b, c There was no difference in tumor size (b) or the percentage of patients with positive lymph nodes (c) in breast cancers with higher versus lower stromal or epithelial FBLN1 Discussion The vast array of molecules involved in breast stromal–epithelial interactions makes it difficult to identify dominant molecules affecting breast cancer initiation and progression. The ambiguity of the spatial and temporal origin of carcinogenesis-related

functional and molecular alterations adds another layer of complexity. mTOR activity Even though these alterations have been identified in both stromal and epithelial compartments early in the carcinogenic process [26–28], it is still unclear which compartment is affected first—the epithelium, stroma or both of them simultaneously. These

complex issues emphasize a need for additional assessment of the molecular and functional signatures of fibroblasts in normal and cancerous tissues that can eventually expand our understanding of the role of fibroblast–epithelial interactions in cancer. Results from the current study complement our previous work demonstrating that NAF have a greater inhibitory effect on the proliferation of breast epithelial cells than CAF [3]. We now show that both soluble and matrix- or membrane-bound molecules are important for the inhibitory signal. The greater inhibition of epithelial growth by NAF in Tanespimycin direct co-cultures is likely a result of the closer proximity of epithelial cells and fibroblasts selleck chemical allowing for direct

contact between different cell types OSBPL9 and their ECM. However, significant inhibition of epithelial cell growth by NAF in transwell cultures indicates that soluble secreted factors are also important. Therefore, our selection of differentially expressed genes for validation included soluble secreted factors, ECM-bound proteins and molecules that contribute to remodeling of the ECM. Remodeling of the ECM is characteristic of the stromal response to cancer, contributes to the tumor microenvironment and results in molecular alterations that affect cancer behavior [29, 30]. In CAF, we observed significant overexpression of several molecules involved in ECM remodeling—PAI2 and PLAT. PAI2 inhibits ECM remodeling by inhibiting urokinase plasminogen activator (uPA) [31–33], while PLAT activates a variety of proteins embedded in the ECM by cleaving plasminogen to plasmin and thereby promoting tissue degeneration and ECM remodeling [34, 35]. Overexpression of TFPI2 in CAF was not confirmed by QRT, but TFPI2 is an inhibitor of coagulation and is proposed to be a maintenance factor of ECM remodeling [36]. Our results indicate a borderline increase in MMP1. MMP1 breaks down collagens and other ECM components and has been reported to be expressed at a higher level in breast cancers, but primarily in cancer epithelial cells rather than stromal fibroblasts [37].

All HBV plasmids expressed detectable HBsAg and HBeAg in mice sera (Figure 6). As compared to the control mice (HBV+L1254), B245 and B376 treatments reduced HBsAg expression by over 99% in all five HBV genotypes. Furthermore, B1581 and B1789 treatments suppressed HBsAg by over selleck chemicals 99% in mice infected with HBV genotypes A, B, C and D. In a novel W29 strain representing genotype I however, B1581 and B1789 treatments only reduced HBsAg expression by about 90%.

With regards to serum HBeAg for genotypes A, B, C, D and I, B245, B376, B1581 and B1789 treatments suppressed HBeAg by 96%~99%, 79%~99%, 94%~99%, and 89%~99%, respectively. The overview of the results shows that B245 is the most

potent agent. Figure 6 Kinetics of serum HBV antigen (HBsAg and HBeAg) of various HBV genotypes in RNAi-treated mice. For each group (each line in the figure), the experiment was repeated using two different groups of five mice. Due to limited serum resources, each sample was diluted 4SC-202 10-fold. (A) Genotype Ae (N10 group), (B) Genotype Ba (C4371 group), (C) Genotype NVP-LDE225 manufacturer C1 (Y1021 group), (D) Genotype D1 (Y10 group), (E) Genotype I1 (W29 group). Discussion Activated RNAi pathway can silence HBV replication and expression [13, 14]. However, in most previous studies, the activity of RNAi against HBV is often evaluated with only one HBV strain [15–18]. Nine HBV genotypes (including a newly identified genotype “”I”"), designated as the letters A through I, have been recognized with an accompanying sequence divergence of >8% over the entire genome Acyl CoA dehydrogenase [19–21]. The influence of genotypes on HBV replication efficacy and antigen expression level had been proved to be various and that may further associate with clinical outcomes and antiviral treatments responses [22]. Hence, RNAi designed for one genotype may not necessarily be effective against another genotype. Given the high heterogeneity of HBV strains and the sensitivity of siRNA to the sequence changes,

designing siRNA targets against the conservative site on HBV genome is essential to ensure activity across all genotypes [23]. In shRNA expression systems, two different promoters are predominantly used: U6 and H1, both driven by human polymerase III (poly III). Compared to Pol II promoters, Pol III promoters generally possess a greater capacity to synthesize RNA transcripts of a higher yield and rarely induce interferon responses [17, 24]. However, a previous study noted that U6 Pol III-expressed shRNAs may cause serious toxicity in vivo by saturating the endogenous miR pathway [25]. In this report, we constructed 40 shRNA plasmids (Table 1) with various targets, using a human H1 Pol III promoter.

PCR cycling consisted of an initial denaturation at 94°C for 6 min; followed by 30 cycles of denaturation at 94°C for 30 s, annealing at 57°C for 45 s, and extension at 72°C for 2 min; and a final extension at 72°C for 3 min. Amplified DNA was verified by electrophoresis on 2% agarose gels. Daporinad manufacturer Restriction digest The PCR products from the four replicates were pooled into two samples, purified with QIAquick PCR purification kit (Qiagen, Hilden, ALK inhibitor Germany), and finally eluted in a volume of 30 μl EB buffer (10 mM Tris, pH 8.5). Then 15 μl purified PCR product was digested overnight (or 3 hours) at 37°C with 0.02 U of Hha1 (Boehringer, Mannheim,

Germany) in a 20 μl reaction mixture. Terminal-restriction fragment length polymorphism Each sample was analysed as two replicate fragments (T-RFs) by electrophoresis on an automatic sequence analyzer (ABI-PRISM-373-DNA-Sequencer; PE Biosystems, Foster City, California). Aliquots (2 μl) of T-RFs were mixed with

2 μl of deionized formamide, 0.4 μl of loading buffer (PE Biosystems), and 0.6 μl of DNA fragment length standard (MegaBace ET900, GE Healthcare, Hillerød, DK). The T-RF mixture was denatured at 94°C for 2 min and chilled on ice prior to electrophoresis. Five check details microliter aliquots of the mixture were loaded on a 36-cm, 6% denaturing polyacrylamide gel. Electrophoresis settings were 2,500 V and 40 mA for 10 h, using the B filter set. Due to sequence species specific variations in the ribosomal gene, a restriction digest will give rise to T-RF

of different size, and when many species are mixed as in the intestinal microbiota this can be visualized as a pattern of peaks in an electropherogram, a fingerprint profile. These profiles were collected by the software and analysed by the use of BioNumerics software (Applied Maths, Sint-Martens-Latem, Belgium). The length of each band was determined by comparing it towards the internal standard Clomifene ladder. From each sample two replicates were compared, and weak bands that were only represented in one of the two were rejected to exclude false T-RFs from the fingerprint. After normalization of all profiles towards the internal standard, they were compared using BioNumerics. The comparisons between cages were based on calculating the Dice similarity coefficient and the unweighted pair group method using arithmetic averages for clustering. Principal Component Analysis (PCA) was performed to reflect the grouping and relatedness of samples. Pyrosequencing of ribosomal genes Samples (n = 10) from the same cage types (CC, FC, and AV), and sampling date (before inoculation and 4 weeks PI.), were pooled by mixing 250 ng of purified DNA from each sample in one tube, in total making up 6 samples.

(144 bp) Ent-F: CCC TTA TTG TTA GTT GCC ATC ATT 60 [41] Ent-R: ACT CGT TGT ACT TCC CAT TGT †Enterobacteriaceae (195 bp) Enterobac-F: CAT TGA CGT TAC CCG CAG AAG AAG C 63 [42] Enterobac-R: CTC TAC GAG ACT CAA GCT TGC †Staphylococcus spp. (370 bp) TStaG422: GGC CGT GTT GAA CGT GGT CAA ATC 55 [43] TStaG765: TIA CCA TTT CAG TAC CTT CTG GTA A †Bacillus spp. (995 bp) BacF: GGGAAACCGGGGCTAATACCGGAT 55 [44] BacR: GTC ACC TTA GAG TGC CC †E. coli

(544 bp) ECP79F: GAA GCT TGC TTC TTT GCT 54 [45] ECP620R: GAG CCC GGG GAT TTC ACA T †SLT-I (614 bp) VT1 (SLTI-F): ACA CTG GAT GAT CTC AGT GG 55 [44] Selleck GF120918 VT2 (SLTI-R): CTG AAT CCC CCT CCA TTA TG †SLT-II (779 bp) VT3 (SLTII-F): CCA TGA CAA CGG ACA GCA GTT 55 VT4 (SLTII-R): CCT GTC AAC TGA GCA CTT T 16S rDNA Sequencing 616V: AGA GTT TGA TYM TGG CTC 52 [46] (~1500 bp) 630R: AAG GAG GTG GAT CCA RCC   CAKAAAGGAGGTGGATCC Random Primer for RAPD DAF4: CGG CAG CGC C 35 [47]   M13V: GTT TTC CCA GTC ACG ACG

TTG 35 [48] Universal Primers HDA1: ACT CCT ACG GGA GGC AGC AG 52 [49]   HDA2: GTA TTA CCG CGG CTG CTG GCA     HDA1 + GC: CGC CCG GGG CGC GCC CCG GGC GGG GCG GGG GGC ACG GGG GGA CTC CTA CGG GAG GCA GCA G   TA Cloning M13Forward (−20): GTA AAA CGA CGG CCA G 55 [50]   M13Reverse: CAG GAA ACA GCT ATG AC   †Pediocin Structural Gene pedA (100 bp) pedA2RTF: GDC-0449 solubility dmso GGC CAA TAT CAT TGG TGG TA 60 [25] pedA2RTR: ATT GAT TAT GCA AGT GGT AGC C TqM-pedA: FAM-ACT TGT GGC AAA CAT TCC TGC TCT GTT GA-TAMRA †Total Bacteria (727 bp) TotalBac-F785: GGA TTA GAT ACC CTG GTA GTC 52 [51–53] TotalBac-R1512r: TAC CTT GTT ACG ACT T TaqMan click here 1400r Probe: 6-FAM-TGA CGG GCG GTG TGT ACA AGG C-TAMRA † All dagger-marked primer pairs were used in the preparation of standards and qPCR analyses. Partial 16S ribosomal rRNA gene amplification and sequencing Isolates differing in origin or RAPD pattern were identified by partial sequencing of 16S rRNA genes. PCR reaction was performed in a this website master mix with a final volume of 50 μL containing 1.5 U Taq DNA Polymerase (Invitrogen), 5 μL of 10X PCR

Reaction Buffer (Invitrogen), 1.5 μL of 25 mmol L-1 MgCl2 (Invitrogen), 25 pmol of universal bacterial primers 616V and 630R (Table 2), 1 μL of 10 mmol L-1 dNTP, and 1 μL of template DNA. PCR product was electrophoresed in 1.0% (w/v) agarose gel, with a 2-log ladder (New England Biolabs). All sequencing data were obtained from sequencing services provided by Macrogen (Rockville, USA). The 16S rRNA gene sequences of isolates were compared with 16S rRNA gene sequences of type strains in the Ribosomal Project Database Project II (RDP-II; Michigan State University, East Lansing, USA, http://​rdp.​cme.​msu.​edu). Identification of E. coli with species-specific PCR and API 20E test system PCR amplification of the hypervariable regions of the E. coli 16S rRNA gene used primers described by Sabat et al.[45]. The PCR reaction mix (final volume 50 μL) consisted of 1.

To further examine this hypothesis, we looked at the presence of TA loci that are known to affect persister formation in 15 E. coli and Shigella taxa, as well as in Escherichia fergusonii. We found significant variation in the presence of TA modules across different E. coli isolates (Figure 6), suggesting that these loci are lost (and/or gained) over relatively short time scales in this clade. Such changes in the number

or types of TA pairs are likely to affect the production of persister cells, as has been shown experimentally [11]. Figure 6 Known persister loci are rapidly gained and/or lost within the E. coli clade. Grey boxes indicate the presence of the orthologue in the indicated genome; white indicates absence. The data suggests that toxin – antitoxin loci undergo rapid loss and/or gain within the E. coli clade. Orthologue presence – absence of toxin-antitoxin NVP-BGJ398 price loci is based on a bidirectional best-hit analyses [33] for 14 E. coli and Shigella taxa and E. fergusonii. The rate of switching from normal to persister state is the primary determinant of persister fractions In the analyses above, we have used information

from cell-killing dynamics to infer the proportion of persister cells that were present at the start of antibiotic killing. These persisters are formed during exponential growth, and the fraction that is present is determined largely by two independent parameters, the rates of switching FGFR inhibitor to and from the persister cell state. To gain additional insight into the Aurora Kinase mechanistic underpinnings of persister formation, we examined the relationship between the persister fraction and these two parameters. We find strong evidence that the primary determinant of the persister fraction is the

rate at which persister cells are formed from normal cells: these two variables are strongly Quisinostat manufacturer correlated across both strains and antibiotics (Figure 7). In contrast, the rate of switching from persister to normal cell has little to no relationship with the persister fraction. Figure 7 The primary determinant of the persister fraction is the rate of switching to the persister state. A: The rate of switching from the normal cellular state to the persister state is strongly correlated with the fraction of persisters in the population. B: There is little to no correlation between the rate of switching from the persister state to the normal state and the fraction of persisters. C: No correlation exists between the rate of death of normal cells and the persister fraction. Discussion In generating antibiotic kill curves from CFU data, we have shown that these curves differ substantially between environmental isolates of E. coli for single antibiotics. In addition, we found that the shape of these curves differs between different antibiotics.

Figure 2 Cross-sectional TEM images, EDS concentration profiles, and AFM images. (a, c) Cross-sectional TEM images

before and after annealing at 1,250°C with SAED images in the insets. (b, d) EDS concentration profiles of Er, Sc, O, and Si for the corresponding inset TEM images (a) and (c), respectively. (e, f) AFM images of the sample after deposition and annealing at 1,250°C. After thermal treatment at 1,250°C in O2, we formed a unique layer with an average thickness of 102 nm as shown in Figure 2c. The SAED images show a single-crystal compound. The interplanar spacings are 1.30, 1.54, and 2.61 Å, corresponding respectively to (203), (33-2), and (220) planes, for Er2Si2O7. The annealing treatment at 1,250°C results in the intermixing between different layers with homogeneous selleck chemicals concentration profiles of Er, Sc, Si, and O in depth (Figure 2c). Indeed, Er and Sc diffuse in the SiO2 layer. EDS measurements show that Er and Sc concentrations are 6.7 × 1021 and 1.4 × 1021 atoms/cm3, respectively, with the Er/Sc ratio of 4.5. This high concentration of Er incorporated into the Sc2O3 matrix is due to the presence of Sc that creates concentration quenching. From the GIXD and TEM analysis, we conclude

that Er2Si2O7 is in mTOR inhibitor the bottom and top layers before annealing and that the Er x Sc2-x Si2O7 phase is dominant after annealing at 1,250°C. In addition, it is considered that the high-temperature annealing Carbohydrate at 1,250°C and long annealing time enhance the reaction of Er-O and Si-O precursors with the SiO2 interlayers, converting most of the Er2SiO5 to Er2Si2O7 [18]. The existence of the Er x Sc2-x SiO5 phase after annealing determined by GIXD analysis may be due to size of the analyzed surface which is much bigger using an X-ray beam than a TEM Selleck PLX3397 electron beam. The surface morphology after deposition and annealing was analyzed by AFM. The AFM images in Figure 2e,f show a flat surface with no cracks after annealing up to 1,250°C. After deposition, the roughness value of approximately 2.7 nm was measured against that of 4.1 nm after annealing because of the increase of the grain size. Er

diffusion at 1,250°C was analyzed by measuring the Er concentration profiles before and after heat treatment in Figure 3. After deposition, the atomic weight of Er is estimated to be 35% to 40%, and these values decrease from 11% to 14% after annealing at 1,250°C due to the homogeneous redistribution of Er atoms in the annealing layers. Er diffuses in the depth with a diffusion length of around 39 nm in the bottom layer of SiO2 compared to the as-grown sample (Figure 3), but we suppose that Er diffuses with the same thickness in the other layers. The diffusion length is given by , where D is the diffusion coefficient and t is the duration of the thermal treatment. For the annealing temperature of 1,250°C, the diffusion coefficient D is 1 × 10-15 cm2/s. This value is fairly consistent with the value of 0.

Ruprecht et al. (2014) studied the genetic diversity of green algal partners (photobionts, find more chlorobionts) in the biocrust-forming lichen P. decipiens along four European sites of the SCIN project. Using phylogenetic analyses based on molecular data, they found a high chlorobiont diversity within P. decipiens, which was associated with several different species of Trebouxia and Asterochloris. Most of the chlorobiont species appeared to be cosmopolitan,

but five clades were unevenly distributed between the sampling sites. The wide range of chlorobiont species observed might contribute to the observed abundance of P. decipiens in areas widely differing in their environmental conditions and geographical location, such as a semi-arid shrubland in Spain and an alpine site in the Austrian Alps. The impacts of climate change on biocrust

constituents and the ecological processes associated with them are being increasingly studied (Escolar et al. 2012; Maphangwa et al. 2012; Zelikova et al. 2012; Reed et al. 2012; Maestre et al. 2010, 2013). Ladrón de Guevara et al. (2014) adds to this growing, but still scarce, body of literature. These authors report results from a manipulative full factorial experiment conducted in central (Aranjuez) and southeastern (Sorbas) Spain aiming to evaluate how precipitation, temperature, and biocrust cover, affect the assimilation and net C balance of biocrusts. They found that warming reduced the fixation of atmospheric C in biocrust-dominated microsites

throughout the year in Sorbas. In Aranjuez, there was an interaction this website either between the three factors: during winter, net photosynthesis was significantly greater in high biocrust cover plots under natural conditions than in the rainfall exclusion treatment. The authors also noted the importance of rainfall and non-rainfall water inputs (NRWI) on responses to the climate change treatments they employed. This work suggests that changes in NRWI regimes as consequence of global warming could have a greater impact on the carbon balance of biocrusts than changes in rainfall amounts. They also indicate that climate change may reduce the photosynthetic ability of lichens, with a consequent possible reduction of their dominance in biocrust communities in the mid- to long term. Raggio et al. (2014) also evaluated results from the simultaneous monitoring of gas exchange, chlorophyll fluorescence, and microclimatic variables, of the most abundant biocrust constituents (the lichens Squamarina cartilaginea, Diploschistes Quizartinib in vitro diacapsis, Toninia albilabra and P. decipiens, and the moss Didymodon rigidulus) in the Tabernas badlands (Almeria, SE Spain). Measurements during typical activity days in the field over 1 year showed a similar physiological performance of the different biocrust constituent types studied.

As Figure 6B shows, most points were located www.selleckchem.com/products/OSI-906.html around the origin point, and only a few points were away from the origin. The significant differences between each group were caused by the compound represented by these scattered points. Inspection of the loading SWCNTs suggested that the metabolic effects following SWCNTs treatments were characterized by significant changes in very low density lipoprotein (VLDL) and LDL, (δ0.82, δ0.86, δ1.26) and phosphatidylcholine (δ3.22) as well as several unknown

materials (δ1.22, δ1.3), which require further study (Figure 6B). The SWCNTs-induced variations in plasma endogenous metabolites are summarized in Table 2. Figure check details 6 LED score plot (A) and loading plot (B) for the endogenous metabolite profiles in plasma samples after exposed to SWCNTs in rats. Control group (diamond), SWCNTs-L (square), SWCNTs-M (triangle), and SWCNTs-H (circle) groups. Table 2 Summary of rat plasma metabolite variations induced by SWCNTs administration Chemical shift (δ, ppm) Metabolites SWCNTs-L group SWCNTs-M group SWCNTs-H group 0.80-0.90, 1.20-1.29 Lipoprotein ↓ ↓ ↑ 0.94 Ile + Leu ↑ ↑ ↑ 1.31-1.33, 4.10-4.12 Lactate ↑ ↑ ↑ 1.48 Alanine ↓ ↓ ↓ 1.91 Acetate ↓ ↓ ↑ 2.03-2.04

NAc ↑ ↑ ↑ 2.13-2.14 OAc ↑ ↑ ↑ 2.42-2.44 Gln-glutamine ↑ ↑ ↑ 3.03 Creatine ↓ ↓ ↑ 3.20 Cho ↑ ↑ ↑ 3.22, 3.23 PCho ↑ ↑ ↑ 3.40-4.00 Glucose ↓ ↓ ↓ 0.70 HDL ↑ ↓ ↑ 0.82, 0.86 E7080 VLDL/LDL ↓ ↓ ↓ 1.10 HDL ↑ ↓ ↑ 1.26 VLDL/LDL ↓ ↓ ↓ 1.58 Lipid CH2CH2CO ↓ ↑ ↓ 2.02 NAc ↑ ↓ ↑ 2.14 OAc ↓ ↑ ↑ 2.26 Lipid CH2CO ↓ ↑ ↓ 3.22 PtdCho ↓ ↑ ↓ 5.30 UFA ↑ ↓ ↑ Ile, isoleucine; Leu, leucine; NAc, n-acetylgalactosamine; OAc, O-acetyl glucoprotein; Cho, choline; PCho, phosphatidylcholine; HDL, high-density lipoprotein; VLDL, very low density lipoprotein; LDL, low-density lipoprotein; PtdCho,

phosphatidylcholine; UFA, unesterified fatty acids. Down arrow indicates decrease, and up arrow indicates increase, compared to control. 1H NMR spectroscopic and pattern recognition analysis of aqueous soluble liver extract Typical 1H NMR spectra of aqueous soluble liver extract following administration of SWCNTs are shown in Figure 7. Examination of the score plot (Figure 8A) from 1H NMR spectra of samples ID-8 from the control and dosed groups indicated that the control group was separated from the three treated groups, but the three treated groups overlapped with each other. It revealed that SWCNTs could cause cell oxidative damage, but the dose-related hepatotoxicity was not obvious. Figure 7 1 H NMR spectra of rat aqueous soluble liver tissue extracts after exposed to SWCNTs in rats. (A) Control group and (B, C, D) SWCNTs-L, SWCNTs-M, and SWCNTs-H groups, respectively. Figure 8 Score (A) and loading (B) plots for the endogenous metabolite profiles in aqueous soluble liver extracts after exposed to SWCNTs in rats. Control (diamond), SWCNTs-L (square), SWCNTs-M (triangle), and SWCNTs-H (circle) groups.

The multidimensional PRI-724 concentration scaling supports this finding in that bee communities of openland plots were highly clustered, while forested habitats covered a larger variety of species compositions. Hence, agroforestry systems

may maintain high regional species richness due to high management diversity and medium-intensity disturbance, enhancing floral abundance and spatiotemporal habitat heterogeneity. Canopy disturbances in primary forests occur frequently due to tree fall gaps, resulting in increased herbaceous vegetation density and insect richness compared to interior forest (Dirzo et al. 1992; Bruna and Ribeiro 2005; Horn et al. 2005; Wunderle et al. 2005). Anthropogenic disturbances in agroforestry systems, such as opening of the canopy (Liow et al. 2001; Winfree et al. 2007), appeared to mTOR inhibitor review simulate and promote the positive effect of natural tree fall on the plant, and thereby, the bee community in our study. Forested habitats offer nesting sites for many bee species (Klein et al. 2003b; Brosi et al. 2007; Brosi et al. 2008), while openland provides better food resources in the herb layer, but bees are known to often bridge different habitats providing different resources (Tscharntke et al. 2005a). Therefore, bee diversity of human-dominated habitats may often depend SRT1720 cost on large areas of natural habitats providing nesting resources (Steffan-Dewenter et al. 2002),

but floral resources may be similarly or even more important (Westphal et al. 2003; Jha and Vandermeer 2009). In conclusion, the different habitat types strongly differed in their relative contribution to the bee community. The land-use

systems in the studied human dominated tropical landscape strongly increased local and regional pollinator species richness through enhanced heterogeneity of the landscape. Local species richness was highest PFKL in openland, but the high beta-diversity of agroforestry systems levelled off this difference, resulting in similar gamma-diversity. However, farmers recently tend to remove shade trees in coffee and cacao agroforestry, thereby simplifying these systems (Perfecto et al. 1996; Steffan-Dewenter et al. 2007). Such reduction of heterogeneity in tropical landscapes will further reduce overall biodiversity and associated ecological services such as pollination of wild and crop plants provided by the native bee communities. Acknowledgments We thank Andrea Holzschuh and Owen T. Lewis for valuable suggestions on the manuscript, Stephan Risch, Leverkusen (Germany) for species identification of bees and Ramadhanil Pitopang, Palu (Indonesia) for identification of herbaceous plant species. We thank the Deutsche Forschungsgemeinschaft (DFG) for financing the Collaborative Research Centre STORMA (SFB 552), LIPI for the research permit and Damayanti Buchori for collaboration.

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