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Abstract Emission Trading System (ETS) is an innovative practice under the progress of green development in China. It is also an important method for China to achieve market-oriented environmental governance in ecological civilization construction. The ETS pilot policy has implemented for more than 10 years. However, the co-benefits of ETS pilot policy by the integration of energy consumption, carbon and sulfur dioxide emissions, and wastewater has not been evaluated. In order to fill this gap, we use the 2003–2017 annual data of 30 China’s provinces (municipalities and autonomous regions), and utilize the Difference-in-Differences (DID) model and Propensity Score Matching (PSM-DID) methodology to evaluate the co-benefits of ETS pilot policy on energy conservation and emission reduction. We find that the ETS pilot policy significantly promote energy conservation and emission reduction. Eastern and central China have significantly benefited from the policy, while the western China has not due to the limited technology and innovation as well as an imbalance of the industrial structure. The results provide the policy reference for China’s government and institutions as well as the governments and institutions around the world to fulfill their commitments to save energy and reduce emissions, and early achieve the carbon peaking and carbon neutralization.

Reward is essential for shaping behavior. Using sensory cues to imply forthcoming rewards, previous studies have demonstrated powerful effects of rewards on behavior. Nevertheless, the impact of reward on the sensorimotor transformation, particularly when reward is linked to behavior remains uncertain. In this study, we investigated how reward modulates smooth pursuit eye movements in monkeys. Three distinct associations between reward and eye movements were conducted in independent blocks. Results indicated that reward increased eye velocity during the steady-state pursuit, rather than during the initiation. The influence depended on the particular association between behavior and reward: a faster eye velocity was linked with reward. Neither rewarding slower eye movements nor randomizing rewards had a significant effect on behavior. The findings support the existence of distinct mechanisms involved in the initiation and steady-state phases of pursuit, and contribute to a deeper understanding of how reward interacts with these two periods of pursuit.

In order to investigate the hydrogen permeation behavior of 316L stainless steel during the microstructural evolution induced by laser peening (LP), an electrochemical hydrogen charging system for initial hydrogen charging of LPed and non-LPed specimen was developed. Afterward, the microhardness, residual stress, and microstructures of the samples were determined and analyzed. Finally, electrochemical hydrogen permeation experiments were undertaken to verify LP's influence on hydrogen permeation parameters of 316L. The results showed that LP reduced the hydrogen-induced hardening rate of the alloy and additionally invoked high magnitude compressive residual stress on its surface. At the layer close to the face of the specimen, the grain refinement rate was as high as 56.18%, which was accompanied by the appearance of high-density dislocations. Compared with the non-LPed sample, the hydrogen permeation time increased significantly, and the saturation current density in steady state hydrogen permeation also decreased gradually.

Lidar multiple scattering measurements provide the probability distribution of the distance laser light travels inside snow. Based on an analytic two-stream radiative transfer solution, the present study demonstrates why/how these lidar measurements can be used to derive snow depth and snow density. In particular, for a laser wavelength with little snow absorption, an analytical radiative transfer solution is leveraged to prove that the physical snow depth is half of the average distance photons travel inside snow and that the relationship linking lidar measurements and the extinction coefficient of the snow is valid. Theoretical formulas that link lidar measurements to the extinction coefficient and the effective grain size of snow are provided. Snow density can also be derived from the multi-wavelength lidar measurements of the snow extinction coefficient and snow effective grain size. Alternatively, lidars can provide the most direct snow density measurements and the effective discrimination between snow and trees by adding vibrational Raman scattering channels.

Surface-enhanced Raman spectroscopy (SERS) microfluidic system, which enables rapid detection of chemical and biological analytes, offers an effective platform to monitor various food contaminants and disease diagnoses. The efficacy of SERS microfluidic systems is greatly dependent on the sensitivity and reusability of SERS detection substrates to ensure repeated use for prolonged periods. This study proposed a novel process of femtosecond laser nanoparticle array (NPA) implantation to achieve homogeneous forward transfer of gold NPA on a flexible polymer film and accurately integrated it within microfluidic chips for SERS detection. The implanted Au-NPA strips show a remarkable electromagnetic field enhancement with the factor of 9 × 10 ^8 during SERS detection of malachite green (MG) solution, achieving a detection limit lower than 10 ppt, far better than most laser-prepared SERS substrates. Furthermore, Au-NPA strips show excellent reusability after several physical and chemical cleaning, because of the robust embedment of laser-implanted NPA in flexible substrates. To demonstrate the performance of Au-NPA, a SERS microfluidic system is built to monitor the online oxidation reaction between MG/NaClO reactants, which helps infer the reaction path. The proposed method of nanoparticle implantation is more effective than the direct laser structuring technique. It provides better performance for SERS detection, robustness of detection, and substrate flexibility and has a wider range of applications for microfluidic systems without any negative impact.

We describe the PACE-MAPP algorithm that simultaneously retrieves aerosol and ocean optical parameters using multiangle and multispectral polarimeter measurements from the SPEXone, Hyper-Angular Rainbow Polarimeter 2 (HARP2), and Ocean Color Instrument (OCI) instruments onboard the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) observing system. PACE-MAPP is adapted from the Research Scanning Polarimeter (RSP) Microphysical Aerosol Properties from Polarimetry (RSP-MAPP) algorithm. The PACE-MAPP algorithm uses a coupled vector radiative transfer model such that the atmosphere and ocean are always considered together as one system. Consequently, this physically consistent treatment of the system across the ultraviolet, (UV: 300–400 nm), visible (VIS: 400–700 nm), near-infrared (NIR: 700–1100 nm), and shortwave infrared (SWIR: 1100–2400 nm) spectral bands ensures that negative water-leaving radiances do not occur. PACE-MAPP uses optimal estimation to simultaneously characterize the optical and microphysical properties of the atmosphere’s aerosol and ocean constituents, find the optimal solution, and evaluate the uncertainties of each parameter. This coupled approach, together with multiangle, multispectral polarimeter measurements, enables retrievals of aerosol and water properties across the Earth’s oceans. The PACE-MAPP algorithm provides aerosol and ocean products for both the open ocean and coastal areas and is designed to be accurate, modular, and efficient by using fast neural networks that replace the time-consuming vector radiative transfer calculations in the forward model. We provide an overview of the PACE-MAPP framework and quantify its expected retrieval performance on simulated PACE-like data using a bimodal aerosol model for observations of fine-mode absorbing aerosols and coarse-mode sea salt particles. We also quantify its performance for observations over the ocean of dust-laden scenes using a trimodal aerosol model that incorporates non-spherical coarse-mode dust particles. Lastly, PACE-MAPP’s modular capabilities are described, and we discuss plans to implement a new ocean bio-optical model that uses a mixture of coated and uncoated particles, as well as a thin cirrus model for detecting and correcting for sub-visual ice clouds.

The year 2023 marked the tenth anniversary of the first published description of global ocean plankton stocks based on measurements from a satellite lidar. Diverse studies have since been conducted to further refine and validate the lidar retrievals and use them to discover new characteristics of plankton seasonal dynamics and marine animal migrations, as well as evaluate geophysical products from traditional passive ocean color sensors. Surprisingly, all of these developments have been achieved with lidar instruments not designed for ocean applications. Over this same decade, we have witnessed unprecedented changes in ocean ecosystems at unexpected rates and driven by a multitude of environmental stressors, with a dominant factor being climate warming. Understanding, predicting, and responding to these ecosystem changes requires a global ocean observing network linking satellite, in situ, and modeling approaches. Inspired by recent successes, we promote here the creation of a lidar global ocean climate record as a key element in this envisioned advanced observing system. Contributing to this record, we announce the development of a new satellite lidar mission with ocean-observing capabilities and then discuss additional technological advances that can be envisioned for subsequent missions. Finally, we discuss how a potential near-term gap in global ocean lidar data might, at least partially, be filled using on-orbit or soon-to-be-launched lidars designed for other disciplinary purposes, and we identify upcoming needs for in situ support systems and science community development.

The extreme marine weather is a very vital factor and has important implications for of marine economic development. However, there is a lack of systematic and quantitative analyses of its impact on the marine economic development. Here, we study the impacts of extreme marine weather on marine economic development of 11 coastal regions in China, using the dynamic panel model. We found that extreme marine weather exerts a significant negative impact on the marine economic development. The marine scientific and technological innovation promotes marine economic development in a prominent manner. The marine scientific and technological innovation slows down the unfavorable impact of extreme marine weather on the marine economy. After considering different industries for marine economic development and heterogeneity, we found that extreme marine weather and marine scientific and technological innovation have a great impact on marine economic development in the tertiary industry and the areas with high development concerning marine economy level, while deliver a small impact on the marine economic development in the primary industry and the areas low development level. This paper empirically studies the relationship between the two variables of marine extreme weather and marine science and technology innovation and its impact on marine economic development, enriches the research perspective of extreme weather on marine economic development, and provides new method evidence for improving the level of marine scientific and technological innovation and promoting the development of marine economy.

This study simulates V-band sea surface reflectance and normalized radar cross-section (NRCS) for sea surface air pressure barometry using a differential absorption radar operating at three spectrally even spaced close frequency bands (65.5, 67.75 and 70.0 GHz) with ± 15° cross-track scanning angle. The reflectance ratios of two neighboring frequency pairs and the ratio of the two ratios or three-channel approach are the focus of this study. Impacts of major sea surface geophysical variables such as sea surface temperature, wind, salinity, whitecap, and incidence angle on these reflection properties are analyzed. The reflection simulation is essentially based on geometric optics of rough sea surface. Simulation shows that NRCS values are sufficiently strong within the scanning angle and sea surface salinity would only introduce minimal variations in the surface reflection. The impact of sea surface reflection variations with sea surface temperature, wind, and whitecaps on sea surface barometry are mitigated when the ratios of frequency-paired radar signals are used. Furthermore, the ratios of a three-channel approach are very close to unity and calibration or compensation for the reflectance ratios may not be needed for sea level pressure retrievals. These results improve our understanding of sea surface reflection variations and would help the system design and development.

Oxygen A-band measurements can provide important information about cloud top height, cloud physical and optical thickness, and surface atmospheric pressure. So far, O2 A-band measurements are typically made with spectrometers at a spectral resolution of 40 p.m. (such as in the OCO-2 satellite) or a coarser resolution. This paper reports a new CubeSat measurement concept with higher spectral resolution over the O2 A-band using integrated photonic circuits and solar photon counting techniques. An integrated Micro-ring filter (MRR) chip with 10 p.m. resonant linewidth with an extinction ratio of 25 dB or higher is designed, fabricated, and used for precise measurements of the atmospheric oxygen A-band absorption line-shapes around 770 nm. With solar-photon counting and using a narrow-band filter made of an integrated, fast-swept MRR on lithium niobate on insulator (LNOI), we have demonstrated a high-resolution measurement of the O2 A-band absorption spectrum, exhibiting good agreement with the HITRAN database.

The strengthening mechanisms behind surface modification processes must be evaluated to optimize surface treatment methods. This study developed a physics-based crystal plasticity finite element framework to evaluate multiple-laser-peening impacts on enhancing the high-cycle fatigue properties of AA2195-T6 friction stir welded joints. For this purpose, different contributions of laser peening effects were quantified on the variations of fatigue indicator parameters in the joint region. Extreme-value fatigue indicator parameters evaluated the laser peening effects in decreasing the driving force for fatigue crack initiation at near-surface grain boundaries. Moreover, grain-average fatigue indicator parameters investigated the fatigue property modification trends with depth. Experimental high-cycle fatigue tests were applied to validate the related modeling outputs. According to the crystal plasticity model, the improvement in joint near-surface fatigue properties majorly stemmed from cyclic mean stress alleviation under laser-peening-induced compressive residual stresses. This effect was followed by the increased resistance against cyclic plastic deformation under the growth in dislocation density and crystallographic texture intensity and the reduction in texture heterogeneity under the crystal morphology homogenization effects after laser peening. Fatigue tests revealed the enhancement in the joint fatigue life under the relocation of fatigue crack initiation regions to depth after LP and validated the numerical approaches.

Inversion of electromagnetic (EM) signals reflected from or transmitted through a medium, or emitted by it due to internal sources can be used to investigate the optical and physical properties of a variety of scattering/absorbing/emitting materials. Such media encompass planetary atmospheres and surfaces (including water/snow/ice), and plant canopies. In many situations the signals emerging from such media can be described by a linear transport equation which in the case of EM radiation is the radiative transfer equation (RTE). Solutions of the RTE can be used as a forward model to solve the inverse problem to determine the medium state parameters giving rise to the emergent (reflected/transmitted/emitted) EM signals. A novel method is developed to determine layer-by-layer contributions to the emergent signals from such stratified, multilayered media based on the solution of the pertinent RTE. As a specific example of how this approach may be applied, the radiation reflected from a multilayered atmosphere is used to solve the problem relevant for EM probing by a space-based lidar system. The solutions agree with those obtained using the standard lidar approach for situations in which single scattering prevails, but this novel approach also yields reliable results for optically thick, multiple scattering aerosol and cloud layers that cannot be provided by the traditional lidar approach.

Well logging data contain abundant information on stratigraphic sedimentology. Artificial identification is usually strongly subjective and time-consuming. Pattern recognition algorithms like SVM may not adequately capture the depth-related variations in logging curve shape. This paper defines logging sedimentary microfacies as unidirectional 2D image segmentation and builds an improved U-net model to meet the requirements of logging sedimentary microfacies acquaintance. The proposed model contains three characteristics: (1) It removes pooling layers to avoid the loss of spatial features; (2) it utilizes multi-scale convolution blocks for mining multi-scale spatial features in logging data; (3) a one dimensional convolution layer is added to achieve deep single-direction segmentation. In this model, a 2D image composed of several standardized logging curves is used as the network’s input. In addition, we propose an effective data enhancement method and calculate the geometric feature attributes of well logging curves to reduce the complexity of the data characteristics. We tested the model on manually annotated validation datasets. Our method automatically measures fine sedimentary microfacies characteristics, improving the accuracy of sedimentary microfacies identification and achieving the desired result. Additionally, the model was tested on unlabeled actual logging data, which shows the generalizability of this deep learning method on different datasets.

We present an overview and several important upgrades to the Vector Discrete Ordinate Radiative Transfer (VDISORT) code. VDISORT is a polarized (vector) radiative transfer code that can be applied to a wide range of research problems including the Earth’s atmosphere and ocean system. First, a solution is developed to the complex algebraic eigenvalue problem resulting when the b2 component of the Stokes scattering matrix is non-zero. This solution is needed to compute the V component of the Stokes vector I=[I∥,I⊥,U,V]T. Second, a significant improvement in computational efficiency is obtained by reducing the dimension of the algebraic eigenvalue by a factor of 2 resulting in a speed increase of about 23 = 8. Third, an important upgrade of the VDISORT code is obtained by developing and implementing a method to enable output at arbitrary polar angles by the integration of the source function (ISF) method for partially reflecting Lambertian as well as general non-Lambertian surfaces. Fourth, a pseudo-spherical treatment has been implemented to provide important corrections for Earth curvature effects at near horizontal solar zenith and observation (viewing) polar angles. Fifth, a post-processing single-scattering correction procedure has been developed to enhance the accuracy and speed for strongly forward-peaked scattering. With these significant improvements the results from the upgraded version of the VDISORT code match published benchmark results for Rayleigh scattering, Mie scattering, and scattering by non-spherical cirrus particles. The performance of VDISORT for a polarized incident beam source is equally satisfactory. The VDISORT vector radiative transfer code is made public and freely available for use by the growing polarimetric research community including the space-borne polarimeters on the future NASA PACE and AOS missions.

In this study, we propose a simple method to derive vertically resolved aerosol particle number concentration (Na) using combined polarimetric and lidar remote sensing observations. This method relies on accurate polarimeter retrievals of the fine-mode column-averaged aerosol particle extinction cross section and accurate lidar measurements of vertically resolved aerosol particle extinction coefficient such as those provided by multiwavelength high spectral resolution lidar. We compare the resulting lidar + polarimeter vertically resolved Na product to in situNa data collected by airborne instruments during the NASA aerosol cloud meteorology interactions over the western Atlantic experiment (ACTIVATE). Based on all 35 joint ACTIVATE flights in 2020, we find a total of 32 collocated in situ and remote sensing profiles that occur on 11 separate days, which contain a total of 322 cloud-free vertically resolved altitude bins of 150 m resolution. We demonstrate that the lidar + polarimeter Na agrees to within 106% for 90% of the 322 vertically resolved points. We also demonstrate similar agreement to within 121% for the polarimeter-derived column-averaged Na. We find that the range-normalized mean absolute deviation (NMAD) for the polarimeter-derived column-averaged Na is 21%, and the NMAD for the lidar + polarimeter-derived vertically resolved Na is 16%. Taken together, these findings suggest that the error in the polarimeter-only column-averaged Na and the lidar + polarimeter vertically resolved Na are of similar magnitude and represent a significant improvement upon current remote sensing estimates of Na.

The application of diffusion theory and Monte Carlo lidar radiative transfer simulations presented in Part I of this series of study suggests that snow depth can be derived from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. These methods are now applied to the satellite ICESat-2 lidar measurements over the Arctic sea ice and land surfaces of Northern Hemisphere. Over the Arctic sea ice, the ICESat-2 retrieved snow depths agree well with co-located IceBridge snow radar measured values with a root-mean-square (RMS) difference of 7.8 cm or 29.2% of the mean snow depth. The terrestrial snow depths derived from ICESat-2 show drastic spatial variation of the snowpack along ICESat-2 ground tracks over the Northern Hemisphere, which are consistent with the University of Arizona (UA) and Canadian Meteorological Centre (CMC) gridded daily snow products. The RMS difference in snow depths between ICESat-2 and UA gridded daily snow products is 14 cm, or 28% of the mean UA snow depth. To better understand these results, we also discuss the possible sources of errors in ICESat-2 derived snow depths, including surface roughness within the laser footprint, atmospheric forward scattering, solar background noise, and detector dark current. Simulation results indicate that the snow depth errors would be less than 5 cm if the standard deviation of pulse spreading due to surface roughness is within 50 cm. Our results demonstrate that the ICESat-2 lidar measurements can be used to reliably derive snow depth, which is a critical geophysical parameter for cryosphere studies including sea ice thickness estimation and also provides important constraints in the modeling of terrestrial hydrological processes.

Snow is a crucial element in the Earth’s system, but snow depth and mass are very challenging to be measured globally. Here, we provide the theoretical foundation for deriving snow depth directly from space-borne lidar (ICESat-2) snow multiple scattering measurements for the first time. First, based on the Monte Carlo lidar radiative transfer simulations of ICESat-2 measurements of 532-nm laser light propagation in snow, we find that the lidar backscattering path length follows Gamma distribution. Next, we derive three simple analytical equations to compute snow depth from the average, second-, and third-order moments of the distribution. As a preliminary application, these relations are then used to retrieve snow depth over the Antarctic ice sheet and the Arctic sea ice using the ICESat-2 lidar multiple scattering measurements. The robustness of this snow depth technique is demonstrated by the agreement of snow depth computed from the three derived relations using both modeled data and ICESat-2 observations.

A radiative transfer simulator was developed to compute the synthetic data of all three instruments onboard NASA’s Plankton Aerosol, Cloud, ocean Ecosystem (PACE) observatory, and at the top of the atmosphere (TOA). The instrument suite includes the ocean color instrument (OCI), the Hyper-Angular Rainbow Polarimeter 2 (HARP2), and the Spectro-Polarimeter for Planetary Exploration 1 (SPEXone). The PACE simulator is wrapped around a monochromatic radiative transfer model based on the successive order of scattering (RTSOS), which accounts for atmosphere and ocean coupling, polarization, and gas absorption. Inelastic scattering, including Raman scattering from pure ocean water, fluorescence due to chlorophyll, and colored dissolved organic matter (CDOM), is also simulated. This PACE simulator can be used to explore the sensitivity of the hyperspectral and polarized reflectance of the Earth system with tunable atmosphere and ocean parameters, which include aerosol and cloud number concentration, refractive indices, and size distribution, ocean particle microphysical parameters, and solar and sensor-viewing geometry. The PACE simulator is used to study two important case studies. One is the impact of the significant uncertainty in pure ocean water absorption coefficient to the radiance field in the ultraviolet (UV) spectral region, which can be as much as 6%. The other is the influence of different amounts of brown carbon aerosols and CDOM on the polarized radiance field at TOA. The percentage variation of the radiance field due to CDOM is mostly for wavelengths smaller than 600 nm, while brown aerosols affect the whole spectrum from 350 to 890 nm, primarily due to covaried soot aerosols. Both case studies are important for aerosol and ocean color remote sensing and have not been previously reported in the literature.

Water droplet size, or effective radius, of water clouds can be retrieved using measurements of the total reflectance in near-infrared channels (bi-spectral method) or the linearly polarized radiance of the cloud rainbow (polarimetric method). The retrieved droplet sizes from the bi-spectral method can be significantly larger than those from the polarimetric method. Droplet size vertical heterogeneity is considered as a likely cause of this difference. In this radiative transfer theoretical study, we find that partially melting droplets in mixed-phase clouds can be another cause of this difference. The theoretical study suggests that, when the clouds are dominated by large particles, the existence of partially melting droplets could cause the polarimetric method to underestimate the particle size; but for moderate-sized particles, the polarimetric method should be able to retrieve accurate particle size no matter if the clouds consist of partially melting droplets or not. However, the droplet effective radius of partially melting droplets retrieved by the bi-spectral method can be overestimated if the infrared channel of the bi-spectral method is at 1.6 μm, but is relatively insensitive to the presence of partially melted spherical droplets when the infrared channel is at 2.1 μm. The retrieved droplet size difference between droplet sizes from 1.6 μm and 2.1 μm channels can be used for detecting partially melting droplets.

Remote sensing measurements from multi-angle polarimeters (MAPs) contain rich aerosol microphysical property information, and these sensors have been used to perform retrievals in optically complex atmosphere and ocean systems. Previous studies have concluded that, generally, five moderately separated viewing angles in each spectral band provide sufficient accuracy for aerosol property retrievals, with performance gradually saturating as angles are added above that threshold. The Hyper-Angular Rainbow Polarimeter (HARP) instruments provide high angular sampling with a total of 90–120 unique angles across four bands, a capability developed mainly for liquid cloud retrievals. In practice, not all view angles are optimal for aerosol retrievals due to impacts of clouds, sunglint, and other impediments. The many viewing angles of HARP can provide resilience to these effects, if the impacted views are screened from the dataset, as the remaining views may be sufficient for successful analysis. In this study, we discuss how the number of available viewing angles impacts aerosol and ocean color retrieval uncertainties, as applied to two versions of the HARP instrument. AirHARP is an airborne prototype that was deployed in the ACEPOL field campaign, while HARP2 is an instrument in development for the upcoming NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission. Based on synthetic data, we find that a total of 20–30 angles across all bands (i.e., five to eight viewing angles per band) are sufficient to achieve good retrieval performance. Following from this result, we develop an adaptive multi-angle polarimetric data screening (MAPDS) approach to evaluate data quality by comparing measurements with their best-fitted forward model. The FastMAPOL retrieval algorithm is used to retrieve scene geophysical values, by matching an efficient, deep learning-based, radiative transfer emulator to observations. The data screening method effectively identifies and removes viewing angles affected by thin cirrus clouds and other anomalies, improving retrieval performance. This was tested with AirHARP data, and we found agreement with the High Spectral Resolution Lidar-2 (HSRL-2) aerosol data. The data screening approach can be applied to modern satellite remote sensing missions, such as PACE, where a large amount of multi-angle, hyperspectral, polarimetric measurements will be collected.

Abstract Remote sensing from Earth‐observing satellites is now providing valuable information about ocean phytoplankton distributions. This paper presents the new ocean subsurface optical properties obtained from two space‐based lidars: the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations satellite and the Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite‐2 satellite. Obtaining reliable estimates of subsurface biomass necessitates removing instrument artifacts peculiar to each sensor, that is, polarization crosstalk artifacts in the CALIOP signals and after pulsing effects arising from the ATLAS photodetectors. We validate the optical properties derived from the corrected lidar backscatter signals using MODerate‐resolution Imaging Spectroradiometer ocean color measurements and autonomous biogeochemical Argo float profiles. Our results support the continued use of present and future spaceborne lidars to study the global plankton system and characterize its vertical structures in the upper ocean.

A neural-network algorithm that uses CALIPSO lidar measurements to infer droplet effective radius, extinction coefficient, liquid-water content, and droplet number concentration for water clouds is described and assessed. These results are verified against values inferred from High-Spectral-Resolution Lidar (HSRL) and Research Scanning Polarimeter (RSP) measurements made on an aircraft that flew under CALIPSO. The global cloud microphysical properties are derived from 14+ years of CALIPSO lidar measurements, and the droplet sizes are compared to corresponding values inferred from MODIS passive imagery. This new product will provide constraints to improve modeling of Earth’s water cycle and cloud-climate interactions.

Abstract The Advanced Topographic Laser Altimeter System (ATLAS) aboard the Ice, Cloud, and land Elevation Satellite‐2 (ICESat‐2) has been making very high resolution measurements of the Earth’s surface elevation since October 2018. ATLAS uses photomultiplier tubes (PMTs) as detectors in photon counting mode, so that a single photon reflected back to the receiver triggers a detection within the ICESat‐2 data acquisition system. However, one characteristic of ICESat‐2 detected photons is the possible presence of afterpulses, defined as small amplitude pulses occurring after the primary signal pulse due to photon arrival. The disadvantage of these afterpulses is that they often confound the accurate measurements of low level signals following a large amplitude of signal and can degrade energy resolution and cause errors in pulse counting applications. This paper discusses and summarizes the after‐pulsing effects exhibited by the ATLAS PMTs based on on‐orbit measurements over different seasons and geographic regions. The potential impacts of these after‐pulsing effects on altimetry and ocean subsurface retrievals are discussed.

Recent studies indicate that the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite provides valuable information about ocean phytoplankton distributions. CALIOP’s attenuated backscatter coefficients, measured at 532 nm in receiver channels oriented parallel and perpendicular to the laser’s linear polarization plane, are significantly improved in the Version 4 data product. However, due to non-ideal instrument effects, a small fraction of the backscattered optical power polarized parallel to the receiver polarization reference plane is misdirected into the perpendicular channel, and vice versa. This effect, known as polarization crosstalk, typically causes the measured perpendicular signal to be higher than its true value and the measured parallel signal to be lower than its true value. Therefore, the ocean optical properties derived directly from CALIOP’s measured signals will be biased if the polarization crosstalk effect is not taken into account. This paper presents methods that can be used to estimate the CALIOP crosstalk effects from on-orbit measurements. The global ocean depolarization ratios calculated both before and after removing the crosstalk effects are compared. Using CALIOP crosstalk-corrected signals is highly recommended for all ocean subsurface studies.

The North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) is an interdisciplinary investigation to improve understanding of Earth's ocean ecosystem-aerosol-cloud system. Specific overarching science objectives for NAAMES are to (1) characterize plankton ecosystem properties during primary phases of the annual cycle and their dependence on environmental forcings, (2) determine how these phases interact to recreate each year the conditions for an annual plankton bloom, and (3) resolve how remote marine aerosols and boundary layer clouds are influenced by plankton ecosystems. Four NAAMES field campaigns were conducted in the western subarctic Atlantic between November 2015 and April 2018, with each campaign targeting specific seasonal events in the annual plankton cycle. A broad diversity of measurements were collected during each campaign, including ship, aircraft, autonomous float and drifter, and satellite observations. Here, we present an overview of NAAMES science motives, experimental design, and measurements. We then briefly describe conditions and accomplishments during each of the four field campaigns and provide information on how to access NAAMES data. The intent of this manuscript is to familiarize the broad scientific community with NAAMES and to provide a common reference overview of the project for upcoming publications.

The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) platform, is an elastic backscatter lidar that has been providing vertical profiles of the spatial, optical, and microphysical properties of clouds and aerosols since June 2006. Distinguishing between feature types (i.e., clouds vs. aerosol) and subtypes (e.g., ice clouds vs. water clouds and dust aerosols from smoke) in the CALIOP measurements is currently accomplished using layer-integrated measurements acquired by co-polarized (parallel) and cross-polarized (perpendicular) 532 nm channels and a single 1064 nm channel. Newly developed deep machine learning (DML) semantic segmentation methods now have the ability to combine observations from multiple channels with texture information to recognize patterns in data. Instead of focusing on a limited set of layer integrated values, our new DML feature classification technique uses the full scope of range-resolved information available in the CALIOP attenuated backscatter profiles. In this paper, one of the convolutional neural networks (CNN), SegNet, a fast and efficient DML model, is used to distinguish aerosol subtypes directly from the CALIOP profiles. The DML method is a 2D range bin-to-range bin aerosol subtype classification algorithm. We compare our new DML results to the classifications generated by CALIOP’s 1D layer-to-layer operational retrieval algorithm. These two methods, which take distinctly different approaches to aerosol classification, agree in over 60% of the comparisons. Higher levels of agreement are found in homogeneous scenes containing only a single aerosol type (i.e., marine, stratospheric aerosols). Disagreement between the two techniques increases in regions containing mixture of different aerosol types. The multi-dimensional texture information leveraged by the DML method shows advantages in differentiating between aerosol types based on their classification scores, as well as in distinguishing vertical distributions of aerosol types within individual layers. However, untangling mixtures of aerosol subtypes is still challenging for both the DML and operational algorithms.

Remote sensing of global ocean color is a valuable tool for understanding the ecology and biogeochemistry of the worlds oceans, and provides critical input to our knowledge of the global carbon cycle and the impacts of climate change. Ocean polarized reflectance contains information about the constituents of the upper ocean euphotic zone, such as colored dissolved organic matter (CDOM), sediments, phytoplankton, and pollutants. In order to retrieve the information on these constituents, remote sensing algorithms typically rely on radiative transfer models to interpret water color or remote-sensing reflectance; however, this can be resource-prohibitive for operational use due to the extensive CPU time involved in radiative transfer solutions. In this work, we report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean bidirectional polarized reflectance given inherent optical properties of ocean waters. This supervised model is trained using a large volume of data derived from radiative transfer simulations for coupled atmosphere and ocean systems using the successive order of scattering technique (SOS-CAOS). The performance of the model is validated against another large independent test dataset generated from SOS-CAOS. The model is able to predict both polarized and unpolarized reflectances with an absolute error (AE) less than 0.004 for 99% of test cases. We have also shown that the degree of linear polarization (DoLP) for unpolarized incident light can be predicted with an AE less than 0.002 for 99% of test cases. In general, the simulation time of SOS-CAOS depends on optical depth, and required accuracy. When comparing the average speeds of the NNRPM against the SOS-CAOS model for the same parameters, we see that the NNRPM is able to predict the Ocean BRDF 6000 times faster than SOS-CAOS. Both ultraviolet and visible wavelengths are included in the model to help differentiate between dissolved organic material and chlorophyll in the study of the open ocean and the coastal zone. The incorporation of this model into the retrieval algorithm will make the retrieval process more efficient, and thus applicable for operational use with global satellite observations.

Earth observing systems have proven to be a unique source of long-term synoptic information on numerous physical, chemical and biological parameters on a global scale. Merging this information for integrated studies that peruse key questions about the ocean-atmosphere interface is, however, very challenging. Such studies require interdisciplinary frameworks and novel insights into ways to address the problem. We present here a perspective review on how current and emerging remote sensing technologies could help address two scientific questions within the Surface Ocean-Lower Atmosphere Study (SOLAS) science plan: (1) to what extent does upper-ocean biology affect the composition and radiative properties of the marine boundary layer; and (2) to what extent does upper-ocean turbulence drive fluxes of mass and energy at the air-sea interface. We provide a thorough review of how these questions have been addressed and discuss novel potential avenues using multiplatform space-borne missions, from visible to microwave, active and passive sensors.

We report the first radiative transfer model that is able to simulate phytoplankton fluorescence with both photochemical and non-photochemical quenching included. The fluorescence source term in the inelastic radiative transfer equation is proportional to both the quantum yield and scalar irradiance at excitation wavelengths. The photochemical and nonphotochemical quenching processes change the quantum yield based on the photosynthetic active radiation. A sensitivity study was performed to demonstrate the dependence of the fluorescence signal on chlorophyll a concentration, aerosol optical depths and solar zenith angles. This work enables us to better model the phytoplankton fluorescence, which can be used in the design of new space-based sensors that can provide sufficient sensitivity to detect the phytoplankton fluorescence signal. It could also lead to more accurate remote sensing algorithms for the study of phytoplankton physiology.

Abstract Background Cold stress poses a serious challenge to tropical fruit production, particularly in mango. N6-methyladenosine (m6A) modifications are key regulators of gene expression, enabling plants to respond to stress responses, enhance adaptation and improve resilience to environmental challenges. Results In our study, transcriptome-wide m6A methylation profiling under cold stress identified 6,499 differentially methylated m6A peaks and 2,164 differentially expressed genes (DEGs) in mango seedlings. Among these genes, six exhibited both significant increases in m6A modification levels and gene expression, 21 showed a significant increase in m6A levels but a concurrent downregulation of gene expression, and 26 showed reduced m6A levels but exhibited increased gene expression, highlighting distinct regulatory patterns in m6A-mediated gene expression control. Gene Ontology (GO) enrichment analysis revealed significant involvement in pathways such as potassium ion import, nitrate response, and transcription regulation. Notably, HSP70 was one of the upregulated genes in response to cold stress. RNA immunoprecipitation (RNA-IP) assays confirmed the association of HSP70 with m6A-modified RNAs in vivo, supporting its role in regulating stress-responsive transcripts. Additionally, immunofluorescence analysis demonstrated the formation of HSP70 condensates in plant cells under cold stress, indicating a potential mechanism for localized RNA stabilization. Fluorescence polarization assays demonstrated that HSP70 binds preferentially to m6A-modified RNAs, suggesting its role in forming protective condensates under cold conditions. This interaction between m6A modification and HSP70 points to a potential mechanism that helps stabilize stress-responsive transcripts, contributing to the plant’s enhanced cold tolerance. Conclusions m6A modifications play a vital role in regulating gene expression under cold stress, offering new insights into mango’s stress responses and potential breeding strategies for cold tolerance.

Abstract Regional variations exist in the implementation of Syphilis Mother-to-Child Transmission Prevention (PMTCT). Thus, it is crucial to assess the effectiveness of this model in the Ningxia region and explore the supplementary role of Health Management Teams (HMT). This study established the PMTCT + HMT model and examined its impact on adverse outcomes in pregnant women with syphilis infection. The majority of participants were urban residents, married, had a minimum high school education, and held public positions; 36.7% and 26.7% were from minority ethnic groups. The PMTCT + HMT model enhanced participants’ knowledge, rates of voluntary counseling, and testing. The incidence of adverse pregnancy outcomes (miscarriages, preterm births, stillbirths) significantly decreased, and adverse neonatal outcomes (low birth weight, neonatal mortality, congenital syphilis) were notably reduced. Simultaneously, we identified factors associated with adverse outcomes, including non-residency, unmarried status, lower educational attainment, minority ethnicity, primary syphilis, and positive titers. Thus, HMT may be an effective intervention to enhance the effect of PMTCT for syphilis. The unique population structure in Ningxia is closely linked to adverse outcomes, highlighting the significance of providing equitable treatment for vulnerable populations.

Abstract Background Rice is a staple crop for over half of the global population, but soil salinization poses a significant threat to its production. As a type of polyamine, spermidine (Spd) has been shown to reduce stress-induced damage in plants, but its specific role and mechanism in protecting rice roots under salt stress require further investigation. Results This study suggested spermidine (Spd) mitigates salt stress on rice root growth by enhancing antioxidant enzyme activity and reducing peroxide levels. Transcriptomic analysis showed that salt stress caused 333 genes to be upregulated and 1,765 to be downregulated. However, adding Spd during salt treatment significantly altered this pattern: 2,298 genes were upregulated and 844 were downregulated, which indicated Spd reverses some transcriptional changes caused by salt stress. KEGG pathway analysis suggested that Spd influenced key signaling pathways, including MAPK signaling, plant hormone signal transduction, and phenylalanine metabolism. Additionally, the bZIP transcription factor OsbZIP73 was upregulated after Spd treatment, which is confirmed by Western blot. Further insights into the interaction between OsbZIP73 and Spd were gained through fluorescence polarization experiments, showing that Spd enhances protein OsbZIP73’s affinity for RNA. Functional enrichment analyses revealed that OsPYL1, OsSPARK1, and various SAUR family genes involved in Spd-affected pathways. The presence of G/A/C-box elements in these genes suggests they are potential targets for OsbZIP73. Conclusions Our findings suggest a strategy of using spermidine as a chemical alleviator for salt stress and provide insights into the regulatory function of OsbZIP73 in mitigating salt stress in rice roots.

How a pair of pollutant parcels deviates from each other with an initial separation distance r0, known as relative dispersion or Richardson dispersion, is relevant in many circumstances. This study examines the spatiotemporal similarity of the Richardson relative dispersion in the Gulf of Mexico by reanalyzing the Lagrangian trajectory of the surface drifter provided by two famous field experiments, that is, the Grand Lagrangian Deployment and the Lagrangian Submesoscale Experiment. The experimental dispersion curve indicates a critical separation time. When above this critical time, the dispersion shows an asymptotic power law growth independent of the initial separation distance r0. Below it, the dispersion curve shows a strong spatiotemporal dependence with two spatiotemporal similarity regimes that can be identified for both experiments by looking at the isoline of the normalized dispersion curve. A new similarity variable is introduced to successfully collapse measured dispersion curves. However, the observed spatiotemporal similarity cannot be reproduced by the submesoscale preserved model. Thus, our results suggest that small-scale fluctuations play a crucial role in the relative dispersion of oceanic flows.

Abstract The IVa subfamily of glycine-rich proteins (GRPs) comprises a group of glycine-rich RNA binding proteins referred to as GR-RBPa here. Previous studies have demonstrated functions of GR-RBPa proteins in regulating stress response in plants. However, the mechanisms responsible for the differential regulatory functions of GR-RBPa proteins in different plant species have not been fully elucidated. In this study, we identified and comprehensively studied a total of 34 GR-RBPa proteins from five plant species. Our analysis revealed that GR-RBPa proteins were further classified into two branches, with proteins in branch I being relatively more conserved than those in branch II. When subjected to identical stresses, these genes exhibited intensive and differential expression regulation in different plant species, corresponding to the enrichment of cis-acting regulatory elements involving in environmental and internal signaling in these genes. Unexpectedly, all GR-RBPa genes in branch I underwent intensive alternative splicing (AS) regulation, while almost all genes in branch II were only constitutively spliced, despite having more introns. This study highlights the complex and divergent regulations of a group of conserved RNA binding proteins in different plants when exposed to identical stress conditions. These species-specific regulations may have implications for stress responses and adaptations in different plant species.

Ocean loading deformation is one of the key processes in geodesy, geophysics, and Isostasy, and is also important for assessment of future coastal environmental changes. Traditionally, the ocean tide loading has been considered the only contributor to the ocean loading deformation by most previous InSAR (Interferometric Synthetic Aperture Radar) studies. In this study, we quantify the non-tidal ocean loading deformation using the multi-temporal InSAR-derived deformation data from the Chaoshan coast in southern China and explore its driving mechanisms. Our results show that InSAR deformation anomalies in the Chaoshan coastal area exhibit a distinct deformation gradient from the coast to inland. A strong scale- and direction-dependent pattern in InSAR data, with significant seasonality, is unveiled by the second-order spatial structure function. To explore the driving mechanism behind this deformation, we conduct a correlation analysis between spatial pattern of land deformation and sea level changes. The results indicate that ocean loading is the predominant driving factor behind the Chaoshan coastal deformation. Analysis using the MERRA2 model demonstrates that terrestrial water storage loading deformation is negligible in comparison. Furthermore, by applying the FES2014b tide model, we find that the contributions of the non-tidal and tide-induced ocean loading deformation to the total deformation anomaly are 50–70% and 30–50%, respectively. Lastly, we have uncovered that the non-tidal ocean loading is primarily attributed to the monsoon-induced seasonal sea level changes, with a time lag ranging from one to two months. Quantifying non-tidal ocean loading deformation provides valuable insights to large-scale high-precision coastal InSAR applications.

Abstract Background Hepatitis B mother-to-child transmission interruption (PMTCT) poses a formidable challenge in underdeveloped regions of China. This study aims to evaluate the effectiveness of PMTCT and the health management team (HMT) model in Ningxia, China, as well as the risk factors for adverse outcomes. Methods The PMTCT + HMT model was established, and 360 pregnant women diagnosed with HBV infection in 2020–2022 were selected and divided into the control and the study groups based on different intervention modes. HBV serum markers and HBV DNA levels were assessed, the indicators of compliance behaviors and adverse outcomes were compared, and the factors influencing adverse outcomes were analyzed. Results The majority of subjects were residents of the local city, married, with secondary school or higher education, and employees of public sectors. The proportion of ethnic minorities was 40.8% and 34.2% in the control group and study group. HBeAg positivity was 23.3% and 26.3%, and the proportion with HBV DNA levels ≥ 2 × 105 IU/mL was 9.2% and 7.1%. Compared with the control group (PMTCT alone), the PMTCT + HMT model led to improved maternal knowledge (17.5% vs. 57.1%), voluntary counseling (34.2% vs. 63.3%), and testing (37.5% vs. 70.4%). The incidence of adverse pregnancy outcomes ((including miscarriage, preterm birth) decreased significantly (17.5% vs. 6.2%), as did adverse neonatal outcomes (low birth weight and congenital HBV) (26.9% vs. 10.5%). Adverse outcomes were associated with low educational attainment, non-locals, unmarried status, and ethnic minority identity. Additionally, HBeAg positivity and HBV DNA levels ≥ 2 × 105 IU/mL were risk factors for adverse outcomes. Conclusions The PMTCT + HMT model demonstrates significant effectiveness in preventing mother-to-child transmission of hepatitis B in Ningxia. The unique demographic structure of Ningxia region is closely linked to poor outcomes, emphasizing the importance of monitoring HBeAg status and HBV DNA viral load level.

Low light stress seriously affects the growth and yield of crops and the phytohormone brassinosteroid (BRs) plays a vital role in regulating plant adaptation to low light conditions. However, the molecular mechanism underlying this process remains largely unknown. In this study, we showed that exogenous BR effectively alleviated damages to photosynthesis and antioxidant systems, improved the plant biomass under low light stress mimicking treatments in tomato (Lycopersicon esculentum Mill.). Comparative transcriptome profiling analysis revealed that genes related with photosynthesis and Calvin cycle pathways were enriched among the differentially expressed genes (DEG) co-regulated by low light stress and BR. The combination of transcriptome and metabolome analysis showed that BR could mitigate the down-regulation of photosynthesis and Calvin cycle caused by low light stress, and partially restore the up-regulation of Glycolysis / Gluconeogenesis and tricarboxylic acid (TCA) cycle through transcriptional and metabolic reprogramming to alleviate the effects of low light stress. Moreover, we further identified the crucial transcription factors, SIEPR1 and SIERF059, and their potential target genes involved in the regulation of low light stress alleviation mediated by BR signaling. Our results shed new light on the molecular mechanisms underlying the alleviation of low light stress by BR.

Histone modification plays a crucial role in chromatin remodeling and regulating gene expression, and participates in various biological processes, including plant development and responses to stress. Several gene families related to histone modification have been reported in various plant species. However, the identification of members and their functions in the rice (Oryza sativa L.) histone modification gene family (OsHM) at the whole-genome level remains unclear. In this study, a total of 130 OsHMs were identified through a genome-wide analysis. The OsHM gene family can be classified into 11 subfamilies based on a phylogenetic analysis. An analysis of the genes structures and conserved motifs indicates that members of each subfamily share specific conserved protein structures, suggesting their potential conserved functions. Molecular evolutionary analysis reveals that a significant number of OsHMs proteins originated from gene duplication events, particularly segmental duplications. Additionally, transcriptome analysis demonstrates that OsHMs are widely expressed in various tissues of rice and are responsive to multiple abiotic stresses. Fourteen OsHMs exhibit high expression in rice anthers and peaked at different pollen developmental stages. RT-qPCR results further elucidate the expression patterns of these 14 OsHMs during different developmental stages of anthers, highlighting their high expression during the meiosis and tetrad stages, as well as in the late stage of pollen development. Remarkably, OsSDG713 and OsSDG727 were further identified to be nucleus-localized. This study provides a fundamental framework for further exploring the gene functions of HMs in plants, particularly for researching their functions and potential applications in rice anthers’ development and male sterility.

Lewis Fry Richardson proposed his famous picture of turbulent flows in 1922, where the kinetic energy is transferred from large-scale to small-scale structures until the viscosity converts it into heat. This cascade idea, also known as the forward energy cascade, is now widely accepted and is treated as the cornerstone of not only turbulent modeling, but also global circulation models of the ocean and atmosphere. In this work, the Filter-Space-Technique is applied to the oceanic flow field provided by the CMEMS reanalysis model to quantify the scale-to-scale energy flux. A rich dynamical pattern associated with different scales is observed. More precisely, either positive or negative fluxes are observed, indicating the direction of the energy cascade, where the energy is transferred from large-scale structures to small-scale ones or vice versa. High-intensity energy exchange is found mainly in the Western Boundary Current Systems and Equatorial Counter Currents. For the latter case, a wavelike pattern is observed on the westward travel. Moreover, strong seasonal variation is evident for some scales and regions. These results confirm the existence of forward and inverse cascades and rich regional dynamics.

Soil salinization has significant deleterious impacts on arable lands and crop productivity worldwide. Plant growth–promoting rhizobacteria (PGPR) have the capability to establish mutualistic associations with plants, resulting in the increase of plant growth under abiotic stress. PGPR strain RL-WG26 was previously isolated from the rhizosphere of rice Sea Rice 86 (SR86). In this study, by genome-based average nucleotide identity (ANI) calculation, RL-WG26 was re-identified as Pseudomonas promysalinigenes here instead of Pseudomonas putida. SR86 seedlings inoculated with RL-WG26 possessed significant increases in plant biomass (fresh weight and dry weight), root surface area and length, and chlorophyll content under either normal or saline conditions, when compared with those without RL-WG26 inoculation. Ion content analysis demonstrated that seedlings inoculated with RL-WG26 contained lower Na+ and Cl– accumulation but higher K+ and Ca2+ uptake under saline condition, which finally alleviated the salt ion toxic in the RL-WG26 inoculated seedlings, when compared with those without RL-WG26 inoculation. In addition, SR86 seedlings inoculated with RL-WG26 also exhibited higher antioxidant activity and higher concentration of osmoregulation factor proline than those without RL-WG26 inoculation. At last, genomic analysis of strain RL-WG26 identified several genes and gene clusters encoding 1-aminocyclopropane-1-carboxylic acid (ACC)-deaminase, the tryptophan-dependent indole-3-acetic acid (IAA) synthesis pathway, and the synthesis of betaine. Taken together, PGPR strain RL-WG26 could enhance the growth and salt stress tolerance in SR86 seedlings through various pathways, which provide a foundation for deciphering the molecular mechanisms of strain RL-WG26-mediated plant salt tolerance and promote the application of this strain.

【Objective】Tolerance to hypoxia germination is one of the key traits for direct-seeding adaptability of rice. The temperate direct-seeding japonica rice variety Francis and multi-panicle indica restorer line R998 were used for QTL mapping of hypoxia germination related traits, aiming to provide new valuable gene resources for developing direct-seeding rice varieties and the promotion of direct-seeding rice production methods.【Method】The coleoptile length, shoot length and maximum root length of rice seedlings submerged in 10-cm-depth water in the dark for 7 days at 28℃ were recorded as indicators of hypoxia germination tolerance capacity. A high-density genetic map containing 3 106 bin markers was constructed by low-coverage genome re-sequencing, and QTL scanning was performed with WinQTL Cart 2.5.【Result】Under the condition of hypoxia germination, the coleoptile length and root length of Francis are significantly higher than those of R998, but there is no significant difference in shoot length between them. The total genetic distance of the constructed genetic map is 3 646.2 cM, in which the number of markers on chromosome 12 is the least, and that on chromosome 1 is the most, with 174 and 389, respectively. The average marker distance in the genetic map is 1.21 cM, the marker distance of each chromosomes is 0.68 -1.84 cM, and the gaps above 5 cM account for 0.36%. By using composite interval mapping (CIM), six QTLs related to hypoxia germination tolerance are detected on 5 chromosomes. Among them, the number of QTLs controlling coleoptile length, shoot length and root length are 3, 1 and 2, respectively. The coleoptile locus qCL9 and the shoot length locus qSL5 had a phenotypic variance explained (PVE) over 10%, which are 13.39% and 10.78%, respectively. The beneficial allele of qCL9 and the root length locus qRL2-1 come from the parent Francis, while the other four beneficial alleles are provided by the parent R998. Three of the six QTLs have not been reported previously, therefore, they may be novel QTLs.【Conclusion】In summary, there is significant difference between Francis and R998 in hypoxia germination, and both parents have beneficial alleles for hypoxia germination tolerance. qCL9 and qSL5 are major effect QTLs controlling hypoxia germination tolerance and have practical value in the molecular breeding of direct-seeding rice.

Selenium (Se) is crucial for both plants and humans, with plants acting as the main source for human Se intake. In plants, moderate Se enhances growth and increases stress resistance, whereas excessive Se leads to toxicity. The physiological mechanisms by which Se influences rice seedlings’ growth are poorly understood and require additional research. In order to study the effects of selenium stress on rice seedlings, plant phenotype analysis, root scanning, metal ion content determination, physiological response index determination, hormone level determination, quantitative PCR (qPCR), and other methods were used. Our findings indicated that sodium selenite had dual effects on rice seedling growth under hydroponic conditions. At low concentrations, Se treatment promotes rice seedling growth by enhancing biomass, root length, and antioxidant capacity. Conversely, high concentrations of sodium selenite impair and damage rice, as evidenced by leaf yellowing, reduced chlorophyll content, decreased biomass, and stunted growth. Elevated Se levels also significantly affect antioxidase activities and the levels of proline, malondialdehyde, metal ions, and various phytohormones and selenium metabolism, ion transport, and antioxidant genes in rice. The adverse effects of high Se concentrations may directly disrupt protein synthesis or indirectly induce oxidative stress by altering the absorption and synthesis of other compounds. This study aims to elucidate the physiological responses of rice to Se toxicity stress and lay the groundwork for the development of Se-enriched rice varieties.

Rice is a major food crop for more than half of the world’s population, while its production is seriously threatened by flooding, a common environmental stress worldwide. Flooding leads to oxygen deficiency, which is a major problem for submerged plants. Over the past three decades, significant progress has been made in understanding rice adaptation and molecular regulatory mechanisms in response to flooding. At the seed germination and seedling establishment stages, the CIPK15-SnRK1A-MYBS1 signaling cascade plays a central role in determining rice submergence tolerance. However, from seedlings to mature plants for harvesting, SUB1A- and SK1/SK2-regulated pathways represent two principal and opposite regulatory mechanisms in rice. In addition, phytohormones, especially gibberellins, induce adaptive responses to flooding throughout the rice growth period. This review summarizes the significant adaptive traits observed in flooded rice varieties and updates the molecular genetics and mechanisms of submergence tolerance in rice.

Rice (Oryza sativa L.), a crucial food crop that sustains over half the world’s population, is often hindered by salt stress during various growth stages, ultimately causing a decrease in yield. However, the specific mechanism of rice roots’ response to salt stress remains largely unknown. In this study, transcriptomics and lipidomics were used to analyze the changes in the lipid metabolism and gene expression profiles of rice roots in response to salt stress. The results showed that salt stress significantly inhibited rice roots’ growth and increased the roots’ MDA content. Furthermore, 1286 differentially expressed genes including 526 upregulated and 760 downregulated, were identified as responding to salt stress in rice roots. The lipidomic analysis revealed that the composition and unsaturation of membrane lipids were significantly altered. In total, 249 lipid molecules were differentially accumulated in rice roots as a response to salt stress. And most of the major phospholipids, such as phosphatidic acid (PA), phosphatidylcholine (PC), and phosphatidylserine (PS), as well as major sphingolipids including ceramide (Cer), phytoceramide (CerP), monohexose ceramide (Hex1Cer), and sphingosine (SPH), were significantly increased, while the triglyceride (TG) molecules decreased. These results suggested that rice roots mitigate salt stress by altering the fluidity and integrity of cell membranes. This study enhances our comprehension of salt stress, offering valuable insights into changes in the lipids and adaptive lipid remodeling in rice’s response to salt stress.

Abstract Salt stress poses physiological drought, ionic toxicity and oxidative stress to plants, which causes premature senescence and death of the leaves if the stress sustained. Salt tolerance varied between different rice varieties, but how different rice varieties respond at the early stage of salt stress has been seldom studied comprehensively. By employing third generation sequencing technology, we compared gene expressional changes in leaves of three rice varieties that varied in their level of tolerance after salt stress treatment for 6 h. Commonly up-regulated genes in all rice varieties were related to water shortage response and carbon and amino acids metabolism at the early stage of salt stress, while reactive oxygen species cleavage genes were induced more in salt-tolerant rice. Unexpectedly, genes involved in chloroplast development and photosynthesis were more significantly down-regulated in the two salt tolerant rice varieties ‘C34’ and ‘Nona Bokra’. At the same time, genes coding ribosomal protein were suppressed to a more severe extent in the salt-sensitive rice variety ‘IR29’. Interestingly, not only variety-specific gene transcriptional regulation, but also variety-specific mRNA alternative splicing, on both coding and long-noncoding genes, were found at the early stage of salt stress. In summary, differential regulation in gene expression at both transcriptional and post-transcriptional levels, determine and fine-tune the observed response in level of damage in leaves of specific rice genotypes at early stage of salt stress.

In a fully developed sea, the significant wave height (Hs) and wind speed (U10) are conventionally related to a pure quadratic equation. This relation is often violated, since in the field the measured local Hs is often contaminated by the swell, which is propagated from distant places. Therefore, a swell partition is required before the establishment of the wind-wave relation. The Spectra Energy Partition (SEP) is regarded as the best way to isolate the swell and the wind wave relation: it is based on the identification of a separation frequency in the ocean wave spectrum. However, for most field observations, the wave spectra information is unavailable, and thus the SEP is inapplicable. This work proposes a probability-based algorithm to identify the averaged swell without knowing the wave spectrum a priori. The local wind-wave relation is established by either a linear or an energy-conserved decomposition. We also find that the local wind-wave relation is a power-law when the wind speed U10 is above 4 m/s. The proposed method is first validated by applying the SEP method to buoy collected wave spectra data. The global pattern of the swell and the local wind waves is retrieved by applying the proposed method to a 17-year wind and wave database from the JASON satellite. Strong seasonal and spatial variations are obtained. Finally, a prediction model based on the empirical wind-wave relation is shown to successfully retrieve the wave field when the wind field is available.

With 45# transformer oil as the base fluid, different concentrations of TiO2 nanomodified transformer oil were prepared via the thermal oscillation method. Nanomodified transformer oil with a concentration of 0.01 g/L was selected for evaluation via the breakdown test. Then, 0.01 g/L nanomodified transformer oil and ordinary 45# transformer oil were subjected to the variable control of different moisture contents and different temperatures, and breakdown tests under different types of electrodes were performed within the temperature range of −30 °C to 30 °C. The test results showed the following: Under severe cold conditions, the improvement effect of the breakdown voltage in different temperature ranges was different. Within the temperature range of −30 °C to −10 °C, the enhancement effect could reach 13% to 15%. Within the temperature range of −10 °C to 0 °C, the enhancement effect could reach 8% to 9%. Within the temperature range of 0 °C to 30 °C, the enhancement effect could reach 18% to 21%. Compared with the test results at high water contents, the improvement in the breakdown voltage amplitude of transformer oil by nanomaterials was more obvious at low water contents. In addition, nanomaterials could reduce the dispersion of the breakdown voltage to make the breakdown voltage more stable. Lastly, COMSOL was used to simulate the polarization process of nanoparticles under a uniform electric field and the influence of the trajectory of charged particles in the oil to further analyze the mechanism of the influence of nanoparticles in oil on the breakdown voltage of the oil gap. The simulation results showed that, when the particles were accelerated by the electric field, they moved irregularly. After adding nanoparticles, the charged particles were adsorbed by the nanoparticles when passing through the nanoparticles, which reduced the migration rate of charged particles in the oil. The breakdown voltage of the oil gap increased.

A novel pomelo peel biochar/MgFe-layered double hydroxide composite (PPBC/MgFe-LDH) was synthesised using a facile coprecipitation approach and applied to remove cadmium ions (Cd (II)). The adsorption isotherm demonstrated that the Cd (II) adsorption by the PPBC/MgFe-LDH composite fit the Langmuir model well, and the adsorption behaviour was a monolayer chemisorption. The maximum adsorption capacity of Cd (II) was determined to be 448.961 (±12.3) mg·g−1 from the Langmuir model, which was close to the actual experimental adsorption capacity 448.302 (±1.41) mg·g−1. The results also demonstrated that the chemical adsorption controlled the rate of reaction in the Cd (II) adsorption process of PPBC/MgFe-LDH. Piecewise fitting of the intra-particle diffusion model revealed multi-linearity during the adsorption process. Through associative characterization analysis, the adsorption mechanism of Cd (II) of PPBC/MgFe-LDH involved (i) hydroxide formation or carbonate precipitation; (ii) an isomorphic substitution of Fe (III) by Cd (II); (iii) surface complexation of Cd (II) by functional groups (-OH); and (iv) electrostatic attraction. The PPBC/MgFe-LDH composite demonstrated great potential for removing Cd (II) from wastewater, with the advantages of facile synthesis and excellent adsorption capacity.

It is of great significance to promote the quantitative research of soil science and the implementation of precision agriculture. On this basis, taking Xuwen County as the research object, this paper comprehensively analyzed the soil characteristics of cultivated land in Xuwen County and clarify the soil nutrient content and spatial distribution characteristics of cultivated land in Xuwen County, this paper comprehensively applied the methods of geostatistics, geographic information system (GIS) and fuzzy mathematics, and referred to the cultivated land quality grade standard (GB/T 33469-2016), to analyze the soil characteristics and evaluate the soil fertility of this region. The results show that the optimal interpolation model of soil pH and available phosphorus (AP) is a Gaussian model, and the optimal interpolation model of soil organic matter (SOM), available nitrogen (AN) and available potassium (AK) is a J-Bessel model. In addition, the spatial correlation of AK is weak, whereas pH, SOM, AN and AP show moderate spatial correlation. The proportion of excellent, good, average, medium and poor comprehensive fertility index are 26.00%, 32.67%, 19.33%, 19.00% and 3.00%, respectively. The overall level of soil fertility in Xuwen County is above the average, and the fertility quality presents an obvious trend of high in the South and low in the North. Areas that above average fertility are mainly distributed in Maichen Town, Qujie Town, Nanshan Town and Chengbei Town. The results can provide theoretical basis for improving the utilization rate of chemical fertilizer, fine management of cultivated land and ecological environment in this region, which can help in decision-making of precision fertilization.

The pathophysiology of cardiac hypertrophy is complex and multifactorial. Both the store-operated Ca2+ entry (SOCE) and excessive autophagy are the major causative factors for pathological cardiac hypertrophy. However, it is unclear whether these two causative factors are interdependent. In this study, we examined the functional role of SOCE and Orai1 in angiotensin II (Ang II)-induced autophagy and hypertrophy using in vitro neonatal rat cardiomyocytes (NRCMs) and in vivo mouse model, respectively. We show that YM-58483 or SKF-96365 mediated pharmacological inhibition of SOCE, or silencing of Orai1 with Orail-siRNA inhibited Ang II-induced cardiomyocyte autophagy both in vitro and in vivo. Also, the knockdown of Orai1 attenuated Ang II-induced pathological cardiac hypertrophy. Together, these data suggest that Ang II promotes excessive cardiomyocyte autophagy through SOCE/Orai1 which can be the prime contributing factors in cardiac hypertrophy.