Testing Approach Research Articles

Clinical pilot of bacterial transcriptional profiling as a combined genotypic and phenotypic antimicrobial susceptibility test.

Antimicrobial resistance is a growing health threat, but standard methods for determining antibiotic susceptibility are slow and can delay optimal treatment, which is especially consequential in severe infections such as bacteremia. Novel approaches for rapid susceptibility profiling have emerged that characterize either bacterial response to antibiotics (phenotype) or detect specific resistance genes (genotype). Genotypic and Phenotypic AST through RNA detection (GoPhAST-R) is a novel assay, performed directly on positive blood cultures, that integrates rapid transcriptional response profiling with the detection of key resistance gene transcripts, thereby providing simultaneous data on both phenotype and genotype. Here, we performed the first clinical pilot of GoPhAST-R on 42 positive blood cultures: 26 growing Escherichia coli, 15 growing Klebsiella pneumoniae, and 1 with both. An aliquot of each positive blood culture was exposed to nine different antibiotics, lysed, and underwent rapid transcriptional profiling on the NanoString platform; results were analyzed using an in-house susceptibility classification algorithm. GoPhAST-R achieved 95% overall agreement with standard antimicrobial susceptibility testing methods, with the highest agreement for beta-lactams (98%) and the lowest for fluoroquinolones (88%). Epidemic resistance genes including the extended spectrum beta-lactamase blaCTX-M-15 and the carbapenemase blaKPC were also detected within the population. This study demonstrates the clinical feasibility of using transcriptional response profiling for rapid resistance determination, although further validation with larger and more diverse bacterial populations will be essential in future work. GoPhAST-R represents a promising new approach for rapid and comprehensive antibiotic susceptibility testing in clinical settings.IMPORTANCEExposure to antibiotics causes differential transcriptional signatures in susceptible vs resistant bacteria. These differences can be leveraged to rapidly predict resistance profiles of Escherichia coli and Klebsiella pneumoniae in clinically positive blood cultures.

Theoretical development and validation of asphalt concrete density measurement using non-contact interdigital coplanar capacitive sensor

Asphalt concrete (AC) density serves as a critical criterion for quality control and quality assurance in asphalt pavement construction. Current non-destructive testing (NDT) approaches have shown potential for AC density measurement, but they present disadvantages such as inefficiency and unstable accuracy. This paper developed a novel NDT approach for AC density measurement based on non-contact interdigital coplanar capacitive sensor (ICCS). The feasibility of this approach was evaluated through both numerical and experimental validations. Firstly, the theoretical model of non-contact ICCS for AC density measurement was established based on conformal mapping technique, partial capacitance technique, and dielectric mixing model. Secondly, numerical simulations were performed to validate the developed theoretical model under two scenarios. Thirdly, laboratory experiments were conducted to measure capacitance data and predict density for three common types of ACs. Finally, the error of non-contact ICCS was discussed and compared with that of other NDT instruments. The results indicated that the developed theoretical model could establish the relationship between AC density and capacitance value, which provided the basis for AC density measurement using non-contact ICCS. It was also observed that the capacitance data obtained by the proposed model closely matched those computed by numerical simulations. The maximum errors between theoretical and numerical data were 4.10% and 1.08% for two cases respectively, which proved the feasibility of the theoretical model. Furthermore, experimental results indicated that the capacitance value of AC specimens increased by about 1 pF as the rolling time increases due to a decrease in the volume of air in AC, which reaffirms the validity of AC density measurement using non-contact ICCS. Additionally, the non-contact ICCS demonstrated excellent accuracy with a maximum error of 6.69%, which is comparable to the current mainstream NDT approaches. And it has the advantages of lower cost, simpler data processing and higher efficiency. The findings of this paper offer a new and reliable method and tool for non-destructive and large-scale AC density measurement.

Synergistic effect of climatic and non-climatic factors on food security in Syria: an autoregressive distributed lag approach

ABSTRACT Food security has always been the main goal of Syrian agricultural strategy. Therefore, the main objective of this study is to examine the dynamic relationship between selected climatic and non-climatic variables and food security in Syria from 1961 to 2018. The autoregressive distributed lag (ARDL) bounds testing approach is applied to estimate the association among the study variables with evidence of long-run and short-run analysis. The long-run estimates revealed that non-climatic variables including crop production, agricultural land, land under cereal production, and population, positively affected food security. Moreover, climatic variables including temperature and CO2 emissions positively affected food security in the long-run. The findings revealed that in the short-run, fertilizer affected food security positively. However, in the long run, the effect of fertilizer consumption turned out to be negative and significant. This study suggests that governments may consider measures to improve food security in Syria. Governments should focus on the issue of using climate-adapted plant varieties. Increasing the area under cereal crops and reducing agricultural land degradation could be an important long-term strategy for the government. There is a need to properly train farmers regarding fertilizer usage and switch from chemical fertilizer to organic fertilizer.

Sustainable bioinspired materials for regenerative medicine: balancing toxicology, environmental impact, and ethical considerations

The pursuit of sustainable bioinspired materials for regenerative medicine demands a nuanced balance between scientific advancement, ethical considerations, and environmental consciousness. This abstract encapsulates a comprehensive perspective paper exploring the intricate dynamics of toxicology, environmental impact, and ethical concerns within the realm of bioinspired materials. As the landscape of regenerative medicine evolves, ensuring the biocompatibility and safety of these materials emerges as a pivotal challenge. Our paper delves into the multidimensional aspects of toxicity assessment, encompassing cytotoxicity, genotoxicity, and immunotoxicity analyses. Additionally, we shed light on the complexities of evaluating the environmental impact of bioinspired materials, discussing methodologies such as life cycle assessment, biodegradability testing, and sustainable design approaches. Amid these scientific endeavors, we emphasize the paramount importance of ethical considerations in bioinspired material development, navigating the intricate web of international regulations and ethical frameworks guiding medical materials. Furthermore, our abstract underscores the envisioned future directions and challenges in toxicology techniques, computational modeling, and holistic evaluation, aiming for a comprehensive understanding of the synergistic interplay between sustainable bioinspired materials, toxicity assessment, environmental stewardship, and ethical deliberation.

Does development in financial markets and institutions affect green growth? Empirical evidence from India

Abstract This study delves into the intricate connection between financial development and green growth in India. The study considers the development in financial institutions and financial markets as a proxy of financial development. By employing an autoregressive distributed lag model for the period 1990–2019, the results reveal a mixed impact of financial development on green growth in India. The study confirms a cointegrating relationship among the interested variables through the autoregressive distributed lag-bound test approach. Further, the results reported that financial institutions have a positive and statistically significant influence on green growth in India both in the short and long terms. However, financial markets have no statistically significant effect on green growth. These findings are important for policymakers, offering valuable insights into India’s intricate interplay between financial development and green growth. These findings suggest the way for targeted policy interventions to achieve green growth and better investment strategies in India’s economic landscape.

Efficacy of a hybrid take home and in class summative assessment for the postsecondary physics classroom

Abstract Assigning course grades to students requires obtaining accurate measures of the students’ understanding and knowledge of the topic. The induced stress from a traditional summative assessment is known to negatively impact student grades, confounding the connection between knowledge and test grades. Documented approaches to reduce stress during examinations can lead to a distracting testing environment (two-stage tests) or are subject to cheating (take-home tests). We have developed a hybrid take-home/in-class exam that avoids such difficulties. We present herein the responses from student surveys conducted after each exam. The results of the surveys indicate that the hybrid exam method reduces test anxiety while improving students’ self-reported mastery of physics, as compared to traditional in-class summative assessments. These findings are significant at the p < 0.05 level. Lastly, we discuss the difficulties encountered in applying this test approach.

Optimization the design of fixed and group sequential three-arm non-inferiority trials with dichotomous endpoints of risk difference and odds ratio

Although the risk difference (RD) is the most common and well explored functional form for testing efficacy with dichotomous endpoint, odds ratio (OR) is also suggested and well applied measure for non-inferiority (NI) trials. Since the construction and interpretation of these function forms are quite different, this study aims to provide detailed discussions and comprehensive comparisons on the design and testing approach for RD and OR scales for the fixed and group sequential three-arm NI trials under various of situations. The sample size determinations and testing approaches for assessing NI of a new treatment in three-arm clinical trials for RD and OR scales were reviewed comprehensively. Simulation studies are conducted for hundreds of scenarios with parameter configurations of the response rates, randomized allocations, NI margins and interim analysis. The operating characteristic (OC) of RD and OR scales based on the MLE and RMLE methods were thoroughly investigated. A trial example was designed and analyzed to demonstrate the methodologies. It is found that sample size determination on OR scale gives smaller sample size and robust procedure compared to RD scale in the majority of situations. When evaluating the behaviors of the attained power, the RMLE methods based on OR scale outperforms the MLE method and tend to have more power to reject the null hypothesis especially under the small sample size situations. Compared to the fixed design, the group sequential design has better OC, which provides a comparable power while needing smaller total average sample sizes for all cases. In addition, we suggest a lower significance level with a higher power for the sample size determination in the superiority test stage in the group sequential design, which can significantly reduce the total sample sizes while the number of subjects in the placebo group does not increase much. It can offer some recommendations for the investigators to choose the optimal endpoints and parameter configurations to design a three-arm NI trial under certain situations.

Targeted next-generation sequencing - a promising approach in the diagnosis of Mycobacterium tuberculosis and drug resistance.

Targeted next-generation sequencing (tNGS) offers a high-throughput, culture-independent approach that delivers a comprehensive resistance profile in a significantly shorter turn-around time, making it promising in enhancing tuberculosis (TB) diagnosis and informing treatment decisions. This study aims to evaluate the performance of tNGS in the TB diagnosis and drug resistance detection of Mycobacterium tuberculosis (MTB) using MTB clinical isolates and bronchoalveolar lavage fluid (BALF) samples. A total of 143 MTB clinical isolates were assessed, tNGS, phenotypic antimicrobial susceptibility testing (AST), and AST based on whole genome sequencing (WGS) exhibited high concordance rates, averaging 95.10% and 97.05%. Among 158 BALF samples, culture, Xpert MTB/RIF, and tNGS reported 29, 70 and 111 positives, respectively. In the confirmed cases with etiological evidence (smears, cultures, or molecular test), the positive rate of tNGS (73/83, 87.95%) was higher than that of Xpert MTB (67/83, 80.72%). Additionally, 45% (27/60) of clinically diagnosed cases (with imaging or immunological evidence) were positive for tNGS. Further validation on the discrepant results between tNGS and Xpert MTB/RIF with droplet digital PCR (ddPCR) yielded 35 positives, tNGS detected all, and Xpert MTB/RIF only identified 6 positives. In conclusion, tNGS demonstrates robust and rapid performance in the identification of MTB and its associated drug resistance, and can be directly applied to clinical samples, positioning it as a promising approach for laboratory testing of tuberculosis.

Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships.

The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. However, few tools exist to support the development and evaluation of predictive models in the context of neuroimaging or lesion-behavior research, creating an obstacle to the widespread adoption of predictive modeling approaches in the field. Further, existing tools for lesion-behavior analysis are often unable to accommodate categorical outcome variables and often impose restrictions on the predictor data. Researchers therefore often must use different software packages and analytical approaches depending on whether they are addressing a classification vs. regression problem and on whether their predictor data correspond to binary lesion images, continuous lesion-network images, connectivity matrices, or other data modalities. To address these limitations, we have developed a MATLAB software toolkit that supports both inferential and predictive modeling frameworks, accommodates both classification and regression problems, and does not impose restrictions on the modality of the predictor data. The toolkit features both a graphical user interface and scripting interface, includes implementations of multiple mass-univariate, multivariate, and machine learning models, features built-in and customizable routines for hyper-parameter optimization, cross-validation, model stacking, and significance testing, and automatically generates text-based descriptions of key methodological details and modeling results to improve reproducibility and minimize errors in the reporting of methods and results. Here, we provide an overview and discussion of the toolkit features and demonstrate its functionality by applying it to the question of how expressive and receptive language impairments relate to lesion location, structural disconnection, and functional network disruption in a large sample of patients with left hemispheric brain lesions. We find that impairments in expressive vs. receptive language are most strongly associated with left lateral prefrontal and left posterior temporal/parietal damage, respectively. We also find that impairments in expressive vs. receptive language are associated with partially overlapping patterns of fronto-temporal structural disconnection, and that the associated functional networks are also similar. Importantly, we find that lesion location and lesion-derived network measures are highly predictive of both types of impairment, with predictions from models trained on these measures explaining ~30-40% of the variance on average when applied to data from patients not used to train the models. We have made the toolkit publicly available, and we have included a comprehensive set of tutorial notebooks to support new users in applying the toolkit in their studies.

The effects of financial development on crop productivity: ARDL evidence from Indian agriculture

Purpose This work aims to empirically investigate the effects of financial development on crop productivity of India. Design/methodology/approach Time series data such as crop production index, International Monetary Fund’s (IMF) financial development index, gross domestic product (GDP) per capita, arable land, rural population, trade openness and physical capital from 1980 to 2020 was used. The autoregressive distributed lag (ARDL) bounds testing approach of cointegration was used to determine the long-run equilibrium relationship between the selected time series. Also, ARDL long- and short-run coefficients were estimated to examine the effects of selected variables on crop productivity. Furthermore, to establish the robustness of results, long-run estimators such as fully modified least squares and the dynamic least squares were also used. Finally, using the vector error-correction model, causality between the selected time series was examined. Findings The ARDL cointegration test confirmed the existence of long-run equilibrium relationship among agricultural productivity, financial development, capital formation, GDP per capita, arable land, rural population and trade openness. The estimated long-run elasticities from all the three techniques and the short-run elasticities of ARDL have consistently suggested that the elasticity of financial development is higher (1.55% and 1.40%, respectively) in explaining the crop productivity of India. The short-run causality estimates indicated the presence of positive bidirectional causality between crop productivity and financial development and seven positive unidirectional causal relationships between the selected variables. Practical implications Agricultural credit being an important non-land input and essential for overall growth and sustenance of agricultural sector, the policymakers should ensure the overall development of its financial sector which will reduce the intermediation, informational and other transactional costs associated with agricultural credit. This will possibly result in timely availability and access to adequate and low-cost credit from institutional sources. Originality/value Though extensive research is available on the effects of financial development on economic growth, limited research is available concerning the impact of financial development on crop productivity, especially for an emerging economy like India. For India, predominantly studies have investigated the impact of farm credit on crop productivity but have not exclusively examined the effects of financial development on agricultural productivity. Therefore, this study not only adds to the empirical literature but also provides new evidence on the nexus between financial development and crop productivity by examining the effects of financial development on crop productivity using the composite financial development index developed by the IMF using the ARDL bounds test for cointegration and other econometric estimators.

The impact of capital flight on economic development: An empirical analysis from Palestine

This study focused on the function of capital flight to ascertain the true impact of the phenomenon of capital flight on economic development in Palestine and aimed to analyze the short- and long-term dynamic relationship between capital flight and economic development, including other affected variables. This research employed a quantitative research design and the descriptive analysis method. The analysis uses quarterly data from 2004 to 2022. This study employed the autoregressive distribution lag (ARDL) bound testing approach. Actual data from Palestine spanning the years 2004-2022 yielded significant findings. Based on heteroskedastic dynamic regression as an ARDL panel model, important findings were reached. First, both local country fundamentals and global variables have an impact on economic development and its rates over the long term, but in the near term, global forces may be predominantly recognized as drivers. Second, the variable of interest, capital flight, has a favorable impact on tax advantages. The empirical findings revealed that the short- and long-run analyses are consistent with each other. This necessitates putting into practice a variety of tactics, from creating efficient judicial and political institutions to encouraging economic development through managing macroeconomic issues. This study provides a fresh insight for policymakers to evaluate the impact of capital flight on economic development factors when coordinating fiscal and monetary policy in Palestine. The monetary and fiscal authorities should create an efficient policy framework. Palestine must reduce and stop the outflow of cash from inside its borders to improve its capacity to pay back its loans and debts to foreign creditors.

Molecular techniques in haematopathology: what and how?

Here we review the 'what and how' of molecular techniques used in the context of haematopathological diagnostics of both lymphoid and myeloid neoplasms. Keeping in mind that the required resources for molecular testing are not universally available, we will not only discuss novel and emerging techniques that allow more high-throughput and sophisticated analyses of lymphoid and myeloid neoplasms, but also the more classical, low-cost alternatives and even some workarounds for molecular testing approaches. In this review we also address other key aspects around molecular techniques for haematopatholgy diagnostics, including preanalytics, data interpretation, and data management, bioinformatics, and interlaboratory precision and performance evaluation.

MDM2 Testing In Lipomatous Tumors: An Institutional Experience with 1,161 Neoplasms

Abstract Introduction/Objective Amplifications of the MDM2 gene is a well-recognized central molecular event in atypical lipomatous tumors, well-differentiated liposarcomas, and dedifferentiated liposarcomas. However, given the fact that most adipocytic neoplasms are benign, the specific testing approaches that will maximize the detection of MDM2 amplified tumors in an accurate and cost-efficient manner remains unclear. The purpose of this study is to present our experience with MDM2 testing by FISH in a large cohort of lipomatous neoplasms. Methods/Case Report The pathologic database of an academic medical center was queried for all lipomatous tumors as well as all tumors for which MDM2 testing was performed during a three-year period. At our center, MDM2 testing for well differentiated adipocytic neoplasms is triggered by the so-called “traditional criteria”: a deep tumor and/or tumor size >10 cm and/or recurrence. We also take an expanded approach for a variety of indications that may include equivocal atypia or clinical/imaging concern, among others. Testing is also performed on overt malignancies for which dedifferentiated liposarcoma is a plausible consideration after review of morphologic features. Clinicopathologic data, including patient age of diagnosis, tumor size, depth of tumor, results from MDM2 testing [when performed], anatomic location(s) and recurrence [where applicable], were collected for each patient. Results (if a Case Study enter NA) 1161 cases were assessed, 456 of whom underwent MDM2 testing, and 108 of which were ultimately classified as MDM2 amplified. Standardized bivariable comparisons found that a deep tumor location (p=0.0014) and older age (p=0.0002), but not tumor size at the >10 cm cut off were associated with MDM2 positivity. However, a logistic regression model showed that neither size, depth or age were significantly associated with MDM2 positivity. Among the MDM2-positive cohort, 91% had tumor sizes <10 cm, 14% were superficial, 23% were non-recurrent and 15% were less than the median cohort patient age of 58 years. If the aforementioned “traditional criteria” were used as the sole basis for MDM2 testing, 50 (46.29%) of the 108 cases that were ultimately classified as MDM2 positive would not have been tested. Conclusion Our findings suggest that a strict adherence to “traditional criteria” may result in a significant subset of MDM2 positive tumors not being tested in lipomatous tumors. A more expansive approach that incorporates additional indications for testing, should be evaluated.

Post-localized blast experimental investigation of mechanical properties and microstructural evolutions in austenitic stainless steel 316L

Mechanical loading causes material deformation, resulting in changes in mechanical properties due to microstructural alterations such as the multiplication of dislocations and evolution of grain morphology. Blast loading, a condition where materials deform at high strain rates, results in significant plastic deformation. This rapid deformation induces intense mechanical stresses, causing complex microstructural changes that influence the mechanical behavior and performance of the materials. Consequently, designing structures to withstand blast loading requires understanding the relationship between microstructure and property evolution. As the primary objective of this study, post-localized blast experiments have been conducted to elucidate the variability in microstructural response of Austenitic stainless steel (ASS) 316 L, characterized by its face-centered cubic crystal structure. Localized blast loads were applied to square test plates, 2 mm thick, with a circular exposed area of 106 mm in diameter. Quantitative mechanical property data from key zones within the deformed dome were determined through using a novel micro-tensile testing approach and nanoindentation tests. Electron backscatter diffraction (EBSD) in scanning electron microscopy (SEM) technique was employed to characterize the microstructural changes in the selected samples. The results revealed that blast loading induced complex mechanical and microstructural changes in ASS 316 L, including enhanced material strength, reduced ductility, and significant alterations in grain orientation and misorientation distributions. The materials underwent significant strain hardening due to the increased stress and deformation, resulting in a more resistant to plastic deformation and the greatest internal strain accumulations. Texture analysis underscored the influence of deformation geometry, with Goss and Copper emerging as predominant texture components.

The outcomes of an audiological management programme for neonates with hyperbilirubinaemia.

Hyperbilirubinaemia is a contributing condition to the prevalence of neonatal hearing loss. Because of its pathophysiology, the use of Otoacoustic Emissions (OAE) and Automated Auditory Brainstem Response (AABR) testing is essential in diagnosing hearing loss. Two-tier screening models are typically used in developing world contexts; however, a combined approach to testing (using both tests) should be used for early detection. Blood serum levels should also be monitored to determine how they impact audiological test results. To determine the outcomes of using a combined testing approach of both OAE and Auditory Brainstem Response (ABR) for both screening and diagnostic testing of neonates with hyperbilirubinaemia and studying the relationship between the test results and the serum bilirubin levels. A cross-sectional, comparative design was utilised. Forty neonates were tested (80 ears). Neonates underwent hearing screening and diagnostic testing (ABR and/or AABR and DPOAE tests). The study was conducted at a hospital in South Africa. One-third (32.5%) of the neonates had comorbidities. Screening results indicated that the AABR test could identify more cases of abnormalities than DPOAEs (p= 0.001). Participants with a serum level greater than 10 mg/dL presented with abnormal diagnostic ABR test results while passing the DPOAE test (p 0.001). Combined use of ABR and DPOAE testing yielded a greater identification of auditory pathology than using either test alone. Serum bilirubin levels can be used as an indicator for combination testing.Contribution:Combined use of ABR and DPOAE testing leads to greater identification of auditory pathology.

Correlation of COVID-19 vaccination and RT-PCR ct value among cases in Addis Ababa, Ethiopia: implication for future preparedness

BackgroundThe COVID-19 disease requires accurate diagnosis to effectively manage infection rates and disease progression. The study aims to assess the relationship between vaccination status and RT-PCR cycle threshold (Ct) values by comparing clinical, RDT and RT-PCR results.MethodsA total of 453 suspected COVID-19 cases were included in this study. Nasopharyngeal swabs were collected for both RDT and RT-PCR testing, with RDTs conducted on-site and RT-PCR at the Ethiopian Public Health Institute (EPHI) genomics laboratory. Detailed clinical, RDT, and RT-PCR results were analyzed. Data analysis included descriptive statistics, cross-tabulation, and Chi-Square tests to investigate the connections between diagnostic outcomes and vaccination status, with a focusing on Ct values.ResultsRDT results showed 34.0% negative and 65.8% positive, while RT-PCR results indicated 35.8% negative and 64.2% positive cases. The discrepancies between RDT and RT-PCR results emphasize the importance of thorough testing. No significant association was found between vaccination status and viral load, as indicated by Ct values. Among RT-PCR positive cases, 49.8% had been vaccinated, suggesting challenges in interpreting results among vaccinated individuals. Further analysis revealed that vaccination (first or second dose) had minimal impact on Ct values, indicating limited influence of vaccination status on viral load dynamics in infected individuals.ConclusionsThe study highlights the significant differences between RDT and RT-PCR outcomes, underscoring the need for a comprehensive testing approach. Additionally, the findings suggest that vaccination status does not significantly impact RT-PCR Ct values, complicating the interpretation of diagnostic results in vaccinated individuals, especially in breakthrough infections and potential false positives.

Operational Research to Support Rapid Evidence-Based Responses to Outbreaks: Learnings from COVID-19.

During the COVID-19 pandemic, the need for making testing readily available was recognized as an important factor for individuals to help make informed decisions, including to isolate or seek care, and for policymakers to control transmission. Toward this end, FIND and the Access to COVID-19 Tools Accelerator funded 16 rapid operational research studies and one implementation project in Africa, the Caribbean, and Asia evaluating the utility, acceptability, and feasibility of different community-based SARS-CoV-2 testing approaches. Here, we discuss common factors and challenges encountered during study implementation. We note six key factors essential for success: 1) collaboration and partnerships; 2) buy-in of local stakeholders, including communities; 3) access to affordable supplies; 4) flexible financing; 5) effective approval systems; and 6) a skilled and motivated workforce. We also note various challenges that must be addressed to fully capitalize on these success factors. In particular, ethics committees are often not well equipped to assess operational research during outbreaks. Outbreaks, especially of novel pathogens, are unpredictable, and transmission dynamics are even more likely to change if the pathogen is prone to frequent mutations, such as SARS-CoV-2. Research that aims to evaluate strategies for curbing transmission must hence be easily and swiftly adaptable. This requires flexibility from researchers, funders, staff conducting the studies, and ethics and other approval committees. International guidelines for evaluating operational research protocols in outbreaks are needed to provide timely evidence to enable informed decisions by individuals, communities, and policymakers, thereby reducing both the human and the economic impact of outbreaks.

Hybrid paper/PDMS microfluidic device integrated with RNA extraction and recombinase polymerase amplification for detection of norovirus in foods.

HuNoV belongs to the family of Caliciviridae and is a leading cause of acute gastroenteritis that can be transmitted through contaminated foods. HuNoV causes around one out of five cases of acute gastroenteritis that lead to diarrhea and vomiting, placing a substantial burden on the healthcare system worldwide. HuNoV outbreaks can occur when food is contaminated at the source (e.g., wild mussels exposed to polluted water), on farms (e.g., during crop cultivation, harvesting, or livestock handling), during packaging, or at catered events. The research outcomes of this study expand the approaches of HuNoV testing, adding value to the framework for routine testing of food products. This microfluidic device can facilitate the monitoring of HuNoV outbreaks, reduce the economic loss of the agri-food industry, and enhance food safety.

Reward Augmentation in Reinforcement Learning for Testing Distributed Systems

Bugs in popular distributed protocol implementations have been the source of many downtimes in popular internet services. We describe a randomized testing approach for distributed protocol implementations based on reinforcement learning. Since the natural reward structure is very sparse, the key to successful exploration in reinforcement learning is reward augmentation. We show two different techniques that build on one another. First, we provide a decaying exploration bonus based on the discovery of new states---the reward decays as the same state is visited multiple times. The exploration bonus captures the intuition from coverage-guided fuzzing of prioritizing new coverage points; in contrast to other schemes, we show that taking the maximum of the bonus and the Q-value leads to more effective exploration. Second, we provide waypoints to the algorithm as a sequence of predicates that capture interesting semantic scenarios. Waypoints exploit designer insight about the protocol and guide the exploration to "interesting" parts of the state space. Our reward structure ensures that new episodes can reliably get to deep interesting states even without execution caching. We have implemented our algorithm in Go. Our evaluation on three large benchmarks (RedisRaft, Etcd, and RSL) shows that our algorithm can significantly outperform baseline approaches in terms of coverage and bug finding.

AAT4IRS: automated acceptance testing for industrial robotic systems.

Industrial robotic systems (IRS) consist of industrial robots that automate industrial processes. They accurately perform repetitive tasks, replacing or assisting with dangerous jobs like assembly in the automotive and chemical industries. Failures in these systems can be catastrophic, so it is important to ensure their quality and safety before using them. One way to do this is by applying a software testing process to find faults before they become failures. However, software testing in industrial robotic systems has some challenges. These include differences in perspectives on software testing from people with diverse backgrounds, coordinating and collaborating with diverse teams, and performing software testing within the complex integration inherent in industrial environments. In traditional systems, a well-known development process uses simple, structured sentences in English to facilitate communication between project team members and business stakeholders. This process is called behavior-driven development (BDD), and one of its pillars is the use of templates to write user stories, scenarios, and automated acceptance tests. We propose a software testing (ST) approach called automated acceptance testing for industrial robotic systems (AAT4IRS) that uses natural language to write the features and scenarios to be tested. We evaluated our ST approach through a proof-of-concept, performing a pick-and-place process and applying mutation testing to measure its effectiveness. The results show that the test suites implemented using AAT4IRS were highly effective, with 79% of the generated mutants detected, thus instilling confidence in the robustness of our approach.