Pre-defined Set Research Articles

Left ventricular filling pressure in Tetralogy of Fallot: Correlation between invasive and noninvasive indices

BackgroundLeft heart filling pressures, as measured by pulmonary artery wedge pressure (PAWP), is associated with heart failure related mortality. Because of the prognostic importance of PAWP, several echocardiographic indices have been proposed for noninvasive assessment of PAWP. However, these indices have not been validated in the congenital heart disease population. The purpose of this study was to determine the correlation between echocardiographic indices of PAWP, and the effect of high PAWP on transplant-free survival in adults with tetralogy of Fallot (TOF). MethodsRetrospective study of adult TOF patients that underwent cardiac catheterization at Mayo Clinic, 1990–2017. We selected these pre-defined set of echocardiographic indices of LV diastolic function: mitral valve early velocity (E), mitral valve early and late velocity ratio (E/A), mitral valve deceleration time (DT), mitral annular tissue Doppler early velocity (e′), and left atrial volume index (LAVI). ResultsOf the echocardiographic indices analyzed among 213 patients (age 37 ± 14 years), only E velocity (β = 5.83, standard error = 1.52, p < 0.001) and LAVI (β = 0.14, standard error = 0.05, p = 0.007) correlated with PAWP. LAVI > 28 ml/m2 had sensitivity of 79% and specificity of 63% (AUC 0.712), and E velocity > 1.0 m/s had sensitivity of 66% and specificity of 89% (AUC 0.692), for detecting PAWP > 15 mmHg. LAVI > 28 ml/m2 was associated with reduced 10-year transplant-free survival (49% vs 90%, p < 0.001). ConclusionThis study supports the use of LAVI for noninvasive assessment of PAWP and for prognostication. Further studies are required to validate these results in a different population.

Pelvic floor disorders following different delivery modes-a population-based cohort analysis.

Identifying protective factors against pelvic floor disorders (PFDs) is important. We investigated whether cesarean delivery (CD) at the indication of abnormal second stage of labor (ASSL) has the same protective effect against future PFDs. This population-based cohort study included deliveries occurring from 1991-2017 in a tertiary medical center. Women were grouped by their delivery mode: patients with vaginal deliveries (VD) only; those with CD only, excluding second-stage indications; and those with CD due to ASSL. The outcome measure, PFDs and related repair diagnoses, included any recorded hospitalization involving a pre-defined set of ICD-9 codes. A Kaplan-Meier survival curve compared cumulative PFD morbidity in the different groups, and a Cox proportional hazards model controlled for confounders. A total of 106,003 patients met the inclusion criteria; 86.7% (n= 91,856) experienced VD only, 11.7% (n= 12,359) underwent CD only and 1.7% (n= 1788) had at least one CD due to ASSL. PFD-related hospitalization incidence was 0.7% (n= 719) for the entire cohort. While a significantly higher PFD-related hospitalization rate was recorded in the VD group (0.7%) compared with the ASSL CD group (0.3%) and the non-ASSL CD group (0.5%, p< 0.001), after controlling for potential confounders, an independent and protective association was noted between CD and later PFDs only in parturients who did not experience ASSL (aHR 0.679, 95% CI 0.51-0.90, p = 0.006). CDs may be protective against later PFD development only if performed prior to the second stage of labor.

The impact of sample selection strategies on genetic diversity and representativeness in germplasm bank collections

BackgroundGermplasm banks maintain collections representing the most comprehensive catalogue of native genetic diversity available for crop improvement. Users of germplasm banks are interested in a fixed number of samples representing as broadly as possible the diversity present in the wider collection. A relevant question is whether it is necessary to develop completely independent germplasm samples or it is possible to select nested sets from a pre-defined core set panel not from the whole collection. We used data from 15,384, maize landraces stored in the CIMMYT germplasm bank to study the impact on 8 diversity criteria and the sample representativeness of: (1) two core selection strategies, a statistical sampling (DM), or a numerical maximization method (CH); (2) selecting samples of varying sizes; and (3) selecting samples of different sizes independently of each other or in a nested manner.ResultsSample sizes greater than 10% of the whole population size retained more than 75% of the polymorphic markers for all selection strategies and types of sample; lower sample sizes showed more variability (instability) among repetitions; the strongest effect of sample size was observed on the CH-independent combination. Independent and nested samples showed similar performance for all the criteria for the DM method, but there were differences between them for the CH method. The DM method achieved better approximations to the known values in the population than the CH method; 2-d multidimensional scaling plots of the collection and samples highlighted tendency of sample selection towards the extremes of diversity in the CH method, compared with sampling more representative of the overall genotypic distribution of diversity under the DM method.ConclusionsThe use of core subsets of size greater than or equal to 10% of the whole collection satisfied well the requirement of representativeness and diversity. Nested samples showed similar diversity and representativeness characteristics as independent samples offering a cost effective method of sample definition for germplasm banks. For most criteria assessed the DM method achieved better approximations to the known values in the whole population than the CH method, that is, it generated more statistically representative samples from collections.

A Semi-Automated Knime-Based Workflow for High Throughput Quantification and Analysis of Centrosome Aberrations

A Semi-Automated Knime-Based Workflow for High Throughput Quantification and Analysis of Centrosome Aberrations

The association between pregnancies complicated with isolated polyhydramnios or oligohydramnios and offspring long-term gastrointestinal morbidity.

Infants born to mothers with pregnancies complicated by polyhydramnios or oligohydramnios are at an increased risk for significant adverse perinatal outcome. We sought to assess whether in utero exposure to amniotic fluid abnormalities increases the risk of long-term gastrointestinal (GI) morbidity in the offspring. In a population-based cohort study, the incidence of GI-related hospitalizations was compared between singletons exposed and unexposed to oligohydramnios or polyhydramnios. Deliveries occurred between the years 1991-2014 in a regional tertiary medical center. Offspring GI morbidity was assessed up to the age of 18years according to a pre-defined set of International Classification of Diseases, ninth revision (ICD-9) codes associated with hospitalization. A Kaplan-Meier survival curve was used to compare cumulative morbidity incidence. A Cox proportional hazard model was performed to control for confounders. During the study period, 186,196 newborns met the inclusion criteria, of which 2.1% (n = 4063) and 3.0% (n = 5684) were born following pregnancies with oligohydramnios and polyhydramnios, respectively. The Kaplan-Meier curve demonstrated that children exposed to isolated oligohydramnios (but not to polyhydramnios) had higher cumulative incidence of GI morbidity (log-rank test, p = 0.001). In the Cox regression model, controlled for maternal age, gestational age, birth weight, and mode of delivery, isolated oligohydramnios (adjusted HR 1.2, 95% CI 1.04-1.34, p = 0.007), but not polyhydramnios (adjusted HR 1.1, 95% CI 0.91-1.13, p = 0.766), was found to be independently associated with long-term GI morbidity of the offspring. In utero exposure to isolated oligohydramnios is an independent risk factor for long-term GI morbidity in the offspring.

Mathematical models to improve the current practice in a Home Healthcare Unit

This paper addresses a home healthcare routing problem in which doctors and nurses visit patients at their homes to provide services. We consider a real-world home healthcare service arising in a particular hospital in Spain. Doctors and nurses are distributed in teams and travel by taxi; taxis transport a pre-defined set of workers who travel together the whole route. The objective is to minimise the transportation costs related to the total taxi journey time, including travelling and waiting costs. The paper presents a mathematical model that considers these current policies and permits the problem to be solved optimally. The paper also explores, after reviewing the hospital’s current model, the benefits that can be obtained by changing some of the current policies of the hospital. In this way, a new model is proposed based on two sustainable strategies that can be extended to other home service fields: (1) workers can walk between houses and (2) the workers transported by a taxi may change during the route. A complex nonlinear mathematical model is presented, and a metaheuristic is designed to provide quality solutions. Computational tests on a set of instances based on real-world data estimate the gap between the solutions of both models.

Increasing affordance potency through process improvement: case study of a healthcare system

ABSTRACTWhile the adoption of electronic health records (EHR) has greatly increased in recent years, the ability of healthcare organizations to leverage their EHRs to improve clinical outcomes has not been clearly evidenced. One reason is that most healthcare organizations implement commercial information systems that are designed to support a pre-defined set of clinical work processes. These systems rarely support every clinical work process in the most effective way and must therefore be modified post-implementation to improve operational alignment in order to better meet the needs of the healthcare organization. This paper investigates that process in the context of a clinical process improvement project. We found that EHR adaptations are most effectively enabled when the goals of the organization and the affordances of system features are known to those using the system. This is because the actualization of the affordances, which result in process improvement, arises from the potency of affordances of the system’s features. In this paper we present the case of a healthcare organization that has successfully modified and leveraged its EHR to improve the clinical process that supports the entire spectrum of care for the identification and treatment of alcohol withdrawal patients. We use the post-implementation adaptation of EHRs to extend the theoretical concept of affordance potency for process improvement from a temporal perspective.

Interval methods and contractor-based branch-and-bound procedures for verified parameter identification of quasi-linear cooperative system models

A large number of dynamic system models in engineering as well as computational physics, chemistry or biology are described after first-principle modeling approaches by sets of either discrete-time difference equations or by sets of ordinary differential equations. In both cases, the structure of those system models results from fundamental physical system properties such as conservation laws of mass, momentum and energy, Newton’s or Kirchhoff’s laws as well as a structural subdivision into subsystems or individual compartments. Regardless of the area of application, all of these system models involve parameters which are not perfectly known due to uncertainty resulting from tolerances in the construction of the respective real-life systems or which are described by an inherent uncertainty coming from model simplifications or even from a lack of knowledge about detailed microscopic dynamic effects. This is especially true for applications in engineering, where a trade-off between the model accuracy and the resulting computational complexity has to be made in order to obtain mathematical representations which can be evaluated numerically in real time. This real-time applicability is inevitable if tasks such as feedback control or model-based state estimation on the basis of incomplete state measurements are of interest. Hence, all aforementioned tasks depend on the reliable estimation of those parameters of the structurally predefined sets of state equations which are feasible with respect to the knowledge about selected measurable state variables under consideration of the associated measurement tolerances. This paper presents computational techniques for a model-based identification of system parameters under the a-priori knowledge of stability of the underlying open-loop dynamics and the structural constraint of cooperativity.

Developing a Standardization Algorithm for Categorical Laboratory Tests for Clinical Big Data Research: Retrospective Study.

BackgroundData standardization is essential in electronic health records (EHRs) for both clinical practice and retrospective research. However, it is still not easy to standardize EHR data because of nonidentical duplicates, typographical errors, or inconsistencies. To overcome this drawback, standardization efforts have been undertaken for collecting data in a standardized format as well as for curating the stored data in EHRs. To perform clinical big data research, the stored data in EHR should be standardized, starting from laboratory results, given their importance. However, most of the previous efforts have been based on labor-intensive manual methods.ObjectiveWe aimed to develop an automatic standardization method for eliminating the noises of categorical laboratory data, grouping, and mapping of cleaned data using standard terminology.MethodsWe developed a method called standardization algorithm for laboratory test–categorical result (SALT-C) that can process categorical laboratory data, such as pos +, 250 4+ (urinalysis results), and reddish (urinalysis color results). SALT-C consists of five steps. First, it applies data cleaning rules to categorical laboratory data. Second, it categorizes the cleaned data into 5 predefined groups (urine color, urine dipstick, blood type, presence-finding, and pathogenesis tests). Third, all data in each group are vectorized. Fourth, similarity is calculated between the vectors of data and those of each value in the predefined value sets. Finally, the value closest to the data is assigned.ResultsThe performance of SALT-C was validated using 59,213,696 data points (167,938 unique values) generated over 23 years from a tertiary hospital. Apart from the data whose original meaning could not be interpreted correctly (eg, ** and _^), SALT-C mapped unique raw data to the correct reference value for each group with accuracy of 97.6% (123/126; urine color tests), 97.5% (198/203; (urine dipstick tests), 95% (53/56; blood type tests), 99.68% (162,291/162,805; presence-finding tests), and 99.61% (4643/4661; pathogenesis tests).ConclusionsThe proposed SALT-C successfully standardized the categorical laboratory test results with high reliability. SALT-C can be beneficial for clinical big data research by reducing laborious manual standardization efforts.

Regulatory annotation of genomic intervals based on tissue-specific expression QTLs.

Annotating a given genomic locus or a set of genomic loci is an important yet challenging task. This is especially true for the non-coding part of the genome which is enormous yet poorly understood. Since gene set enrichment analyses have demonstrated to be effective approach to annotate a set of genes, the same idea can be extended to explore the enrichment of functional elements or features in a set of genomic intervals to reveal potential functional connections. In this study, we describe a novel computational strategy named loci2path that takes advantage of the newly emerged, genome-wide and tissue-specific expression quantitative trait loci (eQTL) information to help annotate a set of genomic intervals in terms of transcription regulation. By checking the presence or the absence of millions of eQTLs in a set of input genomic intervals, combined with grouping eQTLs by the pathways or gene sets that their target genes belong to, loci2path build a bridge connecting genomic intervals to functional pathways and pre-defined biological-meaningful gene sets, revealing potential for regulatory connection. Our method enjoys two key advantages over existing methods: first, we no longer rely on proximity to link a locus to a gene which has shown to be unreliable; second, eQTL allows us to provide the regulatory annotation under the context of specific tissue types. To demonstrate its utilities, we apply loci2path on sets of genomic intervals harboring disease-associated variants as query. Using 1702612 eQTLs discovered by the Genotype-Tissue Expression (GTEx) project across 44 tissues and 6320 pathways or gene sets cataloged in MSigDB as annotation resource, our method successfully identifies highly relevant biological pathways and revealed disease mechanisms for psoriasis and other immune-related diseases. Tissue specificity analysis of associated eQTLs provide additional evidence of the distinct roles of different tissues played in the disease mechanisms. loci2path is published as an open source Bioconductor package, and it is available at http://bioconductor.org/packages/release/bioc/html/loci2path.html. Supplementary data are available at Bioinformatics online.

Many-objective evolutionary algorithm based on adaptive weighted decomposition

Decomposition is a representative method for handling many-objective optimization problems with evolutionary algorithms. Classical decomposition scheme relies on a set of uniformly distributed reference vectors to divide the objective space into multiple subregions. This scheme often works poorly when the problem has an irregular Pareto front due to the inconsistency between the distribution of reference vectors and the shape of Pareto fronts. We propose in this paper an adaptive weighted decomposition based many-objective evolutionary algorithm to tackle complicated many-objective problems whose Pareto fronts may or may not be regular. Unlike traditional decomposition based algorithms that use a pre-defined set of reference vectors, the reference vectors in the proposed algorithm are produced from the population during the search. The experiments show that the performance of the proposed algorithm is competitive with other state-of-the-art algorithms and is less-sensitive to the irregularity of the Pareto fronts.

Assessing land degradation and identifying potential sustainable land management practices at the subnational level in Lebanon.

Assessing land degradation is essentially needed for decision-makers to monitor and mitigate degradation and implement sustainable land management practices. More specifically, monitoring land degradation on a regular basis enables decision-makers to analyze impact of degradation and effects of sustainable land management practices. This requires adopting a precise methodology for assessing land degradation. With information form assessments, decision-makers can establish achievable management goals to attain land degradation neutrality as addressed by the Sustainable Development Goal 15 (i.e., SDG 15). Accordingly, this work aimed at developing a systematic approach for assessing land degradation at the subnational level with the combined use of geo-spatial information and field data. The specific objectives were to (1) assess trends in land degradation and the impact of historical land uses on the current landscape characters, (2) investigate and characterize principal criteria of land degradation, and (3) identify sustainable land management practices. Multi-source and multi-resolution spatial data were employed for assessing the three indicators of land degradation neutrality (i.e., changes in vegetation cover, land productivity, and soil organic carbon). Prioritizing lands prone to degradation was investigated using a set of spatial attributes of different weights. Field data collection involved the use of pre-defined set of questions for assessing the principal criteria of land degradation including type, extent, rate, degree, and causes of land degradation. Sustainable land management practices were identified and categorized accordingly. This assessment provided a means for prioritization of interventions (i.e., prevention, mitigation, and/or rehabilitation) in an area characterized by a climate varying from semi-arid to moist subhumid.

Clusterwise Peak Detection and Filtering Based on Spatial Distribution To Efficiently Mine Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI) has the potential to reveal the localization of thousands of biomolecules such as metabolites and lipids in tissue sections. The increase in both mass and spatial resolution of today’s instruments brings on considerable challenges in terms of data processing; accurately extracting meaningful signals from the large data sets generated by MSI without losing information that could be clinically relevant is one of the most fundamental tasks of analysis software. Ion images of the biomolecules are generated by visualizing their intensities in 2-D space using mass spectra collected across the tissue section. The intensities are often calculated by summing each compound’s signal between predefined sets of borders (bins) in the m/z dimension. This approach, however, can result in mixed signals from different compounds in the same bin or splitting the signal from one compound between two adjacent bins, leading to low quality ion images. To remedy this problem, we propose a novel data processing approach. Our approach consists of a sensitive peak detection method able to discover both faint and localized signals by utilizing clusterwise kernel density estimates (KDEs) of peak distributions. We show that our method can recall more ground-truth molecules, molecule fragments, and isotopes than existing methods based on binning. Furthermore, it automatically detects previously reported molecular ions of lipids, including those close in m/z, in an experimental data set.

Real-Word Errors in Arabic Texts: A Better Algorithm for Detection and Correction

Real-word also known as semantic or context-sensitive spelling error is a class of error that escapes the typical spell checker which relies on dictionary look-up. This kind of error occurs when a user types a correctly spelled word–by mistake–when another is intended, e.g., “I want a peace piece of cake.” Further, these errors commonly arise in text written by people with dyslexia. Real-word errors are harder to detect as we need to consider the context. In this paper, we propose a spell checker that detects and corrects real-word errors for the Arabic language. Our method avoids predefined confusion sets–a simple approach used by many works tackling this problem–which limits the list of words that can be detected and corrected. Thus, our system can detect and correct a larger set of real-word errors. For the detection phase, we employ word and stem n-gram n = 1–3 language model along with machine learning, achieving a precision and recall of 83.5% and 99.2%, respectively. And for the correction phase we use n-gram, which results in an accuracy of 98%. Our scheme is robust, with an excellent performance even when the percentage of real-word error words is high. This makes the system suitable for handling errors in post OCR recognition of Arabic text.

Event-based cooperative global practical output regulation of multi-agent systems with nonlinear leader

This paper presents a distributed event-based high gain feedback design for the cooperative global practical output regulation problem of nonlinear multi-agent systems with unit relative degree subject to the general nonlinear leader system. We first convert global practical output regulation problem into global practical stabilization problem, then prove the input-to-state stability of the closed-loop system by regarding the sampling error and the steady-state of the actuator together as the external input. Based on the high gain feedback technique, the input-to-state gain function can be made sufficiently small, and hence the tracking error can converge to an arbitrary predefined small residual set around the origin. A distinguishing feature of our result is that, our controller is static and purely digital, thus, giving rise to quite a simple structure.

Home-based records’ quality and validity of caregivers’ recall of children’s vaccination in Lebanon

IntroductionHome-based records (HBRs) (also known as vaccination cards) and caregivers’ recall are the main means to ascertain vaccination status; however, data on the quality of HBRs and the validity of recall vaccination data compared to HBRs is scarce. This manuscript presents results from two analyses related to HBRs, one on HBR pictures taken during a vaccination coverage survey, including an assessment of the HBR quality and legibility, and an evaluation of the agreement between caregivers’ recall and the vaccination information in the HBRs. MethodsUsing pictures from 500 randomly selected HBRs collected during the 2016 district-based immunization coverage evaluation survey in Lebanon, two independent researchers assessed the quality of the picture and then of the HBR itself against a pre-defined set of criteria. HBRs were classified into three types: private, public and all others. In addition, caregivers’ recall was compared to data found in vaccination HBRs to assess measures of vaccination status agreement for 5713 children for whom both sources of data were available. ResultsOver 90% of the 500 HBR pictures reviewed were considered adequate to assess the HBR quality. In the sample, most cards were type 1 (41%), followed by type 2 (34%). Most HBRs met the set criteria for quality in terms of physical condition and legibility, while, among the 28 different types of cards, vaccination cards’ content and design met a moderate level of quality. Concordance, sensitivity, specificity, positive and negative predictive values, and the Kappa statistic showed diverse levels of agreement for vaccination status per vaccine dose between caregivers’ recall and vaccination HBRs. ConclusionThis study illustrates that taking pictures of HBRs in a coverage survey is feasible and useful to conduct secondary analyses related to HBRs, such as assessing their quality and comparing recall with HBRs when both sources of data are available.

QCMAP: An Interactive Web-Tool for Performance Diagnosis and Prediction of LC-MS Systems.

The increasing role played by liquid chromatography-mass spectrometry (LC-MS)-based proteomics in biological discovery has led to a growing need for quality control (QC) on the LC-MS systems. While numerous quality control tools have been developed to track the performance of LC-MS systems based on a pre-defined set of performance factors (e.g., mass error, retention time), the precise influence and contribution of the performance factors and their generalization property to different biological samples are not as well characterized. Here, a web-based application (QCMAP) is developed for interactive diagnosis and prediction of the performance of LC-MS systems across different biological sample types. Leveraging on a standardized HeLa cell sample run as QC within a multi-user facility, predictive models are trained on a panel of commonly used performance factors to pinpoint the precise conditions to a (un)satisfactory performance in three LC-MS systems. It is demonstrated that the learned model can be applied to predict LC-MS system performance for brain samples generated from an independent study. By compiling these predictive models into our web-application, QCMAP allows users to benchmark the performance of their LC-MS systems using their own samples and identify key factors for instrument optimization. QCMAP is freely available from: http://shiny.maths.usyd.edu.au/QCMAP/.

Validator – a Web-Based Interactive Tool for Validation of Ocean Models at Oceanographic Stations

Numerical ocean models, like other geoscientific models, are a strongly simplified representation of real oceans. They are used as tools to answer research questions about the real-world systems. Therefore, their thorough validation is essential to ensure that the conclusions drawn from the model experiment are valid in reality. We demonstrate a software which allows an interactive model validation through a web interface based on the R Shiny framework. At pre-defined stations, different kinds of plots can be rendered within a few seconds, according to the user’s choice, allowing a live validation of different model parameters even in model simulations which are still running. This makes it different from validation approaches which generate a pre-defined set of plots after the calculations have finished and make it particularly useful for model tuning purposes. Observation data can be read in from text files or can be extracted from a database. Once set up, the validation tool requires no technical skills to use. It can be used for single- or multi-model validation and allows saving the generated plots as high-resolution images suitable for use in scientific publications. A Linux operating system is required for the Validator app, but via a virtual machine, the software can run on Windows or MacOS hosts as well. A Dockerfile is supplied which allows to test the software with example data without installation. Funding statement: This software was developed at the Leibniz Institute for Baltic Sea Research Warnemunde (IOW) on institutional funding.

Impact of a diagnostic decision support system on GP clinical documentation

BackgroundKostopoulou and colleagues designed and evaluated a diagnostic decision support system (DSS) that presented GPs with differential diagnoses to consider at the start of the consultation. The DSS was integrated with the electronic health record (EHR) and evaluated in simulated consultations (34 GPs consulting with 12 actors), where it was found to improve diagnostic accuracy.AimTo evaluate the impact of the DSS on GP clinical documentation.MethodSecondary data analysis. The analysis dataset consisted of all data items that the 34 GPs recorded during their consultations with the 12 actors (408 consultations). Each GP had conducted six consultations with the EHR alone, and six with the integrated DSS. The 12 patient scenarios had a pre-defined set of clinical cues and differential diagnoses. For each patient scenario, a Delphi panel of five GPs identified which cues were consistent with each differential diagnosis. The data items recorded, either in free text or code, were counted. These were referenced against the Delphi panel’s matrix of cues and diagnoses. For each GP, it was estimated what proportion of the recorded data was consistent with the diagnosis that they finally gave.ResultsUsing the DSS increased the total amount of information documented (b = 3.58 [95% confidence interval {CI} = 2.81 to 4.35] P&lt;0.001) and reduced the proportion of documentation that was consistent with the GP’s final diagnosis (b = −0.08 [95% CI = −0.11 to −0.05] P&lt;0.001), suggesting less bias.ConclusionUsing the DSS led to more complete and less biased documentation. This has implications for the use of routine EHR data to create clinical prediction rules.

Design-Level and Code-Level Security Analysis of IoT Devices

The Internet of Things (IoT) is playing an important role in different aspects of our lives. Smart grids, smart cars, and medical devices all incorporate IoT devices as key components. The ubiquity and criticality of these devices make them an attractive target for attackers. Therefore, we need techniques to analyze their security so that we can address their potential vulnerabilities. IoT devices, unlike remote servers, are user-facing and, therefore, an attacker may interact with them more extensively, e.g., via physical access. Existing techniques for analyzing security of IoT devices either rely on a pre-defined set of attacks and, therefore, have limited effect or do not consider the specific capabilities the attackers have against IoT devices. Security analysis techniques may operate at the design-level, leveraging abstraction to avoid state-space explosion, or at the code-level for ensuring accuracy. In this article, we introduce two techniques, one at the design-level, and the other at the code-level, to analyze security of IoT devices, and compare their effectiveness. The former technique uses model checking, while the latter uses symbolic execution, to find attacks based on the attacker’s capabilities. We evaluate our techniques on an open source smart meter. We find that our code-level analysis technique is able to find three times more attacks and complete the analysis in half the time, compared to the design-level analysis technique, with no false positives.