Learning, Forecasting and Structural Breaks

The literature on structural breaks focuses on ex post identification of break points that may have occurred in the past. While this question is important, a more challenging problem facing econometricians is to provide forecasts when the data generating process is unstable. The purpose of this paper is to provide a general methodology for forecasting in the presence of model instability. We make no assumptions on the number of break points or the law of motion governing parameter changes. Our approach makes use of Bayesian methods of model comparison and learning in order to provide an optimal predictive density from which forecasts can be derived. Estimates for the posterior probability that a break occurred at a particular point in the sample are generated as a byproduct of our procedure. We discuss the importance of using priors that accurately reflect the econometrician's opinions as to what constitutes a plausible forecast. Several applications to macroeconomic time-series data demonstrate the usefulness of our procedure.Bayesian Model Averaging, Markov Chain Monte Carlo, Real GDP Growth, Phillip's Curve

Analyzing P300 Distractors for Target Reconstruction

P300-based brain-computer interfaces (BCIs) are often trained per-user and per-application space. Training such models requires ground truth knowledge of target and non-target stimulus categories during model training, which imparts bias into the model. Additionally, not all non-targets are created equal; some may contain visual features that resemble targets or may otherwise be visually salient. Current research has indicated that non-target distractors may elicit attenuated P300 responses based on the perceptual similarity of these distractors to the target category. To minimize this bias, and enable a more nuanced analysis, we use a generalized BCI approach that is fit to neither user nor task. We do not seek to improve the overall accuracy of the BCI with our generalized approach; we instead demonstrate the utility of our approach for identifying target-related image features. When combined with other intelligent agents, such as computer vision systems, the performance of the generalized model equals that of the user-specific models, without any user specific data.Comment: 4 pages, 3 figure

Death is associated with complement C3 depletion in cerebrospinal fluid of patients with pneumococcal meningitis.

Pneumococcal meningitis can lead to death or serious neurological sequelae as a result of the host inflammatory response. We investigated the association between host response protein expression and outcome in patients with pneumococcal meningitis. Cerebrospinal fluid (CSF) was obtained from 80 patients with pneumococcal meningitis (40 nonsurvivors and 40 survivors) and 10 normal controls. Candidate proteins were analyzed for an association with survival. Complement C3 levels were 5-fold lower in nonsurvivors than in survivors (P < 0.05). This C3 reduction was not associated with lower levels in serum, indicating a compartmentalized CSF response. Transferrin levels were significantly higher in CSF (but not serum) from nonsurvivors than in CSF from survivors, suggestive of blood-brain barrier damage. Classical apoptosis proteins caspase 3 and apoptosis-inducing factor were not present in CSF. Expression of creatine kinase BB in clinically infected CSF suggested neuronal necrosis, but there was no clear association between level of expression and clinical outcome. Increased blood-brain barrier permeability and complement C3 depletion may have a role in determining outcome from bacterial meningitis. Therapeutic use of citicoline or caspase inhibitors is unlikely to have beneficial effects in patients with meningitis. IMPORTANCE: We previously identified proteins associated with clinical outcome in patients diagnosed with pneumococcal meningitis in a pilot proteomics study of cerebrospinal fluid (CSF). In this article, we have quantitatively assayed specific proteins identified from this previous proteomics analysis along with proteins associated with cell death by using Western blotting

Obesity in individuals with schizophrenia:a case controlled study in Scotland

We would like to thank Dr Moira Connolly, Professor Steve Lawrie and Dr Carol Robertson who, along with the authors, were part of a protocol development group funded by the NHS Research Scotland Mental Health Network (NRS MHN) formerly known as the Scottish Mental Health Research Network (SMHRN). We are grateful to the NRS MHN for funding the data acquisition and to the PCCIU for providing the data. We also thank Professor Gordon Murray for statistical advice.Peer reviewedPublisher PD

Spectral emissivity of cirrus clouds

The inference of cirrus cloud properties has many important applications including global climate studies, radiation budget determination, remote sensing techniques and oceanic studies from satellites. Data taken at the Parsons Kansas site during the FIRE II project are used for this study. On November 26 there were initially clear sky conditions gradually giving way to a progressively thickening cirrus shield over a period of a few hours. Interferometer radiosonde and lidar data were taken throughout this event. Two techniques are used to infer the downward spectral emittance of the observed cirrus layer. One uses only measurements and the other involves measurements and FASCODE III calculations. FASCODE III is a line-by line radiance/transmittance model developed at the Air Force Geophysics Laboratory

Upregulation of Defensins in Burn Sheep Small Intestine

Objective: The aim of this study was to visualize and localize the sheep antimicrobials, β-defensins 1, 2, and 3, (SBD-1, SBD-2, SBD-3), sheep neutrophil defensin alpha (SNP-1), and the cathelicidin LL-37 in sheep small intestine after burn injury, our hypothesis being that these compounds would be upregulated in an effort to overcome a compromised endothelial lining. Response to burn injury includes the release of proinflammatory cytokines and systemic immune suppression that, if untreated, can progress to multiple organ failure and death, so protective mechanisms have to be initiated and implemented. Methods: Tissue sections were probed with antibodies to the antimicrobials and then visualized with fluorescently labeled secondary antibodies and subjected to fluorescence deconvolution microscopy and image reconstruction. Results: In both the sham and burn samples, all the aforementioned antimicrobials were seen in each of the layers of small intestine, the highest concentration being localized to the epithelium. SBD-2, SBD-3, and SNP-1 were upregulated in both enterocytes and Paneth cells, while SNP-1 and LL-37 showed increases in both the inner circular and outer longitudinal muscle layers of the muscularis externa following burn injury. Each of the defensins, except SBD-1, was also seen in between the muscle layers of the externa and while burn caused slight increases of SBD-2, SBD-3, and SNP-1 in this location, LL-37 content was significantly decreased. Conclusion: That while each of these human antimicrobials is present in multiple layers of sheep small intestine, SBD-2, SBD-3, SNP-1, and LL-37 are upregulated in the specific layers of the small intestine

Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings

Humans can fluidly adapt their interest in complex environments in ways that machines cannot. Here, we lay the groundwork for a real-world system that passively monitors and merges neural correlates of visual interest across team members via Collaborative Brain Computer Interface (cBCI). When group interest is detected and co-registered in time and space, it can be used to model the task relevance of items in a dynamic, natural environment. Previous work in cBCIs focuses on static stimuli, stimulus- or response- locked analyses, and often within-subject and experiment model training. The contributions of this work are twofold. First, we test the utility of cBCI on a scenario that more closely resembles natural conditions, where subjects visually scanned a video for target items in a virtual environment. Second, we use an experiment-agnostic deep learning model to account for the real-world use case where no training set exists that exactly matches the end-users task and circumstances. With our approach we show improved performance as the number of subjects in the cBCI ensemble grows, and the potential to reconstruct ground-truth target occurrence in an otherwise noisy and complex environment.Comment: 6 pages, 6 figure

Identification of Binding Regions of Bilirubin in the Ligand-Binding Pocket of the Peroxisome Proliferator-Activated Receptor-A (PPARalpha)

Recent work has shown that bilirubin has a hormonal function by binding to the peroxisome proliferator-activated receptor-α (PPARα), a nuclear receptor that drives the transcription of genes to control adiposity. Our previous in silico work predicted three potential amino acids that bilirubin may interact with by hydrogen bonding in the PPARα ligand-binding domain (LBD), which could be responsible for the ligand-induced function. To further reveal the amino acids that bilirubin interacts with in the PPARα LBD, we harnessed bilirubin’s known fluorescent properties when bound to proteins such as albumin. Our work here revealed that bilirubin interacts with threonine 283 (T283) and alanine 333 (A333) for ligand binding. Mutational analysis of T283 and A333 showed significantly reduced bilirubin binding, reductions of 11.4% and 17.0%, respectively. Fenofibrate competitive binding studies for the PPARα LBD showed that bilirubin and fenofibrate possibly interact with different amino acid residues. Furthermore, bilirubin showed no interaction with PPARγ. This is the first study to reveal the amino acids responsible for bilirubin binding in the ligand-binding pocket of PPARα. Our work offers new insight into the mechanistic actions of a well-known molecule, bilirubin, and new fronts into its mechanisms