Are plant growth and photosynthesis limited by pre-drought following rewatering in grass?

Although the relationship between grassland productivity and soil water status has been extensively researched, the responses of plant growth and photosynthetic physiological processes to long-term drought and rewatering are not fully understood. Here, the perennial grass (Leymus chinensis), predominantly distributed in the Euro-Asia steppe, was used as an experimental plant for an irrigation manipulation experiment involving five soil moisture levels [75–80, 60–75, 50–60, 35–50, and 25–35% of soil relative water content (SRWC), i.e. the ratio between present soil moisture and field capacity] to examine the effects of soil drought and rewatering on plant biomass, relative growth rate (RGR), and photosynthetic potential. The recovery of plant biomass following rewatering was lower for the plants that had experienced previous drought compared with the controls; the extent of recovery was proportional to the intensity of soil drought. However, the plant RGR, leaf photosynthesis, and light use potential were markedly stimulated by the previous drought, depending on drought intensity, whereas stomatal conductance (gs) achieved only partial recovery. The results indicated that gs may be responsible for regulating actual photosynthetic efficiency. It is assumed that the new plant growth and photosynthetic potential enhanced by pre-drought following rewatering may try to overcompensate the great loss of the plant's net primary production due to the pre-drought effect. The present results highlight the episodic effects of drought on grass growth and photosynthesis. This study will assist in understanding how degraded ecosystems can potentially cope with climate change

Extreme temperature events reduced carbon uptake of a boreal forest ecosystem in Northeast China: Evidence from an 11-year eddy covariance observation

Boreal forests, the second continental biome on Earth, are known for their massive carbon storage capacity and important role in the global carbon cycle. Comprehending the temporal dynamics and controlling factors of net ecosystem CO2 exchange (NEE) is critical for predicting how the carbon exchange in boreal forests will change in response to climate change. Therefore, based on long-term eddy covariance observations from 2008 to 2018, we evaluated the diurnal, seasonal, and interannual variations in the boreal forest ecosystem NEE in Northeast China and explored its environmental regulation. It was found that the boreal forest was a minor CO2 sink with an annual average NEE of -64.01 (± 24.23) g CO2 m-2 yr-1. The diurnal variation in the NEE of boreal forest during the growing season was considerably larger than that during the non-growing season, and carbon uptake peaked between 8:30 and 9:30 in the morning. The seasonal variation in NEE demonstrated a “U” shaped curve, and the carbon uptake peaked in July. On a half-hourly scale, photosynthetically active radiation and vapor pressure deficit had larger impacts on daytime NEE during the growing season. However, temperature had major control on NEE during the growing season at night and during the non-growing season. On a daily scale, temperature was the dominant factor controlling seasonal variation in NEE. Occurrence of extreme temperature days, especially extreme temperature events, would reduce boreal forest carbon uptake; interannual variation in NEE was substantially associated with the maximum CO2 uptake rate during the growing season. This study deepens our understanding of environmental controls on NEE at multiple timescales and provides a data basis for evaluating the global carbon budget

Localization of BEN1-LIKE protein and nuclear degradation during development of metaphloem sieve elements in Triticum aestivum L.

Metaphloem sieve elements (MSEs) in the developing caryopsis of Triticum aestivum L. undergo a unique type of programmed cell death (PCD); cell organelles gradually degrade with the MSE differentiation while mature sieve elements keep active. This study focuses on locating BEN1-LIKE protein and nuclear degradation in differentiating MSEs of wheat. Transmission electron microscopy (TEM) showed that nuclei degraded in MSE development. First, the degradation started at 2–3 days after flowering (DAF). The degraded fragments were then swallowed by phagocytic vacuoles at 4 DAF. Finally, nuclei almost completely degraded at 5 DAF. We measured the BEN1-LIKE protein expression in differentiating MSEs. In situ hybridization showed that BEN1-LIKE mRNA was a more obvious hybridization signal at 3–4 DAF at the microscopic level. Immuno-electron microscopy further revealed that BEN1-LIKE protein was mainly localized in MSE nuclei. Furthermore, MSE differentiation was tested using a TSQ Zn2+ fluorescence probe which showed that the dynamic change of Zn2+ accumulation was similar to BEN1-LIKE protein expression. These results suggest that nucleus degradation in wheat MSEs is associated with BEN1-LIKE protein and that the expression of this protein may be regulated by Zn2+ accumulation variation

How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers

As a booming research area in the past decade, deep learning technologies have been driven by big data collected and processed on an unprecedented scale. However, privacy concerns arise due to the potential leakage of sensitive information from the training data. Recent research has revealed that deep learning models are vulnerable to various privacy attacks, including membership inference attacks, attribute inference attacks, and gradient inversion attacks. Notably, the efficacy of these attacks varies from model to model. In this paper, we answer a fundamental question: Does model architecture affect model privacy? By investigating representative model architectures from CNNs to Transformers, we demonstrate that Transformers generally exhibit higher vulnerability to privacy attacks compared to CNNs. Additionally, We identify the micro design of activation layers, stem layers, and LN layers, as major factors contributing to the resilience of CNNs against privacy attacks, while the presence of attention modules is another main factor that exacerbates the privacy vulnerability of Transformers. Our discovery reveals valuable insights for deep learning models to defend against privacy attacks and inspires the research community to develop privacy-friendly model architectures.Comment: Under revie

Preparation and Characteristic of PC/PLA/TPU Blends by Reactive Extrusion

To overcome the poor toughness of PC/PLA blends due to the intrinsic properties of materials and poor compatibility, thermoplastic urethane (TPU) was added to PC/PLA blends as a toughener; meantime, catalyst di-n-butyltin oxide (DBTO) was also added for catalyzing transesterification of components in order to modify the compatibility of blends. The mechanical, thermal, and rheological properties of blends were investigated systematically. The results showed that the addition of TPU improves the toughness of PC/PLA blends significantly, with the increase of TPU, the elongation at break increases considerably, and the impact strength increases firstly and then falls, while the tensile strength decreases significantly and the blends exhibit a typical plastic fracture behavior. Meantime, TPU is conducive to the crystallinity of PLA in blends which is inhibited seriously by PC and damages the thermal stability of blends slightly. Moreover, the increased TPU makes the apparent viscosity of blends melt decrease due to the well melt fluidity of TPU; the melt is closer to the pseudoplasticity melt. Remarkably, the transesterification between the components improves the compatibility of blends significantly, and more uniform structure results in a higher crystallinity and better mechanical properties

Mutations associated with no durable clinical benefit to immune checkpoint blockade in Non-S-Cell lung cancer

(1) Background: The immune checkpoint blockade (ICB) has shown promising efficacy in non-small-cell lung cancer (NSCLC) patients with significant clinical benefits and durable responses, but the overall response rate to ICBs is only 20%. The lack of responsiveness to ICBs is currently a central problem in cancer immunotherapy. (2) Methods: Four public cohorts comprising 2986 patients with NSCLC were included in the study. We screened 158 patients with NSCLC with no durable clinical benefit (NDB) to ICBs in the Rizvi cohort and identified NDB-related gene mutations in these patients using univariate and multivariate Cox regression analyses. Programmed death-ligand 1 (PD-L1) expression, tumor mutation burden (TMB), neoantigen load, tumor-infiltrating lymphocytes, and immune-related gene expression were analyzed for identifying gene mutations. A comprehensive predictive classifier model was also built to evaluate the efficacy of ICB therapy. (3) Results: Mutations in FAT1 and KEAP1 were found to correlate with NDB in patients with NSCLC to ICBs; however, the analysis suggested that only mutation in FAT1 was valuable in predicting the efficacy of ICB therapy, and that mutation in KEAP1 acted as a prognostic but not a predictive biomarker for NSCLC. Mutations in FAT1 were associated with a higher TMB and lower multiple lymphocyte infiltration, including CD8 (T-Cell Surface Glycoprotein CD8)+ T cells. We established a prognostic model according to PD-L1 expression, TMB, smoking status, treatment regimen, treatment type, and FAT1 mutation, which indicated good accuracy by receiver operating characteristic (ROC) analysis (area under the curve (AUC) for 6-months survival: 0.763; AUC for 12-months survival: 0.871). (4) Conclusions: Mutation in FAT1 may be a predictive biomarker in patients with NSCLC who exhibit NDB to ICBs. We proposed an FAT1 mutation-based model for screening more suitable NSCLC patients to receive ICBs that may contribute to individualized immunotherapy.info:eu-repo/semantics/publishedVersio

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%.Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages

Monthly land cover-specific evapotranspiration models derived from global eddy flux measurements and remote sensing data

Evapotranspiration (ET) is arguably the most uncertain ecohydrologic variable for quantifying watershed water budgets. Although numerous ET and hydrological models exist, accurately predicting the effects of global change on water use and availability remains challenging because of model deficiency and/or a lack of input parameters. The objective of this study was to create a new set of monthly ET models that can better quantify landscape-level ET with readily available meteorological and biophysical information. We integrated eddy covariance flux measurements from over 200 sites, multiple year remote sensing products from the Moderate Resolution Imaging Spectroradiometer (MODIS), and statistical modelling. Through examining the key biophysical controls on ET by land cover type (i.e. shrubland, cropland, deciduous forest, evergreen forest, mixed forest, grassland, and savannas), we created unique ET regression models for each land cover type using different combinations of biophysical independent factors. Leaf area index and net radiation explained most of the variability of observed ET for shrubland, cropland, grassland, savannas, and evergreen forest ecosystems. In contrast, potential ET (PET) as estimated by the temperaturebased Hamon method was most useful for estimating monthly ET for deciduous and mixed forests. The more data-demanding PET method, FAO reference ET model, had similar power as the simpler Hamon PET method for estimating actual ET. We developed three sets of monthly ET models by land cover type for different practical applications with different data availability. Our models may be used to improve water balance estimates for large basins or regions with mixed land cover types

Rapeseed Seedling Stand Counting and Seeding Performance Evaluation at Two Early Growth Stages Based on Unmanned Aerial Vehicle Imagery

The development of unmanned aerial vehicles (UAVs) and image processing algorithms for field-based phenotyping offers a non-invasive and effective technology to obtain plant growth traits such as canopy cover and plant height in fields. Crop seedling stand count in early growth stages is important not only for determining plant emergence, but also for planning other related agronomic practices. The main objective of this research was to develop practical and rapid remote sensing methods for early growth stage stand counting to evaluate mechanically seeded rapeseed (Brassica napus L.) seedlings. Rapeseed was seeded in a field by three different seeding devices. A digital single-lens reflex camera was installed on an UAV platform to capture ultrahigh resolution RGB images at two growth stages when most rapeseed plants had at least two leaves. Rapeseed plant objects were segmented from images of vegetation indices using typical Otsu thresholding method. After segmentation, shape features such as area, length-width ratio and elliptic fit were extracted from the segmented rapeseed plant objects to establish regression models of seedling stand count. Three row characteristics (the coefficient of variation of row spacing uniformity, the error rate of the row spacing and the coefficient of variation of seedling uniformity) were further calculated for seeding performance evaluation after crop row detection. Results demonstrated that shape features had strong correlations with ground-measured seedling stand count. The regression models achieved R-squared values of 0.845 and 0.867, respectively, for the two growth stages. The mean absolute errors of total stand count were 9.79 and 5.11% for the two respective stages. A single model over these two stages had an R-squared value of 0.846, and the total number of rapeseed plants was also accurately estimated with an average relative error of 6.83%. Moreover, the calculated row characteristics were demonstrated to be useful in recognizing areas of failed germination possibly resulted from skipped or ineffective planting. In summary, this study developed practical UAV-based remote sensing methods and demonstrated the feasibility of using the methods for rapeseed seedling stand counting and mechanical seeding performance evaluation at early growth stages