Floating Waste Discovery by Request via Object-Centric Learning

Discovering floating wastes,especially bottles on water,is a crucial research problem in environmental hygiene.Nevertheless,real-world applications often face challenges such as interference from irrelevant objects and the high cost associated with data collection.Consequently,devising algorithms capable of accurately localizing specific objects within a scene in scenarios where annotated data is limited remains a formidable challenge.To solve this problem,this paper proposes an object discovery by request problem setting and a corresponding algorithmic framework.The proposed problem setting aims to identify specified objects in scenes,and the associated algorithmic framework comprises pseudo data generation and object discovery by request network.Pseudo-data generation generates images resembling natural scenes through various data augmentation rules,using a small number of object samples and scene images.The network structure of object discovery by request utilizes the pre-trained Vision Transformer(ViT)model as the backbone,employs object-centric methods to learn the latent representations of foreground objects,and applies patch-level reconstruction constraints to the model.During the validation phase,we use the generated pseudo datasets as training sets and evaluate the performance of our model on the original test sets.Experiments have proved that our method achieves state-of-the-art performance on Unmanned Aerial Vehicles-Bottle Detection(UAV-BD)dataset and self-constructed dataset Bottle,especially in multi-object scenarios.

Strategies for translating proteomics discoveries into drug discovery for dementia

Tauopathies,diseases characterized by neuropathological aggregates of tau including Alzheimer's disease and subtypes of fro ntotemporal dementia,make up the vast majority of dementia cases.Although there have been recent developments in tauopathy biomarkers and disease-modifying treatments,ongoing progress is required to ensure these are effective,economical,and accessible for the globally ageing population.As such,continued identification of new potential drug targets and biomarkers is critical."Big data"studies,such as proteomics,can generate information on thousands of possible new targets for dementia diagnostics and therapeutics,but currently remain underutilized due to the lack of a clear process by which targets are selected for future drug development.In this review,we discuss current tauopathy biomarkers and therapeutics,and highlight areas in need of improvement,particularly when addressing the needs of frail,comorbid and cognitively impaired populations.We highlight biomarkers which have been developed from proteomic data,and outline possible future directions in this field.We propose new criteria by which potential targets in proteomics studies can be objectively ranked as favorable for drug development,and demonstrate its application to our group's recent tau interactome dataset as an example.

Towards cultivar-oriented gene discovery for better crops

The continued expansion of the world population,increasingly inconsistent climate and shrinking agricultural resources present major challenges to crop breeding.Fortunately,the increasing ability to discover and manipulate genes creates new opportunities to develop more productive and resilient cultivars.Many genes have been described in papers as being beneficial for yield increase.However,few of them have been translated into increased yield on farms.In contrast,commercial breeders are facing gene decidophobia,i.e.,puzzled about which gene to choose for breeding among the many identified,a huge chasm between gene discovery and cultivar innovation.The purpose of this paper is to draw attention to the shortfalls in current gene discovery research and to emphasise the need to align with cultivar innovation.The methodology dictates that genetic studies not only focus on gene discovery but also pay good attention to the genetic backgrounds,experimental validation in relevant environments,appropriate crop management,and data reusability.The close of the gaps should accelerate the application of molecular study in breeding and contribute to future global food security.

Recent Advances of Bioactive Marine Natural Products in Drug Discovery

Marine natural products(MNPs)are valuable resources for drug development.To date,17 drugs from marine sources are in clinical use,and 33 pharmaceutical compounds are in clinical trials.Presently the success of drug development from the marine resources is higher than the industry average.It is a feasible strategy to conduct the discovery of druglead compounds based on marine chemical ecology by fully exploiting the pharmacological potential of marine chemical defense matters.In the search for bioactive MNPs,our group has constructed a biological resources library including more than 1500 strains of fungi.Focusing on the strategy of Blue Drug Library,we have discovered a series of novel MNPs with abundant biological functions.Highly efficient and scalable total synthesis of(+)-aniduquinolone A(44)and pesimquinolone I(48)have been completed,which will facilitate access to sufficient quantities of candidates for in vivo pharmacological and toxicological studies.As a nucleoprotein(NP)inhibitor,QLA(75)possesses significant anti-influenza A virus(IAV)activities both in vitro and in vivo.CHNQD-00803(76)is a potent and selective AMP-activated kinase(AMPK)activator that can effectively inhibit metabolic disorders and metabolic dysfunction-associated steatohepatitis(MASH)progression.Moreover,we identified two new candidate molecules with potent anti-hepatocellular carcinoma effects.Particularly,as a natural guanine-nucleotide exchange factors for ADP-ribosylation factor GTPases(Arf-GEFs)inhibitor prodrug,CHNQD-01255(78)is qualified to be developed as a targeted candidate anticancer drug,which may be promising to apply for cancer immunotherapy.Hence,it is evident that MNPs play an important role in drug development.

UAV-supported intelligent truth discovery to achieve low-cost communications in mobile crowd sensing

Unmanned and aerial systems as interactors among different system components for communications,have opened up great opportunities for truth data discovery in Mobile Crowd Sensing(MCS)which has not been properly solved in the literature.In this paper,an Unmanned Aerial Vehicles-supported Intelligent Truth Discovery(UAV-ITD)scheme is proposed to obtain truth data at low-cost communications for MCS.The main innovations of the UAV-ITD scheme are as follows:(1)UAV-ITD scheme takes the first step in employing UAV joint Deep Matrix Factorization(DMF)to discover truth data based on the trust mechanism for an Information Elicitation Without Verification(IEWV)problem in MCS.(2)This paper introduces a truth data discovery scheme for the first time that only needs to collect a part of n data samples to infer the data of the entire network with high accuracy,which saves more communication costs than most previous data collection schemes,where they collect n or kn data samples.Finally,we conducted extensive experiments to evaluate the UAV-ITD scheme.The results show that compared with previous schemes,our scheme can reduce estimated truth error by 52.25%–96.09%,increase the accuracy of workers’trust evaluation by 0.68–61.82 times,and save recruitment costs by 24.08%–54.15%in truth data discovery.

A Multi-Token Sector Antenna Neighbor Discovery Protocol for Directional Ad Hoc Networks

In this paper,we propose a Multi-token Sector Antenna Neighbor Discovery(M-SAND)protocol to enhance the efficiency of neighbor discovery in asynchronous directional ad hoc networks.The central concept of our work involves maintaining multiple tokens across the network.To prevent mutual interference among multi-token holders,we introduce the time and space non-interference theorems.Furthermore,we propose a master-slave strategy between tokens.When the master token holder(MTH)performs the neighbor discovery,it decides which 1-hop neighbor is the next MTH and which 2-hop neighbors can be the new slave token holders(STHs).Using this approach,the MTH and multiple STHs can simultaneously discover their neighbors without causing interference with each other.Building on this foundation,we provide a comprehensive procedure for the M-SAND protocol.We also conduct theoretical analyses on the maximum number of STHs and the lower bound of multi-token generation probability.Finally,simulation results demonstrate the time efficiency of the M-SAND protocol.When compared to the QSAND protocol,which uses only one token,the total neighbor discovery time is reduced by 28% when 6beams and 112 nodes are employed.

Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

GE Discovery 750W 3.0T磁共振故障维修案例分析与探讨

分析了GE Discovery 750W 3.0T磁共振出现的氦压缩机异常、检查床无法正常移动以及伪影故障的现象、原因,并提出了具体的排除方法,为工程技术人员维修此类故障提供了参考。

Artificial intelligence as a tool in drug discovery and development

The rapidly advancing field of artificial intelligence(AI)has garnered substantial attention for its potential application in drug discovery and development.This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry.AI,encompassing machine learning algorithms,deep learning,and data analytics,offers unprecedented opportunities to streamline and enhance various stages of drug development.This opinion review delved into the current landscape of AI-driven approaches,discussing their utilization in target identification,lead optimization,and predictive modeling of pharmacokinetics and toxicity.We aimed to scrutinize the integration of large-scale omics data,electronic health records,and chemical informatics,highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies.Despite the considerable potential of AI,the review also addressed inherent challenges,including data privacy concerns,interpretability of AI models,and the need for robust validation in realworld clinical settings.Additionally,we explored ethical considerations surrounding AI-driven decision-making in drug development.This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends,presenting critical insights and addressing potential hurdles.In conclusion,this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Sensing and Communication Integrated Fast Neighbor Discovery for UAV Networks

In unmanned aerial vehicle(UAV)networks,the high mobility of nodes leads to frequent changes in network topology,which brings challenges to the neighbor discovery(ND)for UAV networks.Integrated sensing and communication(ISAC),as an emerging technology in 6G mobile networks,has shown great potential in improving communication performance with the assistance of sensing information.ISAC obtains the prior information about node distribution,reducing the ND time.However,the prior information obtained through ISAC may be imperfect.Hence,an ND algorithm based on reinforcement learning is proposed.The learning automaton(LA)is applied to interact with the environment and continuously adjust the probability of selecting beams to accelerate the convergence speed of ND algorithms.Besides,an efficient ND algorithm in the neighbor maintenance phase is designed,which applies the Kalman filter to predict node movement.Simulation results show that the LA-based ND algorithm reduces the ND time by up to 32%compared with the Scan-Based Algorithm(SBA),which proves the efficiency of the proposed ND algorithms.

The dynamics of price discovery between the U.S.and Chinese soybean market:a wavelet approach to understanding the effects of Sino-US trade conflict and COVID-19 pandemic

During geopolitical crises,the price stability of agricultural commodities is critical for national security.Understanding the dynamics of pricing power between the U.S.and China and how it varies over time can help smaller nations navigate unpredictable moments.This study uses a unified framework and wavelet approach to examine soybean price discovery in the U.S.and China from the standpoints of price interdependence and information flows.We begin by illustrating the integrated link between the soybean futures markets in the U.S.and China,which includes multiple structural breaks.The pricing difference between the two nations acts as the primary information spillover route for their integrated relationship.Furthermore,we show that the direction and degree of information spillover change dramatically in proportion to the strength of the U.S.–Chinese soybean interaction.Finally,we find that China’s recent retaliatory tax on the U.S.soybeans gave the Chinese market a more powerful position in soybean futures price discovery.After the first-stage trade deal was reached,and during the epidemic phase of the coronavirus pandemic,the pricing power of the U.S.soybean market showed no signs of full recovery.

Discovery Studio软件在分析中药成分透过血脑屏障中的应用

血脑屏障是药物能否进入脑组织发挥作用的重要屏障。中药有效成分的结构与其透过血脑屏障的能力有一定的关系。Discovery Studio软件对非糖苷类成分通过血脑屏障的分析结果与文献报道基本吻合,而对糖苷类成分则无法准确预测。

GE Discovery XR650 X线系统在儿童双下肢不等长中的临床应用价值

目的:探讨GE Discovery XR650 X线系统在儿童双下肢不等长中的临床应用价值。方法:利用GE DiscoveryXR650 X线系统对18例儿童双下肢不等长的患者进行立位全脊柱、骨盆和全下肢全景拼接,评价图像效果。结果:所得矫正前后36幅图像均满足临床骨科对患者矫正过程中的定性及相对定量评价。结论:GE Discovery XR650 X线系统在儿童双下肢不等长中的应用为骨科医生的全面评估及治疗提供了客观依据,为临床提供准确可靠的影像资料。

GE Discovery 750HD宝石能谱CT典型故障维修2例

0引言 GE公司生产的高端宝石能谱成像Discovery750HD CT采用最新的宝石探测器、动态变焦球管、瞬能高压发生器、自适应统计迭代重建（adaptive statiatical iterative reconstruction,ASiR）引擎和最先进的能谱成像技术,将CT从形态学诊断和单一参数诊断带入到功能学诊断和多参数诊断的崭新领域。

Discovery Studio 2.0的模块扩展及应用:残基水平非键相互作用能量的自动批量计算

非键相互作用对于生物体系中的分子识别和结合过程起着关键作用。然而,传统的方法并不能在残基水平自动批量计算非键相互作用。近年来,已经发展了一些方法和工具进行非键相互作用的计算分析。该文研究发展了一种可以自动计算残基间非键相互作用的方法,即用Perl脚本调用Discovery Studio 2.0(DS 2.0,Accelrys Inc.)底层模块中的非键相互作用协议,实现了直接利用命令行批量计算非键相互作用能量,而无需通过DS2.0的图形界面。该方法扩展了DS2.0的计算模块,并于近期运用到了复合结构的研究分析中。

Autodock Vina与Discovery Studio在虚拟筛选耐药蛋白抑制剂中的比较

随着大量与细菌耐药相关的基因的发现和其表达蛋白结构的成功测定,从已有的化合物中通过计算机模拟方法筛选对耐药蛋白靶点有作用的候选化合物,成为了药物发现的一个标准途径。虚拟筛选在耐药基因抑制剂的发现中可以提高效率、降低实验成本。本文介绍了Autodock Vina和Discovery Studio在基于分子对接法的虚拟筛选中的使用,并对比分析其对β-内酰胺酶活性位点的筛选结果。希望通过这种比较促进虚拟筛选在药物设计领域中的应用,提高耐药基因抑制剂的发现速度。

Discovery Studio软件在药物化学教学中的应用

借助Discovery Studio软件,以3D和2D图形的方式直观展示ATP敏感钾离子通道、雌激素受体等生物大分子的空间结构,对接模拟格列本脲等药物与相关靶点间的作用方式,进而对药物的结构特征或构效关系进行“可视化”解释,帮助学生更好地理解掌握药物化学课程中的重难点内容。

Method and supporting framework for business domain-oriented web service discovery

This paper proposes a new business domain-oriented web service discovery method and framework to solve the low precision results caused by UDDI （universal description,discovery and integration） syntactic discovery and the difficulty in selecting from among functionally equivalent web services.When requesting services,service clusters are extracted from concrete services in terms of functional requests; then, through business information properties consultation,the most suitable services are determined and finally bound to user requests.The whole process is transparent to users. This framework is also tested and supported through a prototype based on a travel domain,IPVita （intelligent platform of virtual travel agency）.

当今科普纪录片运作分析——以Discovery探索频道为例

本文以世界知名品牌Discovery探索频道为案例,主要从节目内容、品牌管理以及经营模式等方面分析其动作的成功因素。

IPv6中Neighbor Discovery协议及其测试

IPv6协议是下一代互联网协议,NeighborDiscovery(邻居发现)协议是IPv6协议的一个重要组成部分,RFC2461是邻居发现协议的标准文本。为了检查各个生产厂家的实现是否与标准文本相一致,文章提出了一种协议一致性测试的方法,并开发了一个进行IPv6一致性测试的系统。该文提出了逻辑测试结构和虚拟测试器的概念,并在这个测试系统中应用了这两种测试结构和概念,还给出了一个邻居发现协议的测试实例。最后,给出了笔者对一种UNIX系统上实现的邻居发现协议进行一致性测试得到的结果,并对其与标准文本不一致的部分进行了分析说明。