Successful treatment of a solitary skull metastasis in a child with Wilms' Tumor

This report presents the successful treatment of a child with a solitary metastatic lesion to the calvarium following treatment for Stage III anaplastic Wilms’ Tumor

Tactical Satellite (TacSat) feasibility study a scenario driven approach

The objective of this project was to examine the feasibility of developing a tactically controlled, operationally responsive satellite system. A specific mission scenario, the Philippine Sea Scenario, was chosen to guide and bound the analysis. Within the bounds of this scenario, this high level space systems engineering exercise provided insights into operations and military utility as well as enough granularity to estimate costs for such a system. The operational approach and high level design concept is based on the Space Mission Analysis and Design (SMAD) process authored by Wiley J. Larson and Kames R. Wertz. The study shows that there are tactical scenarios in which space capabilities provide military utility and cost effectiveness above what is provided by traditional tactical assets such as UAVs. This is particularly true when large operational areas are involved and long periods of service are required.http://archive.org/details/tacticalsatellit109456927N

The record large 2006 Antarctic ozone hole

Póster presentado en: EGU General Assembly 2007 celebrada del 15 al 20 de abril en Viena, Austria.The Antarctic ozone hole of 2006 was unusually large. Several parameters used to measure the extent of ozone destruction and ozone hole severity set new records in 2006. Several ground-based stations measured record low total ozone column amounts. Ozonesonde measurements also revealed in many cases record low values of ozone in certain height intervals. The dynamics of the 2006 ozone hole will be described together with satellite-based measurements and calculations of ozone hole area and mass deficit. These finding will be supplemented with several examples of data from various stations in Antarctica

Observations of the Antarctic ozone hole from 2003-2010

Póster presentado en: EGU General Assembly 2011 celebrada del 3 al 8 de abril en Viena, Austria.The Global Atmosphere Watch of WMO includes several stations in Antarctica that keep a close eye on the ozone layer during the ozone hole season. Observations made during the ozone holes from 2003 to 2010 will be compared to each other and interpreted in light of the meteorological conditions. Satellite observations will be used to get a more general picture of the size and depth of the ozone hole and will also be used to calculate various metrics for ozone hole severity. In 2003, 2005 and 2006, the ozone hole was relatively large with more ozone loss than normal. This is in particular the case for 2006, which by most ozone hole metrics was the most severe ozone holeon record. On the other hand, the ozone holes of 2004, 2007 and 2010 were less severe than normal, and only the very special ozone hole of 2002 had less ozone depletion when one regards the ozone holes of the last decade. The interannual variability will be discussed with the help of meteorological data, such as temperature conditions, possibility for polar stratospheric clouds, vortex shape and vortex longevity. Observations will also be compared to 3-D chemical transport model calculations

A highly invasive human glioblastoma pre-clinical model for testing therapeutics

Animal models greatly facilitate understanding of cancer and importantly, serve pre-clinically for evaluating potential anti-cancer therapies. We developed an invasive orthotopic human glioblastoma multiforme (GBM) mouse model that enables real-time tumor ultrasound imaging and pre-clinical evaluation of anti-neoplastic drugs such as 17-(allylamino)-17-demethoxy geldanamycin (17AAG). Clinically, GBM metastasis rarely happen, but unexpectedly most human GBM tumor cell lines intrinsically possess metastatic potential. We used an experimental lung metastasis assay (ELM) to enrich for metastatic cells and three of four commonly used GBM lines were highly metastatic after repeated ELM selection (M2). These GBM-M2 lines grew more aggressively orthotopically and all showed dramatic multifold increases in IL6, IL8, MCP-1 and GM-CSF expression, cytokines and factors that are associated with GBM and poor prognosis. DBM2 cells, which were derived from the DBTRG-05MG cell line were used to test the efficacy of 17AAG for treatment of intracranial tumors. The DMB2 orthotopic xenografts form highly invasive tumors with areas of central necrosis, vascular hyperplasia and intracranial dissemination. In addition, the orthotopic tumors caused osteolysis and the skull opening correlated to the tumor size, permitting the use of real-time ultrasound imaging to evaluate antitumor drug activity. We show that 17AAG significantly inhibits DBM2 tumor growth with significant drug responses in subcutaneous, lung and orthotopic tumor locations. This model has multiple unique features for investigating the pathobiology of intracranial tumor growth and for monitoring systemic and intracranial responses to antitumor agents

A Sterol-Regulatory Element Binding Protein Is Required for Cell Polarity, Hypoxia Adaptation, Azole Drug Resistance, and Virulence in Aspergillus fumigatus

At the site of microbial infections, the significant influx of immune effector cells and the necrosis of tissue by the invading pathogen generate hypoxic microenvironments in which both the pathogen and host cells must survive. Currently, whether hypoxia adaptation is an important virulence attribute of opportunistic pathogenic molds is unknown. Here we report the characterization of a sterol-regulatory element binding protein, SrbA, in the opportunistic pathogenic mold, Aspergillus fumigatus. Loss of SrbA results in a mutant strain of the fungus that is incapable of growth in a hypoxic environment and consequently incapable of causing disease in two distinct murine models of invasive pulmonary aspergillosis (IPA). Transcriptional profiling revealed 87 genes that are affected by loss of SrbA function. Annotation of these genes implicated SrbA in maintaining sterol biosynthesis and hyphal morphology. Further examination of the SrbA null mutant consequently revealed that SrbA plays a critical role in ergosterol biosynthesis, resistance to the azole class of antifungal drugs, and in maintenance of cell polarity in A. fumigatus. Significantly, the SrbA null mutant was highly susceptible to fluconazole and voriconazole. Thus, these findings present a new function of SREBP proteins in filamentous fungi, and demonstrate for the first time that hypoxia adaptation is likely an important virulence attribute of pathogenic molds

Virtual Patients and Sensitivity Analysis of the Guyton Model of Blood Pressure Regulation: Towards Individualized Models of Whole-Body Physiology

Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a “virtual population” from which “virtual individuals” can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for in silico exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the “virtual individuals” that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models

Unravelling the Scientific Debate on How to Address Wolf-Dog Hybridization in Europe

Anthropogenic hybridization is widely perceived as a threat to the conservation of biodiversity. Nevertheless, to date, relevant policy and management interventions are unresolved and highly convoluted. While this is due to the inherent complexity of the issue, we hereby hypothesize that a lack of agreement concerning management goals and approaches, within the scientific community, may explain the lack of social awareness on this phenomenon, and the absence of effective pressure on decision-makers. By focusing on wolf x dog hybridization in Europe, we hereby (a) assess the state of the art of issues on wolf x dog hybridization within the scientific community, (b) assess the conceptual bases for different viewpoints, and (c) provide a conceptual framework aiming at reducing the disagreements. We adopted the Delphi technique, involving a three-round iterative survey addressed to a selected sample of experts who published at Web of Science listed journals, in the last 10 years on wolf x dog hybridization and related topics. Consensus was reached that admixed individuals should always be defined according to their genetic profile, and that a reference threshold for admixture (i.e., q-value in assignment tests) should be formally adopted for their identification. To mitigate hybridization, experts agreed on adopting preventive, proactive and, when concerning small and recovering wolf populations, reactive interventions. Overall, experts' consensus waned as the issues addressed became increasingly practical, including the adoption of lethal removal. We suggest three non-mutually exclusive explanations for this trend: (i) value-laden viewpoints increasingly emerge when addressing practical issues, and are particularly diverging between experts with different disciplinary backgrounds (e.g., ecologists, geneticists); (ii) some experts prefer avoiding the risk of potentially giving carte blanche to wolf opponents to (illegally) remove wolves, based on the wolf x dog hybridization issue; (iii) room for subjective interpretation and opinions result from the paucity of data on the effectiveness of different management interventions. These results have management implications and reveal gaps in the knowledge on a wide spectrum of issues related not only to the management of anthropogenic hybridization, but also to the role of ethical values and real-world management concerns in the scientific debate

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations