Traditional Approaches Research Articles

Apocalyptic praxis in Evagrius of Pontus and Francis of Assisi

Abstract The Christian mystical tradition approaches the apocalyptic as praxis – a way of living that renounces the world as it is, lives proleptically into a counter-world of God's reign and practices indifferent freedom in the meantime to love God and neighbour. Although concerns about the ethical viability of such a disposition have merit, this essay demonstrates its constructive possibility through recourse to two archives: the writings of Evagrius of Pontus and the witness of Francis of Assisi. By recovering a scriptural distinction between world and creation, and by emphasising the posture of holy indifference, apocalyptic praxis offers a resource and guide.

Nondestructive assessment of ZnO surge arrester operating condition based on time-domain dielectric characteristics

Given the expansion of the economy and the rise in electricity demand, it is imperative to ensure the safe operation of the power system. Zinc oxide (ZnO) surge arresters play a crucial role in preventing overvoltage that can potentially harm the grid. The study presents an innovative non-destructive assessment approach for evaluating the working condition of ZnO surge arresters based on time-domain dielectric characteristics, aiming to guarantee their safe functioning within the power grid. The proposed technique provides a platform for conducting aging experiments updates the procedure for preparing piezoresistors in ZnO surge arresters and examines their performance. Furthermore, a test bed was utilized to investigate polarization/depolarization currents. The findings revealed that the pressure-sensitive voltage exhibited a minimum value of 693.09 U1mA ·cm2 using the traditional solid-phase approach while employing the chemical precipitation method with a 2:1 alcohol-to-water volume ratio resulted in a maximum value of 1028.41 U1mA ·cm2. Under the traditional solid-phase method, the voltage gradient varied from 411.04 V·mm−1 to 585.12 V·mm−1 when using the chemical precipitation method with an alcohol-to-water volume ratio of 2:1 V ·mm−1. The condition of ZnO surge arresters can be evaluated non-destructively by the polarization/depolarisation charge differential, which increases with the average service life. In conclusion, this study provides methodological criteria for assessing ZnO surge arrester conditions to ensure safe operation of power grids.

MoReVis: A Visual Summary for Spatiotemporal Moving Regions.

Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.

A systematic review of in-memory database over multi-tenancy

The significant cost and time are essential to obtain a comprehensive response, the response time to a query across a peer-to-peer database is one of the most challenging issues. This is particularly exact when dealing with large-scale data processing, where the traditional approach of processing data on a single machine may not be sufficient. The need for a scalable, reliable, and secure data processing system is becoming increasingly important. Managing a single in-memory database instance for multiple tenants is often easier than managing separate databases for each tenant. The research work is focused on scalability with multi-tenancy and more efficiency with a faster querying performance using in-memory database approach. We compare the performance of a row-oriented approach and column-oriented approach on our benchmark human resources (HR) schema using Oracle TimesTen in-memory database. Also, we captured some of the key advantages on optimization dimension(s) are the traditional approach, late-materialization, compression and invisible join on column-store (c-store) and row-base. When compression and late materialization are enabled in a query set; it improves the overall performance of query sets. In particular, the paper aims to elucidate the motivations behind multi-tenant application requirements concerning the database engine and highlight major designs over in-memory database for the tenancy approach on cloud.

Demystifying AI: Current State and Future Role in Medical Education Assessment.

Medical education assessment faces multifaceted challenges, including data complexity, resource constraints, bias, feedback translation, and educational continuity. Traditional approaches often fail to adequately address these issues, creating stressful and inequitable learning environments. This article introduces the concept of precision education, a data-driven paradigm aimed at personalizing the educational experience for each learner. It explores how artificial intelligence (AI), including its subsets machine learning (ML) and deep learning (DL), can augment this model to tackle the inherent limitations of traditional assessment methods.AI can enable proactive data collection, offering consistent and objective assessments while reducing resource burdens. It has the potential to revolutionize not only competency assessment but also participatory interventions, such as personalized coaching and predictive analytics for at-risk trainees. The article also discusses key challenges and ethical considerations in integrating AI into medical education, such as algorithmic transparency, data privacy, and the potential for bias propagation.AI's capacity to process large datasets and identify patterns allows for a more nuanced, individualized approach to medical education. It offers promising avenues not only to improve the efficiency of educational assessments but also to make them more equitable. However, the ethical and technical challenges must be diligently addressed. The article concludes that embracing AI in medical education assessment is a strategic move toward creating a more personalized, effective, and fair educational landscape. This necessitates collaborative, multidisciplinary research and ethical vigilance to ensure that the technology serves educational goals while upholding social justice and ethical integrity.

Targeted-BEHRT: Deep Learning for Observational Causal Inference on Longitudinal Electronic Health Records.

Observational causal inference is useful for decision-making in medicine when randomized clinical trials (RCTs) are infeasible or nongeneralizable. However, traditional approaches do not always deliver unconfounded causal conclusions in practice. The rise of "doubly robust" nonparametric tools coupled with the growth of deep learning for capturing rich representations of multimodal data offers a unique opportunity to develop and test such models for causal inference on comprehensive electronic health records (EHRs). In this article, we investigate causal modeling of an RCT-established causal association: the effect of classes of antihypertensive on incident cancer risk. We develop a transformer-based model, targeted bidirectional EHR transformer (T-BEHRT) coupled with doubly robust estimation to estimate average risk ratio (RR). We compare our model to benchmark statistical and deep learning models for causal inference in multiple experiments on semi-synthetic derivations of our dataset with various types and intensities of confounding. In order to further test the reliability of our approach, we test our model on situations of limited data. We find that our model provides more accurate estimates of relative risk [least sum absolute error (SAE) from ground truth] compared with benchmark estimations. Finally, our model provides an estimate of class-wise antihypertensive effect on cancer risk that is consistent with results derived from RCTs.

Point-to-Point Learning Tracking Control via Fading Communication Using Reference Update Strategy.

The networked structure has attracted significant attention due to high demand for industrial systems and rapid developments of network communication. Among various network randomness, fading is a common phenomenon, which can lead to signal attenuation, distortion, loss, and interference. This study concentrates on the point-to-point tracking problem via fading communications by proposing a reference update strategy. Using this strategy, the tracking performance is continuously improved even with faded information as the number of iterations increases. A learning control scheme is established and proved convergent in both mean-square and almost-sure senses under mild conditions. The convergence rate is accelerated by introducing the virtual reference compared with the traditional update approach. Illustrative simulations verify the theoretical results.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts.

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

A High-Throughput Microdroplet-Based Single Cell Transfection Method for Gene Knockout Based on the CRISPR/Cas9 System.

The efficient application of the newly developed gene-editing method CRISPR/Cas9 requires more accurate intracellular gene delivery. Traditional delivery approaches, such as lipotransfection and non-viral delivery methods, must contend with major problems to overcome the drawbacks of low efficiency, high toxicity, and cell-type dependency. The high-throughput microdroplet-based single-cell transfection method presented herein provides an alternative method for delivering genome-editing reagents into single living cells. By accurately controlling the number of exogenous plasmids in microdroplets, this method can achieve high-efficiency delivery of nucleic acids to different types of single cells. This paper presents a high-throughput quantitative DNA transfection method for single cells and explores the optimal DNA transfection conditions for specific cell lines. The transfection efficiency of cells at different concentrations of DNA in microdroplets is measured. Under the optimized transfection conditions, the method is used to construct gene-knockout cancer cell lines to determine specific gene functions through the CRISPR/Cas9 knockout system. In a case study, the migration ability of TRIM72 knockout cancer cells is inhibited, and the tumorigenicity of cells in a zebrafish tumor model is reduced. A single-cell microfluidic chip is designed to achieve CRISPR/Cas9 DNA transfection, dramatically improving the transfection efficiency of difficult-to-transfect cells. This research demonstrates that the microdroplet method developed herein has a unique advantage in CRISPR/Cas9 gene-editing applications.

Decentralized Scheduling for Concurrent Tasks in Mobile Edge Computing via Deep Reinforcement Learning

Mobile Edge Computing (MEC) is a promising solution to enhance the computing capability of resource-limited networks. A fundamental problem in MEC is efficiently offloading tasks from user devices to edge servers. However, there still exists a gap to deploy in real-world environments: 1) traditional centralized approaches needs complete information of edge network, ignoring the communication costs generated by synchronization, 2) previous works do not consider concurrent computation on edge servers, which may cause dynamic changes in the environment, and 3) the scheduling algorithm should deliver individualized decisions for different users independently and with high efficiency To solve this mismatch, we studied a multi-user task offloading problem where user devices make offloading decisions independently. We consider the concurrent execution of tasks and formulate a non-divisible and delay-aware task offloading problem to jointly minimize the dropped task ratio and long-term latency. We propose a decentralized task scheduling algorithm based on DRL that makes offloading decisions without knowing the information of other user devices. We employ Double-DQN, Dueling-DQN, Prioritized Replay Memory, and Recurrent Neural Network (RNN) techniques to improve the algorithm's performance. The results of simulation experiments show that our method can significantly reduce the long-term latency and dropped task ratio compared to the baseline algorithms.

Anomaly Detection in Crowd Surveillance using Edge Computing: A Comprehensive Survey

Abstract: As urban environments continue to witness the proliferation of surveillance systems, ensuring public safety and security has become an increasingly challenging endeavor. Anomaly detection in crowded areas has emerged as a crucial task in these settings, aimed at identifying suspicious activities or potential threats. To meet the demands of real-time monitoring and analysis, edge computing has gained prominence as a critical technology. This survey paper provides a comprehensive overview of the state-of-the-art in anomaly detection, its use in crowd surviellance and the role played by edge computing in anomaly detection .The paper reviews an extensive body of literature encompassing various techniques and methodologies employed for anomaly detection in crowded scenes. It explores the evolution of traditional video-based approaches, such as motion analysis and object tracking, and the recent advancements leveraging deep learning, including various models in machine learning. These techniques are examined for their applicability in crowd surveillance scenarios.Various commonly used datasets to measure the quality of anomaly detection are also explored, along with their attributes and descriptions.The survey analyzes how edge computing solutions, such as edge AI accelerators and edge devices, enable faster and more context-aware processing of video data, while also addressing issues related to bandwidth constraints, privacy concerns, and scalability; paving way for a further research in harnessing the power of edge computing in crowd surveillance anomaly detection.

Strategic human resource management for sustainability in creative industries: A review and framework proposal

Strategic Human Resource Management (SHRM) has emerged as a critical approach for fostering sustainability within the dynamic landscape of creative industries. This review presents a comprehensive review of existing literature on SHRM in the context of creative industries, culminating in a proposed framework aimed at enhancing sustainability in these sectors. The review begins by examining the unique challenges faced by creative industries, characterized by rapid technological advancements, evolving consumer preferences, and intense competition. In this context, traditional HRM approaches are often insufficient, necessitating a strategic alignment between HR practices and organizational objectives to ensure long-term viability. Key themes identified in the literature review include talent management, organizational culture, employee engagement, and leadership development. Effective talent management strategies are crucial for attracting, retaining, and developing skilled individuals who drive innovation and creativity within creative firms. Furthermore, fostering a supportive organizational culture that values diversity, collaboration, and experimentation is vital for promoting sustainability in these industries. Employee engagement emerges as a critical factor influencing organizational performance and resilience, particularly in creative environments where intrinsic motivation plays a significant role. Moreover, leadership development programs tailored to the unique needs of creative professionals can empower individuals to navigate uncertainty and drive strategic initiatives that contribute to organizational sustainability. Building upon these insights, this review proposes a framework for SHRM in creative industries, integrating strategic planning, talent management, culture development, and leadership practices. The framework emphasizes the alignment between HR strategies and business goals, emphasizing the importance of agility, innovation, and adaptability in response to dynamic market conditions. Strategic Human Resource Management plays a pivotal role in enhancing sustainability within creative industries by fostering a culture of innovation, empowering employees, and cultivating leadership capabilities. The proposed framework provides a roadmap for organizations seeking to navigate the complexities of the creative landscape while maximizing long-term success and resilience.

Integrating AI in education: Opportunities, challenges, and ethical considerations

Integrating Artificial Intelligence (AI) in education presents a promising frontier with manifold opportunities, yet it also poses significant challenges and necessitates ethical considerations. This review explores the multifaceted landscape of AI integration in education, highlighting its potential to revolutionize traditional pedagogical approaches, personalize learning experiences, and streamline administrative tasks. However, it also addresses the challenges pertaining to implementation, including issues related to accessibility, data privacy, and the digital divide. The opportunities afforded by AI in education are vast and transformative. AI-driven technologies have the capacity to adapt instruction to individual learning styles, thereby enhancing student engagement and academic outcomes. Additionally, AI-powered tools can automate administrative tasks, allowing educators to allocate more time to meaningful interactions with students. Moreover, AI holds promise in facilitating the creation of immersive learning environments through virtual reality and augmented reality applications, enriching the educational experience. Nevertheless, the integration of AI in education presents ethical considerations that warrant careful examination. Concerns regarding data privacy and security arise as educational institutions collect and analyze vast amounts of student data. Moreover, there are apprehensions about the potential for AI algorithms to perpetuate biases or reinforce inequalities if not implemented with conscientious oversight. Furthermore, questions surrounding the ethical use of AI in assessing student performance and making consequential decisions underscore the importance of establishing transparent and accountable practices. While the integration of AI in education offers unprecedented opportunities for innovation and improvement, it is imperative to navigate the associated challenges with diligence and ethical foresight. By addressing these challenges thoughtfully, stakeholders can harness the full potential of AI to cultivate equitable, inclusive, and effective educational environments.

Determination of the Optimum Test Conditions for Measurement of Glucose Level in Liquids

Diabetes is a disease that affects more than 400 million people worldwide and currently lacks a cure. Monitoring blood sugar levels is crucial in minimizing the effects of this disease and protecting against its complications. Invasive and minimally invasive methods are commonly used traditional approaches for detecting and monitoring blood sugar levels. However, these methods bring along psychological and infectious risks. Currently, efforts are being made to develop a non-invasive method for determining blood sugar levels. Microwaves offer the possibility of non-invasive glucose measurement as they do not cause any harmful effects on human tissue. Furthermore, the complex permeability of blood is sensitive to glucose concentration in the microwave band. In literature, most of the studies are done with vector network analyzers (VNA) to detect blood sugar level noninvasively. In this study, an expensive and bulky VNA is replaced by an affordable microwave source and RMS power detector. The influence of the type and diameter of the test tube material used for non-invasive determination of sugar levels is examined with this setup. Additionally, the effect of the distance between the Vivaldi antennas used during measurements and the test tube is investigated. The results indicate that measurements performed using plastic test tubes yield better results compared to glass test tubes. Moreover, reducing the diameter of the test tube leads to improved outcomes. It has been observed that accurate results cannot be obtained if the antennas and the test tube are too close (4.5cm) from each other.

From impact to impacting: A pragmatist perspective on tackling grand challenges

Scholars have long sought to impact management practice. However, the current conceptualization of impact is grounded in dualisms, separating researchers from managers, means from ends, and thought from action. Such a dualistic understanding of impact hampers researchers’ and managers’ ability to achieve impact. Nowhere is this issue more acute than in the context of grand challenges, which require researchers and managers to work together closely. As a way forward, we propose a pragmatist perspective on impact, where impact is not seen as a one-time, unidirectional event, but rather as a relational and recursive process. By overcoming dualisms in traditional approaches to impact, pragmatist impacting can help advance progress on grand challenges and our current understanding of co-creation. In this article, we illustrate pragmatist impact ing and reflect on its opportunities and challenges through our experience at Innovation North, an innovation laboratory that brought together researchers and managers to co-create a systems innovation process.

Sulfur in Waste-Free Sustainable Synthesis: Advancing Carbon-Carbon Coupling Techniques.

This review explores the pivotal role of sulfur in advancing sustainable carbon-carbon (C-C) coupling reactions. The unique electronic properties of sulfur, as a soft Lewis base with significant mesomeric effect make it an excellent candidate for initiating radical transformations, directing C-H-activation, and facilitating cycloaddition and C-S bond dissociation reactions. These attributes are crucial for developing waste-free methodologies in green chemistry. Our mini-review is focused on existing sulfur-directed C-C coupling techniques, emphasizing their sustainability and comparing state-of-the-art methods with traditional approaches. The review highlights the importance of this research in addressing current challenges in organic synthesis and catalysis. The innovative use of sulfur in photocatalytic, electrochemical and metal-catalyzed processes not only exemplifies significant advancements in the field but also opens new avenues for environmentally friendly chemical processes. By focusing on atom economy and waste minimization, the analysis provides broad appeal and potential for future developments in sustainable organic chemistry.

Quantitative susceptibility mapping for detection of kidney stones, hemorrhage differentiation, and cyst classification in ADPKD.

The objective is to demonstrate feasibility of quantitative susceptibility mapping (QSM) in autosomal dominant polycystic kidney disease (ADPKD) patients and to compare imaging findings with traditional T1/T2w magnetic resonance imaging (MRI). Thirty-three consecutive patients (11 male, 22 female) diagnosed with ADPKD were initially selected. QSM images were reconstructed from the multiecho gradient echo data and compared to co-registered T2w, T1w, and CT images. Complex cysts were identified and classified into distinct subclasses based on their imaging features. Prevalence of each subclass was estimated. QSM visualized two renal calcifications measuring 9 and 10mm and three pelvic phleboliths measuring 2mm but missed 24 calcifications measuring 1mm or less and 1 larger calcification at the edge of the field of view. A total of 121 complex T1 hyperintense/T2 hypointense renal cysts were detected. 52 (43%) Cysts appeared hyperintense on QSM consistent with hemorrhage; 60 (49%) cysts were isointense with respect to simple cysts and normal kidney parenchyma, while the remaining 9 (7%) were hypointense. The presentation of the latter two complex cyst subtypes is likely indicative of proteinaceous composition without hemorrhage. Our results indicate that QSM of ADPKD kidneys is possible and uniquely suited to detect large renal calculi without ionizing radiation and able to identify properties of complex cysts unattainable with traditional approaches.

WITHDRAWAL – Administrative Duplicate Publication: Vs-0 Correction Factors for Input Ground Motions used in Seismic Site Response Analyses

Input motions used in seismic site response analyses are commonly selected based on similarities between the shear wave velocity (Vs) at the recording station, and the reference depth at the site of interest (among other aspects such as the intensity of the expected ground motion). This traditional approach disregards the influence of the attenuation in the shallow crust on site response. Given that this attenuation (damping) can be characterized by the distance-independent high-frequency attenuation parameter 0, a Vs-0 correction framework for input motions is proposed to render them compatible with the assumed properties of the reference depth at the site. The proposed correction factors were applied to a subset of recordings from the KiK-net database, and compared to traditional deconvolution. Results indicate that Vs-0 corrected motions outperform deconvolved motions in the characterization of the spectral energy in the high-frequency range. However, motions recorded at sites with soft deposits are not good candidates for the Vs-0 correction approach. Vs-0 corrections also affect amplification functions which are important in the assessment of site-specific seismic hazards.

ANALYSIS OF THE FUNCTIONS OF PROJECT MANAGEMENT INFORMATION SYSTEMS – TRADITIONAL APPROACH

Project management information systems in enterprises help managers to manage, improve and track the progress in the implementation of projects from their conception to the achievement of results. The article reviews the literature and presents the most commonly used applications for storing, organizing and controlling project information in accordance with the traditional approach to project management. The authors analyzed the possibility of implementing the processes and achieving the results defined for them, presented in the Project Management Body of Knowledge, by means of five selected project management information systems. On the basis of the obtained results of research conducted on a group of Project Managers, conclusions were formulated, also indicating directions for further research.

Abstract 3147: DXC025, a novel anti-MUC1/EGFR bispecific antibody-tubulysin conjugate with a function linker, exhibits potent anti-tumor efficacy

Abstract Antibody-drug conjugates (ADCs) have become a promised drug class in cancer therapy. Armed with three main components-an antibody, a linker molecule, and a cytotoxic agent (“payload”), plus sites of the antibody conjugation and the distribution of tumor-associate antigens, nowadays ADCs have proved the unique ability of the precision of targeted therapy, a great leap forward from traditional chemotherapeutic approaches that cause widespread effects without specificity. Both epidermal growth factor receptor (EGFR) and mucin 1 (MUC1) are tumor-associated antigens (TAA) that are co-expressed in many solid tumors, such as, colon cancer, non-small cell lung cancer (NSCLC), esophageal cancer, epidermal cancer, and pancreatic cancer. In particular, MUC1, a glycoprotein essential for the formation of the epithelial mucous barrier, is hypoglycosylated and dimerizes with EGFR, a potent oncoprotein, in transformed cells. For our anti-Muc1 ADC clinical program, for treatment of colon and pancreatic cancers, it was found out that the highly glycosylated transmembrane Muc1 had much more turning over rates on the luminal surface of epithelial cells. Here, we first designed and generated bispecific Fab/scFv antibodies targeting both MUC1 and EGFR, using the knob-into-hole technology. The addition of the affinity- and internalization-optimized anti-EGFR arm to the other single Muc1 arm was aimed to broaden tumor selectivity and to reduce on-target toxicity, for the treatment of gastrointestinal tumors in comparison to individual full IgG1-ADC, either EGFR or Muc1-ADC. Indeed, many of generated anti- MUC1/EGFR bispecific antibodies (BsAbs) showed strong binding affinity in MUC1modetateEGFRmodetate cells, in addition to both MUC1 and EGFR highly expressed cells. Internalization assays demonstrated that the BsAbs were endocytosed in tumor cells co-expressing EGFR and MUC1 more efficiently than monoclonal antibodies targeting MUC1. The selected BsAbs were subsequently conjugated with tubulysin B analogs with varieties of Tubulysin B analog payloads and function peptidyl spacer/linkers to generate bispecific ADC (DXC025) candidates. DXC025 candidates exhibited very potent cytotoxicity in vitro against epidermal cancer, lung adenocarcinoma, and pancreatic cancer cell lines with IC50 of single or tens digital pM. In in vivo cell-derived xenograft (CDX) models, DXC025 candidates demonstrated much superior efficacies of tumor growth inhibition in comparison with parental (monoclonal), either Muc1- or EGFR- ADC for the treatment of gastrointestinal tumors. These results indicated that the MUC1/EGFR-ADC (DXC025) would be a promising ADC candidate for targeted treatment of either Muc1- or EGFR-expressing gastrointestinal tumors. Citation Format: Xingyan Jiang, Junxiang Jia, Huihui Guo, Xiangfei Kong, Yongfang Xu, Sishi Ye, Yong Du, Zhicang Ye, Qikuang Cen, Lingli Zhang, Yongxiang Chen, Gaituo Chen, Lu Bai, Yunxia Zheng, Wei Zheng, Jun Zheng, Juan Wang, Wenjun Li, Yuanyuan Huang, Linyao Zhao, Yifang Xu, Mengmeng Liu, Binbin Chen, Meng Dai, Miaomiao Chen, Zhixiang Guo, Qingliang Yang, Robert Y. Zhao. DXC025, a novel anti-MUC1/EGFR bispecific antibody-tubulysin conjugate with a function linker, exhibits potent anti-tumor efficacy [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 3147.