A Distinct Cytokine Profile and Stromal Vascular Fraction Metabolic Status without Significant Changes in the Lipid Composition Characterizes Lipedema

Lipedema is an adipose tissue disorder characterized by the disproportionate increase of subcutaneous fat tissue in the lower and/or upper extremities. The underlying pathomechanism remains unclear and no molecular biomarkers to distinguish the disease exist, leading to a large number of undiagnosed and misdiagnosed patients. To unravel the distinct molecular characteristic of lipedema we performed lipidomic analysis of the adipose tissue and serum of lipedema versus anatomically- and body mass index (BMI)-matched control patients. Both tissue groups showed no significant changes regarding lipid composition. As hyperplastic adipose tissue represents low-grade inflammation, the potential systemic effects on circulating cytokines were evaluated in lipedema and control patients using the Multiplex immunoassay system. Interestingly, increased systemic levels of interleukin 11 (p = 0.03), interleukin 28A (p = 0.04) and interleukin 29 (p = 0.04) were observed. As cytokines can influence metabolic activity, the metabolic phenotype of the stromal vascular fraction was examined, revealing significantly increased mitochondrial respiration in lipedema. In conclusion, despite sharing a comparable lipid profile with healthy adipose tissue, lipedema is characterized by a distinct systemic cytokine profile and metabolic activity of the stromal vascular fraction.     

Packet loss detection in networked control systems

One of the main issues related to the reliable operation of network control systems concerns the design of mechanisms able to detect anomalies in the functioning of the communication network through which the control loops are closed. In this article, we address the problem of detecting the occurrence of packet losses and design a mechanism that can detect the occurrence of packet losses directly from process sensor data. Specifically, we propose a moving‐horizon estimation scheme that permits to detect if the packet‐loss rate exceeds prescribed thresholds, which are representative of stability and performance of the control system. We discuss theoretical properties of the proposed solution along with an approximation scheme with reduced computational burden. A numerical example is discussed to substantiate the analysis.     

Milk from cows fed a diet with a high forage:concentrate ratio improves inflammatory state,oxidative stress,and mitochondrial function in rats

Excessive energy intake may evoke complex biochemical processes characterized by inflammation, oxidative stress, and impairment of mitochondrial function that represent the main factors underlying noncommunicable diseases. Because cow milk is widely used for human nutrition and in food industry processing, the nutritional quality of milk is of special interest with respect to human health. In our study, we analyzed milk produced by dairy cows fed a diet characterized by a high forage:concentrate ratio (high forage milk, HFM). In view of the low n-6:n-3 ratio and high content of conjugated linoleic acid of HFM, we studied the effects of this milk on lipid metabolism, inflammation, mitochondrial function, and oxidative stress in a rat model. To this end, we supplemented for 4 wk the diet of male Wistar rats with HFM and with an isocaloric amount (82 kJ, 22 mL/d) of milk obtained from cows fed a diet with low forage:concentrate ratio, and analyzed the metabolic parameters of the animals. Our results indicate that HFM may positively affect lipid metabolism, leptin:adiponectin ratio, inflammation, mitochondrial function, and oxidative stress, providing the first evidence of the beneficial effects of HFM on rat metabolism.     

Application of a multicriteria methodology for evaluation of energy alternatives for hydrogen production for the automotive sector – Case study

This paper presents a case study focusing on fourteen most used energy alternatives in Brazil, possible to feed large scale hydrogen production plants for the automotive sector. The evaluations are made using a Decision Making Support Method, MACBETH - Measuring Attractiveness by a Categorical Based Evaluation Technique, with the computational code M-MACBETH 3.2.0, using criteria that include economic and financial, technological, environmental and social aspects. The selected criteria that were used in the assessment, for each of the energy alternatives are capital to be invested in a plant, leveled cost of electric energy produced, CO₂ emissions, mortality rate due to the technology use and energy efficiency of technology. The main results obtained showed that photovoltaics off grid electric energy is the most attractive alternative, followed by the photovoltaic on grid alternative, for an eventual automotive hydrogen program in Brazil.     

The (Poly)Pharmacology of Cannabidiol in Neurological and Neuropsychiatric Disorders: Molecular Mechanisms and Targets

Cannabidiol (CBD), the major nonpsychoactive Cannabis constituent, has been proposed for the treatment of a wide panel of neurological and neuropsychiatric disorders, including anxiety, schizophrenia, epilepsy and drug addiction due to the ability of its versatile scaffold to interact with diverse molecular targets that are not restricted to the endocannabinoid system. Albeit the molecular mechanisms responsible for the therapeutic effects of CBD have yet to be fully elucidated, many efforts have been devoted in the last decades to shed light on its complex pharmacological profile. In particular, an ever-increasing number of molecular targets linked to those disorders have been identified for this phytocannabinoid, along with the modulatory effects of CBD on their cascade signaling. In this view, here we will try to provide a comprehensive and up-to-date overview of the molecular basis underlying the therapeutic effects of CBD involved in the treatment of neurological and neuropsychiatric disorders.     

Efficient estimation of extreme quantiles using adaptive kriging and importance sampling

This study considers an efficient method for the estimation of quantiles associated to very small levels of probability (up to O(10⁻⁹)), where the scalar performance function J is complex (eg, output of an expensive-to-run finite element model), under a probability measure that can be recast as a multivariate standard Gaussian law using an isoprobabilistic transformation. A surrogate-based approach (Gaussian Processes) combined with adaptive experimental designs allows to iteratively increase the accuracy of the surrogate while keeping the overall number of J evaluations low. Direct use of Monte-Carlo simulation even on the surrogate model being too expensive, the key idea consists in using an importance sampling method based on an isotropic-centered Gaussian with large standard deviation permitting a cheap estimation of small quantiles based on the surrogate model. Similar to AK-MCS as presented in the work of Schöbi et al., (2016), the surrogate is adaptively refined using a parallel infill criterion of an algorithm suitable for very small failure probability estimation. Additionally, a multi-quantile selection approach is developed, allowing to further exploit high-performance computing architectures. We illustrate the performances of the proposed method on several two to eight-dimensional cases. Accurate results are obtained with less than 100 evaluations of J on the considered benchmark cases.