Improving Risk Prediction of Methicillin-Resistant Staphylococcus aureus Using Machine Learning Methods With Network Features: Retrospective Development Study.

BACKGROUND: Health care-associated infections due to multidrug-resistant organisms (MDROs), such as methicillin-resistant Staphylococcus aureus (MRSA) and Clostridioides difficile (CDI), place a significant burden on our health care infrastructure. OBJECTIVE: Screening for MDROs is an important mechanism for preventing spread but is resource intensive. The objective of this study was to develop automated tools that can predict colonization or infection risk using electronic health record (EHR) data, provide useful information to aid infection control, and guide empiric antibiotic coverage. METHODS: We retrospectively developed a machine learning model to detect MRSA colonization and infection in undifferentiated patients at the time of sample collection from hospitalized patients at the University of Virginia Hospital. We used clinical and nonclinical features derived from on-admission and throughout-stay information from the patient's EHR data to build the model. In addition, we used a class of features derived from contact networks in EHR data; these network features can capture patients' contacts with providers and other patients, improving model interpretability and accuracy for predicting the outcome of surveillance tests for MRSA. Finally, we explored heterogeneous models for different patient subpopulations, for example, those admitted to an intensive care unit or emergency department or those with specific testing histories, which perform better. RESULTS: We found that the penalized logistic regression performs better than other methods, and this model's performance measured in terms of its receiver operating characteristics-area under the curve score improves by nearly 11% when we use polynomial (second-degree) transformation of the features. Some significant features in predicting MDRO risk include antibiotic use, surgery, use of devices, dialysis, patient's comorbidity conditions, and network features. Among these, network features add the most value and improve the model's performance by at least 15%. The penalized logistic regression model with the same transformation of features also performs better than other models for specific patient subpopulations. CONCLUSIONS: Our study shows that MRSA risk prediction can be conducted quite effectively by machine learning methods using clinical and nonclinical features derived from EHR data. Network features are the most predictive and provide significant improvement over prior methods. Furthermore, heterogeneous prediction models for different patient subpopulations enhance the model's performance.

An Indoor Fire Detection Method Based on Multi-Sensor Fusion and a Lightweight Convolutional Neural Network.

Indoor fires pose significant threats in terms of casualties and economic losses globally. Thus, it is vital to accurately detect indoor fires at an early stage. To improve the accuracy of indoor fire detection for the resource-constrained embedded platform, an indoor fire detection method based on multi-sensor fusion and a lightweight convolutional neural network (CNN) is proposed. Firstly, the Savitzky-Golay (SG) filter is used to clean the three types of heterogeneous sensor data, then the cleaned sensor data are transformed by means of the Gramian Angular Field (GAF) method into matrices, which are finally integrated into a three-dimensional matrix. This preprocessing stage will preserve temporal dependency and enlarge the characteristics of time-series data. Therefore, we could reduce the number of blocks, channels and layers in the network, leading to a lightweight CNN for indoor fire detection. Furthermore, we use the Fire Dynamic Simulator (FDS) to simulate data for the training stage, enhancing the robustness of the network. The fire detection performance of the proposed method is verified through an experiment. It was found that the proposed method achieved an impressive accuracy of 99.1%, while the number of CNN parameters and the amount of computation is still small, which is more suitable for the resource-constrained embedded platform of an indoor fire detection system.

Alternative approaches for creating a wealth index: the case of Mozambique.

INTRODUCTION: The wealth index is widely used as a proxy for a household's socioeconomic position (SEP) and living standard. This work constructs a wealth index for the Mopeia district in Mozambique using data collected in year 2021 under the BOHEMIA (Broad One Health Endectocide-based Malaria Intervention in Africa) project. METHODS: We evaluate the performance of three alternative approaches against the Demographic and Health Survey (DHS) method based wealth index: feature selection principal components analysis (PCA), sparse PCA and robust PCA. The internal coherence between four wealth indices is investigated through statistical testing. Validation and an evaluation of the stability of the wealth index are performed with additional household income data from the BOHEMIA Health Economics Survey and the 2018 Malaria Indicator Survey data in Mozambique. RESULTS: The Spearman's rank correlation between wealth index ventiles from four methods is over 0.98, indicating a high consistency in results across methods. Wealth rankings and households' income show a strong concordance with the area under the curve value of ~0.7 in the receiver operating characteristic analysis. The agreement between the alternative wealth indices and the DHS wealth index demonstrates the stability in rankings from the alternative methods. CONCLUSIONS: This study creates a wealth index for Mopeia, Mozambique, and shows that DHS method based wealth index is an appropriate proxy for the SEP in low-income regions. However, this research recommends feature selection PCA over the DHS method since it uses fewer asset indicators and constructs a high-quality wealth index.

Alternative approaches for creating a wealth index: the case of Mozambique

Background Residual malaria transmission is the result of adaptive mosquito behavior that allows malaria vectors to thrive and sustain transmission in the presence of good access to bed nets or insecticide residual spraying. These behaviors include crepuscular and outdoor feeding as well as intermittent feeding upon livestock. Ivermectin is a broadly used antiparasitic drug that kills mosquitoes feeding on a treated subject for a dose-dependent period. Mass drug administration with ivermectin has been proposed as a complementary strategy to reduce malaria transmission. Methods A cluster randomized, parallel arm, superiority trial conducted in two settings with distinct eco-epidemio logical conditions in East and Southern Africa. There will be three groups: human intervention, consisting of a dose of ivermectin (400 mcg/kg) administered monthly for 3 months to all the eligible population in the cluster (>15 kg, nonpregnant and no medical contraindication); human and livestock intervention, consisting human treatment as above plus treatment of livestock in the area with a single dose of injectable ivermectin (200 mcg/kg) monthly for 3 months; and controls, consisting of a dose of albendazole (400 mg) monthly for 3 months. The main outcome measure will be malaria incidence in a cohort of children under fve living in the core of each cluster followed prospectively with monthly RDTs Discussion The second site for the implementation of this protocol has changed from Tanzania to Kenya. This sum mary presents the Mozambique-specifc protocol while the updated master protocol and the adapted Kenya-specifc

BOHEMIA a cluster randomized trial to assess the impact of an endectocide-based one health approach to malaria in Mozambique: baseline demographics and key malaria indicators.

BACKGROUND: Many geographical areas of sub-Saharan Africa, especially in rural settings, lack complete and up-to-date demographic data, posing a challenge for implementation and evaluation of public health interventions and carrying out large-scale health research. A demographic survey was completed in Mopeia district, located in the Zambezia province in Mozambique, to inform the Broad One Health Endectocide-based Malaria Intervention in Africa (BOHEMIA) cluster randomized clinical trial, which tested ivermectin mass drug administration to humans and/or livestock as a potential novel strategy to decrease malaria transmission. METHODS: The demographic survey was a prospective descriptive study, which collected data of all the households in the district that accepted to participate. Households were mapped through geolocation and identified with a unique identification number. Basic demographic data of the household members was collected and each person received a permanent identification number for the study. RESULTS: 25,550 households were mapped and underwent the demographic survey, and 131,818 individuals were registered in the district. The average household size was 5 members and 76.9% of households identified a male household head. Housing conditions are often substandard with low access to improved water systems and electricity. The reported coverage of malaria interventions was 71.1% for indoor residual spraying and 54.1% for universal coverage of long-lasting insecticidal nets. The median age of the population was 15 years old. There were 910 deaths in the previous 12 months reported, and 43.9% were of children less than 5 years of age. CONCLUSIONS: The study showed that the district had good coverage of vector control tools against malaria but sub-optimal living conditions and poor access to basic services. The majority of households are led by males and Mopeia Sede/Cuacua is the most populated locality in the district. The population of Mopeia is young (< 15 years) and there is a high childhood mortality. The results of this survey were crucial as they provided the household and population profiles and allowed the design and implementation of the cluster randomized clinical trial. Trial registration NCT04966702.

BOHEMIA a cluster randomized trial to assess the impact of an endectocide-based one health approach to malaria in Mozambique: baseline demographics and key malaria indicators

Many geographical areas of sub-Saharan Africa, especially in rural settings, lack complete and up-to-date demographic data, posing a challenge for implementation and evaluation of public health interventions and carrying out large-scale health research. A demographic survey was completed in Mopeia district, located in the Zambezia province in Mozambique, to inform the Broad One Health Endectocide-based Malaria Intervention in Africa (BOHEMIA) cluster randomized clinical trial, which tested ivermectin mass drug administration to humans and/or livestock as a potential novel strategy to decrease malaria transmission. Methods: The demographic survey was a prospective descriptive study, which collected data of all the households in the district that accepted to participate. Households were mapped through geolocation and identified with a unique identification number. Basic demographic data of the household members was collected and each person received a permanent identification number for the study. Results: 25,550 households were mapped and underwent the demographic survey, and 131,818 individuals were registered in the district. The average household size was 5 members and 76.9% of households identified a male household head. Housing conditions are often substandard with low access to improved water systems and electricity. The reported coverage of malaria interventions was 71.1% for indoor residual spraying and 54.1% for universal coverage of long-lasting insecticidal nets. The median age of the population was 15 years old. There were 910 deaths in the previous 12 months reported, and 43.9% were of children less than 5 years of age. Conclusions: The study showed that the district had good coverage of vector control tools against malaria but sub-optimal living conditions and poor access to basic services. The majority of households are led by males and Mopeia Sede/Cuacua is the most populated locality in the district. The population of Mopeia is young (< 15 years) and there is a high childhood mortality. The results of this survey were crucial as they provided the household and population profiles and allowed the design and implementation of the cluster randomized clinical trial. Trial registration NCT04966702.

Challenges of modeling and analysis in cybermanufacturing: a review from a machine learning and computation perspective.

In Industry 4.0, smart manufacturing is facing its next stage, cybermanufacturing, founded upon advanced communication, computation, and control infrastructure. Cybermanufacturing will unleash the potential of multi-modal manufacturing data, and provide a new perspective called computation service, as a part of service-oriented architecture (SOA), where on-demand computation requests throughout manufacturing operations are seamlessly satisfied by data analytics and machine learning. However, the complexity of information technology infrastructure leads to fundamental challenges in modeling and analysis under cybermanufacturing, ranging from information-poor datasets to a lack of reproducibility of analytical studies. Nevertheless, existing reviews have focused on the overall architecture of cybermanufacturing/SOA or its technical components (e.g., communication protocol), rather than the potential bottleneck of computation service with respect to modeling and analysis. In this paper, we review the fundamental challenges with respect to modeling and analysis in cybermanufacturing. Then, we introduce the existing efforts in computation pipeline recommendation, which aims at identifying an optimal sequence of method options for data analytics/machine learning without time-consuming trial-and-error. We envision computation pipeline recommendation as a promising research field to address the fundamental challenges in cybermanufacturing. We also expect that computation pipeline recommendation can be a driving force to flexible and resilient manufacturing operations in the post-COVID-19 industry.

Red Blood Cell Distribution Width Is Associated With Adverse Kidney Outcomes in Patients With Chronic Kidney Disease.

Background: Chronic kidney disease (CKD) is a global public health issue. Red blood cell distribution width (RDW) is a recently recognized potential inflammatory marker, which mirrors the variability in erythrocyte volume. Studies indicate that elevated RDW is associated with increased risk of mortality in CKD patients, while evidence regarding the impact of RDW on kidney outcome is limited. Methods: Altogether 523 patients with CKD stage 1-4 from a single center were enrolled. We identified the cutoff point for RDW level using maximally selected log-rank statistics. The time-averaged estimated glomerular filtration rate (eGFR) slope was determined using linear mixed effects models. Rapid CKD progression was defined by an eGFR decline >5 ml/min/1.73 m2/year. The composite endpoints were defined as doubling of serum creatinine, a 30% decline in initial eGFR or incidence of eGFR < 15 ml/min/1.73 m2, whichever occurred first. Multivariable logistic regression or Cox proportional hazards regression was performed, as appropriate. Results: During a median follow-up of 26 [interquartile range (IQR): 12, 36] months, 65 (12.43%) patients suffered a rapid CKD progression and 172 (32.89%) composite kidney events occurred at a rate of 32.3/100 patient-years in the high RDW group, compared with 14.7/100 patient-years of the remainder. The annual eGFR change was clearly steeper in high RDW group {-3.48 [95% confidence interval (CI): -4.84, -2.12] ml/min/1.73 m2/year vs. -1.86 [95% CI: -2.27, -1.45] ml/min/1.73 m2/year among those with RDW of >14.5% and ≤14.5%, respectively, P for between-group difference <0.05}. So was the risk of rapid renal function loss (odds ratio = 6.79, 95% CI: 3.08-14.97) and composite kidney outcomes (hazards ratio = 1.51, 95% CI: 1.02-2.23). The significant association remained consistent in the sensitivity analysis. Conclusion: Increased RDW value is independently associated with accelerated CKD deterioration. Findings of this study suggest RDW be a potential indicator for risk of CKD progression.

Decision-adjusted driver risk predictive models using kinematics information.

Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information. We apply the proposed framework to identify the optimal threshold values for elevated longitudinal acceleration (ACC), deceleration (DEC), lateral acceleration (LAT), and other model parameters for predicting driver risk. The Second Strategic Highway Research Program (SHRP 2) naturalistic driving data were used with the decision rule of identifying the top 1% to 20% of the riskiest drivers. The results show that the decision-adjusted model improves prediction precision by 6.3% to 26.1% compared to a baseline model using non-telematics predictors. The proposed model is superior to models based on a receiver operating characteristic curve criterion, with 5.3% and 31.8% improvement in prediction precision. The results confirm that the optimal thresholds for ACC, DEC and LAT are sensitive to the decision rules, especially when predicting a small percentage of high-risk drivers. This study demonstrates the value of kinematic driving behavior in crash risk prediction and the necessity for a systematic approach for extracting prediction features. The proposed method can benefit broad applications, including fleet safety management, use-based insurance, driver behavior intervention, as well as connected-vehicle safety technology development.

Low-Loss Broadband Transverse Electric Pass Hybrid Plasmonic Fiber Polarizers Using Metallic Nanomaterials.

Metals were for decades perceived as devoid of interesting optical properties that could be harnessed for optical components and devices. However, with the development of accurate nanofabrication techniques and precise control over architectural parameters, metals can be structured and characterized on the nanoscale. Metallic plasmonic nanomaterials exhibit a number of unique structural and optical properties, which offer the potential for developing new types of plasmonic devices. Here, we demonstrate a low-loss broadband polarizer based on a hybrid plasmonic fiber structure using metals as polarization-selective absorption materials. The polarization mechanism, design, fabrication, and characteristics of the plasmonic polarizers are investigated theoretically, numerically, and experimentally. The theoretical analysis predicts that the polarization-selective absorption with insensitivity to wavelength enables hybrid plasmonic fibers to function as broadband polarizers. Numerical simulations give the comparison of the polarization-selective absorption of various metallic nanomaterials (Ag, Au, In, Al, Cr) and show that aluminum is regarded as the optimum absorption material for the plasmonic polarizer. Experimental results show that through precise control over geometrical parameters, this device is capable of offering a high polarization extinction ratio (PER) of over 40 dB and a low insertion loss (IL) of less than 1.3 dB in the wavelength region of 810.1-870.0 nm. Compared with commercial birefringent-crystal-fiber polarizers, the plasmonic fiber polarizer has a better PER and IL bandwidth. These merits, combined with a compact and robust configuration, enable the plasmonic polarizer to have great potential in a broad range of applications.

Bayesian auxiliary variable model for birth records data with qualitative and quantitative responses.

Many applications involve data with qualitative and quantitative responses. When there is an association between the two responses, a joint model will produce improved results than fitting them separately. In this paper, a Bayesian method is proposed to jointly model such data. The joint model links the qualitative and quantitative responses and can assess their dependency strength via a latent variable. The posterior distributions of parameters are obtained through an efficient MCMC sampling algorithm. The simulation is conducted to show that the proposed method improves the prediction capacity for both responses. Further, the proposed joint model is applied to the birth records data acquired by the Virginia Department of Health for studying the mutual dependence between preterm birth of infants and their birth weights.

Driving safety assessment for ride-hailing drivers.

Ride-hailing services, which have become increasingly prevalent in the last decade, provide an efficient travel mode by matching drivers and travelers via smartphone apps. Ride-hailing services enable millions of non-traditional taxi drivers to provide travel services, but may also raise safety concerns due to heterogeneity in the driver population. This study evaluated crash risk factors for ride-hailing drivers, including driving history and ride-hailing operational characteristics, using a sample of 189,815 drivers. We utilized the Poisson generalized additive model to accommodate for the potential nonlinear relationship between crash rate and risk factors. Results showed that crash history, the percentage of long-shift bookings, driving distance, operations during peak hours, years of being a ride-hailing driver, and passenger rating were significantly associated with crash risk. Several factors showed nonlinear relationships with crash risk. We adopted the SHapley Additive exPlanation (SHAP) method to assess and visualize the impact of each risk factor. The results indicated that passenger average rating, total driving distance, and crash history were the leading contributing factors. The findings of this study provide critical information for the development of safety countermeasures, driver education programs, as well as safety regulations for the ride-hailing industry.

Dihydromyricetin ameliorates chronic liver injury by reducing pyroptosis.

BACKGROUND: Chronic liver injury (CLI) is now a worldwide disease. However, there is no effective treatment. Pyroptosis plays an essential role in CLI. Dihydromyricetin (DHM) resists oxidation and protects the liver. We hypothesize that the beneficial effect of DHM on CLI is related to its effect on the expression of pyroptosis-related molecules. Therefore, we studied the influence of DHM on CLI and pyroptosis. AIM: To study the role of pyroptosis in the pathogenesis of CLI and the therapeutic mechanism of DHM. METHODS: Thirty-two mice were randomly divided into four groups: The control group was injected with olive oil, the carbon tetrachloride (CCl4) group was injected with CCl4, the vehicle group was injected with hydroxypropyl-ß-cyclodextrin while injecting CCl4 and the DHM group was injected with DHM while injecting CCl4. After four weeks of treatment, liver tissues from the mice were stained with hematoxylin and eosin, and oil red O. Blood was collected from the angular vein for serological analysis. The severity of CLI was estimated. Some liver tissue was sampled for immunohistochemistry, Western blotting and quantitative reverse transcription PCR to observe the changes in pyroptosis-related molecules. RESULTS: Serum total cholesterol, low density lipoprotein, aspartate aminotransferase (AST) and alanine aminotransferase (ALT) in the CCl4 group were higher than those in the control group, and serum total cholesterol, low density lipoprotein, AST and ALT in the DHM group were lower than those in the vehicle group. Hematoxylin and eosin and oil red O staining showed that there were more lipid droplets in the CCl4 group than in the control group, and there were fewer lipid droplets in the DHM group than in the vehicle group. Western blotting showed that the expression of the pyroptosis-related molecules caspase-1, NOD-, LRR- and pyrin domain-containing 3 (NLRP3) and gasdermin D (GSDMD)-N in the CCl4 group was higher than that in the control group, while expression of these proteins in the DHM group was lower than that in the vehicle group. Quantitative reverse transcription PCR results showed that the expression of the pyroptosis-related genes caspase-1, NLRP3, GSDMD and interleukin-1ß (IL-1ß) in the CCl4 group was higher than that in the control group, while there was no significant change in NLRP3 and caspase-1 expression in the DHM group compared with that in the vehicle group, and the expression of GSDMD and IL-1ß was decreased. CONCLUSION: DHM improves CCl4-induced CLI and regulates the pyroptosis pathway in hepatocytes. DHM may be a potential therapeutic agent for CLI.

Data analysis and modeling pipelines for controlled networked social science experiments.

There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.

Efficacy of ß-Blockers on Postural Tachycardia Syndrome in Children and Adolescents: A Systematic Review and Meta-Analysis.

Background: Postural tachycardia syndrome (POTS) is a severe health problem in children. Short-term ß-blockers are recommended for pharmaceutical treatment. However, there have been contradictory data about its efficacy among pediatric patients. Methods and Results: Eight studies comparing ß-blockers to conventional treatments for children with POTS were selected, where 497 cases of pediatric POTS were included. The efficacy of ß-blockers was evaluated using the effective rate, the change of symptom score, the change of heart rate difference and adverse events. The results were stated as relative ratio (RR) and mean difference (MD) with a 95% confidence interval (95% CI). A random-effects meta-analysis for the effective rate indicated that ß-blockers were more effective in treating pediatric POTS than controlled treatment (79.5 vs. 57.3%, RR = 1.50, 95%CI: 1.15-1.96, P < 0.05). A fixed-effects model analysis showed that ß-blockers were more effective in lowering the symptom score and the heart rate increment during standing test than controlled treatment with a mean difference of 0.81 (95% CI: 0.44-1.18, P < 0.05) and 3.78 (95% CI: 2.10-5.46, P < 0.05), respectively. There were no reported severe adverse events in included studies. Conclusion: ß-blockers are effective in treating POTS in children and adolescents, alleviating orthostatic intolerance, and improving hemodynamic abnormalities.

Adjusting finite sample bias in traffic safety modeling.

Poisson and negative binomial regression models are fundamental statistical analysis tools for traffic safety evaluation. The regression parameter estimation could suffer from the finite sample bias when event frequency is low, which is commonly observed in safety research as crashes are rare events. In this study, we apply a bias-correction procedure to the parameter estimation of Poisson and NB regression models. We provide a general bias-correction formulation and illustrate the finite sample bias through a special scenario with a single binary explanatory variable. Several factors affecting the magnitude of bias are identified, including the number of crashes and the balance of the crash counts within strata of a categorical explanatory variable. Simulations are conducted to examine the properties of the bias-corrected coefficient estimators. The results show that the bias-corrected estimators generally provide less bias and smaller variance. The effect is especially pronounced when the crash count in one stratum is between 5 and 50. We apply the proposed method to a case study of infrastructure safety evaluation. Three scenarios were evaluated, all crashes collected in three years, and two hypothetical situations, where crash information was collected for "half-year" and "quarter-year" periods. The case-study results confirm that the magnitude of bias correction is larger for smaller crash counts. This paper demonstrates the finite sample bias associated with the small number of crashes and suggests bias adjustment can provide more accurate estimation when evaluating the impacts of crash risk factors.

Non-invasive assessment of liver quality in transplantation based on thermal imaging analysis.

BACKGROUND AND OBJECTIVE: Liver quality evaluation is one of the vital steps for predicting the success of liver transplantation. Current evaluation methods, such as biopsy and visual inspection, which are either invasive or lack of consistent standards, provide limited predictive value of long-term transplant viability. Objective analytical models, based on the real-time infrared images of livers during perfusion and preservation, are proposed as novel methods to precisely evaluate donated liver quality. METHODS: In this study, by using principal component analysis to extract infrared image features as predictors, we construct a multivariate logistic regression model for single liver quality evaluation, and a multi-task learning logistic regression model for cross-liver quality evaluation. RESULTS: The single liver quality predictions show testing errors of 0%. The leave-one-liver-out predictions show testing errors ranging from 9% to 36%. CONCLUSIONS: It is found that there is a strong correlation between the viability of livers and the infrared image features in both single liver and cross-liver quality evaluations. These analytical methods also determine that the selected significant infrared image features indicate regional difference in viability and show that more stringent pre-implantation evaluation may be needed to predict transplant outcomes.

Sensitivity Analysis of an ENteric Immunity SImulator (ENISI)-Based Model of Immune Responses to Helicobacter pylori Infection.

Agent-based models (ABM) are widely used to study immune systems, providing a procedural and interactive view of the underlying system. The interaction of components and the behavior of individual objects is described procedurally as a function of the internal states and the local interactions, which are often stochastic in nature. Such models typically have complex structures and consist of a large number of modeling parameters. Determining the key modeling parameters which govern the outcomes of the system is very challenging. Sensitivity analysis plays a vital role in quantifying the impact of modeling parameters in massively interacting systems, including large complex ABM. The high computational cost of executing simulations impedes running experiments with exhaustive parameter settings. Existing techniques of analyzing such a complex system typically focus on local sensitivity analysis, i.e. one parameter at a time, or a close "neighborhood" of particular parameter settings. However, such methods are not adequate to measure the uncertainty and sensitivity of parameters accurately because they overlook the global impacts of parameters on the system. In this article, we develop novel experimental design and analysis techniques to perform both global and local sensitivity analysis of large-scale ABMs. The proposed method can efficiently identify the most significant parameters and quantify their contributions to outcomes of the system. We demonstrate the proposed methodology for ENteric Immune SImulator (ENISI), a large-scale ABM environment, using a computational model of immune responses to Helicobacter pylori colonization of the gastric mucosa.

Predictive computational modeling of the mucosal immune responses during Helicobacter pylori infection.

T helper (Th) cells play a major role in the immune response and pathology at the gastric mucosa during Helicobacter pylori infection. There is a limited mechanistic understanding regarding the contributions of CD4+ T cell subsets to gastritis development during H. pylori colonization. We used two computational approaches: ordinary differential equation (ODE)-based and agent-based modeling (ABM) to study the mechanisms underlying cellular immune responses to H. pylori and how CD4+ T cell subsets influenced initiation, progression and outcome of disease. To calibrate the model, in vivo experimentation was performed by infecting C57BL/6 mice intragastrically with H. pylori and assaying immune cell subsets in the stomach and gastric lymph nodes (GLN) on days 0, 7, 14, 30 and 60 post-infection. Our computational model reproduced the dynamics of effector and regulatory pathways in the gastric lamina propria (LP) in silico. Simulation results show the induction of a Th17 response and a dominant Th1 response, together with a regulatory response characterized by high levels of mucosal Treg) cells. We also investigated the potential role of peroxisome proliferator-activated receptor γ (PPARγ) activation on the modulation of host responses to H. pylori by using loss-of-function approaches. Specifically, in silico results showed a predominance of Th1 and Th17 cells in the stomach of the cell-specific PPARγ knockout system when compared to the wild-type simulation. Spatio-temporal, object-oriented ABM approaches suggested similar dynamics in induction of host responses showing analogous T cell distributions to ODE modeling and facilitated tracking lesion formation. In addition, sensitivity analysis predicted a crucial contribution of Th1 and Th17 effector responses as mediators of histopathological changes in the gastric mucosa during chronic stages of infection, which were experimentally validated in mice. These integrated immunoinformatics approaches characterized the induction of mucosal effector and regulatory pathways controlled by PPARγ during H. pylori infection affecting disease outcomes.

Spatial modeling for refining and predicting surface potential mapping with enhanced resolution.

Quantitatively mapping surface properties with nanometer or even subnanometer resolutions is critical for advanced scanning probe microscopy (SPM) characterization. However, the characterization performance often suffers from noises and artifacts due to instrumentation or environmental limitations. In this paper, we proposed a novel statistical approach with bivariate spatial modeling to efficiently refine and predict surface property mapping. Scanning Kelvin probe microscopy (SKPM) was selected as a representative example to test our proposed method on lateral nanowire assemblies. We revealed that the proposed method can effectively retrieve the artifact-free surface potential distribution by automatically identifying topological artifacts from surface potential maps. Furthermore, the statistical model built upon low spatial resolution was successfully used to predict the potential values from higher-resolution topography data. Compared to conventional regression model, our model is able to predict the surface potential distribution from less raw data but yields much higher accuracy. Through this means, the spatial resolution of SKPM surface potential maps can be significantly improved. This statistics-enabled predictive method opens a new route toward high-precision and high-resolution SPM characterizations without the enhancement of instrumentation capabilities.