Manual Steps Research Articles

MT36 Assessment and Comparison of the Manual Steps Required to Perform Whole Blood Processing Using a Semi-Automated Method Versus a Fully Automated Method

MT36 Assessment and Comparison of the Manual Steps Required to Perform Whole Blood Processing Using a Semi-Automated Method Versus a Fully Automated Method

A Closed Formalism for Anatomy-Independent Projection and Optimization of Magnetic Stimulation Coils on Arbitrarily Shaped Surfaces.

Transcranial magnetic stimulation (TMS) is a popular method for the noninvasive stimulation of neurons in the brain. It has become a standard instrument in experimental brain research and has been approved for a range of diagnostic and therapeutic applications. These applications require appropriately shaped coils. Various applications have been established or approved for specific coil designs with their corresponding spatial electric field distributions. However, the specific coil implementation may no longer be appropriate from the perspective of available material and manufacturing opportunities or considering the latest understanding of how to achieve induced electric fields in the head most efficiently. Furthermore, in some cases, field measurements of coils with unknown winding or a user-defined field are available and require an actual implementation. Similar applications exist for magnetic resonance imaging coils. This work aims at introducing a complete formalism free from heuristics, iterative optimization, and ad-hoc or manual steps to form practical stimulation coils with individual turns to either equivalently match an existing coil or produce a given field. The target coil can reside on practically any sufficiently large or closed surface adjacent to or around the head. The method derives an equivalent field through vector projection exploiting the well-known Huygens' and Love's equivalence principle. In contrast to other coil design or optimization approaches recently presented, the procedure is an explicit forward Hilbert-space vector projection or basis change. For demonstration, we map a commercial figure-of-eight coil as one of the most widely used devices and a more intricate coil recently approved clinically for addiction treatment (H4) onto a bent surface close to the head for highest efficiency and lowest field energy. The resulting projections are within ≤4% of the target field and reduce the necessary pulse energy by more than 40%.

DNA repair-retarded isothermal CRISPR amplification for rapid, sensitive base excision repair enzyme assay

BackgroundAs a core enzyme in the base excision repair system, uracil DNA glycosylase (UDG) is indispensable in maintaining genomic integrity and normal cell cycles. Its abnormal activity intervenes in cancers and neurodegerative diseases. Previous UDG assays based on isothermal amplification and Clustered Regularly Interspaced Short Palindromic Repeats/Cas (CRISPR/Cas) system were fine in sensitivity, but exposed to complications in assay flow, time, and probe design. After isothermal amplification, a CRISPR/Cas reagent should be separately added with extra manual steps and its guide RNA (gRNA) should be designed, considering the presence of protospacer adjacent motif (PAM) site. ResultsWe herein describe a UDG-REtarded CRISPR Amplification assay, termed ‘URECA’. In URECA, isothermal nucleic acid (NA) amplification and CRISPR/Cas12a system were tightly combined to constitute a one-pot, isothermal CRISPR amplification system. Isothermal NA amplification for a UDG substrate (US) with uracil (U) bases was designed to activate and boost CRISPR/Cas12a reaction. Such scheme enabled us to envision that UDG would halt the isothermal CRISPR amplification reaction by excising U bases and messing up the US. Based on this principle, the assay detected the UDG activity down to 9.17 x 10−4 U/mL in 50 min. With URECA, we fulfilled the recovery test of UDG activities in plasma and urine with high precision and reproducibility and reliably determined UDG activities in cell extracts. Also, we verified its capability to screen candidate UDG inhibitors, showing its potentials in practical application as well as drug discovery. SignificanceURECA offers further merits: i) the assay is seamless. Following target recognition, the reactions proceed in one-step without any intervening steps, ii) probe design is simple. Unlike the conventional CRISPR/Cas12a-based assays, URECA does not consider the PAM site in probe design as Cas12a activation relies on instantaneous gRNA binding to single-stranded DNA strands. By rationally designing an enzyme substrate probe to be specific to other enzymes, while keeping a role as a template for isothermal CRISPR amplification, the detection principle of URECA will be expanded to devise biosensors for various enzymes of biological, clinical significance.

A Web-Based Intervention for Insufficiently Active College Students: Feasibility and Preliminary Efficacy.

One third of college students do not achieve aerobic activity levels recommended for physical and mental health. The web-based "I Can Be Active!" intervention was designed to help college students increase their physical activity. The intervention was grounded in the Multi-Process Action Control (M-PAC) framework which emphasizes translating intention into sustainable action. The primary purpose was to evaluate the feasibility of the intervention with insufficiently active young adult college students. The secondary purposes were to describe the preliminary effects of the intervention on: (1) the M-PAC constructs and (2) physical activity. Twenty-one college students, ages 18 to 24, were enrolled in the pre-post quasi-experimental study to test the 8-week intervention during Spring 2021. Data were collected via self-report questionnaires, web-analytics, and interviews. Feasibility outcomes included recruitment, retention, acceptability, practicality, and implementation. Preliminary efficacy outcomes were self-report M-PAC constructs and physical activity. Data analyses included descriptive statistics, t tests, Wilcoxon signed rank tests, Hedge's g, and thematic analysis. Recruitment and retention rates were 70% and 71%, respectively. Participants reacted positively to the program, content, and features, except the manual entry step tracker and private social media group. Positive trends and significant increases were found in the regulatory and reflexive M-PAC constructs (self-regulation, habit, and identity) and physical activity. Findings support the feasibility and preliminary effects of the intervention for insufficiently active college students and highlight implications for intervention refinement. Future research will test intervention effectiveness using a randomized controlled trial with a larger diverse sample of college students.

Operationalizing open and restricted-access data—Formulating verifiable criteria for the openness of data sets mentioned in biomedical research articles

Abstract Increasing the availability of research data sets is a goal of many stakeholders in science, and monitoring related practices requires definitions of the entity in question. There are several, largely overlapping, definitions for open data. However, they have so far not been translated into operationalizations that would allow us to detect, in a structured and reproducible way, whether, for a specific research article, the underlying data have been shared. Here, we propose a detailed set of criteria to enable such assessments, focusing on biomedical research. We have used these criteria to distribute performance-oriented funding at a large university hospital and to monitor data sharing practices in a dashboard. In addition to fully open data, we include separate criteria for data sets with restricted access, which we also reward. The criteria are partly inspired by the FAIR principles, particularly findability and accessibility, but do not map onto individual principles. The criteria attribute open data status in a binary fashion, both to individual data sets and, ultimately, articles with which they were shared. The criteria allow a verifiable assessment, based on automated and manual screening steps, which we have implemented and validated, as described elsewhere. Here, we focus conceptually on assessing the presence of shared data.

Workflow for detecting biomedical articles with underlying open and restricted-access datasets.

To monitor the sharing of research data through repositories is increasingly of interest to institutions and funders, as well as from a meta-research perspective. Automated screening tools exist, but they are based on either narrow or vague definitions of open data. Where manual validation has been performed, it was based on a small article sample. At our biomedical research institution, we developed detailed criteria for such a screening, as well as a workflow which combines an automated and a manual step, and considers both fully open and restricted-access data. We use the results for an internal incentivization scheme, as well as for a monitoring in a dashboard. Here, we describe in detail our screening procedure and its validation, based on automated screening of 11035 biomedical research articles, of which 1381 articles with potential data sharing were subsequently screened manually. The screening results were highly reliable, as witnessed by inter-rater reliability values of ≥0.8 (Krippendorff's alpha) in two different validation samples. We also report the results of the screening, both for our institution and an independent sample from a meta-research study. In the largest of the three samples, the 2021 institutional sample, underlying data had been openly shared for 7.8% of research articles. For an additional 1.0% of articles, restricted-access data had been shared, resulting in 8.3% of articles overall having open and/or restricted-access data. The extraction workflow is then discussed with regard to its applicability in different contexts, limitations, possible variations, and future developments. In summary, we present a comprehensive, validated, semi-automated workflow for the detection of shared research data underlying biomedical article publications.

Automatic knowledge graph creation from engineering standards using the example of formulas

AbstractEngineering standards are an important source of knowledge in product development. Despite the increasing digitalisation, the provision and usage of standards is characterised by lots of manual steps. This research paper aims at applying automatic knowledge graph creation in the domain of engineering standards to enable machine-actionable standards. For this, a formula knowledge graph ontology as well as suitable information extraction techniques are developed. The concept is validated using the example of DIN ISO 281, showing the overall capability of automatic knowledge graph creation.

Cyclic reduction of combusted iron powder: A study on the material properties and conversion reaction in the iron fuel cycle

Recently, iron powder has been proposed as a high energy density, easily storable, and CO2-free energy carrier. During the iron combustion, thermal energy is released as heat. The combusted products, the iron oxide particles, are captured and cyclically reduced back into iron powder in a process that is powered by renewable energy. Each combustion step, followed by a reduction, constitutes one cycle in the process. Previous studies predominantly focused on the reduction and combustion as individual steps. This work investigates the impact of cyclic combustion-reduction on material properties, with the specific focus on the reduction of iron oxides using hydrogen under fluidisation conditions. The powder is combusted and reduced for 3 cycles under the same conditions. Afterwards the properties of the powder and the reaction conversion are characterized. The physical techniques used for size and shape analysis include laser diffraction particle size analysis and scanning electron microscopy (SEM). For structure and chemistry, X-ray diffraction (XRD) and energy dispersive X-ray (EDX) analysis are also employed. To study the effect of the reduction conditions on the cycle and on the material properties of the powder, different experiments are conducted using 550 and 575°C as reduction temperatures. The higher conversion is observed at 575°C. During the first combustion particles resulted in large agglomerates and required a manual grinding step before being sent to the next reduction. However, the particle size distribution remains relatively stable in all subsequent cycles. The results suggest that the powder can be effectively utilized in the iron fuel cycle without requiring additional intermediate treatments, such as grinding and sieving after each cycle. This aspect highlights the potential feasibility and simplicity of implementing the cyclic combustion-reduction process for practical applications of iron powder as an energy carrier.

External quality assessments for SARS-CoV-2 genome detection in Austria : Acomparison of the first postpandemic round with results from the pandemic era.

External quality assessment (EQA) schemes provide objective feedback to participating laboratories about the performance of their analytical systems and information about overall regional analytical performance. The EQAs are particularly important during pandemics as they also assess the reliability of individual test results and show opportunities to improve test strategies. With the end of the COVID-19 pandemic, the testing frequency significantly decreased in Austria. Here, we analyzed whether this decrease had an effect on participation and/or performance in SARS-CoV‑2 virus detection EQAs, as compared to the pandemic era. Identical samples were sent to all participating laboratories, and the EQA provider evaluated the agreement of the reported results with defined targets. The EQA was operated under two schemes with identical samples and therefore we analyzed it as asingle EQA round. The performance of testing was reported as true positive ratios, comparing the post-pandemic data to previous rounds. Furthermore, subgroups of participants were analyzed stratified by laboratory type (medical or nonmedical) and the test system format (fully automated or requiring manual steps). While the frequency of false negative results per sample did not change during the 3 years of the pandemic (5.7%, 95% confidence interval [CI] 3.1-8.4%), an average per sample false negative ratio of 4.3% was observed in the first post-pandemic EQA (0%, 1.8%, and 11% for the 3 positive samples included in the test panel, n = 109 test results per sample). In this first post-pandemic EQA medical laboratories (average 0.4% false negative across 3samples, n = 90) and automated test systems (average 1.2% false negative, n = 261) had lower false negative ratios than nonmedical laboratories (22.8%, n = 19) and manual test systems (16.7%, n = 22). These lower average ratios were due to alow concentration sample, where nonmedical laboratories reported 36.8% and manual test systems 54.5% true positive results. Overall ratios of true positive results were below the mean of all results during the pandemic but were similar to the first round of the pandemic. Alower post-pandemic true positive ratio was associated with specific laboratory types and assay formats, particularly for samples with low concentration. The EQAs will continue to monitor the laboratory performance to ensure the same quality of epidemiological data after the pandemic, even if vigilance has decreased.

In-silico Evaluation of Mirror Repeats in Some Selected Genes of Candida albicans

All cellular processes in a living cell are controlled by its genetic material. DNA in majority of the domains acts as a regulatory molecule by controlling various vital functions. The genetic makeup of DNA or RNA (in viruses) is unique in all the domains. Their uniqueness is determined by the presence of various types of repetitive patterns of bases. These includes inverted repeats, tandem repeats, VNTR’s, palindromes etc. Among many repetitive pattern types, Mirror repeats (MR) found to be dispersed throughout the genes or genomes. These sequences are associated with various functional features like their involvement in H-DNA formation, in replication & transcription, nervous system related diseases development etc. The major focus of this investigation is to identify MR sequences from some selected genes of Candida albicans using a bioinformatics based pipeline. The approach refers to as FPCB which utilized some manual steps to extract out MR sequence from any targeted gene or genome. The current study find out that the identified Mirror repeats found to be dispersed throughout the selected genes along with variable length. Among them the maximum & minimum MR sequences were reported in the gene FAS2 (108) & HIS1 (15) respectively. The present study will be helpful to provide a new insight for molecular as well as computational based studies of Candida albicans as well as other related fungal groups.

Automated ELISA for potency measurements of therapeutic antibodies and antibody fragments

Potency assays are essential for the development and quality control of biopharmaceutical drugs, but they are often a time limiting factor due to manual handling steps and consequently low analytical throughput. On the other hand, automation of potency assays can be challenging due to their complexity and the use of biological materials. ELISA (enzyme-linked immunosorbent assay) is widely used for potency determination and is a good candidate for automation as all ELISA types depend on the same basic steps: coating, blocking, sample incubation, detection, and signal measurement. Nevertheless, ELISA for relative potency measurements still require drug-specific development and assay validation thereby complicating automation efforts. To simplify potency testing by ELISA, we first developed a manual protocol generally applicable to different drugs and then adapted this protocol for automated measurements. We identified unexpected critical parameters which had to be adapted to transfer the manual ELISA to an automated liquid handling system and we demonstrated that gravimetric sample dilution is unnecessary with the automated protocol. Both manual and automated protocols were validated and compared using multiple biotherapeutics. The automated protocol showed similar or higher precision and accuracy when compared to the manual method.

A Semantic-Spatial Aware Data Conflation Approach for Place Knowledge Graphs

Recent advances in knowledge graphs show great promise to link various data together to provide a semantic network. Place is an important part in the big picture of the knowledge graph since it serves as a powerful glue to link any data to its georeference. A key technical challenge in constructing knowledge graphs with location nodes as geographical references is the matching of place entities. Traditional methods typically rely on rule-based matching or machine-learning techniques to determine if two place names refer to the same location. However, these approaches are often limited in the feature selection of places for matching criteria, resulting in imbalanced consideration of spatial and semantic features. Deep feature-based methods such as deep learning methods show great promise for improved place data conflation. This paper introduces a Semantic-Spatial Aware Representation Learning Model (SSARLM) for Place Matching. SSARLM liberates the tedious manual feature extraction step inherent in traditional methods, enabling an end-to-end place entity matching pipeline. Furthermore, we introduce an embedding fusion module designed for the unified encoding of semantic and spatial information. In the experiment, we evaluate the approach to named places from Guangzhou and Shanghai cities in GeoNames, OpenStreetMap (OSM), and Baidu Map. The SSARLM is compared with several classical and commonly used binary classification machine learning models, and the state-of-the-art large language model, GPT-4. The results demonstrate the benefit of pre-trained models in data conflation of named places.

Characterization of Plastic Scintillator Detector for In Vivo Dosimetry in Gynecologic Brachytherapy.

(1) Background: High dose gradients and manual steps in brachytherapy treatment procedures can lead to dose errors which make the use of in vivo dosimetry (IVD) highly recommended for verifying brachytherapy treatments. A new procedure was presented to obtain a calibration factor which allows fast and robust calibration of plastic scintillation detector (PSD) probes for the geometry of a compact phantom using Monte Carlo simulations. Additionally, characterization of PSD energy, angular, and temperature dependences was performed. (2) Methods: PENELOPE/PenEasy code was used to obtain the calibration factor. To characterize the energy dependence of the PSD, the signal was measured at different radial and transversal distances. The sensitivity to the angular position was characterized in axial and azimuthal planes. (3) Results: The calibration factor obtained allows for an absorbed dose to water determination in full scatter conditions from ionization measured in a mini polymethylmethacrylate (PMMA) phantom. The energy dependence of the PSD along the radial distances obtained was (2.3 ± 2.1)% (k = 1). The azimuthal angular dependence measured was (2.6 ± 3.4)% (k = 1). The PSD response decreased by (0.19 ± 0.02)%/°C with increasing detector probe temperature. (4) Conclusions: The energy, angular, and temperature dependence of a PSD is compatible with IVD.

RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate

Image registration can map the ground truth extent of prostate cancer from histopathology images onto MRI, facilitating the development of machine learning methods for early prostate cancer detection. Here, we present RAdiology PatHology Image Alignment (RAPHIA), an end-to-end pipeline for efficient and accurate registration of MRI and histopathology images. RAPHIA automates several time-consuming manual steps in existing approaches including prostate segmentation, estimation of the rotation angle and horizontal flipping in histopathology images, and estimation of MRI-histopathology slice correspondences. By utilizing deep learning registration networks, RAPHIA substantially reduces computational time. Furthermore, RAPHIA obviates the need for a multimodal image similarity metric by transferring histopathology image representations to MRI image representations and vice versa. With the assistance of RAPHIA, novice users achieved expert-level performance, and their mean error in estimating histopathology rotation angle was reduced by 51% (12 degrees vs 8 degrees), their mean accuracy of estimating histopathology flipping was increased by 5% (95.3% vs 100%), and their mean error in estimating MRI-histopathology slice correspondences was reduced by 45% (1.12 slices vs 0.62 slices). When compared to a recent conventional registration approach and a deep learning registration approach, RAPHIA achieved better mapping of histopathology cancer labels, with an improved mean Dice coefficient of cancer regions outlined on MRI and the deformed histopathology (0.44 vs 0.48 vs 0.50), and a reduced mean per-case processing time (51 vs 11 vs 4.5 min). The improved performance by RAPHIA allows efficient processing of large datasets for the development of machine learning models for prostate cancer detection on MRI. Our code is publicly available at: https://github.com/pimed/RAPHIA.

VitroJet: new features and case studies.

Single-particle cryo-electron microscopy has become a widely adopted method in structural biology due to many recent technological advances in microscopes, detectors and image processing. Before being able to inspect a biological sample in an electron microscope, it needs to be deposited in a thin layer on a grid and rapidly frozen. The VitroJet was designed with this aim, as well as avoiding the delicate manual handling and transfer steps that occur during the conventional grid-preparation process. Since its creation, numerous technical developments have resulted in a device that is now widely utilized in multiple laboratories worldwide. It features plasma treatment, low-volume sample deposition through pin printing, optical ice-thickness measurement and cryofixation of pre-clipped Autogrids through jet vitrification. This paper presents recent technical improvements to the VitroJet and the benefits that it brings to the cryo-EM workflow. A wide variety of applications are shown: membrane proteins, nucleosomes, fatty-acid synthase, Tobacco mosaic virus, lipid nanoparticles, tick-borne encephalitis viruses and bacteriophages. These case studies illustrate the advancement of the VitroJet into an instrument that enables accurate control and reproducibility, demonstrating its suitability for time-efficient cryo-EM structure determination.

Minimizing Urban Flooding by Optimal Design of Drainage System Using Sequential Least Squares Quadratic Programming and Spatial Datasets

Urban flooding is caused due to poor drainage design and excessive rain. It severely affects the road infrastructure. Existing hydrologic software tools to examine the extent of urban flooding primarily require walking through a series of manual steps and address each study area individually, preventing a collective review of poor storm-drains in an efficient manner. Previous methods for optimal drainage design were inefficient and lacked the ability of solving the underlying optimization problem due to the inherent nonlinearity of the decision variables. In this paper, we develop a nonlinear optimization formulation to minimize urban flooding using underground pipe size as a decision variable. We propose a solution algorithm using sequential least squares quadratic programming and spatial datasets. The proposed method eliminates the need to examine each study area manually using existing hydrologic tools. An example using the storm-drain system for the Baltimore County is performed. The results show that the model is effective in identifying storm-drain deficiencies and correcting them by choosing appropriate storm-drain inlet types to minimize flooding. Future works may include using large datasets and a more sophisticated modeling approach for estimating rainfall intensity based on extreme weather patterns. The method can be applied to other jurisdictions if relevant hydrological and underdrain piping network data were available.

Constructing telomere-to-telomere diploid genome by polishing haploid nanopore-based assembly.

Draft genomes generated from Oxford Nanopore Technologies (ONT) long reads are known to have a higher error rate. Although existing genome polishers can enhance their quality, the error rate (including mismatches, indels and switching errors between paternal and maternal haplotypes) can be significant. Here, we develop two polishers, hypo-short and hypo-hybrid to address this issue. Hypo-short utilizes Illumina short reads to polish an ONT-based draft assembly, resulting in a high-quality assembly with low error rates and switching errors. Expanding on this, hypo-hybrid incorporates ONT long reads to further refine the assembly into a diploid representation. Leveraging on hypo-hybrid, we have created a diploid genome assembly pipeline called hypo-assembler. Hypo-assembler automates the generation of highly accurate, contiguous and nearly complete diploid assemblies using ONT long reads, Illumina short reads and optionally Hi-C reads. Notably, our solution even allows for the production of telomere-to-telomere diploid genomes with additional manual steps. As a proof of concept, we successfully assembled a fully phased telomere-to-telomere diploid genome of HG00733, achieving a quality value exceeding 50.

BioISO: An Objective-Oriented Application for Assisting the Curation of Genome-Scale Metabolic Models.

As the reconstruction of Genome-Scale Metabolic Models (GEMs) becomes standard practice in systems biology, the number of organisms having at least one metabolic model is peaking at an unprecedented scale. The automation of laborious tasks, such as gap-finding and gap-filling, allowed the development of GEMs for poorly described organisms. However, the quality of these models can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. Biological networks constraint-based In Silico Optimisation (BioISO) is a computational tool aimed at accelerating the reconstruction of GEMs. This tool facilitates manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis (FBA) to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). In this assessment, BioISO is better suited to reducing the search space for errors and gaps in metabolic networks by identifying smaller ratios of dead-end metabolites. Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions for filling the gaps.

An approach to detect and predict epileptic seizures with high accuracy using convolutional neural networks and single-lead-ECG signal

One of the epileptic patients’ challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals. The structure of the proposed CNN for seizure detection and prediction is the same. Considering the requirements of a wearable system, after a few light pre-processing steps, the ECG signal can be used as input to the neural network without any manual feature extraction step. The desired neural network learns purposeful features according to the labelled ECG signals and then performs the classification of these signals. Training of 39-layer CNN for seizure detection and prediction has been done separately. The proposed method can detect seizures with an accuracy of 98.84% and predict them with an accuracy of 94.29%. With this approach, the ECG signal can be a promising indicator for the construction of portable systems for monitoring the status of epileptic patients.

Multi-well plate lid for single-step pooling of 96 samples for high-throughput barcode-based sequencing

High-throughput transcriptomics is of increasing fundamental biological and clinical interest. The generation of molecular data from large collections of samples, such as biobanks and drug libraries, is boosting the development of new biomarkers and treatments. Focusing on gene expression, the transcriptomic market exploits the benefits of next-generation sequencing (NGS), leveraging RNA sequencing (RNA-seq) as standard for measuring genome-wide gene expression in biological samples. The cumbersome sample preparation, including RNA extraction, conversion to cDNA and amplification, prevents high-throughput translation of RNA-seq technologies. Bulk RNA barcoding and sequencing (BRB-seq) addresses this limitation by enabling sample preparation in multi-well plate format. Sample multiplexing combined with early pooling into a single tube reduces reagents consumption and manual steps. Enabling simultaneous pooling of all samples from the multi-well plate into one tube, our technology relies on smart labware: a pooling lid comprising fluidic features and small pins to transport the liquid, adapted to standard 96-well plates. Operated with standard fluidic tubes and pump, the system enables over 90% recovery of liquid in a single step in less than a minute. Large scale manufacturing of the lid is demonstrated with the transition from a milled polycarbonate/steel prototype into an injection molded polystyrene lid. The pooling lid demonstrated its value in supporting high-throughput barcode-based sequencing by pooling 96 different DNA barcodes directly from a standard 96-well plate, followed by processing within the single sample pool. This new pooling technology shows great potential to address medium throughput needs in the BRB-seq workflow, thereby addressing the challenge of large-scale and cost-efficient sample preparation for RNA-seq.Graphical abstract