Mix-Key: graph mixup with key structures for molecular property prediction.

Molecular property prediction faces the challenge of limited labeled data as it necessitates a series of specialized experiments to annotate target molecules. Data augmentation techniques can effectively address the issue of data scarcity. In recent years, Mixup has achieved significant success in traditional domains such as image processing. However, its application in molecular property prediction is relatively limited due to the irregular, non-Euclidean nature of graphs and the fact that minor variations in molecular structures can lead to alterations in their properties. To address these challenges, we propose a novel data augmentation method called Mix-Key tailored for molecular property prediction. Mix-Key aims to capture crucial features of molecular graphs, focusing separately on the molecular scaffolds and functional groups. By generating isomers that are relatively invariant to the scaffolds or functional groups, we effectively preserve the core information of molecules. Additionally, to capture interactive information between the scaffolds and functional groups while ensuring correlation between the original and augmented graphs, we introduce molecular fingerprint similarity and node similarity. Through these steps, Mix-Key determines the mixup ratio between the original graph and two isomers, thus generating more informative augmented molecular graphs. We extensively validate our approach on molecular datasets of different scales with several Graph Neural Network architectures. The results demonstrate that Mix-Key consistently outperforms other data augmentation methods in enhancing molecular property prediction on several datasets.

JAK/STAT signaling regulated intestinal regeneration defends insect pests against pore-forming toxins produced by Bacillus thuringiensis.

A variety of coordinated host-cell responses are activated as defense mechanisms against pore-forming toxins (PFTs). Bacillus thuringiensis (Bt) is a worldwide used biopesticide whose efficacy and precise application methods limits its use to replace synthetic pesticides in agricultural settings. Here, we analyzed the intestinal defense mechanisms of two lepidopteran insect pests after intoxication with sublethal dose of Bt PFTs to find out potential functional genes. We show that larval intestinal epithelium was initially damaged by the PFTs and that larval survival was observed after intestinal epithelium regeneration. Further analyses showed that the intestinal regeneration caused by Cry9A protein is regulated through c-Jun NH (2) terminal kinase (JNK) and Janus tyrosine kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathways. JAK/STAT signaling regulates intestinal regeneration through proliferation and differentiation of intestinal stem cells to defend three different Bt proteins including Cry9A, Cry1F or Vip3A in both insect pests, Chilo suppressalis and Spodoptera frugiperda. Consequently, a nano-biopesticide was designed to improve pesticidal efficacy based on the combination of Stat double stranded RNA (dsRNA)-nanoparticles and Bt strain. This formulation controlled insect pests with better effect suggesting its potential use to reduce the use of synthetic pesticides in agricultural settings for pest control.

Deconstruction of Polymers through Olefin Metathesis.

The consumption of synthetic polymers has ballooned; so has the amount of post-consumer waste generated. The current polymer economy, however, is largely linear with most of the post-consumer waste being either landfilled or incinerated. The lack of recycling, together with the sizable carbon footprint of the polymer industry, has led to major negative environmental impacts. Over the past few years, chemical recycling technologies have gained significant traction as a possible technological route to tackle these challenges. In this regard, olefin metathesis, with its versatility and ease of operation, has emerged as an attractive tool. Here, we discuss the developments in olefin-metathesis-based chemical recycling technologies, including the development of new materials and the application of olefin metathesis to the recycling of commercial materials. We delve into structure-reactivity relationships in the context of polymerization-depolymerization behavior, how experimental conditions influence deconstruction outcomes, and the reaction pathways underlying these approaches. We also look at the current hurdles in adopting these technologies and relevant future directions for the field.

Self-assembling paclitaxel-mediated stimulation of tumor-associated macrophages for postoperative treatment of glioblastoma.

The unique cancer-associated immunosuppression in brain, combined with a paucity of infiltrating T cells, contributes to the low response rate and poor treatment outcomes of T cell-based immunotherapy for patients diagnosed with glioblastoma multiforme (GBM). Here, we report on a self-assembling paclitaxel (PTX) filament (PF) hydrogel that stimulates macrophage-mediated immune response for local treatment of recurrent glioblastoma. Our results suggest that aqueous PF solutions containing aCD47 can be directly deposited into the tumor resection cavity, enabling seamless hydrogel filling of the cavity and long-term release of both therapeutics. The PTX PFs elicit an immune-stimulating tumor microenvironment (TME) and thus sensitizes tumor to the aCD47-mediated blockade of the antiphagocytic "don't eat me" signal, which subsequently promotes tumor cell phagocytosis by macrophages and also triggers an antitumor T cell response. As adjuvant therapy after surgery, this aCD47/PF supramolecular hydrogel effectively suppresses primary brain tumor recurrence and prolongs overall survivals with minimal off-target side effects.

Revealing the Distribution of Lithium Compounds in Lithium Dendrites by Four-Dimensional Electron Microscopy Analysis.

Characterizing the microstructure of radiation- and chemical-sensitive lithium dendrites and its solid electrolyte interphase (SEI) is an important task when investigating the performance and reliability of lithium-ion batteries. Widely used methods, such as cryogenic high-resolution transmission electron microscopy as well as related spectroscopy, are able to reveal the local structure at nanometer and atomic scale; however, these methods are unable to show the distribution of various crystal phases along the dendrite in a large field of view. In this work, two types of four-dimensional electron microscopy diffractive imaging methods, i.e., scanning electron nanodiffraction (SEND) and scanning convergent beam electron diffraction (SCBED), are employed to show a new pathway on characterizing the sensitive lithium dendrite samples at room temperature and in a large field of view. Combining with the non-negative matrix factorization (NMF) algorithm, orientations of different lithium metal grains along the lithium dendrite as well as different lithium compounds in the SEI layer are clearly identified.

TGFA expression is associated with poor prognosis and promotes the development of cervical cancer.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are the second most common cancers in women aged 20-39. While HPV screening can help with early detection of cervical cancer, many patients are already in the medium to late stages when they are identified. As a result, searching for novel biomarkers to predict CESC prognosis and propose molecular treatment targets is critical. TGFA is a polypeptide growth factor with a high affinity for the epidermal growth factor receptor. Several studies have shown that TGFA can improve cancer growth and progression, but data on its impact on the occurrence and advancement of CESC is limited. In this study, we used clinical data analysis and bioinformatics techniques to explore the relationship between TGFA and CESC. The results showed that TGFA was highly expressed in cervical cancer tissues and cells. TGFA knockdown can inhibit the proliferation, migration and invasion of cervical cancer cells. In addition, after TGFA knockout, the expression of IL family and MMP family proteins in CESC cell lines was significantly reduced. In conclusion, TGFA plays an important role in the occurrence and development of cervical cancer. Therefore, TGFA may become a new target for cervical cancer treatment.

Oxidative stress is involved in immunosuppression and macrophage regulation in glioblastoma.

Oxidative stress dually affected cancer progression, while its effect on glioblastomas remained unclear. Herein, we clustered the multicenter glioblastoma cohorts based on the oxidative-stress-responsive genes (OSS) expression. We found that cluster 2 with high OSS levels suffered a worse prognosis. Functional analyses and immune-related analyses results exhibited that M2-like pro-tumoral macrophages and neutrophils were enriched in cluster 2, while Natural killer cells' infiltration was decreased. The increased M2-like pro-tumoral macrophages in cluster 2 was confirmed by immunofluorescence. An integrated single-cell analysis validated the malignant features of cluster 2 neoplastic cells and discovered their crosstalk with M2-like pro-tumoral macrophages. Moreover, we observed that SOD3 knockdown might decrease the M2-like pro-tumoral transformation of macrophage in vitro and in vivo. Comprehensively, we revealed oxidative stress' prognostic and immunosuppressive potential in glioblastoma and discovered SOD3's potential role in regulating macrophage M2-like pro-tumoral transformation.

A novel mechanism of major ginsenosides from Panax ginseng against multiple organ aging in middle-aged mice: Phosphatidylcholine-myo-inositol metabolism based on metabolomic analysis.

Aging is a complex, degenerative process associated with various metabolic abnormalities. Ginsenosides (GS) is the main active components of Panax ginseng, which has anti-aging effects and improves metabolism. However, the anti-aging effect and the mechanism of GS in middle-aged mice has not been elucidated. In this study, GS after 3-month treatment significantly improved the grip strength, fatigue resistance, cognitive indices, and cardiac function of 15-month-old mice. Meanwhile, GS treatment reduced the fat content and obviously inhibited histone H2AX phosphorylation at Ser 139 (γ-H2AX), a marker of DNA damage in major organs, especially in the heart and liver. Further, the correlation analysis of serum metabolomics combined with aging phenotype suggested that myo-inositol (MI) upregulated by GS was positively correlated with left ventricular ejection fraction (LVEF) and left ventricular fractional shortening (LVFS), the main indicators of cardiac function. More importantly, liver tissue metabolomic analysis showed that GS increased MI content by promoting the synthesis pathway from phosphatidylcholine (PC) to MI for the inhibition of liver aging. Finally, we proved that MI reduced the percentage of senescence-associated ß-galactosidase staining, γ-H2AX immunofluorescence staining, p21 expression, and the production of reactive oxygen species in H2O2-induced cardiomyocytes. These results suggest that GS can enhance multiple organ functions, especially cardiac function for promoting the healthspan of aging mice, which is mediated by the conversion of PC to MI in the liver and the increase of MI level in the serum. Our study might provide new insights into the potential mechanisms of ginsenosides for prolonging the healthspan of natural aging mice.

Stability Enhancement in All-Inorganic Perovskite Light Emitting Diodes via Dual Encapsulation.

Addressing the challenge of lighting stability in perovskite white light emitting diodes (WLEDs) is crucial for their commercial viability. CsPbX3 (X = Cl, Br, I, or mixed) nanocrystals (NCs) are promising for next-generation lighting due to their superior optical and electronic properties. However, the inherent soft material structure of CsPbX3 NCs is particularly susceptible to the elevated temperatures associated with prolonged WLED operation. Additionally, these NCs face stability challenges in high humidity environments, leading to reduced lighting performance. This study introduces a two-step dual encapsulation method, resulting in CsPbBr3 @SiO2 /Al2 SiO5 composite fibers (CFs) with enhanced optical stability under extreme conditions. In testing, WLEDs incorporating these CFs, even under prolonged operation at high power (100 mA for 9 h), maintain consistent electroluminescence (EL) intensity and optoelectronic parameters, with surface temperatures reaching 84.2 °C. Crucially, when subjected to 85 °C and 85% relative humidity for 200 h, the WLEDs preserve 97% of their initial fluorescence efficiency. These findings underscore the efficacy of the dual encapsulation strategy in significantly improving perovskite material stability, marking a significant step toward their commercial application in optoelectronic lighting.

Toward accurate diagnosis and surveillance of bacterial infections using enhanced strain-level metagenomic next-generation sequencing of infected body fluids.

Metagenomic next-generation sequencing (mNGS) enables comprehensive pathogen detection and has become increasingly popular in clinical diagnosis. The distinct pathogenic traits between strains require mNGS to achieve a strain-level resolution, but an equivocal concept of 'strain' as well as the low pathogen loads in most clinical specimens hinders such strain awareness. Here we introduce a metagenomic intra-species typing (MIST) tool (https://github.com/pandafengye/MIST), which hierarchically organizes reference genomes based on average nucleotide identity (ANI) and performs maximum likelihood estimation to infer the strain-level compositional abundance. In silico analysis using synthetic datasets showed that MIST accurately predicted the strain composition at a 99.9% average nucleotide identity (ANI) resolution with a merely 0.001× sequencing depth. When applying MIST on 359 culture-positive and 359 culture-negative real-world specimens of infected body fluids, we found the presence of multiple-strain reached considerable frequencies (30.39%-93.22%), which were otherwise underestimated by current diagnostic techniques due to their limited resolution. Several high-risk clones were identified to be prevalent across samples, including Acinetobacter baumannii sequence type (ST)208/ST195, Staphylococcus aureus ST22/ST398 and Klebsiella pneumoniae ST11/ST15, indicating potential outbreak events occurring in the clinical settings. Interestingly, contaminations caused by the engineered Escherichia coli strain K-12 and BL21 throughout the mNGS datasets were also identified by MIST instead of the statistical decontamination approach. Our study systemically characterized the infected body fluids at the strain level for the first time. Extension of mNGS testing to the strain level can greatly benefit clinical diagnosis of bacterial infections, including the identification of multi-strain infection, decontamination and infection control surveillance.

Machine learning-based tumor-infiltrating immune cell-associated lncRNAs for predicting prognosis and immunotherapy response in patients with glioblastoma.

Long noncoding ribonucleic acids (RNAs; lncRNAs) have been associated with cancer immunity regulation. However, the roles of immune cell-specific lncRNAs in glioblastoma (GBM) remain largely unknown. In this study, a novel computational framework was constructed to screen the tumor-infiltrating immune cell-associated lncRNAs (TIIClnc) for developing TIIClnc signature by integratively analyzing the transcriptome data of purified immune cells, GBM cell lines and bulk GBM tissues using six machine learning algorithms. As a result, TIIClnc signature could distinguish survival outcomes of GBM patients across four independent datasets, including the Xiangya in-house dataset, and more importantly, showed superior performance than 95 previously established signatures in gliomas. TIIClnc signature was revealed to be an indicator of the infiltration level of immune cells and predicted the response outcomes of immunotherapy. The positive correlation between TIIClnc signature and CD8, PD-1 and PD-L1 was verified in the Xiangya in-house dataset. As a newly demonstrated predictive biomarker, the TIIClnc signature enabled a more precise selection of the GBM population who would benefit from immunotherapy and should be validated and applied in the near future.

Mass spectrometry-based methods for investigating the dynamics and organization of the surfaceome: exploring potential clinical implications.

INTRODUCTION: Cell-surface proteins are extremely important for many cellular events, such as regulating cell-cell communication and cell-matrix interactions. Aberrant alterations in surface protein expression, modification (especially glycosylation), and interactions are directly related to human diseases. Systematic investigation of surface proteins advances our understanding of protein functions, cellular activities, and disease mechanisms, which will lead to identifying surface proteins as disease biomarkers and drug targets. AREAS COVERED: In this review, we summarize mass spectrometry (MS)-based proteomics methods for global analysis of cell-surface proteins. Then, investigations of the dynamics of surface proteins are discussed. Furthermore, we summarize the studies for the surfaceome interaction networks. Additionally, biological applications of MS-based surfaceome analysis are included, particularly highlighting the significance in biomarker identification, drug development, and immunotherapies. EXPERT OPINION: Modern MS-based proteomics provides an opportunity to systematically characterize proteins. However, due to the complexity of cell-surface proteins, the labor-intensive workflow, and the limit of clinical samples, comprehensive characterization of the surfaceome remains extraordinarily challenging, especially in clinical studies. Developing and optimizing surfaceome enrichment methods and utilizing automated sample preparation workflow can expand the applications of surfaceome analysis and deepen our understanding of the functions of cell-surface proteins.

The cell surface contains many important proteins such as receptors and transporters. These proteins are responsible for cells to communicate with each other, take nutrients from outside, and interact with their surroundings. Aberrant changes in surface protein expression, modifications, and interactions with other molecules directly result in various diseases, including infections, immune disorders, and cancer. Currently, mass spectrometry (MS)-based proteomics is very powerful to study proteins on a large scale, and there has been a strong interest in employing MS to investigate cell-surface proteins. In this review, we discuss different methods combining mass spectrometry with other approaches to systematically characterize protein abundance, dynamics, modification, and interaction on the cell surface. These methods help uncover protein functions and specific cell-surface proteins related to human diseases. A better understanding of the functions and properties of cell-surface proteins can facilitate the discovery of surface proteins as effective biomarkers for disease early detection and the identification of drug targets for disease treatment.

Intratumoral microbial heterogeneity affected tumor immune microenvironment and determined clinical outcome of HBV-related HCC.

BACKGROUND AND AIMS: The intratumoral microbiome has been reported to regulate the development and progression of cancers. We aimed to characterize intratumoral microbial heterogeneity (IMH) and establish microbiome-based molecular subtyping of HBV-related HCC to elucidate the correlation between IMH and HCC tumorigenesis. APPROACH AND RESULTS: A case-control study was designed to investigate microbial landscape and characteristic microbial signatures of HBV-related HCC tissues adopting metagenomics next-generation sequencing. Microbiome-based molecular subtyping of HCC tissues was established by nonmetric multidimensional scaling. The tumor immune microenvironment of 2 molecular subtypes was characterized by EPIC and CIBERSORT based on RNA-seq and verified by immunohistochemistry. The gene set variation analysis was adopted to explore the crosstalk between the immune and metabolism microenvironment. A prognosis-related gene risk signature between 2 subtypes was constructed by the weighted gene coexpression network analysis and the Cox regression analysis and then verified by the Kaplan-Meier survival curve.IMH demonstrated in HBV-related HCC tissues was comparably lower than that in chronic hepatitis tissues. Two microbiome-based HCC molecular subtypes, defined as bacteria- and virus-dominant subtypes, were established and significantly correlated with discrepant clinical-pathologic features. Higher infiltration of M2 macrophage was detected in the bacteria-dominant subtype with to the virus-dominant subtype, accompanied by multiple upregulated metabolism pathways. Furthermore, a 3-gene risk signature containing CSAG4 , PIP4P2 , and TOMM5 was filtered out, which could predict the clinical prognosis of HCC patients accurately using the Cancer Genome Atlas data. CONCLUSIONS: Microbiome-based molecular subtyping demonstrated IMH of HBV-related HCC was correlated with a disparity in clinical-pathologic features and tumor microenvironment (TME), which might be proposed as a biomarker for prognosis prediction of HCC.

Versatile Hyper-Cross-Linked Polymers Derived from Waste Polystyrene: Synthesis, Properties, and Intentional Recycling.

Waste polystyrene contributes considerably to environmental pollution due to its persistent nature, prompting a widespread consensus on the urgent need for viable recycling solutions. Owing to the aromatic groups structure of polystyrene, hyper-cross-linked polymers can be synthesized through the Friedel-Crafts cross-linking reaction using Lewis acids as catalysts. In addition, hyper-cross-linked polystyrene and its carbonaceous counterparts can be used in several important applications, which helps in their efficient recycling. This review systematically explores methods for preparing multifunctional hyper-cross-linked polymers from waste polystyrene and their applications in sustainable recycling. We have comprehensively outlined various synthetic approaches for these polymers and investigated their physical and chemical properties. These multifunctional polymers not only exhibit structural flexibility but also demonstrate diversity in performance, making them suitable for various applications. Through a systematic examination of synthetic methods, we showcase the cutting-edge positions of these materials in the field of hyper-cross-linked polymers. Additionally, we provide in-depth insights into the potential applications of these hyper-cross-linked polymers in intentional recycling, highlighting their important contributions to environmental protection and sustainable development. This research provides valuable references to the fields of sustainable materials science and waste management, encouraging further exploration of innovative approaches for the utilization of discarded polystyrene.

Quantification of alkalinity of deep eutectic solvents based on (H-) and NMR.

Alkaline deep eutectic solvents (DESs) have been widely employed across diverse fields. A comprehensive understanding of the alkalinity data is imperative for the comprehension of their performance. However, the current range of techniques for quantifying alkalinity is constrained. In this investigation, we formulated a series of alkaline DESs and assessed their basicity properties through a comprehensive methodology of Hammett functions alongside 1H NMR analysis. A correlation was established between the composition, structure and alkalinity of solvents. Furthermore, a strong linear correlation was observed between the Hammett basicity (H-) of solvents and initial CO2 adsorption rate. Machine learning techniques were employed to predict the significant impact of alkaline functional components on alkalinity levels and CO2 capture capacity. This study offers valuable insights into the design, synthesis and structure-function relationship of alkaline DESs.

Microbial co-culture mediated by intercellular nanotubes during DMAC degradation: Microbial interaction, communication mode, and degradation mechanism.

Microbial co-culture has been proven as an effective technique for environmental remediation. In this study, co-culture mechanism of Rhodococcus ruber HJM-8 and Paracoccus communis YBH-X during N,N-dimethylacetamide (DMAC) degradation was studied. The comparison of degradation performance in monoculture and co-culture was presented; due to the efficient cooperation between the two strains via parallel and cascaded degradation, the removal efficiency of total nitrogen (TN) in co-culture could reach 90.1%, which was 1.35 and 1.21 times higher than that of HJM-8 and YBH-X, respectively. Then the communication mode of co-culture during DMAC degradation was determined as contact-independent and contact-dependent interactions between microorganisms. Meanwhile, intercellular nanotube between HJM-8 and YBH-X was found as a unique contact-dependent interaction. The cell staining experiments and RNA sequencing analyses revealed that the nanotube could be used as a bridge to exchange cytoplasmic molecules, and thus improved material transfer and enhanced cell connection in co-culture. The results of KEGG pathway showed that differentially expressed genes in co-culture have an association with cell metabolism, nanotube generation, and genetic material transfer. Furthermore, a mechanism diagram of DMAC biodegradation was proposed for co-culture, indicating that bidirectional cooperation was established between HJM-8 and YBH-X which was mediated by the conversions of acetate and nitrogen. Finally, the co-culture system was validated for treatment of an actual wastewater; results indicated that removal efficiencies of 100% and 68.2% were achieved for DMAC and TN, respectively, suggesting that co-culture had the potential for application.

"Pseudo-pseudogenes" in bacterial genomes: Proteogenomics reveals a wide but low protein expression of pseudogenes in Salmonella enterica.

Pseudogenes (genes disrupted by frameshift or in-frame stop codons) are ubiquitously present in the bacterial genome and considered as nonfunctional fossil. Here, we used RNA-seq and mass-spectrometry technologies to measure the transcriptomes and proteomes of Salmonella enterica serovars Paratyphi A and Typhi. All pseudogenes' mRNA sequences remained disrupted, and were present at comparable levels to their intact homologs. At the protein level, however, 101 out of 161 pseudogenes suggested successful translation, with their low expression regardless of growth conditions, genetic background and pseudogenization causes. The majority of frameshifting detected was compensatory for -1 frameshift mutations. Readthrough of in-frame stop codons primarily involved UAG; and cytosine was the most frequent base adjacent to the codon. Using a fluorescence reporter system, fifteen pseudogenes were confirmed to express successfully in vivo in Escherichia coli. Expression of the intact copy of the fifteen pseudogenes in S. Typhi affected bacterial pathogenesis as revealed in human macrophage and epithelial cell infection models. The above findings suggest the need to revisit the nonstandard translation mechanism as well as the biological role of pseudogenes in the bacterial genome.

A psychobiographical analysis of Empress Dowager Cixi: exploring the femininity castrated complex.

In Chinese culture, there is a widely circulated phrase, 'A hen crows in the morning'. This phrase is used to humiliate women who steal power and engage in the political field. It demonstrates the complicated relationship between women and power in the context of Chinese culture. Women are not completely excluded from the politics, but women in power are often stigmatised. This study explores the life of Empress Dowager Cixi (1835-1908), the last female dominator in Chinese history, takes psychobiography as the research method, and attempts to understand the complicated relationship between women and power in Chinese culture through analysing Cixi's life from the perspective of complex and cultural complex theory which originated with C.G. Jung and analytical theory. The research findings show that humiliating and suppressing women with political talent can trigger their complexes, both personal and cultural. This study attempts to propose the femininity castrated complex to better describe the conscious and unconscious psychological dynamics impacting on women within patriarchal, political Chinese culture. This complex further relates to (1) denying her biological sex in order to avoid accusations of superego and, (2) the relationship with her son who is not only her son, but also her enemy regarding (political) power.

Microstructure of the Li-Al-O Second Phases in Garnet Solid Electrolytes.

The microstructure of the Li7La3Zr2O12 (LLZO) garnet solid electrolyte is critical for its performance in all-solid-state lithium-ion battery. During conventional high-temperature sintering, second phases are generated at the grain boundaries due to the reaction between sintering aids and LLZO, which have an enormous effect on the performances of LLZO. However, a detailed structure study of the second phases and their impact on physical properties is lacking. Here, crystal structures of the second phases in LLZO pellets are studied in detail by transmission electron microscopy. Three different crystal structures of Li-Al-O second phases, γ-LiAlO2, α-Li5AlO4, and ß-Li5AlO4 were identified, and atomic-scale lattice information was obtained by applying low-dose high-resolution imaging for these electron-beam-sensitive second phases. On this basis, the structure-property relationship of these structures was explored. It was found that sintering aids with a higher Li/Al ratio are beneficial to form Li-rich second phases, which result in more highly ionic conductive LLZO.

Utilizing support vector machines to foster sustainable development and innovation in the clean energy sector via green finance.

As the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. in order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. through the use of Support Vector Machine (SVM) Multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. with wind power base a as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terAs the global demand for clean energy continues to grow, the sustainable development of clean energy projects has become an important topic of research. In order to optimize the performance and sustainability of clean energy projects, this work explores the environmental and economic benefits of the clean energy industry. Through the use of Support Vector Machine (SVM) multi-factor models and a bi-level multi-objective approach, this work conducts comprehensive assessment and optimization. With Wind Power Base A as a case study, the work describes the material consumption of wind turbines, transportation energy consumption and carbon dioxide (CO2) emissions, and infrastructure material consumption through descriptive statistics. Moreover, this work analyzes the characteristics of different wind turbine models in depth. On one hand, the SVM multi-factor model is used to predict and assess the profitability of Wind Power Base A. On the other hand, a bi-level multi-objective approach is applied to optimize the number of units, internal rate of return within the project, and annual average equivalent utilization hours of the Wind Power Base A. The research results indicate that in March, the WilderHill New Energy Global Innovation Index (NEX) was 0.91053, while the predicted value of the SVM multi-factor model was 0.98596. The predicted value is slightly higher than the actual value, demonstrating the model's good grasp of future returns. The cumulative rate of return of Wind Power Base A is 18.83%, with an annualized return of 9.47%, exceeding the market performance by 1.68%. Under the optimization of the bi-level multi-objective approach, the number of units at Wind Power Base A decreases from the original 7004 to 5860, with total purchase and transportation costs remaining basically unchanged. The internal rate of return of the project increases from 8% to 9.3%, and the annual equivalent utilization hours increase to 2044 h, comprehensively improving the investment return and utilization efficiency of the wind power base. Through optimization, significant improvements are achieved in terms of the number of units, internal rate of return within the project, and annual average equivalent utilization hours at Wind Power Base A. The number of units decreases to 5860, with total purchase and transportation costs remaining basically unchanged, the internal rate of return increases to 9.3%, and annual equivalent utilization hours increase to 2044 h. Energy consumption and CO2 emissions are significantly reduced, with energy consumption decreasing by 0.68 × 109 kgce and CO2 emissions decreasing by 1.29 × 109 kg. The optimization effects are mainly concentrated in the production and installation stages, with emission reductions achieved through the recycling and disposal of materials consumed in the early stages. In terms of investment benefits, environmental benefits are enhanced, with a 13.93% reduction in CO2 emissions. Moreover, there is improved energy efficiency, with the energy input-output ratio increasing from 7.73 to 9.31. This indicates that the Wind Power Base A project has significant environmental and energy efficiency advantages in the clean energy industry. This work innovatively provides a comprehensive assessment and optimization scheme for clean energy projects and predicts the profitability of Wind Power Base A using SVM multi-factor models. Besides, this work optimizes key parameters of the project using a bi-level multi-objective approach, thus comprehensively improving the investment return and utilization efficiency of the wind power base. This work provides innovative methods and strong data support for the development of the clean energy industry, which is of great significance for promoting sustainable development under the backdrop of green finance.