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Methotrexate (MTX) is a therapeutic drug that is widely used in clinic for treatment of a variety of cancers. However, MTX has limited selectivity and serious cytotoxicity, at the same time, there are problems such as narrow therapeutic window and large individual variability in metabolism and excretion of MTX. Therefore, blood concentration monitoring is required for MTX administration in clinical practice. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has the advantages of strong specificity, good separation characteristics, high sensitivity and fast detection speed, which has become the internationally recognized “gold standard” for blood concentration monitoring of drugs. In this study, an isotope dilution mass spectrometry method was developed for accurate measurement of MTX in serum by the house-made QLIT-6610MD quadrupole-linear ion trap liquid chromatography-mass spectrometer. Unlike the traditional triple quadrupole QqQ tandem mass spectrometer, the quadrupole-linear ion trap (Q-LIT) mass spectrometer adopts the quadrupole-linear ion trap direct axial coupling design, i.e., a single linear ion trap is used to replace the QqQ tandem mass spectrometer’s back-end fragmentation cell and the quadrupole, and no other components are installed between the quadrupole and linear ion trap, which greatly streamlines the quality and efficiency of the mass spectrometer while ensuring the original performance of the instrument. With this unique configuration, the QLIT-6610MD instrument uses a simultaneous fragmentation and accumulation technique, which remarkedly reduces matrix-induced interferences and effectively reduces space charge effects. Serum samples were pretreated with methanol to precipitate proteins and the supernatants were measured by QLIT-6610MD spectrometer. The linear correlation coefficient (R2＞0.999 0) is obtained for MTX in the range of 10-5 000 μg/L. The limit of quantification is 10 μg/L. The precision of the quality control is better than 6.94%, and the recoveries of spiked serum samples are 86.39%-97.84% with a coefficient of variation less than 8.04%. We used the QLIT-6610MD and AB QTRAP 6500+ liquid chromatography-mass spectrometers to analyze 70 clinical serum samples for comparison of the performance of the house-made instrument by means of Pearson’s coefficient and BLand-ALtman method. The results showed that the QLIT-6610MD mass spectrometry achieves a similar level of performance to AB ATRAP 6500+ instrument, and provides a new domestic instrument option for clinical mass spectrometry. In addition, this study explores the possibility applying the multi-stage fragmentation technique (MSn) to determine MTX in serum, gaining fundamental data for future studies.

We developed a novel site-specific bimodal MRI/fluorescence nanoparticle contrast agent targeting gastrin-releasing peptide receptors (GRPrs), which are overexpressed in aggressive prostate cancers. Biocompatible ultra-small superparamagnetic iron oxide (USPIO) nanoparticles were synthesized using glucose and casein coatings, followed by conjugation with a Cy7.5-K-8AOC-BBN [7-14] peptide conjugate. The resulting USPIO(Cy7.5)-BBN nanoparticles were purified by 100 kDa membrane dialysis and fully characterized using transmission electron microscopy (TEM), dynamic light scattering (DLS), Fourier transform infrared (FTIR) spectroscopy, and magnetic resonance imaging (MRI) relaxivity, as well as evaluated for in vitro and in vivo binding specificity and imaging efficacy in PC-3 prostate cancer cells and xenografted tumor-bearing mice. The USPIO(Cy7.5)-BBN nanoparticles had a core diameter of 4.93 ± 0.31 nm and a hydrodynamic diameter of 35.56 ± 0.58 nm. The r2 relaxivity was measured to be 70.2 ± 2.5 s−1 mM−1 at 7T MRI. The Cy7.5-K-8AOC-BBN [7-14] peptide-to-nanoparticle ratio was determined to be 21:1. The in vitro GRPr inhibitory binding (IC50) value was 2.5 ± 0.7 nM, indicating a very high binding affinity of USPIO(Cy7.5)-BBN to the GRPr on PC-3 cells. In vivo MRI showed significant tumor-to-muscle contrast enhancement in the uptake group at 4 h (31.1 ± 3.4%) and 24 h (25.7 ± 2.1%) post-injection compared to the blocking group (4 h: 15.3 ± 2.0% and 24 h: −2.8 ± 6.8%; p < 0.005). In vivo and ex vivo near-infrared fluorescence (NIRF) imaging revealed significantly increased fluorescence in tumors in the uptake group compared to the blocking group. These findings demonstrate the high specificity of bimodal USPIO(Cy7.5)-BBN nanoparticles towards GRPr-expressing PC-3 cells, suggesting their potential for targeted imaging in aggressive prostate cancer.

The three-dimension (3D) inversion of magnetic data is an effective method of recovering underground magnetic susceptibility distributions using magnetic anomaly data. The conventional regularization inversion method has good data fitting; however, its inversion model has the problem of a poor model-fitting ability due to a low depth resolution. The 3D inversion method based on deep learning can effectively improve the model-fitting accuracy, but it is difficult to guarantee the data-fitting accuracy of the inversion results. The loss function of traditional deep learning 3D inversion methods usually adopts the metric of the absolute mean squared error (MSE). In order to improve the accuracy of the data fitting, we added a forward-fitting constraint term (FFit) on the basis of the MSE. Meanwhile, in order to further improve the accuracy of the model fitting, we added the Dice coefficient to the loss function. Finally, we proposed a multi-constraint deep learning 3D inversion method based on UNet++. Compared with the traditional single-constraint deep learning method, the multi-constraint deep learning method has better data-fitting and model-fitting effects. Then, we designed corresponding test models and evaluation metrics to test the effectiveness and feasibility of the method, and applied it to the actual aeromagnetic data of a test area in Suqian City, Jiangsu Province.

This paper concentrates on the elevator passenger detection task, a pivotal element for subsequent elevator passenger tracking and behavior recognition, crucial for ensuring passenger safety. To enhance the accuracy of detecting passenger positions inside elevators, we improved the YOLOv8 network and proposed the SC-YOLOv8 elevator passenger detection network with soft-pooling and attention mechanisms. The main improvements in this paper encompass the following aspects: Firstly, we transformed the convolution module (ConvModule) of the YOLOv8 backbone network by introducing spatial and channel reconstruction convolution (SCConv). This improvement aims to reduce spatial and channel redundancy in the feature extraction process of the backbone network, thereby improving the overall efficiency and performance of the detection network. Secondly, we propose a dual-branch SPP-Fast module by incorporating a soft-pooling branch into the YOLOv8 network’s SPP-Fast module. This dual-branch SPP-Fast module can preserve essential information while reducing the impact of noise. Finally, we propose a soft-pooling and multi-scale convolution CBAM module to further enhance the network’s performance. This module enhances the network’s focus on key regions, allowing for more targeted feature extraction, thereby further improving the accuracy of object detection. Additionally, the attention module enhances the network’s robustness in handling complex backgrounds. We conducted experiments on an elevator passenger dataset. The results show that the precision, recall, and mAP of our improved YOLOv8 network are 94.32%, 91.17%, and 92.95%, respectively, all surpassing those of the original YOLOv8 network.

Abdominal aortic aneurysm (AAA) is a serious vascular disease which is associated with vascular remodeling. CD38 is a main NAD+-consuming enzyme in mammals, and our previous results showed that CD38 plays the important roles in many cardiovascular diseases. However, the role of CD38 in AAA has not been explored. Here, we report that smooth-muscle-cell-specific deletion of CD38 (CD38SKO) significantly reduced the morbidity of AngII-induced AAA in CD38SKOApoe−/− mice, which was accompanied with a increases in the aortic diameter, medial thickness, collagen deposition, and elastin degradation of aortas. In addition, CD38SKO significantly suppressed the AngII-induced decreases in α-SMA, SM22α, and MYH11 expression; the increase in Vimentin expression in VSMCs; and the increase in VCAM-1 expression in smooth muscle cells and macrophage infiltration. Furthermore, we demonstrated that the role of CD38SKO in attenuating AAA was associated with the activation of sirtuin signaling pathways. Therefore, we concluded that CD38 plays a pivotal role in AngII-induced AAA through promoting vascular remodeling, suggesting that CD38 may serve as a potential therapeutic target for the prevention of AAA.

Septins play a key regulatory role in cell division, cytokinesis, and cell polar growth of the rice blast fungus (Magnaporthe oryzae). We found that the organization of the septin ring, which is essential for appressorium-mediated infection in M. oryzae, requires long-chain fatty acids (LCFAs), which act as mediators of septin organization at membrane interfaces. However, it is unclear how septin ring formation and LCFAs regulate the pathogenicity of the rice blast fungus. In this study, a novel protein was named MoLfa1 because of its role in LCFAs utilization. MoLfa1 affects the utilization of LCFAs, lipid metabolism, and the formation of the septin ring by binding with phosphatidylinositol phosphates (PIPs), thereby participating in the construction of penetration pegs of M. oryzae. In addition, MoLfa1 is localized in the endoplasmic reticulum (ER) and interacts with the ER-related protein MoMip11 to affect the phosphorylation level of Mps1. (Mps1 is the core protein in the MPS1-MAPK pathway.) In conclusion, MoLfa1 affects conidia morphology, appressorium formation, lipid metabolism, LCFAs utilization, septin ring formation, and the Mps1-MAPK pathway of M. oryzae, influencing pathogenicity.

Stachydrine, a prominent bioactive alkaloid derived from Leonurus heterophyllus, is a significant herb in traditional medicine. It has been noted for its anti-inflammatory and antioxidant characteristics. Consequently, we conducted a study of its hepatoprotective effect and the fundamental mechanisms involved in acetaminophen (APAP)-induced liver injury, utilizing a mouse model. Mice were intraperitoneally administered a hepatotoxic dose of APAP (300 mg/kg). Thirty minutes after APAP administration, mice were treated with different concentrations of stachydrine (0, 2.5, 5, and 10 mg/kg). Animals were sacrificed 16 h after APAP injection for serum and liver tissue assays. APAP overdose significantly elevated the serum alanine transferase levels, hepatic pro-inflammatory cytokines, malondialdehyde activity, phospho-extracellular signal-regulated kinase (ERK), phospho-protein kinase B (AKT), and macrophage-stimulating protein expression. Stachydrine treatment significantly decreased these parameters in mice with APAP-induced liver damage. Our results suggest that stachydrine may be a promising beneficial target in the prevention of APAP-induced liver damage through attenuation of the inflammatory response, inhibition of the ERK and AKT pathways, and expression of macrophage-stimulating proteins.

The 3D inversion algorithm for gravity data based on a smooth model constraint has been proven to yield a reasonable density distribution. However, as the amount of observed data and model parameters increases, the algorithm experiences issues with high memory consumption and prolonged computation time. Therefore, the corresponding problem in interpreting gravity inversion lies in developing a fast inversion algorithm. The conventional smooth model constraint inversion algorithm, based on regularization theory, requires the introduction of a model weighting function with a large matrix, and involves storage and operation of a large matrix with intermediate variables during inversion iteration, contributing significantly to the prolonged computation time. In this paper, a diagonal weight matrix is represented by vectorization, and the intermediate variable of the large matrix type in the iteration is replaced with the combination of a small matrix and a vector. Additionally, the algorithm flow of the conjugate gradient method is further optimized to minimize the number of vectors that need to be stored during iteration. As a result of these optimizations, the memory consumption of the algorithm during the operation process is successfully reduced. Finally, the experiments demonstrate the successful development of a fast 3D inversion algorithm for gravity data. Specifically, for a 80 × 80 × 20 mesh number inversion, our accelerated algorithm achieves an average speed of ~0.5 s per iteration, and the iterative process speeds up by a factor of 1000, providing an effective strategy for the fast inversion of large-scale data.

Force-dependent transitions can provide insights into the free-energy landscape of proteins. Here, the authors observe the folding and unfolding dynamics of the model protein Csp using magnetic tweezers, from which a free-energy landscape with two energy barriers and a transient intermediate state is constructed.

Abstract With the rapid development of nanofluidics, more and more unexpected behaviors and bizarre properties have been discovered, which brings more possibility to solve the water and energy problem. Carbon nanotubes (CNTs) with nanoscale diameter and ultrasmooth hydrophobic surface provide strong confinement and unusual water‐carbon couple which lead to many exotic properties, such as flow enhancement, strong ion exclusion, ultrafast proton transport and phase transition. This article reviews the recent progresses of CNT‐based nanofluidic devices in fabrication, property, and applications. Moreover, challenges and opportunities of the CNT‐based nanofluidic devices are discussed.

A human body monitoring system remains a significant focus, and to address the challenges in wearable sensors, a nanotechnology-enhanced strategy is proposed for designing stretchable metal-organic polymer nanocomposites. The nanocomposite comprises reduced graphene oxide (rGO) and in-situ generated silver nanoparticles (AgNPs) within elastic electrospun polystyrene-butadiene-polystyrene (SBS) fibers. The resulting Sandwich Structure Piezoresistive Woven Nanofabric (SSPWN) is a tactile-sensitive wearable sensor with remarkable performance. It exhibits a rapid response time (less than three milliseconds) and high reproducible stability over 5500 cycles. The nanocomposite also demonstrates exceptional thermal stability due to effective connections between rGO and AgNPs, making it suitable for wearable electronic applications. Furthermore, the SSPWN is successfully applied to human motion monitoring, including various areas of the hand and RGB sensing shoes for foot motion monitoring. This nanotechnology-enhanced strategy shows promising potential for intelligent healthcare, health monitoring, gait detection, and analysis, offering exciting prospects for future wearable electronic products.

Narrowband active noise control (NANC) has shown excellent performance in dealing with the low frequency periodic noise generated by rotating machines, such as fans, engines and power transformers. Accommodating large frequency mismatch (FM) and improving its tracking capability is required for the NANC system. The existence of FM influences the noise cancellation performance. In this paper, a frequency correction algorithm based on least mean p-power (LMP) combined with the autoregressive (AR) model is designed for the NANC system, which is simple and feasible, and has a good performance under a large step size. In the NANC system, the reference signal is handled by a delay unit and AR model, and the coefficients of the AR model are adjusted by the LMP algorithm, which fine-tunes the coefficients and offers the reference signals to the NANC system. The stability bounds for the step size parameter have also been derived in the mean sense. The designed mechanism converges fast and enhances the noise decrement. Extensive simulations are performed to demonstrate the superior performance of the proposed NANC in dealing with periodic noises.

Aeromagnetic exploration is a magnetic exploration method that detects changes of the earth’s magnetic field by loading a magnetometer on an aircraft. With the miniaturization of magnetometers and the development of unmanned aerial vehicles (UAV) technology, UAV aeromagnetic surveying plays an increasingly important role in mineral exploration and other fields due to its advantages of low cost and safety. However, in the process of aeromagnetic measurement data, due to the ferromagnetic material of the aircraft itself and the change of flight direction and attitude, magnetic field interference will occur and affect the measurement of the geomagnetic field by the magnetometer. The work of aeromagnetic compensation is to compensate for this part of the magnetic interference and improve the magnetic measurement accuracy of the magnetometer. This paper focused on the problems of UAV aeromagnetic survey data processing and improved the accuracy of UAV based aeromagnetic data measurement. Based on the Tolles–Lawson model, a numerical simulation experiment of magnetic interference of UAV-based aeromagnetic data was carried out, and a radial basis function (RBF) artificial neural network (ANN) algorithm was proposed for the first time to compensate the aeromagnetic data. Compared with classical backpropagation (BP) ANN, the test results of the synthetic data and real measured magnetic data showed that the RBF-ANN has higher compensation accuracy and stronger generalization ability.

Aeromagnetic compensation is a crucial step in the processing of aeromagnetic data. The aeromagnetic compensation method based on the linear regression model has poorer fitting capacity than the neural network aeromagnetic compensation algorithm. The existing gradient updating neural network-based aeromagnetic compensation algorithm is subject to the problem that the gradient disappears during the backpropagation process, resulting in poor fitting ability and affecting aeromagnetic compensation accuracy. In this paper, we propose a neural network compensation algorithm with strong fitting ability: residual backpropagation neural network (Res-bp). The algorithm realizes the cross-layer propagation of the gradient through a residual connection so that the network not only preserves the original information but also acquires additional information during training, successfully solving the problem of gradient disappearance and boosting the network’s fitting capacity. The algorithm is applied to the data collected by unmanned aerial vehicles (UAVs) to verify its effectiveness. The results show that the improvement ratio is improved compared with the traditional neural network, demonstrating that the algorithm has a significant compensation effect on aeromagnetic interference and improves the quality of aeromagnetic data.

In this study, different carbon nanomaterials (carbon nanotubes, solid carbon spheres and graphene nanosheets) for immobilizing bacterial small laccase (SLAC) to enable direct bioelectrocatalysis of oxygen reduction were comparatively investigated. Highest bioelectrocatalytic current response was achieved with ethanol-assisted SLAC adsorption on single-walled carbon nanotubes. Direct electron transfer (DET) rates were determined and generally shown to be larger with carbon nanomaterials in smaller dimensions or higher surface curvature, i.e., 1.92, 1.05, 1.52, 0.62 and 0.63 s−1 for adsorbed SLAC on single-walled carbon nanotubes (d = ca. 1.5 nm), double-walled carbon nanotubes (d = 2–4 nm), thinner multi-walled carbon nanotubes (d = 8–15 nm), thicker multi-walled carbon nanotubes (d = 20–30 nm) and graphene nanosheets, respectively. No obvious catalytic current response was obtained with solid carbon spheres (d > 200 nm). Surface nanostructures on electrodes with decreased cylindrical diameter could enable a favorable protein orientation of immobilized SLAC for efficient electronic communication between the catalytic copper sites and carbon nanomaterials as revealed by surface vibrational spectroscopy. Moreover, such a difference in DET kinetics of SLAC could be an outcome of synergic effects of surface properties (curvature, oxygen-containing groups, etc.) and conductivity of carbon nanomaterials. It is expected to offer useful information to fabricating carbon-based oxygen-reducing biocathodes of wide working pH range and high tolerance to chloride. Keywords: Small laccase, Carbon nanomaterials, Oxygen reduction, Direct electron transfer

Accurate and efficient reconstruction of hidden geological structures under the surface is the main task of high-resolution Velocity Model Building (VMB). The most commonly used methods in practice are Tomography and Full Waveform Inversion (FWI), which rely heavily on the initial model. Recently, deep learning types of methods have received widespread attention and have performed well in many tasks such as image segmentation and classification. Therefore, it is of great significance to introduce deep learning algorithms into the VMB procedure to accelerate the production cycle, especially for the velocity anomalies detection, which is crucial for a high-resolution initial model. In this paper, a modified U-Net framework is proposed and applied directly on the seismic shot gathers to identify anomalies in the early stage of VMB, which can provide a suitable initial guess for the following large-scale VMB procedures such as FWI. The numerical examples show the power of the proposed method on synthetic data.

Investigation of the heterogeneity of protein adsorption on particle surfaces has attracted enormous research attention owing to its great importance in fundamental studies and quality control. Herein we, for the first time, report a new method for label-free analysis of the heterogeneity of protein adsorption on single nanoparticles, based on particle collision events at the orifice of a nanopipette. The dwell time was strongly dependent on the amount of protein adsorbed on polystyrene particles, which could be used to analyze the heterogeneity of protein adsorption at the single particle level. This method presents a label-free, sensitive, reproducible and easily-operated way to analyze adsorption behavior at the single particle level, which opens a new approach to the study of the heterogeneity of physicochemical parameters at the surface of nanoparticles. Keywords: Single particle analysis, Nanopipette, Adsorption heterogeneity, Label-free, Polystyrene particles

As sessile organisms, the precise development phase transitions are very important for the success of plant adaptability, survival and reproduction. The transition from juvenile to the adult phase—referred to as the vegetative phase change—is significantly influenced by numbers of endogenous and environmental signals. Here, we showed that brassinosteroid (BR), a major growth-promoting steroid hormone, positively regulates the vegetative phase change in Arabidopsis thaliana. The BR-deficient mutant det2-1 and BR-insensitive mutant bri1-301 displayed the increased ratio of leaf width to length and reduced blade base angle. The plant specific transcription factors SQUAMOSA PROMOTER BINDING PROTEIN-LIKE (SPL) are key masters for the vegetative phase transition in plants. The expression levels of SPL9, SPL10 and SPL15 were significantly induced by BR treatment, but reduced in bri1-116 mutant compared to wild-type plants. The gain-of-function pSPL9:rSPL9 transgenic plants displayed the BR hypersensitivity on hypocotyl elongation and partially suppressed the delayed vegetative phase change of det2-1 and bri1-301. Furthermore, we showed that BRASSINAZOLE-RESISTANT 1 (BZR1), the master transcription factor of BR signaling pathway, interacted with SPL9 to cooperatively regulate the expression of downstream genes. Our findings reveal an important role for BRs in promoting vegetative phase transition through regulating the activity of SPL9 at transcriptional and post-transcriptional levels.

With the continued development of 5G mobile communications technology, the implementation of high-power communication systems has become a key indicator of developed nations. Communication modules are also trending toward wide bandwidth and high-capacity Multi-Input and Multi-Output systems. As the signal transmission speed and resolution continue with the increasing trend, the power used to operate these communications systems increase, causing extreme heat generation by transmit/receive modules (T/R module). In conditions where computation load increases in micro design systems, chips must operate in environments that are narrow, sealed, and have no convection, which can drastically increase the thermal load within a system. If no proper cooling system is utilized, the system fails or operates at impacted performance due to excessive temperatures. To solve the aforementioned problem, this study aimed to optimize the design of the cooling system in the T/R modules of communications systems by integrating heat pipes, cooling fans, cooling fins, and cooling chips within a limited space. We also proposed four types of cold plates based on the different directional clamp-in configuration methods of heat pipes within copper panels and utilized the finite element method to simulate and analyze the heat dissipation performance. The simulation results reveal that cold plates of types I and II can achieve a better heat dissipation performance. Finally, types I and II cold plates were selected for production and experimental verification. The results show that heat dissipation performances were similar to simulation results. The results also confirmed that type II cold plate has a better temperature uniformity and heat transfer efficiency. Thus, the cooling mechanism depicted in this study is viable in practical applications. The proposed mechanisms can also provide a reference for heat dissipation design patterns in different electronic module settings.

Organic electrochemical transistors (OECTs) are recently developed high-efficient transducers not only for electrochemical biosensor but also for cell electrophysiological recording due to the separation of gate electrode from the transistor device. The efficient integration of OECTs with electrochemical gate electrode makes the as-prepared sensors with improved performance, such as sensitivity, limit of detection, and selectivity. We herein reviewed the recent progress of OECTs-based biosensors and cell electrophysiology recording, mainly focusing on the principle and chemical design of gate electrode and the channel. First, the configuration, work principle, semiconductor of OECT are briefly introduced. Then different kinds of sensing modes are reviewed, especially for the biosensing and electrophysiological recording. Finally, the challenges and opportunities of this research field are discussed.

In this study, thermo-responsive molecularly imprinted hydrogels (T-MIHs) were developed as an effective potential adsorbent for selectively adsorption phenol from wastewater. During the process, N-isopropyl acrylamide (NIPAm) was used as thermal responsive monomer. The obtained materials were characterized in detail by fourier transform infrared (FT-IR) spectrometer, scanning electron microscope (SEM), and thermo gravimetric analysis (TGA). A series of static adsorption studies were performed to investigate the kinetics, specific adsorption equilibrium, and selective recognition ability of phenol. Reversible adsorption and release of phenol were realized by changing temperatures. Three type of phenols, namely 3-chlorophenols (3-CP), 2,4-dichlorophenol (2,4-DCP), and 2,4,6-trichlorophenol (2,4,6-TCP) were selected as model analytes to evaluate the selective recognition performance of T-MIHs. The T-MIHs have good selectivity, temperature response, and reusability, making them ideal in applying in the controlled separation and release of phenol pollutants.

As a preliminary study for bearingless permanent magnet slice motor (BPMSM) development, an effective means for BPMSM mechanical structure optimization is proposed here by developing a virtual prototype based on Ansoft Maxwell to realize overall performance improvements. First, the sensitivity evaluation index of the candidate mechanical structural parameters for individual BPMSM performance is constructed for selection. Orthogonal tests are performed to determine the dominant mechanical structural parameters to be optimized by utilizing monitored data based on Ansoft Maxwell. A linear regression model of the mechanical structural parameters for specific performances is obtained by utilizing the gradient descent method. Then, a multi-structural optimization regression model of the selected dominant mechanical structural parameters for overall performance is established using an analytic hierarchy process and solved using a genetic algorithm. The simulation results show that the performance of the optimized BPMSM has been comprehensively improved. Specifically, the passive axial stiffness, passive tilting stiffness, force-current coefficient, and motor efficiency increased by 56.4%, 71.3%, 19.6%, and 8.7%, respectively.

Van der Waals epitaxial GaN thin films on c-sapphire substrates with a sp2-bonded two-dimensional (2D) MoS2 buffer layer, prepared by pulse laser deposition, were investigated. Low temperature plasma-assisted molecular beam epitaxy (MBE) was successfully employed for the deposition of uniform and ~5 nm GaN thin films on layered 2D MoS2 at different substrate temperatures of 500, 600 and 700 °C, respectively. The surface morphology, surface chemical composition, crystal microstructure, and optical properties of the GaN thin films were identified experimentally by using both in situ and ex situ characterizations. During the MBE growth with a higher substrate temperature, the increased surface migration of atoms contributed to a better formation of the GaN/MoS2 heteroepitaxial structure. Therefore, the crystallinity and optical properties of GaN thin films can obviously be enhanced via the high temperature growth. Likewise, the surface morphology of GaN films can achieve a smoother and more stable chemical composition. Finally, due to the van der Waals bonding, the exfoliation of the heterostructure GaN/MoS2 can also be conducted and investigated by transmission electron microscopy. The largest granular structure with good crystallinity of the GaN thin films can be observed in the case of the high-temperature growth at 700 °C.

We examined the uncertainty of the two-dimensional (2D) resistivity method using conceptual cavity models. The experimental cavity study was conducted to validate numerical model results. Spatial resolution and sensitivity to resistivity perturbations were also assessed using checkerboard tests. Conceptual models were simulated to generate synthetic resistivity data for dipole-dipole (DD), pole-dipole (PD), Wenner–Schlumberger (WS), and pole-pole (PP) arrays. The synthetically measured resistivity data were inverted to obtain the geoelectric models. The highest anomaly effect (1.46) and variance (24,400 Ω·m) in resistivity data were recovered by the DD array, whereas the PP array obtained the lowest anomaly effect (0.60) and variance (2401 Ω·m) for the shallowest target cavity set at 2.2 m depth. The anomaly effect and variance showed direct dependency on the quality of the inverted models. The DD array provided the highest model resolution that shows relatively distinct anomaly geometries. In contrast, the PD and WS arrays recovered good resolutions, but it is challenging to determine the correct anomaly geometries with them. The PP array reproduced the lowest resolution with less precise anomaly geometries. Moreover, all the tested arrays showed high sensitivity to the resistivity contrasts at shallow depth. The DD and WS arrays displayed the higher sensitivity to the resistivity perturbations compared to the PD and PP arrays. The inverted models showed a reduction in sensitivity, model resolution, and accuracy at deeper depths, creating ambiguity in resistivity model interpretations. Despite these uncertainties, our modeling specified that two-dimensional resistivity imaging is a potential technique to study subsurface cavities. We inferred that the DD array is the most appropriate for cavity surveys. The PD and WS arrays are adequate, while the PP array is the least suitable for cavity studies.

Alligator weed is reported to have a strong ability to adapt to potassium deficiency (LK) stress. Leaves are the primary organs responsible for photosynthesis of plants. However, quantitative proteomic changes in alligator weed leaves in response to LK stress are largely unknown. In this study, we investigated the physiological and proteomic changes in leaves of alligator weed under LK stress. We found that chloroplast and mesophyll cell contents in palisade tissue increased, and that the total chlorophyll content, superoxide dismutase (SOD) activity and net photosynthetic rate (PN) increased after 15 day of LK treatment, but the soluble protein content decreased. Quantitative proteomic analysis suggested that a total of 119 proteins were differentially abundant proteins (DAPs). KEGG analysis suggested that most represented DAPs were associated with secondary metabolism, the stress response, photosynthesis, protein synthesis, and degradation pathway. The proteomic results were verified using parallel reaction monitoring mass spectrometry (PRM–MS) analysis and quantitative real-time PCR (qRT-PCR)assays. Additional research suggested that overexpression of cationic peroxidase 1 of alligator weed (ApCPX1) in tobacco increased LK tolerance. The seed germination rate, peroxidase (POD) activity, and K+ content increased, and the hydrogen peroxide (H2O2) content decreased in the three transgenic tobacco lines after LK stress. The number of root hairs of the transgenic line was significantly higher than that of WT, and net K efflux rates were severely decreased in the transgenic line under LK stress. These results confirmed that ApCPX1 played positive roles in low-K+ signal sensing. These results provide valuable information on the adaptive mechanisms in leaves of alligator weed under LK stress and will help identify vital functional genes to apply to the molecular breeding of LK-tolerant plants in the future.