Persistence of instanton connections in chemical reactions with time dependent rates

The evolution of a system of chemical reactions can be studied, in the eikonal approximation, by means of a Hamiltonian dynamical system. The fixed points of this dynamical system represent the different states in which the chemical system can be found, and the connections among them represent instantons or optimal paths linking these states. We study the relation between the phase portrait of the Hamiltonian system representing a set of chemical reactions with constant rates and the corresponding system when these rates vary in time. We show that the topology of the phase space is robust for small time-dependent perturbations in concrete examples and state general results when possible. This robustness allows us to apply some of the conclusions on the qualitative behavior of the autonomous system to the time-dependent situation

An Aphid Effector Targets Trafficking Protein VPS52 in a Host-Specific Manner to Promote Virulence

Plant- and animal-feeding insects secrete saliva inside their hosts, containing effectors, which may promote nutrient release and suppress immunity. Although for plant pathogenic microbes it is well established that effectors target host proteins to modulate host cell processes and promote disease, the host cell targets of herbivorous insects remain elusive. Here, we show that the existing plant pathogenic microbe effector paradigm can be extended to herbivorous insects in that effector-target interactions inside host cells modify critical host processes to promote plant susceptibility. We showed that the effector Mp1 from Myzus persicae associates with the host Vacuolar Protein Sorting Associated Protein52 (VPS52). Using natural variants, we provide a strong link between effector virulence activity and association with VPS52, and show that the association is highly specific to M. persicae-host interactions. Also, coexpression of Mp1, but not Mp1-like variants, specifically with host VPS52s resulted in effector relocalization to vesicle-like structures that associate with prevacuolar compartments. We show that high VPS52 levels negatively impact virulence, and that aphids are able to reduce VPS52 levels during infestation, indicating that VPS52 is an important virulence target. Our work is an important step forward in understanding, at the molecular level, how a major agricultural pest promotes susceptibility during infestation of crop plants. We give evidence that an herbivorous insect employs effectors that interact with host proteins as part of an effective virulence strategy, and that these effectors likely function in a species-specific manner

Sensing Fluid-Shear Stress in the Endothelial System with a Special Emphasis on the Primary Cilium

Fluid shear stress (FSS) is able to generate phenotypic changes in the cells in direct contact with the strain force. In order to detect and transduce FSS into intracellular pathways, biological systems use a specific set of sensors, called mechanosensors. The process involves the conversion of the mechanical force into a chemical or electrical signal. Primary cilium is a non-motile organelle that emanates from the cell surface of most mammalian cell types that act as a mechanosensor. Increasing evidence suggests that primary cilia are key coordinators of signaling pathways in tissue homeostasis and when defective may cause human diseases and developmental disorders. Here, we will describe the endothelial primary cilium as a mechanotransductory organelle sensing FSS. To fulfill this function, primary cilium requires the localization of mechanoproteins, polycystin-1 and -2, in their membrane and the structural gene product, polaris. Physiologically, deflection of primary cilium increases the intracellular calcium concentration triggering a signaling pathway that leads to nitric oxide (NO) formation and vasodilation. Additionally, ciliopathies, such as polycystic kidney disease and atherosclerosis, will also be discussed. We also analyze available information regarding a trio of membrane receptors involved in FSS sensing and transducing such as vascular endothelial growth factor receptors (VEGFRs) and its coreceptor neuropilin (NRP), as well as purinergic receptors (P2Y2). Whether or not they modulate, the primary cilium role in sensing FSS is poorly understood. This chapter highlights the main relevance of primary cilium in sensing blood flow, although exact mechanisms are not fully known yet

A Practical Approach to the Design of a Highly Efficient PSFB DC-DC Converter for Server Applications

The phase shift full bridge (PSFB) is a widely known isolated DC-DC converter topology commonly used in medium to high power applications, and one of the best candidates for the front-end DC-DC converter in server power supplies. Since the server power supplies consume an enormous amount of power, the most critical issue is to achieve high efficiency. Several organizations promoting electrical energy efficiency, like the 80 PLUS, keep introducing higher efficiency certifications with growing requirements extending also to light loads. The design of a high efficiency PSFB converter is a complex problem with many degrees of freedom which requires of a sufficiently accurate modeling of the losses and of e cient design criteria. In this work a losses model of the converter is proposed as well as design guidelines for the efficiency optimization of PSFB converter. The model and the criteria are tested with the redesign of an existing reference PSFB converter of 1400 W for server applications, with wide input voltage range, nominal 400 V input and 12 V output; achieving 95.85% of efficiency at 50% of the load. A new optimized prototype of PSFB was built with the same specifications, achieving a peak efficiency of 96.68% at 50% of the load.This research was financed by Infineon Technologies AG

Biological production of H2, CH4 and CO2 in the deep subsurface of the iberian pyrite belt

Most of the terrestrial deep subsurfaces are oligotrophic environments in which some gases, mainly H2, CH4 and CO2, play an important role as energy and/or carbon sources. In this work, we assessed their biotic and abiotic origin in samples from subsurface hard-rock cores of the Iberian Pyrite Belt (IPB) at three different depths (414, 497 and 520 m). One set of samples was sterilized (abiotic control) and all samples were incubated under anaerobic conditions. Our results showed that H2, CH4 and CO2 remained low and constant in the sterilized controls while their levels were 4, 4.1 and 2.5 times higher respectively, in the unsterilized samples compared to the abiotic controls. The δ13CCH4-values measured in the samples (range −31.2 to −43.0 ‰) reveals carbon isotopic signatures that are within the range for biological methane production. Possible microorganisms responsible for the biotic production of the gases were assessed by CARD-FISH. The analysis of sequenced genomes of detected microorganisms within the subsurface of the IPB allowed to identify possible metabolic activities involved in H2 (Rhodoplanes, Shewanella and Desulfosporosinus), CH4 (Methanobacteriales) and CO2 production. The obtained results suggest that part of the H2, CH4 and CO2 detected in the deep subsurface has a biological originAuthors thank all the IPBSL project team members for facilitating access to the samples. This work was supported by MICINN grant PID2019‐1048126GB‐I00. Thanks are due to A. I. Morato for her valuable technical assistanc

Graph-Based Permutation Patterns for the Analysis of Task-Related fMRI Signals on DTI Networks in Mild Cognitive Impairment

Permutation Entropy ($PE$) is a powerful nonlinear analysis technique for univariate time series. Very recently, Permutation Entropy for Graph signals ($PE_G$) has been proposed to extend $PE$ to data residing on irregular domains. However, $PE_G$ is limited as it provides a single value to characterise a whole graph signal. Here, we introduce a novel approach to evaluate graph signals at the vertex level: graph-based permutation patterns. Synthetic datasets show the efficacy of our method. We reveal that dynamics in graph signals, undetectable with $PE_G$, can be discerned using our graph-based permutation patterns. These are then validated in the analysis of DTI and fMRI data acquired during a working memory task in mild cognitive impairment, where we explore functional brain signals on structural white matter networks. Our findings suggest that graph-based permutation patterns change in individual brain regions as the disease progresses. Thus, graph-based permutation patterns offer promise by enabling the granular scale analysis of graph signals.Comment: 5 pages, 5 figures, 1 tabl

Classification enhancement for post-stroke dementia using fuzzy neighborhood preserving analysis with QR-decomposition

The aim of the present study was to discriminate the electroencephalogram (EEG) of 5 patients with vascular dementia (VaD), 15 patients with stroke-related mild cognitive impairment (MCI), and 15 control normal subjects during a working memory (WM) task. We used independent component analysis (ICA) and wavelet transform (WT) as a hybrid preprocessing approach for EEG artifact removal. Three different features were extracted from the cleaned EEG signals: spectral entropy (SpecEn), permutation entropy (PerEn) and Tsallis entropy (TsEn). Two classification schemes were applied - support vector machine (SVM) and k-nearest neighbors (kNN) - with fuzzy neighborhood preserving analysis with QR-decomposition (FNPAQR) as a dimensionality reduction technique. The FNPAQR dimensionality reduction technique increased the SVM classification accuracy from 82.22% to 90.37% and from 82.6% to 86.67% for kNN. These results suggest that FNPAQR consistently improves the discrimination of VaD, MCI patients and control normal subjects and it could be a useful feature selection to help the identification of patients with VaD and MCI

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Stroke-related mild cognitive impairment detection during working memory tasks using EEG signal processing

The aim of the present study was to reveal markers from the electroencephalography (EEG) using approximation entropy (ApEn) and permutation entropy (PerEn). EEGs' of 15 stroke-related patients with mild cognitive impairment (MCI) and 15 control healthy subjects during a working memory (WM) task have EEG artifacts were removed using a wavelet (WT) based method. A t-test (p <; 0.05) was used to test the hypothesis that the irregularity (ApEn and PerEn) in MCIs was reduced in comparison with control subjects. ApEn and PerEn showed reduced irregularity in the EEGs of MCI patients. Therefore, ApEn and PerEn could be used as markers associated with MCI detection and identification and the EEG could be a valuable tool for inspecting the background activity in the identification of patients with MCI