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Abstract Background Plant cell walls have evolved precise plasticity in response to environmental stimuli. The plant heterotrimeric G protein complexes could sense and transmit extracellular signals to intracellular signaling systems, and activate a series of downstream responses. dep1 (Dense and Erect Panicles 1), the gain-of-function mutation of DEP1 encoding a G protein γ subunit, confers rice multiple improved agronomic traits. However, the effects of DEP1 on cell wall biosynthesis and wall-related agronomic traits remain largely unknown. Results In this study, we showed that the DEP1 mutation affects cell wall biosynthesis, leading to improved lodging resistance and biomass saccharification. The DEP1 is ubiquitously expressed with a relatively higher expression level in tissues rich in cell walls. The CRISPR/Cas9 editing mutants of DEP1 (dep1-cs) displayed a significant enhancement in stem mechanical properties relative to the wild-type, leading to a substantial improvement in lodging resistance. Cell wall analyses showed that the DEP1 mutation increased the contents of cellulose, hemicelluloses, and pectin, and reduced lignin content and cellulose crystallinity (CrI). Additionally, the dep1-cs seedlings exhibited higher sensitivity to cellulose biosynthesis inhibitors, 2,6-Dichlorobenzonitrile (DCB) and isoxaben, compared with the wild-type, confirming the role of DEP1 in cellulose deposition. Moreover, the DEP1 mutation-mediated alterations of cell walls lead to increased enzymatic saccharification of biomass after the alkali pretreatment. Furthermore, the comparative transcriptome analysis revealed that the DEP1 mutation substantially altered expression of genes involved in carbohydrate metabolism, and cell wall biosynthesis. Conclusions Our findings revealed the roles of DEP1 in cell wall biosynthesis, lodging resistance, and biomass saccharification in rice and suggested genetic modification of DEP1 as a potential strategy to develop energy rice varieties with high lodging resistance.

Abstract Reactive oxygen species (ROS) has emerged as potent therapeutic agents for biofilm-associated bacterial infections. Chemodynamic therapy (CDT), involving the generation of high-energy ROS, displays great potential in the therapy of bacterial infections. However, challenges such as insufficient hydrogen peroxide (H2O2) and over-expressed glutathione (GSH) levels within the microenvironment of bacterial biofilms severely limit the antibacterial efficacy of CDT. Herein, we have developed a multifunctional nanoplatform (CuS@CaO2@Dex) by integrating copper sulfide (CuS) and calcium peroxide (CaO2) into dextran (Dex)-coated nanoparticles. This innovative platform enhanced ROS generation for highly efficient biofilm elimination by simultaneously supplying H2O2 and depleting GSH. The Dex-coating facilitated the penetrability of CuS@CaO2@Dex into biofilms, while CaO2 generated a substantial amount of H2O2 in the acidic biofilm microenvironment. CuS, through a Fenton-like reaction, catalyzed the conversion of self-supplied H2O2 into hydroxyl radicals (•OH) and consumed the overexpressed GSH. Additionally, the incorporation of near-infrared II (NIR II) laser irradiation enhanced the photothermal properties of CuS, improving the catalytic efficiency of the Fenton-like reaction for enhanced antibacterial effects. In vivo experiments have demonstrated that CuS@CaO2@Dex exhibited remarkable antibacterial and antibiofilm efficacy, exceptional wound healing capabilities, and notable biosafety. In summary, the Dex-coated nanoplatform proposed in this study, with its self-sterilization capability through ROS, holds significant potential for future biomedical applications.

Iron-chromium redox flow battery (ICRFB) is an electrochemical energy storage technology that plays a vital role in dealing with the problems of discontinuity and instability of massive new energy generation and improving the acceptance capacity of the power grid. Carbon cloth electrode (CC) is the main site where the electrochemical reaction occurs, which always suffers from the disadvantages of poor electrochemical reactivity. A new N-B co-doped co-regulation Ti composite CC electrode (T-B-CC) is firstly generated and applied to ICRFB, where the REDOX reaction can be promoted significantly owing to the plentiful active sites generated on the modified electrode. As contrasted with ICRFB with normal CC electrode, after 50 battery charge/discharge cycles, the discharge capacity (1,990.3 ​mAh vs 1,155.8 ​mAh) and electrolyte utilization (61.88% vs 35.94%) of ICRFB with CC electrode (T-B-CC) are significantly improved. Furthermore, the energy efficiency (EE) is maintained at about 82.7% under 50 cycles, which is 9.3% higher than that of the pristine electrically assembled cells. The co-modulation of heteroatom doping and the introduction of Ti catalysts is a simple and easy method to improve the dynamics of the Cr3+/Cr2+ and Fe3+/Fe2+ reactions, enhancing the performance of ICRFBs.

Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.

Under non-Gauss noise condition, the performance of traditional state estimation methods based on Gauss measurement noise will be greatly reduced. In order to solve this problem, a robust state estimation method based on Mahalanobis distance under non-Gauss noise is proposed in this paper. First of all, based on the Mahalanobis distance, the calculation method of optimal buffer length for PMU measurements is used, which can unify the SCADA measurements with PMU measurements in the same snapshot. Then, Based on the two-stage processing method, in the first stage, the SCADA measurements are used for filtering by using maximum likelihood estimator to obtain the estimated values, and then the estimated values in the first-stage are combined with PMU measurements as the second-stage measurements for filtering, and finally the final results are obtained. Based on the IEEE-39 buses system and IEEE-118 buses system, under Gaussian noise and non-Gaussian noise, the AEE results of proposed method are very small, and which are all within 10−3, numerical tests under different simulation conditions verified the robustness and effectiveness.

Abstract Micron-sized Si anode promises a much higher theoretical capacity than the traditional graphite anode and more attractive application prospect compared to its nanoscale counterpart. However, its severe volume expansion during lithiation requires solid electrolyte interphase (SEI) with reinforced mechanical stability. Here, we propose a solvent-induced selective dissolution strategy to in situ regulate the mechanical properties of SEI. By introducing a high-donor-number solvent, gamma-butyrolactone, into conventional electrolytes, low-modulus components of the SEI, such as Li alkyl carbonates, can be selectively dissolved upon cycling, leaving a robust SEI mainly consisting of lithium fluoride and polycarbonates. With this strategy, raw micron-sized Si anode retains 87.5% capacity after 100 cycles at 0.5 C (1500 mA g−1, 25°C), which can be improved to >300 cycles with carbon-coated micron-sized Si anode. Furthermore, the Si||LiNi0.8Co0.1Mn0.1O2 battery using the raw micron-sized Si anode with the selectively dissolved SEI retains 83.7% capacity after 150 cycles at 0.5 C (90 mA g−1). The selective dissolution effect for tailoring the SEI, as well as the corresponding cycling life of the Si anodes, is positively related to the donor number of the solvents, which highlights designing high-donor-number electrolytes as a guideline to tailor the SEI for stabilizing volume-changing alloying-type anodes in high-energy rechargeable batteries.

Abstract Background Sensory stimulation can play a fundamental role in the activation of the primary sensorimotor cortex (S1-M1), which can promote motor learning and M1 plasticity in stroke patients. However, studies have focused mainly on investigating the influence of brain lesion profiles on the activation patterns of S1-M1 during motor tasks instead of sensory tasks. Therefore, the objective of this study is to explore the lesion-specific activation patterns due to different brain lesion profiles and types during focal vibration (FV). Methods In total 52 subacute stroke patients were recruited in this clinical experiment, including patients with basal ganglia hemorrhage/ischemia, brainstem ischemia, other subcortical ischemia, cortical ischemia, and mixed cortical–subcortical ischemia. Electroencephalograms (EEG) were recorded following a resting state lasting for 4 min and three sessions of FV. FV was applied over the muscle belly of the affected limb’s biceps for 3 min each session. Beta motor-related EEG power desynchronization overlying S1-M1 was used to indicate the activation of S1-M1, while the laterality coefficient (LC) of the activation of S1-M1 was used to assess the interhemispheric asymmetry of brain activation. Results (1) Regarding brain lesion profiles, FV could lead to the significant activation of bilateral S1-M1 in patients with basal ganglia ischemia and other subcortical ischemia. The activation of ipsilesional S1-M1 in patients with brainstem ischemia was higher than that in patients with cortical ischemia. No activation of S1-M1 was observed in patients with lesions involving cortical regions. (2) Regarding brain lesion types, FV could induce the activation of bilateral S1-M1 in patients with basal ganglia hemorrhage, which was significantly higher than that in patients with basal ganglia ischemia. Additionally, LC showed no significant correlation with the modified Barthel index (MBI) in all patients, but a positive correlation with MBI in patients with basal ganglia lesions. Conclusions These results reveal that sensory stimulation can induce lesion-specific activation patterns of S1-M1. This indicates FV could be applied in a personalized manner based on the lesion-specific activation of S1-M1 in stroke patients with different lesion profiles and types. Our study may contribute to a better understanding of the underlying mechanisms of cortical reorganization.

In order to study the effects of soda residue content, particle size, moisture content, and curing age on the unconfined compressive strength (UCS) of soda residue cement lime soil (SRCLS), a 4-factor, 4-level orthogonal experimental design was employed in this study. Different conditions of SRCLS UCS and their impacts were tested and analyzed. The internal microstructure and hydration products of SRCLS were studied using SEM and XRD to explore the strengthening mechanism of SR in SRCLS. The results indicate that as the soda residue content gradually increased, SRCLS UCS initially increased and then decreased, with a maximum increase of up to 67%. With increasing soda residue particle size and moisture content, the UCS of SRCLS gradually decreased. The optimized mix ratio was determined to be soda residue:cement:lime:soil = 3%:3%:6%:100%, with the soda residue dried naturally and an ideal particle size of 0.15 mm. The factors influencing the unconfined compressive strength (UCS) of SRCLS, in order of importance, are curing age, soda residue content, moisture content, and particle size of SR. Among these, curing age and soda residue content have a significant impact on the UCS. An adequate amount of SR can act as a fine aggregate filler, replace lime, promote cement hydration, and enhance chloride ion binding. This improves the grading of SRCLS materials and facilitates the formation of cementitious products from AFm, AFt, and Friedel’s salt, resulting in denser and stronger SRCLS materials. The research findings provide a reference for the mix design of SRCLS and the large-scale utilization of waste soda residue.

Accurately portraying the mechanism of the flow of natural resource consumption between regions and its impact on ecology is of crucial value in deepening the understanding of the coordinated relationship between population, resources, environment and development. Consequently, this promotes the sustainable development of the natural economy and society. Based on a regional-industrial perspective, this study used a localized three-dimensional ecological footprint model to measure and decompose natural resources in Xinjiang from 2005 to 2020. In doing so, the study clarified the supply, demand, and flow utilization of natural capital in Xinjiang, the balance of spatial and temporal allocation of resources, the coupling between economic growth and resource consumption, and the coordination between industrial structure and ecological environment. The results showed that (1) Xinjiang's per capita ecological deficit grew from 2.096 to 11.667 in 2005–2020. Moreover, the energy footprint was a decisive part of the ecological deficit throughout the study period. Furthermore, the trend of increased ecological pressure was higher in northern and eastern Xinjiang than in southern Xinjiang. (2) The overall Gini coefficient of Xinjiang's ecological carrying capacity was at the critical value of spatial equilibrium (0.4), with differences between the groups: Northern & Southern Xinjiang > Northern & Eastern Xinjiang > Eastern & Southern Xinjiang. The reasons for this inter-regional economic disparity are related to fiscal expenditure/GDP, level of urbanization, and regional industrial output. Overall, the decoupling relationship between environmental pressures and economic growth was optimistic. (3) From an industrial perspective, the levels of industrial structural efficiency and the industrial ecological harmony index were still relatively low, but the overall trend was on the rise. (4) Resource endowment, economic development, consumption structure, and population had significant driving effects on the ecological footprint, whereas environmental protection, science, and technology could inhibit its growth to a certain extent. This study aimed to provide an in-depth analysis of the current situation and problems of natural resource use in Xinjiang and provide theoretical and practical references for sustainable development in the region.

BackgroundPrevious studies have examined the gender and geographic diversity within editorial boards across various academic disciplines, excluding the field of education. Thus, the purpose of this study was to address this gap by investigating the extent of gender and geographic disparity within the editorial boards of education journals.MethodsThe selection of top five education journals from each quartile (Q1–Q4) was performed based on Clarivate Analytics’ Journal Citation Reports (JCR) 2021 within the category of “Education & Educational Research.” The information of editors was collected through online sources.ResultsOverall, female editors accounted for 17 out of the 29 editors-in-chief (58.62%), 36 out of the 64 deputy/associate editors (56.25%), 378 out of the 728 editorial/advisory board members (51.92%) and 15 out of the 28 other types of editors (53.57%). There was no significant association between the impact factor (IF) and the proportion of female editors (Pearson’s r = −0.095, p = 0.689). The United States had the highest number of editors (n = 459, 54.06%), followed by the United Kingdom (n = 98, 11.54%), Australia (n = 63, 7.42%), China (n = 29, 3.42%), Germany (n = 25, 2.94%), and Canada (n = 22, 2.59%). Notably, the majority of the included editors were from developed countries (n = 794, 93.52%), while editors from developing countries constituted a significantly smaller proportion (n = 55, 6.48%).ConclusionEditorial boards of education journals exhibit reasonable gender diversity than other disciplines, though still fall short when considering the proportion of women in the discipline. Besides, obvious geographical disparity was observed among editorial boards of education journals. There was a notable lack of representation of researchers associated with institutions from developing countries on the editorial boards of education journals. While maintaining sufficient gender diversity, it is imperative to enhance the geographical diversity in these journals, ensuring a more equitable number of positions to individuals from these underrepresented groups.

The mixing of cotton seeds of different cultivars and qualities can lead to differences in growth conditions and make field management difficult. In particular, except for yield loss, it can also lead to inconsistent cotton quality and poor textile product quality, causing huge economic losses to farmers and the cotton processing industry. However, traditional cultivar identification methods for cotton seeds are time-consuming, labor-intensive, and cumbersome, which cannot meet the needs of modern agriculture and modern cotton processing industry. Therefore, there is an urgent need for a fast, accurate, and non-destructive method for identifying cotton seed cultivars. In this study, hyperspectral images (397.32 nm-1003.58 nm) of five cotton cultivars, namely Jinke 20, Jinke 21, Xinluzao 64, Xinluzao 74, and Zhongmiansuo 5, were captured using a Specim IQ camera, and then the average spectral information of seeds of each cultivar was used for spectral analysis, aiming to estab-lish a cotton seed cultivar identification model. Due to the presence of many obvious noises in the < 400 nm and > 1000 nm regions of the collected spectral data, spectra from 400 nm to 1000 nm were selected as the representative spectra of the seed samples. Then, various denoising techniques, including Savitzky-Golay (SG), Standard Normal Variate (SNV), and First Derivative (FD), were applied individually and in combination to improve the quality of the spectra. Additionally, a successive projections algorithm (SPA) was employed for spectral feature selection. Based on the full-band spectra, a Partial Least Squares-Discriminant Analysis (PLS-DA) model was established. Furthermore, spectral features and textural features were fused to create Random Forest (RF), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) identification models. The results showed that: (1) The SNV-FD preprocessing method showed the optimal denoising performance. (2) SPA highlighted the near-infrared region (800-1000 nm), red region (620-700 nm), and blue-green region (420-570 nm) for identifying cotton cultivar. (3) The fusion of spectral features and textural features did not consistently improve the accuracy of all modeling strategies, suggesting the need for further research on appropriate modeling strategies. (4) The ELM model had the highest cotton cultivar identification accuracy, with an accuracy of 100% for the training set and 98.89% for the test set. In conclusion, this study successfully developed a highly accurate cotton seed cultivar identification model (ELM model). This study provides a new method for the rapid and non-destructive identification of cotton seed cultivars, which will help ensure the cultivar consistency of seeds used in cotton planting, and improve the overall quality and yield of cotton.

With the deployment of renewable energy and the increasing demand for power grid modernization, redox flow battery has attracted a lot of research interest in recent years. Among the available energy storage technologies, the redox flow battery is considered the most promising candidate battery due to its unlimited capacity, design flexibility, and safety. In this review, we summarize the latest progress and improvement strategies of common inorganic redox flow batteries, such as vanadium redox flow batteries, iron-chromium redox flow batteries, and zinc-based redox flow batteries, including electrolyte, membrane, electrode, structure design, etc. In addition, we introduce the latest progress in aqueous and non-aqueous organic redox flow batteries. We also focus on the modification mechanism, optimization design, improvement strategy, and modeling method of the redox flow battery reaction. Finally, this review presents a brief summary, challenges, and perspectives of the redox flow battery.

Due to the intrinsically ultrahigh cooling rate and thermal gradient during laser melting deposition (LMD), columnar-to-equiaxed transition (CET) of grains has been a significant challenge in titanium-based alloys. In this study, two strategies, including the optimization of processing parameters and the addition of ceramics particles, were utilized to promote the CET of Ti6Al4V. The optimal processing parameters of Ti6Al4V were confirmed by response surface methodology (RSM). The width of prior β grains effectively decreases under the minimum dilution of single track. Besides, different contents of TiB2 and La2O3 were added to Ti6Al4V powder and the in situ (TiB + La2O3)/Ti6Al4V composites were manufactured using the identified optimal processing parameters. In situ TiB whiskers segregating at the grain boundaries tailor the coarse columnar to equiaxed grains with La2O3 particles dispersed in the composites. With increasing formation of the reinforcements, equiaxed grain size decreases from 23.3 to 11.9 μm. The ultimate tensile strength of the components was improved by 19.6% and 26.4% compared to that of Ti6Al4V. This work provides systematic solutions to suppress the coarse columnar grains and refine the microstructure for titanium-based alloys.

Plasmonic vortices confining orbital angular momentums to surface have aroused wide research interest in the last decade. Recent advances of near-field microscopes have enabled the study on the spatiotemporal dynamics of plasmonic vortices, providing a better understanding of optical orbital angular momentums in the evanescent wave regime. However, these works only focused on the objective characterization of plasmonic vortex and have not achieved subjectively tailoring of its spatiotemporal dynamics for specific applications. Herein, it is demonstrated that the plasmonic vortices with the same topological charge can be endowed with distinct spatiotemporal dynamics by simply changing the coupler design. Based on a near-field scanning terahertz microscopy, the surface plasmon fields are directly obtained with ultrahigh spatiotemporal resolution, experimentally exhibiting the generation and evolution divergences during the whole lifetime of plasmonic vortices. The proposed strategy is straightforward and universal, which can be readily applied into visible or infrared frequencies, facilitating the development of plasmonic vortex related researches and applications.

This paper discusses a type of mixed-delay quaternion-valued neural networks (QVNNs) under impulsive and stochastic disturbances. The considered QVNNs model are treated as a whole, rather than as complex-valued neural networks (NNs) or four real-valued NNs. Using the vector Lyapunov function method, some criteria are provided for securing the mean-square exponential stability of the mixed-delay QVNNs under impulsive and stochastic disturbances. Furthermore, a type of chaotic QVNNs under stochastic and impulsive disturbances is considered using a previously established stability analysis method. After the completion of designing the linear feedback control law, some sufficient conditions are obtained using the vector Lyapunov function method for determining the mean-square exponential synchronization of drive–response systems. Finally, two examples are provided to demonstrate the correctness and feasibility of the main findings and one example is provided to validate the use of QVNNs for image associative memory.

This study investigates the process by which English as a Foreign Language (EFL) student teachers transmute their Pedagogical Content Knowledge (PCK) into Personal Practical Knowledge (PPK) within a blended learning community. Data sources, including conversation transcripts, reflective journals, and field notes, were meticulously examined utilizing the commonplaces of temporality, sociality, and place. Several key findings were unveiled: (1) the volume and focal points of transformed PPK varied across participants; (2) the metamorphosis of PCK into PPK was found to be selectively partial, filtered by factors such as previous learning experiences, course expectations, and levels of engagement; and (3) the selection process was molded within the dynamic interplay of the internal components of the blended learning community and the external socio-cultural conditions. The study concludes that the cognition mechanism of EFL student teachers’ PPK is characterized by elements of variation, selection, and dynamism.

It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in $\text{R}^{{2}}$ , revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.

Thermal stability and mechanical property of the multicomponent nanocrystalline Fe–ZrNbMoTa alloys were investigated. Single-phase nanocrystalline Fe–(ZrNbMoTa)x (x = 0.1, 0.2, 0.5 and 1 at. %) alloys were prepared by ball milling and followed by annealing in a set of predesigned condition. It was found that the Fe-based multicomponent alloys possess good thermal stability through 10 h annealing at 900 °C, which is attributed to the thermodynamics effect in the reduction of grain boundary energy caused by multicomponent co-segregation of Zr, Nb, Mo and Ta, and kinetics effect related to sluggish diffusion and Zener pinning of a small amount of second-phase precipitation. Additionally, nanocrystalline Fe–(ZrNbMoTa)x alloys show excellent mechanical properties. The hardness is 738.6 HV, and its yield strength and compressive strength reach up to 3100 MPa and 3600 MPa, respectively. These are attributed to the comprehensive effects of nanocrystalline, solid solution and second phase strengthening.

Waterlogging tolerance is important in direct seeding rice cultivation practice. Here, the authors identify a 14-3-3 protein-coding gene OsGF14h in weedy rice that confers anaerobic germination and anaerobic seedling development tolerance by balancing ABA signaling and GA biosynthesis.

Unlike conventional reservoirs, shale is particularly complex in its mineral composition. As typical components in shale reservoirs, clay and organic matter have different pore structures and strong interactions with fluids, resulting in complex fluid occurrence-states in shale. For example, there are both free water and adsorbed water in clay, and both free oil and ad/absorbed oil in organic matter. Key properties such as fluid content, organic/inorganic porosity, and permeability in clay-rich shale have been poorly characterized in previous studies. In this paper, we used a vacuum-imbibition experimental method combined with nuclear magnetic resonance technique and mathematical modeling to characterize the fluid content, organic/inorganic porosity, saturation, and permeability of clay-rich shale. We conducted vacuum-imbibition experiments on both shale samples and pure clay samples to distinguish the adsorbed oil and water in clay and organic matter. The effects of clay content and total organic matter content (TOC) on porosity and adsorbed-fluid content are then discussed. Our results show that, for the tested samples, organic porosity accounts for 26–76% of total porosity. The oil content in organic matter ranges from 29% to 69% of the total oil content, and 2% to 58% of the organic oil content is ad/absorbed in kerogen. The inorganic porosity has a weak positive correlation with clay content, and organic porosity increases with rising levels of organic matter content. The organic permeability is 1–3 orders of magnitude lower than the inorganic permeability.

Polarization control is of paramount importance for various applications. Here, the authors enable extreme control over light polarization spanning the entire Poincaré sphere by combining coherent control of wave phenomena and the physics of bound states in the continuum.

The Asian cultivated rice Oryza sativa can be classified into two major subspecies: japonica/geng and indica/xian. There are large physiological and phenotypic differences between the two subspecies, with each having its advantages and disadvantages. Understanding the differences between xian and geng could provide a foundation for cultivar improvement based on hybridization between subspecies in order to synthesize favorable traits. We review the origin and domestication of xian and geng rice, compare their differences in terms of physiological and phenotypical traits, and describe the molecular mechanism differences between the subspecies. Based on this knowledge, we propose an ideal plant architecture of geng rice varieties for northern regions.

Non-small cell lung cancer (NSCLC) is a prominent etiology of cancer-related mortality. The heterogeneous nature of this disease impedes its accurate diagnosis and efficacious treatment. Consequently, constant advancements in research are imperative in order to comprehend its intricate nature. In addition to currently available therapies, the utilization of nanotechnology presents an opportunity to enhance the clinical outcomes of NSCLC patients. Notably, the burgeoning knowledge of the interaction between the immune system and cancer itself paves the way for developing novel, emerging immunotherapies for treating NSCLC in the early stages of the disease. It is believed that with the novel engineering avenues of nanomedicine, there is a possibility to overcome the inherent limitations derived from conventional and emerging treatments, such as off-site drug cytotoxicity, drug resistance, and administration methods. Combining nanotechnology with the convergence points of current therapies could open up new avenues for meeting the unmet needs of NSCLC treatment.

Plasmonics takes advantage of surface plasmon polaritons (SPPs), which have the characteristics of strong subwavelength field confinement and enhancement. Plasmonic devices that confine light in regions with dimensions that are smaller than the wavelength have important application prospects in on-chip integrated circuits. Nonetheless, at terahertz frequencies, the weak confinement of SPPs and immature near-field technology hinder the development of related devices. Here, a highly confined SPP mode (spoof SPP) is implemented by structuring metal surfaces. Gradient index lenses with variable indices are constructed by fine-tuning the size of the pillars, and the characteristics of several different lenses are examined and explored by scanning near-field terahertz microscopy. Thus, terahertz waves are regulated and more possibilities are provided for further miniaturization and compactness of THz devices.

Electromagnetic induction can effectively induce abundant firing patterns in neurons. In modeling a neuron model with the electromagnetic induction effect, an electromagnetic induction current is frequently added to the state equation of membrane potential. To more properly reflect the non-uniform distribution of the ions inside and outside the neuron membrane, an ideal flux-controlled memristor with sinusoidal memductance function and non-linearly modulated input is raised to depict an electromagnetic induction effect on a Hindmarsh–Rose neuron model, and thereby, a three-dimensional (3D) memristive Hindmarsh–Rose (mHR) neuron model is built in this paper. The proposed mHR neuron model possesses no equilibrium point since the involvement of the ideal flux-controlled memristor, which induces the generation of hidden dynamics. Numerical results declare that the mHR neuron model can generate abundant hidden dynamics, i.e., periodic spiking, chaotic spiking, period-doubling bifurcation route, tangent bifurcation, and chaos crisis. These hidden dynamics are much related to the memristor coupling strength and externally applied stimulus. Afterward, the memristor initial condition-offset boosting behavior is revealed. This can trigger the generation of infinite multiple coexisting firing patterns along the memristor variable coordinate. These coexisting firing patterns have identical attractor topology but different locations in the phase plane. Finally, an analog circuit is designed for implementing the mHR neuron model, and PSIM-based circuit simulation is executed. The circuit-simulated results perfectly verify the generation of hidden infinite multiple coexisting initial condition-offset boosting firing patterns in the proposed mHR neuron model.

Abstract Ep type is an important morphological improvement (following dwarf breeding and ideal plant type) that has contributed to breeding super-high yielding, and shows a pleiotropic effect in increasing grain yield and also nitrogen-use efficiency (NUE) in rice. Nevertheless, it remains unclear whether Ep has adverse effects on eating quality and how it affects nitrogen uptake and assimilation. In this study, we developed a pair of near-isogenic lines (NILs) for panicle type (NIL-Ep, NIL-non Ep) in the Liaogeng 5 (LG5) and Akihikari (AKI) backgrounds. Rice plants of the NIL-Ep had higher grain numbers per panicle in the middle to bottom spike positions than plants of the NIL-non Ep. The increased grain number is not only is the key factor leading to increased yield but also is the reason for reduced the eating quality. The content of prolamin and glutelin was significantly higher in NIL-Ep, which resulted in higher hardness and worse viscosity of rice after cooking. In addition, the activity of several essential enzymes catalyzing nitrogen metabolism was higher in the NIL-Ep line grains than in the NIL-non Ep, especially from the mid to late grain filling stage. Based on these results, we conclude that Ep positively regulates grain protein accumulation, primarily through enhancing the activity of enzymes involved in nitrogen assimilation and redistribution during the mid to late grain-filling stage, resulting in excessive accumulation of grain protein and decreased eating quality.

Topological photonic devices provide a powerful platform to control the flow of light because their topologically protected edge states can be immune to backscattering and be robust to impurities, disorders, and defects. As one of the topological photonic systems, valley photonic crystals have attracted great attention because they can achieve a large bandgap and be easily combined with modern micro-nano fabrication technology. Terahertz photonic devices are of enormous interest in next-generation communications. Here, we design a valley photonic crystal consisting of a triangular lattice with three metal rod scatterers. By rotating the three metal rods in opposite directions to break the inversion symmetry, we achieve a bandgap width of up to 70% and observe the transport of the edge states in the terahertz region. By replacing part of the structure with phase change material VO2, on/off switching of the terahertz photonic topological edge states is realized, and the attenuation can be also modeled by adjusting the size of the VO2 region. The proposed regime has potential applications in sensing and 6G communications.

Background: The hand motor function is lost and activities in daily life (ADLs) are affected due to some illnesses such as stroke and hemiplegia. As a coping way, we present a wearable rehabilitation glove with the bionic actuator for restoring the hand function and the motor control ability lost by stroke patients. Methods: The soft pneumatic bionic actuator (SPBA) is developed on the basis of the research of human hand bone structure and finger joint characteristics and a series of tests are conducted. Besides, we built the rehabilitation glove system based on the proposed SPBAs to verify the availability due to typical gesture, mirror therapy (MT) and grasping experiment for irregular objects. Result: The bending angle of SPBA can reach 260°. The output force of it can reach 5.1N with 0.25 MPa air pressure input. The maximum variance of the bending angle can be concluded at 5.1° in MT. The grasping experiments of the glove worn on the hand or not shows the proposed glove is flexible, the grip force is large and achieve stable grasping of objects. Conclusion: The designed SPBA is satisfied with the requirements of rehabilitation training and the proposed glove restore the normal hand motion of patients in ADLs.

As a powerful tool for elucidating the embedding representation of graph-structured data, Graph Neural Networks (GNNs), which are a series of powerful tools built on homogeneous networks, have been widely used in various data mining tasks. It is a huge challenge to apply a GNN to an embedding Heterogeneous Information Network (HIN). The main reason for this challenge is that HINs contain many different types of nodes and different types of relationships between nodes. HIN contains rich semantic and structural information, which requires a specially designed graph neural network. However, the existing HIN-based graph neural network models rarely consider the interactive information hidden between the meta-paths of HIN in the poor embedding of nodes in the HIN. In this paper, we propose an Attention-aware Heterogeneous graph Neural Network (AHNN) model to effectively extract useful information from HIN and use it to learn the embedding representation of nodes. Specifically, we first use node-level attention to aggregate and update the embedding representation of nodes, and then concatenate the embedding representation of the nodes on different meta-paths. Finally, the semantic-level neural network is proposed to extract the feature interaction relationships on different meta-paths and learn the final embedding of nodes. Experimental results on three widely used datasets showed that the AHNN model could significantly outperform the state-of-the-art models.

Abstract Light is a complex vectorial field characterized by its amplitude, phase, and polarization properties, which can be further represented by four basic parameters, that is, amplitudes and phases of two orthogonally polarized components. Controlling these parameters simultaneously and independently at will using metasurfaces are essential in arbitrarily manipulating the light propagation. However, most of the studies so far commonly require a great number of different meta‐atoms or rely on diffraction under oblique incidence, which lack convenience and flexibility in design and implementation. Here, a new metasurface paradigm is proposed that can completely manipulate the amplitudes and phases of two spin components based on the interference effect, where only two different meta‐atoms are applied. For proof‐of‐concept demonstration, two five‐channel meta‐holograms for imaging and information encryption are designed and experimentally characterized. The interference method provides a simple route toward compact complex and multifunctional meta‐devices.

Manipulating spoof surface plasmon polaritons (SPPs) effectively has promising applications in developing ultracompact plasmonic circuits. Research on versatile components in planar spoof SPP waveguides has attracted widespread attention over the decades. In particular, cascaded chip-scale devices play an important role in enhancive buffering, highly-sensitive sensing and nonlinear interaction. Herein, we propose a novel strategy to manipulate the switching and slow wave features of spoof SPPs on the basis of plasmon-induced transparency (PIT) spectral responses. A combined module consisting of split square-ring resonators and electric-LC resonators is loaded with a spoof SPP waveguide along the propagating trajectory, where the destructive interference between the two resonators contributes to the PIT behavior and the group delay of the spoof SPPs. A modulation contrast of up to 16.2 dB is achieved for the spoof SPP transmission at the PIT peak frequency. The measurements are consistent with both full-wave simulations and theoretical calculations. Cascaded capabilities performed by installing multiple PIT modules at an interval of an equivalent wavelength are accomplished in the planar spoof SPP waveguide. The proposed strategy opens up fascinating prospects on highly integrated plasmonic circuits and networks.

The security of powering systems has been a major problem over the last decade, leading to an increased interest in wireless power and data transfer. In this research paper, a new inductive Wireless Power Transfer (WPT) circuit topology has been used. In traditional WPT circuits, the inverters are used to produce an oscillation for the transmitter coils. The classic WPT system includes intrinsic energy dissipation sources due to the use of switches, necessitating the need of an extra control circuit to ensure proper switching time. Furthermore, they have limited data encryption capabilities. As a result, an unique WPT system based on memristors has been developed, eliminating the need for switches. Furthermore, because this novel topology communicates a synchronised chaotic behaviour, it becomes highly beneficial. This circuit may be used in Near Field Communication (NFC), where chaotic true random numbers (TRNG) can be generated to increase security. The results of simulations indicate the functioning of the Memristor-based WPT (M-WPT) and its ability to generate random numbers. We experimentally proved the chaotic behaviour of the circuit and statistically demonstrated the development of the TRNG, using an Arduino board and the Chua circuit to build the M-WPT circuit.

A periodic intermittent adaptive control method with saturation is proposed to pin the quasi-consensus of nonlinear heterogeneous multi-agent systems with external disturbances in this paper. A new periodic intermittent adaptive control protocol with saturation is designed to control the internal coupling between the follower agents and the feedback gain between the leader and the follower. In particular, we use the saturation adaptive law: when the quasi-consensus error converges to a certain range, the adaptive coupling edge weight and the adaptive feedback gain will not be updated. Furthermore, we propose three saturated adaptive pinning control protocols. The quasi-consensus is achieved through its own pinning as long as the agents remain connected to each other. Using the Lyapunov function method and inequality technique, the convergence range of the quasi-consensus error of a heterogeneous multi-agent system is obtained. Finally, the rationality of the proposed control protocol is verified through numerical simulation. Theoretical derivation and simulation results show that the novel proposed periodic intermittent adaptive control method with saturation can successfully be used to achieve the pinning of quasi-consensus of nonlinear heterogeneous multi-agent systems.

Study DesignA randomized controlled pilot study.BackgroundBimanual therapy (BMT) is an effective neurorehabilitation therapy for the upper limb, but its application to the distal upper limb is limited due to methodological difficulties. Therefore, we applied an exoskeleton hand to perform robot-assisted task-oriented bimanual training (RBMT) in patients with stroke.ObjectiveTo characterize the effectiveness of RBMT in patients with hemiplegic stroke with upper limb motor impairment.InterventionsA total of 19 patients with subacute stroke (1–6 months from onset) were randomized and allocated to RBMT and conventional therapy (CT) groups. The RBMT and CT groups received 90 min of training/day (RBMT: 60 min RBMT + 30 min CT; CT: 60 min CT for hand functional training + 30 min regular CT), 5 days/week, for 4 weeks (20 sessions during the experimental period).AssessmentsClinical assessments, including the Fugl–Meyer assessment of the upper extremity (FMA-UE), action research arm test (ARAT), and wolf motor arm function test (WMFT), were conducted before and after the intervention.ResultsWithin-group analysis showed a significant improvement in the FMA-UE and WMFT in both the CT and RBMT groups. A significant improvement in the Fugl–Meyer assessment (FMA) of the wrist and hand for the distal part in the RBMT group occurred earlier than that in the CT group. A significant improvement in WMFT time was found in both groups, but the WMFT functional ability assessment was only found in the RBMT group. No significant improvements in ARAT assessment were observed in either the CT or RBMT groups. Compared with CT, significant improvements were found in terms of the proportion of minimally clinically important differences after RBMT in FMA-UE (χ2 = 4.34, p = 0.037). No adverse events were reported by any of the participants across all sessions.ConclusionsThis study is the first to apply RBMT to the distal part of the upper limb. Both RBMT and CT are effective in improving the upper limb function in patients with subacute stroke. RBMT shows superior potential efficacy in facilitating recovery of the distal part of upper extremity (UE) motor function in the early stage. Future randomized control studies with a large sample size and follow-up assessments are needed to validate the present conclusions.

BackgroundThis study aims to develop and validate a nomogram for predicting 1- and 2-year generalization probabilities in patients with ocular myasthenia gravis (OMG).MethodsIn total, 501 eligible patients with OMG treated at seven tertiary hospitals in China between January 2015 and May 2019 were included. The primary outcome measure was disease generalization. A nomogram for predicting 1- and 2-year generalization probabilities was constructed using a stepwise Cox regression model. Nomogram performance was quantified using C-indexes and calibration curves. Two-year cumulative generalization rates were analyzed using the Kaplan−Meier method for distinct nomogram-stratified risk groups. The clinical usefulness of the nomogram was evaluated using decision curve analysis (DCA).ResultThe eligible patients were randomly divided into a development cohort (n=351, 70%) and a validation cohort (n=150, 30%). The final model included five variables: sex, onset age, repetitive nerve stimulation findings, acetylcholine receptor antibody test results, and thymic status. The model demonstrated good discrimination (C-indexes of 0.733 and 0.788 in the development and validation cohorts, respectively) and calibration, with good agreement between actual and nomogram-estimated generalization probabilities. Kaplan−Meier curves revealed higher 2-year cumulative generalization rates in the high-risk group than that in the low-risk group. DCA demonstrated a higher net benefit of nomogram-assisted decisions compared to treatment of all patients or none.ConclusionThe nomogram model can predict 1- and 2-year generalization probabilities in patients with OMG and stratified these patients into distinct generalization risk groups. The nomogram has potential to aid neurologists in selecting suitable patients for initiating immunotherapy and for enrolment in clinical trials of risk-modifying treatments.

Abstract Conventional lithium‐ion batteries (LIBs) with graphite anodes are approaching their theoretical limitations in energy density. Replacing the conventional graphite anodes with high‐capacity Si‐based anodes represents one of the most promising strategies to greatly boost the energy density of LIBs. However, the inherent huge volume expansion of Si‐based materials after lithiation and the resulting series of intractable problems, such as unstable solid electrolyte interphase layer, cracking of electrode, and especially the rapid capacity degradation of cells, severely restrict the practical application of Si‐based anodes. Over the past decade, numerous reports have demonstrated that polymer binders play a critical role in alleviating the volume expansion and maintaining the integrity and stable cycling of Si‐based anodes. In this review, the state‐of‐the‐art designing of polymer binders for Si‐based anodes have been systematically summarized based on their structures, including the linear, branched, crosslinked, and conjugated conductive polymer binders. Especially, the comprehensive designing of multifunctional polymer binders, by a combination of multiple structures, interactions, crosslinking chemistries, ionic or electronic conductivities, soft and hard segments, and so forth, would be promising to promote the practical application of Si‐based anodes. Finally, a perspective on the rational design of practical polymer binders for the large‐scale application of Si‐based anodes is presented.

Abstract In this study, we investigate reaction-diffusion complex-valued neural networks with mixed delays. The mixed delays include both time-varying and infinite distributed delays. Criteria are derived to ensure the existence, uniqueness, and exponential stability of the equilibrium state of the addressed system on the basis of the M-matrix properties and homeomorphism mapping theories as well as the vector Lyapunov function method. The results demonstrate the positive effect of reaction-diffusion on the stability, which further improves the existing conditions. Finally, the analysis of several examples is compared to the present results to verify the correctness and reduced conservatism of the primary results.

Conventional metasurfaces often manipulate light propagation in a passive way, which cannot satisfy the dynamic operation requirements of many practical applications. Integrating functional materials or components into the structure design is one effective method, which, however, often introduces additional loss to the resonances, thus greatly affecting the efficiency of modulation. Here, we numerically propose a reflection-type dielectric metasurface design that could allow switchable and efficient wavefront control in the terahertz regime. The metasurfaces are composed of dielectric structures suspended on a ground metallic film, which can exhibit different phase responses at different gap distances between them owing to the variation of the Fabre-Pérot (FP) resonance. Two metasurfaces are designed and numerically demonstrated, which can achieve mechanically switchable beam steering and wave focusing responses, respectively, by only changing the gap distance by a small range of 1/3 working wavelength. The maximum efficiency can reach around 89%. The proposed method provides a new avenue towards efficient and active terahertz wavefront control.

This article introduces a drone image stitching based on mesh-guided deformation and ground constraint, which can closely match the characteristics of images and achieve precise registration and acquire ideal stitching effect. The traditional methods use the homography model to align the image, which causes artifacts in the result of stitching the images with parallax. To overcome this situation, the image is divided into meshes and the mesh vertices of the target image are used to guide the warping. A new energy function is designed to represent the deformation characteristics of the image. We propose a new alignment term by using local homography and a local scale term by using the edge information of the mesh. The established mesh-guided deformation model can overcome image parallax caused by some external factors and eliminate the ghostly parts of the result. Moreover, imaged scene is not effectively planar and some fluctuations exist in the scene of the images, which will distort the stitching result. We propose a ground constraint with the ground plane as the main plane to reduce projection distortions in non-overlapping areas between images. Finally, the method of creating groundtruth is proposed, which can evaluate the naturalness of results and make comparison more reasonable. Several sets of challenging drone images are tested, and the experimental results show that our stitching system has good results.

This study analysed the spatial pattern change of land use from 2005 to 2015 in Xinjiang and proposed a Markov–Future Land Use Simulation composite model to predict land-use change by 2025 under natural growth and ecological protection scenarios. The Criteria Importance Though Intercrieria Correlation method was proposed to construct the landscape ecological risk index based on land-use data. By calculating the risk index centroid and standard deviation ellipses, the spatio-temporal patterns and changing characteristics of landscape ecological risk in different scenarios in 2005, 2015, and 2025 were evaluated, and the driving forces of their evolution were also analysed. The results indicated that: (1) The characteristics of land use pattern change under the natural growth scenario from 2015 to 2025 are similar to those from 2005 to 2015. Under an ecological protection scenario, restricting the expansion of construction land, forest land, and grassland, which are the central ecological lands, can be effectively protected. Compared to the natural growth scenario, these areas increased by 507 and 724 km2, respectively. (2) From 2005 to 2015, landscape ecological risk in Xinjiang generally showed a decreasing trend. Compared to the natural growth scenario in 2025, ecological protection can effectively reduce the ecological risk of the landscape. The highest, higher, and medium ecological risks were reduced by 0.39%, 3.11%, and 4.43%, respectively, while the areas with the lowest and lower ecological risks increased by 0.16% and 7.77%, respectively. (3) Under the natural growth scenario from 2005 to 2025, the centroid of landscape ecological risk in Xinjiang generally shifted to the southwest. To some extent, the ecological protection scenario can make the distribution of the highest, the higher, and the medium ecological risk areas more dispersed and the distribution of the lower ecological risk areas more concentrated. (4) Temperature is positively correlated with landscape ecological risk, while rainfall, Digital Elevation Model, slope, and aspect are negatively correlated with landscape ecological risk. There is also a correlation between soil types and landscape ecological risk. This research can reference the theory and method of landscape ecological risk and provide decision support for the sustainable development of the ecological environment of Xinjiang.

Mental health has always been a prominent public health concern, and it has become more important in the wake of the COVID-19 pandemic. The mental health of airline pilots plays a significant role in their occupational health and overall performance. It is also vital for ensuring the safe operation of aircrafts. Therefore, it is crucial to identify the factors that may improve the mental health of pilots. This study investigates the relationship between proactive coping, perceived stress, social support, and mental health among airline pilots during China's regular prevention and control of COVID-19. Using a sample consisting of 285 Chinese commercial airline pilots, we tested a moderated mediation model to explore whether, how, and when proactive coping affects the mental health of pilots. The results show that proactive coping has a direct and positive effect on pilots' mental health, as well as an indirect effect on mental health through its influence on perceived stress. Social support was found to weaken the relationship between perceived stress and mental health. It also weakened the indirect relationship between proactive coping and mental health through perceived stress. These findings advance our understanding of the underlying mechanisms that affect the mental health of pilots. It also provides empirical evidence for effective mental health interventions for airline pilots during regular prevention and control of COVID-19.

Pathway-level analysis is a powerful approach enabling the interpretation of post-genomic data at a higher level than that of individual molecules. Molecular-targeted therapy focusing on cascade signaling pathways has become a new paradigm in anticancer therapy, instead of a single protein. However, the approaches to narrowing down the long list of biological pathways are limited. Here, we proposed a strategy for in silico Drug Prescription on biological pathways across pan-Cancers (CDP), by connecting drugs to candidate pathways. Applying on a list of 120 traditional Chinese medicines (TCM), we especially identified the “TCM–pathways–cancers” triplet and constructed it into a heterogeneous network across pan-cancers. Applying them into TCMs, the computational prescribing methods deepened the understanding of the efficacy of TCM at the molecular level. Further applying them into Western medicines, CDP could promote drug reposition avoiding time-consuming developments of new drugs.

Abstract Background Precision oncology pharmacotherapy relies on precise patient-specific alterations that impact drug responses. Due to rapid advances in clinical tumor sequencing, an urgent need exists for a clinical support tool that automatically interprets sequencing results based on a structured knowledge base of alteration events associated with clinical implications. Results Here, we introduced the Oncology Pharmacotherapy Decision Support System (OncoPDSS), a web server that systematically annotates the effects of alterations on drug responses. The platform integrates actionable evidence from several well-known resources, distills drug indications from anti-cancer drug labels, and extracts cancer clinical trial data from the ClinicalTrials.gov database. A therapy-centric classification strategy was used to identify potentially effective and non-effective pharmacotherapies from user-uploaded alterations of multi-omics based on integrative evidence. For each potentially effective therapy, clinical trials with faculty information were listed to help patients and their health care providers find the most suitable one. Conclusions OncoPDSS can serve as both an integrative knowledge base on cancer precision medicine, as well as a clinical decision support system for cancer researchers and clinical oncologists. It receives multi-omics alterations as input and interprets them into pharmacotherapy-centered information, thus helping clinicians to make clinical pharmacotherapy decisions. The OncoPDSS web server is freely accessible at https://oncopdss.capitalbiobigdata.com .

Abstract Background The heterotrimeric G protein complex, consisting of Gα, Gβ, and Gγ subunits, are conserved signal transduction mechanism in eukaryotes. Recent molecular researches had demonstrated that G protein signaling participates in the regulation of yield related traits. However, the effects of G protein genes on yield components and stress tolerance are not well characterized. Results In this study, we generated heterotrimeric G protein mutants in rice using CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats) gene-editing technology. The effects of heterotrimeric G proteins on the regulation of yield components and stress tolerance were investigated. The mutants of gs3 and dep1 generated preferable agronomic traits compared to the wild-type, whereas the mutants of rga1 showed an extreme dwarf phenotype, which led to a dramatic decrease in grain production. The mutants showed improved stress tolerance, especially under salinity treatment. We found four putative extra-large G proteins (PXLG)1–4 that also participate in the regulation of yield components and stress tolerance. A yeast two hybrid showed that the RGB1 might interact with PXLG2 but not with PXLG1, PXLG3 or PXLG4. Conclusion These findings will not only improve our understanding of the repertoire of heterotrimeric G proteins in rice but also contribute to the application of heterotrimeric G proteins in rice breeding.

PURPOSE:To evaluate skeletal muscle perfusion in a canine hind limb ischemia model using CT perfusion imaging (CTPI).METHODS:Twelve beagles underwent embolization at the branch of the left deep femoral artery. The right hind limbs were used as controls. CTPI was performed immediately after embolization. The perfusion parameters of the regions of interest (ROI), including blood volume (BV), blood flow (BF), mean transit time (MTT) and permeability (PMB), were obtained in both the lateral and posterior hind limb muscle groups.RESULTS:After embolization, the BV, BF and PMB values in the lateral muscles of the left hind limbs were significantly lower than those in the right hind limbs (P > 0.05), and the MTT was significantly prolonged (P > 0.05). The values for BV, BF, MTT and PMB in the posterior muscles of the left hind limbs were not significantly different from those in the right hind limbs (P > 0.05). The values for BV, BF and PMB in the lateral muscles of the left hind limbs were significantly lower than those in the posterior muscles of the left hind limbs (P > 0.05).CONCLUSION:CTPI could be used to evaluate skeletal muscle perfusion in a canine model, which may have clinical relevance in lower limb ischemia and vascular reconstruction.

Seeking the unprecedented ability of metasurfaces to manipulate terahertz waves in a desired manner has attracted increasing interest. We design and investigate a kind of multi-channel, polarization-dependent transmission-type dielectric metasurface-based terahertz beam splitter. It is numerically demonstrated that such metasurfaces can manipulate terahertz beam, and split it into four channels with alterable angles and orthogonal polarizations, under the linearly polarized incidence with both the x- and y-polarization components. The suggested regime may alleviate the shortage of advanced multifunctional devices and provide extra freedom for terahertz functional device design.