Innovative breakthroughs facilitated by single-cell multi-omics: manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells

BackgroundMelanoma is typically regarded as the most dangerous form of skin cancer. Although surgical removal of in situ lesions can be used to effectively treat metastatic disease, this condition is still difficult to cure. Melanoma cells are removed in great part due to the action of natural killer (NK) and T cells on the immune system. Still, not much is known about how the activity of NK cell-related pathways changes in melanoma tissue. Thus, we performed a single-cell multi-omics analysis on human melanoma cells in this study to explore the modulation of NK cell activity.Materials and methodsCells in which mitochondrial genes comprised > 20% of the total number of expressed genes were removed. Gene ontology (GO), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and AUCcell analysis of differentially expressed genes (DEGs) in melanoma subtypes were performed. The CellChat package was used to predict cell–cell contact between NK cell and melanoma cell subtypes. Monocle program analyzed the pseudotime trajectories of melanoma cells. In addition, CytoTRACE was used to determine the recommended time order of melanoma cells. InferCNV was utilized to calculate the CNV level of melanoma cell subtypes. Python package pySCENIC was used to assess the enrichment of transcription factors and the activity of regulons in melanoma cell subtypes. Furthermore, the cell function experiment was used to confirm the function of TBX21 in both A375 and WM-115 melanoma cell lines.ResultsFollowing batch effect correction, 26,161 cells were separated into 28 clusters and designated as melanoma cells, neural cells, fibroblasts, endothelial cells, NK cells, CD4+ T cells, CD8+ T cells, B cells, plasma cells, monocytes and macrophages, and dendritic cells. A total of 10137 melanoma cells were further grouped into seven subtypes, i.e., C0 Melanoma BIRC7, C1 Melanoma CDH19, C2 Melanoma EDNRB, C3 Melanoma BIRC5, C4 Melanoma CORO1A, C5 Melanoma MAGEA4, and C6 Melanoma GJB2. The results of AUCell, GSEA, and GSVA suggested that C4 Melanoma CORO1A may be more sensitive to NK and T cells through positive regulation of NK and T cell-mediated immunity, while other subtypes of melanoma may be more resistant to NK cells. This suggests that the intratumor heterogeneity (ITH) of melanoma-induced activity and the difference in NK cell-mediated cytotoxicity may have caused NK cell defects. Transcription factor enrichment analysis indicated that TBX21 was the most important TF in C4 Melanoma CORO1A and was also associated with M1 modules. In vitro experiments further showed that TBX21 knockdown dramatically decreases melanoma cells’ proliferation, invasion, and migration.ConclusionThe differences in NK and T cell-mediated immunity and cytotoxicity between C4 Melanoma CORO1A and other melanoma cell subtypes may offer a new perspective on the ITH of melanoma-induced metastatic activity. In addition, the protective factors of skin melanoma, STAT1, IRF1, and FLI1, may modulate melanoma cell responses to NK or T cells

Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma

BackgroundWe explore sphingolipid-related genes (SRGs) in skin melanoma (SKCM) to develop a prognostic indicator for patient outcomes. Dysregulated lipid metabolism is linked to aggressive behavior in various cancers, including SKCM. However, the exact role and mechanism of sphingolipid metabolism in melanoma remain partially understood.MethodsWe integrated scRNA-seq data from melanoma patients sourced from the GEO database. Through the utilization of the Seurat R package, we successfully identified distinct gene clusters associated with patient survival in the scRNA-seq data. Key prognostic genes were identified through single-factor Cox analysis and used to develop a prognostic model using LASSO and stepwise regression algorithms. Additionally, we evaluated the predictive potential of these genes within the immune microenvironment and their relevance to immunotherapy. Finally, we validated the functional significance of the high-risk gene IRX3 through in vitro experiments.ResultsAnalysis of scRNA-seq data identified distinct expression patterns of 4 specific genes (SRGs) in diverse cell subpopulations. Re-clustering cells based on increased SRG expression revealed 7 subgroups with significant prognostic implications. Using marker genes, lasso, and Cox regression, we selected 11 genes to construct a risk signature. This signature demonstrated a strong correlation with immune cell infiltration and stromal scores, highlighting its relevance in the tumor microenvironment. Functional studies involving IRX3 knockdown in A375 and WM-115 cells showed significant reductions in cell viability, proliferation, and invasiveness.ConclusionSRG-based risk signature holds promise for precise melanoma prognosis. An in-depth exploration of SRG characteristics offers insights into immunotherapy response. Therapeutic targeting of the IRX3 gene may benefit melanoma patients

A new fault diagnosis and fault-tolerant control method for mechanical and aeronautical systems with neural estimators

A new method of fault detection and fault tolerant control is proposed in this paper for mechanical systems and aeronautical systems. The faults to be estimated and diagnosed are malfunctions occurred within the control loops of the systems, rather than some static faults, such as gearbox fault, component cracks, etc. In the proposed method two neural networks are used as on-line estimators, the fault will be accurately estimated when the estimators are adapted on-line with the post fault dynamic information. Furthermore, the estimated value of faults are used to compensate for the impact of the faults, so that the stability and performance of the system with the faults are maintained until the faulty components to be repaired. The sliding mode control is used to maintain system stability under the post fault dynamics. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimators are guaranteed to converge to the fault to be diagnosed, while the entire closed-loop system stability is guaranteed with all variables bounded. The main contribution of this paper to the knowledge in this field is that the proposed method cannot only diagnose and tolerant with constant fault, also diagnose and tolerant with the time-varying faults. This is very important because most faults occurred in industrial systems are time-varying in nature. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results are compared with two existing methods that can cope with constant faults only, and the superiority is demonstrated

Image-and-skeleton-based parameterized approach to real-time identification of construction workers' unsafe behaviors

Workers’ unsafe behaviors are one of the main causes for construction accidents. To fully understand the causes to unsafe behaviors on site will benefit their prevention, thus reducing construction accidents. The accurate and timely identification of site workers' unsafe behaviors can aid in the analysis of the causes to unsafe behaviors and prevention of construction accidents. However, the traditional methods (e.g. site observation) of behavior data collection on site is neither efficient nor comprehensive. This paper develops a skeleton-based real-time identification method by combining image-based technologies, construction safety knowledge and ergonomic theory. The proposed method recognizes unsafe behaviors by simplifying dynamic motions into static postures, which can be described by a few parameters. Three basic modules are involved: an unsafe behavior database, real-time data collection module and behavior judgement module. A laboratory test demonstrated the feasibility, efficiency and accuracy of the method. The method has the potential to improve construction safety management by providing comprehensive data for the systematic identification of the causes to workers' unsafe behaviors, such as inappropriate management methods, measures or decisions, personal characteristics, work space and time, etc., as well as warning workers identified as behaving unsafely, if necessary. Thus, this paper contributes to practice and the body of knowledge of construction safety management, as well as research and practice in image-based behavior recognition

An experimental study of real-time identification of construction workers' unsafe behaviors

Construction workers’ unsafe behavior is one of the main reasons leading to construction accidents. However, the existing management approach to unsafe behaviors, e.g. Behavior-Based Safety (BBS), relies primarily on manual observation and recording, which not only consumes lots of time and cost but impossibly cover a whole construction site or all workers. To solve this problem and improve safety performance, an image-skeleton-based parameterized method has been proposed in a previous research to real-time identifying construction workers’ unsafe behaviors. A theoretical framework has been developed with a preliminary test, but still lacking a comprehensive experiment to verify its validity, particularly in the recognition of the types of unsafe behaviors. This will have a serious impact on the extensive application of the method in real construction sites. Based on the method, this research designs and carries out a series of experiments involving three types of unsafe behaviors to examine its feasibility and accuracy, and determines the value ranges of relevant key parameters. The results of the experiment demonstrate the feasibility and efficiency of the method, being able to identify and distinguish unsafe behaviors in real time, as well as its limitations. This research not only benefits the extensive application of the method in construction safety management, but improves the effectiveness and efficiency of the method by identifying relevant future research focuses. Therefore this paper contributes to the practice as well as the body of knowledge of construction safety management

Grammatical Relations in Chinese: GB-Ground Extraction and Data-Driven Parsing

This paper is concerned with building linguistic resources and statistical parsers for deep grammatical relation (GR) analysis of Chinese texts. A set of linguistic rules is defined to explore implicit phrase structural information and thus build high-quality GR annotations that are represented as general directed dependency graphs. The reliability of this linguistically-motivated GR extraction procedure is highlighted by manual evaluation. Based on the converted corpus, we study transition-based, datadriven models for GR parsing. We present a novel transition system which suits GR graphs better than existing systems. The key idea is to introduce a new type of transition that reorders top k elements in the memory module. Evaluation gauges how successful GR parsing for Chinese can be by applying datadriven models. ? 2014 Association for Computational Linguistics.EI

Synthesis of Flexible Aerogel Composites Reinforced with Electrospun Nanofibers and Microparticles for Thermal Insulation

Flexible silica aerogel composites in intact monolith of 12 cm were successfully fabricated by reinforcing SiO2 aerogel with electrospun polyvinylidene fluoride (PVDF) webs via electrospinning and sol-gel processing. Three electrospun PVDF webs with different microstructures (e.g., nanofibers, microparticles, and combined nanofibers and microparticles) were fabricated by regulating electrospinning parameters. The as-electrospun PVDF webs with various microstructures were impregnated into the silica sol to synthesize the PVDF/SiO2 composites followed by solvent exchange, surface modification, and drying at ambient atmosphere. The morphologies of the PVDF/SiO2 aerogel composites were characterized and the thermal and mechanical properties were measured. The effects of electrospun PVDF on the thermal and mechanical properties of the aerogel composites were evaluated. The aerogel composites reinforced with electrospun PVDF nanofibers showed intact monolith, improved strength, and perfect flexibility and hydrophobicity. Moreover, the aerogel composites reinforced with the electrospun PVDF nanofibers had the lowest thermal conductivity (0.028 W·m−1·K−1). It indicates that the electrospun PVDF nanofibers could greatly improve the mechanical strength and flexibility of the SiO2 aerogels while maintaining a lower thermal conductivity, which provides increasing potential for thermal insulation applications

A deep learning-based method for detecting non-certified work on construction sites

The construction industry is a high hazard industry. Accidents frequently occur, and part of them are closely relate to workers who are not certified to carry out specific work. Although workers without a trade certificate are restricted entry to construction sites, few ad-hoc approaches have been commonly employed to check if a worker is carrying out the work for which they are certificated. This paper proposes a novel framework to check whether a site worker is working within the constraints of their certification. Our framework comprises key video clips extraction, trade recognition and worker competency evaluation. Trade recognition is a new proposed method through analyzing the dynamic spatiotemporal relevance between workers and non-worker objects. We also improved the identification results by analyzing, comparing, and matching multiple face images of each worker obtained from videos. The experimental results demonstrate the reliability and accuracy of our deep learning-based method to detect workers who are carrying out work for which they are not certified to facilitate safety inspection and supervision