A Data-Semantic-Conflict-Based Multi-Truth Discovery Algorithm for a Programming Site

With the extensive application of software collaborative development technology,the processing of code data generated in programming scenes has become a research hotspot.In the collaborative programming process,different users can submit code in a distributed way.The consistency of code grammar can be achieved by syntax constraints.However,when different users work on the same code in semantic development programming practices,the development factors of different users will inevitably lead to the problem of data semantic conflict.In this paper,the characteristics of code segment data in a programming scene are considered.The code sequence can be obtained by disassembling the code segment using lexical analysis technology.Combined with a traditional solution of a data conflict problem,the code sequence can be taken as the declared value object in the data conflict resolution problem.Through the similarity analysis of code sequence objects,the concept of the deviation degree between the declared value object and the truth value object is proposed.A multi-truth discovery algorithm,called the multiple truth discovery algorithm based on deviation(MTDD),is proposed.The basic methods,such as Conflict Resolution on Heterogeneous Data,Voting-K,and MTRuths_Greedy,are compared to verify the performance and precision of the proposed MTDD algorithm.

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.

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.

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.

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.

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.

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.

JWST Discoveries and the Hypersphere World-Universe Model: Transformative New Cosmology

Twenty-six years ago, a small committee report built upon earlier studies to articulate a compelling and poetic vision for the future of astronomy. This vision called for an infrared-optimized space telescope with an aperture of at least four meters. With the support of their governments in the US, Europe, and Canada, 20,000 people brought this vision to life as the 6.5-meter James Webb Space Telescope (JWST). The telescope is working perfectly, delivering much better image quality than expected [1]. JWST is one hundred times more powerful than the Hubble Space Telescope and has already captured spectacular images of the distant universe. A view of a tiny part of the sky reveals many well-formed spiral galaxies, some over thirteen billion light-years away. These observations challenge the standard Big Bang Model (BBM), which posits that early galaxies should be small and lack well-formed spiral structures. JWST’s findings are prompting scientists to reconsider the BBM in its current form. Throughout the history of science, technological advancements have led to new results that challenge established theories, sometimes necessitating their modification or even abandonment. This happened with the geocentric model four centuries ago, and the BBM may face a similar reevaluation as JWST provides more images of the distant universe. In 1937, P. Dirac proposed the Large Number Hypothesis and the Hypothesis of Variable Gravitational Constant, later incorporating the concept of Continuous Creation of Matter in the universe. The Hypersphere World-Universe Model (WUM) builds on these ideas, introducing a distinct mechanism for matter creation. WUM is proposed as an alternative to the prevailing BBM. Its main advantage is the elimination of the “Initial Singularity” and “Inflation”, offering explanations for many unresolved problems in Cosmology. WUM is presented as a natural extension of Classical Physics with the potential to bring about a significant transformation in both Cosmology and Classical Physics. Considering JWST’s discoveries, WUM’s achievements, and 87 years of Dirac’s proposals, it is time to initiate a fundamental transformation in Astronomy, Cosmology, and Classical Physics. The present paper is a continuation of the published article “JWST Discoveries—Confirmation of World-Universe Model Predictions” [2] and a summary of the paper “Hypersphere World-Universe Model: Digest of Presentations John Chappell Natural Philosophy Society” [3]. Many results obtained there are quoted in the current work without full justification;interested readers are encouraged to view the referenced papers for detailed explanations.

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.

Rediscovering Don Swanson:The Past,Present and Future of Literature-based Discovery

Purpose： The late Don R. Swanson was well appreciated during his lifetime as Dean of the Graduate Library School at University of Chicago, as winner of the American Society for Information Science Award of Merit for 2000, and as author of many seminal articles. In this informal essay, I will give my personal perspective on Don＇s contributions to science, and outline some current and future directions in literature-based discovery that are rooted in concepts that he developed.Design/methodology/approach： Personal recollections and literature review. Findings： The Swanson A-B-C model of literature-based discovery has been successfully used by laboratory investigators analyzing their findings and hypotheses. It continues to be a fertile area of research in a wide range of application areas including text mining, drug repurposing, studies of scientific innovation, knowledge discovery in databases, and bioinformatics. Recently, additional modes of discovery that do not follow the A-B-C model have also been proposed and explored （e.g. so-called storytelling, gaps, analogies, link prediction, negative consensus, outliers, and revival of neglected or discarded research questions）. Research limitations： This paper reflects the opinions of the author and is not a comprehensive nor technically based review of literature-based discovery. Practical implications： The general scientific public is still not aware of the availability of tools for literature-based discovery. Our Arrowsmith project site maintains a suite of discovery tools that are free and open to the public （http：//arrowsmith.psych.uic.edu）, as does BITOLA which is maintained by Dmitar Hristovski （http：// http：//ibmi.mf.uni-lj.si/bitola）, and Epiphanet which is maintained by Trevor Cohen （http：//epiphanet.uth.tme.edu/）. Bringing user-friendly tools to the public should be a high priority, since even more than advancing basic research in informatics, it is vital that we ensure that scientists actually use discovery tools and that these are actually able to help them make experimental discoveries in the lab and in the clinic. Originality/value： This paper discusses problems and issues which were inherent in Don＇s thoughts during his life, including those which have not yet been fully taken up and studied systematically.

Metabolomics in drug discovery: Restoring antibiotic pipeline

Metabolomics has emerged as a valuable tool in drug discovery and development,providing new insights into the mechanisms of action and toxicity of potential therapeutic agents.Metabolomics focuses on the comprehensive analysis of primary as well as secondary metabolites,within biological systems.Metabolomics provides a comprehensive understanding of the metabolic changes that occur within microbial pathogens when exposed to therapeutic agents,thus allowing for the identification of unique metabolic targets that can be exploited for therapeutic intervention.This approach can also uncover key metabolic pathways essential for survival,which can serve as potential targets for novel antibiotics.By analyzing the metabolites produced by diverse microbial communities,metabolomics can guide the discovery of previously unexplored sources of antibiotics.This review explores some examples that enable medicinal chemists to optimize drug structure,enhancing efficacy and minimizing toxicity via metabolomic approaches.

关于GE Discovery CT 750 HD设备故障分析与维修

GE Discovery CT 750 HD是临床诊断中先进的CT诊疗设备,在运行中,以高灵敏度宝石晶体探测器、能谱成像、动态变焦球管、ASIR重建引擎等技术体系为主,运用这些技术不仅提高了空间分辨力和密度分辨力,而且使扫描速度也相应提高,可以很好地满足临床诊断的需求。但是,在GE Discovery CT 750 HD运行期间,也经常会发生故障,这时就需要我们进行分析诊断,有针对性地进行处理,避免故障产生带来负面影响,影响临床诊疗工作。本文针对该方面,展开分析和阐述,以期为相关工程技术人员研究提供一定的参考。

GE公司Discovery 690 PET/CT设备常见故障的维修分析及维护保养

随着医疗设备的不断发展,PET/CT使用已经越来越广泛,特别是近5年的发展,该设备在世界上配置了近200台,其中亚洲配置近25台,在医学影像设备增长中相对较快。其中GE公司生产的型号为GE Discovery 690 PET/CT设备装机量也大幅增加,该设备除了保持原有传统CT及PET的优点外,还改善了单CT机成像不完善的问题,有效地提升了患者医学检查效率,大大节省了患者等待时间以及医护人员的工作时间。当前GE Discovery 690 PET/CT设备,有着采集图像快、图像质量高、分辨率较高、分析精确的特性,深受医疗机构的喜爱。但是,由于PET/CT装机量不断增加,随之而来的维修、维护及保养问题也日渐突出,需要继续解决。本文就型号为GE Discovery 690 PET/CT设备常见的故障进行分析,总结经验,提出建设性的意见及措施,为设备工程维修人员能够借鉴及相应的技术支持。

Process Mining Discovery Techniques for Software Architecture Lightweight Evaluation Framework

This research recognizes the limitation and challenges of adaptingand applying Process Mining as a powerful tool and technique in theHypothetical Software Architecture (SA) Evaluation Framework with thefeatures and factors of lightweightness. Process mining deals with the largescalecomplexity of security and performance analysis, which are the goalsof SA evaluation frameworks. As a result of these conjectures, all ProcessMining researches in the realm of SA are thoroughly reviewed, and ninechallenges for Process Mining Adaption are recognized. Process mining isembedded in the framework and to boost the quality of the SA model forfurther analysis, the framework nominates architectural discovery algorithmsFlower, Alpha, Integer Linear Programming (ILP), Heuristic, and Inductiveand compares them vs. twelve quality criteria. Finally, the framework’s testingon three case studies approves the feasibility of applying process mining toarchitectural evaluation. The extraction of the SA model is also done by thebest model discovery algorithm, which is selected by intensive benchmarkingin this research. This research presents case studies of SA in service-oriented,Pipe and Filter, and component-based styles, modeled and simulated byHierarchical Colored Petri Net techniques based on the cases’ documentation.Processminingwithin this framework dealswith the system’s log files obtainedfrom SA simulation. Applying process mining is challenging, especially for aSA evaluation framework, as it has not been done yet. The research recognizesthe problems of process mining adaption to a hypothetical lightweightSA evaluation framework and addresses these problems during the solutiondevelopment.

Applications and prospects of cryo-EM in drug discovery

Drug discovery is a crucial part of human healthcare and has dramatically benefited human lifespan and life quality in recent centuries, however, it is usually time-and effort-consuming. Structural biology has been demonstrated as a powerful tool to accelerate drug development. Among different techniques, cryo-electron microscopy(cryo-EM) is emerging as the mainstream of structure determination of biomacromolecules in the past decade and has received increasing attention from the pharmaceutical industry. Although cryo-EM still has limitations in resolution, speed and throughput, a growing number of innovative drugs are being developed with the help of cryo-EM. Here, we aim to provide an overview of how cryo-EM techniques are applied to facilitate drug discovery. The development and typical workflow of cryo-EM technique will be briefly introduced, followed by its specific applications in structure-based drug design, fragment-based drug discovery, proteolysis targeting chimeras, antibody drug development and drug repurposing. Besides cryo-EM, drug discovery innovation usually involves other state-of-the-art techniques such as artificial intelligence(AI), which is increasingly active in diverse areas. The combination of cryo-EM and AI provides an opportunity to minimize limitations of cryo-EM such as automation, throughput and interpretation of mediumresolution maps, and tends to be the new direction of future development of cryo-EM. The rapid development of cryo-EM will make it as an indispensable part of modern drug discovery.

Lightweight privacy-preserving truth discovery for vehicular air quality monitoring

Air pollution has become a global concern for many years.Vehicular crowdsensing systems make it possible to monitor air quality at a fine granularity.To better utilize the sensory data with varying credibility,truth discovery frameworks are introduced.However,in urban cities,there is a significant difference in traffic volumes of streets or blocks,which leads to a data sparsity problem for truth discovery.Protecting the privacy of participant vehicles is also a crucial task.We first present a data masking-based privacy-preserving truth discovery framework,which incorporates spatial and temporal correlations to solve the sparsity problem.To further improve the truth discovery performance of the presented framework,an enhanced version is proposed with anonymous communication and data perturbation.Both frameworks are more lightweight than the existing cryptography-based methods.We also evaluate the work with simulations and fully discuss the performance and possible extensions.

The role of SLC12A family of cation-chloride cotransporters and drug discovery methodologies

The solute carrier family 12(SLC12)of cation-chloride cotransporters(CCCs)comprises potassium chloride cotransporters(KCCs,e.g.KCC1,KCC2,KCC3,and KCC4)-mediated Cl^(-)extrusion,and sodium potassium chloride cotransporters(N[K]CCs,NKCC1,NKCC2,and NCC)-mediated Cl^(-)loading.The CCCs play vital roles in cell volume regulation and ion homeostasis.Gain-of-function or loss-of-function of these ion transporters can cause diseases in many tissues.In recent years,there have been considerable advances in our understanding of CCCs'control mechanisms in cell volume regulations,with many techniques developed in studying the functions and activities of CCCs.Classic approaches to directly measure CCC activity involve assays that measure the transport of potassium substitutes through the CCCs.These techniques include the ammonium pulse technique,radioactive or nonradioactive rubidium ion uptakeassay,and thallium ion-uptake assay.CCCs'activity can also be indirectly observed by measuring gaminobutyric acid(GABA)activity with patch-clamp electrophysiology and intracellular chloride concentration with sensitive microelectrodes,radiotracer^(36)Cl^(-),and fluorescent dyes.Other techniques include directly looking at kinase regulatory sites phosphorylation,flame photometry,22Nat uptake assay,structural biology,molecular modeling,and high-throughput drug screening.This review summarizes the role of CCCs in genetic disorders and cell volume regulation,current methods applied in studying CCCs biology,and compounds developed that directly or indirectly target the CCCs for disease treatments.

Device Discovery in D2D Communication: Scenarios and Challenges

Device to Device (D2D) communication is expected to be anessential part of 5G cellular networks. D2D communication enables closeproximitydevices to establish a direct communication session. D2D communicationoffers many advantages, such as reduced latency, high data rates,range extension, and cellular offloading. The first step to establishing a D2Dsession is device discovery;an efficient device discovery will lead to efficientD2D communication. D2D device further needs to manage its mode of communication,perform resource allocation, manage its interference and mostimportantly control its power to improve the battery life of the device. Thiswork has developed six distinct scenarios in which D2D communication canbe initiated, considering their merits, demerits, limitations, and optimizationparameters. D2D communication procedures for the considered scenarioshave been formulated, based upon the signal flow, containing device discovery,resource allocation, and session teardown. Finally, latency for each scenariohas been evaluated, based on propagation and processing delays.

A Hybrid Approach to Neighbour Discovery in Wireless Sensor Networks

In the contemporary era of unprecedented innovations such as Internet of Things(IoT),modern applications cannot be imagined without the presence of Wireless Sensor Network(WSN).Nodes in WSN use neighbour discovery(ND)protocols to have necessary communication among the nodes.Neighbour discovery process is crucial as it is to be done with energy efficiency and minimize discovery latency and maximize percentage of neighbours discovered.The current ND approaches that are indirect in nature are categorized into methods of removal of active slots from wake-up schedules and intelligent addition of new slots.The two methods are found to have certain drawbacks.Thefirst category disturbs original integrity of wake-up schedules leading to reduced chances of discovering new nodes in WSN as neighbours.When second category is followed,it may have inefficient slots in the wake-up schedules leading to performance degradation.Therefore,the motivation behind the work in this paper is that by combining the two categories,it is possible to reap benefits of both and get rid of the limitations of the both.Making a hybrid is achieved by introducing virtual nodes that help maximize performance by ensuring original integrity of wake-up schedules and adding of efficient active slots.Thus a Hybrid Approach to Neighbour Discovery(HAND)protocol is realized in WSN.The simulation study revealed that HAND outperforms the existing indirect ND models.