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831 results for “Partition”
Data - Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 2. Nonionic micellar systems
<p><strong>Overview</strong></p> <p>The production of recombinant biopharmaceuticals is highly dependent of a proper choice of the downstream processing stages. Particularly, the purification that must ensure that all the endotoxins (lipopolysaccharide - LPS) are efficiently removed from the final product. This dataset contains the raw data and statistical analysis for the research entitled - "Effect of electrolytes as adjuvants in GFP and LPS partitioning on aqueous two-phase systems: 2. Nonionic micellar systems". </p> <p><strong>Info</strong></p> <p>ANOVA_Turkey_Sub.R <- code for ANOVA analysis in R statistic 3.3.3 <br> glm.R <- code for GLM analysis in R statistic 3.3.3<br> K&REC_ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) and recover (REC) for ANOVA analysis</p> <p>K_ORG_ANOVA.docx <- File with ANOVA result of partition coefficient (K) for GFP</p> <p>REC_ORG_ANOVA.docx <- File with ANOVA result of partition coefficient (REC) for GFP</p> <p>REM_LPS_ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of LPS removal for ANOVA analysis</p> <p>REM_LPS_ORG_ANOVA.docx <- File with ANOVA result of removal of LPS</p> <p>Stability__ORG_ANOVA.csv <- File with raw values organized in a spreadsheet of GFP stability for ANOVA analysis</p> <p>Stability__ORG_ANOVA.docx <- File with ANOVA result of GFP stability</p> <p>K_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.05M salt assays </p> <p>K_ORG_glm_005.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.05M salt assays </p> <p>K_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.05M salt assays </p> <p>K_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of GFP partition coefficient (K) for GLM analysis in 0.25M salt assays </p> <p>K_ORG_glm_025.doc <- File with GLM analysis of GFP partition coefficient (K) in 0.25M salt assays </p> <p>K_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis for partition coefficient (K) in 0.25M salt assays </p> <p>REC_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.05M salt assays</p> <p>REC_ORG_glm_005.doc <- File with GLM analysis of GFP recover (REC) in 0.05M salt assays </p> <p>REC_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.05M salt assays </p> <p>REC_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of GFP recover (REC) for GLM analysis in 0.25M salt assays</p> <p>REC_ORG_glm_025.doc <- File with GLM analysis of GFP recover (REC) in 0.25M salt assays </p> <p>REC_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis of GFP recover (REC) in 0.25M salt assays </p> <p>REM_ORG_glm_005.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.05M salt assays</p> <p>REM_ORG_glm_005.doc <- File with GLM analysis of LPS removal (REM) in 0.05M salt assays </p> <p>REM_ORG_glm_005_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p>REM_ORG_glm_025.csv <- File with raw values organized in a spreadsheet of LPS removal (REM) for GLM analysis in 0.25M salt assays</p> <p>REM_ORG_glm_025.doc <- File with GLM analysis of LPS removal (REM) in 0.25M salt assays </p> <p>REM_ORG_glm_025_QQ.png <- Residual quantile plot of GLM analysis of LPS removal (REM) in 0.25M salt assays</p> <p> </p> <p><strong>Annotation</strong></p> <p>12/12 - Concentration of 12% of each polymer PEG/NaPA</p> <p>16/16 - Concentration of 16% of each polymer PEG/NaPA</p> <p>P/N - PEG/NaPA</p> <p>10e4, 10e5, 10e6 - Concentration of LPS in scientific notation - 10000, 100000, 100000 EU/mL</p> <p>poly - Polymer</p> <p>salt - Salt concentration in the assay</p> <p>tsalt - Type of salt in the assay (NaCl, KNO3, KI and Li2SO4)</p> <p>lps - lipopolysaccharide</p> <p>K - GFP partition coefficient</p> <p>REM - LPS removal</p> <p>REC - GFP recover</p> <p>wo_salt - Assay without salt addition</p> <p><strong>Acknowledgements</strong></p> <p>The authors are grateful for financial support from FAPESP (São Paulo Research Foundation, Brazil) through the following projects: 2005/60159-7; 2007/51978-0; 2014/16424-7; and 2014/19793-3. The authors also acknowledge the support from CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brazil) through the process #0366/09-9 and CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil).</p> <p><strong>Consider citing our work. </strong></p> <p>1. Work in progress...</p>
Partitioned Image Data for Machine Learning Analysis of Molecular Biology Figures
<p><strong> Corpus Composition</strong></p> <p>This data collection provides four types of hand-curated images from open access research articles images. The types are:</p> <ol> <li>chart (n=811): data displays such as bar charts, scatterplots, line graphs, etc.</li> <li>diagram (n=816): any general conceptual diagram</li> <li>gel (n=1182): the output of electrophoresis experiments in Northern, Western, or Southern Blot experiments. </li> <li>histology (n=3458): microscope images of tissue with histological staining</li> </ol> <p>The images are simply organized in subdirectories as individual files. File names are based on PubMed Id and Figure number. </p>
SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas
<p>This dataset contains several data, results and processing material from the application of GEOBIA-based, Spatially Partitioned Segmentation Parameter Optimization (SPUSPO) in the city of Ouagadougou. In detail in contains:</p> <ul> <li><strong>A Land Use - Land Cover map of Ouagadougou derived through SPUSPO. The classifier used was Extreme Gradient Boosting (XGBoost). </strong> <p>Labels :</p> <p>2 : Artificial Ground Surface</p> <p>0 : Building</p> <p>5 : Low Vegetation</p> <p>4 : Tree</p> <p>1 : Swimming Pool</p> <p>3 : Bare Ground</p> <p>7 : Shadow</p> <p>6 : Inland Water</p> </li> </ul> <p> </p> <ul> <li><strong>The training and test data used in the study (SPUSPO and benchmark approach). </strong></li> </ul> <p>The data are given in a csv format.</p> <ul> <li><strong>The Jupyter notebook code which involves Python and GRASS GIS to automatize and efficiently perform SPUSPO in a large dataset.</strong></li> </ul> <p>Python code calling GRASS GIS functions for automatizing the procedure.</p> <p> </p> <ul> <li><strong>The segmentation layers coming from SPUSPO and the benchmark approaches (in raster formats due to data limitations).</strong></li> </ul> <p>Segmentation rasters for each approach.</p> <ul> <li><strong>The R code for optimization of XGBoost as well as feature selection with VSURF and classification of the whole dataset.</strong></li> </ul> <p> </p> <ul> <li><strong>Segmentation evaluation metrics.</strong></li> </ul> <p>A csv file with the data sued to compute the Area Fit Index for each approach.</p> <ul> <li><strong>Morphological zones of Ouagadougou as created by Grippa et al. 2017 a shp format.</strong></li> </ul>
Figure 4 in Microhabitat partitioning of closely related Sarawak (Malaysian Borneo) frog species previously assigned to the genus Hylarana (Amphibia: Anura)
Figure 4. NMDS configuration showing ecological groupings from microhabitat characteristics of Sarawak frogs. Each point represents a species: Hba = Pulchrana baramica (N = 62 individuals), Hg = Pulcharana glandulosa (N = 10 individuals), Hsig = Pulcharana signata (N = 26 individuals), Hp = Pulcharana picturata (N = 27 individuals, Hra = Chalcorana raniceps (N = 112 individuals), He = Hylarana erythraea (N = 46 individuals), and Oh = Odorrana hosii (N = 21 individuals).
Figure 2 in Microhabitat partitioning of closely related Sarawak (Malaysian Borneo) frog species previously assigned to the genus Hylarana (Amphibia: Anura)
Figure 2. Dendrogram of Morisita's similarity resulting from average linkage clustering using the unweighted pair-group (UPGMA) method on data based on counts of individuals of frogs' species associated with habitats and microhabitats.
Figure 3 in Microhabitat partitioning of closely related Sarawak (Malaysian Borneo) frog species previously assigned to the genus Hylarana (Amphibia: Anura)
Figure 3. Final coordinate dimension (FDC) 1(A) and 2(B) of NMDS (PROXSCAL) of microhabitat characteristics of Sarawak frogs.
Figure 5 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 5. Station-wise variation in the 0–500 m column integrated mesozooplankton abundance/density and biomass in the central (a) and western (b) Bay of Bengal during spring intermonsoon.
Figure 6 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 6. Depth-wise variation in the number of zooplankton groups at each station in the central (a) and western (b) Bay of Bengal during spring intermonsoon.
Figure 1 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 1. Map of the sampling site in the Bay of Bengal. Stations CB1 to CB5 are located along the central (88°E) and WB1 to WB4 along the western margin of the bay.
Figure 13 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 13. Multivariate cluster analysis of the data of all 129 copepod species combined from all the stations and depths in the central and western bay using the 30% cut-off level of Bray–Curtis similarity. Cluster/Group I are assemblages mostly from the mixed layer (M) and thermocline (T) from central and western transects. Group II comprises assemblages found between the thermocline and 500 m and Group III includes only a few species found exclusively from 200–300 m depth at stations CB3–CB5.
Figure 4 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 4. Vertical profiles of day (D) and night (N) zooplankton biovolume from multinet tows in the western Bay of Bengal during spring intermonsoon. ng: negligible biovolume; NO DATA is where the net failed to open/close. *At WB3, medusae (100 mL 100 m–3) and at WB4 salps (200 mL 100 m–3) were observed at the surface during the day.
Figure 9 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 9. Vertical distribution of abundance (log number 100 m–3) of the major copepod species in the central Bay of Bengal during spring intermonsoon.
Figure 3 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 3. Vertical profiles of day (D) and night (N) zooplankton biovolume from multinet tows in the central Bay of Bengal during spring intermonsoon. ng: Negligible biovolume; NO DATA is where the net failed to open/close. *Swarms of medusae were observed at CB3 (their biovolume 90 mL 100 m–3) and CB4 (200 mL 100 m–3) at the surface at night.
Figure 8 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 8. Vertical distribution of the various types (orders) of copepods in the central (a) and western (b) Bay of Bengal during the spring intermonsoon. The percentages at every depth are averages from 5 stations in the central and 4 stations in the western bay. Data are unavailable at 300–500 m in the central bay due to negligible abundance.
Figure 12 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 12. Variation in multivariate dispersion (MVDISP) indices between different depth strata (9 stations data combined) and between the central and western transects in the Bay of Bengal.
Figure 11 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 11. Variation in Shannon diversity (H'), species richness (d), and evenness (J') of copepods in different depth strata in the upper 500 m of the central (a) and western (b) Bay of Bengal.
Figure 4 in The partitioning of temporal movement patterns of breeding red-crowned crane (Grus japonensis) induced by temperature
Figure 4. Multiple comparisons of the moving distance among the four RCC breeding subseasons (M: mating, B: brooding, W: wading, G: growing).
Figure 3 in The partitioning of temporal movement patterns of breeding red-crowned crane (Grus japonensis) induced by temperature
Figure 3. Principal component analysis for the climatic variables and the moving distance. The first axis (PC1) explains 31.44% of total variation, and the second axis (PC2) accounts for 22.44% of total variation.
Fig. 5 in Niche partitioning in two syntopic mudskipper species (Teleostei: Gobiidae: Oxudercinae) in a Singapore mangrove
Fig. 5. Abiotic characterisation of the demarcated permanent plots (n = 18): (a) two-dimensional MDS on canopy cover, sediment organic content and sediment particle sizes, (b) mean canopy cover and (c) mean organic content of the sediment. sig.—significant; n.s.—not significant; error bars—standard error.
Fig. 1 in Niche partitioning in two syntopic mudskipper species (Teleostei: Gobiidae: Oxudercinae) in a Singapore mangrove
Fig. 1. Plot fidelity exhibited by a Periophthalmus walailakae individual monitored over four spring tides—(a) relative positions of concavities in permanent plot, (b) 30 August 2010: individual in burrow A, (c) 12 September 2010: individual in burrow B, (d) 27 September 2010: individual in burrow C, (e) 11 October 2010: individual in burrow B.
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