Expression of myofibrillar proteins and parvalbumin isoforms in white muscle of the developing turbot <i>Scophthalmus maximus</i> (Pisces, Pleuronectiformes)

Expression of polymorphic myofibrillar and sarcoplasmic proteins was investigated in the fish Scophthalmus maximus (L.) undergoing metamorphosis. A range of electrophoretic techniques was used to monitor sequential synthesis of isoforms from hatching to the adult stage. Two isoforms (larval and adult) of myosin light chain LC2 and troponin-I were successively detected during turbot growth, in addition to variations in the peptide composition of myosin heavy chains. Two isoforms of troponin-T also appeared sequentially, but the first to make its appearance was not detected until the juvenile stage. The composition of alkali light chains, actin, tropomyosin, and troponin-C did not seem to change as the fish progressed through the different stages. Parvalbumin isoforms were isolated and their physico-chemical parameters defined. As in the other fish examined so far, there appeared a succession of larval (PA IIa and PA IIb) and adult (PA V) parvalbumin isoforms through the life of the fish. All these biochemical changes occurred gradually in the course of turbot development, and did not appear particularly related to metamorphosis but rather to physiological needs of the different growth stages

Enhanced Characterization of the Smell of Death by Comprehensive Two-Dimensional Gas Chromatography-Time-of-Flight Mass Spectrometry (GCxGC-TOFMS)

Soon after death, the decay process of mammalian soft tissues begins and leads to the release of cadaveric volatile compounds in the surrounding environment. The study of postmortem decomposition products is an emerging field of study in forensic science. However, a better knowledge of the smell of death and its volatile constituents may have many applications in forensic sciences. Domestic pigs are the most widely used human body analogues in forensic experiments, mainly due to ethical restrictions. Indeed, decomposition trials on human corpses are restricted in many countries worldwide. This article reports on the use of comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC-TOFMS) for thanatochemistry applications. A total of 832 VOCs released by a decaying pig carcass in terrestrial ecosystem, i.e. a forest biotope, were identified by GCxGC-TOFMS. These postmortem compounds belong to many kinds of chemical class, mainly oxygen compounds (alcohols, acids, ketones, aldehydes, esters), sulfur and nitrogen compounds, aromatic compounds such as phenolic molecules and hydrocarbons. The use of GCxGC-TOFMS in study of postmortem volatile compounds instead of conventional GC-MS was successful

Identification of infrastructure related risk factors, Deliverable 5.1 of the H2020 project SafetyCube

The present Deliverable (D5.1) describes the identification and evaluation of infrastructure related risk factors. It outlines the results of Task 5.1 of WP5 of SafetyCube, which aimed to identify and evaluate infrastructure related risk factors and related road safety problems by (i) presenting a taxonomy of infrastructure related risks, (ii) identifying “hot topics” of concern for relevant stakeholders and (iii) evaluating the relative importance for road safety outcomes (crash risk, crash frequency and severity etc.) within the scientific literature for each identified risk factor. To help achieve this, Task 5.1 has initially exploited current knowledge (e.g. existing studies) and, where possible, existing accident data (macroscopic and in-depth) in order to identify and rank risk factors related to the road infrastructure. This information will help further on in WP5 to identify countermeasures for addressing these risk factors and finally to undertake an assessment of the effects of these countermeasures. In order to develop a comprehensive taxonomy of road infrastructure-related risks, an overview of infrastructure safety across Europe was undertaken to identify the main types of road infrastructure-related risks, using key resources and publications such as the European Road Safety Observatory (ERSO), The Handbook of Road Safety Measures (Elvik et al., 2009), the iRAP toolkit and the SWOV factsheets, to name a few. The taxonomy developed contained 59 specific risk factors within 16 general risk factors, all within 10 infrastructure elements. In addition to this, stakeholder consultations in the form of a series of workshops were undertaken to prioritise risk factors (‘hot topics’) based on the feedback from the stakeholders on which risk factors they considered to be the most important or most relevant in terms of road infrastructure safety. The stakeholders who attended the workshops had a wide range of backgrounds (e.g. government, industry, research, relevant consumer organisations etc.) and a wide range of interests and knowledge. The identified ‘hot topics’ were ranked in terms of importance (i.e. which would have the greatest effect on road safety). SafetyCube analysis will put the greatest emphasis on these topics (e.g. pedestrian/cyclist safety, crossings, visibility, removing obstacles). To evaluate the scientific literature, a methodology was developed in Work Package 3 of the SafetyCube project. WP5 has applied this methodology to road infrastructure risk factors. This uniformed approach facilitated systematic searching of the scientific literature and consistent evaluation of the evidence for each risk factor. The method included a literature search strategy, a ‘coding template’ to record key data and metadata from individual studies, and guidelines for summarising the findings (Martensen et al, 2016b). The main databases used in the WP5 literature search were Scopus and TRID, with some risk factors utilising additional database searches (e.g. Google Scholar, Science Direct). Studies using crash data were considered highest priority. Where a high number of studies were found, further selection criteria were applied to ensure the best quality studies were included in the analysis (e.g. key meta-analyses, recent studies, country origin, importance). Once the most relevant studies were identified for a risk factor, each study was coded within a template developed in WP3. Information coded for each study included road system element, basic study information, road user group information, study design, measures of exposure, measures of outcomes and types of effects. The information in the coded templates will be included in the relational database developed to serve as the main source (‘back end’) of the Decision Support System (DSS) being developed for SafetyCube. Each risk factor was assigned a secondary coding partner who would carry out the control procedure and would discuss with the primary coding partner any coding issues they had found. Once all studies were coded for a risk factor, a synopsis was created, synthesising the coded studies and outlining the main findings in the form of meta-analyses (where possible) or another type of comprehensive synthesis (e.g. vote-count analysis). Each synopsis consists of three sections: a 2 page summary (including abstract, overview of effects and analysis methods); a scientific overview (short literature synthesis, overview of studies, analysis methods and analysis of the effects) and finally supporting documents (e.g. details of literature search and comparison of available studies in detail, if relevant). To enrich the background information in the synopses, in-depth accident investigation data from a number of sources across Europe (i.e. GIDAS, CARE/CADaS) was sourced. Not all risk factors could be enhanced with this data, but where it was possible, the aim was to provide further information on the type of crash scenarios typically found in collisions where specific infrastructure-related risk factors are present. If present, this data was included in the synopsis for the specific risk factor. After undertaking the literature search and coding of the studies, it was found that for some risk factors, not enough detailed studies could be found to allow a synopsis to be written. Therefore, the revised number of specific risk factors that did have a synopsis written was 37, within 7 infrastructure elements. Nevertheless, the coded studies on the remaining risk factors will be included in the database to be accessible by the interested DSS users. At the start of each synopsis, the risk factor is assigned a colour code, which indicates how important this risk factor is in terms of the amount of evidence demonstrating its impact on road safety in terms of increasing crash risk or severity. The code can either be Red (very clear increased risk), Yellow (probably risky), Grey (unclear results) or Green (probably not risky). In total, eight risk factors were given a Red code (e.g. traffic volume, traffic composition, road surface deficiencies, shoulder deficiencies, workzone length, low curve radius), twenty were given a Yellow code (e.g. secondary crashes, risks associated with road type, narrow lane or median, roadside deficiencies, type of junction, design and visibility at junctions) seven were given a Grey code (e.g. congestion, frost and snow, densely spaced junctions etc.). The specific risk factors given the red code were found to be distributed across a range of infrastructure elements, demonstrating that the greatest risk is spread across several aspects of infrastructure design and traffic control. However, four ‘hot topics’ were rated as being risky, which were ‘small work-zone length’, ‘low curve radius’, ‘absence of shoulder’ and ‘narrow shoulder’. Some limitations were identified. Firstly, because of the method used to attribute colour code, it is in theory possible for a risk factor with a Yellow colour code to have a greater overall magnitude of impact on road safety than a risk factor coded Red. This would occur if studies reported a large impact of a risk factor but without sufficient consistency to allocate a red colour code. Road safety benefits should be expected from implementing measures to mitigate Yellow as well as Red coded infrastructure risks. Secondly, findings may have been limited by both the implemented literature search strategy and the quality of the studies identified, but this was to ensure the studies included were of sufficiently high quality to inform understanding of the risk factor. Finally, due to difficulties of finding relevant studies, it was not possible to evaluate the effects on road safety of all topics listed in the taxonomy. The next task of WP5 is to begin identifying measures that will counter the identified risk factors. Priority will be placed on investigating measures aimed to mitigate the risk factors identified as Red. The priority of risk factors in the Yellow category will depend on why they were assigned to this category and whether or not they are a hot topic

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCube

Identification of safety effects of infrastructure related measures, Deliverable 5.2 of the H2020 project SafetyCub

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCube

Inventory of assessed infrastructure risk factors and measures, Deliverable 5.4 of the H2020 project SafetyCub

Semi-Empirical Topological Method for Prediction of the Relative Retention Time of Polychlorinated Biphenyl Congeners on 18 Different HR GC Columns

High resolution gas chromatographic relative retention time (HRGC-RRT) models were developed to predict relative retention times of the 209 individual polychlorinated biphenyls (PCBs) congeners. To estimate and predict the HRGC-RRT values of all PCBs on 18 different stationary phases, a multiple linear regression equation of the form RRT = ao + a1 (no. o-Cl) + a2 (no. m-Cl) + a3 (no. p-Cl) + a4 (VM or SM) was used. Molecular descriptors in the models included the number of ortho-, meta-, and para-chlorine substituents (no. o-Cl, m-Cl and p-Cl, respectively), the semi-empirically calculated molecular volume (VM), and the molecular surface area (SM). By means of the final variable selection method, four optimal semi-empirical descriptors were selected to develop a QSRR model for the prediction of RRT in PCBs with a correlation coefficient between 0.9272 and 0.9928 and a leave-one-out cross-validation correlation coefficient between 0.9230 and 0.9924 on each stationary phase. The root mean squares errors over different 18 stationary phases are within the range of 0.0108–0.0335. The accuracy of all the developed models were investigated using cross-validation leave-one-out (LOO), Y-randomization, external validation through an odd–even number and division of the entire data set into training and test sets

Avanços recentes em nutrição de larvas de peixes

Os requisitos nutricionais de larvas de peixes são ainda mal compreendidos, o que leva a altas mortalidades e problemas de qualidade no seu cultivo. Este trabalho pretende fazer uma revisão de novas metodologias de investigação, tais como estudos com marcadores, genómica populacional, programação nutricional, génomica e proteómica funcionais, e fornecer ainda alguns exemplos das utilizações presentes e perspectivas futuras em estudos de nutrição de larvas de peixes

Polychlorinated dibenzo-p-dioxins, dibenzofurans and biphenyls in fish from the Netherlands: concentrations, profiles and comparison with DR CALUX® bioassay results

Fish from Dutch markets were analysed for concentrations of polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (DL-PCBs) and compared with the new European maximum residue levels (MRLs), set in 2006. In a first study on 11 different fish and shellfish from various locations, concentrations of PCDD/Fs were nearly all below the MRL for PCDD/Fs [4 pg toxic equivalents (TEQ) per gram wet weight (ww)] and nearly all below 8 pg total TEQ/g ww, the new MRL for the sum of PCDD/Fs and DL-PCBs. Some samples exceeded the total TEQ MRL, such as anchovy, tuna and sea bass. Furthermore, 20 (out of 39) wild eel samples exceeded the specific MRL for eel (12 pg total TEQ/g ww), as the study revealed PCDD/F TEQ levels of 0.2-7.9 pg TEQ/g ww and total TEQ values of 0.9 to 52 pg/g ww. TEQ levels in farmed and imported eel were lower and complied with the MRLs. Smoking eel, a popular tradition in the Netherlands, only had marginal effects on PCDD/F and DL-PCB concentrations. Owing to volatilization, concentrations of lower-chlorinated PCBs were reduced to below the limit of quantification after smoking. DL-PCBs contributed 61-97% to the total TEQ in all eel samples. This also holds for other fish and shellfish (except shrimps): DL-PCB contributed (on average) from 53 (herring) to 83% (tuna) to the total TEQ. Principal-component analysis revealed distinctive congener profiles for PCDD/Fs and non-ortho PCBs for mussels, pikeperch, herring and various Mediterranean fish. The application of new TCDD toxic equivalency factors (TEFs) set by the World Health Organization in 2006 (to replace the 1997 TEFs) resulted in lower TEQ values, mainly owing to a decreased mono-ortho PCB contribution. This decrease is most pronounced for eel, owing to the relative high mono-ortho PCB concentrations in eel. Consequently, a larger number of samples would comply with the MRLs when the new TEFs are applied. The DR CALUX (R) assay may be used for screening total TEQ levels in eel, in combination with gas chromatography-high resolution mass spectrometry confirmation of suspected samples. An almost 1:1 correlation was found when the 1997 TEFs were applied, but, surprisingly, a 1.4-fold overestimation occurred with application of the 2006 TEFs