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Abstract Doxorubicin’s antitumor effectiveness may be constrained with ineffective tumor penetration, systemic adverse effects, as well as drug resistance. The co-loading of immune checkpoint inhibitors and doxorubicin into liposomes can produce synergistic benefits and address problems, including quick drug clearance, toxicity, and low drug penetration efficiency. In our previous study, we modified a nanobody targeting CTLA-4 onto liposomes (LPS-Nb36) to be an extremely potent CTLA-4 signal blocker which improve the CD8+ T-cell activity against tumors under physiological conditions. In this study, we designed a drug delivery system (LPS-RGD-Nb36-DOX) based on LPS-Nb36 that realized the doxorubicin and anti-CTLA-4 Nb co-loaded and RGD modification, and was applied to antitumor therapy. We tested whether LPS-RGD-Nb36-DOX could targets the tumor by in vivo animal photography, and more importantly, promote cytotoxic T cells proliferation, pro-inflammatory cytokine production, and cytotoxicity. Our findings demonstrated that the combination of activated CD8+ T cells with doxorubicin/anti-CTLA-4 Nb co-loaded liposomes can effectively eradicate tumor cells both in vivo and in vitro. This combination therapy is anticipated to have synergistic antitumor effects. More importantly, it has the potential to reduce the dose of chemotherapeutic drugs and improve safety.

Biodiversity is a vital aspect of an ecosystem and investigating its formation and maintenance is vital. Maintaining biodiversity notably requires a clear understanding of the mechanisms underlying species coexistence. However, in the Anthropocene, the absence of large carnivores in most areas has reduced the number of relatively intact ecosystems suitable for coexistence research. The mountains of Southwest China, a biodiversity hotspot with relatively low anthropogenic disturbance, offer a potentially suitable area for studying carnivore coexistence, especially in the Xinlong region in Sichuan. To understand the mechanisms underlying the coexistence of small carnivores, we used infrared cameras to investigate their spatiotemporal activity. We explored interspecific niche differentiation and small carnivore' responses to top predators and human disturbances using occupancy modeling and avoidance-attraction ratios (AARs). The results were as follows: (1) different small carnivores exhibited unique spatial and temporal responses to top predators, with leopard cats (Prionailurus bengalensis) preferring to occupy areas occupied by leopards (Panthera pardus), whereas yellow-throated martens (Martes flavigula) and red foxes (Vulpes vulpes) remained spatially independent of leopards; (2) human activities did not significantly affect the spatial activities of small carnivores, but small carnivores exhibited time-resistant behavior toward humans and domestic cattle; and (3) we observed temporal and spatial avoidance between leopard cats and other small carnivores (yellow-throated martens and red foxes). Overall, our study highlights the complex and unexpected behavioral responses and survival strategies of animals within an integrated community. In the future, we aim to further investigate the mechanisms underlying the formation of wildlife communities in this region and to elucidate the potential roles of human activities within this ecological community.

Hyperspectral target detection (HTD) is a crucial aspect of remote sensing applications, aiming to identify targets in hyperspectral images (HSIs) based on their known prior spectral signatures. However, the spectral variability resulting from various imaging conditions in multi-temporal hyperspectral images poses a challenge to both classical and deep learning (DL) methods. To overcome the limitations imposed by spectral variability, an implicit contrastive learning-based target detector (ICLTD) is proposed to exploit in-scene spectra in an unsupervised way. First, only prior spectra are utilized for explicit supervision, while an implicit contrastive learning module (ICLM) is designed to normalize the feature distributions of prior and in-scene spectra. This paper theoretically demonstrates that the ICLM can transfer the gradients from prior spectral features to those of in-scene spectra based on their feature similarities and differences. Because of transferred gradient signals, the ICLTD is regularized to extract similar representations for the prior and in-scene target spectra, while augmenting feature differences between the target and background spectra. Additionally, a local spectral similarity constraint (LSSC) is proposed to enhance the capability of scene adaptation by leveraging the spectral similarities among in-scene targets. To validate the performance of the ICLTD under spectral variability, multi-temporal HSIs captured under various imaging conditions are collected to generate prior spectra and in-scene spectra. Comparative evaluations against several DL detectors and classical methods reveal the superior performance of the ICLTD in achieving a balance between target detectability and background suppressibility under spectral variability.

Target detection in hyperspectral images (HSIs) mainly relies on the spectral information of the target prior. However, prior spectra with precise variation information are often hard to obtain, and the “different objects with the same spectrum” phenomena in complex environments make it difficult to detect the variational targets. To mitigate this problem, self-supervised learning (SSL) is introduced to mine the spectral variation in the HSIs to supplement the priors. The SSL-based detector, termed SSDCTD, is optimized with unlabeled spectra using an unsupervised pretext task and restricted with prior spectra in an end-to-end way. A deep clustering-based pretext task is designed, which utilizes the discriminative features among different substances to cluster the spectra in HSIs into several classes. Besides, we simultaneously optimize the detector to classify the priors into the target cluster. The combination of exploiting variational features from HSIs and supervision of target priors helps the proposed detector to make a great balance between target detectability (TD) and background suppressibility (BS). Experiments on four real datasets validate the superior performance of the proposed detector over several state-of-the-art detectors in multiple criteria.

Immune cells are essential for initiating and developing the fibrotic process by releasing cytokines and growth factors that activate fibroblasts and promote extracellular matrix deposition. Immunometabolism describes how metabolic alterations affect the function of immune cells and how inflammation and immune responses regulate systemic metabolism. The disturbed immune cell function and their interactions with other cells in the tissue microenvironment lead to the origin and advancement of fibrosis. Understanding the dysregulated metabolic alterations and interactions between fibroblasts and the immune cells is critical for providing new therapeutic targets for fibrosis. This review provides an overview of recent advances in the pathophysiology of fibrosis from the immunometabolism aspect, highlighting the altered metabolic pathways in critical immune cell populations and the impact of inflammation on fibroblast metabolism during the development of fibrosis. We also discuss how this knowledge could be leveraged to develop novel therapeutic strategies for treating fibrotic diseases.

The habitat plays a crucial role in ensuring the survival of wildlife. However, the increasing disturbances caused by human activities present a substantial threat to habitats, especially for species such as the leopard cat (Prionailurus bengalensis), which is a significant small predator. Currently, research on leopard cats predominantly focuses on low-altitude regions within its distribution range, leaving plateau areas understudied. To enhance our understanding of the impact of human disturbances on leopard cat habitats, we undertook a study employing infrared camera trappings to monitor leopard cats’ activity in Xinlong of southwestern China between 2015 and 2023. We analyzed the spatial distribution and habitat suitability of the leopard cats by utilizing ensemble species distribution models (ESDMs). Moreover, we employed two-species occupancy models to investigate the spatial interaction between leopard cats and human disturbances. The results indicated that (1) the potential suitable habitat area for leopard cats encompassed approximately 1324.93 km2 (14.3%), primarily located along the banks of Yalong river. (2) The distribution of suitable habitat was predominantly influenced by competitors, specifically the yellow-throated marten (YTM), accounting for 52.4% of the influence, as well as environmental factors such as distance to water (DTW) at 12.0% and terrain roughness index (TRI) at 10.0%. Human interference, including cattle presence (4.6%), distance to road (DTD, 4.9%), and distance to settlement (DTS, 3.5%), had a limited impact on the habitat distribution. (3) Within a 5 km radius, habitat suitability increased with proximity to human settlements. (4) Leopard cats exhibited spatial independence from humans and domestic cattle (species interaction factor (SIF) = 1.00) while avoiding domestic horses (SIF = 0.76 ± 0.03). The relatively minor impact of human disturbances in Xinlong could be attributed to traditional cultural practices safeguarding wildlife and the leopard cat’s environmental adaptability. We recommend establishing a novel conservation paradigm based on the living dynamics of wildlife communities in Xinlong, thereby offering a more targeted approach to biodiversity preservation in the future.

Satellite-based PM2.5 estimation is an effective means to achieve large-scale and long-term PM2.5 monitoring and investigation. Currently, most of methods retrieve PM2.5 from satellite-derived aerosol optical depth (AOD) or top-of-atmosphere reflectance (TOAR) during daytime. A few algorithms are also developed to retrieve nighttime PM2.5 from the satellite day–night band and the accuracy is greatly limited by moonlight and artificial light sources. In this study, we utilize the properties of absorption pollutants in infrared spectrum to estimate PM2.5 concentrations from satellite infrared data, thus achieve the PM2.5 estimation in both day and night. Himawari-8 infrared bands data are used for PM2.5 estimation by a specifically designed neural network and loss function. Quantitative results show the satellite derived PM2.5 concentrations correlates with ground-based data well with R2 of 0.79 and RMSE of 15.43 μg · m−3 for hourly PM2.5 estimation. Spatiotemporal distributions of model-estimated PM2.5 over China are also analyzed, and exhibit a highly consistent with ground-based measurements. Dust storms, heavy air pollution and fire smoke events are examined to further demonstrate the efficacy of our model. Our method not only circumvents the intermediate retrievals of AOD, but also enables consistent estimation of PM2.5 concentrations during daytime and nighttime in real-time monitoring.

Background:Dendrobium nobile (D. nobile), a traditional Chinese medicine, has received attention as an anti-tumor drug, but its mechanism is still unclear. In this study, we applied network pharmacology, bioinformatics, and in vitro experiments to explore the effect and mechanism of Dendrobin A, the active ingredient of D. nobile, against pancreatic ductal adenocarcinoma (PDAC).Methods: The databases of SwissTargetPrediction and PharmMapper were used to obtain the potential targets of Dendrobin A, and the differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. The protein-protein interaction (PPI) network for Dendrobin A anti-PDAC targets was constructed based on the STRING database. Molecular docking was used to assess Dendrobin A anti-PDAC targets. PLAU, one of the key targets of Dendrobin A anti-PDAC, was immunohistochemically stained in clinical tissue arrays. Finally, in vitro experiments were used to validate the effects of Dendrobin A on PLAU expression and the proliferation, apoptosis, cell cycle, migration, and invasion of PDAC cells.Results: A total of 90 genes for Dendrobin A anti-PDAC were screened, and a PPI network for Dendrobin A anti-PDAC targets was constructed. Notably, a scale-free module with 19 genes in the PPI indicated that the PPI is highly credible. Among these 19 genes, PLAU was positively correlated with the cachexia status while negatively correlated with the overall survival of PDAC patients. Through molecular docking, Dendrobin A was found to bind to PLAU, and the Dendrobin A treatment led to an attenuated PLAU expression in PDAC cells. Based on clinical tissue arrays, PLAU protein was highly expressed in PDAC cells compared to normal controls, and PLAU protein levels were associated with the differentiation and lymph node metastatic status of PDAC. In vitro experiments further showed that Dendrobin A treatment significantly inhibited the proliferation, migration, and invasion, inducing apoptosis and arresting the cell cycle of PDAC cells at the G2/M phase.Conclusion: Dendrobin A, a representative active ingredient of D. nobile, can effectively fight against PDAC by targeting PLAU. Our results provide the foundation for future PDAC treatment based on D. nobile.

Convolutional neural networks (CNNs) have been widely used in remote sensing (RS) scene classification tasks due to their remarkable feature representation and inference capability. The complexity of RS images not only brings the challenges of high inter-class similarity and large intra-class diversity, but also introduces the problem that category-relevant regions are insufficiently prominent in feature extraction. Siamese CNNs with feature similarity measurement are chosen in some applications to overcome the former issue, but most ignore the randomness of input sample pairs. This makes the Siamese CNNs not focus enough on challenging samples, which limits the training efficiency. We propose the focal cosine metric (FCM) block that combines the cosine similarity metric and the threshold control to achieve sample selection, thereby completing network learning more efficiently. FCM only permits the misclassified focal samples to participate in similarity measurement based on Siamese CNN. It flexibly mitigates the misclassification caused by the high inter-class similarity and large intra-class diversity. Moreover, the adaptive attention (AA) module is designed to stress the pivotal target regions and assist in the similarity measurement of Siamese CNN. This is realized by adaptively assigning high weights to key targets with learnable guided vectors. It enables the model to focus on the details of intra-class similarities or inter-class differences in sample pairs, and thus reduces the difficulty of model optimization. Encouraging experimental results on three public data sets demonstrate the effectiveness of the novel Siamese CNN-based method with FCM and AA and show its superiority compared to other state-of-the-art scene classification methods.

Large terrestrial carnivores play a crucial role in the top–down control of terrestrial ecosystems by maintaining ecosystem stability and biodiversity. However, intense interspecific competition typically occurs among large sympatric carnivores, leading to population reduction or extinction. Spatial partitioning through divergent habitat selection mitigates such competition. In this study, we analyzed the main environmental factors influencing the habitat selection and fragmentation of suitable habitats in Xinlong County, Sichuan Province, using 410 infrared cameras from 2015 to 2023. By employing generalized linear and maximum entropy models, we developed an ensemble model to predict the suitable habitat distribution of leopards (Panthera pardus) and wolves (Canis lupus). The results revealed significant disparities in suitable habitat distributions of leopards and wolves as coexisting large carnivores. Leopards prefer understory, whereas wolves prefer high-altitude meadows. Wolves spatially avoid leopards, who secure relatively superior resources and relegate wolves to inferior habitats. Although suitable habitat patches for both species cluster intensely, habitat connectivity remains low owing to pronounced anthropogenic disturbances, which is especially evident in the higher fragmentation of wolf habitats. These results suggest that sympatric large carnivores can reduce spatial competition intensity and promote spatial partitioning by selecting divergently suitable habitats, thereby facilitating species coexistence.

Pancreatic cancer (PC) is one of the most common human malignancies worldwide and remains a major clinical challenge. Here, we found that benproperine phosphate (BPP), a cough suppressant, showed a significant anticancer effect on PC both in vitro and in vivo via the induction of autophagy‐mediated cell death. Mechanistic studies revealed that BPP triggered AMPK/mTOR‐mediated autophagy initiation and disturbed Ras‐related protein Rab‐11A (RAB11A)‐mediated autophagosome–lysosome fusion, resulting in excessive accumulation of autophagosomes. Inhibition of autophagy or overexpression of RAB11A partially reversed BPP‐induced growth inhibition in PC cells, suggesting that BPP might induce lethal autophagy arrest in PC cells. In conclusion, our results identify BPP as a potent antitumor agent for PC via the induction of autophagy arrest, therefore providing a new potential therapeutic strategy for the treatment of PC.

Image dehazing algorithms based on dark channel prior principle have achieved good results for most scenes. However, the popular dark channel prior tends to underestimate transmissions of bright areas or objects, such as the skies, white areas and self-luminous bodies, which may cause color distortions during dehazing. A complementary prior called the extreme reflectance channel prior (ERC), which combines the dark channel prior with the bright channel prior, is proposed to estimate the transmission map. The extreme reflectance channel is the union of dark and bright channel’s pixels which satisfy the corresponding channel. Based on the scattering analysis results that the intensities of pixels in ERC are often close to 0 or 1 for the natural haze-free images or close to global atmospheric light if hazes occur in the air, the pixels in a hazy image can be recovered according to ERC to calculate the transmission map and then solve the haze imaging mode. Experiments show that ERC method outperforms state-of-the-art methods in PSNR, SSIM and visual perception effects.

Human papillomavirus (HPV) is a common virus, and about 5% of all cancers worldwide is caused by persistent high-risk HPV infections. Here, we reported a comprehensive analysis of the molecular features for HPV-related cancer types using TCGA (The Cancer Genome Atlas) data with HPV status. We found that the HPV-positive cancer patients had a unique oncogenic process, tumor microenvironment, and drug response compared with HPV-negative patients. In addition, HPV improved overall survival for the four cancer types, namely, cervical squamous cell carcinoma (CESC), head and neck squamous cell carcinoma (HNSC), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC). The stronger activity of cell-cycle pathways and lower driver gene mutation rates were observed in HPV-positive patients, which implied the different carcinogenic processes between HPV-positive and HPV-negative groups. The increased activities of immune cells and differences in metabolic pathways helped explain the heterogeneity of prognosis between the two groups. Furthermore, we constructed HPV prediction models for different cancers by the virus infection score (VIS) which was linearly correlated with HPV load and found that VIS was associated with drug response. Altogether, our study reveals that HPV-positive cancer patients have unique molecular characteristics which help the development of precision medicine in HPV-positive cancers.

Blind deconvolution is a highly ill-posed problem for the restoration of degraded images and requires prior knowledge or regularization. Recently, various priors have been proposed and the models based on these priors have achieved state-of-the-art performances. In this paper, we present a blind image deblurring method based on a computationally efficient and effective image regularizer. The proposed regularizer is motivated by the fact that the success of recent priors mainly stems from their properties, which implicitly generate an unnatural latent image suppressing insignificant structures and preserving only salient edges. These salient edges guide the models to estimate an accurate kernel. In this paper, the proposed regularizer termed smoothing-enhancing regularizer, not only assures that only salient structures in the image are preserved but also enhances these salient structures to help the model estimate the more accurate kernel. To efficiently solve the proposed model, we develop an efficient numerical approach based on the half-quadratic splitting algorithm and the lagged-fixed-point iteration scheme. The optimization scheme only requires a few additional shrinkage operations compared with the original half-quadratic splitting algorithm, making our method much faster than recent leading methods. The qualitative and quantitative experimental results show that our algorithm achieves the state-of-the-art results and can be extended to other challenging deblurring tasks, such as those involving text, face, and low-illuminated images. Furthermore, the proposed method is much more computationally efficient than the recent state-of-the-art algorithms with up to more than 10× faster execution time.

Remote sensing image scene classification (RSISC) plays a vital role in remote sensing applications. Recent methods based on convolutional neural networks (CNNs) have driven the development of RSISC. However, these approaches are not adequate considering the contributions of different features to the global decision. In this paper, triplet-metric-guided multi-scale attention (TMGMA) is proposed to enhance task-related salient features and suppress task-unrelated salient and redundant features. Firstly, we design the multi-scale attention module (MAM) guided by multi-scale feature maps to adaptively emphasize salient features and simultaneously fuse multi-scale and contextual information. Secondly, to capture task-related salient features, we use the triplet metric (TM) to optimize the learning of MAM under the constraint that the distance of the negative pair is supposed to be larger than the distance of the positive pair. Notably, the MAM and TM collaboration can enforce learning a more discriminative model. As such, our TMGMA can avoid the classification confusion caused by only using the attention mechanism and the excessive correction of features caused by only using the metric learning. Extensive experiments demonstrate that our TMGMA outperforms the ResNet50 baseline by 0.47% on the UC Merced, 1.46% on the AID, and 1.55% on the NWPU-RESISC45 dataset, respectively, and achieves performance that is competitive with other state-of-the-art methods.

Target detection in hyperspectral images (HSIs) aims to distinguish target pixels from the background using knowledge gleaned from prior spectra. Most traditional methods are based on certain assumptions and utilize handcrafted classifiers. These simple models and assumptions’ failure restrict the detection performance under complicated background interference. Recently, based on the convolutional networks, many supervised deep learning detectors have outperformed the traditional methods. However, these methods suffer from unstable detection, heavy computation burden, and optimization difficulty. This paper proposes a Siamese fully connected based target detector (SFCTD) that comprises nonlinear feature extraction modules (NFEMs) and cosine distance classifiers. Two NFEMs, which extract discriminative spectral features of input spectra-pairs, are based on fully connected layers for efficient computing and share the parameters to ease the optimization. To solve the few samples problem, we propose a pseudo data generation method based on the linear mixed model and the assumption that background pixels are dominant in HSIs. For mitigating the impact of stochastic suboptimal initialization, we parallelly optimize several Siamese detectors with small computation burdens and aggregate them as ensembles in the inference time. The network ensembles outperform every detector in terms of stability and achieve an outstanding balance between background suppression and detection rate. Experiments on multiple data sets demonstrate that the proposed detector is superior to the state-of-the-art detectors.