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Salt stress is a major abiotic factor that negatively impacts the growth, performance, and secondary metabolite production in pepper (Capsicum annuum L.) plants. Brassinosteroids (BRs) play a crucial role in enhancing plant tolerance to abiotic stress, yet their potential in mitigating salt stress in pepper plants, particularly by promoting sugar and glycine betaine accumulation, remains underexplored. In this study, we investigated the effects of the foliar application of 2,4-epibrassinolide (EBR) on salt-stressed pepper seedlings. Our findings revealed that EBR treatment significantly increased the levels of proline, sugar, and glycine betaine under salt stress compared to untreated controls. Moreover, EBR enhanced the antioxidant defense mechanisms in pepper seedlings by increasing sugar and glycine betaine levels, which contributed to the reduction of reactive oxygen species (ROS) and malondialdehyde (MDA) accumulation. [ABSTRACT FROM AUTHOR]

Nonlinear dynamical systems in such applications as design and control often depend on a set of parameters, resulting in parameterized dynamical systems. Establishing mathematical models of such parameterized systems is essential for numerical simulations, where first-principle models are often not affordable although they are desired whenever possible. Data-driven modeling is an important alternative with a trade-off among modeling difficulty, model complexity, and computational efficiency. However, data-driven modeling of such parameterized systems is challenging because not only nonlinear dynamics but also their parametric dependence need to be identified from data; especially for bifurcation systems where small changes in the parameters may cause drastic, qualitative changes of dynamical behaviors. Thus, data-driven modeling of bifurcation systems can be intractable. This work presents a novel method for data-driven modeling of bifurcation systems by transforming the intractable modeling problem into two tractable steps: first, learning an intermediate meta-model that is general for a wide range of bifurcation parameter values; subsequently, using this meta-model to perform efficient adaptation to target/new bifurcation values. Particularly, we leverage the meta-learning to guide the intermediate model to learn to generalize over the bifurcation parameter values, yielding a meta-model which allows a fast and data-efficient adaptation (generalization) to any new bifurcation parameter values. We conduct numerical experiments to validate the presented method on three classic bifurcation systems. It is observed that the adaptation (generalization) to new parameter values enabled by the obtained meta-model is faster than the direct modeling for the new parameter values from scratch. Furthermore, the meta-model-based adaptation yields more accurate models that allow long-term future-state prediction. Finally, we discuss the limitations of this work and potential future studies needed. [ABSTRACT FROM AUTHOR]

Tension force is a crucial indicator in reflecting the stressing state of old cables during the cable replacement process. Even though the vibration‐based method is popular in the cable force identification due to its simple calculation process and low cost, the frequency is hard to be recognized with both high time and frequency resolutions attributed to the Heisenberg uncertainty principle, which hinders its application in identifying time‐varying cable force. In this paper, a novel quasi‐real‐time cable force identification method is presented based on a quasi‐ideal time‐frequency analysis method called multi‐synchrosqueezing transform (MSST), by which the cable frequencies can be identified with appreciable time‐frequency resolution. To achieve the identification in a real‐time manner, an Automatic Frequency Order Identification (AFOI) algorithm is developed to recognize the frequency order automatically depending on the MSST result, in which the interference of fake modes and omitted modes to the identification of the actual frequency order is eliminated to a large extent. The performance of the proposed AFOI algorithm and the quasi‐real‐time cable force identification method is evaluated on a practical cable replacement engineering case. Results show that the correct orders of the multiple frequencies received from MSST can be identified along the time domain, which demonstrates the effectiveness of the proposed method. The variation of the tension force of not only the replaced cable but also its neighbor cables is estimated with desired time‐frequency resolution, which promotes the safety state assessment of a cable in a real‐time manner during the replacement process. [ABSTRACT FROM AUTHOR]

The improper treatment of crop straw not only leads to resource wastage but also adversely impacts the ecological environment. However, the application of microorganisms can accelerate the decomposition of crop straw and improve its utilization. In this study, cellulose-degrading microbial strains were isolated from naturally decayed corn straw and screened using Congo red staining, along with assessing variations in carboxymethyl cellulase (CMCase) activity, filter paper enzyme (FPase) activity and β-glucosidase (β-Gase) activity, as well as the degradation rate. The eight strains, namely Neurospora intermedia isolate 29 (A1), Streptomyces isolate FFJC33 (A2), Gibberella moniliformis isolate FKCB-009 (A3), Fusarium fujikuroi isolate EFS3(2) (A4), Fusarium Fujikuroi isolate FZ04 (A5), Lysine bacillus macroides strain LNHL43 (B1), Bacillus subtilis strain MPF30 (B2) and Paenibacilli lautus strain ALEB-P1 (C), were identified and selected for microbial strain consortium design based on their high activities of CMCase, FPase and β-Gase. The fungi, bacteria and actinomycete strains were combined without antagonistic effects on corn straw decomposition. The results showed the A2B2 combination had a significantly higher FPase at 55.44 U/mL and β-Gase at 25.73 U/mL than the other two strain combinations (p < 0.05). Additionally, the degradation rate of this combination was 40.33%, which was considerably higher than that of the other strains/consortia. The strain combination A4B2C also had superior enzyme activity, including CMCase with a value of 35.03 U/mL, FPase with a value of 63.59 U/mL and β-Gase with a value of 26.15 U/mL, which were significantly different to those of the other three strain combinations (p < 0.05). Furthermore, seven single microbial strains with high cellulase activities were selected to construct various microbial consortiums for in situ composting in order to evaluate their potential. Taken as a whole, the results of composting, including temperature, moisture content, pH, E4/E6 value and seed germination index, indicated that the microbial strain consortium consisting of Neurospora intermediate isolate 29, Fusarium fujikuroi isolate EFS3(2), Fusarium fujikuroi isolate FZ04, Lysinibacillus macrolides, Lysinibacillus sphaericus, Bacillus subtilis and Paenibacillus lautus was advantageous for corn straw decomposition and yielded high-quality compost. The screened flora was able to effectively degrade corn straw. This study provides a novel solution for the construction of a microbial consortium for the composting of corn straw. [ABSTRACT FROM AUTHOR]

Background: Peritoneal carcinomatosis was the main reason leading to gastric cancer (GC)-related death. We aimed to explore the roles of dysregulated microRNAs (miRNAs) and related immune regulation activities in GC-associated malignant ascites. Methods: GSE126399 were downloaded from GEO database. Differentially expressed miRNAs in GC ascites samples was firstly screened, and critical miRNAs were further investigated by LASSO (least absolute shrinkage and selection operator) logistic regression and random forest (RF) algorithm. Receiver operating characteristic of critical miRNAs was also constructed. Moreover, functional analysis, immune cell infiltration associated with differentially expressed mRNAs were further analyzed. After selecting key modules by weighted gene co-expression network analysis, mRNAs related with survival performance and transcription factor (TF)-miRNA-mRNA network were constructed. Results: Hsa-miR-181b-5p was confirmed as critical differentially expressed miRNAs in GC ascites. Then, the tumor samples were divided into high- and low- expression groups divided by mean expression levels of hsa-miR-181b-5p, and subjects with high hsa-miR-181b-5p levels had better survival outcomes. In total, 197 differentially expressed mRNAs associated with hsa-miR-181b-5p levels were obtained, and these mRNAs were mainly enriched in muscle activity and vascular smooth muscle contraction. Hsa-miR-181b-5 was positively related with activated CD4 T cells and negatively related with eosinophil. 17 mRNAs were selected as mRNAs significantly related with prognosis of GC, such as PDK4 and RAMP1. Finally, 75 TF-miRNA-mRNA relationships were obtained, including 15 TFs, hsa-miR-181b-5p, and five mRNAs. Conclusion: Our data suggest that the differentially expressed hsa-miR-181b-5p in ascites samples of GC patients may be a valuable prognostic marker and a potential target for therapeutic intervention, which should be validated in the near future. [ABSTRACT FROM AUTHOR]

Due to the widespread use of intensive cropping patterns, the problem of continuous cropping obstacle, which is dominated by autotoxicity, has been becoming more and more prominent. Although many methods have been proposed to overcome the continuous cropping obstacle of cucumber, no study has reported the screening and evaluation of cucumber germplasm resistant to autotoxicity. In this study, 28 physiological indices related to the cucumber bud stage under cinnamic acid (CA) treatment were determined. In total, 45 cucumber cultivars were classified into three groups using principal component analysis and cluster analysis, and a model for evaluating cucumber resistance to autotoxicity was developed. The evaluation model was validated using autotoxicity-tolerant and non-autotoxicity-tolerant cultivars. The results showed that the growth of non-autotoxicity-tolerant cultivars was significantly inhibited compared to autotoxicity-tolerant cultivars. This indicated that the evaluation model of cucumber autotoxicity tolerance is reliable. The results of this study provide a valuable reference for the application of cucumber autotoxicity-tolerant germplasm resources and the development of autotoxicity-tolerant genes. [ABSTRACT FROM AUTHOR]

The bismuth sulfide nanostructure has become a promising gas sensing material thanks to its exceptional intrinsic properties. However, pristine Bi2S3 as a room-temperature sensing material cannot achieve the highly sensitive detection of ppb-level NO2 gas. Herein, 1D nanorods with self-assembled hierarchical Bi2S3 nanostructures were obtained via a simple hydrothermal process. The as-prepared hierarchical Bi2S3 nanostructures exhibited outstanding NO2 sensing behaviors, such as a high response value (Rg/Ra = 5.8) and a short response/recovery time (τ90 = 28/116 s) upon exposure to 1 ppm NO2. The limit of detection of hierarchical Bi2S3 was down to 50 ppb. Meanwhile, the sensor exhibited excellent selectivity and humidity tolerance. The improved NO2 sensing properties were associated with the self-assembled hierarchical nanostructures, which provided a rich sensing active surface and accelerated the diffusion and adsorption/desorption processes between NO2 molecules and Bi2S3 materials. Additionally, the sensing response of hierarchical Bi2S3 nanostructures is much higher at 100% N2 atmosphere, which is different from the chemisorption oxygen model. [ABSTRACT FROM AUTHOR]

Nonlinear dynamical systems in applications such as design and control generally depend on a set of variable parameters that represent system geometry, boundary conditions, material properties, etc. Modeling of such parameterized nonlinear systems from first principles is often challenging due to insufficient knowledge of the underlying physics (e.g., damping), especially when the physics-associated parameters are considered to be variable. In this study, we present a dynamics-embedded conditional generative adversarial network (Dyn-cGAN) for data-driven modeling and identification of parameterized nonlinear dynamical systems, capturing transient dynamics conditioned on the system parameters. Specifically, a dynamics block is embedded in a modified conditional generative adversarial network, thereby identifying temporal dynamics and its dependence on the system parameters, simultaneously. The data-driven Dyn-cGAN model is learned to perform long-term prediction of the dynamical response of a parameterized nonlinear dynamical system (equivalently a family of nonlinear systems with different parameter values), given any initial conditions and system parameter values. The capability of the presented Dyn-cGAN is evaluated by numerical studies on a variety of parameterized nonlinear dynamical systems including pendulums, Duffing, and Lorenz systems, considering various combinations of initial conditions and system (physical) parameters as inputs and different ranges of nonlinear dynamical behaviors including chaotic. It is observed that the presented data-driven framework is reasonably effective for predictive modeling and identification of parameterized nonlinear dynamical systems. Further analysis also indicates that its prediction accuracy degrades gracefully as the complexity of the nonlinear system increases, such as strongly nonlinear systems and systems with multiple parameters changing. The limitations of this work and potential future work are also discussed. [ABSTRACT FROM AUTHOR]

Efficient regional seismic risk assessment including ground motion prediction and damage risk estimation is needed for emergency response planning. However, a conventional regional assessment suffers from low data‐ and time‐ efficiency as it generally involves a large number of locations and infrastructure systems that have specific soil conditions, and geometric, material, and structural properties, requiring access to large data and massive individual calculations with complicated procedures. To achieve efficient regional seismic risk assessment, this work presents a deep generative learning framework to construct input–output surrogate models of regional seismic risk by learning the underlying complex relation between earthquake source parameters and regional seismic risk involving many locations and structures from data. The learned deep surrogate models directly output the ground motion intensity map and the risk map of a region given earthquake source parameters, circumventing massive individual calculations and data access to individual locations and structures. The presented framework is validated on the bridge network risk assessment using simulated scenario earthquakes of the San Francisco Bay Area. We observe that the obtained deep surrogate models perform well without the need of data access to locations and structures and are time‐efficient. We also discuss the applicability and limitations of the presented framework. [ABSTRACT FROM AUTHOR]

To solve the multi-objective, flexible job-shop scheduling problem, the biogeography-based optimization (BBO) algorithm can easily fall into premature convergence, local optimum and destroy the optimal solution. Furthermore, the symbiotic organisms search (SOS) strategy can be introduced, which integrates the mutualism strategy and commensalism strategy to propose a new migration operator. To address the problem that the optimal solution is easily destroyed, a parasitic natural enemy insect mechanism is introduced, and predator mutation and parasitic mutation strategies with symmetry are defined, which can be guided according to the iterative characteristics of the population. By comparing with eight multi-objective benchmark test functions with four multi-objective algorithms, the results show that the algorithm outperforms other comparative algorithms in terms of the convergence of the solution set and the uniformity of distribution. Finally, the algorithm is applied to multi-objective, flexible job-shop scheduling (FJSP) to test its practical application value, and it is shown through experiments that the algorithm is effective in solving the multi-objective FJSP problem. [ABSTRACT FROM AUTHOR]

Highlights: A rational design of metal halide perovskites for achieving efficient CO2 reduction reaction was demonstrated. The stability of CsPbI3 perovskite nanocrystal (NCs) in aqueous electrolyte was improved by compositing with reduced graphene oxide (rGO). The CsPbI3/rGO catalyst exhibited > 92% Faradaic efficiency toward formate production with high current density which was associated with the synergistic effects between the CsPbI3 NCs and rGO. Transformation of greenhouse gas (CO2) into valuable chemicals and fuels is a promising route to address the global issues of climate change and the energy crisis. Metal halide perovskite catalysts have shown their potential in promoting CO2 reduction reaction (CO2RR), however, their low phase stability has limited their application perspective. Herein, we present a reduced graphene oxide (rGO) wrapped CsPbI3 perovskite nanocrystal (NC) CO2RR catalyst (CsPbI3/rGO), demonstrating enhanced stability in the aqueous electrolyte. The CsPbI3/rGO catalyst exhibited > 92% Faradaic efficiency toward formate production at a CO2RR current density of ~ 12.7 mA cm−2. Comprehensive characterizations revealed the superior performance of the CsPbI3/rGO catalyst originated from the synergistic effects between the CsPbI3 NCs and rGO, i.e., rGO stabilized the α-CsPbI3 phase and tuned the charge distribution, thus lowered the energy barrier for the protonation process and the formation of *HCOO intermediate, which resulted in high CO2RR selectivity toward formate. This work shows a promising strategy to rationally design robust metal halide perovskites for achieving efficient CO2RR toward valuable fuels. [ABSTRACT FROM AUTHOR]

Nonlinear dynamical systems in applications such as design and control typically depend on a set of variable parameters that represent system geometry, boundary conditions, material properties, etc. Such a parameterized dynamical system requires a parameterized model (e.g., a parameterized differential equation) to describe. On the one hand, to discover the wide variety of the parameter-dependent dynamical behaviors, repeated simulations with the parameterized model are often required over a large range of parameter values, leading to significant computational burdens especially when the system is complex (strongly nonlinear and/or high-dimensional) and the high-fidelity model is inefficient to simulate. Thus, seeking surrogate models that mimic the behaviors of high-fidelity parameterized models while being efficient to simulate is critically needed. On the other hand, the governing equations of the parameterized nonlinear dynamical system (e.g., an aerodynamic system with a physical model (full-scale or scaled in the laboratory) for optimization or design tasks) may be unknown or partially unknown due to insufficient physics knowledge, leading to an inverse problem where we need to identify the models from measurement data only. Accordingly, this work presents a novel deep generative framework for data-driven surrogate modeling/identification of parameterized nonlinear dynamical systems from data only. Specifically, the presented framework learns the direct mapping from simulation parameters to visualization images of dynamical systems by leveraging deep generative convolutional neural networks, yielding two advantages: (i) the surrogate simulation is efficient because the calculation of transient dynamics over time is circumvented; (ii) the surrogate output retains characterizing ability and flexibility as the visualization image is customizable and supports any visualization scheme for revealing and representing high-level dynamics feature (e.g., Poincaré map). We study and demonstrate the framework on Lorenz system, forced pendulum system, and forced Duffing system. We present and discuss the prediction performance of the obtained surrogate models. It is observed that the obtained model has promising performance on capturing the sensitive parameter dependence of the nonlinear dynamical behaviors even when the bifurcation occurs. We also discuss in detail the limitation of this work and potential future work. [ABSTRACT FROM AUTHOR]

Objective: At present, it is still uncertain whether single screw has the same stability as double screws in the treatment of ulnar coronal process basal fracture (Regan–Morry type III). So, we aimed to compare the pull‐out force and anti‐rotation torque of anterior single/double screw‐cancellous bone fixation (aSSBF, aDSBF) in this fracture, and further study the influencing factors on anatomical and biomechanical stability of smart screw internal fixations. Methods: A total of 63 adult volunteers with no history of elbow injury underwent elbow CT scanning with associated three‐dimensional reconstruction that enabled the measurements of bone density and fixed length of the proximal ulna and coronoid. The models of coronal process basal fracture, aSSBF and aDSBF, were developed and validated. Using the finite element model test, the sensitivity analysis of pull‐out force and rotational torque was carried out. Results: The pull‐out force of aSSBF model was positively correlated with the density of the cancellous bone and linearly related to the fixed depth of the screw. The load pattern of pull‐out force of aDSBF model was similar to that of aSSBF model. The ultimate torque of aDSBF model was higher than that of aSSBF model, but the load pattern of ultimate torque of both models was similar to each other when the fracture reset was satisfactory, and the screw nut attaches closely to coronoid process. Moreover, with enhancement of initial pre‐tightening force, the increase of ultimate torque of both models was small. Conclusions: In addition to three pull‐out stability factors of smart screw fixations, fracture surface fitting degree and nut fitting degree are the other two important anatomical and biomechanical stability factors of smart screw fixations both for rotational stability. When all pull‐out stability and rotational stability factors meet reasonable conditions simultaneously, single or double screw fixation methods are stable for the treatments of ulnar coronoid basal fractures. [ABSTRACT FROM AUTHOR]

A comprehensive analysis based on Cerasus humilis genome data was used to investigate the implications of amino acid/auxin permease (AAAP) genes on fruit growth and development. A total of 45 ChAAAP genes were identified based on their phylogenetic relationship, and they were further classified into eight types: GAT, ProT, LHT, AUX, ANT, ATLb, ATLa, and AAP, with 7, 3, 10, 3, 4, 9, 8, and 1 ChAAAP genes, respectively. The motif composition and gene structure analysis showed that motif 1 (YFSVAVFGYWAFGBKVLSQIL) was the most conserved motif, whereas no motif was found in ChAAAP11 and ChAAAP12. The numbers of introns and exons varied widely between ChAAAP genes: both ChAAAP30 and ChAAAP32 had 13 exons was the most; ChAAAP26, ChAAAP35, ChAAAP38, and ChAAAP39 had only one exon. Promoter analysis showed that ChAAAP genes play an important role in plant growth and development. The ChAAAP genes were distributed unevenly on eight chromosomes, and 9 gene duplication fragments were observed, which were all segmental, whereas there was no tandem duplication type. These genes contributed to the expansion of the ChAAAP genes in C. humilis. Gene duplication with the other species showed ChAAAP had more orthologous genes with Prunus persica. According to the transcriptome data, the expression level of ChAAAP17 was the highest in fruit, followed by ChAAAP22 and ChAAAP23. However, ChAAAP11 and ChAAAP12 were only expressed in the root. Furthermore, we utilized qPCR to evaluate the 19 candidate genes during fruit development and predicted seven ChAAAP genes (ChAAAP08, ChAAAP16, ChAAAP17, ChAAAP19, ChAAAP32, ChAAAP33, and ChAAAP40) to further explore their involvement in fruit development. Our findings provide a comprehensive understanding of the ChAAAP gene family's functions and could be employed to validate gene function in the future. [ABSTRACT FROM AUTHOR]

Data-driven modeling of nonlinear dynamical systems is essential because of the need for a trade-off among complexity, efficiency, and reliability in analytical or numerical studies as well as the difficulty of deriving fully physics-based models. An important limitation is commonly seen in existing works: modeling of a new dynamical system typically starts from scratch, requiring a large amount of data and intensive computation, though some prior experience or knowledge is available from a previously collected database of similar but different systems. However, on the one hand, the data amount for the new dynamical system is often limited, especially for real-world dynamical systems. On the other hand, the computational resource is also limited and a data-driven modeling task is usually computationally expensive, especially for large-scale systems. To improve data efficiency and computational efficiency in data-driven modeling of nonlinear systems, we present an enhanced data-driven modeling approach by incorporating metalearning into a physics-integrated deep learning framework. The core idea is to learn the metaknowledge about how to model a new system from a previously collected database of similar but different systems. Then this metaknowledge is leveraged to enable efficient modeling of a new system with limited data. For validations we conducted numerical experiments on three sets of fundamental nonlinear systems, including Duffing oscillators, nonlinear pendulums, and van der Pol oscillators. We performed both interpolation and extrapolation modelings to investigate the generalization ability of the presented approach. Furthermore, we conducted a quantitative analysis on data efficiency, addressing two critical issues: how few data are sufficient for the new system modeling and how much prior experience (previously collected database of similar but different systems) is needed for the metalearning. The results show that the presented approach improves both data efficiency and computational efficiency, compared with the conventional data-driven modeling approach (without leverage of the prior database) and the pretraining-based approach (simply using the prior database but without metalearning idea). We also discuss the limitations of this work and potential future study. [ABSTRACT FROM AUTHOR]

In this study, we present a physics-constrained deep learning method to discover and visualize from data the invariant nonlinear normal modes (NNMs) which contain the spatiotemporal dynamics of the fluid flow potentially containing strong nonlinearity. Specifically, we develop a NNM-physics-constrained convolutional autoencoder (NNM-CNN-AE) integrated with a multi-temporal-step dynamics prediction block to learn the nonlinear modal transformation, the NNMs containing the spatiotemporal dynamics of the flow, and reduced-order reconstruction and long-time future-state prediction of the flow fields, simultaneously. In test cases, we apply the developed method to analyze different flow regimes past a cylinder, including laminar flows with low Reynolds number in transient and steady states (RD = 100) and high Reynolds number flow (RD = 1000), respectively. The results indicate that the identified NNMs are able to reveal the nonlinear spatiotemporal dynamics of these flows, and the NNMs-based reduced-order modeling consistently achieves better accuracy with orders of magnitudes smaller errors in construction and prediction of the nonlinear velocity and vorticity fields, compared to the linear proper orthogonal decomposition (POD) method and the Koopman-constrained-CNN-AE using the same number or dimension of modes. We perform an analysis of the modal energy distribution of NNMs and find that compared to POD modes, the few fundamental NNMs capture a very high level of total energy of the flow, which is advantageous for reduced-order modeling and representation of the complex flows. Finally, we discuss the potentials and limitations of the presented method. [ABSTRACT FROM AUTHOR]

BACKGROUND: Asian citrus psyllid (ACP), also known as Diaphorina citri, is the natural vector of Candidatus Liberibacter asiaticus (CLas), which is responsible for Huanglongbing (HLB), a devastating citrus disease. Previously, the pathogen was successfully excluded from diseased citrus plants by using the indigenous endophyte Bacillus subtilis L1‐21. However, the pathogen elimination and colonization potential of B. subtilis L1‐21 in the carrier vector ACP, as well as the recruitment of native microbial communities of psyllid in the presence of endophytes, are still unknown. RESULTS: Initially, we suggested that endophyte L1‐21 reduced the CLas copies in ACP from 6.58 × 106 to 5.04 × 104 per insect after 48 h, however, the pathogen copies remained stable in the negative control. The endophyte was stable for 48 h after application. Among the bacterial genera those highlighted in ACP were Candidatus Liberibacter, Pseudomonas, Candidatus Profftella, Methylobacterium‐Methylorubrum, Pantoea, Curtobacterium, Wolbachia, Actinomycetospora, and Bacillus. Interestingly, B. subtilis L1‐21 easily colonizes the midgut of ACP but cannot be detected in eggs. When ACP with endophyte L1‐21 was allowed to feed on new citrus leaves, the highest colonization was observed. We also found that psyllids carrying endophyte L1‐21 after feeding on citrus leaves reduced the CLas copies in leaves on the 0, 3rd and 5th day from 8.18 × 10,4 2.6 × 10,3 and 0 pathogen copies/g fresh midvein, respectively. CONCLUSIONS: We propose that B. subtilis L1‐21 is a native endophyte in citrus and psyllid, which efficiently reduces the CLas pathogen in both citrus and psyllids, provides a more protective effect by increasing the number of cultivable endophytes, and successfully colonizes the midgut of ACP. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

To study the difference of volatile substances and their relative contents in different fermentation conditions, the optimal fermentation conditions were determined according to the important aroma volatile compounds profiling. The fermentation time, the amounts of SO2, and yeast addition were used as fermentation conditions, using the headspace solid phase microextraction (HS‐SPME) combined with gas chromatography–mass spectrometry (GC–MS) analysis, a total of 182 volatile substances were identified in nine treatments. In nine treatments, a total of 182 volatile compounds were detected. However, the majority of detected volatile compounds were esters, with a total number of 37. Each treatment had the maximum quantity and relative concentration of esters; moreover, five esters (ethyl acetate, isobutyl acetate, and others) were discovered in all treatments. PCA results showed a large positive relation between esters and fermentation treatments (85.79%). The addition of 40 mg/L SO2, 100 mg/L yeast, and a fermentation time of 2 days (T7) were recognized as the optimum fermentation conditions, where 14 ester compounds were detected and a relative ester content of 65.65% was achieved. Ultimately, the results indicate that with the optimized fermentation conditions of this study, grape wines with more fruity, sweet, milky, and better ester concentrations can be produced and could also provide a certain reference for the production of other fruit wines. Novelty impact statement: A total of 182 volatile substances were identified in nine treatments. The number and relative content of esters were the highest in each treatment. Five esters (ethyl acetate, isobutyl acetate, and others) were detected in all the treatments. [ABSTRACT FROM AUTHOR]

Summary: Real‐time health monitoring of stay cables in cable‐stayed bridges is necessary for timely maintenance and to avoid unforeseen fatigue damage due to vortex‐induced vibration—mainly due to combination rain and wind‐related dynamic loads. Conventional contact‐based sensors may often malfunction in harsh weather conditions and are expensive to install and maintain. Therefore, recently, the usage of non‐contact camera‐based measurement is burgeoning in the domain of structural sensing. Non‐contact video‐based sensing provides a higher spatial resolution compared to conventional sensors along with a lower cost. Therefore, in this paper, we present a framework that uses video‐based measurement as multiple sensors to reduce the estimation error in determining the real‐time cable tension. First, we calculate the vibration response using the phase‐based motion estimation algorithm for various locations of interest. We then intuitively fuse the data from all the locations to estimate the real‐time frequency variation using a blind source separation (BSS) technique named complexity pursuit (CP). Finally, the real‐time stay‐cable tension is calculated from the real‐time frequency history using the taut‐string theory. The proposed algorithm is applied to Fred‐Hartman cable‐stayed bridge in Houston, Texas. The algorithm is validated using actual tension in the cable. We also show that the estimation error in the proposed sliding window‐based CP framework is considerably lesser than the conventional real‐time tension estimation technique using Short‐time Fourier Transform (STFT). The accurate estimation of stay‐cable tension from the video‐based measurement shows the significant potential of the proposed framework in the domain of structural health monitoring. [ABSTRACT FROM AUTHOR]

Objectives: Tetralogy of Fallot (TOF) is the most common deformity combined with the unilateral absence of the mediastinal pulmonary artery (UAMPA), and its treatment strategy remains controversial. In this study, we analyzed the effect of bilateral pulmonary reconstruction in patients with TOF combined with UAMPA. Methods: This was a single‐center, retrospective review of 1713 patients with TOF between January 2009 and November 2021. Overall, eight patients were diagnosed with TOF combined with UAMPA. Among them, seven underwent surgery: three underwent one‐stage TOF correction with bilateral pulmonary artery reconstruction; three patients underwent bilateral pulmonary artery reconstruction, followed by two‐stage TOF correction after several months; and one patient underwent two procedures of left pulmonary artery reconstruction, and the ventral septal defect remained open. The left pulmonary arteries were reconstructed with a Goretex conduit in three cases, direct anastomosis in two cases, and the modified autologous tissue extension technique in two cases. Results: All seven patients survived during the postoperative follow‐up and showed good cardiac function and normal oxygen saturation of >97%. During follow‐up echocardiography, we noted that the left pulmonary arteries reconstructed with a Goretex conduit or direct anastomosis had thrombosis or stenosis. However, those reconstructed using the modified autologous tissue extension technique was unobstructed. Conclusions: In patients with TOF and UAMPA, if there is a pulmonary artery confluence in the affected hilum, it is feasible to implement bilateral pulmonary artery reconstruction for one‐stage TOF correction. The use of the pulmonary artery extension technique and autologous tissue for bilateral pulmonary reconstruction could reduce the incidence of anastomotic stenosis. [ABSTRACT FROM AUTHOR]

Stay-cables in the cable-stayed bridge are the most vital components as they carry the bridge deck's load and transmit the force to the bridge pylons. However, dynamics loads due to vortex-induced vibration, ambient wind excitation, and even vehicular vibration cause fatigue in the stay-cable. Hence continuous real-time performance monitoring of such cables is necessary for maintenance to avoid any kind of damage to the cable. Wireless sensors are contact-based sensor that provide accurate measurement, and it does not involve any wiring cost like conventional wired sensors. Monitoring cable health using such wireless sensors is a good choice provided packet loss (which occurs while transmitting the measured data to the base station) that invariably occurs is addressed by data processing. Such discontinuity in data (due to packet loss) may interrupt the real-time/online cable health monitoring process - depending on the window length of the data loss. In general, online health monitoring using multiple sensors reduces the estimation errors. In this paper, we propose a framework that takes the wireless sensor data as the input, then reconstructs the packet lost samples (if any), and finally, provides a real-time tension estimation as an output. The novel framework, first adopts compressive sensing algorithm to reconstruct the data due to packet loss. Subsequently, we synthesize the reconstructed responses from multiple sensors to estimate the real-time frequency variation using Blind Source Separation (BSS) Technique. As the cable response due to ambient vibration contains a large number of modes, the dominant modal response or the corresponding dominant frequency is estimated from very few measurements using a variant of the BSS technique named Sparse Component Analysis (SCA). Finally, real-time cable tension is estimated from the frequency variation using the taut-string theory. The proposed technique is applied to a real full-scale cable-stayed bridge. The mean tension obtained from the framework is comparable with the cable's actual design tension. The accurate estimation of real-time stay-cable tension by the proposed algorithm shows great potential in the field of structural health monitoring. [ABSTRACT FROM AUTHOR]

The Laurentian Great Lakes, one of the world's largest surface freshwater systems, pose a modeling challenge in seasonal forecast and climate projection. While physics-based hydrodynamic modeling is a fundamental approach, improving the forecast accuracy remains critical. In recent years, machine learning (ML) has quickly emerged in geoscience applications, but its application to the Great Lakes hydrodynamic prediction is still in its early stages. This work is the first one to explore a deep learning approach to predicting spatiotemporal distributions of the lake surface temperature (LST) in the Great Lakes. Our study shows that the Long Short-Term Memory (LSTM) neural network, trained with the limited data from hypothetical monitoring networks, can provide consistent and robust performance. The LSTM prediction captured the LST spatiotemporal variabilities across the five Great Lakes well, suggesting an effective and efficient way for monitoring network design in assisting the ML-based forecast. Furthermore, we employed an explainable artificial intelligence (XAI) technique named SHapley Additive exPlanations (SHAP) to uncover how the features impact the LSTM prediction. Our XAI analysis shows air temperature is the most influential feature for predicting LST in the trained LSTM. The relatively large bias in the LSTM prediction during the spring and fall was associated with substantial heterogeneity of air temperature during the two seasons. In contrast, the physics-based hydrodynamic model performed better in spring and fall yet exhibited relatively large biases during the summer stratification period. Finally, we developed a statistical integration of the hydrodynamic modeling and deep learning results based on the Best Linear Unbiased Estimator (BLUE). The integration further enhanced prediction accuracy, suggesting its potential for next-generation Great Lakes forecast systems. [ABSTRACT FROM AUTHOR]

In general, mixed modes and dispersion of guided wave occur at any excitation frequency, while single modes are needed for effective nondestructive evaluation of structures. In this work, we present an approach to generate a selective single-mode guided wave in plate-like structures by exploiting the unique directionality of the d36 type piezoelectric wafer and the symmetry of fundamental guided wave modes. Specifically, we devise a unified fundamental shear horizontal (SH0) wave/fundamental antisymmetric mode (A0) wave directional transducer pair by attaching the d36 type piezoelectric wafers back to back on the opposite sides of the plate to provide selective, directional single-mode guided waves for actuation and sensing. Originally, the d36-type piezoelectric wafer, poled and cut from the lead magnesium niobate-lead titanate crystal, produces a mixture of the in-plane SH0 (symmetric) mode and out-of-plane A0 (asymmetric) and S0 (symmetric) modes. By applying the in-phase or out-of-phase applied electrical field to the coupled d36-type piezoelectric wafer pair, our devised approach generates selective single modes, SH0 or A0, respectively, to propagate in the structure. We describe the theoretical development of our approach and conduct both numerical simulations and laboratory experiments for validations. [ABSTRACT FROM AUTHOR]

Huanglongbing (HLB) has turned into a devastating botanical pandemic of citrus crops, caused by Candidatus Liberibacter asiaticus (C Las). However, until now the disease has remained incurable with very limited control strategies available. Restoration of the affected microbiomes in the diseased host through the introduction of an indigenous endophyte Bacillus subtilis L1-21 isolated from healthy citrus may provide an innovative approach for disease management. A novel half-leaf method was developed in vitro to test the efficacy of the endophyte L1-21 against C Las. Application of B. subtilis L1-21 at 104 colony forming unit (cfu ml−1) resulted in a 1,000-fold reduction in the C Las copies per gram of leaf midrib (107 to 104) in 4 days. In HLB-affected citrus orchards over a period of 2 years, the C Las incidence was reduced to < 3%, and C Las copies declined from 109 to 104 g−1 of diseased leaf midribs in the endophyte L1-21 treated trees. Reduction in disease incidence may corroborate a direct or an indirect biocontrol effect of the endophytes as red fluorescent protein-labeled B. subtilis L1-21 colonized and shared niche (phloem) with C Las. This is the first large-scale study for establishing a sustainable HLB control strategy through citrus endophytic microbiome restructuring using an indigenous endophyte. [ABSTRACT FROM AUTHOR]

Asymmetric hydrogenation based on heterogeneous metal nanoparticles (NPs) has attracted extensive attention for the production of chiral chemicals due to its high conversion efficiency, simple product separation, and convenient catalyst regeneration. Nevertheless, its further practical application is still impeded by the leaching of expensive chiral ligands and by the associated extra purification costs that are caused by the weak absorption of the chiral ligands on the metal NPs. In contrast, chiral metal–organic frameworks (CMOFs) have been proposed to serve as metal NP supports and chirality inducers via biomimicry of the configuration of privileged natural cinchona alkaloids on the single-chiral-site level. Among the presented engineered isoreticular valine (Val)-, serine (Ser)-, and threonine (Thr)-derived CMOFs, the Thr-MOF-supported Pt nanoparticles (NPs) exhibit impressive enantioselectivity in the asymmetric hydrogenation of ethyl pyruvate (EP) in addition to inherently robust catalytic recyclability without the need to add either fresh chiral ligands or Pt NPs. [ABSTRACT FROM AUTHOR]

Background: Coronary malperfusion (CM) secondary to acute type A aortic dissection (ATAAD) is considered rare but has a high mortality rate. This study examined the incidence, management, and outcomes of patients with CM secondary to ATAAD and proposes a modified Neri classification. Methods: Between 2015 and 2020, out of 1018 patients who underwent surgical repair for ATAAD, 137 presented with CM, including 68 (49.6%), 43 (31.3%), and 15 (10.9%) with Neri types A, B, and C, respectively, and 11 (8.0%) with coronary orifice intimal tear (COIT), which we consider a novel category. Results: The occurrence rate of CM was 13.4%. CM was associated with higher in-hospital mortality (18.2% vs. 7.8%, p < 0.001). For Neri type A (98.5%) and most type B lesions (72.1%), coronary repair was adequate. Coronary artery bypass grafting (CABG) was necessary for type B patients unsuited for repair (23.2%) and for all type C patients (100%). Repair of COIT was possible (45.5%). The in-hospital mortality rates differed significantly among the four lesion groups (p = 0.006). Conclusions: The occurrence of CM secondary to ATAAD may be more frequent than previously reported. Surgical management based on lesion classification achieved acceptable outcomes. Repair was adequate for Neri type A and most type B lesions. Other type B and type C lesions could be treated by CABG. Coronary orifice intimal tear is a unique set of lesions, for which orifice repair was also possible. [ABSTRACT FROM AUTHOR]

Studying structure–activity correlations of electrocatalysts is essential for improving the conversion of electrical to chemical energy. Recently, increasing evidence obtained by operando characterization techniques reveal that the structural evolution of catalysts caused by the interplay with electric fields, electrolytes and reactants/intermediates brings about the formation of real active sites. Hence, it is time to summarize structural evolution related research advances and envisage future developments. In this Minireview, we first introduce the fundamental concepts associated with structural evolution (e.g. catalysts, active sites/centers and stability/lifetime) and their relevance. Then, the multiple triggers of structural evolution and advanced operando characterizations are discussed. Significantly, a brief overview of structural evolution and its reversibility in heterogeneous electrocatalysis is provided, especially for the representative electrocatalytic oxygen evolution reaction (OER) and CO2 reduction reaction (CO2RR) processes. Lastly, key challenges and opportunities in this exciting field are highlighted. [ABSTRACT FROM AUTHOR]

Studying structure–activity correlations of electrocatalysts is essential for improving the conversion of electrical to chemical energy. Recently, increasing evidence obtained by operando characterization techniques reveal that the structural evolution of catalysts caused by the interplay with electric fields, electrolytes and reactants/intermediates brings about the formation of real active sites. Hence, it is time to summarize structural evolution related research advances and envisage future developments. In this Minireview, we first introduce the fundamental concepts associated with structural evolution (e.g. catalysts, active sites/centers and stability/lifetime) and their relevance. Then, the multiple triggers of structural evolution and advanced operando characterizations are discussed. Significantly, a brief overview of structural evolution and its reversibility in heterogeneous electrocatalysis is provided, especially for the representative electrocatalytic oxygen evolution reaction (OER) and CO2 reduction reaction (CO2RR) processes. Lastly, key challenges and opportunities in this exciting field are highlighted. [ABSTRACT FROM AUTHOR]

Huanglongbing (HLB) has turned into a devastating botanical pandemic of citrus crops, caused by Candidatus Liberibacter asiaticus (C Las). However, until now the disease has remained incurable with very limited control strategies available. Restoration of the affected microbiomes in the diseased host through the introduction of an indigenous endophyte Bacillus subtilis L1-21 isolated from healthy citrus may provide an innovative approach for disease management. A novel half-leaf method was developed in vitro to test the efficacy of the endophyte L1-21 against C Las. Application of B. subtilis L1-21 at 104 colony forming unit (cfu ml−1) resulted in a 1,000-fold reduction in the C Las copies per gram of leaf midrib (107 to 104) in 4 days. In HLB-affected citrus orchards over a period of 2 years, the C Las incidence was reduced to < 3%, and C Las copies declined from 109 to 104 g−1 of diseased leaf midribs in the endophyte L1-21 treated trees. Reduction in disease incidence may corroborate a direct or an indirect biocontrol effect of the endophytes as red fluorescent protein-labeled B. subtilis L1-21 colonized and shared niche (phloem) with C Las. This is the first large-scale study for establishing a sustainable HLB control strategy through citrus endophytic microbiome restructuring using an indigenous endophyte. [ABSTRACT FROM AUTHOR]

Identifying the characteristic coordinates or modes of nonlinear dynamical systems is critical for understanding, analysis, and reduced-order modeling of the underlying complex dynamics. While normal modal transformation exactly characterizes any linear systems, there exists no such a general mathematical framework for nonlinear dynamical systems. Nonlinear normal modes (NNMs) are natural generalization of the normal modal transformation for nonlinear systems; however, existing research for identifying NNMs has relied on theoretical derivation or numerical computation from the closed-form equation of the system, which is usually unknown. In this work, we present a new data-driven framework based on physics-integrated deep learning for nonlinear modal identification of unknown nonlinear dynamical systems from the system response data only. Leveraging the universal modeling capacity and learning flexibility of deep neural networks, we first represent the forward and inverse nonlinear modal transformations through the physically interpretable deep encoder–decoder architecture, generalizing the modal superposition to nonlinear dynamics. Furthermore, to guarantee correct nonlinear modal identification, the proposed deep learning architecture integrates prior physics knowledge of the defined NNMs by embedding a unique dynamics-coder with physics-based constraints, including generalized modal properties, dynamics evolution, and future-state prediction. We test the proposed method by a series of study on the conservative and non-conservative Duffing systems with cubic nonlinearity and observe that the proposed data-driven framework is able to identify NNMs with invariant manifolds, energy-dependent nonlinear modal spectrum, and future-state prediction for unknown nonlinear dynamical systems from response data only; these identification results are found consistent with those from theoretically derived or numerically computed from closed-form equations. We also discuss its implementations and limitations for nonlinear modal identification of dynamical systems. [ABSTRACT FROM AUTHOR]

Electrocatalysis is at the heart of many significant chemical transformation processes and advanced clean energy technologies. Traditional noble/transition metal oxides are widely used as electrocatalysts; however, they often suffer from intrinsic disadvantages, including low atom utilization, small surface area, and unfavorable tunability. Metal–organic frameworks (MOFs), as a new family of catalytic materials, are attracting extensive attention due to their unique physicochemical properties. The tremendous pristine MOF‐based materials are created using various synthetic approaches and further used for important energy conversions. Herein, a systematic overview on the unique merits and the state‐of‐the‐art design of MOF‐based electrocatalysts is offered. This review also presents recent advances in the development of various pristine MOFs and MOF‐based host–guest composite catalysts for electrocatalysis (i.e., oxygen reduction reaction, hydrogen oxidation reaction, hydrogen evolution reaction, oxygen evolution reaction, and CO2 reduction reaction) and discusses the future challenges and opportunities in this emerging field. [ABSTRACT FROM AUTHOR]

Identifying reduced-order models (ROMs) of nonlinear dynamical systems is difficult, especially when the system equation is unknown with only measurement data available. In such a case, not only a reduced subspace but also the associated dynamics need to be identified from data only, leading to a challenging data-driven ROM problem. In this study, we present a hierarchical deep learning approach to identify ROM from measurement only; it simultaneously identifies the nonlinear normal modal (NNM) subspace with a hierarchical order and the associated nonlinear modal dynamics. We conduct study to validate such an approach on both unforced and forced nonlinear dynamical systems, and find that the identified hierarchical NNMs-spanned subspace enables an efficient and effective dimensional truncation to achieve optimally lowest-dimensional ROM. We discuss in detail its performance and applicability. [ABSTRACT FROM AUTHOR]

Modern infrastructure systems, such as bridges, dams, power generation stations, and buildings increasingly have an intrinsic cyber-physical nature to them. Infrastructure now commonly, includes actuators, network connections, sensors, control systems, and computational resources. It is of increasing concern that modern infrastructure is vulnerable to cyber-attacks that can damage both the cyber and physical nature of the infrastructure. To date, the physical and cyber health of infrastructure has been considered separately. However, the increasing concerns associated with the cyber-physical security of infrastructure coupled with the emergence of 5G networks made using components that are not universally considered trustworthy, and the emergence of techniques for creating deepfakes and adversarial examples suggests the time has come to begin considering cyber health and structural health with a more holistic approach. In this work, a protocol is developed for ensuring the imagery data captured by a structural health monitoring system can be unambiguously attributed to legitimate sensors associated with the structural health monitoring system. A computer vision approach based on the idea of mutual information is then presented to detect damage in an image. This work presents the protocol for authenticating the provenance of imager data and demonstrates that this protocol does not have overly adverse effects when used with the mutual information-based technique for detecting damage in the resulting imagery data. [ABSTRACT FROM AUTHOR]

This article introduces the use of emerging augmented reality technology to enable the next generation of structural infrastructure inspection and awareness. This work is driven by the prevalence of visual structural inspection. It is known that current visual inspection techniques have multiple sources of variance that should be reduced in order to achieve less ambiguous visual inspections. Emerging augmented reality tools feature a variety of sensors, computation, and communication resources that can enable relevant structural inspection data to be collected at very high resolution in an unambiguous manner. This work shows how emerging augmented reality tools can be used to greatly enhance our ability to capture comprehensive, high-resolution, three-dimensional measurements of critical infrastructure. This work also provides detailed information on the software architecture for augmented reality structural inspection applications that helps meet the goals of the framework. The fact that the framework is designed to accommodate the considerations associated with high-consequence infrastructure implies that it is also comprehensive enough to be applied to less hazardous but still high-value infrastructure such as bridges, dams, and tunnels. Augmented reality has great potential to enable the next generation of smart infrastructure, and this work focuses on addressing how augmented reality can be leveraged to enable the next generation of structural awareness for high-consequence, long-lifespan structures. [ABSTRACT FROM AUTHOR]

Subwavelength defect imaging using guided waves has been known to be a difficult task mainly due to the diffraction limit and dispersion of guided waves. In this article, we present a noncontact super-resolution guided wave array imaging approach based on deep learning to visualize subwavelength defects in plate-like structures. The proposed approach is a novel hierarchical multiscale imaging approach that combines two distinct fully convolutional networks. The first fully convolutional network, the global detection network, globally detects subwavelength defects in a raw low-resolution guided wave beamforming image. Then, the subsequent second fully convolutional network, the local super-resolution network, locally resolves subwavelength-scale fine structural details of the detected defects. We conduct a series of numerical simulations and laboratory-scale experiments using a noncontact guided wave array enabled by a scanning laser Doppler vibrometer on aluminate plates with various subwavelength defects. The results demonstrate that the proposed super-resolution guided wave array imaging approach not only locates subwavelength defects but also visualizes super-resolution fine structural details of these defects, thus enabling further estimation of the size and shape of the detected subwavelength defects. We discuss several key aspects of the performance of our approach, compare with an existing super-resolution algorithm, and make recommendations for its successful implementations. [ABSTRACT FROM AUTHOR]

Core‐shell structured bimetallic platinum‐metal (Pt−M) nanoparticles, as a new class of active and stable nanocatalysts, have shown many advantages in increasing the utilization of precious Pt and improving electrocatalytic performances. Here, a core‐shell Pt3Co@Pt supported on porous graphitic carbon (denoted as Pt3Co@Pt/C) is synthesized via a simple thermal method, and further used as an efficient electrocatalyst for oxygen reduction reaction (ORR) in the direct methanol fuel cell. An atomic‐resolution high‐angle annular dark‐field scanning transmission electron microscopy (HAADF‐STEM) measurement combining with line‐profile analysis reveals that the average thickness of Pt‐shell is around 0.4–0.6 nm, forming an ultrathin catalytic layer. Given its unique geometric and electronic structure, the as‐prepared Pt3Co@Pt/C displays highly enhanced electrocatalytic ORR activity and stability, boosted anti‐methanol poisoning ability with a high onset potential and an exceptional half‐wave potential in 0.1 M HClO4 solution. Impressively, its mass activity (0.71 mA mgPt−1) and specific activity (2.75 mA cmPt−2) for ORR are 3.7‐ and 8.1‐fold higher than those of commercial Pt/C catalyst, respectively. The Pt3Co@Pt/C nanocatalysts show remarkable tolerance against methanol poisoning, evidenced by the in situ Fourier‐transform infrared (FTIR) spectroscopy. This work points out a path for the design of high‐performance nanocatalysts for accelerating the development of clean energy technologies involving ORR. [ABSTRACT FROM AUTHOR]

No observations show evidence of ionization in long air gap discharge during dark periods. However, for the first time, weak channel illuminations are observed during the so‐called dark periods in this paper. The length of these faint bright channels is almost constant during each dark period and is nearly equal to that of the remaining low‐density channels created by the previous discharge. The phenomenon, that is, the dark period is not really dark, is more likely to occur at the time when there is a greater change rate of the applied voltage. These findings are based on the high‐speed direct imaging and Schlieren photography together, in which the former is used to visualize the bright channels and the latter to detect the low‐density area. Plain Language Summary: Although a large amount of related experimental and theoretical researches on long positive sparks have been carried out, there are still some phenomena to be explained, such as the abrupt elongation of leader channel, and the intermittent propagation of upward positive leader. Together with the above phenomena, dark periods are often accompanied before the channel re‐illumination, during which no observations show evidence of ionization as no current and light are measured. However, for the first time, this paper shows the direct experimental evidence that channels in dark periods are not always dark in long positive sparks, but have weak illumination with a length equal to the low‐density channel left by previous discharges. It is thought that the capacitive current due to the varying applied voltage may make the left low‐density channels to emit weak light. New findings can inspire researchers to pay attention to the capacitive current in the dark period instead of directly ignoring them as before. This may help to explain the above‐mentioned mysterious phenomena to a certain extent. Key Points: More direct experimental evidence to show that discharge channels during dark periods are not always dark in long positive sparksThe length of faint bright channels in each dark period is almost constant and nearly the same as that of the remaining low‐density channelsCapacitive current due to the varying applied voltage may cause the channel illumination in dark periods [ABSTRACT FROM AUTHOR]

The elongation and branching of long positive spark discharges in the laboratory and in lightning have been attributed to the formation of thermalized channels inside a diffuse, glow-like streamer section at the leader head. It is experimentally shown here that the structured morphology of streamers produce low-density stem channels that elongate and branch well before a new leader channel section is formed. These non-thermalized stems are also shown to develop ahead of a developing leader channel. These findings are based on high-speed photography and Schlieren imaging used to visualize both the morphology of streamer filaments and stem channels. Numerical analysis is also performed to estimate the axial temperature and density of the stem channels. A stem-driven mechanism for the propagation and branching of positive long air gap discharges is proposed and discussed based on the presence of not-yet thermalized, low density channels formed by streamer ensembles at the leader head. [ABSTRACT FROM AUTHOR]

Genic male sterility (GMS) is a common and important trait, which is widely used for the production of hybrid seeds. However, the molecular mechanism of GMS in watermelon remains poorly understood. In this study, we comparatively analyzed the transcriptome profiles of sterile and fertile floral buds using the bulked segregant analysis (BSA) and transcriptome sequencing (RNA-seq). A total of 2507 differentially expressed genes (DEGs) including 593 up-regulated and 1914 down-regulated, were identified to be related to male sterility in watermelon line Se18. Gene ontology (GO) analysis showed that 57 GO terms were significantly enriched, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed plant hormone signal transduction, glycolysis/gluconeogenesis, starch and sucrose metabolism, plant–pathogen interaction, phenylpropanoid biosynthesis pathways were obviously enriched. Furthermore, the efficiency of the RNA-seq analysis was validated by quantitative real-time PCR (qRT-PCR). Among the DEGs, some valuable candidate genes involved in pollen development were identified, such as gene Cla000029, a bHLH transcription factor and homologous to MS1 in Arabidopsis. Moreover, other DEGs including MYB gene Cla012590 (MYB26), Cla017100 (MYB21), etc., also provide useful information for further understanding the function of key genes involved in pollen development. This study provides new insights into the global network of male sterility in watermelon. [ABSTRACT FROM AUTHOR]

Background: Rubinstein-Taybi syndrome (RTS) is a rare, congenital, plurimalformative, and neurodevelopmental disorder. Previous studies have reported that large deletions contribute to more severe RTS phenotypes than those caused by CREBBP point mutations, suggesting a concurrent pathogenetic role of flanking genes, typical of contiguous gene syndromes, but the detailed genetics are unclear.Results: This study presented a rare case of Rubinstein-Taybi (RT) syndrome with serious cardiac abnormalities. Based on the clinical and genetic analysis of the patient, the ADCY9 gene deletion was highlighted as a plausible explanation of cardiac abnormalities. In adcy9 morphant zebrafish, cardiac malformation was observed. Immunofluorescence study disclosed increased macrophage migration and cardiac apoptosis. RNA sequencing in zebrafish model highlighted the changes of a number of genes, including increased expression of the mmp9 gene which encodes a matrix metalloproteinase with the main function to degrade and remodel extracellular matrix.Conclusions: In this study, we identified a plausible new candidate gene ADCY9 of CHD through the clinical and genetic analysis of a rare case of Rubinstein-Taybi (RT) syndrome with serious cardiac abnormalities. By functional study of zebrafish, we demonstrated that deletion of adcy9 is the causation for the cardiac abnormalities. Cardiac apoptosis and increased expression of the MMP9 gene are involved in the pathogenesis. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer-related deaths worldwide, with 50–70% of patients suffering from bone metastasis. Accumulating evidence has demonstrated that miRNAs are involved in cell proliferation, migration, and invasion in malignancy, such as lung cancer bone metastasis. In the present study, we demonstrated that reduced miR-192-5p and increased TRIM44 levels were associated with the proliferation, migration and invasion of lung cancer. Furthermore, the potential functions of miR-192-5p were explored in A549 and NCI-H1299 cells. We found that miR-192-5p upregulation suppressed tumour behaviours in lung cancer cells. To further investigate whether miR-192-5p is associated with TRIM44, we used TargetScan software to predict the binding site between miR-192-5p and TRIM44. Luciferase activity assays were performed to verify this prediction. In addition, the significant role of miR-192-5p in negatively regulating TRIM44 expression was manifested by our research group. our results suggest that miR-192-5p inhibited the proliferation, migration and invasion of lung cancer through TRIM44. [ABSTRACT FROM AUTHOR]

The development of low bandgap perovskite solar cells is important as the current best perovskite solar cells are not based on a light harvesting layer with an ideal bandgap, and low bandgap materials can also be used together with large bandgap materials to construct all-perovskite tandem cells with potentially higher power conversion efficiencies. However, low-bandgap perovskite solar cells based on mixed Pb and Sn generally suffer from inferior performance, hindering the further development of tandem perovskite solar cells. One of the main reasons is the relatively high open-circuit voltage loss in low-bandgap perovskite solar cells, which is generally attributed to the high trap density in perovskite films and energy-level mismatch between different functional layers in the device. In this study, we find another mechanism that can cause high open-circuit voltage loss and degrade device performance, which is silver diffusion towards the perovskite layer. Devices based on a typical inverted structure (indium tin oxide/PEDOT:PSS/FA0.5MA0.5Sn0.5Pb0.5I3/PCBM/Ag) suffered from severe silver diffusion that contaminated the whole perovskite layer during the thermal deposition process of the silver electrode. Such contamination increased the trap density and down-shifted the energy level of the FA0.5MA0.5Sn0.5Pb0.5I3 layer, resulting in high open-circuit voltage loss in the low-bandgap perovskite solar cells. Accordingly, we inserted a ZnO layer between the PCBM layer and silver electrode to significantly suppress silver diffusion, which dramatically improved the open-circuit voltage from 0.59 V to 0.79 V. Ultimately, a highly efficient low-bandgap Sn–Pb mixed perovskite solar cell with a power conversion efficiency of 18.1% and low open-circuit voltage loss of 0.40 V was achieved, which represents one of the state-of-the-art results in this field. [ABSTRACT FROM AUTHOR]

Reverse‐transcription quantitative real‐time PCR (RT‐qPCR) is a primary tool for measuring gene expression levels, and selection of appropriate reference genes is crucial for accurate and reproducible results of gene expression under various experimental conditions. However, no systematic evaluation of reference genes in pitaya (Hylocereus undatus Britt.) has been performed. Here, we examined the expression of five candidate reference genes, namely elongation factor 1‐alpha (HuEF1‐α), 18S ribosomal RNA (Hu18S rRNA), ubiquitin (HuUBQ), actin (HuACT), and ubiquitin‐conjugating enzyme (HuUQT), under different conditions in pitaya. The expression stabilities of these five genes were evaluated using two computation programs: geNorm and NormFinder. The results were further validated by normalizing the expression of the phosphoglycerate kinase (HuPGK) and ethylene‐responsive transcription factor (HuERF) genes. Our results indicate that combined use of HuUBQ and HuUQT is the most stable reference under all of the experimental conditions examined. HuEF1‐α, HuUBQ, and HuUQT are the top three most stable reference genes under salt stress, drought stress, and heat stress, and across different cultivars. HuEF1‐α, HuACT, and HuUQT exhibited the most stable expression patterns across different tissues. Our results will allow researchers to select the most appropriate reference genes for gene expression studies of pitaya under different conditions. [ABSTRACT FROM AUTHOR]

An electrochemical NO2 gas sensor was fabricated by using high temperature co-fired ceramic (HTCC) process with zirconia ceramics, conductive metal paste and room temperature ionic liquid as the main raw materials. The electrochemical performance of the sensor electrode and the output and response characteristics of the sensor were studied. The results showed that the room temperature ionic liquid 1-butyl-3-methylimidazolium bis-trifluoromethylsulfonyl imide salt used in the experiment was suitable as the electrolyte of the electrochemical NO2 gas sensor, and the suitable working voltage was 1.2 V; when the detection range of the sensor was 0~0.010% (volume fraction), and the accuracy was 1.5% FS. The response time (τ90) of the sensor was 25 s, which was superior to the traditional chamber electrochemical gas sensor. [ABSTRACT FROM AUTHOR]

The diameter of a stem, a common origin point for streamers where the transition from the streamer to the leader takes place, has a significant effect on the simulation of streamer-to-leader transition. To measure the diameter of the stem, a schlieren system was established with the temporal and spatial resolutions of 5 μs and 70 μm, respectively. Experiments were conducted under positive lightning and switching impulses; the applied voltage, discharge current, and schlieren images were recorded synchronously. A method was proposed to measure the diameter of the stem based on the schlieren images, with which the stem diameter as a function of time was obtained. Moreover, the initial diameters of the stem under different conditions were obtained through the linear fitting. At last, a relation between the initial cross-sectional area of the stem and the discharge current was given within a certain range. [ABSTRACT FROM AUTHOR]

Recent developments in the ability to automatically and efficiently extract natural frequencies, damping ratios, and full-field mode shapes from video of vibrating structures has great potential for reducing the resources and time required for performing experimental and operational modal analysis at very high spatial resolution. Furthermore, these techniques have the added advantage that they can be implemented remotely and in a non-contact fashion. Emerging full-field imaging techniques therefore have potential to allow the identification of the modal properties of structures in regimes that used to be challenging. For instance, these techniques suggest that the high spatial resolution structural identification could be performed on an aircraft during flight using a ground or aircraft-based imager. They also have the potential to identify the dynamics of microscopic systems. In order to realize this capability it will be necessary to develop techniques that can extract full-field structural dynamics in the presence of non-ideal operating conditions. In this work, we develop a framework for the deployment of emerging algorithms that allow the automatic extraction of high-resolution, full-field modal parameters in the presence of non-ideal operating conditions. One of the most notable non-ideal operating conditions is the rigid body motion of both the structure being measured as well as the imager performing the measurement. We demonstrate an instantiation of the framework by showing how it can be used to address, in-plane, translational, rigid body motion. The development of a frame-to-frame keypoint–based technique for identifying full-field structural dynamics in the presence of either rigid body motion is presented and demonstrated in the context of the framework for the deployment of full-field structural identification techniques in the presence of non-ideal operating conditions. It is expected that this framework will ultimately help enable the collection of full-field structural dynamics using measurement platforms including unmanned aerial vehicles, robotic telescopes, satellites, imagers mounted in high-vibration environments (seismic, industrial, harsh weather), characterization of microscopic structures, and human-carried imagers. If imager-based structural identification techniques mature to the point that they can be used in non-ideal field conditions, it could open up the possibility that the structural health monitoring community will be able to think beyond monitoring individual structures, to full-field structural integrity monitoring at the city scale. [ABSTRACT FROM AUTHOR]

Stem is a special short bright zone for the streamer to leader transition in long air gap discharges, whose characteristics play an important role in leader initiation and development. For the purpose of observing the number of stems around the H.V. electrode at the initial corona discharge, experiments were conducted under positive switching and lightning impulses with different crest values, and the schlieren photographs of stems and the discharge current were recorded synchronously. The associated analysis of the experimental results shows that the number of stems around the electrode at the initial corona discharge is always more than one in most cases. In addition, the average number of stems increases with the radius of the electrode and the crest value of applied impulse. If we focus on the characteristic of only one stem for special study, the conditions with a lower crest value under switching impulse and the electrode with a radius as small as possible are preferable. [ABSTRACT FROM AUTHOR]

Summary: Structures with complex geometries, material properties, and boundary conditions exhibit spatially local dynamic behaviors. A high‐spatial‐resolution model of the structure is thus required for high‐fidelity analysis, assessment, and prediction of the dynamic phenomena of the structure. The traditional approach is to build a highly refined finite element computer model for simulating and analyzing the structural dynamic phenomena based on detailed knowledge and explicit modeling of the structural physics such as geometries, materials properties, and boundary conditions. These physics information of the structure may not be available or accurately modeled in many cases, however. In addition, the simulation on the high‐spatial‐resolution structural model, with a massive number of degrees of freedom and system parameters, is computationally demanding. This study, on a proof‐of‐principle basis, proposes a novel alternative approach for spatiotemporal video‐domain high‐fidelity simulation and realistic visualization of full‐field structural dynamics by an innovative combination of the fundamentals of structural dynamic modeling and the advanced video motion manipulation techniques. Specifically, a low‐modal‐dimensional yet high‐spatial (pixel)‐resolution (as many spatial points as the pixel number on the structure in the video frame) modal model is established in the spatiotemporal video domain with full‐field modal parameters first estimated from line‐of‐sight video measurements of the operating structure. Then in order to simulate new dynamic response of the structure subject to a new force, the force is projected onto each modal domain, and the modal response is computed by solving each individual single‐degree‐of‐freedom system in the modal domain. The simulated modal responses are then synthesized by the full‐field mode shapes using modal superposition to obtain the simulated full‐field structural dynamic response. Finally, the simulated structural dynamic response is embedded into the original video, replacing the original motion of the video, thus generating a new photo‐realistic, physically accurate video that enables a realistic, high‐fidelity visualization/animation of the simulated full‐field vibration of the structure. Laboratory experiments are conducted to validate the proposed method, and the error sources and limitations in practical implementations are also discussed. Compared with high‐fidelity finite element computer model simulations of structural dynamics, the video‐based simulation method removes the need to explicitly model the structure's physics. In addition, the photo‐realistic, physically accurate simulated video provides a realistic visualization/animation of the full‐field structural dynamic response, which was not traditionally available. These features of the proposed method should enable a new alternative to the traditional computer‐aided finite element model simulation for high‐fidelity simulating and realistically visualizing full‐field structural dynamics in a relatively efficient and user‐friendly manner. [ABSTRACT FROM AUTHOR]