Single Test Case Research Articles

Model-domain failing test augmentation with Generative Adversarial Networks

It is undoubtedly that test suites are crucial for fault localization techniques. Through the running of a single test case in the test suite, a fault localization technology is able to obtain the dynamic coverage information of the statements, which is a vector and could be analyzed to identify the corresponding coefficients between statements and program failures. This vector is commonly referred to as a model domain test sample. There are two types of model domain test samples: passing ones and failing ones. However, the number of passing test samples often far exceeds the number of failing ones, which undoubtedly causes the asymmetry phenomenon. Previous studies have proved that the imbalance of model domain test samples may hamper the effectiveness of fault localization techniques. In practice, it is difficult to create failing test cases in input domain. With the rapid development of deep learning techniques, Generative Adversarial Networks (GAN) have achieved promising results in many fields, which may bring a new perspective for augmenting model domain failing test samples. Thus, we try to provide a new method named MAG: Model-domain failing test Augmentation with Generative Adversarial Networks. MAG first constructs a Generative Adversarial Network, and then generates vectors with the abstract representation of execution information as input. At last, MAG utilizes influential global and local contexts to enhance the generated vectors, and to formulate model domain test samples with failing labels. MAG augments model domain failing test samples. Different from traditional methods of generating test cases directly from input domain, MAG seeks to augment failing test samples of model domain, which could improve the effectiveness of existing fault localization techniques. Experimental results illustrate that MAG significantly improves the effectiveness of 13 typical fault localization approaches and is better than two representative data optimization techniques.

Self-adaptive turbulence eddy simulation of flow control for drag reduction around a square cylinder with an upstream rod

The use of an upstream circular rod to control the flow around a downstream square cylinder is found to efficiently reduce the drag of the square cylinder. The complex flow characteristics and mechanism of the rod-square system in tandem arrangement as a passive flow control method for various centre-to- centre gap ratios (L/D) are investigated by numerical simulation. Turbulence modelling of the rod-square cylinder system is challenging. To select a suitable turbulence modelling, the recently developed Self-Adaptive Turbulence Eddy Simulations (SATES), as well as IDDES (Improved Delayed Detached Eddy Simulation) and SBES (Stress Blended Eddy Simulation) are first assessed for single circular and single square cylinder flow test cases. The SATES method performs best among the three models for bluff body flow simulation and is thus used in the drag flow control study. For the rod-square flow control system, the Reynolds number is 34000 based on freestream velocity U0 and the edge length of the square cylinder D. The diameter of the control rod is kept constant at d/D = 0.18. The flow characteristics and mechanism are explored in detail, varying the rod and square cylinder distance of L/D = 1.5, 2.0, 2.5and 3.0. According to the absence or presence of vortex shedding from the upstream rod cylinder, two flow patterns (pattern I and pattern II) are observed. Large drag reduction is observed for all the simulation cases the maximum drag reduction is approximately 63% with L/D = 2.0, and the minimum drag reduction approximately 53% with L/D = 3.0. The secondary lock-frequency, which presents as pattern II, is caused by the excitation of upstream vortices. It is found that the side recirculation is associated with the wake recirculation length of the square cylinder, which is similar to the elongated cylinder in the flow pattern I. The flow mechanism of suppression of the vortices from the rod can be attributed to the effective diffusion of vorticity in the separating shear layer caused by the presence of the front wall of the square cylinder.

Single and Multi-objective Test Cases Prioritization for Self-driving Cars in Virtual Environments

Testing with simulation environments helps to identify critical failing scenarios for self-driving cars (SDCs). Simulation-based tests are safer than in-field operational tests and allow detecting software defects before deployment. However, these tests are very expensive and are too many to be run frequently within limited time constraints. In this article, we investigate test case prioritization techniques to increase the ability to detect SDC regression faults with virtual tests earlier. Our approach, called SDC-Prioritizer , prioritizes virtual tests for SDCs according to static features of the roads we designed to be used within the driving scenarios. These features can be collected without running the tests, which means that they do not require past execution results. We introduce two evolutionary approaches to prioritize the test cases using diversity metrics (black-box heuristics) computed on these static features. These two approaches, called SO-SDC-Prioritizer and MO-SDC-Prioritizer , use single-objective and multi-objective genetic algorithms ( GA ), respectively, to find trade-offs between executing the less expensive tests and the most diverse test cases earlier. Our empirical study conducted in the SDC domain shows that MO-SDC-Prioritizer significantly ( P - value <=0.1 e -10) improves the ability to detect safety-critical failures at the same level of execution time compared to baselines: random and greedy-based test case orderings. Besides, our study indicates that multi-objective meta-heuristics outperform single-objective approaches when prioritizing simulation-based tests for SDCs. MO-SDC-Prioritizer prioritizes test cases with a large improvement in fault detection while its overhead (up to 0.45% of the test execution cost) is negligible.

Semi-analytical fluid-structure model for the analysis of fuel assembly bow in full PWR cores

Fuel assembly bow is an intricate industrial issue occurring in PWR nuclear cores, which relies on multiphysics coupled phenomena based on mechanics, thermal hydraulics and neutronics.Amongst many causes, the hydraulic forces induced by in-core flow redistributions play an important role on the fuel assembly mechanical deflection.Because deformation spread out over days, weeks or months, fuel assembly bow is often studied as a quasistatic fluid-structure interaction with neutronics parameters as inputs only. In order to set up an efficient partitioned two-way coupling between mechanics and hydraulics representing a full PWR core, simplified models must be used.Regarding hydraulics, common strategies imply porous media, or even more time-saving 1D networks. In a previous study, we followed such a 1D approach to simulate the flow redistributions occurring near spacer grids, from fuel assemblies to the bypasses surrounding them or the other way around. In the current paper, we extend the latter local hydraulic networks to build up a full fuel assembly, which is then two-way coupled with an in-house CEA mechanical code. The simulations are compared with a CEA experiment. The lateral force exerting on spacer grids induced by the bypass-fuel assembly redistribution appears to play an important role on the shape of the fuel assembly.Finally, we propose further extensions to a row of fuel assemblies, and then to a whole core. Qualitative comparisons with the literature highlight proper bow patterns for the single row test case, with high sensitivity to the flow inlet and outlet conditions. At last, we demonstrate that hydraulic forces in a full core can indeed be estimated accurately through separated calculations on single rows in orthogonal directions, as suggested in the literature.

Identify the contribution of vehicle non-exhaust emissions: a single particle aerosol mass spectrometer test case at typical road environment

Identify the contribution of vehicle non-exhaust emissions: a single particle aerosol mass spectrometer test case at typical road environment

Automated method for airfield pavement condition index evaluations

Infrastructure inspection and maintenance is a necessary, and often costly, process required for civil engineering structures, which has led to a movement toward automated technologies within this domain. This paper describes a method for conducting a partially automated airfield pavement condition assessment, similar to the manual ASTM Pavement Condition Index (PCI), using drone mounted imaging technology. Aerial images were processed using a convolutional neural network trained for pavement distress detection to generate a condition index. Automated data collected on a single auxiliary airfield in Colorado Springs, CO produced a condition index value of 56.5, which strongly agreed with manual inspection results that calculated a PCI value of 54 for the same runway. These results indicate that automation of some time-intensive aspects of pavement evaluation is feasible using drone-captured images and machine learning. The results of this study represent a single test case; future work should focus on expanding this framework to include the effects of location-dependent variability.

SEAFARER – A new concept for validating radiotherapy patient specific QA for clinical trials and clinical practice

BackgroundThe quality of radiotherapy delivery has been shown to significantly impact clinical outcomes including patient survival. To identify errors, institutions perform Patient Specific Quality Assurance (PSQA) assessing each individual radiotherapy plan prior to starting patient treatments. Externally administered Dosimetry Audits have found problems despite institutions passing their own PSQA. Hence a new audit concept which assesses the institution’s ability to detect errors with their routine PSQA is needed. MethodsPurposefully introduced edits which simulated treatment delivery errors were embedded into radiation treatment plans of participating institutions. These were designed to produce clinically significant changes yet were mostly within treatment delivery specifications. Actual impact was centrally assessed for each plan. Institutions performed PSQA on each plan, without knowing which contained errors. ResultsSeventeen institutions using six radiation treatment planning systems and two delivery systems performed PSQA on twelve plans each. Seventeen erroneous plans (across seven institutions) passed PSQA despite causing >5% increase in spinal cord dose relative to the original plans. Six plans (from four institutions) passed despite a >10% increase. ConclusionsThis novel audit concept evolves beyond testing an institution’s ability to deliver a single test case, to increasing the number of errors caught by institutions themselves, thus increasing quality of radiation therapy and impacting every patient treated. Administered remotely this audit also provides advantages in cost, environmental impact, and logistics.

A New Hybrid Optimization Method, Application to a Single Objective Active Flow Control Test Case

Genetic Algorithms (GA) are useful optimization methods for exploration of the search space, but they usually have slowness problems to exploit and converge to the minimum. On the other hand, gradient based methods converge faster to local minimums, although are not so robust (e.g., flat areas and discontinuities can cause problems) and they lack exploration capabilities. This article presents a hybrid optimization method trying to combine the virtues of genetic and gradient based algorithms, and to overcome their corresponding drawbacks. The performance of the Hybrid Method is compared against a gradient based method and a Genetic Algorithm, both used alone. The rate of convergence of the methods is used to compare their performance. To take into account the robustness of the methods, each one has been executed more than once, with different starting points for the gradient based method and different random seeds for the Genetic Algorithm and the Hybrid Method. The performance of the different methods is tested against an optimization Active Flow Control (AFC) problem over a 2D Selig–Donovan 7003 (SD7003) airfoil at Reynolds number 6×104 and a 14 degree angle of attack. Five design variables are considered: jet position, jet width, momentum coefficient, forcing frequency and jet inclination angle. The objective function is defined as minus the lift coefficient (−Cl), so it is defined as a minimization problem. The proposed Hybrid Method enables working with N optimization algorithms, multiple objective functions and design variables per optimization algorithm.

Deep Learning Based Program Generation From Requirements Text: Are We There Yet?

To release developers from time-consuming software development, many approaches have been proposed to generate source code automatically according to software requirements. With significant advances in deep learning and natural language processing, deep learning-based approaches are proposed to generate source code from natural language descriptions. The key insight is that given a large corpus of software requirements and their corresponding implementations, advanced deep learning techniques may learn how to translate software requirements into source code that fulfill such requirements. Although such approaches are reported to be highly accurate, they are evaluated on datasets that are rather small, lack of diversity, and significantly different from real-world software requirements. To this end, we build a large scale dataset that is composed of longer requirements as well as validated implementations. We evaluate the state-of-the-art approaches on this new dataset, and the results suggest that their performance on our dataset is significantly lower than that on existing datasets concerning the common metrics, i.e., BLEU. Evaluation results also suggest that the generated programs often contain syntactic and semantical errors, and none of them can pass even a single predefined test case. Further analysis reveals that the state-of-the-art approaches learn little from software requirements, and most of the successfully generated statements are popular statements in the training programs. Based on this finding, we propose a popularity-based approach that always generates the most popular statements in training programs regardless of the input (software requirements). Evaluation results suggest that none of the state-of-the-art approaches can outperform this simple statistics-based approach. As a conclusion, deep learning-based program generation requires significant improvement in the future, and our dataset may serve as a basis for future research in this direction.

Hide and seek: The supply and demand of information for household solar photovoltaic investment

Buildings provide an ideal platform for solar photovoltaics (PV) towards sustainable development goals, and the decision to invest in PV lies predominantly with building owners. Information delivery is critical for the diffusion of innovations, and this study aims to improve the quality of information for household PV investors in Sweden. A User Journey Mapping approach is applied with a combination of semi-structured interviews and a review of online solar calculators. The results show that despite a rapid growth in the quantity of information there is still a gap between demand and supply due to the lack of clarity and trustworthiness of information. This is clearly demonstrated in the review of online calculators, which show a high variance in results. Payback time, for example, ranged from 7 to 18 years for a single test case. The information gap can be closed by creating neutral, non-commercial online information sources that focus on transparency and education where household investors can validate supplier offers and analyses. The PV industry risks eroding trust in the market, which will likely slow adoption by the early majority and hinder sustainability goals.

A Hyper-Viscoelastic Continuum-Level Finite Element Model of the Spinal Cord Assessed for Transverse Indentation and Impact Loading.

Finite Element (FE) modelling of spinal cord response to impact can provide unique insights into the neural tissue response and injury risk potential. Yet, contemporary human body models (HBMs) used to examine injury risk and prevention across a wide range of impact scenarios often lack detailed integration of the spinal cord and surrounding tissues. The integration of a spinal cord in contemporary HBMs has been limited by the need for a continuum-level model owing to the relatively large element size required to be compatible with HBM, and the requirement for model development based on published material properties and validation using relevant non-linear material data. The goals of this study were to develop and assess non-linear material model parameters for the spinal cord parenchyma and pia mater, and incorporate these models into a continuum-level model of the spinal cord with a mesh size conducive to integration in HBM. First, hyper-viscoelastic material properties based on tissue-level mechanical test data for the spinal cord and hyperelastic material properties for the pia mater were determined. Secondly, the constitutive models were integrated in a spinal cord segment FE model validated against independent experimental data representing transverse compression of the spinal cord-pia mater complex (SCP) under quasi-static indentation and dynamic impact loading. The constitutive model parameters were fit to a quasi-linear viscoelastic model with an Ogden hyperelastic function, and then verified using single element test cases corresponding to the experimental strain rates for the spinal cord (0.32–77.22 s−1) and pia mater (0.05 s−1). Validation of the spinal cord model was then performed by re-creating, in an explicit FE code, two independent ex-vivo experimental setups: 1) transverse indentation of a porcine spinal cord-pia mater complex and 2) dynamic transverse impact of a bovine SCP. The indentation model accurately matched the experimental results up to 60% compression of the SCP, while the impact model predicted the loading phase and the maximum deformation (within 7%) of the SCP experimental data. This study quantified the important biomechanical contribution of the pia mater tissue during spinal cord deformation. The validated material models established in this study can be implemented in computational HBM.

Estimating the Storativity by Recovery data in Khabat area

The study presented a method for estimating aquifer storage coefficient form recovery test data meanwhile, it is hard to determine especially for single well test cases. The method involves an equation based on the principle of Jacob’s modified Theis equation by measuring residual drawdown data versus recovery time in the production well. The main objectives of this study is to determine the reliability of this equation in the selected area and to evaluate and compare the results with the pumping test results as well. The method is starting by estimating the Aquifer parameters (Transmissivity and Storativity) which involves plotting residual drawdown as a function of time, later substituting the obtained test values in an appropriate transient groundwater flow equations based on the aquifer characteristics and types to find the parameter values. Finally, the study concluded that the proposed equation is reliable for determine the Storativity of the aquifer for the single well test, and also can be used as a check for the parameters that obtained from pumping test results and thus the method is verified the objective of the study.

Simulation of liquid fuel combustion start-up dynamical behavior

A successful ignition is the consequence of corresponding conditions of fuel mass flow rates, mixture ratio, as well as initiation energy in time and space. If ignition does not take place accurately, serious damages can take place within or outside the engine. The dynamical behavior of the combustion chamber was investigated during ignition. Single shear coaxial injector combustion chamber test case is used in the present study. At the time of propellant injection, mostly the pressure and temperature remain subcritical. When injected these increases promptly and convert into supercritical conditions. The thermodynamic state usually changes from subcritical to supercritical during ignition. Navier Stokes equations along with Soave Redlich Kwong equation model has been applied during steady-state, while transient simulations with ideal gas assumptions are performed to analyze the dynamical behavior of the combustor. All the simulations were performed in the present study using the ANSYS Fluent 16.2 code. The computational findings have strong analytical consistency with the experimental data. Due to inappropriate modeling of the equation of state and limited information about initial and boundary conditions for ignition transient results have some differences with experimental data. The difference between the observed chamber pressure during the experiment and numerical computation using 0.4 RFL value is only 2 bar (approximately 7% difference).

Experimental measurements of propulsive factors in following and head waves

The results from resistance measurements in calm water and load-varying self-propulsion tests in calm water and regular head and following waves are presented. The experimental campaign is conducted in the large towing tank at SINTEF Ocean (formerly MARINTEK). The openly accessible hull of the single screw Duisburg Test Case is selected as the test case. The wave added resistance, ship motions RAOs, axial wake fraction, thrust deduction fraction, relative rotative efficiency, hull efficiency, propeller open-water efficiency, propeller efficiency behind ship, and propulsive efficiency are determined.Regarding the calculation of the thrust deduction fraction, the results of the experiments show the effect of utilizing the bare hull resistance instead of the linearly extrapolated ship resistance at zero propeller thrust. If the former is applied, the thrust deduction fraction will be dependent on the load of the propeller. If the latter is utilized, the thrust deduction fraction will be independent of the propeller loading. As expected, the wave added resistance is lower in following waves than in head waves. The heave and pitch motions are larger in head waves, whereas the surge motion is higher in following waves. The effective wake fraction is affected by both the propeller loading and the ship motions. In the case of the former, the higher the propeller loading, the lower the effective wake fraction. For the latter, a general decrease in effective wake fraction is noticed in head waves compared to calm water. On the contrary, the effective wake fraction is higher in following waves in comparison to calm water. The thrust deduction fraction computed with the extrapolated ship resistance appears to be slightly affected by the ship motions. However, a final conclusion was not drawn because of the large uncertainties in the measurements of this propulsive coefficient. The variation in propeller open-water efficiency is mainly related to the change in propeller loading. The relative rotative efficiency is barely affected by both the propeller loading and the motions of the ship. Except in the case of very large wave amplitudes, the hull efficiency is hardly influenced by the ship motions. The propulsive efficiency is primarily affected by the change in propeller open-water efficiency.Based on the results of the experimental campaign, overload tests in calm water provide a good estimation of the propulsion efficiency in waves for the selected case vessel.

The Need for Permit Management within Biodiversity Collection Management Systems to Digitally Track Legal Compliance Documentation and Increase Transparency About Origins and Uses

Abstract A growing number of domestic and international legal issues are confronting biodiversity collections, which require immediate access to information documenting the legal aspects of specimen ownership and restrictions regarding use. The Nagoya Protocol, which entered into force in 2014, established a legal framework for access and benefit-sharing of genetic resources and has notable implications for collecting, researchers working with specimens, and biodiversity collections. Herein, we discuss how this international protocol mandates operating changes within US biodiversity collections. Given the new legal landscape, it is clear that digital solutions for tracking records at all stages of a specimen's life cycle are needed. We outline how the Harvard Museum of Comparative Zoology (MCZ) has made changes to its procedures and museum-wide database, MCZbase (an independent instance of the Arctos collections management system), linking legal compliance documentation to specimens and transactions (i.e., accessions, loans). We used permits, certificates, and agreements associated with MCZ specimens accessioned in 2018 as a means to assess a new module created to track compliance documentation, a controlled vocabulary categorizing these documents, and the automatic linkages established among documentation, specimens, and transactions. While the emphasis of this work was a single year test case, its successful implementation may be informative to policies and collection management systems at other institutions.

Increasing the accuracy of binary neutron star simulations with an improved vacuum treatment

Numerical-relativity simulations are essential for studying the last stages of the binary neutron star coalescence. Unfortunately, for stable simulations there is the need to add an artificial low-density atmosphere. Here we discuss a new framework in which we can effectively set the density surrounding the neutron stars to zero to ensure a more accurate simulation. We test our method with a number of single star test cases and for an equal-mass binary neutron star simulation. While the bulk motion of the system is not influenced, and hence there is no improvement with respect to the emitted gravitational-wave signal, we find that the new approach is superior with respect to mass conservation and it allows a much better tracking of outward moving material. This will allow a more accurate simulation of the ejected material and supports the interpretation of present and future multimessenger observations with more accurate numerical-relativity simulations.

Hybrid scheme for complex flows on staggered grids and application to multiphase flows

A hybrid scheme is developed that enables advanced simulations, such as multiphase flows, in complex flow setups with compressible flow solvers using a staggered arrangement of flow variables. For this purpose, a weighted essentially non-oscillatory (WENO) scheme is adapted for staggered grids and combined with a finite difference central scheme. Hybrid weights that maintain continuity in time are computed based on a limiter constructed from the deviation of the local weights from the optimal weights. A total variation diminishing (TVD) interpolation is introduced to avoid numerical oscillations from the flow field. Additionally, the finite difference operators are reformulated in a flux-based finite volume form for convective terms in order to avoid spurious oscillations for turbulent configurations. For demonstrating the accuracy of the newly developed scheme and its improvements compared to the existing schemes, a modified wavenumber analysis is shown. Furthermore, the new scheme is applied to single and multiphase test cases. Finally, it is used for simulating the needle-opening process of a gasoline direct injector (GDI) and predicting the amount of gas inside the nozzle.

Uncertainty Quantification of the Effects of Blade Damage on the Actual Energy Production of Modern Wind Turbines

Wind turbine blade deterioration issues have come to the attention of researchers and manufacturers due to the relevant impact they can have on the actual annual energy production (AEP). Research has shown how after prolonged exposure to hail, rain, insects or other abrasive particles, the outer surface of wind turbine blades deteriorates. This leads to increased surface roughness and material loss. The trailing edge (TE) of the blade is also often damaged during assembly and transportation according to industry veterans. This study aims at investigating the loss of AEP and efficiency of modern multi-MW wind turbines due to such issues using uncertainty quantification. Such an approach is justified by the stochastic and widely different environmental conditions in which wind turbines are installed. These cause uncertainties regarding the blade’s conditions. To this end, the test case selected for the study is the DTU 10 MW reference wind turbine (RWT), a modern reference turbine with a rated power of 10 MW. Blade damage is modelled through shape modification of the turbine’s airfoils. This is done with a purposely developed numerical tool. Lift and drag coefficients for the damaged airfoils are calculated using computational fluid dynamics. The resulting lift and drag coefficients are used in an aero-servo-elastic model of the wind turbine using NREL’s code OpenFAST. An arbitrary polynomial chaos expansion method is used to estimate the probability distributions of AEP and power output of the model when blade damage is present. Average AEP losses of around 1% are predicted mainly due to leading-edge blade damage. Results show that the proposed method is able to account for the uncertainties and to give more meaningful information with respect to the simulation of a single test case.

Momentum injection by clustered supernovae: testing subgrid feedback prescriptions

ABSTRACT Using a 1D Lagrangian code specifically designed to assess the impact of multiple, time-resolved supernovae (SNe) from a single-star cluster on the surrounding medium, we test three commonly used feedback recipes: delayed cooling (e.g. used in the gasoline-2 code), momentum-energy injection (a resolution-dependent transition between momentum-dominated feedback and energy-dominated feedback used, e.g. in the fire-2 code), and simultaneous energy injection (e.g. used in the EAGLE simulations). Our work provides an intermediary test for these recipes: we analyse a setting that is more complex than the simplified scenarios for which many were designed, but one more controlled than a full galactic simulation. In particular, we test how well these models reproduce the enhanced momentum efficiency seen for an 11 SN cluster simulated at high resolution (0.6 pc; a factor of 12 enhancement relative to the isolated SN case) when these subgrid recipes are implemented in low resolution (20 pc) runs. We find that: (1) the delayed cooling model performs well – resulting in 9 times the momentum efficiency of the fiducial isolated SN value – when SNe are clustered and 1051 erg are injected per SN, while clearly overpredicting the momentum efficiency in the single SN test case; (2) the momentum-energy model always achieves good results, with a factor of 5 boost in momentum efficiency; and (3) injecting the energy from all SNe simultaneously does little to prevent overcooling and greatly underproduces the momentum deposited by clustered SNe, resulting in a factor of 3 decrease in momentum efficiency on the average.

Combined Single-Cell DNA Genotyping and Protein Quantification (DAb-seq) in Acute Myeloid Leukemias Reveals Distinct Immunophenotypic Subsets Among Pathogenic Clones

Genetic and immunophenotypic diversity is a hallmark of hematopoietic malignancies, and clonal evolution has been implicated as a driver of tumor initiation, progression, treatment response, and relapse. Consequently, a deeper understanding of the complex relationship between mutational profiles and disease phenotypes is critical to better understand the underlying disease processes. Probing the question of genotype-immunophenotype interplay at scale has historically proven difficult due to technical limitations permitting capture of only a single datatype from an individual cell. In acute myeloid leukemia (AML), flow cytometry for detection of measurable residual disease (MRD) has become standard of care for monitoring of treatment response and risk for relapse. At the same time, DNA-based molecular profiling of AML provides prognostic information and can guide therapeutic decisions at the time of diagnosis and disease progression. However, it remains a challenge to effectively integrate these assessments into a comprehensive model of disease state that can be used for clinical decision-making. Here we present DNA and Antibody sequencing (DAb-seq), a novel technology for simultaneously performing high-throughput, single-cell measurements of both DNA genotype and protein markers from >5,000 single cells. To our knowledge, DAb-seq is the first technology to directly integrate readouts of DNA genotype and cellular phenotype at the single-cell level. The experimental workflow leverages the single-cell microfluidic technology on the Mission Bio Tapestri platform, a system optimized to conduct multiplex single-cell targeted genomic DNA amplification. In our modified protocol, cells are stained with a pool of monoclonal antibodies conjugated with barcoded oligonucleotides, enabling a sequencing-based readout of single-cell protein signatures. The sequencing data is analyzed with a bioinformatic pipeline that separates antibody signal from targeted genotyping data on a cell-by-cell basis. We apply DAb-seq to the analysis of multiple AML patient samples, using a panel of 50 DNA targets comprising recurrently mutated genes in AML and 23 antibodies commonly used in flow cytometry-based MRD detection. In a single test case of pediatric AML, we identify cells harboring known pathogenic KRAS and FLT3 mutations at diagnosis and relapse. Among these genetically defined cell subsets, we observe differential protein expression of CD56, CD11b, and CD15. Specifically, we find a significant positive correlation between KRAS mutational status and combined expression of CD56 and CD11b. Similarly, FLT3 mutant cells, the dominant clone at relapse, are found to express elevated levels of CD15. We further apply DAb-seq technology to the profiling of mixed phenotype leukemia and monitoring the phenotypic dynamics of genetic clones in response to CD33 targeted therapy. Our data suggest cell populations defined by single variants in reality comprise multiple immunophenotypic subpopulations, underscoring the complex interplay between genotype and protein expression. While the precise mechanism underlying this immunophenotypic diversity remains to be elucidated, our work demonstrates the utility of DAb-seq in uncovering broader patterns of genotypic and phenotypic evolution in large AML patient cohorts. Ultimately, such studies could support further stratification of disease subtypes and increase the precision with which therapies are applied. Figure Disclosures Ruff: Mission Bio Inc.: Employment. Ooi:Mission Bio: Employment, Equity Ownership. Smith:Astellas Pharma: Research Funding; Abbvie: Research Funding; fujiFilm: Research Funding; Revolution Medicines: Research Funding. Abate:Mission Bio, Inc.: Equity Ownership.