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Microbial natural products form the basis of most of the antibiotics used in the clinic. The vast majority has not yet been discovered, among others because the hidden chemical space is obscured by previously identified (and typically abundant) antibiotics in culture extracts. Efficient dereplication is therefore key to the discovery of our future medicines. Here we present an analytical platform for the efficient identification and prioritization of low abundance bioactive compounds at nanoliter scale, called nanoRAPIDS. NanoRAPIDS encompasses analytical scale separation and nanofractionation of natural extracts, followed by the bioassay of interest, automated mass spectrometry identification, and Global Natural Products Social molecular networking (GNPS) for dereplication. As little as 10 μL crude extract is fractionated into 384 fractions. First, bioactive congeners of iturins and surfactins were identified in Bacillus, based on their bioactivity. Subsequently, bioactive molecules were identified in an extensive network of angucyclines elicited by catechol in cultures of Streptomyces sp. This allowed the discovery of a highly unusual N-acetylcysteine conjugate of saquayamycin, despite low production levels in an otherwise abundant molecular family. These data underline the utility and broad application of the technology for the prioritization of minor bioactive compounds in complex extracts. Microbial natural products are an important source for antibiotic discovery, however, their efficient dereplication remains challenging. Here, the authors develop an analytical pipeline, nanoRAPIDS, to prioritize low abundance bioactive compounds at nanoscale, by integrating the bioassay of interest, automated mass spectrometry identification and GNPS-based dereplication, resulting in the discovery of saquayamycin N from Streptomyces sp. MBT84. [ABSTRACT FROM AUTHOR]

The starting point of the operation and maintenance tasks in photovoltaic plants is the continuous monitoring and supervision of its components. The great amount of registered data requires a major improvement in the ways this information is processed and analyzed to rapidly detect any potential fault, without incurring additional costs. In this paper, a procedure to perform a detailed graphical supported analysis of the operation of photovoltaic installations, based on inverter data, and using a self-developed application, is presented. The program carries out the automated processing of the registered data, providing their access and visualization by means of color maps. These graphs allow a large volume of data set to be simultaneously represented in a readable way, enabling operation and maintenance operators to quickly detect patterns that would require any type of intervention. As a case study, the operation of a grid-connected photovoltaic plant located in southern Spain was studied during a period of three years. The average daily efficiency values of the PV modules and inverters were in the range of 7.6-14.6%, and 73.5-94% respectively. Moreover, the presence of shadings, as well as the hours and days mainly affected by this issue, was easily detected. [ABSTRACT FROM AUTHOR]

This paper describes a procedure for experimental determination of metastable and resonant populations along a surface wave argon plasma column at atmospheric pressure. As modeling work progresses extensively, an experimental database is necessary to confirm these models. Obtained results, within the range of values reported in literature, indicate that densities of metastable and resonant levels grow along the plasma column for positions near the energy coupler device, where the energy of the surface wave which creates discharge is the highest. This behavior is also found for electron density and temperature; however, it is not the same for gradient. Therefore, at atmospheric pressure, populations of 4s levels are not only controlled by collision with electrons from ground state, but the dissociative recombination mechanism is also important, in agreement with results found by other authors using collisional-radiative models. [ABSTRACT FROM AUTHOR]

Farnesoid X receptor (FXR) activation reduces renal inflammation, but the underlying mechanisms remain elusive. Neutrophil extracellular traps (NETs) are webs of DNA formed when neutrophils undergo specialized programmed cell death (NETosis). The signaling lipid sphingosine-1-phosphate (S1P) stimulates NETosis via its receptor on neutrophils. Here, we identify FXR as a negative regulator of NETosis via repressing S1P signaling. We determined the effects of the FXR agonist obeticholic acid (OCA) in mouse models of adenosine phosphoribosyltransferase (APRT) deficiency and Alport syndrome, both genetic disorders that cause chronic kidney disease. Renal FXR activity is greatly reduced in both models, and FXR agonism reduces disease severity. Renal NETosis and sphingosine kinase 1 (Sphk1) expression are increased in diseased mice, and they are reduced by OCA in both models. Genetic deletion of FXR increases Sphk1 expression, and Sphk1 expression correlates with NETosis. Importantly, kidney S1P levels in Alport mice are two-fold higher than controls, and FXR agonism restores them back to baseline. Short-term inhibition of sphingosine synthesis in Alport mice with severe kidney disease reverses NETosis, establishing a causal relationship between S1P signaling and renal NETosis. Finally, extensive NETosis is present in human Alport kidney biopsies (six male, nine female), and NETosis severity correlates with clinical markers of kidney disease. This suggests the potential clinical relevance of the newly identified FXR-S1P-NETosis pathway. In summary, FXR agonism represses kidney Sphk1 expression. This inhibits renal S1P signaling, thereby reducing neutrophilic inflammation and NETosis. NEW & NOTEWORTHY Many preclinical studies have shown that the farnesoid X receptor (FXR) reduces renal inflammation, but the mechanism is poorly understood. This report identifies FXR as a novel regulator of neutrophilic inflammation and NETosis via the inhibition of sphingosine-1-phosphate signaling. Additionally, NETosis severity in human Alport kidney biopsies correlates with clinical markers of kidney disease. A better understanding of this signaling axis may lead to novel treatments that prevent renal inflammation and chronic kidney disease. [ABSTRACT FROM AUTHOR]

Reduction of CO2 emissions is a main target in the future smart grid. This goal is boosting the installation of renewable energy resources (RES), as well as a major consumer engagement that seeks for a more efficient utilization of these resources toward the figure of 'prosumers'. Nevertheless, these resources present an intermittent nature, which requires the presence of an energy storage system and an energy management system (EMS) to ensure an uninterrupted power supply. Moreover, network-related issues might arise due to the increasing power of renewable resources installed in the grid, the storage systems also being capable of contributing to the network stability. However, to assess these future scenarios and test the control strategies, a simulation system is needed. The aim of this paper is to analyze the interaction between residential consumers with high penetration of PV generation and distributed storage and the grid by means of a high temporal resolution simulation scenario based on a stochastic residential load model and PV production records. Results of the model are presented for different PV power rates and storage capacities, as well as a two-level charging strategy as a mechanism for increasing the hosting capacity (HC) of the network. [ABSTRACT FROM AUTHOR]

Actinobacteria are a rich source of bioactive molecules, and genome sequencing has shown that the vast majority of their biosynthetic potential has yet to be explored. However, many of their biosynthetic gene clusters (BGCs) are poorly expressed in the laboratory, which prevents discovery of their cognate natural products. To exploit their full biosynthetic potential, better understanding of the signals that promote the expression of BGCs is needed. Here, we show that the human stress hormone epinephrine (adrenaline) elicits siderophore production by Actinobacteria. Catechol was established as the likely eliciting moiety, since similar responses were seen for catechol and for the catechol-containing molecules dopamine and catechin but not for related molecules. Exploration of the catechol-responsive strain Streptomyces sp. MBT84 using mass spectral networking revealed elicitation of a BGC that produces the angucycline glycosides aquayamycin, urdamycinone B and galtamycin C. Heterologous expression of the catechol-cleaving enzymes catechol 1,2-dioxygenase or catechol 2,3-dioxygenase counteracted the eliciting effect of catechol. Thus, our work identifies the ubiquitous catechol moiety as a novel elicitor of the expression of BGCs for specialized metabolites. The biosynthetic gene clusters of Actinobacteria are poorly expressed in the laboratory, necessitating a better understanding of the signals that promote their expression in order to exploit their full biosynthetic potential. Here, the authors show that the stress hormone epinephrine elicits the expression of biosynthetic gene clusters and influences the metabolism of Streptomyces, with the catechol moiety proving key to this response. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic framework for the optimization of microgrids that has the functionality of providing flexibility services to System Operators (SOs) considering uncertainties in the energy forecast. The methodology is developed with the aim of being applied to complex microgrids composed of different distributed energy resources and hybrid energy storage systems (ESS). The associated optimization problem is operated in two stages: the first one performs a stochastic optimization of the microgrid in order to reserve an up/down regulation capacity with which to deal with the energy forecast uncertainties of the microgrid. The different microgrid devices are optimized by considering their operational costs in order to achieve their optimal operation in the Day-Ahead Market (DM). The second stage is used to re-schedule the initial planning according to the signal request and an economic offer from the SO. The control problem is developed using Stochastic Model Predictive Control (SMPC) techniques and Mixed-Integer Quadratic Programming (MIQP), owing to the presence of logic, integer, mixed and probabilistic variables. The simulation results show that the proposed methodology reduces the risk of undergoing up/down-penalty deviations in the Regulation Service Market (RM), also being able to provide flexibility services to the SOs, despite being subject to uncertainties in the energy forecast carried out for the microgrid. [ABSTRACT FROM AUTHOR]

There is, at present, considerable interest in the storage and dispatchability of photovoltaic (PV) energy, together with the need to manage power flows in real-time. This paper presents a new system, PV-on time, which has been developed to supervise the operating mode of a Grid-Connected Utility-Scale PV Power Plant in order to ensure the reliability and continuity of its supply. This system presents an architecture of acquisition devices, including wireless sensors distributed around the plant, which measure the required information. It is also equipped with a high-precision protocol for synchronizing all data acquisition equipment, something that is necessary for correctly establishing relationships among events in the plant. Moreover, a system for monitoring and supervising all of the distributed devices, as well as for the real-time treatment of all the registered information, is presented. Performances were analyzed in a 400 kW transformation center belonging to a 6.1 MW Utility-Scale PV Power Plant. In addition to monitoring the performance of all of the PV plant's components and detecting any failures or deviations in production, this system enables users to control the power quality of the signal injected and the influence of the installation on the distribution grid. [ABSTRACT FROM AUTHOR]

Objective--To evaluate the duration of effects on movement patterns of horses after sedation with equipotent doses of xylazine hydrochloride, detomidine hydrochloride, or romifidine hydrochloride and determine whether accelerometry can be used to quantify differences among drug treatments. Animals--6 healthy horses. Procedures--Each horse was injected IV with saline (0.9% NaCI) solution (10 mL), xylazine diluted in saline solution (0.5 mg/kg), detomidine diluted in saline solution (0.01 mg/kg), or romifidine diluted in saline solution (0.04 mg/kg) in random order. A triaxial accelerometric device was used for gait assessment 15 minutes before and 5, 15, 30, 45, 60, 75, 90, 105, and 120 minutes after each treatment. Eight variables were calculated, including speed, stride frequency, stride length, regularity, dorsoventral power, propulsive power, mediolateral power, and total power; the force of acceleration and 3 components of power were then calculated. Results--Significant differences were evident in stride frequency and regularity between treatments with saline solution and each α2-adrenoceptor agonist drug; in speed, dorsoventral power, propulsive power, total power, and force values between treatments with saline solution and detomldine or romifidine; and in mediolateral power between treatments with saline solution and detomidine. Stride length did not differ among treatments. Conclusions and Clinical Relevance--Accelerometric evaluation of horses administered α2-adrenoceptor agonist drugs revealed more prolonged sedative effects of romifidine, compared with effects of xylazine or detomidine. Accelerometry could be useful in assessing the effects of other sedatives and analgesics. Accelerometric data may be helpful in drug selection for situations in which a horse's balance and coordination are important. [ABSTRACT FROM AUTHOR]