Perl scripts used in designing and evaluating ITS primers of plants.

These perl scripts were designed by Tao Cheng in 2014 for designing and evaluating ITS primers of plants. The related article has been submitted to Molecular Ecology Resources. The descriptions of each script are as follow: Classifier_Fungi.pl Function: Divide the total Fungi sequences into lower classification level. Classifier_Viridiplantae.pl Function: Divide the total sequences into lower classification level. N_Degenerate_Filter_New.pl Function: Get rid of the sequences with 'n' or degenerate character. contaminantsFilter.pl Function: Remove the potential contaminated sequences from GenBank based on mega-blast. GenBanktoFastawithClassName.pl Function: Convert a GB file to fasta format in following style: # >Class_Genus_Species_TaxonID_GI_ACC_length description # >Nelumbonaceae_Nelumbo_lutea_4431_1479989_L75835_1713 Nelumbo lutea 18S ribosomal RNA (18S rDNA) gene # nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnnnn nnnnnnnncc atgcatgtgt # aagtatgaac taattcagac tgtgaaactg cgaatggctc attaaatcag ttatagtttg # tttgatggta tctactactc ggataaccgt agtaattcta gagctaatac gtgcaccaaa # ................................................................. # ttgcaattgt tggtcttcaa cgaggaattc ctagtaagcg cgagtcatca gctcgcgttg # actacgtccc tgccctttgt acacaccgcc cgtcgctcct accgattgaa tggtccggtg # aagtgttcgg atcgcggcga cgtgggcggt tcg Find_Conserved_DNA_Segments.pl Function: This script is used to extract conserved DNA segments from an alignment file. MismatchDetect-Batch.pl Function: Calculate the species coverage between the primer and every target sequence.User set the mismatch allowed and the threshold of nucleotides constrained in the 3'-end. getPCRProductLengthByPrimerBlast.pl Function: This script is used to get the PCR product length of a certain primer-pair based on PrimerBlast

Adsorption of Ethanol Vapor on Mica Surface under Different Relative Humidities: A Molecular Simulation Study

The adsorption of ethanol vapor on a mica surface at 298 K and different relative humidities (RHs) are studied using grand canonical Monte Carlo and molecular dynamics simulations. The simulations show that the adsorbed ethanol molecules form a monolayer on the mica surface, sharply contrasting the behavior of water, which forms multiple adsorption layers on the mica surface. Simulations of an ethanol and water mixture reveal that the adsorbed molecules are segregated into a water-rich domain near the mica surface and an ethanol-rich domain on top of the water-rich domain. The water-rich domain exhibits multilayers unless the RH is extremely low (<1%), whereas the ethanol-rich domain exhibits a monolayer. These findings are supported by calculations of the isosteric heats of adsorption and analyses of configurations, concentrations, and diffusivities of molecules in different layers

A Highly Diastereoselective and Enantioselective Synthesis of Polysubstituted Pyrrolidines via an Organocatalytic Dynamic Kinetic Resolution Cascade

Highly functionalized pyrrolidine and piperidine analogues, with up to three stereogenic centers, were synthesized in good yield (50–95%), excellent dr (single isomer), and high ee (>90%) using a <i>Cinchona</i> alkaloid-derived carbamate organocatalyst. High stereoselective synergy was achieved by combining a reversible <i>aza</i>-Henry reaction with a dynamic kinetic resolution (DKR)-driven <i>aza</i>-Michael cyclization. Whereas both reactions proceed with moderate enantioselectivities (50–60% for each step), high enantioselectivities are obtained for the overall products devoid of dr sacrifice

Nanoscale Titanium Dioxide (nTiO₂) Transport in Natural Sediments: Importance of Soil Organic Matter and Fe/Al Oxyhydroxides

Many engineered nanoparticle (ENP) transport experiments use quartz sand as the transport media; however, sediments are complex in nature, with heterogeneous compositions that may influence transport. Nanoscale titanium dioxide (nTiO₂) transport in water-saturated columns of quartz sand and variations of a natural sediment was studied, with the objective of understanding the influence of soil organic matter (SOM) and Fe/Al-oxyhydroxides and identifying the underlying mechanisms. Results indicated nTiO₂ transport was strongly influenced by pH and sediment composition. When influent pH was 5, nTiO₂ transport was low because positively charged nTiO₂ was attracted to negatively charged minerals and SOM. nTiO₂ transport was slightly enhanced in sediments with sufficient SOM concentrations due to leached dissolved organic matter (DOM), which adsorbed onto the nTiO₂ surface, reversing the zeta potential to negative. When influent pH was 9, nTiO₂ transport was generally high because negatively charged medium repelled negatively charged nTiO₂. However, in sediments with SOM or amorphous Fe/Al oxyhydroxides depleted, transport was low due to pH buffering by the sediments, causing attraction between nTiO₂ and crystalline Fe oxyhydroxides. This was counteracted by DOM adsorbing to nTiO₂, stabilizing it in suspension. Our research demonstrates the importance of SOM and Fe/Al oxyhydroxides in governing ENP transport in natural sediments

Additional file 4: Figure S4. of Digitalization of a non-irradiated acute myeloid leukemia model

Supplemental data for illustrating the differentiation blockade in the hematopoietic cascade. (A). Absolute numbers of LT-HSCs, ST-HSCs and MPPs in leukemic BM [11]. (B–C). Absolute numbers of CMPs, GMPs, MEPs (B) and CLPs (C) in leukemic BM. Data are represented as the mean ± SEM (n = 12, 3 independent experiments). * p < 0.05, ** p < 0.01, *** p < 0.001. + or -, increase or decrease [11]. (D). A pattern showing linear correlation between the reduction and differentiation hierarchy in the hematopoietic cascade. The reduction of LT-HSC was normalized to −1, and the bars indicate normalized reduction level [11]. (E). Flow plots (left panel) and histograms (right panel) show the cell cycle status of LT-HSCs in leukemic BM. Data are represented as the mean ± SEM (n = 12, 3 independent experiments). * p < 0.05 [11]. (F). Flow plots (left panel) and histogram (right panel) show the BrdU incorporation of LT-HSCs in leukemic BM. Data are represented as the mean ± SEM (n = 8, 2 independent experiments). *** p < 0.001 [11]. (PNG 135 kb

Additional file 2: Figure S2. of Digitalization of a non-irradiated acute myeloid leukemia model

Reproduction of the normal control by the model. (A–C). Computational and experimental cell kinetics of PB (A), spleen (B) and BM (C) under the normal condition. The computation results are yielded by directly eliminating the parameters for leukemic effects in the mathematical model. Experimental data are taken from Ref [11]. (D). Computational and experimental kinetics of BM HSCs/HPCs under the normal condition. (E). Computational and experimental kinetics of quiescent and active HSCs in BM under the normal condition. (PNG 79 kb

Additional file 3: Figure S3. of Digitalization of a non-irradiated acute myeloid leukemia model

Supplemental data for illustrating the major factor of HSC loss. (A). The 3D visualization of projections of the HSC dynamics. Expn, Diff and Death are the orthogonal axes; and the rates projected on them are bars with lengths proportional to the values. Prolif is symbolized as the vectorized composition of Diff and Expn. Temporal profiles at day 0, 7, 10, 12, 14 and 21 are given. (B). Flow plots (left panel) and histogram (right panel) show the BrdU incorporation of HSCs (LKS+ cells) in leukemic BM. Data are represented as the mean ± SEM (n = 8, 2 independent experiments). *** p < 0.001 [11]. (PNG 165 kb

Additional file 1: Figure S1. of Digitalization of a non-irradiated acute myeloid leukemia model

Raw data of cell kinetics from experiment. (A–C). Absolute numbers of CD45.1+ normal hematopoietic cells and GFP+ leukemia cells in PB (A), spleen (B) and BM (C) during leukemia development (n = 5-7). Ctrl, Control mice [11]. (D–E). Absolute numbers of CD45.1+LKS+ (D) and CD45.1+LKS− (E) cells in leukemic BM (n = 4-5) [11]. (PNG 71 kb

Additional file 6: of Digitalization of a non-irradiated acute myeloid leukemia model

Descriptions in-detail for the computational model. Descriptions of the modeling process and computational procedures are enclosed; concrete mathematical formulas and (optimized) parameter values are also provided. (DOCX 152 kb

Free-Energy Barriers and Reaction Mechanisms for the Electrochemical Reduction of CO on the Cu(100) Surface, Including Multiple Layers of Explicit Solvent at pH 0

The great interest in the photochemical reduction from CO₂ to fuels and chemicals has focused attention on Cu because of its unique ability to catalyze formation of carbon-containing fuels and chemicals. A particular goal is to learn how to modify the Cu catalysts to enhance the production selectivity while reducing the energy requirements (overpotential). To enable such developments, we report here the <i>free-energy reaction barriers</i> and <i>mechanistic pathways</i> on the Cu(100) surface, which produces only CH₄ (not C₂H₄ or CH₃OH) in acid (pH 0). We predict a threshold potential for CH₄ formation of −0.52 V, which compares well to experiments at low pH, −0.45 to −0.50 V. These <i>quantum molecular dynamics</i> simulations included ∼5 layers of <i>explicit water</i> at the water/electrode interface using enhanced sampling methodology to obtain the free energies. We find that that chemisorbed hydroxyl-methylene (CH–OH) is the key intermediate determining the selectivity for methane over methanol