Bird tolerance to humans in open tropical ecosystems.

Animal tolerance towards humans can be a key factor facilitating wildlife-human coexistence, yet traits predicting its direction and magnitude across tropical animals are poorly known. Using 10,249 observations for 842 bird species inhabiting open tropical ecosystems in Africa, South America, and Australia, we find that avian tolerance towards humans was lower (i.e., escape distance was longer) in rural rather than urban populations and in populations exposed to lower human disturbance (measured as human footprint index). In addition, larger species and species with larger clutches and enhanced flight ability are less tolerant to human approaches and escape distances increase when birds were approached during the wet season compared to the dry season and from longer starting distances. Identification of key factors affecting animal tolerance towards humans across large spatial and taxonomic scales may help us to better understand and predict the patterns of species distributions in the Anthropocene.

Atlas of type 2 dopamine receptors in the human brain: Age and sex dependent variability in a large PET cohort.

BACKGROUND: The dopamine system contributes to a multitude of functions ranging from reward and motivation to learning and movement control, making it a key component in goal-directed behavior. Altered dopaminergic function is observed in neurological and psychiatric conditions. Numerous factors have been proposed to influence dopamine function, but due to small sample sizes and heterogeneous data analysis methods in previous studies their specific and joint contributions remain unresolved. METHODS: In this cross-sectional register-based study we investigated how age, sex, body mass index (BMI), as well as cerebral hemisphere and regional volume influence striatal type 2 dopamine receptor (D2R) availability in the human brain. We analyzed a large historical dataset (n=156, 120 males and 36 females) of [11C]raclopride PET scans performed between 2004 and 2018. RESULTS: Striatal D2R availability decreased through age for both sexes (2-5 % in striatal ROIs per 10 years) and was higher in females versus males throughout age (7-8% in putamen). BMI and striatal D2R availability were weakly associated. There was no consistent lateralization of striatal D2R. The observed effects were independent of regional volumes. These results were validated using two different spatial normalization methods, and the age and sex effects also replicated in an independent sample (n=135). CONCLUSIONS: D2R availability is dependent on age and sex, which may contribute to the vulnerability of neurological and psychiatric conditions involving altering D2R expression.

Probabilistic outlier identification for RNA sequencing generalized linear models.

Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

Qualifying drug dosing regimens in pediatrics using Gaussian processes.

Drug development commonly studies an adult population first and then the pediatric population. The knowledge from the adult population is taken advantage of for the design of the pediatric trials. Adjusted drug doses for these are often derived from adult pharmacokinetic (PK) models which are extrapolated to patients with smaller body size. This extrapolation is based on scaling physiologic model parameters with a body size measure accounting for organ size differences. The inherent assumption is that children are merely small adults. However, children can be subject to additional effects such as organ maturation. These effects are not present in the adult population and are possibly overlooked at the design stage of the pediatric trials. It is thus crucial to qualify the extrapolation assumptions once the pediatric trial data are available. In this work, we propose a model based on a non-parametric regression method called Gaussian process (GP) to detect deviations from the made extrapolation assumptions. We introduce the theoretical background of this model and compare its performance to a parametric expansion of the adult model. The comparison includes simulations and a clinical study data example. The results show that the GP approach can reliably detect maturation trends from sparse pediatric data.

lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data.

MOTIVATION: Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data, however, requires interpretable methods that can model complicated covariance structures and detect non-linear effects of both categorical and continuous covariates, as well as their interactions. Detecting disease effects is hindered by the fact that they often occur rapidly near the disease initiation time, and this time point cannot be exactly observed. An additional challenge is that the effect magnitude can be heterogeneous over the subjects. RESULTS: We present lgpr, a widely applicable and interpretable method for non-parametric analysis of longitudinal data using additive Gaussian processes. We demonstrate that it outperforms previous approaches in identifying the relevant categorical and continuous covariates in various settings. Furthermore, it implements important novel features, including the ability to account for the heterogeneity of covariate effects, their temporal uncertainty, and appropriate observation models for different types of biomedical data. The lgpr tool is implemented as a comprehensive and user-friendly R-package. AVAILABILITY AND IMPLEMENTATION: lgpr is available at jtimonen.github.io/lgpr-usage with documentation, tutorials, test data and code for reproducing the experiments of this article. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interindividual variability and lateralization of µ-opioid receptors in the human brain.

Alterations in the brain's µ-opioid receptor (MOR) system have been associated with several neuropsychiatric disorders. Central MOR availability also varies considerably in healthy individuals. Multiple epidemiological factors have been proposed to influence the MOR system, but due to small sample sizes the magnitude of their influence remains inconclusive. We compiled [11C]carfentanil positron emission tomography scans from 204 individuals with no neurologic or psychiatric disorders, and estimated the effects of sex, age, body mass index (BMI) and smoking on [11C]carfentanil binding potential using between-subject regression analysis. We also examined hemispheric differences in MOR availability. Older age was associated with increase in MOR availability in frontotemporal areas but decrease in amygdala, thalamus, and nucleus accumbens. The age-dependent increase was stronger in males. MOR availability was globally lowered in smokers but independent of BMI. Finally, MOR availability was higher in the right versus the left hemisphere. The presently observed variation in MOR availability may explain why some individuals are prone to develop MOR-linked pathological states, such as chronic pain or psychiatric disorders. Lateralized MOR system may reflect hemispheric work specialization in central emotion and pain processes.

Minimum Mode Saddle Point Searches Using Gaussian Process Regression with Inverse-Distance Covariance Function.

The minimum mode following method can be used to find saddle points on an energy surface by following a direction guided by the lowest curvature mode. Such calculations are often started close to a minimum on the energy surface to find out which transitions can occur from an initial state of the system, but it is also common to start from the vicinity of a first-order saddle point making use of an initial guess based on intuition or more approximate calculations. In systems where accurate evaluations of the energy and its gradient are computationally intensive, it is important to exploit the information of the previous evaluations to enhance the performance. Here, we show that the number of evaluations required for convergence to the saddle point can be significantly reduced by making use of an approximate energy surface obtained by a Gaussian process model based on inverse interatomic distances, evaluating accurate energy and gradient at the saddle point of the approximate surface and then correcting the model based on the new information. The performance of the method is tested with start points chosen randomly in the vicinity of saddle points for dissociative adsorption of an H2 molecule on the Cu(110) surface and three gas phase chemical reactions.

Nudged Elastic Band Calculations Accelerated with Gaussian Process Regression Based on Inverse Interatomic Distances.

Calculations of minimum energy paths for atomic rearrangements using the nudged elastic band method can be accelerated with Gaussian process regression to reduce the number of energy and atomic force evaluations needed for convergence. Problems can arise, however, when configurations with large forces due to short distance between atoms are included in the data set. Here, a significant improvement to the Gaussian process regression approach is obtained by basing the difference measure between two atomic configurations in the covariance function on the inverted interatomic distances and by adding a new early stopping criterion for the path relaxation phase. This greatly improves the performance of the method in two applications where the original formulation does not work well: a dissociative adsorption of an H2 molecule on a Cu(110) surface and a diffusion hop of an H2O molecule on an ice Ih(0001) surface. Also, the revised method works better in the previously analyzed benchmark application to rearrangement transitions of a heptamer island on a surface, requiring fewer energy and force evaluations for convergence to the minimum energy path.

Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra.

Deep learning methods for the prediction of molecular excitation spectra are presented. For the example of the electronic density of states of 132k organic molecules, three different neural network architectures: multilayer perceptron (MLP), convolutional neural network (CNN), and deep tensor neural network (DTNN) are trained and assessed. The inputs for the neural networks are the coordinates and charges of the constituent atoms of each molecule. Already, the MLP is able to learn spectra, but the root mean square error (RMSE) is still as high as 0.3 eV. The learning quality improves significantly for the CNN (RMSE = 0.23 eV) and reaches its best performance for the DTNN (RMSE = 0.19 eV). Both CNN and DTNN capture even small nuances in the spectral shape. In a showcase application of this method, the structures of 10k previously unseen organic molecules are scanned and instant spectra predictions are obtained to identify molecules for potential applications.

An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data.

Biomedical research typically involves longitudinal study designs where samples from individuals are measured repeatedly over time and the goal is to identify risk factors (covariates) that are associated with an outcome value. General linear mixed effect models are the standard workhorse for statistical analysis of longitudinal data. However, analysis of longitudinal data can be complicated for reasons such as difficulties in modelling correlated outcome values, functional (time-varying) covariates, nonlinear and non-stationary effects, and model inference. We present LonGP, an additive Gaussian process regression model that is specifically designed for statistical analysis of longitudinal data, which solves these commonly faced challenges. LonGP can model time-varying random effects and non-stationary signals, incorporate multiple kernel learning, and provide interpretable results for the effects of individual covariates and their interactions. We demonstrate LonGP's performance and accuracy by analysing various simulated and real longitudinal -omics datasets.

Characterization and non-parametric modeling of the developing serum proteome during infancy and early childhood.

Children develop rapidly during the first years of life, and understanding the sources and associated levels of variation in the serum proteome is important when using serum proteins as markers for childhood diseases. The aim of this study was to establish a reference model for the evolution of a healthy serum proteome during early childhood. Label-free quantitative proteomics analyses were performed for 103 longitudinal serum samples collected from 15 children at birth and between the ages of 3-36 months. A flexible Gaussian process-based probabilistic modelling framework was developed to evaluate the effects of different variables, including age, living environment and individual variation, on the longitudinal expression profiles of 266 reliably identified and quantified serum proteins. Age was the most dominant factor influencing approximately half of the studied proteins, and the most prominent age-associated changes were observed already during the first year of life. High inter-individual variability was also observed for multiple proteins. These data provide important details on the maturing serum proteome during early life, and evaluate how patterns detected in cord blood are conserved in the first years of life. Additionally, our novel modelling approach provides a statistical framework to detect associations between covariates and non-linear time series data.

p95HER2 Methionine 611 Carboxy-Terminal Fragment Is Predictive of Trastuzumab Adjuvant Treatment Benefit in the FinHer Trial.

Purpose: Expression of p95HER2 (p95), a truncated form of the HER2 receptor, which lacks the trastuzumab binding site but retains kinase activity, has been reported as a prognostic biomarker for poor outcomes in patients with trastuzumab-treated HER2-positive metastatic breast cancer. The impact of p95 expression on trastuzumab treatment efficacy in early HER2-positive breast cancer is less clear. In the current study, p95 was tested as a predictive marker of trastuzumab treatment benefit in the HER2-positive subset of the FinHer adjuvant phase III trial.Experimental Design: In the FinHer trial, 232 patients with HER2-positive early breast cancer were randomized to receive chemotherapy plus 9 weeks of trastuzumab or no trastuzumab treatment. Quantitative p95 protein expression was measured in formalin-fixed paraffin-embedded samples using the p95 VeraTag assay (Monogram Biosciences), specific for the M611 form of p95. Quantitative HER2 protein expression was measured using the HERmark assay (Monogram Biosciences). Distant disease-free survival (DDFS) was used as the primary outcome measure.Results: In the arm receiving chemotherapy only, increasing log10(p95) correlated with shorter DDFS (HR, 2.0; P = 0.02). In the arm receiving chemotherapy plus trastuzumab (N = 95), increasing log10(p95) was not correlated with a shorter DDFS. In a combined analysis of both treatment arms, high breast tumor p95 content was significantly correlated with trastuzumab treatment benefit in multivariate models (interaction P = 0.01).Conclusions: A high p95HER2/HER2 ratio identified patients with metastatic breast cancer with poor outcomes on trastuzumab-based therapies. Further investigation of the p95HER2/HER2 ratio as a potential prognostic or predictive biomarker for HER2-targeted therapy is warranted. Clin Cancer Res; 24(13); 3046-52. ©2018 AACR.

Brain-to-brain hyperclassification reveals action-specific motor mapping of observed actions in humans.

Seeing an action may activate the corresponding action motor code in the observer. It remains unresolved whether seeing and performing an action activates similar action-specific motor codes in the observer and the actor. We used novel hyperclassification approach to reveal shared brain activation signatures of action execution and observation in interacting human subjects. In the first experiment, two "actors" performed four types of hand actions while their haemodynamic brain activations were measured with 3-T functional magnetic resonance imaging (fMRI). The actions were videotaped and shown to 15 "observers" during a second fMRI experiment. Eleven observers saw the videos of one actor, and the remaining four observers saw the videos of the other actor. In a control fMRI experiment, one of the actors performed actions with closed eyes, and five new observers viewed these actions. Bayesian canonical correlation analysis was applied to functionally realign observers' and actors' fMRI data. Hyperclassification of the seen actions was performed with Bayesian logistic regression trained on actors' data and tested with observers' data. Without the functional realignment, between-subjects accuracy was at chance level. With the realignment, the accuracy increased on average by 15 percentage points, exceeding both the chance level and the accuracy without functional realignment. The highest accuracies were observed in occipital, parietal and premotor cortices. Hyperclassification exceeded chance level also when the actor did not see her own actions. We conclude that the functional brain activation signatures underlying action execution and observation are partly shared, yet these activation signatures may be anatomically misaligned across individuals.

Nudged elastic band calculations accelerated with Gaussian process regression.

Minimum energy paths for transitions such as atomic and/or spin rearrangements in thermalized systems are the transition paths of largest statistical weight. Such paths are frequently calculated using the nudged elastic band method, where an initial path is iteratively shifted to the nearest minimum energy path. The computational effort can be large, especially when ab initio or electron density functional calculations are used to evaluate the energy and atomic forces. Here, we show how the number of such evaluations can be reduced by an order of magnitude using a Gaussian process regression approach where an approximate energy surface is generated and refined in each iteration. When the goal is to evaluate the transition rate within harmonic transition state theory, the evaluation of the Hessian matrix at the initial and final state minima can be carried out beforehand and used as input in the minimum energy path calculation, thereby improving stability and reducing the number of iterations needed for convergence. A Gaussian process model also provides an uncertainty estimate for the approximate energy surface, and this can be used to focus the calculations on the lesser-known part of the path, thereby reducing the number of needed energy and force evaluations to a half in the present calculations. The methodology is illustrated using the two-dimensional Müller-Brown potential surface and performance assessed on an established benchmark involving 13 rearrangement transitions of a heptamer island on a solid surface.

Effect of KIT and PDGFRA Mutations on Survival in Patients With Gastrointestinal Stromal Tumors Treated With Adjuvant Imatinib: An Exploratory Analysis of a Randomized Clinical Trial.

IMPORTANCE: Little is known about whether the duration of adjuvant imatinib influences the prognostic significance of KIT proto-oncogene receptor tyrosine kinase (KIT) and platelet-derived growth factor receptor α (PDGFRA) mutations. OBJECTIVE: To investigate the effect of KIT and PDGFRA mutations on recurrence-free survival (RFS) in patients with gastrointestinal stromal tumors (GISTs) treated with surgery and adjuvant imatinib. DESIGN, SETTING, AND PARTICIPANTS: This exploratory study is based on the Scandinavian Sarcoma Group VIII/Arbeitsgemeinschaft Internistische Onkologie (SSGXVIII/AIO) multicenter clinical trial. Between February 4, 2004, and September 29, 2008, 400 patients who had undergone surgery for GISTs with a high risk of recurrence were randomized to receive adjuvant imatinib for 1 or 3 years. Of the 397 patients who provided consent, 341 (85.9%) had centrally confirmed, localized GISTs with mutation analysis for KIT and PDGFRA performed centrally using conventional sequencing. During a median follow-up of 88 months (completed December 31, 2013), 142 patients had GIST recurrence. Data of the evaluable population were analyzed February 4, 2004, through December 31, 2013. MAIN OUTCOMES AND MEASURES: The main outcome was RFS. Mutations were grouped by the gene and exon. KIT exon 11 mutations were further grouped as deletion or insertion-deletion mutations, substitution mutations, insertion or duplication mutations, and mutations that involved codons 557 and/or 558. RESULTS: Of the 341 patients (175 men and 166 women; median age at study entry, 62 years) in the 1-year group and 60 years in the 3-year group), 274 (80.4%) had GISTs with a KIT mutation, 43 (12.6%) had GISTs that harbored a PDGFRA mutation, and 24 (7.0%) had GISTs that were wild type for these genes. PDGFRA mutations and KIT exon 11 insertion or duplication mutations were associated with favorable RFS, whereas KIT exon 9 mutations were associated with unfavorable outcome. Patients with KIT exon 11 deletion or insertion-deletion mutation had better RFS when allocated to the 3-year group compared with the 1-year group (5-year RFS, 71.0% vs 41.3%; P < .001), whereas no significant benefit from the 3-year treatment was found in the other mutational subgroups examined. KIT exon 11 deletion mutations, deletions that involved codons 557 and/or 558, and deletions that led to pTrp557_Lys558del were associated with poor RFS in the 1-year group but not in the 3-year group. Similarly, in the subset with KIT exon 11 deletion mutations, higher-than-the-median mitotic counts were associated with unfavorable RFS in the 1-year group but not in the 3-year group. CONCLUSIONS AND RELEVANCE: Patients with KIT exon 11 deletion mutations benefit most from the longer duration of adjuvant imatinib. The duration of adjuvant imatinib modifies the risk of GIST recurrence associated with some KIT mutations, including deletions that affect exon 11 codons 557 and/or 558. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00116935.

SLUG transcription factor: a pro-survival and prognostic factor in gastrointestinal stromal tumour.

BACKGROUND: The SLUG transcription factor has been linked with the KIT signalling pathway that is important for gastrointestinal stromal tumour (GIST) tumourigenesis. Its clinical significance in GIST is unknown. METHODS: Influence of SLUG expression on cell proliferation and viability were investigated in GIST48 and GIST882 cell lines. The association between tumour SLUG expression in immunohistochemistry and recurrence-free survival (RFS) was studied in two clinical GIST series, one with 187 patients treated with surgery alone, and another one with 313 patients treated with surgery and adjuvant imatinib. RESULTS: SLUG downregulation inhibited cell proliferation, induced cell death in both cell lines, and sensitised GIST882 cells to lower imatinib concentrations. SLUG was expressed in 125 (25.0%) of the 500 clinical GISTs evaluated, and expression was associated with several factors linked with unfavourable prognosis. SLUG expression was associated with unfavourable RFS both when patients were treated with surgery alone (HR=3.40, 95% CI=1.67-6.89, P=0.001) and when treated with surgery plus adjuvant imatinib (HR=1.83, 95% CI=1.29-2.60, P=0.001). CONCLUSIONS: GIST patients with high tumour SLUG expression have unfavourable RFS. SLUG may mediate pro-survival signalling in GISTs.

Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex.

During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing. The predictive performances of the classifiers were tested by leaving one participant at a time for testing and training the model using the remaining 15 participants. The signature patterns associated with unimodal auditory stimuli encompassed distributed locations mostly in the middle and superior temporal gyrus (STG/MTG). A pattern regression analysis, based on a continuous acoustic model, revealed that activity in some of these MTG and STG areas were associated with acoustic features present in speech and music stimuli. Concurrent visual stimulus modulated activity in bilateral MTG (speech), lateral aspect of right anterior STG (singing), and bilateral parietal opercular cortex (piano). Our results suggest that specific supratemporal brain areas are involved in processing complex natural speech, singing, and piano playing, and other brain areas located in anterior (facial speech) and posterior (music-related hand actions) supratemporal cortex are influenced by related visual information. Those anterior and posterior supratemporal areas have been linked to stimulus identification and sensory-motor integration, respectively.

The current duration design for estimating the time to pregnancy distribution: a nonparametric Bayesian perspective.

This paper was inspired by the studies of Niels Keiding and co-authors on estimating the waiting time-to-pregnancy (TTP) distribution, and in particular on using the current duration design in that context. In this design, a cross-sectional sample of women is collected from those who are currently attempting to become pregnant, and then by recording from each the time she has been attempting. Our aim here is to study the identifiability and the estimation of the waiting time distribution on the basis of current duration data. The main difficulty in this stems from the fact that very short waiting times are only rarely selected into the sample of current durations, and this renders their estimation unstable. We introduce here a Bayesian method for this estimation problem, prove its asymptotic consistency, and compare the method to some variants of the non-parametric maximum likelihood estimators, which have been used previously in this context. The properties of the Bayesian estimation method are studied also empirically, using both simulated data and TTP data on current durations collected by Slama et al. (Hum Reprod 27(5):1489-1498, 2012).

Gastrointestinal stromal tumor: a method for optimizing the timing of CT scans in the follow-up of cancer patients.

PURPOSE: To develop a mathematical model to adjust the timing of computed tomography (CT) scans with the hazard of cancer recurrence in time to facilitate early detection of cancer recurrence. MATERIALS AND METHODS: The clinical data were extracted from the randomized Scandinavian Sarcoma Group (SSG) XVIII/Arbeitsgemeinschaft Internistische Onkologie (AIO) trial database. The SSG XVIII/AIO trial was registered (trial no. NCT00116935) and approved by the national or institutional review boards. In the trial, 1- and 3-year durations of adjuvant imatinib mesylate in the treatment of patients with gastrointestinal stromal tumor (GIST) were compared. A nonhomogeneous Poisson model with a piecewise log-constant hazard in time that accounts for the nonlinear pattern of GIST recurrence was applied to tumor site, mitotic count, and recurrence data. The optimal times to obtain follow-up CT scans were computed by modifying the frequency of CT scans with the hazard of tumor recurrence in time. The hazard-adjusted follow-up schedules were compared with the National Comprehensive Cancer Network (NCCN) guidelines of the United States, which suggest imaging with CT at intervals of 3-6 months for 3-5 years and then annually. RESULTS: Optimized timing of CT scans on the basis of hazard of recurrence resulted in follow-up schedule options where CT is performed more sparsely than in the NCCN guidelines during adjuvant imatinib administration and more frequently, at approximately 3-month intervals, during the first 2 years that follow imatinib discontinuation when the risk of recurrence was the greatest. The number of CT scans could be reduced by a median of 31% (from 13 to nine) compared with the standard schedules within the first 6 years of follow-up without increasing the delay in recurrence detection. CONCLUSION: Detection of GIST recurrence may be enhanced by adjusting the timing of the CT scans with the hazard of recurrence. The method may be applicable to other human tumor types. Online supplemental material is available for this article.

The influence of selective participation in a physical activity intervention on the generalizability of findings.

OBJECTIVE: To evaluate factors that characterize employees who did not participate in a physical activity intervention in an occupational setting and assess how selective participation affects inferences from the data. METHODS: Employees were asked to complete a health risk appraisal. The respondents were invited to participate in a physical activity intervention. We compared predictors of sickness absence (register data) among all respondents and those who participated in the intervention, using Bayesian regression analysis. RESULTS: Of 1116 employees, 817 (73%) responded, of whom 544 (67%) participated in the intervention. Participants had better health behaviors and fewer health problems than those who did not participate. The predictors of sickness absence in all respondents differed from those who participated in the intervention. CONCLUSIONS: Selective participation may reduce the potential benefit of interventions and limit generalizability of findings.