High-quality Test Research Articles

Ultralow Magnetic Susceptibility in Pure and Fe(Bi)-doped Au-Pt Alloys Improved by Structural Strain Regulation.

Designing and manufacturing multi-component alloy samples with ultralow magnetic susceptibility χ (<10-6 cm3/mol) is crucial for producing high-quality test masses to successfully detect gravitational wave in the LISA and TianQin projects. Previous research has idenfified AuPt alloys as a potential candidate for test masses, capable of achieving ultralow magnetic susceptibility that meets the requirements from both theoretical and experimental perspectives. In this study, we discover that the structural strain regulation (i.e., tensile and stress) can effectively optimize and further reduce the ultralow magnetic susceptibility of AuPt allpys, while fully understanding their underlying physical mechanisms. More importantly, even when doped with trace elements such as Fe or Bi impurity, strain regulation can still effectively reduce the magnetic susceptibility of the doped AuPt alloy to the desired range. Our theoretical calculations also reveal that, when the strain ratio η is controlled within in a relatively small range (< 2.0%), the regulaton effect on the ultralow magnetic susceptibilities of pure or doped-AuPt alloys remains significant. This property is beneficial for achieving ultralow or even near-zero magnetic susceptibility in real AuPt alloy samples.

Monotonic and cyclic lateral loading of piles in low- to medium-density chalk

Offshore and other structures often rely on driven piles to carry lateral loads. However, there is currently no established design method to cover lateral loading at chalk sites, which are widespread across Northwest Europe. This paper reports monotonic and cyclic lateral load tests on highly instrumented 508 mm and 1220 mm diameter. open steel piles driven at a well-characterised chalk test site in Kent, UK, for a recent joint industry project that developed new benchmark datasets and analyses, supported by high-quality testing. The ultimate lateral pressures mobilised in the chalk are shown to be relatively low compared to its uniaxial compressive strengths (UCS) due to pile-driving damage, natural fracturing, local yielding and brittleness. Significant gaps opened between the piles and chalk during loading, that led to a substantially softer response on unloading and subsequent reloading, as well as marked axial capacity losses. Reaction curves extracted from the field measurements and applied in a one-dimensional numerical model perform well in reproducing the monotonic lateral tests. As with piles driven in other materials, one-way cyclic lateral loading led to permanent displacement accumulation and stiffness changes that were linked to the cyclic loading parameters. Both effects were more marked under biaxial cyclic lateral loading.

WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models

Compiler correctness is crucial, as miscompilation can falsify program behaviors, leading to serious consequences over the software supply chain. In the literature, fuzzing has been extensively studied to uncover compiler defects. However, compiler fuzzing remains challenging: Existing arts focus on black- and grey-box fuzzing, which generates test programs without sufficient understanding of internal compiler behaviors. As such, they often fail to construct test programs to exercise intricate optimizations. Meanwhile, traditional white-box techniques, such as symbolic execution, are computationally inapplicable to the giant codebase of compiler systems. Recent advances demonstrate that Large Language Models (LLMs) excel in code generation/understanding tasks and even have achieved state-of-the-art performance in black-box fuzzing. Nonetheless, guiding LLMs with compiler source-code information remains a missing piece of research in compiler testing. To this end, we propose WhiteFox, the first white-box compiler fuzzer using LLMs with source-code information to test compiler optimization, with a spotlight on detecting deep logic bugs in the emerging deep learning (DL) compilers. WhiteFox adopts a multi-agent framework: (i) an LLM-based analysis agent examines the low-level optimization source code and produces requirements on the high-level test programs that can trigger the optimization; (ii) an LLM-based generation agent produces test programs based on the summarized requirements. Additionally, optimization-triggering tests are also used as feedback to further enhance the test generation prompt on the fly. Our evaluation on the three most popular DL compilers (i.e., PyTorch Inductor, TensorFlow-XLA, and TensorFlow Lite) shows that WhiteFox can generate high-quality test programs to exercise deep optimizations requiring intricate conditions, practicing up to 8 times more optimizations than state-of-the-art fuzzers. To date, WhiteFox has found in total 101 bugs for the compilers under test, with 92 confirmed as previously unknown and 70 already fixed. Notably, WhiteFox has been recently acknowledged by the PyTorch team, and is in the process of being incorporated into its development workflow. Finally, beyond DL compilers, WhiteFox can also be adapted for compilers in different domains, such as LLVM, where WhiteFox has already found multiple bugs.

SMT2Test: From SMT Formulas to Effective Test Cases

One of the primary challenges in software testing is generating high-quality test inputs and obtaining corresponding test oracles. This paper introduces a novel methodology to mitigate this challenge in testing program verifiers by employing SMT (Satisfiability Modulo Theories) formulas as a universal test case generator. The key idea is to transform SMT formulas into programs and link the satisfiability of the formulas with the safety property of the programs, allowing the satisfiability of the formulas to act as a test oracle for program verifiers. This method was implemented as a framework named SMT2Test, which enables the transformation of SMT formulas into Dafny and C programs. An intermediate representation was designed to augment the flexibility of this framework, streamlining the transformation for other programming languages and fostering modular transformation strategies. We evaluated the effectiveness of SMT2Test by finding defects in two program verifiers: the Dafny verifier and CPAchecker. Utilizing the SMT2Test framework with the SMT formulas from the SMT competition and SMT solver fuzzers, we discovered and reported a total of 14 previously unknown defects in these program verifiers that were not found by previous methods. After reporting, all of them have been confirmed, and 6 defects have been fixed. These findings show the effectiveness of our method and imply its potential application in testing other programming language infrastructures.

NLPLego: Assembling Test Generation for Natural Language Processing Applications

With the development of Deep Learning, Natural Language Processing (NLP) applications have reached or even exceeded human-level capabilities in certain tasks. Although NLP applications have shown good performance, they can still have bugs like traditional software and even lead to serious consequences. Inspired by Lego blocks and syntax structure analysis, we propose an assembling test generation method for NLP applications or models and implement it in NLPLego . The key idea of NLPLego is to assemble the sentence skeleton and adjuncts in order by simulating the building of Lego blocks to generate multiple grammatically and semantically correct sentences based on one seed sentence. The sentences generated by NLPLego have derivation relations and different degrees of variation. These characteristics make it well-suited for integration with metamorphic testing theory, addressing the challenge of test oracle absence in NLP application testing. To validate NLPLego , we conduct experiments on three commonly used NLP tasks (i.e., machine reading comprehension, sentiment analysis, and semantic similarity measures), focusing on the efficiency of test generation and the quality and effectiveness of generated tests. We select five advanced NLP models and one popular industrial NLP software as the tested subjects. Given seed tests from SQuAD 2.0, SST, and QQP, NLPLego successfully detects 1,732, 3,140, and 261,879 incorrect behaviors with around 93.1% precision in three tasks, respectively. The experiment results show that NLPLego can efficiently generate high-quality tests for multiple NLP tasks to detect erroneous behaviors effectively. In the case study, we analyze the testing results provided by NLPLego to obtain intuitive representations of the different NLP capabilities of the tested subjects. The case study confirms that NLPLego can provide developers with clarity on the direction to improve NLP models or applications, laying the foundation for enhancing performance.

Standardization through education of molecular pathology: a spotlight on the European Masters in Molecular Pathology.

Despite advancements in precision medicine, many cancer patients globally, particularly those in resource-constrained environments, face significant challenges in accessing high-quality molecular testing and targeted therapies. The considerable heterogeneity in molecular testing highlights the urgent need to harmonize practices across Europe and beyond, establishing a more standardized and consistent approach in MP laboratories. Professionals, especially molecular pathologists, must move beyond traditional education to cope with this heterogeneity. This perspective addresses critical issues in molecular pathology (MP), such as limited access to high-quality molecular testing, leading to disparities in cancer treatment, and the consequences of inconsistent practices. Recognizing the necessity for a standardized framework for education to address these issues, educational programs play a pivotal role in updating professionals' skills to achieve standardization in MP. European experts from the Steering Committee, the Pathology Section of the European Union of Medical Specialists, and the European Society of Pathology have proposed creating a comprehensive Master's degree program called the "European Masters in Molecular Pathology" (EMMP). This program emerges as a strategic response to the demand for a specialized and standardized framework for education in MP, catering to professionals who concurrently work and study. The program's design aligns with evidence-based education methods, ensuring effective learning and engagement while integrating computational pathology to analyze complex molecular data, enhance diagnostic accuracy, and improve treatment outcomes. EMMP's structured curriculum, strategic partnerships, and regular updates underscore its significance in standardizing MP practices. Exploring future developments, this perspective delves into technology integration and interdisciplinary collaboration, anticipating ongoing advances and harmonization. Challenges and future directions in MP education are discussed, emphasizing the necessity for dynamic curriculum updates, seamless technology integration, and interdisciplinary cooperation. This perspective underscores EMMP's pivotal role in preparing pathologists for this dynamic field, advocating continuous advancements in education and training to uphold excellence in MP practices and maintain the highest patient care standards.

B-185 A Comparison Between Two Oral Sample Collection Devices on Preserving High-Quality Genomic DNA

Abstract Background High-quality genomic testing significantly enhances the knowledge on gene-disease association, and greatly benefits future biomedical research and precision medicine. However, widely used saliva sample collection presents challenges with high-quality and quantity of human DNA and high bacterial DNA ratio. In this study, we validated the performance of two commercially available oral collection devices for their ability to preserve high-quality genomic DNA long-term at room temperature. Methods Fifty-one buccal samples in total from twelve donors were collected using two kinds of commercially available oral sample collection devices (iSWAB®-DNA (Device 1) and OCD-100 (Device 2)). All of the collected samples were stored at ambient temperature for up to 1 year. Collected samples were extracted with QIAamp Blood Mini Kit (Qiagen) at baseline, 30 days, 60 days, and 1 year. All DNA yields were measured using a Nanodrop One Microvolume UV-Vis Spectrophotometer; TapeStation System was used for determining the integrity of DNA. The bacterial DNA ratio was estimated by quantitative PCR with primers targeting the 16S rRNA gene known to be present in prokaryotes but not eukaryotes. Results Device 1 had higher yield (Device 1: 18.59 ± 8.98; Device 2: 6.44 ± 4.62), lower bacterial DNA ratio (Device 1: 2.06 ± 1.93; Device 2: 3.79 ± 4.58), and higher overall DNA integrity (Device 1: 6.1 ± 0.31; Device 2: 5.42 ± 0.96) compared to Device 2. Device 1 demonstrated longer post-collection sample stability at ambient temperature compared with Device 2 regarding DNA yield, bacterial DNA ratio and DNA integrity. No significant changes in DNA yield, integrity, and bacterial DNA ratio were observed after one year of storage in Device 1. No significant changes in DNA yield, integrity, and bacterial DNA ratio were observed after 60 days of storage in Device 2. However, Device 2 showed a significant increase in DNA yield, bacterial DNA ratio, and DNA integrity comparing baseline to 1 year. Conclusions Device 1 preserves higher quantity and quality of DNA from oral samples at ambient temperature for up to 1 year. These advantages significantly enhance efficiency and cost-effectiveness in downstream applications such as genotyping and sequencing, making it a valuable tool in genomics research with a commendable success rate.

The foundation for the microbiology laboratory's essential role in diagnostic stewardship: an ASM Laboratory Practices Subcommittee report.

Diagnostic stewardship (DxS) has gained traction in recent years as a cross-disciplinary method to improve the quality of patient care while appropriately managing resources within the healthcare system. Clinical microbiology laboratorians have been highly engaged in DxS efforts to guide best practices with conventional microbiology tests and more recently with molecular infectious disease diagnostics. Laboratories can experience resistance to their role in DxS, especially when the clinical benefits, motivations for interventions, and underlying regulatory requirements are not clearly conveyed to stakeholders. Clinical laboratories must not only ensure ethical practices but also meet obligatory requirements to steward tests responsibly. In this review, we aim to support clinical microbiology laboratorians by providing the background and resources that demonstrate the laboratory's essential role in DxS. The heart of this review is to collate regulatory and accreditation requirements that, in essence, mandate DxS practices as a long-standing, core element of high-quality laboratory testing to deliver the best possible patient care. While examples of the clinical impact of DxS are plentiful in the literature, here, we focus on the operational and regulatory justification for the laboratory's role in stewardship activities.

Will Transformers change gastrointestinal endoscopic image analysis? A comparative analysis between CNNs and Transformers, in terms of performance, robustness and generalization

Gastrointestinal endoscopic image analysis presents significant challenges, such as considerable variations in quality due to the challenging in-body imaging environment, the often-subtle nature of abnormalities with low interobserver agreement, and the need for real-time processing. These challenges pose strong requirements on the performance, generalization, robustness and complexity of deep learning-based techniques in such safety–critical applications. While Convolutional Neural Networks (CNNs) have been the go-to architecture for endoscopic image analysis, recent successes of the Transformer architecture in computer vision raise the possibility to update this conclusion. To this end, we evaluate and compare clinically relevant performance, generalization and robustness of state-of-the-art CNNs and Transformers for neoplasia detection in Barrett’s esophagus. We have trained and validated several top-performing CNNs and Transformers on a total of 10,208 images (2,079 patients), and tested on a total of 7,118 images (998 patients) across multiple test sets, including a high-quality test set, two internal and two external generalization test sets, and a robustness test set. Furthermore, to expand the scope of the study, we have conducted the performance and robustness comparisons for colonic polyp segmentation (Kvasir-SEG) and angiodysplasia detection (Giana). The results obtained for featured models across a wide range of training set sizes demonstrate that Transformers achieve comparable performance as CNNs on various applications, show comparable or slightly improved generalization capabilities and offer equally strong resilience and robustness against common image corruptions and perturbations. These findings confirm the viability of the Transformer architecture, particularly suited to the dynamic nature of endoscopic video analysis, characterized by fluctuating image quality, appearance and equipment configurations in transition from hospital to hospital. The code is made publicly available at: https://github.com/BONS-AI-VCA-AMC/Endoscopy-CNNs-vs-Transformers.

Proficiency Testing Customization in Clinical Trials: How the pSMILE Project Ensures High-Quality Proficiency Testing Coverage for International Laboratories.

Proficiency Testing Customization in Clinical Trials: How the pSMILE Project Ensures High-Quality Proficiency Testing Coverage for International Laboratories.

QuanTest: Entanglement-Guided Testing of Quantum Neural Network Systems

Quantum Neural Network (QNN) combines the Deep Learning (DL) principle with the fundamental theory of quantum mechanics to achieve machine learning tasks with quantum acceleration. Recently, QNN systems have been found to manifest robustness issues similar to classical DL systems. There is an urgent need for ways to test their correctness and security. However, QNN systems differ significantly from traditional quantum software and classical DL systems, posing critical challenges for QNN testing. These challenges include the inapplicability of traditional quantum software testing methods to QNN systems due to differences in programming paradigms and decision logic representations, the dependence of quantum test sample generation on perturbation operators, and the absence of effective information in quantum neurons. In this paper, we propose QuanTest, a quantum entanglement-guided adversarial testing framework to uncover potential erroneous behaviors in QNN systems. We design a quantum entanglement adequacy criterion to quantify the entanglement acquired by the input quantum states from the QNN system, along with two similarity metrics to measure the proximity of generated quantum adversarial examples to the original inputs. Subsequently, QuanTest formulates the problem of generating test inputs that maximize the quantum entanglement adequacy and capture incorrect behaviors of the QNN system as a joint optimization problem and solves it in a gradient-based manner to generate quantum adversarial examples. Experimental results demonstrate that QuanTest possesses the capability to capture erroneous behaviors in QNN systems (generating 67.48%-96.05% more high-quality test samples than the random noise under the same perturbation size constraints). The entanglement-guided approach proves effective in adversarial testing, generating more adversarial examples (maximum increase reached 21.32%).

Challenges in Practical Button Cell Testing for Hydrogen Production from High Temperature Electrolysis of Water

High temperature electrolysis of water using solid oxide electrochemical cells (SOEC) is a promising technology for hydrogen production with high energy efficiency and may promote decarbonization when coupled with renewable energy sources and excess heat from nuclear reactors. Over the past several decades there have been extensive scientific and engineering studies on cell materials and degradation behaviors that have greatly improved current density, decreased total resistance, and lowered degradation rates. Although the technology is now at a near-commercial level, maintaining consistency in cell testing and minimizing variance in practical testing environments is an often overlooked but crucial topic. To promote high quality data collection, testing procedures and balance of plant component details are extremely important to consider. This work discusses some key factors affecting the reproducibility of practical SOEC testing on the button cell level, namely current collection layers, cell sealing procedures, the reliability of steam and hydrogen delivery systems, cell testing fixture design, and reduction procedures. To provide a baseline and a level of standardization for the SOEC community, this work also discloses details of the standard operating procedure and techniques adopted for o-SOEC testing at Idaho National Laboratory (INL).

Improving Private Well Testing Programs: Experimental Evidence from Iowa.

Approximately 23 million U.S. households rely on private wells for drinking water. This study first summarizes drinking water behaviors and perceptions from a large-scale survey of households that rely on private wells in Iowa. Few households test as frequently as recommended by public health experts. Around 40% of households do not regularly test, treat, or avoid their drinking water, suggesting pollution exposure may be widespread among this population. Next, we utilize a randomized control trial to study how nitrate test strips and information about a free, comprehensive water quality testing program influence households' behaviors and perceptions. The intervention significantly increased testing, including high-quality follow-up testing, but had limited statistically detectable impacts on other behaviors and perceptions. Households' willingness to pay for nitrate test kits and testing information exceeds program costs, suggesting that the intervention was welfare-enhancing.

Threshold strain for the accumulation of excess pore-water pressure in saturated coral sands under complex cyclic loading patterns

The liquefaction of coral sands caused by the accumulation of excess pore-water pressure is a major factor contributing to catastrophic events on coral reefs, and accurately estimating this excess pore-water pressure accumulation holds significant importance. High-quality laboratory test results are essential for analytical or numerical calculations. In this study, a new test method is employed to conduct a series of undrained, multi-staged, stress-controlled multidirectional hollow cylinder tests on saturated coral sand under complex loading conditions. The concept of threshold strain (γt) and the method for determining γt of saturated coral sand specimens under complex loading conditions are proposed. The test results demonstrate that γt of saturated coral sand remains insensitive to cyclic loading conditions (including frequency, stress path, and mode) but increases with increasing relative density. The range of volumetric threshold strain, degradation threshold strain, and flow threshold for saturated coral sand under different initial states and cyclic loading conditions are 0.0183%–0.0341%, 0.0242%–0.0454%, and 1.006%–1.614%, respectively. This research provides a novel approach for accurately determining input parameters required for resolving and implementing coupled models in numerical modeling.

Minute level ultra-rapid and thousand copies level high-sensitive pathogen nucleic acid identification based on contactless impedance detection microsensor

Early screening for pathogens is crucial during pandemic outbreaks. Nucleic acid testing (NAT) is a valuable method for keeping pathogens from spreading. However, the long detection time and large size of the instruments involved significantly limited the efficiency of detection. This work described an integrated NAT microsensor that facilitated rapid and extremely sensitive detection based on nucleic acid amplification (NAA) on a chip. The biochip consisted of two layers incorporating a heater, a thermometer, an interdigital electrode (IDE) and a reaction chamber. The Pt electrode based heater and thermometer were utilized to maintain a specific temperature for the sample in the chamber. The thermometer exhibited a good linear correlation with a sensitivity of 9.36 Ω/°C and the heater achieved a heating efficiency of approximately 6.5 °C/s. Multiple ions were released during NAA, resulting in a decrease in the impedance of the amplification system solution. A large signal of impedance was generated by the released ions due to its linear correlation with the logarithm of the ion concentration. With this detection principle, IDE was employed for real-time monitoring of the in-chip reaction system impedance and NAA process. Specific nucleic acids from two pathogens (SARS-CoV-2, Vibrio vulnificus) were detected with this microsensor. The samples were qualitatively analyzed on microchip within 3 min, with a limit of detection (LOD) of 103 copies/μL. The proposed sensor presented several advantages, including reduced NAT time and increased sensitivity. Consequently, it has shown significant potential in rapid and high-quality nucleic acid testing for the field of epidemic prevention.

Implementation of anterior segment ophthalmic telemedicine.

The growing push to integrate telemedicine into ophthalmic practices requires physicians to have a thorough understanding of ophthalmic telemedicine's applications, limitations, and recent advances in order to provide well tolerated and appropriate clinical care. This review aims to provide an overview of recent advancements in the use of ophthalmic telemedicine for anterior segment eye examinations. Virtual care for anterior segment evaluation relies on appropriate technology, novel workflows, and appropriate clinical case selection. Recent advances, particularly in the wake of the COVID-19 pandemic, have highlighted the utility of home-based assessments for visual acuity, external evaluation, tonometry, and refraction. Additionally, innovative workflows incorporating office-based testing into virtual care, termed 'hybrid telemedicine', enable high-quality ophthalmic testing to inform clinical decision-making. Novel digital tools and workflows enable high-quality anterior segment evaluation and management for select ophthalmic concerns. This review highlights the clinical tools and workflows necessary to enable anterior segment telehealth.

Decentralised [formula omitted] robust control of MTMDs for mitigating vibration of a slender MDOF floor configuration

In this research, a novel robust decentralised H (H∞ and H2) design combined with a pattern search method (PSM), incorporating considerations of multiple modal dynamics, was developed to address the vibration issue in slender structures. To verify the effectiveness of the concept, a full-scale laboratory reconfigurable multiple degrees of freedom (MDOF) test-bed floor panel structure was selected, equipping with a passive multiple-tuned mass damper (MTMD) as a coupling system. The optimal parameters of the MTMDs were tailored using the decentralised H design and the PSM, considering the dynamic engagement of multiple modes. Furthermore, a crucifix-type configuration of 2DOF MTMDs was proposed to mitigate the vibration of the floor panel structure. Full-scale forced vibration testing (FVT) was conducted to validate the dynamic performance of the 2DOF MTMDs. The test results provide satisfactory verification and high-quality curve-fitting testing data for parameter study and dynamic simulation. By examining the field data from the 2DOF MTMDs and performing various numerical simulations and analyses, the control performance robustness of the MTMDs was comprehensively evaluated, considering practical engineering variations such as the number and stiffness of MTMDs, as well as the stiffness and mass of the floor panel structure. As a result, the proposed H control strategies for the MTMDs demonstrated robust control capabilities by effectively dissipating significant amounts of externally applied energy.

Towards assessing the quality of knowledge graphs via differential testing

Knowledge graphs (KG) can aggregate data and make information resources easier to calculate and understand. With tremendous advancements in knowledge graphs, they have been incorporated into plenty of software systems to assist various tasks. However, while KGs determine the performance of downstream software systems, their quality is often measured by the accuracy of test data. Considering the limitation of accessible high-quality test data, an automated quality assessment technique could fundamentally improve the testing efficiency of KG-driven software systems and save plenty of manual labeling resources.In this paper, we propose an automated approach to quantify the quality of KGs via differential testing. It first constructs multiple Knowledge Graph Embedding Models (KGEM) and conducts head prediction tasks on models. Then, it can produce a differential score that reflects the quality of KGs by comparing the proximity of output results. To validate the effectiveness of this approach, we experiment with four open-sourced knowledge graphs. The experiment results show that our approach is capable of accurately evaluating the quality of KGs and producing reliable results on different datasets. Moreover, we compared our method with existing methods and achieved certain advantages. The potential usefulness of our approach sheds light on the development of various KG-driven software systems.

Severely compromised supply of patch test allergens in Europe hampers adequate diagnosis of occupational and non-occupational contact allergy. A European Society of Contact Dermatitis (ESCD), European Academy of Allergy and Clinical Immunology (EAACI), European Academy of Dermatology and Venereology (EADV) task forces 'Contact Dermatitis' and 'Occupational Skin Disease' position paper.

Patch testing is the only clinically applicable diagnostic method for Type IV allergy. The availability of Type IV patch test (PT) allergens in Europe, however, is currently scarce. This severely compromises adequate diagnostics of contact allergy, leading to serious consequences for the affected patients. Against this background, the European Society of Contact Dermatitis (ESCD) has created a task force (TF) (i) to explore the current availability of PT substances in different member states, (ii) to highlight some of the unique characteristics of Type IV vs. other allergens and (iii) to suggest ways forward to promote and ensure availability of high-quality patch testing substances for the diagnosis of Type IV allergies throughout Europe. The suggestions of the TF on how to improve the availability of PT allergens are supported by the ESCD, the European Academy of Allergy and Clinical Immunology, and the European Academy of Dermatology and Venereology and intend to provide potential means to resolve the present medical crisis.

Continuous quality evaluation of the Asanté rapid test for recent infection for robust kit lot quality verification.

The Sedia Biosciences Asanté rapid test for recent infection (RTRI) can identify HIV infections and characterize HIV-1 as recent or long-term infection via the positive verification (V) line and long-term line (LT) line, respectively. Tracking with Recency Assays to Control the Epidemic (TRACE) program uses RTRI assays. Successful implementation of TRACE requires high-quality test performance. The goal of this study is to evaluate the additional quality practices established for new kit lots prior to field use. Asanté lot quality control data from the manufacturer is reviewed by the Centers for Disease Control and Prevention International Laboratory Branch (CDC-ILB) in the Division of Global HIV and TB using. If a lot passes manufacturer quality control and CDC-ILB review, test kits are sent to CDC-ILB for further evaluation. Evaluation by CDC includes inter-rater reliability and linear regressions comparing the V and LT lines against reference data as well as V and LT line data between testers. A Bland-Altman analysis was conducted to assess bias and systematic error. Overall, CDC-ILB passed 29 (91%) out of 32 Sedia Biosciences Asanté kit lots that initially passed manufacturing quality control from July 2017 to May 2020. Regression analyses demonstrate that test kits are performing as expected with consistent R2≥0.92 for both V and LT lines. On average, inter-rater reliability kappa was 0.9, indicating a strong level of agreement. Bland-Altman analyses demonstrate high agreement with little to no systematic error and bias. Ongoing evaluation of new RTRI kit lots is important to ensure high quality test performance. Rejecting 9% of kit lots highlight the importance of continuing to work with manufacturers to ensure consistent kit production and quality assurance (QA) activities. Investing in effective QA measures, conducting both pre- and post-market performance data reviews, could help improve RTRI accuracy and outcomes in similar testing programs.