Can we continue research in splenectomized dogs? Mycoplasma haemocanis: Old problem - New insight

We report the appearance of a Mycoplasma haemocanis infection in laboratory dogs, which has been reported previously, yet, never before in Europe. Outbreak of the disease was triggered by a splenectomy intended to prepare the dogs for a hemorrhagic shock study. The clinical course of the dogs was dramatic including anorexia and hemolytic anemia. Treatment included allogeneic transfusion, prednisone, and oxytetracycline. Systematic follow-up (n=12, blood smears, antibody testing and specific polymerase chain reaction) gives clear evidence that persistent eradication of M. haemocanis is unlikely. We, therefore, had to abandon the intended shock study. In the absence of effective surveillance and screening for M. haemocanis, the question arises whether it is prudent to continue shock research in splenectomized dogs. Copyright (C) 2004 S. Karger AG, Basel

The Physics of Star Cluster Formation and Evolution

© 2020 Springer-Verlag. The final publication is available at Springer via https://doi.org/10.1007/s11214-020-00689-4.Star clusters form in dense, hierarchically collapsing gas clouds. Bulk kinetic energy is transformed to turbulence with stars forming from cores fed by filaments. In the most compact regions, stellar feedback is least effective in removing the gas and stars may form very efficiently. These are also the regions where, in high-mass clusters, ejecta from some kind of high-mass stars are effectively captured during the formation phase of some of the low mass stars and effectively channeled into the latter to form multiple populations. Star formation epochs in star clusters are generally set by gas flows that determine the abundance of gas in the cluster. We argue that there is likely only one star formation epoch after which clusters remain essentially clear of gas by cluster winds. Collisional dynamics is important in this phase leading to core collapse, expansion and eventual dispersion of every cluster. We review recent developments in the field with a focus on theoretical work.Peer reviewe

Taxonomic considerations in listing subspecies under the U.S. Endangered Species Act

The U.S. Endangered Species Act (ESA) allows listing of subspecies and other groupings below the rank of species. This provides the U.S. Fish and Wildlife Service and the National Marine Fisheries Service with a means to target the most critical unit in need of conservation. Although roughly one-quarter of listed taxa are subspecies, these management agencies are hindered by uncertainties about taxonomic standards during listing or delisting activities. In a review of taxonomic publications and societies, we found few subspecies lists and none that stated standardized criteria for determining subspecific taxa. Lack of criteria is attributed to a centuries-old debate over species and subspecies concepts. Nevertheless, the critical need to resolve this debate for ESA listings led us to propose that minimal biological criteria to define disjunct subspecies (legally or taxonomically) should include the discreteness and significance criteria of distinct population segments (as defined under the ESA). Our subspecies criteria are in stark contrast to that proposed by supporters of the phylogenetic species concept and provide a clear distinction between species and subspecies. Efforts to eliminate or reduce ambiguity associated with subspecies-level classifications will assist with ESA listing decisions. Thus, we urge professional taxonomic societies to publish and periodically update peer-reviewed species and subspecies lists. This effort must be paralleled throughout the world for efficient taxonomic conservation to take place

Probability of Streamflow Permanence Model (PROSPER): A spatially continuous model of annual streamflow permanence throughout the Pacific Northwest

The U.S. Geological Survey (USGS) has developed the PRObability of Streamflow PERmanence (PROSPER) model, a GIS raster-based empirical model that provides streamflow permanence probabilities (probabilistic predictions) of a stream channel having year-round flow for any unregulated and minimally-impaired stream channel in the Pacific Northwest region, U.S. The model provides annual predictions for 2004–2016 at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions and static physiographic variables associated with the upstream basin. Predictions correspond to any pixel on the channel network consistent with the medium resolution National Hydrography Dataset channel network stream grid. Total annual precipitation and percent forest cover were consistently the most important predictor variables among global and most subregional models, which had error rates between 17 and 22%. Probabilities were converted to wet and dry streamflow permanence classes with an associated confidence. Wet and dry classifications were used to derive descriptors that characterize the statistical and spatial distribution of streamflow permanence in three focal basins. Predicted dry channel segments account for 52–92% of the stream network across the three focal basins; streamflow permanence decreased during climatically drier years. Predictions are publicly available through the USGS StreamStats platform. Results demonstrate the utility of the PROSPER model as a tool for identifying areas that may be resilient or sensitive to drought conditions, allowing for management efforts that target protecting critical reaches. Importantly, PROSPER’s successful predictive performance can be improved with new datasets of streamflow permanence underscoring the importance of field observations. Keywords: Flow permanence, Intermittent, Ephemeral, Perennial, Non-perennial, Stream

Sensor Mining for User Behavior Profiling in Intelligent Environments

The proposed system exploits sensor mining methodologies to profile user behaviors patterns in an intelligent workplace. The work is based in the assumption that users’ habit profiles are implicitly described by sensory data, which explicitly show the consequences of users’ actions over the environment state. Sensor data are analyzed in order to infer relationships of interest between environmental variables and the user, detecting in this way behavior profiles. The system is designed for a workplace equipped in the context of Sensor9k, a project carried out at the Department of Computer Science of Palermo University