Approaches to estimating the universe of natural history collections data

This contribution explores the problem of recognizing and measuring the universe of specimen-level data existing in Natural History Collections around the world, in absence of a complete, world-wide census or register. Estimates of size seem necessary to plan for resource allocation for digitization or data capture, and may help represent how many vouchered primary biodiversity data (in terms of collections, specimens or curatorial units) might remain to be mobilized. Three general approaches are proposed for further development, and initial estimates are given. Probabilistic models involve crossing data from a set of biodiversity datasets, finding commonalities and estimating the likelihood of totally obscure data from the fraction of known data missing from specific datasets in the set. Distribution models aim to find the underlying distribution of collections’ compositions, figuring out the occult sector of the distributions. Finally, case studies seek to compare digitized data from collections known to the world to the amount of data known to exist in the collection but not generally available or not digitized. Preliminary estimates range from 1.2 to 2.1 gigaunits, of which a mere 3% at most is currently web-accessible through GBIF’s mobilization efforts. However, further data and analyses, along with other approaches relying more heavily on surveys, might change the picture and possibly help narrow the estimate. In particular, unknown collections not having emerged through literature are the major source of uncertainty

Regulation of human lung fibroblast alpha 1(I) procollagen gene expression by tumor necrosis factor alpha, interleukin-1 beta, and prostaglandin E2.

We investigated the participation of prostaglandin (PG) E2 in the regulation of the alpha 1(I) procollagen gene expression by tumor necrosis factor alpha (TNF alpha), and interleukin-1 beta (IL-1 beta) in normal adult human lung fibroblasts. TNF alpha (100 units/ml) and IL-1 beta (100 units/ml) stimulated the production of PGE2 and caused a dose-dependent inhibition of up to 54 and 66%, respectively, of the production of type I procollagen. Preincubation of cultures with indomethacin partially reversed the inhibition of procollagen production induced by the cytokines. Cytokine-stimulated endogenous fibroblast PG accounted for 35 and 68% of the inhibition induced by TNF alpha and IL-1 beta, respectively. Steady-state mRNA levels for alpha 1(I) procollagen paralleled the changes in collagen production. The transcription rate of the alpha 1(I) procollagen gene was reduced by 58% by TNF alpha and by 43% by IL-1 beta. Cytokine-stimulated endogenous PG production accounted for half of these effects. These results indicate that TNF alpha and IL-1 beta inhibit the expression of the alpha 1(I) procollagen gene in human lung fibroblasts at the transcriptional level by a PGE2-independent effect as well as through the effect of endogenous fibroblast PGE2 released under the stimulus of the cytokines

Water Detection Based on Sky Reflections

This software has been designed to detect water bodies that are out in the open on cross-country terrain at mid- to far-range (approximately 20 100 meters), using imagery acquired from a stereo pair of color cameras mounted on a terrestrial, unmanned ground vehicle (UGV). Non-traversable water bodies, such as large puddles, ponds, and lakes, are indirectly detected by detecting reflections of the sky below the horizon in color imagery. The appearance of water bodies in color imagery largely depends on the ratio of light reflected off the water surface to the light coming out of the water body. When a water body is far away, the angle of incidence is large, and the light reflected off the water surface dominates. We have exploited this behavior to detect water bodies out in the open at mid- to far-range. When a water body is detected at far range, a UGV s path planner can begin to look for alternate routes to the goal position sooner, rather than later. As a result, detecting water hazards at far range generally reduces the time required to reach a goal position during autonomous navigation. This software implements a new water detector based on sky reflections that geometrically locates the exact pixel in the sky that is reflecting on a candidate water pixel on the ground, and predicts if the ground pixel is water based on color similarity and local terrain feature

Multi-Sensor Mud Detection

Robust mud detection is a critical perception requirement for Unmanned Ground Vehicle (UGV) autonomous offroad navigation. A military UGV stuck in a mud body during a mission may have to be sacrificed or rescued, both of which are unattractive options. There are several characteristics of mud that may be detectable with appropriate UGV-mounted sensors. For example, mud only occurs on the ground surface, is cooler than surrounding dry soil during the daytime under nominal weather conditions, is generally darker than surrounding dry soil in visible imagery, and is highly polarized. However, none of these cues are definitive on their own. Dry soil also occurs on the ground surface, shadows, snow, ice, and water can also be cooler than surrounding dry soil, shadows are also darker than surrounding dry soil in visible imagery, and cars, water, and some vegetation are also highly polarized. Shadows, snow, ice, water, cars, and vegetation can all be disambiguated from mud by using a suite of sensors that span multiple bands in the electromagnetic spectrum. Because there are military operations when it is imperative for UGV's to operate without emitting strong, detectable electromagnetic signals, passive sensors are desirable. JPL has developed a daytime mud detection capability using multiple passive imaging sensors. Cues for mud from multiple passive imaging sensors are fused into a single mud detection image using a rule base, and the resultant mud detection is localized in a terrain map using range data generated from a stereo pair of color cameras

Assessment of user needs of primary biodiversity data: Analysis, concerns, and challenges

A Content Needs Assessment (CNA) survey has been conducted in order to determine what GBIF-mediated data users may be using, what they would be using if available, and what they need in terms of primary biodiversity data records. The survey was launched in 2009 in six languages, and collected more than 700 individual responses. Analysis of the responses showed some lack of awareness about the availability of accessible primary data, and pointed out some types of data in high demand for linking to distribution and taxonomical data now derived from the GBIF cache. A notable example was linkages to molecular data. Also, the CNA survey uncovered some biases in the design of user needs surveys, by showing demographic and linguistic effects that may have influenced the distribution of responses received in analogous surveys conducted at the global scale

Using Thermal Radiation in Detection of Negative Obstacles

A method of automated detection of negative obstacles (potholes, ditches, and the like) ahead of ground vehicles at night involves processing of imagery from thermal-infrared cameras aimed at the terrain ahead of the vehicles. The method is being developed as part of an overall obstacle-avoidance scheme for autonomous and semi-autonomous offroad robotic vehicles. The method could also be applied to help human drivers of cars and trucks avoid negative obstacles -- a development that may entail only modest additional cost inasmuch as some commercially available passenger cars are already equipped with infrared cameras as aids for nighttime operation