Correlations in superstatistical systems

We review some of the properties of higher-dimensional superstatistical stochastic models. As an example, we analyse the stochastic properties of a superstatistical model of 3-dimensional Lagrangian turbulence, and compare with experimental data. Excellent agreement is obtained for various measured quantities, such as acceleration probability densities, Lagrangian scaling exponents, correlations between acceleration components, and time decay of correlations. We comment on how to proceed from superstatistics to a thermodynamic description.Comment: 8 pages, 4 figures. To appear in the proceedings of CTNEXT07 'Complexity, Metastability and Nonextensivity', Catania 1-5 July 2007, eds. S. Abe, H.J. Herrmann, P. Quarati, A. Rapisarda, C. Tsallis, AIP 200

Parameter Optimisation of a Virtual Synchronous Machine in a Microgrid

Parameters of a virtual synchronous machine in a small microgrid are optimised. The dynamical behaviour of the system is simulated after a perturbation, where the system needs to return to its steady state. The cost functional evaluates the system behaviour for different parameters. This functional is minimised by Parallel Tempering. Two perturbation scenarios are investigated and the resulting optimal parameters agree with analytical predictions. Dependent on the focus of the optimisation different optima are obtained for each perturbation scenario. During the transient the system leaves the allowed voltage and frequency bands only for a short time if the perturbation is within a certain range.Comment: 17 pages, 5 figure

Factors Affecting Performance on an Army Urban Operation Casualty Evacuation for Male and Female Soldiers

Introduction: This study was conducted to determine what physical and physiological characteristics contribute to the performance of an urban operation casualty evacuation (UO) and its predictive test, FORCE combat (FC) and describe the metabolic demand of the UO in female soldiers. Methods: Seventeen military members (9 M and 8 F) completed a loaded walking maximal aerobic test, the UO and FC. Heart rate reserve (HRR) and completion time were used as efficiency/performance measures. Oxygen consumption (VO2) was directly measured for UO on five female participants with a portable indirect calorimetry system, and analysed using descriptive statistics. Stepwise multiple regression analysis were used to determine the contribution of the non-modifiable (age, sex, height) and modifiable characteristics (lean body mass to dead mass ratio (LBM:DM), VO2max corrected for load (L.VO2max), peak force (PF) measured on an isometric mid-thigh pull (IMTP) and medicine ball chest throw distance (Dist) on to the performance of each exercise. Results: LBM:DM and PF were the only factors included in the stepwise regression model for UO, predicting 70% of UO performance (p<0.01). For FC, L.VO2max only was included in the stepwise regression model predicting 54% of FC performance (p<0.01). Sex, age and height were not included in the regression model. The average metabolic cost of UO was 21.4 mL of O2*kg-1*min-1 in female soldiers while wearing PPE Conclusion: This study showed that modifiable factors such as body composition, PF on IMTP and L.VO2max are key contributors to performance on UO and FC performance

Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research

Funding Information: Funding: Odense University Hospital Research Fund (grant no. A3130 and A3329) and The Danish Heart Foundation.The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.publishersversionpublishe

Proteomic Analysis of Anti-Cancerous Scopularide Production by a Marine Microascus brevicaulis Strain and Its UV Mutant

The marine fungus Microascus brevicaulis strain LF580 is a non-model secondary metabolite producer with high yields of the two secondary metabolites scopularides A and B, which exhibit distinct activities against tumour cell lines. A mutant strain was obtained using UV mutagenesis, showing faster growth and differences in pellet formation besides higher production levels. Here, we show the first proteome study of a marine fungus. Comparative proteomics were applied to gain deeper understanding of the regulation of production and of the physiology of the wild type strain and its mutant. For this purpose, an optimised protein extraction protocol was established. In total, 4759 proteins were identified. The central metabolic pathway of strain LF580 was mapped using the KEGG pathway analysis and GO annotation. Employing iTRAQ labelling, 318 proteins were shown to be significantly regulated in the mutant strain: 189 were down- and 129 upregulated. Proteomics are a powerful tool for the understanding of regulatory aspects: The differences on proteome level could be attributed to limited nutrient availability in the wild type strain due to a strong pellet formation. This information can be applied for optimisation on strain and process level. The linkage between nutrient limitation and pellet formation in the non-model fungus M. brevicaulis is in consensus with the knowledge on model organisms like Aspergillus niger and Penicillium chrysogenu

Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics

Funding Information: Funding: This research was partly funded by a “Center of Clinical Excellence” research grant from the Health Region of Southern Denmark to Odense Amyloidosis Center (AmyC). Publisher Copyright: © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically through-out the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril‐forming protein, and its accurate identification is essential to the choice of treat-ment. Mass spectrometry‐based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective inter-pretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model‐assisted method for the unbiased identification of amyloid‐containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid‐containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid‐containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin‐1, vitronectin complement component C9 and also three collagen proteins, as well as the well‐known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid‐negative control biopsies. The trained algorithm performed su-perior in the discrimination of amyloid‐containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected am-yloid‐containing biopsies. Moreover, our method successfully classified amyloidosis patients ac-cording to the subtype in 102 out of 103 blinded cases. Collectively, our model‐assisted approach identified novel amyloid‐associated proteins and demonstrated the use of mass spectrometry‐based data in clinical diagnostics of disease by the unbiased and reliable model‐assisted classification of amyloid deposits and of the specific amyloid subtype.publishersversionpublishe

A comparative analysis of mass spectrometry studies

Funding Information: Funding: R.M. is supported by Fundação para a Ciência e a Tecnologia (CEEC position, 2019–2025 investigator). This article is a result of the projects (iNOVA4Health—UIDB/04462/2020), supported by Lisboa Portugal Regional Operational Programme (Lisboa2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). This work is also funded by FEDER funds through the COMPETE 2020 Programme and National Funds through FCT—Portuguese Foundation for Science and Technology under the projects number PTDC/BTM-TEC/30087/2017 and PTDC/BTM-TEC/30088/2017. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly ex-plored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.publishersversionpublishe

Transcriptome Reprogramming of CD11b(+) Bone Marrow Cells by Pancreatic Cancer Extracellular Vesicles

Funding: JM was supported by “Fundação para a Ciência e a Tecnologia” (PD/BD/105866/2014). This work was supported by the Champalimaud Foundation; grant 751547 from H2020-MSCAIF-2016; EMBO Installation Grant 3921; grant 2017NovPCC1058 from Breast Cancer Now’s Catalyst Programme, which is supported by funding from Pfizer; grant 765492 from H2020-MSCA-ITN-2017; and grant LCF/PR/HR19/52160014 from “La Caixa” Foundation. This work was also funded by FEDER funds through the Operational Programme for Competitiveness Factors-COMPETE and National Funds through the Foundation for Science and Technology (FCT), under the project: PTDC/MED-ONC/28489/2017 (to AM). JP acknowledges the FCT grant SFRH/BD/137319/2018.publishersversionpublishe