Adaptation of the MapMan ontology to biotic stress responses: application in solanaceous species-1

<p><b>Copyright information:</b></p><p>Taken from "Adaptation of the MapMan ontology to biotic stress responses: application in solanaceous species"</p><p>http://www.plantmethods.com/content/3/1/10</p><p>Plant Methods 2007;3():10-10.</p><p>Published online 4 Sep 2007</p><p>PMCID:PMC2018691.</p><p></p>he pathogen which is recognized by the related receptors (putative R genes), transcription of the cascade of the plant defence mechanism is triggered, including oxidative stress changes. Inside the cell, signals are transmitted to lead to the production of defence molecules (PR-proteins, heat shock proteins and secondary metabolites). Genes with experimental indication of involvement in the biotic stress are gathered on the main panel (coloured with dark grey), while genes and pathways that are putatively involved in biotic stress pathway are shown on the left and right sides (coloured in light grey). ) Potato samples 30 minutes after inoculation with potato virus . ) Tobacco samples 24 hours after inoculation with . In both cases, the signal after infection is expressed as a ratio relative to the signal in unifected controls, converted to a scale, and displayed. The scale is shown in the figures

Adaptation of the MapMan ontology to biotic stress responses: application in solanaceous species-0

<p><b>Copyright information:</b></p><p>Taken from "Adaptation of the MapMan ontology to biotic stress responses: application in solanaceous species"</p><p>http://www.plantmethods.com/content/3/1/10</p><p>Plant Methods 2007;3():10-10.</p><p>Published online 4 Sep 2007</p><p>PMCID:PMC2018691.</p><p></p>he pathogen which is recognized by the related receptors (putative R genes), transcription of the cascade of the plant defence mechanism is triggered, including oxidative stress changes. Inside the cell, signals are transmitted to lead to the production of defence molecules (PR-proteins, heat shock proteins and secondary metabolites). Genes with experimental indication of involvement in the biotic stress are gathered on the main panel (coloured with dark grey), while genes and pathways that are putatively involved in biotic stress pathway are shown on the left and right sides (coloured in light grey). ) Potato samples 30 minutes after inoculation with potato virus . ) Tobacco samples 24 hours after inoculation with . In both cases, the signal after infection is expressed as a ratio relative to the signal in unifected controls, converted to a scale, and displayed. The scale is shown in the figures

Table S2 from Parallel analysis of Arabidopsis circadian clock mutants reveals different scales of transcriptome and proteome regulation

The circadian clock regulates physiological processes central to growth and survival. To date, most plant circadian clock studies have relied on diurnal transcriptome changes to elucidate molecular connections between the circadian clock and observable phenotypes in wild-type plants. Here, we have integrated RNA-sequencing and protein mass spectrometry data to comparatively analyse the <i>lhycca1</i>, <i>prr7prr9</i>, <i>gi</i> and <i>toc1</i> circadian clock mutant rosette at the end of day and end of night. Each mutant affects specific sets of genes and proteins, suggesting that the circadian clock regulation is modular. Furthermore, each circadian clock mutant maintains its own dynamically fluctuating transcriptome and proteome profile specific to subcellular compartments. Most of the measured protein levels do not correlate with changes in their corresponding transcripts. Transcripts and proteins that have coordinated changes in abundance are enriched for carbohydrate- and cold-responsive genes. Transcriptome changes in all four circadian clock mutants also affect genes encoding starch degradation enzymes, transcription factors and protein kinases. The comprehensive transcriptome and proteome datasets demonstrate that future system-driven research of the circadian clock requires multi-level experimental approaches. Our work also shows that further work is needed to elucidate the roles of post-translational modifications and protein degradation in the regulation of clock-related processes

Distribution of RNA expression.

<p>Transcript-specific RNA expression values from the Maize Gene Atlas <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004845#pgen.1004845-Sekhon1" target="_blank">[30]</a> were summed to determine total expression for each gene. The log-transformed distribution of maximum expression values are shown for the entire filtered gene set (solid line) or just genes with GWAS hits within 5 kb of their primary transcripts (dashed line); vertical lines indicate the median of each distribution. The GWAS-hit genes show a slight depletion (∼20%) of low-expressed genes. For comparison, the median expression of maize transcription factors in this dataset (as annotated on Grassius, <a href="http://grassius.org/" target="_blank">http://grassius.org/</a>) is indicated by an arrowhead. FPKM, Fragments Per Kilobase of transcript per Million mapped reads.</p

Prediksi Suhu Atmosfer Bumi Berdasarkan Model Transfer Radiasi Termal 1-Dimensi

Terbentuknya profil distribusi vertikal suhu atmosfer bumi ingin dipahami secara teori melalui model lapisan atmosfer dengan mekanisme transfer radiasi antar lapisan dimaksud. Hasil perhitungan dengan menggunakan data nilai solar constant S = 1370 W/m2, albedo r0 = 0,3 serta reflektivitas dan transmisivitas optik dari komponen gelombang pendek (SW) dan gelombang panjang (LW) yang terukur, diperoleh keseauaian kualitatif dengan pola profil suhu yang diperoleh dari hasil pengukuran sebagaimana tercantum dalam literatur standar. Selain dari pada itu, secara kuantitatif diperoleh kesesuaian dengan data hasil perhitungan perataan horizontal model GCM (General Circulation Model), hingga ketinggian ~400 hPa (~7000 meter) dari permukaan bumi. Model transfer radiasi ini juga memberi gambaran tentang profil suhu untuk beberapa skenario menyangkut kondisi iklim global

Phenotypes used in this study.

a<p><a href="http://www.panzea.org/lit/data_sets.html#phenos" target="_blank">http://www.panzea.org/lit/data_sets.html#phenos</a>; the joint-linkage model to create residuals for this data was provided courtesy of Sherry Flint-Garcia.</p><p>Phenotypes used in this study.</p

Different effects of the polymorphism classes.

<p>(A) Variance explained by polymorphism class. Genic and gene-proximal polymorphisms explain the largest amount of unique variation in each trait. Breaking the data into the two components that most influence variance explained—allele frequency (B) and polymorphism effect size (C)—reveals a negative correlation between them such that classes with larger effect sizes (e.g., intergenic) also tend to have rarer polymorphisms. (D) Pairwise p-values testing whether the distributions in (A-C) are significantly different from each other (two-sided Kolmogorov-Smirnov test); values <1×10⁻³ are bolded.</p

Aplikasi Spray Dryer Untuk Pengeringan Larutan Garam Amonium Perklorat Sebagai Bahan Propelan

Ammonium perchlorate ( AP ) is an inorganic oxidizer that is widely used as a component of rocket propellants. In this research, spray drying was used to produce crystalline AP from its saturated solution. A method of spray dryer is viscous liquid or paste contacted with thot air co-currently. Fluid is passed at a nozzle and came out into the form of fine granules ( droplet . Drying method was conducted to run4 variables change, such as the inlet temperature (80, 90, 100, 110, 120oC), flow rate air dryer (9,1 and 16.3 m/s), the material flow rate (5.5 and 5.8 ml/s) and material concentration (5, 10, 15, 20, 25% salt). The drying process lasts for 13 minutes and divided into 3 minutes of time spraying and 10 minutes for residence time in a spray dryer column.At a temperature of80° C, the concentration of20%, materialflow rateof 5.5ml/sand aair flow rate of9.1m /sobtainedsaltparticlediameterof67.144µmthen calculatesimilarityusingWebernumber, obtainedAPdiameter of42.79µm.While ata temperature of 100° C, the concentration of20%, with asame material and air flow rate,obtaineddriedsaltparticle diameterof23.433µm.Afterwards,similaritycalculationusing theWebernumberobtainedAPdiameter13.877µm. It can be seenthatthe result ofAPdiametersmaller than thediameter of theparticlesinLAPAN (National Aeronautics and Space Institute), rangedbetween100-170µm. We can conclude that the higher concentration of salt solution, then the diameter of products are also getting bigger. The higher temperature then the diameter of products are getting smaller. Calculation of similarity both ammonium perchlorate and salt with the weber number has the same graph trends

Polymorphism effect size and allele frequencies.

<p>(A) The standardized effect size of a polymorphism (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004845#s4" target="_blank">Methods</a>) is negatively correlated with minor allele frequency. This correlation is probably due to both biological factors (e.g., large effects are both more likely to deleterious (Fisher 1930; Orr 1998) and more easily selected against than small ones, and thus are more likely to remain rare) and statistical ones (e.g., in order for a rare variant to explain enough variance to be detected in GWAS, it must have a large effect). Similar results were found in a previous analysis of maize inflorescence traits <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004845#pgen.1004845-Brown1" target="_blank">[12]</a>. (B) Minor allele frequency distributions for the different polymorphism classes of GWAS hits. Intergenic hits are strongly enriched for rare alleles. The bimodal distribution in both parts is due to the way NAM was constructed; specifically, since B73 is a parent in all 25 families, any polymorphisms with the rare allele in B73 have their frequency artificially boosted toward 0.5.</p

Distribution of non-genic GWAS hits as a function of gene distance.

<p>The number of SNPs at increasing distances from the nearest gene is plotted; CNVs are excluded due to their large size and the difficulty determining where many (especially insertions) actually occur. The input (whole genome) dataset shows a single peak at ∼25 kb away from a gene. The GWAS dataset, however, shows an additional peak at ∼1–5 kb (shaded), where one would expect to find promoters and short-range regulatory elements. Note that due to the log scale, each bin contains successively more nucleotides that make it appear that most SNPs are far from genes, when the reverse is actually true.</p