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opencc-by-4.0Jul 2023View details →

Primary X-ray diffraction data for the study "Femtosecond X-ray snapshots reveal correlated displacements of specific distal atoms in a protein crystal"

The data set contains the primary X-ray diffraction data collected at the FemtoMAX beamline from bovine trypsin crystals at room temperature (motor gony). Every image represents a 150 fs X-ray pulse diffracting on the crystal. The photon energy was 11.15 keV (ΔE/E = 0.01), approximately 1 × 107 photon/pulse.  The images are stored in compressed numpy file format and the float pixels values describe energy in keV as generated by the time-over-threshold technique. The odd numbered images were recorded without a preceding THz pulse and the even numbered images were recorded after a single cycle THz pulse (2.1 THz peak, FWHM 2 THz) with approximately 50 ps delay. The data set also contain the laser delay associated with a rotation position (motor laserdelay, in seconds). Only one laser delay was recorded per step.

opencc-by-4.0Jul 2023View details →

Dataset: Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production Systems

This online repository contains the accompanying data for the ETFA publication "Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production Systems".   This involves all tables which contain the values of the maturity evaluation criteria of the five examined subject DSLs.

Fig. 5 in Identification and localization of morphological feature-specific metabolites in Reynoutria multiflora roots

Fig. 5. MS/MS-based identification of representative specialised metabolites detected in the DESI-MSI analysis: A, 2,3,5,4′–Tetrahydroxystilbene; B, 3′-O-Methylmyricetin; C, Torachrysone; D, Hypaphorine, and E, LPC.

Fig. 1 in Identification and localization of morphological feature-specific metabolites in Reynoutria multiflora roots

Fig. 1. Schematic diagram of tissue features of R. multiflora roots and their preferred solvent systems applied in DESI-MSI.

Fig. 2 in Identification and localization of morphological feature-specific metabolites in Reynoutria multiflora roots

Fig. 2. Major specialised metabolites detected at AVB by DESI-MSI (A-I, detected at PAB; J-L, detected at XAB).

Fig. 3 in Identification and localization of morphological feature-specific metabolites in Reynoutria multiflora roots

Fig. 3. Major specialised metabolites detected at cork and cortex by DESI-MSI (A, B and D-F, detected at cork and cortex; C, detected at cortex).

restrictedJun 2022View details →

A flexible model to reconstruct education-specific fertility rates: Sub-saharan Africa case study

A flexible model to reconstruct education-specific fertility rates: Sub-saharan Africa case study The fertility rates are consistent with the United Nation World Population Prospects (UN WPP) 2022 fertility rates. The Bayesian model developed to reconstruct the fertility rates using Demographic and Health Surveys and the UN WPP is published in a <a href="https://epub.oeaw.ac.at/0xc1aa5576_0x003e65e0.pdf">working paper</a>.  Abstract The future world population growth and size will be largely determined by the pace of fertility decline in sub-Saharan Africa. Correct estimates of education-specific fertility rates are crucial for projecting the future population. Yet, consistent cross-country comparable estimates of education-specific fertility for sub-Saharan African countries are still lacking. We propose a flexible Bayesian hierarchical model to reconstruct education-specific fertility rates by using the patchy Demographic and Health Surveys (DHS) data and the United Nations’ (UN) reliable estimates of total fertility rates (TFR). Our model produces estimates that match the UN TFR to different extents (in other words, estimates of varying levels of  consistency with the UN). We present three model specifications: consistent but not identical with the UN, fully-consistent (nearly identical) with the UN, and consistent with the DHS. Further, we provide a full time series of education-specific TFR estimates covering five-year periods between 1980 and 2014 for 36 sub-Saharan African countries. The results show that the DHS-consistent estimates are usually higher than the UN-fully-consistent ones. The differences between the three model estimates vary substantially in size across countries, yielding 1980-2014 fertility trends that differ from each other mostly in level only but in some cases also in direction. Funding The data set are part of the <a href="https://www.oeaw.ac.at/vid/research/research-projects/bayesedu">BayesEdu Project </a>at Wittgenstein Centre for Demography and Global Human Capital (IIASA, OeAW, University of Vienna) funded from the “Innovation Fund Research, Science and Society” by the Austrian Academy of Sciences (ÖAW). We provide education-specific total fertility rates (ESTFR) from three model specifications: (1) estimated TFR consistent but not identical with the TFR estimated by the UN (“Main model (UN-consistent)”; (2) estimated TFR fully consistent (nearly identical) with the TFR estimated by the UN ( “UN-fully -consistent”, and (3) estimated TFR consistent only with the TFR estimated by the DHS ( “DHS-consistent”). For education- and age-specific fertility rates that are UN-fully consistent, please see https://doi.org/10.5281/zenodo.8182960 Variables Country: Country names Education: Four education levels, No Education, Primary Education, Secondary Education and Higher Education. Year: Five-year periods between 1980 and 2015. ESTFR: Median education-specific total fertility rate estimate  sd: Standard deviation Upp50: 50% Upper Credible Interval Lwr50: 50% Lower Credible Interval Upp80: 80% Upper Credible Interval Lwr80: 80% Lower Credible Interval Model: Three model specifications as explained above and in the working paper. DHS-consistent, Main model (UN-consistent) and UN-fully consistent. List of countries: Angola, Benin, Burkina Faso, Burundi, Cote D'Ivoire, Cameroon, Central African Republic, Chad, Comoros, Congo, Democratic Republic of Congo, Eswatini, Ethiopia, Gabon, Gambia, Ghana, Guinea, Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Tanzania, Togo, Uganda, Zambia, Zimbabwe

opennotspecifiedOct 2022View details →

Fig. 3 in Microbial transformation of capsaicin by several human intestinal fungi and their inhibitory effects against lysine-specific demethylase 1

Fig. 3. The metabolites of capsaicin transformed by Rhizopus oryzae R2701 and its hypothetical biotransformation pathway.

opennotspecifiedOct 2022View details →

Fig. 1 in Microbial transformation of capsaicin by several human intestinal fungi and their inhibitory effects against lysine-specific demethylase 1

Fig. 1. The metabolites of capsaicin transformed by Aspergillus fumigatus PB4204, Aspergillus japonicus Y4009A, and the hypothetical biotransformation pathway.

Fig. 9 in Tissue specificity of (E)-β-farnesene and germacrene D accumulation in pyrethrum flowers

Fig. 9. Dynamic emission and distribution of the two predominant sesquiterpene volatiles EβF and GD in T. cinerariifolium flower. EβF and GD concentrations peak at the S2 flower stage. Their microdistribution patterns strongly differ though: the peduncle is dominated by EβF but the corolla and stigma by GD. This uneven distribution leads to a dominant EβF emission at the S2 flower stage which attracts ladybird beetles and repels aphids. Presumably the later dominance of GD released from the stigma and corollas when disc florets open up is promoting the pollination process. In peduncle, relatively pure EβF was largely produced in the parenchyma cells and further transported and stored in the secretory ducts. Lines at the S2 and S4 flower stages show the EβF/GD oil ducts. Pyrethrins were increasingly accumulated in the pericarp of the developed ovary.

Fig. 7 in Tissue specificity of (E)-β-farnesene and germacrene D accumulation in pyrethrum flowers

Fig. 7. NADI and aniline blue staining of cross sections of young T. cinerariifolium flower peduncles. (A and B) NADI staining of two cross sections of S0 and S1 upper flower peduncles. The positive NADI stained oil droplets are pointed out by white arrows. (C) Cross section of S2 flower peduncle stained by NADI reagent coupled with aniline blue staining observed under the bright light. (D) Cross section of the same S2 flower peduncles as in C observed under the UV light, callose in the sieve elements was stained by aniline blue and indicated by white arrowhead. The white arrows point to NADI stained oil droplets in C that appear to be outside the cell wall as indicated by red arrows in D. Ph, phloem; Xy, xylem; Co, cortex; Pi, pith; Se, sieve element.. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6 in Tissue specificity of (E)-β-farnesene and germacrene D accumulation in pyrethrum flowers

Fig. 6. Chromatogram profiles of hexane extract of T. cinerariifolium stigma and corolla analyzed by GC–MS. For analysis conditions see text. GC-MS chromatogram of volatiles extracted from stigma and corolla are presented as total extracted ion current (EIC, m/z 93). Methyl laurate was used as an internal standard (IS). The concentration of EβF and GD were showed in the figure and presented as ng/mg fresh tissue. Localization of terpene oils in the closed and open disk floret was shown in the inset drawing pictures with purple (EβF and GD) and blue (pyrethrins) color.. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in Tissue specificity of (E)-β-farnesene and germacrene D accumulation in pyrethrum flowers

Fig. 4. EβF and GD accumulations in different parts of flower head at different developmental stages. Quantitated EβF and GD accumulations in different parts (upper disk floret, lower disk floret, receptacle, bract) of field flower following flower development. Fresh flower tissues were extracted by hexane and analyzed by GC-MS. GD concentration was evaluated by compared with EβF. Error bars indicate ± SD of three biological replicates.

Fig. 5 in Tissue specificity of (E)-β-farnesene and germacrene D accumulation in pyrethrum flowers

Fig. 5. NADI staining for longitudinal sections of flower of T. cinerariifolium. (A) NADI staining for disk floret of S1 flower. (B) NADI staining of the upper section of a disk floret of S1 flower. (C) NADI staining of the upper section of a disk floret of an S2 flower showing the bilobed stained stigma on top of a short style. (D) NADI staining of longitudinal sections of S3 flower. (E) NADI staining of the upper sections of disk florets of S4 flower. Df, disk floret; Rec, receptacle; Sty, style; An, anther; Ac, anther cylinder; Sti, stigma; Co, corolla; GT, glandular trichome. Pure GD, EβF and pyrethrins were individually stained by NADI reagent and shown in A. The white arrows point to the positive NADI stained oils. The inset picture in D represents the top view of the flower.

Fig. 1 in Tissue specificity of (E)-β-farnesene and germacrene D accumulation in pyrethrum flowers

Fig. 1. Morphological and anatomical structures of stage 2 (S2) Tanacetum cinerariifolium flowers. (A) Longitudinal section of an S2 flower with peduncle attached. The arrow points to a cavity in the center of the pith that will become larger when the flower matures. The inset pictures show cross sections of the upper and lower peduncle (4 cm individually). (B) A closed disk flower. (C–G) SEM images of the T. cinerariifolium S2 flower organs and tissues. (C) The corollas of closed disk flowers. (D and E) Different magnifications of bract tissue, with an arrow pointing to the longitudinal serpentine vascular system located in the joining site with receptacle. (F and G) Cross sections of the flower peduncle and a detailed view of the disintegrating pith tissue.

Fig. 2 in Tissue specificity of (E)-β-farnesene and germacrene D accumulation in pyrethrum flowers

Fig. 2. Analysis of terpene volatiles in flower head and peduncle (upper and lower) at early flowering stages (S0–S2). (A) Chromatogram profiles of T. cinerariifolium S1 flower head and upper peduncle hexane extracts analyzed by GC–MS and presented as total extracted ion current (EIC, m/z 93). Chromatogram profile of lower peduncle hexane extraction was not shown here due to the same peak patterns with the upper peduncle. For analysis conditions see text. List of the main terpene components, see Table 1 (B) Quantification of EβF and GD content in flower head and lower and upper peduncle at early flowering stages (S0–S2). Fresh flower tissues were extracted with hexane and analyzed by GC-MS. The GD and EβF concentrations were both estimated using an EβF standard reference curve. Error bars indicate ± SE of three biological replicates.

Fig. 8 in Tissue specificity of (E)-β-farnesene and germacrene D accumulation in pyrethrum flowers

Fig. 8. NADI staining and aniline blue staining for cross and longitudinal sections of young T. cinerariifolium flower peduncles. (A) Bright field micrographs of the cross sections of S2 flower peduncle double-stained by NADI reagent and aniline blue. (B) Cross section of the same S2 flower peduncles as in A observed under the UV light. The arrows point to stained terpene oil droplets. (C and D) NADI staining of two longitudinal sections of flower peduncles. Co, cortex; Vb, vascular bundle. The purple-violet oil droplet in the inset of A is pure EβF standard stained by NADI reagent.. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 6 in Genome-wide transcriptional analysis unveils the molecular basis of organ-specific expression of isosteroidal alkaloids biosynthesis in critically endangered Fritillaria roylei Hook

Fig. 6. Heatmap representing organ specific significantly enriched genes corresponding to (A) Isosteroidal alkaloid biosynthesis, (B) Sucrose and starch metabolism, (C) UGTs and CYPs, (D) aquaporins, (E) ABC transporters, (F) Transcription factor and Transposable elements. The red-blue scale represents positive enrichment (red) and negative enrichment (Blue) of transcripts. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5 in Genome-wide transcriptional analysis unveils the molecular basis of organ-specific expression of isosteroidal alkaloids biosynthesis in critically endangered Fritillaria roylei Hook

Fig. 5. Significantly enriched transcripts (nodes) in predicted interactome network. (A) Steroidal alkaloid biosynthesis pathways (B) Isopentenyl diphosphate biosynthesis pathway, (C) Sucrose and starch metabolic pathways and (D) Aquaporins. The nodes encircled in red color represents higher enrichment in the bulb while the nodes encircled in grey and blue represents higher enrichment in arial organs (leaf and stem). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 7 in Genome-wide transcriptional analysis unveils the molecular basis of organ-specific expression of isosteroidal alkaloids biosynthesis in critically endangered Fritillaria roylei Hook

Fig. 7. qRT-PCR expression-based validation of RNA-seq data using potential 20 genes involved in isosteroidal alkaloid biosynthetic pathway. (A) Bulb vs. Leaf, (B) Leaf vs. Stem and (C) Bulb vs. Stem.