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The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 14. Drawing of autocorrelation function and partial correlation of the residues for primary stage males students

After diagnosing and evaluating the models, the accommodating and the sufficiency of the models must be checked for primary stage female 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 primary stage female students: Ljung-Box Q' = 0.966626, With p-value = P(Chi-square(1) > 0.966626) = 0.3255 However, the Tabulated value   equals 3.841 whilst the Q value is less than Tabulated value, so it accepts the Null Hypothesis which indicates the emptiness of the evaluated model out of the contrast accordance trouble. It's possible to notice the two parameters functions (Autocorrelation and Partial Correlation Functions)  of the residues for  females primary stage students, 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 shown.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 9. Drawing the time series for males and females primary stage students

The Unit Radix Dickey-Fuller Test is used to ensure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.736458 , Statistic Test = 0.380545 , P-Value  = 0.794 We notice from the values above P-Value = 0.794on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis ( The Nonexistence of a Radix Unit) implies that the time series is stable. Figure (9) represents the time series of females and males in the primary stage students.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 7. Drawing the time chain for Females primary stage students after the first difference

We use the Unit Radix Dickey-Fuller Test to assert the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.47242, Statistic Test = 1.22797, P-Value = 0.9445 We notice from the values above P-Value =0.9445 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, it is noticed that the stability of the time series has been accomplished. See Figure 7.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 6. Drawing of auto correlation function and partial correlation for females primary stage students

The instability of the time series is noticed and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to affirm the stability according to the figure 6.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 4. Drawing of autocorrelation function and partial correlation for males primary stage students

We get to notice the stability of the time series, and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure (4).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 5. Drawing the time series for primary stage female students

We use the Unit Radix Dickey-Fuller Test to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.233403, Statistic Test = 0.125769, P-Value = 0.6405 The values above P-Value = 0.6405 is noted on the abstract level of 0.05 which leads to refusing the Null Hypothesis and accepting the Alternative Hypothesis (The Nonexistence of a Radix Unit) implies that the time series is stable. Figure 5 represents the Time series of Female Primary Stage Students.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 8. Drawing of autocorrelation function and partial correlation for Females primary stage students

The stability of the time series is observed and to be more accurate we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to assure the stability according to the figure (8).

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 3. Drawing the time series for Males primary stage students after getting the first difference

The Unit Radix Dickey-Fuller Test is used to assure the series’ stability. The results are: Dickey-Fuller Test Estimated Value = 0.276151, Statistic Test = 0.87796, P-Value = 0.8984 We get to notice from the values above P-Value = 0.8984 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, it is observed that the stability of the Time Series has been accomplished . See figure 3.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 2. Drawing of autocorrelation function and partial correlation for males primary stage

We get to notice the instability of the time series, and to be more precise we draw each (Autocorrelation Function) ACF, and (Partial Autocorrelation Function) PACF in a row to ensure the stability according to the figure 2.

The Prediction of the Rate of the Dropout of the Primary Schools Students by Using the Genetic Algorithm-Figure 1. Drawing the time series for males primary stage

After collecting all the students’ dropout proportion for both males and females in the primary stage, the first step of the Box-Jenkins is to draw the time chain data to understand the chain's attitude.

opencc-by-4.0Jan 2019View details →

Large-scale and fine-grained phenological stage annotation of herbarium specimens datasets

This upload is constituted of four datasets of specimens from American herbaria covering different levels of information precision and different floras - from temperate to equatorial. Three of these datasets consist of selected specimens from herbaria located in different geographic and environmental regions. Each specimen of these three datasets was annotated with the following fields: family, genus, species name, fertile / non-fertile, presence / absence of flower(s), presence / absence of fruit(s). The resulting dataset was composed of 163,233 herbarium specimens belonging to 7,782 species, 1,906 genera, and 236 families. Specimens were annotated as “fertile” if any reproductive structures were present, such as sporangia (ferns), cones (gymnosperms), flowers, or fruits (angiosperms). Non-fertile specimens were those that lacked any reproductive structures. The fourth dataset consists of 20,371 herbarium specimens from 11 genera in the sunflower family (Asteraceae). The main difference in this dataset is that it is annotated with fine-grained phenophase scores rather than presence/absence attributes (see description below). Each of these datasets is described below: <ul> <li> NEVP: this dataset of New England vascular plant (NEVP) specimens was produced by members of the Consortium of Northeastern Herbaria. The dataset comprises 42,658 digitized specimens that belong to 1,375 species and come from several North American institutions. Most of the specimens in this dataset are from the north-temperate region of the northeastern United States. </li> <li> FSU: this dataset was produced by the Florida State University's Robert K. Godfrey Herbarium (FSU), a collection that focuses on northern Florida and the U.S. Southeast Coastal Plain, one of North America's biodiversity hotspots. This dataset contains 54,263 digitized herbarium specimen records that belong to 3,870 species, making it the taxonomically richest dataset in this study. Most species in this dataset grow under subtropical or warm temperate conditions in the southeastern region of the United States. </li> <li> CAY: this dataset comes from the IRD’s Herbarium of French Guiana (CAY). CAY is dedicated to the Guayana Shield flora, with a strong focus on tropical tree species. This dataset is composed of 66,312 herbarium specimens that belong to 3,024 species. All digitized specimens of this herbarium are accessible online. Most specimens were collected in the tropical rainforests of French Guiana, with the remaining specimens coming mostly from Suriname and Guyana. </li> <li> PHENO: this dataset includes 20,371 herbarium specimens of 139 species in the Asteraceae produced in a study of phenological trends in the U.S. Southeast Coastal Plain. The dataset is composed of specimen records from 57 herbaria. Each recorded specimen was annotated for quartile percentages (0, 25, 50, 75, or 100%) of (i) closed buds, (ii) buds transformed into flowers, and (iii) fruits. According to the distribution of these three categories for each specimen, a phenophase code was computed. </li> </ul>   Datasets format These datasets are grouped in 3 tasks: <ol> <li>fertility detection</li> <li>flowers and/or fruit detection</li> <li>phenophase classification</li> </ol> The first 2 tasks are carried on the first 3 previous datasets and thus are based on the same set of images, unlike the third task which has its own disjoint set of images. This is why the dataset is presented into two separated files, one for each set of images. Fertility detection & flower/fruit detection These tasks are contained into the herbarium_fertility_annotations.zip archive. It consists of 3 files: <ul> <li>metadata.csv: general information about all the herbarium specimens for these tasks <ul> <li>id: specimen identifier</li> <li>collection: which of NEVP, FSU or CAY does the specimen come from</li> <li>herbarium: institution of origin of the specimen, especially for NEVP collection</li> <li>clade, family, genus, species: classification of the specimen</li> <li>URL: URL of the scan</li> </ul> </li> <li>fertility_task.csv: specific information regarding the fertility detection task <ul> <li>id: specimen identifier</li> <li>is_fertile: True if the specimen has an expression of fertility, False otherwise</li> <li>train_test_set: which subset does the specimen belong to; possible values are: train, random_test, species_test and herbarium_test</li> </ul> </li> <li>flower_fruit_task.csv: specific information regarding the flower/fruit detection task <ul> <li>id: specimen identifier, note that in this case not all the specimen described in metadata.csv are included in this task</li> <li>has_flower: True if the specimen has at least one flower, False otherwise</li> <li>has_fruit: True if the specimen has at least one fruit, False otherwise</li> <li>train_test_set: which subset does the specimen belong to; possible values are: train, random_test, species_test and herbarium_test</li> </ul> </li> </ul> Phenophase classification These tasks are contained into the herbarium_asteraceae_phenophase_annotations.zip archive. It consists of a single file: <ul> <li>annotations.csv: <ul> <li>id: specimen identifier</li> <li>URL: URL of the scan</li> <li>genus: genus of the specimen</li> <li>phenophase: integer from 1 to 9 describing the phenophase of the specimen</li> <li>train_test_set: which subset does the specimen belong to; possible values are: train and test</li> </ul> </li> </ul>   Additional ressources More information can be found in the related paper: Lorieul, T., K. D. Pearson, E. R. Ellwood, H. Goëau, J.-F. Molino, P. W.  Sweeney, J. M. Yost, J. Sachs, E. Mata-Montero, G. Nelson, P. S. Soltis, P. Bonnet, and A. Joly. 2019. Toward a large-scale and deep phenological stage annotation of  herbarium specimens: Case studies from temperate, tropical, and equatorial floras. Applications in Plant Sciences 7(3): e1233. For an example of usage of these datasets as well as a baseline, see: <a href="http://doi.org/10.5281/zenodo.2549996">http://doi.org/10.5281/zenodo.2549996</a>

opencc-by-4.0Apr 2019View details →

Ultrasound Vevo 2100 data on ascending and abdominal aneurysms in ApoE-deficient mice - baseline and early stage

This dataset contains raw data of ultrasound measurements taken of the ascending and abdominal aorta of Ang II-infused mice. Data were taken at baseline (prior to pump implantation) and at an early stage of disease development. Data can be openend with the Vevo 2100 analysis software provided by Fujifilm.

Integrated analysis of miRNA landscape and cellular networking pathways in stage-specific prostate cancer

Figure S1. Heat map of miRNA-microarray. Expression of miRNAs differentially expressed and assessed in microarray analysis of RNA isolated from four different cell lines of prostate cancer (LNCaP, PC3, DU145, 22Rv1) compared with control cell line of prostate cancer (PrEc). The red color depicts high and green color showed a lower level of expression at p value <0.01. Figure S2A. Drug resistance by drug efflux. The internetworking relationship with miRNAs and plasma membrane protein P-glycoprotein (Pgp-plasma membrane protein). The miRNA-130a and miR-181a are downregulated in this pathway (green color) and linked with Pg and BCRP (breast cancer resistant protein). miR-133a and miR379 are involved in regulating the expression of MRP2. While miR-298, miR27a, miR331-5p and miR-130a are involved in regulating the expression of poly-glycoprotein (P-gp). Figure S2B. Epithelial mesenchymal transition pathway. The miR-200b was downregulated during early-stage prostate cancer and was involved in inhibiting Jagged-2 (JAG2), one of the NOTCH ligands. Figure S2C. Adipogenesis pathway. The expression of miR-326 was upregulated during the metastatic stage of prostate cancer, (red color) and involved in regulating the expression of CCAT/enhancer binding protein β (C/EBPβ), directly linked with the nuclear hormone receptor peroxisome proliferator-activated receptor-gamma (PPAR-γ). Figure S2D. Bone metamorphosis signaling pathway. The expression of miR-140, miR-145, and miR-155 was upregulated, along with miR-140 and miR-145 were associated with modulation of gene SOX9, and miR-155 was involved in regulating the gene FOXO3A. Figure S2E. Th1 pathway. In this pathway, the expression of miR-146a showed a higher level of expression (red color) and modulated the expression of NF-κB signaling. Figure S2F. Th1 and Th2 pathway. In this pathway, the expression of miR-146a showed a lower level of expression (green color) and may modulate the expression of NF-κB signaling. Table S1. List of differentially expressed miRNAs derived from LNCaP cells lines statistically significant as P < 0.001. Table S2. List of differentially expressed miRNAs derived from PC3 cells lines statistically significant as P < 0.001. Table S3. List of differentially expressed miRNAs derived from DU145 cells lines statistically significant as P < 0.001. Table S4. List of differentially expressed miRNAs derived from 22Rv1 cells lines statistically significant as P < 0.001.

Fig. 7 in A new species of Macrostemum Kolenati, 1859 from Brazilian Amazon and the immature stages of M. brasiliense (Fischer, 1970) (Trichoptera: Hydropsychidae)

Fig. 7. Macrostemum brasiliense (Fischer, 1970), larva. A. Larval habitus. B. Thoracic nota, dorsal view. C. Prosternum sclerite. D1. Foreleg. D2. Midleg. D3. Hindleg. E. Sternum VIII, IX and anal prolegs, ventral view. Scale bars: A = 1 mm; B = 0.5 mm; D1–D3, E = 0.2 mm.

Fig. 6 in A new species of Macrostemum Kolenati, 1859 from Brazilian Amazon and the immature stages of M. brasiliense (Fischer, 1970) (Trichoptera: Hydropsychidae)

Fig. 6. Macrostemum brasiliense (Fischer, 1970), larva. A. Head, dorsal view. B. Head, ventral view. C. Head, left lateral view. D. Stridulatory lines of genae, ventral view. E. Labrum, dorsal view. F. Mandibles, dorsal view. Scale bars = 0.2 mm.

Fig. 3 in A new species of Macrostemum Kolenati, 1859 from Brazilian Amazon and the immature stages of M. brasiliense (Fischer, 1970) (Trichoptera: Hydropsychidae)

Fig. 3. Macrostemum araca sp. nov., male genitalia. A. Lateral view. B. Dorsal view. C. Ventral view. D–E. Phallus, lateral view. F–G. Phallus, dorsal view. H–I. Phallus, ventral view. Scale bars: A–D = 0.2 mm; E–I = 0.1 mm.

Fig. 2 in A new species of Macrostemum Kolenati, 1859 from Brazilian Amazon and the immature stages of M. brasiliense (Fischer, 1970) (Trichoptera: Hydropsychidae)

Fig. 2. Macrostemum araca sp. nov., adult male. A. Venation of the forewing (holotype). B. Venation of the hindwing (holotype). Scale bar = 2 mm.

Fig. 5 in A new species of Macrostemum Kolenati, 1859 from Brazilian Amazon and the immature stages of M. brasiliense (Fischer, 1970) (Trichoptera: Hydropsychidae)

Fig. 5. Collection sites of Macrostemum araca sp. nov. and Macrostemum brasiliense (Fischer, 1970) and general views of some of the sampled streams and rivers. A. A map showing the collection sites of M. araca sp. nov. and M. brasiliense in the Amazon and Atlantic Forest, respectively. B. Córrego (stream) Venerando, São Paulo, collection site of M. brasiliense. – C–E. collection sites of M. araca sp. nov. C. Igarapé da (stream) Cobra, Amazonas. D. Igarapé (stream) do Jabuti, Amazonas. E. Rio (river) Aracá, Amazonas.

Fig. 4 in A new species of Macrostemum Kolenati, 1859 from Brazilian Amazon and the immature stages of M. brasiliense (Fischer, 1970) (Trichoptera: Hydropsychidae)

Fig. 4. Macrostemum araca sp. nov., female genitalia. A. Lateral view. B. Dorsal view. C. Ventral view. D. Vaginal apparatus. Scale bars: A–C = 0.2 mm; D = 0.1 mm.

Fig. 1 in A new species of Macrostemum Kolenati, 1859 from Brazilian Amazon and the immature stages of M. brasiliense (Fischer, 1970) (Trichoptera: Hydropsychidae)

Fig. 1. Macrostemum araca sp. nov., adult male. A. Dorsal habitus. B–C. Head, dorsal and frontal views. D. Forewing (holotype). E. Forewing, variation in the apical area (paratype). F. Hindwing (holotype). G. Process of sternite V. Scale bars: A, D–F = 2 mm; B–C = 0.5 mm.