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[This corrects the article DOI: 10.1371/journal.pgen.1010541.].

Plants often adapt to adverse conditions via differential growth, whereby limited resources are discriminately allocated to optimize the growth of one organ at the expense of another. Little is known about the decision-making processes that underly differential growth. In this study, we developed a screen to identify decision making mutants by deploying two tools that have been used in decision theory: a well-defined yet limited budget, as well as conflict-of-interest scenarios. A forward genetic screen that combined light and water withdrawal was carried out. This identified BRASSINOSTEROID INSENSITIVE 2 (BIN2) alleles as decision mutants with "confused" phenotypes. An assessment of organ and cell length suggested that hypocotyl elongation occurred predominantly via cellular elongation. In contrast, root growth appeared to be regulated by a combination of cell division and cell elongation or exit from the meristem. Gain- or loss- of function bin2 mutants were most severely impaired in their ability to adjust cell geometry in the hypocotyl or cell elongation as a function of distance from the quiescent centre in the root tips. This study describes a novel paradigm for root growth under limiting conditions, which depends not only on hypocotyl-versus-root trade-offs in the allocation of limited resources, but also on an ability to deploy different strategies for root growth in response to multiple stress conditions.

Ecological Protection Redline (EPR) is pivotal for constructing an ecological security framework in China. However, the current EPR delineation exhibits deficiencies because the assessment methods predominantly rely on vertical ecological processes, neglecting horizontal interconnections between ecosystems. To address this issue, this study proposed a hydrological distribution model based on water quality purification functions and environmental risk assessment, the research references Chinese drinking water quality standards. We identify the key purification areas and find a high pollutant correlation with a Pearson coefficient of 0.961. Also, the overlap rate between KWPAs and current EPRs is 89.1 %. This paper suggests that the area of the Redline boundary supplement area is 553.46 ha, and suggests that the area of the internal optimization and improvement area is 244.27 ha. Moreover, the study provides a reference tool for identifying areas with significant ecological value of other watersheds.

BackgroundCalcific aortic valve disease (CAVD) is one of the most prevalent valvular diseases and is the second most common cause for cardiac surgery. However, the mechanism of CAVD remains unclear. This study aimed to investigate the role of pyroptosis-related genes in CAVD by performing comprehensive bioinformatics analysis.MethodsThree microarray datasets (GSE51472, GSE12644 and GSE83453) and one RNA sequencing dataset (GSE153555) were obtained from the Gene Expression Omnibus (GEO) database. Pyroptosis-related differentially expressed genes (DEGs) were identified between the calcified and the normal valve samples. LASSO regression and random forest (RF) machine learning analyses were performed to identify pyroptosis-related DEGs with diagnostic value. A diagnostic model was constructed with the diagnostic candidate pyroptosis-related DEGs. Receiver operating characteristic (ROC) curve analysis was performed to estimate the diagnostic performances of the diagnostic model and the individual diagnostic candidate genes in the training and validation cohorts. CIBERSORT analysis was performed to estimate the differences in the infiltration of the immune cell types. Pearson correlation analysis was used to investigate associations between the diagnostic biomarkers and the immune cell types. Immunohistochemistry was used to validate protein concentration.ResultsWe identified 805 DEGs, including 319 down-regulated genes and 486 up-regulated genes. These DEGs were mainly enriched in pathways related to the inflammatory responses. Subsequently, we identified 17 pyroptosis-related DEGs by comparing the 805 DEGs with the 223 pyroptosis-related genes. LASSO regression and RF algorithm analyses identified three CAVD diagnostic candidate genes (TREM1, TNFRSF11B, and PGF), which were significantly upregulated in the CAVD tissue samples. A diagnostic model was constructed with these 3 diagnostic candidate genes. The diagnostic model and the 3 diagnostic candidate genes showed good diagnostic performances with AUC values >0.75 in both the training and the validation cohorts based on the ROC curve analyses. CIBERSORT analyses demonstrated positive correlation between the proportion of M0 macrophages in the valve tissues and the expression levels of TREM1, TNFRSF11B, and PGF.ConclusionThree pyroptosis-related genes (TREM1, TNFRSF11B and PGF) were identified as diagnostic biomarkers for CAVD. These pyroptosis genes and the pro-inflammatory microenvironment in the calcified valve tissues are potential therapeutic targets for alleviating CAVD.

With the development of satellite photography, meteorologists are inclined to rely on methods for the automatic and efficient classification of weather images. However, many popular networks require numerous parameters and a lengthy inference time, making them unsuitable for real-time classification tasks. To solve these problems, a lightweight convolutional network termed the channel-dilation-concatenation network (CDC-net) is constructed for meteorological satellite image classification. When extracting features, CDC-net utilizes depth-wise convolution rather than standard convolution. Additionally, a FeatureCopy operation was employed instead of a half-convolution operation. CDC-net extracts high-dimensional features and contains a local importance-based pooling layer, reducing the network's depth, the number of network parameters and inference time. Based on these techniques, the CDC-net achieves an accuracy of 93.56% on the large-scale satellite cloud image database for meteorological research, with a graphics processing unit (GPU) inference time of 3.261 ms and 1.12 million parameters. Because many weather images reveal multiple weather patterns, multiple labels are necessary. Therefore, we propose a prediction method and conduct experiments on multilabel data. Experiments on single-label and multilabel meteorological satellite image datasets demonstrate the superiority of the CDC-net over other structures. Thus, the proposed CDC-net can provide a faster and lighter solution in meteorological satellite image classification.

With the development of remote-sensing technology, it is important to use semantic segmentation methods to obtain detailed information in remote-sensing images. However, the objects in the images reveal significant intraclass differences and slight interclass differences, thus affecting the acquisition of terrain information. To tackle the problems, this article proposes an improved U-Net for the semantic segmentation of remote-sensing images. First, the local importance-based pooling is introduced to alleviate the loss of feature details in the coding part. Second, a combined attention module with a double-branch structure is designed, which models the local relationship and the global relationship at the same time to obtain more typical features. Finally, in order to make full use of the feature information extracted from the coding part, the combined attention module and the channel attention module are added to different positions in U-Net. In order to validate the proposed method, we conduct experiments on the WHDLD dataset and compare the experimental results with other semantic segmentation methods. On the WHDLD dataset, the MPA, MIOU, and FWIOU of the proposed method reach 76$\%$, 64.11$\%$, and 75.64$\%$, respectively, revealing its priority. To demonstrate the generalization of the proposed method, generalization experiments are conducted via the LandCover.ai dataset and the Massachusetts-building dataset. The simulation results testify that the proposed method provides an excellent generalization ability.

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands.