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Abstract Tumor spread through air spaces (STAS) is a distinctive metastatic pattern affecting prognosis in lung adenocarcinoma (LUAD) patients. Several challenges are associated with STAS detection, including misdetection, low interobserver agreement, and lack of quantitative analysis. In this research, a total of 489 digital whole slide images (WSIs) were collected. The deep learning-based STAS detection model, named STASNet, was constructed to calculate semi-quantitative parameters associated with STAS density and distance. STASNet demonstrated an accuracy of 0.93 for STAS detection at the tiles level and had an AUC of 0.72–0.78 for determining the STAS status at the WSI level. Among the semi-quantitative parameters, T10S, combined with the spatial location information, significantly stratified stage I LUAD patients on disease-free survival. Additionally, STASNet was deployed into a real-time pathological diagnostic environment, which boosted the STAS detection rate and led to the identification of three easily misidentified types of occult STAS.

Abstract Previous studies revealed that MIR155HG possessed an oncogenic role in many types of tumors including lung adenocarcinoma (LUAD), along with higher expression in tumors. However, in our study, we observed a positive correlation between MIR155HG expression and overall survival across different cohorts. The transferred PBMC on the NCG mouse model abrogated the tumor intrinsic oncogenic role of MIR155HG in LUAD. Upregulation of MIR155HG positively correlated with CD8+ T cell infiltration both in vitro and in vivo, as well as LUAD tissues. Mechanistically, we revealed that MIR155HG increased the cytokine CCL5 expression at the transcriptional level, which depended on the interaction between MIR155HG and YBX1 protein, a novel transcription factor of CCL5, resulting in the more protein stability of YBX1 through dampening ubiquitination. Additionally, we also observed that MIR155 could increase PD-L1 expression to hamper the activity of recruited CD8+ T cells, which could be rescued through PD-L1 mAb addition. Finally, we uncovered that patients with high MIR155HG expression had a higher response rate to immunotherapy, and the combination of MIR155HG overexpression and PD-L1 mAb increased the efficacy of PD-L1 mAb. Together, our study provides a novel biomarker and potential combination treatment strategy for patients who received immunotherapy.

Abstract Osimertinib (Osi) is a widely used epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI). However, the emergence of resistance is inevitable, partly due to the gradual evolution of adaptive resistant cells during initial treatment. Here, we find that Osi treatment rapidly triggers adaptive resistance in tumor cells. Metabolomics analysis reveals a significant enhancement of oxidative phosphorylation (OXPHOS) in Osi adaptive-resistant cells. Mechanically, Osi treatment induces an elevation of NCOA4, a key protein of ferritinophagy, which maintains the synthesis of iron-sulfur cluster (ISC) proteins of electron transport chain and OXPHOS. Additionally, active ISC protein synthesis in adaptive-resistant cells significantly increases the sensitivity to copper ions. Combining Osi with elesclomol, a copper ion ionophore, significantly increases the efficacy of Osi, with no additional toxicity. Altogether, this study reveals the mechanisms of NCOA4-mediated ferritinophagy in Osi adaptive resistance and introduces a promising new therapy of combining copper ionophores to improve its initial efficacy.

Abstract Histone deacetylases (HDACs) contribute significantly to the initiation, progression, and prognosis of colorectal adenocarcinoma (COAD). Additionally, HDACs regulate the tumor microenvironment, immune escape, and tumor stem cells, and are closely linked to COAD prognosis. We developed a prognostic model for COAD that incorporates HDACs to evaluate their specific roles. The COAD dataset containing clinical and mutation data was collected using the TCGA and GEO databases to obtain genes associated with HDAC. LASSO analysis and univariate and multivariate Cox regression analysis were used to determine the presence of prognostic genes. Multivariate Cox analysis was also used to determine risk scores for HDAC-related features. Furthermore, genomic alterations, immune infiltration, and drug response were compared between high- and low-risk groups. Cellular experiments validated the potential regulatory role of BRD3 on COAD proliferation, migration, and apoptosis. The median risk scores, calculated based on the characteristics, demonstrated a more significant prognostic improvement in patients in the low-risk group. Furthermore, HDAC-related features were identified as important independent prognostic factors for patients with COAD. Additionally, genomic mutation status, immune infiltration, and function, as well as response to immunotherapy and chemotherapy, were found to be associated with risk scores. Subgroup analyses indicate that anti-PD-1 therapy may be beneficial for patients in the low-risk group. Additionally, a decrease in risk score was associated with a decrease in immune infiltration. Finally, HCT116 and HT29 cells exhibited inhibition of BRD3 gene proliferation and migration, as well as promotion of apoptosis. In patients with COAD, HDAC-related characteristics may be useful in predicting survival and selecting treatment.

Abstract Exciton–polaritons (polaritons) resulting from the strong exciton–photon interaction stimulates the development of novel low-threshold coherent light sources to circumvent the ever-increasing energy demands of optical communications1–3. Polaritons from bound states in the continuum (BICs) are promising for Bose–Einstein condensation owing to their theoretically infinite quality factors, which provide prolonged lifetimes and benefit the polariton accumulations4–7. However, BIC polariton condensation remains limited to cryogenic temperatures ascribed to the small exciton binding energies of conventional material platforms. Herein, we demonstrated room-temperature BIC polariton condensation in perovskite photonic crystal lattices. BIC polariton condensation was demonstrated at the vicinity of the saddle point of polariton dispersion that generates directional vortex beam emission with long-range coherence. We also explore the peculiar switching effect among the miniaturized BIC polariton modes through effective polariton−polariton scattering. Our work paves the way for the practical implementation of BIC polariton condensates for integrated photonic and topological circuits.

Abstract Energy transfer is a ubiquitous phenomenon that delivers energy from a blue-shifted emitter to a red-shifted absorber, facilitating wide photonic applications. Two-dimensional (2D) semiconductors provide unique opportunities for exploring novel energy transfer mechanisms in the atomic-scale limit. Herein, we have designed a planar optical microcavity-confined MoS2/hBN/WS2 heterojunction, which realizes the strong coupling among donor exciton, acceptor exciton, and cavity photon mode. This configuration demonstrates an unconventional energy transfer via polariton relaxation, brightening MoS2 with a record-high enhancement factor of ~440, i.e., two-order-of-magnitude higher than the data reported to date. The polariton relaxation features a short characteristic time of ~1.3 ps, resulting from the significantly enhanced intra- and inter-branch exciton-exciton scattering. The polariton relaxation dynamics is associated with Rabi energies in a phase diagram by combining experimental and theoretical results. This study opens a new direction of microcavity 2D semiconductor heterojunctions for high-brightness polaritonic light sources and ultrafast polariton carrier dynamics.

Abstract Background Lung cancer is the leading cause of cancer related to mortality worldwide, and the main pathological type is lung adenocarcinoma (LUAD). Circular RNAs (circRNAs) have been reported to be modified by N6‐methyladenosine (m6A), which is involved in the progression of diverse tumors. However, the crosstalk between circRNAs and m6A modification has not been well elucidated in LUAD. Methods MeRIP‐seq and YTHDF2‐RIP‐seq datasets were explored to identify candidate circRNAs modified by YTHDF2. Dual‐luciferase reporter assay, RIP, and rescue assays were performed to explore the relationship between circFUT8 and its parent mRNA of FUT8. In vitro and in vivo experiments were utilized to uncover the function of circFUT8. Results In this study, we identified a novel m6A‐modified circFUT8, derived from exon 3 of FUT8, which was elevated in tumor tissues compared with adjacent noncancerous tissues. The m6A reader YTHDF2 recognized and destabilized circFUT8 in an m6A‐dependent manner. YTHDF2 also combined with the line form of FUT8 (mFUT8), and circFUT8 competitively interacted with YTHDF2, blunting its binding to mFUT8, to stabilize the mRNA level of FUT8. Additionally, circFUT8 sponged miR‐186‐5p to elevate the expression of mFUT8. Finally, we revealed that circFUT8 promoted the malignant progression of LUAD dependent on the oncogenic function of FUT8. Conclusions These findings identified a novel m6A‐modified circFUT8 recognized and destabilized by YTHDF2, which competitively interacted with YTHDF2 and miR‐186‐5p to stabilize FUT8 mRNA to promote malignant progression in LUAD.

Abstract The 2D human pose estimation plays an important role in human‐computer interaction and action recognition. Although the method based on high‐resolution network has superior performance, there is still room for improvement in terms of speed and lightweight. Here, a LiteDEKR, a 2D pose estimation method that combines lightweight and accuracy, is proposed by designing a lightweight network based on DEKR and constructing two scientifically valid loss functions. The method, constructs a multi‐instance bias regression loss that matches the true distribution of keypoint bias, improves the accuracy of bias regression, and constructs a keypoint similarity loss with the object keypoint similarity index of keypoints as the optimization objective to achieve end‐to‐end training of the network. In addition, this paper has designed a lightweight DEKR, using LitePose as the backbone network. With the optimization of the above two loss functions, LiteDEKR not only achieves lightweight but also has high accuracy. Comparative experiments on the COCO and CrowdPose datasets show that compared to the current state‐of‐the‐art Contextual Instance Decoupling, LiteDEKR achieves a similar accuracy with only 10% of its network complexity. It also shows better robustness to low‐resolution input images.

Abstract Background Screening for hyperthyroidism using gold-standard diagnostic criteria in the general population is not cost-effective, leading to a relatively high rate of undiagnosed and untreated patients. This study aimed to establish a deep learning-based system to detect hyperthyroidism based on retinal photographs. Methods The multicenter, observational study included retinal photographs taken from participants in two hospitals and 24 health care centers throughout China. We first trained two models to identify hyperthyroidism: in model #1, the non-hyperthyroidism individuals were randomly selected, while in model #2, the non-hyperthyroidism group was matched for age and gender with the hyperthyroidism group. After internal validation, we selected the better model for further evaluation using external validation datasets. Results The study included 22,940 retinal photographs of 11,409 participants for the model development, and 3862 retinal photographs (1870 participants) which were obtained from two hospitals and four medical centers as the external validation datasets. Model #1 achieved a higher area under the receiver operator curve (AUC) than model #2 (0.907, 95% CI: 0.894–0.918 versus 0.850, 95% CI: 0.832–0.866) in the internal validation so that model #1 was used for further evaluation. In external datasets, model #1 reached AUCs ranging from 0.816 (95% CI 0.789–0.846) to 0.849 (95% CI 0.824–0.874) and achieved accuracies between 0.735 (95% CI 0.700–0.773) and 0.796 (95% CI 0.765–0.824). Heatmaps showed a focus of the DL-algorism on large fundus vessels and the optic nerve head. Conclusions Retinal fundus photographs may serve for DL systems for a cost-effective and non-invasive method to detect hyperthyroidism.

Abstract Super-enhancers (SEs) are regulatory element clusters related to cell identity and disease. While the studies illustrating the function of SE-associated long noncoding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains few. In our research, a SE-driven lncRNA, LINC00880, was identified, which showed higher expression in LUAD compared to normal tissues and indicated worse outcomes in stage I LUADs. We found that the transcription factor (TF) FOXP3 could simultaneously occupy the promoter and SE regions of LINC00880 to promote its transcription. The oncogenic function of LINC00880 was validated both in vitro and in vivo. Mechanically, LINC00880 binds to the protein CDK1 to increase its kinase activity, which rely on the phosphorylation state of pT161 in CDK1. LINC00880 also promotes the interaction between CDK1 and PRDX1. Moreover, LINC00880 interacts with PRDX1, which indicates that LINC00880 acts as a protein scaffold between CDK1 and PRDX1 to form a ternary complex, thereby resulting in the activation of PI3K/AKT to promote malignancy. Our results reveal that the SE-associated lncRNA LINC00880 regulates the CDK1/PRDX1 axis to sustain the malignancy of LUAD, providing a novel therapeutic target.

Summary: Retinopathy of prematurity (ROP) is currently one of the leading causes of infant blindness worldwide. Recently significant progress has been made in deep learning-based computer-aided diagnostic methods. However, deep learning often requires a large amount of annotated data for model optimization, but this requires long hours of effort by experienced doctors in clinical scenarios. In contrast, a large number of unlabeled images are relatively easy to obtain. In this paper, we propose a new semi-supervised learning framework to reduce annotation costs for automatic ROP staging. We design two consistency regularization strategies, prediction consistency loss and semantic structure consistency loss, which can help the model mine useful discriminative information from unlabeled data, thus improving the generalization performance of the classification model. Extensive experiments on a real clinical dataset show that the proposed method promises to greatly reduce the labeling requirements in clinical scenarios while achieving good classification performance.

Background: The profile of immune-related adverse events (irAEs) due to programmed death-1 (PD-1) inhibitors-based combination therapy in advanced non-small cell lung cancer (NSCLC) and its relationship with survival have not been fully described. Objective: Designed to capture the spectrum of irAEs and explore the association between irAEs and clinical outcomes in patients with NSCLC. Design: This retrospective single-center study included patients with advanced NSCLC treated with PD-1 inhibitors (mainly in combination with chemotherapy) at Jiangsu Cancer Hospital. Methods: The relationship between irAEs and survival was explored using landmark analysis and time-dependent Cox regression. The subgroup analyses focused on investigating the effects of organ-specific irAE, irAE grade, and steroid dose used to treat irAE. Results: This study included 301 patients, 199 of whom received PD-1 inhibitors plus chemotherapy. The most common irAEs were skin toxicity (19.3%), endocrinopathy (21.3%), and pneumonitis (17.6%). In the entire cohort, the median progression-free survival (PFS) for patients developing and not developing irAE was 12.3 and 10.7 months ( p < 0.001), and the median overall survival (OS) was 23.5 months and 20.1 months ( p = 0.137), respectively. Subgroup analyses indicated that grade 3 or higher irAE, high steroid dose, and immune-related pneumonitis were detrimental to OS, whereas skin toxicity was beneficial to survival. These findings were further corroborated by both landmark analyses and Cox regression models conducted over four time points (1, 3, 6, and 12 months). Conclusion: In the real world, NSCLC patients receiving PD-1 inhibitor-based combination therapy (particularly combined with chemotherapy) experience longer PFS with irAE, though not necessarily OS. Immune-related skin toxicity is associated with a better prognosis, whereas pneumonitis grade ⩾3 irAE and high steroid dose compromise survival. Clinicians should remain cognizant of the organ-specific manifestations of irAE and take proactive measures to mitigate the progression of irAE.

The primary feature of Industry 4.0 is MPP (mass personalization production), which requires that consumers’ individual requests are met in large-scale production. Under MPP, there is a multitude of subtasks decomposed from production tasks that are derived from individualized requests, and allocating these subtasks properly brings high economic benefits. However, existing approaches to achieve MPP, such as cloud manufacturing and social manufacturing, generally can not provide customers with deep participation in the entire production cycle, or respond to consumers’ modification needs by a triggered mechanism. Besides, some methods are of centralized architecture, which is vulnerable to single point error and with large cloud load that is not conducive to quickly responding to consumers’ dynamic demand changes. Therefore, this paper proposes a dynamic edge-cloud manufacturing mode for MPP, which can make subtask allocation with high economic benefit through distributed computing and implementing modifications of alternating direction method of multiplier (ADMM) algorithm. Also, it proposes an original improved ADMM algorithm, named Relaxation-Based ADMM algorithm, to increase the optimization speed in large-scale cases. The experimental results show that the proposed method generally obtains a superior solution under a certain iteration count.

Abstract Background Lung adenocarcinoma (LUAD) is the leading cause of death worldwide. However, the roles of long noncoding RNAs (lncRNAs) hijacked by super-enhancers (SEs), vital regulatory elements of the epigenome, remain elusive in the progression of LUAD metastasis. Methods SE-associated lncRNA microarrays were used to identify the dysregulated lncRNAs in LUAD. ChIP-seq, Hi-C data analysis, and luciferase reporter assays were utilized to confirm the hijacking of LINC01977 by SE. The functions and mechanisms of LINC01977 in LUAD were explored by a series of in vitro and in vivo assays. Results We found that LINC01977, a cancer-testis lncRNA, was hijacked by SE, which promoted proliferation and invasion both in vitro and in vivo. LINC01977 interacted with SMAD3 to induce its nuclear transport, which facilitated the interaction between SMAD3 and CBP/P300, thereby regulating the downstream target gene ZEB1. Additionally, SMAD3 up-regulated LINC09177 transcription by simultaneously binding the promoter and SE, which was induced by the infiltration of M2-like tumor-associated macrophages (TAM2), subsequently activating the TGF-β/SMAD3 pathway. Moreover, LINC01977 expression was positively correlated with TAM2 infiltration and SMAD3 expression, especially in early-stage LUAD. Higher chromatin accessibility in the SE region of LINC01977 was observed with high expression of TGF-β. Early-stage LUAD patients with high LIN01977 expression had a shorter disease-free survival. Conclusions TAM2 infiltration induced a rich TGF-β microenvironment, activating SMAD3 to bind the promoter and the SE of LINC01977, which up-regulated LINC01977 expression. LINC01977 also promoted malignancy via the canonical TGF-β/SMAD3 pathway. LINC01977 hijacked by SE could be a valuable therapeutic target, especially for the treatment of early-stage LUAD.

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI.

A high-throughput liquid chromatography tandem mass spectrometry method was developed for the determination of 85 acid synthetic pigments in sauce. The acid synthetic pigments were extracted with methanol, purified with C18 and diluted with water. Subsequently, the sample was analyzed by liquid chromatography-tandem mass spectrometry under multiple reaction monitoring (MRM) mode, and quantified by external standard method. The results showed that the method had a good linearity in the range of 10~1000 ng/mL which correlation coefficients were greater than 0.99. The detection limit of the method was 8.08~885.2 μg/kg, and the quantitative limit was 26.94~2950.7 μg/kg. The average recoveries of synthetic pigments from spiked samples ranged from 70.2% to 116.5%, and the relative standard deviations were between 0.1% and 15.0%. This method was applied to analyze 50 batches of sauce such as tomato sauce and chili sauce, acid red 13 and acid orange II were screened out. The method has strong universality, wide range of detection objects, short analysis time and high sensitivity. It is suitable for the determination of 85 acid synthetic pigments in sauce.

Abstract Background Lung cancer is among the major diseases threatening human health. Although the immune response plays an important role in tumor development, its exact mechanisms are unclear. Materials and methods Here, we used CIBERSORT and ESTIMATE algorithms to determine the proportion of tumor-infiltrating immune cells (TICs) as well as the number of immune and mesenchymal components from the data of 474 lung cancer patients from the Gene Expression Omnibus database. And we used data from The Cancer Genome Atlas database (TCGA) for validation. Results We observed that immune, stromal, and assessment scores were only somewhat related to survival with no statistically significant differences. Further investigations revealed these scores to be associated with different pathology types. GO and KEGG analyses of differentially expressed genes revealed that they were strongly associated with immunity in lung cancer. In order to determine whether the signaling pathways identified by GO and KEGG signaling pathway enrichment analyses were up- or down-regulated, we performed a gene set enrichment analysis using the entire matrix of differentially expressed genes. We found that signaling pathways involved in hallmark allograft rejection, hallmark apical junction, hallmark interferon gamma response, the hallmark P53 pathway, and the hallmark TNF-α signaling via NF-ĸB were up-regulated in the high-ESTIMATE-score group. CIBERSORT analysis for the proportion of TICs revealed that different immune cells were positively correlated with the ESTIMATE score. Cox regression analysis of the differentially expressed genes revealed that CPA3, C15orf48, FCGR1B, and GNG4 were associated with patient prognosis. A prognostic model was constructed wherein patients with high-risk scores had a worse prognosis (p

Abstract Background The aim of this study was to investigate the co-operative role of CXCR4/CXCL12 axis and IL-1Ra in metastatic processes mechanism by interactions between colorectal cancer cells and stromal cells in their microenvironment. Methods Expression of IL-1α, interleukin-1 receptor type I (IL-1 RI), CXCL12 and CXCR4 mRNA and proteins were determined by RT-PCR and Western blot. The effect of secreted level of CXCL12 by IL-1Ra on fibroblasts was measured by ELISA. CXCL12 regulate metastatic potential of colorectal cancer was evaluated by proliferation, invasion and angiogenesis assays, respectively, in which invasion and angiogenesis assays used an in vitro system consisting of co-cultured colorectal cells and stromal cells. Results IL-1α was expressed in high liver metastatic colorectal cancer cell lines (HT-29 and WiDr). The colorectal cancer cell-derived IL-1α and rIL-1α significantly promoted CXCL12 expression by fibroblasts, and this enhancing effect can be significantly inhibited by IL-1Ra (P

Background and objective Lung cancer is the leading cause of cancer-related death, of which non-small cell lung cancer (NSCLC) is the most common type. Immune checkpoint inhibitors (ICIs) have now become one of the main treatments for advanced NSCLC. This paper retrospectively investigated the effect of peripheral blood inflammatory indexes on the efficacy of immunotherapy and survival of patients with advanced non-small cell lung cancer, in order to find strategies to guide immunotherapy in NSCLC. Methods Patients with advanced non-small cell lung cancer who were hospitalized in The Affiliated Cancer Hospital of Nanjing Medical University from October 2018 to August 2019 were selected to receive anti-PD-1 (pembrolizumab, sintilimab or toripalimab) monotherapy or combination regimens. And were followed up until 10 December 2020, and the efficacy was evaluated according to RECIST1.1 criteria. Progression-free survival (PFS) and overall survival (OS) were followed up for survival analysis. A clinical prediction model was constructed to analyze the predictive value of neutrophil-to-lymphocyte ratio (NLR) based on NLR data at three different time points: before treatment, 6 weeks after treatment and 12 weeks after treatment (0w, 6w and 12w), and the accuracy of the model was verified. Results 173 patients were finally included, all of whom received the above treatment regimen, were followed up for a median of 19.7 months. The objective response rate (ORR) was 27.7% (48/173), the disease control rate (DCR) was 89.6% (155/173), the median PFS was 8.3 months (7.491-9.109) and the median OS was 15.5 months (14.087-16.913). The chi-square test and logistic multi-factor analysis showed that NLR6w was associated with ORR and NLR12w was associated with ORR and DCR. Further Cox regression analysis showed that NLR6w and NLR12w affected PFS and NLR0w, NLR6w and NLR12w were associated with OS. Conclusion In patients with advanced non-small cell lung cancer, NLR values at different time points are valid predictors of response to immunotherapy, and NLR

Abstract V–VI antimony chalcogenide semiconductors have shown exciting potentials for thin film photovoltaic applications. However, their solar cell efficiencies are strongly hampered by anomalously large voltage loss (>0.6 V), whose origin remains controversial so far. Herein, by combining ultrafast pump–probe spectroscopy and density functional theory (DFT) calculation, the coupled electronic and structural dynamics leading to excited state self‐trapping in antimony chalcogenides with atomic level characterizations is reported. The electronic dynamics in Sb2Se3 indicates a ≈20 ps barrierless intrinsic self‐trapping, with electron localization and accompanied lattice distortion given by DFT calculations. Furthermore, impulsive vibrational coherences unveil key SbSe vibrational modes and their real‐time interplay that drive initial excited state relaxation and energy dissipation toward stabilized small polaron through electron–phonon and subsequent phonon–phonon coupling. This study's findings provide conclusive evidence of carrier self‐trapping arising from intrinsic lattice anharmonicity and polaronic effect in antimony chalcogenides and a new understanding on the coupled electronic and structural dynamics for redefining excited state properties in soft semiconductor materials.

The so-called pseudo-bilayer (PB) organic solar cell (OSC) device architecture can promote enhanced exciton dissociation and charge transport, leading to improved device performance. Here, the authors report high-efficiency OSCs that features a PB architecture and optimized ternary system.

Gastric cancer (GC) remains a major public health problem. Ursolic acid (UA) is reported to be effective in inhibiting GC; however, its low solubility and poor biocompatibility have greatly hindered its clinical application. Herein, an innovative reactive oxygen species (ROS)-sensitive UA dimeric prodrug is developed by coupling two UA molecules via a ROS-cleavable linkage, which can self-assemble into stable nanoparticles in the presence of surfactant. This new UA-based delivery system comprises the following major components: (I) dimeric prodrug inner core that can achieve high drug-loading (55%, w/w) and undergo rapid and selective conversion into intact drug molecules in response to ROS; (II) a polyethylene glycol (PEG) shell to improve colloid stability and extend blood circulation, and (III) surface-modified internalizing RGD (iRGD) to increase tumor targeting. Enhancement of the antitumor effect of this delivery system was demonstrated against GC tumors in vitro and in vivo. This novel approach offers the potential for clinical applications of UA.

Abstract Considering the robust and stable nature of the active layers, advancing the power conversion efficiency (PCE) has long been the priority for all‐polymer solar cells (all‐PSCs). Despite the recent surge of PCE, the photovoltaic parameters of the state‐of‐the‐art all‐PSC still lag those of the polymer:small molecule‐based devices. To compete with the counterparts, judicious modulation of the morphology and thus the device electrical properties are needed. It is difficult to improve all the parameters concurrently for the all‐PSCs with advanced efficiency, and one increase is typically accompanied by the drop of the other(s). In this work, with the aids of the solvent additive (1‐chloronaphthalene) and the n‐type polymer additive (N2200), we can fine‐tune the morphology of the active layer and demonstrate a 16.04% efficient all‐PSC based on the PM6:PY‐IT active layer. The grazing incidence wide‐angle X‐ray scattering measurements show that the shape of the crystallites can be altered, and the reshaped crystallites lead to enhanced and more balanced charge transport, reduced recombination, and suppressed energy loss, which lead to concurrently improved and device efficiency and stability.

With the booming development of the cyber-physical system, human society is much more dependent on information technology. Unfortunately, like software, hardware is not trusted at all, due to so many third parties involved in the separated integrated circuit's (IC) design and manufacturing stages for the high profit. The malicious circuits (named Hardware Trojans) can be implanted during any stage of the ICs' design and manufacturing process. However, the existing pre-silicon approaches based on machine learning theory have good performance, they all belong to supervised learning methods, which have a key prerequisite that is numerous already known information. Meanwhile, hardware Trojans are even more unimaginable because today's ICs are becoming more complicated. The known information is even harder to gain. Furthermore, the training process for supervised learning methods tends to be time-consuming and generally requires a huge amount of balanced training data. Therefore, this paper proposes an unsupervised hardware Trojans detection approach by combined the principal component analysis (PCA) and local outlier factor (LOF) algorithm, called PL-HTD. We firstly visualize the distribution features of normal nets and Trojan nets, and then reveal the differences between the two types of nets to reduce the dimension of the feature set. According to the outliers of each net, the abnormal nets are selected and verified by professionals later to confirm whether it is a true Trojan relative to the host circuit to realize the detection. The experiments show that the proposed method can detect hardware Trojans effectively and reduce the cost of manual secondary detection. For the Trust-HUB benchmarks, the PL-HTD achieves up to 73.08% TPR and 97.52% average TNR, moreover, it achieves average 96.00% accuracy, which shows the feasibility and efficiency of hardware Trojans detecting by employing a method without the guidance of class label information.

Electric vehicles are being increasingly adopted worldwide to support the sustainable development of society. However, various technologies that are related to electric vehicles still require further investigation. In this study, we consider a gearshift control architecture that combines offline trajectory planning and online control for improved shifting in multispeed transmission electric vehicles. For gearshift trajectory planning, we establish a dynamic model and apply the multi-objective optimization Radau pseudospectral method. Simulations of this method provide a Pareto solution set under three optimization objectives, namely duration, friction work, and jerk, thereby establishing a new approach for satisfying the requirements for shift quality. Moreover, the relations among these objectives are detailed on the Pareto solution set. We conducted a hardware-in-the-loop simulation to verify the performance of the proposed control architecture and trajectory planning method. The simulation results indicate that the gearshift control delivers a suitable response for different driving intentions based on the obtained Pareto solution set.

BackgroundThis study aims to investigate the effects of ω-3, ω-6 polyunsaturated fatty acids (PUFAs), and their middle metabolites prostaglandin (PGE)2 and PGE3 on proliferation, invasion, and angiogenesis formation of gastric cancer cells and to explore associated mechanism.MethodsRT-PCR and ELISA were used to detect the expression of cyclooxygenase (COX)-1 and COX-2 in gastric cancer cell lines. The effect of ω-3, ω-6, PGE2, and PGE3 on the proliferation, invasion, and angiogenesis of gastric cancer cells were measured by cell proliferation, invasion, and angiogenesis assay in vitro. COX-2 small interfering RNA (siRNA) was transfected into gastric cancer cells, and the expression of COX-2 protein was detected by Western blot. COX-2 gene silencing influencing proliferation, invasion, and angiogenesis potential of gastric cancer cells was detected by WST-1, transwell chamber, and angiogenesis assay, respectively.ResultsCOX-2 was only expressed in MKN74 and MKN45 cells. In gastric cancer cell lines with positive COX-2 expression, ω-6 and PGE2 could significantly enhance the proliferation, invasion, and angiogenesis of gastric cancer cells, and after transfection with COX-2 siRNA, the effects of ω-6 and PGE2 on enhancing the proliferation, invasion, and angiogenesis of gastric cancer cells were significantly attenuated; ω-3 and PEG3 could inhibit the proliferation, invasion, and angiogenesis of gastric cancer cells. In gastric cancer cell lines with negative COX-2 expression, ω-6 and PGE2 had no significant effect on the proliferation, invasion, and angiogenesis of gastric cancer; ω-3 and PGE3 could significantly inhibit the proliferation, invasion, and angiogenesis of gastric cancer.Conclusionω-6 PUFAs reinforce the metastatic potential of gastric cancer cells via COX-2/PGE2; ω-3 PUFAs inhibit the metastatic potential of gastric cancer via COX-1/PGE3 signaling axis.

BackgroundHepatocellular carcinoma (HCC) is the most common pathological type of primary liver cancer. The lack of prognosis indicators is one of the challenges in HCC. In this study, we investigated the combination of tertiary lymphoid structure (TLS) and several systemic inflammation parameters as a prognosis indicator for HCC.Materials and MethodsWe retrospectively recruited 126 postoperative patients with primary HCC. The paraffin section was collected for TLS density assessment. In addition, we collected the systemic inflammation parameters from peripheral blood samples. We evaluated the prognostic values of those parameters on overall survival (OS) using Kaplan-Meier curves, univariate and multivariate Cox regression. Last, we plotted a nomogram to predict the survival of HCC patients.ResultsWe first found TLS density was positively correlated with HCC patients’ survival (HR=0.16, 95% CI: 0.06 − 0.39, p < 0.0001), but the power of TLS density for survival prediction was found to be limited (AUC=0.776, 95% CI:0.772 − 0.806). Thus, we further introduced several systemic inflammation parameters for survival analysis, we found neutrophil-to-lymphocyte ratio (NLR) was positively associated with OS in univariate Cox regression analysis. However, the combination of TLS density and NLR better predicts patient’s survival (AUC=0.800, 95% CI: 0.698-0.902, p < 0.001) compared with using any single indicator alone. Last, we incorporated TLS density, NLR, and other parameters into the nomogram to provide a reproducible approach for survival prediction in HCC clinical practice.ConclusionThe combination of TLS density and NLR was shown to be a good predictor of HCC patient survival. It also provides a novel direction for the evaluation of immunotherapies in HCC.

Interface interactions in 2D vertically stacked heterostructures play an important role in optoelectronic applications, and photodetectors based on graphene/InSe heterostructures show promising performance nowadays. However, nonlinear optical property studies based on the graphene/InSe heterostructure are insufficient. Here, we fabricated a graphene/InSe heterostructure by mechanical exfoliation and investigated the optically induced charge transfer between graphene/InSe heterostructures by taking photoluminescence and pump–probe measurements. The large built-in electric field at the interface was confirmed by Kelvin probe force microscopy. Furthermore, due to the efficient interfacial carrier transfer driven by the built-in electric potential (~286 meV) and broadband nonlinear absorption, the application of the graphene/InSe heterostructure in a mode-locked laser was realized. Our work not only provides a deeper understanding of the dipole orientation-related interface interactions on the photoexcited charge transfer of graphene/InSe heterostructures, but also enriches the saturable absorber family for ultrafast photonics application.

Antimony trisulfide has a proper bandgap of 1.7 eV for making solar cells but the devices suffer from severe voltage loss. Here Yang et al. propose that the photoexcited carriers are self-trapped by lattice deformation, which places a thermodynamic limit of only 0.8 V for the open circuit voltage.

Community detection is an important research direction in the field of complex network analysis. It aims to discover community structures in complex networks. Algorithms based on dynamic distance mechanism can find stable communities with various shapes. However, they still cannot discover overlapping or outlier communities. This paper proposes an overlapping community discovery algorithm based on coarsening and local overlapping modularity. First, to reduce the running time, a new equation for computing the local overlapping modularity increment is derived. This equation finds the overlapping communities, accurately and quickly. Second, a new similarity measuring strategy is designed to reduce the number of outlier communities. The experiments on artificial and real datasets show that the proposed algorithm can discover the overlapping communities, accurately and efficiently.

One-shot video-based person re-identification exploits the unlabeled data by using a single-labeled sample for each individual to train a model and to reduce the need for laborious labeling. Although recent works focusing on this task have made some achievements, most state-of-the-art models are vulnerable to misalignment, pose variation and corrupted frames. To address these challenges, we propose a one-shot video-based person re-identification model based on pose-guided spatial alignment and KFS. First, a spatial transformer sub-network trained using pose-guided regression is employed to perform the spatial alignment. Second, we propose a novel training strategy based on KFS. Key frames with abruptly changing poses are deliberately identified and selected to make the network adaptive to pose variation. Finally, we propose a frame feature pooling method by incorporating long short-term memory with an attention mechanism to reduce the influence of corrupted frames. Comprehensive experiments are presented based on the MARS and DukeMTMC-VideoReID datasets. The mAP values for these datasets reach 46.5% and 68.4%, respectively, demonstrating that the proposed model achieves significant improvements over state-of-the-art one-shot person re-identification methods.

Semantic segmentation plays a critical role in image understanding. Recently, Fully Convolutional Network (FCN)-based models have made significant progress in semantic segmentation. However, achieving the full utilization of contextual information and recovery of lost spatial details remains a huge challenge. In this paper, we present a semantic segmentation model based on pyramid context contrast and a subpixel-aware dense decoder. We propose first using the pyramid context contrast to exploit the capability of contextual information by aggregating multi-scale foreground representations in different background regions via the pyramid context contrast module. Then, we add a subpixel-aware dense decoder architecture to reuse features extracted from different decoder levels by pixel shuffle, which can reasonably resolve resolution inconsistency between feature maps. Next, we refine the boundary by utilizing spatial visual information about low-level features via a boundary refinement branch with addition of auxiliary supervision. The presented model was evaluated using the PASCAL VOC 2012 semantic segmentation benchmark and achieved a performance of 86.9%, demonstrating that the proposed model achieves considerable improvement over most state-of-the-art models.

BackgroundPD-1 inhibitors have been routinely used in the treatment of advanced non-small cell lung cancer (NSCLC), and have demonstrated to significantly improve survivorship when combining with other conventional therapies, such as chemotherapy and anti-angiogenesis therapy. PD-L1 is the most commonly used biomarker to select benefiting groups, while not all patients with high PD-L1 expression benefit from immunotherapy. Therefore, identifying other prognostic and predictive biomarkers, including peripheral blood indexes, is essential.MethodsWe retrospectively collected medical records and hematological data of 151 patients with advanced NSCLC treated with PD-1 inhibitor-based combination therapy in our hospital. The peripheral blood indexes of interest were NLR, PLR, PAR, Hb, LDH, CEA, and NSE. The association between peripheral blood indexes and treatment responses or survival outcomes was examined by multivariable logistic regression and Cox regression, respectively.ResultsThe decreased CEA at week 6 (OR = 4.209, 95%CI: 1.287-13.758) or 12 (OR = 7.267, 95%CI: 1.508-35.006) post-treatment was related to a higher disease control rate. The decrease or NLR at week 6 (OR = 3.081, 95%CI: 1.464-6.483) or 12 (OR = 3.304, 95%CI: 1.560-7.001) post-treatment, or CEA at week 12 post-treatment (OR = 2.469, 95%CI: 1.134-5.375), was associated with a higher objective response rate. Patients whose NLR (HR = 0.610, 95%CI: 0.411-0.907) or CEA (HR = 0.477, 95%CI: 0.320-0.710) decreased at week 6 post-treatment tended to have longer progression-free survival, and similar results were found in those with decreased NLR (HR = 0.587, 95%CI: 0.388-0.886) or CEA (HR = 0.406, 95%CI: 0.270-0.609) at week 12 post-treatment. Patients whose CEA (HR = 0.543, 95%CI: 0.339-0.871) or NSE (HR = 0.619, 95%CI: 0.386-0.994) decreased after 6 weeks post-treatment appeared to have longer overall survival, and the same was found for those whoseCEA (HR = 0.620, 95%CI: 0.390-0.986) or NSE (HR = 0.578, 95%CI: 0.353-0.947) was decreased at 12 weeks after treatment.ConclusionPost-treatment NLR, CEA and NSE changes are suggestive indicators for the prognosis of NSCLC patients after immunotherapy.

To examine the effects of piperine on lipid metabolism in white adipose tissue of high-fat diet induced obese mice, mice were divided into CON, HFD, HFD + Pip15, and HFD + Pip30 groups (n = 10 in each group). The mice in the latter two groups received 15 and 30 mg/kg body weight of piperine, respectively. Piperine decreased body and fat weights. Piperine treatments, especially 30 mg/kg, significantly reduced adipose cell size in SAT and VAT. mRNA expressions associated with adipose tissue expansion in the HFD + Pip30 group was significantly downregulated in SAT and VAT. For lipogenesis, piperine downregulated all of determined genes in VAT. For Lipolysis, only LPL mRNA in the HFD + Pip30 group was significantly upregulated in SAT. In VAT, 30 mg/kg piperine significantly increased lipolytic enzymes and upregulated their mRNA expressions. Piperine protects against obesity and regulates the lipogenic and lipolytic genes, which is more effective in visceral fat than in subcutaneous fat.

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

Domain generation algorithms (DGAs) use specific parameters as random seeds to generate a large number of random domain names to prevent malicious domain name detection. This greatly increases the difficulty of detecting and defending against botnets and malware. Traditional models for detecting algorithmically generated domain names generally rely on manually extracting statistical characteristics from the domain names or network traffic and then employing classifiers to distinguish the algorithmically generated domain names. These models always require labor intensive manual feature engineering. In contrast, most state-of-the-art models based on deep neural networks are sensitive to imbalance in the sample distribution and cannot fully exploit the discriminative class features in domain names or network traffic, leading to decreased detection accuracy. To address these issues, we employ the borderline synthetic minority over-sampling algorithm (SMOTE) to improve sample balance. We also propose a recurrent convolutional neural network with spatial pyramid pooling (RCNN-SPP) to extract discriminative and distinctive class features. The recurrent convolutional neural network combines a convolutional neural network (CNN) and a bi-directional long short-term memory network (Bi-LSTM) to extract both the semantic and contextual information from domain names. We then employ the spatial pyramid pooling strategy to refine the contextual representation by capturing multi-scale contextual information from domain names. The experimental results from different domain name datasets demonstrate that our model can achieve 92.36% accuracy, an 89.55% recall rate, a 90.46% F1-score, and 95.39% AUC in identifying DGA and legitimate domain names, and it can achieve 92.45% accuracy rate, a 90.12% recall rate, a 90.86% F1-score, and 96.59% AUC in multi-classification problems. It achieves significant improvement over existing models in terms of accuracy and robustness.

Diverse and wide-range applications of integrated circuits (ICs) and the development of Cyber Physical System (CPS), more and more third-party manufacturers are involved in the manufacturing of ICs. Unfortunately, like software, hardware can also be subjected to malicious attacks. Untrusted outsourced manufacturing tools and intellectual property (IP) cores may bring enormous risks from highly integrated. Attributed to this manufacturing model, the malicious circuits (known as Hardware Trojans, HTs) can be implanted during the most designing and manufacturing stages of the ICs, causing a change of functionality, leakage of information, even a denial of services (DoS), and so on. In this paper, a survey of HTs is presented, which shows the threatens of chips, and the state-of-the-art preventing and detecting techniques. Starting from the introduction of HT structures, the recent researches in the academic community about HTs is compiled and comprehensive classification of HTs is proposed. The state-of-the-art HT protection techniques with their advantages and disadvantages are further analyzed. Finally, the development trends in hardware security are highlighted.

Drop-and-pull transportation can repeatedly utilize tractors with different trailers and reduce costs, carbon emissions, and the number of tractors to purchase and use. Fuel-cell electric vehicles (FCEV) possess high power and long drive endurance. These performance characteristics complement the performance requirements of drop-and-pull transportation of heavy loads and long mileage. This paper proposes a novel fuel-cell electric articulated vehicle featuring three power sources: fuel cell, power battery, and ultracapacitor. Then, based on the proposed vehicle, we expound on a highly efficient and flexible transport system. To compare economics and durability of fuel-cell electric trailers with two energy sources (i.e., fuel-cell-battery) and three energy sources, we developed and simulated a rule-based energy management strategy under driving conditions. The results indicate that, although similar levels of fuel economy and capacity degradation of the fuel cell occur for the proposed vehicle and its two-energy-source counterpart, the ampere–hour throughput of three-energy-source vehicles was 64% lower than that of two-energy-source vehicles, which indicates the introduction of the ultracapacitor in fuel-cell-battery electric articulated vehicles can offer significant protection to the power battery. This result shows that the three energy sources increase the service life of the energy system.

This paper presents a novel method for scanning duplex-printed documents without incurring the unwanted show-through artifact. The proposed method achieves the goal of eliminating the leaked-out reverse-side content by fusing a white backed scan image with a black backed scan image of the document. The fusion is accomplished using a multilayer perceptron having learned a fusion mapping from manually corrected document images. The main novel contributions of this work include (1) being the first to propose to accomplish the goal of show through free scanning by fusing a white backed scan image with a black backed scan image of the document; (2) proposing a learning approach using a multilayer perceptron to learn the fusion mapping from manually corrected scan images; and (3) proposing to use the pixel value histogram of reverse-side-printed area as well as the pixel value histogram of duplex-printed area to quantitatively indicate show through severity to facilitate objective comparison of the methods in consideration. The experiment results show that the proposed method is remarkably more powerful in eliminating show through than the two state-of-the-art methods in comparison.

Mobile security is one of the most fundamental problems in Wireless Sensor Networks (WSNs). The data transmission path will be compromised for some disabled nodes. To construct a secure and reliable network, designing an adaptive route strategy which optimizes energy consumption and network lifetime of the aggregation cost is of great importance. In this paper, we address the reliable data aggregation route problem for WSNs. Firstly, to ensure nodes work properly, we propose a data aggregation route algorithm which improves the energy efficiency in the WSN. The construction process achieved through discrete particle swarm optimization (DPSO) saves node energy costs. Then, to balance the network load and establish a reliable network, an adaptive route algorithm with the minimal energy and the maximum lifetime is proposed. Since it is a non-linear constrained multi-objective optimization problem, in this paper we propose a DPSO with the multi-objective fitness function combined with the phenotype sharing function and penalty function to find available routes. Experimental results show that compared with other tree routing algorithms our algorithm can effectively reduce energy consumption and trade off energy consumption and network lifetime.

Urban impedance is an important consideration in assessments of transportation and land-use systems. This work leverages check-in records obtained from mobile social networks to build a fine-grained but inexpensive urban impedance model. Check-in records and road networks are collected and used to calculate and adjust the various parameters of the model, including path length, number and angle of turns, number and direction of junctions, and population density. Check-in records can filter functional locations and supply the time factor, thereby providing excellent advantages over traditional models that do not employ this data type. The proposed model is more accurate than traditional impedance models, as verified by experiments using Sina Weibo data in Tianjin City.