Approximate Bayesian Computing for Spatial Extremes

Statistical analysis of max-stable processes used to model spatial extremes has been limited by the difficulty in calculating the joint likelihood function. This precludes all standard likelihood-based approaches, including Bayesian approaches. Here we present a Bayesian approach through the use of approximate Bayesian computing. This circumvents the need for a joint likelihood function and instead relies on simulations from the (unavailable) likelihood. This method is compared with an alternative approach based on the composite likelihood. When estimating the spatial dependence of extremes, we demonstrate that approximate Bayesian computing can provide estimates with a lower mean square error than the composite likelihood approach, though at an appreciably higher computational cost. As this approach very naturally incorporates parameter uncertainty into predictions, it is well suited for use in pricing weather derivatives to manage environmental risks. We discuss the construction and pricing of such weather derivatives. The method described utilizes results from spatial statistics and extreme value theory to first model extremes in the weather as a max-stable process, and then use these models to simulate payments for a general collection of weather derivatives. These simulations capture the spatial dependence of payments. Incorporating results from catastrophe ratemaking, we show how this method can be used to compute risk loads and premiums for weather derivatives which are renewal-additive. We illustrate the performance of the approximate Bayesian computing method and weather derivative pricing with applications to United States temperature data. The first application considers pricing weather derivatives for temperature extremes in the Midwestern United States. The second application demonstrates the use of the approximate Bayesian computing method in estimating the risk of crop loss due to an unlikely freeze event in northern Texas

Klebsiella pneumoniae peptide hijacks a Streptococcus pneumoniae permease to subvert pneumococcal growth and colonization.

Treatment of pneumococcal infections is limited by antibiotic resistance and exacerbation of disease by bacterial lysis releasing pneumolysin toxin and other inflammatory factors. We identified a previously uncharacterized peptide in the Klebsiella pneumoniae secretome, which enters Streptococcus pneumoniae via its AmiA-AliA/AliB permease. Subsequent downregulation of genes for amino acid biosynthesis and peptide uptake was associated with reduction of pneumococcal growth in defined medium and human cerebrospinal fluid, irregular cell shape, decreased chain length and decreased genetic transformation. The bacteriostatic effect was specific to S. pneumoniae and Streptococcus pseudopneumoniae with no effect on Streptococcus mitis, Haemophilus influenzae, Staphylococcus aureus or K. pneumoniae. Peptide sequence and length were crucial to growth suppression. The peptide reduced pneumococcal adherence to primary human airway epithelial cell cultures and colonization of rat nasopharynx, without toxicity. We identified a peptide with potential as a therapeutic for pneumococcal diseases suppressing growth of multiple clinical isolates, including antibiotic resistant strains, while avoiding bacterial lysis and dysbiosis

Adaptive and Behavioral Changes in Kynurenine 3-Monooxygenase Knockout Mice:Relevance to Psychotic Disorders

BACKGROUND: Kynurenine 3-monooxygenase converts kynurenine to 3-hydroxykynurenine, and its inhibition shunts the kynurenine pathway-which is implicated as dysfunctional in various psychiatric disorders-toward enhanced synthesis of kynurenic acid, an antagonist of both α7 nicotinic acetylcholine and N-methyl-D-aspartate receptors. Possibly as a result of reduced kynurenine 3-monooxygenase activity, elevated central nervous system levels of kynurenic acid have been found in patients with psychotic disorders, including schizophrenia. METHODS: In the present study, we investigated adaptive-and possibly regulatory-changes in mice with a targeted deletion of Kmo (Kmo-/-) and characterized the kynurenine 3-monooxygenase-deficient mice using six behavioral assays relevant for the study of schizophrenia. RESULTS: Genome-wide differential gene expression analyses in the cerebral cortex and cerebellum of these mice identified a network of schizophrenia- and psychosis-related genes, with more pronounced alterations in cerebellar tissue. Kynurenic acid levels were also increased in these brain regions in Kmo-/- mice, with significantly higher levels in the cerebellum than in the cerebrum. Kmo-/- mice exhibited impairments in contextual memory and spent less time than did controls interacting with an unfamiliar mouse in a social interaction paradigm. The mutant animals displayed increased anxiety-like behavior in the elevated plus maze and in a light/dark box. After a D-amphetamine challenge (5 mg/kg, intraperitoneal), Kmo-/- mice showed potentiated horizontal activity in the open field paradigm. CONCLUSIONS: Taken together, these results demonstrate that the elimination of Kmo in mice is associated with multiple gene and functional alterations that appear to duplicate aspects of the psychopathology of several neuropsychiatric disorders

Association Between Anterior Nasal and Plasma SARS-CoV-2 RNA Levels and Hospitalization or Death in Nonhospitalized Adults With Mild-to-Moderate COVID-19

BackgroundThere is little information regarding severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA as a predictor for clinical outcomes in outpatients with mild-to-moderate coronavirus disease 2019 (COVID-19).MethodsAnterior nasal (AN) and plasma SARS-CoV-2 RNA data from 2115 nonhospitalized adults who received monoclonal antibodies (mAbs) or placebo in the ACTIV-2/A5401 trial were analyzed for associations with hospitalization or death.ResultsOne hundred two participants were hospitalized or died through 28 days of follow-up. Higher day 0 (pretreatment) AN RNA was associated with increasing risk of hospitalization/death (risk ratio [RR], 1.24 per log10 copies/mL [95% confidence interval {CI}, 1.04-1.49]) among placebo recipients, ranging from 3% to 16% for <2 to ≥6 log10 copies/mL. Although only 1% had quantifiable levels, there was a similar trend across day 0 plasma RNA categories. Higher day 3 AN RNA was associated with subsequent hospitalization/death among placebo recipients (RR, 1.42 per log10 copies/mL [95% CI, 1.00-2.03]), but not mAb recipients (RR, 1.02 per log10 copies/mL [95% CI, 0.68-1.56]). The proportion of treatment effect (reduction in hospitalizations/deaths after day 3 for mAb vs placebo) explained by day 3 AN RNA was 8%.ConclusionsSARS-CoV-2 RNA levels are predictive of hospitalization/death in the natural history setting, but AN RNA levels may not be a reliable surrogate marker of mAb treatment effect in COVID-19 trials. Clinical Trials Registration. NCT04518410

Millennial changes in North American wildfire and soil activity over the last glacial cycle

Climate changes in the North Atlantic region during the last glacial cycle were dominated by the slow waxing and waning of the North American ice sheet as well as by intermittent Dansgaard-­‐Oeschger (DO) events. However prior to the last deglaciation, little is known about the response of North American vegetation to such rapid climate changes and especially about the response of biomass burning, an important factor for regional changes in radiative forcing. Here we use continuous, high-­‐resolution ammonium (NH4+) records derived from the NGRIP and GRIP ice cores to document both North American NH4+ background emissions from soils and wildfire frequency over the last 110,000 yr. Soil emissions increased on orbital timescales with warmer climate, related to the northward expansion of vegetation due to reduced ice-­‐covered areas. During Marine Isotope Stage (MIS) 3 DO warm events, a higher fire recurrence rate is recorded, while NH4+ soil emissions rose only slowly during longer interstadial warm periods, in line with slow ice sheet shrinkage and delayed ecosystem changes. Our results indicate that sudden warming events had little impact on NH4+ soil emissions and NH4+ aerosol transport to Greenland during the glacial but triggered a significant increase in the frequency of fire occurrence.This paper has greatly benefitted from the Sir Nicholas Shackleton fellowship, Clare Hall, University of Cambridge, U.K., awarded to HF in 2014. The Division for Climate and Environmental Physics, Physics Institute, University of Bern acknowledges the long-­‐term financial support of ice core research by the Swiss National Science Foundation (SNSF) and the Oeschger Centre for Climate Change Research. EW is supported by a Royal Society professorship. NGRIP is directed and organized by the Department of Geophysics at the Niels Bohr Institute for Astronomy, Physics and Geophysics, University of Copenhagen. It is supported by funding agencies in Denmark (SNF), Belgium (FNRS-­‐CFB), France (IPEV and INSU/CNRS), Germany (AWI), Iceland (RannIs), Japan (MEXT), Sweden (SPRS), Switzerland (SNSF) and the USA (NSF, Office of Polar Programs).This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/ngeo249

A Baseline for the Multivariate Comparison of Resting-State Networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease

Long COVID After Bamlanivimab Treatment

BackgroundProspective evaluations of long COVID in outpatients with coronavirus disease 2019 (COVID-19) are lacking. We aimed to determine the frequency and predictors of long COVID after treatment with the monoclonal antibody bamlanivimab in ACTIV-2/A5401.MethodsData were analyzed from participants who received bamlanivimab 700 mg in ACTIV-2 from October 2020 to February 2021. Long COVID was defined as the presence of self-assessed COVID symptoms at week 24. Self-assessed return to pre-COVID health was also examined. Associations were assessed by regression models.ResultsAmong 506 participants, median age was 51 years. Half were female, 5% Black/African American, and 36% Hispanic/Latino. At 24 weeks, 18% reported long COVID and 15% had not returned to pre-COVID health. Smoking (adjusted risk ratio [aRR], 2.41 [95% confidence interval {CI}, 1.34- 4.32]), female sex (aRR, 1.91 [95% CI, 1.28-2.85]), non-Hispanic ethnicity (aRR, 1.92 [95% CI, 1.19-3.13]), and presence of symptoms 22-28 days posttreatment (aRR, 2.70 [95% CI, 1.63-4.46]) were associated with long COVID, but nasal severe acute respiratory syndrome coronavirus 2 RNA was not.ConclusionsLong COVID occurred despite early, effective monoclonal antibody therapy and was associated with smoking, female sex, and non-Hispanic ethnicity, but not viral burden. The strong association between symptoms 22-28 days after treatment and long COVID suggests that processes of long COVID start early and may need early intervention.Clinical trials registrationNCT04518410