A Cost Comparison of Treatments of Moderate to Severe Psoriasis

This study of the efficacy and cost-effectiveness of moderate to severe psoriasis treatments compared phototherapy, oral systemic agents, and biologics from a managed health care systems perspective. A literature review was conducted to identify published studies reporting Psoriasis Area and Severity Index (PASI) percentage improvement from baseline (PASI%) for selected treatments. The researchers then calculated total annualized costs. For each treatment, annualized cost-effectiveness was calculated by dividing total annualized costs of treatment by PASI%. The costs necessary to achieve clinically meaningful outcomes (PASI50 and PASI75) were then calculated. Of 3886 articles examined, 16 studies met inclusion criteria. Oral systemic medications, UV therapy, and UV therapy combined with acitretin appear to be the most cost-effective therapies for moderate to severe psoriasis

Using machine learning to infer reasoning provenance from user interaction log data: based on the data/frame theory of sensemaking

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Using Machine Learning to Infer Reasoning Provenance from User Interaction Log Data

The reconstruction of analysts’ reasoning processes (reasoning provenance) during complex sensemaking tasks can support reflection and decision making. One potential approach to such reconstruction is to automatically infer reasoning from low-level user interaction logs. We explore a novel method for doing this using machine learning. Two user studies were conducted in which participants performed similar intelligence analysis tasks. In one study, participants used a standard web browser and word processor; in the other, they used a system called INVISQUE (Interactive Visual Search and Query Environment). Interaction logs were manually coded for cognitive actions based on captured think-aloud protocol and posttask interviews based on Klein, Phillips, Rall, and Pelusos’s data/frame model of sensemaking as a conceptual framework. This analysis was then used to train an interaction frame mapper, which employed multiple machine learning models to learn relationships between the interaction logs and the codings. Our results show that, for one study at least, classification accuracy was significantly better than chance and compared reasonably to a reported manual provenance reconstruction method. We discuss our results in terms of variations in feature sets from the two studies and what this means for the development of the method for provenance capture and the evaluation of sensemaking systems

Serving GODAE Data and Products to the Ocean Community

The Global Ocean Data Assimilation Experiment (GODAE [http:// www.godae.org]) has spanned a decade of rapid technological development. The ever-increasing volume and diversity of oceanographic data produced by in situ instruments, remote-sensing platforms, and computer simulations have driven the development of a number of innovative technologies that are essential for connecting scientists with the data that they need. This paper gives an overview of the technologies that have been developed and applied in the course of GODAE, which now provide users of oceanographic data with the capability to discover, evaluate, visualize, download, and analyze data from all over the world. The key to this capability is the ability to reduce the inherent complexity of oceanographic data by providing a consistent, harmonized view of the various data products. The challenges of data serving have been addressed over the last 10 years through the cooperative skills and energies of many individuals

A geometry of interaction machine for Gödel's System T

Gödel’s System T is the simply typed lambda calculus extended with numbers and an iterator. The higher-order nature of the language gives it enormous expressive power—the language can represent all the primitive recursive functions and beyond, for instance Ackermann’s function. In this paper we use System T as a minimalistic functional language. We give an interpretation using a data-flow model that incorporates ideas from the geometry of interaction and game semantics. The contribution is a reversible model of higher-order computation which can also serve as a novel compilation technique

A shallow layer model for heavy gas dispersion from natural sources: application and hazard assessment at Caldara di Manziana, Italy.

Several non-volcanic sources in central Italy emit a large amount of carbon dioxide (CO2). Under stable atmospheric conditions and/or in presence of topographic depressions, the concentration of CO2, which has a molecular mass greater than that of air, can reach high values that are lethal to humans or animals. Several episodes of this phenomenon were recorded in central Italy and elsewhere. In order to validate a model for the dispersion of a heavy gas and to assess the consequent hazard, we applied and tested the code TWODEE-2, an improved version of the established TWODEE model, which is based on a shallow layer approach that uses depth-averaged variables to describe the flow behavior of dense gas over complex topography. We present results for a vented CO2 release at Caldara di Manziana in central Italy. We find that the model gives reliable results when the input quantity can be properly defined. Moreover, we show that the model can be a useful tool for gas hazard assessment, by evaluating where and when lethal concentrations for humans and animal are reached.Several non-volcanic sources in central Italy emit a large amount of carbon dioxide (CO2). Under stable atmospheric conditions and/or in presence of topographic depressions, the concentration of CO2, which has a molecular mass greater than that of air, can reach high values that are lethal to humans or animals. Several episodes of this phenomenon were recorded in central Italy and elsewhere. In order to validate a model for the dispersion of a heavy gas and to assess the consequent hazard, we applied and tested the code TWODEE-2, an improved version of the established TWODEE model, which is based on a shallow layer approach that uses depth-averaged variables to describe the flow behavior of dense gas over complex topography. We present results for a vented CO2 release at Caldara di Manziana in central Italy. We find that the model gives reliable results when the input quantity can be properly defined. Moreover, we show that the model can be a useful tool for gas hazard assessment, by evaluating where and when lethal concentrations for humans and animal are reached.Several non-volcanic sources in central Italy emit a large amount of carbon dioxide (CO2). Under stable atmospheric conditions and/or in presence of topographic depressions, the concentration of CO2, which has a molecular mass greater than that of air, can reach high values that are lethal to humans or animals. Several episodes of this phenomenon were recorded in central Italy and elsewhere. In order to validate a model for the dispersion of a heavy gas and to assess the consequent hazard, we applied and tested the code TWODEE-2, an improved version of the established TWODEE model, which is based on a shallow layer approach that uses depth-averaged variables to describe the flow behavior of dense gas over complex topography. We present results for a vented CO2 release at Caldara di Manziana in central Italy. We find that the model gives reliable results when the input quantity can be properly defined. Moreover, we show that the model can be a useful tool for gas hazard assessment, by evaluating where and when lethal concentrations for humans and animal are reached.Several non-volcanic sources in central Italy emit a large amount of carbon dioxide (CO2). Under stable atmospheric conditions and/or in presence of topographic depressions, the concentration of CO2, which has a molecular mass greater than that of air, can reach high values that are lethal to humans or animals. Several episodes of this phenomenon were recorded in central Italy and elsewhere. In order to validate a model for the dispersion of a heavy gas and to assess the consequent hazard, we applied and tested the code TWODEE-2, an improved version of the established TWODEE model, which is based on a shallow layer approach that uses depth-averaged variables to describe the flow behavior of dense gas over complex topography. We present results for a vented CO2 release at Caldara di Manziana in central Italy. We find that the model gives reliable results when the input quantity can be properly defined. Moreover, we show that the model can be a useful tool for gas hazard assessment, by evaluating where and when lethal concentrations for humans and animal are reached.Several non-volcanic sources in central Italy emit a large amount of carbon dioxide (CO2). Under stable atmospheric conditions and/or in presence of topographic depressions, the concentration of CO2, which has a molecular mass greater than that of air, can reach high values that are lethal to humans or animals. Several episodes of this phenomenon were recorded in central Italy and elsewhere. In order to validate a model for the dispersion of a heavy gas and to assess the consequent hazard, we applied and tested the code TWODEE-2, an improved version of the established TWODEE model, which is based on a shallow layer approach that uses depth-averaged variables to describe the flow behavior of dense gas over complex topography. We present results for a vented CO2 release at Caldara di Manziana in central Italy. We find that the model gives reliable results when the input quantity can be properly defined. Moreover, we show that the model can be a useful tool for gas hazard assessment, by evaluating where and when lethal concentrations for humans and animal are reached

A shallow-layer model for heavy gas dispersion from natural sources: Application and hazard assessment at Caldara di Manziana, Italy

Several nonvolcanic sources in central Italy emit a large amount of carbon dioxide (CO2). Under stable atmospheric conditions and/or in the presence of topographic depressions, the concentration of CO2, which has a molecular mass greater than that of air, can reach high values that are lethal to humans or animals. Several episodes of this phenomenon were recorded in central Italy and elsewhere. In order to validate a model for the dispersion of a heavy gas and to assess the consequent hazard, we applied and tested the code TWODEE-2, an improved version of the established TWODEE model, which is based on a shallow-layer approach that uses depth-averaged variables to describe the flow behavior of dense gas over complex topography. We present results for a vented CO2 release at Caldara di Manziana in central Italy. We find that the model gives reliable results when the input quantity can be properly defined. Moreover, we show that the model can be a useful tool for gas hazard assessment by evaluating where and when lethal concentrations for humans and animals are reached