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Sucrose controls various developmental and metabolic processes in plants. It also functions as a signaling molecule in the synthesis of carbohydrates, storage proteins, and anthocyanins, as well as in floral induction and defense response. We found that sucrose preferentially induced OsWRKY7, whereas other sugars (such as mannitol, glucose, fructose, galactose, and maltose) did not have the same effect. A hexokinase inhibitor mannoheptulose did not block the effect of sucrose, which is consequently thought to function directly. MG132 inhibited sucrose induction, suggesting that a repressor upstream of OsWRKY7 is degraded by the 26S proteasome pathway. The 3-kb promoter sequence of OsWRKY7 was preferentially induced by sucrose in the luciferase system. Knockout mutants of OsWRKY7 were more sensitive to the rice blast fungus Magnaporthe oryzae, whereas the overexpression of OsWRKY7 enhanced the resistance, indicating that this gene is a positive regulator in the plant defense against this pathogen. The luciferase activity driven by the OsPR10a promoter was induced by OsWRKY7 and this transcription factor bound to the promoter region of OsPR10a, suggesting that OsWRKY7 directly controls the expression of OsPR10a. We conclude that sucrose promotes the transcript level of OsWRKY7, thereby increasing the expression of OsPR10a for the defense response in rice.

The plant hormone gibberellic acid (GA) is important for plant growth and productivity. Actin-related proteins (ARPs) also play central roles in plant growth, including cell elongation and development. However, the relationships between ARPs and GA signaling and biosynthesis are not fully understood. Here, we isolated OsGASD, encoding an ARP subunit from rice (Oryza sativa), using the Ac/Ds knockout system. The osgasd knockout (Ko) mutation reduced GA3 content in shoots as well as plant growth and height. However, GA application restored the plant height of the osgasd Ko mutant to a height similar to that of the wild type (WT). Rice plants overexpressing OsGASD (Ox) showed increased plant height and grain yield compared to the WT. Transcriptome analysis of flag leaves of OsGASD Ox and osgasd Ko plants revealed that OsGASD regulates cell development and the expression of elongation-related genes. These observations suggest that OsGASD is involved in maintaining GA homeostasis to regulate plant development, thereby affecting rice growth and productivity.

Additional file 1: Fig. S1. The mutant phenotype of OsSNDP2. Fig. S2. Mature pollen of ossndp2 ossndp3-1.

3D-printed bone scaffolds hold great promise for the individualized treatment of critical-size bone defects. Among the resorbable polymers available for use as 3D-printable scaffold materials, poly(epsilon-caprolactone) (PCL) has many benefits. However, its relatively low stiffness and lack of bioactivity limit its use in load-bearing bone scaffolds. This study tests the hypothesis that surface-oxidized cellulose nanocrystals (SO-CNCs), decorated with carboxyl groups, can act as multi-functional scaffold additives that (1) improve the mechanical properties of PCL and (2) induce biomineral formation upon PCL resorption. To this end, an in vitro biomineralization study was performed to assess the ability of SO-CNCs to induce the formation of calcium phosphate minerals. In addition, PCL nanocomposites containing different amounts of SO-CNCs (1, 2, 3, 5, and 10 wt%) were prepared using melt compounding extrusion and characterized in terms of Young's modulus, ultimate tensile strength, crystallinity, thermal transitions, and water contact angle. Neither sulfuric acid-hydrolyzed CNCs (SH-CNCs) nor SO-CNCs were toxic to MC3T3 preosteoblasts during a 24 h exposure at concentrations ranging from 0.25 to 3.0 mg/mL. SO-CNCs were more effective at inducing mineral formation than SH-CNCs in simulated body fluid (1x). An SO-CNC content of 10 wt% in the PCL matrix caused a more than 2-fold increase in Young's modulus (stiffness) and a more than 60% increase in ultimate tensile strength. The matrix glass transition and melting temperatures were not affected by the SO-CNCs but the crystallization temperature increased by about 5.5 degrees C upon addition of 10 wt% SO-CNCs, the matrix crystallinity decreased from about 43 to about 40%, and the water contact angle decreased from 87 to 82.6 degrees. The abilities of SO-CNCs to induce calcium phosphate mineral formation and increase the Young's modulus of PCL render them attractive for applications as multi-functional nanoscale additives in PCL-based bone scaffolds. National Science FoundationNational Science Foundation (NSF) [DMR-0907567]; U.S. Department of AgricultureUnited States Department of Agriculture (USDA) [2010-65504-20429]; Virginia Agricultural Experiment Station; Hatch Multistate Program of the National Institute of Food and Agriculture of the U.S. Department of Agriculture; Institute for Critical Technology and Applied Science This project was supported in part by the National Science Foundation under grant number DMR-0907567, the U.S. Department of Agriculture under grant numbers 2010-65504-20429, the Virginia Agricultural Experiment Station, the Hatch Multistate Program of the National Institute of Food and Agriculture of the U.S. Department of Agriculture, and the Institute for Critical Technology and Applied Science.

Additional file 9: Table S2. OsRopGEF isogene-specific primers for qPCR and cloning.

Additional file 6: Figure S5. Bar plot of the integrated abundance the 16 most abundant phyla from sorghum under drought conditions. Ctrl, xxx, and DR refer to Control, well-watered, and drought state, respectively. Samples were grown under well-watered condition for 4 weeks to establish roots and then water was drained at 5 weeks after planting. Drought-stressed plants were sampled every week starting from 5 weeks after planting. Control samples were collected from a well-watered bathtub.

Additional file 4: Figure S3. Bar plot of the integrated abundance of the 16 most dominant phyla from endosphere data collected under drought stress. Each bar represents the mean value under each condition. The 16 most abundant phyla in each treatment are integrated, resulting in the identification of 16 phyla, including unassigned phyla. Only groups within the Proteobacteria phylum are presented as classes. Phyla other than those classified are clustered as “Others.” Detailed information on each sample is presented in Table S11.

Additional file 2: Figure S2. Meta-expression analysis of entire AtRopGEF genes. The heatmap was prepared using the Genevestigator. We chose five representative tissues, including pollen. It revealed that five Arabidopsis RopGEFs were highly expressed in pollen. The dark red color of the heatmap indicated the highest expression; white color, lowest expression.

Additional file 3: Figure S3. Protein sequence alignment domain analysis and conserved phosphorylated amino acid residues of C-termini of RopGEF genes. Every OsRopGEF and AtRopGEF protein sequence was collected and aligned, followed by the PRONE domain (C1, C2, C3) and the WW-motif. At the end part of the sequence, we found some conserved regions. According to previous studies, S510 in the C-terminus of AtRopGEF12 is involved in the C-terminal inhibition of GEF activity. As in AtRopGEF12, the serine residue is conserved in each of the OsRopGEF2, OsRopGEF3, and OsRopGEF8 genes but not in OsRopGEF6. However, K (Lysine) can also be phosphorylated.

Additional file 5: Figure S4. Bar plot of the 16 most abundant phyla from rhizosphere and rhizoplane data collected under drought stress. Each bar represents the mean value under each condition. The 16 most abundant phyla in each treatment are integrated, resulting in the identification of 16 phyla, including unassigned phyla. Only groups within the Proteobacteria are presented as classes. Phyla other than those classified are clustered as “Others.” Detailed information on each sample is presented in Table S11.

Additional file 3: Figure S2. Bar plot of the integrated abundance of the 16 most dominant phyla in each environment. Each bar represents the mean value under each condition. Phyla that are not included in the integrated set of 16 phyla are classified as “others.”

Additional file 6: Figure S6. Single mutant assay using CRISPR-Cas9 system. (a-b, d-e) In-vitro pollen germination test of wild-type (a, d), ropgef2 (b, c), and ropgef3 (e-f) single mutants on solid pollen media, Bar = 50 μm; (c,f) enlarged pollen pictures, Bar = 20 μm. Each control and mutant pollen in-vitro assays were performed in the same environment on the same day. (g) The ratio of pollen germination. Black bar indicates wild type; grey bar indicates ropgef2; white bar indicates ropgef3 mutant pollen. Error bars are standard deviation of three technical repeats. (h) Fertility ratio comparison between wild-type and ropgef2 single mutant plants. Error bars are standard deviation of more than three panicles in the plants.

Additional file 7: Table S1. All amplicon sequence data collected for re-analysis.

Additional file 1:. Materials and Methods.

Additional file 1: Figure S1. Meta-expression analysis and genome-wide identification of RopGEF in rice. The expressions from various rice tissues were examined using Microarray database. Yellow color in the heatmap indicates high level of expression; dark blue, low level of expression. Numeric values indicate the average of the normalized log2 intensity of microarray data.

Additional file 7: Figure S7. Meta-expression analysis and genome-wide identification of the seven OsRac and ten AtRop genes. (a) Heatmap expression analysis of OsRac gene. (b) Heatmap of AtRop using Genevestigator. (c) Phylogenetic tree constructs including every OsRac and AtRop.

Additional file 2: Figure S1. Box plots of indexes for alpha diversity by drought condition (A) or by both factors (B) (Chao1, Faith_PD, Shannon, Simpson). Each index is calculated using the QIIME2 functions: qiime diversity alpha-phylogenetic and qiime diversity alpha.

Additional file 5: Figure S5. Promoter analysis of each OsRopGEF gene. Specific cis-acting element (CRE) known to affect expression in pollen were identified using PLACE database, namely three pollen-CREs and the TATA box. The number above the yellow bar shows the upstream position of the promoter base pair when taken at + 1 of ATG. We analyzed up to the upstream 2000-base pair. The p-values indicate how significantly the four genes exhibiting high expression in pollen differed from the other seven genes.

Table S1. List of SNPs in the SUI4 mapped region of Dongjin plants and sui4 mutants. (DOCX 16 kb)

Transcription factors (TFs) are an important class of regulatory molecules. Despite their importance, only a small number of genes encoding TFs have been characterized in Oryza sativa (rice), often because gene duplication and functional redundancy complicate their analysis. To address this challenge, we developed a web-based tool called the Rice Transcription Factor Phylogenomics Database (RTFDB) and demonstrate its application for predicting TF function. The RTFDB hosts transcriptome and co-expression analyses. Sources include high-throughput data from oligonucleotide microarray (Affymetrix and Agilent) as well as RNA-Seq-based expression profiles. We used the RTFDB to identify tissue-specific and stress-related gene expression. Subsequently, 273 genes preferentially expressed in specific tissues or organs, 455 genes showing a differential expression pattern in response to 4 abiotic stresses, 179 genes responsive to infection of various pathogens and 512 genes showing differential accumulation in response to various hormone treatments were identified through the meta-expression analysis. Pairwise Pearson correlation coefficient analysis between paralogous genes in a phylogenetic tree was used to assess their expression collinearity and thereby provides a hint on their genetic redundancy. Integrating transcriptome with the gene evolutionary information reveals the possible functional redundancy or dominance played by paralog genes in a highly duplicated genome such as rice. With this method, we estimated a predominant role for 83.3% (65/78) of the TF or transcriptional regulator genes that had been characterized via loss-of-function studies. In this regard, the proposed method is applicable for functional studies of other plant species with annotated genome.

A novel classification technique based on combined Genetic Algorithm (GA) and Adaptive Neural Fuzzy Inference System (ANFIS) for diagnosis of heart Attack is reported. Exploiting the combined advantages of neural networks, fuzzy logic and GA, the performance of the proposed system is investigated by evaluation functions such as sensitivity, specificity, precision, accuracy and Root Mean Squared Error (RMSE). Also, the efficiency of the algorithm is evaluated by employing 9-fold cross validation. To address the explainability of the predictions, explainable graphs are provided. The results show that the performance of the proposed algorithm is quite satisfactory. Furthermore, the importance of various symptoms in diagnosis of heart attack is investigated through defining and employing an importance evaluation function. It is shown that some symptoms have key roles in effective prediction of heart Attack.

Table S3. qRT-PCR primer sequences (DOCX 14 kb)

A survey of the artificial radionuclides in rivers and lakes was conducted to investigate their levels in surface water. Water samples were collected at 60 points and analyzed by gamma-ray spectrometry with a measurement time of 10,000 seconds for 134Cs, 137Cs, and 131I. The obained values were lower than MDA for all points, except one point for 131I that was 0.533±0.058 Bq/L. 131I is known as a radioactive material that occurs frequently in sewage treatment plants. Because it is often used for medical treatments and subject to spreading into the environment due to the excretion from the patients. For the point where 131I was detected, we conducted additional investigation on the upstream river point and the effluent points of nearby sewage treatment plant to find the source of 131I. 131I was not detected at the upstream points of one of the upstream sewage treatment plants but found at the downstream points with the level being 0.257±0.034 to 0.799±0.051 Bq/L, proving the sewage treatment plant was the 131Isource.

Figure S1. Expression graph after KMC analysis of anatomical meta-expression data including root hairs. Genes in Cluster 7 (marked with red box) were selected as group showing root hair-preferential pattern of expression. Figure S2. GUS expression pattern of 63 promoter trap candidates. Among them, five lines showed GUS expression (blue and red boxes), and 3 exhibited root hair-preferential pattern (red box). Figure S3. Co-segregation between genotyping and GUS expression, as checked for 3 promoter trap lines: LOC_Os05g45900 (a), LOC_Os10g42750 (b), and LOC_Os12g02240 (c). GUS-positive and GUS-negative are represented by + and -, respectively. Figure S4. Expression profiles of root hair-preferential genes. Analysis of expression patterns via real-time PCR for 6 genes: LOC_Os05g45900 (a), LOC_Os10g42750 (b), LOC_Os12g02240 (c), LOC_Os02g42820 (d), LOC_Os10g38340 (e), and LOC_Os12g38010 (f). Y-axis, gene expression relative to rice OsUbi5 transcript level. Figure S5. New motifs discovered in 409 root hair-preferential genes, based on MEME analysis. Figure S6. Expression graph after KMC analysis of anatomical meta-expression data in Arabidopsis. Clusters 2, 4, and 6 (in red boxes) show root hair-preferential expression patterns. Figure S7. Heatmap for expression profiles of root hair-preferential genes in Arabidopsis. Yellow, high expression; dark-blue, low expression. Figure S8. Functional gene network associated with root hair-preferential genes in rice, as indicated by large circles with red boundaries. Six functionally characterized genes are named. Interactions between nodes with PCC values >â 0.5 are represented by red lines. (DOCX 2500 kb)

Figure S1. a. Order of developmental stages for anatomical samples. ACF, formation of archesporial cells; BG, bi-cellular gametophyte; Fl, flowering; Me, meiosis; Me1, meiotic leptotene; Me2, meiotic zygotene-pachytene; Me3, meiotic diplotene-tetrad; MP, mature pollen; PMe, pre-meiosis; GP, germinating pollen; TG, tri-cellular pollen; UG, uni-cellular gametophyte. Red bar, sample containing late pollen. b. Expression graph of 36 clusters after KMC analysis with 57,382 probes. Clusters 2 and 35 exhibited mature pollen-preferential patterns of expression and are marked with red boxes. Figure S2. Schematic representation of 3 promoter trap lines for T-DNA insertions. a. T-DNA was inserted into 17th intron of SacI homology domain-containing protein (LOC_Os11g20384) in Line 1A-13,819 (mtd1â 1). b. Line 3A-05916 has T-DNA insertion in B12D protein (LOC_Os07g17310). BL, left T-DNA border; RB, right T-DNA border; Gray boxes, exons; lines, introns. Figure S3. Expression graph after KMC analysis of meta-expression data from Arabidopsis. Clusters marked with red box showed late pollen-preferred patterns. Figure S4. Heatmap for expression profiles of late pollen-preferred genes in Arabidopsis. Figure S5. Heatmap of genes involved in GA biosynthesis and signaling. CPS, GA3ox1, KAO, GA20ox3, and KO2 showed late pollen-preferred expression patterns and are outlined with red boxes. (DOCX 227 kb)

Table S1. Six series of microarray data comprising 64 slides (GPL2025) associated with anthers/pollen in rice. Table S2. Locus IDs and putative functions of late pollen-preferred genes from rice. Table S3. Locus IDs and promoter regions of genes used for promoter analysis with GUS reporter. Table S4. Classification of GO terms for biological processes associated with late pollen-preferred genes. Table S5. MapMan classification of late pollen-preferred genes. Table S6. Genes related to hormone metabolism term in MapMan. Table S7. Late pollen-preferred genes in Arabidopsis. Table S8. Assignment of rice orthologs to Arabidopsis late pollen-preferred genes. Locus numbers are shown in by red. Table S9. Assignment of Arabidopsis orthologs to rice late pollen-preferred genes. Locus numbers are shown in red. Table S10. MapMan terms related to cell wall organization and modifications in rice and Arabidopsis. Table S11. Primer sequences used in genotyping and real-time PCR. (DOCX 1980 kb)

Blast2 alignment of amino acid sequences from Os1-CysPrxA and Os1-CysPrxB. (PDF 41Â kb)

To figure out the decay characteristics of naturally occurring radionuclides, eight sampled groundwaters from a monitoring borehole having high levels of uranium and radon concentrations in a two mica granitic area have analyzed by liquid scintillation counters (LSC) for over 1 year. In December 2011, three groundwater samples (DJ1, DJ2, DJ3) were obtained from each aquifer system located at -20 m, -40 m, -60 m of the monitoring borehole below the ground surface, respectively. Five samples (DJ4, DJ5, DJ6, DJ7, DJ8) were additionally gained from each aquifer positioned -20 m, -40 m, -60 m, -100 m, -105 m of the borehole in February 2012, respectively. Temporal variation characteristics of uranium and radon concentrations have showed over maximum 2.1 times and 1.4 times fluctuations of the values in the same sampling intervals over time, respectively. The intervals of -40 m and -105 m in the borehole have the highest values of uranium and radon concentrations, respectively. This may imply that the concentrations of naturally occurring radionuclides such as uranium and radon in groundwater have been changed over time and indicate that the qualities of groundwaters from the aquifers developed at each interval in the borehole are different each other. This discrepancy, moreover, could be caused by behaviour differences between uranium which is in ionic status having a half life of 4.6 billion years and is transported along with the flowing groundwater, and radon which is in gaseous status having a 3.82 day`s half life in the aquifer systems. Physicochemical characteristics of groundwaters from the aquifer systems could be identified by the results of the on-situ measuring items such as pH and Eh, and the major ionic contents. The CPM values of eight groundwater samples analysed by LSC over one year have shown not to follow the theoretical decay curve of the radon. The CPM values of the samples have ranged from 2 to 7.5 after it had passed two months when the theoretical CPM values of the radon started zero since the initial analysis. Alpha and beta particle spectrums have shown the peaks of radium-226, however they have not revealed any peaks of radon and it`s daughter products such as polonium-218 and 214, bismuth-214 for the late stage of the analysis. This implies that the groundwater from the borehole may contain radium-226 having a half life of 1,600 years which decays continuously.

This study has investigated naturally occurring radioactive materials (N.O.R.M; 238 U, 222 Rn) for 353 drinking groundwaterwells in metamorphic rock areas in Korea. Uranium concentrations ranged from N.D (not detected) to 563.56µg/L(median value, 0.68µg/L) and radon concentrations ranged from 108 to 11,612 pCi/L (median value, 1,400 pCi/L).Uranium and radon concentrations in the groundwater generally are similar to USA with similar geological setting.Uranium concentrations in 9 wells (2.6%) exceeded 30µg/L, which is the maximum contaminant level (MCL) by the USenvironmental protection agency (EPA), radon concentrations in 46 wells (13%) exceeded 4,000 pCi/L, which is theAlternative MCL (AMCL) by the US.EPA. The log-log correlation coefficient between uranium and radon was 0.32. Thecorrelation coefficient between uranium and pH was 0.12 and the correlation coefficient between radon and temperaturewas −0.01. The correlation coefficient between uranium and HCO 3 was 0.09 and the correlation coefficient betweenuranium and Ca was 0.11. The median value of uranium was high Chung-Buk (1.78µg/L), Gyeong-Buk (1.37µg/L), In-Cheon (1.06µg/L) for each province. On the other hand, the median value of radon was high In-Cheon (2,962 pCi/L),Chung-Buk (2,339 pCi/L), Jeon-Buk (2,165 pCi/L) for each province. Jeon-Buk for log-log correlation coefficient is thehighest (0.63) among provinces. Key words :NORM (Uranium, Radon), Metamorphic rock, Groundwater, Correlation coefficient, MCL and AMCL

We analyzed natural radionuclides in 80 wells in volcanic rock areas and investigated environmental characteristics. Uranium and radon concentrations ranged from ND to (median value: 0.21) , 38~29,222 pCi/L (median value: 579), respectively. In case of gross-, 26 samples exceeded MDA (minimum detectable activity, ) and gross- (15 pCi/L). But Rn concentrations in 4 samples exceeded USEPA AMCL (Alternative maximum contaminant level, 4,000 pci/L) and one of them showed a significantly higher value (29,222 pCi/L) than the others. The levels of uranium concentrations in volcanic rock aquifer regions were detected in order of andesite, miscellaneous volcanic rocks, rhyolite, basalt aquifer regions. Radon, however, was detected in order of miscellaneous volcanic rocks, rhyolite, andesite, basalt aquifer regions. The correlation coefficient between uranium and radon was r

Topology optimization is applied for the lightweight design of three main parts of a vertical articulated robot: a base frame, a lower and a upper frame. Design domains for optimization are set as large solid regions that completely embrace the original parts, which are discretized by using three-dimensional solid elements. Design variables are parameterized one-to-one to the material properties of each element by using the SIMP method. The objective of optimization is set as the multi-objective form combining the natural frequencies and mean compliances of a structure for which load steps of interest are selected from the multibody dynamics analysis of a robot. The obtained results of topology optimization are post-processed to designs favorable to manufacturability for casting process. The final optimized results are 11.0% (base frame), 12.0% (lower frame) and 10.0% (upper frame) lighter with similar or even higher static and dynamic stiffnesses than the original models.

226 Ra in groundwater could be analyzed by various methods. LSC (liquid scintillation counter) is used to measure itsactivity of Ba co-precipitates with Hisafe III scintillation cocktail solution. Counting efficiency was obtained using NIST 226 Ra standard solution in triplicate and calculated 226 Ra concentration using the efficiency values. 226 Ra values of 19groundwaters having gross-α concentrations of more than 5 pCi/L ranged from ND (≤0.1 pCi/L) to 1.18 pCi/L. Geologicsettings of the 19 areas are composed of granitic rocks of Pre-Cambrian and Jurassic and Cretaceous, gneiss (schist) ofPre-Cambrian, and volcanic rocks of Cretaceous. No relationship was shown among 226 Ra concentrations and in-situ waterquality data, and gross-α, uranium, radon concentrations.Keywords : 226 Ra, LSC, Gross-α, Uranium, Geologic setting 1. 서론 음용수의 대부분을 지표수에 의존하고 있는 국내의 여건으로 인해 그동안 지하수에 대한 관심은 상대적으로 적었으며, 그중 자연방사성물질에 대한 관심은 다른 위해한물질에 비해 상대적으로 낮았으나 최근 지하수 중 자연방사성물질에 대한 함유실태 조사 결과에 따라 국민의 건강과 관련하여 관심이 점점 높아지고 있다(김태승 외, 2007;노회정 외, 2008). 지하수 중 방사성물질은 대부분 인위적인 요소에 의한 것이 아니라 지구의 탄생과 더불어 자연적으로 존재해 오고 있으며 인간에게 피폭되는 연간 방사성 양의 82%는 지질기원의 라듐과 우라늄, 라돈 등 자연방사성 핵종에 의한 것(Egidi, 1997)으로 대부분은 우라늄과 토륨의 234붕괴과정에서 방출되며 238U→

We demonstrate a commercialized injection molding method for nano patterning of polymethyl methacrylate (PMMA) for the purpose of mass production of a label-free optical biosensor chip. Scanning electron microscope (SEM) images clearly show uniform nanopattern transfer on the plastic chip fabricated by an injection molding method. A uniformly deposited silicon nitride layer having a high refractive index by a plasma enhanced chemical vapor deposition (PECVD) technique is also observed. A unique characterization method is described wherein optical resonance reflection is used accurately and quickly to characterize the geometrical sensor structure. The authors anticipate that this robust method will provide an excellent means of producing an optical resonance reflection biosensor (ORRB).

Principal investigators John P. Vogel1, David F. Garvin2, Todd C. Mockler3, Jeremy Schmutz4, Dan Rokhsar5,6, Michael W. Bevan7; DNA sequencing and assembly Kerrie Barry5, Susan Lucas5, Miranda Harmon-Smith5, Kathleen Lail5, Hope Tice5, Jeremy Schmutz4 (Leader), Jane Grimwood4, Neil McKenzie7, Michael W. Bevan7; Pseudomolecule assembly and BACend sequencing Naxin Huo1, Yong Q.Gu1,Gerard R. Lazo1, Olin D.Anderson1, John P. Vogel1 (Leader), Frank M. You8,Ming-Cheng Luo8, Jan Dvorak8, Jonathan Wright7, Melanie Febrer7, Michael W. Bevan7, Dominika Idziak9, Robert Hasterok9, David F. Garvin2; Transcriptome sequencing and analysis Erika Lindquist5, Mei Wang5, Samuel E. Fox3, Henry D. Priest3, Sergei A. Filichkin3, Scott A. Givan3, Douglas W. Bryant3, Jeff H.Chang3, Todd C.Mockler3 (Leader), Haiyan Wu10,24, Wei Wu10, An-Ping Hsia10, Patrick S. Schnable10,24, Anantharaman Kalyanaraman11, Brad Barbazuk12, Todd P.Michael13, Samuel P.Hazen14, Jennifer N. Bragg1, Debbie Laudencia-Chingcuanco1, John P. Vogel1, David F. Garvin2, Yiqun Weng15, Neil McKenzie7, Michael W. Bevan7; Gene analysis and annotation Georg Haberer16, Manuel Spannagl16, Klaus Mayer16 (Leader), Thomas Rattei17, Therese Mitros6, Dan Rokhsar6, Sang-Jik Lee18, Jocelyn K.C. Rose18, Lukas A. Mueller19, Thomas L. York19; Repeats analysis Thomas Wicker20 (Leader), Jan P. Buchmann20, Jaakko Tanskanen21, Alan H. Schulman21 (Leader), Heidrun Gundlach16, Jonathan Wright7, Michael Bevan7, Antonio Costa de Oliveira22, Luciano da C. Maia22, William Belknap1, Yong Q. Gu1, Ning Jiang23, Jinsheng Lai24, Liucun Zhu25, JianxinMa25, Cheng Sun26, Ellen Pritham26; Comparative genomics Jerome Salse27 (Leader), Florent Murat27, Michael Abrouk27, Georg Haberer16, Manuel Spannagl16, Klaus Mayer16, Remy Bruggmann13, Joachim Messing13, Frank M. You8, Ming-Cheng Luo8, Jan Dvorak8; Small RNA analysis Noah Fahlgren3, Samuel E. Fox3, Christopher M. Sullivan3, Todd C. Mockler3, James C. Carrington3, Elisabeth J. Chapman3,28, Greg D.May29, Jixian Zhai30, Matthias Ganssmann30, Sai Guna Ranjan Gurazada30, Marcelo German30, Blake C. Meyers30, Pamela J. Green30 (Leader); Manual annotation and gene family analysis Jennifer N. Bragg1, Ludmila Tyler1,6, Jiajie Wu1,8, Yong Q. Gu1, Gerard R. Lazo1, Debbie Laudencia-Chingcuanco1, James Thomson1, John P. Vogel1 (Leader), Samuel P. Hazen14, Shan Chen14, Henrik V. Scheller31, Jesper Harholt32, Peter Ulvskov32, Samuel E. Fox3, Sergei A. Filichkin3, Noah Fahlgren3, Jeffrey A. Kimbrel3, Jeff H. Chang3, Christopher M. Sullivan3, Elisabeth J. Chapman3,27, James C. Carrington3, Todd C. Mockler3, Laura E. Bartley8,31, Peijian Cao8,31, Ki-Hong Jung8,31, Manoj K Sharma8,31, Miguel Vega-Sanchez8,31, Pamela Ronald8,31, Christopher D.Dardick33, Stefanie De Bodt34,Wim Verelst34, Dirk Inze34, Maren Heese35, Arp Schnittger35, Xiaohan Yang36, Udaya C. Kalluri36, Gerald A. Tuskan36, Zhihua Hua37, Richard D. Vierstra37, David F. Garvin3, Yu Cui24, Shuhong Ouyang24, Qixin Sun24, Zhiyong Liu24, Alper Yilmaz38, Erich Grotewold38, Richard Sibout39, Kian Hematy39, Gregory Mouille39, Herman Höfte39, Todd Michael13, Jérome Pelloux40, Devin O'Connor41, James Schnable41, Scott Rowe41, Frank Harmon41, Cynthia L. Cass42, John C. Sedbrook42, Mary E. Byrne7, Sean Walsh7, Janet Higgins7, Michael Bevan7, Pinghua Li19, Thomas Brutnell19, Turgay Unver43,Hikmet Budak43, Harry Belcram44, Mathieu Charles44, Boulos Chalhoub44, Ivan Baxter45 1 USDA-ARS Western Regional Research Center, Albany, California 94710, USA. 2 USDA-ARS Plant Science Research Unit and University of Minnesota, St Paul, Minnesota 55108, USA. 3 Oregon State University, Corvallis, Oregon 97331-4501, USA. 4 HudsonAlpha Institute, Huntsville, Alabama 35806, USA. 5 US DOE Joint Genome Institute, Walnut Creek, California 94598, USA. 6 University of California Berkeley, Berkeley, California 94720, USA. 7 John Innes Centre, Norwich NR4 7UJ, UK. 8 University of California Davis, Davis, California 95616, USA. 9 University of Silesia, 40-032 Katowice, Poland. 10 Iowa State University, Ames, Iowa 50011, USA. 11 Washington State University, Pullman, Washington 99163, USA. 12 University of Florida, Gainsville, Florida 32611, USA. 13 Rutgers University, Piscataway, New Jersey 08855-0759, USA. 14 University of Massachusetts, Amherst, Massachusetts 01003-9292, USA. 15 USDA-ARS Vegetable Crops Research Unit, Horticulture Department, University of Wisconsin, Madison, Wisconsin 53706, USA. 16 Helmholtz Zentrum München, D-85764 Neuherberg, Germany. 17 Technical University München, 80333 München, Germany. 18 Cornell University, Ithaca, New York 14853, USA. 19 Boyce Thompson Institute for Plant Research, Ithaca, New York 14853-1801, USA. 20 University of Zurich, 8008 Zurich, Switzerland. 21 MTT Agrifood Research and University of Helsinki, FIN-00014 Helsinki, Finland. 22 Federal University of Pelotas, Pelotas, 96001-970, RS, Brazil. 23 Michigan State University, East Lansing, Michigan 48824, USA. 24 China Agricultural University, Beijing 10094, China. 25 Purdue University, West Lafayette, Indiana 47907, USA. 26 The University of Texas, Arlington, Arlington, Texas 76019, USA. 27 Institut National de la Recherche Agronomique UMR 1095, 63100 Clermont-Ferrand, France. 28 University of California San Diego, La Jolla, California 92093, USA. 29 National Centre for Genome Resources, Santa Fe, New Mexico 87505, USA. 30 University of Delaware, Newark, Delaware 19716, USA. 31 Joint Bioenergy Institute, Emeryville, California 94720, USA. 32 University of Copenhagen, Frederiksberg DK-1871, Denmark. 33 USDA-ARS Appalachian Fruit Research Station, Kearneysville, West Virginia 25430, USA. 34 VIB Department of Plant Systems Biology, VIB and Department of Plant Biotechnology and Genetics, Ghent University, Technologiepark 927, 9052 Gent, Belgium. 35 Institut de Biologie Moléculaire des Plantes du CNRS, Strasbourg 67084, France. 36 BioEnergy Science Center and Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831-6422, USA. 37 University of Wisconsin-Madison, Madison, Wisconsin 53706, USA. 38 The Ohio State University, Columbus, Ohio 43210, USA. 39 Institut Jean-Pierre Bourgin, UMR1318, Institut National de la Recherche Agronomique, 78026 Versailles cedex, France. 40 Université de Picardie, Amiens 80039, France. 41 Plant Gene Expression Center, University of California Berkeley, Albany, California 94710, USA. 42 Illinois State University and DOE Great Lakes Bioenergy Research Center, Normal, Illinois 61790, USA. 43 Sabanci University, Istanbul 34956, Turkey. 44 Unité de Recherche en Génomique Végétale: URGV (INRA-CNRS-UEVE), Evry 91057, France. 45 USDA-ARS/Donald Danforth Plant Science Center, St Louis, Missouri 63130, USA.; Three subfamilies of grasses, the Ehrhartoideae, Panicoideae and Pooideae, provide the bulk of human nutrition and are poised to become major sources of renewable energy. Here we describe the genome sequence of the wild grass Brachypodium distachyon (Brachypodium), which is, to our knowledge, the first member of the Pooideae subfamily to be sequenced. Comparison of the Brachypodium, rice and sorghum genomes shows a precise history of genome evolution across a broad diversity of the grasses, and establishes a template for analysis of the large genomes of economically important pooid grasses such as wheat. The high-quality genome sequence, coupled with ease of cultivation and transformation, small size and rapid life cycle, will help Brachypodium reach its potential as an important model system for developing new energy and food crops.

Coal fly ash (CF) and synthetic coal fly ash aggregates (SCFAs) were evaluated as low-cost reactive media for the remediation of groundwater contaminated with Zn. The SCFAs were prepared by mixing CF, sodium silicate, and deionized (DI) water. Serial batch kinetic and static tests were conducted on both CF and SCFAs, under various conditions (i.e., pH, initial Zn concentration, reaction time, and solid dosage), using Zn(NO(3))(2).6H(2)O solutions. Serial column tests were also conducted on both CF and SCFAs. The final rather than the initial pH of the solution had a greater effect on the removal of Zn. At pH7.0, the removal of Zn was due to precipitation, whereas at7.0, the removal of Zn was due to adsorption onto the reactive media. The removal of Zn increased with increasing dosage of the reactive medium and decreasing initial Zn concentration. The results of the column and batch tests were comparable. Preferential flow paths were observed with CF, but not SCFA. The hydraulic conductivity of CF was more significantly decreased than that of SCFA with increasing dry density of the specimen.

Enantioselective esterification of racemic ibuprofen catalyzed by Candida rugosa lipase in isooctane was coupled with pervaporation of water by-produced by the reaction. A cross-linked poly(vinyl alcohol) (PVA) membrane and commercially available perfluorinated membranes (Nafion® 117 and Nafion® NE 450) were tested for selective removal of water from the reaction mixture. Effects of the reaction temperature, the concentration and nature of alcohol on the reaction rate and enantioselectivity were investigated systematically with and without pervaporation. The PVA pervaporation membrane appeared to be more suitable for the enantioselective esterification of ibuprofen than the Nafion® membranes. The highest initial rate and enantiomeric ratio were obtained at an equimolar ratio of 1-octanol/1-decanol to racemic ibuprofen regardless of the application of pervaporation. Raising the reaction temperature from 30 to 60 °C increased the reaction rate, but decreased the enantiomeric ratio. However, in most cases, the concomitant pervaporation operation brought about the enhancement of the reaction rate and the enantiomeric ratio. This was thought to be due to the selective removal of excess water by pervaporation, leading to the reduction of undesirable effects on the enzymatic reaction by the excess water.

Effects of Ag nanodots on silicon nanocrystal (nc-Si) light-emitting diodes (LEDs) are investigated. The electrical property of the nc-Si LED with Ag nanodots was enhanced compared to that of the nc-Si LED without ones. This was attributed to the increase in the electric field due to the formation of Ag nanodots at the contact interface, indicating that the current could flow more efficiently from the indium tin oxide layer to n-SiC film. The formation of Ag nanodots with a size of 3~6 nm was confirmed by using a high-resolution transmission electron microscope analysis. Moreover, light output power of the nc-Si LED with Ag nanodots was enhanced

Electric power equipment must be operated in the state of high reliability with its responsibility in the seamless power supply. Therefore, persistent maintenance is very important. There are many maintenance methods well known in industrial business. Among them, the reliability-centered maintenance (RCM) is a state of the art maintenance technique. This paper shows a RCM method for electric power equipments. This reliability-centered maintenance (RCM) has been applied in the fields of aerospace, nuclear power plants and rail system, etc. In this paper, we design the FMEA flow for power transformer and evaluate the importance level based on the fault information. The importance level is applied to the inspection period, fault detection method and operator selection. The results of the system are provided as a worksheet containing the information that needs to be maintained.

Batch leach tests (BLTs) and column leach tests (CLTs) were conducted on bottom ash (BA), lagoon ash (LA), soil (S), and bottom ash–soil (BA–S) and lagoon ash–soil (LA–S) mixtures to evaluate the environmental feasibility of using these as fill materials to raise the ground level at construction sites. For the BLTs, the leachability of the elements (i.e., Cr, Mn, Ni, and Zn) generally increased with decreasing pH from 6.3 to 3.0. In addition, the concentration of all the elements analyzed in the leachates increased with decreasing the liquid-to-solid (L/S) ratio. Freezing–thawing cycles had no significant effect on the leachability of the elements found in the BLTs. The initial concentrations of the leachates from the CLTs were higher than those of the leachates from the BLTs regardless of the materials tested due to the lower L/S ratio. The initial concentrations of As, Ni, Mn, Se, Cl − , and SO 4 2− in the leachates from the CLTs for the LA, BA, and LA–S and BA–S mixtures exceeded the drinking water standards (DWSs), which suggests that these materials may pose a risk to groundwater when these materials are used in areas where there is little dilution.

Time-resolved PL characteristic of the nc-Si QDs in SiNx matrix grown by PECVD is studied. The measured PL lifetime of nc-Si QDs is about 0.43 ns