Advanced Model Research Articles

Macroeconomic drivers effect on housing sale prices in China

Purpose The purpose of this study is to conduct a thorough empirical investigation of the intricate relationship between urban housing sales prices and land supply prices in China, with the aim of elucidating the underlying economic principles governing this dynamic interplay. Design/methodology/approach Using monthly data of China, the authors use the asymmetry nonlinear autoregressive distributed lag (NARDL) model to test for nonlinearity in the relationship between land supply price and urban housing prices. Findings The empirical results confirm the existence of an asymmetric relationship between land supply price and urban housing prices. The authors find that land supply price has a positive and statistically significant impact on urban housing prices when land supply is increasing. Policymakers should strive to strike a balance between safeguarding residents’ housing rights and maintaining market stability. Research limitations/implications Although the asymmetric effect of land supply price has been identified as a significant contributor in this study, it is important to note that the research primarily relies on time series data and focuses on analysis at the national level. Although time series data offer a macroscopic perspective of overall trends within a country, they fail to adequately showcase the structural variations among different cities. Practical implications To ensure a stable housing market and meet residents’ housing needs, policymakers must reexamine current land policies. Solely relying on restricting land supply to control housing prices may yield counterproductive results. Instead, increasing land supply could be a more viable option. By rationally adjusting land supply prices, the government can not only mitigate excessive growth in housing prices but also foster the healthy development of the housing market. Originality/value First, the authors have comprehensively evaluated the impact of land supply prices in China on urban housing sales prices, examining whether they play a facilitating or mitigating role in the fluctuation of these prices. Second, departing from traditional linear analytical frameworks, the authors have explored the possibility of a nonlinear relationship existing between land supply prices and urban housing sales prices in China. Finally, using an advanced NARDL model, the authors have delved deeper into the asymmetric effects of land supply prices on urban housing sales prices in China.

Robust hyperspectral image classification using generative adversarial networks

This paper introduces Sill-Rgan, a novel Generative Adversarial Network (GAN) designed to improve hyperspectral image (HSI) classification under varying lighting conditions. Sill-Rgan uniquely maps different light condition domains, enhancing sample classification robustness and generating new virtual samples. Addressing challenges like high spectral dimensionality and noise in HSI classification, our approach utilizes a deep proxy-based learning framework. It integrates and improves advanced GAN models and multitask networks for optimal training stability and loss function optimization. The model's mapping network is adept at generating domain-specific latent codes, enabling the transformation of original hyperspectral data into enhanced versions. Extensive experiments conducted on hyperspectral datasets of agricultural products under diverse indoor and outdoor lighting conditions confirm the effectiveness of Sill-Rgan. The results highlight the model's adaptability in both supervised and semi-supervised learning scenarios, yielding exceptional classification accuracy and enhanced data quality. The versatile potential of Sill-Rgan extends its applicability to a broad range of spectral data classifications, underlining its significant contribution to hyperspectral imaging. This advancement opens new avenues in machine vision systems, particularly in scenarios with dynamic lighting challenges.

The Dark Side of Language Models: Exploring the Potential of LLMs in Multimedia Disinformation Generation and Dissemination

Disinformation - the deliberate spread of false or misleading information poses a significant threat to our society by undermining trust, exacerbating polarization, and manipulating public opinion. With the rapid advancement of artificial intelligence and the growing prominence of large language models (LLMs) such as ChatGPT, new avenues for the dissemination of disinformation are emerging. This review paper explores the potential of LLMs to initiate the generation of multi-media disinformation, encompassing text, images, audio, and video. We begin by examining the capabilities of LLMs, highlighting their potential to create compelling, context-aware content that can be weaponized for malicious purposes. Subsequently, we examine the nature of disinformation and the various mechanisms through which it spreads in the digital landscape. Utilizing these advanced models, malicious actors can automate and scale up disinformation effectively. We describe a theoretical pipeline for creating and disseminating disinformation on social media. Existing interventions to combat disinformation are also reviewed. While these efforts have shown success, we argue that they need to be strengthened to effectively counter the escalating threat posed by LLMs. Digital platforms have, unfortunately, enabled malicious actors to extend the reach of disinformation. The advent of LLMs poses an additional concern as they can be harnessed to significantly amplify the velocity, variety, and volume of disinformation. Thus, this review proposes augmenting current interventions with AI tools like LLMs, capable of assessing information more swiftly and comprehensively than human fact-checkers. This paper illuminates the dark side of LLMs and highlights their potential to be exploited as disinformation dissemination tools.

Dhad—A Children’s Handwritten Arabic Characters Dataset for Automated Recognition

This study delves into the intricate realm of recognizing handwritten Arabic characters, specifically targeting children’s script. Given the inherent complexities of the Arabic script, encompassing semi-cursive styles, distinct character forms based on position, and the inclusion of diacritical marks, the domain demands specialized attention. While prior research has largely concentrated on adult handwriting, the spotlight here is on children’s handwritten Arabic characters, an area marked by its distinct challenges, such as variations in writing quality and increased distortions. To this end, we introduce a novel dataset, “Dhad”, refined for enhanced quality and quantity. Our investigation employs a tri-fold experimental approach, encompassing the exploration of pre-trained deep learning models (i.e., MobileNet, ResNet50, and DenseNet121), custom-designed Convolutional Neural Network (CNN) architecture, and traditional classifiers (i.e., Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP)), leveraging deep visual features. The results illuminate the efficacy of fine-tuned pre-existing models, the potential of custom CNN designs, and the intricacies associated with disjointed classification paradigms. The pre-trained model MobileNet achieved the best test accuracy of 93.59% on the Dhad dataset. Additionally, as a conceptual proposal, we introduce the idea of a computer application designed specifically for children aged 7–12, aimed at improving Arabic handwriting skills. Our concluding reflections emphasize the need for nuanced dataset curation, advanced model architectures, and cohesive training strategies to navigate the multifaceted challenges of Arabic character recognition.

Enhancing Maritime Navigational Safety: Ship Trajectory Prediction Using ACoAtt–LSTM and AIS Data

Predicting ship trajectories plays a vital role in ensuring navigational safety, preventing collision incidents, and enhancing vessel management efficiency. The integration of advanced machine learning technology for precise trajectory prediction is emerging as a new trend in sophisticated geospatial applications. However, the complexity of the marine environment and data quality issues pose significant challenges to accurate ship trajectory forecasting. This study introduces an innovative trajectory prediction method, combining data encoding representation, attribute correlation attention module, and long short-term memory network. Initially, we process AIS data using data encoding conversion technology to improve representation efficiency and reduce complexity. This encoding not only preserves key information from the original data but also provides a more efficient input format for deep learning models. Subsequently, we incorporate the attribute correlation attention module, utilizing a multi-head attention mechanism to capture complex relationships between dynamic ship attributes, such as speed and direction, thereby enhancing the model’s understanding of implicit time series patterns in the data. Finally, leveraging the long short-term memory network’s capability for processing time series data, our approach effectively predicts future ship trajectories. In our experiments, we trained and tested our model using a historical AIS dataset. The results demonstrate that our model surpasses other classic intelligent models and advanced models with attention mechanisms in terms of trajectory prediction accuracy and stability.

Prediction of ORF for Optimized CO2 Flooding in Fractured Tight Oil Reservoirs via Machine Learning

Tight reservoirs characterized by complex physical properties pose significant challenges for extraction. CO2 flooding, as an EOR technique, offers both economic and environmental advantages. Accurate prediction of recovery rate plays a crucial role in the development of tight oil and gas reservoirs. But the recovery rate is influenced by a complex array of factors. Traditional methods are time-consuming and costly and cannot predict the recovery rate quickly and accurately, necessitating advanced multi-factor analysis-based prediction models. This study uses machine learning models to rapidly predict the recovery of CO2 flooding for tight oil reservoir development, establishes a numerical model for CO2 flooding for low-permeability tight reservoir development based on actual blocks, studies the effects of reservoir parameters, horizontal well parameters, and injection-production parameters on CO2 flooding recovery rate, and constructs a prediction model based on machine learning for the recovery. Using simulated datasets, three models, random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), were trained and tested for accuracy evaluation. Different levels of noise were added to the dataset and denoised, and the effects of data noise and denoising techniques on oil recovery factor prediction were studied. The results showed that the LightGBM model was superior to other models, with R2 values of 0.995, 0.961, 0.921, and 0.877 for predicting EOR for the original dataset, 5% noise dataset, 10% noise dataset, and 15% noise dataset, respectively. Finally, based on the optimized model, the key control factors for CO2 flooding for tight oil reservoirs to enhance oil recovery were analyzed. The novelty of this study is the development of a machine-learning-based method that can provide accurate and cost-effective ORF predictions for CO2 flooding for tight oil reservoir development, optimize the development process in a timely manner, significantly reduce the required costs, and make it a more feasible carbon utilization and EOR strategy.

Thermophoretic particle deposition in a nanofluid flow across a disc with non-fourier heat flux: An investigation using tangent hyperbolic model

Non-Newtonian nanofluids are attracting the attention of researchers and academics due to the high heat transmission rates that they possess. To improve thermal devices and exchangers, the researchers come up with innovative and expense-effective methods. One of the advanced technological approaches that can be utilized to enhance the thermal performance of devices is the utilization of nanofluids. This non- Newtonian nanofluid possesses the advanced properties of increasing heat transfer and destroying harmful bacteria. For this purpose, the present theoretical work is to explore the heat and thermophoretic particle deposition analysis on the tangent hyperbolic nanofluid flow over the rotating porous disk with presence of non-Fourier heat flux model and nanoparticle aggregation effect. Darcy-Forchheimer fluid model is adopted to develop the fluid flow system. Furthermore, the nanofluid’s viscosity and thermal conductivity are expressed through advanced modeling, employing the modified Krieger-Dougherty model for viscosity and the Maxwell-Bruggeman model for thermal conductivity. The nanoparticles and base fluid involved in this context are Molybdenum disulfide ( Mo S 2 ) and Water/carboxyl-methyl cellulose (CMC) respectively. The mathematical representation of the fluid flow and energy propagation involves a system of coupled partial differential equations (PDEs). The system of partial differential equations (PDEs) is transformed into non-dimensional ordinary differential equations (ODEs), which are then solved both analytically and numerically using the Homotopy Analysis Method(HAM) and Runge-Kutta-Fehlberg (RKF) method along with shooting technique. Exploring the graphical representation reveals a comprehensive analysis of key factors influencing velocity, temperature, and concentration. Through innovative visualization, these parameters take center stage, providing insights into their intricate interplay and dynamic relationships. The heat transfer rate is enhanced 0.24% and 1.4% by rising the values of thermal relaxation parameter and Weissenberg number. Also, the mass transfer rate is accelerated 0.8% and 0.14% due to enhancing the values of thermophoretic coefficient and thermophoresis parameter.

Backdoor Two-Stream Video Models on Federated Learning

Video models on federated learning (FL) enable continual learning of the involved models for video tasks on end-user devices while protecting the privacy of end-user data. As a result, the security issues on FL, e.g., the backdoor attacks on FL and their defense have increasingly becoming the domains of extensive research in recent years. The backdoor attacks on FL are a class of poisoning attacks, in which an attacker, as one of the training participants, submits poisoned parameters and thus injects the backdoor into the global model after aggregation. Existing backdoor attacks against videos based on FL only poison RGB frames, which makes that the attack could be easily mitigated by two-stream model neutralization. Therefore, it is a big challenge to manipulate the most advanced two-stream video model with a high success rate by poisoning only a small proportion of training data in the framework of FL. In this paper, a new backdoor attack scheme incorporating the rich spatial and temporal structures of video data is proposed, which injects the backdoor triggers into both the optical flow and RGB frames of video data through multiple rounds of model aggregations. In addition, the adversarial attack is utilized on the RGB frames to further boost the robustness of the attacks. Extensive experiments on real-world datasets verify that our methods outperform the state-of-the-art backdoor attacks and show better performance in terms of stealthiness and persistence.

Chinese text dual attention network for aspect-level sentiment classification.

English text has a clear and compact subject structure, which makes it easy to find dependency relationships between words. However, Chinese text often conveys information using situational settings, which results in loose sentence structures, and even most Chinese comments and experimental summary texts lack subjects. This makes it challenging to determine the dependency relationship between words in Chinese text, especially in aspect-level sentiment recognition. To solve this problem faced by Chinese text in the field of sentiment recognition, a Chinese text dual attention network for aspect-level sentiment recognition is proposed. First, Chinese syntactic dependency is proposed, and sentiment dictionary is introduced to quickly and accurately extract aspect-level sentiment words, opinion extraction and classification of sentimental trends in text. Additionally, in order to extract context-level features, the CNN-BILSTM model and position coding are also introduced. Finally, to better extract fine-grained aspect-level sentiment, a two-level attention mechanism is used. Compared with ten advanced baseline models, the model's capabilities are being further optimized for better performance, with Accuracy of 0.9180, 0.9080 and 0.8380 respectively. This method is being demonstrated by a vast array of experiments to achieve higher performance in aspect-level sentiment recognition in less time, and ablation experiments demonstrate the importance of each module of the model.

Nanomedicines Promote Cartilage Regeneration in Osteoarthritis by Synergistically Enhancing Chondrogenesis of Mesenchymal Stem Cells and Regulating Inflammatory Environment.

Osteoarthritis (OA) is a degenerative joint disease characterized by progressive erosion of the articular cartilage and inflammation. Mesenchymal stem cells' (MSCs) transplantation in OA treatment is emerging, but its clinical application is still limited by the low efficiency in oriented differentiation. In our study, to improve the therapeutic efficiencies of MSCs in OA treatment by carbonic anhydrase IX (CA9) siRNA (siCA9)-based inflammation regulation and Kartogenin (KGN)-based chondrogenic differentiation, the combination strategy of MSCs and the nanomedicine codelivering KGN and siCA9 (AHK-CaP/siCA9 NPs) was used. In vitro results demonstrated that these NPs could improve the inflammatory microenvironment through repolarization of M1 macrophages to the M2 phenotype by downregulating the expression levels of CA9 mRNA. Meanwhile, these NPs could also enhance the chondrogenesis of bone marrow-derived mesenchymal stem cells (BMSCs) by upregulating the pro-chondrogenic TGF-β1, ACAN, and Col2α1 mRNA levels. Moreover, in an advanced OA mouse model, compared with BMSCs alone group, the lower synovitis score and OARSI score were found in the group of BMSCs plus AHK-CaP/siCA9 NPs, suggesting that this combination approach could effectively inhibit synovitis and promote cartilage regeneration in OA progression. Therefore, the synchronization of regulating the inflammatory microenvironment through macrophage reprogramming (CA9 gene silencing) and promoting MSCs oriented differentiation through a chondrogenic agent (KGN) may be a potential strategy to maximize the therapeutic efficiency of MSCs for OA treatment.

Stem Cell-Based Approaches in Parkinson's Disease Research.

Parkinson's disease (PD) is a neurodegenerative condition characterized by the loss of midbrain dopaminergic neurons, leading to motor symptoms. While current treatments provide limited relief, they don't alter disease progression. Stem cell technology, involving patient-specific stem cell-derived neurons, offers a promising avenue for research and personalized regenerative therapies. This article reviews the potential of stem cell-based research in PD, summarizing ongoing efforts, their limitations, and introducing innovative research models. The integration of stem cell technology and advanced models promises to enhance our understanding and treatment strategies for PD.

Virtual laboratory enabled constitutive modelling of dual phase steels

Accurate and efficient characterization and modelling of deformation responses are critically essential in the development of advanced metal forming processes. This work presents a virtual laboratory enabled constitutive framework for modelling complex deformation responses of dual phase (DP) steels under complex stress states, based on multi-phase full-field crystal plasticity (CP) and advanced phenomenological modelling. In the CP based virtual testing method, kinematic hardening and degradation of elastic modulus are modelled in particular to improve the capability for describing evolving microstructure induced mechanical responses of DP steels. The workflow of this framework is completely built and numerically implemented, including (i) representative volume element (RVE) generation based on microstructure characterization, (ii) identification of multiphase CP model parameters, (iii) prediction of elastic modulus degradation, (iv) prediction of yield stresses and plastic potentials in uniaxial tension and biaxial tension with various stress ratios, and (v) prediction of stress–strain curves in reverse tension compression. Using DP780 and DP980 as case materials, the corresponding physical experiments are conducted to verify the accuracy of the virtual tests, showing a good agreement between the virtual and experimental approaches. Both experimental and virtual tests are used to calibrate advanced phenomenological constitutive models that include non-linear elasticity, anisotropic yielding, and kinematic hardening. The calibrated models are implemented into the finite element (FE) codes to predict complex deformation and springback behaviours of DP780 and DP980 sheets in U-bending processes. In comparison with forming experiments of U-channel parts, the virtually calibrated models are validated and show good performance in predicting deformation and springback behaviours, providing a high capability for process analysis. The findings support that the virtual laboratory enabled modelling approach could be a substitute for extensive, expensive, and hard-to-access physically mechanical experiments required in the model calibration for a more effective and efficient analysis of metal forming processes.

A Two-Generation, Early Childhood Advanced Primary Care Model.

It is well recognized that early experiences produce long-term impacts on health outcomes, yet many children are at risk of not achieving their full potential because of health and service disparities related largely to poverty and racism. Although many pediatric primary care (PPC) models address these needs, most are isolated, add-on efforts that struggle to be scalable and sustainable. We describe 3-2-1 IMPACT (Integrated Model for Parents and Children Together), an initiative to transform the model of PPC delivered within New York City Health + Hospitals, the largest public hospital system in the United States, to address the full range of child and family needs in early childhood. Taking advantage of the frequent contact with PPC in the early years and linking to prenatal services, the model assesses family mental, social, and physical health needs and offers evidence-based parenting supports and integrated mental health services. Launching and sustaining the model in our large health system has required coalition building and sustained advocacy at the state, city, and health system levels. Long-term sustainability of the IMPACT model will depend on the implementation of early childhood-focused advanced payment models, on which we have made substantial progress with our major contracted Medicaid managed care plans. By integrating multiple interventions into PPC and prenatal care across a large public-healthcare system, we hope to synergize evidence-based and evidence-informed interventions that individually have relatively small effect sizes, but combined, could substantially improve child and maternal health outcomes and positively impact health disparities.

Virtual Laboratory in Mixed Reality with 3D Modelling for Sustainable Design

Immersive experiences through Mixed Reality (MR) are revolutionizing the way people connect and interact with each other and things around them. MR is the umbrella term that binds all spatial computing technologies which blends real and virtual environments. It holds the potential to provide things beyond imagination while simultaneously making deeper and more meaningful engagement possible. A new engineer to be equipped with the know-how of an OP (Operational Procedure) of machinery, this virtual laboratory helps to know the intricate details of the machinery and tools. The virtual laboratory combines the use of MR technologies, such as augmented reality (AR) and virtual reality (VR), with advanced 3D modeling tools. This virtual laboratory has the potential to revolutionize the sustainable design process by providing an immersive and flexible platform for experimentation, analysis, and collaboration. This project focuses on helping the user to visualize the machine (i.e., Single Drum Piston) and its functionalities through an immersive experience and understand the visual empathy of it. Through 3D Modelling technology, Plane detection and algorithms written with C# script we built this solution with the incorporation of Autodesk Maya and Unity Game Engine. The built solution can be experienced immersive by viewing it through a mobile placed in a headset (Aryzon Headset). This MR experience provides realistic virtual presentations which eases learning of complex modules while analytics ensure understanding of outcomes. It can empower designers and researchers to explore innovative design solutions, optimize resource utilization, and ultimately contribute to the development of more sustainable built environments. This can be incorporated in various other fields like automobile manufacturing, machine industries, construction tool manufacturing, real estate buildings & landscapes, home appliances and furniture, interior design, medical institution, and educational institutions.

A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations

Climate change is profoundly affecting the global water cycle, increasing the likelihood and severity of extreme water-related events. Better decision-support systems are vital to accurately predict and monitor water-related environmental disasters and optimally manage water resources. These must integrate advances in remote sensing, in situ, and citizen observations with high-resolution Earth system modeling, artificial intelligence (AI), information and communication technologies, and high-performance computing. Digital Twin Earth (DTE) models are a ground-breaking solution offering digital replicas to monitor and simulate Earth processes with unprecedented spatiotemporal resolution. Advances in Earth observation (EO) satellite technology are pivotal, and here we provide a roadmap for the exploitation of these methods in a DTE for hydrology. The 4-dimensional DTE Hydrology datacube now fuses high-resolution EO data and advanced modeling of soil moisture, precipitation, evaporation, and river discharge, and here we report the latest validation data in the Mediterranean Basin. This system can now be explored to forecast flooding and landslides and to manage irrigation for precision agriculture. Large-scale implementation of such methods will require further advances to assess high-resolution products across different regions and climates; create and integrate compatible multidimensional datacubes, EO data retrieval algorithms, and models that are suitable across multiple scales; manage uncertainty both in EO data and models; enhance computational capacity via an interoperable, cloud-based processing environment embodying open data principles; and harness AI/machine learning. We outline how various planned satellite missions will further facilitate a DTE for hydrology toward global benefit if the scientific and technological challenges we identify are addressed.

Genetic mouse models in opioid research: current status and future directions.

Synthetic and semi-synthetic opioids are prescribed for the management of severe pain conditions, but their long-term use is often leading to physical dependence and addiction disorders. Understanding the complex neurobiology of the opioid system in preclinical models will be essential for the development of safe and efficacious analgesics. With rising numbers of synthetic opioid users and overdose cases, a better understanding of the neuroanatomical and cellular pathways associated with physical dependence and addiction is expected to guide treatment approaches for opioid use disorders. In this commentary, we highlight the importance of advanced genetic mouse models for studying the regional effects of opioid receptors, and we discuss the need of genetic mouse models for the investigation of the regional, circuit and cell compartment-specific role of intracellular mediators of opioid actions.

Abstract B084: Triple checkpoint blockade, but not anti-PD1 alone, enhances the efficacy of engineered adoptive T cell therapy in advanced ovarian cancer

Abstract Background: Over 20,000 women are diagnosed with ovarian cancer annually, and more than half will die within 5 years. This rate has changed little in the last 30 years, highlighting the need for therapy innovation. Although immunotherapy has revolutionized cancer treatment, efforts to harness endogenous patient immune responses have yielded limited therapeutic activity in ovarian cancer patients. T cells engineered to express a T cell receptor (TCR) targeting proteins uniquely overexpressed in tumors have the potential to control tumor growth without toxicity. Mesothelin (Msln) is over-expressed in ovarian cancer, contributes to the malignant and invasive phenotype, and has limited expression in healthy cells, making it a candidate immunotherapy target in these tumors. Methods: The ID8VEGF mouse cell line was used to evaluate if T cells engineered to express a mouse Msln-specific high-affinity T cell receptor (TCRMsln) can kill ovarian cancer. Tumor-bearing mice were treated with TCRMsln T cells plus anti-PD-1, anti-Tim-3 or anti-Lag-3 checkpoint-blocking antibodies alone or in combination, ultimately targeting up to three inhibitory receptors simultaneously. Single-cell RNA-sequencing (scRNAseq) was used to profile the impact of combination checkpoint blockade on engineered T cells and the tumor microenvironment (TME). Results: In a disseminated ID8 tumor model, adoptively transferred TCRMsln T cells preferentially accumulated in established tumors, delayed ovarian tumor growth, and prolonged mouse survival. However, elements in the TME limited engineered T cell persistence and cytolytic function. Triple checkpoint blockade, but not single- or double-agent treatment, dramatically increased antitumor function by intratumoral TCRMsln T cells. scRNAseq of tumor-infiltrating cells revealed distinct transcriptome changes in engineered and endogenous T cells and myeloid-derived cells. Engineered T cells, when combined with triple checkpoint blockade, increased expression of genes associated with effector and memory gene signatures, including proliferation and metabolic function, and reduced expression of genes associated with exhaustion. Moreover, combining adoptive immunotherapy with triple checkpoint blockade significantly prolonged survival in the cohort of treated tumor-bearing mice, relative to mice that received TCRMsln T cells alone or with anti-PD1 or double-agent treatments. Conclusions: Inhibitory receptor/ligand interactions within the TME can dramatically reduce T cell function, suggesting tumor cells may upregulate the ligands for PD-1, Tim-3 and Lag-3 for protection from tumor-infiltrating lymphocytes. In an advanced ovarian cancer model, triple checkpoint blockade significantly improved engineered T cell function and outcomes in mice in a setting where single checkpoint blockade had no significant activity. These results suggest that disrupting multiple inhibitory pathways simultaneously, which can be more safely pursed in a cell intrinsic form through genetic engineering, may be necessary for improved efficacy in patients. Citation Format: Kristin G. Anderson, Yapeng Su, Madison G. Burnett, Breanna M. Bates, Magdalia L. Rodgers Suarez, Susan L. Ruskin, Valentin Voillet, Raphael Gottardo, Philip D. Greenberg. Triple checkpoint blockade, but not anti-PD1 alone, enhances the efficacy of engineered adoptive T cell therapy in advanced ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B084.

Swimming behavior as a potential metric to detect satiation levels of European seabass in marine cages

Aquaculture is anticipated to contribute to two-thirds of the world’s fish consumption by 2030, emphasizing the need for innovative methods to optimize practices for economic viability, social responsibility, and environmental sustainability. Feeding practices play a pivotal role in aquaculture success and the feeding requirements are dynamic, influenced by factors like fish size, environmental conditions, and health status necessitating ongoing improvements in feeding practices. This study addresses a critical gap in feeding control systems in sea cages. It introduces a continuous, real-time monitoring system for analyzing the feeding behavior of European seabass, employing advanced AI models (YOLO and DEEPSORT) and computer vision techniques. The investigation focuses on key parameters, including speed and the newly defined feeding behavior index (FBI), to evaluate swimming responses under varying feeding scenarios exploring meal frequency, feeding time, and feeding quantity. The findings reveal a sensitivity of fish speed and the feeding behavior index (FBI) to different feeding scenarios, elucidating distinct behavioral patterns in response to varying frequencies, times, and quantities of feeding, such as increased activity in the morning relative to later times and the emergence of asymmetric activity patterns when fish are underfed or overfed. Notably, this study is one of the few in the field, presenting the development of a continuous, real-time monitoring system for feeding control in sea cages. Simultaneously, it explores reference curves and threshold values to enhance the overall efficacy of feeding control measures.

Unravelling the reaction mechanism for H2 production via formic acid decomposition over Pd: Coverage-Dependent microkinetic modeling

As a key liquid organic hydrogen carrier, investigating the decomposition of formic acid (HCOOH) on the Pd (111) transition metal surface is imperative for harnessing hydrogen energy. Despite a multitude of studies, the major mechanisms and key intermediates involved in the dehydrogenation process of formic acid remain a great topic of debate due to ambiguous adsorbate interactions. In this research, we develop an advanced microkinetic model based on first-principles calculations, accounting for adsorbate–adsorbate interactions. Our study unveils a comprehensive mechanism for the Pd (111) surface, highlighting the significance of coverage effects in formic acid dehydrogenation. Our findings unequivocally demonstrate that H coverage on the Pd (111) surface renders formic acid more susceptible to decompose into H2 and CO2 through COOH intermediates. Consistent with experimental results, the selectivity of H2 in the decomposition of formic acid on the Pd (111) surface approaches 100 %. Considering the influence of H coverage, our kinetic analysis aligns perfectly with experimental values at a temperature of 373 K.

Abstract B002: Exploiting JAK/STAT signaling to inhibit highly advanced and resistant forms of ovarian cancer

Abstract Ovarian cancer is the most fatal of all female reproductive cancers due to its late detection, aggressive nature, and propensity to develop resistance to current standards of care. Despite initial responses to first line platinum- and paclitaxel-based chemotherapies, >70% of ovarian cancers recur with increasingly resistant disease. Targeted therapies, including PARP-inhibitors (PARPi) and VEGF-inhibitors, are being included in the front-line setting; and while they have improved progression-free survival in a subset of patients, they have thus far failed to improve overall survival in patients without specific genetic signatures. With the broad uptake of PARPi, PARPi-resistance is becoming common. Once resistance develops, nearly all patients die of their disease and there is an obvious need to elucidate the basis for chemotherapy- and PARPi-resistance and devise novel approaches to overcome resistance. Using a small molecule drug screen, we identified lestaurtinib, a known tyrosine kinase inhibitor as a potent inhibitor of treatment naive and highly advanced models of ovarian cancer. Lestaurtinib was originally developed to target FLT3, a protein not expressed in any ovarian cancer models we’ve tested. To identify lestaurtinib targets, we preformed phospho-proteomics in parallel with RNAseq and discovered the JAK/STAT signally pathway as a top hit. We confirmed that essential components of JAK/STAT signaling are constitutively activated in chemotherapy- and PARPi-resistant cells, suggesting that induction of this pathway may drive resistance. Importantly, we found that lestaurtinib-sensitive patient-derived-xenografts (PDXs) exhibited higher expression of multiple JAK/STAT signaling pathway members compared to PDXs that did not respond to lestaurtinib. Genetic knockdown of STAT1 and STAT3 via siRNA, or knockout via CRISPR/Cas9, resulted in significant growth inhibition of sensitive and resistant models, confirming their importance in maintaining cell viability and progression. Combining lestuartinib with standard-of-care cisplatin or olaparib, a PARPi, was shown to be synergistic in multiple models, indicating that pharmacological inhibition of JAK/STAT signaling has the potential to alleviate drug resistance. Finally, we assessed a panel of JAK/STAT inhibitors that interfere at various levels of the pathway and found profound differences in their ability to block STAT-phosphorylation and inhibit cell proliferation. Thus, it is critical to further define the mechanisms by which these drugs function to selectively suppress JAK/STAT signaling in order to elucidate specific nodes of the pathway that drive ovarian cancer progression. Ongoing studies are aimed at identifying the unique roles of STAT1 and STAT3, the specific contributions of their tyrosine and serine phosphorylation, and the relevance of up-/down-stream mediators of the JAK/STAT pathway on ovarian cancer cell growth with the goal of informing future clinical trials. Citation Format: Esther Rodman, Michael Emch, Archit Bajaj, Xiaojia Tang, Xiaonan Hou, Scott Kaufmann, Krishna Kalari, John Weroha, John Hawse. Exploiting JAK/STAT signaling to inhibit highly advanced and resistant forms of ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr B002.