Multiparameter Sensitivity Research Articles

Efficient Productivity-Aware Control Parameter Optimization in Cutter Suction Dredger Construction Using Machine Learning with Parallel Global Search

This paper proposes an efficient productivity-aware optimization framework that utilizes hybrid machine learning with parallel global search to timely and appropriately adjust the critical control parameters (CCPs) of a cutter suction dredger (CSD) during construction. This optimization framework consists of three main parts. First, a hybrid Jaya–multilayer perceptron (MLP) algorithm was developed to rapidly construct a model that captures the interaction between construction parameters and slurry concentration. Next, the preliminary coarse results for the CCPs are determined through multi-parameter sensitivity analysis. Finally, the proposed resilient-zone parallel global search algorithm was employed to further optimize the CCPs, yielding more precise optimization results. To validate the proposed optimization framework and implement the in-situ service, it is applied to a real-world case study involving “Tianda” CSD construction. The results demonstrated that the average optimization duration is 6.7 s, which is shorter than the data acquisition interval of 8 s. Our approach improves the computational efficiency by 9.4 times compared with traditional optimization control methods. Additionally, there is a significant increase in the slurry concentration, with the maximum growth rate reaching 81.64%.

A computational analysis of the role of integrins and Rho-GTPases in the emergence and disruption of apical-basal polarization in renal epithelial cells.

Apical-basal polarization in renal epithelial cells is crucial to renal function and an important trigger for tubule formation in kidney development. Loss of polarity can induce epithelial-to-mesenchymal transition (EMT), which can lead to kidney pathologies. Understanding the relative and combined roles of the involved proteins and their interactions that govern epithelial polarity may provide insights for controlling the process of polarization via chemical or mechanical manipulations in an in vitro or in vivo setting. Here, we developed a computational framework that integrates several known interactions between integrins, Rho-GTPases Rho, Rac and Cdc42, and polarity complexes Par and Scribble, to study their mutual roles in the emergence of polarization. The modeled protein interactions were shown to induce the emergence of polarized distributions of Rho-GTPases, which in turn led to the accumulation of apical and basal polarity complexes Par and Scribble at their respective poles, effectively recapitulating polarization. Our multiparametric sensitivity analysis suggested that polarization depends foremost on the mutual inhibition between Rac and Rho. Next, we used the computational framework to investigate the role of integrins and GTPases in the generation and disruption of polarization. We found that a minimum concentration of integrins is required to catalyze the process of polarization. Furthermore, loss of polarization was found to be only inducible via complete degradation of the Rho-GTPases Rho and Cdc42, suggesting that polarization is fairly stable once it is established. Comparison of our computational predictions against data from in vitro experiments in which we induced EMT in renal epithelial cells while quantifying the relative Rho-GTPase levels, displayed that EMT coincides with a large reduction in the Rho-GTPase Rho. Collectively, these results demonstrate the essential roles of integrins and Rho-GTPases in the establishment and disruption of apical-basal polarity and thereby provide handles for the in vitro or in vivo regulation of polarity.

Parameter sensitivity analysis for CO-mediated sickle cell de-polymerization

This study investigates the impact of melting/binding rates (referred to hereafter as the parameters) over the polymers and monomers on the dynamics of carbon-monoxide-mediated sickle cell hemoglobin (HbS) de-polymerization. Two approaches, namely the traditional sensitivity analysis (TSA) and the multi-parameter sensitivity analysis (MPSA), have been developed and applied to the mathematical model system to quantify the sensitivities of polymers and monomers to the parameters. The Runge-Kutta method and the Monte-Carlo simulation are employed for the implementation of the sensitivity analyses. The TSA utilizes the traditional sensitivity functions (TSFs). The MPSA enumerates the overall effect of the model input parameters on the output by perturbing the model input parameters simultaneously within large ranges. All four concentrations (namely, de-oxy HbS monomers, CO-bound HbS monomers, de-oxy Hbs polymer and CO-bound HbS polymer) as model outputs, and all four binding/melting rates (namely, the CO binding and melting rates for polymers and monomers) as input parameters are considered in this study. The sensitivity results suggest that TSA and MPSA are essentially consistent.

Region optimization and sensitivity analysis of THT assembly process parameters based on flexible soldering robot platform

In order to solve the problem that the selection of key process parameters of a flexible soldering robot depends on manual experience, the empirical model of its THT assembly process parameters is established with the response surface method and the reliability of the model is verified. On this basis, the multi-parameter relative sensitivity of the key process parameters is analyzed through the Monte-Carlo simulation, and their single-parameter sensitivity curve is drawn. Then, based on the sensitivity analysis, the stability region division method is designed, and the stability region of process parameters for soldering quality comprehensive score is obtained. The optimal region of process parameters of the soldering robot are acquired by compressing the stability region: soldering temperature from 350-368 ℃, soldering time from 1.31-1.48 s and solder feeding volume from 1.5-2.0 mm3. The average value of the soldering quality comprehensive score within the optimal region of process parameters is 90.2 points.

Decoding Human Somatosensory Sensitivity Through Resting EEG and Behavioral Analysis: A Multimodal Fusion Approach.

In precision medicine and clinical pain management, the creation of quantitative, objective indicators to assess somatosensory sensitivity was essential. This study proposed a fusion approach for decoding human somatosensory sensitivity, which combined multimodal (quantitative sensory test and neurophysiology) features to classify the dataset on individual somatosensory sensitivity and reveal distinct types of brain activation patterns. Sixty healthy participants took part in the experiment on somatosensory sensitivity that implemented cold, heat, mechanical punctate, and pressure stimuli, and the resting-state electroencephalography (EEG) was collected using BrainVision. The quantitative sensory testing (QST) scores of the participants were clustered using the unsupervised k-means algorithm into four subgroups: generally hypersensitive (HS), generally non-sensitive (NS), predominantly thermally sensitive (TS), and predominantly mechanically sensitive (MS). Furthermore, two types of power spectral density (PSD), band-based PSD (BB-PSD) and frequency-based PSD (FB-PSD), and two types of inter-electrode connectivity (IEC), band-based connectivity (BBC) and frequency-based connectivity (FBC), derived from resting-state EEG were subjected to feature selection with a proposed prior-compared minimum-redundancy maximum-relevance (PCMRMR) protocol. Their effectiveness was then tested by the supervised classification tasks using support vector machine (SVM), k-nearest neighbor (kNN), random forest (RF), and Gaussian classifier (GC). Brain networks of four somatosensory types were revealed by decoding fused multimodal data, namely type-averaged connectivity. The data from sixty healthy individuals were divided into training (n =59) and validation (n =1) datasets according to leave-one-subject-out (LOSO) criteria. The FBC was identified, which can serve as better brain signatures than BB-PSD, FB-PSD, and BBC to classify subjects as HS, NS, TS, or MS groups. Using the SVM, kNN, RF, and GC models, the best accuracy of 87% was obtained when classifying participants into HS, NS, TS, or MS groups. Moreover, the brain networks were decoded from HS, NS, TS, and MS groups by decoding the type-averaged connectivity fused from somatosensory phenotypes and selected FBC. It indicated that quantified multi-parameter somatosensory sensitivity could be achieved with acceptable accuracy, leading to considerable possibilities for using objective pain perception evaluation in clinical practice.

The Impact of the Peptide Drug Conjugate Melflufen on the Myeloma Tumour Microenvironment

Introduction Melphalan flufenamide (melflufen), is a peptide-drug conjugate that delivers alkylating agents in tumors upon cleavage by aminopeptidases, and recently approved by the EMA for the treatment of relapsed/refractory multiple myeloma (MM). Although melflufen's MoA on tumor cells had been extensively studied, little is known about the drug impact on the tumor microenvironment. By applying single cell, multi-parametric, flow cytometry-based analysis to bone marrow mononuclear cells (BM-MNC) from MM patient samples treated ex vivo, we investigated the effect of melflufen on immune cell populations to assess for potential immune modulatory effects and to identify potential immune-based biomarkers of response. Methods Viably frozen BM-MNC from 26 samples of patients diagnosed with MM were used for multi-parametric flow cytometry-based drug sensitivity and resistance testing evaluation to melflufen after written informed consent following approved protocols in compliance with the Declaration of Helsinki. Drug sensitivity was assessed using five different concentrations of melflufen ranging between 0.5-5000 nM and cells incubated with drug or DMSO control for 72 hrs incubation. For analysis, we used antibody panels allowing us to identify and phenotype T and B lymphocytes, NK, progenitor, and malignant plasma cells (PC). Based on drug sensitivity scores (DSS) of gated PCs from drug treated and controls samples, we divided the samples into two groups - high sensitivity (HS, Q3 of PC-DSS) and low sensitivity (LS, Q1 of PC-DSS). Samples with no PCs detected (n=11) were excluded from PC subset analysis. Results The MM samples were divided based on the sensitivity of the PC population to melflufen with DSS > 21.25 defining a highly sensitive group and DSS < 15.9 defining a less sensitive group. We found that HS samples have higher abundance of mature NK and iNKT cells, although significance was low, likely due to the small cohort size. In addition, we discovered that effector memory (EM) and terminal effector (TE) T cells have higher sensitivity to melflufen, compared to naïve and central memory (CM) phenotypes (Fig. 1). More mature NK cells (Stage 5 and 6) also showed higher sensitivity than NK cells of earlier stages of maturation. Importantly, we observed that in many of the tested samples, progenitor-like CD34+CD38- cells either were not sensitive to the cytotoxic effect of the drug, or even proliferated in higher drug concentrations (Fig. 2). Interestingly, HS samples had significantly higher baseline abundance of this cell population (p<0.05). At the same time, response profiles of healthy B and malignant PCs were similar. Finally, in our tests, PCs in samples with trisomy 11 abnormality showed lower sensitivity to melflufen (p<0.05). Conclusion In a previous study, we found that effector-like cells could be associated with better response to melflufen. In this study, we observed the same trends and confirm our earlier findings demonstrating an association of NK and NKT cells with higher sensitivity to melflufen. Moreover, we identified differential response of CD4, CD8 and NK cell populations comprising the tumor microenvironment to melflufen suggesting that in addition to direct tumor cell killing, melflufen may have modulating effects on different tumor associated immune cell populations. Finally, we observed proliferation of CD34+CD38- progenitor cells, which might be relevant for lymphocyte proliferation and function, and additionally supporting potential modulating effect. Further analyses are being done to validate these findings.

Application of cell mapping to control optimization for an antenna servo system on a disturbed carrier

Abstract. The cell-mapping method, due to its global optimality, has been applied to solve multi-objective optimization problems (MOPs) and optimal control problems. However, the curse of dimensionality limits its application in high-dimensional systems. In this paper, the multi-parameter sensitivity analysis is investigated to reduce the parameter space dimension, which broadens the application of cell mapping to MOPs in high-dimensional parameter space. A post-processing algorithm for MOPs is introduced to help choose proper control parameters from the Pareto set. The proposed scheme is applied successfully in the control parameter optimization of an adaptive nonsingular terminal sliding-mode control for an antenna servo system on a disturbed carrier. Moreover, as the existing global optimal tracking control with an adjoining cell-mapping method may generate tracking-phase differences, an optimal-sliding-mode combined-control strategy is proposed. By using the combined-control strategy, the azimuth and pitch angles of the antenna system are controlled to catch up to a target trajectory with the minimum cost function and to keep high-precision tracking after that.

Feasible domain of cycling operating conditions and model parameters for Holby–Morgan model of platinum catalyst degradation in PEMFC

In the paper, electrochemical behavior of platinum based cathode catalyst in a polymer electrolyte membrane fuel cell is studied under non-symmetric square-wave electric potential cycling applied in accelerated stress test. Following the modeling by Holby and Morgan, degradation due to Pt dissolution, ion diffusion, and oxide coverage is performed with respect to cycling operating conditions and model parameters. The computer simulation demonstrates impact of the electrochemical surface area loss rate under variation of the temperature, pH, platinum particle diameter, loading, Pt to Carbon volume fraction, and their simultaneous effect. From physical consistency and multi-parametric sensitivity analysis, a statement on feasible domain of the parameters for the PEMFC operation is concluded.

An investigation on structural parameters optimization and coupled heat transfer of LNG submerged combustion vaporizer

Submerged combustion vaporizer (SCV) owns an extremely efficient multiphase heat exchanger adopted in the coastal liquefied natural gas (LNG) receiving terminal. Overall vaporization efficiency of SCV mostly depends on the matching relationship of structural parameters and coupled heat transfer characteristics, which has been rarely addressed in previous studies. In the present work, an integrated SCV experimental platform with handling capacity of 50 kg/h was set up to validate the reliability of numerical model and method. Additionally, on account of multi-parameter orthogonal combination optimization method, the effects and sensitivity orders of structural parameters (transverse tube pitch, longitudinal tube pitch, orifice size and number of distributor branch) on dynamics performance of SCV were investigated. Finally, the coupled heat transfer between gas–liquid two-phase flow and supercritical LNG gasification was revealed in view of optimized parameters. Results indicated that the transient numerical tube-side outlet temperature could be consistent with experimental data. For the shell-side heat transfer coefficient, the sensitivity order was transverse tube pitch > orifice size > longitudinal tube pitch > number of distributor branch. Nevertheless, for the shell-side pressure drop, the sensitivity order was longitudinal tube pitch > transverse tube pitch > orifice size > number of distributor branch. Under the condition of optimization scheme, the heat provided by gas–liquid two-phase flow could meet the requirements of supercritical LNG gasification, the NG outlet temperature might achieve approximately 272 K. The present outcomes could lay a foundation for the optimization design and deep understanding for actual SCV facility.

Can population models be a useful tool for evaluating the status of data-deficient species?

Population and life history data remains sparse for many species, with low prospects for improvement due to the costs of research and difficulties associated with gathering field data. However, data-deficient species may nevertheless face substantial threats. Effective approaches are required to quantitatively assess threats and the effects of data uncertainty in the assessments. We explore the use of population models to assess the impact of uncertain threatening processes in the data-deficient Tasmanian masked owl Tyto novaehollandiae castanops. We addressed data-deficiency with a multi-parameter sensitivity analysis. This showed that reproductive output, adult mortality and juvenile mortality had the greatest impact on population size and growth. Using literature on a related but better studied species, the barn owl (Tyto alba), we then used population models to simulate the impacts of rodenticide exposure by increasing stochastic variation in reproductive output, adult, and juvenile mortality. We investigated rodenticide exposure because, although there is evidence of secondary poisoning of Tasmanian masked owls through consumption of contaminated prey, the effects of potentially widespread exposure on the population are unclear. The effect on population size and growth from increased stochasticity in the rodenticide sensitive parameters was negative. However, the magnitude of impacts depended on the baseline juvenile mortality rate used in each model, highlighting the importance of this uncertain parameter on our assessment of risk. Our study serves as an example of the use of population models for exploring hypothetical scenarios for data-deficient populations. Population models are an important tool for setting priorities for research and sounding a precautionary alarm where potential threats may be concealed by uncertainty.

A simplified ANN-based evaluation method for creep sensitive girder bridge identification at preliminary design stage

Excessive deflection which exceeded expectations has been identified as one of the main problems contributing to long-term performance degradation of long-span girder bridges. Although the 3D refined finite element model of the full bridge can improve the prediction accuracy of long-term creep deformation, there is still a lack of efficient creep-sensitive structural judgment methods in the preliminary design stage. This study was thus intended to develop a new evaluation method and model to identify the possibility of excessive deflection will occur by utilizing simple structural design parameters, which contributed to new understandings of the creep deformation of bridge structural level. A finite element model database established by 20 long-span prestressed concrete girder bridges with actual project backgrounds was used for providing training, testing, and validation sample datasets for the artificial neural networks (ANN). The Back Propagation (BP) model was chosen for creating mappings among variables and results and solving complex problems with a fairly accurate prediction even without sufficient information. The indicator of deflection-span ratio difference between creep models was proposed as the key analysis factor. A case study was conducted to validate the effectiveness of the ANN model. The mean absolute error (MAE) between the results of the ANN model and FE analysis was 6.25%, which indicated a high consistency. The analysis of bridge design parameters using the ANN model revealed that it had a high risk of excessive deformation, which was consistent with the conclusion of the actual situation. Multiparameter sensitivity analysis results from the model also suggest that the side-span ratio, the main span, and the deflection caused by the pavement are the most three significant impacts compared to different input variables in this model. Based on the results, it can be concluded that the performance of the ANN-based technique can predict possible excessive creep problems in advance.

Molten salt reactor system dynamics in simulink and modelica, a code to code comparison

Understanding reactor dynamics is essential for the development of MSRs. Modern computational software allows rapid development and simulation of multiphysics systems. As such, MSRs can be simulated in various programming languages to study their behavior. Meanwhile, employing various software environments and methods can help ensure that simulation results are accurate and openly accessible to the broader research community. In this publication, two variations of software implementation for an MSR dynamic modeling tool in MATLAB Simulink and OpenModelica are compared. A code-to-code comparison is conducted, and both implementations are benchmarked for accuracy and simulation run time. In addition to the benchmark, a pilot quality assurance program is introduced for the implementation of the OpenModelica model. Finally, a multi-parameter sensitivity analysis is performed to demonstrate possible applications of the Modelica model.

Multiparameter quantum metrology and mode entanglement with spatially split nonclassical spin ensembles

We identify the multiparameter sensitivity of entangled spin states, such as spin-squeezed and Dicke states that are spatially distributed into several addressable spatial modes. Analytical expressions for the spin-squeezing matrix of families of states that are accessible by current atomic experiments reveal the quantum gain in multiparameter metrology, as well as the optimal strategies to maximize the sensitivity gain for the estimation of any linear combination of parameters. We further study the mode entanglement of these states by deriving a witness for genuine k-partite mode entanglement from the spin-squeezing matrix. Our results highlight the advantage of mode entanglement for distributed sensing, and outline optimal protocols for multiparameter estimation with nonclassical spatially-distributed spin ensembles. We illustrate our findings with the design of a protocol for gradient sensing with a Bose–Einstein condensate in an entangled spin state in two modes.

Pore-scale parametric sensitivity analysis of liquid water transport in the gas diffusion layer of polymer electrolyte membrane fuel cell

Water management in the gas diffusion layer (GDL) is crucial for improving the performance of polymer electrolyte membrane fuel cells (PEMFCs). In this study, a modified stochastic method that fully took into account the fiber deflection and layered structure in the thickness direction was proposed to reconstruct the realistic porous structures of three-dimensional (3D) GDL. The liquid water transport within the GDL was then numerically studied using the 3D Lattice Boltzmann method (LBM). Subsequently, the contributions of different parameters (porosity, fiber diameter, thickness, wettability, and pressure difference) to the transport process of liquid water were elucidated by implementing a multi-parametric sensitivity analysis (MPSA). The numerical results indicate that the liquid water penetration process can be divided into initial injection, selective percolation, and diffusion stages. With the decreases in porosity or increase in contact angle, the flow is inclined to transform from gentle flow to accumulation and secondary breakthrough owing to the increased percolation resistance. Further accumulation of the liquid within GDL can be identified when the evolution time progresses from the breakthrough point to the creep point. Moreover, for GDL, the degree of influence of the structural parameters on the breakthrough velocity is ranked in the order of GDL porosity, fiber diameter, GDL thickness, pressure difference, and GDL wettability. The total liquid saturation is dominated by GDL wettability and is insensitive to GDL porosity and fiber diameter owing to the conflict between the percolation resistance of the pores and the percolation time within GDL. The current work consolidates the understanding of the influencing mechanisms of different structural parameters on liquid water transport characteristics, contributing to the optimal design of the gas diffusion layer.

Thermal management research for a 2.5 MWh energy storage power station on airflow organization optimization and heat transfer influential characteristics

Most of the thermal management for the battery energy storage system (BESS) adopts air cooling with the air conditioning. However, the air-supply distance impacts the temperature uniformity. To improve the BESS temperature uniformity, this study analyzes a 2.5 MWh energy storage power station (ESPS) thermal management performance. It optimizes airflow organization with louver fins and simulates its heat transfer behavior. To improve the flow rate distribution along the airflow passage, the air-supply organization of louver fins for the overhead air conditioner is optimized. It indicates that the air-supply angle (A) for louver fins and the battery spacing (D) impacts greatly on BESS thermal management performance. The air supply optimal organization with the A of 90° and the D of 18mm was determined, of which the average temperature (TA) rise of the battery can be reduced by 18%, and the temperature difference (TD) is decreased by 23%. Furthermore, a multi-parametric sensitivity analysis is conducted, and the different heat-flow parameter influential sequence for the TA and TD are revealed. The air supply volume flow rate (L) and the air supply temperature (AST) has a significant effect on the TA reduction, and the AST shows significance on the TD decreasing. When AST is reduced to 290 K, the TA rise of batteries decreased by 71%. When the L is increased 10 times, the TA rise of batteries decreased by 227%. When the AST is decreased to 290 K, the TD is reduced by 39%. When the L for air supply is increased by 10 times, the TD will increase by 28%. In this research, the louver fins are firstly adopted for orifices air supply organization to improve thermal field uniformity for BESS.

Impact of nickel–titanium super-elastic material properties on the mechanical performance of self-expandable transcatheter aortic valves

Self-expandable transcatheter aortic valves (TAVs) elastically resume their initial shape when implanted without the need for balloon inflation by virtue of the nickel–titanium (NiTi) frame super-elastic properties. Experimental findings suggest that NiTi mechanical properties can vary markedly because of a strong dependence on the chemical composition and processing operations. In this context, this study presents a computational framework to investigate the impact of the NiTi super-elastic material properties on the TAV mechanical performance. Finite element (FE) analyses of TAV implantation were performed considering two different TAV frames and three idealized aortic root anatomies, evaluating the device mechanical response in terms of pullout force magnitude exerted by the TAV frame and peak maximum principal stress within the aortic root. The widely adopted NiTi constitute model by Auricchio and Taylor (1997) was used. A multi-parametric sensitivity analysis and a multi-objective optimization of the TAV mechanical performance were conducted in relation to the parameters of the NiTi constitutive model. The results highlighted that: five NiTi material model parameters (EA, σtLS, σtUS, σtUE and σcLS) are significantly correlated with the FE outputs; the TAV frame geometry and aortic root anatomy have a marginal effect on the level of influence of each NiTi material parameter; NiTi alloy candidates with pareto-optimal characteristics in terms of TAV mechanical performance can be successfully identified. In conclusion, the proposed computational framework supports the TAV design phase, providing information on the relationship between the super-elastic behavior of the supplied NiTi alloys and the device mechanical response.

A Potential Role of NK Cells and ABCB9 in Modulating the Response to Melflufen in Multiple Myeloma

Introduction Melphalan flufenamide (melflufen), is a novel peptide-drug conjugate that delivers alkylating agents in tumours upon hydrolysis by aminopeptidases. Melflufen was recently approved by the EMA for the treatment of relapsed/refractory multiple myeloma (MM). Considering the challenges in treating this group of patients and their low overall survival, information that can support treatment selection and success is urgently needed. To discover possible biomarkers of melflufen sensitivity, we applied a multiparametric drug sensitivity assay to MM patient samples ex vivo and analysed the samples by single cell RNA sequencing (scRNAseq) and flow cytometry. Drug testing identified groups of MM samples with high and low sensitivity to melflufen, while differential gene expression and flow cytometry analysis revealed markers associated with response and cytotoxicity. Methods Bone marrow mononuclear cells from 12 newly diagnosed (ND) and 12 relapsed/refractory (RR) patients were used for multi-parametric flow cytometry-based drug sensitivity and resistance testing (DSRT) evaluation to melflufen and melphalan, and for scRNAseq after written informed consent following approved protocols in compliance with the Declaration of Helsinki. Based on the results from the DSRT assay and drug sensitivity scores (DSS), we divided the samples into three groups - high sensitivity (HS, DSS > 40 (melflufen) or DSS > 16 (melphalan)), intermediate sensitivity (IS, 31 ≤ DSS ≤ 40 (melflufen) or 10 ≤ DSS ≤ 16 (melphalan)), and low sensitivity (LS, DSS < 31 (melflufen) or DSS < 10 (melphalan)). To identify genes, responsible for the general sensitivity to we conducted differential gene expression (DGE) analyses between HS and LS samples for both drugs ("HS vs. LS melphalan” and "HS vs. LS for melflufen", respectively). In addition, we conducted flow cytometry analysis to evaluate the levels of HLA-B protein, associated with increased cytotoxicity of NK cells. Results We analyzed the DSRT and scRNAseq data for the MM samples of different melflufen sensitivity groups (HS, IS, LS). DGE analysis showed significantly lower levels of the ABCB9 gene in plasma cells populations of the HS compared to LS group (2.2-fold, HS vs. LS groups for melflufen, p < 0.01). ABCB9 belongs to a family of proteins known for drug efflux and multidrug resistance. A similar pattern was detected for the melphalan HS vs. LS (3.2-fold, p < 0.01) comparison suggesting that this alteration might be a common indicator of sensitivity to melflufen and melphalan. As a confirmation, we found that MM cell lines treated with melphalan (Fig. 1) also tend to express higher levels of ABCB9, as compared to control cells (1.2-fold, p<0.001), indicating its potential role in drug resistance. Based on scRNAseq and flow cytometry analysis, we also discovered that NK cells may be associated with melflufen cytotoxic effect. Our results demonstrated higher levels of NK and NKT subpopulations, as well as lower surface HLA-B protein level (p<0.01) and activation of NFKB signalling in the melflufen HS group in comparison to the LS group (Fig. 2). The latter two features are associated with increased NK cells cytotoxicity, thus, suggesting synergistic effect of innate immunity with melflufen. Conclusion In summary, we demonstrate the association of ABCB9 expression with resistance both to melphalan or melflufen. Furthermore, we observed that MM samples that were highly sensitive to melflufen exhibited markers associated with NK-mediated cytotoxicity, suggesting a potential role of NK cells for enhancing the efficacy of melflufen. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The Novel Peptide Drug Conjugate OPDC3 Is Highly Active in Different Hematological Malignancies

Background: Peptide drug conjugates (PDCs) are an important emerging class of molecules that allow for targeted delivery of therapeutic agents. Melflufen, which was recently approved by the European Medicines Agency for relapsed refractory multiple myeloma patients, is one such PDC that allows for targeted delivery of the payload drug melphalan. Like melflufen, the novel PDC OPDC3 was designed to target cells with high peptidase activity to deliver toxic payload. In this study, we evaluated the activity of OPDC3 in models of acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and diffuse large B-cell lymphoma (DLBCL). Since the majority of AML patients eventually relapse, and only a limited set of therapies are available for MDS and DLBCL patients, new treatment options are urgently needed. Methods: The Finnish Hematology Research Biobank provided viably frozen bone marrow mononuclear cells from 29 AML and 6 MDS patients. All 29 AML samples and 1 MDS sample were from relapsed/refractory (R/R) patients, and 5 MDS samples were taken at diagnosis. Multi-parametric flow cytometry-based drug sensitivity and resistance testing (DSRT) was used to assess sample sensitivities to OPDC3, melflufen and venetoclax. Samples were incubated for 72h hours in seven different concentrations ranging from 0.01 nM to 10 000 nM of OPDC3, melflufen and venetoclax. Cells were stained using antibody panels that allowed detection of immature and mature blasts. Cell death and viability were assessed using Annexin V and DRAQ7. Dose response curves were generated for each detected cell subset. Drug sensitivity scores (DSS) and EC50 values were calculated. OPDC3 tumor growth inhibition efficacy was validated on xenografts in ovo initiated from SUDHL-4 cell line. OPDC3 and bendamustine effects as single agents were tested in three different concentrations (8.4; 33.5; 167.5 μM) on DLBCL cell line SUDHL-4. OPDC3 in combination with rituximab and bendamustine in combination with rituximab were also tested. The grafted tumors were quantitatively evaluated by tumor weight using the ChorioAllantoic Membrane (CAM) assay. Toxicity was followed daily by embryonic lethality and final quantitative evaluation was done on day18 after treatment per treatment group. Results: OPDC3 was effective in killing blast cells in samples from R/R AML patients. The most sensitive sample having the highest DSS 33 (IC50: 0.9 nM), least sensitive sample having the lowest DSS 15 (IC50: 19.5 nM), and median DSS being 24.1 (IC50: 8.5 nM) (Figure 1). For venetoclax the highest DSS was 27.9 (IC50: 2 nM), lowest DSS 0 (IC50: >10 000 nM), and median DSS 19.4 (Figure 1). The 3 least sensitive samples to OPDC3 were clearly sensitive to venetoclax (Figure 1). OPDC3 was also able to kill more mature malignant CD14+ cells (median DSS 21.5; median EC50 17.1 nM). OPDC3 was more effective in killing both blast and CD14+ cells than melflufen. Melflufen blast median DSS was 20.6 (EC50: 19.9 nM) and for CD14+ cells median DSS was 16.4 (EC50: 84.7 nM). OPDC3 was also effective in killing MDS blast cells in 4/6 of the patient samples. The highest OPDC3 DSS was 27.2 (IC50: 3.1 nM), lowest DSS 0 (IC50: >10 000), and median DSS 15.3. OPDC3 reduced the DLBCL cell line SUDHL-4 tumor growth at 8.4 μM when compared to the negative control (Figure 2). Bendamustine had little effect on tumor growth at 8.4 μM. At 33.5 μM bendamustine was unable to reduce tumor growth as much as 8.4 μM of OPDC3. OPDC3 also caused less toxicity at 33.5 and 167.5 μM compared to bendumustine at the same concentrations. OPDC3 in combination with rituximab was more effective in reducing tumor growth than both compounds alone, or bendamustine in combination with rituximab. OPDC3 in combination with rituximab caused also the least embryonic toxicity. Conclusions: OPDC3 can efficiently target patient derived AML and MDS blast cells at relatively low nM concentrations. It is also able to kill more differentiated malignant CD14+ AML cells. Especially in R/R AML samples in which the blast cells and CD14+ cells are resistant to venetoclax, OPDC3 was effective at targeting both cell populations. OPDC3 is more effective than bendamustine as a single drug in killing SUDHL-4 tumor cells, and when combined with rituximab compared to bendamustine in combination with rituximab. OPDC3 shows promise as a novel therapeutic for different hematological malignancies. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Conjugate heat transfer study of various cooling structures and sensitivity analysis of overall cooling effectiveness

The conjugate heat transfer of a turbine blade is influenced by several factors. To analyze the influence of each factor, the published one-dimensional conjugate heat transfer model was improved through theoretical analysis in this study. An overall cooling effectiveness equation containing three dimensionless parameters (adiabatic film cooling effectiveness η, Biot number on the mainstream side Big, and ratio between the heat transfer coefficients of the external and internal walls hg/hi) was obtained. The sensitivity of the overall cooling effectiveness ϕ to these three parameters was obtained through a multi-parameter sensitivity analysis. The results showed that increasing η could improve ϕ the most effectively. The interactions between the dimensionless parameters were analyzed by developing sensitivity charts. The results showed that increasing η from 0.4 to 0.5 could reduce the sensitivity of ϕ to the other two parameters by approximately 15%, whereas increasing Big had little effect on the sensitivity of ϕ to each dimensionless parameter. Increasing hg/hi could improve the sensitivity to η. The above conclusions could also be applied to the plate film hole and plate impingement effusion structures. The effects of different internal cooling structures and film hole structures on the three dimensionless parameters were studied by performing numerical simulations, which verified the accuracy of the one-dimensional conjugate heat transfer model in this study. The results showed that the internal cooling structures had little effect on the distribution of η and Big. The heat transfer coefficient on the coolant side could be effectively improved by installing film holes. The film hole structures mainly affected ϕ by influencing the distribution of η.

Study on noise reduction method of main noise transmission path of axial piston pump with new damping material

Axial piston pump is complex fluid machinery with mechanical fluid coupling, and it has the characteristics of multiple noise sources. In order to solve the high noise problem of axial piston pump, this paper proposes a noise reduction method by identifying the main noise sources of axial piston pump and laying new damping materials to reduce noise. It breaks through the existing method of optimizing the parameters of piston pump shell and valve plate structure. A multi-parameter trajectory sensitivity identification and noise reduction method for vibration transmission path of axial piston pump is proposed in this paper. First, the lumped parameter method is used to establish the mathematical model of vibration transmission path of axial piston pump. The sensitivity of structural vibration response to transmission path multi-parameters are analyzed based on trajectory sensitivity method. The most sensitive parameters in the main noise transmission path are determined. Then, the experimental study of radiated noise transmission path of axial piston pump is carried out. A new type of viscous damping material is proposed to reduce the noise on the transmission path of the main noise source. The experimental results show that the noise of piston pump is reduced by 2.32 dB. The research results provide a new method and way for the design and manufacture of low noise piston pump.