Testing Process Research Articles

Perspectives of healthcare professionals on training for quantitative G6PD testing during implementation of tafenoquine in Brazil (QualiTRuST Study).

Effective radical cure of Plasmodium vivax malaria is essential for malaria elimination in Brazil. P. vivax radical cure requires administration of a schizonticide, such as chloroquine, plus an 8-aminoquinoline. However, 8-aminoquinolines cause hemolysis in individuals with glucose-6-phosphate dehydrogenase (G6PD) deficiency, requiring prior screening to exclude those at risk. Brazil is pioneering the implementation of tafenoquine, a single-dose 8-aminoquinoline indicated for P. vivax patients with >70% of normal G6PD activity. Tafenoquine implementation in Manaus and Porto Velho, two municipalities located in the western Brazilian Amazon, included comprehensive training of healthcare professionals (HCPs) on point-of-care quantitative G6PD testing and a new treatment algorithm for P. vivax radical cure incorporating tafenoquine. Training was initially provided to higher-level facilities (phase one) and later adapted for primary care units (phase two). This study analyzed HCP experiences during training and implementation and identified barriers and facilitators. In-depth interviews and focus discussion groups were conducted 30 days after each training for a purposive random sample of 115 HCPs. Thematic analysis was employed using MAXQDA software, analyzing data through inductive and deductive coding. Analysis showed that following the initial training for higher-level facilities, some HCPs did not feel confident performing quantitative G6PD testing and prescribing the tafenoquine regimen. Modifications to the training in phase two resulted in an improvement in understanding the implementation process of the G6PD test and tafenoquine, as well as in the knowledge acquired by HCPs. Additionally, knowledge gaps were addressed through in situ training, peer communication via a messaging app, and educational materials. Training supported effective deployment of the new tools in Manaus and Porto Velho and increased awareness of the need for pharmacovigilance. A training approach for nationwide implementation of these tools was devised. Implementing quantitative G6PD testing and tafenoquine represents a significant shift in P. vivax malaria case management. Consistent engagement with HCPs is needed to overcome challenges in fully integrating these tools within the Brazilian health system.

Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3

The application of an electronic violation detection system has begun to be implemented in many countries using CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in the form of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat belts, the use of cell phones while driving, which is influenced by the number of vehicles, vehicle speed and lighting, which can increase the difficulty in the detection process. This research developed a traffic violation detection system using a hybrid model, namely the CNN and LSTM algorithms for the application of discipline using seat belts. The dataset was obtained from RoboFlow Universe with a total of 199 front view car images consists of 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while the LSTM algorithm plays a role in the prediction process. Additionally, the performance evaluation of the CNN+LSTM algorithm will be measured using the accuracy value to measure the performance of the training process and testing process. When measuring the performance of the training process, it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, Yolo3, Yolo3+LSTM. The test results show that Yolo3+LSTM has a higher accuracy compared to the others, at 89%. Next, in the testing process, the CNN+LSTM model will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models have a higher accuracy of 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%.

Experimental study on the flexural performance of FRP grid-reinforced ultra-high-performance concrete I-beams with different steel fiber contents

To adapt to harsh engineering environments, the use of structural components with fiber-reinforced polymer (FRP) grid-reinforced ultra-high-performance concrete (UHPC) is considered a new development trend. Twelve FRP grid-reinforced UHPC I-beams are fabricated to investigate the effects of the thicknesses of FRP grids (0 mm, 1 mm, 3 mm, and 5 mm) and the steel fiber contents of UHPC (0%, 1%, and 2%) on their flexural performance. Four-point bending tests are conducted to obtain the failure modes, load-deflection characteristics, and strain responses of each testing beam, and a simplified calculation method for cracking loading and flexural bearing capacity is proposed. The results reveal that the increase in FRP grid thickness, the ultimate load bearing capacity of FRP grid-reinforced UHPC structural beams increases and the ductility also can be enhanced. The presence of steel fiber effectively inhibits the cracking and increases the first cracking load of the beam during the test process, although its influence on the ultimate bearing capacity is limited. Besides, until to the cracking stage, there is no noticeable change in the tensile strain of the FRP grids as the applied load increases. It has been used to simplify theoretical calculations, which shows good agreement with the experimental results.

Test Input Prioritization for 3D Point Clouds

3D point cloud applications have become increasingly prevalent in diverse domains, showcasing their efficacy in various software systems. However, testing such applications presents unique challenges due to the high-dimensional nature of 3D point cloud data and the vast number of possible test cases. Test input prioritization has emerged as a promising approach to enhance testing efficiency by prioritizing potentially misclassified test cases during the early stages of the testing process. Consequently, this enables the early labeling of critical inputs, leading to a reduction in the overall labeling cost. However, applying existing prioritization methods to 3D point cloud data is constrained by several factors: (1) inadequate consideration of crucial spatial information, and (2) susceptibility to noises inherent in 3D point cloud data. In this article, we propose PCPrior , the first test prioritization approach specifically designed for 3D point cloud test cases. The fundamental concept behind PCPrior is that test inputs closer to the decision boundary of the model are more likely to be predicted incorrectly. To capture the spatial relationship between a point cloud test and the decision boundary, we propose transforming each test (a point cloud) into a low-dimensional feature vector, toward indirectly revealing the underlying proximity between a test and the decision boundary. To achieve this, we carefully design a group of feature generation strategies, and for each test input, we generate four distinct types of features, namely spatial features, mutation features, prediction features, and uncertainty features. Through a concatenation of the four feature types, PCPrior assembles a final feature vector for each test. Subsequently, a ranking model is employed to estimate the probability of misclassification for each test based on its feature vector. Finally, PCPrior ranks all tests based on their misclassification probabilities. We conducted an extensive study based on 165 subjects to evaluate the performance of PCPrior, encompassing both natural and noisy datasets. The results demonstrate that PCPrior outperforms all of the compared test prioritization approaches, with an average improvement of 10.99% to 66.94% on natural datasets and 16.62% to 53% on noisy datasets.

The Utilization of the Game Technique as a Test Instrument to Determine the Persistence of Applicants

This research was conducted at PT. XYZ, which utilizes the game elements in the process of psychological test. This game-based test is intended to measure the persistence of applicants (management-trainee position) and to replace Pauli Test, which is administered through a paper and pencil test method. Results of the game-based persistence test are proven to have a positive correlation with the measurement of persistence in the measurement of persistence on the Pauli Test (p-value <.05, koef. Correlation (+)). This project was developed using iOS platform from Apple, which automates the process of psychological test by applying Computer Based Test (CBT) which uses iPad media.

Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage

Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood. To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US. In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024. Diagnosis of prostate cancer. The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard. A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy. This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.

The Development of Javanese Glossary Website as a Form of Language Maintenance and Revitalization

As a vital component of cultural identity, language is under pressure as a result of globalization. This article discusses the creation of a website that provides a dictionary of Javanese phrases to help preserve and revitalize the language. In this study, we collect, categorize, and display Javanese words on electronic resources. In addition, the system usability scale (SUS) was used to conduct usability tests on the investigated websites to determine how user-friendly they actually were. Gathering terms from multiple sources, categorizing them, and developing a user-friendly interface with a search bar are all steps in the process of making a website. Users from all walks of life fill out the SUS questionnaire as part of the usability testing process. The test results reveal how well the website satisfies its users' requirements. Creating a database of Javanese words online and putting it through the SUS test is a great example of how technology can be used to help preserve a language and its heritage. It is believed that by taking this step, more people will become familiar with the Javanese language and become invested in its continued existence in the modern world. The usability testing results demonstrate that the development strategy and interface design effectively fostered a positive user experience. High scores on the SUS questionnaire, with an average rating of 80.25, indicate that users find the website satisfactory and user-friendly.

A novel framework for analyzing internet of things datasets for machine learning and deep learning-based intrusion detection systems

To generate a machine learning (ML) and deep learning (DL) architecture with good performance, we need a decent dataset for the training and testing phases of the development process. Starting with the knowledge discovery and data mining (KDD) Cup 99 dataset, numerous datasets have been produced since 1998 to be utilized in the ML and DL-based intrusion detection systems (IDS) training and testing process. Because there are so many datasets accessible, it might be challenging for researchers to choose which dataset to employ. Therefore, a framework for evaluating dataset appropriateness with the research to be conducted is becoming increasingly crucial as new datasets are regularly created. Additionally, given the growing popularity of internet of things (IoT) devices and an increasing number of specific datasets for IoT in recent years, it is essential to have a specific framework for IoT datasets. Therefore, this research aims to develop a new framework for evaluating IoT datasets for ML and DL-based IDS. The study's findings include, first, a novel framework for assessing IoT datasets, second, a comparison of this novel framework to other existing frameworks, and third, an analysis of five IoT datasets by using the new framework.

Palembang songket fabric motif image detection with data augmentation based on ResNet using dropout

A good way to spread knowledge about Palembang songket woven cloth patterns is to use information technology, especially artificial intelligence technology. This study's main goal is to develop a ResNet model with dropout regularization methods and find out how dropout regularization affects the ResNet model for detecting Palembang songket fabric motif with more data. Data was collected in places like tujuh saudara songket, Zainal songket, songket PaSH, AMS songket, and batik, Ernawati songket, Nabilah collections, Ilham songket, and Marissa songket. We used eight class of data for this research. A dataset of 7,680 data for training, 960 data for validation, and 960 data for testing is a dataset that has been prepared to be implemented in experiments. In the final results, the experimental results for DResNet demonstrated that accuracy at the training stage was 92.16%, accuracy at the validation stage was 78.60%, and accuracy at the submission stage was 80.3%. The experimental results also show that dropouts are able to increase the accuracy of the ResNet model by adding +1.10% accuracy in the training process, adding +1.80% accuracy in the validation process, and adding +0.40% accuracy in the testing process.

Assessing the prognostic value of transcriptomic data in multiple myeloma through machine learning methodologies.

7513 Background: Multiple myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of plasma cells in the bone marrow. Identifying the characteristics within transcriptomic data of MM patients could be imperative for elucidating long-term cancer prognosis. Prediction and bioinformatic evidence could be valuable for therapeutic intervention. Methods: To discover a precise collection of biomarkers, a large-scale disproportionality analysis of MM patients using the FDA Spontaneous Reporting System database is conducted. This analysis aims to provide pharmacovigilance evidence regarding severe adverse events (AEs) due to medications. Additionally, pharmacogenomic insights from OMIM and MM drug targets are integrated into the analysis to carefully select 84 essential biomarkers. Seven different machine learning (ML) algorithms, namely AdaBoost, K-Nearest Neighbors (KNN), Decision Tree (DT), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP) were then employed to assess the classification of disease outcomes. Specifically, the focus was on distinguishing deceased patients (due to MM) from surviving patients in the MM Research Foundation (MMRF) dataset based on the identified essential biomarker. Results: The classification involved a total of 787 patients from MMRF (624 alive and 163 deceased). Four sampling strategies (original raw data, undersampling, oversampling, and simultaneous sampling) were included in the training and testing process on ML models to alleviate the influence of imbalanced data. SVM achieved the highest accuracy while RF achieved highest mean across four sampling methods (Table). We then performed the Kaplan-Meier survival analysis of the prognostic values of essential genes. The expression of two genes (BRAC1 and CTLA4) from MMRF data when comparing deceased to survival patients was associated with patient outcomes. Additionally, seven genes were potentially prognostic factors in MM (Positive prognostic: ATM, CYBA, NR3C1, PIK3CA, and PIK3CG; Negative prognostic: IFNG and NTRK2). Conclusions: This study successfully showcased a computational methodology that holds potential for optimizing future clinical trials in MM. The approach includes identifying the most suitable patient population for a particular MM regimen, thereby contributing to improved patient care. It also offers strategies for reducing AEs, lowering mortality rates, and ensuring the optimal allocation of healthcare resources. [Table: see text]

Research on AFDX network configuration testing technology

The terminal system, as a key part of AFDX network data transmission, determines the communication performance of the entire AFDX network. In order to test the functionality of the verification end system and evaluate the transmission performance of the AFDX network, this article proposes an AFDX network configuration testing method. Firstly, the configuration testing specifications for the end system is designed, an overall framework for end system testing is established; the testing response mechanism of the end system is studied; the testing process is standardized; and generated configuration files are as input information for algorithm design in the following stage. Secondly, incentive data generation algorithms is designed, incentive data generation frameworks is established, the constraints for data transmission are formalized, and incentive data transmission schedules are generated using SMT solvers. Next, an AFDX network configuration testing tool is designed and implemented; the performance from the perspectives of operational stability and response time is evaluated to prove the effectiveness of the tool. Finally, the types and performance of the seven clock function modules in the overlay network configuration are tested, and the experimental results prove the effectiveness of the network configuration testing method.

Analysis and Discussion on High Insulation Performance of 20 kV Generator Motor Stator in the Large Pumped Storage Unit

Abstract The terminal voltage of 400 MW 500 r/min generator motor in a large pumped storage power station is 20 kV, in which the height of stator core is 3400 mm and the stator bar adopts less glue VPI (Vacuum Pressure Impregnating) process. The characteristic of this generator motor is high voltage level, large external size and high technical requirements. As the pumped storage unit with the highest voltage level that has been put into operation in China, its stator system of 20 kV generator motor shows excellent insulation performance during the test process. This paper analyzes and discusses from two perspectives of development and installation, and summarizes the key technologies in the process, which can be used for further reference in similar projects.

In-depth simulation of rainfall–runoff relationships using machine learning methods

ABSTRACT Measurement inaccuracies and the absence of precise parameters value in conceptual and analytical models pose challenges in simulating the rainfall–runoff modeling (RRM). Accurate prediction of water resources, especially in water scarcity conditions, plays a distinctive and pivotal role in decision-making within water resource management. The significance of machine learning models (MLMs) has become pronounced in addressing these issues. In this context, the forthcoming research endeavors to model the RRM utilizing four MLMs: Support Vector Machine, Gene Expression Programming (GEP), Multilayer Perceptron, and Multivariate Adaptive Regression Splines (MARS). The simulation was conducted within the Malwathu Oya watershed, employing a dataset comprising 4,765 daily observations spanning from July 18, 2005, to September 30, 2018, gathered from rainfall stations, and Kappachichiya hydrometric station. Of all input combinations, the model incorporating the input parameters Qt−1, Qt−2, and R̄t was identified as the optimal configuration among the considered alternatives. The models' performance was assessed through root mean square error (RMSE), mean average error (MAE), coefficient of determination (R2), and developed discrepancy ratio (DDR). The GEP model emerged as the superior choice, with corresponding index values (RMSE, MAE, R2, DDRmax) of (43.028, 9.991, 0.909, 0.736) during the training process and (40.561, 10.565, 0.832, 1.038) during the testing process.

Effect of Printing Orientation on the Mechanical Properties of 3D-Printed Cu–10Sn Alloys by Laser Powder Bed Fusion Technology

This article focuses on investigating the effect of printing direction on the mechanical properties of Cu–10Sn alloys prepared by laser powder bed fusion (LPBF) technology. Specimens with different forming angles (0°, 15°, 30°, 45°, 60°, 75°, and 90°) were fabricated using LPBF technology, and their mechanical properties were systematically tested. During the testing process, we used an Instron 5985 electronic universal material testing machine to accurately evaluate the mechanical properties of the material at a constant strain rate of 10−3/s. The experimental results showed that the mechanical properties of the specimens were the best when the test direction was perpendicular to the growth direction (i.e., the 0° direction). As the angle between the test direction and the growth direction increased, the mechanical properties of the material exhibited a trend of first decreasing, then increasing, and then decreasing again, which was consistent with the direction of the microtexture of the specimens. The root cause of this trend lies in the significant change in the stress direction borne by the columnar crystals under different load directions. Specifically, as the load direction gradually transitions from being parallel to the columnar crystals to perpendicular to them, the stress direction of the columnar crystals also shifts from the radial direction to the axial direction. Due to the differences in the number and strength of grain boundaries in different stress directions, this directly leads to changes in mechanical properties. In particular, when the specimen is loaded in the radial direction of the columnar crystals, the grain boundary density is higher, and these grain boundaries provide greater resistance during dislocation migration, thus significantly hindering tensile deformation and enabling the material to exhibit superior tensile properties. Among all the tested angles, the laser powder bed fusion specimen with a forming angle of 0° exhibited the best mechanical properties, with a tensile strength of 723 MPa, a yield strength of 386 MPa, and an elongation of 33%. In contrast, the specimen with a forming angle of 90° performed the worst in terms of tensile properties. These findings provide important insights for us to deeply understand the mechanical properties of Cu–10Sn alloys prepared by LPBF.

Technological Process of Assembly and QA Testing of Silicone Tracking Modules with Silicon Strip Sensor

Technological Process of Assembly and QA Testing of Silicone Tracking Modules with Silicon Strip Sensor

Perspectives on clinical adoption barriers to blood-based multi-cancer early detection tests across stakeholders.

e13574 Background: Current United States Preventive Services Task Force (USPSTF) recommendations include routine screening for breast, cervical, colorectal, and lung cancer; however, two out of every three cancer cases occur in other indications leading to diagnosis in advanced stages of the disease and higher likelihood of mortality. Current recommended cancer screening and early detection tests also face several hurdles hindering equitable access in a real-world setting. Blood-based MCED tests can impact cancer screening and early detection by monitoring for multiple different cancer types at once via a blood draw that can detect cancers in multiple indications, including indications where screening is not performed routinely today. Given their nascency, the role of blood-based MCED tests in clinical practice is currently undefined. To help facilitate the appropriate integration of MCED tests into patient care, it is important to understand the perspectives of stakeholders on the anticipated challenges to implementation. Methods: We conducted three separate double-blinded surveys in July 2023 of representative cohorts of U.S. Healthcare providers (HCPs) (n=238) that are or act as primary care providers (general practitioners and obstetrics and gynecologists (OB-GYNs)), payers (n=40), and patients (n=116), to evaluate the current cancer screening and early detection landscape and gather perspectives on barriers to implementation of new cancer screening technologies, including blood-based MCED tests. Results: Of the surveyed HCPs and payers, >90% and 100% respectively reported to be aware of MCED tests. However, <10% of HCPs reported having ordered an MCED test and 80% of payers had not yet evaluated an MCED test for coverage under their plan at the time of the survey. The survey results indicated that there is broad agreement on the value proposition that blood-based MCED tests would improve patient outcomes with earlier intervention. Despite this recognition, the survey highlighted that there is limited use today due to the perceived lack of clinical accuracy and utility data, high out-of-pocket patient costs, and lack of payer coverage. Conclusions: To overcome the hurdles for future widespread adoption of blood-based MCED tests, increased investment in data generation, education, and implementation of logistical support for HCPs will be critical. Initiatives include collaborations between stakeholders to generate near-term clinical utility data, equipping HCPs with the ability to educate patients on test accuracy, and seamlessly integrating the testing process into the patient visit workflow. The findings of this study can help build understanding of the anticipated barriers and inform strategies to alleviate them and build a foundation for clinical adoption of blood-based MCED tests.

ВИКОРИСТАННЯ КОМП’ЮТЕРНИХ ТЕСТ-ТРЕНАЖЕРІВ ДЛЯ ВИЗНАЧЕННЯ І РОЗВИТКУ ПСИХОФІЗІОЛОГІЧНИХ ЯКОСТЕЙ ЮНИХ СПОРТСМЕНІВ

The current task for coaches nowadays is to choose an adequate method for assessing psychophysiological abilities, diagnosing, and further developing the psychophysiological qualities of young athletes to organize and correct training and competitive processes. The purpose of the study was to evaluate the effectiveness of computer test simulator programs for developing the psychophysiological qualities of young athletes, individualizing, and optimizing their training process. Using computer test simulators with the programs "MY_TIMER" and "FISHKI.INI", psychophysiological indicators were measured before and after training in five repetitions among 14 schoolchildren aged 12-14 from a secondary education institution. Statistical data processing was conducted using Microsoft Excel. Results: Unreliable differences (p ≥ 0.5) were found between the psychophysiological parameters of young swimmers before and after training. During the computer testing process, increases were observed in the total working time for each of the five tasks (indicating improved speed of thinking) by 18.5%, and in minimum, average, and maximum movement time by 14.3%, 11.1%, and 8.3%, respectively. However, the thinking factor decreased by 16% after training, which impedes the ability to perform operational tasks impromptu. Exercising in areas of high and moderate power leads to fatigue in the cortex of the large hemispheres of the brain, where operational control is carried out. Consequently, young athletes experience both physical and mental fatigue after training. The use of computer test simulators for determining and developing the psychophysiological indicators of young athletes is relevant, promising, and effective. These tools allow for the scientific substantiation of individualized training loads and the rapid assessment of athletes' current functional states. The "FISHKI.INI" program should be used for diagnosing and further developing psychophysiological qualities such as attention, operational thinking, and the dominance of figurative or visual-action thinking. This program enables the quantitative assessment of young athletes' characteristics and improves these qualities with high efficiency through the use of computer test simulators.

Development and Validation of Miniaturized Assays to Assess Protein Techno-functional Properties.

Techno-functional properties of protein isolates such as emulsification, foaming, and gelling serve as key indicators to determine their food applications. Conventional macro-volume techniques used to measure these techno-functional properties are usually time consuming, require large amounts of protein samples, and are impractical when diverse protein samples are handled at the early screening stage. To overcome these issues, we have developed scaled-down (miniaturized) assays to test techno-functional properties of protein samples. These assays are simple, efficient, and require <400 μl of protein solution. Specifically, the miniaturized emulsification and gelling assays require 25-fold less protein than conventional macro-volume techniques and the miniaturized foaming assay requires 100-fold less sample. The performance of these assays has been thoroughly validated using conventional techno-functional tests for each parameter. The protocols described herein offer high-throughput screening capabilities, accelerating the testing process for protein techno-functional properties and allowing for quick identification of samples of interest from diverse samples. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Miniaturized emulsification assay Alternate Protocol 1: Conventional macro-volume emulsification assay Basic Protocol 2: Miniaturized foaming assay Alternate Protocol 2: Conventional macro-volume foaming assay Basic Protocol 3: Miniaturized gelling assay Alternate Protocol 3: Conventional macro-volume gelling assay.

Pairwise test case generation with harmony search, one-parameter-at-at-time, seeding, and constraint mechanism integration

Pairwise testing is a method for identifying defects through combinatorial analysis. It involves testing all possible combinations of input parameters in pairs within a system, ensuring that each pair is tested at least once. The field of test case generation is highly active in the realm of combinatorial interaction testing. Research in this area is particularly encouraged, as it falls under the category of non-deterministic polynomial-time hardness. A big challenge in this field is the combinatorial explosion problem. It is about finding the best test suite that covers all possible combinations of interaction strength. In this paper, we present the task of discovering a pairwise test set as a search problem and introduce an innovative testing tool referred to as pairwise test case generation in harmony search algorithm with seeding and constraint mechanism (PHOSC). Experimental results show that PHOSC performs better compared to some existing pairwise strategies in terms of test suite size. Additionally, PHOSC provides a comprehensive framework and serves as a research platform for the generation of pairwise test sets employing the harmony search algorithm. It adopts an approach that focuses on one parameter at a time (OPAT) and incorporates seeding and constraint mechanisms at the same time, thereby enhancing the efficiency and effectiveness of the testing process.

Pathways for provider initiated genetic testing to bridge the gap in germline genetic testing for metastatic breast cancer.

e23239 Background: Genetic testing for BRCA 1/2 in breast cancer patients have important implications on systemic therapy options, particularly for qualification of PARP inhibitor therapy. NCCN guidelines currently recommend genetic testing for all patients with metastatic or recurrent breast cancer, triple negative breast cancer, as well as high-risk HER2-negative breast cancer. Despite these recommendations, germline genetic testing in these populations remain suboptimal in part due to limited access to genetic counselors. Introducing pathways to identify patients who qualify for genetic testing through nurse navigator screening and increasing provider ordered testing can help bridge the gap to expedite genetic testing. Methods: A retrospective review of metastatic breast cancer patients who were seen at MD Anderson Cancer Center (MDA) between 01/01/2021 and 12/31/2021 was performed to determine a baseline rate at which this population met with genetic counselors. A new protocol was established to aid nurse navigators in identifying patients establishing care at MDA who qualify for testing and guide providers on ordering genetic testing at initial appointment. Nurse navigators and providers underwent a formal training session on protocol with guidelines on patient qualification. Additionally, a best practice advisory (BPA) was implemented into the electronic medical record to alert providers to patients who should be tested. Results: At baseline, 29.6% of patients with metastatic breast cancer had met with genetic counselors at our institution in 2021. Based on this finding, the provider initiated genetic testing process was implemented, where providers order genetic testing at the initial appointment. Initial uptake of this process was low with less than 10 tests ordered in the first three months of intervention. Therefore, a nurse navigator pathway was implemented as well as a BPA within our electronic medical records to alert providers on patients who qualify for testing. This BPA flags patients with stage IV breast cancer that have not seen genetic counselors at our institution and guides providers to either order genetic testing, place a referral for genetic counseling, or dismiss the BPA due to specified reasons. Conclusions: It is imperative that patients with metastatic breast cancer have access to genetic testing at diagnosis to determine optimal therapy. With the expansion of genetic testing recommendations, moving toward a provider driven testing model may expedite and increase access to genetic testing. By implementing additional screening through our nurse navigators and BPA advisories, we hope to increase provider driven testing at our institution. Additionally, we aim to expand these interventions to other subgroups of breast cancer who would qualify for adjuvant PARP therapy in the next phase of intervention.