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Fig. 2 in Fig. 8 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 2. Mean (± 95% confidence intervals) defence score of male (M) and female (F) Red-backed Shrikes in relation to interspecific territory overlapping. (BW –) – pairs breeding in areas beyond the breeding territories of the associated species (Barred Warbler); (BW +) – pairs breeding within the boundaries of associate species.

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Fig. 1 in Fig. 8 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 1. Mean (± 95% confidence intervals) defence score of male (M) and female (F) Barred Warblers in relation to interspecific territory overlapping. (RBS –) – pairs breeding in areas beyond the breeding territories of the associated species (Red–backed Shrike); (RBS +) – pairs breeding within the boundaries of the associated species.

Fig. 7 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 7. in vitro detection of arα and arβ expression during ovarian tissue culture within 24 hours. The serial time course of mRNA expression was screened within 24 hours. mRNA expression patterns of (A) arα and (B) arβ within 24 hours in L-15 medium tissue culture without treatment. Quantitative mRNA expression data are presented as the mean ± SD, and statistically significant differences were determined by one-way ANOVA and LSD post hoc tests (p <0.05).

Fig. 6 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 6. in vivo mRNA expression of fshr following AR's agonist and antagonist treatments. Ovarian tissue was collected from female eels. (A) Controls, n = 4; SPH, n = 4; SPH + FLUT, n = 4. (B) Controls, n = 4; MT, n = 7; MT + FLUT, n = 4. The mRNA expression data from female eels' ovaries are presented as the mean ± SD, and statistically significant differences were determined via one-way ANOVA and LSD post hoc tests (p <0.05).

Fig. 3 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 3. The ovarian composition of female eel undergoing hormonal induction in three stages: IA, IB, and IC (the grey tint from light to dark). Stage was categorized as the characteristics for follicle stage calculation. GSI was measured and is shown in each individual. The ovary composition is shown in each percentage bar. The stage composition is shown among female eels in (A) the control group, (B) weekly SPH injection for three weeks and (C) weekly SPH + MT injection for three weeks.

Fig. 1 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 1. Histological comparison of ovarian development among the control and three weekly SPH- and SPH + MT- injected groups. Histological analyses are shown in controls (A, GSI = 0.42%; B, GSI = 0.46%; C, GSI = 0.5%; D, GSI = 0.75%), weekly SPH injection for three weeks (E, GSI = 0.63%; F, GSI = 1.02%; G, GSI = 1.07%) and weekly SPH + MT for three weeks (H, GSI = 0.93%; I, GSI = 1.34%; J, GSI = 1.38%; K, GSI = 1.41%). Stage IA follicles are labeled as A; Stage IB follicles are labeled as B; Stage IC follicles are labeled as C. Ovarian tissue was sampled 72 hours after the third injection. Sections underwent HE staining. 10-fold magnified LM images were obtained by digital camera photography. Scale bar = 100 μm.

Fig. 8 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 8. in vitro detection of arα and arβ expression during ovarian tissue culture with and without hormone treatment. mRNA expression was detected after FSH, MT, or FSH + MT treatments. ara expression is shown after (A) 1 and (B) 12 hours of hormonal treatments; arβ expression is shown after (C) 1 and (D) 12 hours of hormonal treatments. Relative mRNA expression data are presented as mean ± SD, and statistically significant differences were determined by one-way ANOVA and LSD post hoc tests (p <0.05).

Fig. 5 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 5. Correlations between gene (arα, arβ, and fshr) expression and GSI. Correlations between gene expression and GSI are displayed. (A, B, and C) are correlations between fshr expression and GSI. (D, E and F) are correlations between arα expression and GSI. (G, H, and I) are correlations between arβ expression and GSI. The three groups displayed are the control (A, D, and G; n = 6), weekly SPH injection for three weeks (B, E and H; n = 9) and weekly SPH+MT injection for three weeks (C, F and I; n = 6). The correlation between mRNA expression and previtellogenic ovary growth condition was analyzed using Spearman's rho correlations. GSI is plotted against the mRNA expression level. p <0.05 indicates significant correlation, and r-value represents the positive or negative correlation coefficient.

Fig. 2 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 2. Calculation of follicle stage among female eels undergoing hormonal induction of ovary development. (A) Ovarian development was demonstrated by GSI, and GSI percentage was calculated as mean ± SD (control, n = 4; SPH, n = 3 and SPH + MT, n = 4). Stage IA, IB, and IC follicles were categorized as the characteristics for follicle stage calculation among female eels in the control, weekly SPH-injected, and weekly SPH + MT-injected groups. Each stage calculation is displayed in (B) stage IA follicles, (C) stage IB follicles, and (D) stage IC follicles. Significant differences are compared using one-way ANOVA and LSD post hoc tests; p <0.05.

Fig. 4 in Androgenic Modulation in the Primary Ovarian Growth of the Japanese eel, .

Fig. 4. in vivo mRNA expression of arα, arβ, and fshr after weekly hormone injection for three weeks. Relative mRNA expression of (A) arα, (B) arβ, and (C) fshr among the control (n = 6), SPH (n = 9) and SPH + MT (n = 6) groups. The mRNA of 18S rRNA was used as the internal standard for relative mRNA normalized quantification. The mRNA expression data from female eels' ovaries were collected and calculated as mean ± SD. Statistically significant differences are identified by one-way ANOVA and LSD post hoc tests. p <0.05 is considered significant.

opencc-by-4.0Jul 2024View details →

Рис. 3. Оценка окраски меΛанином первостепенных маховых P6–P10 чайки КумΛиена (баΛΛы по критерию ИнгоΛфссона) Fig. 3. Primary Pattern Score of Kumlien's gull assessed using the Ingolfsson criteria (1970) in The first documented record of the Kumlien's gull Larus glaucoides kumlieni Brewster, 1883 in Russia

Рис. 3. Оценка окраски меΛанином первостепенных маховых P6–P10 чайки КумΛиена (баΛΛы по критерию ИнгоΛфссона) Fig. 3. Primary Pattern Score of Kumlien's gull assessed using the Ingolfsson criteria (1970)

opencc-by-4.0Dec 2018View details →

Fig. 3 in Ectoparasites are unlikely to be a primary cause of population declines of bent-winged bats in south-eastern Australia

Fig. 3. Ventral view of idiosoma of female Spinturnix loricata. Rectangle in inset photograph denotes position of sternal shield. Note elongate sternal shield (Arrow).

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Fig. 4 in Ectoparasites are unlikely to be a primary cause of population declines of bent-winged bats in south-eastern Australia

Fig. 4. Ventral view of idiosoma of female Spinturnix psi. Rectangle in inset photograph denotes position of sternal shield. Note subcircular sternal shield (Arrow).

opencc-by-4.0Jul 2024View details →

Dataset of Vocabulary in Uzbek Primary Education

This dataset compiles words from two main sources: the "Explanatory Vocabulary of the Uzbek Language" (EDUL) and textbooks used across grades 1-4 in Uzbek primary schools (UPSC). The EDUL.txt file contains 29,190 words meticulously compiled by Urgench State University between 2019 and 2023. Additionally, the UPSC dataset includes 208,204 words extracted from primary school textbooks, sorted into separate files for each grade level. The dataset also identifies specific vocabulary words for each grade, supporting the enhancement of Uzbek language education and facilitating the development of natural language processing tools. <ul> <li> Grade 1 lemma vocabulary: 3,188 words (all new words) </li> <li> Grade 2 lemma vocabulary: 4,630 words (including 1,997 new words) </li> <li> Grade 3 lemma vocabulary: 5,700 words (including 1,578 new words) </li> <li> Grade 4 lemma vocabulary: 6,397 words (including 1,356 new words) </li> </ul> All files are conveniently packaged into a single ZIP archive for easy access and distribution.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 18. Drawing of the time series for males and females of primary stage students and its prediction)

Note that the Tabulated value   equals 3.841 while the Q value is less than   Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions)  of the residues for male females primary stage, in which the residues  value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 17. Drawing of the time series for females of primary stage students and its prediction

It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions)  of the residues for male females primary stage, in which the residues  value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 16. Drawing of the time series for males of primary stage students and its prediction

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 15. Drawing of autocorrelation function and partial correlation of the residues for males and females primary stage students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for males and females of primary stage students, through applying the compute (Ljung-Box Q) to check the model accommodation on the Function level 0.05 so the Q value occurs of males and females of primary stage students: Ljung-Box Q' = 1.10306, With p-value = P(Chi-square(1) > 1.10306) = 0.2936 Note that the Tabulated value   equals 3.841 while the Q value is less than   Tabulated value, so it takes the Null Hypothesis which manifests that the emptiness of the evaluated model out of the contrast in accordance trouble. It's possible to notice that the two parameters functions (Autocorrelation and Partial correlation Functions)  of the residues for male females primary stage, in which the residues  value is located within confidence interval limits, which means the residues series is random and the evaluated model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 13. Drawing of autocorrelation Function and partial correlation of the residues for primary stage males students

It's possible to notice the two parameters functions (Autocorrelation and Partial correlation Functions) of the residues for males primary stage, in which the residues  value is located within the confidence interval limits which means the residues series is random and the Evaluated Model is good and convenient as it is presented.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm- Figure 12. Drawing of autocorrelation function and partial correlation for females primary stage students

We use the Unit Radix Dickey-Fuller Test to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.369693, Statistic Test =1.01829, P-Value=0.9194 We notice from the values above P-Value = 0.9194 on the abstract level of 0.05 which leads to accepting the Null Hypothesis and refusing the Alternative Hypothesis (Existence of a Radix Unit) implies that the time series is instable. By taking the first difference, we notice that the stability of the time series has been achieved. See Figure 11.