HIDROCARBONETOS EM SOLOS E SEDIMENTOS DO ENTORNO DA ESTAÇÃO ANTÁRTICA BRASILEIRA COMANDANTE FERRAZ

Despite being remote and uninhabited, the Antartic Continent is not free from the impact of human presence. Among the possible impacts, the presence of oilderived hydrocarbons has been a matter of concern. The ships operating in the region and the transportation of diesel oil to the power plant of Comandante Ferraz Station (EACF) have contributed to the introduction of oil hydrocartbons into the environment. This work, as part of Criosols project, aimed to evaluate the presence of oil hydrocarbons at the surrounds of EACF. Soils samples were collected in a prestablished area during the summer of 2003. The aliphatic and aromatic polycyclic hydrocarbons were extracted by Soxhlet. The hydrocarbons were isolated, identified and quantified by CGMS. The aliphatic and aromatic polycyclic hydrocarbons found in the soil and sediments were similar to those found in the diesel fuel and their areas of dispersion, mainly in the sub-surface, were larger than those of the volatile aromatic hydrocarbons. The aliphatic hydrocarbons with the highest concentrations were pentadecane, octadecane and pentacosane. Among the aromatic polycyclic hydrocarbons, acenaftene, fluorantene and chysene had the highest concentrations and a larger area of dispersion.    Por mais remoto e desabitado que o continente Antártico seja, ele não está livre do impacto da presença humana. Dentre os possíveis impactos, a presença de hidrocarbonetos derivados de petróleo têm sido objeto de preocupação. A ocorrência de embarcações operando na região e o transporte de óleo diesel para os geradores da Estação Antártica Comandante Ferraz (EACF) tem contribuído para a introdução de hidrocarbonetos de petróleo no ambiente. Este trabalho, como parte do projeto Criossolos, teve como objetivo avaliar a presença de hidrocarbonetos de petróleo no entorno da EACF. As amostras de solos foram coletadas numa grade rígida durante o verão de 2003. Os hidrocarbonetos alifáticos e policíclicos aromáticos foram extraídos por Soxhlet. Os hidrocarbonetos foram separados, identificados e quantificados por meio de CGMS. Os hidrocarbonetos alifáticos e policíclicos aromáticos encontrados no solo e sedimentos assemelharam-se àqueles do diesel e a sua área de dispersão, principalmente em sub-superfície, foi maior que aquela dos hidrocarbonetos voláteis. Os hidrocarbonetos alifáticos com maiores concentrações foram o pentadecano, octadecano e o pentacosano. Dentre os hidrocarbonetos policíclicos aromáticos os compostos acenafteno, fluoranteno e criseno obtiveram maiores concentrações e uma maior área de dispersão

Renal Sodium Gradient Orchestrates a Dynamic Antibacterial Defense Zone.

Lower urinary tract infections are among the most common human bacterial infections, but extension to the kidneys is rare. This has been attributed to mechanical forces, such as urine flow, that prevent the ascent of bladder microbes. Here, we show that the regional hypersalinity, required for the kidney's urine-concentrating function, instructs epithelial cells to produce chemokines that localize monocyte-derived mononuclear phagocytes (MNPs) to the medulla. This hypersaline environment also increases the intrinsic bactericidal and neutrophil chemotactic activities of MNPs to generate a zone of defense. Because MNP positioning and function are dynamically regulated by the renal salt gradient, we find that patients with urinary concentrating defects are susceptible to kidney infection. Our work reveals a critical accessory role for the homeostatic function of a vital organ in optimizing tissue defense

Explaining co-movements between equity and CDS bid-ask spreads

In this paper I show that the co-movements between bid-ask spreads of equities and credit default swaps vary over time and increase over crisis periods. The co-movements are strongly related to systematic risk factors and to the theoretical debt-to-equity hedge ratio. I document that hedging and asymmetric information, besides higher funding costs and market volatility risk, are driving factors of the commonality and are significantly priced in CDS bid-ask spreads

GLRB allelic variation associated with agoraphobic cognitions, increased startle response and fear network activation : a potential neurogenetic pathway to panic disorder

The molecular genetics of panic disorder (PD) with and without agoraphobia (AG) are still largely unknown and progress is hampered by small sample sizes. We therefore performed a genome-wide association study with a dimensional, PD/AG - related anxiety phenotype based on the Agoraphobia Cognition Questionnaire (ACQ) in a sample of 1,370 healthy German volunteers of the CRC TRR58 MEGA study wave 1. A genome-wide significant association was found between ACQ and single non-coding nucleotide variants of the GLRB gene (rs78726293, p=3.3x10-8; rs191260602, p=3.9x10-8). We followed up on this finding in a larger dimensional ACQ sample (N=2,547) and in independent samples with a dichotomous AG phenotype based on the Symptoms Checklist (SCL-90; N=3,845) and a case control sample with the categorical phenotype PD/AG (Ncombined =1,012) obtaining highly significant p-values also for GLRB single nucleotide variants rs17035816 (p=3.8x10-4) and rs7688285 (p=7.6x10-5). GLRB gene expression was found to be modulated by rs7688285 in brain tissue as well as cell culture. Analyses of intermediate PD/AG phenotypes demonstrated increased startle reflex and increased fear network as well as general sensory activation by GLRB risk gene variants rs78726293, rs191260602, rs17035816 and rs7688285. Partial Glrb knockout-mice demonstrated an agoraphobic phenotype. In conjunction withthe clinical observation that rare coding GLRB gene mutations are associated with the neurological disorder hyperekplexia characterized by a generalized startle reaction and agoraphobic behavior, our data provide evidence that non-coding, though functional GLRB gene polymorphisms may predispose to PD by increasing startle response and agoraphobic cognitions.PostprintPeer reviewe

Beyond ecosystem modeling: a roadmap to community cyberinfrastructure for ecological data‐model integration

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks to informing models with observations have reduced our capacity to fully exploit the growing volume and variety of available data. Here, we take a critical look at the information infrastructure that connects ecosystem modeling and measurement efforts, and propose a roadmap to community cyberinfrastructure development that can reduce the divisions between empirical research and modeling and accelerate the pace of discovery. A new era of data‐model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, including both modelers and empiricists. This roadmap focuses on five key opportunities for community tools: the underlying foundationsof community cyberinfrastructure; data ingest; calibration of models to data; model‐data benchmarking; and data assimilation and ecological forecasting. This community‐driven approach is key to meeting the pressing needs of science and society in the 21st century

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease