Combining VPS34 inhibitors with STING agonists enhances type I interferon signaling and anti-tumor efficacy.

An immunosuppressive tumor microenvironment promotes tumor growth and is one of the main factors limiting the response to cancer immunotherapy. We have previously reported that inhibition of vacuolar protein sorting 34 (VPS34), a crucial lipid kinase in the autophagy/endosomal trafficking pathway, decreases tumor growth in several cancer models, increases infiltration of immune cells and sensitizes tumors to anti-programmed cell death protein 1/programmed cell death 1 ligand 1 therapy by upregulation of C-C motif chemokine 5 (CCL5) and C-X-C motif chemokine 10 (CXCL10) chemokines. The purpose of this study was to investigate the signaling mechanism leading to the VPS34-dependent chemokine increase. NanoString gene expression analysis was applied to tumors from mice treated with the VPS34 inhibitor SB02024 to identify key pathways involved in the anti-tumor response. We showed that VPS34 inhibitors increased the secretion of T-cell-recruitment chemokines in a cyclic GMP-AMP synthase (cGAS)/stimulator of interferon genes protein (STING)-dependent manner in cancer cells. Both pharmacological and small interfering RNA (siRNA)-mediated VPS34 inhibition increased cGAS/STING-mediated expression and secretion of CCL5 and CXCL10. The combination of VPS34 inhibitor and STING agonist further induced cytokine release in both human and murine cancer cells as well as monocytic or dendritic innate immune cells. Finally, the VPS34 inhibitor SB02024 sensitized B16-F10 tumor-bearing mice to STING agonist treatment and significantly improved mice survival. These results show that VPS34 inhibition augments the cGAS/STING pathway, leading to greater tumor control through immune-mediated mechanisms. We propose that pharmacological VPS34 inhibition may synergize with emerging therapies targeting the cGAS/STING pathway.

A robust Bayesian bias-adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis.

Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study design and execution (ie, bias). The risk of bias is usually assessed qualitatively using critical appraisal, and quantitative bias analysis can be used to evaluate the influence of bias on the quantity of interest. We propose a way to consider ignorance or ambiguity in how to quantify bias terms in a bias analysis by characterizing bias with imprecision (as bounds on probability) and use robust Bayesian analysis to estimate the overall effect. Robust Bayesian analysis is here seen as Bayesian updating performed over a set of coherent probability distributions, where the set emerges from a set of bias terms. We show how the set of bias terms can be specified based on judgments on the relative magnitude of biases (ie, low, unclear, and high risk of bias) in one or several domains of the Cochrane's risk of bias table. For illustration, we apply a robust Bayesian bias-adjusted random effects model to an already published meta-analysis on the effect of Rituximab for rheumatoid arthritis from the Cochrane Database of Systematic Reviews.

Assessment of the FilmArray ME panel in 4199 consecutively tested cerebrospinal fluid samples.

OBJECTIVES: In central nervous system infections, early and correct management is of utmost importance. Rapid syndromic panel testing can potentially provide valuable guidance. The FilmArray meningitis/encephalitis (ME) panel detects 14 pathogens through multiplex PCR. Our study objectives were to assess its performance compared with established diagnostic procedures, especially real-time quantitative PCR for detection of viruses, and to determine the diagnostic and clinical significance of discrepant results. METHODS: All cerebrospinal fluid samples sent for viral diagnostics to our microbiological laboratory over 34 months were analysed with the ME panel and in-house real-time PCR for herpes simplex virus type 1 (HSV-1), HSV-2, varicella zoster virus and enteroviruses. Other pathogens detected by the panel were confirmed by routine diagnostic procedures. Discrepant results were analysed through interpretation of biological and clinical data, and performance data were calculated for individual pathogens. RESULTS: Altogether, 315 pathogens were detected by the ME panel in 4199 cerebrospinal fluid samples (7.5%) and an additional 21 viral targets were identified using real-time PCR. Thirty-four ME panel detections were not confirmed, totalling 55 discrepant results. After discrepancy analysis, 20 false-positive and 21 false-negative ME panel results remained. Performance varied between pathogens. Sensitivity for HSV-1 was calculated at 82.4% (59.0%-93.8%) with three false-negative results. Also noteworthy were 13 false-negative enterovirus and eight false-positive Streptococcus pneumoniae results. CONCLUSIONS: Our analysis shows good performance for the ME panel in diagnosing central nervous system infection. The risk of false-negative HSV-1 results, however, warrants additional testing when encephalitis is suspected. Uncertainties in interpretation of enterovirus and S. pneumoniae results represent other limitations.

CXCL13 in patients with facial palsy caused by varicella zoster virus and Borrelia burgdorferi: a comparative study.

High cerebrospinal fluid (CSF) concentrations of the chemokine CXCL13 have been associated with Lyme neuroborreliosis (LNB), and have recently been studied as a potential diagnostic marker. It has proven difficult to establish a reliable diagnostic cut-off, possibly in part due to heterogenicity of case-control groups. Our purpose was to investigate CSF CXCL13 concentrations in patients with similar clinical presentations, facial palsy. We retrospectively included patients with facial palsy associated with LNB (n = 21), or varicella zoster virus (VZV) (n = 26). Median CXCL13 concentrations were significantly higher in patients with LNB facial palsy compared to VZV facial palsy. Receiver-operating characteristic analyses yielded an optimal cut-off concentration at 34.5 pg/mL (sensitivity 85.7%, specificity of 84.6%), lower than that in previous studies. Although the analysis has potential, it is still not adequately established that CXCL13 provides additional, clinically useful, diagnostic information over current recommendations.

An unexpectedly high occurrence of aciclovir-induced neuropsychiatric symptoms in patients treated for herpesvirus CNS infection: a prospective observational study.

BACKGROUND: Aciclovir is effective in herpesvirus infections of the CNS. Aciclovir-induced neuropsychiatric symptoms (AINS) have been reported and are associated with high CSF concentrations of aciclovir metabolite 9-carboxymethoxymethylguanine (CMMG). Risk factors except for renal failure have not been explored, and disruption of the blood-brain barrier (BBB) in acute CNS infection may be of interest. OBJECTIVES: To investigate the impact of risk factors on aciclovir and CMMG concentrations, and to relate the results to AINS. METHODS: We investigated 21 consecutively included, consenting patients treated with aciclovir or valaciclovir for herpesvirus CNS infection. Regression models were constructed to study the impact of risk factors including BBB disruption, as measured with CSF:serum albumin ratio, on CSF aciclovir and CMMG concentrations. Medical records were assessed retrospectively to identify patients with AINS. RESULTS: Increased CSF:serum albumin ratio, as well as decreased renal function and high aciclovir doses, was associated with increased aciclovir and CMMG concentrations in the CSF. We identified five patients with new neuropsychiatric symptoms; four of those were considered to have AINS and had increased CSF CMMG concentrations. Only one patient without suspicion of AINS had an increased CSF CMMG concentration. CONCLUSIONS: In patients with herpesvirus CNS infections, BBB disruption is associated with increasing aciclovir and CMMG CSF concentrations. We also found an unexpectedly high number of patients with AINS. Evaluation of CSF:serum albumin ratios, renal function and CSF concentrations of aciclovir and CMMG may all contribute to the optimization of aciclovir dosing and avoidance of AINS.

New satellite-based estimates show significant trends in spring phenology and complex sensitivities to temperature and precipitation at northern European latitudes.

Recent climate warming has altered plant phenology at northern European latitudes, but conclusions regarding the spatial patterns of phenological change and relationships with climate are still challenging as quantitative estimates are strongly diverging. To generate consistent estimates of broad-scale spatially continuous spring plant phenology at northern European latitudes (> 50° N) from 2000 to 2016, we used a novel vegetation index, the plant phenology index (PPI), derived from MODerate-resolution Imaging Spectroradiometer (MODIS) data. To obtain realistic and strong estimates, the phenology trends and their relationships with temperature and precipitation over the past 17 years were analyzed using a panel data method. We found that in the studied region the start of the growing season (SOS) has on average advanced by 0.30 day year-1. The SOS showed an overall advancement rate of 2.47 day °C-1 to spring warming, and 0.18 day cm-1 to decreasing precipitation in spring. The previous winter and summer temperature had important effects on the SOS but were spatially heterogeneous. Overall, the onset of SOS was delayed 0.66 day °C-1 by winter warming and 0.56 day °C-1 by preceding summer warming. The precipitation in winter and summer influenced the SOS in a relatively weak and complex manner. The findings indicate rapid recent phenological changes driven by combined seasonal climates in northern Europe. Previously unknown spatial patterns of phenological change and relationships with climate drivers are presented that improve our capacity to understand and foresee future climate effects on vegetation.

Creation of a Novel Class of Potent and Selective MutT Homologue 1 (MTH1) Inhibitors Using Fragment-Based Screening and Structure-Based Drug Design.

Recent literature has both suggested and questioned MTH1 as a novel cancer target. BAY-707 was just published as a target validation small molecule probe for assessing the effects of pharmacological inhibition of MTH1 on tumor cell survival, both in vitro and in vivo. (1) In this report, we describe the medicinal chemistry program creating BAY-707, where fragment-based methods were used to develop a series of highly potent and selective MTH1 inhibitors. Using structure-based drug design and rational medicinal chemistry approaches, the potency was increased over 10,000 times from the fragment starting point while maintaining high ligand efficiency and drug-like properties.

Trends and uncertainties in budburst projections of Norway spruce in Northern Europe.

Budburst is regulated by temperature conditions, and a warming climate is associated with earlier budburst. A range of phenology models has been developed to assess climate change effects, and they tend to produce different results. This is mainly caused by different model representations of tree physiology processes, selection of observational data for model parameterization, and selection of climate model data to generate future projections. In this study, we applied (i) Bayesian inference to estimate model parameter values to address uncertainties associated with selection of observational data, (ii) selection of climate model data representative of a larger dataset, and (iii) ensembles modeling over multiple initial conditions, model classes, model parameterizations, and boundary conditions to generate future projections and uncertainty estimates. The ensemble projection indicated that the budburst of Norway spruce in northern Europe will on average take place 10.2 ± 3.7 days earlier in 2051-2080 than in 1971-2000, given climate conditions corresponding to RCP 8.5. Three provenances were assessed separately (one early and two late), and the projections indicated that the relationship among provenance will remain also in a warmer climate. Structurally complex models were more likely to fail predicting budburst for some combinations of site and year than simple models. However, they contributed to the overall picture of current understanding of climate impacts on tree phenology by capturing additional aspects of temperature response, for example, chilling. Model parameterizations based on single sites were more likely to result in model failure than parameterizations based on multiple sites, highlighting that the model parameterization is sensitive to initial conditions and may not perform well under other climate conditions, whether the change is due to a shift in space or over time. By addressing a range of uncertainties, this study showed that ensemble modeling provides a more robust impact assessment than would a single phenology model run.

Novel Class of Potent and Cellularly Active Inhibitors Devalidates MTH1 as Broad-Spectrum Cancer Target.

MTH1 is a hydrolase responsible for sanitization of oxidized purine nucleoside triphosphates to prevent their incorporation into replicating DNA. Early tool compounds published in the literature inhibited the enzymatic activity of MTH1 and subsequently induced cancer cell death; however recent studies have questioned the reported link between these two events. Therefore, it is important to validate MTH1 as a cancer dependency with high quality chemical probes. Here, we present BAY-707, a substrate-competitive, highly potent and selective inhibitor of MTH1, chemically distinct compared to those previously published. Despite superior cellular target engagement and pharmacokinetic properties, inhibition of MTH1 with BAY-707 resulted in a clear lack of in vitro or in vivo anticancer efficacy either in mono- or in combination therapies. Therefore, we conclude that MTH1 is dispensable for cancer cell survival.

Cerebrospinal fluid viral load and biomarkers of neuronal and glial cells in Ramsay Hunt syndrome.

Reactivation of varicella zoster virus (VZV) can manifest with facial palsy diagnosed as Ramsay Hunt Syndrome (RHS) or Ramsay Hunt Syndrome zoster sine herpete (RHS-ZSH). These syndromes are associated with poor prognosis despite treatment with antivirals and corticosteroids. Concentrations of biomarkers such as neurofilament protein (NFL), S-100ß protein and glial fibrillary acidic protein (GFAp) have previously been measured in cerebrospinal fluid (CSF) to assess neuronal damage and glial pathology. We employed immunochemical methods to measure concentrations of NFL, S-100ß protein and GFAp in CSF from patients with RHS (n = 15) and RHS-ZSH (n = 13) diagnosed by detection of VZV DNA in the CSF by quantitative PCR, and compared with a control group (n = 52). The biomarker concentrations were correlated with CSF viral load and outcome measured by House-Brackmann score. NFL and GFAp concentrations were increased compared with controls (P = 0.008 and P = 0.04), while S-100ß levels were decreased. This pattern was more pronounced in patients with RHS compared to the patients with RHS-ZSH (NS and P = 0.028). The amount of viral DNA in CSF correlated with increased GFAp (P = 0.003) and NFL (P = 0.006). No correlations were found between biomarker concentrations and patient outcome. Patients with facial palsy caused by VZV had biochemical signs of neuronal damage and astrogliosis. High amounts of viral DNA may be associated with the degree of damage on neuronal and astroglial cells. Prospective studies are warranted to elucidate the association of elevated biomarkers in the CSF and outcome assessed by more sensitive tests.

What a difference a CRP makes. A prospective observational study on how point-of-care C-reactive protein testing influences antibiotic prescription for respiratory tract infections in Swedish primary health care.

OBJECTIVE: To explore how C-reactive protein (CRP) tests serve to support physicians in decisions concerning antibiotic prescription to patients with respiratory tract infections (RTI). DESIGN: Prospective observational study. SETTING: Primary health care centres in western Sweden. SUBJECTS: Physicians in primary health care. Patients with acute RTI. MAIN OUTCOME MEASURES: Physician willingness to measure CRP, their ability to estimate CRP, and changes in decision-making concerning antibiotic treatment based on error estimate and the physician's opinion of whether CRP measurement was crucial. RESULTS: Data from 340 consultations were gathered. CRP testing was found to be crucial in 130 cases. In 86% of visits decisions regarding antibiotic prescription were unchanged. Physicians considering CRP crucial and physicians making an error estimate of CRP altered their decisions concerning antibiotic prescription after CRP testing more often than those who considered CRP unnecessary, and those making a more accurate estimate. Physicians changed their decision on antibiotic prescription in 49 cases. In the majority of these 49 cases physicians underestimated CRP levels, and the majority of changes were from "no" to "yes" as to whether to prescribe antibiotics. CONCLUSION: CRP is an important factor in the decision on whether to prescribe antibiotics for RTIs. Error estimates of CRP and willingness to measure CRP are important factors leading to physicians changing decisions on antibiotic treatment. Key points There is a generally low antibiotic prescription rate and a high frequency of C-reactive protein (CRP) testing for respiratory tract infections (RTIs) in Sweden. CRP testing was considered essential to further management in 38% of cases. In 86% of visits decisions concerning antibiotic prescription were unchanged. The strongest predictors for revised decisions on antibiotic treatment were error estimates of CRP and the physician's opinion that CRP measurement was crucial.

A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution.

BACKGROUND: Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time. OBJECTIVES: We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). METHODS: We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants' homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations. RESULTS: Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92. CONCLUSIONS: This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies.

A Flexible Spatio-Temporal Model for Air Pollution with Spatial and Spatio-Temporal Covariates.

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of NOx in the Los Angeles area during a ten year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated R2 of approximately 0.7 at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.

Segmentation of B-mode cardiac ultrasound data by Bayesian Probability Maps.

In this paper we present a model for describing the position distribution of the endocardium in the two-chamber apical long-axis view of the heart in clinical B-mode ultrasound cycles. We propose a novel Bayesian formulation, including priors for spatial and temporal smoothness, and preferred shapes and position. The shape model takes into account both endocardium, atrial region and apex. The likelihood is built using a statistical signal model, which attempts to closely model a censored signal. In addition, the use of a censored Gamma mixture model with unknown censoring point, to handle artefacts resulting from left-censoring of the in US clinical B-mode, is to our knowledge novel. The posterior density is sampled by the Gibbs method to estimate the expected latent variable representation of the endocardium, which we call the Bayesian Probability Map; the map describes the probability of pixels being classified as being within the endocardium. The regularization parameters of the model are estimated by cross-validation, and the results are compared against the two-chamber apical model of Chen et al.

Reduced-Rank Spatio-Temporal Modeling of Air Pollution Concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution.

There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a flexible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large data sets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of oxides of nitrogen (NO x )-a pollutant of primary interest in MESA Air-in the Los Angeles metropolitan area via cross-validated R2. Our findings suggest that use of reduced-rank models can improve computational efficiency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.

New aminoimidazoles as ß-secretase (BACE-1) inhibitors showing amyloid-ß (Aß) lowering in brain.

Amino-2H-imidazoles are described as a new class of BACE-1 inhibitors for the treatment of Alzheimer's disease. Synthetic methods, crystal structures, and structure-activity relationships for target activity, permeability, and hERG activity are reported and discussed. Compound (S)-1m was one of the most promising compounds in this report, with high potency in the cellular assay and a good overall profile. When guinea pigs were treated with compound (S)-1m, a concentration and time dependent decrease in Aß40 and Aß42 levels in plasma, brain, and CSF was observed. The maximum reduction of brain Aß was 40-50%, 1.5 h after oral dosing (100 µmol/kg). The results presented highlight the potential of this new class of BACE-1 inhibitors with good target potency and with low effect on hERG, in combination with a fair CNS exposure in vivo.

Design and synthesis of ß-site amyloid precursor protein cleaving enzyme (BACE1) inhibitors with in vivo brain reduction of ß-amyloid peptides.

The evaluation of a series of aminoisoindoles as ß-site amyloid precursor protein cleaving enzyme 1 (BACE1) inhibitors and the discovery of a clinical candidate drug for Alzheimer's disease, (S)-32 (AZD3839), are described. The improvement in permeability properties by the introduction of fluorine adjacent to the amidine moiety, resulting in in vivo brain reduction of Aß40, is discussed. Due to the basic nature of these compounds, they displayed affinity for the human ether-a-go-go related gene (hERG) ion channel. Different ways to reduce hERG inhibition and increase hERG margins for this series are described, culminating in (S)-16 and (R)-41 showing large in vitro margins with BACE1 cell IC(50) values of 8.6 and 0.16 nM, respectively, and hERG IC(50) values of 16 and 2.8 µM, respectively. Several compounds were advanced into pharmacodynamic studies and demonstrated significant reduction of ß-amyloid peptides in mouse brain following oral dosing.

Aminoimidazoles as BACE-1 inhibitors: the challenge to achieve in vivo brain efficacy.

The evaluation of a series of bicyclic aminoimidazoles as potent BACE-1 inhibitors is described. The crystal structures of compounds 14 and 23 in complex with BACE-1 reveal hydrogen bond interactions with the protein important for achieving potent inhibition. The optimization of permeability and efflux properties of the compounds is discussed as well as the importance of these properties for attaining in vivo brain efficacy. Compound (R)-25 was selected for evaluation in vivo in wild type mice and 1.5h after oral co-administration of 300µmol/kg (R)-25 and efflux inhibitor GF120918 the brain Aß40 level was reduced by 17% and the plasma Aß40 level by 76%.

Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

BACKGROUND: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.

Performance and autonomic responses during motion sickness.

OBJECTIVE: The aim of the study was to investigate how motion sickness, triggered by an optokinetic drum, affects short-term memory performance and to explore autonomic responses to perceived motion sickness. BACKGROUND: Previous research has found that motion sickness decreases performance, but it is not known how short-term memory in particular is affected. METHOD: Thirty-eight healthy participants performed a listening span test while seated in a rotating optokinetic drum. Measurements of motion sickness, performance, heart rate, skin conductance, blood volume pulse, and pupil size were performed simultaneously throughout the experiment. RESULTS: A total of 16 participants terminated the trial because of severe nausea, and the other 22 endured the full 25 min. Perceived motion sickness increased over time in both groups but less among those who endured the trial. Short-term memory performance decreased toward the end for those who terminated but increased in the other group. Results from the measured autonomic responses were ambiguous. CONCLUSION: We conclude that performance, measured as short-term memory, declines as perceived motion sickness progresses. APPLICATION: This research has potential implications for command and control personnel at risk of developing motion sickness.