Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes...Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.展开更多
Regulated cell death is a form of cell death that is actively controlled by biomolecules.Several studies have shown that regulated cell death plays a key role after spinal cord injury.Pyroptosis and ferroptosis are ne...Regulated cell death is a form of cell death that is actively controlled by biomolecules.Several studies have shown that regulated cell death plays a key role after spinal cord injury.Pyroptosis and ferroptosis are newly discovered types of regulated cell deaths that have been shown to exacerbate inflammation and lead to cell death in damaged spinal cords.Autophagy,a complex form of cell death that is interconnected with various regulated cell death mechanisms,has garnered significant attention in the study of spinal cord injury.This injury triggers not only cell death but also cellular survival responses.Multiple signaling pathways play pivotal roles in influencing the processes of both deterioration and repair in spinal cord injury by regulating pyroptosis,ferroptosis,and autophagy.Therefore,this review aims to comprehensively examine the mechanisms underlying regulated cell deaths,the signaling pathways that modulate these mechanisms,and the potential therapeutic targets for spinal cord injury.Our analysis suggests that targeting the common regulatory signaling pathways of different regulated cell deaths could be a promising strategy to promote cell survival and enhance the repair of spinal cord injury.Moreover,a holistic approach that incorporates multiple regulated cell deaths and their regulatory pathways presents a promising multi-target therapeutic strategy for the management of spinal cord injury.展开更多
Neutrophils have emerged as key players in tumor progression and are often associated with poor prognosis.Despite ongoing efforts to target neutrophil functions in cancer,therapeutic success has been limited.In this s...Neutrophils have emerged as key players in tumor progression and are often associated with poor prognosis.Despite ongoing efforts to target neutrophil functions in cancer,therapeutic success has been limited.In this study,we addressed the possibility of blocking STAT3 signaling in neutrophils as a targeted therapeutic intervention in cancer.Conditional deletion of Stat3 in a neutrophil-specific manner(Ly6GcreStat3fl/fl mice)significantly impaired tumor growth and metastasis in mice.Neutrophils isolated from these mice exhibited a strong antitumoral phenotype,with increased MHCII,CD80/86 and ICAM-1 expression.Immune profiling of tumors and tumor-draining lymph nodes of these mice revealed significant enrichment of CD8^(+)T cells(granzymeB^(hi),perforin^(hi) and IFN-γ^(hi))with strong cytotoxic activity.To further translate these findings to human settings,we blocked STAT3 signaling in cancer patient neutrophils via the small molecule in^(hi)bitor LLL12 and assessed its effects on patient-derived tumor explants.In agreement with the in vivo mouse data,we observed the expansion and activation of cytotoxic CD8^(+)T cells in such explants.To test the therapeutic applicability of STAT3 targeting,we utilized myeloid cell-selective STAT3 antisense oligonucleotide(CpG-STAT3ASO)to target neutrophils in vivo in tumor-bearing mice.Consistent with previous results,neutrophil-specific STAT3 knockdown impaired tumor growth and enhanced cytotoxic T cell activity in the tumors and tumor-draining lymph nodes of treated mice.These findings highlight STAT3 signaling as a deleterious pathway supporting the protumoral activity of neutrophils and suggest that neutrophil-targeted STAT3 in^(hi)bition is a promising opportunity for cancer immunotherapy,providing novel insights into targeted therapeutic avenues.展开更多
As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the...As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.展开更多
Natural products(NPs)have historically been a fundamental source for drug discovery.Yet the complex nature of NPs presents substantial challenges in pinpointing bioactive constituents,and corresponding targets.In the ...Natural products(NPs)have historically been a fundamental source for drug discovery.Yet the complex nature of NPs presents substantial challenges in pinpointing bioactive constituents,and corresponding targets.In the present study,an innovative natural product virtual screening-interaction-phenotype(NP-VIP)strategy that integrates virtual screening,chemical proteomics,and metabolomics to identify and validate the bioactive targets of NPs.This approach reduces false positive results and enhances the efficiency of target identification.Salvia miltiorrhiza(SM),a herb with recognized therapeutic potential against ischemic stroke(IS),was used to illustrate the workflow.Utilizing virtual screening,chemical proteomics,and metabolomics,potential therapeutic targets for SM in the IS treatment were identified,totaling 29,100,and 78,respectively.Further analysis via the NP-VIP strategy highlighted five high-confidence targets,including poly[ADP-ribose]polymerase 1(PARP1),signal transducer and activator of transcription 3(STAT3),amyloid precursor protein(APP),glutamate-ammonia ligase(GLUL),and glutamate decarboxylase 67(GAD67).These targets were subsequently validated and found to play critical roles in the neuroprotective effects of SM.The study not only underscores the importance of SM in treating IS but also sets a precedent for NP research,proposing a comprehensive approach that could be adapted for broader pharmacological explorations.展开更多
Peripheral immunity forms the foundation of tumor immunity,while tumor immunity represents a more refined adaptation of peripheral immune responses.The tumor microenvironment(TME),a localized niche surrounding tumor c...Peripheral immunity forms the foundation of tumor immunity,while tumor immunity represents a more refined adaptation of peripheral immune responses.The tumor microenvironment(TME),a localized niche surrounding tumor cells,is inherently immunosuppressive(1,2).Effective tumor therapy necessitates the dismantling of this microenvironment,aiming to eradicate tumors from the host system.展开更多
Pancreatic cancer remains one of the most challenging malignancies to treat,with a poor prognosis and limited therapeutic options.Despite the success of immunotherapy and targeted therapies for other cancers,these app...Pancreatic cancer remains one of the most challenging malignancies to treat,with a poor prognosis and limited therapeutic options.Despite the success of immunotherapy and targeted therapies for other cancers,these approaches have not yet transformed the treatment landscape for pancreatic cancer.The unique tumor microenvironment(TME)of pancreatic cancer,characterized by dense fibrotic stroma and immunosuppressive myeloid cells,poses significant barriers to effective immunotherapy.Current research highlights the need for an in-depth understanding of the TME and the development of strategies to overcome its immunosuppressive properties.Recent studies have explored various immunotherapeutic approaches,including immune checkpoint inhibitors,cancer vaccines,and adoptive cell therapies,some of which have shown promising results in preclinical and early clinical trials.Furthermore,combining immunotherapy with traditional treatments,such as chemotherapy and radiotherapy,has shown potential for enhancing antitumor efficacy,although targeted therapies for pancreatic cancer are still in their early stages and are being investigated for their ability to disrupt specific molecular pathways involved in tumor growth and survival.This review provides a comprehensive overview of the advances in immunotherapy and targeted therapies for pancreatic cancer,discussing the current state of research,clinical outcomes,and future directions for improving patient prognosis.展开更多
Chronic atrophic gastritis(CAG)is an important stage of precancerous lesions of gastric cancer.Effective treatment and regulation of CAG are essential to prevent its progression to malignancy.Traditional Chinese medic...Chronic atrophic gastritis(CAG)is an important stage of precancerous lesions of gastric cancer.Effective treatment and regulation of CAG are essential to prevent its progression to malignancy.Traditional Chinese medicine(TCM)has shown multi-targeted efficacy in CAG treatment,with advantages in enhancing gastric mucosal barrier defense,improving microcirculation,modulating inflammatory and immune responses,and promoting lesion healing,etc.Clinical studies and meta-analyses indicate that TCM provides significant benefits,with specific Chinese herbal compounds and monomers demonstrating protective effects on the gastric mucosa through mechanisms including anti-inflammation,antioxidation,and regulation of cellular proliferation and apoptosis,etc.Finally,it is pointed out that the efficacy of TCM in the treatment of CAG requires standardized research and unified standards,and constantly clarifies and improves the evaluation criteria of each dimension of gastric mucosal barrier function.展开更多
An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyram...An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.展开更多
Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power sta...Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.展开更多
Metal complexes hold significant promise in tumor diagnosis and treatment.However,their potential applications in photodynamic therapy(PDT)are hindered by issues such as poor photostability,low yield of reactive oxyge...Metal complexes hold significant promise in tumor diagnosis and treatment.However,their potential applications in photodynamic therapy(PDT)are hindered by issues such as poor photostability,low yield of reactive oxygen species(ROS),and aggregation-induced ROS quenching.To address these challenges,we present a molecular self-assembly strategy utilizing aggregation-induced emission(AIE)conjugates for metal complexes.As a proof of concept,we synthesized a mitochondrial-targeting cyclometalated Ir(Ⅲ)photosensitizer Ir-TPE.This approach significantly enhances the photodynamic effect while mitigating the dark toxicity associated with AIE groups.Ir-TPE readily self-assembles into nanoaggregates in aqueous solution,leading to a significant production of ROS upon light irradiation.Photoirradiated Ir-TPE triggers multiple modes of death by excessively accumulating ROS in the mitochondria,resulting in mitochondrial DNA damage.This damage can lead to ferroptosis and autophagy,two forms of cell death that are highly cytotoxic to cancer cells.The aggregation-enhanced photodynamic effect of Ir-TPE significantly enhances the production of ROS,leading to a more pronounced cytotoxic effect.In vitro and in vivo experiments demonstrate this aggregation-enhanced PDT approach achieves effective in situ tumor eradication.This study not only addresses the limitations of metal complexes in terms of low ROS production due to aggregation but also highlights the potential of this strategy for enhancing ROS production in PDT.展开更多
Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models...Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion,their rigid architectures struggle with multi-scale adaptability,as exemplified by YOLOv8n’s 36.4%mAP and 13.9%small-object AP on VisDrone2019.This paper presents YOLO-LE,a lightweight framework addressing these limitations through three novel designs:(1)We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters,thereby improving model efficiency.(2)An adaptive feature fusion module is designed to dynamically integrate multi-scale feature maps,optimizing the neck structure,reducing neck complexity,and enhancing overall model performance.(3)We replace the original loss function with a distributed focal loss and incorporate a lightweight self-attention mechanism to improve small-object recognition and bounding box regression accuracy.Experimental results demonstrate that YOLO-LE achieves 39.9%mAP@0.5 on VisDrone2019,representing a 9.6%improvement over YOLOv8n,while maintaining 8.5 GFLOPs computational efficiency.This provides an efficient solution for UAV object detection in complex scenarios.展开更多
This paper proposes that China,under the challenge of balancing its development and security,can aim for the Paris Agreement's goal to limit global warming to no more than 2℃by actively seeking carbonpeak and car...This paper proposes that China,under the challenge of balancing its development and security,can aim for the Paris Agreement's goal to limit global warming to no more than 2℃by actively seeking carbonpeak and carbon-neutrality pathways that align with China's national conditions,rather than following the idealized path toward the 1.5℃target by initially relying on extensive negative-emission technologies such as direct air carbon capture and storage(DACCS).This work suggests that pursuing a 1.5℃target is increasingly less feasible for China,as it would potentially incur 3–4 times the cost of pursuing the 2℃target.With China being likely to achieve a peak in its emissions around 2028,at about 12.8 billion tonnes of anthropogenic carbon dioxide(CO_(2)),and become carbon neutral,projected global warming levels may be less severe after the 2050s than previously estimated.This could reduce the risk potential of climate tipping points and extreme events,especially considering that the other two major carbon emitters in the world(Europe and North America)have already passed their carbon peaks.While natural carbon sinks will contribute to China's carbon neutrality efforts,they are not expected to be decisive in the transition stages.This research also addresses the growing focus on climate overshoot,tipping points,extreme events,loss and damage,and methane reductions in international climate cooperation,emphasizing the need to balance these issues with China's development,security,and fairness considerations.China's pursuit of carbon neutrality will have significant implications for global emissions scenarios,warming levels,and extreme event projections,as well as for climate change hotspots of international concern,such as climate tipping points,the climate crisis,and the notion that the world has moved from a warming to a boiling era.Possible research recommendations for global emissions scenarios based on China's 2℃target pathway are also summarized.展开更多
With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions...With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.展开更多
Underwater target detection is extensively applied in domains such as underwater search and rescue,environmental monitoring,and marine resource surveys.It is crucial in enabling autonomous underwater robot operations ...Underwater target detection is extensively applied in domains such as underwater search and rescue,environmental monitoring,and marine resource surveys.It is crucial in enabling autonomous underwater robot operations and promoting ocean exploration.Nevertheless,low imaging quality,harsh underwater environments,and obscured objects considerably increase the difficulty of detecting underwater targets,making it difficult for current detection methods to achieve optimal performance.In order to enhance underwater object perception and improve target detection precision,we propose a lightweight underwater target detection method using You Only Look Once(YOLO)v8 with multi-scale cross-channel attention(MSCCA),named YOLOv8-UOD.In the proposed multiscale cross-channel attention module,multi-scale attention(MSA)augments the variety of attentional perception by extracting information from innately diverse sensory fields.The cross-channel strategy utilizes RepVGGbased channel shuffling(RCS)and one-shot aggregation(OSA)to rearrange feature map channels according to specific rules.It aggregates all features only once in the final feature mapping,resulting in the extraction of more comprehensive and valuable feature information.The experimental results show that the proposed YOLOv8-UOD achieves a mAP50 of 95.67%and FLOPs of 23.8 G on the Underwater Robot Picking Contest 2017(URPC2017)dataset,outperforming other methods in terms of detection precision and computational cost-efficiency.展开更多
Obesity has become a global threat to health;however,the available drugs for treating obesity are limited.We investigated the anti-obesity effect of hydroxy-α-sanshool(HAS),an amide derived from the fruit of Zanthoxy...Obesity has become a global threat to health;however,the available drugs for treating obesity are limited.We investigated the anti-obesity effect of hydroxy-α-sanshool(HAS),an amide derived from the fruit of Zanthoxylum bungeanum,which promotes the management of obesity by triggering the browning of white adipose tissue(WAT)targeting the membrane receptor of transient receptor potential vanilloid 1(TRPV1).However,HAS easily undergoes configuration transformation and oxidative degradation.The short peptide CKGGRAKDC or adipose-targeting sequence(ATS)binds specifically to prohibitin on the surface of WAT cells and can be used as recognition assembly to enhance adipocyte targetability.Furthermore,mesoporous silica nanoparticles(MSNs)are widely used in drug delivery systems because of their large specific surface area and pore volume.Therefore,HAS-loaded adipose-targeted MSNs(MSNs-ATS)were developed to enhance the adipocyte targetability,safety,and efficacy of HAS,and tested on mature 3T3-L1 cells and obese mouse models.MSNs-ATS showed higher specificity for adipocyte targetability without obvious toxicity.HAS-loaded MSNs-ATS showed anti-obesity effects superior to those of HAS alone.In conclusion,we successfully developed adipocyte-targeted,HAS-loaded MSNs with good safety and anti-obesity effects.展开更多
It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sens...It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sensor Networks(UWSNs).To this end,this paper investigates the relationship between the Degree of Target Change(DoTC)and the detection period,as well as the impact of individual nodes.A Hierarchical Detection and Tracking Approach(HDTA)is proposed.Firstly,the network detection period is determined according to DoTC,which reflects the variation of target motion.Secondly,during the network detection period,each detection node calculates its own node detection period based on the detection mutual information.Taking DoTC as pheromone,an ant colony algorithm is proposed to adaptively adjust the network detection period.The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25%and the network energy consumption by 10%simultaneously,compared to the traditional adaptive period detection schemes.展开更多
This article provides a comprehensive review of various approaches to targeted drug delivery for liver cancer, an area of significant need due to the limited effectiveness of current treatments. The article begins by ...This article provides a comprehensive review of various approaches to targeted drug delivery for liver cancer, an area of significant need due to the limited effectiveness of current treatments. The article begins by highlighting the role of the liver in metabolism and discusses the high mortality associated with hepatocellular carcinoma (HCC). The shortcomings of traditional chemotherapy, such as multidrug resistance and off-target effects, necessitate the exploration of novel therapeutic strategies, with a focus on targeted approaches. The review details both passive and active targeting strategies. Passive targeting leverages the enhanced permeability and retention (EPR) effect and unique features of the tumor microenvironment, while active targeting employs specific ligands, such as peptides, antibodies, and proteins, to bind to overexpressed receptors on liver and tumor cells. The article further details many examples of active targeting using the asialoglycoprotein receptor (ASGPR), glycyrrhetinic acid (GA), transferrin receptor (TfR), and folate receptor (FR) on hepatocytes and tumor cells, demonstrating that there has been significant research effort put into this field. The importance of non-parenchymal cells in the liver is also discussed, and the article examines methods of targeting Kupffer cells, sinusoidal endothelial cells, and hepatic stellate cells for therapeutic benefit. The review goes on to cover the emerging field of subcellular targeting, including specific strategies to target the nucleus, mitochondria, and the endoplasmic reticulum/Golgi apparatus, noting that although there has been some progress, further research is needed in this area. The text finishes with a summary which acknowledges that while targeted therapies, including enzyme-activated prodrugs, such as Pradefovir, and other novel methods for drug delivery have shown significant promise, challenges remain in translating these therapies into clinical use due to limitations in understanding the sequential transport and the mechanisms of action. Ultimately, the article emphasizes the need for in-depth research to fully realize the potential of precision cancer therapies for liver cancer.展开更多
The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on...The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on the YOLOv8 model was proposed in this paper.Firstly,the Global Attention Module(GAM)was introduced to enhance data prediction capability and model expression ability.Secondly,the Space-to-Depth(SPD)module was incorporated into the backbone network for fine-grained feature information learning to mitigate feature information loss due to down-sampling.Finally,a 160 pixels×160 pixels feature layer was added to expand small target feature information and effectively reduce instances of missed targets.Experimental validation on the public VisDrone2019 UAV small target detaset demonstrated that the proposed model achieves significant performance improvement in small target detection tasks compared to existing models,exhibiting higher accuracy.展开更多
基金supported by grants from the Natural Science Foundation of Tianjin(General Program),Nos.23JCYBJC01390(to RL),22JCYBJC00220(to XC),and 22JCYBJC00210(to QL).
文摘Peripheral nerve injury is a common neurological condition that often leads to severe functional limitations and disabilities.Research on the pathogenesis of peripheral nerve injury has focused on pathological changes at individual injury sites,neglecting multilevel pathological analysis of the overall nervous system and target organs.This has led to restrictions on current therapeutic approaches.In this paper,we first summarize the potential mechanisms of peripheral nerve injury from a holistic perspective,covering the central nervous system,peripheral nervous system,and target organs.After peripheral nerve injury,the cortical plasticity of the brain is altered due to damage to and regeneration of peripheral nerves;changes such as neuronal apoptosis and axonal demyelination occur in the spinal cord.The nerve will undergo axonal regeneration,activation of Schwann cells,inflammatory response,and vascular system regeneration at the injury site.Corresponding damage to target organs can occur,including skeletal muscle atrophy and sensory receptor disruption.We then provide a brief review of the research advances in therapeutic approaches to peripheral nerve injury.The main current treatments are conducted passively and include physical factor rehabilitation,pharmacological treatments,cell-based therapies,and physical exercise.However,most treatments only partially address the problem and cannot complete the systematic recovery of the entire central nervous system-peripheral nervous system-target organ pathway.Therefore,we should further explore multilevel treatment options that produce effective,long-lasting results,perhaps requiring a combination of passive(traditional)and active(novel)treatment methods to stimulate rehabilitation at the central-peripheral-target organ levels to achieve better functional recovery.
基金supported by the Natural Science Foundation of Fujian Province,No.2021J02035(to WX).
文摘Regulated cell death is a form of cell death that is actively controlled by biomolecules.Several studies have shown that regulated cell death plays a key role after spinal cord injury.Pyroptosis and ferroptosis are newly discovered types of regulated cell deaths that have been shown to exacerbate inflammation and lead to cell death in damaged spinal cords.Autophagy,a complex form of cell death that is interconnected with various regulated cell death mechanisms,has garnered significant attention in the study of spinal cord injury.This injury triggers not only cell death but also cellular survival responses.Multiple signaling pathways play pivotal roles in influencing the processes of both deterioration and repair in spinal cord injury by regulating pyroptosis,ferroptosis,and autophagy.Therefore,this review aims to comprehensively examine the mechanisms underlying regulated cell deaths,the signaling pathways that modulate these mechanisms,and the potential therapeutic targets for spinal cord injury.Our analysis suggests that targeting the common regulatory signaling pathways of different regulated cell deaths could be a promising strategy to promote cell survival and enhance the repair of spinal cord injury.Moreover,a holistic approach that incorporates multiple regulated cell deaths and their regulatory pathways presents a promising multi-target therapeutic strategy for the management of spinal cord injury.
基金support from the Open Access Publication Fund of the University of Duisburg-Essensupported by the Deutsche Forschungsgemeinschaft(DFG/JA-2461/7-1)+1 种基金CRC TRR332 project A05 to JJthe Stiftung Tumorforschung Kopf-Hals to CK.
文摘Neutrophils have emerged as key players in tumor progression and are often associated with poor prognosis.Despite ongoing efforts to target neutrophil functions in cancer,therapeutic success has been limited.In this study,we addressed the possibility of blocking STAT3 signaling in neutrophils as a targeted therapeutic intervention in cancer.Conditional deletion of Stat3 in a neutrophil-specific manner(Ly6GcreStat3fl/fl mice)significantly impaired tumor growth and metastasis in mice.Neutrophils isolated from these mice exhibited a strong antitumoral phenotype,with increased MHCII,CD80/86 and ICAM-1 expression.Immune profiling of tumors and tumor-draining lymph nodes of these mice revealed significant enrichment of CD8^(+)T cells(granzymeB^(hi),perforin^(hi) and IFN-γ^(hi))with strong cytotoxic activity.To further translate these findings to human settings,we blocked STAT3 signaling in cancer patient neutrophils via the small molecule in^(hi)bitor LLL12 and assessed its effects on patient-derived tumor explants.In agreement with the in vivo mouse data,we observed the expansion and activation of cytotoxic CD8^(+)T cells in such explants.To test the therapeutic applicability of STAT3 targeting,we utilized myeloid cell-selective STAT3 antisense oligonucleotide(CpG-STAT3ASO)to target neutrophils in vivo in tumor-bearing mice.Consistent with previous results,neutrophil-specific STAT3 knockdown impaired tumor growth and enhanced cytotoxic T cell activity in the tumors and tumor-draining lymph nodes of treated mice.These findings highlight STAT3 signaling as a deleterious pathway supporting the protumoral activity of neutrophils and suggest that neutrophil-targeted STAT3 in^(hi)bition is a promising opportunity for cancer immunotherapy,providing novel insights into targeted therapeutic avenues.
基金funded by the Fundamental Research Funds for the Central Universities(J2023-024,J2023-027).
文摘As an important complement to cloud computing, edge computing can effectively reduce the workload of the backbone network. To reduce latency and energy consumption of edge computing, deep learning is used to learn the task offloading strategies by interacting with the entities. In actual application scenarios, users of edge computing are always changing dynamically. However, the existing task offloading strategies cannot be applied to such dynamic scenarios. To solve this problem, we propose a novel dynamic task offloading framework for distributed edge computing, leveraging the potential of meta-reinforcement learning (MRL). Our approach formulates a multi-objective optimization problem aimed at minimizing both delay and energy consumption. We model the task offloading strategy using a directed acyclic graph (DAG). Furthermore, we propose a distributed edge computing adaptive task offloading algorithm rooted in MRL. This algorithm integrates multiple Markov decision processes (MDP) with a sequence-to-sequence (seq2seq) network, enabling it to learn and adapt task offloading strategies responsively across diverse network environments. To achieve joint optimization of delay and energy consumption, we incorporate the non-dominated sorting genetic algorithm II (NSGA-II) into our framework. Simulation results demonstrate the superiority of our proposed solution, achieving a 21% reduction in time delay and a 19% decrease in energy consumption compared to alternative task offloading schemes. Moreover, our scheme exhibits remarkable adaptability, responding swiftly to changes in various network environments.
基金supported by the National Natural Science Foundations of China(Grant No.:82204584)Liaoning Provincial Science and Technology Projects,China(Project No.:2021JH1/10400055).
文摘Natural products(NPs)have historically been a fundamental source for drug discovery.Yet the complex nature of NPs presents substantial challenges in pinpointing bioactive constituents,and corresponding targets.In the present study,an innovative natural product virtual screening-interaction-phenotype(NP-VIP)strategy that integrates virtual screening,chemical proteomics,and metabolomics to identify and validate the bioactive targets of NPs.This approach reduces false positive results and enhances the efficiency of target identification.Salvia miltiorrhiza(SM),a herb with recognized therapeutic potential against ischemic stroke(IS),was used to illustrate the workflow.Utilizing virtual screening,chemical proteomics,and metabolomics,potential therapeutic targets for SM in the IS treatment were identified,totaling 29,100,and 78,respectively.Further analysis via the NP-VIP strategy highlighted five high-confidence targets,including poly[ADP-ribose]polymerase 1(PARP1),signal transducer and activator of transcription 3(STAT3),amyloid precursor protein(APP),glutamate-ammonia ligase(GLUL),and glutamate decarboxylase 67(GAD67).These targets were subsequently validated and found to play critical roles in the neuroprotective effects of SM.The study not only underscores the importance of SM in treating IS but also sets a precedent for NP research,proposing a comprehensive approach that could be adapted for broader pharmacological explorations.
文摘Peripheral immunity forms the foundation of tumor immunity,while tumor immunity represents a more refined adaptation of peripheral immune responses.The tumor microenvironment(TME),a localized niche surrounding tumor cells,is inherently immunosuppressive(1,2).Effective tumor therapy necessitates the dismantling of this microenvironment,aiming to eradicate tumors from the host system.
文摘Pancreatic cancer remains one of the most challenging malignancies to treat,with a poor prognosis and limited therapeutic options.Despite the success of immunotherapy and targeted therapies for other cancers,these approaches have not yet transformed the treatment landscape for pancreatic cancer.The unique tumor microenvironment(TME)of pancreatic cancer,characterized by dense fibrotic stroma and immunosuppressive myeloid cells,poses significant barriers to effective immunotherapy.Current research highlights the need for an in-depth understanding of the TME and the development of strategies to overcome its immunosuppressive properties.Recent studies have explored various immunotherapeutic approaches,including immune checkpoint inhibitors,cancer vaccines,and adoptive cell therapies,some of which have shown promising results in preclinical and early clinical trials.Furthermore,combining immunotherapy with traditional treatments,such as chemotherapy and radiotherapy,has shown potential for enhancing antitumor efficacy,although targeted therapies for pancreatic cancer are still in their early stages and are being investigated for their ability to disrupt specific molecular pathways involved in tumor growth and survival.This review provides a comprehensive overview of the advances in immunotherapy and targeted therapies for pancreatic cancer,discussing the current state of research,clinical outcomes,and future directions for improving patient prognosis.
基金Supported by the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences,No.CI2021A00806High Level Chinese Medical Hospital Promotion Project,No.HLCMHPP2023086the Fundamental Research Funds for the Central Public Welfare Research Institutes,No.ZZ17-XRZ-041.
文摘Chronic atrophic gastritis(CAG)is an important stage of precancerous lesions of gastric cancer.Effective treatment and regulation of CAG are essential to prevent its progression to malignancy.Traditional Chinese medicine(TCM)has shown multi-targeted efficacy in CAG treatment,with advantages in enhancing gastric mucosal barrier defense,improving microcirculation,modulating inflammatory and immune responses,and promoting lesion healing,etc.Clinical studies and meta-analyses indicate that TCM provides significant benefits,with specific Chinese herbal compounds and monomers demonstrating protective effects on the gastric mucosa through mechanisms including anti-inflammation,antioxidation,and regulation of cellular proliferation and apoptosis,etc.Finally,it is pointed out that the efficacy of TCM in the treatment of CAG requires standardized research and unified standards,and constantly clarifies and improves the evaluation criteria of each dimension of gastric mucosal barrier function.
基金supported by the National Natural Science Foundation of China(No.62241109)the Tianjin Science and Technology Commissioner Project(No.20YDTPJC01110)。
文摘An improved model based on you only look once version 8(YOLOv8)is proposed to solve the problem of low detection accuracy due to the diversity of object sizes in optical remote sensing images.Firstly,the feature pyramid network(FPN)structure of the original YOLOv8 mode is replaced by the generalized-FPN(GFPN)structure in GiraffeDet to realize the"cross-layer"and"cross-scale"adaptive feature fusion,to enrich the semantic information and spatial information on the feature map to improve the target detection ability of the model.Secondly,a pyramid-pool module of multi atrous spatial pyramid pooling(MASPP)is designed by using the idea of atrous convolution and feature pyramid structure to extract multi-scale features,so as to improve the processing ability of the model for multi-scale objects.The experimental results show that the detection accuracy of the improved YOLOv8 model on DIOR dataset is 92%and mean average precision(mAP)is 87.9%,respectively 3.5%and 1.7%higher than those of the original model.It is proved the detection and classification ability of the proposed model on multi-dimensional optical remote sensing target has been improved.
基金supported in part by the National Natural Science Foundation of China under Grant No.61473066in part by the Natural Science Foundation of Hebei Province under Grant No.F2021501020+2 种基金in part by the S&T Program of Qinhuangdao under Grant No.202401A195in part by the Science Research Project of Hebei Education Department under Grant No.QN2025008in part by the Innovation Capability Improvement Plan Project of Hebei Province under Grant No.22567637H
文摘Recently,one of the main challenges facing the smart grid is insufficient computing resources and intermittent energy supply for various distributed components(such as monitoring systems for renewable energy power stations).To solve the problem,we propose an energy harvesting based task scheduling and resource management framework to provide robust and low-cost edge computing services for smart grid.First,we formulate an energy consumption minimization problem with regard to task offloading,time switching,and resource allocation for mobile devices,which can be decoupled and transformed into a typical knapsack problem.Then,solutions are derived by two different algorithms.Furthermore,we deploy renewable energy and energy storage units at edge servers to tackle intermittency and instability problems.Finally,we design an energy management algorithm based on sampling average approximation for edge computing servers to derive the optimal charging/discharging strategies,number of energy storage units,and renewable energy utilization.The simulation results show the efficiency and superiority of our proposed framework.
基金support from the National Natural Science Foundation of China(Nos.22277056,21977052)the Distinguished Young Scholars of Jiangsu Province(No.BK20230006)+2 种基金the Natural Science Foundation of Jiangsu Province(Nos.BK20230977,BK20231090)the Natural Science Foundation of the Higher Education Institutions of Jiangsu Province(No.23KJB150020)the Jiangsu Excellent Postdoctoral Program(No.2022ZB758)。
文摘Metal complexes hold significant promise in tumor diagnosis and treatment.However,their potential applications in photodynamic therapy(PDT)are hindered by issues such as poor photostability,low yield of reactive oxygen species(ROS),and aggregation-induced ROS quenching.To address these challenges,we present a molecular self-assembly strategy utilizing aggregation-induced emission(AIE)conjugates for metal complexes.As a proof of concept,we synthesized a mitochondrial-targeting cyclometalated Ir(Ⅲ)photosensitizer Ir-TPE.This approach significantly enhances the photodynamic effect while mitigating the dark toxicity associated with AIE groups.Ir-TPE readily self-assembles into nanoaggregates in aqueous solution,leading to a significant production of ROS upon light irradiation.Photoirradiated Ir-TPE triggers multiple modes of death by excessively accumulating ROS in the mitochondria,resulting in mitochondrial DNA damage.This damage can lead to ferroptosis and autophagy,two forms of cell death that are highly cytotoxic to cancer cells.The aggregation-enhanced photodynamic effect of Ir-TPE significantly enhances the production of ROS,leading to a more pronounced cytotoxic effect.In vitro and in vivo experiments demonstrate this aggregation-enhanced PDT approach achieves effective in situ tumor eradication.This study not only addresses the limitations of metal complexes in terms of low ROS production due to aggregation but also highlights the potential of this strategy for enhancing ROS production in PDT.
文摘Unmanned aerial vehicle(UAV)imagery poses significant challenges for object detection due to extreme scale variations,high-density small targets(68%in VisDrone dataset),and complex backgrounds.While YOLO-series models achieve speed-accuracy trade-offs via fixed convolution kernels and manual feature fusion,their rigid architectures struggle with multi-scale adaptability,as exemplified by YOLOv8n’s 36.4%mAP and 13.9%small-object AP on VisDrone2019.This paper presents YOLO-LE,a lightweight framework addressing these limitations through three novel designs:(1)We introduce the C2f-Dy and LDown modules to enhance the backbone’s sensitivity to small-object features while reducing backbone parameters,thereby improving model efficiency.(2)An adaptive feature fusion module is designed to dynamically integrate multi-scale feature maps,optimizing the neck structure,reducing neck complexity,and enhancing overall model performance.(3)We replace the original loss function with a distributed focal loss and incorporate a lightweight self-attention mechanism to improve small-object recognition and bounding box regression accuracy.Experimental results demonstrate that YOLO-LE achieves 39.9%mAP@0.5 on VisDrone2019,representing a 9.6%improvement over YOLOv8n,while maintaining 8.5 GFLOPs computational efficiency.This provides an efficient solution for UAV object detection in complex scenarios.
基金supported by the top-level design of the National Natural Science Foundation of China(NSFC)Major Project“Realization of optimal carbon neutral pathway and coupling of multi-scale interaction patterns of natural-social systems in China”(42341202)the Basic Scientific Research Fund of the Chinese Academy of Meteorological Sciences(2021Z014)。
文摘This paper proposes that China,under the challenge of balancing its development and security,can aim for the Paris Agreement's goal to limit global warming to no more than 2℃by actively seeking carbonpeak and carbon-neutrality pathways that align with China's national conditions,rather than following the idealized path toward the 1.5℃target by initially relying on extensive negative-emission technologies such as direct air carbon capture and storage(DACCS).This work suggests that pursuing a 1.5℃target is increasingly less feasible for China,as it would potentially incur 3–4 times the cost of pursuing the 2℃target.With China being likely to achieve a peak in its emissions around 2028,at about 12.8 billion tonnes of anthropogenic carbon dioxide(CO_(2)),and become carbon neutral,projected global warming levels may be less severe after the 2050s than previously estimated.This could reduce the risk potential of climate tipping points and extreme events,especially considering that the other two major carbon emitters in the world(Europe and North America)have already passed their carbon peaks.While natural carbon sinks will contribute to China's carbon neutrality efforts,they are not expected to be decisive in the transition stages.This research also addresses the growing focus on climate overshoot,tipping points,extreme events,loss and damage,and methane reductions in international climate cooperation,emphasizing the need to balance these issues with China's development,security,and fairness considerations.China's pursuit of carbon neutrality will have significant implications for global emissions scenarios,warming levels,and extreme event projections,as well as for climate change hotspots of international concern,such as climate tipping points,the climate crisis,and the notion that the world has moved from a warming to a boiling era.Possible research recommendations for global emissions scenarios based on China's 2℃target pathway are also summarized.
文摘With the rapid expansion of social media,analyzing emotions and their causes in texts has gained significant importance.Emotion-cause pair extraction enables the identification of causal relationships between emotions and their triggers within a text,facilitating a deeper understanding of expressed sentiments and their underlying reasons.This comprehension is crucial for making informed strategic decisions in various business and societal contexts.However,recent research approaches employing multi-task learning frameworks for modeling often face challenges such as the inability to simultaneouslymodel extracted features and their interactions,or inconsistencies in label prediction between emotion-cause pair extraction and independent assistant tasks like emotion and cause extraction.To address these issues,this study proposes an emotion-cause pair extraction methodology that incorporates joint feature encoding and task alignment mechanisms.The model consists of two primary components:First,joint feature encoding simultaneously generates features for emotion-cause pairs and clauses,enhancing feature interactions between emotion clauses,cause clauses,and emotion-cause pairs.Second,the task alignment technique is applied to reduce the labeling distance between emotion-cause pair extraction and the two assistant tasks,capturing deep semantic information interactions among tasks.The proposed method is evaluated on a Chinese benchmark corpus using 10-fold cross-validation,assessing key performance metrics such as precision,recall,and F1 score.Experimental results demonstrate that the model achieves an F1 score of 76.05%,surpassing the state-of-the-art by 1.03%.The proposed model exhibits significant improvements in emotion-cause pair extraction(ECPE)and cause extraction(CE)compared to existing methods,validating its effectiveness.This research introduces a novel approach based on joint feature encoding and task alignment mechanisms,contributing to advancements in emotion-cause pair extraction.However,the study’s limitation lies in the data sources,potentially restricting the generalizability of the findings.
基金supported in part by the National Natural Science Foundation of China Grants 62402085,61972062,62306060the Liaoning Doctoral Research Start-Up Fund 2023-BS-078+1 种基金the Dalian Youth Science and Technology Star Project 2023RQ023the Liaoning Basic Research Project 2023JH2/101300191.
文摘Underwater target detection is extensively applied in domains such as underwater search and rescue,environmental monitoring,and marine resource surveys.It is crucial in enabling autonomous underwater robot operations and promoting ocean exploration.Nevertheless,low imaging quality,harsh underwater environments,and obscured objects considerably increase the difficulty of detecting underwater targets,making it difficult for current detection methods to achieve optimal performance.In order to enhance underwater object perception and improve target detection precision,we propose a lightweight underwater target detection method using You Only Look Once(YOLO)v8 with multi-scale cross-channel attention(MSCCA),named YOLOv8-UOD.In the proposed multiscale cross-channel attention module,multi-scale attention(MSA)augments the variety of attentional perception by extracting information from innately diverse sensory fields.The cross-channel strategy utilizes RepVGGbased channel shuffling(RCS)and one-shot aggregation(OSA)to rearrange feature map channels according to specific rules.It aggregates all features only once in the final feature mapping,resulting in the extraction of more comprehensive and valuable feature information.The experimental results show that the proposed YOLOv8-UOD achieves a mAP50 of 95.67%and FLOPs of 23.8 G on the Underwater Robot Picking Contest 2017(URPC2017)dataset,outperforming other methods in terms of detection precision and computational cost-efficiency.
基金supported by the Natural Science Foundation of Sichuan Province(No.2022NSFSC0720)Research Center for the Development of the Comprehensive Health Industry and Rural Revitalization of Sichuan TCM(No.DJKYB202306)State Administration of Traditional Chinese Medicine of Sichuan Province of China(No.2020HJZX001).
文摘Obesity has become a global threat to health;however,the available drugs for treating obesity are limited.We investigated the anti-obesity effect of hydroxy-α-sanshool(HAS),an amide derived from the fruit of Zanthoxylum bungeanum,which promotes the management of obesity by triggering the browning of white adipose tissue(WAT)targeting the membrane receptor of transient receptor potential vanilloid 1(TRPV1).However,HAS easily undergoes configuration transformation and oxidative degradation.The short peptide CKGGRAKDC or adipose-targeting sequence(ATS)binds specifically to prohibitin on the surface of WAT cells and can be used as recognition assembly to enhance adipocyte targetability.Furthermore,mesoporous silica nanoparticles(MSNs)are widely used in drug delivery systems because of their large specific surface area and pore volume.Therefore,HAS-loaded adipose-targeted MSNs(MSNs-ATS)were developed to enhance the adipocyte targetability,safety,and efficacy of HAS,and tested on mature 3T3-L1 cells and obese mouse models.MSNs-ATS showed higher specificity for adipocyte targetability without obvious toxicity.HAS-loaded MSNs-ATS showed anti-obesity effects superior to those of HAS alone.In conclusion,we successfully developed adipocyte-targeted,HAS-loaded MSNs with good safety and anti-obesity effects.
文摘It is difficult to improve both energy consumption and detection accuracy simultaneously,and even to obtain the trade-off between them,when detecting and tracking moving targets,especially for Underwater Wireless Sensor Networks(UWSNs).To this end,this paper investigates the relationship between the Degree of Target Change(DoTC)and the detection period,as well as the impact of individual nodes.A Hierarchical Detection and Tracking Approach(HDTA)is proposed.Firstly,the network detection period is determined according to DoTC,which reflects the variation of target motion.Secondly,during the network detection period,each detection node calculates its own node detection period based on the detection mutual information.Taking DoTC as pheromone,an ant colony algorithm is proposed to adaptively adjust the network detection period.The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25%and the network energy consumption by 10%simultaneously,compared to the traditional adaptive period detection schemes.
文摘This article provides a comprehensive review of various approaches to targeted drug delivery for liver cancer, an area of significant need due to the limited effectiveness of current treatments. The article begins by highlighting the role of the liver in metabolism and discusses the high mortality associated with hepatocellular carcinoma (HCC). The shortcomings of traditional chemotherapy, such as multidrug resistance and off-target effects, necessitate the exploration of novel therapeutic strategies, with a focus on targeted approaches. The review details both passive and active targeting strategies. Passive targeting leverages the enhanced permeability and retention (EPR) effect and unique features of the tumor microenvironment, while active targeting employs specific ligands, such as peptides, antibodies, and proteins, to bind to overexpressed receptors on liver and tumor cells. The article further details many examples of active targeting using the asialoglycoprotein receptor (ASGPR), glycyrrhetinic acid (GA), transferrin receptor (TfR), and folate receptor (FR) on hepatocytes and tumor cells, demonstrating that there has been significant research effort put into this field. The importance of non-parenchymal cells in the liver is also discussed, and the article examines methods of targeting Kupffer cells, sinusoidal endothelial cells, and hepatic stellate cells for therapeutic benefit. The review goes on to cover the emerging field of subcellular targeting, including specific strategies to target the nucleus, mitochondria, and the endoplasmic reticulum/Golgi apparatus, noting that although there has been some progress, further research is needed in this area. The text finishes with a summary which acknowledges that while targeted therapies, including enzyme-activated prodrugs, such as Pradefovir, and other novel methods for drug delivery have shown significant promise, challenges remain in translating these therapies into clinical use due to limitations in understanding the sequential transport and the mechanisms of action. Ultimately, the article emphasizes the need for in-depth research to fully realize the potential of precision cancer therapies for liver cancer.
文摘The YOLOv8 model faces challenges with dense target distribution and small size,resulting in lower accuracy in dense small target detection.To address these issues,an improved small target detection algorithm based on the YOLOv8 model was proposed in this paper.Firstly,the Global Attention Module(GAM)was introduced to enhance data prediction capability and model expression ability.Secondly,the Space-to-Depth(SPD)module was incorporated into the backbone network for fine-grained feature information learning to mitigate feature information loss due to down-sampling.Finally,a 160 pixels×160 pixels feature layer was added to expand small target feature information and effectively reduce instances of missed targets.Experimental validation on the public VisDrone2019 UAV small target detaset demonstrated that the proposed model achieves significant performance improvement in small target detection tasks compared to existing models,exhibiting higher accuracy.
