Nonparametric Modeling Approach Research Articles

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Investigating the dynamic effects of service value added on CO2 emissions: Novel insights from a non-parametric approach

Carbon dioxide (CO2) emissions are the primary contributors to climate change. Addressing and mitigating climate change necessitates the effective management and utilization of renewable energy consumption, which poses a substantial challenge for the forthcoming decades. This study explores the dynamic effects of service value added (SVA) and renewable energy on environmental quality, particularly focusing on CO2 emissions. Unlike previous studies, we employ a non-parametric modeling approach to uncover the time-varying influence of service sector growth on CO2 emissions. Specifically, we apply the local linear dummy variable estimation (LLDVE) method to a panel of the 17 highest-emitting nations over the period 1980–2021. Our study uncovers a non-linear relationship between CO2 emissions and SVA. From 1980 to 2003, we observe a negative correlation. However, starting from 2005 to 2020, we witness a shift towards a positive correlation, indicating a rise in energy consumption within the service sector. The results indicate that significant emitter economies have yet to achieve sustainability, with the service sector continuing to contribute to pollution. Addressing this issue necessitates more robust climate change policies and increased investment in clean energy, specifically targeting the service sector, including buildings and transport.

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

A nonparametric Bayesian item response modelling approach for clustering items and individuals simultaneously

Item response theory (IRT) is a popular modelling paradigm for measuring subject latent traits and item properties according to discrete responses in tests or questionnaires. There are very limited discussions on heterogeneity pattern detection for both items and individuals. In this paper, we introduce a nonparametric Bayesian approach for clustering items and individuals simultaneously under the Rasch model. Specifically, our proposed method is based on the mixture of finite mixtures (MFM) model, which obtains the number of clusters and the clustering configurations for both items and individuals simultaneously. The performance of parameter estimation and parameter clustering under the MFM Rasch model is evaluated by simulation studies, and two real examples are applied to further illustrate the MFM Rasch modelling.

Using Unmanned Aerial Vehicles and Multispectral Sensors to Model Forage Yield for Grasses of Semiarid Landscapes

Forage yield estimates provide relevant information to manage and quantify ecosystem services in grasslands. We fitted and validated prediction models of forage yield for several prominent grasses used in restoration projects in semiarid areas. We used field forage harvests from three different sites in Northern Utah and Southern California, USA, in conjunction with multispectral, high-resolution UAV imagery. Different model structures were tested with simple models using a unique predictor, the forage volumetric 3D space, and more complex models, where RGB, red edge, and near-infrared spectral bands and associated vegetation indices were used as predictors. We found that for most dense canopy grasses, using a simple linear model structure could explain most (R2 0.7) of the variability of the response variable. This was not the case for sparse canopy grasses, where a full multispectral dataset and a non-parametric model approach (random forest) were required to obtain a maximum R2 of 0.53. We developed transparent protocols to model forage yield where, in most circumstances, acceptable results could be obtained with affordable RGB sensors and UAV platforms. This is important as users can obtain rapid estimates with inexpensive sensors for most of the grasses included in this study.

Exploring clozapine pharmacokinetics in Tunisian schizophrenic patients: A population-based modelling approach investigating the impact of genetic and non-genetic variables.

Clozapine is characterized by a large within- and between-patient variability in its pharmacokinetics, attributed to non-genetic and genetic factors. A cross-sectional analysis of clozapine trough concentration (Clz C0) issued from Tunisian schizophrenic patients was collected and analysed using a nonparametric modelling approach. We assessed the impact of demographic covariates (age, weight and sex), patient's habits (smoking status, alcohol and caffeine intake) and the genetic factors (CYP1A2*1C, CYP1A2*1F and CYP2C19*2 polymorphisms) on each pharmacokinetic parameter. An external validation of this pharmacokinetic model using an independent data set was performed. Fit goodness between observed- and individual-predicted data was evaluated using the mean prediction error (% MPE), the mean absolute prediction error (% MAPE) as a measure of bias, and the root mean squared error (% RMSE) as a measure of precision. Sixty-three CLz C0 values issued from 51 schizophrenic patients were assessed in this study and divided into building and validation groups. CYP1A2*1F polymorphism and smoking status were the only covariates significantly associated with clozapine clearance. Precision parameters were as follows: 1.02%, 0.95% and 22.4%, respectively, for % MPE, % MAPE and % RMSE. We developed and validated an accurate pharmacokinetic model able to predict Clz C0 in Tunisian schizophrenic patients using the two parameters CYP1A2*1F polymorphism and smoking.

Combining Nonparametric and Parametric Item Response Theory to Explore Data Quality: Illustrations and a Simulation Study

ABSTRACT Researchers who use measurement models for evaluation purposes often select models with stringent requirements, such as Rasch models, which are parametric. Mokken Scale Analysis (MSA) offers a theory-driven nonparametric modeling approach that may be more appropriate for some measurement applications. Researchers have discussed using MSA as a preliminary procedure with which to evaluate data quality before applying a parametric model. However, the literature includes only a few examples in which researchers have integrated MSA techniques with parametric models throughout the analytic procedure. We consider a systematic approach for integrating results from nonparametric MSA techniques with parametric measurement models to evaluate measurement quality and construct scales with useful measurement properties. We use real-data illustrations and a simulation study to demonstrate and systematically explore our approach. We discuss implications for research and practice.

Abstract 13339: Proteomic Associations of Cardiac Function Trajectories Through Mid- to Late Life

Introduction: Using a novel Bayesian nonparametric modeling approach based jointly on LVEF and E/A ratio, we previously identified 3 trajectories of cardiac function through mid- to late life that associated with heighted risk of incident heart failure (HF): trajectory 1 demonstrated failure to increase LVEF with age; trajectory 2 had progressive LVEF decline; and trajectory 3 had steeper than expected rise in E/A ratio. Trajectories 1 and 2 associated with higher risk of HFrEF while trajectory 3 associated with HFpEF. Whether circulating proteins differ between trajectories is unknown. Methods: We assigned 4,419 HF-free participants with available proteomic data (SomaScan v4) at study Visit 5 of the Atherosclerosis Risk in Communities study to their most likely trajectory (2,201 trajectory 1, 162 trajectory 2, 348 to trajectory 3). Associations of plasma proteins with trajectory membership were assessed using multinominal logistic regression analyses adjusted for age and sex at a false discovery rate <0.05. Two-sample Mendelian randomization (MR) was performed to assess potential causal effects between trajectory-associated proteins and LV volume and LVEF. Results: Mean age was 75±5 years and 58% were women. Several unique and shared proteins associated with each high-risk trajectory (Figure). MR identified potential causal effects of 13 trajectory-associated proteins on LV volume and LVEF. These included trajectory 3-associated proteins (B3GNT2, MFAP4) with genetically higher levels associated with smaller LV size and higher LVEF, consistent with the observed association with HFpEF. One trajectory 1-associated protein (HADH) had MR association with lower LVEF, consistent with the observed association with HFrEF. Conclusions: A Bayesian nonparametric model using multidimensional imaging data identifies trajectories of cardiac function with distinct proteomic associations, a subset of which have potential causal effects on cardiac function.

A Bayesian Nonparametric Modeling Approach to Settlement Patterns of Pastoralists Population in Kenya

Pastoralists' settlement patterns in Kenya have been studied for decades using various statistical and mathematical models. However, traditional models have often relied on restrictive assumptions, such as the normality of the data or the linearity of relationships. In this paper, we apply a Bayesian nonparametric approach to model the settlement patterns of pastoralists in Kenya, allowing for more flexible and realistic representations of the data. We first collected settlement data for pastoralists in Kenya and compiled a database of environmental covariates, such as distance to water sources, vegetation cover, and road networks. We then applied a Bayesian nonparametric clustering method to identify distinct settlement patterns and tested the performance of the model against other commonly used clustering techniques. Our results indicate that the Bayesian nonparametric approach outperforms other clustering techniques in terms of model fit and accuracy in identifying distinct settlement patterns. Additionally, we conducted a spatial regression analysis to investigate the relationship between settlement patterns and environmental covariates, revealing that distance to water sources and road networks are significant predictors of settlement patterns. Overall, our study highlights the usefulness of Bayesian nonparametric methods in modelling settlement patterns of pastoralists in Kenya and provides valuable insights into the relationship between environmental factors and settlement patterns.

Learning latent functions for causal discovery

Causal discovery from observational data offers unique opportunities in many scientific disciplines: reconstructing causal drivers, testing causal hypotheses, and comparing and evaluating models for optimizing targeted interventions. Recent causal discovery methods focused on estimating the latent space of the data to get around a lack of causal sufficiency or additivity constraints. However, estimating the latent space significantly increases model complexity, compromising causal identifiability and making it hard to compare models that correspond to different causal hypotheses. We propose a kernel, non-parametric latent-space modelling approach and deal with the difficulty of comparing causal directions by measuring and controlling for the level of causal assumption fulfilment. We introduce a latent noise causal inference framework to estimate latent factors associated with the hypothesized causal direction by optimizing a loss function with kernel independence criteria. We extend the framework to work with time series using an additional time-dependent kernel regularizer. We discuss the additivity assumption and model complexity and give empirical evidence of performance in a wide range of synthetic and real causal discovery problems.

How do disease control measures impact spatial predictions of schistosomiasis and hookworm? The example of predicting school-based prevalence before and after preventive chemotherapy in Ghana.

Schistosomiasis and soil-transmitted helminth infections are among the neglected tropical diseases (NTDs) affecting primarily marginalized communities in low- and middle-income countries. Surveillance data for NTDs are typically sparse, and hence, geospatial predictive modeling based on remotely sensed (RS) environmental data is widely used to characterize disease transmission and treatment needs. However, as large-scale preventive chemotherapy has become a widespread practice, resulting in reduced prevalence and intensity of infection, the validity and relevance of these models should be re-assessed. We employed two nationally representative school-based prevalence surveys of Schistosoma haematobium and hookworm infections from Ghana conducted before (2008) and after (2015) the introduction of large-scale preventive chemotherapy. We derived environmental variables from fine-resolution RS data (Landsat 8) and examined a variable distance radius (1-5 km) for aggregating these variables around point-prevalence locations in a non-parametric random forest modeling approach. We used partial dependence and individual conditional expectation plots to improve interpretability of results. The average school-level S. haematobium prevalence decreased from 23.8% to 3.6% and that of hookworm from 8.6% to 3.1% between 2008 and 2015. However, hotspots of high-prevalence locations persisted for both infections. The models with environmental data extracted from a buffer radius of 2-3 km around the school location where prevalence was measured had the best performance. Model performance (according to the R2 value) was already low and declined further from approximately 0.4 in 2008 to 0.1 in 2015 for S. haematobium and from approximately 0.3 to 0.2 for hookworm. According to the 2008 models, land surface temperature (LST), modified normalized difference water index, elevation, slope, and streams variables were associated with S. haematobium prevalence. LST, slope, and improved water coverage were associated with hookworm prevalence. Associations with the environment in 2015 could not be evaluated due to low model performance. Our study showed that in the era of preventive chemotherapy, associations between S. haematobium and hookworm infections and the environment weakened, and thus predictive power of environmental models declined. In light of these observations, it is timely to develop new cost-effective passive surveillance methods for NTDs as an alternative to costly surveys, and to focus on persisting hotspots of infection with additional interventions to reduce reinfection. We further question the broad application of RS-based modeling for environmental diseases for which large-scale pharmaceutical interventions are in place.

Analysis of cuttings concentration experimental data using exploratory data analysis

Cuttings transportation is a complex phenomenon involving many inter-acting variables. Experimental investigations on cuttings transport are carried out by different research groups for decades and varying findings are reported which points out to the need for a methodical data analysis approach. In the current paper, six experimental datasets (702 observations) are analyzed using exploratory data analysis (EDA) in a two-fold manner – univariate and multivariate analysis. Univariate analysis shows the asymmetry in distribution for each experimental parameter indicating the need for a nonparametric modeling approach. Multivariate analysis shows the interaction of the experimental parameters among themselves and their influence on downhole cuttings concentration (Cc) using 6D scatter plots and correlation coefficients (Kendall's τ). EDA of the current experimental data reveals the following major findings:•Smaller Cc in concentric vertical wells compared to concentric non-vertical wells.•Drilling fluid flow rate is a dominant operational parameter in vertical wellbore cleaning while string rotation (RPM) is dominant in non-vertical wellbore cleaning.•Little impact of RPM in concentric vertical well and negative eccentric deviated/highly deviated well cleaning. However, RPM together with drilling fluid flow rate provides better cleaning of non-vertical wells with positive eccentricity.•RPM has higher influence on cuttings transport in narrow annulus compared to that in wide annulus.•Assuming drilling fluid of sufficient viscosity and drill string rotation present, low viscous fluid under turbulent flow and high viscous fluid under laminar flow provide better hole cleaning. Further, Kendall's τ indicates apparent viscosity playing a more significant role in cleaning deviated wellbores compared to other inclinations for the current dataset.•Drilling fluid flow rate influences the transport of heavier cuttings and larger cuttings more while RPM has higher influence on the transport of lighter cuttings and smaller cuttings.•Better hole cleaning by heavier drilling fluids than that by lighter fluids.

Inverse Dynamics Modeling and Analysis of Healthy Human Data for Lower Limb Rehabilitation Robots

Bio-controllers inspired by the characteristics of the human lower limb play an important role in the study of lower limb rehabilitation robots (LLRRs). However, the inverse dynamics modeling of robots for human lower limb rehabilitation remains a challenging issue due to the non-linear and strong coupling characteristics of the bio-controller. To further improve the inverse dynamics model’s accuracy, this paper proposes the use of a non-parametric modeling approach in order to learn it. In detail, the main idea is to use the motion data of the main joints of the lower limbs of healthy people as an input and the corresponding joint moments as an output, which are learned through the training of a neural network. To ensure that the learned model can be used on LLRRs, all data collected in this paper are real data from human lower limbs. In addition, since the type of data collected is time series, this paper proposes the use of the long short-term memory (LSTM) and gated recurrent unit (GRU) networks to learn the inverse dynamics model of the robot-like human lower limb and to compare the learning effects of the two networks. The evaluation metric for both network models is the root mean square error (RMSE). The experimental results show that both networks have sound learning effects, and that the GRU network has a more significant learning ability than the LSTM network.

Evaluation of parametric and non-parametric stem taper modeling approaches: A case study for Betula platyphylla in Northeast China

The accurate estimation of tree volume and biomass are crucial for forest inventories, forest sales, and carbon stock evaluations. Taper models were widely used and regarded as a standard method for estimating tree volume, but in practice it can be challenging to get an accurate and convenient method for taper models when calibration data is not available. Therefore, our main objectives were to compare fitting methods and present a reliable method to be used in forest inventories. In this work, the parametric modeling approaches ordinary nonlinear least squares, fixed-effects models at single-level and multilevel effects, and non-parametric modeling approach quantile generalized additive models were evaluated by predicting diameter, volume (total, partial, and merchantable), and merchantable height of 868 Betula platyphylla samples taken in three regions in Northeast China. The fixed-effects models were evaluated with fixed part parameters estimated from mixed-effects models at tree-level, plot-tree-level, and region-plot-tree-level. Evaluation statistics and box plots revealed that considering plot and region as random effects didn’t get the prediction accuracy improved for fixed-effects taper models. Therefore, both single-level and multilevel mixed-effects models are not recommended for prediction if the calibration data is not available. Although all models showed a bias in predicting merchantable height, this limitation does not significantly affect the performance for diameter and volume prediction. Results showed that non-parametric approach quantile generalized additive models had the highest prediction accuracy for stem diameter and volume prediction, closely followed by the ordinary nonlinear least squares and then the fixed-effects models at single-level. Therefore, the quantile generalized additive models are highly recommended in practice. The recommended model in this study can estimate the diameter and volume for a large area. Moreover, it has broader applicability in the situation when measuring upper-stem diameter may not be realized in practice.

Prediction of inpatient pressure ulcers based on routine healthcare data using machine learning methodology

Despite the relevance of pressure ulcers (PU) in inpatient care, the predictive power and role of care-related risk factors (e.g. anesthesia) remain unclear. We investigated the predictability of PU incidence and its association with multiple care variables. We included all somatic cases between 2014 and 2018 with length of stay ≥ 2d in a German university hospital. For regression analyses and prediction we used Bayesian Additive Regression Trees (BART) as nonparametric modeling approach. To assess predictive accuracy, we compared BART, random forest, logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) using area under the curve (AUC), confusion matrices and multiple indicators of predictive performance (e.g. sensitivity, specificity, F1, positive/ negative predictive value) in the full dataset and subgroups. Analysing 149,006 cases revealed high predictive variable importance and associations between incident PU and ventilation, age, anesthesia (≥ 1 h) and number of care-involved wards. Despite high AUCs (range 0.89–0.90), many false negative predictions led to low sensitivity (range 0.04–0.10). Ventilation, age, anesthesia and number of care-involved wards were associated with incident PU. Using anesthesia as a proxy for immobility, an hourly repositioning is indicated. The low sensitivity indicates major challenges for correctly predicting PU based on routine data.

Associations of Antipsychotic Doses with Dysregulated Rest-Activity Rhythms in People with Chronic Schizophrenia

<h3>Introduction</h3> Peoplewith schizophrenia (PwS) have a shortened lifespan by 15 to 20 years, primarily due to metabolic disturbances and cardiovascular disease. Increased metabolic risk in PwS is partly due to sedentary lifestyles, unhealthy diets, inadequate healthcare, and side effects from antipsychotics. Dysregulated rest-activity rhythms (RARs) are a key risk factor for metabolic health that are common yet understudied in PwS and could be a potential treatment target. This study aims to objectively assess and compare RAR variables in PwS and a non-psychiatric comparison group (NCs). We also examined how RAR variables were associated with sociodemographic factors and daily antipsychotic dose. We hypothesized that PwS would have more dysregulated RAR measures than NCs. Among PwS, we explored how antipsychotic dose was associated with RAR variables. <h3>Methods</h3> Studyparticipants included 26 PwS (DSM-IV-TR criteria) and 36 NCs from the greater San Diego Area. We collected sociodemographic variables, clinical data (e.g., antipsychotic dose as defined by the WHO, symptom severity, physical well-being measured by the 36-Item Short Form Survey or SF-36), and subjective sleep quality (Pittsburgh Sleep Quality Index or PSQI). Participants wore wrist-worn actigraphy devices for up to 7 days to record 24-hour sleep-wake activity patterns. We analyzed the raw data using non-parametric and extended-cosine modeling approaches (RAR variables included: interdaily stability [IS], interdaily variability [IV], relative amplitude [RA], most active 10 hours [M10], least active 5 hours [L5], peak activity time [acrophase], active period start-time [t-left], period start-time [t-right], and relative width of active to rest periods [alpha]). We compared sociodemographic, physical well-being, and subjective sleep between PwS and NCs using independent sample t-tests and chi-square tests. Among the PwS group, we used general linear models to examine associations of antipsychotic doses, age, race, sex, and education with RAR variables. <h3>Results</h3> Meanage and % female of PwS and NCs were similar to each other (51.4 years vs. 53.0 years and 50% vs. 53.8%, respectively). PwS were 34.6% of non-Caucasian ethnicity, and PwS were 19.4% of non-Caucasian ethnicity. PwS had worse physical well-being (p=0.004) and poorer sleep quality (p=0.004) compared to NCs. Among PwS, 91.3% of the group was currently taking atypical antipsychotic medications. The PwS group had significantly higher alpha (mean -0.3 [SD 0.3] vs. mean 0.8 [SD 0.2], t(60) = 3.03, p=0.004, d=0.75), lower RA (mean 0.8 [SD 0.3] vs. mean 0.9 [SD 0.2], t(60) = -2.18, p=0.03, d=-0.53), and lower M10 (mean 2160 [SD 816] vs. mean 2650 [SD 673], t(60) = -2.58, p=0.01, d=-0.65), compared to the NCs. AmongPwS, female sex was associated with lower alpha (B=0.262, p=0.024, η_p²=0.038) and having earlier t_right (B=-2.905, p=0.029, η_p²=0.216). Among PwS, Caucasian ethnicity was associated with higher RA (B=0.15, p=0.028, η_p²=0.22) and lower L5 (B=-272.814, p=0.015, η_p²=0.263). Higher antipsychotic doses were associated with lower IS (B=-0.027, p=0.036, η_p²=0.203). <h3>Conclusions</h3> Wefound that RAR variables differed between PwS and NCs, and that among PwS, higher antipsychotic doses were associated with less day-to-day stability of RARs. This finding is consistent with two other studies that showed antipsychotic exposure may be related to decreased motor activity. The impact of antipsychotics on dysregulated RARs suggests a possible mechanistic pathway for antipsychotic-mediated metabolic risk. Limitations of the study include the modest sample size and predominant use of atypical antipsychotic medications. Further understanding of the impact of antipsychotic medications on RARs could help to develop and tailor interventions to mitigate antipsychotic-related side effects and improve health among PwS. <h3>This research was funded by</h3> This work was supported, in part, by the National Institute of Mental Health [an R01 grant (R01MH094151-01 to Dilip Jeste [PI]), and a K23 grant (K23 MH119375-01 to EEL [PI], National Institute on Aging T35 grant AG26757 (PI: Dilip V. Jeste, MD and Alison Moore, MD, MPH), and National Institutes of Health Grant NIH UL1TR001442 of CTSA (PI: Gary Firestein, MD), as well as the Stein Institute for Research on Aging at the University of California San Diego. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Aside from the funding sources listed above, the authors have no conflicts of interest to report.

A Comparison between Parametric and Nonparametric Volatility Forecasting of Stock Index Futures in China

ABSTRACT In this study, the volatilities of CSI300 index futures, SSE50 index futures, and CSI500 index futures are modeled and predicted based on both parametric and nonparametric modeling approaches. Four ARMA-GARCH-type models and four HAR-type models are taken as the framework of volatility prediction. The last one-third of transaction data are used as the testing sample and the rolling window approach is adopted for prediction. The best predictive models for these three stock index futures vary with the properties of the futures, while volatility prediction based on the HAR-type models always has a higher accuracy than the ARMA-GARCH-type models. Moreover, we find that the property of target assets influences the performance of models, and the choice of extended models in prediction is suggested to be based on the peculiarity in the sample.

Erlang mixture modeling for Poisson process intensities

We develop a prior probability model for temporal Poisson process intensities through structured mixtures of Erlang densities with common scale parameter, mixing on the integer shape parameters. The mixture weights are constructed through increments of a cumulative intensity function which is modeled nonparametrically with a gamma process prior. Such model specification provides a novel extension of Erlang mixtures for density estimation to the intensity estimation setting. The prior model structure supports general shapes for the point process intensity function, and it also enables effective handling of the Poisson process likelihood normalizing term resulting in efficient posterior simulation. The Erlang mixture modeling approach is further elaborated to develop an inference method for spatial Poisson processes. The methodology is examined relative to existing Bayesian nonparametric modeling approaches, including empirical comparison with Gaussian process prior based models, and is illustrated with synthetic and real data examples.

Building Optimal Three-Drug Combination Chemotherapy Regimens To Eradicate Mycobacterium tuberculosis in Its Slow-Growth Acid Phase.

Mycobacterium tuberculosis metabolic state affects the response to therapy. Quantifying the effect of antimicrobials in the acid and nonreplicating metabolic phases of M. tuberculosis growth will help to optimize therapy for tuberculosis. As a brute-force approach to all possible drug combinations against M. tuberculosis in all different metabolic states is impossible, we have adopted a model-informed strategy to accelerate the discovery. Using multiple concentrations of each drug in time-kill studies, we examined single drugs and two- and three-drug combinations of pretomanid, moxifloxacin, and bedaquiline plus its active metabolite against M. tuberculosis in its acid-phase metabolic state. We used a nonparametric modeling approach to generate full distributions of interaction terms between pretomanid and moxifloxacin for susceptible and less susceptible populations. From the model, we could predict the 95% confidence interval of the simulated total bacterial population decline due to the 2-drug combination regimen of pretomanid and moxifloxacin and compare this to observed declines with 3-drug regimens. We found that the combination of pretomanid and moxifloxacin at concentrations equivalent to average or peak human concentrations effectively eradicated M. tuberculosis in its acid growth phase and prevented emergence of less susceptible isolates. The addition of bedaquiline as a third drug shortened time to total and less susceptible bacterial suppression by 8 days compared to the 2-drug regimen, which was significantly faster than the 2-drug kill.

Bayesian Nonparametric Object Association for Semantic SLAM

Semantic simultaneous localization and mapping (SLAM) can provide the foundation for robots to perform more advanced tasks than traditional geometric SLAM. Object association is a crucial factor in semantic SLAM. Previous work utilizes observations at the current moment and a single geometric feature to associate objects. The number and measurement of objects change over time. The ambiguity of object association reduces the performance of semantic SLAM. In this letter, we present a Bayesian Nonparametric model approach for object association. The temporal Dirichlet process mixture (TDPM) model is introduced to provide prior information. We couple the evolution of time with the object association, which considers the influence of historical association information on the current association results. Besides, we propose a Multi-Rules Likelihood (MRL) model to eliminate the ambiguity of object association by considering multiple object properties. We validate our approach both in simulation and real data from the KITTI dataset. The experimental results show that our approach improves the performance of object association and semantic SLAM.