Dimensionality Reduction Analysis Research Articles

PCA-ICA-LSTM: A Hybrid Deep Learning Model Based on Dimension Reduction Methods to Predict S&P 500 Index Price

AbstractIn this paper, we propose a new hybrid model based on a deep learning network to predict the prices of financial assets. The study addresses two key limitations in existing research: (1) the lack of standardized datasets, time scales, and evaluation metrics, and (2) the focus on prediction return. The proposed model employs a two-stage preprocessing approach utilizing Principal Component Analysis (PCA) for dimensionality reduction and de-noising, followed by Independent Component Analysis (ICA) for feature extraction. A Long Short-Term Memory (LSTM) network with five layers is fed with this preprocessed data to predict the price of the next day using a 5 day time horizon. To ensure comparability with existing literature, experiments employ an 18 year dataset of the Standard & Poor's 500 (S&P500) index and include over 40 technical indicators. Performance evaluation encompasses six metrics, highlighting the model's superiority in accuracy and return rates. Comparative analyses demonstrate the superiority of the proposed PCA-ICA-LSTM model over single-stage statistical methods and other deep learning architectures, achieving notable improvements in evaluation metrics. Evaluation against previous studies using similar datasets corroborates the model's superior performance. Moreover, extensions to the study include adjustments to dataset parameters to account for the COVID-19 pandemic, resulting in improved return rates surpassing traditional trading strategies. PCA-ICA-LSTM achieves a 220% higher return compared to the “hold and wait” strategy in the extended S&P500 dataset, along with a 260% higher return than its closest competitor in the comparison. Furthermore, it outperformed other models in additional case studies. Graphical Abstract

Single-Cell Data Analysis Reveals Critical Hepatic Cells Subpopulations in the Progression of Non-alcoholic Fatty Liver Disease to Non-Alcoholic Steatohepatitis.

The aim of this study was to reveal the hepatic cell landscape and function in the progression of NAFLD to NASH. Non-alcoholic steatohepatitis (NASH) is the progressive form and turning point of nonalcoholic fatty liver disease (NAFLD), which severely causes irreversible cirrhosis as well as hepatocellular carcinoma. The mechanism underlying the progression of NAFLD to NASH has not been revealed. Unraveling the mechanism of action of NAFLD-NASH is an important goal in improving the survival of patients with liver disease. The aim of this study is to discover heterogeneous hepatic cells during the progression of NAFLD to NASH. Single-nucleus RNA-seq (snRNA-seq) data containing NASH in NAFLD samples were obtained from the Gene Expression Omnibus (GEO) database. Cell types in liver tissues from NASH and NAFLD were identified after dimensionality reduction analysis, cluster analysis, and cell annotation. The cell pathways in which differences existed were identified by analyzing metabolic pathways in Hepatic cells. We also identified cell subpopulations in Hepatic cells. The developmental trajectories of Hepatic cells were characterized by pseudotime trajectory analysis. Single-cell regulatory network inference and clustering analysis identified key transcription factors and gene regulatory networks in Hepatic cells. Moreover, cell communication analysis determined the potential interactions between Hepatic cells and immune cells, and heapatic stellate cells. Seven cell types were identified in NAFLD and NASH. The proportion of Hepatic cells was lower in NASH and showed greater energy metabolism and glucose metabolism activity. Hepatic cells exhibited heterogeneity, showing two cell subpopulations, Hepatic cells 1 and Hepatic cells 2. Dysregulation of lipid metabolism in Hepatic Cell 2 resulted in lipid accumulation in the liver, which might be involved in the progression of NAFLD. Four key transcription factors, BHLHE40, NFEL2L, RUNX1, and INF4A, were primarily found in Hepatic cells 2. The transcription factors within the hepatic cells 2 subpopulation mainly regulated genes related to lipid metabolism, energy metabolism, and inflammatory response. The cell communication analysis showed that hepatocyte interactions with immune cells were associated with inflammatory responses, while interactions with hepatic astrocytes were associated with liver injury and hepatocyte fibrosis. The hepatic cells 2 might promote the progression of NAFLD to NASH by regulating metabolic activity, which might contribute to liver injury through inflammation.

SNP-SNP positive interaction between MMP2 and MMP12 increases the risk of COPD.

Determining SNP-SNP interaction of the disease has become important for further investigation of pathogenesis and experimental research. Although many studies have been published on the effect of MMPs gene polymorphisms on chronic obstructive pulmonary disease (COPD), there is a lack of information on SNP-SNP and SNP-environment interactions. This study aimed to investigate the interaction between the polymorphisms of MMP1, MMP2, MMP9 and MMP12 genes and its combined effect with smoking on the risk of developing COPD. Totally 181 COPD patients and 292 healthy individuals were involved. Blood samples from the participants were tested for genotyping and data were collected through questionnaires. Genotyping was performed with nested allele-specific polymerase chain reaction (AS-PCR) and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). SNP-SNP and SNP-environment interactions were investigated using multifactor dimensionality reduction and logistic regression analysis. The result showed that participants with high nicotine dependence and heavy smokers had a higher risk of COPD than non-smokers. Also, G/G genotype (cOR = 5.83; 95% CI, 1.19-28.4, p = 0.029) of MMP2 rs243864 and T/T genotype (cOR = 1.79; 95% CI, 1.16-2.76, p = 0.008) of MMP12 rs652438 independently contributes to the susceptibility of COPD. For SNP-SNP interaction, the positive interaction between rs243864 G/G genotype of MMP2 and rs652438 T/T genotype of MMP12 was found, and the combination of risk genotypes has a high risk of COPD (OR = 12.92; 95% CI, 1.46-114.4, p = 0.021). Moreover, the combination of T/T genotype of MMP12 rs652438 and smoking-related factors increases the risk of COPD approximately 4.5 to 6-fold. The results suggests that there is a combination of MMP2, MMP12, and smoking-related factors may increase the risk of developing COPD.

Association between NUDT17 polymorphisms and breast cancer risk.

Breast cancer (BC) is the leading cause of cancer death among women worldwide. The nudix hydrolase 17 (NUDT17) may play notable roles in cancer growth and metastasis. In this study, we explored the importance of NUDT17 gene polymorphism in patients with BC. In our study, 563 BC patients and 552 healthy controls participated. We used logistic regression analysis to calculate odds ratios (OR) and 95% confidence intervals (CI), and multifactor dimension reduction (MDR) analysis of SNP-SNP interactions. Finally, UALCAN and THPA databases were used for bioinformatics analysis. The rs9286836 G allele was associated with a decreased the BC risk (p = 0.022), and the carriers of rs2004659 G allele had a 32% decreased risk of BC than individuals with allele A (p = 0.004). In the four genetic models, rs9286836 and rs2004659 reduced the risk of BC. Additionally, we found that the NUDT17 SNPs were associated with BC risk under age, tumor size, and clinical stage stratification. The MDR analysis showed that the five-locus interaction model was the best in the multi-locus model. Our study found that NUDT17 single nucleotide polymorphisms are associated with BC susceptibility in Chinese Han population.

Bridging Dense and Sparse Maximum Inner Product Search

Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top- \(k\) retrieval in Information Retrieval. This duality exists because sparse and dense vectors serve different end goals. That is despite the fact that they are manifestations of the same mathematical problem. In this work, we ask if algorithms for dense vectors could be applied effectively to sparse vectors, particularly those that violate the assumptions underlying top- \(k\) retrieval methods. We study clustering-based approximate MIPS where vectors are partitioned into clusters and only a fraction of clusters are searched during retrieval. We conduct a comprehensive analysis of dimensionality reduction for sparse vectors, and examine standard and spherical KMeans for partitioning. Our experiments demonstrate that clustering-based retrieval serves as an efficient solution for sparse MIPS. As byproducts, we identify two research opportunities and explore their potential. First, we cast the clustering-based paradigm as dynamic pruning and turn that insight into a novel organization of the inverted index for approximate MIPS over general sparse vectors. Second, we offer a unified regime for MIPS over vectors that have dense and sparse subspaces, that is robust to query distributions.

Raman and Mid-Infrared Spectroscopy Coupled With Machine–Deep Learning for Adulterant Detection in Ground Turmeric

The intricate nature of the global food supply chain and the presence of regulations spanning multiple jurisdictions contribute to an increased likelihood of food adulteration. This underscores the need for effective monitoring methods to guarantee the safety and nutritional quality of our food. In this context, the application of infrared spectroscopy-based techniques emerges as an environmentally friendly, non-invasive, and waste-minimizing solution for authenticating food products. Infrared spectra serve as unique molecular fingerprints, offering a multidimensional representation of how chemical bonds in the material interact with infrared light. Chemometrics, which are primarily linear-based models, play a crucial role in extracting essential information from spectral data, enabling dimensionality reduction, classification, and predictive analysis. Recent progress in the field of big data science and artificial intelligence has brought forth machine learning and deep learning algorithms explicitly designed to uncover features from complex multidimensional data, encompassing both linear and nonlinear relationships. These advancements have the potential to enhance the detection of adulterants in food products. This study assesses the accuracy of various shallow machine learning models and a deep learning model based on a one-dimensional convolutional neural network (1D CNN). The evaluation is conducted using Raman and infrared spectral data obtained from ground turmeric samples that were deliberately adulterated with five distinct substances. The study highlights the improved classification accuracy achieved through the implementation of the 1D CNN model.

Early life stress unravels epistatic genetic associations of cortisol pathway genes with depression

Dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis represents one of the most consistent pathophysiological findings in depressive disorders. Cortisol signaling is affected by proteins that mediate its cellular responses or alters its availability to mineralocorticoid and glucocorticoid receptors. In our study, we evaluated candidate genes that may influence the risk for depression and suicide due to its involvement in cortisol signaling. The aim of the study was to assess whether the genotypes of these genes are associated with the risk for depression, severity of depressive symptoms, suicidal ideation, and suicide attempts. And whether there is interaction between genes and early-life stress. In this study, 100 healthy controls and 140 individuals with depression were included. The subjects were clinically assessed using the 21-item GRID-Hamilton questionnaires (GRID-HAMD-21), Beck Scale for Suicidal Ideation (BSI), and the Childhood Trauma Questionnaire (CTQ). A robust multifactorial dimensionality reduction analysis was used to characterize the interactions between the genes HSD11B1, NR3C1, NR3C2, and MDR1 and early-life stress. It was found a significant association of the heterozygous genotype of the MDR1 gene rs1128503 polymorphism with reduced risk of at least one suicide attempt (OR: 0.08, p = 0.003*) and a reduction in the number of suicide attempts (β = −0.79, p = 0.006*). Furthermore, it was found that the MDR1 rs1228503 and NR3C2 rs2070951 genes interact with early-life stress resulting in a strong association with depression (p = 0.001). Our findings suggest that polymorphisms in the MDR1 and NR3C2 genes and their interaction with childhood trauma may be important biomarkers for depression and suicidal behaviors.

Abstract PO4-09-09: Deep Immunoprofiling Demonstrates Racial Differences in the Peripheral Immune System in Women at Risk for Breast Cancer

Abstract Background: Disparities in breast cancer outcomes between Non-Hispanic Black (NHB) and Non-Hispanic White (NHW) women have been attributed to NHB women presenting with more aggressive subtypes and later stage disease, differences in treatment receipt and adherence, access to care and tumor biology. However, previous work has shown that these known factors do not fully account for the observed disparities in outcomes. Systemic immune fitness is a factor not previously studied that may play a role in dictating disease progression and therapeutic responses. Numerous factors, including age and race, impact immune system function and understanding these changes is critical to address disparities in breast cancer outcomes. Here, we developed a pipeline for deep immunoprofiling of peripheral blood mononuclear cells (PBMC) at single cell resolution by full-spectrum flow cytometry. Using this pipeline, we sought to investigate whether there are differences in immune fitness between NHB and NHW women at high risk for developing breast cancer. Methods: PBMC were collected and processed from age-matched NHB and NHW women (n=20/cohort) identified as being at high risk for developing breast cancer through our institution’s B-PREP (Breast Cancer Personalized Risk Assessment Education and Prevention) clinic. Two custom antibody panels and protocols for comprehensive immunoprofiling of T and B cell subsets (T/B Panel), as well as monocytic, NK, and dendritic cell subsets (M/N/D Panel), along with functional and exhaustion markers were designed, optimized, and implemented. Traditional gating strategies were used to quantify proportions of canonical cell populations, and unbiased high dimensionality reduction analysis was performed using the OMIQ analysis platform to assess phenotypes and functional status of various immune cell subsets. Results: Significant differences between cohorts in abundance as well as phenotype of various cell populations were observed. Specifically, CD4+ follicular helper T cells, TEMRA CD8, and CD45RA Terminal Effector CD8+ cells were enriched in NHB women while PD-1+/CD3+ cells and PD-1+/CD8+ cells were enriched in the NHW women (Table). Additionally, several immunosuppressive and systemic inflammatory immune cell types were enriched in NHB women (Table). Conclusions: Deep immunoprofiling using single-cell spectral flow cytometry revealed differences in peripheral immune cells between NHB and NHW women at high risk for breast cancer. The significant enrichment of immunosuppressive monocytes and several activated NK cell populations in PBMC samples from NHB women relative to those from NHW women indicate an altered immune environment that may influence breast cancer development. Additional analysis, including a larger sample size and patients with invasive cancer, are ongoing to further understand if differences in immune fitness between NHB and NHW women are associated with disparities in breast cancer outcomes. Table: PBMC populations (% of CD45+) that are significantly enriched in NHB relative to NHW patients Citation Format: Esther Ogayo, Milos Spasic, Tasnim Rahman, Olga Kantor, Tari King, Peter van Galen, Sandra McAllister, Elizabeth Mittendorf. Deep Immunoprofiling Demonstrates Racial Differences in the Peripheral Immune System in Women at Risk for Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-09-09.

Bridging Modalities: A Multimodal Machine Learning Approach for Parkinson’s Disease Diagnosis Using EEG and MRI Data

Parkinson’s disease (PD) is a slowly progressing neurological disorder with symptoms that overlap with those of other conditions, making early detection and accurate diagnosis vital for effective treatment and a patient’s quality of life. Symptoms such as tremors, stiffness, slow movements, and balance issues, along with psychiatric manifestations, are typical of PD. This study introduces a groundbreaking approach to PD diagnosis, utilizing a multimodal machine learning framework that integrates Electroencephalography (EEG) and Magnetic Resonance Imaging (MRI) data. Focusing on the early detection and accurate classification of PD, the proposed research leverages the distinct yet complementary nature of EEG and MRI datasets to enhance diagnostic precision. We employed a robust algorithmic strategy, including LightGBM and machine learning techniques, to analyze the complex patterns inherent in neurological data. The key steps of the proposed research are preprocessing and feature extraction from both EEG and MRI modalities, followed by their fusion using Principal Component Analysis (PCA) for dimensionality reduction. The fused dataset was then analyzed using a LightGBM model and validated through a 10-fold cross-validation process to ensure reliability and stability. The model’s efficacy was further tested on independent datasets, demonstrating its robustness across diverse patient demographics. The obtained results showcased an accuracy of 97.17%, sensitivity of 96.58%, and specificity of 96.82% in PD classification, outperforming traditional multimodal as well as single-modality diagnostic methods. The integration of EEG and MRI data provided a more comprehensive view of the neurophysiological and neuroanatomical changes associated with PD. Additionally, the use of advanced machine learning algorithms allowed for a nuanced analysis, capturing subtle patterns indicative of early PD stages.

Computational modeling of animal behavior in T-mazes: Insights from machine learning

This study investigates the intricacies of animal decision-making in T-maze environments through a synergistic approach combining computational modeling and machine learning techniques. Focusing on the binary decision-making process in T-mazes, we examine how animals navigate choices between two paths. Our research employs a mathematical model tailored to the decision-making behavior of fish, offering analytical insights into their complex behavioral patterns. To complement this, we apply advanced machine learning algorithms, specifically Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and a hybrid approach involving Principal Component Analysis (PCA) for dimensionality reduction followed by SVM for classification to analyze behavioral data from zebrafish and rats. The above techniques result in high predictive accuracies, approximately 98.07% for zebrafish and 98.15% for rats, underscoring the efficacy of computational methods in decoding animal behavior in controlled experiments. This study not only deepens our understanding of animal cognitive processes but also showcases the pivotal role of computational modeling and machine learning in elucidating the dynamics of behavioral science.

End-to-end dimensionality reduction and regression from 3D geological uncertainties to estimate oil reservoir simulations

Managing geological uncertainties in reservoir engineering involves significant challenges mainly due to the prohibitive computational costs of traditional simulation methods. These simulations, essential for generating geological models, often require extensive computational resources and can take days or weeks to complete. The computational load limits the number of viable models while the high dimensionality of properties compounds the challenge, and the abundance of producing wells, each associated with different objective functions, further complicates the problem. Current methodologies have focused on training an Artificial Neural Network (ANN) for each producer well to create a proxy model. Instead, we propose the development of a single ANN designed to simultaneously predict the behavior of multiple objective functions. This work proposes a novel end-to-end deep neural network that can handle 3D geological uncertainties and replicate the simulator’s response for several cumulative production curves. This approach leverages 3D convolutions to process spatial and depth dimensions within heterogeneous reservoirs, including diverse geostatistical realizations. The end-to-end solution was successfully implemented in a Brazilian offshore field and accurately replicated the simulator’s behavior and yielded results that outperformed state-of-the-art methods. The results indicate correlations that exceeded 0.95 between the proxy response and the numerical simulator. Additionally, our approach demonstrated robustness even when trained with a limited number of simulation models, and it notably reduced computational costs. The findings highlight our architecture’s capacity to integrate dimensional reduction and regression analysis within a unified framework, effectively predicting different fluid behaviors in the reservoir and showcasing robustness against high dimensionality and sparse data.

DPF3 polymorphisms increased the risk of pulmonary tuberculosis in the Northwest Chinese Han population

PurposeThis study aimed to investigate the association between single nucleotide polymorphisms (SNPs) in DPF3 and susceptibility to pulmonary tuberculosis (PTB) in the Northwest Chinese Han population. MethodsGenotyping of four DPF3 SNPs (rs10140566, rs75575287, rs202075571, and rs61986330) was performed using Agena MassARRAY from 488 PTB patients and 488 healthy controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression. Multifactor dimensionality reduction (MDR) analysis was employed to investigate the effect of SNP-SNP interactions on PTB risk. The GSE54992 dataset was analyzed using R software to ascertain DPF3 expression levels. ResultsOverall analysis revealed that rs202075571 (allele: OR = 1.31, p = 0.015; CC vs. TT: OR = 1.97, p = 0.049; dominant: OR = 1.33, p = 0.032) and rs61986330 (allele: OR = 1.38, p = 0.010; CA vs. CC: OR = 1.35, p = 0.044; dominant: OR = 1.40, p = 0.019) were associated with an increased PTB risk. Stratified analysis showed that rs10140566 was a PTB risk factor in females, those aged ≤40 and non-smokers, and rs202075571 was associated with PTB risk in individuals aged >40 and smokers, and rs61986330 was associated with PTB risk in males, those aged >40 and smokers. The four SNPs model demonstrated significant predictive potential for PTB risk. Furthermore, DPF3 exhibited higher expression in PTB compared to healthy controls. ConclusionDPF3 polymorphisms (rs10140566, rs202075571, and rs61986330) are associated with an increased risk of PTB, providing valuable new insights into the mechanism of PTB.

MRI-based clinical-radiomics nomogram model for predicting microvascular invasion in hepatocellular carcinoma.

Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients. Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC). The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC). Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance. This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis.

Development of New Predictive Equations for the Resting Metabolic Rate (RMR) of Women with Lipedema.

This study aimed to develop a novel predictive equation for calculating resting metabolic rate (RMR) in women with lipedema. We recruited 119 women diagnosed with lipedema from the Angiology Outpatient Clinic at Wroclaw Medical University, Poland. RMR was assessed using indirect calorimetry, while body composition and anthropometric measurements were conducted using standardized protocols. Due to multicollinearity among predictors, classical multiple regression was deemed inadequate for developing the new equation. Therefore, we employed machine learning techniques, utilizing principal component analysis (PCA) for dimensionality reduction and predictor selection. Regression models, including support vector regression (SVR), random forest regression (RFR), and k-nearest neighbor (kNN) were evaluated in Python's scikit-learn framework, with hyperparameter tuning via GridSearchCV. Model performance was assessed through mean absolute percentage error (MAPE) and cross-validation, complemented by Bland-Altman plots for method comparison. A novel equation incorporating body composition parameters was developed, addressing a gap in accurate RMR prediction methods. By incorporating measurements of body circumference and body composition parameters alongside traditional predictors, the model's accuracy was improved. The segmented regression model outperformed others, achieving an MAPE of 10.78%. The proposed predictive equation for RMR offers a practical tool for personalized treatment planning in patients with lipedema.

Image-Based Evaluation of Cracking Degrees on Wood Fiber Bundles: A Machine Learning Approach

In this study, a machine learning-based method to assess and predict the cracking degree (CD) on wood fiber bundles (WFB) was developed, which is crucial for enhancing the quality control and refining the production process of wood scrimber (WS). By roller-cracking poplar wood one to three times, three distinct CD levels were established, and 361 WFB specimens were analyzed, using their water absorption rate (WAR) as the foundation for CD prediction. Through crack image analysis, four key quantitative parameters were identified—cracking density, coherence degree, crack count, and average width—and this study found through discriminant analysis that the discrimination accuracy on the CD levels by cracking density or coherence degree over 90%, emphasizing their significance in evaluation. Cluster analysis grouped the specimens into three clusters based on four key quantitative parameters, aligning with the CD levels. This study developed specialized prediction models for each CD level, integrating principal component analysis for dimensionality reduction with polynomial fitting, achieving mean squared error (MSE) of 0.0132, 0.0498, and 0.0204 for levels 1, 2, and 3, respectively. An integrated model, with an accuracy of 94.3% and predictions within a 20% error margin, was created, demonstrating the effectiveness of using surface crack image features to predict WAR of WFB. This research establishes a methodological framework for assessing CDs on WFB, contributing to enhancing WS product quality and helping to better understand wood cracking and water absorption mechanisms.

An Improved Transformer Model for Remaining Useful Life Prediction of Lithium-Ion Batteries under Random Charging and Discharging

With the development of artificial intelligence and deep learning, deep neural networks have become an important method for predicting the remaining useful life (RUL) of lithium-ion batteries. In this paper, drawing inspiration from the transformer sequence-to-sequence task’s transformation capability, we propose a fusion model that integrates the functions of the stacked denoising autoencoder (SDAE) and the Transformer model in order to improve the performance of RUL prediction. Firstly, the health factors under three different conditions are extracted from the measurement data as model inputs. These conditions include constant current and voltage, random discharge, and the application of principal component analysis (PCA) for dimensionality reduction. Then, SDAE is responsible for denoising and feature extraction, and the Transformer model is utilized for sequence modeling and RUL prediction of the processed data. Finally, accurate prediction of the RUL of the four battery cells is achieved through cross-validation and four sets of comparison experiments. Three evaluation metrics, MAE, RMSE, and MAPE, are selected, and the values of these metrics are 0.170, 0.202, and 19.611%, respectively. The results demonstrate that the proposed method outperforms other prediction models in terms of prediction accuracy, robustness, and generalizability. This provides a new solution direction for the daily life prediction research of lithium-ion batteries.

Global dynamics and cytokinin participation of salt gland development trajectory in recretohalophyte Limonium bicolor.

Salt gland is an epidermal Na+ secretory structure that enhances salt resistance in the recretohalophyte sea lavender (Limonium bicolor). To elucidate the salt gland development trajectory and related molecular mechanisms, we performed single-cell RNA sequencing of L. bicolor protoplasts from young leaves at salt gland initiation and differentiation stages. Dimensionality reduction analyses defined 19 transcriptionally distinct cell clusters, which were assigned into four broad populations-promeristem, epidermis, mesophyll, and vascular tissue-verified by in situ hybridization. Cytokinin was further proposed to participate in salt gland development by the expression patterns of related genes and cytological evidence. By comparison analyses of scRNA-seq with exogenous application of 6-benzylaminopurine, we delineated five salt gland development-associated sub-clusters and defined salt gland specific differentiation trajectories from sub-clusters 8, 4, or 6 to sub-cluster 3 and 1. Additionally, we validated the participation of TRIPTYCHON and the interacting protein Lb7G34824 in salt gland development, which regulated the expression of cytokinin metabolism and signaling related genes such as GLABROUS INFLORESCENCE STEMS 2 to maintain cytokinin homeostasis during salt gland development. Our results generated a gene expression map of young leaves at single-cell resolution for the comprehensive investigation of salt gland determinants and cytokinin participation that helps elucidate cell fate determination during epidermis formation and evolution in recretohalophytes.

Internet of Vehicles (IoV) Based Framework for electricity Demand Forecasting in V2G

The integration of smart grids with Advanced Metering Infrastructure (AMI) has bridged the realms of the Internet of Vehicles (IoV) and Electric Vehicles (EVs), yet challenges persist in managing EV Battery Range and charging infrastructure effectively. This paper presents an innovative IoV-based framework tailored for EVs, with a specific focus on forecasting electricity consumption in a Vehicle-to-Grid (V2G) scenario. By exploring the hurdles surrounding electric vehicle usage, the research lays the foundation for Electric Vehicles (EVs). The proposed IoV model optimizes IoT sensor utilization through a fog layer and employs a Back Propagation (BP) neural network for battery State of Charge (SoC) estimation, integrating Principal Component Analysis (PCA) for data dimensionality reduction. Leveraging substantial computing capabilities, the cloud layer predicts Electricity Consumption Data (ECD) associated with EVs in V2G scenarios. Performance evaluation metrics like Akaike Information Criteria (AIC), Mean Error (ME), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE) are assessed across state-of-the-art forecasting algorithms. Incorporating EV potential into the optimal model reveals a significant 10% reduction in electricity demand. This research advances IoV-based frameworks, offering insights to enhance EV efficiency within the broader energy infrastructure.

Detection of cardiac abnormalities from 12-lead ecg using complex wavelet sub-band features

Aim of the study. This research endeavours to optimize cardiac anomaly detection by introducing a method focused on selecting the most effective Daubechis wavelet families. The principal aim is to differentiate between cardiac states that are normal and abnormal by utilizing longer electrocardiogram (ECG) signal events based on the Apnea ECG dataset. Apnea ECG is often used to detect sleep apnea, a sleep disorder characterized by repeated interruptions in breathing during sleep. By using machine learning methods, such as Principal Component Analysis (PCA) and different classifiers, the goal is to improve the precision of cardiac irregularity identification. Used method. To extract important statistical and sub-band information from lengthy ECG signal episodes, the study uses a novel method that combines discrete wavelet transform with Principal Component Analysis (PCA) for dimension reduction. The methodology focuses on successfully categorizing ECG signals by utilizing several classifiers, including multilayer perceptron (MLP) neural network, Ensemble Subspace K-Nearest Neighbour(KNN), and Ensemble Bagged Trees, together with varied Daubechis wavelet families (db2, db3, db4, db5, db6). Brief Description of Results. The results emphasize the importance of the chosen Daubechis wavelet family, db5, and its superiority in ECG representation. The method distinguishes normal and abnormal ECG signals well on the Physionet Apnea ECG database. The Neural Network-based method accurately recognizes 100% of healthy signals and 97.8% of problematic ones with 98.6% accuracy. Findings. The Ensemble Subspace K-Nearest Neighbour (KNN) and Ensemble Bagged Trees methods got 87.1% accuracy and 0.89 and 0.87 AOC curve values on this dataset, showing that the method works. Precision values of 0.96, 0.86, and 0.86 for MLP Neural Network, KNN Subspace, and Ensemble Bagged Trees confirm their robustness. These findings suggest wavelet families and machine learning can improve cardiac abnormality detection and categorization.

Exogenous compound bacteria enhance the nutrient removal efficiency of integrated bioremediation systems: Functional genes and microorganisms play key roles

With the continuous development of intensive mariculture, the application of the integrated bioremediation system of aquaculture wastewater (IBSAW) is increasingly promoted. However, the process and nutrients removal performance of the IBSAW need to be further optimized due to its immature technologies. In this study, exogenous compound bacteria (ECB) were added to IBSAW to investigate its pollutants removal efficiency and the relevant mechanisms. High-throughput sequencing and Geochip gene array were used to analyze the correlation between nutrients and bacteria, and the abundance of N and P cycling genes were quantified. Multivariable statistics, dimensionality reduction analysis, and network analysis were applied to explore the mechanisms of IBSAW operation. The results showed that the nutrients decreased significantly after adding ECB, with the brush treatment group significantly outperforming the ceramsite in removing NO3− and PO43−. Ceramsite has an advantage in removing NO2−-N. The addition of ECB and different substrates significantly affected the composition of bacterial communities. The contents of nosZ and nirKS related to denitrification in the treatment groups were significantly higher than those in the control group, and the contents in the brush treatment group were significantly higher than that of ceramsite. The biomarkers Psychroserpens and Ruegeria on the biofilm of the brush treatment group were positively correlated with nirKS, while Mycobacterium, Erythrobacter and Paracoccus, Pseudohaliea in the ceramsite group were positively correlated with nirS and nirK, respectively. Therefore, it is speculated that the ECB significantly promoted the increase of denitrification bacteria by affecting the composition of bacterial communities, and the ECB combined with functional genera improved the efficiency of nutrients removal in the system. This study provided a reference for understanding the process and mechanism of nutrients removal, optimizing the wastewater purification technology of the IBSAW and improving the performance of the system.