Polymeric elastomers play an increasingly important role in the development of stretchable electronics. A highly demanded elastic matrix is preferred to own not only excellent mechanical properties, ...but also additional features like high toughness and fast self‐healing. Here, a polyurethane (DA‐PU) is synthesized with donor and acceptor groups alternately distributed along the main chain to achieve both intra‐chain and inter‐chain donor‐acceptor self‐assembly, which endow the polyurethane with toughness, self‐healing, and, more interestingly, thermal repair, like human muscle. In detail, DA‐PU exhibits an amazing mechanical performance with elongation at break of 1900% and toughness of 175.9 MJ m−3. Moreover, it shows remarkable anti‐fatigue and anti‐stress relaxation properties as manifested by cyclic tensile and stress relaxation tests, respectively. Even in case of large strain deformation or long‐time stretch, it can almost completely restore to original length by thermal repair at 60 °C in 60 s. The self‐healing speed of DA‐PU is gradually enhanced with the increasing temperature, and can be 1.0–6.15 µm min−1 from 60 to 80 °C. At last, a stretchable and self‐healable capacitive sensor is constructed and evaluated to prove that DA‐PU matrix can ensure the stability of electronics even after critical deformation and cut off.
A highly desirable elastic matrix is preferred to own not only excellent mechanical properties, but also additional features high toughness and fast self‐healing. Here, a polyurethane is synthesized with donor and acceptor groups alternately distributed along the main chain to achieve both intra‐chain and inter‐chain donor‐acceptor self‐assembly, which endow the polyurethane with toughness, self‐healing, and thermal repair, like muscle.
At the end of 2019, a novel coronavirus outbreak causative organism has been subsequently designated the 2019 novel coronavirus (2019-nCoV). The effectiveness of adjunctive glucocorticoid therapy in ...the management of 2019-nCoV-infected patients with severe lower respiratory tract infections is not clear, and warrants further investigation.
The present study will be conducted as an open-labeled, randomized, controlled trial. We will enrol 48 subjects from Chongqing Public Health Medical Center. Each eligible subject will be assigned to an intervention group (methylprednisolone via intravenous injection at a dose of 1-2 mg/kg/day for 3 days) or a control group (no glucocorticoid use) randomly, at a 1:1 ratio. Subjects in both groups will be invited for 28 days of follow-up which will be scheduled at four consecutive visit points. We will use the clinical improvement rate as our primary endpoint. Secondary endpoints include the timing of clinical improvement after intervention, duration of mechanical ventilation, duration of hospitalization, overall incidence of adverse events, as well as rate of adverse events at each visit, and mortality at 2 and 4 weeks.
The present coronavirus outbreak is the third serious global coronavirus outbreak in the past two decades. Oral and parenteral glucocorticoids have been used in the management of severe respiratory symptoms in coronavirus-infected patients in the past. However, there remains no definitive evidence in the literature for or against the utilization of systemic glucocorticoids in seriously ill patients with coronavirus-related severe respiratory disease, or indeed in other types of severe respiratory disease. In this study, we hope to discover evidence either supporting or opposing the systemic therapeutic administration of glucocorticoids in patients with severe coronavirus disease 2019.
ClinicalTrials.gov, ChiCTR2000029386, http://www.chictr.org.cn/showproj.aspx?proj=48777.
Uncertain differential equation is a type of differential equation driven by canonical process. In this paper, a concept of -path to uncertain differential equation is first introduced, which is a ...type of deterministic function that solves an associate ordinary differential equation. Then, a numerical method is designed for solving uncertain differential equations, which essentially solves each -path and produces an inverse uncertainty distribution of the solution. To illustrate the efficiency of the numerical method, several examples are given.
Molecular quantum gases (that is, ultracold and dense molecular gases) have many potential applications, including quantum control of chemical reactions, precision measurements, quantum simulation ...and quantum information processing
. For molecules, to reach the quantum regime usually requires efficient cooling at high densities, which is frequently hindered by fast inelastic collisions that heat and deplete the population of molecules
. Here we report the preparation of two-dimensional Bose-Einstein condensates (BECs) of spinning molecules by inducing pairing interactions in an atomic condensate near a g-wave Feshbach resonance
. The trap geometry and the low temperature of the molecules help to reduce inelastic loss, ensuring thermal equilibrium. From the equation-of-state measurement, we determine the molecular scattering length to be + 220(±30) Bohr radii (95% confidence interval). We also investigate the unpairing dynamics in the strong coupling regime and find that near the Feshbach resonance the dynamical timescale is consistent with the unitarity limit. Our work demonstrates the long-sought transition between atomic and molecular condensates, the bosonic analogue of the crossover from a BEC to a Bardeen-Cooper-Schrieffer (BCS) superfluid in a Fermi gas
. In addition, our experiment may shed light on condensed pairs with orbital angular momentum, where a novel anisotropic superfluid with non-zero surface current is predicted
, such as the A phase of
He.
Abstract
The dbPTM (http://dbPTM.mbc.nctu.edu.tw/) has been maintained for over 10 years with the aim to provide functional and structural analyses for post-translational modifications (PTMs). In ...this update, dbPTM not only integrates more experimentally validated PTMs from available databases and through manual curation of literature but also provides PTM-disease associations based on non-synonymous single nucleotide polymorphisms (nsSNPs). The high-throughput deep sequencing technology has led to a surge in the data generated through analysis of association between SNPs and diseases, both in terms of growth amount and scope. This update thus integrated disease-associated nsSNPs from dbSNP based on genome-wide association studies. The PTM substrate sites located at a specified distance in terms of the amino acids encoded from nsSNPs were deemed to have an association with the involved diseases. In recent years, increasing evidence for crosstalk between PTMs has been reported. Although mass spectrometry-based proteomics has substantially improved our knowledge about substrate site specificity of single PTMs, the fact that the crosstalk of combinatorial PTMs may act in concert with the regulation of protein function and activity is neglected. Because of the relatively limited information about concurrent frequency and functional relevance of PTM crosstalk, in this update, the PTM sites neighboring other PTM sites in a specified window length were subjected to motif discovery and functional enrichment analysis. This update highlights the current challenges in PTM crosstalk investigation and breaks the bottleneck of how proteomics may contribute to understanding PTM codes, revealing the next level of data complexity and proteomic limitation in prospective PTM research.
Robotics education has received an increasing attention in recent years as a means to build students' motivation, team collaboration skills, and other valuable 21st century competencies. Yet there is ...a lack of experimental studies to investigate and identify strategies to facilitate robotics education. This study adopted a 2 × 2 quasi‐experimental design to investigate two strategies: the incorporation of augmented reality (AR) and the introduction of competition in robotics activities. Students' robotics task performance, team collaboration processes, 21st century learning competencies and learning motivation were measured as dependent variables. The results indicated that AR significantly improved students' motivation, team processes, and 21st century competencies. Moreover, the effects of AR were more pronounced with the competition groups. Implications are drawn to provide guidelines on the use of AR and competition in robotics education.
Lay Description
What is currently known about the subject matter?
Robots in education have been recognized as an effective approach to enhance learning competencies.
The benefits of AR in learning gains and motivation.
What their paper adds to this?
To design robotics curricula that can be used in middle schools.
To understand what middle school students can learn from the robotics curricula.
The implications of study findings for practitioners
How to utilize AR in robotics education.
What are the structure and pace of robotics curricula for middle school students?
3 SARS-like coronaviruses have been isolated from Chinese horseshoe bats, and may attach to and utilize the angiotensin-converting enzyme 2 receptor in human lower respiratory tract cells to gain ...entry into these cells, thus facilitating transmission to, and initiating infection in, humans. The complete ban on market trading and sale of wild game meat in China on January 26th, 2020 will help prevent zoonotic transmission of 2019-nCoV in the current epidemic and, to a certain degree, help prevent emergence of new zoonotic infections. Each preventable zoonotic outbreak costs the country of origin and the world vast amounts of money and resources, and an inestimable cost in human lives, and if emerging zoonotic outbreaks can be prevented by severely limiting human exposure to wild animals and their trade, then effective measures to ensure that this occurs should be implemented by regulatory government authorities globally as soon as it is practicable. Additionally, urgent international attention to and curtailment of the hitherto unregulated and commonplace trade in wild game, meat and products is essential if a repeat of the human and economic loss, and public fear and social disruption wreaked by the current 2019-nCoV outbreak is to be avoided in the future.
Methicillin-resistant Staphylococcus aureus (MRSA), one of the most important clinical pathogens, conducts an increasing number of morbidity and mortality in the world. Rapid and accurate strain ...typing of bacteria would facilitate epidemiological investigation and infection control in near real time. Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry is a rapid and cost-effective tool for presumptive strain typing. To develop robust method for strain typing based on MALDI-TOF spectrum, machine learning (ML) is a promising algorithm for the construction of predictive model. In this study, a strategy of building templates of specific types was used to facilitate generating predictive models of methicillin-resistant Staphylococcus aureus (MRSA) strain typing through various ML methods. The strain types of the isolates were determined through multilocus sequence typing (MLST). The area under the receiver operating characteristic curve (AUC) and the predictive accuracy of the models were compared. ST5, ST59, and ST239 were the major MLST types, and ST45 was the minor type. For binary classification, the AUC values of various ML methods ranged from 0.76 to 0.99 for ST5, ST59, and ST239 types. In multiclass classification, the predictive accuracy of all generated models was more than 0.83. This study has demonstrated that ML methods can serve as a cost-effective and promising tool that provides preliminary strain typing information about major MRSA lineages on the basis of MALDI-TOF spectra.
Macrophages undergoing M1- versus M2-type polarization differ significantly in their cell metabolism and cellular functions. Here, global quantitative time-course proteomics and phosphoproteomics ...paired with transcriptomics provide a comprehensive characterization of temporal changes in cell metabolism, cellular functions, and signaling pathways that occur during the induction phase of M1- versus M2-type polarization. Significant differences in, especially, metabolic pathways are observed, including changes in glucose metabolism, glycosaminoglycan metabolism, and retinoic acid signaling. Kinase-enrichment analysis shows activation patterns of specific kinases that are distinct in M1- versus M2-type polarization. M2-type polarization inhibitor drug screens identify drugs that selectively block M2- but not M1-type polarization, including mitogen-activated protein kinase kinase (MEK) and histone deacetylase (HDAC) inhibitors. These datasets provide a comprehensive resource to identify specific signaling and metabolic pathways that are critical for macrophage polarization. In a proof-of-principle approach, we use these datasets to show that MEK signaling is required for M2-type polarization by promoting peroxisome proliferator-activated receptor-γ (PPARγ)-induced retinoic acid signaling.
Display omitted
•Time-course quantitative proteomics/phosphoproteomics of polarizing macrophages•Distinct changes in cell metabolism and signaling in M1- and M2-type macrophages•Chemical screens identify pharmacologic M2-type macrophage polarization inhibitors•MEK induces PPARγ that promotes M2-type polarization by activating RA signaling
He et al. provide a detailed characterization of dynamic temporal changes in cell signaling and metabolism during macrophage polarization by using quantitative time-course proteomics and phosphoproteomics and identify pharmacologic inhibitors of M2-type macrophage polarization. These data uncover a critical role of MEK/ERK signaling for PPARγ/retinoic acid-induced M2-type macrophage polarization.
This study mainly focuses on the development of two newly designed control strategies including an adaptive control scheme and a fuzzy neural network (FNN) control system for a single-stage boost ...inverter. First, the dynamic model of a single-stage boost inverter is analyzed and built for the later control manipulation. Then, a model-based adaptive control scheme and a model-free FNN control system with varied learning rates are designed sequentially. The effectiveness of the proposed adaptive control scheme and the proposed FNN control system is verified by experimental results of a 1-kW single-stage boost inverter prototype, and their merits are indicated in comparison with a traditional double-loop proportional-integral (PI) control framework. Experimental results show that the superior FNN control system has significant improvements of 45.2% total harmonic distortion and 37.1% normalized mean square error compared to the conventional double-loop PI control framework under nonlinear loads.
PDF
