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opencc-by-4.0Feb 2020View details →

Measurement of Absolute Retinal Blood Flow Using a Laser Doppler Velocimeter Combined with Adaptive Optics

Data set of measurements related to the following: Purpose: Development and validation of an absolute laser Doppler velocimeter (LDV) based on an adaptive optical fundus camera which provides simultaneously high definition images of the fundus vessels and absolute maximal red blood cells (RBCs) velocity in order to calculate the absolute retinal blood flow. Methods: This new absolute laser Doppler velocimeter is combined with the adaptive optics fundus camera (rtx1, Imagine Eyes©,Orsay, France) outside its optical wavefront correction path. A 4 seconds recording includes 40 images, each synchronized with two Doppler shift power spectra. Image analysis provides the vessel diameter close to the probing beam and the velocity of the RBCs in the vessels are extracted from the Doppler spectral analysis. Combination of those values gives an average of the absolute retinal blood flow. An in vitro experiment consisting of latex microspheres flowing in water through a glass-capillary to simulate a blood vessel and in vivo measurements on six healthy human retinal venous junctions were done to assess the device. Results: In the in vitro experiment, the calculated flow varied between 1.75 μl/min and 25.9 μl/min and was highly correlated (r2 = 0.995) with the imposed flow by a syringe pump. In the in vivo experiment, the error between the flow in the parent vessel and the sum of the flow in the daughter vessels was between −25% and 17% (mean±sd −2 ± 17%). Retinal blood flow in the main temporal retinal veins of healthy subjects varied between 1.3 μL/min and 28.7 μL/min Conclusion: This adaptive optics LDV prototype (aoLDV) allows the measurement of absolute retinal blood flow derived from the retinal vessel diameter and the maximum RBCs velocity in that vessel.

Figure 6 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105

Figure 6 Phylogenetic relationships of the subgenera of Bombus from the Bayesian Inference tree (Fig. 2); nearly all of them are well supported by posterior probabilities.

Figure 4 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105

Figure 4 Estimated phylogeny of the same samples using both new sequences and sequences from Cameron et al. (2007), based on five combined gene sequences (mitochondrial gene 16S rRNA, nuclear genes Opsin, ArgK, EF-1α, and PEPCK) analyzed by Maximum Likelihood. Values on the branches are the bootstrap values, (C) represents Cameron et al.'s species, and "BG" represents our species. The bold font indicates species from both datasets that did not cluster together into monophyletic clades.

opencc-zeroJan 2021View details →

Supplementary material 1 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105

Key to the 26 species of the genus Bombus

Figure 2 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105

Figure 2 Estimated phylogeny of Bombus based on six combined gene sequences (mitochondrial genes 16S rRNA and COI, nuclear genes Opsin, ArgK, EF-1α, and PEPCK) analyzed by Bayesian Inference and Maximum Likelihood. Subgeneric clades are noted at the right of the figure, values above branches are posterior probabilities (BI), values below branches are bootstrap values (ML). Species in bold font were collected by the authors in China and a black spot indicates species that were not included in the previous phylogeny of Bombus of Cameron et al. (2007). The outgroups are at the top of the tree. Abbreviations: SF, short-faced clade; LF, long-faced clade. Subgenera that were synonymized are in parentheses.

Figure 3 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105

Figure 3 Estimated phylogeny of Bombus based on six combined gene sequences (mitochondrial genes 16S rRNA and COI, nuclear genes Opsin, ArgK, EF-1α, and PEPCK) analyzed by Maximum Parsimony. Subgeneric clades are noted at the right of the figure and values on branches are the bootstrap values. Species in bold font were collected in China and a black spot indicates species that were not included in the phylogeny of Bombus of Cameron et al. (2007). The outgroups are at the top of the tree. Abbreviations: SF, short-faced clade; LF, long-faced clade. Subgenera that were synonymized are in parentheses.

Figure 5 from: Wang L-H, Liu S, Tang Y-J, Chen Y-P, Wu J, Li J-L (2020) Using the combined gene approach and multiple analytical methods to improve the phylogeny and classification of Bombus (Hymenoptera, Apidae) in China. ZooKeys 1007: 1-21. https://doi.org/10.3897/zookeys.1007.34105

Figure 5 Estimated phylogeny of Bombus based on six combined gene sequences (mitochondrial genes 16S rRNA and COI, nuclear genes Opsin, ArgK, EF-1α, and PEPCK) analyzed by Neighbor Joining. Subgeneric clades are noted at the right of the Figure, and values on branches are the bootstrap values of NJ. Species in bold font were collected in China and a black spot indicates species that were not included in the phylogeny of Bombus of Cameron et al. (2007). The outgroups are at the bottom of the tree. Subgenera that were synonymized are in parentheses.

opencc-zeroDec 2016View details →

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Data from: High invasion potential of Hydrilla verticillata in the Americas predicted using ecological niche modeling combined with genetic data

Ecological niche modeling is an effective tool to characterize the spatial distribution of suitable areas for species, and it is especially useful for predicting the potential distribution of invasive species. The widespread submerged plant Hydrilla verticillata (hydrilla) has an obvious phylogeographical pattern: Four genetic lineages occupy distinct regions in native range, and only one lineage invades the Americas. Here, we aimed to evaluate climatic niche conservatism of hydrilla in North America at the intraspecific level and explore its invasion potential in the Americas by comparing climatic niches in a phylogenetic context. Niche shift was found in the invasion process of hydrilla in North America, which is probably mainly attributed to high levels of somatic mutation. Dramatic changes in range expansion in the Americas were predicted in the situation of all four genetic lineages invading the Americas or future climatic changes, especially in South America; this suggests that there is a high invasion potential of hydrilla in the Americas. Our findings provide useful information for the management of hydrilla in the Americas and give an example of exploring intraspecific climatic niche to better understand species invasion.

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Data from: Boundary strength analysis: combining colour pattern geometry and coloured patch visual properties for use in predicting behaviour and fitness

1.Colour patterns are used by many species to make decisions that ultimately affect their Darwinian fitness. Colour patterns consist of a mosaic of patches that differ in geometry and visual properties. Although traditionally pattern geometry and colour patch visual properties are analysed separately, these components are likely to work together as a functional unit. Despite this, the combined effect of patch visual properties, patch geometry, and the effects of the patch boundaries on animal visual systems, behaviour and fitness are relatively unexplored. 2.Here we describe Boundary Strength Analysis (BSA), a novel way to combine the geometry of the edges (boundaries among the patch classes) with the receptor noise estimate (ΔS) of the intensity of the edges. The method is based upon known properties of vertebrate and invertebrate retinas. The mean and SD of ΔS (mΔS, sΔS) of a colour pattern can be obtained by weighting each edge class ΔS by its length, separately for chromatic and achromatic ΔS. This assumes those colour patterns, or parts of the patterns used in signalling, with larger mΔS and sΔS are more stimulating and hence more salient to the viewers. BSA can be used to examine both colour patterns and visual backgrounds. 3.BSA was successful in assessing the estimated conspicuousness of colour pattern variants in two species, guppies (Poecilia reticulata) and Gouldian finches (Erythrura gouldiae), both polymorphic for patch colour, luminance and geometry. 3D representations of the ΔS of patch edges (Fort Diagrams) of both species show that there is little or negative geometric correspondence between the chromatic and achromatic edges. All individuals have mΔS > 1.5 for both chromatic and achromatic measures, indicating the high within‐pattern contrast expected for display signals. In contrast from what one would expect from sexual selection, all guppies have mΔS less than expected from random contacts between all pairs of patch colour/luminance classes. The correlation between chromatic and luminance ΔS is negative in both species but zero when correlating all possible kinds of edges between the colours of each species and morph indicating non‐random colour geometry. 4.The pattern difference between chromatic and achromatic edges in both species reveals the possibility that chromatic and achromatic edges could function differently. The smaller than random expected mΔS values in guppies suggests an anti‐predator function because guppies are never found without predators. Moreover, mΔS could vary with predation intensity within and among species. BSA can be applied to any colour pattern used in intraspecific and interspecific behaviour. Seven predictions and four questions about colour patterns are presented. 5.In species which are very convex, both chromatic and luminance mΔS change with viewing angle; geometry of signalling is as important as signal geometry.

opencc-zeroDec 2017View details →

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Data from: Non-destructive geographical traceability of sea cucumber (Apostichopus japonicus) using near infrared spectroscopy combined with chemometric methods

Sea cucumber is the major tonic seafood worldwide, and geographical origin traceability is an important part of its quality and safety control. In this work, a non-destructive method for origin traceability of sea cucumber (Apostichopus japonicus) from northern China Sea and East China Sea using near infrared spectroscopy (NIRS) and multivariate analysis methods was proposed. Total fat contents of 189 fresh sea cucumber samples were determined and partial least squares (PLS) regression was used to establish the quantitative NIRS model. The ordered predictors selection (OPS) algorithm was performed to select feasible wavelength regions for the construction of PLS and identification models. The identification model was developed by the principal component analysis combined with Mahalanobis distance (PCA-MD) and Scaling to the first range algorithms. In the test set of the optimum PLS models, the root mean square errors of prediction (RMSEP) was 0.45, and correlation coefficients (R2) was 0.90. The correct classification rates of 100% were obtained both in identification calibration model and test model. The overall results indicated that NIRS method combined with chemometric analysis was a suitable tool for origin traceability and identification of fresh sea cucumber samples from nine origins in China.

opencc-zeroDec 2016View details →

Figure 4 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997

Figure 4 The anti-proliferative assay (MTT) was used to examine the EMT-6/P and EMT-6/CPR cell lines' sensitivity to cisplatin at varying doses.

Figure 16 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997

Figure 16 Effect of TQ (25 mg/kg), silymarin (50 mg/kg), their combinations, cisplatin (0.7 mg/kg), and control group on serum ALT level measured by (IU/L).

Figure 10 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997

Figure 10 Folds increase in caspase-3 activity and apoptosis induction in concentrations of TQ (10 µM), silymarin (10 µM), and their combination in EMT-6/CPR cell line.

Figure 15 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997

Figure 15 Changes of tumor sizes in EMT-6/CPR after dissection at day 10 in all groups compared to each other, n = 7.

Figure 14 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997

Figure 14 A plot of change in average tumor size (mm³) vs. time in (days) of treatment in EMT-6/CPR cell line.

Figure 21 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997

Figure 21 Showed that there is no significant difference in serum creatinine between the healthy group and other treatment groups except cisplatin group. This graph was obtained by GraphPad Prism.

Figure 9 from: Hamed RA, Talib WH (2024) Targeting cisplatin resistance in breast cancer using a combination of Thymoquinone and Silymarin: an in vitro and in vivo study. Pharmacia 71: 1-19. https://doi.org/10.3897/pharmacia.71.e117997

Figure 9 Demonstrates concentration of Caspase-3 in ng/ml in each treatment group along with the control group.