White spot syndrome virus (WSSV) transmission risk through infected cooked shrimp products assessed by polymerase chain reaction (PCR) and bio-inoculation studies

The aim of the study was to evaluate the resistance of white spot syndrome virus (WSSV) in shrimps (Penaeus monodon) to the process of cooking. The cooking was carried out at 1000C six different durations 5, 10, 15, 20, 25 and 30 min. The presence of WSSV was tested by single step and nested polymerase chain reaction (PCR). In the single step PCR, the primers 1s5 & 1a16 and IK1 & IK2 were used. While in the nested PCR, primers IK1 &IK2 – IK3 & IK4 were used for the detection of WSSV. WSSV was detected in the single step PCR with the primers 1s5 and 1a16 and the nested PCR with the primers IK1 and IK2 – IK3 & IK4 from the cooked shrimp samples. The cooked shrimps, which gave positive results for WSSV by PCR, were further confirmed for the viability of WSSV by conducting the bio-inoculation studies. Mortality (100%) was observed within 123 h of intra-muscular post injection (P.I) into the live healthy WSSV-free shrimps (P. monodon). These results show that the WSSV survive the cooking process and even infected cooked shrimp products may pose a transmission risk for WSSV to the native shrimp farming systems.

Analysis of multivariate stochastic signals sampled by on-line particle analyzers: Application to the quantitative assessment of occupational exposure to NOAA in multisource industrial scenarios (MSIS)

In multisource industrial scenarios (MSIS) coexist NOAA generating activities with other productive sources of airborne particles, such as parallel processes of manufacturing or electrical and diesel machinery. A distinctive characteristic of MSIS is the spatially complex distribution of aerosol sources, as well as their potential differences in dynamics, due to the feasibility of multi-task configuration at a given time. Thus, the background signal is expected to challenge the aerosol analyzers at a probably wide range of concentrations and size distributions, depending of the multisource configuration at a given time. Monitoring and prediction by using statistical analysis of time series captured by on-line particle analyzers in industrial scenarios, have been proven to be feasible in predicting PNC evolution provided a given quality of net signals (difference between signal at source and background). However the analysis and modelling of non-consistent time series, influenced by low levels of SNR (Signal-Noise Ratio) could build a misleading basis for decision making. In this context, this work explores the use of stochastic models based on ARIMA methodology to monitor and predict exposure values (PNC). The study was carried out in a MSIS where an case study focused on the manufacture of perforated tablets of nano-TiO2 by cold pressing was performed

Analytical strategies to solve problems in industrial and official laboratories

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Options for achieving a 50% cut in industrial carbon emissions by 2050.

Carbon emissions from industry are dominated by production of goods in steel, cement plastic, paper, and aluminum. Demand for these materials is anticipated to double at least by 2050, by which time global carbon emissions must be reduced by at least 50%. To evaluate the challenge of meeting this target the global flows of these materials and their associated emissions are projected to 2050 under five technical scenarios. A reference scenario includes all existing and emerging efficiency measures but cannot provide sufficient reduction. The application of carbon sequestration to primary production proves to be sufficient only for cement The emissions target can always be met by reducing demand, for instance through product life extension, material substitution, or "light-weighting". Reusing components shows significant potential particularly within construction. Radical process innovation may also be possible. The results show that the first two strategies, based on increasing primary production, cannot achieve the required emissions reductions, so should be balanced by the vigorous pursuit of material efficiency to allow provision of increased material services with reduced primary production.

Prioritizing county-level well-being: Moving toward assessment of Gulf Coast counties impacted by the Deepwater Horizon Industrial Disaster

To develop a portfolio of indicators and measures that could best measure changes in the social, economic, environmental and health dimensions of well-being in coastal counties we convened a group of experts March 8-9, 2011 in Charleston, SC, U.S.A. The region of interest was of the northern Gulf of Mexico, specifically, those coastal counties most impacted during the explosion and subsequent oil spill from the Macondo Prospect wellhead during the summer of 2010. Over the course of the two-day workshop participants moved through presentations and facilitated sessions to identify and prioritize potential indicators and measures deemed most valuable for capturing changes in well-being related to changes in or disruption of ecosystem services. The experts reached consensus on a list of indicators that are now being operationalized by NOAA researchers. The ultimate goal of this research project is to determine whether a meaningful set of social and economic indicators can be developed to document changes in well-being that occur as a result of changes in ecosystem services. The outcomes and outputs from the workshop that is the subject of this report helped us to identify high-quality indicators useful for measuring well-being.