Neurotrophin growth factors and their receptors as promising blood biomarkers for Alzheimer's Disease: a gene expression analysis study

BACKGROUND

Alzheimer's disease (AD) is a multifaceted neurological ailment affecting more than 50 million individuals globally, distinguished by a deterioration in memory and cognitive abilities. Investigating neurotrophin growth factors could offer significant contributions to understanding AD progression and prospective therapeutic interventions.

METHODS AND RESULTS

The present investigation collected blood samples from 50 patients diagnosed with AD and 50 healthy individuals serving as controls. The mRNA expression levels of neurotrophin growth factors and their receptors were measured using quantitative PCR. A Bayesian regression model was used in the research to assess the relationship between gene expression levels and demographic characteristics such as age and gender. The correlations between variables were analyzed using Spearman correlation coefficients, and the diagnostic potential was assessed using a Receiver Operating Characteristic curve. NTRK2, TrkA, TrkC, and BDNF expression levels were found to be considerably lower (p-value < 0.05) in the blood samples of AD patients compared to the control group. The expression of BDNF exhibited the most substantial decrease in comparison to other neurotrophin growth factors. Correlation analysis indicates a statistically significant positive association between the genes. The ROC analysis showed that BDNF exhibited the greatest Area Under the Curve (AUC) value of 0.76, accompanied by a sensitivity of 70% and specificity of 66%. TrkC, TrkA, and NTRK2 demonstrated considerable diagnostic potential in distinguishing between cases and controls.

CONCLUSION

The observed decrease in the expression levels of NTRK2, TrkA, TrkC, and BDNF in AD patients, along with the identified associations between specific genes and their diagnostic capacity, indicate that these expressions have the potential to function as biomarkers for the diagnosis and treatment of AD.

An improved intermittent control model of postural sway during quiet standing implemented by a data driven approach

This paper proposes a new intermittent control model during human quiet standing, which is consisted of postulated "regular intervention" and "imminent intervention". The regular intervention is within the main control loop, and its trigger condition is equivalent to the switching frequency of center of pressure (COP) data calculated by wavelet transform. The imminent intervention will only be triggered after the postural sway angle exceeds a certain threshold. In order to prove the effectiveness of the new model, the simulation results of the new model and the model proposed by Asai et al. (2009) are compared with the experimental data. The setting parameters of both models are retrieved by Bayesian regression from the experimental data. The results show that the new model not only could exhibit two power law scaling regimes of power spectral density (PSD) of COP, but also show that indices of the probability density function distance, root mean square (RMS), Total Sway Path, displacement Range, 50% power frequency of center of mass (COP) between the simulation results and the experimental data are closer compared to the existing model. Moreover, the limit cycle oscillations (LCOs) obtained from the simulation results of the new model have a higher degree of matching with those retrieved from the experimental data.

Interception of vertically approaching objects: temporal recruitment of the internal model of gravity and contribution of optical information

Introduction: Recent views posit that precise control of the interceptive timing can be achieved by combining on-line processing of visual information with predictions based on prior experience. Indeed, for interception of free-falling objects under gravity's effects, experimental evidence shows that time-to-contact predictions can be derived from an internal gravity representation in the vestibular cortex. However, whether the internal gravity model is fully engaged at the target motion outset or reinforced by visual motion processing at later stages of motion is not yet clear. Moreover, there is no conclusive evidence about the relative contribution of internalized gravity and optical information in determining the time-to-contact estimates. Methods: We sought to gain insight on this issue by asking 32 participants to intercept free falling objects approaching directly from above in virtual reality. Object motion had durations comprised between 800 and 1100 ms and it could be either congruent with gravity (1 g accelerated motion) or not (constant velocity or -1 g decelerated motion). We analyzed accuracy and precision of the interceptive responses, and fitted them to Bayesian regression models, which included predictors related to the recruitment of a priori gravity information at different times during the target motion, as well as based on available optical information. Results: Consistent with the use of internalized gravity information, interception accuracy and precision were significantly higher with 1 g motion. Moreover, Bayesian regression indicated that interceptive responses were predicted very closely by assuming engagement of the gravity prior 450 ms after the motion onset, and that adding a predictor related to on-line processing of optical information improved only slightly the model predictive power. Discussion: Thus, engagement of a priori gravity information depended critically on the processing of the first 450 ms of visual motion information, exerting a predominant influence on the interceptive timing, compared to continuously available optical information. Finally, these results may support a parallel processing scheme for the control of interceptive timing.

Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation

Generative approaches to molecular design are an area of intense study in recent years as a method to generate new pharmaceuticals with desired properties. Often though, these types of efforts are constrained by limited experimental activity data, resulting in either models that generate molecules with poor performance or models that are overfit and produce close analogs of known molecules. In this paper, we reduce this data dependency for the generation of new chemotypes by incorporating docking scores of known and de novo molecules to expand the applicability domain of the reward function and diversify the compounds generated during reinforcement learning. Our approach employs a deep generative model initially trained using a combination of limited known drug activity and an approximate docking score provided by a second machine learned Bayes regression model, with final evaluation of high scoring compounds by a full docking simulation. This strategy results in molecules with docking scores improved by 10-20% compared to molecules of similar size, while being 130 × faster than a docking only approach on a typical GPU workstation. We also show that the increased docking scores correlate with (1) docking poses with interactions similar to known inhibitors and (2) result in higher MM-GBSA binding energies comparable to the energies of known DDR1 inhibitors, demonstrating that the Bayesian model contains sufficient information for the network to learn to efficiently interact with the binding pocket during reinforcement learning. This outcome shows that the combination of the learned latent molecular representation along with the feature-based docking regression is sufficient for reinforcement learning to infer the relationship between the molecules and the receptor binding site, which suggest that our method can be a powerful tool for the discovery of new chemotypes with potential therapeutic applications.

Updating constructions: additive effects of prior and current experience during sentence production

While much earlier work has indicated that prior verb bias from lifelong language experience influences language processing, recent findings highlight the fact that verb biases induced during lab-based exposure sessions also influence processing. We investigated the nature of updating, i.e., how prior and current experience might interact in guiding subsequent sentence production. Participants underwent a short training session where we manipulated the bias of known English dative verbs. The prior bias of each verb for the double-object (DO) versus the prepositional-object (PO) dative was estimated using a corpus. Current verb bias was counterbalanced and controlled experimentally. Bayesian mixed-effects logistic models of participants' responses (DO or PO) during subsequent free-choice production showed that both the prior and current verb biases affected speakers' construction choice. These effects were additive and not interactive, contrary to the prediction from error-based learning models. Semantic similarity to other verbs and their experimentally manipulated biases influenced sentence production, consistent with item-based analogy and exemplar theory. These results shed light on the potential mechanisms underlying language updating and the adaptation of sentence production to ongoing experience.

Decay rate estimation of respiratory viruses in aerosols and on surfaces under different environmental conditions

Majority of the viral outbreaks are super-spreading events established within 2-10 h, dependent on a critical time interval for successful transmission between humans, which is governed by the decay rates of viruses. To evaluate the decay rates of respiratory viruses over a short span, we calculated their decay rate values for various surfaces and aerosols. We applied Bayesian regression and ridge regression and determined the best estimation for respiratory viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), severe acute respiratory syndrome coronavirus (SARS-CoV), middle east respiratory syndrome coronavirus (MERS-CoV), influenza viruses, and respiratory syncytial virus (RSV); the decay rate values in aerosols for these viruses were 4.83 ± 5.70, 0.40 ± 0.24, 0.11 ± 0.04, 2.43 ± 5.94, and 1.00 ± 0.50 h^-1, respectively. The highest decay rate values for each virus type differed according to the surface type. According to the model performance criteria, the Bayesian regression model was better for SARS-CoV-2 and influenza viruses, whereas ridge regression was better for SARS-CoV and MERS-CoV. A simulation using a better estimation will help us find effective non-pharmaceutical interventions to control virus transmissions.

Design of New Dispersants Using Machine Learning and Visual Analytics

Artificial intelligence (AI) is an emerging technology that is revolutionizing the discovery of new materials. One key application of AI is virtual screening of chemical libraries, which enables the accelerated discovery of materials with desired properties. In this study, we developed computational models to predict the dispersancy efficiency of oil and lubricant additives, a critical property in their design that can be estimated through a quantity named blotter spot. We propose a comprehensive approach that combines machine learning techniques with visual analytics strategies in an interactive tool that supports domain experts' decision-making. We evaluated the proposed models quantitatively and illustrated their benefits through a case study. Specifically, we analyzed a series of virtual polyisobutylene succinimide (PIBSI) molecules derived from a known reference substrate. Our best-performing probabilistic model was Bayesian Additive Regression Trees (BART), which achieved a mean absolute error of 5.50±0.34 and a root mean square error of 7.56±0.47, as estimated through 5-fold cross-validation. To facilitate future research, we have made the dataset, including the potential dispersants used for modeling, publicly available. Our approach can help accelerate the discovery of new oil and lubricant additives, and our interactive tool can aid domain experts in making informed decisions based on blotter spot and other key properties.

A Bayesian (meta-)regression model for treatment effects on the risk difference scale

In clinical settings, the absolute risk reduction due to treatment that can be expected in a particular patient is of key interest. However, logistic regression, the default regression model for trials with a binary outcome, produces estimates of the effect of treatment measured as a difference in log odds. We explored options to estimate treatment effects directly as a difference in risk, specifically in the network meta-analysis setting. We propose a novel Bayesian (meta-)regression model for binary outcomes on the additive risk scale. The model allows treatment effects, covariate effects, interactions and variance parameters to be estimated directly on the linear scale of clinical interest. We compared effect estimates from this model to (1) a previously proposed additive risk model by Warn, Thompson and Spiegelhalter ("WTS model") and (2) backtransforming the predictions from a logistic model to the natural scale after regression. The models were compared in a network meta-analysis of 20 hepatitis C trials, as well as in the analysis of simulated single trial settings. The resulting estimates diverged, in particular for small sample sizes or true risks close to 0% or 100%. Researchers should be aware that modelling untransformed risk can yield very different results from default logistic models. The treatment effect in participants with such extreme predicted risks weighed more heavily on the overall treatment effect estimate from our proposed model compared to the WTS model. In our network meta-analysis, this sensitivity of our proposed model was needed to detect all information in the data.

A Bayesian Regression Analysis of the Effects of Alert Presence and Scenario Criticality on Automated Vehicle Takeover Performance

OBJECTIVE

This study investigates the impact of silent and alerted failures on driver performance across two levels of scenario criticality during automated vehicle transitions of control.

BACKGROUND

Recent analyses of automated vehicle crashes show that many crashes occur after a transition of control or a silent automation failure. A substantial amount of research has been dedicated to investigating the impact of various factors on drivers' responses, but silent failures and their interactions with scenario criticality are understudied.

METHOD

A driving simulator study was conducted comparing scenario criticality, alert presence, and two driving scenarios. Bayesian regression models and Fisher's exact tests were used to investigate the impact of alert and scenario criticality on takeover performance.

RESULTS

The results show that silent failures increase takeover times and the intensity of posttakeover maximum accelerations and decrease the posttakeover minimum time-to-collision. While the predicted average impact of silent failures on takeover time was practically low, the effects on minimum time-to-collision and maximum accelerations were safety-significant. The analysis of posttakeover control interaction effects shows that the effect of alert presence differs by the scenario criticality.

CONCLUSION

Although the impact of the absence of an alert on takeover performance was less than that of scenario criticality, silent failures seem to play a substantial role-by leading to an unsafe maneuver-in critical automated vehicle takeovers.

APPLICATION

Understanding the implications of silent failure on driver's takeover performance can benefit the assessment of automated vehicles' safety and provide guidance for fail-safe system designs.

Inferring meaningful change in quality of life with posterior predictive distribution: an alternative to standard error of measurement

OBJECTIVE

In the absence of population-based information, distribution-based meaningful change metrics have been previously found to perform similarly. Yet, it is unknown how a Bayesian approach derived from Posterior Predictive Distribution (PPD) of anticipated changes would compare against existing metrics.

METHODS

PPD defines meaningful change as change scores that exceed the amount expected from the posterior predictive distribution given a previous score. The PPD adjusts for common statistical phenomena that arise in a pre-test-post-test setting, such as regression to the mean and post-test drift. The PPD was compared to Reliable Change Index (RCI) and Gulliksen-Lord-Novick (GLN) methods using published real-world data and simulated hypothetical data, respectively.

RESULTS

Real-world data showed that the methods made similar classifications when the measurement reliability was above 0.80. When reliability was low at 0.50 and thus more susceptible to regression to the mean effects, PPD and GLN were able to correct for it but not the RCI. However, PPD was more conservative and sensitive to biased priors. The simulation study showed that the three methods performed similarly overall, but PPD was slightly better in detecting prevalent changes, e.g., at time 2 (against RCI at p < 0.0001; against GLN at p < 0.0001) and time 3 (p = 0.024, p = 0.002).

CONCLUSIONS

When measurement reliability is high, as is frequent in HRQOL development efforts, the three methods performed similarly. At a cost of more conservative cutoffs and complex calculations, the Bayesian PPD nevertheless confers practical advantages when reliability is low. It may be worthy of further research and applications.

Impact of mass vaccination on the spatiotemporal dynamics of FMD outbreaks in India, 2008-2016

Foot-and-mouth disease (FMD) is endemic in India, where circulation of serotypes O, A and Asia1 is frequent. Here, we provide an epidemiological assessment of the ongoing mass vaccination programs in regard to post-vaccination monitoring and outbreak occurrence. The objective of this study was assessing the contribution of mass vaccination campaigns in reducing the risk of FMD in India from 2008 to 2016 by evaluating sero-monitoring data and modelling the spatiotemporal dynamics of reported outbreaks. Through analyzing antibody titre data from >1 million animals sampled as part of pre- and post-vaccination monitoring, we show that the percent of animals with inferred immunological protection (based on ELISA) was highly variable across states but generally increased through time. In addition, the number of outbreaks in a state was negatively correlated with the percent of animals with inferred protection. We then analyzed the distribution of reported FMD outbreaks across states using a Bayesian space-time model. This approach provides better acuity to disentangle the effect of mass vaccination programs on outbreak occurrence, while accounting for other factors that contribute to spatiotemporal variability in outbreak counts, notably proximity to international borders and inherent spatiotemporal correlations in incidence. This model demonstrated a ∼50% reduction in the risk of outbreaks in states that were part of the vaccination program. In addition, after controlling for spatial autocorrelation in the data, states that had international borders experienced heightened risk of FMD outbreaks. These findings help inform risk-based control strategies for India as the country progresses towards reducing reported clinical disease.

Optimal criteria and their asymptotic form for data selection in data-driven reduced-order modelling with Gaussian process regression

We derive criteria for the selection of datapoints used for data-driven reduced-order modelling and other areas of supervised learning based on Gaussian process regression (GPR). While this is a well-studied area in the fields of active learning and optimal experimental design, most criteria in the literature are empirical. Here we introduce an optimality condition for the selection of a new input defined as the minimizer of the distance between the approximated output probability density function (pdf) of the reduced-order model and the exact one. Given that the exact pdf is unknown, we define the selection criterion as the supremum over the unit sphere of the native Hilbert space for the GPR. The resulting selection criterion, however, has a form that is difficult to compute. We combine results from GPR theory and asymptotic analysis to derive a computable form of the defined optimality criterion that is valid in the limit of small predictive variance. The derived asymptotic form of the selection criterion leads to convergence of the GPR model that guarantees a balanced distribution of data resources between probable and large-deviation outputs, resulting in an effective way of sampling towards data-driven reduced-order modelling. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.

Association between lipid profiles and viral respiratory infections in human sputum samples

Background

Respiratory infections such as influenza account for significant global mortality each year. Generating lipid profiles is a novel and emerging research approach that may provide new insights regarding the development and progression of priority respiratory infections. We hypothesized that select clusters of lipids in human sputum would be associated with specific viral infections (Influenza (H1N1, H3N2) or Rhinovirus).

Methods

Lipid identification and semi-quantitation was determined with liquid chromatography and high-resolution mass spectrometry in induced sputum from individuals with confirmed respiratory infections (influenza (H1N1, H3N2) or rhinovirus). Clusters of lipid species and associations between lipid profiles and the type of respiratory viral agent was determined using Bayesian profile regression and multinomial logistic regression.

Results

More than 600 lipid compounds were identified across the sputum samples with the most abundant lipid classes identified as triglycerides (TG), phosphatidylethanolamines (PE), phosphatidylcholines (PC), Sphingomyelins (SM), ether-PC, and ether-PE. A total of 12 lipid species were significantly different when stratified by infection type and included acylcarnitine (AcCar) (10:1, 16:1, 18:2), diacylglycerols (DG) (16:0_18:0, 18:0_18:0), Lysophosphatidylcholine (LPC) (12:0, 20:5), PE (18:0_18:0), and TG (14:1_16:0_18:2, 15:0_17:0_19:0, 16:0_17:0_18:0, 19:0_19:0_19:0). Cluster analysis yielded three clusters of lipid profiles that were driven by just 10 lipid species (TGs and DGs). Cluster 1 had the highest levels of each lipid species and the highest prevalence of influenza A H3 infection (56%, n = 5) whereas cluster 3 had lower levels of each lipid species and the highest prevalence of rhinovirus (60%; n = 6). Using cluster 3 as the reference group, the crude odds of influenza A H3 infection compared to rhinovirus in cluster 1 was significantly (p = 0.047) higher (OR = 15.00 [95% CI: 1.03, 218.29]). After adjustment for confounders (smoking status and pulmonary comorbidities), the odds ratio (OR) became only marginally significant (p = 0.099), but the magnitude of the effect estimate was similar (OR = 16.00 [0.59, 433.03]).

Conclusions

In this study, human sputum lipid profiles were shown to be associated with distinct types of viral infection. Better understanding the relationship between respiratory infections of global importance and lipids contributes to advancing knowledge of pathogenesis of infections including identifying populations with increased susceptibility and developing effective therapeutics and biomarkers of health status.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02091-w.

Generalized additive regression for group testing data

In screening applications involving low-prevalence diseases, pooling specimens (e.g., urine, blood, swabs, etc.) through group testing can be far more cost effective than testing specimens individually. Estimation is a common goal in such applications and typically involves modeling the probability of disease as a function of available covariates. In recent years, several authors have developed regression methods to accommodate the complex structure of group testing data but often under the assumption that covariate effects are linear. Although linearity is a reasonable assumption in some applications, it can lead to model misspecification and biased inference in others. To offer a more flexible framework, we propose a Bayesian generalized additive regression approach to model the individual-level probability of disease with potentially misclassified group testing data. Our approach can be used to analyze data arising from any group testing protocol with the goal of estimating multiple unknown smooth functions of covariates, standard linear effects for other covariates, and assay classification accuracy probabilities. We illustrate the methods in this article using group testing data on chlamydia infection in Iowa.

Prediction of fatty acid composition using milk spectral data and its associations with various mid-infrared spectral regions in Michigan Holsteins

Fatty acid composition in milk is not only reflective of nutritional quality but also potentially predictive of other attributes (e. g. including the cow's energy balance and its relative output of methane emissions). Furthermore, a higher ratio of long-chain to short-chain fatty acids or mean carbon number has been associated with negative energy balance in dairy cows, whereas enhanced nutritional properties have been generally associated with higher levels of unsaturation. We set out to directly compare Bayesian regression strategies with partial least squares for the prediction of various milk fatty acids using Fourier-transform infrared spectrum data on 777 milk samples taken from 579 cows on 4 Michigan dairy herds between 5 and 90 d in milk. We also set out to identify those spectral regions that might be associated with fatty acids and whether carbon number or level of unsaturation might contribute to the strength of these associations. These associations were based on adaptively clustered windows of wavenumbers to mitigate the distorting effects of severe multicollinearity on marginal associations involving individual wavenumbers. In general, Bayesian regression methods, particularly the variable selection method BayesB, outperformed partial least squares regression for cross-validation prediction accuracy for both individual fatty acids and fatty acid groups. Strong signals for wavenumber associations using BayesB were well distributed throughout the mid-infrared spectrum, particularly between 910 and 3,998 cm^-1. Carbon number appeared to be linearly related to strength of wavenumber associations for 38 moderately to highly predicted fatty acids within the spectral regions of 2,286 to 2,376 and 2,984 to 3,100 cm^-1, whereas nonlinear associations were determined within 1,141 to 1,205; 1,570 to 1,630; and 1,727 to 1,768 cm^-1. However, no such associations were detected with level of unsaturation. Spectral regions where there were significant relationships between strength of association and carbon number may be useful targets for inferring the relative proportion of long-chain to short-chain fatty acids, and hence energy balance.

To pool or not to pool: That is the question in microbial kinetics

Variation observed in heat inactivation of Salmonella strains (data from Combase) was characterized using multilevel modeling with two case studies. One study concerned repetitions at one temperature, the other concerned isothermal experiments at various temperatures. Multilevel models characterize variation at various levels and handle dependencies in the data. The Weibull model was applied using Bayesian regression. The research question was how parameters varied with experimental conditions and how data can best be analyzed: no pooling (each experiment analyzed separately), complete pooling (all data analyzed together) or partial pooling (connecting the experiments while allowing for variation between experiments). In the first case study, level 1 consisted of the measurements, level 2 of the group of repetitions. While variation in the initial number parameter was low (set by the researchers), the Weibull shape factor varied for each repetition from 0.58-1.44, and the rate parameter from 0.006-0.074 h. With partial pooling variation was much less, with complete pooling variation was strongly underestimated. In the second case study, level 1 consisted of the measurements, level 2 of the group of repetitions per temperature experiment, level 3 of the cluster of various temperature experiments. The research question was how temperature affected the Weibull parameters. Variation in initial numbers was low (set by the researchers), the rate parameter was obviously affected by temperature, the estimate of the shape parameter depended on how the data were analyzed. With partial pooling, and one-step global modeling with a Bigelow-type model for the rate parameter, shape parameter variation was minimal. Model comparison based on prediction capacity of the various models was explored. The probability distribution of calculated decimal reduction times was much narrower using multilevel global modeling compared to the usual single level two-step approach. Multilevel modeling of microbial heat inactivation appears to be a suitable and powerful method to characterize and quantify variation at various levels. It handles possible dependencies in the data, and yields unbiased parameter estimates. The answer on the question "to pool or not to pool" depends on the goal of modeling, but if the goal is prediction, then partial pooling using multilevel modeling is the answer, provided that the experimental data allow that.

Genome-Wide Association Studies Reveal Susceptibility Loci for Noninfectious Claw Lesions in Holstein Dairy Cattle

Sole ulcers (SUs) and white line disease (WLD) are two common noninfectious claw lesions (NICL) that arise due to a compromised horn production and are frequent causes of lameness in dairy cattle, imposing welfare and profitability concerns. Low to moderate heritability estimates of SU and WLD susceptibility indicate that genetic selection could reduce their prevalence. To identify the susceptibility loci for SU, WLD, SU and/or WLD, and any type of noninfectious claw lesion, genome-wide association studies (GWAS) were performed using generalized linear mixed model (GLMM) regression, chunk-based association testing (CBAT), and a random forest (RF) approach. Cows from five commercial dairies in California were classified as controls having no lameness records and ≥6 years old (n = 102) or cases having SU (n = 152), WLD (n = 117), SU and/or WLD (SU + WLD, n = 198), or any type of noninfectious claw lesion (n = 217). The top single nucleotide polymorphisms (SNPs) were defined as those passing the Bonferroni-corrected suggestive and significance thresholds in the GLMM analysis or those that a validated RF model considered important. Effects of the top SNPs were quantified using Bayesian estimation. Linkage disequilibrium (LD) blocks defined by the top SNPs were explored for candidate genes and previously identified, functionally relevant quantitative trait loci. The GLMM and CBAT approaches revealed the same regions of association on BTA8 for SU and BTA13 common to WLD, SU + WLD, and NICL. These SNPs had effects significantly different from zero, and the LD blocks they defined explained a significant amount of phenotypic variance for each dataset (6.1-8.1%, p < 0.05), indicating the small but notable contribution of these regions to susceptibility. These regions contained candidate genes involved in wound healing, skin lesions, bone growth and mineralization, adipose tissue, and keratinization. The LD block defined by the most significant SNP on BTA8 for SU included a SNP previously associated with SU. The RF models were overfitted, indicating that the SNP effects were very small, thereby preventing meaningful interpretation of SNPs and any downstream analyses. These findings suggested that variants associated with various physiological systems may contribute to susceptibility for NICL, demonstrating the complexity of genetic predisposition.

Dynamic model updating (DMU) approach for statistical learning model building with missing data

Background

Developing statistical and machine learning methods on studies with missing information is a ubiquitous challenge in real-world biological research. The strategy in literature relies on either removing the samples with missing values like complete case analysis (CCA) or imputing the information in the samples with missing values like predictive mean matching (PMM) such as MICE. Some limitations of these strategies are information loss and closeness of the imputed values with the missing values. Further, in scenarios with piecemeal medical data, these strategies have to wait to complete the data collection process to provide a complete dataset for statistical models.

Method and results

This study proposes a dynamic model updating (DMU) approach, a different strategy to develop statistical models with missing data. DMU uses only the information available in the dataset to prepare the statistical models. DMU segments the original dataset into small complete datasets. The study uses hierarchical clustering to segment the original dataset into small complete datasets followed by Bayesian regression on each of the small complete datasets. Predictor estimates are updated using the posterior estimates from each dataset. The performance of DMU is evaluated by using both simulated data and real studies and show better results or at par with other approaches like CCA and PMM.

Conclusion

DMU approach provides an alternative to the existing approaches of information elimination and imputation in processing the datasets with missing values. While the study applied the approach for continuous cross-sectional data, the approach can be applied to longitudinal, categorical and time-to-event biological data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04138-z.

Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices

The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.

Projecting the course of COVID-19 in Turkey: A probabilistic modeling approach

Background/aim

The COVID-19 pandemic originated in Wuhan, China, in December 2019 and became one of the worst global health crises ever. While struggling with the unknown nature of this novel coronavirus, many researchers and groups attempted to project the progress of the pandemic using empirical or mechanistic models, each one having its drawbacks. The first confirmed cases were announced early in March, and since then, serious containment measures have taken place in Turkey.

Materials and methods

Here, we present a different approach, a Bayesian negative binomial multilevel model with mixed effects, for the projection of the COVID-19 pandemic and we apply this model to the Turkish case. The model source code is available at https:// github.com/kansil/covid-19. We predicted the confirmed daily cases and cumulative numbers from June 6th to June 26th with 80%, 95%, and 99% prediction intervals (PI).

Results

Our projections showed that if we continued to comply with the measures and no drastic changes were seen in diagnosis or management protocols, the epidemic curve would tend to decrease in this time interval. Also, the predictive validity analysis suggests that the proposed model projections should have a PI around 95% for the first 12 days of the projections.

Conclusion

We expect that drastic changes in the course of COVID-19 in Turkey will cause the model to suffer in predictive validity, and this can be used to monitor the epidemic. We hope that the discussion on these projections and the limitations of the epidemiological forecasting will be beneficial to the medical community, and policy makers.

Comparing Analytical Methods for the Gut Microbiome and Aging: Gut Microbial Communities and Body Weight in the Osteoporotic Fractures in Men (MrOS) Study

Determining the role of gut microbial communities in aging-related phenotypes, including weight loss, is an emerging gerontology research priority. Gut microbiome datasets comprise relative abundances of microbial taxa that necessarily sum to 1; analysis ignoring this feature may produce misleading results. Using data from the Osteoporotic Fractures in Men (MrOS) study (n = 530; mean [SD] age = 84.3 [4.1] years), we assessed 163 genera from stool samples and body weight. We compared conventional analysis, which does not address the sum-to-1 constraint, to compositional analysis, which does. Specifically, we compared elastic net regression (for variable selection) and conventional Bayesian linear regression (BLR) and network analysis to compositional BLR and network analysis; adjusting for past weight, height, and other covariates. Conventional BLR identified Roseburia and Dialister (higher weight) and Coprococcus-1 (lower weight) after multiple comparisons adjustment (p < .0125); plus Sutterella and Ruminococcus-1 (p < .05). No conventional network module was associated with weight. Using compositional BLR, Coprococcus-2 and Acidaminococcus were most strongly associated with higher adjusted weight; Coprococcus-1 and Ruminococcus-1 were most strongly associated with lower adjusted weight (p < .05), but nonsignificant after multiple comparisons adjustment. Two compositional network modules with respective hub taxa Blautia and Faecalibacterium were associated with adjusted weight (p < .01). Findings depended on analytical workflow. Compositional analysis is advocated to appropriately handle the sum-to-1 constraint.

Prognostic value of laboratory values in older patients with cervical spine fractures

OBJECTIVES

To understand the prognostic value of laboratory markers at presentation on post-treatment survival of patients 50 and older following cervical spine fracture.

PATIENTS AND METHODS

We obtained clinical data on patients 50 and older treated for cervical spine fracture in a single healthcare system (2006-2016). Our primary outcome consisted of 1-year mortality, with mortality within 3-months of presentation considered secondarily. Our primary predictors included serum glucose, serum creatinine, platelet-lymphocyte ratio (PLR) and neutrophil-lymphocyte ratio (NLR) at presentation. We used multivariable logistic regression to adjust for confounding from sociodemographic and clinical characteristics. Point estimates and 95 % confidence intervals (CI) from the final model were refined using Bayesian regression techniques.

RESULTS

We included 1781 patients in this analysis, with an average age of 75.3 (SD 12.0). The mortality rate at 3-months was 12 % and 17 % at 1-year. In multivariable testing, neither elevated PLR or NLR were significant predictors of 1-year mortality. Elevated serum creatinine was associated with increased mortality at 1-year (OR 1.89; 95 % CI 1.30, 2.74), as was hyperglycemia (OR 1.50; 95 % CI 1.06, 2.13). Elevated serum creatinine remained influential (OR 1.64; 95 % CI 1.06, 2.54) on mortality at 3-months.

CONCLUSIONS

This is the first study to evaluate laboratory values at presentation in conjunction with survival following cervical fractures. The results can be used to help forecast natural history and in expectation management. They may also help formulate treatment plans, especially when the need for surgical intervention is not clearly defined.

Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples

For many important problems the quantity of interest is an unknown function of the parameters, which is a random vector with known statistics. Since the dependence of the output on this random vector is unknown, the challenge is to identify its statistics, using the minimum number of function evaluations. This problem can be seen in the context of active learning or optimal experimental design. We employ Bayesian regression to represent the derived model uncertainty due to finite and small number of input-output pairs. In this context we evaluate existing methods for optimal sample selection, such as model error minimization and mutual information maximization. We show that for the case of known output variance, the commonly employed criteria in the literature do not take into account the output values of the existing input-output pairs, while for the case of unknown output variance this dependence can be very weak. We introduce a criterion that takes into account the values of the output for the existing samples and adaptively selects inputs from regions of the parameter space which have an important contribution to the output. The new method allows for application to high-dimensional inputs, paving the way for optimal experimental design in high dimensions.

Multiresponse Kinetic Modeling of Heat-Induced Equilibrium of β-Carotene cis-trans Isomerization in Medium-Chain Triglyceride Oil

The kinetics and mechanism of the stepwise cis-trans isomerization reactions of all-trans-β-carotene dissolved in MCT (medium-chain triglyceride) oil at temperatures in the range of 80-160 °C have been analyzed using multiresponse modeling. Quantitation of the cis-isomers was performed using HPLC-DAD and quantitation at the reaction isosbestic point at 421 nm. Multiresponse kinetic modeling using the Bayesian criterion was initially performed at 120 °C to determine the best model. Subsequently, the reparametrized Arrhenius equation was used to calculate the activation energies of all reactions. The equilibrium constants for the individual isomerization reactions were determined from the rate constants and the final product distributions. The enthalpies and entropies of the isomerization reactions were determined from the temperature dependence of the equilibrium constants. The 13-cis and 13,13'-di-cis isomers were found to be the fastest formed isomers followed by the 9-cis, 9,13-di-cis, and 13,15-di-cis isomers, where the latter was found to be formed from 13-cis and not the 15-cis isomer. The relative free energies of the β-carotene isomers were determined as all-trans < 13-cis < 9-cis < 13,13'-di-cis < 9,13-di-cis ≈ 15-cis < 13,15-di-cis. The entropic contribution of each reaction was found to play an important role in the ordering. It is concluded that the β-carotene system is quite labile at temperatures ranging from 80 to 160 °C and resulting in equilibrium distributions of the cis-trans isomers.

Bayesian regression-based developmental norms for the Benton Facial Recognition Test in males and females

Face identity recognition is important for social interaction and is impaired in a range of clinical disorders, including several neurodevelopmental disorders. The Benton Facial Recognition Test (BFRT; Benton & Van Allen, 1968), a widely used assessment of identity recognition, is the only standardized test of face identity perception, as opposed to face memory, that has been normed on children and adolescents. However, the existing norms for the BFRT are suboptimal, with several ages not represented and no established time limit (which can lead to inflated scores by allowing individuals with prosopagnosia to use feature matching). Here we address these issues with a large normative dataset of children and adolescents (ages 5-17, N = 398) and adults (ages 18-55; N = 120) who completed a time-limited version of the BFRT. Using Bayesian regression, we demonstrate that face identity perception increases asymptotically from childhood through adulthood, and provide continuous norms based on age and sex that can be used to calculate standard scores. We show that our time limit of 16 seconds per item yields scores comparable to the existing norms without time limits from the non-prosopagnostic samples. We also find that females (N = 156) score significantly higher than males (N = 362), supporting the existence of a female superiority effect for face identification. Overall, these results provide more robust norms for the BFRT and promote future research on face identity perception in developmental populations.

A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data

Microbiome omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with identification of specific bacteria taxa associated with diseases, recent scientific advancements provide mounting evidence that metabolism, genetics, and environmental factors can all modulate these microbial effects. However, the current methods for integrating microbiome data and other covariates are severely lacking. Hence, we present an integrative Bayesian zero-inflated negative binomial regression model that can both distinguish differentially abundant taxa with distinct phenotypes and quantify covariate-taxa effects. Our model demonstrates good performance using simulated data. Furthermore, we successfully integrated microbiome taxonomies and metabolomics in two real microbiome datasets to provide biologically interpretable findings. In all, we proposed a novel integrative Bayesian regression model that features bacterial differential abundance analysis and microbiome-covariate effects quantifications, which makes it suitable for general microbiome studies.

Bias in estimates of variance components in populations undergoing genomic selection: a simulation study

Background

After the extensive implementation of genomic selection (GS), the choice of the statistical model and data used to estimate variance components (VCs) remains unclear. A primary concern is that VCs estimated from a traditional pedigree-based animal model (P-AM) will be biased due to ignoring the impact of GS. The objectives of this study were to examine the effects of GS on estimates of VC in the analysis of different sets of phenotypes and to investigate VC estimation using different methods. Data were simulated to resemble the Danish Jersey population. The simulation included three phases: (1) a historical phase; (2) 20 years of conventional breeding; and (3) 15 years of GS. The three scenarios based on different sets of phenotypes for VC estimation were as follows: (1) Pheno₁: phenotypes from only the conventional phase (1–20 years); (2) Pheno_1 + 2: phenotypes from both the conventional phase and GS phase (1–35 years); (3) Pheno₂: phenotypes from only the GS phase (21–35 years). Single-step genomic BLUP (ssGBLUP), a single-step Bayesian regression model (ssBR), and P-AM were applied. Two base populations were defined: the first was the founder population referred to by the pedigree-based relationship (P-base); the second was the base population referred to by the current genotyped population (G-base).

Results

In general, both the ssGBLUP and ssBR models with all the phenotypic and genotypic information (Pheno_1 + 2) yielded biased estimates of additive genetic variance compared to the P-base model. When the phenotypes from the conventional breeding phase were excluded (Pheno₂), P-AM led to underestimation of the genetic variance of P-base. Compared to the VCs of G-base, when phenotypes from the conventional breeding phase (Pheno₂) were ignored, the ssBR model yielded unbiased estimates of the total genetic variance and marker-based genetic variance, whereas the residual variance was overestimated.

Conclusions

The results show that neither of the single-step models (ssGBLUP and ssBR) can precisely estimate the VCs for populations undergoing GS. Overall, the best solution for obtaining unbiased estimates of VCs is to use P-AM with phenotypes from the conventional phase or phenotypes from both the conventional and GS phases.

Bayesian approach to quantify morphological impact on performance in international elite freestyle swimming

Objectives

The purpose of this study was to quantify the impact of morphological characteristics on freestyle swimming performance by event and gender.

Design

Height, mass, body mass index (BMI) and speed data were collected for the top 100 international male and female swimmers from 50 to 1500 m freestyle events for the 2000–2014 seasons.

Methods

Several Bayesian hierarchical regressions were performed on race speed with height, mass and BMI as predictors. Posterior probability distributions were computed using Markov chain Monte Carlo algorithms.

Results

Regression results exhibited relationships between morphology and performance for both genders and all race distances. Height was always positively correlated with speed with a 95% probability. Conversely, mass plays a different role according to the context. Heavier profiles seem favourable on sprint distances, whereas mass becomes a handicap as distance increases. Male and female swimmers present several differences on the influence of morphology on speed, particularly about the mass. Best morphological profiles are associated with a gain of speed of 0.7%–3.0% for men and 1%–6% for women, depending on race distance. BMI has been investigated as a predictor of race speed but appears as weakly informative in this context.

Conclusion

Morphological indicators such as height and mass strongly contribute to swimming performance from sprint to distance events, and this contribution is quantified for each race distance. These profiles may help swimming federations to detect athletes and drive them to compete in specific distances according to their morphology.

Association between results of diagnostic tests for bovine tuberculosis and Johne's disease in cattle

BACKGROUND

Bovine tuberculosis (bTB) diagnosis is impaired by numerous factors including cross-reactivity with Mycobacterium avium subspecies paratuberculosis, which causes Johne's disease (JD). In addition, the effect of repeated bTB-intradermal testing on the performance of JD diagnostic tests is not fully understood. This study aimed to evaluate the impact of repeated bTB-intradermal tests under field conditions in Spain on the JD serological status of cattle.

METHODS

bTB-positive herds (n=264) from Castilla-y-Leon region were selected and matched with officially tuberculosis-free control herds. The association between JD and bTB status at the herd level was assessed using conditional logistic regression and, in herds with both JD-positive and bTB-positive animals, a Bayesian hierarchical mixed-effect model was used for individual-level analysis.

RESULTS

A significantly higher risk of being JD positive (OR: 1.48; 95 per cent CI: 1.01 to 2.15) was found for bTB-positive herds compared with controls. Individual results indicated that cattle tested more than three times per year, within the last 90 days and more than 12 months were more likely to be JD positive. A skin test-related boost in antibody response could be the cause of an apparent increase of the sensitivity of the JD-absorbed ELISA.

CONCLUSION

The results demonstrate the interaction between bTB repeated testing and JD individual and herd-level results and this improved knowledge will facilitate the design of more effective control programmes in herds coinfected with two of the most important endemic diseases affecting cattle in Spain.

Frog occupancy of polluted wetlands in urban landscapes

Urban sprawl and the rising popularity of water-sensitive urban design of urban landscapes has led to a global surge in the number of wetlands constructed to collect and treat stormwater runoff in cities. However, contaminants, such as heavy metals and pesticides, in stormwater adversely affect the survival, growth, and reproduction of animals inhabiting these wetlands. A key question is whether wildlife can identify and avoid highly polluted wetlands. We investigated whether pond-breeding frogs are attempting to breed in wetlands that affect the fitness of their offspring across 67 urban wetlands in Melbourne, Australia. Frog species richness and the concentration of contaminants (heavy metals and pesticides) were not significantly related, even in the most polluted wetlands. The proportion of fringing vegetation at a wetland had the greatest positive influence on the number of frog species present and the probability of occurrence of individual species, indicating that frogs inhabited wetlands with abundant vegetation, regardless of their pollution status. These wetlands contained contaminant levels similar to urban wetlands around the world at levels that reduce larval amphibian survival. These results are, thus, likely generalizable to other areas, suggesting that urban managers could inadvertently be creating ecological traps in countless cities. Wetlands are important tools for the management of urban stormwater runoff, but their construction should not facilitate declines in wetland-dependent urban wildlife.

High correlations between MRI brain volume measurements based on NeuroQuant® and FreeSurfer

NeuroQuant^® (NQ) and FreeSurfer (FS) are commonly used computer-automated programs for measuring MRI brain volume. Previously they were reported to have high intermethod reliabilities but often large intermethod effect size differences. We hypothesized that linear transformations could be used to reduce the large effect sizes. This study was an extension of our previously reported study. We performed NQ and FS brain volume measurements on 60 subjects (including normal controls, patients with traumatic brain injury, and patients with Alzheimer's disease). We used two statistical approaches in parallel to develop methods for transforming FS volumes into NQ volumes: traditional linear regression, and Bayesian linear regression. For both methods, we used regression analyses to develop linear transformations of the FS volumes to make them more similar to the NQ volumes. The FS-to-NQ transformations based on traditional linear regression resulted in effect sizes which were small to moderate. The transformations based on Bayesian linear regression resulted in all effect sizes being trivially small. To our knowledge, this is the first report describing a method for transforming FS to NQ data so as to achieve high reliability and low effect size differences. Machine learning methods like Bayesian regression may be more useful than traditional methods.

GWAS by GBLUP: Single and Multimarker EMMAX and Bayes Factors, with an Example in Detection of a Major Gene for Horse Gait

Bayesian models for genomic prediction and association mapping are being increasingly used in genetics analysis of quantitative traits. Given a point estimate of variance components, the popular methods SNP-BLUP and GBLUP result in joint estimates of the effect of all markers on the analyzed trait; single and multiple marker frequentist tests (EMMAX) can be constructed from these estimates. Indeed, BLUP methods can be seen simultaneously as Bayesian or frequentist methods. So far there is no formal method to produce Bayesian statistics from GBLUP. Here we show that the Bayes Factor, a commonly admitted statistical procedure, can be computed as the ratio of two normal densities: the first, of the estimate of the marker effect over its posterior standard deviation; the second of the null hypothesis (a value of 0 over the prior standard deviation). We extend the BF to pool evidence from several markers and of several traits. A real data set that we analyze, with ours and existing methods, analyzes 630 horses genotyped for 41711 polymorphic SNPs for the trait “outcome of the qualification test” (which addresses gait, or ambling, of horses) for which a known major gene exists. In the horse data, single marker EMMAX shows a significant effect at the right place at Bonferroni level. The BF points to the same location although with low numerical values. The strength of evidence combining information from several consecutive markers increases using the BF and decreases using EMMAX, which comes from a fundamental difference in the Bayesian and frequentist schools of hypothesis testing. We conclude that our BF method complements frequentist EMMAX analyses because it provides a better pooling of evidence across markers, although its use for primary detection is unclear due to the lack of defined rejection thresholds.

Using Bayesian regression to test hypotheses about relationships between parameters and covariates in cognitive models

An important tool in the advancement of cognitive science are quantitative models that represent different cognitive variables in terms of model parameters. To evaluate such models, their parameters are typically tested for relationships with behavioral and physiological variables that are thought to reflect specific cognitive processes. However, many models do not come equipped with the statistical framework needed to relate model parameters to covariates. Instead, researchers often revert to classifying participants into groups depending on their values on the covariates, and subsequently comparing the estimated model parameters between these groups. Here we develop a comprehensive solution to the covariate problem in the form of a Bayesian regression framework. Our framework can be easily added to existing cognitive models and allows researchers to quantify the evidential support for relationships between covariates and model parameters using Bayes factors. Moreover, we present a simulation study that demonstrates the superiority of the Bayesian regression framework to the conventional classification-based approach.

Genomic Prediction from Multiple-Trait Bayesian Regression Methods Using Mixture Priors

Bayesian multiple-regression methods incorporating different mixture priors for marker effects are used widely in genomic prediction. Improvement in prediction accuracies from using those methods, such as BayesB, BayesC, and BayesCπ, have been shown in single-trait analyses with both simulated and real data. These methods have been extended to multi-trait analyses, but only under the restrictive assumption that a locus simultaneously affects all the traits or none of them. This assumption is not biologically meaningful, especially in multi-trait analyses involving many traits. In this paper, we develop and implement a more general multi-trait BayesC[Formula: see text] and BayesB methods allowing a broader range of mixture priors. Our methods allow a locus to affect any combination of traits, e.g., in a 5-trait analysis, the "restrictive" model only allows two situations, whereas ours allow all 32 situations. Further, we compare our methods to single-trait methods and the "restrictive" multi-trait formulation using real and simulated data. In the real data analysis, higher prediction accuracies were observed from both our new broad-based multi-trait methods and the "restrictive" formulation. The broad-based and restrictive multi-trait methods showed similar prediction accuracies. In the simulated data analysis, higher prediction accuracies to the "restrictive" method were observed from our general multi-trait methods for intermediate training population size. The software tool JWAS offers open-source routines to perform these analyses.

Genomic Prediction from Multiple-Trait Bayesian Regression Methods Using Mixture Priors

Bayesian multiple-regression methods incorporating different mixture priors for marker effects are used widely in genomic prediction. Improvement in prediction accuracies from using those methods, such as BayesB, BayesC, and BayesCπ, have been shown in single-trait analyses with both simulated and real data. These methods have been extended to multi-trait analyses, but only under the restrictive assumption that a locus simultaneously affects all the traits or none of them. This assumption is not biologically meaningful, especially in multi-trait analyses involving many traits. In this paper, we develop and implement a more general multi-trait BayesC[Formula: see text] and BayesB methods allowing a broader range of mixture priors. Our methods allow a locus to affect any combination of traits, e.g., in a 5-trait analysis, the "restrictive" model only allows two situations, whereas ours allow all 32 situations. Further, we compare our methods to single-trait methods and the "restrictive" multi-trait formulation using real and simulated data. In the real data analysis, higher prediction accuracies were observed from both our new broad-based multi-trait methods and the "restrictive" formulation. The broad-based and restrictive multi-trait methods showed similar prediction accuracies. In the simulated data analysis, higher prediction accuracies to the "restrictive" method were observed from our general multi-trait methods for intermediate training population size. The software tool JWAS offers open-source routines to perform these analyses.

Geospatial Associations Between Tobacco Retail Outlets and Current Use of Cigarettes and e-Cigarettes among Youths in Texas

Introduction

To identify the geospatial association between the presence of tobacco retail outlets (TRO) around schools’ neighborhoods, and current use of cigarettes and e-cigarettes among adolescents in four counties in Texas.

Methods

Students in grades 6, 8 and 10th were surveyed in their schools in 2014–2015. The schools’ addresses was geocoded to determine the presence of at least one TRO within half a mile of the school. Two outcomes were considered: past 30-day use of (a) cigarettes and (b) e-cigarettes. Bayesian structured additive regression models and Kriging methods were used to estimate the geospatial associations between the presence of TRO and use in three counties: Dallas/Tarrant, Harris, and Travis.

Results

We observed a geospatial association between the presence of TRO around the schools and current use of cigarettes in the eastern area of Dallas County and in the southeastern area of Harris County. Also, a geospatial association between the presence of TRO around the schools and current use of e-cigarettes was observed in the entire Tarrant County and in the northeastern area of Harris County.

Conclusions

There were geospatial associations between the presence of TRO around some schools and cigarette/e-cigarette use among students, but this association was not consistent across all the counties. More research is needed to determine why some areas are at higher risk for this association.

BAYESIAN LARGE-SCALE MULTIPLE REGRESSION WITH SUMMARY STATISTICS FROM GENOME-WIDE ASSOCIATION STUDIES

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors, they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a "Regression with Summary Statistics" (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS to a GWAS of human height that contains 253,288 individuals typed at 1.06 million SNPs, for which analyses of individual-level data are practically impossible. Estimates of heritability (52%) are consistent with, but more precise, than previous results using subsets of these data. We also identify many previously unreported loci that show evidence for association with height in our analyses. Software is available at https://github.com/stephenslab/rss.

Variations in Obesity Rates between US Counties: Impacts of Activity Access, Food Environments, and Settlement Patterns

There is much ongoing research about the effect of the urban environment as compared with individual behaviour on growing obesity levels, including food environment, settlement patterns (e.g., sprawl, walkability, commuting patterns), and activity access. This paper considers obesity variations between US counties, and delineates the main dimensions of geographic variation in obesity between counties: by urban-rural status, by region, by area poverty status, and by majority ethnic group. Available measures of activity access, food environment, and settlement patterns are then assessed in terms of how far they can account for geographic variation. A county level regression analysis uses a Bayesian methodology that controls for spatial correlation in unmeasured area risk factors. It is found that environmental measures do play a significant role in explaining geographic contrasts in obesity.

Comparison of Bayesian regression models and partial least squares regression for the development of infrared prediction equations

The objective of this study was to compare the prediction accuracy of 92 infrared prediction equations obtained by different statistical approaches. The predicted traits included fatty acid composition (n = 1,040); detailed protein composition (n = 1,137); lactoferrin (n = 558); pH and coagulation properties (n = 1,296); curd yield and composition obtained by a micro-cheese making procedure (n = 1,177); and Ca, P, Mg, and K contents (n = 689). The statistical methods used to develop the prediction equations were partial least squares regression (PLSR), Bayesian ridge regression, Bayes A, Bayes B, Bayes C, and Bayesian least absolute shrinkage and selection operator. Model performances were assessed, for each trait and model, in training and validation sets over 10 replicates. In validation sets, Bayesian regression models performed significantly better than PLSR for the prediction of 33 out of 92 traits, especially fatty acids, whereas they yielded a significantly lower prediction accuracy than PLSR in the prediction of 8 traits: the percentage of C18:1n-7 trans-9 in fat; the content of unglycosylated κ-casein and its percentage in protein; the content of α-lactalbumin; the percentage of α_S2-casein in protein; and the contents of Ca, P, and Mg. Even though Bayesian methods produced a significant enhancement of model accuracy in many traits compared with PLSR, most variations in the coefficient of determination in validation sets were smaller than 1 percentage point. Over traits, the highest predictive ability was obtained by Bayes C even though most of the significant differences in accuracy between Bayesian regression models were negligible.

Antidepressant sales and regional variations of suicide mortality in Germany

Suicides account for over one million deaths per year worldwide with depression among the most important risk factors. Epidemiological research into the relationship between antidepressant utilization and suicide mortality has shown heterogeneous and contradictory results. Different methodological approaches and limitations could at least partially explain varying results. This is the first study assessing the association of suicide mortality and antidepressant sales across Germany using complex statistical approaches in order to control for possible confounding factors including spatial dependency of data. German suicide counts were analyzed on a district level (n = 402) utilizing ecological Poisson regressions within a hierarchical Bayesian framework. Due to significant spatial effects between adjacent districts spatial models were calculated in addition to a baseline non-spatial model. Models were adjusted for several confounders including socioeconomic variables, quality of psychosocial care, and depression prevalence. Separate analyses were performed for Eastern and Western Germany and for different classes of antidepressants (SSRIs and TCAs). Overall antidepressant sales were significantly negatively associated with suicide mortality in the non-spatial baseline model, while after adjusting for spatially structured and unstructured effects the association turned out to be insignificant. In sub-analyses, analogue results were found for SSRIs and TCAs separately. Suicide risk shows a distinct heterogeneous pattern with a pronounced relative risk in Southeast Germany. In conclusion, the results reflect the heterogeneous findings of previous studies on the association between suicide mortality and antidepressant sales and point to the complexity of this hypothesized link. Furthermore, the findings support tailored suicide preventive efforts within high risk areas.

Regression DCM for fMRI

The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.

Prediction of non-muscle invasive bladder cancer outcomes assessed by innovative multimarker prognostic models

Background

We adapted Bayesian statistical learning strategies to the prognosis field to investigate if genome-wide common SNP improve the prediction ability of clinico-pathological prognosticators and applied it to non-muscle invasive bladder cancer (NMIBC) patients.

Methods

Adapted Bayesian sequential threshold models in combination with LASSO were applied to consider the time-to-event and the censoring nature of data. We studied 822 NMIBC patients followed-up >10 years. The study outcomes were time-to-first-recurrence and time-to-progression. The predictive ability of the models including up to 171,304 SNP and/or 6 clinico-pathological prognosticators was evaluated using AUC-ROC and determination coefficient.

Results

Clinico-pathological prognosticators explained a larger proportion of the time-to-first-recurrence (3.1 %) and time-to-progression (5.4 %) phenotypic variances than SNPs (1 and 0.01 %, respectively). Adding SNPs to the clinico-pathological-parameters model slightly improved the prediction of time-to-first-recurrence (up to 4 %). The prediction of time-to-progression using both clinico-pathological prognosticators and SNP did not improve. Heritability (ĥ²) of both outcomes was <1 % in NMIBC.

Conclusions

We adapted a Bayesian statistical learning method to deal with a large number of parameters in prognostic studies. Common SNPs showed a limited role in predicting NMIBC outcomes yielding a very low heritability for both outcomes. We report for the first time a heritability estimate for a disease outcome. Our method can be extended to other disease models.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2361-7) contains supplementary material, which is available to authorized users.

Bayesian tsunami fragility modeling considering input data uncertainty

Empirical tsunami fragility curves are developed based on a Bayesian framework by accounting for uncertainty of input tsunami hazard data in a systematic and comprehensive manner. Three fragility modeling approaches, i.e. lognormal method, binomial logistic method, and multinomial logistic method, are considered, and are applied to extensive tsunami damage data for the 2011 Tohoku earthquake. A unique aspect of this study is that uncertainty of tsunami inundation data (i.e. input hazard data in fragility modeling) is quantified by comparing two tsunami inundation/run-up datasets (one by the Ministry of Land, Infrastructure, and Transportation of the Japanese Government and the other by the Tohoku Tsunami Joint Survey group) and is then propagated through Bayesian statistical methods to assess the effects on the tsunami fragility models. The systematic implementation of the data and methods facilitates the quantitative comparison of tsunami fragility models under different assumptions. Such comparison shows that the binomial logistic method with un-binned data is preferred among the considered models; nevertheless, further investigations related to multinomial logistic regression with un-binned data are required. Finally, the developed tsunami fragility functions are integrated with building damage-loss models to investigate the influences of different tsunami fragility curves on tsunami loss estimation. Numerical results indicate that the uncertainty of input tsunami data is not negligible (coefficient of variation of 0.25) and that neglecting the input data uncertainty leads to overestimation of the model uncertainty.

Genomic selection in maritime pine

A two-generation maritime pine (Pinus pinaster Ait.) breeding population (n=661) was genotyped using 2500 SNP markers. The extent of linkage disequilibrium and utility of genomic selection for growth and stem straightness improvement were investigated. The overall intra-chromosomal linkage disequilibrium was r(2)=0.01. Linkage disequilibrium corrected for genomic relationships derived from markers was smaller (rV(2)=0.006). Genomic BLUP, Bayesian ridge regression and Bayesian LASSO regression statistical models were used to obtain genomic estimated breeding values. Two validation methods (random sampling 50% of the population and 10% of the progeny generation as validation sets) were used with 100 replications. The average predictive ability across statistical models and validation methods was about 0.49 for stem sweep, and 0.47 and 0.43 for total height and tree diameter, respectively. The sensitivity analysis suggested that prior densities (variance explained by markers) had little or no discernible effect on posterior means (residual variance) in Bayesian prediction models. Sampling from the progeny generation for model validation increased the predictive ability of markers for tree diameter and stem sweep but not for total height. The results are promising despite low linkage disequilibrium and low marker coverage of the genome (∼1.39 markers/cM).

Assessment of safety effects for widening urban roadways in developing crash modification functions using nonlinearizing link functions

Since a crash modification factor (CMF) represents the overall safety performance of specific treatments in a single fixed value, there is a need to explore the variation of CMFs with different roadway characteristics among treated sites over time. Therefore, in this study, we (1) evaluate the safety performance of a sample of urban four-lane roadway segments that have been widened with one through lane in each direction and (2) determine the relationship between the safety effects and different roadway characteristics over time. Observational before-after analysis with the empirical Bayes (EB) method was assessed in this study to evaluate the safety effects of widening urban four-lane roadways to six-lanes. Moreover, the nonlinearizing link functions were utilized to achieve better performance of crash modification functions (CMFunctions). The CMFunctions were developed using a Bayesian regression method including the estimated nonlinearizing link function to incorporate the changes in safety effects of the treatment over time. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed and used for EB estimation. The results indicated that the conversion of four-lane roadways to six-lane roadways resulted in a crash reduction of 15 percent for total crashes, and 24 percent for injury crashes on urban roadways. The results show that the safety effects vary across the sites with different roadway characteristics. In particular, LOS changes, time changes, and shoulder widths are significant parameters that affect the variation of CMFs. Moreover, it was found that narrowing shoulder and median widths to make space for an extra through lane shows a negative safety impact. It was also found that including the nonlinearizing link functions in developing CMFunctions shows more reliable estimates, if the variation of CMFs with specific parameters has a nonlinear relationship. The findings provide insights into the selection of roadway sites for adding through lanes.

Mortality attributable to seasonal influenza A and B infections in Thailand, 2005-2009: a longitudinal study

Influenza epidemiology differs substantially in tropical and temperate zones, but estimates of seasonal influenza mortality in developing countries in the tropics are lacking. We aimed to quantify mortality due to seasonal influenza in Thailand, a tropical middle-income country. Time series of polymerase chain reaction–confirmed influenza infections between 2005 and 2009 were constructed from a sentinel surveillance network. These were combined with influenza-like illness data to derive measures of influenza activity and relationships to mortality by using a Bayesian regression framework. We estimated 6.1 (95% credible interval: 0.5, 12.4) annual deaths per 100,000 population attributable to influenza A and B, predominantly in those aged ≥60 years, with the largest contribution from influenza A(H1N1) in 3 out of 4 years. For A(H3N2), the relationship between influenza activity and mortality varied over time. Influenza was associated with increases in deaths classified as resulting from respiratory disease (posterior probability of positive association, 99.8%), cancer (98.6%), renal disease (98.0%), and liver disease (99.2%). No association with circulatory disease mortality was found. Seasonal influenza infections are associated with substantial mortality in Thailand, but evidence for the strong relationship between influenza activity and circulatory disease mortality reported in temperate countries is lacking.

Bayesian genomic-enabled prediction as an inverse problem

Genomic-enabled prediction in plant and animal breeding has become an active area of research. Many prediction models address the collinearity that arises when the number (p) of molecular markers (e.g. single-nucleotide polymorphisms) is larger than the sample size (n). Here we propose four Bayesian approaches to the problem based on commonly used data reduction methods. Specifically, we use a Gaussian linear model for an orthogonal transformation of both the observed data and the matrix of molecular markers. Because shrinkage of estimates is affected by the prior variance of transformed effects, we propose four structures of the prior variance as a way of potentially increasing the prediction accuracy of the models fitted. To evaluate our methods, maize and wheat data previously used with standard Bayesian regression models were employed for measuring prediction accuracy using the proposed models. Results indicate that, for the maize and wheat data sets, our Bayesian models yielded, on average, a prediction accuracy that is 3% greater than that of standard Bayesian regression models, with less computational effort.

Efficient Gaussian process regression for large datasets

Gaussian processes are widely used in nonparametric regression, classification and spatiotemporal modelling, facilitated in part by a rich literature on their theoretical properties. However, one of their practical limitations is expensive computation, typically on the order of n³ where n is the number of data points, in performing the necessary matrix inversions. For large datasets, storage and processing also lead to computational bottlenecks, and numerical stability of the estimates and predicted values degrades with increasing n. Various methods have been proposed to address these problems, including predictive processes in spatial data analysis and the subset-of-regressors technique in machine learning. The idea underlying these approaches is to use a subset of the data, but this raises questions concerning sensitivity to the choice of subset and limitations in estimating fine-scale structure in regions that are not well covered by the subset. Motivated by the literature on compressive sensing, we propose an alternative approach that involves linear projection of all the data points onto a lower-dimensional subspace. We demonstrate the superiority of this approach from a theoretical perspective and through simulated and real data examples.

Bayesian latent structure models with space-time-dependent covariates

Spatial-temporal data requires flexible regression models which can model the dependence of responses on space- and time-dependent covariates. In this paper, we describe a semiparametric space-time model from a Bayesian perspective. Nonlinear time dependence of covariates and the interactions among the covariates are constructed by local linear and piecewise linear models, allowing for more flexible orientation and position of the covariate plane by using time-varying basis functions. Space-varying covariate linkage coefficients are also incorporated to allow for the variation of space structures across the geographical location. The formulation accommodates uncertainty in the number and locations of the piecewise basis functions to characterize the global effects, spatially structured and unstructured random effects in relation to covariates. The proposed approach relies on variable selection-type mixture priors for uncertainty in the number and locations of basis functions and in the space-varying linkage coefficients. A simulation example is presented to evaluate the performance of the proposed approach with the competing models. A real data example is used for illustration.