Proprietary Applications Research Articles

22-LB: Use of a Novel Virtual Patient Simulation Application to Assess Providers’ Knowledge in Management of T2 Diabetes

The goals of Type 2 diabetes (T2DM) management include achievement of glycemic, blood pressure (BP), and lipid control. All three variables must be addressed adequately in order to prevent diabetic complications. Aim: Using a proprietary smartphone-based application (APP) developed by Edocate Ltd, that simulates virtual visits, we assessed how well healthcare providers (HCPs) manage T2DM. Materials & Methods: A total of 461 HCPs (247 MD, MD/PhD, or DO and 214 NPs) downloaded and used the APP to treat their virtual patients over multiple visits. All patients presented with poorly controlled T2DM, hypertension (HTN) and hyperlipidemia. HCPs chose the first, second and third lines of medications, made diagnostic adjustments, initiated appropriate referrals to specialists, and ordered laboratory tests of their choice. Based on their selections, the APP simulated the course of diabetes and its complications, requiring diverse management decisions on the subsequent virtual visits. Results: In the Edocate virtual clinic, only 53% of HCPs achieved good glycemic control (A1c < 7%), 41% achieved BP control (<130/80 mmHg), and 66% prescribed high-intensity statins. NPs performed slightly but not significantly better than the physician cohort. Only 48% of HCPs checked microalbuminuria, 54% ordered creatinine and 55% ordered LDL. Interestingly, 79% of HCPs either kept or started Metformin as the first line medication. The most commonly ordered second line of medications for glycemic control was either GLP-1 receptor agonists (39%) or SGLT2 inhibitors (36%), while 23% of HCPs kept their patients on or added sulfonylurea or basal insulin (11%). Conclusions: Despite a worldwide effort in diabetes education, the knowledge of T2DM management remains suboptimal (as assessed by the simulation APP) and underscores the need for better professional education. Innovative patient simulation-based learning applications can dramatically improve the level of competency in HCPs. Disclosure B. Draznin: None. I. Iancu: None.

WITHDRAWN: Development of internet of things and cloud based applications using Kivy for mobile and smartphone integration

Abstract Now days, every business organization is striving for the development and delivery of their services using mobile apps. This approach allows for the wider coverage as well as worldwide connectivity of enterprise resources. An overview of this growing use of mobile applications distributes a range of software and library systems under numerous licences including proprietary, free and open source applications. These development interfaces provide programmers with user-friendly APIs for fast and productive programmes. The application programming interface. With the increased use of smartphone applications, new mobile apps for different applications and smartphones are expanding throughout the technology market.

Addressing Semantics Standards for Cloud Portability and Interoperability in Multi Cloud Environment

The increasing demand for cloud computing has shifted business toward a huge demand for cloud services, which offer platform, software, and infrastructure for the day-to-day use of cloud consumers. Numerous new cloud service providers have been introduced to the market with unique features that assist service developers collaborate and migrate services among multiple cloud service providers to address the varying requirements of cloud consumers. Many interfaces and proprietary application programming interfaces (API) are available for migration and collaboration services among cloud providers, but lack standardization efforts. The target of the research work was to summarize the issues involved in semantic cloud portability and interoperability in the multi-cloud environment and define the standardization effort imminently needed for migrating and collaborating services in the multi-cloud environment.

A CONTROLLED CLINICAL STUDY TO EVALUATE A PROPRIETARY NON-INVASIVE SMARTPHONE BASED DIGITAL BIOMARKER TOOL LYFAS® IN ENABLING EARLY DETECTION OF COVID-19 INFECTION AMONG ASYMPTOMATIC INDIVIDUALS

Coronavirus disease 2019 (COVID-19), is a pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the increasing number of individuals infected with COVID-19, there is a growing need for easy, dependable, accurate, scalable, and cost-effective tools. These tools should serve the purpose of population screening/surveillance, diagnosis, and prognosis. Unlike the other pandemics in the past, the current viral infection presents itself with long incubation period up to 14days. This situation is challenging because it results in an extremely high proportion of asymptomatic individuals (up to 80% and more). These individuals in turn contribute to high risk of transmission among the vulnerable population with coexisting diseases and the aged. Currently, the real time PCR test and serology is being largely employed only in symptomatic individuals. Therefore, there is a need for a dependable additional test to identify the asymptomatic individuals, to qualify them for the conrmatory rtPCR and/or serology tests. To meet these the above requirement, we decided to repurpose and test our proprietary non-invasive smartphone-based health screening mobile application Lyfas. The validated parameters captured from Lyfas were found to be inherent indicators of several cardiorespiratory, cardiovascular, autonomic nervous system, hematology and biochemistry anomalies. Apparently, these anomalies were also found and reported as early indicators of COVID-19 infection. Therefore, by means of rationale selection of these parameters, we derived a unique LYFAS_COVID_SCORE to enable detection and prioritize conrmatory testing for asymptomatic individuals. A controlled clinical study was conducted in 25(n=25) subjects to prove the hypothesis and establish the difference in LYFAS_COVID_SCORE between an infected and non-infected group of individuals. The LYFAS_COVID_SCORE derived out of this clinical study was found to be different between the two groups. The positive infected test group had a signicantly high(p=.00012) median score of 19.8(range 9.9 to 22.1) compared to 6.9(range 2.2 to 7.8) that of the healthy uninfected control group. The accuracy, sensitivity and the specicity of the study was found to be 92%, 93.2% and 90% respectively. The result of this study is therefore an evidence to use Lyfas as a potential tool in the identication, screening, and surveillance of asymptomatic individuals. The ndings from the study deserves to be extended to a large population conrmatory clinical study to add proof and value to the tool.

A Comprehensive Rubric for Evaluating EduVR

Aim/Purpose: This article presents a comprehensive rubric for evaluating educational virtual reality experiences for mobile devices. The aim of this article is to systematically analyze research to address the quality of virtual reality experiences on mobile applications in order to extend the work of Lee and Cherner (2015) and their instructional application rubric. Background: Ratings in proprietary mobile application stores – The App Store and Google Play, etc. – are generic and do not provide meaningful evaluations of the virtual reality. This article utilizes research in the areas of virtual reality and education to present a comprehensive rubric for evaluating educational virtual reality for mobile applications, which continues to advance previously published, research-based rubrics. Methodology: The methodology uses a systematic process that spans multiple stages. The first stage was to locate pre-existing rubrics for virtual reality, followed by a review of literature focused on it. The third stage was to develop and vet a research-supported rubric for evaluating educational virtual reality. Contribution: The main contribution from this article is that it fills a gap in the literature by presenting a criterion-referenced, research-supported rubric for evaluating the quality of educational virtual reality for mobile devices (e.g., smartphones, tablets, and app-connected goggles). Findings: This paper’s findings include the domains, dimensions, and criterion-referenced Likert scale indicators in the form of rubric dimensions for evaluating educational virtual reality. The evaluative domains consist of (1) Positioning of the EduVR, (2) Avatar Level, (3) Virtual Environment, and (4) Virtual Experience. Recommendations for Practitioners: This rubric is a tool for instructional coaches, teacher educators, and instructional technologists to use when recommending virtual reality experiences for instructional purposes. Recommendation for Researchers: Researchers can use this tool to monitor the quality of educational virtual reality being developed for classroom use. They can also use this rubric to examine educational virtual reality experiences they would use in their studies and evaluate how those educational virtual reality experiences impact student learning, engagement, and collaboration. Impact on Society: We foresee this rubric being an aid in the development, selection, and purchase of educational virtual reality by educational institutions, educators, researchers, edtech developers, and edu-philanthropists, thus advancing the quality and expectations for educational virtual reality experiences. Future Research: Future researchers can further enhance the validity of this rubric by collecting large amounts of data from a diverse set of end users and stakeholders. Also, subsequent rubrics for evaluating augmented reality and extended reality comprise additional research avenues.

An open-source tool to extract natural continuity and hierarchy of urban street networks

Urban streets exhibit a hierarchical structure. From a network analysis perspective, continuity of streets based on the interior angle between street segments can be used to define ‘natural streets’ or ‘strokes’. The length of these ‘strokes’ can then be used to generate a hierarchy of street network. While researchers have described methods for defining such strokes and released tools that enable such analysis, the existing tools are dependent on proprietary applications and only an outline of the algorithm is available in the literature. This paper addresses these limitations and advances past approaches by (a) describing an efficient algorithm ‘COINS’ for conducting street continuity and hierarchy analysis and (b) releasing the python script and QGIS plugin which will enable users to implement this analysis independent of proprietary software and enable automation to process multiple datasets. The paper demonstrates the application of this tool using street network data from OpenStreetMap for 10 Indian cities and two international ones. Results indicate that our algorithm can detect the skeletal structure of a city which is visually very similar to the OpenStreetMap user-generated hierarchy, both at the city level and at the neighbourhood level. Analysing the results from 10 Indian cities using log–log plots of stroke length and stroke rank, we find that the strokes appear to follow Zipf’s law, but only in the mid-range of stroke lengths. Consistent with existing literature, we see that in our sample of Indian cities also there is a strong deviation from Zipf’s law for strokes that are approximately 100 m or lower.

Verification of the precision of GSM and GPS signals available on mobile devices

The research work presents the study of the quality of telecommunications and satellite signals. For this purpose, a proprietary application for mobile devices has been designed, which allowed the collection data. The tests were performed on several models of cell phones and for different operators. The test checked the impact of such things as weather conditions, device architecture, device age, type of terrain. Based on the results, heat maps were created to visualize the research.

Diagnostic Accuracy of Pan-Degenerate Amplification and Adaptation Assay for HIV-1 Drug Resistance Mutation Analysis in Low- and Middle-Income Countries.

HIV drug resistance (HIVDR) is a barrier to sustained virologic suppression in low- and middle-income countries (LMICs). Point mutation assays targeting priority drug resistance mutations (DRMs) are being evaluated to improve access to HIVDR testing. In a cross-sectional study (June 2018 to September 2019), we evaluated the diagnostic accuracy of a simple and rapid HIVDR assay (the pan-degenerate amplification and adaptation [PANDAA] assay targeting the mutations K65R, K103NS, M184VI, Y181C, and G190A) compared to Sanger sequencing and next-generation sequencing (NGS). Plasma samples from adolescents and young adults (aged 10 to 24 years) failing antiretroviral therapy (viral load, >1,000 copies/ml on 2 consecutive occasions 1 month apart) were analyzed. Sensitivity and specificity of the PANDAA assay were determined by a proprietary application designed by Aldatu Biosciences. Agreement between genotyping methods was evaluated using Cohen's kappa coefficient. One hundred fifty samples previously characterized by Sanger sequencing were evaluated using PANDAA. For all DRMs detected, PANDAA showed a sensitivity and specificity of 98% and 94%, respectively. For nucleotide reverse transcriptase inhibitor DRMs, sensitivity and specificity were 98% (95% confidence interval [CI], 92% to 100%) and 100% (94% to 100%), respectively. For non-nucleotide reverse transcriptase inhibitor DRMs, sensitivity and specificity were 100% (97% to 100%) and 76% (61% to 87%), respectively. PANDAA showed strong agreement with Sanger sequencing for K65R, K103NS, M184VI, and G190A (kappa > 0.85) and substantial agreement for Y181C (kappa = 0.720). Of the 21 false-positive samples genotyped by PANDAA, only 6 (29%) were identified as low-abundance variants by NGS. With the high sensitivity and specificity to detect major DRMs, PANDAA could represent a simple and rapid alternative HIVDR assay in LMICs.

Frenemies in Platform Markets: Heterogeneous Profit Foci as Drivers of Compatibility Decisions

We study compatibility decisions of two competing platform owners that generate profits through both hardware sales and royalties from content sales. We consider a game-theoretic model in which two platforms offer different standalone utilities to users. We find that incentives to establish one-way compatibility—the platform owner with smaller standalone value grants access to its proprietary content application to users of the competing platform—can arise from the difference in their profit foci. As the difference in the standalone utilities increases, royalties from content sales become less important to the platform owner with greater standalone value, but more important to the other platform owner. One-way compatibility can thus increase asymmetry between the platform owners’ profit foci and, given a sufficiently large difference in the standalone utilities, yields greater profits for both platform owners. We further show that social welfare is greater under one-way compatibility than under incompatibility. We also investigate how factors such as exclusive content and hardware-only adopters affect compatibility incentives. This paper was accepted by Chris Forman, information systems.

We are at an Inflection Point for Publishing and the Web

Publishing technology is at an important inflection point. It has evolved from consisting mostly of proprietary and incompatible applications and systems to being almost entirely based on web technologies. Those provide fundamental benefits such as interoperability, accessibility, security, and stability. Now with the inclusion of EPUB, the ongoing work in the Publishing@W3C activity is entering a new phase which promises to both establish EPUB as an ever more crucial and stable standard for ebooks and to make the tent bigger for publications and the web, bringing many more sectors of publishing into the fold.

Sensors for Self-Diagnosis of Electrochemical Treatment Technologies

Electrochemical oxidation appears to be a practical technology for decentralized treatment of wastewater in regions where sewer-based wastewater treatment is too expensive or otherwise impossible to construct and maintain.1 As of now, the control processes used by many systems rely principally on successive events that are time dependent and come with little level of monitoring but little feedback into the system process.2-3 One reason is the high cost of sensors and automation systems used in industrial installations cannot easily translate into household or public bathroom products for developing countries where capital expenditure is limited.Additionally, failures associated with onsite treatment technology have to be addressed otherwise the health and the environment of the users in direct contact would suffer. In the technology developed by Hoffmann et al. 3 , most repairs are simple, requiring only a screwdriver, yet without interactive instructions, local electricians and plumbers have been unable to get units properly serviced. From ethnography research, we also found the potential for service interruption is something we must eliminate for true user adoption of our sanitation solution.As an answer to this challenge, we integrated a suite of inexpensive sensors (e.g. ORP, color, turbidity, voltage, current, water level, leakage) in and around the electrochemical treatment system (picture attached) and to a local computer (Raspberry Pi + Arduino) that can diagnose failure modes and send alerts to on-demand repair technicians and users4. Our M2M, IoT technology uses a proprietary mobile application to deliver straight-forward, pictorial instructions that guide maintenance technicians regardless of literacy level.In this study we present the results from the field testing of the system “at scale” in two identical prototypes using electrochemical disinfection and treatment of toilet wastewater. The results from this study enable a refinement in the choice of sensors and the algorithms used to diagnose electrochemical water treatment quality and prevent system failures.

Deriving a usage-independent software quality metric

Context:The extent of post-release use of software affects the number of faults, thus biasing quality metrics and adversely affecting associated decisions. The proprietary nature of usage data limited deeper exploration of this subject in the past. Objective: To determine how software faults and software use are related and how an accurate quality measure can be designed. Method: New users, usage intensity, usage frequency, exceptions, and release date and duration measured for complex proprietary mobile applications for Android and iOS. Utilized Bayesian Network and Random Forest models to explain the interrelationships and to derive the usage independent release quality measure. Investigated the interrelationship among various code complexity measures, usage (downloads), and number of issues for 520 NPM packages and derived a usage-independent quality measure from these analyses, applied it on 4430 popular NPM packages to construct timelines for comparing the perceived quality (issues) and our derived measure of quality for these packages.Results: We found the number of new users to be the primary factor determining the number of exceptions, and found no direct link between the intensity and frequency of software usage and software faults. Release quality expressed as crashes per user was independent of other usage-related predictors, thus serving as a usage independent measure of software quality. Usage also affected quality in NPM, where downloads were strongly associated with numbers of issues, even after taking the other code complexity measures into consideration. Conclusions: We expect our result and our proposed quality measure will help gauge release quality of a software more accurately and inspire further research in this area.

A semantic-based methodology for digital forensics analysis

Nowadays, more than ever, digital forensics activities are involved in any criminal, civil or military investigation and represent a fundamental tool to support cyber-security. Investigators use a variety of techniques and proprietary software forensics applications to examine the copy of digital devices, searching hidden, deleted, encrypted, or damaged files or folders. Any evidence found is carefully analysed and documented in a “finding report” in preparation for legal proceedings that involve discovery, depositions, or actual litigation. The aim is to discover and analyse patterns of fraudulent activities. In this work, a new methodology is proposed to support investigators during the analysis process, correlating evidence found through different forensics tools. The methodology was implemented through a system able to add semantic assertion to data generated by forensics tools during extraction processes. These assertions enable more effective access to relevant information and enhanced retrieval and reasoning capabilities.

P110 Using Wearable Devices to Predict Length of Stay For IBD Patients After Bowel Surgery

BACKGROUND: Understanding the factors that contribute to a prolonged length of stay (LOS) for IBD patients after bowel surgery is essential to improving the quality of care provided to these patients. Early ambulation and sleep are important for post-operative recovery (1,2). We used activity and sleep data collected from a wearable biosensor to predict LOS for IBD patients undergoing bowel surgery. METHODS: We conducted a prospective study of IBD patients undergoing bowel surgery. Patients were provided a Fitbit (Charge or Alta HR, San Francisco, CA) and asked to download and install a proprietary smart phone application (Litmus Health, Austin, TX) for collection of step and sleep data. We also collected sociodemographic and clinical data via review of all patients’ medical records. Associations were assessed using Pearson’s correlation coefficient (r). RESULTS: Our final sample included 22 patients (17 CD, 5 UC), 4 of whom were excluded from analysis of sleep data only. At time of surgery, mean age was 40.6 years, and the mean BMI was 27.33 kg/m2. The median LOS was 5 days (IQR, 4-6.75 days). Compliance wearing Fitbit was 81.5% for step data collection and 64.1% for sleep data collection. There was a significant increase in step count from post-operative day 1 (POD1) to POD5 (r = 0.33, P = 0.015). Between POD1 and POD5, an additional POD was associated with a 421-step increase in step count. However, Pearson’s correlation analysis failed to reveal significant associations of sleep duration and sleep efficiency with post-operative days (P > 0.05). Furthermore, mean daily steps, mean sleep duration, or mean sleep efficiency was not significantly associated with post-operative LOS (r = -0.14, P = 0.52; r = 0.22, P = 0.37; r = -0.12, P = 0.64 respectively). However, a longer sleep duration on POD5 was associated with a longer LOS (r = 0.87, P = 0.026), while a higher sleep efficiency on POD5 was associated with a shorter LOS (r = -0.88, P = 0.019). Moreover, a more positive change in sleep duration from POD4-5 was associated with a longer LOS (r = 0.95, P = 0.003), whereas a more positive change in sleep efficiency from POD4-5 was associated with a shorter LOS (r = -0.92, P = 0.011). CONCLUSION(S): This is the first study to characterize associations of physical activity and sleep quality with LOS for IBD patients after bowel surgery. We found a significant increase in step count from POD1 to POD5. We also demonstrated that longer sleep duration or lower sleep efficiency on POD5 was associated with a longer LOS. More positive change in sleep duration or more negative change in sleep efficiency from POD4-5 was associated with a longer LOS. We plan to conduct an interventional study to assess the effect of physical activity and sleep quality on LOS for IBD patients after bowel surgery.

The Effect of Modifiers and Method of Application on Fine-Coal Combustion

This research work presents the results of studies on the effect of modifiers comprising the salts CuSO4·2H2O, NaCl, NH4Cl, MgSO4·7H2O, CaCl2, and urea at various concentrations on the combustion of fine coal. The tests were carried out in a 12-kW laboratory boiler equipped with a rotary-grate retort furnace. The emission levels and concentrations of CO, CO2, SO2, NOx, and TOC in the flue gas were measured using analyzers. A modifier composed of 350 ppm Cu, Na, Mg, NH4+, Ca, and urea showed particularly high activity in the combustion of fine coal. The flue-gas levels of CO, NOx, and SO2 were reduced by approximately 9%, 12%, and 10%, respectively, in comparison with the modifier-free sample. In this case, the boiler efficiency also increased from 65% in the tests with no modifier to 76% in the tests with the modifier. The proprietary application system, enabling the modifier to be added in exact amounts to the variable flow of fine coal, is also described. It was found that the use of the modifier in coal combustion tests results in lower emissions of harmful fuel components, a higher amount of heat obtained from the fuel unit mass, lower corrosive impact of the fuels, lower boiler maintenance costs, and extended service life.

SYNERGY-AI: Artificial intelligence-based precision oncology clinical trial matching and registry.

22 Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimized cancer care. Artificial Intelligence (AI), telemedicine and value-based care may optimize clinical trial enrollment (CTE) and overall cost-benefit. This ongoing, international registry for cancer pts evaluates the feasibility and clinical utility of an AI-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate CTE, as well as the financial impact, and potential outcomes of the intervention. Methods: The SYNERGY-AI Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CTs. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into registry, which is non-interventional with no protocol-mandated tests/procedures—all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as ~36 mo. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥ 1500 patients. Key inclusion criteria: Pts with solid and hematological malignancies; Pts cancer-related biomarkers. Key exclusion: ECOG PS > 2; abnormal organ function; hospice. Results: To be presented. Conclusions: AI-based, patient-driven CTE is feasible, highly effective and paradigm-changing. Clinical trial information: NCT03452774.

A Highly Efficient Remote Access Trojan Detection Method

Nowadays, machine learning is popular in remote access Trojan (RAT) detection which can create patterns for decision-making. However, most research focus on improving the detection rate and reducing the false negative rate, therefore they ignore the result of abnormal samples. In addition, most classifiers select several proprietary applications and RATs as their training set, which makes them difficult to adapt to the real environment. In this article, the authors address the issue of imbalance dataset between normal and RAT samples, and propose a highly efficient method of detecting RATs in real traffic. In the authors method, they generate eight features by combining the size, the inter-arrival and the flag from one packet sequence. Then, they preprocess the imbalance dataset and implement a classifier by XGBoost algorithm. The classifier achieves a false negative rate of less than 0.18%. Moreover, the authors demonstrate that their classifier is capable of detecting unknown RAT.

A Screening Test for Disclosed Vulnerabilities in FOSS Components

Free and Open Source Software (FOSS) components are ubiquitous in both proprietary and open source applications. Each time a vulnerability is disclosed in a FOSS component, a software vendor using this component in an application must decide whether to update the FOSS component, patch the application itself, or just do nothing as the vulnerability is not applicable to the older version of the FOSS component used. This is particularly challenging for enterprise software vendors that consume thousands of FOSS components and offer more than a decade of support and security fixes for their applications. Moreover, customers expect vendors to react quickly on disclosed vulnerabilities—in case of widely discussed vulnerabilities such as Heartbleed, within hours. To address this challenge, we propose a screening test: a novel, automatic method based on thin slicing, for estimating quickly whether a given vulnerability is present in a consumed FOSS component by looking across its entire repository. We show that our screening test scales to large open source projects (e.g., Apache Tomcat, Spring Framework, Jenkins) that are routinely used by large software vendors, scanning thousands of commits and hundred thousands lines of code in a matter of minutes. Further, we provide insights on the empirical probability that, on the above mentioned projects, a potentially vulnerable component might not actually be vulnerable after all.

Abstract CT218: SYNERGY-AI: Artificial intelligence based precision oncology clinical trial matching and registry

Abstract Background: Precision oncology encompasses the implementation of high level of evidence disease-specific and biomarker-driven diagnostic and treatment recommendations for optimized cancer care. Artificial Intelligence (AI), telemedicine and value-based care may optimize clinical trial enrollment (CTE) and overall cost-benefit. This ongoing, international registry for cancer pts evaluates the feasibility and clinical utility of an AI-based precision oncology clinical trial matching tool, powered by a virtual tumor boards (VTB) program, and its clinical impact on pts with advanced cancer to facilitate CTE, as well as the financial impact, and potential outcomes of the intervention. Methods: The SYNERGY-AI Registry is an international prospective, observational cohort study of eligible adult and pediatric pts with advanced solid and hematological malignancies, for whom the decision to consider CTE has already been made by their primary providers (PP). Using a proprietary application programming interface (API) linked to existing electronic health records (EHR) platforms, individual clinical data is extracted, analyzed and matched to a parametric database of existing institutional and non-institutional CTs. Machine learning algorithms allow for dynamic matching based on CT allocation and availability for optimized matching. Patients voluntarily enroll into registry, which is non-interventional with no protocol-mandated tests/procedures—all treatment decisions are made at the discretion of PP in consultation with their pts, based on the AI CT matching report, and VTB support. CTE will be assessed on variables including biomarkers, barriers to enrollment. Study duration anticipated as ~36 mo (~24-mo enrollment followed by 12 mo of data collection, to occur every 3 mo). The primary analysis will be performed 12 mo after last pt enrolled. The impact time to initiation of CTE on PFS and OS will be estimated by Kaplan-Meier and Cox multivariable survival analysis. Enrollment is ongoing, with a target of ≥ 1500 patients. Key inclusion criteria: Pts with solid and hematological malignancies; cancer-related biomarkers. Key exclusion criteria: ECOG PS > 2; abnormal organ function; hospice enrollment. ClinicalTrials.gov Identifier: NCT03452774 https://clinicaltrials.gov/ct2/show/NCT03452774 Citation Format: Selin Kurnaz, Arturo Loaiza-Bonilla. SYNERGY-AI: Artificial intelligence based precision oncology clinical trial matching and registry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr CT218.

A Robotic Framework for Simulation and Control of SCARA Robot Based on ROS

Robot Operating System (ROS) is a popular robot development platform that provides various features such as passing various messages, distributed computing, reusing of code, and so on. The ROS community is growing very fast and there are several users and developers worldwide. Most of the high-end robotics companies are switching from proprietary robotic application to ROS. In industry, SCARA robots are widely used as they are faster than similar types of Cartesian robot systems. In this paper, we propose the design and control of the SCARA robot using ROS. For implementation, an Arduino UNO board is used as an interface between the robot model and the ROS environment. Rviz simulator is used to visualize the model and MoveIt! Setup Assistant is used to controlling the robotic arm with the help of joint state publisher. This paper gives the overall idea about ROS and its advantages for controlling industrial robots.