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Lithium-ion batteries raise safety, environmental, and cost concerns, which mostly arise from their nonaqueous electrolytes. The use of aqueous alternatives is limited by their narrow electrochemical stability window (1.23 volts), which sets an intrinsic limit on the practical voltage and energy output. We report a highly concentrated aqueous electrolyte whose window was expanded to ~3.0 volts with the formation of an electrode-electrolyte interphase. A full lithium-ion battery of 2.3 volts using such an aqueous electrolyte was demonstrated to cycle up to 1000 times, with nearly 100% coulombic efficiency at both low (0.15 coulomb) and high (4.5 coulombs) discharge and charge rates.

Controlling the coherence of chaotic soliton bunch holds the promise to explore novel light-matter interactions and manipulate dynamic events such as rogue waves. However, the coherence control of chaotic soliton bunch remains challenging, as there is a lack of dynamic equilibrium mechanism for stochastic soliton interactions. Here, we develop a strategy to effectively control the coherence of chaotic soliton bunch in a laser. We show that by introducing a lumped fourth-order-dispersion (FOD), the soliton oscillating tails can be formed and generate the potential barriers among the chaotic solitons. The repulsive force between neighboring solitons enabled by the potential barriers gives rise to an alleviation of the soliton fusion/annihilation from stochastic interactions, endowing the capability to control the coherence in chaotic soliton bunch. We envision that this result provides a promising test-bed for a variety of dynamical complexity science and brings new insights into the nonlinear behavior of chaotic laser sources. Controlling the coherence of chaotic waveforms holds the promise to explore novel light‐matter interactions. Here, the authors demonstrate a fourth‐order‐dispersion driven laser that delivers the coherence‐controlled chaotic soliton bunch.

In this article, a novel dynamic system, the fractional-order complex Lorenz system, is proposed. Dynamic behaviors of a fractional-order chaotic system in complex space are investigated for the first time. Chaotic regions and periodic windows are explored as well as different types of motion shown along the routes to chaos. Numerical experiments by means of phase portraits, bifurcation diagrams and the largest Lyapunov exponent are involved. A new method to search the lowest order of the fractional-order system is discussed. Based on the above result, a synchronization scheme in fractional-order complex Lorenz systems is presented and the corresponding numerical simulations demonstrate the effectiveness and feasibility of the proposed scheme.

Conventional approaches to femtosecond (fs) pulse characterization depend on nonlinearities without exception, thereby impeding their utilization in measuring weak fs pulses. Their reliance on iterative phase retrieval or spectrometer-based readout further prevents single-shot full-field characterization over high-repetition-rate fs pulse trains. Here, we present linear spectral shearing interferometry (LSSI) based intelligent single-shot full-field characterization (ISFC) towards high-repetition-rate fs pulse trains, where both intensity and phase of fs pulses are reconstructed in single shot from temporal interferograms through a well-trained fully-connected neural network. The spectral shear in LSSI is created completely by linear effects enabling measurement of weak fs pulses. We validate the proposed method on a megahertz fs pulse train with picojoule-level energy. Moreover, the switching dynamics of a programmable spectral filter are successfully resolved with the proposed method at a megahertz frame rate. We anticipate LSSI-based ISFC to be a particularly powerful tool for characterizing weak ultraviolet fs pulses, and even attosecond pulses with high repetition rates. Approaches to femtosecond pulse characterization have inherent limitations due to their reliance on nonlinearity and iterative retrieval algorithms. Here, the authors implement AI-assisted linear spectral shearing interferometry, showing single-shot characterization of femtosecond pulse trains with high repetition rate and low energies.

Acute kidney injury (AKI) is a common and potential life-threatening disease in patients admitted to hospital, affecting 10%–15% of all hospitalizations and around 50% of patients in the intensive care unit. Severe, recurrent, and uncontrolled AKI may progress to chronic kidney disease or end-stage renal disease. AKI thus requires more efficient, specific therapies, rather than just supportive therapy. Mesenchymal stem cells (MSCs) are considered to be promising cells for cellular therapy because of their ease of harvesting, low immunogenicity, and ability to expand in vitro . Recent research indicated that the main therapeutic effects of MSCs were mediated by MSC-derived extracellular vesicles (MSC-EVs). Furthermore, compared with MSCs, MSC-EVs have lower immunogenicity, easier storage, no tumorigenesis, and the potential to be artificially modified. We reviewed the therapeutic mechanism of MSCs and MSC-EVs in AKI, and considered recent research on how to improve the efficacy of MSC-EVs in AKI. We also summarized and analyzed the potential and limitations of EVs for the treatment of AKI to provide ideas for future clinical trials and the clinical application of MSC-EVs in AKI.

In this study, 145 peaches and nectarines displaying typical brown rot symptoms were collected from multiple provinces in China. A subsample of 26 single-spore isolates were characterized phylogenetically and morphologically to ascertain species. Phylogenetic analysis of internal transcribed spacer (ITS) regions 1 and 2, glyceraldehyde-3-phosphate dehydrogenase (G3PDH), β-tubulin (TUB2) revealed the presence of three distinct Monilinia species. These species included Monilinia fructicola, Monilia mumecola, and a previously undescribed species designated Monilia yunnanensis sp. nov. While M. fructicola is a well-documented pathogen of Prunus persica in China, M. mumecola had primarily only been isolated from mume fruit in Japan. Koch's postulates for M. mumecola and M. yunnanensis were fulfilled confirming pathogenicity of the two species on peach. Phylogenetic analysis of ITS, G3PDH, and TUB2 sequences indicated that M. yunnanensis is most closely related to M. fructigena, a species widely prevalent in Europe. Interestingly, there were considerable differences in the exon/intron structure of the cytochrome b (Cyt b) gene between the two species. Morphological characteristics, including spore size, colony morphology, lesion growth rate, and sporulation, support the phylogenetic evidence suggesting the designation of M. yunnanensis as a new species. A new multiplex PCR method was developed to facilitate the detection of M. yunnanensis and differentiation of Monilinia spp. causing brown rot of peach in China.

Isoprothiolane (IPT) resistance has emerged in Magnaporthe oryzae , due to the long-term usage of IPT to control rice blast in China, yet the mechanisms of the resistance remain largely unknown. Through IPT adaptation on PDA medium, we obtained a variety of IPT-resistant mutants. Based on their EC 50 values to IPT, the resistant mutants were mainly divided into three distinct categories, i.e., low resistance (LR, 6.5 ≤ EC 50 < 13.0 μg/mL), moderate resistance 1 (MR-1, 13.0 ≤ EC 50 < 25.0 μg/mL), and moderate resistance 2 (MR-2, 25.0 ≤ EC 50 < 35.0 μg/mL). Molecular analysis of MoIRR ( Magnaporthe oryzae isoprothiolane resistance related) gene demonstrated that it was associated only with the moderate resistance in MR-2 mutants, indicating that other mechanisms were associated with resistance in LR and MR-1 mutants. In this study, we mainly focused on the characterization of low resistance to IPT in M . oryzae . Mycelial growth and conidial germination were significantly reduced, indicating fitness penalties in LR mutants. Based on the differences of whole genome sequences between parental isolate and LR mutants, we identified a conserved MoVelB gene, encoding the velvet family transcription factor, and genetic transformation of wild type isolate verified that MoVelB gene was associated with the low resistance. Based on molecular analysis, we further demonstrated that the velvet family proteins VelB and VeA were indispensable for IPT toxicity and the deformation of the VelB-VeA-LaeA complex played a vital role for the low IPT-resistance in M . oryzae , most likely through the down-regulation of the secondary metabolism-related genes or CYP450 genes to reduce the toxicity of IPT.

Peach is a popular and important tree fruit widely produced in the world, and the production of high-quality peach fruit does require management of pests and diseases. In this review, major peach diseases from China, Spain, and USA are described in detail for the benefit of producers, consultants, researchers, and other interested parties. Minor diseases of concern in these countries are also described. Current progress on pathogen resistance to major chemical classes of fungicides as well as current resistance management practices are discussed. Specific cultural practices applied in China, Spain, and USA are also described to provide an overview of peach disease management. A ‘Future Outlook’ section is included at the end of this review to highlight the challenges and opportunities for disease management in the future.

Background: Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an accurate machine-learning model to predict EF in intensive care units (ICUs). Methods: Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model. Results: Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction. Conclusions: In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.