Reproducible Data Research Articles

High-Quality RNA Extraction and Evaluation of Reference Genes for qPCR Assay of <i>Pinus sylvestris</i> L. Trunk Tissues

Scots pine (Pinus sylvestris L.) is a species of tree with heartwood (HW), which is forming during aging of sapwood (SW). Due to clear-cut border between SW and HW P. sylvestris should be used as a model woody plant for studying patterns of HW formation. Currently, molecular genetic methods are used to study the processes of trunk tissues formation in woody plants often. A feature of trunk tissues of coniferous trees is a high content of secondary metabolites, a low content of nucleic acids, and potential partial degradation of RNA. In this work we discuss the choice of most successful method for extraction a high-quality RNA for real-time PCR (RT-PCR) in P. sylvestris trunk tissues along the radial vector “conductive phloem/cambial zone – differentiating xylem – exterior part of SW (1–2 annual rings) – interior part of SW (1–2 annual rings afore transition zone (TZ)) – TZ (2 annual rings afore HW)” for reproducible RT-PCR data. The expression stability of six potential reference genes (Actin1, α-Tubulin, β-Tubulin, Ef1a, GAPDH, UBQ) was assessed in all describe tissues. Differences in expression levels of target genes are shown by data normalization using reference genes with different stability of expression.

The future of ecosystem assessments is automation, collaboration, and artificial intelligence

Abstract Robust and routine ecosystem assessments will be fundamental to track progress towards achieving this decade’s global environmental and sustainability goals. Here we examine four needs that address common failure points of ecosystem assessments. These are (1) developing rapid, reproducible, and repeatable ecological data workflows, (2) harmonizing in situ and remotely sensed data, (3) integrating socioeconomic and biophysical data, and (4) increasing access to the digital resources and cyberinfrastructure needed to perform assessments. These four needs have profound potential to help us achieve our environmental objectives through cross-sector collaborations that leverage advancements in digital resources, remote data streams, and data science.

Towards more reproducible and FAIRer research data: documenting provenance during data acquisition using the Infofile format

Documenting data during their acquisition is an essential aspect of science, though rarely done well. The Infofile format is a simple, powerful and proven solution minimising overhead and thus enhancing reproducibility and quality of science.

Nanopore Direct RNA Sequencing Data Processing and Analysis Using MasterOfPores.

This chapter describes MasterOfPores v.2 (MoP2), an open-source suite of pipelines for processing and analyzing direct RNA Oxford Nanopore sequencing data. The MoP2 relies on the Nextflow DSL2 framework and Linux containers, thus enabling reproducible data analysis in transcriptomic and epitranscriptomic studies. We introduce the key concepts of MoP2 and provide a step-by-step fully reproducible and complete example of how to use the workflow for the analysis of S. cerevisiae total RNA samples sequenced using MinION flowcells. The workflow starts with the pre-processing of raw FAST5 files, which includes basecalling, read quality control, demultiplexing, filtering, mapping, estimation of per-gene/transcript abundances, and transcriptome assembly, with support of the GPU computing for the basecalling and read demultiplexing steps. The secondary analyses of the workflow focus on the estimation of RNA poly(A) tail lengths and the identification of RNA modifications. The MoP2 code is available at https://github.com/biocorecrg/MOP2 and is distributed under the MIT license.

A Fiji process for quantifying fluorescent puncta in linear cellular structures.

Understanding the cell biology of protein trafficking and homeostasis requires reproducible methods for identifying and quantifying proteins within cells or cellular structures. Imaging protocols for measuring punctate protein accumulation in linear structures, for example the neurites of C. elegans, have relied on proprietary software for a full range of analysis capabilities. Here we describe a set of macros written for the NIH-supported imaging software ImageJ or Fiji (Fiji is Just ImageJ) that reliably identify protein puncta so that they can be analyzed with respect to intensity, density, and width at half-maximum intensity (Full-Width, Half-Maximum, FWHM). We provide an explanation of the workflow, data outputs, and limitations of the Fiji macro. As part of this integration, we also provide two independent data sets with side-by-side analyses using the proprietary IgorPro software and the Fiji macro (Hulsey-Vincent, et al. A, B., 2023 submitted). The Fiji macro is an important new tool because it provides robust, reproducible data analysis in a free, open-source format.

Validation of the Detection Kit for Pathogenic Bacteria Salmonella typhi Causes Food Poisoning with Real Time Polymerase Chain Reaction

Salmonella typhi is a gram-negative bacteria that causes food poisoning. Salmonella typhi bacteria can cause typhus, which can produce lipopolysaccharide complex endotoxin, which plays an important role in the pathogenesis of typhoid fever. Cases of food poisoning are still common and are one of the causes of death and death in Indonesia. In 2019, there were 77 cases of food poisoning in Indonesia. A fast, accurate, and specific detection method is needed to detect poisoning. In previous studies, the optimum conditions and formulas for detecting these bacteria have been obtained using Real Time PCR. The results of previous studies are used as the basis for the development of a prototype detection kit. In this study, validation was carried out, which aimed to confirm the results of previous studies so that a reproducible and accurate product for the detection of Salmonella typhi bacteria could be obtained. The results of this study showed that fim-C primers for Salmonella typhi were amplified at 95 base pairs (bp) with an annealing temperature of 60ºC and a standard DNA concentration of 50 ng/µL. The results of the Real Time PCR confirmation test of Salmonella typhi bacteria at a pre-denaturation of 3 minutes with a concentration of 10 pmol obtained the Ct value according to thestandard with previous studies. The Ct value obtained was 13.96 for S. typhi bacteria. Based on the results obtained, it can be conclude that the condition validation stage for pure cultures was successfully carried out by producing consistentand reproducible data.

Validation of the Detection Kit for Pathogenic Bacteria Salmonella typhi Causes Food Poisoning with Real Time Polymerase Chain Reaction

Salmonella typhi is a gram-negative bacteria that causes food poisoning. Salmonella typhi bacteria can cause typhus, which can produce lipopolysaccharide complex endotoxin, which plays an important role in the pathogenesis oftyphoid fever. Cases of food poisoning are still common and are one of the causes of death and death in Indonesia. In 2019, there were 77 cases of food poisoning in Indonesia. A fast, accurate, and specific detection method is needed to detect poisoning. In previous studies, the optimum conditions and formulas for detecting these bacteria have been obtained using Real Time PCR. The results of previous studies are used as the basis for the development of a prototypedetection kit. In this study, validation was carried out, which aimed to confirm the results of previous studies so that a reproducible and accurate product for the detection of Salmonella typhi bacteria could be obtained. The results of this study showed that fim-C primers for Salmonella typhi were amplified at 95 base pairs (bp) with an annealing temperature of 60ºC and a standard DNA concentration of 50 ng/µL. The results of the Real Time PCR confirmation test of Salmonellatyphi bacteria at a pre-denaturation of 3 minutes with a concentration of 10 pmol obtained the Ct value according to thestandard with previous studies. The Ct value obtained was 13.96 for S. typhi bacteria. Based on the results obtained, it can be conclude that the condition validation stage for pure cultures was successfully carried out by producing consistentand reproducible data.

Analyzing rice (Oryza sativa L.) panicle structure of 122 RILs using P-TRAP software for spikelet related traits

Panicle architecture is shown to have a direct impact on rice grain yield, making it an important characteristic for rice varietal improvement. In this study, we focus on the Panicle Trait Phenotyping Tool (P-TRAP), freely accessible, platform-independent software for analyzing rice panicle architecture, as one of the ways for generating comprehensive and reproducible panicle structure data and identifying superior breeding lines. In this analysis, the rice panicle structures of 122 RILs along with their parents were analyzed using P-TRAP software. P-TRAP assesses nine spikelet characteristics and its impact on grain yield. All traits had positive and significant associations and have a direct influence on grain yield. The findings indicate that most characteristics’ coefficients of variation had lower values, with the exception of the characteristics spikelet number, spikelet area and grain yield. In comparison to other traits, the standard error for the traits spikelet number and grain yield had a larger value. To better understand their genetic basis, these quantitative traits can be exploited in genome-wide association analyses. Hence, analyzing these traits is crucial. The P-TRAP tool is preferred because it reduces human effort and provides data in a shorter time span when compared to the traditional method. Keywords: Rice, spikelet, p-trap, grain yield

Better antimicrobial resistance data analysis and reporting in less time.

Insights about local antimicrobial resistance (AMR) levels and epidemiology are essential to guide decision-making processes in antimicrobial use. However, dedicated tools for reliable and reproducible AMR data analysis and reporting are often lacking. We aimed to compare traditional data analysis and reporting versus a new approach for reliable and reproducible AMR data analysis in a clinical setting. Ten professionals who routinely work with AMR data were provided with blood culture test results including antimicrobial susceptibility results. Participants were asked to perform a detailed AMR data analysis in a two-round process: first using their software of choice and next using our newly developed software tool. Accuracy of the results and time spent were compared between both rounds. Finally, participants rated the usability using the System Usability Scale (SUS). The mean time spent on creating the AMR report reduced from 93.7 to 22.4 min (P < 0.001). Average task completion per round changed from 56% to 96% (P < 0.05). The proportion of correct answers in the available results increased from 37.9% in the first to 97.9% in the second round (P < 0.001). Usability of the new tools was rated with a median of 83.8 (out of 100) on the SUS. This study demonstrated the significant improvement in efficiency and accuracy in standard AMR data analysis and reporting workflows through open-source software. Integrating these tools in clinical settings can democratize the access to fast and reliable insights about local microbial epidemiology and associated AMR levels. Thereby, our approach can support evidence-based decision-making processes in the use of antimicrobials.

Proteomics-based trapping with single or multiple inactive mutants reproducibly profiles histone deacetylase 1 substrates

Histone deacetylase 1 (HDAC1) plays a key role in diverse cellular processes. With the aberrant expression of HDAC1 linked to many diseases, including cancers, HDAC inhibitors have been used successfully as therapeutics. HDAC1 has been predominantly associated with histone deacetylation and gene expression. Recently, non-histone substrates have revealed diverse roles of HDAC1 beyond epigenetics. To augment discovery of non-histone substrates, we introduced “substrate trapping” to enrich HDAC1 substrates using an inactive mutant. Herein, we performed a series of proteomics studies to test the robustness of HDAC1 substrate trapping. Based on our recent results documenting that different HDAC1 mutants preferentially bound different substrates, which suggested that multiple mutants could be used for efficient trapping, trapping with three single point mutants simultaneously identified several potential substrates uniquely compared to a single mutant alone. However, a greater number of biologically interesting hits were observed using only a single mutant, which suggests that the C151A HDAC1 mutant is the optimal trap. Importantly, comparing independent trials with a single mutant performed by different experimentalists and HEK293 cell populations, trapping was robust and reproducible. Based on the reproducible trapping data, carnosine N-methyltransferase 1 (CARNMT1) was validated as an HDAC1 substrate. The data document that mutant trapping is an effective method for discovery of unanticipated HDAC substrates. SignificanceHistone deacetylase (HDAC) proteins are well established epigenetic transcriptional regulators that deacetylate histone substrates to control gene expression. More recently, deacetylation of non-histone substrates has linked HDAC activity to functions outside of epigenetics. Given the use of HDAC inhibitor drugs as anti-cancer therapeutics, understanding the full functions of HDAC proteins in cell biology is essential to future drug design. To discover unanticipated non-histone substrates and further characterize HDAC functions, inactive mutants have been used to “trap” putative substrates, which were identified with mass spectrometry-based proteomics analysis. Here multiple trapping studies were performed to test the robustness of using inactive mutants and proteomics for HDAC substrate discovery. The data confirm the value of trapping mutants as effective tools to discover HDAC substrates and link HDAC activity to unexpected biological functions.

Cellsnake: a user-friendly tool for single-cell RNA sequencing analysis.

Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptome data to understand the heterogeneity of cell populations at the single-cell level. The analysis of scRNA-seq data requires the utilization of numerous computational tools. However, nonexpert users usually experience installation issues, a lack of critical functionality or batch analysis modes, and the steep learning curves of existing pipelines. We have developed cellsnake, a comprehensive, reproducible, and accessible single-cell data analysis workflow, to overcome these problems. Cellsnake offers advanced features for standard users and facilitates downstream analyses in both R and Python environments. It is also designed for easy integration into existing workflows, allowing for rapid analyses of multiple samples. As an open-source tool, cellsnake is accessible through Bioconda, PyPi, Docker, and GitHub, making it a cost-effective and user-friendly option for researchers. By using cellsnake, researchers can streamline the analysis of scRNA-seq data and gain insights into the complex biology of single cells.

Automated image analysis of keratin 7 staining can predict disease outcome in primary sclerosing cholangitis.

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that obstructs the bile ducts and causes liver cirrhosis and cholangiocarcinoma. Efficient surrogate markers are required to measure disease progression. The cytokeratin 7 (K7) load in a liver specimen is an independent prognostic indicator that can be measured from digitalized slides using artificial intelligence (AI)-based models. A K7-AI model 2.0 was built to measure the hepatocellular K7 load area of the parenchyma, portal tracts, and biliary epithelium. K7-stained PSC liver biopsy specimens (n=295) were analyzed. A compound endpoint (liver transplantation, liver-related death, and cholangiocarcinoma) was applied in Kaplan-Meier survival analysis to measure AUC values and positive likelihood ratios for each histological variable detected by the model. The K7-AI model 2.0 was a better prognostic tool than plasma alkaline phosphatase, the fibrosis stage evaluated by Nakanuma classification, or K7 score evaluated by a pathologist based on the AUC values of measured variables. A combination of parameters, such as portal tract volume and area of K7-positive hepatocytes analyzed by the model, produced an AUC of 0.81 for predicting the compound endpoint. Portal tract volume measured by the model correlated with the histological fibrosis stage. The K7 staining of histological liver specimens in PSC provides significant information on disease outcomes through objective and reproducible data, including variables that cannot be measured by a human pathologist. The K7-AI model 2.0 could serve as a prognostic tool for clinical endpoints and as a surrogate marker in drug trials.

851 REPEATABILITY OF BOLUS AND CONTINUOUS THERMODILUTION FOR ASSESSING CORONARY MICROVASCULAR FUNCTION

Abstract Introduction The bolus thermodilution-derived index of microcirculatory resistance (IMR) has emerged over years as the standard of reference to invasively define coronary microvascular dysfunction (CMD). However, the technique still presents some limitations, mainly related to the fact that manual injection of saline bolus accounts for some variance in the measurements. Continuous intracoronary thermodilution has been recently introduced as a tool to directly quantify absolute coronary flow and microvascular resistance both at rest and during hyperemia and has shown to be safe and operator independent. Microvascular resistance reserve (MRR), derived from continuous thermodilution, has been validated as novel index specific for microcirculation and independent from myocardial mass. Purpose To compare head-to-head the intra-observer repeatability of bolus and continuous thermodilution for assessing microvascular function. Methods Patients undergoing coronary angiography in the absence of obstructive coronary artery disease were prospectively enrolled. Bolus and continuous intracoronary thermodilution measurements were performed in duplicates in the left anterior descending artery (LAD). Patients were randomly assigned in a 1:1 ratio to undergo first bolus thermodilution or first continuous thermodilution assessment. Results A total of 102 patients were enrolled. Average FFR was 0.86±0.06. Coronary Flow Reserve (CFR) calculated with continuous thermodilution (CFRthermo) was significantly lower than bolus thermodilution-derived CFR (CFRbolus) (2.63±0.65 and 3.29±1.17, respectively, p&amp;lt;0.001). CFRthermo showed a lower variability and a higher agreement than CFRbolus (variability 12.74 ± 10.41% vs 31.26±24.85%, respectively, p&amp;lt;0.001; ICC= 0.78 (0.70-0.85) and 0.48 (0.32-0.62), respectively, p&amp;lt;0.001, Figure 1). Both MRR and IMR showed a good agreement (ICC 0.81 (0.74-0.87) and 0.80 (0.71-0.86)) but the variability of the MRR was significantly lower (12.44 ± 10.06% vs 24.24±19.27, respectively, p&amp;lt;0.001, figure 1). Reproducibility data of all indices derived from duplicated measurements of bolus and continuous thermodilution are reported in Figure 2. Conclusion Continuous intracoronary thermodilution has a higher repeatability than bolus thermodilution in the assessment of CMD.

Community Survey Results Show that Standardisation of Preclinical Imaging Techniques Remains a Challenge

PurposeTo support acquisition of accurate, reproducible and high-quality preclinical imaging data, various standardisation resources have been developed over the years. However, it is unclear the impact of those efforts in current preclinical imaging practices. To better understand the status quo in the field of preclinical imaging standardisation, the STANDARD group of the European Society of Molecular Imaging (ESMI) put together a community survey and a forum for discussion at the European Molecular Imaging Meeting (EMIM) 2022. This paper reports on the results from the STANDARD survey and the forum discussions that took place at EMIM2022.ProceduresThe survey was delivered to the community by the ESMI office and was promoted through the Society channels, email lists and webpages. The survey contained seven sections organised as generic questions and imaging modality-specific questions. The generic questions focused on issues regarding data acquisition, data processing, data storage, publishing and community awareness of international guidelines for animal research. Specific questions on practices in optical imaging, PET, CT, SPECT, MRI and ultrasound were further included.ResultsData from the STANDARD survey showed that 47% of survey participants do not have or do not know if they have QC/QA guidelines at their institutes. Additionally, a large variability exists in the ways data are acquired, processed and reported regarding general aspects as well as modality-specific aspects. Moreover, there is limited awareness of the existence of international guidelines on preclinical (imaging) research practices.ConclusionsStandardisation of preclinical imaging techniques remains a challenge and hinders the transformative potential of preclinical imaging to augment biomedical research pipelines by serving as an easy vehicle for translation of research findings to the clinic. Data collected in this project show that there is a need to promote and disseminate already available tools to standardise preclinical imaging practices.

Influence of high temperature GPC/SEC detection modes on the accurate molar mass estimation of slurry phase multimodal HDPE reactor materials

In the present study, trimodal high-density polyethylene (tri-HDPE) slurry phase samples from the polymerization reactors were subjected to high temperature gel permeation chromatography/size exclusion chromatography (HT-GPC/SEC) analysis. The molecular weight (Mw) and its distributions were evaluated using commercially available HT-GPC instruments equipped with different detection modes (DRI, IR5 MCT, and light scattering). The SEC studies reveal the fact that there is a considerable challenge in generating a repeatable and reproducible data for the reactor grade HDPE samples which could be attributed to different detection modes employed and instability of HDPE reactor grades which are susceptible to undergo thermo-oxidative degradation during HT-GPC analysis. Among various instrumentation and detection modes investigated, the HT-GPC with IR5 MCT detection showed reliable results (%Error &lt;15) when compared with absolute Mw data obtained from MALS detection. Based on the findings, proposed suitable SEC mode to characterize the HDPE reactor grade polymer samples.

Low-cost in-situ luminescence: Opportunities for MOFs water vapor sensing

Luminescence is the most exploited phenomena for chemosensing. Real-time measurements must be accurate and reproducible along cycles for the selection of the best sensing materials. At the same time, sample degradation by excitation-light irradiation must be kept minimal. Last but not least, expensive equipment should be avoided, whenever possible. In this work, we present a low-cost in-situ luminescence measuring experimental array designed for solid-state sensing of vapors. It allows repeated activation and subsequent guest entrance along many measuring cycles. A common halogen lamp is used as heating element for the activation of the sample under vacuum in a sealed aluminium chamber. Then, vapor guest molecules are forced into the chamber by a laboratory vacuum pump. Luminescence is LED-excited and measured by a portable reflectance probe connected to a miniature spectrometer. The chosen case study is a luminescent polycrystalline metal-organic framework composed by europium(III), zinc(II) and oxydiacetate ligand. This compound has a honeycomb porous structure that can be repeatedly activated and rehydrated. The associated structural distortion is accompanied by a remarkable variation in the luminescence profile with minimal chemical degradation or crystallinity loss. Results are analyzed and critically compared to those obtained by conventional luminescence. They show that, while conventional luminescence struggles to provide reproducible absolute results, the presented method stands as a simple, robust and trustworthy approach, in which accurate and reproducible in- situ data can be obtained along many cycles. Furthermore, the presented experimental array comparatively minimizes the light-induced degradation of the tested materials. The effect of glue-immobilizing the sample is also addressed using the presented method. • A low-cost in-situ luminescence array for solid-state sensing is designed • Performance of the new array is compared to conventional luminescence • A water-sensitive luminescent Eu-Zn MOF is explored as case-study • Accurate and reproducible data can be obtained along multiple cycles • Light-induced degradation of the tested materials is reduced

Challenges of Comparing Marine Microbiome Community Composition Data Provided by Different Commercial Laboratories and Classification Databases

In the high-throughput sequencing (HTS) era, a metabarcoding technique based on the bacterial V3–V4 hypervariable region of 16S rRNA analysis requires sophisticated bioinformatics pipelines and validated methods that allow researchers to compare their data with confidence. Many commercial laboratories conduct extensive HTS analyses; however, there is no available information on whether the results generated by these vendors are consistent. In our study, we compared the sequencing data obtained for the same marine microbiome community sample generated by three commercial laboratories. Additionally, as a sequencing control to determine differences between commercial laboratories and two 16S rRNA databases, we also performed a “mock community” analysis of a defined number of microbial species. We also assessed the impact of the choice of two commonly used 16S rRNA databases, i.e., Greengenes and SILVA, on downstream data analysis, including taxonomic classification assignment. We demonstrated that the final results depend on the choice of the laboratory conducting the HTS and the reference database of ribosomal sequences. Our findings showed that the number of produced ASVs (amplicon sequence variants) ranged from 137 to 564. Different putative bacterial endosymbionts could be identified, and these differences correspond to the applied 16S rRNA database. The results presented might be of particular interest to researchers who plan to perform microbiome community analysis using the 16S rRNA marker gene, including the identification of putative bacterial endosymbionts, and serve as a guide for choosing the optimum pipeline to obtain the most accurate and reproducible data.

Open Science Infrastructure as a key component of Open Science

The Open Science movement is a response to the accumulated problems in scholarly communication, like the "reproducibility crisis", "serials crisis", and "peer review crisis". The European Commission defines priorities of Open Science as Findable, Accessible, Interoperable and Reproducible (FAIR) data, infrastructure and services in the European Open Science Cloud (EOSC), Next generation metrics, altmetrics and rewards, the future of scientific communication, research integrity and reproducibility, education and skills and citizen science. Open Science Infrastructure is also one of four key components of Open Science defined by UNESCO. Mainly represented among Open Science Infrastructures are institutional and thematic repositories for publications, research data, software and code. Furthermore, the Open Science Infrastructure services range may include discovery, mining, publishing, the peer review process, archiving and preservation, social networking tools, training, high-performance computing, and tools for processing and analysis. Successful Open Science Infrastructure should be based on community values and responsive to needed changes. Preferably the Open Science Infrastructure should be distributed, enabling machine-actionable tools and services, supporting reusability and reproducibility, quality FAIR data, interoperability, sustainability, long-term preservation and funding.

EOdal: An open-source Python package for large-scale agroecological research using Earth Observation and gridded environmental data

Earth Observation by means of remote sensing imagery and gridded environmental data opens tremendous opportunities for systematic capture, quantification and interpretation of plant–environment interactions through space and time. The acquisition, maintenance and processing of these data sources, however, requires a unified software framework for efficient and scalable integrated spatio-temporal analysis taking away the burden of data and file handling from the user. Existing software products either cover only parts of these requirements, exhibit a high degree of complexity, or are closed-source, which limits reproducibility of research. With the open-source Python library EOdal (Earth Observation Data Analysis Library) we propose a novel software that enables the development of fully reproducible spatial data science chains through the strict use of open-source developments. Thanks to its modular design, EOdal enables advanced data warehousing especially for remote sensing data, sophisticated spatio-temporal analysis and intersection of different data sources, as well as nearly unlimited expandability through application programming interfaces (APIs).

Validation of the Assurance® GDS for Cronobacter Tq II in Infant Formulas, Infant Cereals, Ingredients, and Environmental Samples Collaborative Study: First Action 2021.08.

The Assurance® GDS for Cronobacter Tq II assay is a nucleic acid amplification system for the qualitative detection of Cronobacter. The method uses an upfront concentration of the target organism from the enrichment by immunomagnetic separation (IMS) using the PickPen®device. The Assurance GDS for Cronobacter Tq II method was evaluated for Official Methods of AnalysisSM certification. The matrix was compared to the ISO 22964:2017: Microbiology of the Food Chain-Horizontal Method for the Detection of Cronobacter spp. standard and using an alternative confirmation procedure. The alternative method was evaluated using 10 g test portions in an unpaired study design for powdered infant formula (milk based with iron and DHA) containing probiotics. Eleven technicians from eight laboratories located within the United States and Europe participated in the collaborative study. Statistical analysis was conducted according to the probability of detection (POD) statistical model as presented in the AOAC validation guidelines. The difference in laboratory POD (dLPODC) values with 95% confidence intervals across collaborators was calculated for each level between the candidate and reference method results and between the candidate presumptive and confirmed results. Results obtained for the low inoculum level test portions produced a dLPOD value with a 95% confidence interval of 0.03 (-0.18, 0.15). The dLPOD results indicate equivalence between the candidate method and reference method for the matrix evaluated. The method also demonstrated acceptable inter-laboratory reproducibility as determined in the collaborative evaluation. There were no false negative results; the false positive rate was determined and produced a value of <2%. Based on the data generated, the method demonstrated Assurance GDS for Cronobacter Tq II assay produced acceptable interlaboratory reproducibility data and statistical analysis. The Assurance GDS for Cronobacter Tq II method is suitable for the rapid qualitative detection of Cronobacter in infant formulas, infant cereals, ingredients, and environmental samples.