Effect of Ceftazidime on Systemic Cytokine Concentrations in Rats

The effect of a single dose of ceftazidime on circulating concentrations of interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) in a rat model of sepsis was studied. IL-6 concentrations were significantly elevated (100 to 200 times the baseline) 6 h after ceftazidime administration in both septic and nonseptic (control) rats. TNF-α concentrations increased significantly in nonseptic (∼40 times the baseline) rats but not septic (∼2 to 3 times the baseline) rats. Ceftazidime administration was not associated with an increase in endotoxin concentrations. These findings suggest that ceftazidime modulation of proinflammatory cytokine concentrations may be independent of its antimicrobial properties

Safety and Pharmacokinetics of Multiple Doses of Intravenous Ofloxacin in Healthy Volunteers

The safety and pharmacokinetics of ofloxacin in 48 healthy male volunteers were studied in a two-center, randomized, double-blind, placebo-controlled study. Ofloxacin (200 or 400 mg) or placebo was administered as 1-h infusions every 12 h for 7 days. Plasma ofloxacin concentrations were measured by high-performance liquid chromatography. Mean harmonic half-lives ranged from 4.28 to 4.98 h in the 200-mg dosing group and from 5.06 to 6.67 h in the 400-mg dosing group. Intragroup comparisons of trough plasma concentration-versus-time data from study days 2 through 7 revealed that steady state was achieved by day 2 of both multiple-dose regimens. Intergroup comparisons of mean harmonic half-lives, the areas under the concentration-time curve from 0 to 12 and 0 to 60 h, clearance, and apparent volume of distribution (area method) revealed that the pharmacokinetics of ofloxacin are dose independent. Both ofloxacin dosage regimens appeared to be reasonably well tolerated. The two dosage regimens of ofloxacin, 200 or 400 mg every 12 h, appear to be safe and provide serum drug concentrations in excess of the MICs for most susceptible pathogens over the entire dosing interval

ENMTools 1.0: an R package for comparative ecological biogeography

The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages

Validation of Statistical Sampling Algorithms in Visual Sample Plan (VSP): Summary Report

The U.S. Department of Homeland Security, Office of Technology Development (OTD) contracted with a set of U.S. Department of Energy national laboratories, including the Pacific Northwest National Laboratory (PNNL), to write a Remediation Guidance for Major Airports After a Chemical Attack. The report identifies key activities and issues that should be considered by a typical major airport following an incident involving release of a toxic chemical agent. Four experimental tasks were identified that would require further research in order to supplement the Remediation Guidance. One of the tasks, Task 4, OTD Chemical Remediation Statistical Sampling Design Validation, dealt with statistical sampling algorithm validation. This report documents the results of the sampling design validation conducted for Task 4. In 2005, the Government Accountability Office (GAO) performed a review of the past U.S. responses to Anthrax terrorist cases. Part of the motivation for this PNNL report was a major GAO finding that there was a lack of validated sampling strategies in the U.S. response to Anthrax cases. The report (GAO 2005) recommended that probability-based methods be used for sampling design in order to address confidence in the results, particularly when all sample results showed no remaining contamination. The GAO also expressed a desire that the methods be validated, which is the main purpose of this PNNL report. The objective of this study was to validate probability-based statistical sampling designs and the algorithms pertinent to within-building sampling that allow the user to prescribe or evaluate confidence levels of conclusions based on data collected as guided by the statistical sampling designs. Specifically, the designs found in the Visual Sample Plan (VSP) software were evaluated. VSP was used to calculate the number of samples and the sample location for a variety of sampling plans applied to an actual release site. Most of the sampling designs validated are probability based, meaning samples are located randomly (or on a randomly placed grid) so no bias enters into the placement of samples, and the number of samples is calculated such that IF the amount and spatial extent of contamination exceeds levels of concern, at least one of the samples would be taken from a contaminated area, at least X% of the time. Hence, "validation" of the statistical sampling algorithms is defined herein to mean ensuring that the "X%" (confidence) is actually met

An environmental sampling model for combining judgment and randomly placed samples

In the event of the release of a lethal agent (such as anthrax) inside a building, law enforcement and public health responders take samples to identify and characterize the contamination. Sample locations may be rapidly chosen based on available incident details and professional judgment. To achieve greater confidence of whether or not a room or zone was contaminated, or to certify that detectable contamination is not present after decontamination, we consider a Bayesian model for combining the information gained from both judgment and randomly placed samples. We investigate the sensitivity of the model to the parameter inputs and make recommendations for its practical use

Modeling Transport in Gas Chromatography Columns for the Micro-ChemLab

The gas chromatography (GC) column is a critical component in the microsystem for chemical detection ({mu}ChemLab{trademark}) being developed at Sandia. The goal is to etch a meter-long GC column onto a 1-cm{sup 2} silicon chip while maintaining good chromatographic performance. Our design strategy is to use a modeling and simulation approach. We have developed an analytical tool that models the transport and surface interaction process to achieve an optimized design of the GC column. This analytical tool has a flow module and a separation module. The flow module considers both the compressibility and slip flow effects that may significantly influence the gas transport in a long and narrow column. The separation module models analyte transport and physico-chemical interaction with the coated surface in the GC column. It predicts the column efficiency and performance. Results of our analysis will be presented in this paper. In addition to the analytical tool, we have also developed a time-dependent adsorption/desorption model and incorporated this model into a computational fluid dynamics (CFD) code to simulate analyte transport and separation process in GC columns. CFD simulations can capture the complex three-dimensional flow and transport dynamics, whereas the analytical tool cannot. Different column geometries have been studied, and results will be presented in this paper. Overall we have demonstrated that the modeling and simulation approach can guide the design of the GC column and will reduce the number of iterations in the device development

Correlation between investment in sexual traits and valve sexual dimorphism in Cyprideis species (Ostracoda)

Assessing the long-term macroevolutionary consequences of sexual selection has been hampered by the difficulty of studying this process in the fossil record. Cytheroid ostracodes offer an excellent system to explore sexual selection in the fossil record because their readily fossilized carapaces are sexually dimorphic. Specifically, males are relatively more elongate than females in this superfamily. This sexual shape difference is thought to arise so that males carapaces can accommodate their very large copulatory apparatus, which can account for up to one-third of body volume. Here we test this widely held explanation for sexual dimorphism in cytheroid ostracodes by correlating investment in male genitalia, a trait in which sexual selection is seen as the main evolutionary driver, with sexual dimorphism of carapace in the genus Cyprideis. We analyzed specimens collected in the field (C. salebrosa, USA; C. torosa, UK) and from collections of the National Museum of Natural History, Washington, DC (C. mexicana). We digitized valve outlines in lateral view to obtain measures of size (valve area) and shape (elongation, measured as length to height ratio), and obtained several dimensions from two components of the hemipenis: the muscular basal capsule, which functions as a sperm pump, and the section that includes the intromittent organ (terminal extension). In addition to the assessment of this primary sexual trait, we also quantified two dimensions of the male secondary sexual trait-where the transformed right walking leg functions as a clasping organ during mating. We also measured linear dimensions from four limbs as indicators of overall (soft-part) body size, and assessed allometry of the soft anatomy. We observed significant correlations in males between valve size, but not elongation, and distinct structural parts of the hemipenis, even after accounting for their shared correlation with overall body size. We also found weak but significant positive correlation between valve elongation and the degree of sexual dimorphism of the walking leg, but only in C. torosa. The correlation between the hemipenis parts, especially basal capsule size and male valve size dimorphism suggests that sexual selection on sperm size, quantity, and/or efficiency of transfer may drive sexual size dimorphism in these species, although we cannot exclude other aspects of sexual and natural selection

The Genetic Signatures of Noncoding RNAs

The majority of the genome in animals and plants is transcribed in a developmentally regulated manner to produce large numbers of non–protein-coding RNAs (ncRNAs), whose incidence increases with developmental complexity. There is growing evidence that these transcripts are functional, particularly in the regulation of epigenetic processes, leading to the suggestion that they compose a hitherto hidden layer of genomic programming in humans and other complex organisms. However, to date, very few have been identified in genetic screens. Here I show that this is explicable by an historic emphasis, both phenotypically and technically, on mutations in protein-coding sequences, and by presumptions about the nature of regulatory mutations. Most variations in regulatory sequences produce relatively subtle phenotypic changes, in contrast to mutations in protein-coding sequences that frequently cause catastrophic component failure. Until recently, most mapping projects have focused on protein-coding sequences, and the limited number of identified regulatory mutations have been interpreted as affecting conventional cis-acting promoter and enhancer elements, although these regions are often themselves transcribed. Moreover, ncRNA-directed regulatory circuits underpin most, if not all, complex genetic phenomena in eukaryotes, including RNA interference-related processes such as transcriptional and post-transcriptional gene silencing, position effect variegation, hybrid dysgenesis, chromosome dosage compensation, parental imprinting and allelic exclusion, paramutation, and possibly transvection and transinduction. The next frontier is the identification and functional characterization of the myriad sequence variations that influence quantitative traits, disease susceptibility, and other complex characteristics, which are being shown by genome-wide association studies to lie mostly in noncoding, presumably regulatory, regions. There is every possibility that many of these variations will alter the interactions between regulatory RNAs and their targets, a prospect that should be borne in mind in future functional analyses

Bayes factors for mixed models: A discussion

van Doorn et al. (2021) outlined various questions that arise when conducting Bayesian model comparison for mixed effects models. Seven response articles offered their own perspective on the preferred setup for mixed model comparison, on the most appropriate specification of prior distributions, and on the desirability of default recommendations. This article presents a round-table discussion that aims to clarify outstanding issues, explore common ground, and outline practical considerations for any researcher wishing to conduct a Bayesian mixed effects model comparison

Bayesian analysis of biogeography when the number of areas is large.

Historical biogeography is increasingly studied from an explicitly statistical perspective, using stochastic models to describe the evolution of species range as a continuous-time Markov process of dispersal between and extinction within a set of discrete geographic areas. The main constraint of these methods is the computational limit on the number of areas that can be specified. We propose a Bayesian approach for inferring biogeographic history that extends the application of biogeographic models to the analysis of more realistic problems that involve a large number of areas. Our solution is based on a data-augmentation approach, in which we first populate the tree with a history of biogeographic events that is consistent with the observed species ranges at the tips of the tree. We then calculate the likelihood of a given history by adopting a mechanistic interpretation of the instantaneous-rate matrix, which specifies both the exponential waiting times between biogeographic events and the relative probabilities of each biogeographic change. We develop this approach in a Bayesian framework, marginalizing over all possible biogeographic histories using Markov chain Monte Carlo (MCMC). Besides dramatically increasing the number of areas that can be accommodated in a biogeographic analysis, our method allows the parameters of a given biogeographic model to be estimated and different biogeographic models to be objectively compared. Our approach is implemented in the program, BayArea