Drosophila PGRP. The PGRP-LA gene is located in cluster with PGRP-LC and PGRP-LF, which encode a receptor and a negative regulator of the Imd pathway, respectively. Structure predictions indicate that PGRP-LA would not bind to peptidoglycan, pointing to a regulatory role of this PGRP. PGRP-LA expression was enriched in barrier epithelia, but low in the fat body. Use of a newly generated PGRP-LA deficient mutant indicates that PGRP-LA is not required for the production of antimicrobial peptides by the fat body in response to a systemic infection. Focusing on the respiratory tract, where PGRP-LA is strongly expressed, we conducted a genome-wide microarray analysis of the tracheal immune response of wild-type, Relish, and PGRP-LA mutant larvae. Comparing our data to previous microarray studies, we report that a majority of genes regulated in the trachea upon infection differ from those induced in the gut or the fat body. Importantly, antimicrobial peptide gene expression was reduced in the tracheae of larvae and in the adult gut of PGRP-LA-deficient Drosophila upon oral bacterial infection. Together, our results suggest that PGRP-LA positively regulates the Imd pathway in barrier epithelia.PeptidoGlycan Recognition Proteins (PGRPs) are key regulators of the insect innate antibacterial response. Even if they have been intensively studied, some of them have yet unknown functions. Here, we present a functional analysis of PGRP-LA, an as yet uncharacterized  Drosophila, in contrast to mammals, lacks adaptive immunity and therefore relies entirely on innate immunity for defense against invading pathogens Drosophila, the Toll and Imd pathways are the two major signaling cascades regulating the massive expression of antimicrobial peptide genes and other immune genes by the fat body following a systemic infection Drosomycin; the Imd pathway is strongly induced by Gram-negative and bacillus-shaped Gram-positive bacteria and regulates the expression of genes such as Diptericin, encoding an antibacterial peptide Bacillus and Listeria species meso-DAP-D-Ala, also known as tracheal cytotoxin (TCT), has been identified as the minimal peptidoglycan motif capable of efficient induction of the Imd pathway Drosophila genome encodes 13 PGRPs, some of which retain amidase properties. The PGRPs of this subgroup, referred to as catalytic PGRPs, have demonstrated  or predicted  zinc-dependent amidase activity, which reduces or eliminates the ability of peptidoglycan to elicit an immune response PGRP-LC, PGRP-LF encodes a transmembrane protein with two PGRP domains. Studies have indicated that PGRP-LF does not bind peptidoglycan but inhibits the activation of PGRP-LC by competing with PGRP-LC dimerization PGRPs form a conserved family of proteins sharing a 160 amino acid domain (the PGRP domain) with similarities to bacteriophage T7 lysozyme, a zinc-dependent N-acetylmuramoyl-L-alanine amidase that removes peptides from the glycan chains of peptidoglycan PGRP-LC genomic cluster. PGRP-LA expression is enriched in several barrier epithelia such as the hindgut and tracheae whereas its expression in the fat body is low PGRP-LA has a regulatory role and is involved in the fine-tuning of the Imd pathway in barrier epithelia. Our study also includes a genome-wide analysis of gene expression in tracheae in the presence or absence of PGRP-LA and Relish. Comparing this analysis with previous studies monitoring the fat body and gut responses to bacterial infection reveals a high tissue-specificity of the pool of genes regulated upon infection.In this study, we report a functional analysis of PGRP-LA, a non-catalytic PGRP encoded by a gene of the PGRP-LA is located at the 5′ boundary of a cluster of three genes that includes PGRP-LC and PGRP-LF. It encodes three isoforms, which are referred to here as PGRP-LAD, LAF and LAC (PGRP-LA differ considerably in their protein domain organizations. PGRP-LAD encodes a putative transmembrane protein with an intracellular domain containing a RIP Homotypic Interaction Motif (RHIM) F contains both a putative transmembrane domain and a PGRP domain, a structure similar to that of the PGRP-LC receptor, except its lack of a RHIM domain. PGRP-LAC encodes a short protein of 138 amino acids composed exclusively of a N-terminus-truncated PGRP domain: although the typical PGRP domain structure comprises a central β-sheet composed of six β-strands surrounded by three α-helices, PGRP-LAC lacks the β1 and β2 sheets and a part of the α1 helix . Data from FlyAtlas reveal a strong expression of PGRP-LA in barrier epithelia, especially in salivary glands and tracheae of larvae and in the hindgut and eyes of adults, while it was weakly expressed in the fat body . Previous studies have shown that PGRP-LA expression is induced about two-fold in adults upon septic injury Ecc15, PGRP-LA was also shown to be induced 1.6-fold and 10-fold in adult gut and larval tracheae, respectively PGRP-LA expression is induced in whole flies after septic injury and in the midgut after oral infection with the Gram-negative bacterium Erwinia carotovora carotovora 15 (Ecc15) . Together, these data indicate that PGRP-LA is induced after epithelial and septic infection.da-Gal4 driver, over-expression in unchallenged flies of PGRP-LAD but not that of PGRP-LAF or PGRP-LAC was sufficient to induce a very high expression level of Diptericin, an antibacterial peptide gene used as a read-out of the Imd pathway. Diptericin induction by PGRP-LAD required Dredd and Tak1, but not PGRP-LC . The observation that PGRP-LAD can activate the Imd pathway, the presence of a RHIM domain, and the location of PGRP-LA in the same cluster as PGRP-LC and LF are suggestive of a role of PGRP-LA in the regulation of the Imd pathway.Over-expression of PGRP-LE and PGRP-LC is sufficient to activate the Imd pathway, in agreement with their function upstream of this signaling cascade PGRP-LA in vivo, we generated a Drosophila strain deficient for PGRP-LA by imprecise excision of the P-element G14937 (from the Korea Advanced Institute of Science and Technology library). 2APGRP-LA mutant bears a deletion of 1401 bp upstream of the P-element insertion site, uncovering the PGRP domain sequence. This deletion includes the exons encoding the whole of the PGRP-LAC and LAF isoforms and the last four exons of the PGRP-LAD isoform (2APGRP-LA adults did not express PGRP-LA mRNA (see below). In addition, 2APGRP-LA mutants were viable and fertile and did not show any apparent developmental defects as observed for all the other PGRP deficient lines described so far. We introgressed the 2APGRP-LA mutation into the wild-type SCanton background by backcrossing 2APGRP-LA males with SCanton females for three generations in order to reduce possible effects of the genetic background.In order to investigate the role of PGRP-LA expression is induced upon infection and as its over-expression up-regulates antibacterial gene transcription, we hypothesized that this gene was involved in the immune response. In order to clarify its role, we analyzed the effect of PGRP-LA deletion on the systemic immune response to different classes of microorganisms injected into the body cavity. Inactivation of PGRP-LA did not impact fly survival to injection with Gram-negative bacteria , Gram-positive bacteria , or fungi (Aspergillus glaucus), whereas inhibition of the Imd pathway in a Relish mutant or the Toll pathway in a Spätzle mutant had a dramatic effect upon survival . Consistent with these survival analyses, we did not detect an effect of 2APGRP-LA mutation on the expression levels of Diptericin after systemic infection with Ecc15 in larvae and adults, infection with L. monocytogenes or injection of DAP-type peptidoglycan or TCT in adults, nor on levels of Drosomycin (a read-out of the Toll pathway) after systemic infection with the Gram-positive bacterium Micrococcus luteus in adults . These data indicate that PGRP-LA does not function as an essential recognition receptor in either the Toll or the Imd pathway during the systemic immune response of adults. Given that PGRP-LA expression is enriched in epithelia, we hypothesized that this PGRP might be involved in peptidoglycan translocation and long-range activation of the systemic response observed upon oral bacterial infection in PGRP-LB deficient flies or upon genital infection Ecc15 or P. entomophila, or genital infections with Ecc15 . We conclude that PGRP-LA does not play a major role in the systemic immune response.Finally, we generated a PGRP-LA expression is enriched . Using a global classification, more than half of the tracheae-regulated genes were assigned to six functional categories: immunity, stress response, signaling, proteases and inhibitors, metabolism and transport, and chitin/cuticle metabolism (Table S1). To determine the contribution of the Imd pathway to antimicrobial defense in the tracheae, we examined the effect of the Relish mutation on gene expression. The expression of 54 up-regulated genes and 20 down-regulated genes was altered at least 2-fold in a Relish background compared to wild-type, with clear enrichment of Relish target genes among the most strongly induced genes (Table S1). We found that 79% (19/24) of the genes annotated as immune genes were affected in the tracheae of Relish mutant flies (Table S1). Of these immune genes 71% (17/24) have been previously reported to be induced in the systemic or gut immune responses in adults PGRP-SD, SB1 and LF, most antibacterial peptide genes, genes coding for Imd pathway components , as well as TepII and Transferrin 1.To determine the genes specifically induced in tracheae, we investigated transcriptome variations in dissected tracheae of larvae infected with the Gram-negative bacterium 18°C see , using ATwdl family, of which 7 members are down-regulated in the tracheae, suggesting a remodeling of the highly structured intima, thin chitinous cuticle covering the tracheae Ecc15 alters the physiology of larval tracheae, with a repression of chitin metabolism and the stimulation of immune and stress responses, as well as changes in signaling and metabolism.The tracheal response to bacteria appears quantitatively less complex than the response occurring in the gut: 224 genes were modulated in the trachea using a two-fold criteria compared to 900 genes in the gut 2APGRP-LA deletion on the transcriptome of tracheae. We confirmed that the expression of PGRP-LA was lost in the mutant . We observed that 143 genes were more than 2-fold up- or down-regulated in 2APGRP-LA as compared to wild-type  or by an indirect influence of the microbiota that is known to influence epithelial responses Drosomycin-GFP in wild-type and 2APGRP-LA tracheae of unchallenged and Ecc15-infected larvae. After infection with Ecc15, we observed that the proportion of Drosomycin-GFP expressing larvae was smaller in 2APGRP-LA mutants  and that even when selecting larvae expressing the reporter, the fluorescence intensity was lower in 2APGRP-LA mutant than in wild-type tracheae . In both the tracheae and the midgut, the expression of PGRP-LA in the rescue line  was similar to wild-type levels . In order to elude any effect of the microbiota, we generated axenic (germ-free) 2APGRP-LA and [PGRP-LA]2A2M; PGRP-LA lines, reconstituted a gnotobiotic microbiota composed of Lactobacillus plantarum and Lactobacillus brevis only, two bacteria commonly found in Drosophila microbiota . The effect of PGRP-LA on tracheal antimicrobial genes upon Ecc15 infection was still observed when larvae were raised in germ-free conditions. The results were however variable and statistical significance could only be observed when monitoring Defensin after infecting germ-free larvae . Consistently, previous studies on S2-cells did not reveal any role of PGRP-LA in the induction of antimicrobial peptides by peptidoglycan or Gram-negative bacteria Using a Drosophila exhibits major differences depending on the tissue Drosomycin) in the midgut or the tracheae relies only on the Imd pathway 2APGRP-LA tracheae of both Ecc15-infected and unchallenged larvae. The idea that PGRP-LA could establish the basal level of Imd pathway in unchallenged conditions is intriguing. These results were confirmed in RT-qPCR . Such immune communication between tissues occurs between several epithelia and the fat body in PGRP-LC is flanked by both a positive (PGRP-LA) and a negative (PGRP-LF) regulator of the pathway. Future studies should elucidate the mechanisms by which PGRP-LA modulates the Imd pathway, notably to determine which PGRP-LA isoforms are involved. Another question to address will be the respective contributions of PGRP-LA, LC, and LE in the sensing of bacteria in the intestine. Thus, our data add a layer of complexity to the mechanism regulating the Imd pathway and further investigation is needed to fully characterize the role of PGRP-LA.The implication of several pattern-recognition receptors in the gut highlights the complexity of mechanisms underlying bacterial sensing in barrier epithelia. The conservation of PGRP-LA in mosquito (contrary to PGRP-LE or PGRP-LF) where it is also located in cluster with PGRP-LC suggests the conservation of its function in other insect species. The genomic organization of the PGRP-LA, LC, LF cluster is intriguing since the Imd-receptor gene Ecc15 not only induces an immune and stress response, but also alters the metabolism and physiology of tracheae. Interestingly, microarray comparison of the immune response during systemic (fat body), gut, and tracheal immune response reveals that only a small group of common genes are induced, all regulated by the Imd pathway and encoding mainly antimicrobial peptides and other pathway components. These genes may therefore represent the “core” of Imd pathway that are complemented by tissue-specific genes to achieve an optimal immune response.The Drosophila tracheal immune response remained poorly characterized ROregon flies were used as wild-type controls for the 2APGRP-LA original strain , and SCanton flies were used as wild-type controls of 2APGRP-LA introgressed into the SCanton background . Over-expression experiments were controlled by crossing the da-Gal4 driver to 1118w, the strain in which the UAS construct insertions were generated. E20 Relish, B118Dredd, 1Tak1, E12PGRP-LC and rm7Spätzle are described elsewhere da-Gal4 line expresses Gal4 ubiquitously and constitutively. The CUAS-PGRP-LA (insertion R1), DUAS-PGRP-LA (insertion R4) and FUAS-PGRP-LA (insertion R2) lines were obtained as follows. A full-length cDNA of each isoform of PGRP-LA  was placed downstream of the UAS sequence using the pUASt vector. F1 progeny young adults carrying both the UAS construct and the Gal4 driver were transferred to 29°C for optimal efficiency of the UAS/Gal4 system.Stocks were reared at 25°C on media prepared as follows: per liter of water, 58.8 g inactivated yeast , 58.8 grams maize flour (Westhove Farigel Maize H1), 7.5 g agar, 58 mL of 1∶1 mix of grape and multi-fruit juice were combined with water and boiled at 80°C for 30 min. When the mixture had cooled to 65°C, 4.85 ml of 99% proprionic acid and 30 ml of a 10% solution of methyl paraban in 85% ethanol were added. After cooling to room temperature, live yeast was added on the surface of the media, except for germ-free and gnotobiotic flies, which were reared on autoclaved media in glass tubes without the addition of live yeast.2APGRP-LA mutant was obtained by imprecise excision of the G14937 P-element (KAIST library) and ΔPGRP- by FRT mediated deletion of the region between the P-elements 1930 and 4396, following previously published methods [PGRP-LA]2M rescue line was generated using gap-repair and recombineering, and final rescue construct carried by P[acman] vectors was inserted into the PhiC31 landing site 51C on chromosome 2 (BDSC strain 24482) PGRP-LA contain the PGRP-LA gene including the following sequence (based on Flybase release r5.47): 3L: 9323736–9331619.Drosophila stocks and crosses were maintained at 25°C in yeasted tubes containing corn-meal fly medium. Germ-free lines were generated by egg bleaching and kept in autoclaved fly medium. Gnotobiotic lines were generated by introducing cultured L. plantarum and L. brevis previously isolated from our fly lines into the medium of germ-free lines and were also kept in autoclaved fly medium.Erwinia carotovora carotovora 15 (Ecc15), Escherichia coli, Enterococcus faecalis, Pseudomonas entomophila, and Micrococcus luteus were described previously 600 = 200, i.e. the OD600 of a 1/1000th dilution of the pellet in PBS was 0.2 corresponding to 4.1011 CFU.mL−1 (exceptions are mentioned below). Salmonella typhimurium, Listeria monocytogenes and Candida albicans were described previously Aspergillus glaucus was kept as a spore-suspension at 4°C and injected as such into the flies. While testing the susceptibility to septic injury Ecc15 was tested at OD600 200 and 50, Salmonella typhimurium at OD600 0.65 and 10−5, L. monocytogenes at OD600 0.65 and 10−5, Enterococcus faecalis at OD600 5, 10, 15.All bacteria were stored as frozen stocks (15% DMSO). 600 = 200), peptidoglycan (5 mg/ml), or TCT  applied to a filter disk completely covering the surface of standard fly medium. Flies were maintained at 29°C and guts were dissected 16–24 h after contact with infected food. In , S1, midgut was defined as the section of the gut between the proventriculus (included) and the pylorus (midgut/hindgut junction), while hindgut corresponds to the section between the pylorus and the anus. Malpighian tubules were excluded from both midgut and hindgut samples. In Septic injuries were made by pricking adults in the thorax with a thin needle dipped into a concentrated bacterial pellet. Genital infections were performed by touching the tip of the abdomen with a 200 µL pipette-tip containing 10 µL of bacterial pellet Method 1 : adults were allowed to lay eggs for 3 days then removed, and 500 µL Ecc15 pellet (OD600 = 200) were added on the 4th day into 28.5 mm-wide vials where larvae were developing. The vials were then put at 18°C for 24 h and non-wandering 3rd-instar larvae (with hand-shaped anterior spiracles) were dissected. Method 2 ( and S4): larvae were incubated for 30 min in a 2-mL tube with a 1∶1 mixture of mashed banana and Ecc15 (OD600 = 200), then the tube content was transferred in a fresh fly vial and kept at 29°C To monitor the immune response in the tracheae, two different methods of infection were used. RpL32 mRNA quantification by RT-qPCR was performed as described previously PGRP-LA, the primers were designed to amplify a region included in all isoforms and in the deleted part of 2APGRP-LA to allow both expression quantification and deletion control (sequences of the qPCR primers: Fp: CCT-TTA-TGG-GCG-ACT-ATG-GC and Rg: CTT-GGC-GTC-CCA-CGA-TTC) , and DNase treated. The samples were controlled for fat body contamination by RT-qPCR on Fat body protein P6 (Fbp2). RNA quality was controlled on Agilent 2100 Bioanalyzer chips. As the quality of some samples was not good enough after this first purification, RNA of all samples was ethanol precipitated to pass Bioanalyzer quality control. For each sample, 100 ng of total RNA was amplified and labeled using the GeneChip IVT Labeling Kit according to the protocol provided by the supplier. Affymetrix Drosophila Genome 2.0 arrays were hybridized with 30 mg of labeled cRNA, washed, stained, and scanned according to the protocol described in the Affymetrix Manual. Three independent repeats were performed for each condition and gene expression profiles from challenged larvae were normalized to their controls. Statistical analyses were performed using the R and Bioconductor statistical packages. Full dataset can be found at http://lemaitrelab.epfl.ch/ and has been deposited at EMBL-EBI database (Accession number: E-MEXP-3925).Larvae were infected with method 1 and dissected by gently pulling the posterior spiracles backwards until the whole tracheae went out. If needed, the anterior part of the tracheae was pulled out in a second similar step. RNA pools from the tracheae (including anterior spiracles) of 50 3For GFP observation, flies were dissected in PBS and either directly observed under a Leica MZ16F dissecting microscope, or mounted in PBS for imaging with a Zeiss Axioimager Z1. Images were captured with a Leica DFC300FX camera and Leica Application Suite or with an Axiocam MRn camera and Axiovision respectively.S. aureus or E. coli bioparticles (20 mg.mL−1) were injected in the fly abdomen. Flies were left for 30–40 min at 25°C and injected with 6×69 nL of 0.4% trypan blue.The phagocytosis assay was performed as previously published www.flybase.org) accession numbers for genes mentioned in the microarray are indicated in the data . The accession numbers for genes mentioned in the rest of this study are: Defensin (CG1385), Diptericin (CG12763), Drosocin (CG10816), Drosomycin (CG10816), PGRP-LA (CG32042), PGRP-LB (CG14704), PGRP-LC (CG4432), PGRP-LF (CG4437), da , pirk (CG15678) and Relish (CG11992). The vectorbase (www.vectorbase.org) accession numbers for Culex and Anopheles homologs of PGRP-LA are CPIJ006558 and AGAP005205 respectively.The Flybase Click here for additional data file.Figure S1PGRP-LA expression in tissues. RT-qPCR analysis of PGRP-LA expression in wild-type adult female tissues (A) and in adult female midguts (B) and larval tracheae (C) of wild-type and 2A[PGRP-LA]2M; PGRP-LA strains. Data are normalized to RpL32 and shown as a ratio of the expression in the wild-type. In A, a single experiment was performed. In B, data are the mean of three independent experiments, error bars indicate standard errors and data were analyzed by 2-way ANOVA with Bonferroni post-tests. In C, data are the mean of two independent experiments and error bars indicate data variation.(TIF)Click here for additional data file.Figure S2PGRP-LA is not required for the systemic immune response.A–C. Survival analysis of flies after injection with S. typhimurium , L. monocytogenes , A. glaucus . D,E.Dpt expression after injection of 9.2 nL of monomeric  or polymeric peptidoglycan  (A) and after septic injury with Ecc15 in larvae (B).(TIF)Click here for additional data file.Figure S3Tracheal Drs response in wt and 2APGRP-LA deficient larvae (GFP). Fluorescence observed in the tracheae of wild-type and 2APGRP-LA larvae expressing the Drs-GFP reporter gene 4 days after bacterial infection with Ecc15 at 18°C. (−) no fluorescence (+) fluorescence in the spiracles only, (++) in the tracheal trunks, (+++) in the tracheae in less than half of the larva or (++++) in the tracheae in more than half of the larva. •, ••, •••: increasing intensity of fluorescence. Data of one experiment representative of 3 independent experiments are shown.(TIF)Click here for additional data file.Figure S4Tracheal Drs response in wt and 2APGRP-LA deficient larvae (RT-qPCR). RT-qPCR quantification of Drs expression in the larval tracheae 24 h after Ecc15 infection in 2ALA and 2A[PGRP-LA]2M;LA lines raised in germ-free conditions or gnotobiotic conditions (LpLb) where the flora is composed of L. plantarum and L. brevis. Data show the mean of 4 repeats and error bars indicate standard errors. Data were analyzed by Mann-Whitney tests, differences are non significant.(TIF)Click here for additional data file.Figure S5PGRP-LA is not required for the phagocytosis. Fluorescent images of fly abdomens after injection of S. aureus or E. coli nanoparticles. Data show representative results of one experiment.(TIF)Click here for additional data file.


A range of metathesis substrates was assembled from triplets of unsaturated building blocks. The approach involved the iterative attachment of a propagating and a terminating building block to a fluorous-tagged initiating building block. Metathesis cascade chemistry was used to “reprogram” the molecular scaffolds. Remarkably, in one case, a cyclopropanation reaction competed with the expected metathesis cascade process. Finally, it was demonstrated that the metathesis products could be derivatised to yield the final products. At each stage, purification was facilitated by the presence of a fluorous-tagged protecting group. Our collective understanding of the biological relevance of chemical space has been shaped, in large part, by the historic exploration of chemical space by chemical synthesis (and biosynthesis) . The scaKnown organic molecules populate chemical space unevenly and unsystematically. Around half of all known organic compounds are based on only 0.25% of the known molecular scaffolds ! This un1 . Subsequently, metathesis cascade reactions were used to “reprogram” the molecular scaffolds, concomitantly releasing the products from the linker  [We have previously developed a robust approach for the synthesis of skeletally diverse small molecules  14]. Th. Th14].  4 or 5) . The app1, which is an allyl alcohol or allyl amine equivalent, all of the products were inevitably allylic alcohols or cyclic allylic amines. Here, we demonstrate that the approach is considerably more general, and that it is feasible to exploit triplets of building blocks, extending the range of diverse molecular scaffolds that may be prepared.Although powerful, this general approach to skeletally diverse molecules had only been exemplified by varying pairs of unsaturated building blocks . Thus, b6a or 7) bearing a fluorous tag to facilitate the purification of synthetic intermediates . Tr. Tr7 was8–11, and the terminating building block 12b, were prepared by using established methods [11 was determined by conversion into the corresponding diastereomeric O-methyl mandelate esters. The terminating building blocks 12a and 13 were prepared by straightforward derivatisation of commercially available starting materials . In each case, an excess of the propagating building block, DEAD and triphenylphosphine was used. In general, the crude product was directly deacetylated. At each stage, the required fluorous-tagged product was isolated by fluorous-solid-phase extraction (F-SPE), and its purity determined by analysis by 500 MHz 1H NMR spectroscopy. These results are summarised in Initially, the propagating building blocks 12a, 12b or 13) (see 1H NMR spectroscopy.The metathesis substrates were prepared by subsequent attachment of a terminating building block ( 13) see . In eachtert-butyl methyl ether [o-nitrophenylsulfonyl groups were removed from the products. The results are summarised in The scaffolds of the metathesis substrates were “reprogrammed” by treatment with Hoveyda–Grubbs second-generation catalyst in either dichloromethane or yl ether  (TBME). yl ether  of the c39, however, the cyclopentene did not participate in the metathesis reaction, and the bridged macrocycle 53 was obtained in low yield. We have previously observed the formation of macrocyclic metathesis products in similar metathesis cascade reactions [46 and 47 as byproducts in the metathesis cascade reaction of 32 was remarkable [59 (45 after deprotection), or cyclopropanate [46 or 47 after deprotection)  .60–71  to yield a range of amides and ureas . The flu60–71  were obtMetathesis is an extremely powerful reaction for diversity-oriented synthesis. It was demonstrated that metathesis substrates could be assembled efficiently from triplets of building blocks. Thereafter, metathesis cascades yielded a diverse range of molecular scaffolds. The diversity of the products was increased through variation of all three of the building blocks used: the initiating, the propagating, and the terminating building block.The overall approach was facilitated by fluorous tagging of the initiating building block, allowing easy purification (by F-SPE) of synthetic intermediates and metathesis products. The presence of a fluorous tag also facilitated the purification of the functionalised products. Evaluation of the biological activity of the final products will be reported in due course.File 1Experimental and compound characterisation.


Neuroimaging-based diagnostics could potentially assist clinicians to make more accurate diagnoses resulting in faster, more effective treatment. We participated in the 2011 ADHD-200 Global Competition which involved analyzing a large dataset of 973 participants including Attention deficit hyperactivity disorder (ADHD) patients and healthy controls. Each participant's data included a resting state functional magnetic resonance imaging (fMRI) scan as well as personal characteristic and diagnostic data. The goal was to learn a machine learning classifier that used a participant's resting state fMRI scan to diagnose (classify) that individual into one of three categories: healthy control, ADHD combined (ADHD-C) type, or ADHD inattentive (ADHD-I) type. We used participants' personal characteristic data , without any fMRI data, as input to a logistic classifier to generate diagnostic predictions. Surprisingly, this approach achieved the highest diagnostic accuracy (62.52%) as well as the highest score (124 of 195) of any of the 21 teams participating in the competition. These results demonstrate the importance of accounting for differences in age, gender, and other personal characteristics in imaging diagnostics research. We discuss further implications of these results for fMRI-based diagnosis as well as fMRI-based clinical research. We also document our tests with a variety of imaging-based diagnostic methods, none of which performed as well as the logistic classifier using only personal characteristic data. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder characterized by impulsiveness, inattention, and hyperactivity. This condition affects about 5% of children and adolescents worldwide  The ADHD-200 Consortium made available a large Training Dataset that contained data from 776 participants collected at multiple institutions. Each participant's data included a resting state fMRI scan, a structural MRI scan, and individual characteristics data . The test (holdout) dataset was comprised of data from 197 additional participants for whom diagnostic information was not provided. Competition entrants had to build and train a diagnostic procedure using the 776 training participants and then submit predicted diagnostic labels for the 197 test set participants. Teams were ranked based on the accuracies of their predicted diagnostic labels. The ADHD-200 dataset is the first publicly-available dataset with fMRI scans from on-the-order-of one thousand participants, including both psychiatric patients and healthy controls. In contrast, previous studies on fMRI-based diagnosis , the Fourier transform, and the functional connectivity (FC) analysis based on independent components analysis (ICA) of Calhoun et al.  and ErhaData analyzed in this paper came from the ADHD-200 dataset, comprising data from 973 participants  scores by the data curators. For the work described here, we refer to this dataset as the “Training Dataset.” . Our Training Dataset included 668 participants, whose details are shown in Table IQ scores were provided for most Original Training Dataset participants except for those from NeuroIMAGE.  participants, except for the 26 participants from the BrownU site. In some analyses performed after the competition, we used the subset of the ADHD-200 Holdout Dataset consisting of the 171 participants for whom diagnostic data were released. We refer to this subset as the “Holdout Dataset.”  For details of Holdout Dataset participants, see Table IQ scores were provided for the Original Holdout Dataset participants. (See Table Each data collection site used its own scanner(s) and its own MR scanning parameters. Full details are available at ADHD-200-Webpage .The ADHD-200 Global Competition required three-way diagnostic classification, but we also explored binary diagnosis because we suspected that it might be easier than three-way diagnosis. In binary diagnosis, we classified participants as healthy control vs. ADHD, collapsing across ADHD subtype. In three-way diagnosis, we classified participants as healthy control vs. ADHD-C vs. ADHD-I.Depending on the analysis, we used as input to the diagnosis process either personal characteristic data or fMRI data. We first preprocessed the data, applied dimensionality reduction algorithms in the case of fMRI data, and then extracted a feature vector for each participant. Personal characteristic feature vectors included site of data collection, age, gender, handedness, and IQ scores (for details see section 2.3). We tested four different types of fMRI feature vector: mean fMRI signal intensity over time in each voxel , projections of voxels' timecourses into a PCA space, low frequency Fourier components of voxel's timecourses, and voxels' weightings on FC maps derived from ICA.  Participant feature vectors and diagnostic labels were input into the Weka machine learning software package . We also compared accuracy results to the baseline chance accuracy achieved by guessing healthy control  for every participant. Note that this definition of chance accuracy is different from the one used by the ADHD-200 Global Competition organizers , preprocessing, feature extraction, and learning algorithm as a “Diagnostic Pipeline.” To test the binary and three-way diagnostic performance of various Diagnostic Pipelines, we used 10-fold cross validation. That is, we defined a standard set of 10-folds where each fold included a (disjoint) subset of 66 or 67 participants from the 668 Training Dataset participants. The subsets were approximately counterbalanced for diagnostic class, gender, age, handedness, IQ, medication status, and site of data collection.  For each iteration For the ADHD-200 Global Competition, we tested various Diagnostic Pipelines on the three-way diagnosis task using 10-fold cross validation. We selected the best-performing Diagnostic Pipeline, which turned out to use the logistic classifier with only personal characteristic data as input. Since fMRI data quality did not affect personal characteristic data, we trained the logistic classifier on all 776 participants from the Original Training Dataset. We applied this trained classifier to the 197 ADHD-200 Global Competition Original Holdout Dataset participants to generate predicted diagnostic labels.In follow-up analyses after the competition, we tested some other Diagnostic Pipelines (see below) using 10-fold cross validation. Testing dozens of different Diagnostic Pipelines introduced the problem of multiple comparisons and over-fitting. To address this issue, we selected the best-performing Diagnostic Pipeline in a given context  and trained its classifier on all 668 participants from the Training Dataset. We then tested the trained classifier on the 171 participants from our Holdout Dataset. For these analyses, the Holdout Dataset was not used in any way to select a Diagnostic Pipeline nor to train its classifier. The Holdout Dataset was used only to test the ability of a given trained Diagnostic Pipeline to generalize its performance to completely new data.PCs1) included 7 features: data collection site, gender, age, handedness, Verbal IQ, Performance IQ, and Full 4 IQ. Personal characteristic selection 2 (PCs2) was identical to PCs1 except for the addition of two more features (IQ Measure and Full 2 IQ) as well as different preprocessing of handedness data from the NeuroIMAGE site. PCs2 included nine features: data collection site, gender, age, handedness, IQ Measure, Verbal IQ, Performance IQ, Full 2 IQ, and Full 4 IQ. Handedness data from all sites except the NeuroIMAGE site were categorical . NeuroIMAGE handedness values were continuous scores from the Edinburgh Handedness Inventory  and all negative scores with a 0 (left-handed). The second preprocessing step filled in missing values using mean imputation for continuous  features and mode imputation for categorical features. After being imported into the Weka machine learning package, feature vectors were standardized such that each feature element fell in the range 0–1  be an index into the length η feature vector. For PCA1−5, k = 1 denotes the first PCA component weight, k = 2 denotes the second, and so on. We define k,pV  as the 3D volume comprised of the kth feature value at each voxel. For PCA1−5, 1,pV contained the weights for the first PCA component for participant p.  For each k ∈ , we identified clusters of voxels (regions of interest) exhibiting significant differences in two-sample t-tests between groups. Specifically, statistical contrast maps were generated with massively univariate t-tests performed at each voxel location on the values from participants' volumes k,pV. Diagnostic Pipelines included clusters either from the comparison of ADHD patients vs. healthy controls (ncomparisons = 1) or from the comparison of both ADHD patients vs. healthy controls as well as ADHD-C vs. ADHD-I patients (ncomparisons = 2). That is, a total of η × ncomparisons statistical maps were computed for a given Diagnostic Pipeline. Statistical contrast maps were thresholded voxelwise such that |t| > θt, where the value of θt was specific to each Diagnostic Pipeline. Statistical maps were then cluster size thresholded (see Appendix I) to zero out those voxels belonging to clusters with volumes (in mm3) less than the threshold volume θcs. The value for θcs was also specific to each Diagnostic Pipeline. Voxel clusters were extracted from the thresholded contrast maps using an automated algorithm (see Appendix H). To avoid over-fitting, on a given fold i of 10-fold cross validation, the statistical cluster selection was done on the training data only (participants not in fold i). The parameter nclusters governed how many significant clusters were retained from each statistical map in a given Diagnostic Pipeline. Clusters were ordered by statistical mass, defined as the sum of absolute t-values for all voxels in a cluster. The nclusters most statistically massive clusters from each statistical map were retained, with less massive clusters discarded. Setting nclusters = ∞ resulted in all clusters' being kept. Finally, for a given participant p, for each cluster identified in each of the η × ncomparisons statistical maps, we took the appropriate k,pV and computed its mean value across the voxel locations in that cluster. All such mean values were then concatenated to form a feature vector for participant p.In detail, the cluster identification procedure was as follows. Each of the feature extraction methods from section 2.5 produced a length η feature vector at each voxel location that passed masking. η took on the following values: T Avg:1, PCACluster feature extraction method was combined with no SWA spatial reduction , masking, and scan-based intensity normalization (PSCS-s). PCA1,Cluster and PCA1−5,Cluster feature extraction were combined with SWA (r = 8), masking, and either timecourse- or scan-based intensity normalization (PSCS-tc or PSCS-s). FFTCluster feature extraction was combined with SWA (r = 3), masking, and either PSCS-tc or PSCS-s intensity normalization. The ALFFCluster feature extraction method was combined with no SWA spatial reduction, masking, and either PSCS-tc or PSCS-s. FCDMN,Cluster and FC1−20,Cluster feature extraction were always combined with no SWA spatial reduction, masking, and timecourse-based intensity normalization (PSCS-tc). The T AvgCluster, PCA1,Cluster, PCA1−5,Cluster, FFTCluster, ALFFCluster, FCDMN,Cluster, and FC1−20,Cluster procedures were governed by five parameters, including θt, θcs, and nclusters defined above, as well as the minimum inter-peak distance dmin and minimum extracted cluster size vmin parameters used by the automated cluster extractor (see Appendix H). We tested Diagnostic Pipelines with different settings for these parameters. Let us consider the parameter vector . This parameter vector took on a specific value for each Diagnostic Pipeline. For T AvgCluster, we tested the parameter vector value . For PCA1,Cluster and PCA1−5,Cluster, we tested the parameter vectors  and . For FFTCluster, we tested the parameter vector . For ALFFCluster, we tested the parameter vector . For FCDMN,Cluster, we tested the following parameter vectors: , , and . For FC1−20,Cluster, we tested the parameter vector .The T AvgRobust, PCA1,Robust, PCA1−5,Robust, FFTRobust, ALFFRobust, FCDMN,Robust, and FC1−20,Robust. For a given iteration i of 10-fold cross validation, a set Si of statistical comparison maps was computed from all participants not in fold i. The set Si included η × ncomparisons statistical comparisons on the volumes k,pV where k ∈  and p ∈  (see above for definitions). For example, in FCDMN,Robust, Si included statistical comparisons on the DMN FC weighting map, as in FCDMN,Cluster. For FC1−20,Robust, Si included statistical comparisons on all 20 FC weighting maps, as in FC1−20,Cluster. Then, we also computed nine additional sets of comparisons maps Si, j in nine sub-iterations j, where j ∈ {1, …, 10}\{i}. A given Si, j was equivalent to a given Si except that Si, j was computed from participants not in fold i nor in fold j. The Si, j maps underwent local |t|-value thresholding as well as cluster size thresholding (see Appendix I). Here, we retained only those voxel locations in Si that also exhibited significance on all nine sets Si, j. That is, after all nine sub-iterations j, the maps in Si were modified by zeroing out those voxel locations not showing significance in the appropriate maps from the sets Si, j. Clusters were then extracted from the filtered maps Si and feature vectors generated for each participant as in the cluster-based feature extraction method described above. This somewhat elaborate process was meant to introduce robustness into the cluster selection process, as it only considered voxel locations showing consistent statistical differences across different subsets of participants.We tested a variant of the cluster-based feature extraction method described above. This variant was intended to reduce cluster variability across the different iterations of 10-fold cross validation. Feature extraction methods utilizing this variant were denoted T AvgRobust, PCA1,Robust, PCA1−5,Robust, FFTRobust, ALFFRobust, FCDMN,Robust, and FC1−20,Robust feature extraction methods were combined with preprocessing steps in the same way, respectively, as the T AvgCluster, PCA1,Cluster, PCA1−5,Cluster, FFTCluster, ALFFCluster, FCDMN,Cluster, and FC1−20,Cluster methods. (See above for details.) The robust feature extraction methods were controlled by the same vector of parameters  introduced above for the cluster-based methods. For T AvgRobust, we tested the parameter vector value . For PCA1,Robust and PCA1−5,Robust, we tested the parameter vector . For FFTRobust, we tested the parameter vector . For ALFFRobust, we tested the parameter vector . For FCDMN,Robust, we tested the parameter vector . For FC1−20,Robust, we tested the following values for the parameter vector: , , , , , and .The T Avgt-tests on the weighting values for each voxel location for each map. We considered two different t-tests. The localizer test compared all participants' weighting values against zero. The patients vs. controls t-test contrasted ADHD participants' FC weighting maps (collapsing across ADHD subtype) against controls. The resulting statistical maps were thresholded voxel-wise at |t|≥2.582 . To correct for multiple comparisons across the voxel population at a global p < 0.05, all maps were cluster size thresholded (see Appendix I) with a minimum cluster size of 63 voxels . The cluster size threshold of 63 was determined using Monte Carlo simulation.We performed statistical group comparison analyses on the FC weighting maps (see last paragraph of section 2.5). These comparisons took the form of massively univariate PCs1) or selection 2 (PCs2) features (see section 2.3). The RBF SVM classifier always guessed healthy control. The best binary diagnostic accuracy (75.0 ± 4.5%) was better than the best three-way diagnostic accuracy (69.0 ± 8.3) at p = 0.01 .Table PCs1) provided the best accuracy on three-way diagnosis of any Diagnostic Pipeline we had tested, including the fMRI-based Diagnostic Pipelines discussed below.  The logistic classifier with PCs2 input achieved slightly better accuracy than with PCs1 on the Training Dataset, but we did not test PCs2 until after the competition. Therefore, we used the logistic classifier with PCs1 input features trained on the entire ADHD-200 Original Training Dataset to generate our predicted diagnostic submissions for the competition based on the ADHD-200 Original Holdout Dataset. Our submission achieved the highest prediction accuracy (62.52%) as well as the best competition score (124 out of 195) of any of the 21 competing teams. The competition scoring system assigned one point for every correct diagnosis and 0.5 point for a diagnosis of ADHD but with the wrong subtype . For more details of competition results, see ADHD-200-Results-Webpage . None of these fMRI-based features provided better diagnostic accuracy than the best results using personal characteristic data (section 3.1) on either diagnostic task.We tested binary diagnosis Figure  top and r = 3, scan-based percent signal change intensity normalization (PSCS-s), T Avg, and the RBF SVM classifier. This accuracy was significantly better than the 64.2 ± 3.8% chance accuracy . However, this accuracy was worse than the 75.0 ± 4.5% obtained with the best performing Diagnostic Pipeline using personal characteristic data (section 3.1) at p = 0.04 .The best accuracy on binary diagnosis of the tests shown in Figure r = 8, timecourse-based percent signal change intensity normalization (PSCS-tc), PCA1 feature extraction, and the linear SVM classifier. Though this accuracy was barely above the baseline chance accuracy of 64.2 ± 3.8%, this small difference was significant . This accuracy was worse than the 69.0 ± 8.3% obtained with the best performing Diagnostic Pipeline using personal characteristic data (section 3.1) at p = 0.03 .The best accuracy on three-way diagnosis of the tests shown in Figure Cluster, PCA1,Cluster, PCA1−5,Cluster, FFTCluster, ALFFCluster, FCDMN,Cluster, and FC1−20,Cluster procedures (see section 2.6). Results are shown in Figure Binary and three-way diagnosis were tested with feature extraction methods based on significant clusters identified in group comparisons. These feature extraction methods included the T Avgp = 0.002, t = 3.88, df = 9). This Diagnostic Pipeline used SWA with reduction factor r = 8, scan-based percent signal change intensity normalization (PSCS-s), PCA1,Cluster feature extraction based on comparison of ADHD patients vs. controls as well as ADHD-C vs. ADHD-I patients, and the cubic SVM classifier. In comparison to this cluster-based approach, the best personal characteristic-based Diagnostic Pipeline (section 3.1) achieved a significantly better accuracy of 75.0 ± 4.5% . For three-way diagnosis, the best cluster-based Diagnostic Pipeline achieved 66.0 ± 5.1% accuracy. Comparison of this accuracy with the baseline chance accuracy of 64.2 ± 3.8% approached significance . This Diagnostic Pipeline used SWA with reduction factor r = 8, scan-based percent signal change intensity normalization (PSCS-s), PCA1−5,Cluster feature extraction based on comparison of ADHD patients vs. controls as well as ADHD-C vs. ADHD-I patients, and the cubic SVM classifier. Compared to this cluster-based approach, the best personal characteristic-based Diagnostic Pipeline (section 3.1) achieved an accuracy of 69.0 ± 8.3%, and the comparison of these accuracies approached significance .For binary diagnosis, the best cluster-based Diagnostic Pipeline achieved 69.5 ± 5.5% accuracy, which was significantly better than the baseline chance accuracy of 64.2 ± 3.8% . See Figure Binary and three-way diagnosis were also tested with feature extraction methods utilizing the robust cluster extraction procedure. These feature extraction methods included the T Avgp = 0.03, t = 2.23, df = 9). This Diagnostic Pipeline used SWA with reduction factor r = 8, scan-based percent signal change intensity normalization (PSCS-s), PCA1−5,Robust feature extraction based on comparison of ADHD patients vs. controls as well as ADHD-C vs. ADHD-I patients, and the cubic SVM classifier. In comparison to this robust cluster-based method, the best personal characteristic-based Diagnostic Pipeline (section 3.1) achieved a significantly better accuracy of 75.0 ± 4.5% .For binary diagnosis, the best robust cluster extraction Diagnostic Pipeline achieved 67.8 ± 6.2% accuracy, which was significantly better than the baseline chance accuracy of 64.2 ± 3.8% . This Diagnostic Pipeline used FC1−20,Robust feature extraction based on comparison of ADHD patients vs. controls and the RBF SVM classifier. In comparison to this robust cluster-based approach, the best personal characteristic-based Diagnostic Pipeline (section 3.1) achieved a significantly better accuracy of 69.0 ± 8.3% .For three-way diagnosis, the best robust cluster extraction Diagnostic Pipeline achieved 65.1 ± 4.7% accuracy. This was not significantly better than the baseline chance accuracy of 64.2 ± 3.8% though it approached significance  yielded 100 ± 0.0% accuracy on the training set for three-way diagnosis with the logistic, quadratic SVM, and cubic SVM classifiers. This feature set included 200 features per participant , which was well below the number of participants (668) used for 10-fold cross validation. All Diagnostic Pipelines using FC1−20,Cluster features performed at or below chance when tested on the test data. We can conclude that there is diagnostically-useful information in the fMRI data and that some Diagnostic Pipelines were able to utilize this information at least for the data on which they were trained. These Diagnostic Pipelines were not able to generalize well to new test data, resulting in performance at or below baseline chance accuracy in most cases and only 6.5% better than chance in the best case.As a control experiment, Diagnostic Pipelines were also trained and then tested on the same training data. Many of the Diagnostic Pipelines whose test accuracies are shown in Figures p < 0.05 corrected) in posterior cingulate cortex, bilateral anterior superior temporal sulcus, and bilateral thalamus. ADHD patients exhibited greater DMN weighting than controls in left anterior middle frontal gyrus, right temporal pole, and superior cerebellum. Though these regions exhibited group differences between controls and patients, there was substantial overlap among participants from the two groups . Previous studies have reported successes with fMRI-based diagnosis of ADHD . For example, suppose one were to test a dozen different classifiers using Thirdly, the ADHD-200 dataset presented new challenges in terms of participant heterogeneity with which previous studies did not have to contend. The ADHD-200 sample contains 973 participants. This is many more participants than were included in the previous studies cited above. Those twelve studies included from 14 to 104 participants with a median of 39 participants. In a study with 20 participants, it is possible to select them to be fairly homogeneous in terms of age, gender, socio-economic background, medication history, and clinical sequelae. In contrast, the ADHD-200 sample pooled hundreds of participants from eight different sites in various countries. We expect that the ADHD-200 data were more heterogeneous compared to datasets used in previous fMRI diagnosis studies. Combining data from multiple sites introduced additional potential heterogeneity in terms of participant nationality, genetic background, and cultural background, as well as potential differences in how clinical groups in different institutions applied ADHD diagnostic criteria. MRI scanner hardware and settings certainly differed across the eight sites contributing to the ADHD-200 dataset, in contrast to previous studies that collected all data on a single scanner. Previous studies of fMRI-based diagnosis did not have to contend with heterogeneity on this scale. The ADHD-200 sample is the first fMRI dataset to combine such large numbers of psychiatric patients and control participants. That the ADHD-200 Global Competition teams did not achieve high accuracy diagnosis with this data may simply reflect the need for new methodological approaches to address this heterogeneity. More research into new fMRI-based diagnostic methods that are robust against participant heterogeneity is clearly warranted and may enable high accuracy diagnosis with the ADHD-200 dataset in the future. Because data from clinical settings also vary across sites, scanners, patients populations, and so on, robustness against these sources of data heterogeneity will be important as we work toward deploying fMRI-based diagnosis in clinical settings.To reduce inter-site variability, we tested diagnosis using resting state fMRI data from ADHD-200 participants all taken from a specific site. Within-site diagnosis was not more successful on average than using the entire Training Dataset (results not shown). Our robust cluster-based feature extraction methods were an attempt to improve diagnostic performance generalization to novel test data by reducing feature heterogeneity, but these attempts were unsuccessful. Most of the individual ADHD-200 sites included relatively large numbers of participants, for example 216 from NYU. The data from a single site likely still exhibited substantial heterogeneity among participants, and the diagnostic algorithms we tested may not have been robust against this heterogeneity.The ADHD-200 Global Competition teams did not achieve high accuracy diagnosis using the resting state fMRI scans in the ADHD-200 sample. It is possible that resting state fMRI may not be an ideal imaging protocol for diagnosing ADHD specifically. Before adopting this strong conclusion, one would obviously need to exclude the possibility that the ADHD-200 competition teams may have used non-optimal diagnostic approaches, as discussed above. One other group has reported 85% accuracy on binary diagnosis of ADHD patients vs. controls using resting state fMRI data . Between-groups statistical comparisons did not produce features (biomarkers) capable of diagnosing individual participants with high accuracy. Because the ADHD-200 dataset combined large numbers of participants from different data collection sites, it imposed novel challenges in terms of inter-participant heterogeneity. Future studies of fMRI-based diagnosis must develop methods that are robust against such heterogeneity. One potential approach may be to adapt existing machine learning methods, which were developed in other contexts, to the identification of individual differences in fMRI datasets. Such individual differences may have better diagnostic utility than differences derived from group comparisons. Novel methods that are robust against heterogeneity will be particularly important as we work toward delivering new fMRI-based clinical diagnostic tools that could be deployed in diverse, real-world health care settings.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We also demonstrate the existence of short grain aromatic landraces perhaps with independently evolved aroma trait; unlike introgression from japonica into indica group, as evidenced in Basmati-type long grain varieties. The independent origin of aroma in indica rice is fascinating as it explores lesser known aspects of indica rice domestication and diversification.Crop domestication is a remarkable example of evolution of wildly growing plants into cultivable forms through human selection. Following the domestication of rice almost 10,000 years ago, ancient farmers selected many rice lineages for diverse agronomic and cultural traits, like grain size, shape and colour; awn length; pest resistance; and aroma etc. In this study, examining phenotypic traits of a large collection of Indian rice landraces (all accessed from Vrihi, rice seed bank,  indica group, unlike introgression of specific alleles of the BADH2 gene from the japonica group as in Basmati. We presume that this parallel origin and evolution of aroma in short-grain indica landraces are linked to the long history of rice domestication that involved inheritance of several traits from Oryza nivara, in addition to O. rufipogon. We conclude with a note that the insights from the phenotypic analysis essentially comprise the first part, which will likely be validated with subsequent molecular analysis.Rice landraces are lineages developed by farmers through artificial selection during the long-term domestication process. Despite huge potential for crop improvement, they are largely understudied in India. Here, we analyse a suite of phenotypic characters from large numbers of Indian landraces comprised of both aromatic and non-aromatic varieties. Our primary aim was to investigate the major determinants of diversity, the strength of segregation among aromatic and non-aromatic landraces as well as that within aromatic landraces. Using principal component analysis, we found that grain length, width and weight, panicle weight and leaf length have the most substantial contribution. Discriminant analysis can effectively distinguish the majority of aromatic from non-aromatic landraces. More interestingly, within aromatic landraces long-grain traditional Basmati and short-grain non-Basmati aromatics remain morphologically well differentiated. The present research emphasizes the general patterns of phenotypic diversity and finds out the most important characters. It also confirms the existence of very unique short-grain aromatic landraces, perhaps carrying signatures of independent origin of an additional aroma quantitative trait locus in the  Oryza rufipogon gradually gave rise to the domesticated O. sativa that now feeds billions of people globally  to investigate the major determinants of phenotypic diversity. We further investigated the morphological distinction between aromatic and non-aromatic landraces as well as that within aromatic landraces.Vrihi (www.cintdis.org/vrihi), the country's largest non-governmental seed bank conserving 920 folk rice varieties. All these varieties have been grown every year on Vrihi's conservation farms, located in the district of Bankura  and Rayagada , over the past 17 years (www.cintdis.org/basudha). Genetic purity of each landrace is maintained by periodic rouging of ‘off types’, in addition to obviating chances of cross-pollination between varieties grown on neighbouring farm plots by employing the flowering asynchrony method (Folk rice varieties were collected from farmers' fields of various Indian states . Seeds oy method . From thwww.cintdis.org/vrihi) upon request for scientific study except for commercial research affiliated to corporate sector.The germplasms of most of the rice landraces are publically available at Vrihi  based on the broad trend observed in the PCA and made an attempt to find the best variable(s) that can discriminate the groups. Subsequent analyses involved forward stepwise discriminant function analysis (DFA) with eight characters  to understand the combination of variables which can best explain the grouping in STATISTICA ver. 10 . We performed DFA considering F to enter as 0.01, F to remove at 0.0 and minimum tolerance at the default value of 0.01. We also obtained a classification function, which can possibly explain the grouping. This function can be used for further cross-validation. Following this, we checked whether group means are significantly different by Welch two-sample t-test in R 2.15.2  using all the 29 variables in PAST  of the variance, respectively. Grain length and DL are highly correlated with component 1 (r2 = 0.97 for both), GW, DW and SW are correlated with component 2  and HT, LL and PD are related to component 3   depicted effective segregation of a majority of aromatic and non-aromatic landraces .


Attention deficit hyperactivity disorder (ADHD) is a frequently diagnosed disorder in child- and adulthood with a high impact affecting multiple facets of social life. Therefore, patients suffering from ADHD are at high risk to be confronted with stigma, prejudices, and discrimination. A review of the empirical research in the field of ADHD with regard to stigma was performed. The findings of investigations in this field were clustered in different categories, including stigma in children with ADHD, stigma in adults with ADHD, stigma in relatives or in people close to a patient with ADHD, and the influence of stigma on authorities’ attitudes toward patients with ADHD. Variables identified to contribute to stigma in ADHD are public’s uncertainty concerning the reliability/validity of an ADHD diagnosis and the related diagnostic assessment, public’s perceived dangerousness of individuals with ADHD, socio-demographical factors as age, gender, and ethnicity of the respondent or the target individual with ADHD, stigmatization of ADHD treatment, for example public’s skepticism toward ADHD medication and disclosure of diagnostic status as well as medication status of the individual with ADHD. The contribution of stigma associated with ADHD can be conceptualized as an underestimated risk factor, affecting treatment adherence, treatment efficacy, symptom aggravation, life satisfaction, and mentally well-being of individuals affected by ADHD. Public as well as health professionals’ concepts about ADHD are highly diverse, setting individuals with an ADHD diagnosis at greater risk to get stigmatized. Investigations on stigma in ADHD cite from 1994 to 2011 with the majority of studies being conducted in the first decade of the twenty-first century. Further elaboration on themes as social representations, rejection, and perceptions associated with ADHD led to another 5 articles, discussing the relevance of stigma in ADHD. The literature overview will start with studies dealing with The first article considering the impact of stigma on children diagnosed with ADHD was published by Stine  who mentFurthermore, medication disclosure has not been found to lead to increased reports of peer rejections or social rejection by others Sandberg . In lineAlarming high numbers of children with ADHD stated personality changes due to medication intake  but also on the characteristics of the addressed audience, the stigmatized group, and interactional factors arising from the complexity of social processes  did not reveal any further explanation of the overall negative ratings of participants. Law et al.  thereforWalker et al.  also invCorrelation analysis between expressed social distance and potential causative attributions indicated that peers who rated the vignette-child’s disorders to stem from parental failure, substance abuse, or the individual’s own failure (low effort) were also more likely to prefer to distance from the target , and characteristics of the adult participants . In general, better-educated participants were more cautious in ascribing deviant behavior to be signs of a mental disorder  used discrepancies between the attitudes toward ADHD of parents of children without ADHD and the expectations of parents with children with ADHD to indicate the degree of perceived stigma of parents with children with ADHD. Results contradicted former studies showing heightened levels of depression in parents of children with ADHD Johnston . Measure used disHarpur et al.  found paKoro-Ljungberg and Bussing  evaluateSchmitz et al.  call attdosReis et al.  acknowleThe present paper reviewed empirical knowledge regarding stigma associated with ADHD. ADHD is known for its lifetime persistency and is characterized by clinical symptoms of inattention, impulsivity, and hyperactivity (American Psychiatric Association However, recently emerging studies investigating the effects on stigma reduction of co-operational behavior of children with and without ADHD diagnosis are very promising. For example, Sandberg  showed t


Attention deficit/hyperactivity disorder (ADHD) is the most frequently diagnosed neurobehavioral disorder of childhood, yet its etiology is not well understood. In this review we present evidence that environmental chemicals, particularly polychlorinated biphenyls (PCBs) and lead, are associated with deficits in many neurobehavioral functions that are also impaired in ADHD.Human and animal studies of developmental PCB or lead exposures that assessed specific functional domains shown to be impaired in ADHD children were identified via searches of PubMed using “lead” or “PCB exposure” in combination with key words, including “attention,” “working memory,” “response inhibition,” “executive function,” “cognitive function,” “behavior,” and “ADHD.”Children and laboratory animals exposed to lead or PCBs show deficits in many aspects of attention and executive function that have been shown to be impaired in children diagnosed with ADHD, including tests of working memory, response inhibition, vigilance, and alertness. Studies conducted to date suggest that lead may reduce both attention and response inhibition, whereas PCBs may impair response inhibition to a greater degree than attention. Low-level lead exposure has been associated with a clinical diagnosis of ADHD in several recent studies. Similar studies of PCBs have not been conducted.We speculate that exposures to environmental contaminants, including lead and PCBs, may increase the prevalence of ADHD. Because the literature on the effects of lead on cognition in children is so extensive, we included only lead studies published after 1990 that either tested specific neurobehavioral functions relevant to ADHD or directly assessed the relationship between lead exposure and a diagnosis of ADHD. We applied similar criteria to PCB studies, with the exception that we included studies before 1990. For studies of lead in animals, we included those that evaluated effects of developmental exposure on cognitive domains relevant to ADHD and reported steady-state blood lead levels (BLLs) < 100 μg/dL. Findings from animal studies that reported steady state BLLs > 100 μg/dL were not included because they are of questionable relevance to humans. We included PCB studies in animals that evaluated individual noncoplanar congeners or mixtures, but we excluded studies of coplanar PCB congeners because exposure to these congeners results in few cognitive deficits in animal models . They found that children with higher BLLs had poorer verbal working memory as assessed on the Wide Range Assessment of Memory and Learning (WRAML).Impaired response inhibition is another hallmark of ADHD , and theThe impact of lead exposure on cognitive flexibility has been assessed in a number of studies, most often using the Wisconsin Card Sorting Test (WCST). Increased perseverative responding  on the WCST was associated with increased BLL in all three studies that used this test . This paPlanning is another cognitive domain that is impaired in ADHD children . AlthougVigilance and alertness are two aspects of attention that are impaired in ADHD children, and both appear to be affected by lead exposure. When CPTs were used to assess attention, impaired vigilance, marked by a decrease in the number of correct responses and an increase in omission errors, was associated with BLL in both of the Detroit cohorts , 2007 anDeficits in alertness are also suggested by associations of BLL or hair lead with longer reaction times while performing several other tests, including simple reaction time tests, the Sternberg Memory task, a mental rotation task, and a stimulus discrimination test in the Detroit 1 , the AmsIn summary, studies of lead exposure in children provide evidence for impairments in several aspects of executive function and attention that are impaired in ADHD children. The functions most consistently affected across studies include cognitive flexibility, vigilance, and alertness. There is also some evidence that impairments in working memory, planning, and response inhibition are associated with lead exposure, but the findings for these cognitive domains are less consistent across studies.p = 0.05 threshold for statistical significance.In addition to the parallels between the behavioral domains affected in lead-exposed and ADHD children, an association between BLL and a diagnosis of ADHD has been reported in a number of recent studies . ImportaA study using the NHANES data  reportedAlthough evidence is beginning to emerge linking lead exposure to ADHD even at relatively low levels of exposure, the data are currently insufficient to infer causality. Furthermore, it is not clear whether behaviors exhibited by ADHD children could result in increased exposure to lead rather than (or in addition to) a direct contribution of lead exposure to the pathogenesis of ADHD. At this juncture, we are not aware of any studies that have attempted to address this difficult but important question.Numerous studies have used rodent and primate models to assess the effects of early lead exposure on tests of executive functions similar to those affected in ADHD children [Lead has been shown to impair performance on the delayed spatial alternation (DSA) task, a commonly used test of spatial working memory. Despite differences in experimental procedures, developmentally lead-exposed monkeys in several studies conducted in two different labs were impaired in their ability to learn the DSA task . In bothAlthough the findings are inconsistent in children, impairments of response inhibition after developmental lead exposure are well documented in animal models . Early lFI and DRL schedules examine a different aspect of response inhibition than the stopping tasks often used in ADHD children. On FI and DRL schedules, the subject must withhold responding in order to achieve efficient performance, whereas on stopping tasks the subject must stop a response after it has already been initiated . This asTemporal information processing deficits, which are present in ADHD children , also coDiscrimination reversal learning, which has parallels with the WCST, has been used to assess cognitive flexibility in lead-exposed animals. As discussed above, relatively low BLLs have been associated with impairments on the WCST in several human studies . ImpairmAn early study found that juvenile rhesus monkeys with high BLLs showed impairments on spatial, color, and size reversal learning tasks, with the most striking deficits observed on the first reversal after original learning , 1979b. Concurrent random interval–random interval (RI-RI) schedules, which allow pressing on two response levers to be reinforced at different frequencies, are another way to evaluate the ability to change response strategies. Lead-exposed monkeys were found to be impaired in their ability to transition between levers in an RI-RI task .Relatively few animal studies have assessed attentional processes after lead exposure. Performance on vigilance tasks was impaired in rats postnatally exposed to lead . SignifiLead-exposed animals also have underlying neurochemical deficits that share commonalities with ADHD, including reduced dopamine signaling e.g., . HoweverIn summary, the cognitive domains impaired in lead-exposed animals parallel those affected in children exposed to lead  and who Researchers of both PCBs  and ADHDn = 178), suggesting that children from more disadvantaged backgrounds may be more at risk. In a separate prospective birth cohort from Oswego, Scores on working memory tests appear to be inversely associated with prenatal PCB exposure. In a prospective study, n = 205–219) (n = 145–152) (n = 44–52)  , 1992 an145–152) . These a= 44–52) . Later t= 44–52) . These fIn conclusion, PCB exposure appears to affect performance on verbal working memory tests, but there has not been sufficient research to determine whether performance on nonverbal working memory tests is affected. By comparison, lead appears to affect performance on working memory tests to a lesser extent, whereas ADHD children are impaired on both verbal and nonverbal working memory tasks, with moderate effect sizes .n = 197) (n = 182) (n = 183)  , 8 yearsn = 182) , and 9.5n = 183) . The clen = 183) . ChildreIn the Michigan cohort, n = 207) who had the lowest (n = 42) and highest (n = 41) prenatal PCB exposure on tests of planning ability. Deficits were observed on the TOL task but not on the ROCF in the children with higher exposure. Performance on these and other planning tasks is impaired in ADHD children  on CPTs. This was true for children at different ages: 4.5, 8, and 9.5 years in the Oswego cohort , 2005 anIn the Michigan cohort, higher prenatal PCB exposure was associated with slower reaction times in a visual discrimination task at 4 years  and in aIn summary, PCB-exposed children show evidence of impaired verbal working memory, response inhibition, cognitive flexibility, and alertness on various tasks, but vigilance, as assessed on CPTs, appears to be relatively intact. In comparison, lead-exposed children demonstrate impaired cognitive flexibility, vigilance, and alertness, as well as less consistent deficits in working memory and planning, but only limited effects on response inhibition. Deficits in all of these functions are typical in ADHD children .A variety of studies have used animal models to assess the effects of PCB exposure on executive functions impaired in ADHD children . Many ofPerformance on the DSA working memory task is impaired by early PCB exposure in a number of studies , althougPerformance on tests of response inhibition including FI and DRL operant schedules is also impaired by early PCB exposure. The findings of impaired response inhibition in monkeys have been mirrored in several rodent studies. Lactational exposure of rats to PCB-153 produced increased responding during FI training and extinction in male rats  but not Discrimination reversal learning tasks have been used to assess the effects of PCBs on cognitive flexibility in monkeys  and in rOnly one study has scrutinized the effects of PCBs on attentional processes. in vitro, resulting in altered synaptic and cytosolic dopamine clearance. Thus, dysregulation of synaptic catecholamines could mediate some of the cognitive impairments observed after PCB exposure. Of interest, Neurochemical studies demonstrate that PCBs cause reductions in dopamine in the prefrontal cortex and striatum , 1997. PIn summary, the findings from studies in animal models reveal deficits on tasks that assess many of the same cognitive domains affected by PCB exposure in humans including tests of working memory, response inhibition, and cognitive flexibility. Performance on these same tasks has been reported to be impaired in laboratory animals and children exposed to lead during early development and in ADHD children . HoweverLead was phased out of gasoline and PCBs were phased out of use in electrical equipment in the 1970s. Since that time, environmental concentrations and human body burdens of both “legacy” contaminants have been slowly decreasing. In contrast, human exposure to other chemicals, including brominated flame retardants , bisphenin vitro  and BPA, which have been shown to disrupt dopamine signaling in vitro , should in vitro . Studiesin vitro . PBDEs hin vitro , parallein vitro  and furtA recently published study reported that prenatal exposure to low-molecular-weight phthalates was associated with poorer parent-rated scores on attention and externalizing problems (including impulsivity and hyperactivity) in 188 children 4–9 years of age using the Behavior Assessment System for Children scales . Precedin = 288) reported an association between prenatal chlorpyrifos exposure and scores in the clinical problems range on both the Attention Problems scale and ADHD scale of the Child Behavior Checklist at 3 years of age (n = 331) and 5 years (n = 323), with several significant associations observed at 5 years of age (A study of an inner-city minority population (s of age . Althougs of age , the chis of age . Specifis of age . Only a s of age , whereass of age .Very recently, a positive association between parentally reported ADHD and serum PFC levels was reported based on an analysis of NHANES data . To our Although in this review we focus on environmental contaminants, a number of other potential environmental factors, including premature birth, low birth weight, and psychosocial adversity, are known or suspected to be risk factors in the development of ADHD on their own and could potentially interact with environmental exposures to modify risk . ConvergWith the addition of studies conducted in the last decade, the specific profiles of cognitive deficits associated with exposure to lead and PCBs are coming into sharper focus. The deficits associated with both contaminants bear many similarities to the behavioral problems observed in ADHD children. Both lead and PCBs have effects on tests designed to assess working memory, with deficits observed in humans  and animInterestingly, ADHD children show more pronounced deficits on tests that assess spatial working memory than on those that assess verbal working memory . Few epiAlthough uncertainty remains, studies in children and animal models suggest that lead may impair both response inhibition and attentional processes , whereasIn addition to deficits in response inhibition and vigilance on CPTs, ADHD children are also impaired on the stop signal task . CommissBoth contaminants also cause impairments on tasks that involve components of both response inhibition and temporal information processing, such as FI and DRL schedules. These findings are consistent across species, from rats to monkeys [see reviews by Another interesting pattern that emerges when recent human studies are considered together with older animal research is a relatively consistent association between lead exposure and deficits in cognitive flexibility using such tasks as WCST in humans  and reveTo date, most ADHD research has focused on the combined phenotype or has not differentiated among the three phenotypes . AnotherFinally, there is a relatively extensive body of literature reporting the effects of neurodevelopmental exposure to lead and PCBs in animals and humans. It is hoped that the knowledge gained from this review of the literature on these two legacy contaminants will assist researchers attempting to understand the potential contribution of emerging contaminants, including PBDEs, BPA, phthalates, pesticides and PFCs, to ADHD risk.