Food‐induced immediate response of the esophagus—A newly identified syndrome in patients with eosinophilic esophagitis

Background Dysphagia is the main symptom of adult eosinophilic esophagitis (EoE). We describe a novel syndrome, referred to as “food-induced immediate response of the esophagus” (FIRE), observed in EoE patients. Methods Food-induced immediate response of the esophagus is an unpleasant/painful sensation, unrelated to dysphagia, occurring immediately after esophageal contact with specific foods. Eosinophilic esophagitis experts were surveyed to estimate the prevalence of FIRE, characterize symptoms, and identify food triggers. We also surveyed a large group of EoE patients enrolled in the Swiss EoE Cohort Study for FIRE. Results Response rates were 82% (47/57) for the expert and 65% (239/368) for the patient survey, respectively. Almost, 90% of EoE experts had observed the FIRE symptom complex in their patients. Forty percent of EoE patients reported experiencing FIRE, more commonly in patients who developed EoE symptoms at a younger age (mean age of 46.4 years vs 54.1 years without FIRE; P < .01) and in those with high allergic comorbidity. Food-induced immediate response of the esophagus symptoms included narrowing, burning, choking, and pressure in the esophagus appearing within 5 minutes of ingesting a provoking food that lasted less than 2 hours. Symptom severity rated a median 7 points on a visual analogue scale from 1 to 10. Fresh fruits/vegetables and wine were the most frequent triggers. Endoscopic food removal was significantly more commonly reported in male patients with vs without FIRE (44.3% vs 27.6%; P = .03). Conclusions Food-induced immediate response of the esophagus is a novel syndrome frequently reported in EoE patients, characterized by an intense, unpleasant/painful sensation occurring rapidly and reproducibly in 40% of surveyed EoE patients after esophageal contact with specific foods

Genome-wide association analysis of eosinophilic esophagitis provides insight into the tissue specificity of this allergic disease

Eosinophilic esophagitis (EoE) is a chronic inflammatory disorder associated with allergic hypersensitivity to food. We interrogated >1.5 million genetic variants in European EoE cases and subsequently in a multi-site cohort with local and out-of-study control subjects. In addition to replication of the 5q22 locus (meta-analysis p = 1.9×10−16), we identified association at 2p23 (encoding CAPN14, p = 2.5×10−10). CAPN14 was specifically expressed in the esophagus, dynamically upregulated as a function of disease activity and genetic haplotype and after exposure of epithelial cells to IL-13, and located in an epigenetic hotspot modified by IL-13. There was enriched esophageal expression for the genes neighboring the top 208 EoE sequence variants. Multiple allergic sensitization loci were associated with EoE susceptibility (4.8×10−2 < p < 5.1×10−11). We propose a model that elucidates the tissue specific nature of EoE that involves the interplay of allergic sensitization with an EoE-specific, IL-13–inducible esophageal response involving CAPN14

Impact of ozone cross-section choice on WFDOAS total ozone retrieval applied to GOME, SCIAMACHY, and GOME-2 (1995-present)

Technical Note Issue 2 (January 2011) with updates from November 2013. A contribution to ACSO http://igaco-o3.fmi.fi/ACSO/. In this technical note we investigate how the choice of ozone cross-sections impact the WFDOAS (Weighting Function Differential Optical Absorption Spectroscopy) total ozone retrieval (Coldewey-Egbers et al., 2005) for the satellite instruments GOME, GOME-2, and SCIAMACHY

Two Major Themes in the Design of Sensor Networks: Data Integrity and Sampling.

In this poster, we consider two major themes in the design of sensor networks: data integrity, and sampling strategies. For the data integrity problem, we propose a signature-based fault detection system for identifying both intermittent faults and persistent faults. Data-dependent features using temporal, spatial, and spatio-temporal information that are useful for detecting faults are identified. These features are combined into signatures that characterize each of the different fault types. We also discuss the problem of simultaneous parameter estimation and fault detection. In this case, parameters must be estimated from a distribution that is truncated in various ways as a result of the fault detection algorithm, which can lead to biased estimates. We propose several methods to account for the bias in parameter estimates. For the sampling problem, we describe two on-going projects. The first one deals with situations where sampling as you move (using sampling paths) is more effective than contemplating sampling points. A PAR sensor riding on a NIMS 3D node is one such situation. This configuration is especially well-suited for sampling phenomena that exhibit latent geometric structure, such as light fields in forest understories. We will consider the case where the phenomena can be approximated by a piecewise-constant field and suggest a novel estimation approach when we have sample paths as observations. The second project considers the problem of finding a sampling strategy to optimize the selection of the correct regression model from a set of competing regression models. The solution is driven by minimizing the probability of error in the selection and consists of a sequential algorithm that directs the collection of measurements. We develop an adaptive sampling algorithm to sample the field with a set of static sensors and one mobile sensor. The algorithm aims to jointly minimize the probability of error in the selection and the mobility cost. The algorithm presented provides a significant improvement in the probability of error in the selection of the correct model over the random collection of measurements

Two Major Themes in the Design of Sensor Networks: Data Integrity and Sampling.

In this poster, we consider two major themes in the design of sensor networks: data integrity, and sampling strategies. For the data integrity problem, we propose a signature-based fault detection system for identifying both intermittent faults and persistent faults. Data-dependent features using temporal, spatial, and spatio-temporal information that are useful for detecting faults are identified. These features are combined into signatures that characterize each of the different fault types. We also discuss the problem of simultaneous parameter estimation and fault detection. In this case, parameters must be estimated from a distribution that is truncated in various ways as a result of the fault detection algorithm, which can lead to biased estimates. We propose several methods to account for the bias in parameter estimates. For the sampling problem, we describe two on-going projects. The first one deals with situations where sampling as you move (using sampling paths) is more effective than contemplating sampling points. A PAR sensor riding on a NIMS 3D node is one such situation. This configuration is especially well-suited for sampling phenomena that exhibit latent geometric structure, such as light fields in forest understories. We will consider the case where the phenomena can be approximated by a piecewise-constant field and suggest a novel estimation approach when we have sample paths as observations. The second project considers the problem of finding a sampling strategy to optimize the selection of the correct regression model from a set of competing regression models. The solution is driven by minimizing the probability of error in the selection and consists of a sequential algorithm that directs the collection of measurements. We develop an adaptive sampling algorithm to sample the field with a set of static sensors and one mobile sensor. The algorithm aims to jointly minimize the probability of error in the selection and the mobility cost. The algorithm presented provides a significant improvement in the probability of error in the selection of the correct model over the random collection of measurements

Sensor Network Data Fault Types

Little of the work in sensor network studies related to data quality has presented a detailed study of sensor faults and fault models. We provide a comprehensive look at sensor network data fault types and a unified basis for describing sensor faults backed up by real world deployment examples. We also identify several considerations one must take into account when developing a fault detection or diagnosis system. We suggest a broad framework of important considerations when developing a data fault detection system and discuss some assumptions that can be made in this context. Based upon experience and previous work we define a series of features to consider when modeling sensor data for either fault detection or fault correction. We define three main headings of types of features and explore the effect that each type has on a sensor. Then we list all common faults that we have observed in actual sensor network deployments. We provide an example of how one may use such a taxonomy of faults