Anna Orłowska, Ewelina Iwan, Marcin Smreczak and Jerzy Rola
trimmed by Trimmomatic; the operation consisted of removal of low quality reads (PHRED score below 33) and reads shorter than 36 bp (2). Non-viral data was filtered by BBDuk with three different approaches: positive filtration of virus reads based on the Kraken database, positive filtration according to the RABV reference sequence, and negative filtration of host reads. Evaluation of RABV data was performed in both Kraken and Centrifuge software ( 30 , 14 ). Cleaned RABV data was then assembled de novo by metaSPAdes software ( 19 ).
In order to evaluate
O. Fenton, S. Vero, R.P.O. Schulte, L. O’Sullivan, G. Bondi and R.E. Creamer
soil horizons with OC >6% and B d values <1 g/cm 3 , or excessively drained sandy profiles, are not suitable for S i estimation. Removal of such horizons and profiles allowed for good correlations among S i , OC% and B d (regardless of texture). Future work should extend this approach to a national scale dataset and examine the relationship of these estimates to site-specific visual examination approaches ( Emmet-Booth et al., 2016b ).
It is important to note that the S i consistently scored a higher SPQ value than the S d equivalent and could result in an
D. Ó hUallacháin, J.A. Finn, B. Keogh, R. Fritch and H. Sheridan
-Blanquet Scale ( Braun-Blanquet et al ., 1932 ). Data analysis techniques required that the Braun-Blanquet values be converted to actual percentage cover values. This was done by assigning a percentage score to the midpoint of each category (according to Wikum and Shanholtzer, 1978 ). The 20 quadrats per site were summed, and the mean value was used to calculate a mean percentage cover value for each species per site. To address the limitations with species richness metrics ( Fleishman et al ., 2006 ), both Shannon diversity index (H′) and effective species richness (expH
M. Ryan, T. Hennessy, C. Buckley, E.J. Dillon, T. Donnellan, K. Hanrahan and B. Moran
dairy farms and on 20% of tillage farms, but there is evidence of poor sustainability on 28% of cattle farms and 25% of sheep farms. In relation to education, dairy and tillage farmers in Ireland tend to be better educated than other farmers. The low sustainability scoring across systems for the education indicator generally is likely to be a scaling issue as education is measured as a count variable with values from one to five. However, the differences between the farming systems are less pronounced with regard to the demographic variables; high age profile and
band pattern or AFLP profile comparable to bar codes used for product identification in commerce. Here it determines a genetic fingerprint. For subsequent data analysis, the resulting AFLP profile is finally converted into a binary presence or absence (+/− or 1/0) code, a process known as “scoring” ( Kück et al., 2012 ). The bin code obtained is specific for a species and represents the basis for determining the relatedness of strains ( Figure 4 ). AFLP has been very useful for taxonomic studies because it clearly classifies bacteria belonging to the same genomic
4. Puetz V et al. The Alberta Stroke Program Early CT Score in clinical practice: what have we learned?. Int J Stroke. 2009;4 (5): 354-64. doi:10.1111/j.1747-4949.2009.00337
5. Kothari RU et al. Cincinnati Prehospital Stroke Scale: reproducibility and validity. Ann Emerg Med. 1999 Apr;33(4):373-8.
Mehdi Ghazanfari, Saeed Rouhani and Mostafa Jafari
Journal, 2008. 19(1), pp. 37-53
14. Yu L., Wang S., K. K. Lai: An intelligent-agent-based fuzzy group decision making model for financial multicriteria decision support: the case of credit scoring. European Journal of Operational Research, 2009. 195(3), pp. 942-959
15. Zack M. H.: The role of decision support systems in an indeterminate world. Decision Support Systems, 2007. 43(4): pp. 1664-1674
16. Lamptey G., Labi S., Li Z.: Decision support for optimal scheduling of highway pavement preventive maintenance within
Fat reserves and body mass in some passerines migrating in autumn through the southern Baltic coast
The aim of the present paper is to serve with a huge data set on the fat and body mass of birds that have been caught during the field work of the Operation Baltic since 1983 (earlier data are still not available in a digitalised form). There are given fat score valuations of 38 species and correction factors for the body mass standardisation. Some comments on observed fat scores in species of different migratory habits are added. They should encourage students to continue the research process on a wider scale.
Stopover Ecology of Some Passerines at Ankara (Central Turkey)
In autumn 2002 we studied little known passerine migration at a woodland patch within the Middle East Technical University (METU) campus in Ankara (Turkey). A total of 954 individuals of 35 passerine species were mist-netted, ringed, measured, weighed and fat-scored (after Busse 2000). Blackcap (Sylvia atricapilla) and Willow Warbler (Phylloscopus trochilus) were the two most common species, with 308 and 145 individuals caught, respectively. Both are passage migrants at METU, recorded from mid-August to late October, representing several waves.
Only 11.5% of Blackcaps had the fat score of T6 and above, and among 20 retraps only 2 gained fat significantly. All the other retraps lost fat, stayed the same, or increased 1-2 scores at most. In contrast, Willow Warblers, as true trans-Saharan migrants, had the much higher proportion (46%) of individuals with fat scores of T6 and above. Most retrapped individuals gained fat, some with already high levels stayed the same, while none lost fat. We interpret these data in terms of known migratory ranges, diet types and habitat patch quality.
Although daily catches were low, a diverse range of species used METU as a stopover site. Fat deposition rates (of up to 50% of body weight within a week) suggest that the study site provided a high quality stopover habitat for most migrants. In Central Turkey, such suitable habitats with trees or tall shrubs are scarce, and therefore, crucial for migrants.
Gohar Afrooz, Naser Sabaghnia, Rahmatollah Karimizadeh and Fariborz Shekari
Knowledge about the extent of variability and the association among traits are of a high value for any breeding efforts. The objective of this investigation is to evaluate the agro-morphological traits in a set of durum wheat genotypes under supplemental irrigation and dry land conditions. Results showed that principal component (PC) analysis had grouped the measured traits into four main components that altogether accounted for 77% of the total variation under non-stressed condition and 87% under water-stressed condition. With regard to the first four PCs, peduncle length, agronomic score, grain yield, vigority, test weight, days to physiological maturity and thousand kernel weight have shown to be the most important variables affecting the performance of durum wheat under non-stressed condition. In the first four PCs at the water- stressed condition, agronomic score, grain yield, vigority, days to physiological maturity, test weight and peduncle length have been shown to be the important variables under water-stressed condition. The results of factor analysis relatively confirmed the results of PC analysis. Our findings indicated that a selection strategy should take into consideration of agronomic score and days to physiological maturity under non-stressed condition while plant height and spike length under water-stressed condition. Therefore, the above-mentioned traits could be used as indirect selection criteria for genetic improvement of grain yield in durum wheat, especially in early generations of breeding programmes