Twelve percent of 853 California ground squirrels (Spermophilus beecheyi) from six different geographic locations in Kern County, Calif., were found to be shedding on average 44,482 oocysts g of feces(-1). The mean annual environmental loading rate of Cryptosporidium oocysts was 57,882 oocysts squirrel(-1) day(-1), with seasonal patterns of fecal shedding ranging from <10,000 oocysts squirrel(-1) day(-1) in fall, winter, and spring to levels of 2 x 10(5) oocysts squirrel(-1) day(-1) in summer. Juveniles were about twice as likely as adult squirrels to be infected and shed higher concentrations of oocysts than adults did, with particularly high levels of infection and shedding being found among juvenile male squirrels. Based on DNA sequencing of a portion of the 18S small-subunit rRNA gene, there existed three genotypes of Cryptosporidium species in these populations of squirrels (Sbey03a, Sbey03b, and Sbey03c; accession numbers AY462231 to AY462233, respectively). These unique DNA sequences were most closely related (96 to 97% homology) to porcine C. parvum (AF115377) and C. wrairi (AF115378). Inoculating BALB/c neonatal mice with up to 10,000 Sbey03b or Sbey03c fresh oocysts from different infected hosts did not produce detectable levels of infection, suggesting that this common genotype shed by California ground squirrels is not infectious for mice and may constitute a new species of Cryptosporidium.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC525134 | PMC |
| http://dx.doi.org/10.1128/AEM.70.11.6748-6752.2004 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Tensor Network Computations That Capture Strict Variationality, Volume Law Behavior, and the Efficient Representation of Neural Network States.
Phys Rev Lett
December 2024
California Institute of Technology, Division of Chemistry and Chemical Engineering, Pasadena, California 91125, USA.
We introduce a change of perspective on tensor network states that is defined by the computational graph of the contraction of an amplitude. The resulting class of states, which we refer to as tensor network functions, inherit the conceptual advantages of tensor network states while removing computational restrictions arising from the need to converge approximate contractions. We use tensor network functions to compute strict variational estimates of the energy on loopy graphs, analyze their expressive power for ground states, show that we can capture aspects of volume law time evolution, and provide a mapping of general feed-forward neural nets onto efficient tensor network functions.
View Article and Find Full Text PDFValidation of a Deep Learning Tool for Detection of Incidental Vertebral Compression Fractures.
J Comput Assist Tomogr
January 2025
Department of Radiological Sciences.
Objective: This study evaluated the performance of a deep learning-based vertebral compression fracture (VCF) detection tool in patients with incidental VCF. The purpose of this study was to validate this tool across multiple sites and multiple vendors.
Methods: This was a retrospective, multicenter, multinational blinded study using anonymized chest and abdominal CT scans performed for indications other than VCF in patients ≥50 years old.
Design for Telecom-Wavelength Quantum Emitters in Silicon Based on Alkali-Metal-Saturated Vacancy Complexes.
ACS Nano
January 2025
Division of Physical Sciences, College of Letters and Science, University of California Los Angeles, Los Angeles, California 90095, United States.
Defect emitters in silicon are promising contenders as building blocks of solid-state quantum repeaters and sensor networks. Here, we investigate a family of possible isoelectronic emitter defect complexes from a design standpoint. We show that the identification of key physical effects on quantum defect state localization can guide the search for telecom-wavelength emitters.
View Article and Find Full Text PDFRethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer's disease.
Brain Inform
January 2025
Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer's disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals.
View Article and Find Full Text PDFEpisodic retrieval for model-based evaluation in sequential decision tasks.
Psychol Rev
January 2025
Department of Cognitive Science, University of California, San Diego.
It has long been hypothesized that episodic memory supports adaptive decision making by enabling mental simulation of future events. Yet, attempts to characterize this process are surprisingly rare. On one hand, memory research is often carried out in settings that are far removed from ecological contexts of decision making.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!