Information Transfer Research Articles

Intercultural Communication: Linguistic and Cultural Approach of Etiquette Units (In Kazakh And English)

The article argues the linguistic and cultural specificity of the etiquette units used in intercultural communication. The etiquette units of the Kazakh and English languages have their own linguistic and cultural features. Determining the scope of application of etiquette units in intercultural communication and the study of linguistic and cultural specifics is one of the urgent problems facing modern linguistics. Intercultural communication is an important aspect of interaction between different cultures. It involves the transfer of information, ideas and values between people who speak different languages and belong to different cultures. One of the main aspects of intercultural communication is etiquette, which includes rules and norms of behavior adopted in a particular culture. Based on this, in the main part of the article will be focused on the linguistic and cultural features of the etiquette units in the Kazakh and English languages. Will be studied the influence of national cognition on linguistic units. A special place in human life is occupied by etiquette units of intercultural communication, in addition to language use. In the process of using etiquette units, the speaker and the listener must have cognitive knowledge. In accordance with this, the main section of the article offers its own definition based on the works of domestic and foreign scientists written in connection with etiquette units. According to the results of the study, the interrelation of etiquette units in intercultural communication with the folk worldview is studied and the examples given are evaluated from the point of view of language and culture. In the final part of the article to the main section, will be formulated a concept and offer a final thought with the help of a clear analysis. Will be also evaluated a number of searches in modern Kazakh linguistics. Will be defined the specifics of etiquette units in intercultural communication, studied from the point of view of language and culture. The most basic ethnic unit of the population is language. Given this, the value of this research object will be determined.

The use of different types of grammar translation approach on enhancing grade 7's English comprehension

This research discusses a coherent method for determining the level of translation skills among 40 high school students at Masapang Integrated National High School before and after an intervention. The study focuses on the types of Grammar-Translation Approaches and the accuracy of English translations to the students. To gather data, researchers used an adapted questionnaire from DepEd, distributing the questionnaires in person before the intervention. Subsequently, an online questionnaire was distributed via Google Forms after the intervention. The results revealed that there was a significant difference between the translation skills of the students before and after the intervention. Before the intervention, the mean level of translation skills of the students was 2.28 and interpreted verbally as intermediate. This means that most of the students have achieved a mean score that ranges from 2.00 to 3.00. After the intervention, the mean level of translation skills of the students was 3.97 and interpreted verbally as advanced. This means that most of the students from intermediate and beginner levels have made progress and achieved the advanced level. The results imply that the interventions significantly affected the translation skills of the students. Therefore, the research hypothesis was rejected based on the collected data. This paper suggests that High School students must continue practicing and improving translation which will benefit them for future educational purposes. It also shows that learning translation skills enables the transfer of information, ideas, and cultural expressions from one language to another, fostering global understanding and collaboration.

Air Quality Prediction in Smart Environment using Lightweight Residual Network: Sustainable Light-AirNet Approach

In densely populated places, air pollution prediction is crucial since it directly affects human health and the local governance. The main objective of this work is to analyze the spatial and temporal patterns of the concentration of the main air pollutants in Bangalore, India. In this paper, a lightweight residual network with an attention mechanism is created using a collection of residual concatenation blocks layered with recursive residual blocks. This aids in the adaptive extraction of useful features, the learning of more expressive spatial context information, and the efficient transfer of information through gradient flow in the network. A unique attention mechanism, known as the Two-Fold Attention Module, has been created with the purpose of enhancing the model’s ability to represent information. The Light-AirNet model was designed to provide hourly forecasts by using past pollution data and three measured weather variables were collected from weather stations. Light-AirNet is compared with existing approaches in terms of different metrics and it was found that it achieves 24.5% of root-mean-square error, 21.5% of mean square error, 12.59% of mean absolute error, and 97.45% of prediction accuracy.

Real-time feedback improves imagined 3D primitive object classification from EEG

ABSTRACT Brain-computer interfaces (BCI) enable movement-independent information transfer from humans to computers. Decoding imagined 3D objects from electroencephalography (EEG) may improve design ideation in engineering design or image reconstruction from EEG for application in brain-computer interfaces, neuro-prosthetics, and cognitive neuroscience research. Object-imagery decoding studies, to date, predominantly employ functional magnetic resonance imaging (fMRI) and do not provide real-time feedback. We present four linked studies in a study series to investigate: (1) whether five imagined 3D primitive objects (sphere, cone, pyramid, cylinder, and cube) could be decoded from EEG; and (2) the influence of real-time feedback on decoding accuracy. Studies 1 (N = 10) and 2 (N = 3) involved a single-session and a multi-session design, respectively, without real-time feedback. Studies 3 (N = 2) and 4 (N = 4) involved multiple sessions, without and with real-time feedback. The four studies involved 69 sessions in total of which 26 sessions were online with real-time feedback (15,480 trials for offline and at least 6,840 trials for online sessions in total). We demonstrate that decoding accuracy over multiple sessions improves significantly with biased feedback (p = 0.004), compared to performance without feedback. This is the first study to show the effect of real-time feedback on the performance of primitive object-imagery BCI.

Overcoming barriers to circularity for internal ICT management in organizations: A change management approach

Circularity-conscious management of information and communications technology (ICT) owned by organizations is important to achieving a circular economy. However, changes in organizational management toward circularity has been met with multiple challenges. This study uses 11 semi-structured interviews with on-the-ground ICT decision-makers in organizations to determine what barriers prevent the development and implementation of circularity-related changes in organizational ICT management. We identified 13 barriers relating to information and knowledge transfer, access to circular equipment, finances and contracts, and prioritization over circularity. Barrier-based interventions were further structured by Lewin's 3-step change management model – unfreeze, change, refreeze – highlighting the role of information access, relationships with contracted partners, and internal accountability and priority structures. These results bridge a currently underdeveloped link between circularity and management research as well as provide policy makers, researchers, and ICT managers insight on facilitating ICT's impactful role in society's transition to circularity.

Altered excitatory and inhibitory ionotropic receptor subunit expression in the cortical visuospatial working memory network in schizophrenia.

Dysfunction of the cortical dorsal visual stream and visuospatial working memory (vsWM) network in individuals with schizophrenia (SZ) likely reflects alterations in both excitatory and inhibitory neurotransmission within nodes responsible for information transfer across the network, including primary visual (V1), visual association (V2), posterior parietal (PPC), and dorsolateral prefrontal (DLPFC) cortices. However, the expression patterns of ionotropic glutamatergic and GABAergic receptor subunits across these regions, and alterations of these patterns in SZ, have not been investigated. We quantified transcript levels of key subunits for excitatory N-methyl-D-aspartate receptors (NMDARs), excitatory alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors (AMPARs), and inhibitory GABAA receptors (GABAARs) in postmortem total gray matter from V1, V2, PPC, and DLPFC of unaffected comparison (UC) and matched SZ subjects. In UC subjects, levels of most AMPAR and NMDAR mRNAs exhibited opposite rostral-to-caudal gradients, with AMPAR GRIA1 and GRIA2 mRNA levels highest in DLPFC and NMDAR GRIN1 and GRIN2A mRNA levels highest in V1. GABRA5 and GABRA1 mRNA levels were highest in DLPFC and V1, respectively. In SZ, most transcript levels were lower relative to UC subjects, with these differences largest in V1, intermediate in V2 and PPC, and smallest in DLPFC. In UC subjects, these distinct patterns of receptor transcript levels across the cortical vsWM network suggest that the balance between excitation and inhibition is achieved in a region-specific manner. In SZ subjects, the large deficits in excitatory and inhibitory receptor transcript levels in caudal sensory regions suggest that abnormalities early in the vsWM pathway might contribute to altered information processing in rostral higher-order regions.

Antifreeze protein-based ultrastretchable and highly sensitive hydrogel for human-machine interaction

Intelligent and convenient human–computer interfaces realize efficient and close cooperation between people and the digital world. As windows of human–machine interaction (HMI), sensors directly determine the ability and quality of acquired signals and the utilization potential of HMI systems. However, it still remains challenging to develop antifreeze hydrogel sensors with freeze-resistant properties while avoiding the loss of other properties as much as possible to achieve stable HMI applications in low-temperature environments. Here, we present an Agar/PAAm dual-network hydrogel (Na+-Agar/PAAm-AFP) doped with both antifreeze protein (AFP) and sodium chloride (NaCl), which exhibits excellent antifreeze capability (−20 °C). The hydrogel integrates transparency (>90%), ultra stretchability (up to 2100%), good adhesion properties to metal, glass, and plastic, high electrical conductivity (3.41 S m−1), high sensitivity (GF = 10.23), and non-irritating to the skin. The hydrogel can be used for monitoring daily activities and glove-based HMI systems for intelligent control. In addition, based on this hydrogel, we have successfully developed an interactive system for real-time information transfer over long distances. It is expected to provide better health management monitoring in several fields and also help to advance the development of innovative and IoT technologies.

Ruppert‐Prakash Reagent (TMSCF3)‐Catalyzed Chemoselective Esterification of Weinreb Amides

AbstractA straightforward TMSCF3‐catalyzed conversion of Weinreb amides into esters through the treatment with sodium alkoxides is reported. The procedure documents a genuine selectivity for the esterification of primary alcohols with Weinreb amides featuring diverse substitution pattern. The constitutive N‐methoxy fragment of these acylating agents is responsible for the unique reactivity they display, thus enabling to explore alkoxides as competent nucleophilic elements. The protocol is compatible with the fully transfer of stereochemical information embodied in the starting material, as well as, guarantees the preparation of pharmaceuticals. Mechanistic investigations revealed that under the adopted reaction conditions (NaOR, R primary) TMSCF3 does not release the trifluoromethyl (CF3−) anion – as occurs with tertiary alkoxides – but rather it catalytically activates the amidic bond.

Controlling the helicity of light by electrical magnetization switching.

Controlling the intensity of emitted light and charge current is the basis of transferring and processing information1. By contrast, robust information storage and magnetic random-access memories are implemented using the spin of the carrier and the associated magnetization in ferromagnets2. The missing link between the respective disciplines of photonics, electronics and spintronics is to modulate the circular polarization of the emitted light, rather than its intensity, by electrically controlled magnetization. Here we demonstrate that this missing link is established at room temperature and zero applied magnetic field in light-emitting diodes2-7, through the transfer of angular momentum between photons, electrons and ferromagnets. With spin-orbit torque8-11, a charge current generates also a spin current to electrically switch the magnetization. This switching determines the spin orientation of injected carriers into semiconductors, in which the transfer of angular momentum from the electron spin to photon controls the circular polarization of the emitted light2. The spin-photon conversion with the nonvolatile control of magnetization opens paths to seamlessly integrate information transfer, processing and storage. Our results provide substantial advances towards electrically controlled ultrafast modulation of circular polarization and spin injection with magnetization dynamics for the next-generation information and communication technology12, including space-light data transfer. The same operating principle in scaled-down structures or using two-dimensional materials will enable transformative opportunities for quantum information processing with spin-controlled single-photon sources, as well as for implementing spin-dependent time-resolved spectroscopies.

A phase field-immersed boundary-lattice Boltzmann coupling method for fluid-structure interaction analysis

Fluid-structure interaction (FSI) is critical in numerous engineering and scientific applications. This paper presents a numerical model for FSI simulations that integrates the lattice Boltzmann method (LBM) for fluid dynamics, the phase field method (PFM) for structural deformation and fracture, and the immersed boundary method (IBM) for fluid-solid coupling. The fluid employs an explicit time integration scheme with a smaller time step than the solid, which uses an implicit time integration scheme, satisfying the CFL condition. During the synchronization of fluid-solid interaction information, a multi-substep strategy is applied. The IBM is implemented using the iterative velocity method for boundary treatment. This approach facilitates the information transfer between domains with different time steps, enhancing the coupling accuracy through iterative computation. The effectiveness of the improved immersed boundary-lattice Boltzmann (IB-LBM) has been verified by the classical cylindrical flow case, which effectively prevents the particle penetration phenomenon and improves the calculation accuracy. The phase field-immersed boundary-lattice Boltzmann method (PF-IB-LBM) is subsequently deployed to simulate the deformation of an aluminum plate under hydrostatic pressure and the aftermath of a dam-break impact on an elastic plate. Verification of the coupling method substantiates its feasibility and efficacy. In addition, the proposed PF-IB-LBM is implemented to simulate the entire process of structural deformation and failure in response to fluid dynamics, which further confirms its capability to predict discontinuous mechanical behaviors such as damage and crack propagation under intricate fluid flows. The results emphasize the flexibility and efficiency of the proposed approach, providing a robust framework for solving more complex scenarios in engineering FSI problems.

Estimating the impact on patient safety of enabling the digital transfer of patients’ prescription information in the English NHS

ObjectivesTo estimate the number and burden of medication errors associated with prescription information transfer within the National Health Service (NHS) in England and the impact of implementing an interoperable prescription...

DRAMS: Double-RIS assisted multihop routing scheme for device-to-device communication

Reconfigurable intelligent surfaces (RISs) is a promising solution for enhancing the performance of multihop wireless communication networks. In this paper, we propose a double-RIS assisted multihop routing scheme for a device-to-device (D2D) communication network. Specifically, the scheme is dependent on the already deployed RISs and users in the surroundings. Besides the RISs, the emphasis of this work is to make more use of the existing intermediate users (IUs), which can act as relays. Hence, the density of RIS deployment in the surroundings can be reduced, which leads to the avoidance of resource wastage. However, we cannot solely depend on the IUs because this implies complete dependence on their availability for relaying and as a result, the aspect of reliability in terms of delay-constrained information transfer cannot be guaranteed. Moreover, the IUs are considered capable of energy harvesting and as a result, they do not waste their own energy in the process of volunteering to act as a relay for other users. Numerical results demonstrate the advantage of the proposed scheme over some existing approaches and lastly, useful insights related to the scheme design are also drawn, where we characterize the maximum acceptable delay at each hop under different set-ups.

Implementing a ward-level intervention to improve nursing handover communication with a focus on bedside handover-A qualitative study.

To improve the effectiveness of nursing clinical handover through a qualitative, tailored communication intervention. A multisite before and after intervention using qualitative ethnography combined with discourse analysis of nursing handover interactions. We implemented a tailored ward-based intervention to redesign nursing handover practice with co-constructed recommendations for organisational and cultural change on seven wards across three affiliated metropolitan hospitals between February 2020 and November 2022. The intervention was informed by pre-implementation interviews and focus groups with nursing, medical and allied health staff and patients (n = 151) and observed and/or audio-recorded handover events (n = 233). Post-intervention we conducted interviews and focus groups (n = 79) and observed and/or audio-recorded handover events (n = 129) to qualitatively evaluate impact. Our translational approach resulted in substantial changes post-intervention. Nurses conducted more shift-to-shift handovers at the bedside, with greater patient interaction and better structured and more comprehensive information transfer, supported by revised handover documentation. Redesigned group handovers were focused and efficient, communicating critical patient information. Contextualised training combined with changes to ward-level systemic factors impeding communication results in improved nursing handovers. Practice change requires strong executive leadership and project governance, combined with effective ward-level leadership, collaboration and mentoring. The speed and degree of change post-intervention demonstrates the power of interdisciplinary collaborative research between hospital executive, ward leadership and communication specialists. Nurses are more likely to conduct efficient group handovers and informative, patient-centred bedside handovers in line with policy when they understand the value of handover and have practical strategies to support communication. Communication training should be combined with broader ward-level changes to handover practice tailored to the ward context. A multilevel approach results in more effective practice change. We adhered to the COREQ guidelines. We interviewed patients on study wards pre and post intervention.

Multi-task learning regression via convex clustering

Multi-task learning (MTL) is a methodology that aims to improve the general performance of estimation and prediction by sharing common information among related tasks. In the MTL, there are several assumptions for the relationships and methods to incorporate them. One of the natural assumptions in the practical situation is that tasks are classified into some clusters with their characteristics. For this assumption, the group fused regularization approach performs clustering of the tasks by shrinking the difference among tasks. This enables the transfer of common information within the same cluster. However, this approach also transfers the information between different clusters, which worsens the estimation and prediction. To overcome this problem, an MTL method is proposed with a centroid parameter representing a cluster center of the task. Because this model separates parameters into the parameters for regression and the parameters for clustering, estimation and prediction accuracy for regression coefficient vectors are improved. The effectiveness of the proposed method is shown through Monte Carlo simulations and applications to real data.

Learning Accurate and Bidirectional Transformation via Dynamic Embedding Transportation for Cross-Domain Recommendation

With the rapid development of Internet and Web techniques, Cross-Domain Recommendation (CDR) models have been widely explored for resolving the data-sparsity and cold-start problem. Meanwhile, most CDR models should utilize explicit domain-shareable information (e.g., overlapped users or items) for knowledge transfer across domains. However, this assumption may not be always satisfied since users and items are always non-overlapped in real practice. The performance of many previous works will be severely impaired when these domain-shareable information are not available. To address the aforementioned issues, we propose the Joint Preference Exploration and Dynamic Embedding Transportation model (JPEDET) in this paper which is a novel framework for solving the CDR problem when users and items are non-overlapped. JPEDET includes two main modules, i.e., joint preference exploration module and dynamic embedding transportation module. The joint preference exploration module aims to fuse rating and review information for modelling user preferences. The dynamic embedding transportation module is set to share knowledge via neural ordinary equations for dual transformation across domains. Moreover, we innovatively propose the dynamic transport flow equipped with linear interpolation guidance on barycentric Wasserstein path for achieving accurate and bidirectional transformation. Our empirical study on Amazon datasets demonstrates that JPEDET significantly outperforms the state-of-the-art models under the CDR setting.

PMAC: Personalized Multi-Agent Communication

Communication plays a crucial role in information sharing within the field of multi-agent reinforcement learning (MARL). However, how to transmit information that meets individual needs remains a long-standing challenge. Some existing work focus on using a common channel for information transfer, which limits the capability for local communication. Meanwhile, other work attempt to establish peer-to-peer communication topologies but suffer from quadratic complexity. In this paper, we propose Personalized Multi-Agent Communication (PMAC), which enables the formation of peer-to-peer communication topologies, personalized message sending, and personalized message receiving. All these modules in PMAC are performed using only multilayer perceptrons (MLPs) with linear computational complexity. Empirically, we show the strength of personalized communication in a variety of cooperative scenarios. Our approach exhibits competitive performance compared to existing methods while maintaining notable computational efficiency.

Towards the Disappearing Truth: Fine-Grained Joint Causal Influences Learning with Hidden Variable-Driven Causal Hypergraphs in Time Series

Causal discovery under Granger causality framework has yielded widespread concerns in time series analysis task. Nevertheless, most previous methods are unaware of the underlying causality disappearing problem, that is, certain weak causalities are less focusable and may be lost during the modeling process, thus leading to biased causal conclusions. Therefore, we propose to introduce joint causal influences (i.e., causal influences from the union of multiple variables) as additional causal indication information to help identify weak causalities. Further, to break the limitation of existing methods that implicitly and coarsely model joint causal influences, we propose a novel hidden variable-driven causal hypergraph neural network to meticulously explore the locality and diversity of joint causal influences, and realize its explicit and fine-grained modeling. Specifically, we introduce hidden variables to construct a causal hypergraph for explicitly characterizing various fine-grained joint causal influences. Then, we customize a dual causal information transfer mechanism (encompassing a multi-level causal path and an information aggregation path) to realize the free diffusion and meticulous aggregation of joint causal influences and facilitate its adaptive learning. Finally, we design a multi-view collaborative optimization constraint to guarantee the characterization diversity of causal hypergraph and capture remarkable forecasting relationships (i.e., causalities). Experiments are conducted to demonstrate the superiority of the proposed model.

Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation

Adversarial Robustness Distillation (ARD) is a promising task to solve the issue of limited adversarial robustness of small capacity models while optimizing the expensive computational costs of Adversarial Training (AT). Despite the good robust performance, the existing ARD methods are still impractical to deploy in natural high-security scenes due to these methods rely entirely on original or publicly available data with a similar distribution. In fact, these data are almost always private, specific, and distinctive for scenes that require high robustness. To tackle these issues, we propose a challenging but significant task called Data-Free Adversarial Robustness Distillation (DFARD), which aims to train small, easily deployable, robust models without relying on data. We demonstrate that the challenge lies in the lower upper bound of knowledge transfer information, making it crucial to mining and transferring knowledge more efficiently. Inspired by human education, we design a plug-and-play Interactive Temperature Adjustment (ITA) strategy to improve the efficiency of knowledge transfer and propose an Adaptive Generator Balance (AGB) module to retain more data information. Our method uses adaptive hyperparameters to avoid a large number of parameter tuning, which significantly outperforms the combination of existing techniques. Meanwhile, our method achieves stable and reliable performance on multiple benchmarks.

Plasmid-mediated phenotypic noise leads to transient antibiotic resistance in bacteria

The rise of antibiotic resistance is a critical public health concern, requiring an understanding of mechanisms that enable bacteria to tolerate antimicrobial agents. Bacteria use diverse strategies, including the amplification of drug-resistance genes. In this paper, we showed that multicopy plasmids, often carrying antibiotic resistance genes in clinical bacteria, can rapidly amplify genes, leading to plasmid-mediated phenotypic noise and transient antibiotic resistance. By combining stochastic simulations of a computational model with high-throughput single-cell measurements of blaTEM-1 expression in Escherichia coli MG1655, we showed that plasmid copy number variability stably maintains populations composed of cells with both low and high plasmid copy numbers. This diversity in plasmid copy number enhances the probability of bacterial survival in the presence of antibiotics, while also rapidly reducing the burden of carrying multiple plasmids in drug-free environments. Our results further support the tenet that multicopy plasmids not only act as vehicles for the horizontal transfer of genetic information between cells but also as drivers of bacterial adaptation, enabling rapid modulation of gene copy numbers. Understanding the role of multicopy plasmids in antibiotic resistance is critical, and our study provides insights into how bacteria can transiently survive lethal concentrations of antibiotics.

Abstract 540: Identification of therapy resistance mediating extracellular vesicles in single-cell level via a novel microfluidic co-culture device development

Abstract Once cancer has metastasized, it is incurable because tumors evolve resistance to all systemically administered anti-cancer therapies. Resistance to therapy has classically been attributed to the presence of a cancer stem cell or genetic tumor cell heterogeneity. Extracellular vesicles (EVs) are nano-sized (40-1000 nm) vesicles that are released by all cells into the extracellular space. EVs carry diverse cargo dependent on the cell of origin and have been studied as a source of novel cancer-specific liquid biopsy biomarkers and contributors to tumor progression and metastasis. However, the paracrine function of EVs in promoting therapy resistance is under-studied, and the impact of EVs released from therapy-resistant cancer cells on surrounding therapy-sensitive cancer cells is unknown. In this study, we develop an innovative microfluidic device, TREVCo, to enable simultaneous single-cell trap, EV detection, and co-culture modules to evaluate EVs-mediated transfer of cellular information between therapy-resistant cancer cells and adjacent cells, and to characterize EVs released from resistant cells and determine their impact on the resistance phenotype of adjacent therapy-sensitive cancer cells. We utilize resistant cells from multiple prostate (PCa) cell lines that have been selected for therapy resistance by culture with increasing dosages of cancer drugs such as cisplatin, docetaxel. We use the matched untreated therapy-sensitive parental cell lines as the sensitive cells. We hypothesize a novel model of therapy resistance in lethal PCa: EVs released from resistant cells contain pro-resistance factors that are transferred to adjacent sensitive cells upon EV uptake, thus directly enabling therapeutic resistance. We will assess the impact of resistant cell-EVs at a single cell level via a novel device, TREVCo, that enables comprehensive downstream analysis. This single-cell approach enables dissection of critical EV-associated therapy-resistant factors within the large heterogeneity of resistant PCa. Co-culture of a single resistant cell and a hundred adjacent sensitive cells mimicking intra-tumoral interactions is also critical to demonstrate evidence of resistant cell secreting factors inducing a pro-resistance phenotype. These studies will be the first-in-field to assess the impact of resistant PCa cell-derived EVs at single-cell resolution. An understanding of resistant cell-derived EVs and the impact of that EV cargo on adjacent sensitive cells will provide critical insight into the acquisition of therapy resistance in lethal PCa. The novel approach to apply resistant cell-derived EVs in co-culture assay with sensitive cells will enable the screening of transmittable resistance-associated factors and provides critical opportunities for accelerating precision medicine for advanced PCa patients. Citation Format: Chi-Ju Kim, Sarah Amend, Kenneth Pienta. Identification of therapy resistance mediating extracellular vesicles in single-cell level via a novel microfluidic co-culture device development [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 540.