Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain les...Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.展开更多
The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of trea...The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.展开更多
As H-and J-aggregation receive more and more attention in the research of organic solar cells(OSCs),especially in small molecular systems,deep understanding of aggregation behavior is needed to guide the design of con...As H-and J-aggregation receive more and more attention in the research of organic solar cells(OSCs),especially in small molecular systems,deep understanding of aggregation behavior is needed to guide the design of conjugated small molecular structure and the fabrication process of OSC device.For this end,this review is written.Here,the review firstly introduced the basic information about H-and J-aggregation of conjugated small molecules in OSCs.Then,the characteristics of H-and J-aggregation and the methods to identify them were summarized.Next,it reviewed the research progress of H-and J-aggregation of conjugated small molecules in OSCs,including the factors influencing H-and J-aggregation in thin film and the effects of H-and J-aggregation on OPV performance.展开更多
Small nucleolar RNAs(snoRNAs)represent a class of non-coding RNAs that play pivotal roles in post-transcriptional RNA processing and modification,thereby contributing significantly to the maintenance of cellular funct...Small nucleolar RNAs(snoRNAs)represent a class of non-coding RNAs that play pivotal roles in post-transcriptional RNA processing and modification,thereby contributing significantly to the maintenance of cellular functions related to protein synthesis.SnoRNAs have been discovered to possess the ability to influence cell fate and alter disease progression,holding immense potential in controlling human diseases.It is suggested that the dysregulation of snoRNAs in cancer exhibits differential expression across various cancer types,stages,metastasis,treatment response and/or prognosis in patients.On the other hand,colorectal cancer(CRC),a prevalent malignancy of the digestive system,is characterized by high incidence and mortality rates,ranking as the third most common cancer type.Recent research indicates that snoRNA dysregulation is associated with CRC,as snoRNA expression significantly differs between normal and cancerous conditions.Consequently,assessing snoRNA expression level and function holds promise for the prognosis and diagnosis of CRC.Nevertheless,current comprehension of the potential roles of snoRNAs in CRC remains limited.This review offers a comprehensive survey of the aberrant regulation of snoRNAs in CRC,providing valuable insights into the discovery of novel biomarkers,therapeutic targets,and potential tools for the diagnosis and treatment of CRC and furnishing critical cues for advancing research into CRC and the judicious selection of therapeutic targets.展开更多
Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unman...Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.展开更多
●AIM:To investigate the long-term changes of corneal densitometry(CD)and its contributing elements after small incision lenticule extraction(SMILE).●METHODS:Totally 31 eyes of 31 patients with mean spherical equival...●AIM:To investigate the long-term changes of corneal densitometry(CD)and its contributing elements after small incision lenticule extraction(SMILE).●METHODS:Totally 31 eyes of 31 patients with mean spherical equivalent of-6.46±1.50 D and mean age 28.23±7.38y were enrolled.Full-scale examinations were conducted on all patients preoperatively and during followup.Visual acuity,manifest refraction,axial length,corneal thickness,corneal higher-order aberrations,and CD were evaluated.●RESULTS:All surgeries were completed successfully without complications or adverse events.Ten-year safety index was 1.17±0.20 and efficacy 1.04±0.28.CD value of 0–6 mm zones in central layer was statistically significantly lower 10y postoperatively,compared with preoperative values(0–2 mmΔ=-1.62,2–6 mmΔ=-1.24,P<0.01).There were no correlations between CD values and factors evaluated.●CONCLUSION:SMILE is a safe and efficient procedure for myopia on a long-term basis.CD values get lower 10y postoperatively,whose mechanism is to be further discussed.展开更多
The present letter to the editor is related to the study with the title“Automatic detection of small bowel(SB)lesions with different bleeding risk based on deep learning models”.Capsule endoscopy(CE)is the main tool...The present letter to the editor is related to the study with the title“Automatic detection of small bowel(SB)lesions with different bleeding risk based on deep learning models”.Capsule endoscopy(CE)is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate.The development of artificial intelligence(AI)in CE could simplify physicians’tasks.The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk,and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.展开更多
Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variati...Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.展开更多
To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight arc...To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.展开更多
Small mountainous rivers are characterized by large instantaneous fluxes and susceptible to extreme weather events,which can rapidly transport materials into the sea and have a significant impact on the ecological env...Small mountainous rivers are characterized by large instantaneous fluxes and susceptible to extreme weather events,which can rapidly transport materials into the sea and have a significant impact on the ecological environment of estuaries and bays.In order to investigate the seasonal characteristics of nutrients in small mountainous rivers in the subtropical monsoon region and the output pattern to the sea during heavy precipitation,surveys on the mountainous rivers were carried out in Baixi watershed in August 2020(wet season),March 2021(dry season)and June 2021(Meiyu period).The results showed that the dissolved inorganic nitrogen(DIN)of the rivers has an average concentration of 752μg L^(−1)in the wet season and 1472μg L^(−1)in the dry season.The concentrations of dissolved inorganic phosphorus(DIP)in wet season and dry season were 63μg L^(−1)and 51μg L^(−1),respectively.Influenced by the changes of land use in sub-watersheds,DIN concentrations in the mainstream increased from 701μg L^(−1)in the upper reaches to 1284μg L^(−1)in the middle reaches.Two rainstorms during the Meiyu period in the watershed caused the pulse runoff in the river.The maximum daily runoff reached 70 times that before rains.The maximum daily fluxes of DIN and DIP were 109 and 247 times that before rains,respectively.In view that the watershed experienced several rainstorms in the wet season,the river,with pulse runoff,carries a large amount of nutrients into the sea in a short time,which will have a significant impact on the environment of Sanmen bay and its adjacent sea.展开更多
Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable...Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.展开更多
In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks.Despite these efforts,the detection of small objects in re...In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks.Despite these efforts,the detection of small objects in remote sensing remains a formidable challenge.The deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers.Additionally,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection accuracy.Moreover,the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty.In this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote sensing.To address feature loss in deep layers,we have devised a cross-layer attention fusion module.Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule.Furthermore,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)losses.The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets.The experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.展开更多
●AIM:To study the changes and effect factors of posterior corneal surface after small incision lenticule extraction(SMILE)with different myopic diopters.●METHODS:Ninety eyes of 90 patients who underwent SMILE were i...●AIM:To study the changes and effect factors of posterior corneal surface after small incision lenticule extraction(SMILE)with different myopic diopters.●METHODS:Ninety eyes of 90 patients who underwent SMILE were included in this retrospective study.Patients were allocated into three groups based on the preoperative spherical equivalent(SE):low myopia(SE≥-3.00 D),moderate myopia(-3.00 D>SE>-6.00 D)and high myopia(SE≤-6.00 D).Posterior corneal surfaces were measured by a Scheimpflug camera preoperatively and different postoperative times(1wk,1,3,6mo,and 1y).Posterior mean elevation(PME)at 25 predetermined points of 3 concentric circles(2-,4-,and 6-mm diameter)above the best fit sphere was analyzed.●RESULTS:All surgeries were completed uneventfully and no ectasia was found through the observation.The difference of myopia group was significant at the 2-mm ring at 1 and 3mo postoperatively(1mo:P=0.017;3mo:P=0.018).The effect of time onΔPME was statistically significant(2-mm ring:P=0.001;4-mm ring:P<0.001;6-mm ring:P<0.001).The effect of different corneal locations onΔPME was significant except 1wk postoperatively(1mo:P=0.000;3mo:P=0.000;6mo:P=0.001;1y:P=0.001).Posterior corneal stability was linearly correlated with SE,central corneal thickness,ablation depth,residual bed thickness,percent ablation depth and percent stromal bed thickness.●CONCLUSION:The posterior corneal surface changes dynamically after SMILE.No protrusion is observed on the posterior corneal surface in patients with different degrees of myopia within one year after surgery.SMILE has good stability,accuracy,safety and predictability.展开更多
Detection of maneuvering small targets has always been an important yet challenging task for radar signal processing.One primary reason is that target variable motions within coherent processing interval generate ener...Detection of maneuvering small targets has always been an important yet challenging task for radar signal processing.One primary reason is that target variable motions within coherent processing interval generate energy migrations across multiple resolution bins,which severely deteriorate the parameter estimation performance.A coarse-to-fine strategy for the detection of maneuvering small targets is proposed.Integration of small points segmented coherently is performed first,and then an optimal inter-segment integration is utilized to derive the coarse estimation of the chirp rate.Sparse fractional Fourier transform(FrFT)is then employed to refine the coarse estimation at a significantly reduced computational complexity.Simulation results verify the proposed scheme that achieves an efficient and reliable maneuvering target detection with-16dB input signal-to-noise ratio(SNR),while requires no exact a priori knowledge on the motion parameters.展开更多
Background: Neuroendocrine neoplasms are those that develop from a neuroendocrine cell. They most commonly affect the lungs, gastrointestinal tract, and pancreas, being rare conditions in the female genital tract. Whe...Background: Neuroendocrine neoplasms are those that develop from a neuroendocrine cell. They most commonly affect the lungs, gastrointestinal tract, and pancreas, being rare conditions in the female genital tract. When present, these neoplasms often manifest with nonspecific signs and symptoms such as pain, itching, swelling, single-focus lesions, bleeding, and enlargement of inguinal lymph nodes, in addition to the presence of progressively enlarging vulvar nodules. Consequently, the diagnostic investigation involves histopathological examination and confirmation through immunohistochemistry. Objective: To present a comprehensive understanding of this rarely studied pathology. The primary objective is to provide valuable insights that could aid in the future development of universally applicable treatment guidelines. Case Presentation: A 57-year-old female, with no prior comorbidities, menopause at 36, who presented with a left vulvar nodule accompanied by intense pain and swelling, later diagnosed with small cell neuroendocrine carcinoma in the vulva. Conclusion: This case report highlights the importance of enhancing our knowledge regarding small cell neuroendocrine carcinoma in the vulva, given its scarcity in medical literature. The information presented here underscores the need for standardized diagnostic and treatment approaches, paving the way for future consensus on managing this uncommon but challenging neoplasm.展开更多
Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sol...Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sold to bring in some income. Timely decisions on farm practices such as farm preparation and planting are critical determinants of the seasonal outcomes. In Kenya, most small-scale farmers have no reliable source of information that would help them make timely and accurate decisions. County governments have extension officers who are mandated with giving farmers advisory services to farmers but they are not able to reach most farmers due to facilitation constraints. The mode and format of sharing information is also critical since it’s important to ensure that it’s timely, well-understood and usable. This study sought to assess access to geospatial derived and other crop production information by farmers in four selected counties of Kenya. Specific objectives were to determine the profile of small-scale farmers in terms of age, education and farm size;to determine the type of information that is made available to them by County and Sub-County extension officers including the format and mode of provision;and to determine if the information provided was useful in terms of accuracy, timeliness and adequacy. The results indicated that over 80% of the farmers were over 35 years of age and over 56% were male. Majority had attained primary education (34%) or secondary education (29%) and most farmers in all the counties grew maize (71%). Notably, fellow farmers were a source of information (71%) with the frequency of sharing information being mostly seasonal (37%) and when information was available (43%). Over 66% of interviewed farmers indicating that they faced challenges while using provided information. The results from the study are insightful and helpful in determining effective ways of providing farmers with useful information to ensure maximum benefits.展开更多
One of the many renewable energy sources that offer advantages is solar energy, which also lowers energy prices and promotes environmental sustainability and energy security. Despite these advantages, various barriers...One of the many renewable energy sources that offer advantages is solar energy, which also lowers energy prices and promotes environmental sustainability and energy security. Despite these advantages, various barriers, such as installation costs, have prevented small and medium-sized enterprises from investigating this invention. Malawi has a significant energy shortfall such that most businesses have been hindered from their profit maximization goals. The “Photovoltaic systems” (PV) that transform sunlight into electricity are the subject of this study. This type of solar energy system is situated on the building’s roof and generally produces electricity for businesses and even homes. Solar energy offers a great impact to small and medium enterprises in Mzuzu city with a cost-effective and dependable alternative to energy that has the potential to change the game. Therefore the aim of the study was to identify factors that encourage the adoption of solar energy among small medium enterprises in the city of Mzuzu city. And to identify some of barriers faced when adopting solar energy among small and medium enterprises in the city of Mzuzu. The research approach employed in the study was a survey. A survey is a type of research methodology in which primary data is gathered from a sample using a questionnaire. When information is to be gathered from a wider sample, a survey is employed. A bigger sample size was needed in this study in order to facilitate hypothesis testing. It is advised to apply a logical approach while using the survey. The survey utilized a five-point Likert scale. The study used convenience sampling to select study participants. The sample size in this study was determined using Cochran’s sample size formula. Statistical Package for Social Sciences (SPSS) and Microsoft Excel were used for statistical analysis. About 97.2% of the participants were aware of solar as a source of energy compared to 2.8 % who were unaware. The majority of participants use solar energy for lighting only, seconded by those who use electricity. The least number of participants use solar energy for cooling only. The majority of participants 21.5% indicated partnership and collaboration as the most motivating factor for the adoption of solar energy. This was followed by technical expertise 19.1 % the least number of participants 10.8% expressed that policy and regulatory frameworks were associated with the adoption of solar energy. This study found that there are no statistically significant factors influencing barriers to the adoption of solar energy. The price of solar energy adoption was identified as the least factor associated with the acceptance or rejection of solar energy. Nonetheless, the reasons given by the homes that had embraced solar technology aligned with the findings of other studies. This survey also found that although the public was aware of solar energy, and technology, there were still a number of factors that mattered, especially for non-adopters.展开更多
Objective:To determine the genetic diversity of Plasmodium(P.)knowlesi isolates from Sabah,Malaysian Borneo and Peninsular Malaysia,targeting the S-type SSU rRNA gene and including aspects of natural selection and hap...Objective:To determine the genetic diversity of Plasmodium(P.)knowlesi isolates from Sabah,Malaysian Borneo and Peninsular Malaysia,targeting the S-type SSU rRNA gene and including aspects of natural selection and haplotype.Methods:Thirty-nine blood samples infected with P.knowlesi were collected in Sabah,Malaysian Borneo and Peninsular Malaysia.The S-type SSU rRNA gene was amplified using polymerase chain reaction,cloned into a vector,and sequenced.The natural selection and haplotype of the S-type SSU rRNA gene sequences were determined using DnaSP v6 and illustrated using NETWORK v10.This study's 39 S-type SSU rRNA sequences and eight sequences from the Genbank database were subjected to phylogenetic analysis using MEGA 11.Results:Overall,the phylogenetic analysis showed no evidence of a geographical cluster of P.knowlesi isolates from different areas in Malaysia based on the S-type SSU rRNA gene sequences.The S-type SSU rRNA gene sequences were relatively conserved and with a purifying effect.Haplotype sharing of the S-type SSU rRNA gene was observed between the P.knowlesi isolates in Sabah,Malaysian Borneo,but not between Sabah,Malaysian Borneo and Peninsular Malaysia.Conclusions:This study suggests that the S-type SSU rRNA gene of P.knowlesi isolates in Sabah,Malaysian Borneo,and Peninsular Malaysia has fewer polymorphic sites,representing the conservation of the gene.These features make the S-type SSU rRNA gene suitable for comparative studies,such as determining the evolutionary relationships and common ancestry among P.knowlesi species.展开更多
Immune checkpoint inhibitors(ICIs)are used to relieve and refuel anti-tumor immunity by blocking the interaction,transcription,and translation of co-inhibitory immune checkpoints or degrading co-inhibitory immune chec...Immune checkpoint inhibitors(ICIs)are used to relieve and refuel anti-tumor immunity by blocking the interaction,transcription,and translation of co-inhibitory immune checkpoints or degrading co-inhibitory immune checkpoints.Thousands of small molecule drugs or biological materials,especially antibody-based ICIs,are actively being studied and antibodies are currently widely used.Limitations,such as anti-tumor efficacy,poor membrane permeability,and unneglected tolerance issues of antibody-based ICIs,remain evident but are thought to be overcome by small molecule drugs.Recent structural studies have broadened the scope of candidate immune checkpoint molecules,as well as innovative chemical inhibitors.By way of comparison,small molecule drug-based ICIs represent superior oral bioavailability and favorable pharmacokinetic features.Several ongoing clinical trials are exploring the synergetic effect of ICIs and other therapeutic strategies based on multiple ICI functions,including immune regulation,anti-angiogenesis,and cell cycle regulation.In this review we summarized the current progression of small molecule ICIs and the mechanism underlying immune checkpoint proteins,which will lay the foundation for further exploration.展开更多
文摘Effective small object detection is crucial in various applications including urban intelligent transportation and pedestrian detection.However,small objects are difficult to detect accurately because they contain less information.Many current methods,particularly those based on Feature Pyramid Network(FPN),address this challenge by leveraging multi-scale feature fusion.However,existing FPN-based methods often suffer from inadequate feature fusion due to varying resolutions across different layers,leading to suboptimal small object detection.To address this problem,we propose the Two-layerAttention Feature Pyramid Network(TA-FPN),featuring two key modules:the Two-layer Attention Module(TAM)and the Small Object Detail Enhancement Module(SODEM).TAM uses the attention module to make the network more focused on the semantic information of the object and fuse it to the lower layer,so that each layer contains similar semantic information,to alleviate the problem of small object information being submerged due to semantic gaps between different layers.At the same time,SODEM is introduced to strengthen the local features of the object,suppress background noise,enhance the information details of the small object,and fuse the enhanced features to other feature layers to ensure that each layer is rich in small object information,to improve small object detection accuracy.Our extensive experiments on challenging datasets such as Microsoft Common Objects inContext(MSCOCO)and Pattern Analysis Statistical Modelling and Computational Learning,Visual Object Classes(PASCAL VOC)demonstrate the validity of the proposedmethod.Experimental results show a significant improvement in small object detection accuracy compared to state-of-theart detectors.
文摘The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.
基金financially supported by the National Natural Science Foundation of China(52203024,22225504)the Shandong Provincial Natural Science Foundation(ZR2022QE135)+2 种基金the Youth Innovation Team Project of Shandong Provincial University(2023KJ330)the Qilu University of Technology strong base plan(2023PY001)Guangdong Provincial Key Laboratory of Catalysis(2020B121201002)。
文摘As H-and J-aggregation receive more and more attention in the research of organic solar cells(OSCs),especially in small molecular systems,deep understanding of aggregation behavior is needed to guide the design of conjugated small molecular structure and the fabrication process of OSC device.For this end,this review is written.Here,the review firstly introduced the basic information about H-and J-aggregation of conjugated small molecules in OSCs.Then,the characteristics of H-and J-aggregation and the methods to identify them were summarized.Next,it reviewed the research progress of H-and J-aggregation of conjugated small molecules in OSCs,including the factors influencing H-and J-aggregation in thin film and the effects of H-and J-aggregation on OPV performance.
基金the National Natural Science Foundation of China,No.82273457Guangdong Basic and Applied Basic Research Foundation,No.2021A1515012180 and No.2023A1515012762+1 种基金Special Grant for Key Area Programs of Guangdong Department of Education,No.2021ZDZX2040and Science and Technology Special Project of Guangdong Province,No.210715216902829.
文摘Small nucleolar RNAs(snoRNAs)represent a class of non-coding RNAs that play pivotal roles in post-transcriptional RNA processing and modification,thereby contributing significantly to the maintenance of cellular functions related to protein synthesis.SnoRNAs have been discovered to possess the ability to influence cell fate and alter disease progression,holding immense potential in controlling human diseases.It is suggested that the dysregulation of snoRNAs in cancer exhibits differential expression across various cancer types,stages,metastasis,treatment response and/or prognosis in patients.On the other hand,colorectal cancer(CRC),a prevalent malignancy of the digestive system,is characterized by high incidence and mortality rates,ranking as the third most common cancer type.Recent research indicates that snoRNA dysregulation is associated with CRC,as snoRNA expression significantly differs between normal and cancerous conditions.Consequently,assessing snoRNA expression level and function holds promise for the prognosis and diagnosis of CRC.Nevertheless,current comprehension of the potential roles of snoRNAs in CRC remains limited.This review offers a comprehensive survey of the aberrant regulation of snoRNAs in CRC,providing valuable insights into the discovery of novel biomarkers,therapeutic targets,and potential tools for the diagnosis and treatment of CRC and furnishing critical cues for advancing research into CRC and the judicious selection of therapeutic targets.
基金This research was funded by the Natural Science Foundation of Hebei Province(F2021506004).
文摘Transformer-based models have facilitated significant advances in object detection.However,their extensive computational consumption and suboptimal detection of dense small objects curtail their applicability in unmanned aerial vehicle(UAV)imagery.Addressing these limitations,we propose a hybrid transformer-based detector,H-DETR,and enhance it for dense small objects,leading to an accurate and efficient model.Firstly,we introduce a hybrid transformer encoder,which integrates a convolutional neural network-based cross-scale fusion module with the original encoder to handle multi-scale feature sequences more efficiently.Furthermore,we propose two novel strategies to enhance detection performance without incurring additional inference computation.Query filter is designed to cope with the dense clustering inherent in drone-captured images by counteracting similar queries with a training-aware non-maximum suppression.Adversarial denoising learning is a novel enhancement method inspired by adversarial learning,which improves the detection of numerous small targets by counteracting the effects of artificial spatial and semantic noise.Extensive experiments on the VisDrone and UAVDT datasets substantiate the effectiveness of our approach,achieving a significant improvement in accuracy with a reduction in computational complexity.Our method achieves 31.9%and 21.1%AP on the VisDrone and UAVDT datasets,respectively,and has a faster inference speed,making it a competitive model in UAV image object detection.
文摘●AIM:To investigate the long-term changes of corneal densitometry(CD)and its contributing elements after small incision lenticule extraction(SMILE).●METHODS:Totally 31 eyes of 31 patients with mean spherical equivalent of-6.46±1.50 D and mean age 28.23±7.38y were enrolled.Full-scale examinations were conducted on all patients preoperatively and during followup.Visual acuity,manifest refraction,axial length,corneal thickness,corneal higher-order aberrations,and CD were evaluated.●RESULTS:All surgeries were completed successfully without complications or adverse events.Ten-year safety index was 1.17±0.20 and efficacy 1.04±0.28.CD value of 0–6 mm zones in central layer was statistically significantly lower 10y postoperatively,compared with preoperative values(0–2 mmΔ=-1.62,2–6 mmΔ=-1.24,P<0.01).There were no correlations between CD values and factors evaluated.●CONCLUSION:SMILE is a safe and efficient procedure for myopia on a long-term basis.CD values get lower 10y postoperatively,whose mechanism is to be further discussed.
文摘The present letter to the editor is related to the study with the title“Automatic detection of small bowel(SB)lesions with different bleeding risk based on deep learning models”.Capsule endoscopy(CE)is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate.The development of artificial intelligence(AI)in CE could simplify physicians’tasks.The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk,and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.
基金the Key Research and Development Program of Hainan Province(Grant Nos.ZDYF2023GXJS163,ZDYF2024GXJS014)National Natural Science Foundation of China(NSFC)(Grant Nos.62162022,62162024)+2 种基金the Major Science and Technology Project of Hainan Province(Grant No.ZDKJ2020012)Hainan Provincial Natural Science Foundation of China(Grant No.620MS021)Youth Foundation Project of Hainan Natural Science Foundation(621QN211).
文摘Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.
基金funded by the General Project of Key Research and Develop-ment Plan of Shaanxi Province(No.2022NY-087).
文摘To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.
基金financially supported by the Postdoctoral Foundation of Qingdao(Pb Isotopes of Oujiang River to Quantitatively Identify Sediment Provenance in Oujiang Estuary and Adjacent Area)the China Geological Survey Project(No.DD20190276)the Fund of Ministry of Science and Technology(Nos.2013FY112200 and 2019YFE0127200).
文摘Small mountainous rivers are characterized by large instantaneous fluxes and susceptible to extreme weather events,which can rapidly transport materials into the sea and have a significant impact on the ecological environment of estuaries and bays.In order to investigate the seasonal characteristics of nutrients in small mountainous rivers in the subtropical monsoon region and the output pattern to the sea during heavy precipitation,surveys on the mountainous rivers were carried out in Baixi watershed in August 2020(wet season),March 2021(dry season)and June 2021(Meiyu period).The results showed that the dissolved inorganic nitrogen(DIN)of the rivers has an average concentration of 752μg L^(−1)in the wet season and 1472μg L^(−1)in the dry season.The concentrations of dissolved inorganic phosphorus(DIP)in wet season and dry season were 63μg L^(−1)and 51μg L^(−1),respectively.Influenced by the changes of land use in sub-watersheds,DIN concentrations in the mainstream increased from 701μg L^(−1)in the upper reaches to 1284μg L^(−1)in the middle reaches.Two rainstorms during the Meiyu period in the watershed caused the pulse runoff in the river.The maximum daily runoff reached 70 times that before rains.The maximum daily fluxes of DIN and DIP were 109 and 247 times that before rains,respectively.In view that the watershed experienced several rainstorms in the wet season,the river,with pulse runoff,carries a large amount of nutrients into the sea in a short time,which will have a significant impact on the environment of Sanmen bay and its adjacent sea.
基金State Grid Jiangsu Electric Power Co.,Ltd.of the Science and Technology Project(Grant No.J2022004).
文摘Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.
基金supported in part by the National Natural Science Foundation of China under Grant 62006071part by the Science and Technology Research Project of Henan Province under Grant 232103810086.
文摘In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks.Despite these efforts,the detection of small objects in remote sensing remains a formidable challenge.The deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers.Additionally,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection accuracy.Moreover,the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty.In this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote sensing.To address feature loss in deep layers,we have devised a cross-layer attention fusion module.Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule.Furthermore,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)losses.The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets.The experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.
基金Supported by Shandong Provincial Natural Science Foundation(No.ZR2022QH384).
文摘●AIM:To study the changes and effect factors of posterior corneal surface after small incision lenticule extraction(SMILE)with different myopic diopters.●METHODS:Ninety eyes of 90 patients who underwent SMILE were included in this retrospective study.Patients were allocated into three groups based on the preoperative spherical equivalent(SE):low myopia(SE≥-3.00 D),moderate myopia(-3.00 D>SE>-6.00 D)and high myopia(SE≤-6.00 D).Posterior corneal surfaces were measured by a Scheimpflug camera preoperatively and different postoperative times(1wk,1,3,6mo,and 1y).Posterior mean elevation(PME)at 25 predetermined points of 3 concentric circles(2-,4-,and 6-mm diameter)above the best fit sphere was analyzed.●RESULTS:All surgeries were completed uneventfully and no ectasia was found through the observation.The difference of myopia group was significant at the 2-mm ring at 1 and 3mo postoperatively(1mo:P=0.017;3mo:P=0.018).The effect of time onΔPME was statistically significant(2-mm ring:P=0.001;4-mm ring:P<0.001;6-mm ring:P<0.001).The effect of different corneal locations onΔPME was significant except 1wk postoperatively(1mo:P=0.000;3mo:P=0.000;6mo:P=0.001;1y:P=0.001).Posterior corneal stability was linearly correlated with SE,central corneal thickness,ablation depth,residual bed thickness,percent ablation depth and percent stromal bed thickness.●CONCLUSION:The posterior corneal surface changes dynamically after SMILE.No protrusion is observed on the posterior corneal surface in patients with different degrees of myopia within one year after surgery.SMILE has good stability,accuracy,safety and predictability.
基金supported in part by the National Natural Science Foundation of China (Nos.62171029,61931015,U1833203)Natural Science Foundation of Beijing Municipality (No.4172052)supported in part by the Basic Research Program of Jiangsu Province (No.SBK2019042353)。
文摘Detection of maneuvering small targets has always been an important yet challenging task for radar signal processing.One primary reason is that target variable motions within coherent processing interval generate energy migrations across multiple resolution bins,which severely deteriorate the parameter estimation performance.A coarse-to-fine strategy for the detection of maneuvering small targets is proposed.Integration of small points segmented coherently is performed first,and then an optimal inter-segment integration is utilized to derive the coarse estimation of the chirp rate.Sparse fractional Fourier transform(FrFT)is then employed to refine the coarse estimation at a significantly reduced computational complexity.Simulation results verify the proposed scheme that achieves an efficient and reliable maneuvering target detection with-16dB input signal-to-noise ratio(SNR),while requires no exact a priori knowledge on the motion parameters.
文摘Background: Neuroendocrine neoplasms are those that develop from a neuroendocrine cell. They most commonly affect the lungs, gastrointestinal tract, and pancreas, being rare conditions in the female genital tract. When present, these neoplasms often manifest with nonspecific signs and symptoms such as pain, itching, swelling, single-focus lesions, bleeding, and enlargement of inguinal lymph nodes, in addition to the presence of progressively enlarging vulvar nodules. Consequently, the diagnostic investigation involves histopathological examination and confirmation through immunohistochemistry. Objective: To present a comprehensive understanding of this rarely studied pathology. The primary objective is to provide valuable insights that could aid in the future development of universally applicable treatment guidelines. Case Presentation: A 57-year-old female, with no prior comorbidities, menopause at 36, who presented with a left vulvar nodule accompanied by intense pain and swelling, later diagnosed with small cell neuroendocrine carcinoma in the vulva. Conclusion: This case report highlights the importance of enhancing our knowledge regarding small cell neuroendocrine carcinoma in the vulva, given its scarcity in medical literature. The information presented here underscores the need for standardized diagnostic and treatment approaches, paving the way for future consensus on managing this uncommon but challenging neoplasm.
文摘Small-scale farming accounts for 78% of total agricultural production in Kenya and contributes to 23.5% of the country’s GDP. Their crop production activities are mostly rainfed subsistence with any surplus being sold to bring in some income. Timely decisions on farm practices such as farm preparation and planting are critical determinants of the seasonal outcomes. In Kenya, most small-scale farmers have no reliable source of information that would help them make timely and accurate decisions. County governments have extension officers who are mandated with giving farmers advisory services to farmers but they are not able to reach most farmers due to facilitation constraints. The mode and format of sharing information is also critical since it’s important to ensure that it’s timely, well-understood and usable. This study sought to assess access to geospatial derived and other crop production information by farmers in four selected counties of Kenya. Specific objectives were to determine the profile of small-scale farmers in terms of age, education and farm size;to determine the type of information that is made available to them by County and Sub-County extension officers including the format and mode of provision;and to determine if the information provided was useful in terms of accuracy, timeliness and adequacy. The results indicated that over 80% of the farmers were over 35 years of age and over 56% were male. Majority had attained primary education (34%) or secondary education (29%) and most farmers in all the counties grew maize (71%). Notably, fellow farmers were a source of information (71%) with the frequency of sharing information being mostly seasonal (37%) and when information was available (43%). Over 66% of interviewed farmers indicating that they faced challenges while using provided information. The results from the study are insightful and helpful in determining effective ways of providing farmers with useful information to ensure maximum benefits.
文摘One of the many renewable energy sources that offer advantages is solar energy, which also lowers energy prices and promotes environmental sustainability and energy security. Despite these advantages, various barriers, such as installation costs, have prevented small and medium-sized enterprises from investigating this invention. Malawi has a significant energy shortfall such that most businesses have been hindered from their profit maximization goals. The “Photovoltaic systems” (PV) that transform sunlight into electricity are the subject of this study. This type of solar energy system is situated on the building’s roof and generally produces electricity for businesses and even homes. Solar energy offers a great impact to small and medium enterprises in Mzuzu city with a cost-effective and dependable alternative to energy that has the potential to change the game. Therefore the aim of the study was to identify factors that encourage the adoption of solar energy among small medium enterprises in the city of Mzuzu city. And to identify some of barriers faced when adopting solar energy among small and medium enterprises in the city of Mzuzu. The research approach employed in the study was a survey. A survey is a type of research methodology in which primary data is gathered from a sample using a questionnaire. When information is to be gathered from a wider sample, a survey is employed. A bigger sample size was needed in this study in order to facilitate hypothesis testing. It is advised to apply a logical approach while using the survey. The survey utilized a five-point Likert scale. The study used convenience sampling to select study participants. The sample size in this study was determined using Cochran’s sample size formula. Statistical Package for Social Sciences (SPSS) and Microsoft Excel were used for statistical analysis. About 97.2% of the participants were aware of solar as a source of energy compared to 2.8 % who were unaware. The majority of participants use solar energy for lighting only, seconded by those who use electricity. The least number of participants use solar energy for cooling only. The majority of participants 21.5% indicated partnership and collaboration as the most motivating factor for the adoption of solar energy. This was followed by technical expertise 19.1 % the least number of participants 10.8% expressed that policy and regulatory frameworks were associated with the adoption of solar energy. This study found that there are no statistically significant factors influencing barriers to the adoption of solar energy. The price of solar energy adoption was identified as the least factor associated with the acceptance or rejection of solar energy. Nonetheless, the reasons given by the homes that had embraced solar technology aligned with the findings of other studies. This survey also found that although the public was aware of solar energy, and technology, there were still a number of factors that mattered, especially for non-adopters.
基金This study was supported by the Ministry of Higher Education,Malaysia(FRGS0322-SG-1/2013)Universiti Malaysia Sabah(GUG0521-2/2020).
文摘Objective:To determine the genetic diversity of Plasmodium(P.)knowlesi isolates from Sabah,Malaysian Borneo and Peninsular Malaysia,targeting the S-type SSU rRNA gene and including aspects of natural selection and haplotype.Methods:Thirty-nine blood samples infected with P.knowlesi were collected in Sabah,Malaysian Borneo and Peninsular Malaysia.The S-type SSU rRNA gene was amplified using polymerase chain reaction,cloned into a vector,and sequenced.The natural selection and haplotype of the S-type SSU rRNA gene sequences were determined using DnaSP v6 and illustrated using NETWORK v10.This study's 39 S-type SSU rRNA sequences and eight sequences from the Genbank database were subjected to phylogenetic analysis using MEGA 11.Results:Overall,the phylogenetic analysis showed no evidence of a geographical cluster of P.knowlesi isolates from different areas in Malaysia based on the S-type SSU rRNA gene sequences.The S-type SSU rRNA gene sequences were relatively conserved and with a purifying effect.Haplotype sharing of the S-type SSU rRNA gene was observed between the P.knowlesi isolates in Sabah,Malaysian Borneo,but not between Sabah,Malaysian Borneo and Peninsular Malaysia.Conclusions:This study suggests that the S-type SSU rRNA gene of P.knowlesi isolates in Sabah,Malaysian Borneo,and Peninsular Malaysia has fewer polymorphic sites,representing the conservation of the gene.These features make the S-type SSU rRNA gene suitable for comparative studies,such as determining the evolutionary relationships and common ancestry among P.knowlesi species.
基金supported by the National Natural Science Foundation of China(Grant Nos.82203539 and 92259102)Provincial Cooperation Project of Science and Technology Department of Sichuan Province(Grant No.2023YFSY0043)the National Key Research and Development Program of China(Grant No.2023YFC3402100).
文摘Immune checkpoint inhibitors(ICIs)are used to relieve and refuel anti-tumor immunity by blocking the interaction,transcription,and translation of co-inhibitory immune checkpoints or degrading co-inhibitory immune checkpoints.Thousands of small molecule drugs or biological materials,especially antibody-based ICIs,are actively being studied and antibodies are currently widely used.Limitations,such as anti-tumor efficacy,poor membrane permeability,and unneglected tolerance issues of antibody-based ICIs,remain evident but are thought to be overcome by small molecule drugs.Recent structural studies have broadened the scope of candidate immune checkpoint molecules,as well as innovative chemical inhibitors.By way of comparison,small molecule drug-based ICIs represent superior oral bioavailability and favorable pharmacokinetic features.Several ongoing clinical trials are exploring the synergetic effect of ICIs and other therapeutic strategies based on multiple ICI functions,including immune regulation,anti-angiogenesis,and cell cycle regulation.In this review we summarized the current progression of small molecule ICIs and the mechanism underlying immune checkpoint proteins,which will lay the foundation for further exploration.