Parallel Samples Research Articles

Development of a High‐Frequency Polytetrafluoroethylene (PTFE)‐Based Laminate With An Ultra‐Low Dielectric Constant by Combination of Ceramic Hollow Spheres and PTFE Resin

AbstractCeramic hollow spheres and polytetrafluoroethylene (PTFE) resin are essential for the fabrication of ultra‐low dielectric constant laminates for high‐frequency printed circuit boards. The poor bonding between ceramic hollow spheres and PTFE is the fundamental obstacle to the homogeneity of the composite. In this work, the broken ceramic hollow spheres were first removed by flotation, and then the surface of the floated hollow spheres was modified using different silane coupling agents at various concentrations. By comprehensively considering the results of the orthogonal experiment, the optimal coupling agent is determined to be 3‐(methacryloyloxy) propyltrimethoxysilane (KH560). The effect of KH560 content, ethanol/water volume ratio, and pH value on the dielectric constant, dielectric layer density, water absorption, and Z‐axis thermal expansion coefficient of the parallel samples was investigated in detail. The optimum surface modification parameters are found to be 6 wt % KH560, an ethanol/water volume ratio of 9 : 1 and a pH of 4. Under optimum technological conditions, the fabricated composites exhibit superior properties with dielectric constant less than 2, indicating a potential application in high performance high frequency copper clad laminates.

Optimization Design of Medium-Voltage High-Frequency Transformers Based on Parallel Sampling Kriging Surrogate Model

In the design process of medium-voltage high-frequency transformers, numerical simulation methods often result in long design cycles and significant computational resource consumption. To achieve rapid and high-precision design for medium-voltage high-frequency transformers, a parallel kriging surrogate model is proposed based on the maximum expected improvement criterion. Initially, Latin hypercube sampling is employed to establish the initial sampling points, and corresponding three-dimensional models of medium-voltage high-frequency transformers are constructed for each sampling point configuration. Finite element simulation software is then used to compute the transformer loss and volume for each model. Subsequently, a kriging surrogate model is developed based on the initial sampling point configuration, and an enhanced parallel sampling strategy is implemented to enhance the fitting accuracy of the kriging surrogate model. Finally, the high-precision kriging surrogate model is utilized as the fitness function for a particle swarm optimization algorithm, with the objectives of minimizing transformer loss and volume. The Pareto optimal solution is sought under multi-objective optimization. The effectiveness of the proposed optimization method is verified through experimental measurements of the loss in a prototype of a medium-voltage high-frequency transformer.

PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus

We present a real-time method for robust estimation of multiple instances of geometric models from noisy data. Geometric models such as vanishing points, planar homographies or fundamental matrices are essential for 3D scene analysis. Previous approaches discover distinct model instances in an iterative manner, thus limiting their potential for speedup via parallel computation. In contrast, our method detects all model instances independently and in parallel. A neural network segments the input data into clusters representing potential model instances by predicting multiple sets of sample and inlier weights. Using the predicted weights, we determine the model parameters for each potential instance separately in a RANSAC-like fashion. We train the neural network via task-specific loss functions, i.e. we do not require a ground-truth segmentation of the input data. As suitable training data for homography and fundamental matrix fitting is scarce, we additionally present two new synthetic datasets. We demonstrate state-of-the-art performance on these as well as multiple established datasets, with inference times as small as five milliseconds per image.

Abstract 4411: Enhancing scalability and consistency in clinical multiomics via an optimized fixed cell ATAC-seq method​

Abstract ATAC-seq has an emerging role in decoding mechanisms of gene regulation, offering valuable insights into pathology and treatment response in disease models. However, clinical adoption of ATAC-seq methods has been limited by logistical hurdles, including time-sensitive processing of fresh samples and compromised viability of cryopreserved cells. These constraints, compounded by changes in open chromatin regions (OCRs) following cryopreservation, introduce unintended bias and pose significant obstacles for the translational impact of ATAC-seq experiments. ​ Here, we introduce an optimized fixed-cell ATAC-seq approach to overcome these limitations and unlock new sample types for ATAC-seq analyses. Our solution improves and simplifies the workflow from sample collection to clinical deliverable. This method enables ATAC-seq investigation of a diverse range of samples and facilitates the execution of complex experimental designs, including time course studies and high throughput screening.​To demonstrate the effectiveness of this method, we compared our optimized fixed-cell method with traditional ATAC-seq preparations in both fresh and cryopreserved GM12878 cells in parallel. Human GM12878 cell line was obtained from Coriell Institute for Medical Research5. Remarkably, we observed consistent genome-wide patterns of OCR enrichment at key regulatory elements across the three sample preparation methods. We observed consistent OCR enrichment across the promoter region of known highly-expressed B-cell genes including CD48 and LCP1, underscoring this assay’s ability to detect chromatin changes at key genes in human disease models. ​ To investigate the potential for multiomic analysis using this method, we prepared RNA-seq libraries from fixed-cell samples in parallel to ATAC-seq. We observed significantly elevated gene expression related to B-cell function and B-cell diseases, demonstrating our method’s compatibility with RNA-seq data collection and integration. This optimized fixed-cell ATAC-seq approach offers enhanced scalability and consistency over conventional methods and presents new opportunities for the multiomic analysis of chromatin and transcriptional activity genome-wide from a single sample in both clinical and research settings. Citation Format: Dafne Alves Winders, Riley Graham, Xiangying Mao, Ilaria De Vito, Andrea O'Hara, Laure Turner, Haythem Latif. Enhancing scalability and consistency in clinical multiomics via an optimized fixed cell ATAC-seq method​ [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 4411.

Dynamic joint decision of matching parameters and relocation strategies in ride-sourcing systems interacting with traffic congestion

As the ride-sourcing market expands, ride-sourcing fleets have increased urban traffic congestion, and in turn, road congestion is affecting ride-sourcing operations. It is crucial to incorporate this interaction mechanism into the operational decision of ride-sourcing systems. This mechanism can be described in detail by simulation models. Applying simulation-based optimization methods to the real-time operation of ride-sourcing systems is challenging due to the limitations of simulation efficiency. To tackle this issue, this study first proposes a modular simulation model combining time-driven and event-driven mechanisms. The simulation model comprises two layers: the traffic flow module at the bottom, defined by trip-based and multi-region macroscopic fundamental diagrams and cell transmission models, and the ride-sourcing operation module at the top, which considers the interaction between the traffic congestion and ride-sourcing operation and models the random behavior of passengers and drivers. We verify the efficiency and accuracy of the simulation model. The simulation model is utilized as an evaluator of the objective functions and performance measures. Then, we integrate the Bayesian optimization, parallel sampling, and rolling horizon approaches to develop an efficient and effective simulation-based optimization framework for the dynamic joint decision-making of matching radius, matching intervals, and threshold relocation strategies. Six groups of experiments for the traffic scenario in Berlin reveal some interesting findings. (1) The matching rate first increases and then decreases with the matching radius (a concave function), while the closest study does not think so. (2) As the fleet size grows, passenger waiting time reaches its minimum and then rises because of the increased endogenous congestion generated by ride-sourcing fleets. (3) The optimal operation strategy considering endogenous congestion not only leads to an increase in the system revenue and efficiency but also leads to congestion relief, even though the latter was not our initial objective. This benefit is the most pronounced when the fleet size and background traffic are moderate.

Deformation Behavior and Seismic Characteristics of Sandy Facies Opalinus Clay During Triaxial Deformation Under Dry and Wet Conditions

Unconsolidated, undrained triaxial deformation tests were performed on sandy facies Opalinus Clay at 50 MPa confining pressure to characterize the effect of water and microfabric orientation on the deformation behavior, mechanical properties, and P-wave velocity evolution. Dry and wet (≈ 8 and > 95% initial water saturation, respectively) samples with 12.6 ± 0.4 vol% porosity were deformed parallel and perpendicular to the bedding direction at a constant strain rate of 5 × 10–6 s−1. Dry samples revealed semi-brittle behavior and exhibited strain localization at failure, while deformation was more ductile at saturated conditions, promoting stable, slow faulting. Peak strength, Young’s modulus, and number of cumulative acoustic emissions decreased significantly for wet samples compared to dry samples; the opposite was observed for Poisson’s ratio. P-wave velocity anisotropy was significantly altered by differential stress, primarily due to the interplay between pore and fracture closure and stress-induced microcrack formation. For samples that were deformed perpendicular to bedding, we observed a reduction and reversal of P-wave velocity anisotropy with increasing differential stress, whereas anisotropy of parallel samples increased. The results suggest that water saturation reduces the pressure at the brittle-ductile transition and that the elastic properties and anisotropy of sandy facies Opalinus Clay can be significantly altered in an anisotropic stress field, e.g., adjacent to fault zones or tunnel excavations. Changes in elastic anisotropy are primarily controlled by the orientation between the pre-existing microfabric and the maximum principal stress direction, stress magnitude, and the degree of water saturation.

Comparison of SARS-CoV-2 variants of concern in primary human nasal cultures demonstrates Delta as most cytopathic and Omicron as fastest replicating.

Comparative analysis of infections by SARS-CoV-2 ancestral virus and variants of concern, including Alpha, Beta, Delta, and Omicron, indicated that variants were selected for efficiency in replication. In infections of patient-derived primary nasal cultures grown at air-liquid interface to model upper respiratory infection, Omicron reached the highest titers at early time points, a finding that was confirmed by parallel population sampling studies. While all infections overcame dsRNA-mediated host responses, infections with Omicron induced the strongest interferon and interferon-stimulated gene response. In both primary nasal cultures and lower respiratory cell line, infections by Delta were most damaging to the cells as indicated by syncytia formation, loss of cell barrier integrity, and nasal ciliary function.

Utilization of spent coffee grounds as charring agent to prepare flame retardant poly(lactic acid) composites with improved toughness

The objective was to utilize spent coffee grounds (SCG) as charring agent to combine with ammonium polyphosphate (APP) to prepare flame retardant poly(lactic acid) (PLA) composites with improved toughness. PLA/APP-SCG and PLA/APP-SCG/KH560 composites were prepared, and silane coupling agent KH560 was applied to improve particle-matrix interfacial compatibility. The particle-matrix interface, char formation, flame retardancy, mechanical properties and fracture morphology of PLA composites were studied. Results showed that PLA/APP-SCG5% and PLA/APP-SCG20% passed UL-94 V-0 rating, and increase in charred residues was favorable for improving flame retardancy. Improved toughness was also obtained compared to PLA, attributed to debonding of APP from matrix under external force as well as plasticization effect of coffee oil contained in SCG. PLA/APP-SCG5%/KH560 and PLA/APP-SCG20%/KH560 showed smaller elongation at break and impact strength compared to PLA/APP-SCG5% and PLA/APP-SCG20%, respectively. The improved interfacial compatibility was unfavorable for debonding of APP from matrix, and both APP and SCG played the role of enhancing strength, thus decreasing toughness. PLA/APP-SCG/KH560 counterparts were actually set as parallel samples to prove that PLA/APP-SCG composites showed improved toughness with weak interfacial compatibility. This study has provided a practical approach to utilize bio-derived wastes as charring agent to prepare flame retardant PLA composites with enhanced toughness.

KMS Infrastructure for KM Practice in Two Mobile Telecommunication Companies in Namibia

Knowledge management system infrastructure is at the forefront of knowledge management practice. Knowledge must be identified, captured, and shared to improve KM practices; however, little is known about the successful implementation of KMS infrastructure by Namibian mobile telecommunications companies. This mixed-methods research study employed a convergent parallel design and parallel sampling techniques. Three hundred and nine online questionnaires were distributed to a representative sample, with a response rate of 57%. Computer software packages were used to analyse quantitative data quantitatively, and qualitative data from interviews with 11 participants and analysis of documents were analysed thematically. Findings showed that infrastructure, management support, and employee participation were necessary for a successful KMS infrastructure implementation for effective and efficient KM practices. The study provides MT companies with a foundational understanding of how the infrastructure for KMS is essential for effective and efficient KM practice.

Experimental study on the Klinkenberg effect for gas permeability in carboniferous shales, Eastern Qaidam Basin, China

It is crucial to understand the conditions and influencing mechanisms of shale gas slip effect for predicting the productivity of shale gas reservoirs. This study focuses on the Carboniferous Hurleg Formation shales in the eastern Qaidam Basin and conducts gas permeability tests using different gases (He/N2), as well as geochemical and pore-structure tests. The slip behavior of different gases in micro- and nanopores as well as the anisotropy of gas permeability were analyzed and discussed. The results show that helium permeability is 1.81–3.56 times higher than nitrogen permeability, with a greater difference at lower pore pressures. These permeability differences are attributed to variations in gas molecule size and slip effects. Specifically, the slip effect of helium gas has a greater contribution to permeability at lower pore pressures, with a helium slip factor averaging 2.79 times that of nitrogen. The effective pore size of shale, calculated based on the helium slip factor, is 0.74 to 1.51 times larger than when nitrogen is used, with an average of 1.67 times. Helium molecules have smaller diameters and longer average molecular free paths, resulting in a more pronounced slip effect compared to nitrogen. While helium does not adsorb, nitrogen exhibits some adsorption, causing radial expansion during gas penetration. Furthermore, when testing with different gases, the horizontal permeability (S043∥; S052∥) is higher than the vertical permeability (S043⊥; S052⊥). The anisotropy of permeability is controlled by the pore system formed by the arrangement and combination of minerals. Calcium-rich samples (S052) tend to exhibit higher anisotropy compared to calcite-rich samples (S043). The effective pore size in the vertical sample is larger than that in the parallel sample, and the gas slip effect is significantly greater in the vertical sample. These findings provide valuable data for future studies on shale gas slip effects and productivity prediction.

Recycling CF/PEEK offcut waste from laser assisted tape placement: Influence of overlaps and gaps

A recycling method for waste CF/PEEK prepreg tapes is proposed that uses offcuts from laser assisted tape placement (LATP) processing, without shredding, grinding or further cutting. Laminates (using the prepreg waste) representing different configurations were manufactured considering overlaps and gaps reflecting observed defects in LATP. Interlaminar shear strengths (ILSS) with 0° fibre (Parallel) and bending strength for 90° fibre (Normal) directions were measured. The parallel samples with 3 mm overlap had the highest ILSS (100.3 MPa) while the 2 mm gapped samples had the lowest ILSS (58.7 MPa). The Normal samples failed at the surface due to matrix-dominated failure providing bending strengths between 44.0 MPa and 82.1 MPa. Failure mechanisms were identified similar to that of non-recycled composites reported in the literature (50–120 MPa), indicating that the recycled prepreg tapes retained approximately 84 % of the ILSS of the highest reported values.

PCGen: A Fully Parallelizable Point Cloud Generative Model.

Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation methods are not fast enough for these applications. We introduce a vector-quantized variational autoencoder model (VQVAE) that can synthesize high-quality point clouds in milliseconds. Unlike previous work in VQVAEs, our model offers a compact sample representation suitable for conditional generation and data exploration with potential applications in rapid prototyping. We achieve this result by combining architectural improvements with an innovative approach for probabilistic random generation. First, we rethink current parallel point cloud autoencoder structures, and we propose several solutions to improve robustness, efficiency and reconstruction quality. Notable contributions in the decoder architecture include an innovative computation layer to process the shape semantic information, an attention mechanism that helps the model focus on different areas and a filter to cover possible sampling errors. Secondly, we introduce a parallel sampling strategy for VQVAE models consisting of a double encoding system, where a variational autoencoder learns how to generate the complex discrete distribution of the VQVAE, not only allowing quick inference but also describing the shape with a few global variables. We compare the proposed decoder and our VQVAE model with established and concurrent work, and we prove, one by one, the validity of the single contributions.

A stochastic simulation-based chance-constrained programming model for optimizing watershed best management practices for nonpoint source pollution control under uncertainty

In this study, a stochastic simulation-based chance-constrained programming (SSCP) model is developed by integrating soil and water assessment tool (SWAT), generalized likelihood uncertainty estimation (GLUE), chance-constrained programming (CCP) and genetic algorithm (GA) for managing non-point source (NPS) pollution under uncertainty. The SSCP model improves upon conventional simulation-based optimization methods by effectively (i) reflecting the propagation of stochastic uncertainties from the SWAT to the optimization framework by the joint GLUE and CCP (GLUE-CCP) approach, and (ii) identifying the optimal implementation levels of best management practices (BMPs) under different constraint-violation risk levels. The SSCP is solved using a GA with parallel sampling technique after conversion into the corresponding deterministic version. The model is applied to determine the optimal types, sizes and locations of BMPs for NPS pollution control in the Hanjiang River basin, China. The results indicate that, under low-risk conditions, grassed waterways and filter strips should be implemented in the northern part of the basin and larger detention ponds of about 637.8 ∼ 850.4 ha should be installed in the northern and eastern parts of the basin; whereas under high-risk conditions, filter strips should be installed in the northeastern part of the basin and larger detention ponds of about 562.1 ∼ 749.5 ha should be installed in the northern and southwestern parts of the basin. Moreover, as the risk level decreases, stricter control of constraint violations leads to higher BMP implementation costs in order to mitigate the risk of excessive NPS pollution loads. Thus, the proposed model provides valuable insights into the selection of BMP placement schemes at various risk levels, facilitating the reduction of NPS pollution at the outlet of the Hanjiang River basin to acceptable levels while considering the tradeoff between system cost and risk.

Forecasting model of building energy consumption based on parallel Kriging sampling algorithm

Parallel Kriging sampling approach (PEIGF) with minimum computing resourcesis proposed to promote the speed ofbuilding energy consumption. In each iteration of PEIGF, the initial sample point is obtained by maximizing the standard expected improvement global fit (EIGF) criterion andthe parallel EIGF function is constructed to obtain multiple new sample points by maximizing the PEIGF criterion. Performance of PEIGF is evaluated by comparing the obtained results with four other existing sequence approaches and four artificial intelligence (AI) based models. Theresults reveal that PEIGF stands out for most of the test cases with goodpredictive accuracy and computing efficiency. PEIGF runs 4.7–11.6 times, 5.1–12.4 times, 5.0–14.8 times, and 19.7–158.6 times faster than thoseof combined expectation (CE), EIGF, expected improvement for global fit based on gradient (EIGFG) and maximum mean square error (MMSE) sampling.Itobtains the least root mean squared errors for energy consumption prediction, and reduces the total number of samples by 14.9% (heating load) and 14.1% (cooling load), as well as 16.7%compared with other four AI-based models.It isprovento bea promising technique toplan low-cost energy managementandfacilitate early designs or renovation of energy conserving buildings.

Bacteria undergo significant shifts while archaea maintain stability in Pocillopora damicornis under sustained heat stress

Global warming reportedly poses a critical risk to coral reef ecosystems. Bacteria and archaea are crucial components of the coral holobiont. The response of archaea associated with warming is less well understood than that of the bacterial community in corals. Also, there have been few studies on the dynamics of the microbial community in the coral holobiont under long-term heat stress. In order to track the dynamic alternations in the microbial communities within the heat-stressed coral holobiont, three-week heat-stress monitoring was carried out on the coral Pocillopora damicornis. The findings demonstrate that the corals were stressed at 32 °C, and showed a gradual decrease in Symbiodiniaceae density with increasing duration of heat stress. The archaeal community in the coral holobiont remained relatively unaltered by the increasing temperature, whereas the bacterial community was considerably altered. Sustained heat stress exacerbated the dissimilarities among parallel samples of the bacterial community, confirming the Anna Karenina Principle in animal microbiomes. Heat stress leads to more complex and unstable microbial networks, characterized by an increased average degree and decreased modularity, respectively. With the extension of heat stress duration, the relative abundances of the gene (nifH) and genus (Tistlia) associated with nitrogen fixation increased in coral samples, as well as the potential pathogenic bacteria (Flavobacteriales) and opportunistic bacteria (Bacteroides). Hence, our findings suggest that coral hosts might recruit nitrogen-fixing bacteria during the initial stages of suffering heat stress. An environment that is conducive to the colonization and development of opportunistic and pathogenic bacteria when the coral host becomes more susceptible as heat stress duration increases.

Understanding of the concept of knowledge management for knowledge management system implementation in two mobile telecommunication companies in Namibia

This study investigates the understanding of knowledge management (KM) for knowledge management systems (KMS) implementation for efficient KM practice in two mobile telecommunications (MT) companies in Namibia. Using a mixed-methods approach, the study employed convergent parallel design via parallel sampling techniques to gather insights. Three hundred and twenty-nine questionnaires were disseminated via online surveys using simple random sampling from 1,584 employees. The questionnaires returned were then accepted based on the comprehension of KM concepts for KMS implementation to introduce KM practice. Quantitative data was subjected to descriptive analysis using SPSS. Qualitative data underwent thematic analysis using Atlas TI. The findings of this study show that Namibia's two MT companies stand to gain significant advantages by implementing KM practices based on their understanding and implementation of KM. By enhancing their KM capabilities, these companies can leverage this organisational knowledge (OK) to drive innovation and adequately meet the demands of a fiercely competitive, knowledge-based society (KBS). This article is of significant value to KM scholars, professionals, decision-makers, and policymakers as an introductory guide to the comprehension of KM for KMS and how they can facilitate effective KM practices within Namibia.

Exposure Assessment of Young Adults to Pesticides That Have Effects on the Thyroid—A Contribution to “One Health”

The objective of this research was to evaluate the cumulative exposure of the population aged 10–24 years to pesticides that have a chronic effect on the thyroid. A consumption study covering fresh fruits and vegetables was collected from 377 respondents. In parallel, 2369 fruit and vegetable samples were chemically analyzed for pesticide residues. As a result, cumulative exposure was calculated for four different scenarios (as is, maximum residue level, 70% of MRL and below detection limit) using two recall methods. The results show that, depending on the scenario, cyprodinil from green lettuce and strawberries, fluopyram blueberries and strawberries, and fluxapyroxad, detected in grapes, contribute most to exposure. More stringent scenarios, with limits at 70% of the MRLs (0.7 MRL) and below the detection limit (“zero residue” approach), show that the estimated total margin of exposure increases by up to 50% in the “0.7 MRL” model, while levels almost triple in the “zero residue” model. The optimization of pesticide use has a beneficial effect on human health and the environment, contributing to the “One Health” approach.

Time-dependent reliability analysis of structural systems based on parallel active learning Kriging model

Time-dependent reliability analysis quantifies the failures of structural systems due to time-dependent uncertainties, such as material degradation and dynamic loads. The active learning Kriging model methods are widely used in structural reliability analysis to replace extensive time-consuming finite element simulations. However, they can only update one training sample and one failure mode per iteration, which limits their application to time-dependent, parallel computing, and multiple failure modes problems. In this study, we propose a new parallel active learning Kriging model for time-dependent reliability analysis, which can update multiple training samples and multiple failure modes per iteration. It includes the following strategies: (1) a novel parallel learning function is proposed, which combines the correlation function and U learning function to allow for the selection of multiple training samples per iteration; (2) an adaptive adjustment strategy for the number of parallel samples is proposed, which takes into account the prediction probability of parallel samples; (3) the proposed parallel learning function is integrated into time-dependent reliability analysis with multiple failure modes, enabling simultaneous updates of multiple training samples and failure modes, thus greatly reducing the number of iterations and computational time; and (4) a new stopping criterion is proposed to improve the efficiency of the estimation of failure probability. The proposed method can be applied to series or parallel time-dependent structural systems with multiple failure modes. We demonstrate the effectiveness of the proposed method through three examples, and the proposed method can achieve a balance between the computational time and function calls while maintaining a high level of accuracy in the estimation of time-dependent failure probability.

A rotationally-driven dynamic solid phase sodium bisulfite conversion disc for forensic epigenetic sample preparation.

The approaches to forensic human identification (HID) are largely comparative in nature, relying upon the comparison of short tandem repeat profiles to known reference materials and/or database profiles. However, many profiles are generated from evidence materials that either do not have a reference material for comparison or do not produce a database hit. As an alternative to individualizing analysis for HID, researchers of forensic DNA have demonstrated that the human epigenome can provide a wealth of information. However, epigenetic analysis requires sodium b̲is̲ulfite c̲onversion (BSC), a sample preparation method that is time-consuming, labor-intensive, prone to contamination, and characterized by DNA loss and fragmentation. To provide an alternative method for BSC that is more amenable to integration with the forensic DNA workflow, we describe a rotationally-driven, microfluidic method for dynamic solid phase-BSC (dSP-BSC) that streamlines the sample preparation process in an automated format, capable of preparing up to four samples in parallel. The method permitted decreased incubation intervals by ∼36% and was assessed for relative DNA recovery and conversion efficiency and compared to gold-standard and enzymatic approaches.

Drug Screening Using Normal Cell and Cancer Cell Mixture in an Automated 3D Cell Culture System.

Three-dimensional (3D) cell culture creates a more physiologically relevant environment for enhanced drug screening capabilities using microcarriers. An automated 3D system that integrates robotic manipulators, liquid handling systems, sensors, and environment control systems has the capacity to handle multiple samples in parallel, perform repetitive tasks, and provide real-time monitoring and analysis. This chapter describes a potential 3D cell culture drug screening model by combining renal proximal tubule cells as a representative normal cell line with cancer cell lines. This combination is subjected to drug screening to evaluate the drug's efficacy in suppressing cancer cells while minimizing impact on normal cells with the added benefit of having the ability to separate the two cell types by magnetic isolation for high content screens including mass spectrometry-based proteomics. This study presents advancements in 3D cell culture techniques, emphasizing the importance of automation and the potential of microcarriers in drug screening and disease modeling.