The ecoinvent Database: Overview and Methodological Framework (7 pp)

Introduction: This paper provides an overview on the content of the ecoinvent database and of selected metholodogical issues applied on the life cycle inventories implemented in the ecoinvent database. Goal, Scope and Background: In the year 2000, several Swiss Federal Offices and research institutes of the ETH domain agreed to a joint effort to harmonise and update life cycle inventory (LCI) data for its use in life cycle assessment (LCA). With the ecoinvent data-base and its actual data v1.1, a consistent set of more than 2'500 product and service LCIs is now available. Method: Nearly all process datasets are transparently documented on the level of unit process inputs and outputs. Methodological approaches have been applied consistently throughout the entire database content and thus guarantee for a coherent set of LCI data. This is particularly true for market and trade modelling (see, for example, electricity modelling), for the treatment of multi-out-put and of recycling processes, but also for the recording and reporting of elementary flows. The differentiation of diameter size for particulate matter emissions, for instance, allows for a more comprehensive impact assessment of human health effects. Data quality is quantitatively reported in terms of standard deviations of the amounts of input and output flows. In many cases qualitative indicators are reported additionally on the level of each individual input and output. The information sources used vary from extensive statistical works to individual (point) measurements or assumptions derived from process descriptions. However, all datasets passed the same quality control procedure and all information relevant and necessary to judge the suitability of a dataset in a certain context are provided in the database. Data documentation and exchange is based on the EcoSpold data format, which complies with the technical specification ISO/TS 14048. Free access to process information via the Internet helps the user to judge the appropriateness of a dataset. Concluding Remarks: The existence of the ecoinvent database proves that it is possible and feasible to build up a large interlinked system of LCI unit processes. The project work proved to be demanding in terms of co-ordination efforts required and consent identification. One main characteristic of the database is its transparency in reporting to enable individual assessment of data appropriateness and to support the plurality in methodological approaches. Outlook: Further work on the ecoinvent database may comprise work on the database content (new or more detailed data-sets covering existing or new economic sectors), LCI (modelling) methodology, the structure and features of the data-base system (e.g. extension of Monte Carlo simulation to the impact assessment phase) or improvements in eco-invent data supply and data query. Furthermore, the deepening and building up of international co-operations in LCI data collection and supply is in the focus of future activitie

array CGH screening of 134 unrelated families

Background A growing number of non-coding regulatory mutations are being identified in congenital disease. Very recently also some exons of protein coding genes have been identified to act as tissue specific enhancer elements and were therefore termed exonic enhancers or “eExons”. Methods We screened a cohort of 134 unrelated families with split-hand/split-foot malformation (SHFM) with high resolution array CGH for CNVs with regulatory potential. Results In three families with an autosomal dominant non-syndromic SHFM phenotype we detected microdeletions encompassing the exonic enhancer (eExons) 15 and 17 of DYNC1I1. In a fourth family, who had hearing loss in addition to SHFM, we found a larger deletion of 510 kb including the eExons of DYNC1I1 and, in addition, the human brain enhancer hs1642. Exons 15 and 17 of DYNC1I1 are known to act as tissue specific limb enhancers of DLX5/6, two genes that have been shown to be associated with SHFM in mice. In our cohort of 134 unrelated families with SHFM, deletions of the eExons of DYNC1I1 account for approximately 3% of the cases, while 17p13.3 duplications were identified in 13% of the families, 10q24 duplications in 12%, and TP63 mutations were detected in 4%. Conclusions We reduce the minimal critical region for SHFM1 to 78 kb. Hearing loss, however, appears to be associated with deletions of a more telomeric region encompassing the brain enhancer element hs1642. Thus, SHFM1 as well as hearing loss at the same locus are caused by deletion of regulatory elements. Deletions of the exons with regulatory potential of DYNC1I1 are an example of the emerging role of exonic enhancer elements and their implications in congenital malformation syndromes

Deletions of exons with regulatory activity at the DYNC1I1 locus are associated with split-hand/split-foot malformation: array CGH screening of 134 unrelated families

Background: A growing number of non-coding regulatory mutations are being identified in congenital disease. Very recently also some exons of protein coding genes have been identified to act as tissue specific enhancer elements and were therefore termed exonic enhancers or "eExons". Methods: We screened a cohort of 134 unrelated families with split-hand/split-foot malformation (SHFM) with high resolution array CGH for CNVs with regulatory potential. Results: In three families with an autosomal dominant non-syndromic SHFM phenotype we detected microdeletions encompassing the exonic enhancer (eExons) 15 and 17 of DYNC1I1. In a fourth family, who had hearing loss in addition to SHFM, we found a larger deletion of 510 kb including the eExons of DYNC1I1 and, in addition, the human brain enhancer hs1642. Exons 15 and 17 of DYNC1I1 are known to act as tissue specific limb enhancers of DLX5/6, two genes that have been shown to be associated with SHFM in mice. In our cohort of 134 unrelated families with SHFM, deletions of the eExons of DYNC1I1 account for approximately 3% of the cases, while 17p13.3 duplications were identified in 13% of the families, 10q24 duplications in 12%, and TP63 mutations were detected in 4%. Conclusions: We reduce the minimal critical region for SHFM1 to 78 kb. Hearing loss, however, appears to be associated with deletions of a more telomeric region encompassing the brain enhancer element hs1642. Thus, SHFM1 as well as hearing loss at the same locus are caused by deletion of regulatory elements. Deletions of the exons with regulatory potential of DYNC1I1 are an example of the emerging role of exonic enhancer elements and their implications in congenital malformation syndromes

Diffusion-Weighted Imaging in Meningioma: Prediction of Tumor Grade and Association with Histopathological Parameters

OBJECTIVES: To analyze diffusion-weighted imaging (DWI) findings of meningiomas and to compare them with tumor grade, cell count, and proliferation index and to test a possibility of use of apparent diffusion coefficient (ADC) to differentiate benign from atypical/malignant tumors. METHODS: Forty-nine meningiomas were analyzed. DWI was done using a multislice single-shot echo-planar imaging sequence. A polygonal region of interest was drawn on ADC maps around the margin of the lesion. In all lesions, minimal ADC values (ADCmin) and mean ADC values (ADCmean) were estimated. Normalized ADC (NADC) was calculated in every case as a ratio ADCmean meningioma/ADCmean white matter. All meningiomas were surgically resected and analyzed histopathologically. The tumor proliferation index was estimated on Ki-67 antigen–stained specimens. Cell density was calculated. Collected data were evaluated by means of descriptive statistics. Analyses of ADC/NADC values were performed by means of two-sided t tests. RESULTS: The mean ADCmean value was higher in grade I meningiomas in comparison to grade II/III tumors (0.96 vs 0.80 × 10−3 mm2s−1, P = .006). Grade II/III meningiomas showed lower NADC values in comparison to grade I tumors (1.05 vs 1.26, P = .015). There was no significant difference in ADCmin values between grade I and II/III tumors (0.69 vs 0.63 × 10−3 mm2s−1, P = .539). The estimated cell count varied from 486 to 2091 (mean value, 1158.20 ± 333.74; median value, 1108). There were no significant differences in cell count between grade I and grade II/III tumors (1163.93 vs 1123.86 cells, P = .77). The mean level of the proliferation index was 4.78 ± 5.08%, the range was 1% to 18%, and the median value was 2%. The proliferation index was statistically significant higher in grade II/III meningiomas in comparison to grade I tumors (15.43% vs 3.00%, P = .001). Ki-67 was negatively associated with ADCmean (r = −0.61, P < .001) and NADC (r = −0.60, P < .001). No significant correlations between cell count and ADCmean (r = −0.20, P = .164) or NADC (r = −0.25, P = .079) were found. ADCmin correlated statistically significant with cell count (r = −0.44, P = .002) but not with Ki-67 (r = −0.22, P = .129). Furthermore, the association between ADCmin and cell count was stronger in grade II/III tumors (r = −0.79, P = .036) versus grade I meningiomas (r = −0.41, P = .008). An ADCmean value of less than 0.85 × 10−3 mm2s−1 was determined as the threshold in differentiating between grade I and grade II/III meningiomas (sensitivity 72.9%, specificity 73.1%, accuracy 73.0%). The positive and negative predictive values were 33.3% and 96.8%, respectively. The same threshold ADCmean value was used in differentiating between tumors with Ki-67 level ≥5% and meningiomas with low proliferation index (Ki-67 <5%). This threshold yielded a sensitivity of 70.6%, a specificity of 81.2%, and an accuracy of 77.6%. The positive and negative predictive values were 66.6% and 83.9%, respectively. CONCLUSIONS: Grade II/III tumors had lower ADCmean values than grade I meningiomas. ADCmean correlated negatively with tumor proliferation index and ADCmin with tumor cell count. These associations were different in several meningiomas. ADCmean can be used for distinguishing between benign and atypical/malignant tumors

The ecoinvent database: Overview and methodological framework

ISSN:0948-3349ISSN:1614-750