The use of cell therapies has recently increased for the treatment of pulmonary diseases. Mesenchymal stem/stromal cells (MSCs) and alveolar type II cells (ATII) are the main cell-based therapies used for the treatment of acute respiratory distress syndrome (ARDS). Many pre-clinical studies have shown that both therapies generate positive outcomes; however, the differences in the efficiency of MSCs or ATII for reducing lung damage remains to be studied. We compared the potential of both cell therapies, administering them using the same route and dose and equal time points in a sustained acute lung injury (ALI) model. We found that the MSCs and ATII cells have similar therapeutic effects when we tested them in a hydrochloric acid and lipopolysaccharide (HCl-LPS) two-hit ALI model. Both therapies were able to reduce proinflammatory cytokines, decrease neutrophil infiltration, reduce permeability, and moderate hemorrhage and interstitial edema. Although MSCs and ATII cells have been described as targeting different cellular and molecular mechanisms, our data indicates that both cell therapies are successful for the treatment of ALI, with similar beneficial results. Understanding direct cell crosstalk and the factors released from each cell will open the door to more accurate drugs being able to target specific pathways and offer new curative options for ARDS.Piezoelectric energy harvesters can transform the mechanical strain into electrical energy. The microelectromechanical transformation device is often composed of piezoelectric cantilevers and has been largely experimented. Most resonances have been developed to harvest nonlinear vibratory energy except for combination resonances. This paper is to analyze several secondary resonances of a cantilever-type piezoelectric energy harvester with a tip magnet. The conventional Galerkin method is improved to truncate the continuous model, an integro-partial differential equation with time-dependent boundary conditions. Then, more resonances on higher-order vibration modes can be obtained. The stable steady-state response is formulated approximately but analytically for the first two subharmonic and combination resonances. The instability boundaries are discussed for these secondary resonances from quadratic nonlinearity. A small damping and a large excitation readily result in an unstable response, including the period-doubling and quasiperiodic motions that can be employed to enhance the voltage output around a wider band of working frequency. Runge-Kutta method is employed to numerically compute the time history for stable and unstable motions. The stable steady-state responses from two different methods agree well with each other. The outcome enriches structural dynamic theory on nonlinear vibration.The scarcity of labelled time-series data can hinder a proper training of deep learning models. This is especially relevant for the growing field of ubiquitous computing, where data coming from wearable devices have to be analysed using pattern recognition techniques to provide meaningful applications. To address this problem, we propose a transfer learning method based on attributing sensor modality labels to a large amount of time-series data collected from various application fields. Using these data, our method firstly trains a Deep Neural Network (DNN) that can learn general characteristics of time-series data, then transfers it to another DNN designed to solve a specific target problem. In addition, we propose a general architecture that can adapt the transferred DNN regardless of the sensors used in the target field making our approach in particular suitable for multichannel data. We test our method for two ubiquitous computing problems-Human Activity Recognition (HAR) and Emotion Recognition (ER)-and compare it a baseline training the DNN without using transfer learning. For HAR, we also introduce a new dataset, Cognitive Village-MSBand (CogAge), which contains data for 61 atomic activities acquired from three wearable devices (smartphone, smartwatch, and smartglasses). Our results show that our transfer learning approach outperforms the baseline for both HAR and ER.Long-term non-progressors (LTNP) and elite controllers (EC) represent spontaneous natural models of efficient HIV-1 response in the absence of treatment. The main purposes of this work are to describe the miRNome of HIV-1 infected patients with different extreme phenotypes and identify potentially altered pathways regulated by differentially expressed (DE) miRNAs. The miRNomes from peripheral blood mononuclear cells (PBMCs) of dual phenotype EC-LTNP or LTNP with detectable viremia and HIV-infected patients with typical progression before and after treatment, were obtained through miRNA-Seq and compared among them. The administration of treatment produces 18 DE miRNAs in typical progressors. LTNP condition shows 14 DE miRNA when compared to typical progressors, allowing LTNP phenotype differentiation.  https://www.selleckchem.com/products/VX-770.html  A set of four miRNAs miR-144-3p, miR-18a-5p, miR-451a, and miR-324 is strongly downregulated in LTNP and related to protein regulation as AKT, mTOR, ERK or IKK, involved in immune response pathways. Deregulation of 28 miRNA is observed between EC-LTNP and viremic-LTNP, including previously described anti-HIV miRNAs miR-29a, associated with LTNP phenotype, and miR-155, targeting different pre-integration complexes such as ADAM10 and TNPO3. A holistic perspective of the changes observed in the miRNome of patients with different phenotypes of HIV-control and non-progression is provided.This study aimed to evaluate the perceptions of dietitians' wellbeing at work before and during the SARS-COV-2 pandemic in Brazil. This cross-sectional study was performed using a previously validated instrument to investigate the wellbeing of dietitians at work in Brazil. The questionnaire on the wellbeing of dietitians was composed of 25 items (with a 5-point scale), characteristics, and questions about the SARS-COV-2 period. The application was carried out with GoogleForms® tool from 26 May to 7 June 2020. The weblink to access the research was sent via email, messaging apps, and social networks. Volunteers were recruited nationwide with the help of the Brazilian Dietitians Councils, support groups, as well as media outreach to reach as many dietitians as possible. Volunteers received, along with the research link, the invitation to participate, as well as the consent form. A representative sample of 1359 dietitians from all the Brazilian regions answered the questionnaire-mostly female (92.5%), Catholic (52.