Ideally, the inner (the upper or lower arm) current of a modular multilevel converter (MMC) is ideally assumed to be the sum of a dc component and an ac component of the fundamental frequency. ...However, as ac current flows through the submodule (SM) capacitors, the capacitor voltages fluctuate with time. Consequently, the inner current is usually distorted and the peak/RMS value of it is increased compared with the theoretical value. The increased currents will increase power losses and may threaten the safe operation of the power devices and capacitors. This paper proposes a closed-loop method for suppression of the inner current in an MMC. This method is very simple and is implemented in a stationary frame, and no harmonic extraction algorithm is needed. Hence, it can be applied to single-phase or three-phase MMCs. Besides, this method does not influence the balancing of the SM capacitor voltages. Simulation and experimental results show that the proposed method can suppress the peak and RMS values of the inner currents dramatically.
Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as “brain fingerprinting” to identify an individual from a pool of subjects. Both ...common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter‐subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest–rest pair). Furthermore, high‐level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high‐order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions.
Our main contribution is to enhance the individual uniqueness based on a framework applying an autoencoder network. Our approach is validated using six modalities of fMRI (resting‐state 1, resting‐state 2, working memory, motor, language, and emotion) from the HCP data set.
Summary
Arsenic can be biomethylated to form a variety of organic arsenicals differing in toxicity and environmental mobility. Trivalent methylarsenite (MAs(III)) produced in the methylation process ...is more toxic than inorganic arsenite (As(III)). MAs(III) also serves as a primitive antibiotic and, consequently, some environmental microorganisms have evolved mechanisms to detoxify MAs(III). However, the mechanisms of MAs(III) detoxification are not well understood. In this study, we identified an arsenic resistance (ars) operon consisting of three genes, arsRVK, that contribute to MAs(III) resistance in Ensifer adhaerens ST2. ArsV is annotated as an NADPH‐dependent flavin monooxygenase with unknown function. Expression of arsV in the arsenic hypersensitive Escherichia coli strain AW3110Δars conferred resistance to MAs(III) and the ability to oxidize MAs(III) to MAs(V). In the presence of NADPH and either FAD or FMN, purified ArsV protein was able to oxidize both MAs(III) to MAs(V) and Sb(III) to Sb(V). Genes with arsV‐like sequences are widely present in soils and environmental bacteria. Metagenomic analysis of five paddy soils showed the abundance of arsV‐like sequences of 0.12–0.25 ppm. These results demonstrate that ArsV is a novel enzyme for the detoxification of MAs(III) and Sb(III) and the genes encoding ArsV are widely present in soil bacteria.
Review on sepsis mediators, and roles in innate and adaptive immune systems, as well as implications for therapeutics.
Sepsis refers to severe systemic inflammation in response to invading pathogens. ...An overwhelming immune response, as mediated by the release of various inflammatory mediators, can lead to shock, multiple organ damage, and even death. Cytokines, proteases, lipid mediators, gaseous substances, vasoactive peptides, and cell stress markers play key roles in sepsis pathophysiology. Various adhesion molecules and chemokines sequester and activate neutrophils into the target organs, further augmenting inflammation and tissue damage. Although the anti‐inflammatory substances counterbalance proinflammatory mediators, prolonged immune modulation may cause host susceptibility to concurrent infections, thus reflecting enormous challenge toward developing effective clinical therapy against sepsis. To understand the complex interplay between pro‐ and anti‐inflammatory phenomenon in sepsis, there is still an unmet need to study newly characterized mediators. In addition, revealing the current trends of novel mediators will upgrade our understanding on their signal transduction, cross‐talk, and synergistic and immunomodulating roles during sepsis. This review highlights the latest discoveries of the mediators in sepsis linking to innate and adaptive immune systems, which may lead to resolution of many unexplored queries.
Extracellular vesicles (EVs), including exosomes and microvesicles, are present in a variety of bodily fluids, and the concentration of these sub-cellular vesicles and their associated biomarkers ...(proteins, nucleic acids, and lipids) can be used to aid clinical diagnosis. Although ultracentrifugation is commonly used for isolation of EVs, it is highly time-consuming, labor-intensive and instrument-dependent for both research laboratories and clinical settings. Here, we developed an integrated double-filtration microfluidic device that isolated and enriched EVs with a size range of 30-200 nm from urine, and subsequently quantified the EVs via a microchip ELISA. Our results showed that the concentration of urinary EVs was significantly elevated in bladder cancer patients (n = 16) compared to healthy controls (n = 8). Receiver operating characteristic (ROC) analysis demonstrated that this integrated EV double-filtration device had a sensitivity of 81.3% at a specificity of 90% (16 bladder cancer patients and 8 healthy controls). Thus, this integrated device has great potential to be used in conjunction with urine cytology and cystoscopy to improve clinical diagnosis of bladder cancer in clinics and at point-of-care (POC) settings.
•A new uncertainty model is proposed for better describing renewable energies.•A solvable optimization problem is formulated to minimize fuel costs in microgrids.•A two-stage algorithm is developed ...to solve the problem.•The proposed scheme can significantly cut down energy expenses.•Numerical results provide useful insights for future policy making.
An important task of power demand and supply management in microgrids is to maintain a good match between power generation and consumption at the minimum cost. Since the highly fluctuant renewable energies constitute a significant portion of the power resources in microgrids, the microgrid system central controller (MGCC) faces the challenge of effectively utilizing the renewable energies while fulfilling the requirements of customers. To tackle the problem, a novel power demand and supply management scheme is proposed in this paper, which mainly includes three parts as follows. Firstly, a novel uncertainty model is developed to capture the randomness of renewable energy generation which, by introducing a reference distribution according to past observations and empirical knowledge and defining a distribution uncertainty set to confine the uncertainty of renewable energies, allows the renewable energies to fluctuate around the reference distribution. An optimization problem is then formulated to determine the optimal power consumption and generation scheduling for minimizing the fuel cost. Finally, a two-stage optimization approach is proposed to transform and then solve the prime problem. Numerical results indicate that the proposed scheme helps effectively reduce the energy cost. Detailed studies on the impacts of different factors on the proposed scheme provide some interesting insights which shall be useful for policy making for the future MGCC.
Raman spectroscopy and surface‐enhanced Raman spectroscopy (SERS) have been widely applied for characterizing biological samples. Nowadays, it is routine to use plastic products for biological sample ...preparation and analysis during spectral acquisition. However, plastic containers produce undesirable signals severely interfering with the spectra of biological samples, which is neglectable and misleading. Herein, we unveiled the physical and chemical interference of plastic products in the process of Raman spectroscopy characterization. Firstly, we revisited a representative case of interference from polystyrene plastics on normal Raman and SERS spectra of cysteine and demonstrated its physical interference due to misfocusing. Furthermore, we testified SERS spectra of aqueous solution stored in several plastic materials commonly used in biological experiments. The spectra indicate strong spectral interference from the leaching chemicals. To address these problems, we proposed a series of strategies to avoid interference from plastic products in the Raman and SERS characterization of biological samples.
Plastic containers produce undesirable signals severely interfering with the spectra of biological samples, which is neglectable and misleading. In this work, a systematic and comprehensive understanding of how to avoid the impact of plasticware on Raman experiments is proposed to ensure the accuracy and reliability of these measurements.
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