Reference Model Research Articles

Investigation of FGM membrane wrinkling using reduced models based on a multi-scale method

In this article, we show that the reduced model proposed in our recent article to study the wrinkling of homogeneous elastic membranes does not reproduce the wrinkling observed in the case of an FGM-type composite membrane. New reduced models are then proposed to investigate the wrinkling of membranes made from Functionally Graded Materials (FGM). We examine, by this way, the influence of material functional parameters on determining the critical load and post-critical behavior under different loads. These reduced models, created using a multi-scale method based on Fourier series with slowly varying coefficients, enrich the reference model. In addition, these reduced models also allow us to significantly decrease the computation time and the number of finite elements compared to the complete model. The robustness of these reduced models is demonstrated in the case of wrinkling of membranes modeled by very thin shells, which are discretized using DKT18-type finite elements as described in literature. The resulting nonlinear problems are solved using a High-Order Continuation Method (HOCM).

Digital Documentation of the Reconstruction of Notre Dame De Paris

This paper demonstrates how digital documentation projects support engagement, analysis and interpretation of the reconstruction of Notre Dame de Paris, as well as demonstrating the underpinning processes by which the documentation itself is generated and managed. The digital documentation project is the scientific website which forms a digital ecosystem to support the reconstruction project, the reuse of the datasets is analysed in the original heritage discipline and across other disciplines by the OAIS reference model. Then, critically consider the main challenges to be for this project and how they are addressed.

Approaches to simplify industrial energy models for operational optimisation

Minimising energy consumption in today's industrial sector is a crucial objective for achieving established climate goals. One effective strategy to enhance efficiency is optimising energy system operations within industries. The initial step in establishing operational optimisation involves developing a comprehensive model of the energy system. This model necessitates a specific structure to meet optimisation requirements. However, creating a model from scratch incurs substantial effort. While numerous companies possess energy models, they often lack the requisite structure for optimisation. Consequently, simplifying existing models can significantly reduce the effort needed to implement operational optimisation.This paper investigates the simplification of intricate industrial energy system models for optimisation purposes. The subsequent sections analyse two distinct approaches. One approach involves linearisation, while the other utilises neural networks. To facilitate a comparative analysis of these approaches, a reference model is developed. The assessment of these methodologies includes an investigation into optimisation robustness, computation time, accuracy concerning the reference model, and the effort required for developing and maintaining the simplified models. It proved that both approaches are suitable for operational optimisation. Linearisation exhibits superior computational efficiency compared to the neural network approach. The linearisation modelling approach together with the optimisation only required a few milliseconds for the calculation. The neural network approach needed 3 h for the calculation of the optimum with the genetic algorithm. The simulation of the neural network itself only required a few milliseconds. Hence, an improvement of the genetic algorithm is needed. However, the accuracy of linearisation falls short of that achieved by neural networks. The linearisation achieves a mean average percentage error from only 13%. In comparison the neural network's mean average error is 2.3%. Therefore, the linearisation must be improved. The impact using a piecewise linearisation on the results will be analysed in further research.

Flexible Quadrotor Unmanned Aerial Vehicles: Spatially Distributed Modeling and Delay-Resistant Control

This paper introduces an analytical framework for the derivation of distributed-parameter equations of motion of a flexible quadrotor. This approach helps obtain rigid and elastic equations of motion simultaneously, in a decoupled form, to facilitate the controller design. A delay-resistant low-frequency adaptive controller is employed, which prevents excessive oscillations due to flexible dynamics, compensates uncertainties, and addresses the inherent time delay. In addition to this, a delay-dependent stability condition for the overall system dynamics is obtained including the human operator with reaction time delay, the adaptive controller, and the flexible quadrotor dynamics with input delay. With comparative simulation studies, it is demonstrated that the flexible arm tip oscillations are significantly reduced when the low-frequency delay-resistant closed-loop reference model adaptive controller is used, compared to a closed-loop reference model adaptive controller and a conventional model reference adaptive controller.

Down-sampling from hierarchically structured corpus data

Abstract Resource constraints often force researchers to downsize the list of tokens returned by a corpus query. This paper sketches a methodology for down-sampling and offers a survey of current practices. We build on earlier work and extend the evaluation of down-sampling designs to settings where tokens are clustered by text file and lexeme. Our case study deals with third-person present-tense verb inflection in Early Modern English and focuses on five predictors: year, gender, genre, frequency, and phonological context. We evaluate two strategies for selecting 2,000 (out of 11,645) tokens: simple down-sampling, where each hit has the same selection probability; and structured down-sampling, where this probability is inversely proportional to the author- and verb-specific token count. We form 500 subsamples using each scheme and compare regression results to a reference model fit to the full set of cases. We observe that structured down-sampling shows better performance on several evaluation criteria.

Model-based adaptive security control strategy against false data injection attacks in cyber–physical systems

This paper addresses the problem of adaptive security control for cyber–physical systems (CPSs) under external nonlinear disturbances and false data injection (FDI) attacks on actuators. To address FDI attacks, a full-order disturbance estimator is proposed for eliminating disturbances in the forward channel. Furthermore, an attack model based on the system state vector is developed to estimate the attack vector by considering potential attacks that can induce state errors between the reference model and the actual system. Subsequently, a novel model-based adaptive security control (MASC) strategy is devised using attack compensation and state feedback. The key advantage of our proposed strategy lies in its ability to effectively mitigate the adverse effects of attacks while accounting for nonlinear disturbances. Finally, experimental validation conducted on a brushless DC motor speed control system demonstrates the capability of achieving high output tracking accuracy.

Model characteristics of technical and tactical activity of basketball players at the stage of preparation for higher achievements

The article examines the features of the competitive activity of basketball players who are at the stage of preparation for higher achievements. Presented are model characteristics of implementation of technical and tactical actions by basketball players aged 18-20, taking into account their role in the game. The need to take playing time into account when building model characteristics of technical and tactical actions is well-founded. The players of the youth national teams (U 20) show a tendency to increase the quality of the implementation of various shots in the match (two-point, three-point, free throw) in comparison with the players of the youth national teams (U 18). Significant differences (p<0.05) in terms of these indicators were observed in basketball players playing the central player position (49,91±17,06 vs. 55,06±12,47).
 According to such technical and tactical actions as block shots and steals of the ball, the basketball players of the youth national teams (U 18) were reliably ahead of the players of the youth national teams (U 20) (0,18±0,30 vs. 0,10±0.21, 2,19±1, 24 vs. 1,87±1,07, respectively). Considered possible directions of using the developed models during the training of basketball players of youth teams and prospective methodical provisions for creating reference models of technical and tactical activity for the best basketball players within the framework of a specific game role.

Abstract 6088: Predicting the risk of any-type cancer in asymptomatic adults using noninvasive glycosaminoglycan profiling

Abstract Background: Early detection of cancers through screening can reduce mortality. However, screening excludes individuals not considered at an increased risk of developing cancer, as determined by age, sex, or tobacco use. No molecular biomarker is routinely used to predict risk. Here, we investigated urine glycosaminoglycan profiles (GAGomes) as noninvasive biomarkers for any-cancer risk prediction. Methods: In this population-based case-control study, we included adults aged over 18 years from the Lifelines Cohort Study, Netherlands who were presumed healthy at baseline. All cases who self-reported any-type cancer or died by the 5-year study visit were confirmed in the Dutch Cancer Registry and matched 1:3 to randomly selected controls (1:1 for the interim analysis). We first developed a logistic regression reference model, including age, sex, and clinical variables to predict any-type cancer. Next, we added urine GAGomes (as a single aggregate score) to the model and assessed model improvement using the likelihood ratio test. We categorized the output of the saturated model (clinical variables plus urine GAGome score) into four groups based on bespoke specificity cut-offs: low-, moderate-, intermediate-, and high-risk. We estimated odds-ratios (ORs) for their association with any-type cancer compared to the moderate-risk group, as well as their sensitivity across subsets. Using the so-estimated OR in the high-risk group, we calculated the minimum pre-test 5-year absolute risk needed to reclassify an adult into a post-test 5-year absolute risk >3.00%. Results: In the interim analysis, we included 2054 adults (median age = 51 years, 57% females). Of them, 924 were diagnosed with any-type cancer over a 6 year-follow-up period (median time to diagnosis = 1.4 years). Urine GAGomes were found to be independent risk factors when added to the reference model, explaining 27% of the variance (Nagelkerke R2 = 0.26 vs. 0.21, p < 0.0001). The ORs in the low-, intermediate-, and high-risk groups were 0.05 (95% CI: 0.02-0.13 p < 0.0001), 5.31 (95% CI: 3.84-7.48, p < 0.0001) and 27.6 (95% CI: 14.3 - 51.6, p < 0.0001), respectively. The high-risk group detected +80% more cancers than the reference model at the same specificity (99.29%), particularly in situ and stage I (+204% and +64%) and among those aged 50-69 (+247%). Adults with a pre-test 5-year absolute risk > 0.25% returning a high-risk group would be reclassified to a post-test 5-year absolute risk > 3.00% for any-type cancer. Conclusion: Implementing urine GAGomes as a strategy for risk-stratified targeted screening could identify adults with increased risk of any-type cancer who are currently excluded from screening. Citation Format: Francesco Gatto. Predicting the risk of any-type cancer in asymptomatic adults using noninvasive glycosaminoglycan profiling [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 6088.

Abstract 7600: Synergy of ZEN3694 and VIP152 for dual transcriptional targeting of BET and CDK9 in patient derived organoid models of pancreatic cancer

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) remains a leading cause of cancer related mortality in the United States, largely due to ineffective systemic therapy. While novel KRAS target strategies are in development, few agents have been investigated to target the transcriptional machinery for global inhibition of expression. Recent small molecule inhibitors targeting transcription (i.e., CDK7) have shown preliminary evidence of disease control; however, combination therapies have lagged, likely due to uncertainty in clinical toxicity. Text mining analysis suggests combination BET and CDK9 inhibition may be promising against PDAC. Here, we present preclinical evidence of synergistic activity for the combination of bromodomain and extraterminal (BET) inhibitor (ZEN3694) and CDK9 inhibitor (VIP152) using patient derived cancer organoids (PCOs). Methods: Dose response curves were generated for ZEN3694 and VIP152 to assess their single agent activity against PCOs using a low volume format. ZEN3694 was treated continuously while VIP152 was removed after 24h to mimic pharmacokinetics with assay endpoint at 144h. Synergy assays were designed using a combination dose titration grid. Viability was determined in all assays using 3D CellTiter Glo (CTG, 50% v/v). Synergy scores were produced using SynergyFinder 3.0 including Zero Interaction Potency (ZIP), Bliss Independence (Bliss), Highest Single Agent (HSA), and Loewe Additivity (Loewe). Western blot analysis was performed against global markers of active RNA polymerase II (phosphorylation at serine 2, 5, 7) and antiapoptotic protein, BCL xL, and MCL 1. Results: ZEN3694 was observed to have a single agent IC50 in the single uM range across 3 independent PDAC PCO lines (PDAC1 3.7uM, PDAC2 1.1uM, PDAC3 2.9uM). VIP152 showed significant potency in the same 3 PDAC PCO lines (PDAC1 140.2nM, PDAC2 30.5nM, PDAC3 43.4nM). The combination of ZEN3694 and VIP152 was found to have increased therapeutic activity with double digit synergy scores across all reference models: PDAC1 (Loewe 17.3, HSA 19.8, ZIP 20.2, and Bliss 17.2) and PDAC2 (Loewe 34.7, HSA 28.3, ZIP 19.7, and Bliss 19.6). Western blots of protein collected from organoids treated for 48h showed on target activity, including decrease in the phosphorylation of Rbp1 (Ser 2, Ser 5, and Ser 7) compared to both control and single agents and decreased expression in antiapoptotic proteins (MCL 1 and BCL xL). Conclusion: Here, we show the activity of dual transcription targeting with BET and CDK9 inhibition in patient derived pancreatic cancer organoid models. The combination of ZEN3694 and VIP152 has in vitro synergy in PDAC PCO models with on target activity. Ongoing work is validating this combination in animal models to assess toxicity, in vivo activity, and RNA expression profiling to understand transcriptional pathway dependency and therapeutic resistance. Citation Format: Lucas J. Koeppel, Austin Stram, Robert J. Millikin, Ellie Riedl, Md Shahadat Hossan, Ethan Samuel Lin, Ron Stewart, Jeremy D. Kratz. Synergy of ZEN3694 and VIP152 for dual transcriptional targeting of BET and CDK9 in patient derived organoid models of pancreatic cancer [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 7600.

Abstract 694: Evaluation of a synergistic drug combination with 177Lu-rhPSMA-10.1 for prostate cancer: Results of an in vitro screen and in vivo proof of concept study

Abstract Purpose: Prostate-specific membrane antigen (PSMA)-targeted radioligand therapy (RLT) has been shown to extend survival in men with advanced prostate cancer (PCa). Novel radiohybrid (rh) PSMA-targeted 177Lu-rhPSMA-10.1 has shown promising preclinical efficacy and advantageous radiation dosimetry in humans. We conducted an in vitro screen to identify known anticancer drugs with potential for synergistic interaction with 177Lu-rhPSMA-10.1. Here we present key screening data and a subsequent in vivo efficacy analysis of the lead novel drug combination in PSMA-expressing 22Rv1 PCa xenografts. Methods: Over 150 FDA-approved anticancer drugs were screened in a clonogenic survival assay of 22Rv1 cells using the test drug alone, at a range of concentrations <20 µM to determine the IC50, and results compared to incubations of the drug + 15 MBq/mL 177Lu-rhPSMA-10.1 after 10 d. A focused screen of 5 lead candidates was then conducted to determine the impact of 177Lu-rhPSMA-10.1 (0-25 MBq/mL) on the drug IC50. A synergy score was determined using the zero interaction potency (ZIP) reference model and the multi-dimensional synergy of combinations (MuSyc) platform. Subsequently, to evaluate the efficacy of the lead combination, 177Lu-rhPSMA-10.1 (single 30 MBq iv dose) and Cobimetinib (0.25 mg orally per day for 21 d) alone and in combination were administered to 22Rv1 tumor-bearing NMRI nude mice (n = 8 per group plus untreated controls). Tumor volume was measured 2x week for 69 d. Two-way ANOVA and Tukey’s multiple comparisons test (data analyzed until n = 3 remained per group) and Kaplan-Meier Log-rank survival analyses were performed. Results: The in vitro screen identified MEK inhibitor Cobimetinib as a lead candidate for synergistic combination with 177Lu-rhPSMA-10.1 across a wide concentration range, with a ZIP synergy score of 13.25% (95% CI ± 2.17) and promising results on MuSyc analysis. The 177Lu-rhPSMA-10.1 + Cobimetinib combination significantly suppressed tumor growth in vivo vs untreated controls (from day 13-30; p<0.01) and 177Lu-rhPSMA-10.1 alone (from day 17-30; p<0.001). The median survival in the combination group (49 d) was significantly longer vs the untreated group (23 d; p=0.001) and the group treated with 177Lu-rhPSMA-10.1 alone (36 d; p=0.002). Conclusions: Through an extensive in vitro screen, we identified Cobimetinib to have potential for a synergistic anti-tumor effect in combination with 177Lu-rhPSMA-10.1. This may be due to inhibition of the MEK-MAPK pathway by Cobimetinib during DNA damage response, resulting in radiosensitization of cancer cells to 177Lu-labeled RLT agents such as 177Lu-rhPSMA-10.1. This novel combination showed an enhanced therapeutic efficacy vs the single agents in 22Rv1 xenografts and the lack of overlapping monotherapy toxicity reported in the clinic supports clinical investigation in men with PCa. Citation Format: Caroline Foxton, Bart Cornelissen, Edward O'Neill, Bradley Waldron, Freja Pretzmann, Rikke Veggerby Grønlund, Mathias Wikke Hallund, Daniel J. Stevens. Evaluation of a synergistic drug combination with 177Lu-rhPSMA-10.1 for prostate cancer: Results of an in vitro screen and in vivo proof of concept study [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 694.

Drying characteristics of sewage sludge pre-conditioned by CaO and sawdust under low-temperature drying conditions

ABSTRACT Heat pump drying is a low-carbon method of sludge drying. The operating temperature of a heat pump is generally not more than 70℃. To improve the drying efficiency of heat pump dryers, the effects of air parameters and additives on sludge drying characteristics at low temperatures were studied. The sludge drying experiments were conducted at an air temperature 50-70℃ and an air velocity of 0.5-1.7 m/s. The experimental results showed that the increase of air temperature, velocity and the addition ratio of additives can accelerate the sludge drying process. The average and maximum drying rates of sludge pre-conditioned by CaO and sawdust increased by 14.23% and 25.71%, respectively, compared with those of pure sludge. The two-way analysis of variance (ANOVA) revealed that the influence of air temperature on the sludge drying was higher than that of air velocity. Five reference models were fitted by the drying experiment data. The Page model has the highest R2, so it is the most suitable model to predict the drying time of sludge at low temperatures.

Does intra-oral scan improve the impression accuracy of full-arch implant-supported prostheses: A systematic review and meta-analysis.

The present study aimed to systematically review the studies comparing the accuracy of intraoral scan (IOS) and conventional implant impressions (CI) in completely edentulous patients. Electronic searches were performed in PubMed, Embase and Cochrane CENTRAL up to December 1, 2023. Clinical studies and in vitro studies reporting the accuracy of digital full arch impressions were included. The primary outcome is the 3-dimensional deviations between the study reference models. A risk of bias assessment was performed for clinical studies. A stratified meta-analysis and a single-armed meta-analysis were conducted. A total of 49 studies were included, with 8 clinical studies and 41 in vitro studies. For comparison between IOS and conventional impressions, studies were categorized into two groups based on the different measurement methods employed: RMS and CMM. In studies using RMS, the result favored the IOS in the unparalleled situation with the mean difference of -99.29 μm (95% CI: [-141.38, -57.19], I2 = 81%), while the result was opposite with the mean difference of 13.62 μm (95% CI: [10.97, 16.28], I2 = 26%) when implants were paralleled. For different brands of IOS, the accuracy ranged from 76.11 μm (95% CI: [42.36, 109.86]) to 158.63 μm (95% CI: [-14.68, 331.93]). Accuracy of intraoral scan is clinically acceptable in edentulous arches, especially for unparalleled implants. More clinical studies are needed to verify the present finding.

Vibration response characteristics of Jujube trees based on finite element method and structure-from-motion

It is an indispensable step to choose optimal vibration parameters in jujube fruits vibration harvesting. Excessive vibration frequency will cause jujube tree damage, otherwise, the separation of fruit and fruit stalk cannot be achieved, resulting in harvesting failure. In our work, finite element method and vibration tests are applied to find optimal vibration parameters. To improve the accuracy of finite element method, a low-cost 3D reconstruction method is proposed to build jujube tree model. Experimental result showed that the phenotypic information of the reconstructed jujube tree and the real jujube tree have a high similarity, and their determination coefficient R2 reach to 0.984. Moreover, the natural frequencies of jujube tree are analyzed by modal analysis, transient analysis is applied to analyze vibration transfer characteristics. The simulation and experimental results show that the optimal vibration frequency range of jujube tree is 25∼30 Hz, the optimal vibration excitation location is on the main trunk and as high as possible close to the secondary branches, which can obtain maximum vibration response for optimum harvesting efficiency. The average determination coefficient is 0.66 between test and simulation results at different frequencies. This study can provide references for accurate 3D modeling of jujube trees, selection of appropriate vibration parameter range and the development of jujube harvesting machinery.

Encoding prior knowledge in ensemble refinement.

The proper balancing of information from experiment and theory is a long-standing problem in the analysis of noisy and incomplete data. Viewed as a Pareto optimization problem, improved agreement with the experimental data comes at the expense of growing inconsistencies with the theoretical reference model. Here, we propose how to set the exchange rate a priori to properly balance this trade-off. We focus on gentle ensemble refinement, where the difference between the potential energy surfaces of the reference and refined models is small on a thermal scale. By relating the variance of this energy difference to the Kullback-Leibler divergence between the respective Boltzmann distributions, one can encode prior knowledge about energy uncertainties, i.e., force-field errors, in the exchange rate. The energy uncertainty is defined in the space of observables and depends on their type and number and on the thermodynamic state. We highlight the relation of gentle refinement to free energy perturbation theory. A balanced encoding of prior knowledge increases the quality and transparency of ensemble refinement. Our findings extend to non-Boltzmann distributions, where the uncertainty in energy becomes an uncertainty in information.

Evaluation of the SCOR Model Research

With the rapid development of logistics in China, the supply chain process operation of enterprises is more and more valued by enterprises, how to improve efficiency and maximize the benefits of enterprises at the same time of cost reduction is the core concern of enterprises. In supply chain operation and management, the supply chain operation reference model, or SCOR model, is a very important tool. This paper provides a brief description of the current status and model introduction of the SCOR model, as well as the SCOR-DS model, which is proposed to be improved based on the SCOR model at the end of 2022, for the reference of model users.

A new intelligently optimized model reference adaptive controller using GA and WOA-based MPPT techniques for photovoltaic systems

Recently, the integration of renewable energy sources, specifically photovoltaic (PV) systems, into power networks has grown in significance for sustainable energy generation. Researchers have investigated different control algorithms for maximum power point tracking (MPPT) to enhance the efficiency of PV systems. This article presents an innovative method to address the problem of maximum power point tracking in photovoltaic systems amidst swiftly changing weather conditions. MPPT techniques supply maximum power to the load during irradiance fluctuations and ambient temperatures. A novel optimal model reference adaptive controller is developed and designed based on the MIT rule to seek global maximum power without ripples rapidly. The suggested controller is also optimized through two popular meta-heuristic algorithms: The genetic algorithm (GA) and the whale optimization algorithm (WOA). These meta-heuristic approaches have been exploited to overcome the difficulty of selecting the adaptation gain of the MRAC controller. The reference voltage for MPPT is generated in the study through an adaptive neuro-fuzzy inference system. The suggested controller’s performance is tested via MATLAB/Simulink software under varying temperature and radiation circumstances. Simulation is carried out using a Soltech 1sth-215-p module coupled to a boost converter, which powers a resistive load. Furthermore, to emphasize the recommended algorithm’s performance, a comparative study was done between the optimal MRAC using GA and WOA and the conventional incremental conductance (INC) method.

Fail-safe topology optimization for a four-leg jacket structure of offshore wind turbines

Topology optimization (TO) has experienced substantial progress in both scholarly research and practical engineering applications. Recent years have witnessed extensive research on TO approaches incorporating the fail-safe concept, with a particular emphasis on potential failures. This paper proposes a TO methodology on the offshore wind turbine (OWT) jacket structure, focusing on the unexpected failure modes. The initial step involves constructing the formulation for maximum-minimization TO. A series of optimized structures can be generated by varying the sizes of individual damage populations and overall failure zones. A 5 MW OWT and jacket structure have been selected as the reference model. The external loads acting on the jacket structure are determined by given environmental conditions, as assessed using the DNV Bladed ™ package. The feasibility of an optimized structure is evaluated by comparing its static behaviors, such as maximum displacement and von Mises stress, to those of the reference structure. The investigation focuses on the fundamental frequencies of the optimized structure under various damage populations. The discovery demonstrates the superiority of the suggested methodology in the case of a potential failure.

PANC-1 Cell Line as an Experimental Model for Characterizing PIVKA-II Production, Distribution, and Molecular Mechanisms Leading to Protein Release in PDAC.

Pancreatic ductal adenocarcinoma (PDAC) represents a highly aggressive malignancy with a lack of reliable diagnostic biomarkers. Protein induced by vitamin K absence (PIVKA-II) is a protein increased in various cancers (particularly in hepatocellular carcinoma), and it has recently exhibited superior diagnostic performance in PDAC detection compared to other biomarkers. The aim of our research was to identify an in vitro model to study PIVKA-II production, distribution, and release in PDAC. We examined the presence of PIVKA-II protein in a panel of stabilized pancreatic cancer cell lines by Western blot analysis and indirect immunofluorescence (IFA). After quantitative evaluation of PIVKA-II in PaCa 44, H-Paf II, Capan-1, and PANC-1, we adopted the latter as a reference model. Subsequently, we analyzed the effect of glucose addiction on PIVKA-II production in a PANC-1 cell line in vitro; PIVKA-II production seems to be directly related to an increase in glucose concentration in the culture medium. Finally, we evaluated if PIVKA-II released in the presence of increasing doses of glucose is concomitant with the expression of two well-acknowledged epithelial-mesenchymal transition (EMT) markers (Vimentin and Snail). According to our experimental model, we can speculate that PIVKA-II release by PANC-1 cells is glucose-dependent and occurs jointly with EMT activation.

A Novel Distributed Adaptive Controller for Multi-Agent Systems with Double-Integrator Dynamics: A Hedging-Based Approach

In this paper, we focus on designing a model reference adaptive control-based distributed control law to drive a set of agents with double-integrator dynamics in a leader–follower fashion in the presence of system anomalies such as agent-based uncertainties, unknown control effectiveness, and actuator dynamics. In particular, we introduce a novel hedging-based reference model with second-order dynamics to allow an adaptation in the presence of actuator dynamics. We show the stability of the overall closed-loop multi-agent system by utilizing the Lyapunov Stability Theory, where we analyze the stability condition by using the Linear Matrix Inequalities method to show the boundedness of the reference model and actuator states. Finally, we illustrate the efficacy of the proposed distributed adaptive controller on an undirected and connected line graph in five cases.

Multi-level cancer profiling through joint cell-graph representations

Computer-aided analysis of digitized pathology samples has significantly advanced with the rapid progression of machine and Deep Learning (DL) methods. However, most existing approaches primarily focus on features extracted from patches due to the large image sizes. This focus limits the ability of Convolutional Neural Networks (CNNs) to capture global information from the samples, resulting in an incomplete phenotypical and topological representation and thereby restricting the diagnostic capabilities of these methods. The recent emergence of Graph Neural Networks (GNNs) offers new opportunities to overcome these limitations through graph-driven representations of pathological samples. This work introduces a graph-based framework that encompasses diverse cancer types and integrates different imaging modalities. In this framework, histopathology samples are represented as graphs, and a pipeline facilitating cell-wise and disease classification is developed. The results support this motivation: for cell-wise classification, we achieved an average accuracy of 88%, and for disease-wise classification, an average accuracy of 83%, outperforming reference models such as XGBoost and standard CNNs. This approach not only provides flexibility in combining various diseases but also extends to integrating different staining techniques.