DPpackage: Bayesian Semi- and Nonparametric Modeling in R

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code.

DPpackage: Bayesian Semi- and Nonparametric Modeling in R

Data analysis sometimes requires the relaxation of parametric assumptions in order to gain modeling flexibility and robustness against mis-specification of the probability model. In the Bayesian context, this is accomplished by placing a prior distribution on a function space, such as the space of all probability distributions or the space of all regression functions. Unfortunately, posterior distributions ranging over function spaces are highly complex and hence sampling methods play a key role. This paper provides an introduction to a simple, yet comprehensive, set of programs for the implementation of some Bayesian nonparametric and semiparametric models in R, DPpackage. Currently, DPpackage includes models for marginal and conditional density estimation, receiver operating characteristic curve analysis, interval-censored data, binary regression data, item response data, longitudinal and clustered data using generalized linear mixed models, and regression data using generalized additive models. The package also contains functions to compute pseudo-Bayes factors for model comparison and for eliciting the precision parameter of the Dirichlet process prior, and a general purpose Metropolis sampling algorithm. To maximize computational efficiency, the actual sampling for each model is carried out using compiled C, C++ or Fortran code

A Successful Portable Computer Lab Training Program

Penn State Cooperative Extension and the Pennsylvania Farm Credit System joined forces to fund a portable computer laboratory. A simplified lab management procedure allowed Extension agents to offer 33 computer operation workshops for 300 participants at minimal participant cost. Participants indicated their future use of computers would focus on farm financial, crop, and livestock management. Although considerable competence was gained, more than 50% viewed themselves with poor to moderate computer skills at the end of the workshops. The lab has enabled agents to contact a preciously under-served population as 54% of the participants had not attended any Extension workshops in the previous year

ACCOMMODATING MIXED-SEVERITY FIRE TO RESTORE AND MAINTAIN ECOSYSTEM INTEGRITY WITH A FOCUS ON THE SIERRA NEVADA OF CALIFORNIA, USA

Existing fire policy encourages the maintenance of ecosystem integrity in fire management, yet this is difficult to implement on lands managed for competing economic, human safety, and air quality concerns. We discuss a fire management approach in the mid-elevations of the Sierra Nevada, California, USA, that may exemplify similar challenges in other fire-adapted regions of the western USA. We also discuss how managing for pyrodiversity through mixed-severity fires can promote ecosystem integrity in Sierran mixed conifer and ponderosa pine (Pinus ponderosa Laws) forests. To illustrate, we show how coarse-filter (landscape-level) and complementary fine-filter (species-level) approaches can enhance forest management and conservation biology objectives as related to wildfire management. At the coarse-filter level, pyrodiverse mixed-severity fires provide landscape heterogeneity. Species and ecosystem characteristics associated with pyrodiversity can be maintained or enhanced by accommodating moderately severe fires, which hasten restoration by recreating a complex vegetation mosaic otherwise at risk from suppression. At the fine-filter level, managers can select focal species and species of conservation concern based on the degree to which those species depend on fire and accommodate their specific conservation needs. The black-backed woodpecker (Picoides arcticus [Swainson, 1832]) is an ideal focal species for monitoring the ecological integrity of forests restored through mixed-severity fire, and the California spotted owl (Strix occidentalis occidentalis [Xantus de Vesey, 1860]) is a species of conservation concern that uses post-fire habitat mosaics and is particularly vulnerable to logging. We suggest a comprehensive approach that integrates wildland fire for ecosystem integrity and species viability with strategic deployment of fire suppression and ecologically based restoration of pyrodiverse landscapes. Our approach would accomplish fire management goals while simultaneously maintaining biodiversity

Unscented Kalman Filter for Brain-Machine Interfaces

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation

A Brain-Machine Interface Instructed by Direct Intracortical Microstimulation

Brain–machine interfaces (BMIs) establish direct communication between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a) vibrotactile stimulation of the monkey's hands or (b) multi-channel intracortical microstimulation (ICMS) delivered to the primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PP) in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine–brain recursive input. After 2 weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices

Effect of the Goals of Care Intervention for Advanced Dementia: A Randomized Clinical Trial

In advanced dementia, goals of care decisions are challenging and medical care is often more intensive than desired

Individual differences in regulatory focus predict neural response to reward

Although goal pursuit is related to both functioning of the brain's reward circuits and psychological factors, the literatures surrounding these concepts have often been separate. Here, we use the psychological construct of regulatory focus to investigate individual differences in neural response to reward. Regulatory focus theory proposes two motivational orientations for personal goal pursuit: (1) promotion, associated with sensitivity to potential gain, and (2) prevention, associated with sensitivity to potential loss. The monetary incentive delay task was used to manipulate reward circuit function, along with instructional framing corresponding to promotion and prevention in a within-subject design. We observed that the more promotion oriented an individual was, the lower their ventral striatum response to gain cues. Follow-up analyses revealed that greater promotion orientation was associated with decreased ventral striatum response even to no-value cues, suggesting that promotion orientation may be associated with relatively hypoactive reward system function. The findings are also likely to represent an interaction between the cognitive and motivational characteristics of the promotion system with the task demands. Prevention orientation did not correlate with ventral striatum response to gain cues, supporting the discriminant validity of regulatory focus theory. The results highlight a dynamic association between individual differences in self-regulation and reward system function

Applying phylogenomics to understand the emergence of Shiga Toxin producing Escherichia coli O157:H7 strains causing severe human disease in the United Kingdom

Shiga Toxin producing Escherichia coli (STEC) O157:H7 is a recently emerged zoonotic pathogen with considerable morbidity. Since the serotype emerged in the 1980s, research has focussed on unravelling the evolutionary events from the E. coli O55:H7 ancestor to the contemporaneous globally dispersed strains. In this study the genomes of over 1000 isolates from human clinical cases and cattle, spanning the history of STEC O157:H7 in the United Kingdom were sequenced. Phylogenetic analysis reveals the ancestry, key acquisition events and global context of the strains. Dated phylogenies estimate the time to the most recent common ancestor of the current circulating global clone to 175 years ago, followed by rapid diversification. We show the acquisition of specific virulence determinates occurred relatively recently and coincides with its recent detection in the human population. Using clinical outcome data from 493 cases of STEC O157:H7 we assess the relative risk of severe disease including HUS from each of the defined clades in the population and show the dramatic effect Shiga toxin complement has on virulence. We describe two strain replacement events that have occurred in the cattle population in the UK over the last 30 years; one resulting in a highly virulent strain that has accounted for the majority of clinical cases in the UK over the last decade. This work highlights the need to understand the selection pressures maintaining Shiga-toxin encoding bacteriophages in the ruminant reservoir and the study affirms the requirement for close surveillance of this pathogen in both ruminant and human populations