On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles.

Thermodynamic modeling is still the most widely used method to characterize aerosol acidity, a critical physicochemical property of atmospheric aerosols. However, it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamic models are employed to estimate the acidity of coarse particles. In this work, field measurements were conducted at a coastal city in northern China across three seasons, and covered wide ranges of temperature, relative humidity and NH3 concentrations. We examined the performance of different modes of ISORROPIA-II (a widely used aerosol thermodynamic model) in estimating aerosol acidity of coarse and fine particles. The M0 mode, which incorporates gas-phase data and runs the model in the forward mode, provided reasonable estimation of aerosol acidity for coarse and fine particles. Compared to M0, the M1 mode, which runs the model in the forward mode but does not include gas-phase data, may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles; M2, which runs the model in the reverse mode, results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity calculations. However, M1 significantly underestimates liquid water contents for both fine and coarse particles, while M2 provides reliable estimation of liquid water contents. In summary, our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity, and thus may help improve our understanding of acidity of coarse particles.

Discovery of perfluoroalkyl sulfonyl quaternary ammonium substances in the environment and their environmental behaviors.

A variety of per- and polyfluoroalkyl substances (PFASs) have been released into the environment via wastewater treatment plant (WWTP) effluent, with current target and nontarget analytical methods typically focusing on negatively ionized PFASs while largely overlooking positively ionized ones. In this study, five cationic PFASs, perfluoroalkyl sulfonyl quaternary ammonium substances (PFAQASs), were first identified in surface water impacted by the WWTP effluent, applying a metabolomics-based nontarget analysis method. Environmental behaviors of identified novel PFAQASs were further investigated. In surface water, sediment, and fish (Coilia mystus) samples collected from the Yangtze River, 8:3 PFAQA was consistently the predominant PFAQASs, with the mean concentrations of 90 ng/L (< LOD-558 ng/L), 92 ng/g dw (< LOD-421 ng/g dw), and 2.3 ng/g ww (< LOD-4.6 ng/g ww), respectively. This study highlights the necessity to discover other cationic PFASs in the environment. Among PFAQASs, 8:4 PFAQA (4.2, range 3.4 - 4.6) had the highest mean sediment-water partitioning coefficient (log Koc), followed by 8:3 PFAQA (3.9, 2.8 - 4.5) and 6:3 PFAQA (3.7, 3.3 - 4.1). The log Koc of PFAQASs showed a general increase trend with the increasing carbon chain length. Mean bioaccumulation factor (BAF) values of PFAQASs calculated in the collected fish from the Yangtze River ranged from 1.9 ± 0.32 (4:3 PFAQA) to 2.9 ± 0.34 (8:4 PFAQA). The mean BAF values of PFAQASs generally increased with the carbon chain length. Further studies are warranted to elucidate the environmental fate, potential toxicity, and human exposure implications for these identified novel PFASs.

Species-specific effect of particle viscosity and particle-phase reactions on the formation of secondary organic aerosol.

Secondary organic aerosol (SOA) is a major component of atmospheric fine particulate matter. Both particle viscosity and particle-phase chemistry play a crucial role in the formation and evolution of SOA; however, our understanding on how these two factors together with gas-phase chemistry collectively determine the formation of SOA is still limited. Here we developed a kinetic aerosol multilayer model coupled with gas-phase and particle-phase chemistry to simulate SOA formation. We take the atmospherically important α-pinene + OH oxidation system as an example application of the model. The simulations show that although the particle viscosity has negligible to small influences on the total SOA mass concentration, it strongly changes the concentration and distribution of individual compounds within the particle. This complicated effect of particle viscosity on SOA formation is a combined result of inhibited condensation or evaporation of specific organics due to slowed particle-phase diffusion. Furthermore, the particle-phase reactions alter the volatility and abundance of specific compounds and exacerbate their non-uniform distribution in highly viscous particles. Our results highlight an important species-specific effect of particle viscosity and particle-phase chemistry on SOA formation and demonstrate the capability of our model for quantifying such complicated effects on SOA formation and evolution.

Aptamer Screening: Current Methods and Future Trend towards Non-SELEX Approach.

Aptamers are nucleic acid sequences that specifically bind with target molecules and are vital to applications such as biosensing, drug development, disease diagnostics, etc. The traditional selection procedure of aptamers is based on the Systematic Evolution of Ligands by an Exponential Enrichment (SELEX) process, which relies on repeating cycles of screening and amplification. With the rapid development of aptamer applications, RNA and XNA aptamers draw more attention than before. But their selection is troublesome due to the necessary reverse transcription and transcription process (RNA) or low efficiency and accuracy of enzymes for amplification (XNA). In light of this, we review the recent advances in aptamer selection methods and give an outlook on future development in a non-SELEX approach, which simplifies the procedure and reduces the experimental costs. We first provide an overview of the traditional SELEX methods mostly designed for screening DNA aptamers to introduce the common tools and methods. Then a section on the current screening methods for RNA and XNA is prepared to demonstrate the efforts put into screening these aptamers and the current difficulties. We further predict that the future trend of aptamer selection lies in non-SELEX methods that do not require nucleic acid amplification. We divide non-SELEX methods into an immobilized format and non-immobilized format and discuss how high-resolution partitioning methods could facilitate the further improvement of selection efficiency and accuracy.

How to Partition a Quantum Observable.

We present a partition of quantum observables in an open quantum system that is inherited from the division of the underlying Hilbert space or configuration space. It is shown that this partition leads to the definition of an inhomogeneous continuity equation for generic, non-local observables. This formalism is employed to describe the local evolution of the von Neumann entropy of a system of independent quantum particles out of equilibrium. Crucially, we find that all local fluctuations in the entropy are governed by an entropy current operator, implying that the production of entanglement entropy is not measured by this partitioned entropy. For systems linearly perturbed from equilibrium, it is shown that this entropy current is equivalent to a heat current, provided that the system-reservoir coupling is partitioned symmetrically. Finally, we show that any other partition of the coupling leads directly to a divergence of the von Neumann entropy. Thus, we conclude that Hilbert-space partitioning is the only partition of the von Neumann entropy that is consistent with the laws of thermodynamics.

Development and validation of extraction and clean-up procedures for UPLC-MS/MS analysis of aflatoxins in spices.

UPLC-MS/MS analytical conditions for the analysis of aflatoxins in spices were optimized and validated in this study. Liquid-liquid partition-based protocols for the cleaning up of extracts using common organic solvents such as acetonitrile, hexane, and ethyl acetate were developed and validated. The developed liquid-liquid partition methods were compared with immuno-affinity column and QuEChERS clean-up methods for the UPLC-MS/MS analysis of aflatoxins in 8 spices. The reduction of lipophilic components using the partition with hexane is particularly useful in spices like red pepper that have higher levels of fatty acids, carotenoids, sterols, triterpenoids, etc. The subsequent partitioning with ethyl acetate considerably reduced the matrix interference from the polar components and increased the sensitivity. The cleaning up of spice extracts using liquid-liquid partition techniques resulted in limits of quantification (LOQ) of 2-5 µgL-1 in UPLC-MS/MS analysis. Trueness, repeatability, and reproducibility of the methods were in acceptable ranges. The accuracy of the developed methods was further verified by analyzing aflatoxins in naturally incurred samples of spices and comparing the results with those obtained from the immuno-affinity column cleanup-HPLC-FD method.

Researching the CNN Collaborative Inference Mechanism for Heterogeneous Edge Devices.

Convolutional Neural Networks (CNNs) have been widely applied in various edge computing devices based on intelligent sensors. However, due to the high computational demands of CNN tasks, the limited computing resources of edge intelligent terminal devices, and significant architectural differences among these devices, it is challenging for edge devices to independently execute inference tasks locally. Collaborative inference among edge terminal devices can effectively utilize idle computing and storage resources and optimize latency characteristics, thus significantly addressing the challenges posed by the computational intensity of CNNs. This paper targets efficient collaborative execution of CNN inference tasks among heterogeneous and resource-constrained edge terminal devices. We propose a pre-partitioning deployment method for CNNs based on critical operator layers, and optimize the system bottleneck latency during pipeline parallelism using data compression, queuing, and "micro-shifting" techniques. Experimental results demonstrate that our method achieves significant acceleration in CNN inference within heterogeneous environments, improving performance by 71.6% compared to existing popular frameworks.

Exploring fingerprints for antidiabetic therapeutics related to peroxisome proliferator-activated receptor gamma (PPARγ) modulators: A chemometric modeling approach.

This study demonstrated the correlation of molecular structures of Peroxisome proliferator-activated receptor gamma (PPARγ) modulators and their biological activities. Bayesian classification, and recursive partitioning (RP) studies have been applied to a dataset of 323 PPARγ modulators with diverse scaffolds. The results provide a deep insight into the important sub-structural features modulating PPARγ. The molecular docking analysis again confirmed the significance of the identified sub-structural features in the modulation of PPARγ activity. Molecular dynamics simulations further underscored the stability of the complexes formed by investigated modulators with PPARγ. Overall, the integration of many computational approaches unveiled key structural motifs essential for PPARγ modulatory activity that will shed light on the development of effective modulators in the future.

Invariances in relations between aging, exposure to external hazards, and mortality reflected in life table aging rate (LAR) patterns examined through the lens of generalized Gompertz-Makeham law.

According to the Gompertz law, the age-dependent change in the logarithm of mortality (life-table aging rate, LAR) is equal to the population-averaged age-independent biological aging rate (γ), and LAR would be constant if aging were the only cause of mortality increase. However, LAR is influenced by population exposures to the external hazards. If they were constant, according to the Gompertz-Makeham law (GML), LAR would be below γ at lower ages and asymptotically and monotonically approach γ with increasing age. Actually, LAR trajectories derived from data on mortality in different countries and historical periods feature systematic undulations. In the present investigation, mortality-vs.-age trajectories were modeled based on a generalized GML (gGML). Unlike the canonical GML terms, which are population-specific constants, the respective terms of the gGML are represented with some population-specific functions of age. Invariant in gGML are the modes of translation of these functions into the dependency of mortality on age: linear for population exposure to the irresistible external hazards or exponential for population-averaged ability to withstand the resistible external and internal hazards. Modeling suggests that, at earlier ages, LAR undulations are attributable to changes in population exposures to the former hazards. However, only their unrealistically high levels can produce the transient increase in LAR at about 65 to 90 years. This pervasive undulation of LAR-vs.-age trajectory is rather caused by an increment in γ. Reasons to regard gGML as a genuine natural law, which defines relations between mortality, aging and environment, are discussed.

Estimating Octanol-Water Partition Coefficients of Novel Brominated Flame Retardants by Reversed-Phase High-Performance Liquid Chromatography and Computational Models.

Legacy brominated flame retardants, including polybrominated diphenyl ethers (PBDEs), have been classified as persistent organic pollutants and replaced with novel brominated flame retardants (NBFRs). The octanol-water partition coefficients (log KOW) of NBFRs have been computationally estimated, but the log KOW values provided by these methods can differ by 1 to 3 orders of magnitude. Given the importance of this parameter in fate and toxicity models, we indirectly measured the log KOW values of eight NBFRs by their capacity factor (k') on a reversed-phase high-performance liquid chromatography (HPLC) C18 column by isocratic elution and compared these measured values with those estimated by nine computational models. Log KOW values were obtained for the NBFRs 1,2-bis(2,4,6-tribromophenoxy) ethane, pentabromobenzene, pentabromoethylbenzene, pentabromotoluene, 2-ethylhexyl 2,3,4,5-tetrabromobenzoate, allyl 2,4,6-tribromophenylether, 2,3-dibromopropyl-2,4,6-tribromophenyl ether, and bis(2-ethylhexyl) tetrabromophthalate. A training set of phthalates, polychlorinated biphenyls, PBDEs, and halogenated benzenes were chosen to obtain the log k'-log KOW calibration for the NBFRs. The computational models KowWIN, XLogP3, EAS-E Suite, COSMOtherm, DirectML, and Abraham polyparameter linear free energy relationships all predicted the log KOW values of the calibration compounds to within 1 order of magnitude without significant bias. The median of these models predicted log KOW values for the calibration compounds that were close to those known in the literature with root mean square error (RMSE) = 0.224 and for the NBFRs that were close to those measured by HPLC (RMSE = 0.334). Environ Toxicol Chem 2024;00:1-10. © 2024 SETAC.

The vertical partitioning between denitrification and dissimilatory nitrate reduction to ammonium of coastal mangrove sediment microbiomes.

Mangrove aquatic ecosystems receive substantial nitrogen (N) inputs from both land and sea, playing critical roles in modulating coastal N fluxes. The microbially-mediated competition between denitrification and dissimilatory nitrate reduction to ammonium (DNRA) in mangrove sediments significantly impacts the N fate and transformation processes. Despite their recognized role in N loss or retention in surface sediments, how these two processes vary with sediment depths and their influential factors remain elusive. Here, we employed a comprehensive approach combining 15N isotope tracer, quantitative PCR (qPCR) and metagenomics to verify the vertical dynamics of denitrification and DNRA across five 100-cm mangrove sediment cores. Our results revealed a clear vertical partitioning, with denitrification dominated in 0-30 cm sediments, while DNRA played a greater role with increasing depths. Quantification of denitrification and DNRA functional genes further explained this phenomenon. Taxonomic analysis identified Pseudomonadota as the primary denitrification group, while Planctomycetota and Pseudomonadota exhibited high proportion in DNRA group. Furthermore, genome-resolved metagenomics revealed multiple salt-tolerance strategies and aromatic compound utilization potential in denitrification assemblages. This allowed denitrification to dominate in oxygen-fluctuating and higher-salinity surface sediments. However, the elevated C/N in anaerobic deep sediments favored DNRA, tending to generate biologically available NH4+. Together, our results uncover the depth-related variations in the microbially-mediated competition between denitrification and DNRA, regulating N dynamics in mangrove ecosystems.

Recursive partitioning analysis for survival stratification and early imaging prediction of molecular biomarker in glioma patients.

BACKGROUND: Glioma is the most common primary brain tumor with high mortality and disability rates. Recent studies have highlighted the significant prognostic consequences of subtyping molecular pathological markers using tumor samples, such as IDH, 1p/19q, and TERT. However, the relative importance of individual markers or marker combinations in affecting patient survival remains unclear. Moreover, the high cost and reliance on postoperative tumor samples hinder the widespread use of these molecular markers in clinical practice, particularly during the preoperative period. We aim to identify the most prominent molecular biomarker combination that affects patient survival and develop a preoperative MRI-based predictive model and clinical scoring system for this combination. METHODS: A cohort dataset of 2,879 patients was compiled for survival risk stratification. In a subset of 238 patients, recursive partitioning analysis (RPA) was applied to create a survival subgroup framework based on molecular markers. We then collected MRI data and applied Visually Accessible Rembrandt Images (VASARI) features to construct predictive models and clinical scoring systems. RESULTS: The RPA delineated four survival groups primarily defined by the status of IDH and TERT mutations. Predictive models incorporating VASARI features and clinical data achieved AUC values of 0.85 for IDH and 0.82 for TERT mutations. Nomogram-based scoring systems were also formulated to facilitate clinical application. CONCLUSIONS: The combination of IDH-TERT mutation status alone can identify the most distinct survival differences in glioma patients. The predictive model based on preoperative MRI features, supported by clinical assessments, offers a reliable method for early molecular mutation prediction and constitutes a valuable scoring tool for clinicians in guiding treatment strategies.

Evaluation and modelling of polycyclic aromatic hydrocarbon (PAH) bioavailability in soils affected by coal tar asphalt.

There are large masses of coal tar asphalt present in old roads, containing high concentrations of polycyclic aromatic hydrocarbons (PAHs). Uncertainty surrounding the risk they pose causes problems during road reconstruction and for the reuse of the asphalt present. To help elucidate potential risks, a parsimonious linear equilibrium partitioning model for the bioavailability of PAHs in soils contaminated by tar asphalt particles was developed. Furthermore, a set of partitioning coefficients for PAHs between sampled coal tar binders and water were determined experimentally, as well as measurements of freely dissolved concentrations using polyoxymethylene samplers in batch tests and column recirculation experiments with various mixtures of different soils (peat and sandy loam) and tar asphalts. The model predictions of freely dissolved concentrations were conservative and within an order of magnitude of measurements in both batch and column tests. The model presented here only relies on soil organic carbon content and the fraction coal tar binder in the soil to model PAH partitioning. This model could be used for more realistic. Low tier risk assessments towards rational prioritization of sensitive areas for risk reduction efforts.

Occurrence, spatial distribution and ecological risk assessment of Organophosphate Esters in surface water and sediments from the Ravi River and its tributaries.

Organophosphate esters (OPEs) are widely used as substitutes for brominated flame retardants and characterized as emerging contaminants. Due to their toxicity and persistent nature, OPEs are becoming a matter of greater concern worldwide. However, information about the pollution profile of OPEs and associated ecological risks is still scarce in environmental matrices of the South Asian region, particularly Pakistan. Hence, the current study was conducted to investigate the occurrence, spatial distribution patterns, ecological risks and riverine flux of 10 organophosphate esters in surface water and sediments of Ravi River and its four tributaries. The concentrations of ∑10OPEs were in the range of 19.2 - 105 ng/L, with the dominance of chlorinated-OPEs (51 %) in surface water, whereas in case of sediments, the ∑10OPEs concentrations ranged from 20.7 to 149 ng/g dw, with high abundance of non - chlorinated alkyl-OPEs, which contributed about 56 % to total OPE concentration. The correlation analysis signified a strong positive relation of OPEs with TOC (p < 0.05, R = 0.76) in sediments; and in addition to this, field-based LogKoc values were estimated to be higher than predicted LogKoc. Moreover, a significantly positive correlation (p < 0.05, R = 0.88) was observed between LogKoc and LogKow, implying that hydrophobicity plays a significant role in OPE distribution in different environmental matrices. The global comparison revealed that contamination status of OPEs in the present study was comparatively lower than other regional findings, furthermore, principal component analysis suggested vehicular emissions, industrial discharges, household supplies and atmospheric deposition as main sources of OPEs occurrence in current study region. Furthermore, the riverine flux of ∑10OPEs was estimated to be 0.68 tons/yr and the ecological risk assessment indicated that all OPEs, except EHDPP and TCrP, showed negligible or insignificant ecological risks for aquatic organisms.

Evidence that spatial scale and environment factors explain grassland community assembly in woodland-grassland ecotones.

How communities of living organisms assemble has long been a central question in ecology. The impact of habitat filtering and limiting similarity on plant community structures is well known, as both processes are influenced by individual responses to environmental fluctuations. Yet, the precise identifications and quantifications of the potential abiotic and biotic factors that shape community structures at a fine scale remains a challenge. Here, we applied null model approaches to assess the importance of habitat filtering and limiting similarity at two spatial scales. We used 63 natural vegetation plots, each measuring 5 × 5 m, with three nested subplots measuring 1 × 1 m, from the 2021 field survey, to examine the alpha diversity as well as beta diversity of plots and subplots. Linear mixed-effects models were employed to determine the impact of environmental variables on assembly rules. Our results demonstrate that habitat filtering is the dominant assembly rules at both the plot and subplot levels, although limiting similarity assumes stronger at the subplot level. Plot-level limiting similarity exhibited a positive association with fine-scale partitioning, suggesting that trait divergence originated from a combination of limiting similarity and spatial partitioning. Our findings also reveal that the community assembly varies more strongly with the mean annual temperature gradient than the mean annual precipitation. This investigation provides a pertinent illustration of non-random assembly rules from spatial scale and environmental factors in plant communities in the loess hilly region. It underscores the critical influence of spatial and environmental constraints in understanding the assembly of plant communities.

Emission characteristics of lead and particulate matter from lead and zinc smelters in China.

Understanding the emission characteristics of particulate matter and associated heavy metals is essential for assessing their environmental and health impacts post-emission, as well as for identifying potential control technologies for the sources. Here, a field test was conducted at two advanced smelting plants equipped with comprehensive air pollution control devices. The particles emitted from different stages of lead and zinc smelting exhibited bi-modal size distributions, with peaks observed in PM0.1-1.0 and PM2.5-10, respectively. Particulate-bound Pb was identified as the predominant Pb species in the flue gas, primarily originating from ore crushing. Consequently, over 80 % of Pb was emitted in the form of coarse particles, a marked contrast to coal-fired power plants where Pb concentrated on fine particles. High efficiencies in Pb removal were achieved by dust collectors, flue gas purification systems, and acid plants with desulfurization systems, resulting in overall Pb emission factors in lead and zinc smelting were only 89.3 and 2.60 g t-1 (of metal production), respectively. Importantly, the contribution of gas-phase Pb, which accounts for approximately 16.6 % of total emissions, must not be neglected in future emission monitoring and control efforts.

Experimentally derived partitioning coefficients of carbamazepine and sulfadiazine in landfill refuse-leachate phase: Effects of refuse and leachate properties.

Pharmaceuticals have been detected at high concentrations in landfill leachate and refuse, which may pose potential long-term environmental impacts. The interaction of pharmaceuticals between leachate and refuse contributes to their retention through in situ sorption, thereby mitigating this impact. However, limited efforts have been made to describe the distribution characteristics of pharmaceuticals in the refuse-leachate phase. In this study, two refuse and three leachate samples were used to obtain partitioning coefficients (Kd) for two typical pharmaceuticals, carbamazepine (CBZ) and sulfadiazine (SD), with campus soil as a comparison. Landfill refuse exhibited higher Kd values (12.36 ± 0.90 and 19.76 ± 1.96 mL/g for CBZ and 1.90 ± 0.34 and 6.27 ± 0.58 mL/g for SD in two samples, respectively) than campus soil (3.73 ± 1.31 mL/g for CBZ and 0.81 ± 0.26 mL/g for SD), influenced by refuse properties such as higher organic matter (OM) content and specific surface area (SSA). The influence of leachate pH on Kd values depended on the electrostatic interaction between the species of target pollutants and negatively charged refuse. The effect of humic acid (HA) was related to its binding with target pollutants in solution and its competition with them for sorption sites. Electrostatic repulsion, hydrogen bonding and π-π interaction were the proposed mechanisms in SD sorption on refuse, while hydrogen bonding participated in the sorption of CBZ. The results will help aid the understanding of the distribution of pharmaceuticals in the refuse-leachate system and improve corresponding management strategies.

Exploration of Free Energy Surface of the Au10 Nanocluster at Finite Temperature.

The first step in comprehending the properties of Au10 clusters is understanding the lowest energy structure at low and high temperatures. Functional materials operate at finite temperatures; however, energy computations employing density functional theory (DFT) methodology are typically carried out at zero temperature, leaving many properties unexplored. This study explored the potential and free energy surface of the neutral Au10 nanocluster at a finite temperature, employing a genetic algorithm coupled with DFT and nanothermodynamics. Furthermore, we computed the thermal population and infrared Boltzmann spectrum at a finite temperature and compared it with the validated experimental data. Moreover, we performed the chemical bonding analysis using the quantum theory of atoms in molecules (QTAIM) approach and the adaptive natural density partitioning method (AdNDP) to shed light on the bonding of Au atoms in the low-energy structures. In the calculations, we take into consideration the relativistic effects through the zero-order regular approximation (ZORA), the dispersion through Grimme's dispersion with Becke-Johnson damping (D3BJ), and we employed nanothermodynamics to consider temperature contributions. Small Au clusters prefer the planar shape, and the transition from 2D to 3D could take place at atomic clusters consisting of ten atoms, which could be affected by temperature, relativistic effects, and dispersion. We analyzed the energetic ordering of structures calculated using DFT with ZORA and single-point energy calculation employing the DLPNO-CCSD(T) methodology. Our findings indicate that the planar lowest energy structure computed with DFT is not the lowest energy structure computed at the DLPN0-CCSD(T) level of theory. The computed thermal population indicates that the 2D elongated hexagon configuration strongly dominates at a temperature range of 50-800 K. Based on the thermal population, at a temperature of 100 K, the computed IR Boltzmann spectrum agrees with the experimental IR spectrum. The chemical bonding analysis on the lowest energy structure indicates that the cluster bond is due only to the electrons of the 6 s orbital, and the Au d orbitals do not participate in the bonding of this system.

Optimal Planting Density Increases the Seed Yield by Improving Biomass Accumulation and Regulating the Canopy Structure in Rapeseed.

Planting density is an important factor affecting plant growth and yield formation in rapeseed. However, the understanding of the mechanism underlying the impact of planting density on biomass, canopy, and ultimate seed yield remains limited. A field experiment was conducted to investigate the effect of planting density on seed yield, yield components, biomass accumulation and partitioning, and canopy structure. Five planting density levels were set as D1 (2.4 × 105 plants ha-1), D2 (3.6 × 105 plants ha-1), D3 (5.4 × 105 plants ha-1), D4 (6.0 × 105 plants ha-1), and D5 (7.2 × 105 plants ha-1). The results showed that with planting density increasing from D1 to D3, the seed yield, number of pods in population, and 1000-seed weight increased, while seedling survival rate, yield per plant, number of pods per plant, and number of seeds per plant decreased. When planting density increased to D4 and D5, seed yield dramatically decreased due to a decreased number of seeds per pod and 1000-seed weight. Increasing planting density from D1 to D3 increased biomass accumulation in all organs. D3 produced the highest biomass partitioning in seeds. In addition, D2 and D3 treatments had a high level of pod area index (5.3-5.8), which caused an approximately 93% of the light to be intercepted. The distribution of light in D2 and D3 was more evenly spread, with the upper and lower parts of the canopy displaying a distribution ratio of roughly 7:3. Therefore, D2 and D3 produced the highest seed yields. In conclusion, D2 and D3 are recommended in rapeseed production due to their role in improving biomass accumulation and partitioning and canopy structure.

Effects of Drought Stress on Leaf Functional Traits and Biomass Characteristics of Atriplex canescens.

Drought is a critical factor constraining plant growth in arid regions. However, the performance and adaptive mechanism of Atriplex canescens (A. canescens) under drought stress remain unclear. Hence, a three-year experiment with three drought gradients was performed in a common garden, and the leaf functional traits, biomass and biomass partitioning patterns of A. canescens were investigated. The results showed that drought stress had significant effects on A. canescens leaf functional traits. A. canescens maintained the content of malondialdehyde (MDA) and the activity of superoxide dismutase (SOD), but the peroxidase (POD) and catalase (CAT) activity decreased, and the content of proline (Pro) and soluble sugar (SS) increased only under heavy drought stress. Under drought stress, the leaves became smaller but denser, the specific leaf area (SLA) decreased, but the dry matter content (LDMC) maintained stability. Total biomass decreased 60% to 1758 g under heavy drought stress and the seed and leaf biomass was only 10% and 20% of non-stress group, but there had no significant difference on root biomass. More biomass was allocated to root under drought stress. The root biomass allocation ratio was doubled from 9.62% to 19.81% under heavy drought, and the root/shoot ratio (R/S) increased from 0.11 to 0.25. The MDA was significantly and negatively correlated with biomass, while the SPAD was significantly and positively correlated with total and aboveground organs biomass. The POD, CAT, Pro and SS had significant correlations with root and seed allocation ratio. The leaf morphological traits related to leaf shape and weight had significant correlations with total and aboveground biomass and biomass allocation. Our study demonstrated that under drought stress, A. canescens made tradeoffs between growth potential and drought tolerance and evolved with a conservative strategy. These findings provide more information for an in-depth understanding of the adaption strategies of A. canescens to drought stress and provide potential guidance for planting and sustainable management of A. canescens in arid and semi-arid regions.