Analysis Tools Research Articles

Historical heritage – MultiCriteria Decision Method (H-MCDM) to prioritize intervention strategies for the adaptive reuse of valuable architectural assets

Identifying the most suitable strategy for the adaptive reuse of historic architectural heritage is a complex challenge. This is because each rehabilitation project of an abandoned historic building must not only ensure the simultaneous pursuit of social, cultural and financial goals, but also the transmission of tangible and intangible values to future generations.In this context, the use of multi-criteria approaches helps to synthesise stakeholders' preferences based on multiple and conflicting criteria, with the aim of promoting sustainable solutions for the restoration and valorisation of valuable architectural assets. To this end, we characterise the Historical heritage - MultiCriteria Decision Method (H-MCDM), an analysis and evaluation tool aimed at identifying the best adaptive reuse of historic buildings considering the principles of architectural conservation and both economic and socio-cultural values. It is a model based on the Analytic Hierarchy Process (AHP), which employs 7 evaluation indicators, chosen after a critical and interpretative analysis of both the sustainability principles promoted by the United Nations and UNESCO, and the ICOMOS quality principles.The H-MCDM is structured on the following five logical-operational steps: (i) identification of strategies for adaptive reuse of the historic building; (ii) construction of the matrices of the pairwise comparisons of the criteria and estimation of the vector of the weights of the criteria; (iii) construction of the matrices of the pairwise comparisons of the alternatives against each evaluation criterion and estimation of the local scores of the alternatives; (iv) consistency checks of the decision matrices; (v) calculation of the total priority and ranking of the HBU strategies for historic assets.The application to a case study demonstrates that, due to its adaptability and versatility, the H-MCDM can be a valid and consistent decision support in public resource allocation processes for the rehabilitation of architectural heritage.

Phage-Displayed SH2 Domain Libraries: From Ultrasensitive Tyrosine Phosphoproteome Probes to Translational Research.

Tyrosine phosphorylation is a critical regulator of cell signaling. A large fraction of the tyrosine phosphoproteome, however, remains uncharacterized, largely due to a lack of robust and scalable methods. The Src homology 2 (SH2) domain, a structurally conserved protein domain present in many intracellular signal-transducing proteins, naturally binds phosphorylated tyrosine (pTyr) residues, providing an ideal scaffold for the development of sensitive pTyr probes. Its modest affinity, however, has greatly limited its application. Phage display is an in vitro technique used for identifying ligands for proteins and other macromolecules. Using this technique, researchers have been able to engineer SH2 domains to increase their affinity and customize their specificity. Indeed, highly diverse phage display libraries have enabled the engineering of SH2 domains as affinity-purification (AP) tools for proteomic analysis as well as probes for aberrant tyrosine signaling detection and rewiring, and represent a promising class of novel diagnostics and therapeutics. This review describes the unique structure-function characteristics of SH2 domains, highlights the fundamental contribution of phage display in the development of technologies for the dissection of the tyrosine phosphoproteome, and highlights prospective uses of SH2 domains in basic and translational research.

Yantiao Formula intervention in Rats with Sepsis: Network Pharmacology and Experimental Analysis.

Traditional Chinese Medicine prescribes Yantiao Formula (YTF; peach kernel, mirabilite, Angelica sinensis, Radix Scrophulariae, raw rhubarb, Radix Paeoniae, Flos Lonicerae, Forsythia, and Ophiopogon japonicus) to treat sepsis. Clinically, it reduced the inflammatory response of sepsis. It also reduced lung damage by decreasing the level of TNF-α in septic rats' serum. Using network pharmacology analysis, we investigated the efficacy network and mechanism of YTF in treating sepsis. We used the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and a Bioinformatics Analysis Tool for Molecular Mechanisms of Traditional Chinese Medicine (BATMAN-TCM) combined with literature to collect the main components in YTF and their targets. DisGeNET and GENECARDS databases were used for sepsis-related targets. Cytoscape 3.7.1 software was used to construct the herbcomponent- target and ingredient-target-disease interaction protein-protein interaction networks of YTF. The jvenn was used to perform the intersection of YTF targets and sepsis targets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed． We also created a sepsis rat model using cecal ligation and perforation and stimulated alveolar macrophages (NR8383) with endotoxin to investigate the mechanisms of YTF. GO, and KEGG enrichment analysis revealed that these targets involved mineralocorticoid secretion, aldosterone secretion, active regulation of chronic inflammatory response, the exogenous coagulation pathway, and other pathophysiology. It was linked to various inflammatory factors and the MAPK pathway. YTF inhibits the p38MAPK pathway and decreases TNF- α, IL-6, and CXCL8 levels. YTF has a multi-component, multi-target, and multi-channel role in treating sepsis. The primary mechanisms may involve inhibiting the p38MAPK pathway to reduce the inflammatory response.

A cognitive level evaluation method based on machine learning approach and Bloom of taxonomy for online assessments

Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners’ cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners’ cognitive levels during the online learning process. However, most of the currently used techniques for evaluating cognitive levels rely on labour-intensive and time-consuming manual coding. In this study, we explore the machine learning (ML) algorithms and taxonomy of Bloom’s cognitive levels to explore features that affect learner’s cognitive level in online assessments and the ability to automatically predict learner’s cognitive level and thus, come up with a recommendation or pedagogical intervention to improve learner’s acquisition. The analysis of 15,182 learners’ assessments of a specific learning concept affirms the effectiveness of our approach. We attain an accuracy of 82.21% using ML algorithms. These results are very encouraging and have implications for how automated cognitive-level analysis tools for online learning will be developed in the future.

Faktor yang Mempengaruhi Tingkat Kecurangan Karyawan PT. BUM Pabrik Perikanan Samudera Kendari

The purpose of this study is to provide empirical evidence of the influence of employee attitudes toward fraud on PT. BUM Kendari ocean fisheries, provide empirical evidence of the effect of pressure on the employee to fraud PT. BUM Kendari ocean fisheries, provide empirical evidence influence employees the opportunity to fraud at PT. BUM Kendari ocean fisheries, provide empirical evidence of the influence of employee capability to fraud at PT. BUM Kendari ocean fisheries, provide empirical evidence influence attitudes, the pressure occasion, and employee capability simultaneously to fraud on PT. BUM Kendari ocean fisheries. The population in this study were employees of PT. BUM Kendari ocean fisheries as many as 42 employees. The sampling technique using census method. Census method is sampling technique when all members of the population used as a sample. The analysis tool used is the Multiple Linear Regression Analysis. The results showed that There is a significant influence on the attitude (Rationalization) against fraud with a significance value of 0.045. There is a significant influence of pressure against fraud with a significance value of 0.008. There is a significant influence of opportunities against fraud with a significance value of 0.027. There is a significant influence on the capability of the fraud with a significance value of 0.002. There is a significant influence on the attitude (Rationalization), Pressure, opportunity and capability against fraud with a significance value of 0.009.

Optimizing the Performance and Governance of MSMEs in the Special Region of Yogyakarta Through Digitalization

A significant feature of technology is its capacity to change the routines and behavior of business players, especially MSMEs actors. MSMEs players in the Special Area have been impacted by the Covid-19 outbreak and are now looking to the MSMEs sector for business opportunities. It is necessary to overcome the difficulty of attaining the digitization of MSMEs. The fact that the MSMEs actors are not fundamentally and mentally equipped is another problem. The study's objective is to evaluate the effectiveness of using digital literacy, e-commerce, and skills as a tactic to enhance MSMEs governance and performance. The main sources of data for the study were imaginative MSMEs Daerah Istimewa Yogyakarta actors. The analytical unit of the study is the manager or owner of MSMEs in Daerah Istimewa Yogyakarta. In this study, primary data are gathered by a questionnaire. The sample size was established using a simple proportionate random sampling approach. The AMOS SEM analysis tool and a sample of 20% of all MSMEs in each district and city of the Daerah Istimewa Yogyakarta are used to achieve this. According to the study's findings, E-Commerce affects the MSMEs Governance, Digital Expertise affects MSMEs Governance and Performance, MSMEs Governance the performance of MSMEs. However, E-Commerce has no effect on the performance of MSMEs.

An improved IPA approach driven by big data and its application to customer satisfaction research of energy-saving appliance

Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users’ opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers.

Linearized Models of the Coupled Rotorcraft Flight Dynamics and Acoustics for Real-Time Noise Prediction

This article demonstrates the linearization of the coupled rotorcraft flight dynamics and aeroacoustics to provide realtime acoustic predictions in generalized maneuvering flight. To demonstrate the methodology, the study makes use of a nonlinear simulation model of a generic utility helicopter (PSUHeloSim) that is coupled with an aeroacoustic solver based on a marching cubes algorithm. A periodic equilibrium of the coupled rotorcraft flight dynamics and acoustics is first found at a desired flight condition using a modified harmonic balance trim solution method. Next, the nonlinear time-periodic dynamics are linearized about that periodic equilibrium and transformed into an equivalent higher order linear time-invariant system in harmonic decomposition form. Composite aeroacoustic measures are included as an output of this system. To speed up runtime and make control design tractable, the order of these harmonic decomposition models is reduced via residualization to an 8-state model where the states are representative of the rigid-body dynamics of the aircraft. This 8-state model is shown to provide accurate acoustic response predictions for small-amplitude pilot inputs and to abate runtime by a factor of approximately 104, thus enabling acoustic predictions in generalized maneuvering flight that are significantly faster than real time. The 8-state model is subsequently used to demonstrate the use of linear system tools for the dynamic analysis of the coupled rotorcraft flight dynamics and acoustics.

Sliding-window cross-correlation and mutual information methods in the analysis of solar wind measurements

Context. When describing the relationships between two data sets, four crucial aspects must be considered, namely: timescales, intrinsic lags, linear relationships, and non-linear relationships. We present a tool that combines these four aspects and visualizes the underlying structure where two data sets are highly related. The basic mathematical methods used here are cross-correlation and mutual information (MI) analyses. As an example, we applied these methods to a set of two-month’s worth of solar wind density and total magnetic field strength data. Aims. Two neighboring solar wind parcels may have undergone different heating and acceleration processes and may even originate from different source regions. However, they may share very similar properties, which would effectively “hide” their different origins. When this hidden information is mixed with noise, describing the relationships between two solar wind parameters becomes challenging. Time lag effects and non-linear relationships between solar wind parameters are often overlooked, while simple time-lag-free linear relationships are sometimes insufficient to describe the complex processes in space physics. Thus, we propose this tool to analyze the monotonic (or linear) and non-monotonic (or non-linear) relationships between a pair of solar wind parameters within a certain time period, taking into consideration the effects of different timescales and possible time lags. Methods. Our tool consists of two parts: the sliding-window cross-correlation (SWCC) method and sliding-window mutual information (SWMI) method. As their names suggest, both parts involve a set of sliding windows. By independently sliding these windows along the time axis of the two time series, this technique can assess the correlation coefficient (and mutual information) between any two windowed data sets with any time lags. Visualizing the obtained results enables us to identify structures where two time series are highly correlated, while providing information on the relevant timescales and time lags. Results. We applied our proposed tool to solar wind density and total magnetic field strength data. Structures with distinct timescales were identified. Our tool also detected the presence of short-term anti-correlations coexisting with long-term positive correlations between solar wind density and magnetic field strength. Some non-monotonic relationships were also found. Conclusions. The visual products of our tool (the SWCC+SWMI maps) represent an innovative extension of traditional numerical methods, offering users a more intuitive perspective on the data. The SWCC and SWMI methods can be used to identify time periods where one parameter has a strong influence on the other. Of course, they can also be applied to other data, such as multi-wavelength photometric and spectroscopic time series, thus providing a new tool for solar physics analyses.

Learning Aligned Vertex Convolutional Networks for Graph Classification.

Graph convolutional networks (GCNs) are powerful tools for graph structure data analysis. One main drawback arising in most existing GCN models is that of the oversmoothing problem, i.e., the vertex features abstracted from the existing graph convolution operation have previously tended to be indistinguishable if the GCN model has many convolutional layers (e.g., more than two layers). To address this problem, in this article, we propose a family of aligned vertex convolutional network (AVCN) models that focus on learning multiscale features from local-level vertices for graph classification. This is done by adopting a transitive vertex alignment algorithm to transform arbitrary-sized graphs into fixed-size grid structures. Furthermore, we define a new aligned vertex convolution operation that can effectively learn multiscale vertex characteristics by gradually aggregating local-level neighboring aligned vertices residing on the original grid structures into a new packed aligned vertex. With the new vertex convolution operation to hand, we propose two architectures for the AVCN models to extract different hierarchical multiscale vertex feature representations for graph classification. We show that the proposed models can avoid iteratively propagating redundant information between specific neighboring vertices, restricting the notorious oversmoothing problem arising in most spatial-based GCN models. Experimental evaluations on benchmark datasets demonstrate the effectiveness.

Agile fusion: developing 'Eat at Right Place' sentiment analysis tool

This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.

InVADo: Interactive Visual Analysis of Molecular Docking Data.

Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.

Функционально-трудоемкостный анализ кадровых процессов системы менеджмента качества организации

The article discusses an approach to improving personnel (supporting) processes in the organisation’s quality management system based on the methodology tool modification of the functional-cost analysis (FCA) and the qualimetric substantiation of critical indicators of the studied process function parameters. The paper analyses tools and technologies for structuring, correcting, and improving personnel processes in the organization’s quality management system as well as problems associated with their application in practice. The expediency for operational determination of the direction for improving the personnel process quality is substantiated by studying a limited number of their parameters. Importance (significance) and labour intensity of personnel process functions are considered as the studied parameters. A procedure for functional and labour-intensive analysis (FLIA) is proposed, which is the personnel process improvement based on analysing the importance-labour intensity ratio of function implementation, allowing the process owner to quickly obtain information about the process state. The procedure of functional and labour-intensive analysis is considered through the approbation on the example of the personnel process “Personnel Recruitment.” Critical indicators of “Personnel Recruitment” structure are compared with similar indicators obtained based on applying functional-cost analysis to the process under consideration. The article argues for the promising use of the proposed procedure for enhancing the personnel processes of the organization’s quality management system, aimed at identifying the direction for their improvement.

Impact of internal pre-damage due to drying shrinkage under various humidity conditions on DEF expansion in cement paste and mortar

This study explores the impact of internal pre-damage from early drying shrinkage on the delayed ettringite formation (DEF) expansion behavior of cement-based materials subjected to high-temperature processes. Specimens of cement paste and mortar with water-cement (W/C) ratios of 0.4, 0.5, and 0.6 were characterized after curing under relative humidity (RH) conditions of 50, 70, and 100 for 56 days. The parameters assessed were drying shrinkage, dynamic elastic modulus, and free water content. Subsequent DEF expansion over time was quantified, and the underlying mechanism was analyzed using XRD and SEM techniques after 270 days of underwater curing. Results indicate that: (1) a lack of adequate early curing humidity significantly reduces the dynamic elastic modulus with more than a 10% drop in cement paste following 56 days at RH50; (2) a higher W/C ratio and lower early curing humidity lead to more internal damage due to shrinkage, thereby promoting DEF expansion; (3) drying intensifies internal pre-damage in the interfacial transition zone (ITZ), causing an earlier deterioration of DEF expansion in mortar compared to cement paste. However, the presence of aggregates constrains this, leading to lesser DEF expansion in mortar; (4) dynamic elastic modulus changes as measured by ultrasonic methods can quantitatively characterize the extent of deterioration caused by DEF expansion, thus offering a tool for non-destructive testing and DEF expansion deterioration analysis.

Misuse of XPS in Analyzing Solid Polymer Electrolytes for Lithium Batteries

One of the most powerful spectroscopic tools for battery analysis is X-ray photoelectron spectroscopy (XPS); however, its great power, must be accompanied by great responsibility for authenticity. Fluorine is documented to be unstable under XPS conditions, and fluorinated salts used in Li batteries show photodecomposition. As all-solid-state batteries advance, demand for surface characterization is increasing. Here, a popular solid polymer electrolyte comprising a fluorinated salt in a PEO matrix was measured by XPS. Rapid photodecomposition after few minutes produced mainly LiF, initially not found on the surface. Not being aware of such artifacts may lead to an erroneous analysis of the characterized electrochemical system.

Abstract 7426: The GenePattern ecosystem for cancer bioinformatics

Abstract As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The GenePattern ecosystem, first introduced in 2004 and updated continually to support the changing needs of cancer genomics research, supports the analytical, computational, and reproducibility needs of the world-wide cancer analysis community.The GenePattern server, available at www.genepattern.org, provides hundreds of analysis methods for scientists at all levels of computational sophistication, with the only requirement for use being a web browser. The GenePattern server offers bulk and single-cell RNA-seq, copy number variation, flow cytometry, network analysis, general machine learning, gene set enrichment analysis, proteomics, and many other modalities. Analysis steps can be linked together into pipelines that can then be edited, shared, and made public. All parameters of an analysis, including the code version, are recorded so that an analytical result can be reproduced at any point in the future.The GenePattern Notebook system, notebook.genepattern.org, is an integration of the Jupyter Notebook environment with the GenePattern server. It combines the research narrative capabilities of Jupyter with the non-programming approach and breadth of analyses available in GenePattern. Scientists using GenePattern Notebook can create documents that include richly-formatted text and multimedia, executable code, and GenePattern analyses. A single GenePattern notebook can therefore comprise a sophisticated analytical workflow that runs on multiple remote servers.We have recently expanded GenePattern Notebook into a new notebook environment, Genomics to Notebook, g2nb.org, which adds the ability to include analyses on any public Galaxy server as well as using the Integrative Genomics Viewer (IGV) and Cytoscape within notebooks. Each analysis or visualization appears a cell within a notebook, preserving the accessibility and ease of use of the notebook metaphor while retaining the essential aspects of each tool’s user interface. When run, the entire analysis appears to execute seamlessly within the notebook. The online workspace also includes a library of featured genomic analysis notebooks, including templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. Citation Format: Michael M. Reich, Thorin Tabor, John Liefeld, Edwin Huang, Forrest Kim, Jill P. Mesirov. The GenePattern ecosystem for cancer bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7426.

Abstract 920: Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research

Abstract Data sharing plays a crucial role in accelerating biomedical research discoveries, promoting rigorous and reproducible research, enabling research data reuse, and fostering public trust. To facilitate data sharing, a coordinated, sustainable, and streamlined infrastructure is required to store, preserve, retrieve, and make data findable, accessible, interoperable, and reusable. Starting January 25, 2023, researchers funded by the National Institutes of Health (NIH) are required to comply with the NIH Data Management and Sharing Policy (https://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-and-sharing-policies/data-management-and-sharing-policy-overview) and indicate how research data funded by the NIH will be managed and broadly shared. As institutions prepare to meet these requirements, the National Cancer Institute (NCI) is looking to understand the existing processes of data storage, preservation, and sharing including what infrastructure and services are available to facilitate data sharing. To carry out this goal, the NCI’s Office of Data Sharing (ODS), dedicated to promoting responsible and efficient sharing of cancer research data, issued a Request for Information (RFI) to understand the existing data sharing processes for NIH-Funded Research. The RFI was designed to collect information on services (e.g. guidance on data standards and file formats), technologies (e.g. storage, compression, retrieval, archival, analysis tools, workbenches), and processes (e.g. governance, communication, education, outreach) that promote data sharing. ODS utilized the RFI to gather input from stakeholders involved in facilitating data sharing. This included personnel from various fields such as research laboratories, scientific instrumentation core facilities, offices of research and/or sponsored programs, provost offices or other research administration, libraries, information technology, and institutional review boards. We also engaged with bioinformaticians and data scientists who are actively contributing to data curation, formatting, and analysis. Here, we present analysis of the responses received, including insights relating to gaps and challenges in data sharing, and proposed next steps and recommendations. Citation Format: Mousumi Ghosh, Nathan Boyd, Subhashini Jagu. Insights from the National Cancer Institute’s request for information on existing data sharing processes for NIH-funded research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 920.

Water Energy Food Nexus in Urbanizing Wetlands: Challenges and Solutions Explored through Choremes and Focus Maps

Addressing complex interactions within water, energy, and food (WEF) resources, innovative tools for in-depth analysis and decision-making are imperative. This study introduces chorematic focus maps (CFMs) as a groundbreaking method to visualize and tackle the WEF nexus’s complexities, focusing specifically on the Danube Delta Biosphere Reserve (DDBR). By merging geospatial analysis with on-site validation, this research reveals intricate interdependencies within the nexus and positions CFMs as an effective tool for stakeholders. This study adopts a methodological approach that focuses on identifying human activities and evaluating their impacts on the WEF nexus, with the goal of developing practical and grounded strategies for managing these essential resources. By testing this approach within the DDBR, the potential for wider application is demonstrated, offering a promising framework for addressing similar socio-environmental challenges across various regions. Future research directions include refining CFMs’ precision and practicality through extended fieldwork and stakeholder engagement, testing the framework’s adaptability across various locations and nexus dynamics. Additionally, incorporating cutting-edge technologies such as machine learning could provide deeper insights and reinforce CFMs’ role in decision support for the WEF nexus. Conclusively, this investigation into the WEF nexus through CFMs emphasizes the critical need for strategies that navigate the complexities of environmental management and resource optimization, marking CFMs as a significant tool for both decision-makers and researchers.

A Universal and Programmable Platform based on Fluorescent Peptide-Conjugated Probes for Detection of Proteins in Organelles of Living Cells.

Realizing protein analysis in organelles of living cells is of great significance for developing diagnostic and therapeutic methods of diseases. Fluorescent-labeled antibodies with well imaging performance and high affinity are classical biochemical tools for protein analysis, while due to the inability to effectively enter into cells, not to mention organelles and the uncontrollable reaction sites that might cause antibodies inactivation when chemically modification, they are hard to apply to living cells. Inspired by the structure of fluorescent-labeled antibodies, we designed as a universal detection platform that was based on the peptide-conjugated probes (PCPs) and consisted of three parts: a) a rotor type fluorescent molecular scaffold for conjugation and signal output; b) the cell penetration protein recognition unit; c) the subcellular organelle targeting unit. In living cells, PCPs could firstly localize at organelles and then proceed protein specific recognition, thus jointly leading to the restriction of twisted intramolecular charge transfer and activation of fluorescence signal. As a proof-of-concept, six different proteins in three typical intracellular organelles could be detected by our platform through simply replacing the recognition sequence of proteins and matching organelle targeting units. The position and intensity of fluorescence signals demonstrated specificity of PCPs and universality of the platform.

A Universal and Programmable Platform based on Fluorescent Peptide‐Conjugated Probes for Detection of Proteins in Organelles of Living Cells

AbstractRealizing protein analysis in organelles of living cells is of great significance for developing diagnostic and therapeutic methods of diseases. Fluorescent‐labeled antibodies with well imaging performance and high affinity are classical biochemical tools for protein analysis, while due to the inability to effectively enter into cells, not to mention organelles and the uncontrollable reaction sites that might cause antibodies inactivation when chemically modification, they are hard to apply to living cells. Inspired by the structure of fluorescent‐labeled antibodies, we designed as a universal detection platform that was based on the peptide‐conjugated probes (PCPs) and consisted of three parts: a) a rotor type fluorescent molecular scaffold for conjugation and signal output; b) the cell penetration protein recognition unit; c) the subcellular organelle targeting unit. In living cells, PCPs could firstly localize at organelles and then proceed protein specific recognition, thus jointly leading to the restriction of twisted intramolecular charge transfer and activation of fluorescence signal. As a proof‐of‐concept, six different proteins in three typical intracellular organelles could be detected by our platform through simply replacing the recognition sequence of proteins and matching organelle targeting units. The position and intensity of fluorescence signals demonstrated specificity of PCPs and universality of the platform.