4D Printed Soft Microactuator for Particle Manipulation via Surrounding Medium Variation.

Soft actuators have assumed vital roles in a diverse number of research and application fields, driving innovation and transformative advancements. Using 3D molding of smart materials and combining these materials through structural design strategies, a single soft actuator can achieve multiple functions. However, it is still challenging to realize soft actuators that possess high environmental adaptability while capable of different tasks. Here, the response threshold of a soft actuator is modulated by precisely tuning the ratio of stimulus-responsive groups in hydrogels. By combining a heterogeneous bilayer membrane structure and in situ multimaterial printing, the obtained soft actuator deformed in response to changes in the surrounding medium. The response medium is suitable for both biotic and abiotic environments, and the response rate is fast. By changing the surrounding medium, the precise capture, manipulation, and release of micron-sized particles of different diameters in 3D are realized. In addition, static capture of a single red blood cell is realized using biologically responsive medium changes. Finally, the experimental results are well predicted using finite element analysis. It is believed that with further optimization of the structure size and autonomous navigation platform, the proposed soft microactuator has significant potential to function as an easy-to-manipulate multifunctional robot.

AabHLH48, a novel basic helix-loop-helix transcription factor from Adonis amurensis, promotes early flowering in Arabidopsis by activating FRUITFULL expression.

Basic helix-loop-helix (bHLH) transcription factors play various important roles in plant growth and development. In this study, a AabHLH48 was identified in the floral organ of Adonis amurensis, a perennial herb that can naturally complete flowering at extreme low temperatures. AabHLH48 was widely expressed in various tissues or organs of A. amurensis and was localized in the nucleus. Overexpression of AabHLH48 promotes early flowering in Arabidopsis under both photoperiod (12 h light/12 h dark and 16 h light/8 h dark) and temperature (22 and 18 °C) conditions. Transcriptome sequencing combined with quantitative real-time PCR analysis showed that overexpression of AabHLH48 caused a general upregulation of genes involved in floral development in Arabidopsis, especially for AtAGAMOUS-LIKE 8/FRUITFULL (AtAGL8/FUL). The yeast one-hybrid assay revealed that AabHLH48 has transcriptional activating activity and can directly bind to the promoter region of AtAGL8/FUL. These results suggest that the overexpression of AabHLH48 promoting early flowering in Arabidopsis is associated with the upregulated expression of AtAGL8/FUL activated by AabHLH48. This indicates that AabHLH48 can serve as an important genetic resource for improving flowering-time control in other ornamental plants or crops.

Improving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.

Timely and accurate estimation of cotton seedling emergence rate is of great significance to cotton production. This study explored the feasibility of drone-based remote sensing in monitoring cotton seedling emergence. The visible and multispectral images of cotton seedlings with 2 - 4 leaves in 30 plots were synchronously obtained by drones. The acquired images included cotton seedlings, bare soil, mulching films, and PE drip tapes. After constructing 17 visible VIs and 14 multispectral VIs, three strategies were used to separate cotton seedlings from the images: (1) Otsu's thresholding was performed on each vegetation index (VI); (2) Key VIs were extracted based on results of (1), and the Otsu-intersection method and three machine learning methods were used to classify cotton seedlings, bare soil, mulching films, and PE drip tapes in the images; (3) Machine learning models were constructed using all VIs and validated. Finally, the models constructed based on two modeling strategies [Otsu-intersection (OI) and machine learning (Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)] showed a higher accuracy. Therefore, these models were selected to estimate cotton seedling emergence rate, and the estimates were compared with the manually measured emergence rate. The results showed that multispectral VIs, especially NDVI, RVI, SAVI, EVI2, OSAVI, and MCARI, had higher crop seedling extraction accuracy than visible VIs. After fusing all VIs or key VIs extracted based on Otsu's thresholding, the binary image purity was greatly improved. Among the fusion methods, the Key VIs-OI and All VIs-KNN methods yielded less noises and small errors, with a RMSE (root mean squared error) as low as 2.69% and a MAE (mean absolute error) as low as 2.15%. Therefore, fusing multiple VIs can increase crop image segmentation accuracy. This study provides a new method for rapidly monitoring crop seedling emergence rate in the field, which is of great significance for the development of modern agriculture.

Exploring the impact of stress on the electronic structure and optical properties of graphdiyne nanoribbons for advanced optoelectronic applications.

Graphdiyne (GDY), a two-dimensional carbon material with sp- and sp2-hybridization, is recognized for its unique electronic properties and well-dispersed porosity. Its versatility has led to its use in a variety of applications. The precise control of this material's properties is paramount for its effective utilization in nano-optical devices. One effective method of regulation, which circumvents the need for additional disturbances, involves the application of external stress. This technique provides a direct means of eliciting changes in the electronic characteristics of the material. For instance, when subjected to uniaxial stress, electron transfer occurs at the triple bond. This results in an armchair-edged graphdiyne nanoribbon (A(3)-GDYNR) with a planar width of 2.07 nm, which exhibits a subtle plasmon effect at 500 nm. Conversely, a zigzag-edged graphdiyne nanoribbon (Z(3)-GDYNR) with a planar width of 2.86 nm demonstrates a pronounced plasmon effect within the 250-1200 nm range. This finding suggests that the zigzag nanoribbon surpasses the armchair nanoribbon in terms of its plasmon effect. First principles calculations and ab initio molecular dynamics further confirmed that under applied stress Z(3)-GDYNR exhibits less deformation than A(3)-GDYNR, indicating superior stability. This work provides the necessary theoretical basis for understanding graphene nanoribbons (GDYNRs).

Charge transfer excitons and directional fluorescence of in-plane lateral MoSe2-WSe2 heterostructures.

In this article, the linear and nonlinear optical properties of in-plane lateral MoSe2-WSe2 heterostructures are theoretically investigated. The polarization-dependent strongest optical absorption in one-photon absorption occurs in charge transfer excited states, where electrons transfer from WSe2 to MoSe2. This phenomenon is supported by the LUMO (lowest unoccupied molecular orbital) and HUMO (highest occupied molecular orbital) imaging obtained through scanning tunneling microscopy. The charge difference density and transition density matrix are used to interpret the electronic transitions, and these interpretations rely on the concept of transition density. The optical properties of two-photon absorption in its nonlinear optical process are significantly different from the excitation in one-photon absorption, where the strongest optical absorption is contributed from direct transition from the ground state to the final state without going through an intermediate excited state, due to the very large difference of permanent dipole moments between the excited and ground states. Our results also reveal directional fluorescence and physical mechanism of in-plane lateral MoSe2-WSe2 heterostructures. Our work can provide insights into the physical mechanism of the optical properties of in-plane lateral MoSe2-WSe2 heterostructures.

Stiffness-tunable substrate fabrication by DMDbased optical projection lithography for cancer cell invasion studies.

OBJECTIVE: Cancer cell invasion is a critical cause of fatality in cancer patients. Physiologically relevant tumor models play a key role in revealing the mechanisms underlying the invasive behavior of cancer cells. However, most existing models only consider interactions between cells and extracellular matrix (ECM) components while neglecting the role of matrix stiffness in tumor invasion. Here, we propose an effective approach that can construct stiffness-tunable substrates using digital mirror device (DMD)-based optical projection lithography to explore the invasion behavior of cancer cells. The printability, mechanical properties, and cell viability of three-dimensional (3D) models can be tuned by the concentration of prepolymer and the exposure time. The invasion trajectories of gastric cancer cells in tumor models of different stiffness were automatically detected and tracked in real-time using a deep learning algorithm. The results show that tumor models of different mechanical stiffness can yield distinct regulatory effects. Moreover, owing to the biophysical characteristics of the 3D in vitro model, different cellular substructures of cancer cells were induced. The proposed tunable substrate construction method can be used to build various microstructures to achieve simulation of cancer invasion and antitumor screening, which has great potential in promoting personalized therapy.

Research on Residual-Current Measurement System of Substation Considering Magnetic Shielding Effect.

Residual current is an important monitoring quantity of a power system, and a current sensor plays an important role in detecting current. The substation environment is complex. In addition to the power frequency signal, residual current also has AC and DC components. But it is also affected by the stray magnetic field of the substation. Therefore, the accuracy of the current sensor demands higher requirements. The tunnel magnetoresistive sensor has the advantages of a stable operation, high efficiency, and energy saving, but it is easily affected by the external stray magnetic field during measurements, resulting in a large error. Therefore, this paper proposes a residual-current sensing monitoring system considering the magnetic shielding effect. The root mean square error of the magnetic shielding structure is only 0.572 mA, which can effectively reduce the influence of the external magnetic field and improve the detection accuracy. At the same time, the DC measurement error is less than 1%, the AC measurement error is less than 5%, and the hybrid AC/DC error is less than 8%. It has good response ability and can accurately detect residual current.

Graphyne and graphdiyne nanoribbons: from their structures and properties to potential applications.

Graphyne (GY) and graphdiyne (GDY) have properties including unique sp- and sp2-hybrid carbon atomic structures, natural non-zero band gaps, and highly conjugated π electrons. GY and GDY have good application prospects in many fields, including catalysis, solar cells, sensors, and modulators. Under the influence of the boundary effect and quantum size effect, quasi-one-dimensional graphyne nanoribbons (GYNRs) and graphdiyne nanoribbons (GDYNRs) show novel physical properties. The various structures available give GYNRs and GDYNRs greater band structure and electronic properties, and their excellent physical and chemical properties differ from those of two-dimensional GY and GDY. However, the development of GYNRs and GDYNRs still faces problems, including issues with accurate synthesis, advanced structural characterization, the structure-performance correlation of materials, and potential applications. In this review, the structures and physical properties of quasi-one-dimensional GYNRs and GDYNRs are reviewed, their advantages and disadvantages are summarized, and their potential applications are highlighted. This review provides a meaningful basis and research foundation for the design and development of high-performance materials and devices based on GYNRs and GDYNRs in the field of energy.

Skin-Inspired Capacitive Flexible Tactile Sensor with an Asymmetric Structure for Detecting Directional Shear Forces.

Flexible pressure sensors based on micro-/nanostructures can be integrated into robots to achieve sensitive tactile perception. However, conventional symmetric structures, such as pyramids or hemispheres, can sense only the magnitude of a force and not its direction. In this study, a capacitive flexible tactile sensor inspired by skin structures and based on an asymmetric microhair structure array to perceive directional shear force is designed. Asymmetric microhair structures are obtained by two-photon polymerization (TPP) and replication. Owing to the features of asymmetric microhair structures, different shear force directions result in different deformations. The designed device can determine the directions of both static and dynamic shear forces. Additionally, it exhibits large response scales ranging from 30 Pa to 300 kPa and maintains high stability even after 5000 cycles; the final relative capacitive change (ΔC/C0 ) is <2.5%. This flexible tactile sensor has the potential to improve the perception and manipulation ability of dexterous hands and enhance the intelligence of robots.

Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm.

Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, blue-green (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R2c, R2p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R2c, R2p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas.

Study on Asymmetric Vibrational Coherent Magnetic Transitions and Origin of Fluorescence in Symmetric Structures.

In this work, the physical mechanisms of three highly efficient circularly polarized luminescent materials are introduced. The UV-vis spectra are plotted; the transition properties of their electrons at the excited states are investigated using a combination of the transition density matrix (TDM) and the charge difference density (CDD); combining the distribution of electron clouds, the essence of charge transfer excitation in three structures is explained. The resonance Raman spectrum of the three structures at the S1 and S2 excited states are calculated. The M, M-4 and M, M-5 structures are found to produce novel chirality by electronic circular dichroism (ECD) spectrum, and the reasons for the chirality of the M, M-4 and M, M-5 structures are discussed by analyzing the density of transition electric/magnetic dipole moments (TEDM/TMDMs) in different orientations. Finally, the Raman optical activity (ROA) of M, M-4, and M, M-5 are calculated, and the spectra are plotted. This study will provide guidance for the application of carbon-based nanomaterials in organic electronic devices, solar cells, and optoelectronics.

Relationship Between Stress Modulated Metallicity and Plasmon in Graphene Nanoribbons.

Nanoscale quantum plasmon is an important technology that restricts the application of optics, electricity, and graphene photoelectric devices. Establishing a structure-effect relationship between the structure of graphene nanoribbons (GNRs) under stress regulation and the properties of plasmons is a key scientific issue for promoting the application of plasmons in micro-nano photoelectric devices. In this study, zigzag graphene nanoribbon (Z-GNR) and armchair graphene nanoribbon (A-GNR) models of specific widths were constructed, and density functional theory (DFT) was used to study their lattice structure, energy band, absorption spectrum, and plasmon effects under different stresses. The results showed that the Z-GNR band gap decreased with increasing stress, and the A-GNR band gap changed periodically with increasing stress. The plasmon effects of the A-GNRs and Z-GNRs appeared in the visible region, whereas the absorption spectrum showed a redshift trend, indicating the range of the plasmon spectrum also underwent significant changes. This study provides a theoretical basis for the application of graphene nanoribbons in the field of optoelectronics under strain-engineering conditions.

Wireless Electrical Signals Induce Functional Neuronal Differentiation of BMSCs on 3D Graphene Framework Driven by Magnetic Field.

Bone marrow mesenchymal stem cells (BMSCs) are suggested as candidates for neurodegeneration therapy by autologous stem cells to overcome the lack of neural stem cells in adults. However, the differentiation of BMSCs into functional neurons is a major challenge for neurotherapy. Herein, a methodology has been proposed to induce functional neuronal differentiation of BMSCs on a conductive three-dimensional graphene framework (GFs) combined with a rotating magnetic field. A wireless electrical signal of about 10 µA can be generated on the surface of GFs by cutting the magnetic field lines based on the well-known electromagnetic induction effect, which has been proven to be suitable for inducing neuronal differentiation of BMSCs. The enhanced expressions of the specific genes/proteins and apparent Ca2+ intracellular flow indicate that BMSCs cultured on GFs with 15 min/day rotating magnetic field stimulation for 15 days can differentiate functional neurons without any neural inducing factor. The animal experiments confirm the neural differentiation of BMSCs on GFs after transplantation in vivo, accompanied by stimulation of an external rotating magnetic field. This study overcomes the lack of autologous neural stem cells for adult neurodegeneration patients and provides a facile and safe strategy to induce the neural differentiation of BMSCs, which has potential for clinical applications of neural tissue engineering.

Improving the ability of straw biochar to remediate Cd contaminated soil: KOH enhanced the modification of K3PO4 and urea on biochar.

In recent years, the improvement of soil cadmium (Cd) contamination remediation effect of biochar by modification has received wide attention. However, the effect of combined modification on biochar performance in soil Cd contamination remediation and the mechanism are still unclear. In this study, cotton straw biochar and maize straw biochar were co-modified by KOH (0, 3, 5 mol L-1), K3PO4, and urea. Then, two modified biochars with high Cd adsorption capacity were selected to test the soil Cd contamination remediation effect through a pot experiment. The results showed that the combined modification by using KOH, K3PO4, and urea significantly increased the specific surface area and nitrogen (N) and phosphorus (P) contents of biochar, providing more adsorption sites for Cd. Among the modified biochar, the cotton straw biochar modified with KOH (3 mol L-1), K3PO4, and urea (m3-CSB) had the highest adsorption capacity (111.25 mg g-1), which was 7.86 times that of cotton straw biochar (CSB). The m3-CSB for adsorption isotherm and kinetics of Cd conformed to the Langmuir model and Pseudo-second-order kinetic equation, respectively. In the pot experiment, under different exogenous Cd levels (0 (Cd0), 4 (Cd4), and 8 (Cd8) mg kg-1), m3-CSB treatment decreased soil available Cd content the most (51.68%-63.4%) compared with other biochar treatments. Besides, m3-CSB treatment significantly promoted the transformation of acid-soluble Cd to reducible, oxidizable, and residual Cd, reducing the bioavailability of Cd. At the Cd4 level, the application of m3-CSB significantly reduced cotton Cd uptake compared to CK, and the maximum reduction of Cd content in cotton fibers was as high as 81.95%. Therefore, cotton straw biochar modified with KOH (3 mol L-1), K3PO4, and urea has great potential in the remediation of soil Cd contamination.

Synergistic Enhancement of Photocatalytic CO2 Reduction by Built-in Electric Field/Piezoelectric Effect and Surface Plasmon Resonance via PVDF/CdS/Ag Heterostructure.

Photocatalytic reduction of CO2 using solar energy is an effective means to achieve carbon neutrality. However, the photocatalytic efficiency still requires improvements. In this study, polyvinylidene fluoride (PVDF) ferroelectric/piezoelectric nanofiber membranes are prepared by electrospinning. Cadmium sulfide (CdS) nanosheets are assembled in situ on the surface of PVDF based on coordination between F- and Cd2+ , and then Ag nanoparticles are deposited on CdS. Because of the synergistic effect between localized surface plasmon resonance of Ag nanoparticles and the built-in electric field of PVDF, the CO2 photocatalytic reduction efficiency using PVDF/CdS/Ag under visible light irradiation is significantly higher than that of any combination of CdS, CdS/Ag, or PVDF/CdS. Under micro-vibration to simulate air flow, the CO2 reduction efficiency of PVDF/CdS/Ag is three times higher than that under static conditions, reaching 240.4 µmol g-1 h-1 . The piezoelectric effect caused by micro-vibrations helps prevent the built-in electric field from becoming saturated with carriers and provides a continuous driving force for carrier separation.

SALA-LSTM: a novel high-precision maritime radar target detection method based on deep learning.

Radar detection of maritime targets plays an important role in marine environment monitoring. For civil maritime detection in the areas of inshore coastal, pulse-compression radar is universally used owing to its low cost. The complex sea clutter in the practical application will greatly affect the received radar echoes. Due to the inability to accurately describe the differences in characteristics between sea clutter and maritime targets, the detection performance of methods based on mathematical derivation is not satisfactory in actual deployment. Recently, neural-based methods have made strides in many pattern recognition tasks, such as computer vision and natural language processing. The sophisticated deep neural models can be applied to different downstream tasks due to their powerful learning ability. Inspired by this idea, we propose a maritime radar target detection method in sea clutter based on deep learning. To better model the sequence correlation of radar echoes, we propose a Self-Adaption Local Augmented Long Short-Term Memory (SALA-LSTM) structure. The proposed SALA-LSTM integrates adaptive convolution into vanilla LSTM cells, which not only maintains the inherent overall sequence modeling ability of vanilla LSTM, but also strengthens its ability to perceive the correlation on a small scale in the local scope. Based on SALA-LSTM and other neural structures, we propose a radar target detection network. A measured dataset containing different typical scenarios is utilized to evaluate the detection probability and false alarm rate. The detection performance of our proposed network is superior to that of the existing methods.

PDK inhibition promotes glucose utilization, reduces hepatic lipid deposition, and improves oxidative stress in largemouth bass (Micropterus salmoides) by increasing pyruvate oxidative phosphorylation.

In omnivorous fish, the pyruvate dehydrogenase kinases (PDKs)-pyruvate dehydrogenase E1α subunit (PDHE1α) axis is essential in the regulation of carbohydrate oxidative catabolism. Among the existing research, the role of the PDKs-PDHE1α axis in carnivorous fish with poor glucose utilization is unclear. In the present study, we determined the effects of PDK inhibition on the liver glycolipid metabolism of largemouth bass (Micropterus salmoides). DCA is a PDK-specific inhibitor that inhibits PDK by binding the allosteric sites. A total of 160 juvenile largemouth bass were randomly divided into two groups, with four replicates of 20 fish each, fed a control diet and a control diet supplemented with dichloroacetate (DCA) for 8 weeks. The present results showed that DCA supplementation significantly decreased the hepatosomatic index, triglycerides in liver and serum, and total liver lipids of largemouth bass compared with the control group. In addition, compared with the control group, DCA treatment significantly down-regulated gene expression associated with lipogenesis. Furthermore, DCA supplementation significantly decreased the mRNA expression of pdk3a and increased PDHE1α activity. In addition, DCA supplementation improved glucose oxidative catabolism and pyruvate oxidative phosphorylation (OXPHOS) in the liver, as evidenced by low pyruvate content in the liver and up-regulated expressions of glycolysis-related and TCA cycle/OXPHOS-related genes. Moreover, DCA consumption decreased hepatic malondialdehyde (MDA) content, enhanced the activities of superoxide dismutase (SOD), and increased transforming growth factor beta (tgf-ß), glutathione S-transferase (gst), and superoxide dismutase 1 (sod1) gene expression compared with the control diet. This study demonstrated that inhibition of PDKs by DCA promoted glucose utilization, reduced hepatic lipid deposition, and improved oxidative stress in largemouth bass by increasing pyruvate OXPHOS. Our findings contribute to the understanding of the underlying mechanism of the PDKs-PDHE1α axis in glucose metabolism and improve the utilization of dietary carbohydrates in farmed carnivorous fish.

Design and Optimization of Multi-Stage TMR Sensors for Power Equipment AC/DC Leakage Current Detection.

Tunnel magnetoresistance (TMR) can measure weak magnetic fields and has significant advantages for use in alternating current/direct current (AC/DC) leakage current sensors for power equipment; however, TMR current sensors are easily perturbed by external magnetic fields, and their measurement accuracy and measurement stability are limited in complex engineering application environments. To enhance the TMR sensor measurement performance, this paper proposes a new multi-stage TMR weak AC/DC sensor structure with high measurement sensitivity and anti-magnetic interference capability. The front-end magnetic measurement characteristics and interference immunity of the multi-stage TMR sensor are found to be closely related to the multi-stage ring size design via finite element simulation. The optimal size of the multipole magnetic ring is determined using an improved non-dominated ranking genetic algorithm (ACGWO-BP-NSGA-II) to derive the optimal sensor structure. Experimental results demonstrate that the newly designed multi-stage TMR current sensor has a measurement range of 60 mA, a fitting nonlinearity error of less than 1%, a measurement bandwidth of 0-80 kHz, a minimum AC measurement value of 85 µA and a minimum DC measurement value of 50 µA, as well as a strong external electromagnetic interference. The TMR sensor can effectively enhance measurement precision and stability in the presence of intense external electromagnetic interference.

Potential candidate genes and pathways related to cytoplasmic male sterility in Dianthus spiculifolius as revealed by transcriptome analysis.

KEY MESSAGE: We introduced the candidate gene DsHSP70 into Arabidopsis thaliana, resulting in male gametophyte sterility and abnormal degeneration of sepals and petals. Cytoplasmic male sterility (CMS) is a useful tool for hybrid production. However, the regulatory mechanism of CMS in Dianthus spiculifolius remains unclear. In this study, we investigated whether male-sterile line of D. spiculifolius has a malformed tapetum and fails to produce normal fertile pollen. RNA sequencing technology was used to compare the gene expression patterns of the D. spiculifolius male-sterile line and its male fertility maintainer line during anther development. A total of 12,365 differentially expressed genes (DEGs) were identified, among which 1765 were commonly expressed in the S1, S2 and S3 stages. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that these DEGs were mainly involved in oxidation-reduction processes, signal transduction and programmed cell death. Additionally, weighted correlation network analysis (WGCNA) showed that three modules may be related to male sterility. A putative regulatory pathway for the male sterility traits was constructed based on the reproductive development network. After introducing the candidate DsHSP70 gene into Arabidopsis thaliana, we found that overexpressing plants showed anther abortion and shorter filaments, and accompanied by abnormal degeneration of sepals and petals. In summary, our results identified potential candidate genes and pathways related to CMS in D. spiculifolius, providing new insights for further research on the mechanism of male sterility.

Improving the monitoring of root zone soil salinity under vegetation cover conditions by combining canopy spectral information and crop growth parameters.

Soil salinization is one of the main causes of land degradation in arid and semi-arid areas. Timely and accurate monitoring of soil salinity in different areas is a prerequisite for amelioration. Hyperspectral technology has been widely used in soil salinity monitoring due to its high efficiency and rapidity. However, vegetation cover is an inevitable interference in the direct acquisition of soil spectra during crop growth period, which greatly limits the monitoring of soil salinity by remote sensing. Due to high soil salinity could lead to difficulty in plants' water absorption, and inhibit plant dry matter accumulation, a method for monitoring root zone soil salinity by combining vegetation canopy spectral information and crop aboveground growth parameters was proposed in this study. The canopy spectral information was acquired by a spectroradiometer, and then variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and random frog algorithm (RFA) were used to extract the salinity spectral features in cotton canopy spectrum. The extracted features were then used to estimate root zone soil salinity in cotton field by combining with cotton plant height, aboveground biomass, and shoot water content. The results showed that there was a negative correlation between plant height/aboveground biomass/shoot water content and soil salinity in 0-20, 0-40, and 0-60 cm soil layers at different growth stages of cotton. Spectral feature selection by the three methods all improved the prediction accuracy of soil salinity, especially CARS. The prediction accuracy based on the combination of spectral features and cotton growth parameters was significantly higher than that based on only spectral features, with R2 increasing by 10.01%, 18.35%, and 29.90% for the 0-20, 0-40, and 0-60 cm soil layer, respectively. The model constructed based on the first derivative spectral preprocessing, spectral feature selection by CARS, cotton plant height, and shoot water content had the highest accuracy for each soil layer, with R2 of 0.715,0.769, and 0.742 for the 0-20, 0-40, 0-60 cm soil layer, respectively. Therefore, the method by combining cotton canopy hyperspectral data and plant growth parameters could significantly improve the prediction accuracy of root zone soil salinity under vegetation cover conditions. This is of great significance for the amelioration of saline soil in salinized farmlands arid areas.