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Fig. 6 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 6. Weighted usable area (WUA) in the upper and lower reach of the riacho São Pedro, for the three fish species.

Fig. 4 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 4. Habitat suitability curves for depth (m), mean water column velocity (m/s) and type of substrate for the three fish species in the riacho São Pedro. Substrate types are; 1- large boulder, 2- boulder, 3- cobble, 4- gravel, 5- fine gravel, 6- sand and 7- vegetation.

Fig. 3 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 3. Frequencies of habitat availability by classes of depth (m), mean velocity (m/s) and type of substrate (1- large boulder; 2- boulder; 3- cobble; 4- gravel; 5- fine gravel; 6- sand and 7- vegetation), obtained by direct survey in the riacho São Pedro.

Fig. 1 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 1. Map of the rio Guandu basin, with indication of the studied reach in riacho São Pedro. Solid dash = small substation of CEDAE (Water and Sewer Treatment Works of Rio de Janeiro State). WTP: Water Treatment Plant.

Fig. 2 in Application of the physical habitat simulation for fish species to assess environmental flows in an Atlantic Forest Stream in South-eastern Brazil

Fig. 2. Isometric view of the cross-sections in the two target reaches of the riacho São Pedro. Grey color indicates water; solid line indicates the contour of the cross-section (in wet areas and banks).

opencc-by-4.0Oct 2019View details →

Figure 1 in Applicability of the vital dyes neutral red and fluorescein diacetate to differentiate between alive and dead non-copepod zooplankton

Figure 1. Intensity of staining of the Black Sea zooplankton with neutral red (NR) and fluorescein diacetate (FDA). 1 (FDA), 2 (NR) – Penilia avirostris; 3, 4 (FDA), 5, 6 (NR) – Pleopis polyphemoides; 7, 8 (FDA), 9 (NR) – Evadne spinifera; 10 (FDA), 11 (NR) – Pseudevadne tergestina; 12, 13 (FDA), 14, 15 (NR) – Cirripedia nauplii; 16 (NR), 17 (FDA) – Rotifera; 18, 19 (FDA), 20, 21 (NR) – Polychaeta larvae; 22, 23 (FDA), 24 (NR) – Decapoda larvae; 25 (NR), 26 (FDA) – Pisces ova; 27 (NR) – Pisces larvae; 28 (FDA), 29 (NR) – Parasagitta setosa; 30 (FDA), 31 (NR) – Oikopleura dioica; 32, 33 (FDA), 34 (NR) – Noctiluca sсintillans; 35 (NR) – Hydromedusae; 36 (NR), 37 (FDA) – Bivalvia larvae; 38, 41 (NR), 39, 40 (FDA) – Gastropoda larvae.

Figure 1 in Methods of application of salicylic acid as attenuator of salt stress in cherry tomato

Figure 1. Air temperature (maximum and minimum) and mean relative air humidity observed in the internal area of the greenhouse during the experimental period.

Figure 2 in Methods of application of salicylic acid as attenuator of salt stress in cherry tomato

Figure 2. Two-dimensional projection of the scores of the principal components for the factors salinity levels (S) and methods of application of salicylic acid (M) (A) and the variables analyzed (B) in the first two principal components (PC and PC ).

Figure 3 in Exogenous application of bio-stimulant and growth retardant improved the productivity of cotton cultivars under different planting arrangement

Figure 3. Influence of MLE and MC on bolls per plant of cotton genotypes cultivated under different planting arrangement in Multan (a) and Shujabad (b).

Figure 2 in Exogenous application of bio-stimulant and growth retardant improved the productivity of cotton cultivars under different planting arrangement

Figure 2. Influence of MLE and MC on flowers per plant of cotton genotypes cultivated under different planting arrangement in Multan (a) and Shujabad (b).

Figure 1 in Exogenous application of bio-stimulant and growth retardant improved the productivity of cotton cultivars under different planting arrangement

Figure 1. Influence of MLE and MC on squares per plant of cotton genotypes cultivated under different planting arrangement in Multan (a) and Shujabad (b).

Figure 3 in Influence of foliar application of glycinebetaine on Tagetes erecta L yield cultivated under salinity conditions

Figure 3. Effect of NaCl stress and glycinebetaine on total phenols content in Tagetes erecta L leaves. C = control; S1 = NaCl, 100 mM; S2 = NaCl, 150 mM; GB = Glycinebetaine, 200 mM.Values are means ± S.D (n=6). The means with the different letters are statistically different from the others based on Duncan's multiple range test at P = 0.05.

Figure 1 in Influence of foliar application of glycinebetaine on Tagetes erecta L yield cultivated under salinity conditions

Figure 1. Effect of NaCl stress and glycinebetaine on relative water content (RWC) and membrane stability index (MSI) in Tagetes erecta L leaves. C = control; S1 = NaCl, 100 mM; S2 = NaCl, 150 mM; GB = Glycinebetaine, 200 mM. Values are means ± S.D (n=6). The means with the different letters are statistically different from the others based on Duncan's multiple range test at P = 0.05.

Figure 4 in Carangoides bartholomaei (Cuvier, 1833) stomach: a source of aspartic proteases for industrial and biotechnological applications

Figure 4. Yield of Nile tilapia collagen extraction using different methods.ASC (Acid Soluble Collagen), PSC/S (Pepsin Soluble Collagen obtained using porcine commercial pepsin) and PSC/PE (Pepsin Soluble Collagen obtained using PE). Different superscript letters mean statistical differences between extraction methods (p<0.05).

Figure 2 in Carangoides bartholomaei (Cuvier, 1833) stomach: a source of aspartic proteases for industrial and biotechnological applications

Figure 2. pH and temperature effect on PE acidic proteases. (A) optimal pH; (B) optimal temperature; (C) pH stability; (D) temperature stability.

opencc-by-4.0Jul 2024View details →

Decentralized Applications Network Structure

This dataset encompasses the network structure of decentralized applications (dApps) mainly deployed in the Ethereum blockchain and other platforms such as Binance, Optimism, Polygon, Astar, Shiden, and Thundercore. Each dApp's network structure is represented through a CSV which includes the following information: File: The name of the Solidity file Source_Contract: The name of the contract that calls the Target contract. Target_Contract: The name of the contract that is called by the Source contract. Source_Function: The name of the function that calls the Target function. Target_Function: The name of the function called by the Source function. Chain: The chain of function calls.

Figure 5 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management

Figure 5. Illustration of the linear relationships between fetal age and growth measurements of biparietal diameter (BPD: top graph), thoracic diameter (TD: middle graph) and thoracic circumference (TC: bottom graph) in belugas.

Figure 4 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management

Figure 4. Comparisons of regression curves of TL growth during the first (●) and second half (○) of gestation (top graph) and the early (●), mid (○) and late (▲) pregnancy (bottom graph). The slopes of the regression lines for first half of gestation (F = 63.31, P <0.0001, df1 = 1, df2 = 33) and for early (F = 50.05, P <0.0001, df1 = 1, df2 = 37) and mid pregnancy (F = 135.04, P <0.0001, df1 = 1, df2 = 32) were different than those for the second half of gestation and late pregnancy, respectively. Note that the animals double in length during late pregnancy (315–473 d).

Figure 3 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management

Figure 3. Individual growth rate data from three animals (Animal 1, 2, 3). Regression line slopes during the first two-thirds of pregnancy (top graph) were similar (F = 0.48, P = 0.62, df1 = 2, df2 =18), while regression slopes where different (F = 15.13, P = 0.03, df1 = 2, df2 =3) from the second half to term. Animal 1 (▲) did not have any TL data beyond the first half of gestation so TL length data were used from the farthest in gestation and then again at term. Note that while growth rates were similar during the first two-thirds of pregnancy, fetuses were already different in size when initially detected.

Figure 2 in Development of predictive models for determining fetal age-at-length in belugas (Delphinapterus leucas) and their application toward in situ and ex situ population management

Figure 2. Fetal growth curve comparison illustrating different growth rates resulting in wide range in estimated gestation length as compared to known gestation length determined in this study. Data from Heide-Jørgensen and Teilmann (1994; dotted line) predicts a gestation length of 310 d for a 150 cm calf and similar to our study used a 2nd order polynomial regression to describe their data. Kleinenberg et al. ([1964] 1969: dashed line) developed a curve of the average monthly embryo/fetal growth. They did not provide the curve, only the predicted age at TL, which we then used to fit to a growth curve, which predicts 150 cm calf as 338 d.