Blog Dast Richtlinie 009 Pdf Reader. Adobe Acrobat Reader DC. DASt Richtlinie 009 (2005). System requirements for Adobe Acrobat Reader DC. Guide to Stability Design Criteria for Metal Structures. Guide to Stability Design Criteria for Metal Structures. The reader is referred to the paper.

Abstract Cultivated for centuries, the varieties of rose have been selected based on a number of flower traits. Understanding the genetic and molecular basis that contributes to these traits will impact on future improvements for this economically important ornamental plant. In this study, we used scanning electron microscopy and sections of meristems and flowers to establish a precise morphological calendar from early rose flower development stages to senescing flowers. Global gene expression was investigated from floral meristem initiation up to flower senescence in three rose genotypes exhibiting contrasted floral traits including continuous versus once flowering and simple versus double flower architecture, using a newly developed Affymetrix microarray (Rosa1_Affyarray) tool containing sequences representing 4765 unigenes expressed during flower development. Data analyses permitted the identification of genes associated with floral transition, floral organs initiation up to flower senescence.

Quantitative real time PCR analyses validated the mRNA accumulation changes observed in microarray hybridizations for a selection of 24 genes expressed at either high or low levels. Our data describe the early flower development stages in Rosa sp, the production of a rose microarray and demonstrate its usefulness and reliability to study gene expression during extensive development phases, from the vegetative meristem to the senescent flower. Introduction Roses are widely used as garden ornamental plants and cut flowers.

A few flowering traits of roses are essential for the plants commercial value. Examples of these traits are plant architecture, continuous flowering, flower development, function and senescence, scent biosynthesis, reproduction and resistance to biotic and abiotic stresses. However, little is known about the molecular mechanisms that control these traits.

This dearth of information limits the scope of rational selection to improve the ornamental plants. During the past decade, using model species such as Arabidopsis thaliana, tobacco, Brachypodium distachyon, rice or maize, researchers significantly enhanced our understanding of the various aspects of plant development and resistance to biotic and abiotic stresses, and of the molecular and genetic pathways associated with these aspects. However, these model species are not suitable for the studies of other flowering traits such as recurrent blooming, scent production and double flower character. Rose represents an interesting ornamental model species to address some of these aspects. Cultivated roses have a very ancient history. The two major areas of rose domestication were China and the peri-mediterranean area encompassing part of Europe and Middle East, where Rosa chinensis Jacq. (respectively) were bred and contributed predominantly to the subsequent selection process.

Artificial crossing between Asian and European roses gave birth to “modern rose cultivars”. Although testimonies and historical records have documented major crosses that led to modern roses, the genetic basis on which the modern rose cultivars are established is still poorly understood. It has been reported that about 8 to 20 species out of about 200 wild species have contributed to the origin of present cultivars,,. In Rosa sp., EST sequencing has identified novel genes whose expression is associated with several rose traits, such as the scent associated genes O-methyltransferases and alcohol acetyltransferase and floral associated genes,,,,,,,. EST sequences were also used to generate a rose DNA microarray comprising 350 selected ESTs. Using this microarray, researchers discovered several novel floral initiation genes and flower scent–related candidate genes (i.e.

Germacrene D-synthase encoding genes). However, this array contains only a limited number of sequences that represent genes expressed at late petal development stages. With publicly available rose gene sequences, we generated a microarray and studied the gene expression throughout floral development, from the initial floral transition to floral senescence.

We created an annotated flower EST database corresponding to 4834 genes and used the sequences to develop an Affymetrix microarray. With this microarray, we compared the transcriptome at different floral development stages.

We found a good correlation between the microarray data and real time quantitative RT-PCR (qPCR) data for selected genes whose expression coincides with early, mid and late flower development stages. This dataset can help identify new rose genes associated with floral initiation, flower development and senescence. Rose flower development stages. (a) to (f): Morphology of the floral transition in one-time flowering roses ( R. Wichurana) Schematic representation of the different stages observed during the floral transition in spring is shown in the upper panel from a vegetative meristem (VM) to a floral meristem (FM). A to c: Light microscopy of cross section of meristems. D to f: Environmental scanning electron microscopy images.

Black bar: 10 µm. (g) to (k): Rose flower organogenesis stages. Cross sections of floral meristem and young flower buds. Images representing initiation of sepals (stages 1, g), petals (stage 2, i), stamens (stages 3, h) and carpels (stage 4, j). K: hypanthium starts introverting below the floral organs (stages 5). Black bar: 50 µm (g,h,i); 200 µm (j,k).

Visible rose flower stages. Pictures of rose flowers at flower bud with visible petals (stage VP), open flower stage (OF) and senescing Flower stage (SF). Sections of floral meristem and young flower buds () were used to define the floral organogenesis steps in R.

Chinensis cv. Five morphologically distinct developmental stages were easily distinguished under a dissecting microscope. At flower development stage 1, the floral bud is surrounded by bracts, the floral meristem is flat and five sepal primordia are visible. Floral organs subsequently form following a radial gradient so that the most external organs are the more differentiated.

At stage 2, petal primordia are apparent on the flank of the hypanthium. At development stage 3 stamens primordia appear on the flank of the hypanthium while petal primordia continue developing. At stage 4, carpel primordia are the last organs that appear in the center of the hypanthium, while the other organs continue developing. At stage 5, all floral organs are apparent, and the hypanthium starts to sink below the perianth and stamens. During the onward development stages the hypanthium continues to form and the flower becomes clearly visible (). The four types of floral organs continue developing and flowers start opening (VP stage for visible petals) (). Then the flower fully opens (OF stage for open flower), and finally senesces (SF stage for senescing flowers).

Rose EST database creation and Rosa1_Affymetrix custom array design We collected the available rose genes sequences (ESTs and mRNA) and built a comprehensive database. Using sequence clustering, we generated a dataset comprising 4765 unique sequences (clusters and singletons) and deposited them in. For most of the clusters, one representative EST was chosen based the following criteria. Its sequence is larger than 600 nucleotides and preferably corresponding to the 5′ end gene sequence. Because the rose is highly heterozygous, such strategy should prevent using chimerical sequences that might have been obtained during the clustering process. However, 343 clusters did not meet the criteria above. For these 343 clusters, two or more ESTs representing the unique sequence were used.

In total, 5175 unique rose EST sequences representing 4765 unique sequences were used for the Rosa1_Affymetrix array design and a total of 6,289 probe sets including Affymetrix control probesets were designed. The arrays were manufactured by Affymetrix (). Array sequences annotation We used the Blastx algorithm against the nr database to identify the best protein hits for the 5175 unique rose sequences, and analyzed these results using Blast2go software. 3959 sequences (76.5%) produced a significant match with one or more entry in the database. Among the 3959 sequences, 222 (5.6%) could not be mapped with GO terms and 3737 had at least 1 GO term. For 1439 sequences, full automatic annotations were obtained. Analysis of GO biological process mapping showed that out of these 1439 sequences, 700 (48.6% of mapped sequences) were annotated as involved in primary metabolism processes and only 43 were annotated as putative secondary metabolism genes.

120 sequences (8.33% of mapped sequences) were mapped with the GO:0010468 annotation corresponding to regulation of gene expression. GO molecular function analysis showed that 38 sequences (2.6% of mapped sequences) had putative transcription factor activity (GO:00037000). The complete list of these sequences represented in the array, giving the first Blastx hit, the Blast2go computed annotation and gene ontology, is shown in.

About 23.5% of the rose sequences produced no significant Blast hit in the gene databases. It is likely that the sequences of these genes have diverged far enough to render the annotation difficult. These highly divergent genes may have evolved functions that are be specific to the Rosa genus or Rosaceae family and are therefore of particular interest. Real time quantitative RT-PCR (qPCR) analysis of six selected differentially expressed genes during rose floral organogenesis, floral opening and senescence in R. Chinensis cv.

QPCR data (black histograms) are compared to the microarray hybridization data (white histograms). Microarray data is presented regardless of Bonferroni test success.

Each pair of histograms represent successive comparisons between floral development stages 1+2, 3+4, 5, visible petals (VP), open flower (OF) and senescing flowers (SF). Gene expression associated with rose floral development We harvested six pools of samples corresponding to different flower development stages in R. Chinensis cv. Old Blush () and compared the transcriptome in successive stages (). We found three distinct groups with common genes (T-test). Super Mario Galaxy 2 Wii Iso Ntsc.

These groups corresponded to early, mid and late floral development (). A total of 135, 401 and 456 sequences appeared significantly and differentially regulated at least once during early, mid and late flower development stages, respectively. To validate and evaluate the accuracy of the microarray data, we performed quantitative real-time PCR (qPCR). Twenty four genes were selected from the microarray transcriptomics comparisons based on previous bibliographic reports and/or deregulation levels, then, using qPCR, we further characterized the expression profiles (; ). The correlation between the microarray results and those obtained by qPCR was assessed by calculating the Pearson's product moment correlation coefficient, ().

Pearson's correlation coefficient was calculated between each pair of fold change as estimated by microarray and qPCR experiments. The statistical significance of each Pearson's correlation coefficient was assessed using the cor.test routine in R.

A global correlation coefficient of 0.858 calculated by the average of every gene was observed. These results indicate that our microarrays are able to detect consistently both low and high fold-changes with high accuracy in different experimental conditions (). Transcriptome analyses during early flower development 135 genes were differentially expressed at during early floral organogenesis. Among these genes, 46 were found differentially expressed between stages 1+2 and 3+4 and 105 genes were differentially expressed between stages 3+4 and 5 ( and ).

An ACC synthase (AY803737) putative homologue was among the highly up-regulated genes between stages 1+2 and 3+4. In Arabidopsis, there are nine ACC synthases, many of which are expressed in the flower,. The floral organ identity MADS-box encoding genes,,, such as an APETALA3 homologue ( RhTM6/MASAKOB3 AB055966, ), the AGAMOUS ortholog ( RhAG, AB025645, ), or the rose PISTILLATA ortholog ( RhPI/ MASAKO BP, AB038462), were among the genes whose expression was up-regulated between stages 1+2 and 3+4 or between stages 3+4 and 5. Interestingly, genes that are predicted to have functions in cell wall remodelling, such as putative extracellular lipases (BQ106293, EC586717, EC588243, BI978064, BI977386, BQ105800), xyloglucan endotransglucosylase/hydrolase 2 ( XTH2, DQ320658), expansins (BI977621, EC589557), putative pectin esterase (BQ105504) and pectate lyase (BQ103887, BQ105987) were up-regulated between stages 3+4 and 5.

This result supports the idea that very active cell wall remodeling coincides with the beginning of organ elongation that occurs mainly at stage 5. A putative gibberellin 2-oxidase (BQ105545) was up-regulated early during flower development.

In Arabidopsis, a similar up-regulation of genes implicated in gibberellins metabolism and signaling have been described at early floral development,. In agreement with previously published data, our microarray analysis suggests that gibberellins are important during early floral development of rose plants,. Among the genes that showed strong down-regulation between stages 1+2 and 3+4, we found the putative orthologues of PERIANTHIA ( PAN), AP1 and SOC1 (AGL20). In Arabidopsis, PAN, AP1 and SOC1 are expressed in the floral meristem, but their expression is down-regulated in the subsequent steps during floral organs differentiation,,,, hence in agreement with the observed down-regulation of the rose homologues between flower development stages 1+2 and 3+4. Early to late floral development transition Sequences corresponding to 401 genes were detected as differentially regulated between stages 5 and VP. Among these genes, 233 were down-regulated and 168 were up-regulated (see for a selection of genes and for full list).

Genes that exhibit strong similarities to genes involved in carotene, flavonoid and anthocyanin biosynthesis are up-regulated between stages 5 and VP. Among these genes, putative phytoene synthase (BI979026), zeta carotene desaturase (CF349648), lycopene beta-cyclase (BQ105122) are likely to be involved in carotenoid biosynthesis.

The expression of UDP-glucose anthocyanidin-o-glucosyltransferase (AB201048/ RhGT1), previously involved in anthocyanin synthesis, was strongly up-regulated. A similar strong up-regulation was observed for genes encoding putative phenylalanine ammonia-lyase (BQ105227), chalcone synthase (EC587811), flavonol synthase (AB038247) and anthocyanidin synthase (BI977949) (). Altogether, these genes are likely good candidates involved in anthocyanins biosynthesis in rose petals.

List of selected genes associated with early to late flower development in R. Chinensis cv Old Blush. Interestingly, genes predicted to encode five putative cyclins (EC586028, EC586517, EC587578, EC588351, and EC588489) and a putative cyclin dependent kinase (EC589228) are strongly down-regulated during floral organ morphogenesis. This down-regulation may reflect the transition from mitotic growth to post-mitotic growth where floral organs grow through cell expansion.

Recently, Vanneste et al. Showed that the transcriptional down-regulation of A2 type cyclins is a direct link between developmental programming and cell-cycle exit in Arabidopsis thaliana.

Fifteen genes encoding putative transcription factors were up-regulated, while nine were down-regulated. Among the up-regulated transcription factors, we found the putative orthologue of SHP (AB025643) and a putative NAC domain protein (BI978992, ).

BI978992 is homologous to Arabidopsis NAC2, a gene expressed in ovule integuments. The differential expression of NAC2 between stages 5 and visible petals (VP) suggests its putatively conserved function with the Arabidopsis NAC2. Three putative MYB transcription factors were also up-regulated (CF349636, BQ104100 and BI978095, ). These rose MYBs may be involved in organ elongation, as they share about 67% protein sequence similarity with AtMYB21, known to be involved in gibberellins/jasmonate-mediated control of stamen filament elongation. Late floral development 456 genes were differentially regulated at least once during the late phases of floral development, i.e.

From visible petal (VP) stage to senescent flower (SF) stage. Most of these genes showed similar expression pattern when we compared stages VP to OF ( open flower) or stages VP to SF (See for top list, and for full data). This result indicates that the transcriptome becomes less dynamic at senescence stages and thus not so many differences are detected when comparing samples OF and SF to the VP sample. Gene ontology analysis showed that among the up-regulated genes, the three GO terms chlorophyll catabolic process, heterocycle catabolic process and cellular nitrogen compound catabolic process were significantly overrepresented as compared to the whole annotated set; the four GO terms nucleus, macromolecule biosynthetic process, intracellular non-membrane-bounded organelle and ribonucleoprotein complex were underrepresented. We could identify two genes encoding stay-green protein homologues (BI978267 and BQ106457) that are strongly up-regulated upon petal elongation and remain highly expressed throughout the final petal senescing process.

Stay-green proteins have a major role in chlorophyll and photosynthetic pigments degradation and have been repeatedly described to be associated with the processes of fruit ripening and organ senescence. Surprisingly, no gene related to ethylene biosynthesis or signaling was detected as differentially expressed during late floral development. However the RbXTH1 and RbEXPA1 genes, both induced during ethylene-triggered and field abscission,, were strongly up-regulated between VP and OF stages and remained as such in senescing flowers. Among the down-regulated genes, the two GO terms protein metabolic process and plasma membrane were underrepresented as compared to the whole set (whole microarray GO terms) and the eight GO terms acyltransferase activity, acyl-carrier-protein biosynthetic process, acyl carrier activity, cellular carbohydrate metabolic process, polysaccharide metabolic process, fatty acid biosynthetic process, lipase activity and defense response to fungus were overrepresented (). The enrichment in the latter set may represent a slowdown of general metabolic pathways at the onset of flower senescence. Similar results were reported in A.

Thaliana during organs senescence where a down-regulation of the photosynthetic machinery accompanied by a reduction in expression of many cell wall biosynthetic genes reflecting a cessation of growth during senescence. Conclusions We established a calendar of the floral initiation and development for the rose and developed a rose microarray that harbors sequence from genes expressed during the floral transition and whole floral development process in Rosa sp, from initiation up to senescing flowers. This microarray and the floral development calendar were successfully used to identify genes whose expression correlated with different flower development stages.These multiple datasets represent an extensive study of rose floral development.

This resource can be helpful to select candidate genes potentially involved in different horticultural traits, such as flowering, floral architecture, scent production and emission, senescence and abscission. We used the microarray developed herein to identify genes whose expression is associated with some of these rose important traits, such as flower initiation, development and senescence. Rosa1_Affyarray harbors sequences from ESTs found in petals of different rose genotypes, () and thus may be helpful to identify genes associated with other rose traits such as scent biosynthesis and/or emission genes. The rose is among the species that exhibit the highest scent complexity – and some scent biosynthesis pathways are unique to the rose or not yet identified in other model species including other members of the Rosaceae genus,. QTLs have been identified to be associated to several important traits of the rose.

However, the heterozygous genome of the rose complicates the breeding programs to select for several traits simultaneously. The identification of genes whose expression correlates with important ornamental traits can facilitate and accelerate candidate gene identification for rose breeding by marker assisted selection or genomic selection. For example, this dataset can provide researchers with a useful resource on the expression of candidate genes within a given mapping interval.

Furthermore, the rapidly progressing high throughput sequencing technologies should allow the generation of precise genetic maps for the rose that could be combined to refined transcriptomics approaches to identify the genes responsible for important horticultural traits in the rose, and allow subsequent marker-assisted selection. Light microscopy and SEM imaging of meristems and early flower development Samples were dissected under a binocular stereomicroscope and then fixed in 4% glutaraldehyde (v/v) in 0.1 M phosphate buffer (pH 7.2) for 2 h at 4°C under vacuum.

Samples were dehydrated in a graded ethanol series and embedded in Technovit 7100. Sections of 1.5 to 2.0 µm (Leica RM 2165 microtome) were stained with toluidine blue and examined under an Olympus BH2-RFC microscope coupled to a 3CCD Sony camera. For scanning electron microscopy, terminal part of the shoot was carefully dissected. After a fixation in 4% glutaraldehyde (v/v), followed by post-fixation with osmium tetroxide, the sample was dehydrated in a graded alcohol series and in acetone. Dehydration was completed by critical point drying. Sample were then coated with gold (MED 020 BALTEC) and observed with a JEOL JSM-63017 scanning electron microscope.

AFFYMETRIX Array hybridization RNA samples were checked for their integrity on The Agilent 2100 bioanalyzer according to the Agilent Technologies (Waldbroon, Germany). Two µg of total RNA were used to synthesize biotin-labeled cRNAs with the One-cycle cDNA synthesis kit (Affymetrix, Santa Clara, CA). Superscript II reverse transcriptase and T7-oligo (dT) primers were used to synthesize the single strand of cDNA at 42°C during 1 hour followed by the synthesis of the double stranded cDNA by using DNA ligase, DNA polymerase I and RNaseH during 2 hours at 16°C. Clean up of the double-stranded cDNA was performed with Sample Cleanup Module (Affymetrix) followed by in vitro transcription (IVT) in presence of biotin-labeled UTP using GeneChip® IVT labelling Kit (Affymetrix). Quantity of the labelled-cRNA with RiboGreen® RNA Quantification Reagent (Turner Biosystems, Sunnyvale, CA) was determined after cleanup by the Sample Cleanup Module (Affymetrix). Fragmentation of 10 µg of labelled-cRNA was carried out for 35 minutes at 94°C, followed by hybridization during 16 hours at 45°C to Affymetrix GeneChip® Rosa1 Genome Array representing approximately 4869 genes. After hybridization, the arrays were washed with 2 different buffers (stringent: 6× SSPE, 0.01% Tween-20 and non-stringent: 100 mM MES, 0.1 M [Na+], 0.01% Tween-20) and stained with a complex solution including Streptavidin R-Phycoerythrin conjugate (Invitrogen/molecular probes, Carlsbad, CA) and anti Streptavidin biotinylated antibody (Vectors laboratories, Burlingame, CA).

The washing and staining steps were performed in a GeneChip® Fluidics Station 450 (Affymetrix). The Affymetrix GeneChip® Rosa1 Genome Arrays were finally scanned with the GeneChip® Scanner 3000 7G piloted by the GeneChip® Operating Software (GCOS). Statistical Analysis of Microarray Data The data were normalized with the gcrma algorithm, available in the Bioconductor package. To determine differentially expressed genes, we performed a usual two group t-test that assumes equal variance between groups. The variance of the gene expression per group is a homoscedastic variance, where genes displaying extremes of variance (too small or too large) were excluded.

The raw P values were adjusted by the Bonferroni method, which controls the Family Wise Error Rate (FWER). A gene is declared differentially expressed if the Bonferroni P-Value is less than 0.05. Validation of genes expression using quantitative real-time PCR Only genes that were involved in floral development were analyzed for microarray data validation.

One microgram total RNA (treated with DNAse) was used in a reverse transcription assay with RevertAid M-MuLV Reverse Transcriptase (Fermentas, Burlington, Ontario). Target cDNAs were quantified by qPCR using FastStart universal SYBR green master (Roche, Basel, Switzerland) on a Step-OnePlus Real-Time PCR System (Applied Biosystems, Foster City, CA USA). Expression levels were normalized with RhαTubuline, RhGAPDH and RhEF1α reference genes. These genes were validated as reference genes using the GeNorm application. Three independent biological replicates (pools of dissected flowers from at least 5 different plants) were used for each experiment and two qPCR technical replicates were performed for each biological replicate.

Primer sequences are available in. The correlation between the microarray results, and those obtained by qPCR was assessed by calculating the Pearson's product moment correlation coefficient,.

References • 1. Martin M, Piola F, Chessel D, Jay M, Heizmann P (2001) The domestication process of the Modern Rose: genetic structure and allelic composition of the rose complex. Theoretical-and-Applied-Genetics 102: 398–404.M.

Heizmann2001The domestication process of the Modern Rose: genetic structure and allelic composition of the rose complex.Theoretical-and-Applied-Genetics102398404 • • • • 2. De Vries DP, Dubois L (1996) Rose breeding: past, present, prospects. Acta Horticulturae 424: 241–248.DP De VriesL. Dubois1996Rose breeding: past, present, prospects.Acta Horticulturae424241248 • • • • 3. Gudin S (2001) Rose breeding technologies. Acta Horticulturae 547: 23–26.S.

Gudin2001Rose breeding technologies.Acta Horticulturae5472326 • • • • 4. Reynders-Aloisi S, Bollereau P (1996) Characterisation of genetic diversity in genus Rosa by Randomly Amplified Polymorphic DNA. Acta Horticulturae 424: 253–259.S. Bollereau1996Characterisation of genetic diversity in genus Rosa by Randomly Amplified Polymorphic DNA.Acta Horticulturae424253259 • • • • 5. Channeliere S, Riviere S, Scalliet G, Szecsi J, Jullien F, et al. (2002) Analysis of gene expression in rose petals using expressed sequence tags.

FEBS Lett 515: 35–38.S. Jullien2002Analysis of gene expression in rose petals using expressed sequence tags.FEBS Lett5153538 • • • • 6.

Foucher F, Chevalier M, Corre C, Soufflet-Freslon V, Legeai F, et al. (2008) New resources for studying the rose flowering process. Genome 51: 827–837.F.

Legeai2008New resources for studying the rose flowering process.Genome51827837 • • • • 7. Guterman I, Masci T, Chen X, Negre F, Pichersky E, et al. (2006) Generation of phenylpropanoid pathway-derived volatiles in transgenic plants: rose alcohol acetyltransferase produces phenylethyl acetate and benzyl acetate in petunia flowers. Plant Mol Biol 60: 555–563.I. Pichersky2006Generation of phenylpropanoid pathway-derived volatiles in transgenic plants: rose alcohol acetyltransferase produces phenylethyl acetate and benzyl acetate in petunia flowers.Plant Mol Biol60555563 • • • • 8.

Lavid N, Wang J, Shalit M, Guterman I, Bar E, et al. (2002) O-methyltransferases involved in the biosynthesis of volatile phenolic derivatives in rose petals. Plant Physiol 129: 1899–1907.N.

Bar2002O-methyltransferases involved in the biosynthesis of volatile phenolic derivatives in rose petals.Plant Physiol7 • • • • 9. Scalliet G, Journot N, Jullien F, Baudino S, Magnard JL, et al. (2002) Biosynthesis of the major scent components 3,5-dimethoxytoluene and 1,3,5-trimethoxybenzene by novel rose O-methyltransferases. Febs Letters 523: PII S0014-5793(0002)3.G. BaudinoJL Magnard2002Biosynthesis of the major scent components 3,5-dimethoxytoluene and 1,3,5-trimethoxybenzene by novel rose O-methyltransferases.Febs Letters523PII S0014-5793(0002)3 • • • • 10. Scalliet G, Lionnet C, Le Bechec M, Dutron L, Magnard JL, et al. (2006) Role of petal-specific orcinol O-methyltransferases in the evolution of rose scent.

Plant Physiology 140: 18–29.G. DutronJL Magnard2006Role of petal-specific orcinol O-methyltransferases in the evolution of rose scent.Plant Physiology1401829 • • • • 11. Scalliet G, Piola F, Douady CJ, Rety S, Raymond O, et al. (2008) Scent evolution in Chinese roses. Proceedings Of The National Academy Of Sciences Of The United States Of America 105: 5927–5932.G. PiolaCJ DouadyS. Raymond2008Scent evolution in Chinese roses.Proceedings Of The National Academy Of Sciences Of The United States Of America2 • • • • 12.

Shalit M, Guterman I, Volpin H, Bar E, Tamari T, et al. (2003) Volatile ester formation in roses. Identification of an acetyl-coenzyme A. Geraniol/Citronellol acetyltransferase in developing rose petals. Plant Physiol 131: 1868–1876.M. Tamari2003Volatile ester formation in roses. Identification of an acetyl-coenzyme A.

Geraniol/Citronellol acetyltransferase in developing rose petals.Plant Physiol6 • • • • 13. Remay A, Lalanne D, Thouroude T, Le Couviour F, Hibrand-Saint Oyant L, et al.

(2009) A survey of flowering genes reveals the role of gibberellins in floral control in rose. Theor Appl Genet 119: 767–781.A. Le CouviourL. Hibrand-Saint Oyant2009A survey of flowering genes reveals the role of gibberellins in floral control in rose.Theor Appl Genet119767781 • • • • 14. Guterman I, Shalit M, Menda N, Piestun D, Dafny-Yelin M, et al.

(2002) Rose scent: Genomics approach to discovering novel floral fragrance-related genes. Plant Cell 14: 2325–2338.I. Dafny-Yelin2002Rose scent: Genomics approach to discovering novel floral fragrance-related genes.Plant Cell • • • • 15. Guterman I, Masci T, Chen XL, Negre F, Pichersky E, et al. (2006) Generation of phenylpropanoid pathway-derived volatiles in transgenic plants: Rose alcohol acetyltransferase produces phenylethyl acetate and benzyl acetate in petunia flowers. Plant Molecular Biology 60: 555–563.I. MasciXL ChenF.

Pichersky2006Generation of phenylpropanoid pathway-derived volatiles in transgenic plants: Rose alcohol acetyltransferase produces phenylethyl acetate and benzyl acetate in petunia flowers.Plant Molecular Biology60555563 • • • • 16. Gudin S (2000) Rose: Genetics and breeding.

Plant Breeding Reviews 17: 159–189.S. Gudin2000Rose: Genetics and breeding.Plant Breeding Reviews17159189 In. Krussmann G (1981) The complete book of roses.

Portland: Timber Press. Krussmann1981The complete book of rosesPortlandTimber Pressxii. Smyth DR, Bowman JL, Meyerowitz EM (1990) Early flower development in Arabidopsis. Plant Cell 2: 755–767.DR SmythJL BowmanEM Meyerowitz1990Early flower development in Arabidopsis.Plant Cell2755767 • • • • 19. Chimonidou D (2003) Morphology and Anatomy: ‘Flower Development and Abscission zone’.

ENCYCLOPEDIA OF ROSE SCIENCE. Amesterdam: Elsevier Academic Press. Chimonidou2003Morphology and Anatomy: ‘Flower Development and Abscission zone’. ENCYCLOPEDIA OF ROSE SCIENCEAmesterdamElsevier Academic Press504512 • 20. Foster T, Johnston R, Seleznyova A (2003) A morphological and quantitative characterization of early floral development in apple (Malus×domestica Borkh.). Ann Bot 92: 199–206.T. Seleznyova2003A morphological and quantitative characterization of early floral development in apple (Malus×domestica Borkh.).Ann Bot92199206 • • • • 21.

Gotz S, Garcia-Gomez JM, Terol J, Williams TD, Nagaraj SH, et al. (2008) High-throughput functional annotation and data mining with the Blast2GO suite. Nucleic Acids Res 36: 3420–3435.S. GotzJM Garcia-GomezJ. TerolTD WilliamsSH Nagaraj2008High-throughput functional annotation and data mining with the Blast2GO suite.Nucleic Acids Res • • • • 22.

Amasino R (2010) Seasonal and developmental timing of flowering. Plant J 61: 1001–1013.R. Amasino2010Seasonal and developmental timing of flowering.Plant J • • • • 23. Fornara F, de Montaigu A, Coupland G (2010) SnapShot: Control of flowering in Arabidopsis. Cell 141: 550, 550 e551–552.F. De MontaiguG.

Coupland2010SnapShot: Control of flowering in Arabidopsis.Cell141550, 550 e551552 • • • • 24. Bennett MJ, Swarup R, Perry P, Hagenbeek D, Van Der Straeten D, et al. (2007) Ethylene upregulates auxin biosynthesis in Arabidopsis seedlings to enhance inhibition of root cell elongation. Plant Cell 19: 2186–2196.MJ BennettR. Van Der Straeten2007Ethylene upregulates auxin biosynthesis in Arabidopsis seedlings to enhance inhibition of root cell elongation.Plant Cell • • • • 25. Kang BG, Newcomb W, Burg SP (1971) Mechanism of Auxin-Induced Ethylene Production. Plant Physiology 47: 504–&.BG KangW.

NewcombSP Burg1971Mechanism of Auxin-Induced Ethylene Production.Plant Physiology47504& • • • • 26. Ma N, Tan H, Liu X, Xue J, Li Y, et al. (2006) Transcriptional regulation of ethylene receptor and CTR genes involved in ethylene-induced flower opening in cut rose (Rosa hybrida) cv. J Exp Bot 57: 2763–2773.N. Li2006Transcriptional regulation of ethylene receptor and CTR genes involved in ethylene-induced flower opening in cut rose (Rosa hybrida) cv. Samantha.J Exp Bot • • • • 27.

Helariutta Y, Bishopp A, Mahonen AP (2006) Signs of change: hormone receptors that regulate plant development. Development 133: 1857–1869.Y.

BishoppAP Mahonen2006Signs of change: hormone receptors that regulate plant development.Development9 • • • • 28. Theologis A, Tsuchisaka A, Yu GX, Jin HL, Alonso JM, et al. (2009) A Combinatorial Interplay Among the 1-Aminocyclopropane-1-Carboxylate Isoforms Regulates Ethylene Biosynthesis in Arabidopsis thaliana. Genetics 183: 979–1003.A. TsuchisakaGX YuHL JinJM Alonso2009A Combinatorial Interplay Among the 1-Aminocyclopropane-1-Carboxylate Isoforms Regulates Ethylene Biosynthesis in Arabidopsis thaliana.Genetics • • • • 29.

Tsuchisaka A, Theologis A (2004) Heterodimeric interactions among the 1-amino-cyclopropane-1-carboxylate synthase polypeptides encoded by the Arabidopsis gene family. Proc Natl Acad Sci U S A 101: 2275–2280.A. Theologis2004Heterodimeric interactions among the 1-amino-cyclopropane-1-carboxylate synthase polypeptides encoded by the Arabidopsis gene family.Proc Natl Acad Sci U S A0 • • • • 30. Yamagami T, Tsuchisaka A, Yamada K, Haddon WF, Harden LA, et al. (2003) Biochemical diversity among the 1-amino-cyclopropane-1-carboxylate synthase isozymes encoded by the Arabidopsis gene family.

J Biol Chem 278: 2.T. YamadaWF HaddonLA Harden2003Biochemical diversity among the 1-amino-cyclopropane-1-carboxylate synthase isozymes encoded by the Arabidopsis gene family.J Biol Chem112 • • • • 31. Dubois A, Raymond O, Maene M, Baudino S, Langlade NB, et al. (2010) Tinkering with the C-function: a molecular frame for the selection of double flowers in cultivated roses. PLoS One 5: e9288.A. BaudinoNB Langlade2010Tinkering with the C-function: a molecular frame for the selection of double flowers in cultivated roses.PLoS One5e9288 • • • • 32.

Kitahara K, Hibino Y, Aida R, Matsumoto S (2004) Ectopic expression of the rose AGAMOUS-like MADS-box genes ‘MASAKO C1 and D1’ causes similar homeotic transformation of sepal and petal in Arabidopsis and sepal in Torenia. Plant Science 166: 1245–1252.K. Matsumoto2004Ectopic expression of the rose AGAMOUS-like MADS-box genes ‘MASAKO C1 and D1’ causes similar homeotic transformation of sepal and petal in Arabidopsis and sepal in Torenia.Plant Science2 • • • • 33. Kitahara K, Hirai S, Fukui H, Matsumoto S (2001) Rose MADS-box genes ‘MASAKO BP and B3’ homologous to class B floral identity genes.

Plant Science 161: 549–557.K. Matsumoto2001Rose MADS-box genes ‘MASAKO BP and B3’ homologous to class B floral identity genes.Plant Science161549557 • • • • 34. Singh AP, Tripathi SK, Nath P, Sane AP (2011) Petal abscission in rose is associated with the differential expression of two ethylene-responsive xyloglucan endotransglucosylase/hydrolase genes, RbXTH1 and RbXTH2. AP SinghSK TripathiP. NathAP Sane2011Petal abscission in rose is associated with the differential expression of two ethylene-responsive xyloglucan endotransglucosylase/hydrolase genes, RbXTH1 and RbXTH2.J Exp Bot • • • • 35. Kaufmann K, Wellmer F, Muino JM, Ferrier T, Wuest SE, et al.

(2010) Orchestration of floral initiation by APETALA1. Science 328: 85–89.K. WellmerJM MuinoT.

FerrierSE Wuest2010Orchestration of floral initiation by APETALA1.Science3288589 • • • • 36. Wellmer F, Alves-Ferreira M, Dubois A, Riechmann JL, Meyerowitz EM (2006) Genome-wide analysis of gene expression during early Arabidopsis flower development. PLoS Genet 2: e117.F. DuboisJL RiechmannEM Meyerowitz2006Genome-wide analysis of gene expression during early Arabidopsis flower development.PLoS Genet2e117 • • • • 37. Roberts AV, Blake PS, Lewis R, Taylor JM, Dunstan DI (1999) The Effect of Gibberellins on Flowering in Roses. J Plant Growth Regul 18: 113–119.AV RobertsPS BlakeR. LewisJM TaylorDI Dunstan1999The Effect of Gibberellins on Flowering in Roses.J Plant Growth Regul18113119 • • • • 38.

Borner R, Kampmann G, Chandler J, Gleissner R, Wisman E, et al. (2000) A MADS domain gene involved in the transition to flowering in Arabidopsis. Plant J 24: 591–599.R.

Wisman2000A MADS domain gene involved in the transition to flowering in Arabidopsis.Plant J24591599 • • • • 39. Das P, Ito T, Wellmer F, Vernoux T, Dedieu A, et al.

(2009) Floral stem cell termination involves the direct regulation of AGAMOUS by PERIANTHIA. Development 136: 1605–1611.P. Dedieu2009Floral stem cell termination involves the direct regulation of AGAMOUS by PERIANTHIA.Development1 • • • • 40. Maier AT, Stehling-Sun S, Wollmann H, Demar M, Hong RL, et al.

(2009) Dual roles of the bZIP transcription factor PERIANTHIA in the control of floral architecture and homeotic gene expression. Development 136: 1613–1620.AT MaierS. DemarRL Hong2009Dual roles of the bZIP transcription factor PERIANTHIA in the control of floral architecture and homeotic gene expression.Development0 • • • • 41. Ogata J, Kanno Y, Itoh Y, Tsugawa H, Suzuki M (2005) Plant biochemistry: anthocyanin biosynthesis in roses. Nature 435: 757–758.J.

Suzuki2005Plant biochemistry: anthocyanin biosynthesis in roses.Nature435757758 • • • • 42. Vanneste S, Coppens F, Lee E, Donner TJ, Xie Z, et al. (2011) Developmental regulation of CYCA2s contributes to tissue-specific proliferation in Arabidopsis.

EMBO J 30: 3430–3441.S. LeeTJ DonnerZ.

Xie2011Developmental regulation of CYCA2s contributes to tissue-specific proliferation in Arabidopsis.EMBO J • • • • 43. Cheng H, Song S, Xiao L, Soo HM, Cheng Z, et al. (2009) Gibberellin acts through jasmonate to control the expression of MYB21, MYB24, and MYB57 to promote stamen filament growth in Arabidopsis. PLoS Genet 5: e1000440.H.

Cheng2009Gibberellin acts through jasmonate to control the expression of MYB21, MYB24, and MYB57 to promote stamen filament growth in Arabidopsis.PLoS Genet5e1000440 • • • • 44. Hortensteiner S (2009) Stay-green regulates chlorophyll and chlorophyll-binding protein degradation during senescence. Trends Plant Sci 14: 155–162.S. Hortensteiner2009Stay-green regulates chlorophyll and chlorophyll-binding protein degradation during senescence.Trends Plant Sci14155162 • • • • 45. Nath P, Sane AP, Tripathi SK (2007) Petal abscission in rose (Rosa bourboniana var Gruss an Teplitz) is associated with the enhanced expression of an alpha expansin gene, RbEXPA1. Plant Science 172: 481–487.P. NathAP SaneSK Tripathi2007Petal abscission in rose (Rosa bourboniana var Gruss an Teplitz) is associated with the enhanced expression of an alpha expansin gene, RbEXPA1.Plant Science172481487 • • • • 46.

Stead A, Rogers HJ, Roberts JA, Wagstaff C (2007) Programmed cell death during floral development and senescence. Comparative Biochemistry and Physiology a-Molecular & Integrative Physiology 146: S199–S200.A.

SteadHJ RogersJA RobertsC. Wagstaff2007Programmed cell death during floral development and senescence.Comparative Biochemistry and Physiology a-Molecular & Integrative Physiology146S199S200 • • • • 47. Kovats ES (1987) Composition of essential oils: Part 7. Bulgarian oil of rose (Rosa Damascena mill.). Journal of Chromatography A 406: 185–222.ES Kovats1987Composition of essential oils: Part 7. Bulgarian oil of rose (Rosa Damascena mill.).Journal of Chromatography A406185222 • • • • 48. Nakamura S (1987) Scent and component analysis of the Hybrid Tea Rose.

Perfumer & flavorist 112: 43–45.S. Nakamura1987Scent and component analysis of the Hybrid Tea Rose.Perfumer & flavorist1124345 • • • • 49. Kaminaga Y, Schnepp J, Peel G, Kish CM, Ben-Nissan G, et al. (2006) Plant phenylacetaldehyde synthase is a bifunctional homotetrameric enzyme that catalyzes phenylalanine decarboxylation and oxidation. J Biol Chem 281: 6.Y. PeelCM KishG. Ben-Nissan2006Plant phenylacetaldehyde synthase is a bifunctional homotetrameric enzyme that catalyzes phenylalanine decarboxylation and oxidation.J Biol Chem366 • • • • 50.

Spiller M, Berger RG, Debener T (2010) Genetic dissection of scent metabolic profiles in diploid rose populations. Theor Appl Genet 120: 1461–1471.M. SpillerRG BergerT. Debener2010Genetic dissection of scent metabolic profiles in diploid rose populations.Theor Appl Genet1 • • • • 51. Baayen RP, Kroes GMLW, Lange W (1998) Histology of root rot of flax seedlings (Linum usitatissimum) infected by Fusarium oxysporum f.sp. European Journal of Plant Pathology 104: 725–736.RP BaayenGMLW KroesW.

Lange1998Histology of root rot of flax seedlings (Linum usitatissimum) infected by Fusarium oxysporum f.sp. Lini.European Journal of Plant Pathology104725736 • • • • 52. Irizarry RA, Ooi SL, Wu Z, Boeke JD (2003) Use of mixture models in a microarray-based screening procedure for detecting differentially represented yeast mutants.

Stat Appl Genet Mol Biol 2: Article1.RA IrizarrySL OoiZ. WuJD Boeke2003Use of mixture models in a microarray-based screening procedure for detecting differentially represented yeast mutants.Stat Appl Genet Mol Biol2Article1 • • • • 53. Gentleman R, Carey V (2002) Bioconductor. RNews 2: 1116.R. Carey2002Bioconductor.RNews21116 • • • • 54. Ge Y, Dudoit S, Speed TP (2003) Resampling-based multiple testing for microarray data analysis.

TEST 12: 1–44.Y. DudoitTP Speed2003Resampling-based multiple testing for microarray data analysis.TEST12144 • • • • 55. Gagnot S, Tamby JP, Martin-Magniette ML, Bitton F, Taconnat L, et al. (2008) CATdb: a public access to Arabidopsis transcriptome data from the URGV-CATMA platform. Nucleic Acids Research 36: D986–D990.S. GagnotJP TambyML Martin-MagnietteF.

Taconnat2008CATdb: a public access to Arabidopsis transcriptome data from the URGV-CATMA platform.Nucleic Acids Research36D986D990 • • • • 56. Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, et al. (2007) NCBI GEO: mining tens of millions of expression profiles–database and tools update. Nucleic Acids Res 35: D760–765.T. BarrettDB TroupSE WilhiteP. Rudnev2007NCBI GEO: mining tens of millions of expression profiles–database and tools update.Nucleic Acids Res35D760765 • • • • 57.

Speleman F, Vandesompele J, De Preter K, Pattyn F, Poppe B, et al. (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 3: F. Poppe2002Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes.Genome Biology3 • • • • 58. Coppack SW (1990) Limitations of the Pearson product-moment correlation. Clin Sci (Lond) 79: 287.SW Coppack1990Limitations of the Pearson product-moment correlation.Clin Sci (Lond)79287 • • • • 59.

Liu Y, Meng Q, Chen R, Wang J, Jiang S, et al. (2004) A new method to evaluate the similarity of chromatographic fingerprints: weighted pearson product-moment correlation coefficient. J Chromatogr Sci 42: 545–550.Y. Jiang2004A new method to evaluate the similarity of chromatographic fingerprints: weighted pearson product-moment correlation coefficient.J Chromatogr Sci42545550 • • •.

Translation of knowledge and skills from classroom settings to clinical practice is a major challenge in healthcare training, especially for behavioral interventions. Methods Intended for use after classroom training occurs, the mobile app has three primary functions designed to increase behavioral intent to deliver SBIRT: (1) review skills (ie, address knowledge and beliefs about SBIRT), (2) apply skills with patients (ie, build confidence and perceived behavioral control), and (3) report performance data (ie, increase accountability and social norms and/or influence). The app includes depression and anxiety screening tools due to high comorbidity with substance use. A randomized controlled trial (RCT) is in progress among health and social service learners (N=200) recruited from 3 universities and 6 different training programs in nursing, social work, internal medicine, psychiatry, and psychology. Participants are randomized to SBIRT classroom instruction alone or classroom instruction plus app access prior to beginning their field placement rotations. TPB-based data are collected via Qualtrics or via the mobile app pre-post and SBIRT utilization, weekly for 10 weeks. Key outcomes include the frequency of and self-reported confidence in delivery of SBIRT.

Introduction The transition from learning clinical skills to sustained changes in health provider behavior is a known problem. Even when evidence-based treatments are available and implementation decisions have been made, workforce development and sustained intervention delivery present formidable challenges []. For example, maintaining fidelity to evidence-based treatments (eg, cognitive behavioral therapy []), often requires strategies to support ongoing learning such as supervision and coaching [].

Providers must learn the new skills, practice the skills, build confidence (in themselves and the intervention), and be able to change past practice norms all within an environment that supports such changes. However, these personnel-intensive strategies can be costly and time-consuming, and have limited reach due to resource constraints.

Yet with effective strategies to support skill translation [,], behavioral healthcare providers can effectively deliver the interventions they are trained to use. Thus, a major challenge for the implementation of evidence-based behavioral practices concerns how to deliver cost-effective support for skill translation in healthcare.

Screening, brief intervention, and referral to treatment (SBIRT) for unhealthy alcohol and drug use is an important example of a widely-trained skill that has fallen short in translation []. SBIRT is designed to reach individuals in health or social service settings who use substances at a range of levels, including those who may not yet meet criteria for alcohol or drug use disorders. Components include screening for hazardous drinking and drug use and related problems, delivering brief motivational interviewing-based interventions for patients at low to moderate risk, and providing referrals to addiction specialty care for those with significant problems []. Available evidence supports the effectiveness of screening and brief intervention in addressing hazardous drinking within primary care [-], although evidence for effectiveness in reducing drug use is weak and trials have been mixed [-]. Based on the strength of this literature, national practice guidelines for SBIRT integration into primary care and other health and social service settings have been developed [,].

In spite of these practice recommendations and a proliferation of SBIRT training programs, optimal skill translation to direct clinical care remains unrealized. Trainees often demonstrate classroom skill proficiency yet fail to use SBIRT in subsequent clinical placements. Commonly cited barriers to translation include provider attitudes about substance use interventions, problems with knowledge recall at the point-of-care, lack of confidence, inadequate knowledge of referral resources, as well as structural barriers in clinical settings such as limited time and competing medical demands, especially in primary care [-]. Studies of SBIRT skill translation and implementation have found a decrease in post-training SBIRT delivery rates over time [,], variability in delivery rates across health disciplines [,], and low fidelity to screening questions []. Fewer than one in six Americans report being asked about or discussing their drinking with a health professional [], and screening is rarely conducted in US primary care settings [] outside the Veteran’s Affairs Health System [].

Similarly, a minority of patients in mental health settings report that providers advise them to reduce hazardous drinking or drug use [], and a recent meta-analysis demonstrated that fidelity to motivational interviewing by clinicians is often poor []. These findings highlight the importance of improving skill translation in real-world health and social service settings. Digital learning tools have been incorporated into some SBIRT training programs but have not been effectively integrated with clinical care. For example, online training modules sometimes supplement didactic presentations and demonstrations, role play with feedback, and patient encounters [-].

With some variability, these digital health training components have been rated as relevant and useful by trainees. Outcome studies have found that such training resulted in increased confidence in SBIRT delivery and more positive attitudes towards patients who use alcohol [].

Yet digital tools such as online learning models have not supported skill translation over time. To our knowledge, the one SBIRT mobile app that is currently available does not incorporate background materials targeted towards trainees (eg, review of prevalence of substance use and evidence for SBIRT efficacy), nor does it include detailed support in conducting screening, delivering interventions, and treatment referral resources [].

If designed with skill translation in mind, a point-of-care mobile app with this additional content could help providers apply newly learned SBIRT skills []. In other health delivery contexts, mobile apps are gaining acceptance and appear to enhance training. Bullock et al (2015) found that providing a mobile app containing the Dr Companion software with 5 key medical textbooks (the iDoc app) to newly qualified doctors increased access to reference materials and was effective in supporting learning and practice [].

The use of mobile technology, including medical apps in nursing education, has demonstrated success in improving learning outcomes and learner confidence []. Tablet-based patient self-administered alcohol and drug screening [,] and intervention [] can increase efficiency in healthcare settings. Yet prior studies have not examined how mobile apps may enhance SBIRT training and skill translation.

Theoretical models identifying barriers and facilitators regarding learning and skill translation may help guide the development of intervention strategies to enhance skill transfer and implementation. On the level of individual provider (or learner) behavior, the theory of planned behavior (TPB) provides a well-validated, conceptual model that identifies both internal and external key factors that could influence SBIRT skill translation []. In the TPB model, behavioral intent (to perform the behavior of interest) is determined by attitudes and/or beliefs about the behavior, perceived social norms, and perceived behavioral control. The TPB is contextualized for SBIRT skill translation in, allowing us to assess learners on each of these variables and to provide matched interventions as needed to promote SBIRT usage. For example, we provide information on the extent of substance use and related problems, and SBIRT efficacy to shape attitudes regarding the value of screening and treatment. We provide information on standards of care (eg, that SBIRT has been recommended by the US Preventive Task Force and many health professional bodies) to influence perceived social norms. Moreover, we provide tools to support practicing SBIRT to positively impact both attitudes and perceived behavioral control such as trainees’ confidence in successfully performing SBIRT and integrating it into clinical care.

Theory of planned behavior as applied to screening, brief intervention, and referral to treatment (SBIRT) skill translation. Model adapted from Ajzen (1991). SUD: substance use disorder; SUS: system usability scale. The aim of this paper is to describe the process of TPB-based mobile alcohol and drug SBIRT app development, beta testing, and protocol for a randomized controlled trial (RCT) comparing health professional learners with access to the app (intervention arm) to learners without access to the app (control arm). We hypothesize that participants in the intervention arm will be more likely to deliver SBIRT in clinical placements than those in the control arm and will be more likely to report intention to deliver SBIRT in the future. We also hypothesize that intervention participants will report more positive beliefs about SBIRT, greater knowledge, and greater perceived control over SBIRT delivery in clinic.

App Development An interprofessional team worked to develop the TPB-based app. The team was comprised of faculty members with prior research expertise in SBIRT training and implementation and included two clinical psychologists with doctorate degrees, an internal medicine physician, an advanced practice nurse with a doctorate degree, and an experienced project manager.

Existing mobile apps were identified to determine needs in the field and to learn about components often emphasized in enhancing behavior change, for example, mobile apps for SBIRT [], Change Talk for Childhood Obesity [], and Epocrates []. Digital product design principles were reviewed including the creation of a product vision and end goal, character sketches of potential users (“personas”), features of currently available products, and emerging mobile app tools that simplify the user interface and promote app usage [].

The app’s purpose and vision evolved based on faculty and learner responses to initial designs. As follow-up to a needs assessment questionnaire, faculty members from different training programs were asked about the potential utility of a mobile app to increase learners’ use of SBIRT. Learners identified specific features and content they wanted while using the app during clinic placements. For example, learners wanted point-of-care screening tools and SBIRT frequency of use measures integrated directly into the app. Based on this initial feedback, the app was designed as a tool for learners to use at their clinic sites that could function as both an SBIRT information resource and as a tool to assist in skills practice and implementation. Content was designed to primarily address alcohol and drug use, but screeners for depression and anxiety, which commonly co-occur with problematic substance use, were added to broaden the scope of the app and to increase its perceived value to both learners and preceptors.

A key design principle was to ensure the app fit within the clinical environment and did not disrupt other training or patient care activities. Because of data security concerns and the range of service settings and medical record systems in which trainees could use SBIRT, the app was not designed to connect with local electronic health records and does not record any protected health information. Given the expense and complexity of integrating screening into healthcare records, with which the faculty had prior experience (eg, integration of alcohol screening into electronic health records in Kaiser Permanente Northern California []), and the fact that not all learners are placed in settings that have electronic records, we anticipated that keeping the app separate from patient record systems would maximize learner flexibility across various clinical placements and assuage concerns about loss of patient privacy. Flow diagrams and wireframes (page schematics and screenshots) were drafted to correspond to the key components of the app. Open Health Network app developers [] were selected as a development partner based on their prior experience in developing mobile apps for healthcare. The wireframes were given to the developers, who provided an initial alpha version.

Team members tested the alpha version individually and worked with the developers to continue refining the flow and content for subsequent beta testing. Beta Testing Once the final beta version of the app was developed, our team chose a small cohort of nurse practitioner learners (N=22) at the University of San Francisco to beta test the app for 3 months. Our testers included learners in an advanced practice nursing training program who were enrolled in a clinic placement. Learners completed the questionnaires and processes to be used in the larger RCT. Full TPB-based surveys (see “Pre/Post Assessment Questions”) were repeated at the end of the beta-testing period, followed by a debrief focus group.

The team tested and refined the app prior to starting the controlled trial. Beta testing results are described below. Participants Study participants (trainees, N=200) are adult health professional learners in one of the designated training programs. Participants must have had prior classroom or online training in SBIRT within the past year and may not have previously used an SBIRT app. For learners who have not yet completed classroom-based SBIRT instruction, they are required to complete the following 3 online training modules developed by the research team: (1) Introduction to SBIRT, (2) Screening, and (3) Brief Intervention. The learner must be enrolled in a field placement and is required to have a mobile device to be in the study (Android or iOS). Field placements include a range of private and public healthcare and social service agencies in the San Francisco Bay Area.

Recruitment and Randomization Project faculty identified SBIRT educators at the participating training programs and received permission to recruit students. Students are recruited during scheduled classroom time using a detailed information sheet that specifies expectations, timing, and types of data to be collected. Absent learners are invited to participate via email recruitment. Participants provide informed consent to participate either live in classroom settings or via email. Standard randomization procedures used in behavioral intervention studies are followed [] using a variable block size with a 1:1 allocation to the intervention or control arm design. These procedures are carried out by the study project manager.

Learners are assigned an identification number that is used for the randomization. A Web-based randomization tool is used to generate group assignments. Randomization is stratified by training program in order to have an even distribution of learners in the intervention and control groups from each program. Intervention Arm Participants in the intervention group are asked to download the app, use it in their clinical rotations (either on their personal mobile phones or tablets), and complete periodic questionnaires via a Qualtrics link. The learners have the opportunity to use the app as much as they need to review SBIRT, receive guidance on structured steps in SBIRT delivery, and receive tailored recommendations on what they can do to improve. We included modest incentives for app use to maximize our ability to measure the potential effects of app use in the trial. Data Collection At baseline, all participants answer a TPB-based questionnaire via Qualtrics (control) or directly on the app (intervention).

At the end of each week, all participants are asked to respond to a brief Qualtrics survey about how often they used SBIRT either by text message (short message service, SMS) or by email. Upon completion of their clinical rotation, all learners are asked to repeat the original TPB-based questionnaire and to provide general feedback about either the app usage (intervention) or their general satisfaction with SBIRT (control). Incentives Learners in both groups receive incentives for participating. The incentives are intended to enhance motivation of the learners to use the mobile app and complete study questionnaires. The learners receive Amazon gift cards throughout the study valued at US $20 at baseline, $2.50 for each completed SBIRT usage questionnaire, and $20 at the end of the study for answering the final questionnaire. Maximum payment is US $65 plus participation in a US $50 gift card lottery based on the completion of the SBIRT usage questionnaires.

Pre-Post Assessment Questions The team developed a 22-item questionnaire based on the TPB model. Likert-scaled items assess attitudes and beliefs including importance and efficacy of SBIRT, perceived patient willingness to participate in SBIRT, substance use epidemiology and clinical significance, and subjective norms and perceived behavioral control in the clinic setting. Three items assess confidence in the respondents’ ability to screen for alcohol or drug use problems, deliver a brief intervention, and to make referrals. One item assesses intent to perform SBIRT “whenever possible in my clinical/field placement.” All participants complete this questionnaire at baseline and again at 10 weeks. For intervention participants, baseline TPB responses are used to tailor their app experience by making specific recommendations of what the learner might need to review within the app’s library.

The system usability scale (SUS) [] is a 10-item Likert scale instrument developed to measure aspects of usability including system complexity and need for support and training. It yields a single score ranging from 0 to 100. Intervention group participants complete this measure at follow-up. Satisfaction We developed a 10-item Likert scale questionnaire to measure the experiences of control group participants, as a counterpart to the SUS. Items include barriers and challenges to SBIRT delivery to determine why some participants might not have used SBIRT in the context of their clinical placement. We included the satisfaction questionnaire for control participants only because we want to ensure they have an equivalent number of questions to the intervention participants, and we were concerned about survey burden with our intervention participants. Design and Function The SBIRT app has the following three primary functions to address TPB concepts: (1) Review SBIRT skills (to help change beliefs and attitudes), (2) Apply SBIRT skills in clinic practice (to help impact attitudes, perceived behavioral control), and (3) Report SBIRT use (to report norms, as well as study outcomes).

A fourth component, the Tools section, includes additional reference material and links (). After downloading the app, intervention learners create an account and complete the pre-TPB survey. Immediate TPB results are given to the learners along with tailored recommendations on what they should do next. For example, if learners score low on SBIRT knowledge, they are directed to the Review section. A progress checklist in the Tools section reminds them of “homework” they still need to complete.

Throughout the study intervention learners are reminded to use the app via the weekly SBIRT usage surveys and periodic text messages promoting app usage. Review The Review section includes content taught in the classroom (which is also available to learners online), as well as additional material. Apply The Apply section assists learners in using SBIRT while with a patient in clinic placements. This section includes screener instruments, scoring tools, scripts, and step-by-step guidance for delivering a brief intervention or referring a patient to alcohol or drug treatment. For example, the app allows users to specify what they want to screen (eg, alcohol, drugs, depression, anxiety), tailors those questions by gender and age group (18 to 64 versus 65 and up), includes single-question hazardous drinking and drug use screeners, as well as the Alcohol Use Disorders Identification Test (AUDIT) [], CRAFFT (CRAFFT is a mnemonic acronym of first letters of key words in six screening questions) [], and Drug Abuse Screening Test (DAST) []. Depression screening using the Patient Health Questionnaire (PHQ-2 and PHQ-9) [] and anxiety screening using the Generalized Anxiety Disorder (GAD-2 and GAD-7) [] measures are also included due to high comorbidity with substance use and commonalities in intervention approaches. Other subsections include tips for delivering brief interventions, including brief negotiated interviews/motivational interviewing, and suggestions for making referrals (eg, referral processes, lists of local treatment resources, and national treatment locators).

Report The Report section was originally conceptualized as a library of tools for instructors and clinical preceptors to track and evaluate their learners. We initially included a collection of pre-post surveys and weekly SBIRT usage items that intervention participants would complete. In order to standardize the data collection procedures across control and intervention participants, all weekly SBIRT usage surveys were completed via Qualtrics.

Intervention participants still completed the pre-post TPB surveys and final satisfaction surveys on the app. However, instructors do not receive reports regarding use of the app or SBIRT by learners. Tools The Tools section includes social networking, feedback and tracking, and gamification or incentive-building tools. Social networking tools include the “social connection” to send message questions to the study team or other app users. The “Progress Checklist” allows learners to check their progress on which pages they have visited and which pages they still need to review.

“Technological Support” is included in this section for those who have technical difficulties and can contact the app developers directly for help. “Leaderboard” is a page on which other learners who are using the app are listed, along with a point system indicating frequency of app use. Leaderboard rankings were tied to lottery tickets and bonus incentives (). Analyses Analyses will be conducted using SAS (SAS Institute Inc., 2011).

Descriptive statistics, including distributions, means, standard deviations, skewness, and kurtosis will be obtained for all variables. Continuous measures will be tested for normality and homogeneity of variance. If the distribution is normal, Likert-scale responses will be analyzed as continuous scores []. Chi-square tests, student t tests, and analysis of variance (ANOVA) tests will be used to determine inclusion in multivariate regression models. Bivariate analyses will examine rate of SBIRT delivery in the two arms and comparison of TPB-based measures (eg, beliefs about SBIRT, social norms and influence, and perceived behavioral control). Multivariate analyses (logistic and multiple regression) will examine the impact of these factors on SBIRT delivery. Beta Testing Initial results focus on beta testing with student learners from the University of San Francisco School of Nursing.

Beta testing was completed in summer 2016. The SUS mean was 65.8 (n=19) which indicates that the SBIRT app is acceptable but needs improvements before rolling out to a larger study sample. Debrief participants reported satisfaction with the Apply and Review sections, which included brief intervention scripts, video demonstrations, the level of detail included in the “Referral to Treatment” section, and inclusion of the PHQ-9 (because this is often required in clinic settings to screen for depression). Suggestions for improvement focused on ease of sign-on and reducing the need for navigation (eg, by having multiple scale items appear on a single screen). These formative beta test data were used for app improvement in preparation for the RCT. Principal Findings The study team found that the TBP model was a useful framework for SBIRT mobile app development and that beta testers responded positively overall to the content and features of the app. The app was developed as a tool to promote translation of substance use screening and intervention skills from classroom to clinical settings.

Our intent was to assist in workforce development and promote the broader use of evidence-based interventions to reduce alcohol and drug problems among patients in healthcare and social service settings. We used an app to support SBIRT skill translation, embedded in a TPB-based approach to learning, in order to inform the field regarding how mobile app technology may be used to reinforce pedagogy, improve implementation, and enhance patient care. The app, “UCSF OHN SBIRT App,” has been positively reviewed online [] and is now publicly available for free downloading (iOS only) via the iTunes store [] (). The RCT in process will determine whether the app has a significant impact on SBIRT skill translation, including rates of SBIRT delivery, learner attitudes, and intent to deliver SBIRT. Icon of the screening, brief intervention, and referral to treatment (SBIRT) mobile app. Based on the evidence and the need for intervention tools usable across settings to reduce alcohol- and drug-related problems, SBIRT instruction in both graduate training programs and continuing education settings for healthcare professionals has been spearheaded by the US Substance Abuse and Mental Health Services Administration, and training opportunities have expanded rapidly over the past 10 years. If efficacy is demonstrated, the mobile app developed by the study team may serve as a useful tool to improve training for healthcare providers and enhance patient care.

This theory-based mobile app serves as a reference guide, a clinical tool, and a data collection instrument. Learners are expected to complete the initial TPB assessment questions before starting their clinical rotations and are then asked to use the app as often as possible during the course of providing direct care. The reporting function frequency of completion is dependent on the structure of the clinical rotation and the needs of the training program and/or preceptors. Although learners’ use of SBIRT is ultimately limited by what their clinical rotation and preceptor allows, this tool may increase the likelihood of effective SBIRT delivery in healthcare and social service settings. This initial presentation describes our mobile app development process, beta testing, and randomized trial methods, which aim to determine the potential impact of this digital tool. Limitations The RCT is conducted in the context of graduate training in the schools in nursing, psychology, social work, and medicine, and may not generalize to other types of professional training or to providers learning SBIRT in the context of continuing education. A limitation of the trial methodology that could impact our study results is the inclusion of incentives within the intervention arm for participants to use the app, which likely would not exist in actual clinical settings.

In addition, some clinical settings and supervisors may not be support the use of SBIRT, and this could impact a learner’s ability to use the app. Although use of mobile devices is becoming widespread, limitations in access to technology could impact the reach of this tool [,]. The app was not designed to integrate responses to screening measures to electronic health records, which could limit its applicability in some clinical settings. Similarly, issues such as adherence to app usage, appropriate use of technology in the workplace, etiquette, and distraction need to be addressed in future studies [] to effectively integrate mobile apps into health and social service settings. Conclusions In behavioral health, mobile apps have primarily been directed toward patients, including alcohol and drug use reduction [-], smoking cessation [], management of depression [], and other mental health conditions [,].

Our approach is innovative in that it uses a skill translation theory-based intervention to target care providers and improve service delivery for important behavioral health problems. If effective, the mobile app could be scaled-up to reach a wider clinical audience and may be useful in future work on developing models of SBIRT fidelity and broader approaches to improving skill translation.