Imprints, Vol. 3

Imprints, Vol. 3 Laura Lundgren, Stephen F Austin State UniversitySandra L. Standley, Stephen F Austin State UniversityMelissa Miller, Stephen F Austin State UniversityCurtis Simmons, Stephen F Austin State UniversityVaughn Hamilton, Stephen F Austin State UniversitySteve Geissen, Stephen F Austin State UniversityEdward Shelton, Stephen F Austin State UniversityJames L. Choron, Stephen F Austin State UniversityAnderson Kelley, Stephen F Austin State UniversityAndrew J. Urbanus, Stephen F Austin State UniversityGordon Garrett Conner, Stephen F Austin State UniversityJames Chionsini Jr., Stephen F Austin State UniversityPaul M. Thomason, Stephen F Austin State UniversityCarol McBrayerJessica Anton, Stephen F Austin State University Download Download Full Text (5.7 MB) Description Imprints is the official publication for Sigma Tau Delta, the honorary English fraternity. The editors welcome creative works submitted by contributors and also publish winners of the annual T. E. Ferguson Writing Contest. Especially welcom are poems, fiction pieces and essays of no more than 5,000 words in length. At this time, we would like to express our gratitude to David Whitescarver, Sigma Tau Delta faculty advisor, for his unrelenting optimism and valuable help in the preparation of this journal

Review of Tier 1 workplace exposure estimates for petroleum substances in REACH dossiers

For the exposure assessment in the 2010 REACH dossiers of petroleum substances, Concawe has used the Tier 1 exposure model ECETOC TRA v.2. In order to account for the heavier, less volatile and more complex petroleum substances and the corresponding exposure situations, several modifications not originally within the scope of the ECETOC TRA were developed. These modifications include an approach to estimate liquid aerosol along with some risk management measures describing the use and handling of petroleum substances commonly in use in the European oil refining industry. In this project, Chemical Safety Assessments (CSAs) on these petroleum substances were evaluated concerning relevant industry areas and included scenarios. Measured data were collated in order to evaluate the exposure estimates in general and the modifications made by Concawe. The comparison exercise showed some discrepancies depending on substance group and the specific scenarios. These discrepancies may be partly attributed to new modifiers or other changes of the ECETOC standard algorithm (e.g. concentration modifier in case of naphthas). In general, for most measures both under- and overestimations can be found; therefore, it is difficult to reach a final conclusion concerning their applicability. Other possible reasons for the observed underestimations were variations within an exposure scenario or the age of datasets. Concerning aerosol exposure, measured data for OLBOs and HFOs could be identified. No significant underestimations were found for the evaluated scenario in case of OLBOs while in the case of HFOs results were inconclusive (partly underestimations but only few data points). Overall it is recognised that available sampling methods for liquid aerosol often tend to give biased or at least variable exposure results and this has to be taken into account for future investigations concerning risk assessment of petroleum substances or validation of the existing CSAs. Measurements made for HFOs show higher overall and vapour concentrations compared to the aerosol values which may suggest that either vapour may be more relevant than previously assumed for high boiling petroleum substances or the corresponding aerosol measurements may not be suitable for a comparison with DNELs or model estimates. Comparable difficulties will probably exist for other semi- or low volatile substances which tend to form aerosols. Although some underestimations have been observed, there are also cases where clear overestimations were observed and thus, a further refinement with higher Tier tools may be possible. Two possible tools, STOFFENMANAGER© and ART were discussed and illustrated with an example scenario. Petroleum substances and the resulting exposure types (vapour and aerosols) are within the scope of both models; however, the new modifiers introduced by Concawe are only implemented to a limited extent (vapour recovery in the case of ART). A qualitative evaluation of the updates made when changing from ECETOC TRA v.2 to v.3 suggested that inhalation exposure estimates will probably be lower if the more recent version is used. This is partly due to newly introduced or changed measures or operational conditions and partly due to modified initial exposure estimates. Overall, there are a number of situations where the comparison of measurements and estimates suggests reasonable results and a controlled risk. There are other situations, however, where, due to different reasons, the contrary is observed. A particular problem seems to be the lack of high quality aerosol data

Review of Tier 1 workplace exposure estimates for petroleum substances in REACH dossiers

For the exposure assessment in the 2010 REACH dossiers of petroleum substances, Concawe has used the Tier 1 exposure model ECETOC TRA v.2. In order to account for the heavier, less volatile and more complex petroleum substances and the corresponding exposure situations, several modifications not originally within the scope of the ECETOC TRA were developed. These modifications include an approach to estimate liquid aerosol along with some risk management measures describing the use and handling of petroleum substances commonly in use in the European oil refining industry. In this project, Chemical Safety Assessments (CSAs) on these petroleum substances were evaluated concerning relevant industry areas and included scenarios. Measured data were collated in order to evaluate the exposure estimates in general and the modifications made by Concawe. The comparison exercise showed some discrepancies depending on substance group and the specific scenarios. These discrepancies may be partly attributed to new modifiers or other changes of the ECETOC standard algorithm (e.g. concentration modifier in case of naphthas). In general, for most measures both under- and overestimations can be found; therefore, it is difficult to reach a final conclusion concerning their applicability. Other possible reasons for the observed underestimations were variations within an exposure scenario or the age of datasets. Concerning aerosol exposure, measured data for OLBOs and HFOs could be identified. No significant underestimations were found for the evaluated scenario in case of OLBOs while in the case of HFOs results were inconclusive (partly underestimations but only few data points). Overall it is recognised that available sampling methods for liquid aerosol often tend to give biased or at least variable exposure results and this has to be taken into account for future investigations concerning risk assessment of petroleum substances or validation of the existing CSAs. Measurements made for HFOs show higher overall and vapour concentrations compared to the aerosol values which may suggest that either vapour may be more relevant than previously assumed for high boiling petroleum substances or the corresponding aerosol measurements may not be suitable for a comparison with DNELs or model estimates. Comparable difficulties will probably exist for other semi- or low volatile substances which tend to form aerosols. Although some underestimations have been observed, there are also cases where clear overestimations were observed and thus, a further refinement with higher Tier tools may be possible. Two possible tools, STOFFENMANAGER© and ART were discussed and illustrated with an example scenario. Petroleum substances and the resulting exposure types (vapour and aerosols) are within the scope of both models; however, the new modifiers introduced by Concawe are only implemented to a limited extent (vapour recovery in the case of ART). A qualitative evaluation of the updates made when changing from ECETOC TRA v.2 to v.3 suggested that inhalation exposure estimates will probably be lower if the more recent version is used. This is partly due to newly introduced or changed measures or operational conditions and partly due to modified initial exposure estimates. Overall, there are a number of situations where the comparison of measurements and estimates suggests reasonable results and a controlled risk. There are other situations, however, where, due to different reasons, the contrary is observed. A particular problem seems to be the lack of high quality aerosol data

SGA output for analysis sets 31-37.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 31-37

SGA output for analysis sets 11-20.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 11-20

SGA output for analysis sets 21-30.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 21-30

SGA output for analysis sets 1-10.

Query strains that express one effector were mated to an array of ~330 effectors in groups of ~10 queries at a time ("Analysis Set"). The arrays were then imaged using a high-resolution camera and the spot sizes were quantified using SGAtools (http://sgatools.ccbr.utoronto.ca/). Outlier spot sizes flagged by the Jackknife filter (JK) in SGAtools were removed and the average and standard deviation of the remaining values were calculated and normalized to the average empty vector control. This .zip archive includes spreadsheets that encompass the raw SGAtools data output from the paper for analysis sets 1-10

Data from: Diverse mechanisms of metaeffector activity in an intracellular bacterial pathogen, Legionella pneumophila

Pathogens deliver complex arsenals of translocated effector proteins to host cells during infection, but the extent to which these proteins are regulated once inside the eukaryotic cell remains poorly defined. Among all bacterial pathogens, Legionella pneumophila maintains the largest known set of translocated substrates, delivering over 300 proteins to the host cell via its Type IVB, Icm/Dot translocation system. Backed by a few notable examples of effector–effector regulation in L. pneumophila, we sought to define the extent of this phenomenon through a systematic analysis of effector–effector functional interaction. We used Saccharomyces cerevisiae, an established proxy for the eukaryotic host, to query > 108,000 pairwise genetic interactions between two compatible expression libraries of ~330 L. pneumophila‐translocated substrates. While capturing all known examples of effector–effector suppression, we identify fourteen novel translocated substrates that suppress the activity of other bacterial effectors and one pair with synergistic activities. In at least nine instances, this regulation is direct—a hallmark of an emerging class of proteins called metaeffectors, or “effectors of effectors”. Through detailed structural and functional analysis, we show that metaeffector activity derives from a diverse range of mechanisms, shapes evolution, and can be used to reveal important aspects of each cognate effector's function. Metaeffectors, along with other, indirect, forms of effector–effector modulation, may be a common feature of many intracellular pathogens—with unrealized potential to inform our understanding of how pathogens regulate their interactions with the host cell