The Exploration of spatial relationships through a personal iconography

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Criminal RICO Enterprise via Salerno, Milken, and Cianci, The

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Variables in VBAC Success: A Retrospective Review of Trial of Labor After Cesarean (TOLAC) and Labor Support

For most of the 20th century, the saying “once a cesarean, always a cesarean” was a rule in the United States. Today, the National Institutes of Health (NIH) opposes the dictum and urges women to consider trial of labor after cesarean (TOLAC). However, the factors that lead to a successful outcome remain unclear, as research continues to be conducted in hopes of creating a predictive model for vaginal birth after cesarean (VBAC) success. The NIH’s request for more research in this area of obstetrics led to this retrospective cohort study of all TOLACs at Marin General Hospital (MGH) from 2000-2013. All labor trials were studied for patient demographics, details of labor, maternal and neonatal morbidities, insurance, and provider type. After confirming the quality of the data, verifying inclusion criteria and ignoring cases with missing data, a data set of 745 TOLACs with 13 explanatory variables of interest was prepared. A forward stepwise (Likelihood Ratio) binary logistic regression was run in IBM® SPSS® Statistics in order to create a model that could determine which variables were most predictive of delivery outcome in TOLAC patients. Ultimately, seven variables were predictive and were included in the model. Of the seven, the most predictive variable in determining VBAC success was provider type. The model concluded that a woman’s odds of having a successful VBAC were almost four times greater if she began her delivery with a certified nurse midwife, than if she began her deliver with a physician (odds ratio 0.27, 95% CI 0.17-0.44; \u3c 0.01). The results from this study mimic the results of other models, and introduce labor support as a key factor in predicting VBAC success

Rapid proliferation of pandemic research: implications for dual-use risks

The COVID-19 pandemic has demonstrated the world’s vulnerability to biological catastrophe and elicited unprecedented scientific efforts. Some of this work and its derivatives, however, present dual-use risks (i.e., potential harm from misapplication of beneficial research) that have largely gone unaddressed. For instance, gain-of-function studies and reverse genetics protocols may facilitate the engineering of concerning SARS-CoV-2 variants and other pathogens. The risk of accidental or deliberate release of dangerous pathogens may be increased by large-scale collection and characterization of zoonotic viruses undertaken in an effort to understand what enables animal-to-human transmission. These concerns are exacerbated by the rise of preprint publishing that circumvents a late-stage opportunity for dual-use oversight. To prevent the next global health emergency, we must avoid inadvertently increasing the threat of future biological events. This requires a nuanced and proactive approach to dual-use evaluation throughout the research life cycle, including the conception, funding, conduct, and dissemination of research

Improved understanding of biorisk for research involving microbial modification using annotated sequences of concern

Regulation of research on microbes that cause disease in humans has historically been focused on taxonomic lists of ‘bad bugs’. However, given our increased knowledge of these pathogens through inexpensive genome sequencing, 5 decades of research in microbial pathogenesis, and the burgeoning capacity of synthetic biologists, the limitations of this approach are apparent. With heightened scientific and public attention focused on biosafety and biosecurity, and an ongoing review by US authorities of dual-use research oversight, this article proposes the incorporation of sequences of concern (SoCs) into the biorisk management regime governing genetic engineering of pathogens. SoCs enable pathogenesis in all microbes infecting hosts that are ‘of concern’ to human civilization. Here we review the functions of SoCs (FunSoCs) and discuss how they might bring clarity to potentially problematic research outcomes involving infectious agents. We believe that annotation of SoCs with FunSoCs has the potential to improve the likelihood that dual use research of concern is recognized by both scientists and regulators before it occurs

Biosecurity and biodefense

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Assessing the uncertainties of model estimates of primary productivity in the tropical Pacific Ocean

Author Posting. © Elsevier B.V., 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Journal of Marine Systems 76 (2009): 113-133, doi:10.1016/j.jmarsys.2008.05.010.Depth-integrated primary productivity (PP) estimates obtained from satellite ocean color based models (SatPPMs) and those generated from biogeochemical ocean general circulation models (BOGCMs) represent a key resource for biogeochemical and ecological studies at global as well as regional scales. Calibration and validation of these PP models are not straightforward, however, and comparative studies show large differences between model estimates. The goal of this paper is to compare PP estimates obtained from 30 different models (21 SatPPMs and 9 BOGCMs) to a tropical Pacific PP database consisting of ~1000 14C measurements spanning more than a decade (1983- 1996). Primary findings include: skill varied significantly between models, but performance was not a function of model complexity or type (i.e. SatPPM vs. BOGCM); nearly all models underestimated the observed variance of PP, specifically yielding too few low PP (< 0.2 gC m-2d-2) values; more than half of the total root-mean-squared model-data differences associated with the satellite-based PP models might be accounted for by uncertainties in the input variables and/or the PP data; and the tropical Pacific database captures a broad scale shift from low biomass-normalized productivity in the 1980s to higher biomass-normalized productivity in the 1990s, which was not successfully captured by any of the models. This latter result suggests that interdecadal and global changes will be a significant challenge for both SatPPMs and BOGCMs. Finally, average root-mean-squared differences between in situ PP data on the equator at 140°W and PP estimates from the satellite-based productivity models were 58% lower than analogous values computed in a previous PP model comparison six years ago. The success of these types of comparison exercises is illustrated by the continual modification and improvement of the participating models and the resulting increase in model skill.This research was supported by a grant from the National Aeronautics and Space Agency Ocean Biology and Biogeochemistry program (NNG06GA03G), as well as by numerous other grants to the various participating investigator