To identify putative biomarkers of porcine spermatogonial stem cells (pSSCs), total RNA sequencing (RNA-seq) analysis was performed on 5- and 180-day-old porcine testes and on pSSC colonies that were established under low temperature culture conditions as reported previously. In total, 10,184 genes were selected using Cufflink software, followed by a logarithm and quantile normalization of the pairwise scatter plot. The correlation rates of pSSCs compared to 5- and 180-day-old testes were 0.869 and 0.529, respectively and that between 5- and 180-day-old testes was 0.580. Hierarchical clustering data revealed that gene expression patterns of pSSCs were similar to 5-day-old testis. By applying a differential expression filter of four fold or greater, 607 genes were identified between pSSCs and 5-day-old testis, and 2118 genes were identified between the 5- and 180-day-old testes. Among these differentially expressed genes, 293 genes were upregulated and 314 genes were downregulated in the 5-day-old testis compared to pSSCs, and 1106 genes were upregulated and 1012 genes were downregulated in the 180-day-old testis compared to the 5-day-old testis. The following genes upregulated in pSSCs compared to 5-day-old testes were selected for additional analysis: matrix metallopeptidase 9 (MMP9), matrix metallopeptidase 1 (MMP1), glutathione peroxidase 1 (GPX1), chemokine receptor 1 (CCR1), insulin-like growth factor binding protein 3 (IGFBP3), CD14, CD209, and Kruppel-like factor 9 (KLF9). Expression levels of these genes were evaluated in pSSCs and in 5- and 180-day-old porcine testes. In addition, immunohistochemistry analysis confirmed their germ cell-specific expression in 5- and 180-day-old testes. These finding may not only be useful in facilitating the enrichment and sorting of porcine spermatogonia, but may also be useful in the study of the early stages of spermatogenic meiosis.

A within-species trade-off between growth rates and lifespan has been observed across different taxa of trees, however, there is some uncertainty whether this trade-off also applies to shade-intolerant tree species. The main objective of this study was to investigate the relationships between radial growth, tree size and lifespan of shade-intolerant mountain pines. For 200 dead standing mountain pines (Pinus montana) located along gradients of aspect, slope steepness and elevation in the Swiss National Park, radial annual growth rates and lifespan were reconstructed. While early growth (i.e. mean tree-ring width over the first 50 years) correlated positively with diameter at the time of tree death, a negative correlation resulted with lifespan, i.e. rapidly growing mountain pines face a trade-off between reaching a large diameter at the cost of early tree death. Slowly growing mountain pines may reach a large diameter and a long lifespan, but risk to die young at a small size. Early gro...

The "Iris Flower Visualization using Python" project is a data science project that focuses on exploring and visualizing the famous Iris flower dataset. The Iris dataset is a well-known dataset in the field of machine learning and data science, containing measurements of four features (sepal length, sepal width, petal length, and petal width) for three different species of Iris flowers (Setosa, Versicolor, and Virginica).

In this project, Python is used as the primary programming language along with popular libraries such as pandas, matplotlib, seaborn, and plotly. The project aims to provide a comprehensive visual analysis of the Iris dataset, allowing users to gain insights into the relationships between the different features and the distinct characteristics of each Iris species.

The project begins by loading the Iris dataset into a pandas DataFrame, followed by data preprocessing and cleaning if necessary. Various visualization techniques are then applied to showcase the dataset's characteristics and patterns. The project includes the following visualizations:

1. Scatter Plot: Visualizes the relationship between two features, such as sepal length and sepal width, using points on a 2D plane. Different species are represented by different colors or markers, allowing for easy differentiation.

2. Pair Plot: Displays pairwise relationships between all features in the dataset. This matrix of scatter plots provides a quick overview of the relationships and distributions of the features.

3. Andrews Curves: Represents each sample as a curve, with the shape of the curve representing the corresponding Iris species. This visualization technique allows for the identification of distinct patterns and separability between species.

4. Parallel Coordinates: Plots each feature on a separate vertical axis and connects the values for each data sample using lines. This visualization technique helps in understanding the relative importance and range of each feature for different species.

5. 3D Scatter Plot: Creates a 3D plot with three features represented on the x, y, and z axes. This visualization allows for a more comprehensive understanding of the relationships between multiple features simultaneously.

Throughout the project, appropriate labels, titles, and color schemes are used to enhance the visualizations' interpretability. The interactive nature of some visualizations, such as the 3D Scatter Plot, allows users to rotate and zoom in on the plot for a more detailed examination.

The "Iris Flower Visualization using Python" project serves as an excellent example of how data visualization techniques can be applied to gain insights and understand the characteristics of a dataset. It provides a foundation for further analysis and exploration of the Iris dataset or similar datasets in the field of data science and machine learning.

Scatter plots of all pairwise combinations of test conditions

Graph theory is useful for estimating time-dependent model parameters via weighted least-squares using interferometric synthetic aperture radar (InSAR) data. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence graph. The edge-vertex incidence matrix and the normalized edge Laplacian matrix are factors in the covariance matrix for the pair-wise data. Using empirical measures of residual scatter in the pair-wise observations, we estimate the variance at each epoch by inverting the covariance of the pair-wise data. We evaluate the rank deficiency of the corresponding least-squares problem via the edge-vertex incidence matrix. We implement our method in a MATLAB software package called GraphTreeTA available on GitHub (https://github.com/feigl/gipht). We apply temporal adjustment to the data set described in Lu et al. (2005) at Okmok volcano, Alaska, which erupted most recently in 1997 and 2008. The data set contains 44 differential volumetric changes and uncertainties estimated from interferograms between 1997 and 2004. Estimates show that approximately half of the magma volume lost during the 1997 eruption was recovered by the summer of 2003. Between June 2002 and September 2003, the estimated rate of volumetric increase is (6.2 +/- 0.6) x 10^6 m^3/yr. Our preferred model provides a reasonable fit that is compatible with viscoelastic relaxation in the five years following the 1997 eruption. Although we demonstrate the approach using volumetric rates of change, our formulation in terms of incidence graphs applies to any quantity derived from pair-wise differences, such as wrapped phase or wrapped residuals. Date of final oral examination: 05/19/2016 This thesis is approved by the following members of the Final Oral Committee: Kurt L. Feigl, Professor, Geoscience Michael Cardiff, Assistant Professor, Geoscience Clifford H. Thurber, Vilas Distinguished Professor, Geoscience

Figure S1 - Distribution of SNPs of the 65K SNP array on the Nile tilapia genome. Figure S2 - Pairwise scatter plots of the first six principal components from the PCA of the genome-wide SNP data obtained from the 65K SNP array. Figure 3 - Fraction of the variance accounted for by the first 40 PCs.Table S1 - Details of the seven previously identified sex-associated markers included on the 65K Nile tilapia SNP array.File S1 - Genome position and probes for all SNPs included on the 65K SNP array.

Additional file 2: Supplementary Figure S1. Structure analysis in the spring wheat association-mapping panel. a) The STRUCTURE analysis showed four hypothetical subpopulations represented by different colors. b) First two components (PC1 and PC2) of a principal component analysis of the spring wheat accessions color coded by breeding program (adapted from Zhang, et al. [45]). c) Neighbor-joining phylogenetic tree showing the subpopulations corresponding to the structure analysis. Supplementary Figure S2. Quantile-quantile (QQ) plots of the observed and the expected p values of the GWAS model. a) QQ-plot for seedling resistance (infection type -IT- for the races Yr19-71 and Yr20-161); b) QQ-plot for Adult plant resistance (Disease Severity and area under disease progress curve –AUDPC-). Supplementary Figure S3. Linkage disequilibrium (LD) decay over physical distance. The scatter plots showing pairwise SNP markers LD r2 value as a function of inter-marker physical distances (Mbp) of (a) 1B chromosome; (b) 2A chromosome; (c) 3A chromosome; (d) 3B chromosome; (e) 5B chromosome; (f) 7A chromosome. The red curve represents the model fit to LD decay. The blue dashed line represents the specific critical r2 value beyond which LD is likely due to linkage. The green dashed line represents the confidence interval for the quantitative trait loci regions in which LD r2 = critical r2 value.