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Background Dendritic cells (DCs) are central for the initiation and regulation of innate and adaptive immunity in the tumor microenvironment. As such, many kinds of DC-targeted vaccines have been developed to improve cancer immunotherapy in numerous clinical trials. Targeted delivery of antigens and adjuvants to DCs in vivo represents an important approach for the development of DC vaccines. However, nonspecific activation of systemic DCs and the preparation of optimal immunodominant tumor antigens still represent major challenges. Methods We loaded the immunogenic cell death (ICD) inducers human neutrophil elastase (ELANE) and Hiltonol (TLR3 agonist) into α-lactalbumin (α-LA)-engineered breast cancer-derived exosomes to form an in situ DC vaccine (HELA-Exos). HELA-Exos were identified by transmission electron microscopy, nanoscale flow cytometry, and Western blot analysis. The targeting, killing, and immune activation effects of HELA-Exos were evaluated in vitro. The tumor suppressor and immune-activating effects of HELA-Exos were explored in immunocompetent mice and patient-derived organoids. Results HELA-Exos possessed a profound ability to specifically induce ICD in breast cancer cells. Adequate exposure to tumor antigens and Hiltonol following HELA-Exo-induced ICD of cancer cells activated type one conventional DCs (cDC1s) in situ and cross-primed tumor-reactive CD8 + T cell responses, leading to potent tumor inhibition in a poorly immunogenic triple negative breast cancer (TNBC) mouse xenograft model and patient-derived tumor organoids. Conclusions HELA-Exos exhibit potent antitumor activity in both a mouse model and human breast cancer organoids by promoting the activation of cDC1s in situ and thus improving the subsequent tumor-reactive CD8 + T cell responses. The strategy proposed here is promising for generating an in situ DC-primed vaccine and can be extended to various types of cancers. Graphic Abstract Scheme 1. Schematic illustration of HELA-Exos as an in situ DC-primed vaccine for breast cancer. (A) Allogenic breast cancer-derived exosomes isolated from MDA-MB-231 cells were genetically engineered to overexpress α-LA and simultaneously loaded with the ICD inducers ELANE and Hiltonol (TLR3 agonist) to generate HELA-Exos. (B) Mechanism by which HELA-Exos activate DCs in situ in a mouse xenograft model ofTNBC. HELA-Exos specifically homed to the TME and induced ICD in cancer cells, which resulted in the increased release of tumor antigens, Hiltonol, and DAMPs, as well as the uptake of dying tumor cells by cDC1s. The activated cDC1s then cross-primed tumor-reactive CD8+ T cell responses. (C) HELA-Exos activated DCs in situ in the breast cancer patient PBMC-autologous tumor organoid coculture system. Abbreviations: DCs: dendritic cells; α-LA: α-lactalbumin; HELA-Exos: Hiltonol-ELANE-α-LA-engineered exosomes; ICD: immunogenic cell death; ELANE: human neutrophil elastase; TLR3: Toll-like receptor 3; TNBC: triple-negative breast cancer; TME: tumor microenvironment; DAMPs: damage-associated molecular patterns; cDC1s: type 1 conventional dendritic cells; PBMCs: peripheral blood mononuclear cells

Road traffic accidents caused by fatigue driving are common causes of human casualties. In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. The algorithm consists of four parts: (1) Positioning mouth and eye with multi-task cascaded convolutional neural networks (MTCNNs). (2) Extracting the static features from a partial facial image. (3) Extracting the dynamic features from a partial facial optical flow. (4) Combining both static and dynamic features using a two-stream neural network to make the classification. The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. Two-stream networks can combine static and dynamic image information, while partial facial images as network inputs can focus on fatigue-related information, which brings better performance. Moreover, we applied gamma correction to enhance image contrast, which can help our method achieve better results, noted by an increased accuracy of 2% in night environments. Finally, an accuracy of 97.06% was achieved on the National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset.

Turbomachinery from a life cycle perspective involves sustainability-oriented development activities such as design, production, and operation. Digital Twin is a technology with great potential for improving turbomachinery, which has a high volume of investment and a long lifespan. This study presents a general framework with different digital twin enabling technologies for the turbomachinery life cycle, including the design phase, experimental phase, manufacturing and assembly phase, operation and maintenance phase, and recycle phase. The existing digital twin and turbomachinery are briefly reviewed. New digital twin technologies are discussed, including modelling, simulation, sensors, Industrial Internet of Things, big data, and AI technologies. Finally, the major challenges and opportunities of DT for turbomachinery are discussed.

Automated diagnostic microscopy powered by deep learning is useful, particularly in rural areas. However, there is no general method for object detection of different cells. In this study, we developed GFS-ExtremeNet, a geometry-aware deep-learning method which is based on the detection of four extreme key points for each object (topmost, bottommost, rightmost, and leftmost) and its center point. A postprocessing step, namely, adjacency spectrum, was employed to measure whether the distances between the key points were below a certain threshold for a particular cell candidate. Our newly proposed geometry-aware deep-learning method outperformed other conventional object detection methods and could be applied to any type of cell with a certain geometrical order. Our GFS-ExtremeNet approach opens a new window for the development of an automated cell detection system. Analyzing cells and tissues under a microscope is a cornerstone of biological research and clinical practice. However, the challenge faced by conventional microscopy image analysis is the fact that cell recognition through a microscope is still time-consuming and lacks both accuracy and consistency. Despite enormous progress in computer-aided microscopy cell detection, especially with recent deep-learning-based techniques, it is still difficult to translate an established method directly to a new cell target without extensive modification. The morphology of a cell is complex and highly varied, but it has long been known that cells show a nonrandom geometrical order in which a distinct and defined shape can be formed in a given type of cell. Thus, we have proposed a geometry-aware deep-learning method, geometric-feature spectrum ExtremeNet (GFS-ExtremeNet), for cell detection. GFS-ExtremeNet is built on the framework of ExtremeNet with a collection of geometric features, resulting in the accurate detection of any given cell target. We obtained promising detection results with microscopic images of publicly available mammalian cell nuclei and newly collected protozoa, whose cell shapes and sizes varied. Even more striking, our method was able to detect unicellular parasites within red blood cells without misdiagnosis of each other. IMPORTANCE Automated diagnostic microscopy powered by deep learning is useful, particularly in rural areas. However, there is no general method for object detection of different cells. In this study, we developed GFS-ExtremeNet, a geometry-aware deep-learning method which is based on the detection of four extreme key points for each object (topmost, bottommost, rightmost, and leftmost) and its center point. A postprocessing step, namely, adjacency spectrum, was employed to measure whether the distances between the key points were below a certain threshold for a particular cell candidate. Our newly proposed geometry-aware deep-learning method outperformed other conventional object detection methods and could be applied to any type of cell with a certain geometrical order. Our GFS-ExtremeNet approach opens a new window for the development of an automated cell detection system.

A coupled smoothing technique, λ S-FEM, is introduced to improve the accuracy of numerical simulations in the mechanical analysis of twist drills. This method combines the edge-based smoothing finite element method (ES-FEM) with the node-based smoothing finite element method (NS-FEM). The λ S-FEM model is designed to evaluate the mechanical properties of twist drills made from tungsten carbide (WC), titanium nitride (TiN) coatings, and high-speed steel (M35), providing a theoretical basis for lifespan estimation and wear prediction. Linear tetrahedral elements construct the smoothing domain, and optimized weighting parameters balance and combine the smoothed strains from ES-FEM and NS-FEM. This integration enhances the accuracy of solutions for displacements, stresses, and strain energies, constructing stiffness matrices with optimal precision. The method’s feasibility is demonstrated through numerical case studies involving flange and shell extractor components. Analyses of straight shank twist drills compare displacement and stress magnitudes across FEM, S-FEM, and λ S-FEM under various degrees of freedom (DOF). Results show λ S-FEM significantly reduces errors, particularly with coarse meshes, validating its practical application in solving engineering challenges.

The combination of anti-angiogenic treatment and immunotherapy presents a promising strategy against colon cancer. Interleukin-17F (IL-17F) emerges as a critical immune cell cytokine expressed in colonic epithelial cells, demonstrating potential in inhibiting angiogenesis. In order to clarify the roles of IL-17F in the colon cancer microenvironment and elucidate its mechanism, we established a mouse colon carcinoma cell line CT26 overexpressing IL-17F and transplanted it subcutaneously into syngeneic BALB/c mice. We also analyzed induced colon tumor in IL-17F knockout and wild type mice. Our results demonstrated that IL-17F could suppress colon tumor growth in vivo with inhibited angiogenesis and enhanced recruitment of cysteine-cysteine motif chemokine receptor 6 (CCR6) positive immune cells. Additionally, IL-17F suppressed the tube formation, cell growth and migration of endothelial cells EOMA in vitro. Comprehensive bioinformatics analysis of transcriptome profiles between EOMA cells and those treated with three different concentrations of IL-17F identified 109 differentially expressed genes. Notably, a potential new target, Caspase 4, showed increased expressions after IL-17F treatment in endothelial cells. Further molecular validation revealed a novel downstream signaling for IL-17F: IL-17F enhanced Caspase 4/GSDMD signaling of endothelial cells, CT26 cells and CT26 transplanted tumors, while IL-17F knockout colon tumors exhibited decreased Caspase 4/GSDMD signaling. The heightened expression of the GSDMD N-terminus, coupled with increased cellular propidium iodide (PI) uptake and lactate dehydrogenase (LDH) release, revealed that IL-17F promoted pyroptosis of endothelial cells. Altogether, IL-17F could modulate the colon tumor microenvironment with inhibited angiogenesis, underscoring its potential as a therapeutic target for colon cancer.

In practical industrial applications, obtaining a sufficient number fault samples for specific types of equipment fault can be challenging. As a result, there are frequently significantly fewer defect samples obtained than healthy samples, and the data samples that are obtained typically have a high noise level. To overcome these issues, this paper introduces a novel approach termed the improved hybrid dilated convolution network (HDCN) to address these limitations and enhance classification accuracy. The proposed method involves transforming the time domain vibration signal into a time-frequency domain image using short time fourier transform (STFT), enabling simultaneous extraction of frequency domain and time domain features. A multi-scale hybrid dilated convolution network is constructed to extract multiple scale fault features and identify characteristic information. Subsequently, an adaptive weight long short-term memory (LSTM) unit is designed to perform weighted fusion of multi-scale features. It can be amplifying the contribution of important features and minimizing the influence of non-relevant features. The scaled exponential linear unit (SELU) is utilized to mitigate the significant suppression of the activation function on a few class samples. Finally, the network model is simulated using the focal loss function to make it more suitable for the case where the fault samples are small and confusing. To assess the effectiveness of the suggested approach, extensive tests are carried out on simulated datasets as well as a public dataset.

Interleukin-17F (IL-17F), produced by Th17 cells and other immune cells, is a member of IL-17 cytokine family with highest homology to IL-17A. IL-17F has been shown to have multiple functions in inflammatory responses. While IL-17A plays important roles in cancer development, the function of IL-17F in tumorigenesis has not yet been elucidated. In the current study, we found that IL-17F is expressed in normal human colonic epithelial cells, but this expression is greatly decreased in colon cancer tissues. To examine the roles of IL-17F in colon cancer, we have used IL-17F over-expressing colon cancer cell lines and IL-17F-deficient mice. Our data showed decreased tumor growth of IL-17F-transfected HCT116 cells comparing to mock transfectants when transplanted in nude mice. Conversely, there were increased colonic tumor numbers and tumor areas in Il-17f(-/-) mice than those from wild-type controls after colon cancer induction. These results indicate that IL-17F plays an inhibitory role in colon tumorigenesis in vivo. In IL-17F over-expressing tumors, there was no significant change in leukocyte infiltration; instead, we found decreased VEGF levels and CD31(+) cells. While the VEGF levels were increased in the colon tissues of Il-17f(-/-) mice with colon cancer. Together, our findings demonstrate a protective role for IL-17F in colon cancer development, possibly via inhibiting tumor angiogenesis.

Hepatocellular carcinoma (HCC) is a highly lethal malignant tumor, and the current non‐invasive diagnosis method based on serum markers, such as α‐fetoprotein (AFP), and des‐γ‐carboxy‐prothrombin (DCP), has limited efficacy in detecting it. Therefore, there is a critical need to develop novel biomarkers for HCC. Recent studies have highlighted the potential of exosomes as biomarkers. To enhance exosome enrichment, a silicon dioxide (SiO2) microsphere‐coated three‐dimensional (3D) hierarchical porous chip, named a SiO2‐chip is designed. The features of the chip, including its continuous porous 3D scaffold, large surface area, and nanopores between the SiO2 microspheres, synergistically improved the exosome capture efficiency. Exosomes from both non‐HCC and HCC subjects are enriched using an SiO2‐chip and performed RNA sequencing to identify HCC‐related long non‐coding RNAs (lncRNAs) in the exosomes. This study analysis reveales that LUCAT‐1 and EGFR‐AS‐1 are two HCC‐related lncRNAs. To further detect dual lncRNAs in exosomes, quantitative real time polymerase chain reaction (qRT‐PCR) is employed. The integration of dual lncRNAs with AFP and DCP significantly improves the diagnostic accuracy. Furthermore, the integration of dual lncRNAs with DCP effectively monitors the prognosis of patients with HCC and detects disease progression. In this study, a liquid biopsy‐based approach for noninvasive and reliable HCC detection is developed. Hepatocellular carcinoma (HCC), a deadly tumor, lacks effective non‐invasive diagnosis via current serum markers. The innovative SiO2‐chip enriches exosomes efficiently, unveiling HCC‐specific lncRNAs like LUCAT‐1 and EGFR‐AS‐1. Validated by qRT‐PCR, integrating these lncRNAs with traditional markers significantly enhances diagnostic accuracy. Moreover, their combination with DCP aids in monitoring HCC progression. This liquid biopsy approach offers reliable non‐invasive HCC detection.

Integrated energy system (IES) is an important direction for the future development of the energy industry, and the stable operation of the IES can ensure heat and power supply. This study established an integrated system composed of an IES and advanced adiabatic compressed air energy storage (AA-CAES) to guarantee the robust operation of the IES under failure conditions. Firstly, a robust operation method using the AA-CAES is formulated to ensure the stable operation of the IES. The method splits the energy release process of the AA-CAES into two parts: a heat-ensuring part and a power-ensuring part. The heat-ensuring part uses the high-temp tank to maintain the balance of the heat subnet of the IES, and the power-ensuring part uses the air turbine of the first stage to maintain the balance of the power subnet. Moreover, another operation method using a spare gas boiler is formulated to compare the income of the IES with two different methods under failure conditions. The results showed that the AA-CAES could guarantee the balance of heat subnet and power subnet under steady conditions, and the dynamic operation income of the IES with the AA-CAES method was a bit higher than the income of the IES with the spare gas boiler method.