Hit Rate Research Articles

Innovative measures of student management by counselors in higher education institutions under a big data environment

Abstract Along with the rapid development of big mobile data and the popularization and deepening of education informatization, big data is an irresistible and inescapable new environment and new opportunity, and the way of student management by counselors in higher education institutions is constantly developing and changing. Based on this, the use of big data to promote the innovation of counselor student management in higher education institutions is an inevitable issue to deepen the comprehensive reform in the field of education and a real issue in front of the majority of educators. In this study, the performance of the K-Means algorithm and PageRank are tested from the perspective of big data to find the most effective ways and methods to improve the counselors’ student management work, and the performance and hit rate of each strategy is tested in the application of K-Means. This means application, the LRFU strategy (step = 0) also achieved relatively good performance, using HiBench to run PageRank algorithm test cases. The results showed that the LRU strategy in the PageRank test case also improved the hit rate by 25% compared with other strategies and the hit rate by 13% compared with the LRFU strategy (step = 0). Student management efficiency. Higher education institutions can reform counselors’ student management methods by establishing a large student database management center, strengthening big data analysis, improving the ability of student data mining, processing, and management work, and striving to maximize the efficiency of student management data and information.

Knee pain among athletes influenced by several factors at Bangladesh

Objectives: To expose the injured participant’s age, training duration and sporting event, identify other associated knee injuries, extract the types of injury, identify recurrence of injuries among the athlete, and know about the management of injury (medication, physiotherapy or both). Methods: A quantitative cross-sectional study design was chosen to achieve the objectives of the study. 40 subjects were selected through convenience sampling technique from the athlete, who trained in BKSP by using a structural questionnaire to collect data. Results: The factors of knee pain 78% (n=29) among the 40 participants. Among the injured participants most affected age was 17, 20.0% (n=8). Male athlete are more affected at the percentage of 77.5% (n=31). 42.5% (n=17) participants are suffered from ACL injury who are trainees of athletics and 41% (n=16) participants are injured whom training event is 2 years. Along with ACL injury 10.0% (n=4) participants are suffered from meniscus injury. 90.0% (n=36) injured participants are affected by direct hit and recurrence rate is 10.0% (n=4). Among 40 affected participants 41% (n=16) participants do their warm up cool down activities for 15 to 20 minutes and most of them about 60.0% (n=24) taken both medicines and physiotherapy treatment. Conclusion: The vulnerable age range 15-20 was frequent injury occurring among Athlete and noticeably flexibility and overuse are the key issues to cause of injury. Health education and perform regular physical activity along with physio therapeutic exercises can prevent injury.

Qualification of DIRICH readout chain

The DIRICH front-end board (FEB) is a 32+1 channel FPGA based TDC readout module for Multianode Photomultipliers (MAPMT), and also Multianode MCPs. Due to its low cost and excellent timing precision in single photon measurement applications it will be used in the RICH detector of the Compressed Baryonic Matter (CBM) experiment at the future FAIR facility at GSI Darmstadt, Germany.In the regions of highest photon density at the CBM RICH one expects an average hit rate of ≃300 kHz per MAPMT pixel (6 mm × 6 mm) and ∼15% occupancy for minimum bias Au+Au collisions at 35 A GeV at an interaction rate of 10 MHz. In order to validate the high-rate capability of the DIRICH readout, a dedicated lab setup simulating realistic detector signals by employing a pulsed picosecond laser light source in combination with a constant current driven LED was commissioned. In this paper the setup is introduced and first results are discussed. We show that individual readout channels can withstand photon rates up to 2.2 MHz per pixel, limited only by maximum data rate capability and buffer size on the front-end board. Using the same setup, also effects of high photon occupancy on the MAPMTs are investigated, which might cause additional signals due to capacitive cross-talk within the MAPMT or readout chain. Occupancies of up to 55% (simultaneous photon hits on more than half of the MAPMT pixels) are investigated, indicating that in the expected occupancy range of 10%–15% the readout works flawlessly with very low cross-talk.

6Scan: A High-Efficiency Dynamic Internet-Wide IPv6 Scanner With Regional Encoding

Efficient Internet-wide scanning plays a vital role in network measurement and cybersecurity analysis. While Internet-wide IPv4 scanning is a solved problem, Internet-wide scanning for IPv6 is still a mission yet to be accomplished due to its vast address space. To tackle this challenge, IPv6 scanning generally needs to use pre-defined seed addresses to guide further IPv6 scanning directions. Under this general principle, various solutions have been developed, but all suffer from two primary pitfalls, low hit rate and low probing speed, caused by the inherent sparse distribution of active IPv6 addresses and the high computational complexity of the search algorithms, respectively. We develop 6Scan, a novel asynchronous IPv6 scanner that effectively addresses the above two problems. To increase the hit rate, 6Scan infers the promising search directions by encoding the regional identifiers of the target addresses within the probing packets and recording the regional activities from the asynchronously arrived replies. It then dynamically adjusts the search directions according to the scanning result of the previous steps. To speed up the search algorithm, 6Scan leverages the regional identifier encoding to quickly adjust search direction without excessive computation. Real-world experiments over the IPv6 Internet in a billion-scale probing budget show that compared with the state-of-the-art solutions, on average 6Scan can discover <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6\%$</tex-math> </inline-formula> more active addresses with nearly the same scanning time.

Novel Edge Caching Approach Based on Multi-Agent Deep Reinforcement Learning for Internet of Vehicles

Along with the development of Internet of Vehicles (IoV) and wireless technology, the usage of applications that require low latency, such as autonomous driving and intelligent navigation, is increasing rapidly, and the demand for content is increasing greatly. This paper proposes an edge caching approach for the IoV based on multi-agent deep reinforcement learning (ECSMADRL) so as to resolve the problem of excessive response delay due to the large increase of data traffic in the IoV. The approach jointly considers content distribution and caching in dynamic environments. In other words, each moving vehicle in the IoV can be seen as an agent, and it can make decisions about content caching and content access adaptively according to the changing environment to minimize the delay in the process of content distribution. It is proved by experiments that compared with other methods, the proposed edge caching (EC) approach has better performance in reducing content distribution delay, improving content hit rate and success rate.

Sparse Bandit Learning Based Location Management for Space-Ground Integrated Networks

The Space-Ground Integrated Network (SGIN) concept constitutes a promising solution for providing seamless global coverage. However, the mobility of satellites and wireless terminals imposes unprecedented challenges on the location management in SGIN. We tackle this challenge by conceiving a split identifier (ID) and Network Address (NA) based design for providing natural mobility support, and characterize the ID-NA mapping allocation problem by exploiting the storage capacity of both Geostationary Earth Orbit Satellites (GEOSs) and Low Earth Orbiting Satellites (LEOSs) to form a spatially distributed binding resolution system and optimize the caching reward in each LEOS. By considering the large quantity of ID-NA mapping and the sparsity of popular mapping having positive mean caching rewards, we formulate the mapping allocation problem as a sparse Multi-Armed Bandit (MAB) learning procedure, where the mappings are treated as the arms and the LEOSs act as the players. A distributed learning algorithm, namely the Sparse Upper confidence bound based Learning aided Caching algorithm (SULC), is proposed for estimating the mean caching rewards of mappings and selecting the optimal mappings for caching. Moreover, we derive a sub-linear upper bound of the cumulative learning regret to prove the learning efficiency of the proposed SULC. Extensive simulations have been conducted to show that the proposed SULC can quickly identify the popular mappings and provide near-optimal content hit rates. In contrast with the existing solutions, SULC has higher caching rewards and can significantly reduce the cumulative regret after a short period of learning.

Multitask learning-driven identification of novel antitrypanosomal compounds.

Background: Chagas disease and human African trypanosomiasis cause substantial death and morbidity, particularly in low- and middle-income countries, making the need for novel drugsurgent. Methodology & results: Therefore, an explainable multitask pipeline to profile the activity of compounds against three trypanosomes (Trypanosoma brucei brucei, Trypanosoma brucei rhodesiense and Trypanosoma cruzi)were created. These models successfully discovered four new experimental hits (LC-3, LC-4, LC-6 and LC-15). Among them, LC-6 showed promising results, with IC50 values ranging0.01-0.072μM and selectivity indices >10,000. Conclusion: These results demonstratethat themultitask protocol offers predictivity and interpretability in the virtual screening of new antitrypanosomal compounds and has the potential to improve hit rates in Chagas and human African trypanosomiasis projects.

A New Biometric Identification System Based on Plantar Pressure

Biometric recognition based on physiological characteristics has been widely used for security reasons. In these systems, unique features such as fingerprints, iris patterns, face structure, and voice are used to recognize a person instead of codes that can be easily transferred. The biometric identification system based on plantar pressure proposed in this work applies a support vector machine procedure to classify footprint patterns detected by a platform instrumented with fiber Bragg gratings. Two sets of 7 in-series gratings, with Bragg wavelengths within 1526.96 nm and 1553.86 nm range, delimit two sensing regions corresponding to the left and right footprints. The coupled and non-linear responses provided by the sensors are pre-processed and organized in a matrix. An algorithm based on support vector machine was used to extract and recognize foot features. The pattern recognition system constructed in this way returned hit rates greater than 86% for the 12 classes, corresponding to different stepping patterns, with data not belonging to the training set.

Spatiotemporal Evolution and Nowcasting of the 2022 Yangtze River Mega-Flash Drought

Flash droughts challenge early warnings due to their rapid onset, which requires a proper drought index and skillful nowcasting system. A few studies have assessed the nowcast skill for flash droughts using a one-dimensional index, but whether the models can capture their spatiotemporal evolution remains unclear. In this study, a three-dimensional meteorological flash drought index based on the percentile of 15-day moving average precipitation minus evapotranspiration (P-ET) is developed. The index is then used to investigate the spatiotemporal evolution of a mega-flash drought that occurred in the Yangtze River basin during the summer of 2022. The results show that the mega-flash drought started at the beginning of July in the upper reaches of the river and expanded to the middle and lower reaches at the beginning of August due to the spread of the high-pressure system. The evolution is well captured by the proposed three-dimensional index. The spatial correlations between the China Meteorological Administration global medium-range ensemble forecast system (CMA-GFS)’s nowcast and reanalysis ranged from 0.58 to 0.85, and the hit rate and equitable threat score are 0.54 and 0.26, respectively. This study shows that the CMA-GFS nowcast of the P-ET index roughly captured the drought’s evolution, which can be used for flash drought early warnings and water resource management.

CGSA optimized LSTM auto encoder for outlier detection

In recent years, outlier detection has attained great attention with machine learning techniques due to its wide range of applications. By considering the input data’s distributive nature and large dimensionality, outlier detection becomes a challenging issue. Robust outlier detection systems are crucial for data pattern prediction without labeled data. This research develops a novel approach based on stacking auto encoders over Long-Short Term Memory (LSTM) for outlier prediction. The detection accuracy of outlier detection is improved with the hyperparameters optimized with the Chaotic Gravitational Search Algorithm (CGSA). CGSA minimizes the training loss with enhanced detection accuracy in the proposed outlier detection process. The auto encoder in outlier detection transforms the input into a latent space representation to generate the original input sequence. The involvement of learning parameters computes and minimizes the errors between input and generated sequences. The proposed work is experimented and compared with state-of-the-art approaches of recent research. Using the proposed approach, the performance of outlier prediction is improved with an accuracy of98.6%, sensitivity of 96.1%, specificity of 97.8%, G-mean of 96%, Area Under Curve (AUC) of 0.935, Hit rate of 92.3%. Also, the outlier detection errors are minimized, showing the proposed approach’s efficiency.

Characteristics and Establishment of Objective Identification Criteria and Predictors for Foehn Winds in Urumqi, China

The special terrain of Urumqi (in the valley area) often triggers strong foehn winds, causing huge losses to local people’s lives and social economies. By using the surface observation data of the hourly temperature, pressure, humidity, and wind from the downwind Urumqi Meteorological Station and the upwind Dabancheng Meteorological Station in the Middle Tianshan Canyon and the NCEP/NCAR reanalysis data during 2008–2022, this paper establishes the dataset of foehn processes at Urumqi Station in the past 15 years and analyzes the variation rules of the associated meteorological variables during the foehn processes. In addition, based on the physical mechanism of the occurrence of foehn, a three-element identification criterion (i.e., 94° ≤ 2 min average wind direction ≤ 168°, 2 min average wind speed ≥ 2.0 m/s, and Δθ between Urumqi station and Dabancheng station ≥ 0.29 K) for foehn in Urumqi is established by comparing and analyzing the variations of wind direction (WD), wind speed (WS), and the potential temperature difference (Δθ) between the two weather stations during the periods of foehn and non-foehn winds from 2013 to 2022. In addition, the performance of the three-element identification criterion is verified, and the results suggest that this criterion has an accuracy of 82.96% and a hit rate of 89.50% for the 2008–2012 foehn events in Urumqi. Moreover, the hit rate of this criterion for foehn wind of gale or above level (i.e., a 2 min wind ≥ 10.8 m/s on average) is 100%. In addition, combined with two predictors of sea-level pressure difference (ΔP) and Δθ between downwind stations and upwind stations, the foehn forecast can be more accurate than that provided by a single predictor. With ΔP ≤ −12 hPa and Δθ ≥ 5 K, the chances for foehn to occur are over 90%. This finding would have some reference and application values for the foehn forecasting.

AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1.

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

Comparison of Acoustic Emission Data Acquired During Tensile Deformation of Maraging Steel M250 Welded Specimens

Safety and reliability are primary concerns in launch vehicle performance due to the involved costs and risk. Pressure vessels are one of the significant subsystems of launch vehicles. In order to have minimal weight, high strength material viz. maraging steel M250 grade is used in realizing the pressure vessel casing hardware. Despite the best efforts in design methodology, quality evaluation in production and effective structural integrity assessment is still a farfetched goal. The evolution of such a system requires, first, identification of an appropriate technique and next its adoption to meet the challenges posed by advanced materials like maraging steels. In fact, a quick survey of the available Non-Destructive Evaluation (NDE) techniques suggests Acoustic Emission (AE) as an effective structural integrity assessment tool capable of identifying any impending failure or degradation at an earlier stage. Experience shows that the longitudinal welds in the pressure vessels are quite vulnerable to failure due to the fact that they experience the maximum stress (i.e. hoop stress). Loading welded tensile samples are quite synonymous to the hoop stress experienced by longitudinal welds. An attempt is made to compare the Acoustic Emission data acquired during tensile deformation of maraging steel welded specimens. A total of 16 welded specimen’s with known defects were studied for their tensile behaviour is in connection with Acoustic Emission data. The lowest failure load was 70.5 kN and the highest being 84.8 kN. AE activity graphs viz. cumulative AE activity, hit rate, energy rate, count rate, AE amplitude history, AE count history, AE energy history, amplitude-count correlation and hit amplitude distribution have been investigated and salient features with respect to the data have been critically studied and relevant correlations are arrived at.

Measured and projected beam backgrounds in the Belle II experiment at the SuperKEKB collider

The Belle II experiment at the SuperKEKB electron–positron collider aims to collect an unprecedented data set of 50ab−1 to study CP-violation in the B-meson system and to search for Physics beyond the Standard Model. SuperKEKB is already the world’s highest-luminosity collider. In order to collect the planned data set within approximately one decade, the target is to reach a peak luminosity of 6 × 1035cm−2s−1 by further increasing the beam currents and reducing the beam size at the interaction point by squeezing the betatron function down to βy∗=0.3mm. To ensure detector longevity and maintain good reconstruction performance, beam backgrounds must remain well controlled. We report on current background rates in Belle II and compare these against simulation. We find that a number of recent refinements have significantly improved the background simulation accuracy. Finally, we estimate the safety margins going forward. We predict that backgrounds should remain high but acceptable until a luminosity of at least 2.8 × 1035cm−2s−1 is reached for βy∗=0.6mm. At this point, the most vulnerable Belle II detectors, the Time-of-Propagation (TOP) particle identification system and the Central Drift Chamber (CDC), have predicted background hit rates from single-beam and luminosity backgrounds that add up to approximately half of the maximum acceptable rates.

In vitro Antibacterial Activity of Dye Compounds

Background: Methylene blue and some of its analogues have known antibacterial activity, however their exact mechanism of action is unknown Objective: In this study, the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of several methylene blue analogues were determined against five bacterial strains, whereafter the data were used to create and validate a pharmacophore model. Methods: The agar dilution method was used to screen the analogues for antibacterial activity, while the broth microdilution method was used to determine their MIC and MBC. A pharmacophore model was constructed and validated using the rank score, fit value, enrichment factor (EF10%), hit rate (HR10%) and receiver operating characteristic area under the curve (ROC-AUC) as metrics. Results: Against Staphylococcus aureus, pyronin B (0.125 µg/ml) was more active than tetracycline (1 µg/ml) and pyronin Y (0.5 µg/ml), 1,9-dimethylmethylene blue (2 µg/ml), basic blue 3 (2 µg/ml), new methylene blue (2 µg/ml) and Nile blue (2 µg/ml) had similar activity compared to tetracycline. Pyronin B, 1,9-dimethylmethylene blue and new methylene blue were bactericidal. A pharmacophore model was created (rank score: 36.55, max. fit value: 3), which was able to identify active analogues out of the test set (EF10%: 2.83, HR10%: 28.57%, ROC-AUC: 0.84 ± 0.04). The pharmacophore model highlighted that a positive ionisable, aromatic ring as well as a hydrophobic moiety are important for antibacterial activity. Conclusion: Methylene blue analogues were found to have potent antibacterial activity and a pharmacophore model was created to understand the structural requirements for activity.

Network Bandwidth Utilization Optimization with Quantum-Inspired Approach

As the popularity of mobile devices and services continues to increase, the demand for more efficient and effective algorithms to manage network utility is increased. Here, we aim to optimize Network Bandwidth Utilization (NBU) by managing user equipments (UEs) and base stations (BSs) association in 5G ORAN architecture. We consider the network total throughput as an objective function and try to find the suitable Cell Individual Offset (CIO) to trigger UE handover with the A3 event such that the network throughput is optimal. We formulate this problem into a Quadratic Unconstrained Binary Optimization (QUBO) problem such that we can explore the power of Quantum Annealing (QA), which is a promising approach to finding the optimal solution for the NP-Hard problem. We also analyze the penalty coefficients setting problem which impacts the outcome of QA significantly. We test our QUBO formulation on a dataset of 7 BSs and 150 UEs with Fujitsu’s third generation Digital Annealer (DA3). Using our QUBO formulation with DA3 solver with fine-tuning, we can improve the throughput with 60% hit rate on this dataset.

Evaluation of wastewater-based epidemiology of COVID-19 approaches in Singapore's ‘closed-system’ scenario: A long-term country-wide assessment

With the COVID-19 pandemic the use of WBE to track diseases spread has rapidly evolved into a widely applied strategy worldwide. However, many of the current studies lack the necessary systematic approach and supporting quality of epidemiological data to fully evaluate the effectiveness and usefulness of such methods. Use of WBE in a very low disease prevalence setting and for long-term monitoring has yet to be validated and it is critical for its intended use as an early warning system. In this study we seek to evaluate the sensitivity of WBE approaches under low prevalence of disease and ability to provide early warning. Two monitoring scenarios were used: (i) city wide monitoring (population 5,700,000) and (ii) community/localized monitoring (population 24,000 to 240,000). Prediction of active cases by WBE using multiple linear regression shows that a multiplexed qPCR approach with three gene targets has a significant advantage over single-gene monitoring approaches, with R2 = 0.832 (RMSE 0.053) for an analysis using N, ORF1ab and S genes (R2 = 0.677 to 0.793 for single gene strategies). A predicted disease prevalence of 0.001% (1 in 100,000) for a city-wide monitoring was estimated by the multiplexed RT-qPCR approach and was corroborated by epidemiological data evidence in three ‘waves’. Localized monitoring setting shows an estimated detectable disease prevalence of ∼0.002% (1 in 56,000) and is supported by the geospatial distribution of active cases and local population dynamics data. Data analysis also shows that this approach has a limitation in sensitivity, or hit rate, of 62.5 % and an associated high miss rate (false negative rate) of 37.5 % when compared to available epidemiological data. Nevertheless, our study shows that, with enough sampling resolution, WBE at a community level can achieve high precision and accuracies for case detection (96 % and 95 %, respectively) with low false omission rate (4.5 %) even at low disease prevalence levels.

A Mechanistic Model Based on Statistics for the Prediction of a Converter’s End-Point Molten Steel Temperature

With the high efficiency and automation of converter smelting, it is becoming increasingly important to predict and control the endpoint temperature of the converter. Based on the heat balance, a model for predicting the molten pool temperature in a converter was established. Moreover, the statistical method of multiple linear regression was used to calculate the converter heat loss coefficient, greatly improving the prediction accuracy of the mechanistic model. Using the model, the oxidation process for each element in the molten pool, the melting processes of scrap, and the flux were also calculated. The model could better approximate the actual smelting process. Data from a 130 t converter were collected to validate the model. When the error ranges were limited to ±20 and ±15 °C, the model hit rates were 96 and 86.7%, respectively.

GENERATION OF NOVEL ANTIBODY CANDIDATES USING TRANSFORMER AND GAN-BASED DEEP LEARNING ARTIFICIAL INTELLIGENCE

Abstract Introduction Conventional library-based antibody display can only explore a small fraction of the sequences generated from animal immunization, not even to exhaust the potential sequence diversity that can be turned into antibody therapies. This is because screening for antibody is limited to sequences that can be displayed, which only constitute a subset of the entire sequences generated by B cells, whereas screening for antibody directly from single B cells can be costly. Here, we introduce a novel Artificial Intelligence-enabling tool to navigate antibody discovery from a broader range of search space with reduced cost. We trained a transformer-based model from sequences of an immunized library to cluster the clones and a generative adversarial network (GAN)-based model to generate novel sequences that can be potentially developed into antibody therapies. Background and significance One limitation in the early discovery of antibody is the number of functional candidates that can be selected. Our work provides an AI-enabling tool to discover and generate a panel of antibodies of differentiated binding strengths to a broad range of epitopes to ensure functional coverage. Methods &amp; Results We extracted 104 sequences from the FACS-enriched yeast pool from a fully immunized alpaca (Lama pacos) using Next Generation Sequencing, from which we assembled 103 unique sdAb sequences. We fine-tuned a transformer-based deep learning model, which was previously trained from our dataset containing 100,000 antibody sequences, on such pre-processed sdAb sequences giving representation that correlates to the sequence homology for the clustering of clonal types. We postulate such representation also encodes long-range amino acid interactions in the 3D structure, making the accuracy exceeds the performance of bioinformatics-based primary sequence homology analysis. This process is fully automated and optimized to require minima computational resources. We selected 15 candidates from AI-clustered clonal groups and experimentally measured their binding activity. Kd of 12 candidates were of 10−9 affinity and 1 candidates were of 10−8 affinity, the rest one candidate was non-binding (hence a hit rate of 87%). The large sequence diversity of the CDR3 show these nanobodies are potentially good binders for a wide range of epitopes. We generated a CDR-diversifying virtual library (103) of each binding candidate by training a GAN-based models using the sequences of the same clonal group of the binder sequences. This method incorporates the probability of amino acid residues on each specific location that provides a more precise mutagenesis route than PCR-based affinity maturation. The generated sequences provided a wider CDR sequence diversity for the selection of antibodies of differentiated affinity and epitopes, which could generate candidates of different functionality. Conclusion Antibody discovery is a central step in early drug development that identification of a wide range of functional candidates could increase the success rate and reduce risks in later developments. We built an AI-enabling tool for the searching and generation of functional antibodies from animal immunization library. We believe this technology would help deliver candidates of fine-tuned affinity and functionality.

Evaluating H295R steroidogenesis assay data for robust interpretation

The in vitro H295R steroidogenesis assay (OECD TG 456) is used to determine a chemical's potential to interfere with steroid hormone synthesis/metabolism. As positive outcomes in this assay can trigger significant higher tiered testing, we compiled a stakeholder database of reference and test item H295R data to characterize assay outcomes. Information concerning whether a Level 5 reproductive toxicity study was triggered due to a positive outcome in the H295R assay was also included.Quality control acceptance criteria were not always achieved, suggesting this assay is challenging to conduct within the guideline specifications. Analysis of test item data demonstrated that pairwise significance testing to controls allowed for overly sensitive statistically significant positive outcomes, which likely contribute to the assay's high positive hit rate. Complementary interpretation criteria (e.g., 1.5-fold change threshold) markedly reduced the rate of equivocal and positive outcomes thus improving identification of robust positive effects in the assay.Finally, a case study (positive H295R outcome and no endocrine adversity in vivo) is presented, which suggests that stricter data interpretation criteria could refine necessary in vivo follow-up testing. Overall, the described additional criteria could improve H295R data interpretation and help inform on how to best leverage this assay for regulatory purposes.