PESSTO: Survey description and products from the first data release by the Public ESO Spectroscopic Survey of Transient Objects

Context. The Public European Southern Observatory Spectroscopic Survey of Transient Objects (PESSTO) began as a public spectroscopic survey in April 2012. PESSTO classifies transients from publicly available sources and wide-field surveys, and selects science targets for detailed spectroscopic and photometric follow-up. PESSTO runs for nine months of the year, January - April and August - December inclusive, and typically has allocations of 10 nights per month. 

Aims. We describe the data reduction strategy and data products that are publicly available through the ESO archive as the Spectroscopic Survey data release 1 (SSDR1). 

Methods. PESSTO uses the New Technology Telescope with the instruments EFOSC2 and SOFI to provide optical and NIR spectroscopy and imaging. We target supernovae and optical transients brighter than 20.5^m for classification. Science targets are selected for follow-up based on the PESSTO science goal of extending knowledge of the extremes of the supernova population. We use standard EFOSC2 set-ups providing spectra with resolutions of 13-18 Å between 3345-9995 Å. A subset of the brighter science targets are selected for SOFI spectroscopy with the blue and red grisms (0.935-2.53 μm and resolutions 23-33 Å) and imaging with broadband JHK<inf>s</inf> filters. 

Results. This first data release (SSDR1) contains flux calibrated spectra from the first year (April 2012-2013). A total of 221 confirmed supernovae were classified, and we released calibrated optical spectra and classifications publicly within 24 h of the data being taken (via WISeREP). The data in SSDR1 replace those released spectra. They have more reliable and quantifiable flux calibrations, correction for telluric absorption, and are made available in standard ESO Phase 3 formats. We estimate the absolute accuracy of the flux calibrations for EFOSC2 across the whole survey in SSDR1 to be typically ∼15%, although a number of spectra will have less reliable absolute flux calibration because of weather and slit losses. Acquisition images for each spectrum are available which, in principle, can allow the user to refine the absolute flux calibration. The standard NIR reduction process does not produce high accuracy absolute spectrophotometry but synthetic photometry with accompanying JHK<inf>s</inf> imaging can improve this. Whenever possible, reduced SOFI images are provided to allow this. 

Conclusions. Future data releases will focus on improving the automated flux calibration of the data products. The rapid turnaround between discovery and classification and access to reliable pipeline processed data products has allowed early science papers in the first few months of the survey.

Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

Industrial accidents involving release of chemicals into the environment:Ecotoxicology

Major industrial accidents pose a serious threat to surrounding habitats. Each accident is unique in terms of pollutants released, pollutant concentrations and pollutant dispersal. The habitats receiving the pollutant(s) are also unique. These factors mean that assessing the environmental and ecological impact of any given pollution event will be complex. Case histories of the biological impact of chemicals released from industrial accidents are reviewed to determine how to assess ecotoxicity of pollutants involved.

Improving antimicrobial release kinetics from hydrophobic polymer implants via ion pairing to combat biomedical-device related infection

Purpose The aim of this study is to improve the drug release properties of antimicrobial agents from hydrophobic biomaterials using using an ion pairing strategy. In so doing antimicrobial agents may be eluted and maintained over a sufficient time period thereby preventing bacterial colonisation and subsequent biofilm formation on medical devices. Methods The model antimicrobial agent was chlorhexidine and the selected fatty acid counter ions were capric acid, myristic acid and stearic acid. The polymethyl methacrylate films were loaded with 2% of fatty acid:antimicrobial agent at the following molar ratios; 0.5:1M, 1:1M and 2:1M and thermally polymerized using azobisisobutyronitrile initiator. Drug release experiments were subsequently performed over a 3-month period and the mass of drug released under sink conditions (pH 7.0, 37oC) quantified using a validated HPLC-UV method. Results In all platforms, a burst of chlorhexidine release was observed over the initial 24-hour period. Similar release kinetics were observed between the formulations during the initial 28 days. However, as time progressed, the chlorhexidine baseline plateaued after 56 days whereas formulations containing the counterions appeared to continuously elute linearly with time. As can be observed in figure 1, the rank order of total chlorhexidine release in the presence of 0.5M fatty acid was myristic acid (40%) > capric acid (35%) > stearic acid (30%)> chlorhexidine baseline (15%). Conclusion The incorporation of fatty acids within the formulation significantly improved chlorhexidine solubility within both the monomer and the polymer and enhanced the drug release kinetics over the period of study. This is attributed to the greater diffusivity of chlorhexidine through PMMA in the presence of fatty acids. In th absence of fatty acids, chlorhexidine release was facilitated by dissolution of surface associated drug particles. This study has illustrated the ability of fatty acids to modulate chlorhexidine release from a model biomaterial through enhanced diffusivity. This strategy may prove advantageous for improved medical devices with enhanced resistance to infection.