Impact of malnutrition on immunity and infection

Malnutrition may be a consequence of energy deficit or micronutrient deficiency. It is considered the most relevant risk factor for illness and death, particularly in developing countries. In this review we described the magnitude of this problem, as well as its direct effect on the immune system and how it results in higher susceptibility to infections. A special emphasis was given to experimental models used to investigate the relationship between undernutrition and immunity. Malnutrition is obviously a challenge that must be addressed to health authorities and the scientific community

praja2 regulates KSR1 stability and mitogenic signaling

The kinase suppressor of Ras 1 (KSR1) has a fundamental role in mitogenic signaling by scaffolding components of the Ras/MAP kinase pathway. In response to Ras activation, KSR1 assembles a tripartite kinase complex that optimally transfers signals generated at the cell membrane to activate ERK. We describe a novel mechanism of ERK attenuation based on ubiquitin-dependent proteolysis of KSR1. Stimulation of membrane receptors by hormones or growth factors induced KSR1 polyubiquitination, which paralleled a decline of ERK1/2 signaling. We identified praja2 as the E3 ligase that ubiquitylates KSR1. We showed that praja2-dependent regulation of KSR1 is involved in the growth of cancer cells and in the maintenance of undifferentiated pluripotent state in mouse embryonic stem cells. The dynamic interplay between the ubiquitin system and the kinase scaffold of the Ras pathway shapes the activation profile of the mitogenic cascade. By controlling KSR1 levels, praja2 directly affects compartmentalized ERK activities, impacting on physiological events required for cell proliferation and maintenance of embryonic stem cell pluripotency

Zinc Supplementation Attenuates Cardiac Remodeling After Experimental Myocardial Infarction

Background/Aims: The objective of our study was to evaluate the effects of zinc supplementation on cardiac remodeling following acute myocardial infarction in rats. Methods: Animals were subdivided into 4 groups and observed for 3 months: 1) Sham Control; 2) Sham Zinc: Sham animals receiving zinc supplementation; 3) Infarction Control; 4) Infarction Zinc. After the followup period, we studied hypertrophy and ventricular geometry, functional alterations in vivo and in vitro, changes related to collagen, oxidative stress, and inflammation, assessed by echocardiogram, isolated heart study, western blot, flow cytometer, morphometry, and spectrophotometry. Results: Infarction induced a significant worsening of the functional variables. On the other hand, zinc attenuated both systolic and diastolic cardiac dysfunction induced by infarction. Considering the infarct size, there was no difference between the groups. Catalase and superoxide dismutase decreased in infarcted animals, and zinc increased its activity. We found higher expression of collagens I and III in infarcted animals, but there was no effect of zinc supplementation. Likewise, infarcted animals had higher levels of IL-10, but without zinc interference. Nrf-2 values were not different among the groups. Infarction increased the amount of Treg cells in the spleen as well as the amount of total lymphocytes. Zinc increased the amount of CD4+ in infarcted animals, but we did not observe effects in relation to Treg cells. Conclusion: zinc attenuates cardiac remodeling after infarction in rats; this effect is associated with modulation of antioxidant enzymes, but without the involvement of collagens I and III, Nrf-2, IL-10, and Treg cells

Estimating Koopman operators for nonlinear dynamical systems: A nonparametric approach

The Koopman operator provides a linear description of non-linear systems exploiting an embedding into an infinite dimensional space. Dynamic Mode Decomposition and Extended Dynamic Mode Decomposition are amongst the most popular finite dimensional approximations of the Koopman Operator. In this paper we capture their core essence as a dual version of the same problem, embedding them into the Kernel framework. To do so, we leverage the RKHS as a suitable space for learning the Koopman dynamics. Learning from finite length data automatically provides a finite dimensional approximation induced by data. Simulations and comparison with standard procedures are included

Data-Driven Control of Nonlinear Systems: Learning Koopman Operators for Policy Gradient

Data-driven control of nonlinear dynamical systems is a largely open problem. In this paper, building upon the theory of Koopman operators and exploiting ideas from policy gradient methods in reinforcement learning, a novel approach for data-driven optimal control of unknown nonlinear dynamical systems is introduced

Non-iterative control-oriented regularization for linear system identification

In the context of regularization methods for linear system identification, we introduce a new kernel design procedure that accounts for control objectives. We consider a model-reference control setup and assume data from one experiment is available. Exploiting the frequency response of the reference model, we design a new kernel that is able to extract the least amount of information from the data to the purpose of matching the desired closed-loop, with particular attention to user-defined frequency bands. Unlike the recently proposed CoRe algorithm, the proposed method is non-iterative and does not require any preliminary controller estimation. Simulation results on a benchmark example show that, when the model is used for control design, the proposed regularization procedure outperforms traditional kernel-based techniques as well as bias-shaping through data prefiltering

Non-iterative control-oriented regularization for linear system identification

In the context of regularization methods for linear system identification, we introduce a new kernel design procedure that accounts for control objectives. We consider a model-reference control setup and assume data from one experiment is available. Exploiting the frequency response of the reference model, we design a new kernel that is able to extract the least amount of information from the data to the purpose of matching the desired closed-loop, with particular attention to user-defined frequency bands. Unlike the recently proposed CoRe algorithm, the proposed method is non-iterative and does not require any preliminary controller estimation. Simulation results on a benchmark example show that, when the model is used for control design, the proposed regularization procedure outperforms traditional kernel-based techniques as well as bias-shaping through data prefiltering

Simultaneous distributed estimation and classification in sensor networks

In this work we consider the problem of simultaneously classifying sensor types and estimating hidden parameters in a network of sensors subject to gossip-like communication limitations. In particular, we consider a network of scalar noisy sensors which measure a common unknown parameter. We assume that a fraction of the nodes is subject to the same (but possibly unknown) offset. The goal for each node is to simultaneously identify the class the node belongs to and to estimate the common unknown parameter, only through local communication and computation. We propose a distributed estimator based on the maximum likelihood (ML) approach and we show that, in case the offset is known, this estimator converges to the centralized ML as the number N of sensor nodes goes to infinity. We also compare this strategy with a distributed implementation of estimationmaximization (EM) algorithm; we show tradeoffs via numerical simulations in terms of robustness, speed of convergence and implementation simplicity