MAVERICK: A Synthetic Murder Mystery Network Dataset to Support Sensemaking Research

AbstractThe MAVERICK dataset was created to support a series of empirical studies looking at the effectiveness of network visualizations intended to support information foraging and human sensemaking within the domain of counterinsurgency intelligence analysis. This synthetic dataset is structured as a forensic mystery with the central goal of solving a fictional murder. The dataset includes 181 text-based reports, with additional media included with some messages as attachments, collected from various sources of varying reliability. The reports are framed as being collected from the perspective of a reporter investigating the murder through interviews with suspects and observations taken at the site the murder. The dataset includes intentional and unintentional deception along with calculated source reliabilities based on available evidence. The dataset is dynamic in nature, as the information in the dataset evolves and expands over a simulated period of time. This is done to both to simulate a real-world scenario, and to allow for evolutionary tasks and experiments to be performed using the dataset. The dataset is designed to be complex enough to simulate a real-world, while remaining accessible to individuals without experience in a specific domain of interest. This meant that it had to be on a topic that did not require prior domain knowledge to understand available information or to understand what strategies should be applied during analysis of the dataset. The solution to these challenges was the development of a fictional murder mystery. The plot involves a murder that took place over the course of a weekend with several possible suspects at a large private estate. This scenario allowed for a great deal of complexity; however, it was also a subject matter that could be easily understood by participants without prerequisite domain experience

Thermal Conductivity of Chirality-Sorted Carbon Nanotube Networks

The thermal properties of single-walled carbon nanotubes (SWNTs) are of significant interest, yet their dependence on SWNT chirality has been, until now, not explored experimentally. Here, we used electrical heating and infrared thermal imaging to simultaneously study thermal and electrical transport in chirality-sorted SWNT networks. We examined solution processed 90% semiconducting, 90% metallic, purified unsorted (66% semiconducting), and as-grown HiPco SWNT films. The thermal conductivities of these films range from 80 to 370 W m-1 K-1 but are not controlled by chirality, instead being dependent on the morphology (i.e., mass and junction density, quasi-alignment) of the networks. The upper range of the thermal conductivities measured is comparable to that of the best metals (Cu and Ag), but with over an order of magnitude lower mass density. This study reveals important factors controlling the thermal properties of light-weight chirality-sorted SWNT films, for potential thermal and thermoelectric applications

An Introduction to Hard and Soft Data Fusion via Conceptual Spaces Modeling for Space Event Characterization

This paper describes an AFOSR-supported basic research program that focuses on developing a new framework for combining hard with soft data in order to improve space situational awareness. The goal is to provide, in an automatic and near real-time fashion, a ranking of possible threats to blue assets (assets trying to be protected) from red assets (assets with hostile intentions). The approach is based on Conceptual Spaces models, which combine features from traditional associative and symbolic cognitive models. While Conceptual Spaces are revolutionary, they lack an underlying mathematical framework. Several such frameworks have attempted to represent Conceptual Spaces, but by far the most robust is the model developed by Holender. His model utilizes integer linear programming in order to obtain an overall similarity value between observations and concepts that support the formation of hypotheses. This paper will describe a method for building Conceptual Spaces models for threats that utilizes ontologies as a means to provide a clear semantic foundation for this inferencing process; in particular threat ontologies and space domain ontologies are developed and employed in this approach. A space situational awareness use-case is presented involving a kinetic kill scenario and results are shown to assess the performance of this fusion-based inferencing framework

A Mathematical model for Astrocytes mediated LTP at Single Hippocampal Synapses

Many contemporary studies have shown that astrocytes play a significant role in modulating both short and long form of synaptic plasticity. There are very few experimental models which elucidate the role of astrocyte over Long-term Potentiation (LTP). Recently, Perea & Araque (2007) demonstrated a role of astrocytes in induction of LTP at single hippocampal synapses. They suggested a purely pre-synaptic basis for induction of this N-methyl-D- Aspartate (NMDA) Receptor-independent LTP. Also, the mechanisms underlying this pre-synaptic induction were not investigated. Here, in this article, we propose a mathematical model for astrocyte modulated LTP which successfully emulates the experimental findings of Perea & Araque (2007). Our study suggests the role of retrograde messengers, possibly Nitric Oxide (NO), for this pre-synaptically modulated LTP.Comment: 51 pages, 15 figures, Journal of Computational Neuroscience (to appear

Modulation of a protein free-energy landscape by circular permutation

Circular permutations usually retain the native structure and function of a protein while inevitably perturb its folding dynamics. By using simulations with a structure-based model and a rigorous methodology to determine free-energy surfaces from trajectories we evaluate the effect of a circular permutation on the free-energy landscape of the protein T4 lysozyme. We observe changes which, while subtle, largely affect the cooperativity between the two subdomains. Such a change in cooperativity has been previously experimentally observed and recently also characterized using single molecule optical tweezers and the Crooks relation. The free-energy landscapes show that both the wild type and circular permutant have an on-pathway intermediate, previously experimentally characterized, where one of the subdomains is completely formed. The landscapes, however, differ in the position of the rate-limiting step for folding, which occurs before the intermediate in the wild-type and after in the circular permutant. This shift of transition state explains the observed change in the cooperativity. The underlying free-energy landscape thus provides a microscopic description of the folding dynamics and the connection between circular permutation and the loss of cooperativity experimentally observed

PlasmoDraft: a database of Plasmodium falciparum gene function predictions based on postgenomic data

<p>Abstract</p> <p>Background</p> <p>Of the 5 484 predicted proteins of <it>Plasmodium falciparum</it>, the main causative agent of malaria, about 60% do not have sufficient sequence similarity with proteins in other organisms to warrant provision of functional assignments. Non-homology methods are thus needed to obtain functional clues for these uncharacterized genes.</p> <p>Results</p> <p>We present PlasmoDraft <url>http://atgc.lirmm.fr/PlasmoDraft/</url>, a database of Gene Ontology (GO) annotation predictions for <it>P. falciparum </it>genes based on postgenomic data. Predictions of PlasmoDraft are achieved with a <it>Guilt By Association </it>method named Gonna. This involves (1) a predictor that proposes GO annotations for a gene based on the similarity of its profile (measured with transcriptome, proteome or interactome data) with genes already annotated by GeneDB; (2) a procedure that estimates the confidence of the predictions achieved with each data source; (3) a procedure that combines all data sources to provide a global summary and confidence estimate of the predictions. Gonna has been applied to all <it>P. falciparum </it>genes using most publicly available transcriptome, proteome and interactome data sources. Gonna provides predictions for numerous genes without any annotations. For example, 2 434 genes without any annotations in the Biological Process ontology are associated with specific GO terms (<it>e.g</it>. Rosetting, Antigenic variation), and among these, 841 have confidence values above 50%. In the Cellular Component and Molecular Function ontologies, 1 905 and 1 540 uncharacterized genes are associated with specific GO terms, respectively (740 and 329 with confidence value above 50%).</p> <p>Conclusion</p> <p>All predictions along with their confidence values have been compiled in PlasmoDraft, which thus provides an extensive database of GO annotation predictions that can be achieved with these data sources. The database can be accessed in different ways. A global view allows for a quick inspection of the GO terms that are predicted with high confidence, depending on the various data sources. A gene view and a GO term view allow for the search of potential GO terms attached to a given gene, and genes that potentially belong to a given GO term.</p

Transcriptional Profiling of Plasmodium falciparum Parasites from Patients with Severe Malaria Identifies Distinct Low vs. High Parasitemic Clusters

Background: In the past decade, estimates of malaria infections have dropped from 500 million to 225 million per year; likewise, mortality rates have dropped from 3 million to 791,000 per year. However, approximately 90% of these deaths continue to occur in sub-Saharan Africa, and 85% involve children less than 5 years of age. Malaria mortality in children generally results from one or more of the following clinical syndromes: severe anemia, acidosis, and cerebral malaria. Although much is known about the clinical and pathological manifestations of CM, insights into the biology of the malaria parasite, specifically transcription during this manifestation of severe infection, are lacking. Methods and Findings: We collected peripheral blood from children meeting the clinical case definition of cerebral malaria from a cohort in Malawi, examined the patients for the presence or absence of malaria retinopathy, and performed whole genome transcriptional profiling for Plasmodium falciparum using a custom designed Affymetrix array. We identified two distinct physiological states that showed highly significant association with the level of parasitemia. We compared both groups of Malawi expression profiles with our previously acquired ex vivo expression profiles of parasites derived from infected patients with mild disease; a large collection of in vitro Plasmodium falciparum life cycle gene expression profiles; and an extensively annotated compendium of expression data from Saccharomyces cerevisiae. The high parasitemia patient group demonstrated a unique biology with elevated expression of Hrd1, a member of endoplasmic reticulum-associated protein degradation system. Conclusions: The presence of a unique high parasitemia state may be indicative of the parasite biology of the clinically recognized hyperparasitemic severe disease syndrome

Gamma Power Is Phase-Locked to Posterior Alpha Activity

Neuronal oscillations in various frequency bands have been reported in numerous studies in both humans and animals. While it is obvious that these oscillations play an important role in cognitive processing, it remains unclear how oscillations in various frequency bands interact. In this study we have investigated phase to power locking in MEG activity of healthy human subjects at rest with their eyes closed. To examine cross-frequency coupling, we have computed coherence between the time course of the power in a given frequency band and the signal itself within every channel. The time-course of the power was calculated using a sliding tapered time window followed by a Fourier transform. Our findings show that high-frequency gamma power (30–70 Hz) is phase-locked to alpha oscillations (8–13 Hz) in the ongoing MEG signals. The topography of the coupling was similar to the topography of the alpha power and was strongest over occipital areas. Interestingly, gamma activity per se was not evident in the power spectra and only became detectable when studied in relation to the alpha phase. Intracranial data from an epileptic subject confirmed these findings albeit there was slowing in both the alpha and gamma band. A tentative explanation for this phenomenon is that the visual system is inhibited during most of the alpha cycle whereas a burst of gamma activity at a specific alpha phase (e.g. at troughs) reflects a window of excitability