Open-source Computational Platform Research Articles

Analytical model for RC coupling beam considering axial restraint

A novel three-dimensional distributed plasticity model element is formulated and implemented into an open-source computational platform (OpenSees) to enhance nonlinear modeling approaches for lateral force resisting systems utilizing diagonally reinforced concrete coupling beams. The model incorporates a fiber-based concrete cross-section along with truss elements to represent the diagonal reinforcement and contact elements to model reinforcing bar slip at the beam-wall interface. The model is validated using test results from five different experiments that include variations in cross-section geometries, reinforcement configurations, and boundary conditions. Notably, two test specimens shared identical attributes except for their cross-sectional shapes (with and without a slab), whereas another pair were identical except for the degree of axial restraint provided. The final specimen utilized both longitudinal and diagonal reinforcement along the length of the beam. The model accurately predicted overall shear versus chord rotation responses with errors < 5 % and axial growth responses with errors < 30 %. The proposed model provides enhanced modeling options for nonlinear analysis by enabling sensitivity studies to address the potential impacts of coupling beam axial restraint on coupling beam and coupled wall behavior.

PSW-Designer: An Open-Source Computational Platform for the Design and Virtual Screening of Photopharmacological Ligands.

Photoswitchable (PSW) molecules offer an attractive opportunity for the optical control of biological processes. However, the successful design of such compounds remains a challenging multioptimization endeavor, resulting in several biological target classes still relatively poorly explored by photoswitchable ligands, as is the case for G protein-coupled receptors (GPCRs). Here, we present the PSW-Designer, a fully open-source computational platform, implemented in the KNIME Analytics Platform, to design and virtually screen novel photoswitchable ligands for photopharmacological applications based on privileged scaffolds. We demonstrate the applicability of the PSW-Designer to GPCRs and assess its predictive capabilities via two retrospective case studies. Furthermore, by leveraging bioactivity information on known ligands, typical and atypical strategies for photoswitchable group incorporation, and the increasingly structural information available for biological targets, the PSW-Design will facilitate the design of novel photoswitchable molecules with improved photopharmacological properties and increased binding affinity shifts upon illumination for GPCRs and many other protein targets.

Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system.

As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop Gell (GPU Cell), a fast and memory-efficient open-source GPU-based hybrid computational modeling platform for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement.

Newton-X Platform: New Software Developments for Surface Hopping and Nuclear Ensembles.

Newton-X is an open-source computational platform to perform nonadiabatic molecular dynamics based on surface hopping and spectrum simulations using the nuclear ensemble approach. Both are among the most common methodologies in computational chemistry for photophysical and photochemical investigations. This paper describes the main features of these methods and how they are implemented in Newton-X. It emphasizes the newest developments, including zero-point-energy leakage correction, dynamics on complex-valued potential energy surfaces, dynamics induced by incoherent light, dynamics based on machine-learning potentials, exciton dynamics of multiple chromophores, and supervised and unsupervised machine learning techniques. Newton-X is interfaced with several third-party quantum-chemistry programs, spanning a broad spectrum of electronic structure methods.

AnFiSA: An open-source computational platform for the analysis of sequencing data for rare genetic disease

Despite genomic sequencing rapidly transforming from being a bench-side tool to a routine procedure in a hospital, there is a noticeable lack of genomic analysis software that supports both clinical and research workflows as well as crowdsourcing. Furthermore, most existing software packages are not forward-compatible in regards to supporting ever-changing diagnostic rules adopted by the genetics community. Regular updates of genomics databases pose challenges for reproducible and traceable automated genetic diagnostics tools. Lastly, most of the software tools score low on explainability amongst clinicians.We have created a fully open-source variant curation tool, AnFiSA, with the intention to invite and accept contributions from clinicians, researchers, and professional software developers. The design of AnFiSA addresses the aforementioned issues via the following architectural principles: using a multidimensional database management system (DBMS) for genomic data to address reproducibility, curated decision trees adaptable to changing clinical rules, and a crowdsourcing-friendly interface to address difficult-to-diagnose cases. We discuss how we have chosen our technology stack and describe the design and implementation of the software. Finally, we show in detail how selected workflows can be implemented using the current version of AnFiSA by a medical geneticist.

Open-source modelling of aerosol dynamics and computational fluid dynamics: Bipolar and unipolar diffusion charging and photoelectric charging

Electric charging is one of the essential aerosol dynamic mechanisms and is harnessed for detection, capture and control of ultrafine aerosol particles in a range of devices. For simplicity, charging and transport mechanisms are commonly modelled with zero spatial dimensions (0-D) and averaged properties such as mean charge or mean particle diameter. These models often neglect localised effects of the flow distribution, diffusion, discrete charge states, and particle polydispersity, often proving inadequate to explain experimental data. This work aims to provide an open-source three-dimensional (3-D) aerosol charging and transport model including bipolar and unipolar diffusion charging, and photoelectric charging algorithms for use in detailed design and analyses of aerosol systems. The computational model consists of more than 200 particle transport equations for discrete charge states and polydisperse sizes coupled with ion conservation equations in the framework of OpenFOAM, an open-source computational fluid dynamics platform. Three test cases are introduced to verify implementation of three charging models by comparison with published literature: bipolar and unipolar diffusion charging, and photoelectric charging. Tutorial cases, which model three distinct aerosol sensors, are described and demonstrate the capabilities of the 3-D aerosol charging and transport models within the predetermined flow field. The aerosolChargingFoam code is available at https://openaerosol.sourceforge.io for widespread use and can be further modified under the GNU general public licence. Program summaryProgram title: aerosolChargingFoamCPC Library link to program files:https://doi.org/10.17632/4pf4n2vg9f.1Developer's repository link:https://openaerosol.sourceforge.io/Licensing provisions: GNU General Public License 3Programming language: C++Nature of problem: aerosolChargingFoam solves generalised aerosol electrical charging and transport equations coupled with computational fluid dynamics using the open-source computational platform, OpenFOAM [1]. The electric charging algorithm including unipolar diffusion charging, bipolar diffusion charging, and photoelectric charging is verified with zero-dimensional test cases [2-4] and generalized to three-dimensional monodisperse or polydisperse particle distributions for more than 50 particle charge states. aerosolChargingFoam can be straightforwardly coupled with other existing solvers, which enables computations of complex multi-physics aerosol charging processes in practical conditions.Solution method: aerosolChargingFoam employs an explicit time-stepping for the time-dependent source terms for generalised aerosol charging. The solution methods and schemes provided by OpenFOAM 9 are used for solving the spatial and time derivatives in the transport equations.

Modeling methods of self-centering energy dissipation braces using OpenSees

Self-centering energy dissipation (SCED) braces as a high-performance structural member has been extensively investigated in the last decade. The unique hysteretic behavior of the SCED brace generally dominates the seismic performance of building structures. This paper presents three available simulation methods for predicting the hysteretic behavior of a typical SCED brace in the widely-used open-source computational platform OpenSees. The simulation methods are implemented using the built-in material and element libraries in OpenSees, and aim to investigate the advantages/disadvantages between different modeling methods. Furthermore, two general-purpose self-centering (SC) uniaxial hysteretic models are developed and implemented for modeling different types of SCED braces. Representative validation studies are then presented through using the developed material models to predict the experimental results of the SCED braces under cyclic loading. The SC hysteretic models can capture primary characteristics of hysteretic responses with reasonable accuracy. Additionally, an improved truss element is also proposed for straightforward application of the developed SC models. The users are able to define the experimentally-observed hysteretic features of the SCED braces in line with the envelopes established in the developed material models. A shaking table test of the SCED braced frame in the literature is further simulated to verify the feasibility of the developed material models and modified truss element under seismic loadings. The research outcomes validate a more convenient and accurate method for predicting the behavior of SCED braces and provide some useful insights on the modeling methods in OpenSees.

Machine learning-based adaptive degradation model for RC beams

Reliable seismic damage assessment of structural systems requires analytical models that are able to capture the cyclic deterioration of components. In this paper, an adaptive-updated degradation model (AUDM) for reinforced concrete (RC) beams is proposed based on the machine learning approach. The proposed model is capable of updating its constitutive parameters from the embedded artificial neural networks, based on the analysis results derived from the previous step. It offers the possibility for generating deterioration without constructing empirical equations, updating the hysteretic shape as the accumulation of damage, and accounting for the effect of variable shear-flexural interaction in nonlinear analysis. The details of the proposed model, including the backbone curve, the hysteretic behavior and the cyclic deterioration rule, were first described. An experimental database consisting of 100 cantilever rectangular RC beams under cyclic loading was collected from the existing literature to identify the constitutive parameters in the proposed model. Artificial neural networks for predicting the constitutive parameters in the proposed model were developed and trained based on the identified data, which was then embedded into the open-source computational platform OpenSees to implement AUDM. The accuracy of the proposed model over the existing commonly-used degradation model is revealed in OpenSees based on the test results of RC beams under cyclic loading. It was found that the proposed AUDM was shown to be superior to the existing degradation models in terms of the lateral bearing capacity, the dissipated energy, the cyclic deterioration and the hysteretic shapes under different failure modes, with the relative error of energy dissipation capacity in the range between −0.19 and 0.19 and the maximum root square error for strength deterioration less than 0.259.

Abstract 1198: MatchMiner: An open-source computational platform that accelerates patient enrollment on to precision medicine trials

Abstract With the advent of next generation sequencing in cancer care, patients' tumors can be genomically profiled and specific genetic alterations can be targeted with precision medicine drugs. However, the abundance of patient sequencing data coupled with complex clinical trial eligibility has made it challenging to match patients to precision medicine trials. To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials. MatchMiner supports two distinct workflows: (1) patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. In MatchMiner at DFCI, there are currently 330+ precision medicine trials and genomic and genomic and clinical data from 39,000+ patients. Although MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017, its impact on patient care has not yet been extensively studied. In this study, we analyzed temporal trends of 170 MatchMiner-driven trial enrollments. We compared these 170 MatchMiner-driven trial enrollments to non-MatchMiner-driven trial enrollments to determine how MatchMiner has impacted patient enrollments. To compare MatchMiner-driven trial enrollments to non-MatchMiner-driven enrollments, we limited the non-MatchMiner group by choosing patients who enrolled on the same trials. We also ensured that all patients in both enrollment groups had a genomic report present in MatchMiner before their consent date. We then analyzed temporal trends between genomic report dates, patient consent and on-study dates, and patient views in MatchMiner. MatchMiner-driven enrollments had a significant decrease in time from genomic report date to consent date compared to non-MatchMiner-driven enrollments. Thus, clinical use of MatchMiner decreased time to enroll in a precision medicine study, and suggests that use of precision medicine trial matching tools such as MatchMiner are important for the future of patient care. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). We are committed to supporting MatchMiner as an open-source software; to our knowledge, at least five cancer centers are implementing MatchMiner. Citation Format: Harry Klein, Tali Mazor, Priti Kumari, James Lindsay, Andrea Ovalle, Ethan Siegel, Pavel Trukhanov, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform that accelerates patient enrollment on to precision medicine trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1198.

Designing and assessing solar energy neighborhoods from visual impact

The optimal diffusion of Building Integrated PhotoVoltaics (BIPV) in cities requires diligent planning to arrange temporal and spatial distribution of energy, while preserving the aesthetics of city landscapes. Benefitting from an increasing quality of 3D models of cities, a comprehensive methodology to estimate potential energy generation from visually-acceptable PV, energy use from buildings, and economically viable micro-grid operation is proposed, by combining validated dynamic energy simulation tools into an open-source computational platform. The platform intends to be useful for urban planners and officials in charge of planning a large-scale BIPV installation into an existing neighborhood: simulations are carried out at the city scale, inclusive of façade potential, shading from vegetation and detailed roof shapes with super-structures.Social acceptability is investigated through a novel visual impact assessment methodology and grid integration solutions are analyzed with reference to their associated costs. Under a conservative scenario, a median of 10 kW hAC-electricity / m2 heated floor area per year is available from BIPV production in Geneva (Switzerland), covering 32 % electricity demand for space heating from heat pumps, or, alternatively, almost 10 times the one for space cooling. Visual impact has demonstrated not to be concurrent to grid integration constraints at the current stage, but rather to contribute to filtering building envelope surface and avoid grid curtailment of excess electricity. In the near future, with an increased grid efficiency, visual impact is expected to become a crucial criterion to limit the integration extent.

Elucidating the Mechanical Energy for Cyclization of a DNA Origami Tile

DNA origami has emerged as a versatile method to synthesize nanostructures with high precision. This bottom-up self-assembly approach can produce not only complex static architectures, but also dynamic reconfigurable structures with tunable properties. While DNA origami has been explored increasingly for diverse applications, such as biomedical and biophysical tools, related mechanics are also under active investigation. Here we studied the structural properties of DNA origami and investigated the energy needed to deform the DNA structures. We used a single-layer rectangular DNA origami tile as a model system and studied its cyclization process. This origami tile was designed with an inherent twist by placing crossovers every 16 base-pairs (bp), corresponding to a helical pitch of 10.67 bp/turn, which is slightly different from that of native B-form DNA (~10.5 bp/turn). We used molecular dynamics (MD) simulations based on a coarse-grained model on an open-source computational platform, oxDNA. We calculated the energies needed to overcome the initial curvature and induce mechanical deformation by applying linear spring forces. We found that the initial curvature may be overcome gradually during cyclization and a total of ~33.1 kcal/mol is required to complete the deformation. These results provide insights into the DNA origami mechanics and should be useful for diverse applications such as adaptive reconfiguration and energy absorption.

Open-source modelling of aerosol dynamics and computational fluid dynamics: Nodal method for nucleation, coagulation, and surface growth

Understanding formation, growth and transport of aerosols is critical to processes ranging from cloud formation to disease transmission. In this work, a numerical algorithm of aerosol dynamics including nucleation, coagulation, and surface growth was coupled with flow and heat transfer equations enabling the solution of three-dimensional multi-physics aerosol processes in an open-source platform. The general dynamic equation was solved by a nodal method where the particle size distribution was represented by a finite number of nodes. The models were verified by comparing four test cases, (1) pure coagulation, (2) nucleation and coagulation, (3) pure surface growth, and (4) a general dynamic equation that includes the three mechanisms provided in literature. A high temperature aerosol flow in a cooled pipe is chosen as a tutorial case of coupled computational aerosol and fluid dynamics. The aerosolGDEFoam code is available at https://openaerosol.sourceforge.io and can be further modified under GNU general public licence. Programme summaryProgramme title: aerosolGDEFoamCPC Library link to programme files: https://doi.org/10.17632/3s368jpdx2.1Developer’s repository link: https://openaerosol.sourceforge.io/Licencing provisions: GNU General Public Licence 3Programming language: C++Nature of problem: aerosolGDEFoam solves the general dynamic equation coupled with flow and heat transfer equations enabling the solution of three-dimensional multi-physics aerosol processes using the open-source computational platform, OpenFOAM [1]. The general dynamic equation describes changes in aerosols due to e.g. nucleation, coagulation and evaporation/condensation, processes which depend on local conditions such as temperature and humidity. A zero-dimensional form of the general dynamic equation from Prakash et al. [2] has been implemented and verified with previously published examples.Solution method: aerosolGDEFoam employs an explicit time-stepping for the time-dependent source terms for aerosol dynamics. The solution methods and schemes provided by OpenFOAM 6 are used for spatial derivatives.

Seismic performance evaluation of a pile-supported wharf system at two seismic hazard levels

This study presents a framework for assessing the seismic performance of a pile-supported wharf at different seismic hazard levels. A refined Three-Dimensional (3D) Finite Element (FE) model is conducted using the open-source computational platform OpenSees. The modeling strategies of the refined numerical model are given in detail, including refined modeling of free-field boundaries and soil-pile interaction. Two seismic hazard levels adopted by the design standard for seismic design of piers and wharves (ASCE 2014) are considered, namely, Contingency Level Earthquake (CLE) and Design Earthquake (DE). The seismic performance of the wharf-ground system is systematically explored in the following two aspects. First, the evaluation of seismic performance is based on the time history response for CLE (and DE) motion. Second, the seismic performance is assessed by averaging maximum response for all 28 DE and CLE motions. The results from this study reveal that (i) the seismic response of the wharf-ground system is larger under a DE motion than under a CLE motion; (ii) the pile foundation with a short free length is more vulnerable to damage for both seismic hazard levels; and (iii) the fiber strains of piles are significantly lower than the strain limits in the design standard (ASCE 2014).

ALKEMIE: An intelligent computational platform for accelerating materials discovery and design

Developing new materials with target properties via the traditional trial-and-error ways is cost-inefficient, and sometimes ends up with fruitlessness, therefore, simulation-driven materials design plays an important role in the past decades. Nevertheless, the advent of the era of data-driven material science requires an intelligent computational platform to accelerate the discovery and design for advanced materials. Here, we present an open-source computational platform named as ALKEMIE, acronyms for Artificial Learning and Knowledge Enhanced Materials Informatics Engineering, which enables easy access of data-driven techniques to broad communities. ALKEMIE is incorporated with three key components for the computational design of materials for the forthcoming data-driven sciences: data generation via high-throughput calculations, data management and data mining via machine learning models. Briefly speaking, the high-throughput calculations in ALKEMIE are implemented through the integration of automatic frameworks of model constructions, calculation performances and data analysis. And the used high-level application programming interface for the database makes the data mining through machine learning more applicable in material science. In particular, ALKEMIE is integrated with a module for the generation of machine-learned interatomic potential for large-scale molecular dynamic simulations where the dataset is obtained from high-throughput first-principles calculations. More importantly, ALKEMIE has an elaborately designed user-friendly graphical user-interface which makes the workflow and dataflow more maneuverable and transparent, facilitating its easy-to-use for scientists with broad backgrounds. Finally, the main characters of ALKEMIE are demonstrated using three computational examples.

Three-Dimensional Printed Molds for Image-Guided Surgical Biopsies: An Open Source Computational Platform.

PURPOSESpatial heterogeneity of tumors is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood because it relies on the accurate coregistration of medical images and tissue biopsies. Tumor molds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumor heterogeneity data.METHODSWe have developed an open-source computational framework to automatically produce patient-specific 3-dimensional–printed molds that can be used in the clinical setting. Our approach achieves accurate coregistration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging, and pathology workflows.RESULTSWe applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalized molds for 6 patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.96 for tumor regions and between 0.70 and 0.76 for healthy kidneys. The framework required minimal manual intervention, producing the final mold design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes.CONCLUSIONOur work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumor heterogeneity on multiple spatial scales.

Abstract 3382: MatchMiner: An open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials

Abstract To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials based on clinical and genomic features. MatchMiner supports two distinct workflows: (1) patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017. There are currently 275+ trials curated in the system and genomic data from 26,000+ patients. Over 80% of living patients match to at least one open clinical trial, with an average of 6 trial matches per patient. At least 98 patients have enrolled on a clinical trial as a result of MatchMiner. To enable computational matching, we developed clinical trial markup language (CTML), a structured format to encode detailed information about a trial. CTML utilizes boolean logic to define clinical (e.g. cancer type), demographic (e.g. age) and genomic (e.g. specific mutations, copy number alterations, structural variants or mutational signatures) eligibility, which can be applied to individual arms of a trial. MatchMiner is an open-source two-tier web application with a Python-based REST API server and an AngularJS front-end. MatchMiner utilizes Security Assertion Markup Language (SAML)-based authentication and is fully HIPAA-compliant when hosted behind a secure institutional firewall. MatchMiner ingests clinical and genomic data, and connects to existing clinical systems, including clinical trial management systems for real-time trial status. We recently refactored the core matching algorithm (the matchengine), which improves upon the original matchengine in several ways: (1) increased granularity in reporting the reason for a match; (2) can match all patients/trials or individual patients/trials; (3) easily extensible to match based on additional data types. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). We are committed to supporting MatchMiner as an open-source software; to our knowledge, at least five cancer centers are implementing MatchMiner at their own institutions. In summary, we have defined a standard for encoding clinical trial information in a structured and computable form, and we have developed an open-source computational trial matching platform to support patient-specific trial identification as well as trial-specific patient recruitment. We are actively collaborating with clinical groups at Dana-Farber Cancer Institute and other institutions to understand the role of MatchMiner in their clinical workflows, and we are committed to continuing to evolve MatchMiner to meet clinical needs. Citation Format: Tali Mazor, Rachel B. Keller, Priti Kumai, James Lindsay, Eric Marriott, Andrea Ovalle, Ethan Siegel, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3382.

MAPDP: A Cloud-Based Computational Platform for Immunopeptidomics Analyses.

The immunopeptidome corresponds to the repertoire of peptides presented at the cell surface by the major histocompatibility complex (MHC) molecules. Cytotoxic T cells scan this repertoire to identify nonself antigens that can arise from tumors or infected cells. The identification of actionable antigenic targets is key to the development of therapeutic cancer vaccines, T-cell therapy, and other T-cell receptor-based biologics. The growing clinical interest for immunopeptidomics has accelerated the development of high throughput proteogenomic platforms that provide a system-level analysis of MHC-associated peptides. Improvement in sensitivity and throughput of mass spectrometers now allows the detection of a few thousands of peptides from less than 100 million cells. To manage the amount of data generated by these instruments, we have developed the MHC-associated peptide discovery platform (MAPDP), a novel open-source cloud-based computational platform for immunopeptidomic analyses. It provides convenient access from a web portal to immunopeptidomes stored in the database, filtering tools, various visualizations, annotations (e.g., IEDB, dbSNP, gnomAD), peptide-binding affinity prediction (mhcflurry, NetMHC), HLA genotyping, and the generation of personalized proteome databases. MAPDP functionalities are demonstrated here by the discovery of MHC peptides featuring new genetic variants identified in two previously published ovarian carcinoma data sets.

Abstract A024: MatchMiner: An open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials

Abstract To facilitate interpretation of complex tumor sequencing data and clinical trial genomic eligibility criteria, we developed MatchMiner, an open-source platform to computationally match cancer patients to precision medicine clinical trials based on clinical and genomic features. MatchMiner supports two distinct workflows: (1) a patient-centric mode, in which an oncologist can find clinical trial matches for a specific patient, and (2) a trial-centric mode, in which a clinical trial investigator can identify and recruit patients for a specific trial. MatchMiner has been operational at Dana-Farber Cancer Institute since early 2017. There are currently 250+ trials curated in the system and genomic data from 24,000+ patients. Over 82% of living patients match to at least one open clinical trial, with an average of 6 trial matches per patient. At least 85 patients have enrolled on a clinical trial as a result of MatchMiner. The MatchMiner open-source software package is available through GitHub (https://github.com/dfci/matchminer). MatchMiner is a two-tier web application with a Python-based REST application programming interface (API) server and an AngularJS front-end. MatchMiner utilizes Security Assertion Markup Language (SAML)-based authentication and, when hosted behind a secure institutional firewall, is fully HIPAA-compliant. MatchMiner can connect to existing clinical systems, including clinical trial management systems for real-time trial status. To enable computational matching to clinical trials, we developed clinical trial markup language (CTML), a structured format to encode detailed information about a trial. CTML utilizes boolean logic to define clinical (e.g. cancer type), demographic (e.g. age) and genomic (e.g. specific mutations, copy number alterations, structural variants or mutational signatures) eligibility, which can be applied to individual arms of a trial. We recently refactored the core matching algorithm (the matchengine), which improves upon the original matchengine in several ways. While the original matchengine reported the reason for a patient-trial match, the refactored matchengine provides additional, more granular details. In addition, the original matchengine ran at the cohort level, matching all patients to all trials, whereas the refactored matchengine can also run against individual patients or trials, speeding up the matching process. The refactored matchengine is also easily extensible to match based on additional data types. In summary, we have defined a standard for encoding clinical trial information in a structured and computable form, and we have developed an open-source computational trial matching platform to support patient-specific trial identification as well as trial-specific patient recruitment. We are actively collaborating with clinical groups at Dana-Farber Cancer Institute to understand the role of MatchMiner in their clinical workflows, and we are committed to continuing to evolve MatchMiner to meet clinical needs. Citation Format: Tali Mazor, Rachel B Keller, Priti Kumari, James Lindsay, Eric Marriott, Andrea Ovalle, Ethan Siegel, Elizabeth H Williams, Joyce Yu, Michael Hassett, Ethan Cerami. MatchMiner: An open-source computational platform for genomically-driven matching of cancer patients to precision medicine clinical trials [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A024. doi:10.1158/1535-7163.TARG-19-A024

Analytical hysteretic model for reinforcing bars

This paper proposes an analytical hysteretic model for reinforcing bars considering the effects of inelastic buckling and low-cycle fatigue. A strain-dependent post-yield stiffness ratio formula is introduced into the traditional Bouc-Wen model to make it possess the asymmetrical characteristic, so that the compressive stress softening effect generally exhibited by buckling bars can be reasonably described. Meanwhile, the low-cycle fatigue degradation is also contained in the model. Based on the collected experimental hysteresis loops of reinforcing bars, the model control parameters are identified using the genetic algorithm. The corresponding prediction equations of control parameters in terms of physical parameters of the reinforcing bar, such as the slenderness ratio, yield strength and ultimate strength-to-yield strength ratio, are then established by regression analysis. Furthermore, the proposed model is implemented in OpenSees, an open-source computational platform. The effectiveness of the proposed model is verified by performing the comparison with the experimental results of both isolated reinforcing bars and reinforced concrete columns.

The effect of earthquake motion characteristics on potentially liquefiable pile-pinned sloping ground

The economic benefits of utilizing numerical simulations versus experimental approaches in geotechnical studies have made them viable tools for engineering assessments. Within this framework, the damages due to earthquake-induced forces and displacements in liquefied ground are amongst the phenomena investigated by many researchers. In the current study, numerical simulation of mildly-sloping liquefied ground under dynamic loading is conducted by the graphical user interface OpenSeesPL, which uses the open-source computational platform OpenSees. Pile-pinning lateral displacement mitigation method in saturated sand is modeled and the effects of artificial earthquake time histories covering a range of different durations and frequency contents on the system response are scrutinized. It is observed that duration and frequency content of the earthquakes changed the initiation time of large deformations. Higher pore pressures were generated in low frequency systems and larger lateral displacements of the improved ground with loose soil were countered with lower accelerations due to the liquefied ground isolating effects. The reliability of different intensity measures for enabling seismic performance assessments are discussed and finally, recommendations for practical hazard evaluations are made.