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No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. For TFs, gateway-compatible entry clones (Invitrogen) containing the open reading frames (ORFs) lacking stop codons were obtained from ref. 11. Drosophila Act5C-promoter driven expression clones were created using the Gateway system. The TF ORFs were shuttled into the GAL4-DNA binding domain (DBD) containing destination vector pAGW-GAL4-DBD (cloned as described below) by mixing 100 ng of TF entry clone, 100 ng of pAGW-GAL4-DBD and 0.7 μl of LR clonase II enzyme mix (Invitrogen). The identities of all TF entry clones have been confirmed by Sanger sequencing using the primers 5′-CCCAGTCACGACGTTG-3′ and 5′-CACAGGAAACAGCTATG-3′. Note that we tested the full-length transcription factors, including their DBDs, as trans-activating and DNA-binding functions might not always reside in entirely separate protein domains. While this implies that the fusion proteins might bind via the TFs’ DBDs in addition to the GAL4-DBD mediated recruitment, this does not influence the results of the assay: the assay itself measures transcriptional activation independently of where TF binding occurs and we expect that the TFs’ DBDs have at most minor effects on binding strengths as the GAL4-DBD binds to DNA already very strongly. For cofactors, we compiled a list of 338 cofactors based on several criteria. We included proteins containing Pfam domains typical for transcriptional cofactors (for example, HAT, HDAC, SET, Chromo, Bromo), proteins which are part of chromatin modifying or remodelling complexes or part of complexes associated with RNA polymerases (for example, SAGA, Polycomb, TFIID, Mediator), and Drosophila proteins which are homologues of mammalian chromatin-associated proteins (Supplementary Table 3). We amplified the cofactor ORFs from cDNA using oligonucleotides containing Gateway-compatible attB-sites (5′-GGGGACAAGTTTGTACAAAAAAGCAGGCTTC-3′ and 5′-GGGACCACTTTGTACAAGAAAGCTGGGTC-3′) for subsequent entry clone creation. The primer sequences have been chosen to be as close as possible to an annealing temperature of 60 °C which we calculated using the formula with cA, cT, cG and cC being the number of adenines, thymines, guanines and cytosines, respectively. The full list of resulting primer sequences (lacking the attB sequences) is listed in Supplementary Table 3; for 18 of the cofactors no primer sequences are available because we obtained these entry clones from ref. 11 categorized as TFs but manually re-categorized them as cofactors based on their annotation in FlyBase31 or their protein domain content32. For cDNA generation, RNA was isolated from S2 cells and reverse transcribed as described in ref. 33. For PCR amplification, KOD and KOD XL DNA (Merck Millipore) and KAPA HiFi (KAPA) polymerases were used according to manufacturer’s specifications. We created Gateway entry clones by mixing 1 μl of PCR reaction, 100 ng of pDONR221, and 1 μl of BP clonase II enzyme mix (Invitrogen). The identities and correctness of all entry clones have been ensured using Sanger and next-generation sequencing (see below) and we deposited them at Addgene (http://www.addgene.org/Alexander_Stark/). The cofactor ORFs were then shuttled to the Drosophila Act5C-promoter driven destination vector pAGW-GAL4-DBD as described for TFs. The insert flanks of all obtained cofactor entry clones have been Sanger-sequenced and automatically checked to cover the TSS and TTS of one of the isoforms annotated by FlyBase. All entry clones passing additional manual visual inspection using BLAT and the UCSC genome browser have been subjected to further verification by next-generation sequencing as follows. A pool of 100–300 entry clones corresponding to a total of 5 μg DNA solved in 50 μl TE buffer was sonicated (duty cycle, 20%; intensity, 5; 200 cycles per burst; time, 90 s) to 200–400 bp using a S220 Focused-ultrasonicator (Covaris) as described in ref. 11. The fragmented plasmid pool was then prepared for deep sequencing using the Illumina DNA Sample Prep kit and sequenced using a HiSeq2000 (Illumina) producing 50-nt reads. The resulting reads have been assembled and analysed using PrInSes-C34. All insert sequences not starting with ATG, containing a stop codon or a frameshift were immediately rejected. All sequences with less than five mutations leading to non-synonymous amino acid changes were immediately accepted. The remaining sequences were translated, aligned against the respective protein sequence, and manually decided. The next-generation sequencing reads have been deposited at the NCBI Sequence Read Archive (SRA) under the accession SRS806429; the PrInSes-C-generated full-length transcript sequences are available at http://factors.starklab.org and in Supplementary Data 1, and the cofactor Gateway entry clones from Addgene (http://www.addgene.org/Alexander_Stark/). We cloned a destination vector to conveniently create vectors expressing N-terminally V5- and GAL4-DBD-tagged TFs and cofactors under the control of the Drosophila Act5C promoter using the Gateway cloning system. pAGW-GAL4-DBD was cloned by amplifying the GAL4-DBD from pBPGUw35 using one oligonucleotide containing the V5-tag (peptide sequence MGKPIPNPLLGLDST) 5′-TCTGATATCATGGGGAAGCCAATCCCTAATCCCCTTCTGGGACTCGACTCTACCGGCGGCTCTATGAAGCTACTGTCTTCTATCGAACA-3′ and the oligonucleotide 5′-TATACCGGTGGCCGCCGCCCGACGATACAGTCAACTGTCTTTGAC-3′. Amplification was performed using KOD Polymerase (Merck Millipore) according to the manufacturer’s instructions. The resulting PCR product was digested using EcoRV and AgeI and ligated into pAGW (Drosophila Gateway Vector Collection), which was digested using the same enzymes, thereby replacing eGFP with V5-GAL4-DBD. We created Gateway-compatible (Invitrogen) destination vectors to conveniently clone reporter vectors for different regulatory contexts based on firefly luciferase transcribed from a housekeeping core promoter (hkCP; promoter of ribosomal gene RpS1213) or a developmental core promoter (dCP; Drosophila synthetic core promoter (DSCP) derived from Eve35). We created the destination vector attR_dCP_luc by digesting pGL4.26 (Promega) with FseI and BglII and ligating a fragment containing DSCP and luc+, thereby replacing the minimal promoter and luc2 with DSCP-luc+. We digested the resulting vector with KpnI and BglII and ligated a fragment containing the attR Gateway cassette, yielding attR_dCP_luc. We created two hkCP-driven destination vectors containing a Gateway cassette either upstream (attR_hkCP_luc) or downstream (hkCP_luc_attR) of the luciferase reporter gene by using the plasmid pGL3 (Promega) as a basis and replacing the SV40 promoter with the promoter of RpS12 as described in ref. 13. The resulting vector was digested using either KpnI and BglII (to create attR_hkCP_luc) or AfeI (to create hkCP_luc_attR); in both cases, we amplified a Gateway attR cassette using oligonucleotides containing the respective restriction sites, and digested and ligated it into the digested plasmid. All enhancers, motif mutant contexts and other motif or backbone mutant variants were either PCR amplified with primers containing attB Gateway sites or ordered as synthesized fragments (IDT), shuttled into entry clones using TOPO or BP Clonase II (both Invitrogen), and shuttled into the luciferase destination vectors using the LR clonase II enzyme mix (Invitrogen) by mixing 1 μl of PCR product or synthesized DNA solved in TE buffer, 100 ng of destination vector and 0.7 μl of LR clonase II enzyme mix (Invitrogen). We used a modified version of pRL-TK (Promega) to normalize the firefly signal for transfection efficiency and cell number. Ubi-RL has been created by cloning a region upstream of the gene Ubi-p63E (chr3L: 3901760-3902637) upstream of the Renilla luciferase gene in reverse orientation using NheI and BglII. S2 cells, derived from embryos36, were obtained from Life Technologies and grown in Schneider’s Drosophila Medium (Life Technologies 21720-024) supplemented with 10% FBS (Sigma F7524) and 1% penicillin/streptomycin (Life Technologies 15140-122) grown in T75 flasks (ThermoScientific 156499) at 27 °C and passaged every 2–4 days. BG3 neuroblast-like cells, derived from larvae37, were obtained from the Drosophila Genomics Resource Center (DGRC) and grown in Schneider’s Drosophila Medium supplemented with 10% FBS, 1% penicillin/streptomycin, and 10 μg ml−1 Insulin (Sigma-Aldrich I1882) in T75 flasks at 27 °C and passaged every 3–4 days. Kc167 cells, derived from embryos38, were obtained from DGRC and grown in M3/BPYE Medium containing 5% FBS and 1% penicillin/streptomycin in T75 flasks at 27 °C and passaged every 2–3 days. Ovarian somatic cells (OSCs), derived from adult ovaries39, were obtained from the laboratory of J. Brennecke and grown in Shields and Sang M3 Insect Medium (Sigma-Aldrich S8398) supplemented with 10% FBS, 1% insulin, 1% glutathione, 1% fly extract, and 1% penicillin/streptomycin in T75 flasks at 27 °C and passaged every 2–3 days. All cell lines used are regularly checked for mycoplasma contamination. S2 cell transfections were performed using jetPEI (peqlab 13-101-40N). Four hours before transfection, 30,000 cells (30 μl of a 106 cells per ml suspension) were seeded in clear polystyrene 384-well plates (ThermoScientific 164688). For each transfection, we used 30 ng firefly luciferase reporter plasmid, 3 ng Renilla luciferase expressing plasmid Ubi-RL, and 3 ng GAL4-DBD-TF/cofactor or GAL4-DBD-GFP fusion protein expressing plasmid. Beforehand, we assayed the effects of using different amounts of GAL4-DBD fusion protein expressing plasmid and chose 3 ng (Extended Data Fig. 9). The DNA solution containing 36 ng DNA in 5 μl TE buffer was filled up to 15 μl using sterile 150 mM NaCl (polyplus) and prepared in 96-well plates. Transfection reagent (15 μl total: 13.95 μl 150 mM NaCl, 1.05 μl jetPEI) was added to each well of the 96-well plates and mixed rigorously. After 30 min incubation at 25 °C, cells were transfected in quadruplicates by transferring each transfection mix four times (6 μl each) to four adjacent wells of a 384-well plate containing the seeded cells. Luciferase assays were performed after 48 h of growth at 27 °C. Handling the transfection mixes and all subsequent pipetting steps have been performed using a Bravo Automated Liquid Handling Platform (Agilent). Kc167, BG3, and OSC cell transfections were performed using jetPEI in the same way as described above for S2 cells with the exception of transfection reagent composition: 15 μl total containing 14.1 μl 150 mM NaCl and 0.9 μl jetPEI. Human HeLa cells (gift from the laboratory of J. M. Peters) were grown in DMEM medium (Gibco 52100-047) supplemented with 10% heat-inactivated FBS, 1% penicillin/streptomycin and 2 mM L-glutamine (Sigma G7513) in T75 flasks at 37 °C in an atmosphere of 95% air and 5% carbon dioxide. All cell lines used are regularly checked for mycoplasma contamination. We performed HeLa cell transfections using a self-prepared 1 mg ml−1 PEI (25,000 MW, Polysciences 23966) stock solution in PBS (pH adjusted to pH 4.5 and sterile filtered). On the day before transfection we seeded 30 μl of a suspension containing 4,000 HeLa cells in medium (DMEM, 10% FBS, penicillin/streptomycin) into each well of a 384-well plate. Three microlitres of a PEI/DMEM mix (0.24 μl PEI filled to a total of 4.5 μl using DMEM without FBS and penicillin/streptomycin and incubated at room temperature for 5 min) were added to 3 μl of a DNA/DMEM mix (44.5 ng firefly luciferase reporter vector, 4.45 ng TF expression vector (created using pAGW-CMV_ GAL4-DBD, see below) and 4.45 ng pRL-CMV vector for transfection normalization (Promega #E2261) in DMEM without FBS and penicillin/streptomycin. The resulting DNA/PEI mix in DMEM was incubated at room temperature for 30 min and subsequently added to the seeded cells. We performed cell lysis and luciferase assays using the Promega dual-luciferase reporter assay system (Promega E1910) according to the manual. We created the Gateway destination vector pAGW-CMV_GAL4-DBD by replacing the Drosophila Act5C promoter in pAGW-GAL4-DBD with a region containing the CMV enhancer and the T7 promoter amplified from pRL-CMV using the primers 5′-CGACAGATCTTCAATATTGGCCATTAGCCATAT-3′ and 5′-GGTGGCTAGCCTATAGTGAGTCGTATTA-3′. Dual-luciferase assays were performed using self-prepared substrate solutions (D-Luciferin and Coelenterazine have been obtained from GoldBio LUCK-250 and pjk-Gmbh 102111) and lysis buffer as described in ref. 40. For cell lysis, the supernatant was removed and 30 μl of lysis buffer added and incubated gently shaking for 30 min. Ten microlitres of the cell lysates were transferred to black 384-well plates for luminescence assays (Nunc MaxiSorp, Sigma-Aldrich P6491-1CS). All pipetting steps have been performed using a Bravo Automated Liquid Handling Platform (Agilent). Luminescence was measured after adding 20 μl of each substrate, for firefly and Renilla luciferase respectively, using a Biotek Synergy H1 plate reader coupled to a plate stacker. We normalized all firefly luciferase signals to the signal of Renilla luciferase to control for transfection efficiency and cell number (the relative luciferase signal). We then further normalized all relative luciferase signals for TF- and cofactor-GAL4-DBD transfections to relative luciferase signals obtained for GAL4-DBD-GFP transfections (fold-change over GFP). We assessed statistical significance by two-sided unpaired t-tests on the two sets of quadruplicate relative luciferase signals (GAL4-DBD-TF/COF versus GAL4-DBD-GFP). Throughout the paper, ‘activation’ was defined as a fold-change ≥1.5 (P < 0.05), and ‘repression’ was defined as a fold-change ≤1/1.5 (P < 0.05), both compared to the signal for GAL4-DBD-GFP. We corrected the P values for multiple testing using the Benjamini and Hochberg method as implemented in R (p.adjust with method ‘BH’ or its alias ‘fdr’). All statistical calculations and graphical displays, if not stated otherwise, have been performed using version 2.15.3 of the R software suite41. Enrichment analyses have been performed for each of the 15 clusters and for 6 types of features. To first obtain a coarse functional characterization of the clusters, we assessed the enrichments and depletions of TFs which are able to activate or repress a developmental (dCP) or housekeeping (hkCP) core promoter on their own (≥1.5-fold activation or repression (P < 0.05), both compared to the signal for GAL4-DBD-GFP when tested on a context comprised of UAS sites upstream of a developmental core promoter (4×UAS-dCP) or a housekeeping core promoter hkCP (4×UAS upstream hkCP)). Homopolymeric amino acid repeat motifs have been de novo discovered using MEME42 (version 4.8.1, q-value threshold of 1 × 10−5) in TFs that activated or repressed on their own outside enhancer contexts (tested in the 4×UAS dCP context; ≥1.5-fold; P < 0.05). Pfam domain32 signature matches in the Drosophila proteome have been generated using hmmer43 (version 3.0b3, e-value threshold of 0.01). Eukaryotic Linear Motifs44 (ELM; version 08/2014) were matched to the amino acid sequences of the tested TF protein isoforms, after masking the TFs’ Pfam. Additionally, Gene Ontology45 (GO) annotations, and gene expression patterns in the Drosophila embryo as annotated by ref. 46 (IMAGO) have been subjected to enrichment and depletion analyses. To control for multiple testing, we empirically determined false-discovery rates (FDRs) for the different hypergeometric P values. For this, we repeated the feature enrichment analyses 1,000 times, each after randomly shuffling the TF-to-cluster assignments, and recorded the best (that is, most significant) P values. We then adjusted the original P values such that only 10% of the 1,000 random controls reached the P values of the original data (FDR < 10%). Following this protocol, we separately adjusted the FDR cut-off for each cluster (15) and feature type (ELM, MEME, Pfam, GO, IMAGO). To assess if tethering via the GAL4-DBD reflects the different TFs’ regulatory functions when bound to their endogenous motifs, we selected two sets of TFs, three TFs that preferentially activated the CGCG- versus the GATA-context (Fig. 1e) and four TFs that preferentially activated the hormone-receptor contexts; Fig. 2c). We replaced each UAS site in the enhancer mutant contexts S2-1 CGCG, S2-1 GATA, and Nhe2 EcR3, 12 (which also corresponds to an endogenous TF motif in the wild-type enhancers, for example, the EcR motif for the hormone contexts) with a sequence corresponding to the consensus motif of the respective TF as reported in refs 47, 48. (Dfd: CTTAATGA, Hey: CAGCCGACACGTGCCCC, Ets21C: ATTTCCGGT, Ato: AACAGGTGG, Ets96B: ACCGGAAGTAC, Gl: ATTTCAAGAATA, HLH4C: AAAAACACCTGCGCC). The enhancer rescue constructs were synthesized by IDT, shuttled into the luciferase reporter vector attR_dCP_luc using the Gateway system and tested in luciferase assays in S2 cells exactly as described above. To assess potential functional associations of assigned TFs and cofactors, we followed the strategy from ref. 30, recruiting TFs via GAL4-DBD and providing untagged cofactors. For this, we chose contexts in which the different TFs (Clk of cluster 8, Bsh of cluster 10, and CG17186 of cluster 14) were active (4×UAS-dCP for Clk and 4×UAS-upstream-hkCP for Bsh and CG17186). We prepared DNA mixes to be transfected containing 29 ng firefly luciferase reporter plasmid, 3 ng Renilla luciferase expressing plasmid Ubi-RL, 1 ng (Bsh and CG17186) or 0.5 ng (Clk) of GAL4-DBD–TF fusion protein expressing plasmid and an increasing series of untagged cofactor expressing plasmid (0 ng, 0.003 ng, 0.006 ng, 0.012 ng, 0.023 ng, 0.047 ng, 0.094 ng, 0.188 ng, 0.375 ng, 0.75 ng, 1.5 ng, 3 ng). We kept the total amount of transfected plasmid DNA constant at 36 ng for all experiments using a GFP-expressing plasmid. To clone the expression plasmids for the untagged cofactors and GFP, we used the Gateway-compatible vector pAW (Drosophila Gateway Vector Collection). The remaining experimental procedure and analysis was performed as described above. We clustered the 474 TFs based on the log -transformed fold-change values (TF over GFP) from all 24 contexts. First, we standardized all contexts and constructed a k-nearest-neighbour graph (k = 15). We used the Euclidean distance as distance measure as it reflects both the variation of the enhancer activity profile across contexts and the effect sizes within each context; that is, it is able to discriminate between strong and weak activators and repressors even if they vary similarly across the 24 contexts. Next, we took a symmetrized (A + AT) adjacency matrix of this graph and solved multiclass spectral clustering as described in ref. 49 and implemented in the Python package scikit-learn50. In order to decide about the number of clusters and to assess the clustering validity, we analysed the clustering stability upon bootstrapping the data set51. In order to visualize the data, we mapped the data onto a plane by a specialized nonlinear dimensionality reduction technique (t-SNE)52. The algorithm provides the visualization by mapping data points close in the original space to nearby locations in the plane, preserving the local structure. We extended the k-nearest-neighbour graph to include cofactors by comparing the log -transformed fold-change values (cofactor over GFP) of cofactors and TFs (k = 5, Euclidean distance). The locations of the cofactors in the visualization were obtained from spring layout. We know that UAS sites in the enhancer mutant contexts most probably replace binding sites that are functional3 but we do not know which TFs bind them in vivo. In order to check whether we recover these positive controls in the enhancer mutants, we took all the TFs expressed in S2 cells (RPKM > 1) (ref. 53) for which motifs are known3. We scanned the wild-type enhancer sequences (S2-1-wt, S2-2-wt, S2-3-wt, Ubi-1-wt, Ubi-2-wt, Ubi-3-wt) for motif matches with P < 9.76 × 10−4 (1/4,096) using an in-house motif-detection program. For each mutant context, we considered only those TFs for which any of its motif matches had at least 5 mutated base pairs. In the resulting set of TFs (Extended Data Table 1) there is at least one TF per each of the enhancer mutant contexts that activated the respective context when recruited via the GAL4-DBD (≥1.5-fold activation compared to GFP; P < 0.05). We tested a subset of the original 472 TFs in four different cell types (S2, Kc167, BG3 and OSC). This subset consists of 171 TFs covering all the 15 clusters by 9–17 TFs, including all the TFs mentioned in the main text. In each cell type, we computed Euclidean distances after standardizing the log -transformed fold-change values in each context. Then we compared the distances of intra-cluster TF–TF pairs (both TFs belong to the same cluster) to inter-cluster TF–TF pairs (each of the TFs belongs to a different cluster). In order to test whether the medians of these two groups of distances are significantly different, we determined empirical P values as follows. We randomly shuffled TF-to-cluster assignments 106 times and each time computed the medians of the distances for both groups. We mark the P values P < 1 × 10−6 as we never obtained a difference between the medians of intra- and inter-cluster distances as large as for the actual data for any of the cell types.

Skonieczny C.,French National Center for Scientific Research | Bory A.,French National Center for Scientific Research | Bout-Roumazeilles V.,French National Center for Scientific Research | Abouchami W.,Max Planck Institute for Chemistry | And 11 more authors.
Journal of Geophysical Research: Atmospheres | Year: 2011

Mineral dust deposits were collected at Mbour, Senegal, throughout the spring of 2006 and especially during the well-documented March 7-13 large Saharan dust outbreak. During this 7-day period, significant changes in mass flux, grain-size, clay mineralogy and Sr and Nd isotopic compositions were recorded, indicating distinct provenances for the dust transported and deposited during and outside the event. All these terrigenous proxies, as well as freshwater diatom taxa, also showed significant temporal variations during the outbreak, implying contributions from at least two different provenance regions. Tri-dimensional back-trajectories and satellite imaging enabled us to link those distinct signatures to regions increasingly to the southeast within a large area covering Mauritania, Mali and southern Algeria, identified by the Total Ozone Mapping Spectrometer (TOMS) as the main source of the prominent winter/spring plume over the tropical Atlantic. The multiproxy characterization of the March 7-13 dust fall therefore enables us to typify the terrigenous signature of two different regions supplying dust off West Africa, and provide valuable clues for the interpretation of Northeastern Tropical Atlantic Ocean dust sedimentary records in terms of changes in provenance regions and transport systems. Additionally, because dust deposition data are scarce, flux and grain size data obtained in this study, among other parameters such as clay assemblages, provide important constraints for atmospheric transport models and dust deposition budget estimates in this area. © 2011 by the American Geophysical Union.

News Article | November 9, 2016
Site: www.newsmaker.com.au

This report studies Image Processing Systems in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, focuses on top manufacturers in global market, with Production, price, revenue and market share for each manufacturer, covering  Barco  DELTA  GOPEL Electronic  Datalogic Automation  CARL ZEISS Industrielle Messtechnik  IBG Automation  IMAGO Technologies  Planar Systems  SCANLAB  Visicontrol Market Segment by Regions, this report splits Global into several key Regions, with production, consumption, revenue, market share and growth rate of Image Processing Systems in these regions, from 2011 to 2021 (forecast), like  North America  Europe  China  Japan  Southeast Asia  India Split by product type, with production, revenue, price, market share and growth rate of each type, can be divided into  Type I  Type II  Type III Split by application, this report focuses on consumption, market share and growth rate of Image Processing Systems in each application, can be divided into  Application 1  Application 2  Application 3 Global Image Processing Systems Market Research Report 2016  1 Image Processing Systems Market Overview  1.1 Product Overview and Scope of Image Processing Systems  1.2 Image Processing Systems Segment by Type  1.2.1 Global Production Market Share of Image Processing Systems by Type in 2015  1.2.2 Type I  1.2.3 Type II  1.2.4 Type III  1.3 Image Processing Systems Segment by Application  1.3.1 Image Processing Systems Consumption Market Share by Application in 2015  1.3.2 Application 1  1.3.3 Application 2  1.3.4 Application 3  1.4 Image Processing Systems Market by Region  1.4.1 North America Status and Prospect (2011-2021)  1.4.2 Europe Status and Prospect (2011-2021)  1.4.3 China Status and Prospect (2011-2021)  1.4.4 Japan Status and Prospect (2011-2021)  1.4.5 Southeast Asia Status and Prospect (2011-2021)  1.4.6 India Status and Prospect (2011-2021)  1.5 Global Market Size (Value) of Image Processing Systems (2011-2021) 7 Global Image Processing Systems Manufacturers Profiles/Analysis  7.1 Barco  7.1.1 Company Basic Information, Manufacturing Base and Its Competitors  7.1.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.1.3 Barco Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.1.4 Main Business/Business Overview  7.2 DELTA  7.2.1 Company Basic Information, Manufacturing Base and Its Competitors  7.2.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.2.3 DELTA Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.2.4 Main Business/Business Overview  7.3 GOPEL Electronic  7.3.1 Company Basic Information, Manufacturing Base and Its Competitors  7.3.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.3.3 GOPEL Electronic Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.3.4 Main Business/Business Overview  7.4 Datalogic Automation  7.4.1 Company Basic Information, Manufacturing Base and Its Competitors  7.4.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.4.3 Datalogic Automation Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.4.4 Main Business/Business Overview  7.5 CARL ZEISS Industrielle Messtechnik  7.5.1 Company Basic Information, Manufacturing Base and Its Competitors  7.5.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.5.3 CARL ZEISS Industrielle Messtechnik Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.5.4 Main Business/Business Overview  7.6 IBG Automation  7.6.1 Company Basic Information, Manufacturing Base and Its Competitors  7.6.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.6.3 IBG Automation Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.6.4 Main Business/Business Overview  7.7 IMAGO Technologies  7.7.1 Company Basic Information, Manufacturing Base and Its Competitors  7.7.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.7.3 IMAGO Technologies Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.7.4 Main Business/Business Overview  7.8 Planar Systems  7.8.1 Company Basic Information, Manufacturing Base and Its Competitors  7.8.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.8.3 Planar Systems Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.8.4 Main Business/Business Overview  7.9 SCANLAB  7.9.1 Company Basic Information, Manufacturing Base and Its Competitors  7.9.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.9.3 SCANLAB Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.9.4 Main Business/Business Overview  7.10 Visicontrol  7.10.1 Company Basic Information, Manufacturing Base and Its Competitors  7.10.2 Image Processing Systems Product Type, Application and Specification Type I Type II  7.10.3 Visicontrol Image Processing Systems Production, Revenue, Price and Gross Margin (2015 and 2016)  7.10.4 Main Business/Business Overview

Kaly F.,LTI | Marticorena B.,LISA | Chatenet B.,LISA | Rajot J.L.,LISA | And 12 more authors.
Atmospheric Research | Year: 2015

The "Sahelian belt" is known as a region where mineral dust content is among the highest in the world. In the framework of the AMMA international Program, a transect of three ground based stations, the "Sahelian Dust Transect" (SDT), has been deployed in order to obtain quantitative information on the mineral dust content over the Sahel. These three stations: Banizoumbou (Niger), Cinzana (Mali) and M'Bour (Senegal) are aligned at 13°N along the east-west main pathway of the Saharan and Sahelian dust toward the Atlantic Ocean. The SDT provides a set of aerosol measurements and local meteorological parameters to describe and understand the mechanisms that control the temporal and regional variability of mineral dust content in these regions. In this work we analyze the seasonal and diurnal variability of the dust concentrations over the period 2006-2010. The analysis of the dust concentrations measured between 2006 and 2010 confirmed a regional seasonal cycle characterized by a maximum in the dry season, with median concentration ranging from 205μgm-3 at Banizoumbou to 144μgm-3 at M'Bour, and a minimum (11-32μgm-3) in the wet season. The five year data set allowed the quantification of the variability of the monthly concentrations. The range between the percentiles 75 and 25 varies linearly with the median concentration: it is of the same order than the median value in M'Bour, 17% slightly higher in Cinzana and 50% higher in Banizoumbou. The range between the accepted maximum and minimum is also correlated with the median value, with slopes ranging from 14 in Banizoumbou to 7 in M'Bour. Part of the variability of the concentration at the monthly scale is due to interannual variability. Extremely high or low monthly concentration can be recorded that significantly impacts the five year median concentration and its range. Compared to the 3-year data set analyzed by Marticorena et al. (2010), the two additional years used in this work appear as the less dusty year (2009) and one of the dustier years (2010).The sampling time step and the high recovery rates of the measurement stations allowed to investigate the diurnal cycle of the dust concentration for the first time. During the dry season, the hourly median concentrations range from 80 to 100μgm-3 during the night to 100-160μgm-3 during the day-time maximum. The diurnal cycle of the PM10 concentrations is phased with the diurnal cycle of the surface wind speed and thus modulated by the interactions between the nocturnal lower level jet (NLLJ) and the surface boundary layer. The NLLJ appears as a major agent to transport Saharan dust toward the Sahel. During the wet season, the median PM10 concentrations are maximum at night-time (<50μgm-3). The night-time concentrations are associated with a large range of variability and coincide with the periods of higher occurrence of meso-scale convective systems. The amplitude of the diurnal cycle is of the order of 60μgm-3 in the dry season and 20μgm-3 in the wet season. Both in the dry and in the wet season, despite a month to month variability of the daily dust concentration, a typical diurnal pattern has been established suggesting that this temporal pattern is mainly driven by local meteorological conditions. © 2015 Elsevier B.V.

Alory G.,Toulouse 1 University Capitole | Delcroix T.,Toulouse 1 University Capitole | Techine P.,Toulouse 1 University Capitole | Techine P.,French National Center for Scientific Research | And 16 more authors.
Deep-Sea Research Part I: Oceanographic Research Papers | Year: 2015

Sea Surface Salinity (SSS) is an essential climate variable that requires long term in situ observation. The French SSS Observation Service (SSS-OS) manages a network of Voluntary Observing Ships equipped with thermosalinographs (TSG). The network is global though more concentrated in the tropical Pacific and North Atlantic oceanic basins. The acquisition system is autonomous with real time transmission and is regularly serviced at harbor calls. There are distinct real time and delayed time processing chains. Real time processing includes automatic alerts to detect potential instrument problems, in case raw data are outside of climatic limits, and graphical monitoring tools. Delayed time processing relies on a dedicated software for attribution of data quality flags by visual inspection, and correction of TSG time series by comparison with daily water samples and collocated Argo data. A method for optimizing the automatic attribution of quality flags in real time, based on testing different thresholds for data deviation from climatology and retroactively comparing the resulting flags to delayed time flags, is presented. The SSS-OS real time data feed the Coriolis operational oceanography database, while the research-quality delayed time data can be extracted for selected time and geographical ranges through a graphical web interface. Delayed time data have been also combined with other SSS data sources to produce gridded files for the Pacific and Atlantic oceans. A short review of the research activities conducted with such data is given. It includes observation-based process-oriented and climate studies from regional to global scale as well as studies where in situ SSS is used for calibration/validation of models, coral proxies or satellite data. © 2015 Elsevier Ltd.

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