continuously updating it as the data from the users own specific input signals arrives. This is possible due to the nature of Semi-supervised learning, which trains on both labeled and unlabeled data, making it possible to keep learning even after reaching its final user, without the need of any manual input.

A General Introduction to Data Analytics

A General Introduction to Data Analytics is an essential guide to understand and use data analytics. This book is written using easy-tounderstand terms and does not require familiarity with statistics or programming. The authors—noted experts in the field—highlight an explanation of the intuition behind the basic data analytics techniques. The text also contains exercises and illustrative examples.