Many engineering and healthcare systems are featured with spatiotemporal dynamic processes. The optimal control of such systems often involves sequential decision making. However, traditional sequential decision-making methods are not applicable to optimize dynamic systems that involves complex 3D geometries. Simulation modeling offers an unprecedented opportunity to evaluate alternative decision options and search for the optimal plan. In this paper, we develop a novel simulation optimization framework for sequential optimization of 3D dynamic systems. We first propose to measure the similarity between functional simulation outputs using coherence to assess the effectiveness of decision actions. Second, we develop a novel Gaussian Process (GP) model by constructing a valid kernel based on Hausdorff distance to estimate the coherence for different decision paths. Finally, we devise a new Monte Carlo Tree Search (MCTS) algorithm, i.e., Normal-Gamma GP MCTS (NG-GP-MCTS), to sequentially optimize the spatiotemporal dynamics. We implement the NG-GP-MCTS algorithm to design an optimal ablation path for restoring normal sinus rhythm (NSR) from atrial fibrillation (AF). We evaluate the performance of NG-GP-MCTS with spatiotemporal cardiac simulation in a 3D atrial geometry. Computer experiments show that our algorithm is highly promising for designing effective sequential procedures to optimize spatiotemporal dynamics in complex geometries.Note to Practitioners—This article proposes a novel simulation optimization framework for sequential decision making to optimize spatiotemporal dynamics in complex geometries. This framework incorporates the advantage of Bayesian modeling and Gaussian Process inference into Monte-Carlo tree search to effectively solve the sequential optimization problem. It has significant potential to contribute to the emerging discipline of computational engineering and medicine, and further realize precision control/treatment planning in various manufacturing and healthcare systems. This paper will be interesting to practitioners who are seeking effective computational and optimization tools for decision support to optimally control dynamic systems for restoring normal system functionality.

The histopathological image analysis is one of the most crucial diagnostic procedures to identify Invasive ductal carcinoma (IDC) in breast cancers. However, this diagnosis process is currently time-consuming and heavily dependent on human expertise. Prior research has shown that different degrees of tumors present various microstructures in the histopathological images. However, very little has been done to utilize spatial recurrence features of microstructures for identifying IDC. This paper presents a novel recurrence analysis methodology for automatic image-guided IDC detection. We first utilize wavelet decomposition to delineate the subtle information in the images. Then, we model the patches with a weighted recurrence network approach to characterize the recurrence patterns of the histopathological images. Finally, we develop automated IDC detection models leveraging machine learning methods with spatial recurrence features extracted. The developed recurrence analysis models successfully characterize the complex microstructures of histopathological images and achieve the IDC detection performances of at least AUC = 0.96. This research developed a spatial recurrence analysis methodology to effectively identify IDC regions in histopathological images for BC. It shows a high potential to assist physicians in the decision-making process. The proposed methodology can further be applicable to image processing for other medical or biological applications.

Most real-world networks are time-varying, and many are subject to the stochastic functioning of their nodes and edges. Examples can be seen in the human brain undergoing an epileptic seizure, spontaneous infection and recovery in epidemics, and intermittent functioning of devices in the Internet of Things. Moreover, such networks are becoming increasingly large due to rapid technological advances. However, little has been done to study time-varying, large-scale, stochastic networks (SNs) from a reliability engineering perspective. Toward this goal, this article develops a fault-tolerance model for a type of time-varying network in which nodes (and/or edges) stochastically switch between active and inactive states. It considers fault tolerance from a global connectivity point of view, which has applications in many natural and engineered networks. Specifically, this article presents a Markov chain framework that models the dynamic behavior of nodes and allows for the computation of quantitative measures, including availability and time-to-failure metrics. To accommodate large-scale networks and emphasize global connectivity, this framework utilizes percolation theory, which has recently been of interest in the reliability engineering discipline, to characterize network failure. This article makes several contributions: it proposes a Markov chain framework for computing fault-tolerance metrics that is tractable for large-scale networks, it shows the existence of a phase transition in network availability of a time-varying SN, and it accounts for finite-size effects of percolation in the fault-tolerance model. The proposed methodology is applied to Erdös–Rényi random graphs and a real, large-scale power grid. Experimental results provide insights into network design, maintenance, and failure prevention of time-varying SNs.Note to Practitioners—This work develops a fault-tolerance model for time-varying stochastic networks in which nodes (and/or edges) randomly switch between active and inactive states. To address increasingly large-scale networks that are being studied, this article appeals to percolation theory. Fault tolerance is, thus, studied from a global connectivity perspective where the existence of a large connected component containing most of the nodes characterizes the functioning of the network. Specifically, this article presents a continuous-time Markov chain (CTMC) framework that models node dynamics and allows for the computation of fault-tolerance metrics, including network availability and mean time to failure. The proposed framework computes metrics efficiently for large networks and allows for studying their asymptotics. The percolation threshold describing the dissolution of the large connected component is used as the failure criterion in the CTMC. The practitioner should note that several assumptions are made in the proposed CTMC framework: nodes possess identical and time-invariant failure and recovery rates, node failure and recovery times are exponentially distributed, and node dynamics are independent of one another. In addition, fault-tolerance metrics computed for finite networks are estimators of the true metric values. The proposed framework is advantageous for quantifying the fault tolerance of large-scale, time-varying networks where the combinatorial explosion and a changing network topology pose challenges to the use of traditional reliability methods. A case study of a power grid network shows how to apply the proposed methodology to real networks.

Rapid advances in the smartphone, wearable sensing, and wireless communication provide an unprecedented opportunity to develop mobile systems for smart health management. Mobile cardiac sensing collects health-related data from individuals and enables the extraction of information pertinent to cardiac conditions. However, wireless sensors in ambulatory care settings operate on batteries. All-time sensing and monitoring will result in fast depletion of the battery in the mobile system. There is an urgent need to develop optimal sensing schemes that will reduce energy consumption while satisfying the requirements in the detection of cardiac events. In this article, we develop a constrained Markov decision process (CMDP) framework to optimize mobile electrocardiography (ECG) sensing under the constraint of the energy budget. We first characterize the cardiac states from ECG signals using the heterogeneous recurrence analysis. Second, we model the stochastic dynamics in cardiac processes as a continuous-time Markov chain (CTMC). Third, we optimize the ECG sensing through a CMDP framework under the constraint of energy budget. Finally, we validate and evaluate the performance of our CMDP policy in both simulation and real-world case studies. Experimental results demonstrate that the proposed CMDP policy significantly outperforms the traditional uniform and mean-time-to-event (MTTE) policies. Specifically, the error of state estimation is reduced by 34.0% in the real-world case study for energy-constrained sensing of cardiac events.Note to Practitioners—This article is motivated by the markedly increasing applications of mobile health (mHealth) in cardiac care. MHealth systems enable the real-time monitoring, tracking, and transmitting of heart-health information but suffer from the problem with limited battery life. This article presents a novel energy-efficient framework for mobile ECG sensing by integrating ECG signal analysis with sensing-policy optimization under the constraint of the energy budget. Experimental results demonstrate the effectiveness of the proposed framework to realize energy-efficient mobile sensing for cardiac events detection.

The input of low-quality feedstocks triggers the interdependence between workpiece quality and machine reliability, which will further adversely impact the performance of manufacturing systems. Considering the interconnected manufacturing system structures, our primary goal is to provide an effective method to compute the performance of networked manufacturing systems suffering from machine and low-quality feedstock interdependence. The strength of our work first lies in the model for the compound degradation process of machines and dissemination of low-quality feedstocks, which enables us to construct a response chain to model the interdependence between machines and feedstocks in the manufacturing network. Then, the second strength is the effective algorithm for the computation of route connectivity and quality loss of a manufacturing network based on the interdependence model. A computational experiment shows our models and algorithm can work well for evaluating the operational performance of manufacturing networks.

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to the supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of six sigma (6S) has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics, and optimization. 6S entails a data-driven DMAIC methodology of five steps-define, measure, analyze, improve, and control. Notwithstanding the sustained successes of the 6S knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics, and beyond, there is a dearth of concentrated application of 6S quality management approaches in the context of AM. In this article, we propose to design, develop, and implement the new DMAIC methodology for the 6S quality management of AM. First, we define the specific quality challenges arising from AM layerwise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, and environment, to postbuild inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems and emphasize the need for analytical methods and tools. We propose and delineate the utility of new data-driven analytical methods, including deep learning, machine learning, and network science, to characterize and model the interrelationships between engineering design, machine setting, process variability, and final build quality. Fourth, we present the methodologies of ontology analytics, design of experiments (DOE), and simulation analysis for AM system improvements. In closing, new process control approaches are discussed to optimize the action plans, once an anomaly is detected, with specific consideration of lead time and energy consumption. We posit that this work will catalyze more in-depth investigations and multidisciplinary research efforts to accelerate the application of 6S quality management in AM.

The Internet of Things (IoT) has propelled the evolution of medical sensing technologies to greater heights. Thus, traditional health systems have been transformed into new data-rich environments. This provides an unprecedented opportunity to develop new analytical methods and tools towards a new paradigm of smart and interconnected health systems. Nevertheless, there are risks pertinent to increasing levels of system connectivity and data accessibility. Cyber-attacks become more prevalent and complex, leading to greater likelihood of data breaches. These events bring sudden disruptions to routine operations and cause the loss of billions of dollars. Adversaries often attempt to leverage models to learn a target's sensitive attributes or extrapolate its inclusion within a database. As healthcare systems are critical to improving the wellbeing of our society, there is an urgent need to protect the privacy of patients and minimize the risk of model inversion attacks. This paper presents a new approach, named Mosaic Gradient Perturbation (MGP), to preserve privacy in the framework of predictive modeling, which meets the requirement of differential privacy while mitigating the risk of model inversion. MGP is flexible in fine-tuning the trade-offs between model performance and attack accuracy while being highly scalable for large-scale computing. Experimental results show that the proposed MGP method improves upon traditional gradient perturbation to mitigate the risk of model inversion while offering greater preservation of model accuracy. The MGP technique shows strong potential to circumvent paramount costs due to privacy breaches while maintaining the quality of existing decision-support systems, thereby ushering in a privacy-preserving smart health system.

Recent reports show that 1 in 4 families has at least one member with a mental disorder. In the current practice, most diagnosis methods in psychiatry are based on clinical interviews and questionnaires, which are subjective and can lead to recalls and interviewer biases. In the healthcare context, Virtual Reality (VR) has shown a strong potential to improve decision making and help patients to better connect with reality, cope with pain, and overcome mental disorders such as anxiety and depression. This study integrates sensing technology (i.e., eye tracking) with a VR simulation of healthcare environments to improve the clinical decision-support system for diagnosis and assessment of mental disorders. Traditional scenario-based patient simulations are used as a basis for the development of VR modules. Data collected via the eye-tracking sensing technology are utilized to develop analytical models for predicting the risk of mental illness. Moreover, artificial intelligence (AI) tools for VR-based healthcare training help medical students learn faster and make smarter decisions. This research helps contribute to improved population health by developing new methods for promoting health and effectively predicting and treating mental disorders.

Fierce global competition drives automated manufacturing systems (AMSs) to be increasingly complex, which poses significant challenges on performance analysis and production control. The multistage production via serial stations will lead to the propagation of failures in AMSs, which will affect system performance by triggering complex competitions among multiple failure modes. Although machine performance and product quality have been considered, very little has been done to investigate the effect of imperfect quality inspection on competing failures. Focusing on a time balance serial AMS, this article presents a new competing failure model to investigate the complex interactions among machine failures, product quality, and inspection process, which enables the characterizations of time-delayed propagation of failure, accumulation of degradation, and dynamics of states in serial AMSs. In order to further analyze the impact of competing behaviors on system performance, we have also developed decision diagram models and algorithms, which are evaluated and validated on serial AMSs with imperfect inspection, revealing the characteristic of multistate interactions. Experimental results show that the proposed methods have strong potentials for performance modeling and analysis of serial AMSs and also demonstrate general applicability for manufacturing decision making.

Heart diseases alter the rhythmic behaviors of cardiac electrical activity. Recent advances in sensing technology bring the ease to acquire space-time electrical activity of the heart such as vectorcardiogram (VCG) signals. Recurrence analysis of successive heartbeats is conducive to detect the disease-altered cardiac activities. However, conventional recurrence analysis is more concerned about homogeneous recurrences, and overlook heterogeneous types of recurrence variations in VCG signals (i.e., in terms of state properties and transition dynamics). This paper presents a new framework of heterogeneous recurrence analysis for the characterization and modeling of disease-altered spatiotemporal patterns in multi-channel cardiac signals. Experimental results show that the proposed approach yields an accuracy of 96.9%, a sensitivity of 95.0%, and a specificity of 98.7% for the identification of myocardial infarctions. The proposed method of heterogeneous recurrence analysis shows strong potential to be further extended for the analysis of other physiological signals such as electroencephalogram (EEG) and electromyography (EMG) signals towards medical decision making.

Abstract Local delivery networks expect drivers to make deliveries to and/or pickups from customers using the shortest routes in order to minimize costs, delivery time, and environmental impact. However, in real‐world applications, it is often the case that not all customers are known when planning the initial delivery route. Instead, additional customers become known while the driver is making deliveries or pickups. Before serving the new demand requests, the vehicle will return to the depot for restocking. In other words, there exists a precedence relation in the delivery route to visit the depot before delivering new orders. The uncertainty in new customer locations can lead to expensive rerouting of the tour, as drivers revisit previous neighborhoods to serve the new customers. We address this issue by constructing the delivery route with the knowledge that additional customers will appear, using historical demand patterns to guide our predictions for the uncertainty. We model this network delivery problem as a precedence‐constrained asymmetric traveling salesman problem using mixed‐integer optimization. Experimental results show that the proposed robust optimization approach provides an effective delivery route under the uncertainty of customer demands.

There has been growing interest in healthcare delivery systems worldwide coupled with a recent influx of funding into the area. Due to rapid development in information and network technology, smartness and interconnectivity have become a central issue in healthcare delivery. Automation is important for healthcare delivery systems engineering. In recent years, the significant changes in healthcare delivery and the rapid development in data analytics, artificial intelligence, robotics, and wearable devices have generated numerous opportunities for innovation in automation for smart and interconnected healthcare delivery systems. In addition, many new challenges have emerged in order to apply and implement these innovations. Such opportunities and challenges have significantly expanded the scopes of traditional automation science and engineering. Therefore, to show the state-of-the-art research and applications in the general area of healthcare delivery systems automation and to address the needs and challenges for the integration of new automation technologies in healthcare delivery, this Special Issue serves as a forum to bring together researchers, clinicians, and healthcare practitioners to present efficient scientific and engineering solutions and to provide visions for future research and development.

Cardiac ion channels are highly glycosylated membrane proteins with up to 30% of the protein's mass containing glycans. Heart diseases often accompany individuals with congenital disorders of glycosylation (CDG). However, cardiac dysfunction among CDG patients is not yet fully understood. There is an urgent need to study how aberrant glycosylation impacts cardiac electrical signaling. Our previous works reported that congenitally reduced sialylation achieved through deletion of the sialyltransferase gene, ST3Gal4, leads to altered gating of voltage-gated Na+and K+channels (Navand Kv, respectively). However, linking the impact of reduced sialylation on ion channel gating to the action potential (AP) is difficult without performing computer experiments. Also, decomposing the sum of K+ currents is difficult because of complex structures and components of Kvchannels (e.g., Kv4.2, and Kv1.5). In this study, we developed in-silico models to describe the functional role of reduced sialylation in both Navand Kvgating and the AP using in vitro experimental data. Modeling results showed that reduced sialylation changes Kvgating as follows: 1) The steady-state activation voltages of Kvisoforms are shifted to a more depolarized potential. 2) Aberrant K+currents (IKslow and Ito) contribute to a prolonged AP duration, and altered Na+current (INa) contributes to a shortened AP refractory period. This study contributes to a better understanding of the functional role of reduced sialylation in cardiac dysfunction that shows strong potential to provide new pharmaceutical targets for the treatment of CDG-related heart diseases.

Myocardial infarction (MI) is among the leading causes of death in the United States. It is imperative to identify and characterize MIs for timely delivery of life-saving medical interventions. Cardiac electrical activity propagates in space and evolves over time. Traditional works focus on the analysis of time-domain ECG (e.g., 12-lead ECG) on the body surface for the detection of MIs, but tend to overlook spatiotemporal dynamics in the heart. Body surface potential mappings (BSPMs) provide high-resolution distribution of electric potentials over the entire torso, and therefore provide richer information than 12-lead ECG. However, BSPM are available on the body surface. Clinicians are in need of a closer look of the electric potentials in the heart to investigate cardiac pathology and optimize treatment strategies. In this paper, we applied the method of spatiotemporal inverse ECG (ST-iECG) modeling to map electrical potentials from the body surface to the heart, and then characterize the location and extent of MIs by investigating the reconstructed heart-surface electrograms. First, we investigate the impact of mesh resolution on the inverse ECG modeling. Second, we solve the inverse ECG problem and reconstruct heart-surface electrograms using the ST-iECG model. Finally, we propose a wavelet-clustering method to investigate the pathological behaviors of heart-surface electrograms, and thereby characterize the extent and location of MIs. The proposed methodology is evaluated and validated with real data of MIs from human subjects. Experimental results show that negative QRS waves in heart-surface electrograms indicate potential regions of MI, and the proposed ST-iECG model yields superior characterization results of MIs on the heart surface over existing methods.

Wireless sensor network has emerged as a key technology for monitoring space-time dynamics of complex systems, e.g., environmental sensor network, battlefield surveillance network, and body area sensor network. However, sensor failures are not uncommon in traditional sensing systems. As such, we propose the design of stochastic sensor networks to allow a subset of sensors at varying locations within the network to transmit dynamic information intermittently. Realizing the full potential of stochastic sensor network hinges on the development of novel information-processing algorithms to support the design and exploit the uncertain information for decision making. This paper presents a new approach of sparse particle filtering to model spatiotemporal dynamics of big data in the stochastic sensor network. Notably, we developed a sparse kernel-weighted regression model to achieve a parsimonious representation of spatial patterns. Further, the parameters of spatial model are transformed into a reduced-dimension space, and thereby sequentially updated with the recursive Bayesian estimation when new sensor observations are available over time. Therefore, spatial and temporal processes closely interact with each other. Experimental results on real-world data and different scenarios of stochastic sensor networks (i.e., spatially, temporally, and spatiotemporally dynamic networks) demonstrated the effectiveness of sparse particle filtering to support the stochastic design and harness the uncertain information for modeling space-time dynamics of complex systems.

Glycan structures account for up to 35% of the mass of cardiac sodium ( Nav ) channels. To question whether and how reduced sialylation affects Nav activity and cardiac electrical signaling, we conducted a series of in vitro experiments on ventricular apex myocytes under two different glycosylation conditions, reduced protein sialylation (ST3Gal4(-/-)) and full glycosylation (control). Although aberrant electrical signaling is observed in reduced sialylation, realizing a better understanding of mechanistic details of pathological variations in INa and AP is difficult without performing in silico studies. However, computer model of Nav channels and cardiac myocytes involves greater levels of complexity, e.g., high-dimensional parameter space, nonlinear and nonconvex equations. Traditional linear and nonlinear optimization methods have encountered many difficulties for model calibration. This paper presents a new statistical metamodeling approach for efficient computer experiments and optimization of Nav models. First, we utilize a fractional factorial design to identify control variables from the large set of model parameters, thereby reducing the dimensionality of parametric space. Further, we develop the Gaussian process model as a surrogate of expensive and time-consuming computer models and then identify the next best design point that yields the maximal probability of improvement. This process iterates until convergence, and the performance is evaluated and validated with real-world experimental data. Experimental results show the proposed algorithm achieves superior performance in modeling the kinetics of Nav channels under a variety of glycosylation conditions. As a result, in silico models provide a better understanding of glyco-altered mechanistic details in state transitions and distributions of Nav channels. Notably, ST3Gal4(-/-) myocytes are shown to have higher probabilities accumulated in intermediate inactivation during the repolarization and yield a shorter refractory period than WTs. The proposed statistical design of computer experiments is generally extensible to many other disciplines that involve large-scale and computationally expensive models.

Ca(2+) plays an important role in the regulation of cellular functions. Local calcium events, e.g., calcium sparks, not only bring insights into Ca(2+) signaling but also contribute to the understanding of various cellular processes. However, it is challenging to detect calcium sparks, due to their transient properties and high level of nonstationary noises in microscopic images. Most of existing algorithms tend to have limitations for the detection of calcium sparks, e.g., empirically defined hard thresholds or poor applicability to nonstationary conditions. This paper presents a novel two-phase greedy pursuit (TPGP) algorithm for automatic detection and characterization of calcium sparks. In Phase I, a coarse-grained search is conducted across the whole image to identify the predominant sparks. In Phase II, adaptive basis function model is developed for the fine-grained representation of detected sparks. It may be noted that the proposed TPGP algorithms overcome the drawback of hard thresholding in most of previous approaches. Furthermore, the morphology of detected sparks is effectively modeled with multiscale basis functions in Phase II, thereby facilitating the analysis of physiological features. We evaluated and validated the TPGP algorithms using both real-word and synthetic images with multiple noise levels and varying baselines. Experimental results show that TPGP algorithms yield better performances than previous hard-thresholding approaches in terms of both sensitivities and positive predicted values. The present research provides the community a robust tool for the automatic detection and characterization of transient calcium signaling.

Cardiac action potentials (AP) are produced by the orchestrated functions of ion channels. A slight change in ion channel activity may affect the AP waveform, thereby potentially increasing susceptibility to abnormal cardiac rhythms. Cardiac ion channels are heavily glycosylated, with up to 30% of a mature protein's mass comprised of glycan structures. However, little is known about how reduced glycosylation impacts the gating of hERG (human ether-a-go-go related gene) channel, which is partially responsible for late phase 2 and phase 3 of the AP. This paper integrates the data from in vitro experiments with in-silico models to predict the glycosylation modulation dynamics in hERG ion channels and cardiac electrical signals. The gating behaviors of hERG channels expressed in Chinese Hamster Ovary (CHO) cells were measured under four glycosylation conditions, i.e., full glycosylation, reduced sialylation, mannose-rich. and N-glycanase treated. Further, we developed in-silico models to simulate glycosylation-channel interactions and predict the effects of reduced glycosylation on multiscale cardiac processes (i.e., cardiac cells, 1-D and 2-D tissues). From the in-silico models, reduced glycosylation was shown to shorten the repolarization phase of cardiac APs, thereby influencing electrical propagation in cardiac fibers and tissues. In addition, the patterns of derived electrocardiogram show that reduced glycosylation of hERG channel shortens the QT interval and decreases the re-entry rate of spiral waves. This work suggests new pharmaceutical targets for the long QT syndrome and potentially other cardiac disorders.

High-quality experimental data are important when developing predictive models for studying nanomaterial environmental impact (NEI). Given that raw data from experimental laboratories and manufacturing workplaces are usually proprietary and small-scaled, extracting information from publications is an attractive alternative for collecting data. We developed an information extraction system that can extract useful information from full-text nanotoxicity related publications. This information extraction system consists of five components: raw data transformation into machine readable format, data preprocessing, ontology-based named entity recognition, rule-based numerical attribute extraction from both tables and unstructured text, and relation extraction among entities and attributes. The information extraction system is applied on a dataset made of 94 publications, and results in an acceptable accuracy. By storing extracted data into a table according to relations among the data, a dataset that can be used to predict nanomaterial environmental impact is obtained. Such a system is unique in current nanomaterial community, and can help nanomaterial scientists and practitioners quickly locate useful information they need without spending lots of time reading articles.

The selection and prioritization of research directions are always challenges. This paper aims to make sense of nanomaterial toxicity publication and keywords data through quantitative metrics and network visualization. We have adapted a combined approach of network analysis, co-occurrence analysis, clustering analysis and visual analytics, to characterize important relational properties of network structures and features of entities. The results show that both co-authorship network and keywords network on nanomaterial toxicity follow the power-law degree distribution. In addition, the co-authorship network appears to be of scale-free pattern. We also investigate and visualize the research trends in field of nanomaterial toxicity by studying top influence researchers and keywords over years. These findings offer researchers various insights of the patterns and trends in the nanomaterial toxicity.

NEIMiner is an integrated information system for studying the nanomaterial environmental impact (NEI). However, there is a lack of visual analytic tools that efficiently query and present large-scaled bibliography meta-data and NEI characterizations in a meaningful way. This paper presents the design and implementation efforts of developing the information visualization (InfoVis) module for NEIMiner. We first describe a user centered design approach to identify the analysis tasks, to select suitable visual representations, and to iteratively validate and improve the development. We then show that how existing techniques, such as graph simplification, enriched visualization algorithms and interactive features, can be usefully combined to aid users gaining insights. We demonstrate the utility of Info Vis through scenarios of constructing co-authorship network, bibliography keywords network, and nanomaterial terms co-occurrence network. We implement our techniques as a Drupal module. Our design is supportive for analysts and researchers to identify concepts and relationships in studying environmental impact of nanomaterial.

Myocardial infarction (MI), also known as a heart attack, is the leading cause of death in the U.S. It often occurs due to the occlusion of coronary arteries, thereby leading to insufficient blood and oxygen supply that damage cardiac muscle cells. Because blood vessels are branching throughout the heart, MI occurs at different spatial locations (e.g., anterior and inferior portions) of the heart. The spatial location of the diseased is rupts normal excitation and propagation of cardiac electrical activity in space and time. Most previous studies focused on the relationships between disease and time-domain biomarkers from 12-lead ECG signals (e.g., Q wave, QT interval, ST elevation/depression, T wave). Few, if any, previous approaches investigated how the spatial location of diseases will alter cardiac vectorcardiogram (VCG) signals in both space and time. This paper presents a novel spatiotemporal warping approach to quantify the dissimilarity of disease-altered patterns in 3-lead spatiotemporal VCG signals. The hypothesis testing shows that there are significant spatiotemporal differences between healthy control, MI-anterior, MI-anterior-septal, MI-anterior-lateral, MI-inferior, and MI-inferior-lateral. Furthermore, we optimize the embedding of each functional recording as a feature vector in the high-dimensional space that preserves the dissimilarity distance matrix. This novel spatial embedding approach facilitates the construction of classification models and yields an averaged accuracy of 95.1% for separating MIs and Healthy Controls (HCs) and an averaged accuracy of 95.8% in identifying anterior-related MIs and inferior-related MIs.

Mathematical modeling of cardiac electrical signals facilitates the simulation of realistic cardiac electrical behaviors, the evaluation of algorithms, and the characterization of underlying space-time patterns. However, there are practical issues pertinent to model efficacy, robustness, and generality. This paper presents a multiscale adaptive basis function modeling approach to characterize not only temporal but also spatial behaviors of vectorcardiogram (VCG) signals. Model parameters are adaptively estimated by the "best matching" projections of VCG characteristic waves onto a dictionary of nonlinear basis functions. The model performance is experimentally evaluated with respect to the number of basis functions, different types of basis function (i.e., Gaussian, Mexican hat, customized wavelet, and Hermitian wavelets), and various cardiac conditions, including 80 healthy controls and different myocardial infarctions (i.e., 89 inferior, 77 anterior-septal, 56 inferior-lateral, 47 anterior, and 43 anterior-lateral). Multiway analysis of variance shows that the basis function and the model complexity have significant effects on model performances while cardiac conditions are not significant. The customized wavelet is found to be an optimal basis function for the modeling of spacetime VCG signals. The comparison of QT intervals shows small relative errors (<;5%) between model representations and realworld VCG signals when the model complexity is greater than 10. The proposed model shows great potentials to model space-time cardiac pathological behaviors and can lead to potential benefits in feature extraction, data compression, algorithm evaluation, and disease prognostics.