Determination of Chlormequat and Mepiquat Residues in Tomato Plants Using Accelerated Solvent Extraction-Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry

An Accelerated-Solvent Extraction-Ultra performance Liquid Chromatography-Tandem Mass Spectrometry (ASE-UPLC-MS/MS) method using purified water as extraction solvent for quantitative analysis of chromequat (CQ) and mepiquat (MQ) in samples of tomato plants with higher sensibility and shorter extraction time was developed. The CQ and MQ residues and their dissipation rate were both covered in this paper. The limits of detection (S/N>3) and limits of quantitation (S/N>10) for CQ and MQ were 0.02 μg/kg and 0.1 μg/kg respectively. The linear range was 0.2~10 μg/kg and the correlation coefficients (r2) was no less than 0.9990, The average recoveries of CQ and MQ from tomato root, stem and leaf in the three spiked range of 1.0, 2.0 and 5.0 μg/kg were in the range of 100.0%~118.8% and 93.2%~110.7% respectively. The dissipation experiment showed that, on average, 98.8% of CQ residues and 99.7% of MQ residues had dissipated after 33 days, with a half-life of 3.67d and 3.66d, which can provide with guideline for using CQ and MQ on tomato in safe range.Key words: Tomato plants; Accelerated solvent extraction; Ultra-performance liquid chromatography-tandem mass spectrometry; Chlormequat; Mepiqua

Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective

Benefiting from the injection of human prior knowledge, graphs, as derived discrete data, are semantically dense so that models can efficiently learn the semantic information from such data. Accordingly, graph neural networks (GNNs) indeed achieve impressive success in various fields. Revisiting the GNN learning paradigms, we discover that the relationship between human expertise and the knowledge modeled by GNNs still confuses researchers. To this end, we introduce motivating experiments and derive an empirical observation that the human expertise is gradually learned by the GNNs in general domains. By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance. By exploring the intrinsic mechanism behind such observations, we elaborate the Structural Causal Model for the graph representation learning paradigm. Following the theoretical guidance, we innovatively introduce the auxiliary causal logic learning paradigm to improve the model to learn the expertise logic causally related to the graph representation learning task. In practice, the counterfactual technique is further performed to tackle the insufficient training issue during optimization. Plentiful experiments on the crafted and real-world domains support the consistent effectiveness of the proposed method

Cuproptosis/ferroptosis-related gene signature is correlated with immune infiltration and predict the prognosis for patients with breast cancer

Background: Breast invasive carcinoma (BRCA) is a malignant tumor with high morbidity and mortality, and the prognosis is still unsatisfactory. Both ferroptosis and cuproptosis are apoptosis-independent cell deaths caused by the imbalance of corresponding metal components in cells and can affect the proliferation rate of cancer cells. The aim in this study was to develop a prognostic model of cuproptosis/ferroptosis-related genes (CFRGs) to predict survival in BRCA patients.Methods: Transcriptomic and clinical data for breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cuproptosis and ferroptosis scores were determined for the BRCA samples from the TCGA cohort using Gene Set Variation Analysis (GSVA), followed by weighted gene coexpression network analysis (WGCNA) to screen out the CFRGs. The intersection of the differentially expressed genes grouped by high and low was determined using X-tile. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) were used in the TGCA cohort to identify the CFRG-related signature. In addition, the relationship between risk scores and immune infiltration levels was investigated using various algorithms, and model genes were analyzed in terms of single-cell sequencing. Finally, the expression of the signature genes was validated with quantitative real-time PCR (qRT‒PCR) and immunohistochemistry (IHC).Results: A total of 5 CFRGs (ANKRD52, HOXC10, KNOP1, SGPP1, TRIM45) were identified and were used to construct proportional hazards regression models. The high-risk groups in the training and validation sets had significantly worse survival rates. Tumor mutational burden (TMB) was positively correlated with the risk score. Conversely, Tumor Immune Dysfunction and Exclusion (TIDE) and tumor purity were inversely associated with risk scores. In addition, the infiltration degree of antitumor immune cells and the expression of immune checkpoints were lower in the high-risk group. In addition, risk scores and mTOR, Hif-1, ErbB, MAPK, PI3K/AKT, TGF-β and other pathway signals were correlated with progression.Conclusion: We can accurately predict the survival of patients through the constructed CFRG-related prognostic model. In addition, we can also predict patient immunotherapy and immune cell infiltration

Single-cell mapping of N6-methyladenosine in esophageal squamous cell carcinoma and exploration of the risk model for immune infiltration

BackgroundN6-methyladenosine (m6A) modification is the most common RNA modification, but its potential role in the development of esophageal cancer and its specific mechanisms still need to be further investigated.MethodsBulk RNA-seq of 174 patients with esophageal squamous carcinoma from the TCGA-ESCC cohort, GSE53625, and single-cell sequencing data from patients with esophageal squamous carcinoma from GSE188900 were included in this study. Single-cell analysis of scRNA-seq data from GSE188900 of 4 esophageal squamous carcinoma samples and calculation of PROGENy scores. Demonstrate the scoring of tumor-associated pathways for different cell populations. Cell Chat was calculated for cell populations. thereafter, m6A-related differential genes were sought and risk models were constructed to analyze the relevant biological functions and impact pathways of potential m6A genes and their impact on immune infiltration and tumor treatment sensitivity in ESCC was investigated.ResultsBy umap downscaling analysis, ESCC single-cell data were labelled into clusters of seven immune cell classes. Cellchat analysis showed that the network interactions of four signaling pathways, MIF, AFF, FN1 and CD99, all showed different cell type interactions. The prognostic risk model constructed by screening for m6A-related differential genes was of significant value in the prognostic stratification of ESCC patients and had a significant impact on immune infiltration and chemotherapy sensitivity in ESCC patients.ConclusionIn our study, we explored a blueprint for the distribution of single cells in ESCC based on m6A methylation and constructed a risk model for immune infiltration analysis and tumor efficacy stratification in ESCC on this basis. This may provide important potential guidance for revealing the role of m6A in immune escape and treatment resistance in esophageal cancer

Contrasting fate of western Third Pole's water resources under 21st century climate change

Seasonal melting of glaciers and snow from the western Third Pole (TP) plays important role in sustaining water supplies downstream. However, the future water availability of the region, and even today’s runoff regime, are both hotly debated and inadequately quantified. Here, we characterize the contemporary flow regimes and systematically assess the future evolution of total water availability, seasonal shifts, and dry and wet discharge extremes in four most meltwater-dominated basins in the western TP, by using a process-based, well-established glacier-hydrology model, well-constrained historical reference climate data, and the ensemble of 22 global climate models with an advanced statistical downscaling and bias correction technique. We show that these basins face sharply diverging water futures under 21st century climate change. In RCP scenarios 4.5 and 8.5, increased precipitation and glacier runoff in the Upper Indus and Yarkant basins more than compensate for decreased winter snow accumulation, boosting annual and summer water availability through the end of the century. In contrast, the Amu and Syr Darya basins will become more reliant on rainfall runoff as glacier ice and seasonal snow decline. Syr Darya summer river-flows, already low, will fall by 16–30% by end-of-century, and striking increases in peak flood discharge (by >60%), drought duration (by >1 month) and drought intensity (by factor 4.6) will compound the considerable water-sharing challenges on this major transboundary river

Identification of cuproptosis and immune-related gene prognostic signature in lung adenocarcinoma

BackgroundCuproptosis is a novel form of programmed cell death that differs from other types such as pyroptosis, ferroptosis, and autophagy. It is a promising new target for cancer therapy. Additionally, immune-related genes play a crucial role in cancer progression and patient prognosis. Therefore, our study aimed to create a survival prediction model for lung adenocarcinoma patients based on cuproptosis and immune-related genes. This model can be utilized to enhance personalized treatment for patients.MethodsRNA sequencing (RNA-seq) data of lung adenocarcinoma (LUAD) patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The levels of immune cell infiltration in the GSE68465 cohort were determined using gene set variation analysis (GSVA), and immune-related genes (IRGs) were identified using weighted gene coexpression network analysis (WGCNA). Additionally, cuproptosis-related genes (CRGs) were identified using unsupervised clustering. Univariate COX regression analysis and least absolute shrinkage selection operator (LASSO) regression analysis were performed to develop a risk prognostic model for cuproptosis and immune-related genes (CIRGs), which was subsequently validated. Various algorithms were utilized to explore the relationship between risk scores and immune infiltration levels, and model genes were analyzed based on single-cell sequencing. Finally, the expression of signature genes was confirmed through quantitative real-time PCR (qRT-PCR), immunohistochemistry (IHC), and Western blotting (WB).ResultsWe have identified 5 Oncogenic Driver Genes namely CD79B, PEBP1, PTK2B, STXBP1, and ZNF671, and developed proportional hazards regression models. The results of the study indicate significantly reduced survival rates in both the training and validation sets among the high-risk group. Additionally, the high-risk group displayed lower levels of immune cell infiltration and expression of immune checkpoint compared to the low-risk group

Quantitative Trait Locus Mapping of Soybean Maturity Gene E6

Soybean [ Glycine max (L.) Merr.] sensitivity to photoperiod determines adaptation to a specific range of latitudes for soybean cultivars. When temperate-adapted soybean cultivars are grown in low latitude under short day conditions, they flower early, resulting in low grain yield, and consequently limiting their utility in tropical areas. Most cultivars adapted to low-latitude environments have the trait of delayed flowering under short day conditions, and this trait is commonly called long juvenile (LJ). In this study, the E6 locus, the classical locus conditioning the LJ trait, was molecularly mapped on Gm04 near single-nucleotide polymorphism marker HRM101. Testcross, genetic mapping, and sequencing suggest that the E6 and J loci might be tightly linked. Genetic interaction evaluation between E6 and E1 suggests that E6 has a suppressive effect on E1 and that the function of E6 is dependent on E1. The tagging markers for E6 are very useful for molecular breeding for wide adaptation and stable productivity of soybean under lowlatitude environments. Molecular identification and functional characterization of the E6 gene will greatly facilitate the understanding of the genetic and molecular mechanisms underlying the LJ trait

Spatial and temporal evolution of urban resilience in China and analysis of barriers: Based on a sustainable development perspective.

With the increasing uncertainty of urban security, urban resilience construction with risk awareness and bottom-line thinking has become essential for promoting sustainable urban development. This paper measures China's urban resilience development index (CRDI) based on 284 cities in China (except Tibet) using the entropy method from four dimensions: economic, social, environmental, and infrastructure, and analyzes it by combining coupling coordination degree and barrier factor analysis. We find that: (1) At the national level, CRDI and its sub-dimensions show an increasing trend in time, a decreasing spatial layout from coastal to inland, and a "high-high-low-low" clustering feature in space. (2) At the regional level, the CRDI is from high to low in the east, middle, and west order. The sub-dimensions are from high to low in the order of east, middle, and west for economic, social, and infrastructure resilience and from high to low in the order of east, west, and middle for environmental resilience. (3) To coupling coordination degree, the CRDI index coupling coordination is increasing in time trend but is still on the verge of dissonance. (4) Social resilience is the main obstacle factor. In the indicator layer, human resources, innovation, education, security, living, and environmental protection are the areas where CRDI coordinated development is the key to improvement. Based on the above empirical evidence, this paper proposes countermeasures to optimize urban resilience construction from four perspectives: economic, social, environmental, and infrastructure