Class Of States Research Articles

A Frustrated Antipolar Phase Analogous to Classical Spin Liquids.

The study of magnetic frustration in classical spin systems is motivated by the prediction and discovery of classical spin liquid states. These uncommon magnetic phases are characterized by a massive degeneracy of their ground state implying a finite magnetic entropy at zero temperature. While the classical spin liquid state is originally predicted in the Ising triangular lattice antiferromagnet in 1950, this state has never been experimentally observed in any triangular magnets. The discovery of an electric analogue of classical spin liquids on a triangular lattice of uniaxial electric dipoles in EuAl12O19 is reported here. This new type of frustrated antipolar phase is characterized by a highly-degenerate state at low temperature implying an absence of long-range antiferroelectric order, despite short-range antipolar correlations. Its dynamics are governed by a thermally activated process, slowing down upon cooling toward a complete freezing at zerotemperature.

Hybrid deep models for parallel feature extraction and enhanced emotion state classification

Emotions play a vital role in recognizing a person’s thoughts and vary significantly with stress levels. Emotion and stress classification have gained considerable attention in robotics and artificial intelligence applications. While numerous methods based on machine learning techniques provide average classification performance, recent deep learning approaches offer enhanced results. This research presents a hybrid deep learning model that extracts features using AlexNet and DenseNet models, followed by feature fusion and dimensionality reduction via Principal Component Analysis (PCA). The reduced features are then classified using a multi-class Support Vector Machine (SVM) to categorize different types of emotions. The proposed model was evaluated using the DEAP and EEG Brainwave datasets, both well-suited for emotion analysis due to their comprehensive EEG signal recordings and diverse emotional stimuli. The DEAP dataset includes EEG signals from 32 participants who watched 40 one-minute music videos, while the EEG Brainwave dataset categorizes emotions into positive, negative, and neutral based on EEG recordings from participants exposed to six different film clips. The proposed model achieved an accuracy of 95.54% and 97.26% for valence and arousal categories in the DEAP dataset, respectively, and 98.42% for the EEG Brainwave dataset. These results significantly outperform existing methods, demonstrating the model’s superior performance in terms of precision, recall, F1-score, specificity, and Mathew correlation coefficient. The integration of AlexNet and DenseNet, combined with PCA and multi-class SVM, makes this approach particularly effective for capturing the intricate patterns in EEG data, highlighting its potential for applications in human-computer interaction and mental health monitoring, marking a significant advancement over traditional methods.

Intelligent Human Operator Mental Fatigue Assessment Method Based on Gaze Movement Monitoring.

Modern mental fatigue detection methods include many parameters for evaluation. For example, many researchers use human subjective evaluation or driving parameters to assess this human condition. Development of a method for detecting the functional state of mental fatigue is an extremely important task. Despite the fact that human operator support systems are becoming more and more widespread, at the moment there is no open-source solution that can monitor this human state based on eye movement monitoring in real time and with high accuracy. Such a method allows the prevention of a large number of potential hazardous situations and accidents in critical industries (nuclear stations, transport systems, and air traffic control). This paper describes the developed method for mental fatigue detection based on human eye movements. We based our research on a developed earlier dataset that included captured eye-tracking data of human operators that implemented different tasks during the day. In the scope of the developed method, we propose a technique for the determination of the most relevant gaze characteristics for mental fatigue state detection. The developed method includes the following machine learning techniques for human state classification: random forest, decision tree, and multilayered perceptron. The experimental results showed that the most relevant characteristics are as follows: average velocity within the fixation area; average curvature of the gaze trajectory; minimum curvature of the gaze trajectory; minimum saccade length; percentage of fixations shorter than 150 ms; and proportion of time spent in fixations shorter than 150 milliseconds. The processing of eye movement data using the proposed method is performed in real time, with the maximum accuracy (0.85) and F1-score (0.80) reached using the random forest method.

Deep Learning‐Based Variational Autoencoder for Classification of Quantum and Classical States of Light

AbstractAdvancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics. However, characterizing light and its sources through single photon measurements requires efficient detectors and longer measurement times to obtain high‐quality photon statistics. Here, a deep learning‐based variational autoencoder (VAE) method is introduced for classifying single photon added coherent state (SPACS), single photon added thermal state (SPATS), and mixed states between coherent and SPACS as well as between thermal and SPATS of light. The semi‐supervised learning‐based VAE efficiently maps the photon statistics features of light to a lower dimension, enabling quasi‐instantaneous classification with low average photon counts. The proposed VAE method is robust and maintains classification accuracy in the presence of losses inherent in an experiment, such as finite collection efficiency, non‐unity quantum efficiency, finite number of detectors, etc. Additionally, leveraging the transfer learning capabilities of VAE enables successful classification of data of any quality using a single trained model. It is envisioned that such a deep learning methodology will enable better classification of quantum light and light sources even in poor detection.

EEG-based optimization of eye state classification using modified-BER metaheuristic algorithm

This article introduces the Modified Al-Biruni Earth Radius (MBER) algorithm, which seeks to improve the precision of categorizing eye states as either open (0) or closed (1). The evaluation of the proposed algorithm was assessed using an available EEG dataset that applied preprocessing techniques, including scaling, normalization, and elimination of null values. The MBER algorithm’s binary format is specifically designed to select features that can significantly enhance the accuracy of classification. The proposed algorithm and competing ones, namely, Al-Biruni Earth Radius (BER), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WAO), Grey Wolf Optimizer (GWO) and Genetic Algorithm (GA) were evaluated using predefined sets of assessment criteria. The statistical analysis employed the ANOVA and Wilcoxon signed-rank tests and assessed the effectiveness and significance of the proposed algorithm compared to the other five algorithms. Furthermore, A series of visual depictions were presented to validate the effectiveness and robustness of the proposed algorithm. Thus, the MBER algorithm outperformed the other optimizers on the majority of the unimodal benchmark functions due to these considerations. Different ML models were used for classification, e.g., DT, RF, KNN, SGD, GNB, SVC, and LR. The KNN model achieved the highest values of Precision (PPV) (0.959425), Negative Predictive Value (NPV) (0.964969), FScore (0.963431), accuracy (0.9612), Sensitivity (0.970578) and Specificity (0.949711). Thus, KNN serves as a fitness function and is optimized by the utilization of Modified Al-Biruni earth radius (MBER). Finally, the accuracy of eye state classification achieved 96.12% using the proposed algorithm.

Bolt loosening status classification via wave energy dissipation measurements with temperature-compensated deep learning by piezoelectric active sensing

Abstract In industrial applications, bolts, serving as crucial components, endure substantial loads and are susceptible to loosening problems exacerbated by intricate external environmental factors. The active sensing method based on wave energy dissipation exhibits pronounced sensitivity to axial load fluctuations in bolts and demonstrates extensive applicability, with its measurement indicators directly applicable for discerning the bolt’s status. However, environmental factors, notably temperature, can significantly influence signal energy measurements, and the oversight of temperature impact may result in erroneous state discrimination. To tackle this challenge, this paper introduces a cascaded model comprising a temperature compensation subnetwork and a bolt state discrimination subnetwork. The temperature compensation subnetwork takes temperature and signal energy as inputs and outputs the temperature-compensated signal energy, and conveys the outcomes to the bolt state discrimination subnetwork for state classification. In model design, we quantitatively analyzed the number of convolutional blocks and training epochs for the temperature compensation subnetwork with the aim of enhancing the model’s generalization ability, ultimately determining the model architecture. By comparing the experimental results between a single-task model and a model incorporating the temperature compensation subnetwork, we verified the effectiveness of the temperature compensation subnetwork. The experimental outcomes demonstrate that the proposed cascaded model achieved a state classification accuracy of 98.6% on over 1300 temperature-generalized data points. To comprehensively assess our approach, we conducted detailed comparisons with other bolt loosening monitoring methods, elucidating the effectiveness based on data trends and model design. Experimental results demonstrate that the proposed temperature compensation cascaded model accurately identifies bolt states amidst complex temperature variations.

Classification of melt pool states for defect detection in laser directed energy deposition using FixConvNeXt model

Abstract Laser directed energy deposition (L-DED) has emerged as a promising technique for rapid prototyping due to its cost-effectiveness and efficiency. However, the intricate and multi-scale physics of the process hinder its widespread application. This paper addresses the challenge by focusing on real-time identification of melt pool states to detect defects early and minimize resource wastage. To achieve this, a FixConvNeXt model was developed for fast and accurate monitoring of melt pool states. This model was trained using 5000 melt pool images captured during the printing of single-track deposits from a charge-coupled device. To evaluate its performance, FixConvNeXt was compared with other models using various metrics. Experimental results demonstrated that FixConvNeXt achieved superior performance in accurately identifying melt pool states with 99.1% accuracy, while also reducing computation burden and processing time. The mechanism of classification by FixConvNeXt was explained using gradient-weighted class activation mapping. The research findings highlight the potential application of online process monitoring in L-DED. This study lays the foundation for future development of an efficient deep learning network for automatic defect detection and feedback control.

Advancing IoMT security: A two-factor authentication model employing PUF and Fuzzy logic techniques

The rapid integration of Internet of Things technologies in healthcare has catalyzed the development of the Internet of Medical Things, markedly enhanced patient care while posing significant security risks. This paper introduces a comprehensive computational framework to safeguard Internet of Medical Things devices and healthcare providers through a sophisticated registration and authentication process. Our model incorporates cryptographic technologies such as Physical Unclonable Functions, fuzzy extractors, and hash functions to bolster the security during the registration and authentication processes for Internet of Medical Things devices and healthcare providers. The Physical Unclonable Function module enhances device security by producing unique, non-replicable responses for device authentication, significantly reinforcing the system's defense against physical and cloning attacks. Furthermore, the model leverages fuzzy logic for the real-time classification of patient health states, enhancing the decision-making accuracy. A comparative analysis confirms that our model exceeds existing models in communication cost, computational efficiency and security. The proposed scheme has been rigorously tested against various attacks using the Scyther tool. By employing a unique identifier generation method through Physical Unclonable Function and utilizing fuzzy logic for secure data transmission and patient classification, our framework addresses vulnerabilities such as man-in-the-middle, denial of service, impersonation, identity guessing, password guessing and replay attacks, which are prevalent in current Internet of Medical Things frameworks.

Geometry of Kirkwood–Dirac classical states: a case study based on discrete Fourier transform

Abstract The characterization of Kirkwood-Dirac (KD) classicality or non-classicality is very important in quantum information processing. In general, the set of KD classical states with respect to two bases is not a convex polytope[J. Math. Phys. \textbf{65} 072201 (2024)], which makes us interested in finding out in which circumnstances they do form a polytope. In this paper, we focus on the characterization of KD classicality of mixed states for the case where the transition matrix between two bases is a discrete Fourier transform (DFT) matrix in Hilbert space with dimensions $p^2$ and $pq$, respectively, where $p, q$ are prime. For the two particular cases we investigate, the sets of extremal points are finite, implying that the set of KD classical states we characterize forms a convex polytope. We show that for $p^2$ dimensional system, the set $\rm{KD}_{\mathcal{A},\mathcal{B}}^+$ is a convex hull of the set $\rm {pure}({\rm {KD}_{\mathcal{A},\mathcal{B}}^+})$ based on DFT, where $\rm{KD}_{\mathcal{A},\mathcal{B}}^+$ is the set of KD classical states with respect to two bases and $\rm {pure}({\rm {KD}_{\mathcal{A},\mathcal{B}}^+})$ is the set of all the rank-one projectors of KD classical pure states with respect to two bases. In $pq$ dimensional system, we believe that this result also holds. Unfortunately, we do not completely prove it, but some meaningful conclusions are obtained about the characterization of KD classicality.

Quantum lozenge tiling and entanglement phase transition

While volume violation of area law has been exhibited in several quantum spin chains, the construction of a corresponding ground state in higher dimensions, entangled in more than one direction, has been an open problem. Here we construct a 2D frustration-free Hamiltonian with maximal violation of the area law. We do so by building a quantum model of random surfaces with color degree of freedom that can be viewed as a collection of colored Dyck paths. The Hamiltonian may be viewed as a 2D generalization of the Fredkin spin chain. It relates all the colored random surface configurations subject to a Dirichlet boundary condition and hard wall constraint from below to one another, and the ground state is therefore a superposition of all such classical states and non-degenerate. Its entanglement entropy between subsystems undergoes a quantum phase transition as the deformation parameter is tuned. The area- and volume-law phases are similar to the one-dimensional model, while the critical point scales with the linear size of the system L as Llog⁡L. Further it is conjectured that similar models with entanglement phase transitions can be built in higher dimensions with even softer area law violations at the critical point.

Topological data analysis assisted machine learning for polar topological structures in oxide superlattices

Ferroelectric topological phases and phase transitions have been extensively investigated recently due to the rich physical insights and potential applications in next-generation electronic devices. However, precisely predicting the topological phase transitions under different internal and external conditions in polar oxide superlattice systems is challenging due to the complex energy competitions and highly nonlinear kinetics involved. Herein, we adopted a state-of-the-art mathematical concept called “persistent homology” from topological data analysis to extract the essential topological features for the polarization data in various topological structures. By implementing the persistent image as the descriptor, support vector regression (SVR) based convolutional neural network (CNN) models are developed for the automated and high precision classification and regression of topological states based on high-dimensional phase-field simulation data of the PTO/STO superlattice. Using this method, we can automatically construct the strain and electric field phase diagrams in seconds with high throughput phase-field data. We hope to spur further interest in the integration of state-of-the-art mathematical tools, machine learning algorithms, and condensed matter physics for predictions of topological phase transitions.

Assessing large multimodal models for one-shot learning and interpretability in biomedical image classification.

Image classification plays a pivotal role in analyzing biomedical images, serving as a cornerstone for both biological research and clinical diagnostics. We demonstrate that large multimodal models (LMMs), like GPT-4, excel in one-shot learning, generalization, interpretability, and text-driven image classification across diverse biomedical tasks. These tasks include the classification of tissues, cell types, cellular states, and disease status. LMMs stand out from traditional single-modal classification approaches, which often require large training datasets and offer limited interpretability.

Deriving the PedsUtil health state classification system to measure health utilities for pediatric populations based on the PedsQL: a confirmatory factor analysis.

An important methodological challenge in conducting pediatric economic evaluations is estimating the preference-based health-related quality of life (HRQoL) of children. Current methods are highly variable and there is no single instrument available to value HRQoL consistently across multiple pediatric age groups. The Pediatric Quality of Life Inventory (PedsQL) is a non-preference-based generic HRQoL instrument validated for children 2-18 years, but it cannot be directly used in economic evaluations. The aim of this study was to establish the core dimension structure of the PedsUtil health state classification system using confirmatory factor analysis, which is the first step of deriving a preference-based measure of HRQoL based on the PedsQL. Four competing dimension structures of the PedsUtil health state classification system were developed based on published literature and expert opinion. Using data from the Longitudinal Study of Australian Children (LSAC) (n = 45,207), the 4 dimension structures were evaluated using the robust weighted least squares estimation method. The analyses were stratified by 2-year age intervals (from 2 to 17 years) to reflect the study design of the LSAC, as well as special healthcare needs status of the child. Model fit was evaluated by examining standardized factor loadings and various fit indices including the comparative fit index (CFI), Tucker-Lewis Index (TLI), and the root mean square error of approximation (RMSEA). Modification indices and residual correlations were examined to re-specify the models to improve model fit when necessary. The findings supported a 7-dimension structure (i.e., Physical Functioning, Pain, Fatigue, Emotional Functioning, Social Functioning, School Functioning, and School Absence) of the PedsUtil health state classification system. The 7-dimension model exhibited adequate fit across subgroups with CFI values that ranged from 0.929 to 0.954, TLI values from 0.916 to 0.946, and RMSEA values from 0.058 to 0.102. This study established the core dimension structure of the PedsUtil health state classification system using confirmatory factor analysis. The 7-dimension structure was found to be applicable across diverse pediatric populations. Research is currently ongoing to select the most representative item within each dimension of the PedsUtil health state classification system and valuation surveys will be fielded to estimate the PedsUtil scoring system.

A novel two-stage method via adversarial strategy for remaining useful life prediction of bearings under variable conditions

It is critical to accurately predict the remaining useful life (RUL) of rolling bearings to avoid severe accidents and financial losses in the industry. Nevertheless, accurately determining the initial prediction time (IPT) continues to pose a challenge, and significant differences in the data distribution of bearings under different operating conditions are frequently overlooked. To deal with these problems, we propose a novel two-stage method based on the adversarial strategy for RUL prediction of bearings under variable conditions. Firstly, we create reliable health indicators in an unsupervised manner by recording the coded characteristics of the bearing’s state of health. Secondly, an adaptive threshold method based on rate-of-change (ATMROC) is developed to perform accurate health state classification. Finally, we propose a RUL prediction network based on the attention depth-gated recurrent unit with domain invariance (DIADGRU) to handle the inconsistent distribution of degradation features under different operating conditions. Experiments of RUL prediction on PHM2012 and XITU-SY datasets are implemented, and the promising results validate the effectiveness of the proposed method.

Supervised machine learning on ECG features to classify sleep in non-critically ill children.

Despite frequent sleep disruption in the paediatric intensive care unit (PICU), bedside sleep monitoring in real-time is currently not available. Supervised machine learning (ML) applied to electrocardiography (ECG) data may provide a solution, since cardiovascular dynamics are directly modulated by the autonomic nervous system (ANS) during sleep. Retrospective study using hospital-based polysomnography (PSG) recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years and 13-18 years. Features were derived in time, frequency and non-linear domain from pre-processed ECG data. Sleep classification models were developed for two, three, four and five state using logistic regression (LR), random forest (RF) and XGBoost (XGB) classifiers during five-fold nested cross-validation. Models were additionally validated across age categories. A total of 90 non-critically ill children were included with a median (Q1, Q3) recording length of 549.0 (494.8, 601.3) minutes. The three models obtained AUROC 0.72 - 0.78 with minimal variation across classifiers and age categories. Balanced accuracies were 0.70 - 0.72, 0.59 - 0.61, 0.50 - 0.51 and 0.41 - 0.42 for two, three, four and five state. Generally, the XGB model obtained the highest balanced accuracy (p < 0.05), except for five state where LR excelled (p = 0.67). ECG-based ML models are a promising and non-invasive method for automated sleep classification directly at the bedside of non-critically ill children aged 6 months to 18 years. Models obtained moderate-to-good performance for two and three state classification.

Heisenberg-Limited Quantum Lidar for Joint Range and Velocity Estimation.

We propose a quantum lidar protocol to jointly estimate the range and velocity of a target by illuminating it with a single beam of pulsed displaced squeezed light. In the lossless scenario, we show that the mean-squared errors of both range and velocity estimations are inversely proportional to the squared number of signal photons, simultaneously attaining the Heisenberg limit. This is achieved by engineering the multiphoton squeezed state of the temporal modes and adopting standard homodyne detection. To assess the robustness of the quantum protocol, we incorporate photon losses and detuning of the homodyne receiver. Our findings reveal a quantum advantage over the best-known classical strategy across a wide range of round-trip transmissivities. Particularly, the quantum advantage is substantial for sufficiently small losses, even when compared to the optimal-potentially unattainable-classical performance limit. The quantum advantage also extends to the practical case where quantum engineering is done on top of a strong classical coherent state with watts of power. This, together with the robustness against losses and the feasibility of the measurement with state-of-the-art technology, make the protocol highly promising for near-term implementation.

Theoretical Kinetics of Radical-Radical Reaction NH2ṄH + ṄH2 and Its Implications for Monomethylhydrazine Pyrolysis Mechanism.

Significant discrepancies were observed between the experiments and the simulations for ṄH2 time-histories in monomethylhydrazine pyrolysis with the robust mechanism proposed by Pascal and Catoire. The rate of formation analyses for ṄH2 indicated the significance of the reaction NH2ṄH + ṄH2 = H2NN + NH3, which has not been well-defined. In this study, ab initio calculations were performed for the theoretical description of the NH2ṄH + ṄH2 chemistry. Most stationary points on the potential energy surface were quantified at the CCSD(T)/CBS//M06-2X/aug-cc-pVTZ level, and the multireference methods were employed for barrier-less reaction and some transition states. The temperature- and pressure-dependent rate coefficients were determined using classical and microcanonical variational transition state theories. Four primary reaction channels were identified as competitive: 1) The H atom abstraction reaction yielding N2H2(T) + NH3, dominating at 1350-3000 K across the 0.001-100 atm pressure range. 2) The H atom abstraction reaction forming N2H2(S) + NH3, dominating at 800-1350 K and competing with the processes of chemical activation and collisional stabilization below 800 K. 3) The chemical-activated reaction resulting in H2NN(S) + NH3, dominating below 800 K at 0.001 atm. 4) The collisional-stabilized recombination reaction leading to N3H5, becoming significant as pressure increases and dominating below 600 and 650 K at 1 and 100 atm, respectively. The implications of newly calculated NH2ṄH + ṄH2 kinetics for the monomethylhydrazine pyrolysis mechanism were evaluated, and updates were implemented. Sensitivity analyses indicated the necessity of additional research efforts to comprehend the dynamics of CH3NH2 unimolecular and N2H2 + ṄH2 reaction systems. The rate coefficients presented in this study can be employed to develop the chemical kinetic model of nitryl-containing systems.

Quantum Information Driven Ansatz (QIDA): Shallow-Depth Empirical Quantum Circuits from Quantum Chemistry.

Hardware-efficient empirical variational ansätze for Variational Quantum Eigensolver (VQE) simulations of quantum chemistry often lack a direct connection to classical quantum chemistry methods. In this work, we propose a method to bridge this gap by introducing a novel approach to constructing a starting point for variational quantum circuits, leveraging quantum mutual information from classical quantum chemistry states to design simple yet effective heuristic ansätze with a topology reflecting the molecular system's correlations. As a first step, we make use of quantum chemistry calculations, such as Mo̷ller-Plesset (MP2) perturbation theory, to initially provide approximate Natural Orbitals, which have been shown to be the best candidate one-electron basis for developing compact empirical wave functions.1 Second, we evaluate the quantum mutual information matrix, which provides insights about the main correlations between qubits of the quantum circuit, and enables a direct design of entangling blocks for the circuit. The resulting ansatz is then used with a VQE to obtain a short-depth variational ground state of the electronic Hamiltonian. To validate our approach, we perform a comprehensive statistical analysis through simulations of various molecular systems (H2, LiH, H2O) and apply it to the more complex NH3 molecule. The reported results demonstrate that the proposed methodology gives rise to highly effective ansätze, outperforming the standard empirical ladder-entangler ansatz. Overall, our approach can be used as an effective state preparation, providing a promising route for designing efficient variational quantum circuits for large molecular systems.

Abstract C066: Identifying Transcriptional Drivers of Plasticity in Pancreatic Cancer

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains a deadly disease with few effective treatments. Even with the advent of mutationally-directed therapies targeting the KRAS oncogene, PDAC transcriptional plasticity remains a major mechanism of therapeutic resistance. As a result, there is a desperate need to develop additional strategies to treat this deadly disease. In addition to the canonical genomic alterations in KRAS, TP53, CDKN2A, and SMAD4, PDAC tumors exhibit diverse transcriptional programs, or "cell states," with prognostic implications: PDAC tumors in the “classical state” are more responsive to upfront chemotherapy and have better outcomes, while those in the “basal state” are more aggressive with poorer prognoses. Currently, PDAC cell states are not used to direct therapy. Cell state is an integrative property shaped by both cell-intrinsic (e.g., mutations, epigenetics) and cell-extrinsic (e.g., tumor microenvironment) factors. In our prior work, single-cell analyses of clinical PDAC samples demonstrated notable cell state heterogeneity and plasticity. In addition, longitudinal monitoring of patient-derived organoids showed that cells can shift from classical to basal or coexpressor states as the environment changes, and that these cell states can drastically influence drug response. However, it remains unknown which signaling and transcriptional pathways induce basal or classical states, and whether these gene regulatory networks can be targeted therapeutically. Thus, there is a pressing need to devise scalable methods to determine essential drivers for prognostically relevant cancer cell states.Here, we aim to systematically and unbiasedly identify transcription factors (TFs) that drive basal and classical cell states in PDAC. We have successfully established a high-throughput screening pipeline to 1) over-express all human TF isoforms in a pooled format within a cohort of PDAC models; 2) sort cells using antibodies against state-specific cell surface markers; and 3) perform single-cell RNA sequencing on sorted cells to nominate individual TFs or combinations that induce specific cell state transitions. Preliminary results identify both previously reported (e.g., GATA4) and new TFs that can induce the classical state. By overexpressing TFs individually or in combination, we are able to engineer PDAC cell line models that more faithfully reflect the transcriptional phenotypes observed in patient tumors. Ultimately, we aim to use this approach to construct high-fidelity isogenic but state-variant PDAC models for use both in therapeutic screening and as a substrate for investigating mechanisms governing cancer cell state plasticity. Citation Format: Yuzhou Evelyn Tong, Aswanth Mahalingam, Walaa E Katan, Julia Joung, Alex K Shalek, Peter S Winter, Srivatsan Raghavan. Identifying Transcriptional Drivers of Plasticity in Pancreatic Cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C066.

Abstract C001: Transforming Growth Factor-β Blockade in Pancreatic Cancer Enhances Sensitivity to Combination Chemotherapy in Mice

Abstract Background: TGFβ plays a critical role in promoting pancreatic tumor progression, attenuating CD8 T cell proliferation and cytokine production, enhancing the differentiation of myofibroblasts and the deposition of extracellular matrix. However, single agent TGFβ blockade has shown limited efficacy in mouse models of pancreatic cancer. Methods: We evaluated the TGFβ- blocking antibody NIS793 in combination with gemcitabine/nanoparticle (albumin-bound)- paclitaxel or FOLFIRINOX (folinic acid [FOL], 5-fluorouracil [F], irinotecan [IRI] and oxaliplatin [OX]) in orthotopic pancreatic cancer models. Single-cell RNA sequencing and immunofluorescence were used to evaluate changes in tumor cell state and the tumor microenvironment. Results: Blockade of TGFβ with chemotherapy reduced tumor burden in poorly immunogenic pancreatic cancer, without affecting the metastatic rate of cancer cells. Efficacy of combination therapy was not dependent on CD8 T cells, because response to TGFβ blockade was preserved in CD8-depleted or recombination activating gene 2 (RAG2-/-) mice. TGFβ blockade decreased total α-smooth muscle actin-positive fibroblasts but had minimal effect on fibroblast heterogeneity. Bulk RNA sequencing on tumor cells sorted ex vivo revealed that tumor cells treated with TGFβ blockade adopted a classical lineage consistent with enhanced chemosensitivity, and immunofluorescence for cleaved caspase 3 confirmed that TGFβ blockade increased chemotherapy-induced cell death in vivo. Conclusions: TGFβ regulates pancreatic cancer cell plasticity between classical and basal cell states. TGFβ blockade in orthotropic models of pancreatic cancer enhances sensitivity to chemotherapy by promoting a classical malignant cell state. This study provides scientific rationale for evaluation of NIS793 with FOLFIRINOX or gemcitabine/nanoparticle (albumin-bound) paclitaxel chemotherapy backbone in the clinical setting and supports the concept of manipulating cancer cell plasticity to increase the efficacy of combination therapy regimens. This study provided the scientific rationale for the evaluation of TGFβ with both chemotherapy backbones in the clinical setting. Ongoing evaluation of samples from the stopped clinical trials will inform as to the role of TGFb in human PDAC. Citation Format: Li Qiang, Megan Hoffman, Lestat Ali, Jaime Ivan Castillo Silva, Lauren Kageler, Ayantu Temesgen, Patrick Lenehan, Jennifer Wang, Elisa Bello, Victoire Cardot-Ruffino, Giselle Uribe, Annan Yang, Michael Dougan, Andrew Aguirre, Srivatsan Raghavan5, Marc Pelletier, Viviana Cremasco, Stephanie Dougan. Transforming Growth Factor-β Blockade in Pancreatic Cancer Enhances Sensitivity to Combination Chemotherapy in Mice [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C001.