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opennotspecifiedJun 2023View details →

FIGURE 5 in Amphibians and reptiles in North Sweden: distribution, habitat affinities, and abundance (Classes: Amphibia and Reptilia)

FIGURE 5. Distribution of Rana temporaria in North Sweden. Although the entire Scandic Mountain range is colored, the species is absent from the High-Alpine zone in the Alpine region (see Figure 1 for outline of the latter). In other words, the species is missing from areas higher than 1400 m in Jämtland, decreasing to 1000 m in Torne lappmark. Colored parts of the Baltic are areas with widespread occurrence on offshore islands.

opencc-by-4.0Jun 2023View details →

Treating the End of the Data Life Cycle as a First-Class Citizen in Data Engineering - Datasets

Additional data for paper "Treating the End of the Data Life Cycle as a First-Class Citizen in Data Engineering".

opencc-by-4.0Jul 2023View details →

Artifact of Enhancing Search-Based Unit Test Generation with Method-Level Java Class Splitter

# Artifact of Enhancing Search-Based Unit Test Generation with Method-Level Java Class Splitter   This artifact contains: 1. The binary folder. Its instruction can be found in [./binary/README.md](./binary/README.md). 2. All classes used in the experiments [./classes.csv](./classes.csv). 3. The experimental data zip file ([experimental_data.zip](./experimental_data.zip)).

opencc-by-4.0Aug 2023View details →

Detailed information of 4179 calculated molecular descriptors from 22 different classes.

Detailed information of 4179 calculated molecular descriptors from 22 different classes.

opennotspecifiedOct 2021View details →

Fig. 2 in Volatile phenolics: A comprehensive review of the anti-infective properties of an important class of essential oil constituents

Fig. 2. World map showing the publications of the different countries involved in scholarly publication of the anti-infective properties of volatile phenolics (1985–2019) as retrieved from the Scopus database.

opennotspecifiedOct 2021View details →

Fig. 1 in Volatile phenolics: A comprehensive review of the anti-infective properties of an important class of essential oil constituents

Fig. 1. The number of publications retrieved from Scopus database in 35 years dealing with anti-infective properties of VP's (n = 2310 publications).

opencc-by-4.0Aug 2023View details →

Basal Area Estimates for DBH Classes from ARRI Modelled Outputs

Summary Basal area per acre (BAA) for ‘All’ trees, ‘Pine’ trees, and ‘Non-Pine’ trees across three diameter at breast height (DBH) size classes, 2- to 10-inch, 10- to 14-inch, and 14+ inch. Models were informed by relative density rasters from 2018 Light Detection and Ranging (lidar) point clouds. Description Three modelling method raster outputs of basal area per acre across three diameter at breast height (DBH) size classes are contained here. Single model raster outputs are linear regression models, ensemble linear models (LM) are mean estimates from 50 linear models, and ensemble generalized additive models (GAM) are mean estimates from 50 GAMs. The units for these single band rasters’ are square foot per acre. Rasters are divided into three tree species groups: 'All' trees, 'Pine' trees (defined as trees of the genus Pinus), and 'Non-Pine' trees, and three size classes: LT 10 for trees with DBH between 2- and 10-inches, 10-14 for trees with DBH between 10- and 14- inch DBH, and GT 14 for trees with DBH greater than 14-inches. Ensemble generalized additive regression models (GAM) and Ensemble linear regression models (LM) of estimated tree basal area per DBH class were created from Restore field plots and relative density canopy cover rasters, or RDCC (St. Peter, et al., 2023). This ensemble GAM model was created using the R script detailed in Hogland, 2021. The ensemble LM models were created using a modification of the Hogland, 2021 script that uses the lm() function in place of the GAM modelling functions. The parameters used were 0.75 for the percent of data used to train the model (selected using random sampling with replacement), 50 models, and using gaussian family. The estimated BAA for each of the 50 ensemble GAM, and LM, models for each cell were averaged to produce the mean estimate, additionally the variability between these estimates was used to create the standard error estimate (SEE) for each cell.     The 246 Restore field plots used to train the models of basal area include measurements of all trees within four non-overlapping 9m radius circular subplots within a 36m square plot. Tree diameter at breast height (DBH), species, count, and condition measurements were recorded. Measurements were summarized to the plot and DBH (square inches) was converted to basal area per acre (square feet per acre) using the formula 0.005454 * DBH^2. Restore field plots were measured in the Spring of 2018. The RDCC metrics are 5m resolution multiband rasters produced by applying a custom r software function that uses the r software’s ‘lidR’ package to produce forest metrics summarized from Light Imaging Detection and Ranging (LiDAR) point clouds. ARSA LiDAR is a combination of three collections, Block 2 and 3 were collected in early 2018 and has a NPS of 0.7-m using a Riegl VQ-1560i lidar system. Leon county LiDAR data has a nominal pulse spacing (NPS) of 0.35-m and was acquired between February 05, 2018 and April 25, 2018 using the Leica ALS80 HP SN8137 and SN8235 lidar systems.  Choctawhatchee data was acquired in early 2017, using the Riegl LMS-Q1560 lidar system and has a NPS of 0.7-m. Rasters were generated in their vendor provided spatial projection before being reprojected to UTM Zone 16.   The 5m resolution RDCC bands were summarized to 40x40m to correspond with the area of our plots and used as predictor variables in the models of all trees basal area. Each 5m pixel value represents the estimated basal area per acre as if it was the center of a 40x40m (8x8 cells) plot surrounding that pixel.     Reference Hogland, J. (2021). Ensemble Generalized Additive Models (EGAM). Retrieved from Jupyter Notebook: <a href="https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing">https://colab.research.google.com/drive/1GnRagruTUCoPJQZSkZ2vMKS9aAKgnhEw?usp=sharing</a> St. Peter, Joseph, Drake, Jason, Medley, Paul, & Ibeanusi, Victor. (2023). Relative Density Canopy Cover Outputs for Leon Lidar data in the Florida Panhandle 2018 [Data set]. In Remote Sensing (Vol. 13, Number 23, p. 4763). Zenodo. https://doi.org/10.5281/zenodo.8222114  Credits This dataset was built by Joseph St. Peter of FAMU’s Center for Spatial Ecology and Restoration using 2018 LiDAR data funded by Leon County, Northwest Florida Water Management District, US Geological Survey and the USDA Forest Service and processed using the r package lidR. Restore plots were funded by the Gulf Coast Ecosystem Restoration Council (RESTORE Council) through an interagency agreement with the USDA Forest Service (17-IA-11083150-001) for the Apalachicola Tate’s Hell Strategy 1 project. Use Limitations This spatial data is based on various data collection and processing techniques as well as on modeling or interpretation. While this data uses the most current and complete information available at the time of production, spatial data and derivative products may vary in accuracy. Spatial data are often developed from sources of differing accuracy which may be accurate only at certain scales. This data has been quality checked but may contain spurious errors or be incomplete or inappropriate for certain uses. Spatial data products used for purposes other than those for which they were created, may yield inaccurate or misleading results. The USDA Forest Service, Florida A&M University, and the Center for Spatial Ecology & Restoration (CSER) reserves the right to correct, update, modify, remove or replace GIS products at any time and without notification. This data may not be distributed without written permission from the Center for Spatial Ecology & Restoration (CSER) at Florida A&M University, and/or the USDA Forest Service.

Fig. 5 in In silico characterization of class II plant defensins from Arabidopsis thaliana

Fig. 5. Secondary structure evolution during the simulations. The overall secondary structures of (A) A7REG2 and (B) A7REG4 are maintained during the simulations, with the exception of the first β-strand, which could transit to β-bridge and/or coils. The final three-dimensional structures at 300 ns of simulation are displayed on the right side of DSSP. Disulfide bridges are represented in ball and sticks.

Fig. 2 in In silico characterization of class II plant defensins from Arabidopsis thaliana

Fig. 2. Alignment between sequences obtained at the end of semi-automatic search and known class II plant defensins. Signal peptide, mature defensin and Cterminal prodomain regions are indicated by the bottom ruler. The "GXC" γ-core motif is highlighted by a magenta box; none of A. thaliana sequences have the characteristic γ-core GXCX3-9C motif between the IV and VI cysteine residues. Cysteine residues are highlighted in yellow and the lines above the sequences indicate the bond pattern between them. Cis-proline motifs are marked in grey. Predicted C-terminal prodomain recognition site is highlighted by a green box, comprising a pair of charged residues, of which the first is always a negative one. Based on this signature, we propose the RegEx "CX8-10CX2-18CX3CX2-10[GAPSIDERYW]XCX4- 17CXCX3CX2[DE][DERK]" for future studies regarding the identification of class II defensins. Charged residues of C-Terminal prodomain are highlighted in blue (positively charged) or red (negatively charged). ZmERS6, NaD1, PhD1, PhD2 presented a negatively charged C-terminal prodomain, in contrast to A7REG2 (positively charged) and A7REG4 (neutral). Sd5 is a partial sequence, and despite the known terminal residues indicated a positively charged C-terminal prodomain, its actual charge is unknown. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in In silico characterization of class II plant defensins from Arabidopsis thaliana

Fig. 4. Tridimensional molecular models of the sequences. (A) After removal of signal peptide and C-terminal prodomain, both sequences resulted in a 43 amino acid residue mature chain with 72% of identity. The α-core region is highlighted by a green box, showing the accumulated mutations between both sequences. The "AXC" motif from γ-core is highlighted by a red box. (B) A7REG2 and (C) A7REG4 structures. Disulfide bonds are represented in ball and sticks. Both models were generated using the sugar cane (Saccharum officinarum) defensin 5 (SD5, PDB ID: 2KSK) (de Paula et al., 2011). On the Ramachandran plot, A7REG2 model presented 83.3% of residues in favored regions, 13.9% in allowed regions and 2.8% in generously allowed regions; while A7REG4 model presented model presented 77.8% of residues in favored regions, 13.9% in allowed regions and 8.3% in generously allowed regions. In addition, models also presented a Z-Score on ProSa II of 4.47 and 5.74, respectively. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in In silico characterization of class II plant defensins from Arabidopsis thaliana

Fig. 1. Flowchart of identification of class II defensins. The indigo boxes indicated steps performed by Perl scripts and the red boxes, steps curated by hand. The white boxes indicated the number of sequences for each respective step. The sequences from A. thaliana were retrieved from UniProt database; the defensin RegEx "CX2-18CX3CX2-10[GAPSIDERYW]XCX4-17CXC" was determined by Zhu (Zhu, 2008). The complete sequences were retrieved according the UniProt annotations; and the sequences predicted to be secreted were selected according to the Phobius prediction, presenting signal peptide and no transmembrane domains. The selected sequences at the end of the process represented less than 1% of all defensin sequences from A. thaliana. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in In silico characterization of class II plant defensins from Arabidopsis thaliana

Fig. 3. Expression profile of coding genes for A7REG2 and A7REG4 (AGI AT1G73603 and AT1G73607, respectively). (A) Expression in flowers. Y axis represents the number of flowers in the inflorescence, at the moment of the anthesis of the first flower (B) Expression in ovules. Y axis represents the number of the silique from which ovules were taken, at the moment when the first silique is 1 cm long (C) Expression in seeds. Y axis represents the number of the silique from which seeds were taken at the moment of the abscission of the first flower.

Fig. 6 in Structural and functional features of a class VI chitinase from cashew (Anacardium occidentale L.) with antifungal properties

Fig. 6. Morphological alterations induced in Lasiodiplodia theobromae hyphae by Anacardium occidentale chitinase. SEM images of untreated mycelium(A-B), mycelium treated with 500 μg AoChi (C–I) and mycelium treated with 5 μg Carbendazim (J–M) are shown. The assays to evaluate the morphological alterations in L. theobromae grown in vitro in the presence of AoChi and acquisition of SEM images were performed as described in the Methods section. SEM images from fungus grown in the presence of water (A–B) or Carbendazim (J–M) were included for comparison.

Fig. 7 in Structural and functional features of a class VI chitinase from cashew (Anacardium occidentale L.) with antifungal properties

Fig. 7. Three-dimensional molecular model of AoChi and its interaction with a chitin oligomer. (A) Cartoon representation of the three-dimensional molecular model of AoChi, which was generated by homology modeling. Side chains of putative catalytic residues are shown as sticks. Disulfide bonds are colored orange. (B) Detailed view of a chito-oligosaccharide (yellow) docked in the substrate-binding cleft of AoChi. Side chains of AoChi residues that probably interact with the docked ligand through hydrogen bonds (represented as yellow dotted lines) are shown as sticks (cyan). N and O atoms are colored blue and red, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 5 in Structural and functional features of a class VI chitinase from cashew (Anacardium occidentale L.) with antifungal properties

Fig. 5. Effects of temperature (A), pH (B), metal ions (C) and chemical reagents (D) on the hydrolytic activity (A–D) and stability (A–B) of AoChi. Enzymatic assays were performed using the purified recombinant protein (200 μg/mL) and colloidal chitin as substrate, as described in the Methods section. In panels C and D, means that are significantly different (P <0.05; Bonferroni's multiple comparisons test) when compared to control are indicated by asterisks.

Fig. 4 in Structural and functional features of a class VI chitinase from cashew (Anacardium occidentale L.) with antifungal properties

Fig. 4. Polyacrylamide gel electrophoresis (SDS-PAGE) of the purified recombinant chitinase from Anacardium occidentale, a member of class VI that has chitinolytic activity. (A) The recombinant chitinase (lane 1; 35 μg) was reduced with β-mercaptoethanol and subjected to denaturing gel electrophoresis (15% polyacrylamide), as described in the Methods section. Lane M: protein markers. (B) Representative fragmentation mass spectrum (LC-MS/MS) of a tryptic peptide, obtained from in gel-digestion of the 45 kDa protein band, that matched a segment of 17 residues of the primary structure of the recombinant product. (C) Colorimetric enzymatic assay, showing the ability of the recombinant cashew chitinase to degrade colloidal chitin. (D) Plot of initial reaction velocities vs substrate concentration. The enzymatic assays (panels C and D) were performed at pH 6.0 and 40 ◦C, and using colloidal chitin as substrate, as described in the Methods section.

Fig. 3 in Structural and functional features of a class VI chitinase from cashew (Anacardium occidentale L.) with antifungal properties

Fig. 3. Alignment of the amino acid sequence of the CatD of AoChi with known three-dimensional structures of GH19 chitinases from plants. The CatD sequence of AoChi was aligned to GH19 structures from: Carica papaya (PDB ID: 3CQL), Vigna unguiculata subsp. sesquipedalis (PDB ID: 4TX7), Secale cereale (PDB ID: 4DWX), Hordeum vulgare (PDB ID: 2BAA), Oryza sativa subsp. japonica (PDB ID: 2DKV), Canavalia ensiformis (PDB ID: 1DXJ), Brassica juncea (PDB ID: 2Z37), Cryptomeria japonica (PDB ID: 5H7T) and Picea abies (PDB ID: 3HBD). Sites containing residues involved in catalysis (triangles) and substrate-binding (hash marks) are indicated. Disulfide bonds, as observed in the three-dimensional model of AoChi (Fig. 7), are indicated by orange lines. Loop regions (I–V), as assigned by Taira et al. (Taira et al., 2011), are boxed. Loop VI (this work), which is present only in AoChi and other class VI chitinases, is also boxed. Alignment columns are colored according to the ALSCRIPT Calcons convention, as implemented in ALINE (Bond and Schüttelkopf, 2009), using a predefined color scheme, which reflects the conservation of amino acid properties in each column (dark blue: identical residues; white: dissimilar residues). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Structural and functional features of a class VI chitinase from cashew (Anacardium occidentale L.) with antifungal properties

Fig. 2. Maximum likelihood (ML) tree showing the phylogenetic relationship of Anacardium occidentale chitinase with representative plant chitinases from classes I, II, IV, VI and VII. The evolutionary history of the sequences was inferred by using the ML method and Whelan and Goldman + Freq. model (Whelan and Goldman, 2001). The tree with the highest log likelihood (12837.58) is shown. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 1.5328)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 3.91% sites). The percentage of trees in which the associated sequences clustered together, as determined by the bootstrap test (100 replicates), is shown next to the branches. This analysis involved 62 amino acid sequences, and the final dataset contained a total of 371 positions (Table S2 and Fig. S7). Evolutionary analyses were conducted in MEGA X (Kumar et al., 2018).

Fig. 1 in Structural and functional features of a class VI chitinase from cashew (Anacardium occidentale L.) with antifungal properties

Fig. 1. Domain architecture of Anacardium occidentale chitinase (AoChi). (A) Graphical representation of AoChi domains (SP: signal peptide; GH19 CatD: GH19 catalytic domain). The catalytic residues of AoChi, Lys(K)128, Glu(E)150 and Tyr(Y)183, are highlighted. (B) Alignment of a stretch of 61 residues from the primary structure of AoChi (from His124 to Trp184), which contains the 3 catalytic residues, with corresponding segments from homologous proteins belonging to plant species from different families and orders (Table S5). Alignment columns are colored according to the ALSCRIPT Calcons convention, as implemented in ALINE (Bond and Schüttelkopf, 2009), using a predefined color scheme, which reflects the conservation of amino acid properties in each column (dark red: identical residues; white: dissimilar residues). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)