Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms

NMF typing and machine learning algorithm-based exploration of preeclampsia-related mechanisms on ferroptosis signature genes

BACKGROUND

Globally, pre-eclampsia (PE) poses a major threat to the health and survival of pregnant women and fetuses, contributing significantly to morbidity and mortality. Recent studies suggest a pathological link between PE and ferroptosis. We aim to utilize non-negative matrix factorization (NMF) clustering and machine learning algorithms to pinpoint disease-specific genes related to the process of ferroptosis in PE and investigate likely underlying biochemistry mechanisms.

METHODS

The acquisition of four microarray datasets from the Gene Expression Omnibus (GEO) repository, the integration of these datasets, and the elimination of batch effects formed the core procedure. Genes related to ferroptosis in PE (DE-FRG) were identified. NMF clustering was performed on DE-FRG for unsupervised analysis, generating a heatmap for clustering validation via principal component analysis. Immunocyte infiltration differences between different subtypes were compared to elucidate the impact of ferroptosis on immune infiltration in the placental tissue of PE patients. The application of weighted gene co-expression network analysis (WGCNA) revealed important module genes linked to sample subtypes and disease status. The screening of PE feature genes involved employing SVM, RF, GLM, and XGB machine learning algorithms, and their predictive performance was validated using various analyses and an external dataset. The iRegulon tool was utilized to predict upstream transcription factors associated with ferroptosis feature genes, from which differentially expressed transcription factors were screened to construct a "Transcription Factor-FRG-ferroptosis" regulatory network. Finally, in vitro (cultured cells) and in vivo (rat) models were utilized to evaluate the regulatory mechanisms of ferroptosis in normal and PE placental tissues.

RESULTS

Differential analysis of the four merged GEO datasets identified 41 DE-FRGs. NMF clustering based on DE-FRGs revealed two PE subtypes. Immunocyte infiltration analysis indicated significant differences in immune levels between these subtypes. Further WGCNA analysis identified module genes associated with PE and these two subtypes. Subsequently, we developed an integrated machine learning model incorporating five FRGs and validated its predictive efficacy using various analyses and an external validation dataset. Finally, based on the transcription factor ARID3A and ferroptosis feature genes EPHB3 and PAPPA2, we constructed a "Transcription Factor-FRG-ferroptosis" regulatory network, with in vitro and in vivo experiments confirming that ARID3A promotes the progression of PE and ferroptosis by activating the expression of EPHB3 and PAPPA2.

CONCLUSION

This analytical journey illuminated a critical regulatory nexus in PE, underscoring the central influence of ARID3A on PE through ferroptosis-mediated pathways.

Eighteen years of upland grassland carbon flux data: reference datasets, processing, and gap-filling procedure

Plant-atmosphere exchange fluxes of CO₂ measured with the Eddy covariance method are used extensively for the assessment of ecosystem carbon budgets worldwide. The present paper describes eddy flux measurements for a managed upland grassland in Central France studied over two decades (2003-2021). We present the site meteorological data for this measurement period, and we describe the pre-processing and post-processing approaches used to overcome issues of data gaps, commonly associated with long-term EC datasets. Recent progress in eddy flux technology and machine learning now paves the way to produce robust long-term datasets, based on normalised data processing techniques, but such reference datasets remain rare for grasslands. Here, we combined two gap-filling techniques, Marginal Distribution Sampling (short gaps) and Random Forest (long gaps), to complete two reference flux datasets at the half-hour and daily-scales respectively. The resulting datasets are valuable for assessing the response of grassland ecosystems to (past) climate change, but also for model evaluation and validation with respect to future global change research with the carbon-cycle community.

Feature Sequencing Method of Industrial Control Data Set Based on Multidimensional Evaluation Parameters

The industrial control data set has many features and large redundancy, which has a certain impact on the training speed and classification results of the neural network anomaly detection algorithm. However, features are independent of each other, and dimension reduction often increases the false positive rate and false negative rate. The feature sequencing algorithm can reduce this effect. In order to select the appropriate feature sequencing algorithm for different data sets, this paper proposes an adaptive feature sequencing method based on data set evaluation index parameters. Firstly, the evaluation index system is constructed by the basic information of the data set, the mathematical characteristics of the data set, and the association degree of the data set. Then, the selection model is obtained by the decision tree training with the data label and the evaluation index, and the suitable feature sequencing algorithm is selected. Experiments were conducted on 11 data sets, including Batadal data set, CICIDS 2017, and Mississippi data set. The sequenced data sets are classified by ResNet. The accuracy of the sequenced data sets increases by 2.568% on average in 30 generations, and the average time reduction per epoch is 24.143%. Experiments show that this method can effectively select the feature sequencing algorithm with the best comprehensive performance.