A spectroscopic investigation of the weathering of a heritage Sydney sandstone

Infrared spectroscopy has been employed in a study of the degradation of heritage Sydney sandstone used in St. Mary's Cathedral in Sydney, Australia. Spectra were used to characterise the clay components taken from weathered and unweathered sandstone blocks removed from the Cathedral as part of a restoration programme. Two types of kaolin clays - kaolinite and its polymorph, dickite - have been identified. A higher amount of dickite present in the clay of weathered sandstone indicated that a kaolinite-to-dickite transformation occurs upon weathering. X-ray photoelectron spectroscopy was also used to confirm the presence of a more thermally stable polymorph of the kaolinite in the sandstone. © 2008 Elsevier B.V. All rights reserved

ESEM-EDS investigation of the weathering of a heritage sydney sandstone

The degradation of Sydney sandstone used to build the heritage St Mary's Cathedral in Sydney, Australia, has been investigated using environmental scanning electron microscopy combined with energy dispersive X-ray spectroscopy. This technique provided the structural details of the cementing clay and an elemental characterization the sandstone. The observed differences in the elemental composition of the unweathered and weathered sandstones were associated with changes to the clay microstructure upon weathering. The results support the substitution theory that Fe3+ replaces Al3+ in the kaolinite clay component upon weathering. An examination of the impurities present prior to a nonstructural iron removal treatment revealed the presence of minerals that may provide a source of the elements responsible for the substitution process. © 2011 Microscopy Society Of America

Integrating Light Curve and Atmospheric Modeling of Transiting Exoplanets

Spectral retrieval techniques are currently our best tool to interpret the observed exoplanet atmospheric data. Said techniques retrieve the optimal atmospheric components and parameters by identifying the best fit to an observed transmission/emission spectrum. Over the past decade, our understanding of remote worlds in our galaxy has flourished thanks to the use of increasingly sophisticated spectral retrieval techniques and the collective effort of the community working on exoplanet atmospheric models. A new generation of instruments in space and from the ground is expected to deliver higher quality data in the next decade; it is therefore paramount to upgrade current models and improve their reliability, their completeness, and the numerical speed with which they can be run. In this paper, we address the issue of reliability of the results provided by retrieval models in the presence of systematics of unknown origin. More specifically, we demonstrate that if we fit directly individual light curves at different wavelengths (L-retrieval), instead of fitting transit or eclipse depths, as it is currently done (S-retrieval), the said methodology is more sensitive against astrophysical and instrumental noise. This new approach is tested, in particular, when discrepant simulated observations from Hubble Space Telescope/Wide Field Camera 3 and Spitzer/IRAC are combined. We find that while S-retrievals converge to an incorrect solution without any warning, L-retrievals are able to flag potential discrepancies between the data sets

Nicotinic acetylcholine receptor expression in human airway correlates with lung function

Nicotine and its derivatives, by binding to nicotinic acetylcholine receptors (nAChRs) on bronchial epithelial cells, can regulate cellular signaling and inflammatory processes. Delineation of nAChR subtypes and their responses to nicotine stimulation in bronchial epithelium may provide information for therapeutic targeting in smoking-related inflammation in the airway. Expression of nAChR subunit genes in 60 bronchial epithelial biopsies and immunohistochemical staining for the subcellular locations of nAChR subunit expression were evaluated. Seven human bronchial epithelial cell lines (HBECs) were exposed to nicotine in vitro for their response in nAChR subunit gene expression to nicotine exposure and removal. The relative normalized amount of expression of nAChR α4, α5, and α7 and immunohistochemical staining intensity of nAChR α4, α5, and β3 expression showed significant correlation with lung function parameters. Nicotine stimulation in HBECs resulted in transient increase in the levels of nAChR α5 and α6 but more sustained increase in nAChR α7 expression. nAChR expression in bronchial epithelium was found to correlate with lung function. Nicotine exposure in HBECs resulted in both short and longer term responses in nAChR subunit gene expression. These results gave insight into the potential of targeting nAChRs for therapy in smoking-related inflammation in the airway.postprin

Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals

Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being "black boxes." It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us–among other things–of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions

Peeking inside the Black Box: Interpreting Deep-learning Models for Exoplanet Atmospheric Retrievals

Deep-learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly nonlinear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine-learning algorithms like deep neural networks (DNNs). Yet, despite their high predictive power, DNNs are also infamous for being "black boxes." It is their apparent lack of explainability that makes the astrophysics community reluctant to adopt them. What are their predictions based on? How confident should we be in them? When are they wrong, and how wrong can they be? In this work, we present a number of general evaluation methodologies that can be applied to any trained model and answer questions like these. In particular, we train three different popular DNN architectures to retrieve atmospheric parameters from exoplanet spectra and show that all three achieve good predictive performance. We then present an extensive analysis of the predictions of DNNs, which can inform us–among other things–of the credibility limits for atmospheric parameters for a given instrument and model. Finally, we perform a perturbation-based sensitivity analysis to identify to which features of the spectrum the outcome of the retrieval is most sensitive. We conclude that, for different molecules, the wavelength ranges to which the DNNs predictions are most sensitive do indeed coincide with their characteristic absorption regions. The methodologies presented in this work help to improve the evaluation of DNNs and to grant interpretability to their predictions

Gestational Exposure to Antidepressants and Risk of Seizure in Offspring: A systematic review and meta-analysis

In spite of the preliminary evidence suggesting a link between gestational use of antidepressant and neurodevelopmental disorders in their offspring, the association between maternal use of antidepressants during pregnancy and the risk of neurologically-related adverse outcomes such as neonatal seizure is still unclear. This study summarises the available evidence on the association between gestational exposure to any antidepressants and the risk of seizure in neonates and children. We found that gestational antidepressant exposure is associated with a 2.3-fold higher incidence of seizure in offspring. Although a causal relationship cannot be confirmed in view of other potential confounders, our findings warrant future research on related clinical aspects, and possibly more careful monitoring of foetal neurodevelopment in pregnant women taking antidepressants during pregnancy. However, this does not suggest the abrupt withdrawal of antidepressants during pregnancy for all cases at risk of seizure in offspring as this must be balanced with the risk of negative consequences caused by untreated maternal depression, and decision-making should be individualised for each patient

The effect of age and recent influenza vaccination history on the immunogenicity and efficacy of 2009-10 seasonal trivalent inactivated influenza vaccination in children

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A new model using routinely available clinical parameters to predict significant liver fibrosis in chronic hepatitis B

Objective: We developed a predictive model for significant fibrosis in chronic hepatitis B (CHB) based on routinely available clinical parameters. Methods: 237 treatment-naïve CHB patients [58.4% hepatitis B e antigen (HBeAg)-positive] who had undergone liver biopsy were randomly divided into two cohorts: training group (n = 108) and validation group (n = 129). Liver histology was assessed for fibrosis. All common demographics, viral serology, viral load and liver biochemistry were analyzed. Results: Based on 12 available clinical parameters (age, sex, HBeAg status, HBV DNA, platelet, albumin, bilirubin, ALT, AST, ALP, GGT and AFP), a model to predict significant liver fibrosis (Ishak fibrosis score ≥3) was derived using the five best parameters (age, ALP, AST, AFP and platelet). Using the formula log(index+1) = 0.025+0.0031(age)+0.1483 log(ALP)+0.004 log(AST)+0.0908 log(AFP+1)-0.028 log(platelet), the PAPAS (Platelet/Age/Phosphatase/AFP/AST) index predicts significant fibrosis with an area under the receiving operating characteristics (AUROC) curve of 0.776 [0.797 for patients with ALT <2×upper limit of normal (ULN)] The negative predictive value to exclude significant fibrosis was 88.4%. This predictive power is superior to other non-invasive models using common parameters, including the AST/platelet/GGT/AFP (APGA) index, AST/platelet ratio index (APRI), and the FIB-4 index (AUROC of 0.757, 0.708 and 0.723 respectively). Using the PAPAS index, 67.5% of liver biopsies for patients being considered for treatment with ALT <2×ULN could be avoided. Conclusion: The PAPAS index can predict and exclude significant fibrosis, and may reduce the need for liver biopsy in CHB patients. © 2011 Seto et al.published_or_final_versio