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Many studies have examined rules governing two aspects of protein structures: short segments and proteins' structural domains. Nevertheless, the organization and nature of the conformational space of segments with intermediate length between short segments and domains remain unclear. Conformational spaces of intermediate length segments probably differ from those of short segments. We investigated the identification and characterization of the boundary(s) between peptide-like (short segment) and protein-like (long segment) distributions. We generated ensembles embedded in globular proteins comprising segments 10–50 residues long. We explored the relationships between the conformational distribution of segments and their lengths, and also protein structural classes using principal component analysis based on the intra-segment Cα-Cα atomic distances.
Our statistical analyses of segment conformations and length revealed critical dual transitions in their conformational distribution with segments derived from all four structural classes. Dual transitions were identified with the intermediate phase between the short segments and domains. Consequently, protein segment universes were categorized. i) Short segments (10–22 residues) showed a distribution with a high frequency of secondary structure clusters. ii) Medium segments (23–26 residues) showed a distribution corresponding to an intermediate state of transitions. iii) Long segments (27–50 residues) showed a distribution converging on one huge cluster containing compact conformations with a smaller radius of gyration. This distribution reflects the protein structures' organization and protein domains' origin. Three major conformational components (radius of gyration, structural symmetry with respect to the N-terminal and C-terminal halves, and single-turn/two-turn structure) well define most of the segment universes. Furthermore, we identified several conformational components that were unique to each structural class. Those characteristics suggest that protein segment conformation is described by compositions of the three common structural variables with large contributions and specific structural variables with small contributions.
The present results of the analyses of four protein structural classes show the universal role of three major components as segment conformational descriptors. The obtained perspectives of distribution changes related to the segment lengths using the three key components suggest both the adequacy and the possibility of further progress on the prediction strategies used in the recent de novo structure-prediction methods.
Vast amounts of three-dimensional (3D) protein data from structural genomic studies and other individual efforts have been added to our knowledge, thereby enhancing our understanding of protein structures. To date, only two extremes of protein structural data have been studied. One extreme includes local features of proteins: those of short protein segments, typically of 10 residues long or less. The other extreme includes global features of proteins: protein folds or structural domains.
Regarding the short protein segments, abundant research examples exist partly because of the existence of variations of methods to analyze the local features of proteins. Various measures, such as RMSDs after structural superposition [1-3], Cα-Cα atomic distances coupled with the torsion angles [4,5], dihedral angles [6], and so on have been used to define the conformational similarity of protein segments. Different clustering techniques, such as k-means clustering [7,8], hierarchical methods [9], competitive learning [6,10], and other methods [11], have been used to describe the organization of the segments' conformational space. The abundance of research results in this area is also partly attributable to various applications of the clustering results of the short segments. A set of representatives from the resulting clusters are often called structural building blocks (SBBs). Even when using different procedures, clustering resolutions of SBBs can be categorized into only a few levels depending mainly on their respective applications, such as structural modeling, verification, comparison, and prediction [6,12]. The most dominant cluster of the short segments, which is common in all studies, corresponds to α-helices, whereas the variability of β-strands is observed at the high-resolution clustering. Regarding global features of proteins, understanding of their organization and analysis of the protein-fold (or structural domain) space studies are progressing well.
As reviewed recently [13], both hierarchical and continuous aspects of fold space have been realized. Regarding hierarchical classification, widely used databases such as CATH [14] and SCOP [15] have been constructed. Other databases such as FSSP [16] and VAST [17] have been developed. They are based on continuous measurements of protein structural similarity. Several studies have provided insights into the nature of fold space. Holm and Sander first described the conformational distribution of protein folds in a fold universe with multi-dimensional scaling methods based on an all-on-all comparison using the Dali program [18]. Using the same measurement, Hou et al. [19] showed visual representations of the protein fold universe and identified three major components which characterize the fold space: secondary structure compositions, chain topologies, and the protein domain size.
Compared to these two extremes, limited surveys have been done on the conformational space of medium size segments between protein short segments and folds. Specifically, supersecondary structures such as α-hairpin, βαβ-unit, and β-hairpin are typical structural motifs of medium size; those motifs have been analyzed. For example, Salem et al. reported that most superfolds contain a higher proportion of their α-helical or β-strand residues in one such supersecondary structure [20]. Szustakowski et al. built a dictionary of supersecondary structures [21]. Kurgan and Kedarisetti studied regularity among twilight zone protein structures at the level of the sequence segments that correspond to the secondary structure fragments of varying length [22]. However, the organization and statistical properties of the whole conformational space of medium-to-long segments remain unclear. Statistical and systematic analyses should be done on the 'segment universe' from short to long lengths to bridge this gap.
Our previous study identified structural clusters and visualized the uneven distribution of short segments in the conformational spaces of 6–22 residues, where known and novel secondary-structure motifs are distributed as isolated clusters [23]. The general features of the segment distribution were consistent for these lengths. However, the question we sought to answer is: Do spaces of long segments differ from those of short segments? In this study, we explore the relationships between the conformational distribution of segments and their length: 10–50 residues, thereby providing a global view of a 'segment universe' and showing critical dual changes (i.e. dual transitions) of the distribution shape in the conformational space of short to long segments. The critical changes might reflect changes of the protein structures' organization. Therefore, the present results suggest the adequacy and the possibility of further progress of the hierarchical treatment used in the recent de novo structure prediction methods. Furthermore, by comparing conformational components among structural classes (i.e., all-α, all-β, α/β, and α+β), we demonstrate the specificity and generality of protein fold classes.
Transitions of segment distribution: short, medium, and long segments
The coverage of segments in cluster(s) was calculated as described below. A densely populated region in the 3D principal component analysis (PCA) space was defined as a cluster [23]. Given a density threshold, the segments are classifiable into two groups: those in regions of a density larger than the threshold and those outside the regions. The coverage of segments in clusters is defined as a ratio of the segments in the regions to all the segments.
Figure 1a portrays the coverage of segments versus the density threshold for the conformational spaces of 10, 20, 30, 40, and 50 residue lengths. The coverage curves exhibited a transition from concave shapes for short lengths (10 and 20 residues long) to convex ones for long lengths (30, 40, and 50 residues long). Notably, the differences of coverage at a density of 0.2 or less show a transition between the short and long segments. For instance, at a density of 0.1, the coverage is only 16.3% for 10 residues, although the coverage is greater than 50% for 30 residues. In addition, at a density of 0.01, the coverage for 10 residues is 45.6%, although coverage for 30 residues is 91.9%. These quantitatively indicate that the density gradient of the conformational space changes markedly with segment elongation.
Figure 1. Coverage versus the density threshold. Coverage of segments in clusters versus the density threshold for segment lengths of 10, 20, 30, 40, and 50 residues (A), and those of 21–30 residues (B). Density is presented on a logarithmic scale.
Further analyses of the coverage graphs between the short and long segments were meaningful to discover the boundaries of distribution changes. Figure 1b shows coverage curves for lengths of 21–30 residues. The dual and critical transitions, with an intermediate phase for segment lengths of 23–26 residues, can be recognized clearly, as presented in Fig. 1b. The transitions at intermediate length are also characterized by the distributional alteration of the radius of gyration of segments in the populated region with density of 0.10–0.35 (Fig. 2). To adjust the effect of different segment lengths, we defined here the relative score (F_Rg) of the radius of gyration for a segment as (Rg(i,j) - Min Rg(j))/(Max Rg(j) - Min Rg(j)), where Rg(i,j) denotes the radius of gyration of a segment i with length j, and where Max Rg(j) and Min Rg(j) represent the maximal and minimal radius of gyration of the entire segment dataset with length j. Based on these observations, the segment length is categorized into the following three groups: short (10–22 residues), medium (23–26 residues), and long (27–50 residues). We were able to show that changes in the density gradient are associated with distributional alterations in the segment universe in subsequent analyses of visualizing the 3D PCA space. In fact, the difference in the coverage between lengths of 10 and 30 residues was attributable to the increase in the volume for the most populated region, as discussed below. The typical global images of segment universes from the three categories are depicted in Fig. 3d. The segment universes here were generated by the first three principal components derived from the entire segment dataset: PCall1, PCall2, and PCall3 (see Methods).
Figure 2. Probability density function of the F_Rg score. Distribution of F_Rg score of segments in the region with density from 0.10 (magenta in Fig. 3) to 0.35 (red). Distributions for segment lengths of 21, 23, 25, 27, and 29 residues are shown.
Figure 3. 3D representation of segment universes. A 3D representation of short (A, 10 residues), medium (B, 26 residues), and long (C, 30 residues) segment universes. The 3D representations were generated using the first three PCall axes. They were expressed by four iso-density contours: 0.005 (blue), 0.01 (orange), 0.1 (magenta), and 0.35 (red). The PC axis numbers (1, 2, and 3) are given near the axes. Front and side views of images are shown, respectively, in the upper and lower areas of the figure. D: Schematic diagram of the relationship between properties of the cluster(s) in the segment universe and segment length. The properties of the cluster(s) in the segment conformational space changed according to increased segment length.
Short length (10–22 residues long)
The conformational space of short segments showed a distribution with an extreme density gradient that originated from secondary structure clusters: α-helix and β-strand clusters were discriminated using a density of 0.01 (shown in orange in Fig. 3a). Between the lengths of 10 and 20 residues, spatial arrangements of the segment distribution, especially for α-helical, β-strand, and β-hairpin clusters, were conserved in short conformational spaces. The highly populated core of the α-helix cluster exhibited a density of 0.1 (shown as magenta in Fig. 3a), consisting of completed α-helical segments. The surrounding area of the central region consisted of various types of helical conformations including helix-capping motifs [12]. The central region of the β-strand cluster consisted of fully extended segments that originated mainly from β-sheets and loop regions. The β-hairpin conformations were separated into several clusters at a density of 0.005. Then they were discriminated using the coordinate c2 along PCall2 (see Methods for the definition of c2). The β-hairpin clusters showed a symmetrical relationship related to the N-terminal and C-terminal halves. They were arranged symmetrically around an edge of segment universes of short length.
Medium length (23–26 residues long)
The segment distribution for medium lengths differed from that for short lengths. The distributional change from short to medium lengths is characterized using a diminishing β-strand cluster and a growing α-helix cluster. The overall distribution was shortened in the direction of PCall1, and enlarged in the direction of PCall2 and PCall3. In the segment universe of 26 residues, the α-helix cluster was discriminated using a density of 0.1 (magenta in Fig. 3b). Interestingly, the shape of the α-helix cluster was a ring (designated as a helix ring cluster). The helix ring cluster that is specific to the medium-length universe consisted not only of the extended α-helices but also of various α-helical conformations, as presented in the inset of Fig. 3b. This cluster included conformations that had originated mainly from all-α, α/β, and α +β proteins (Fig. 4a). The average content of the α-helical residues per segment in the helix ring cluster was about 50% (Fig. 4b); 24.9% of all segments were included within the helix ring cluster. The long-α-helical segments, whose conformation was not compact, were located near the origin of the conformational space (red in Fig. 3b). In contrast, the α-hairpin conformations with a small radius of gyration were located on the opposite side of the position on PCall1. The various α-hairpin conformations with the different turn positions were located symmetrically along PCall2. For medium lengths, the β-strand clusters were diminished because long extended β-strands are rarely found in proteins. The β-hairpin conformations were located symmetrically along PCall2, although the cluster separation of β-hairpins was not clear in medium lengths.
Figure 4. Relationships between segment properties and their length in the populated regions. Upper figures show that the percentages of structural classes from which segments in the clusters with density of 0.10–0.35 (A) and 0.35–1.0 (C) were derived. Lower figures show the percentages of structural properties of the segments in the clusters with density of 0.10–0.35 (B) and 0.35–1.0 (D). The magenta and green lines in lower figures represent the percentage of helical and strand segments in the cluster. A helical segment is defined as the segment with the rate of α-helical residues >= 0.5. A strand segment is defined as the segment with the rate of β-strand residues >= 0.5. The percentages of helix (H) and sheet (E), that are determined using the DSSP program [40], are also represented by the red and blue lines, respectively.
Long length (27–50 residues long)
Conformational spaces for the long lengths were further shortened in the direction of PCall1 and enlarged in that of PCall3. The segment distribution converged on a large populated region that exhibited a density of 0.1 (magenta in Fig. 3c) in the conformational space. With a length of 30 residues, there were two clusters consisting of compact segments and long α-helical segments, respectively, with densities of 0.35 (red in Fig. 3c) in the populated region. The emergence of the compact-segment cluster was attributable to an increase in various types of segments with a small radius of gyration (see inset of Fig. 3c). Various types of conformations are mixed up in the compact-segment cluster. The α-hairpins are derived mainly from all-α proteins. The compact β-sheet structures are derived mainly from all-β proteins. Compact conformations of other types are derived from α/β and α +β proteins (Fig. 4c). About 2% of all segments were included in the compact-segment cluster for 27-residue length. In contrast, long α-helical segments with a large radius of gyration were located on the opposite side of the cluster of the compact segments along the PCall1 axis. For lengths greater than 30 residues, the proportion of the conformations with a small radius of gyration in the compact-segment cluster increased rapidly to around 14% for 50-residue lengths. Those conformations were derived from various folds (Fig. 4c). The supersecondary structures, such as βαβ units and β-sheets, were included in the compact-segment cluster (Fig. 4d).
Contribution ratios of principal axes
Distributional alterations were observed associated with the changes of segment length. For principal component analyses, the contribution ratios (see Methods for the contribution ratios) of the principal components (i.e. PC axes) to the entire distribution indicate how well the PC axes can cover the variation in the original data. Figure 5 portrays contribution ratios of the first five PC axes (PCall1 – PCall5) for segment lengths of 10–50 residues. Even with a length of 43 residues, the cumulative contribution ratio of the first three PC axes, Q123 (= Q1 + Q2 + Q3), was greater than 60%, although Q123 decreased constantly with increased segment length. Each of Q4 and Q5 was always less than 8%. The contribution ratios for higher-order PC axes than PCall5 did not exceed 5% for the examined segment lengths. Therefore, it is sufficient to use only the first three PC axes (or the first five PC axes occasionally) to explain the original structural variation.
Figure 5. Contribution ratios for the PC axes. Contribution ratios for the first five PC axes for segment lengths of 10–50 residues are shown here. The cumulative contribution ratio for the first three PC axes (Q123, solid line with no symbol) and the individual contribution ratios of PCall1 (Q1, crosses), PCall2 (Q2, asterisks), PCall3 (Q3, squares), PCall4 (Q4, filled squares), and PCall5 (Q5, circles) are shown.
With respect to the individual contribution ratios (Q1-Q3) of the first three PC axes, Q1 was overwhelmingly higher than those of the other PC axes up to 50-residue length (Fig. 5), which indicates that PCall1 is a meaningful and fundamental descriptor for segment conformation. Actually, Q1 decreased rapidly, and Q2 increased in the short segment lengths (i.e. 10–22 residues). Thereafter, both Q1 and Q2 decreased slowly. In addition, Q3 increased gradually with lengths up to 33 residues, with a maximum value of 11.5%.
Investigation of structural properties of conformational axes
An eigenvector was analyzed for each PC axis with a triangle map to elucidate the physical and conformational meaning of the PC axes of the conformational space of the short to long segments. The eigenvector can be regarded as a collective variable to describe the segment conformation. Figure 6 shows triangle maps of the first five PC axes (PCall1 – PCall5) for short (10 residues), medium (26 residues), and long segments (30 residues). The triangle map clearly portrays residue pairs, with large or small deviations of Cα-Cα distances along each PC axis from the average distance <qi>. In the triangle map, positive (red) and negative (blue) areas correspond to residue pairs with mutually inverse deviations. The patterns of red and blue areas are conserved in the universes of short to long segments, indicating that conformational deviations related to the PC axes are conserved among the universes. Figure 7 depicts the conformational changes along the PC axis using colored arrows.
Figure 6. Distance matrices for the PC axes. Distance maps express eigenvectors for the first five PC axes for 10, 26, and 30 residue lengths. Each map shows deviations of Cα-Cα distances along each eigenvector from the mean Cα-Cα distances. The scale bar indicates the relative deviation. The color indicates whether in this particular mode the distances are increasing (red) with respect to the mean value or decreasing (blue). The eigenvector values are scaled by the square root of eigenvalue of the PC axis k, λk1/2. Residue numbers are displayed with horizontal and vertical sides of each triangle map. Two segment conformations were picked up from both ends on each PC axis; they are displayed under each triangle map.
Figure 7. Visualization of collective variables for the first five PC axes of 26 residue lengths. Collective variables (eigenvectors) for the first five PC axes of 26 residue lengths are visualized by the vectors onto Cα atoms of a segment. The vector for the Cα atom of residue i is calculated as ri = Σi ≠ jqijevkijλk1/2, where qij is the vector from residue i to j, evkij is the element of eigenvector of the PC axis k (vk) corresponding to the Cα-Cα pair between residue i and j, and λk is the eigenvalue of the PC axis k. Positive (blue) and negative values (red) are shown for elements of eigenvectors. The reference segment used in this figure is designed to clarify the difference between structural variables.
Actually, PCall1 corresponds to the change of the radius of gyration (Rg). The triangle map for PCall1 has only one positive area, shown as red in Fig. 6, which is located near the residue pairs at the N-terminal and C-terminal sides. This single area indicates that the distant residue pairs in the sequence have a larger conformational deviation along PCall1. The correlation coefficient of the conformational deviation along PCall1 with Rg was greater than 0.9 in segment lengths of 10–50 residues (Fig. 8). The arrows in Fig. 7 point to the center of the segment, which indicates clearly that the conformational changes along PCall1 are involved with expansions or compressions of the conformation. For short lengths, PCall1 also shows a strong correlation with the changes of the segment end-to-end distance (Dend), which is defined as the Cα-Cα distance between the first and last residues of segments. Correlation between PCall1 and Dend slowly weakened with increased segment length: 0.91 for 10 residues, 0.79 for 26 residues, and 0.77 for 30 residues.
Figure 8. Correlation coefficients between conformational deviation along each PC axis and physical indicator. The radius of gyration (Rg) for PCall1, structural symmetry related to the N-terminal and C-terminal halves (Dsim) for PCall2, a simple indicator of β-hairpin (Dmn+mc) and Dmn+mc/end-to-end distance (Dend) for PCall3, and the radius of gyration (midRg) around the midpoint of the segment for PCall4 were used in these analyses. Correlation coefficients were calculated at every 10 residues of 10–50 residues.
The PCall2 correlates to a degree of structural symmetry (Dsym) of a segment with respect to the N-terminal and C-terminal halves. The Dsym is defined as follows: Given a distance matrix for a segment, where element (i,j) is the distance (denoted as rij) between Cα atoms of residue i and j. Then, the degree of structural symmetry is defined as the sum of the squared differences of symmetric elements in a distance matrix for a segment: Dsym = Σ1 ≤ i < j ≤ n (rij - rn-(j-1)n-(i-1))2, where n is the segment length. The triangle map for PCall2 was separated into one positive area (red) and one negative area (blue). The correlation coefficient of the conformational deviation along PCall2 with structural symmetry, Dsym was greater than 0.90 in the segment lengths of 10–50 residues (Fig. 8). Both conformations displayed mirrored symmetry about a plane constructed by PCall1 and PCall3 when two conformations were picked from opposite positions along PCall2. The segment conformations picked up along PCall2 are shown in Figs. 3a–3c.
The PCall3 correlated with a physical indicator that describes a conformational transition between structures with one turn and ones with two turns (PCall3 in Fig. 6). The picked conformations along PCall3 indicate that segregation of a β-hairpin structure exists along with conformational changes by PCall3. We defined the physical indicator (Dmn+mc) of the β-hairpin formation: Dmn+mc is the sum of the norms of two vectors, which were generated by the middle point of the segment for both the N-terminal and C-terminal residues: Dmn+mc = , where and respectively denote the vectors from the midpoint to the N-terminal and C-terminal residues of the segment. Good correlation was found between PCall3 and Dmn+mc (Fig. 8). The correlation coefficient was greater than 0.7 for the 10–50 residues. The triangle map of PCall3 indicated a separation of one positive area (red) and two negative areas (blue). It is noteworthy that the triangle map of PCall3 for short segments differed slightly from those of medium and long segments. A positive area is visible near the residue pair of the N-terminal and C-terminal in the short map, suggesting that PCall3 has a (negative) correlation with Dend. For medium and long lengths, the positive area was close to the center of the triangle map. Therefore, the correlation between PCall3 and Dmn+mc/Dend was necessarily smaller in medium and long lengths.
The triangle map of PCall4 had one negative area and one positive area. The positive area, located at the map center, suggests that PCall4 is correlated with the radius of gyration (midRg) of the middle region of the segment – except for both the N-terminal and C-terminal quarter portions – in the medium and long segments. The respective correlation coefficients for the 26 and 30 residue lengths were 0.73 and 0.72. The PCall4 also has a weak (negative) correlation with Dend. The respective correlation coefficients between PCall4 and Dend for the 26 and 30 residue lengths were -0.45 and -0.42.
We identified no simple physical indicator for conformational changes along PCall5. However, visual inspection from conformations picked along PCall5 suggests that PCall5 is a conformational axis that represents segregated β-sheet structures. Conformations picked up from both ends on PCall5 are depicted in Fig. 6. In the triangle map for PCall5, two positive and two negative areas exist along the diagonal line, which might indicate that PCall5 segregates segment conformations with double turns. The PCβ5 contribution ratio, which was derived from all-β proteins, was higher than that derived from other structural classes, which suggests that PC5 is important for describing the structural variation of β-structures.
Segment universes derived from different structural classes
The segment universes described above are those derived from proteins of the four structural classes. Therefore, decomposition of the universe into four classes is helpful to evaluate the influence of each structural class on the segment universe. To this end, a segment universe was constructed for each structural class separately, and compared the PC axes derived from each universe with those of all segments (i.e., PCall1-PCall3). The first three largest eigenvectors of each structural class were also compared respectively with PCall1, PCall2, and PCall3 to elucidate the structural properties of PC axes derived from each universe.
Figure 9 depicts the contribution ratios of the first three PC axes, PCx1 -PCx3 (x = α, β, α +β, or α/β), in each structural class. The marks on the curves in Fig. 9 indicate that the correlation coefficient (vxi·valli) between PCx1 -PCx3 and PCall1 -PCall3 (i.e., i = 1, 2, 3) is greater than 0.7, which was used here as a threshold of conservation of structural properties. The properties of the first two PC axes corresponding to the PCall1 and PCall2 were highly conserved in all four structural classes. The characteristics of PCall3 were also conserved in all four structural classes, although exceptions were apparent for the 20-residue-long and 10–16-residue-long all-α and all-β classes. Therefore, it is confirmed that the first three PC axes (Rg, symmetry, and one/two turn(s)) are important in almost all cases to describe the conformation of segments embedded in globular proteins.
Figure 9. Contribution ratios for the PC axes for each structural class. Contribution ratios (Q1, crosses; Q2, asterisks; and Q3, squares) of PCx1-PCx3 vs. segment lengths of 10–50 residues for each structural class, where x = α, β, α+β, or α/β. The correlation coefficient between PCxi and PCalli (i = 1, 2, 3) is 0.7 or less if no mark is present at a segment length. For the all-β class, no axis exhibited a correlation coefficient greater than 0.7 up to PCall3 for segment lengths of 10–16 residues.
However, the curves for the contribution ratios of both all-α and all-β classes (see two panels of Fig. 9) differ clearly from those of PCall1 – PCall3 (i.e. Q1 – Q3 in Fig. 5). The Qα1, contribution ratio was always higher than 40%, which indicates that the distribution of the all-α segments has a large deviation with respect to Rg. In contrast, the Qβ1 contribution ratio decreased rapidly with increasing segment length. The value of Qα2 increased moderately with increasing segment length. In contrast, the Qβ2 had a maximum value greater than 20% at a length of 22 residues. This rapid increase of Qβ2 might reflect a typical feature for β-sheet conformations. For PC3, the curves for the contribution ratios of the all-α and all-β classes also mutually differed. Although Qβ3 peaked at a length of 35 residues, Qα3 peaked with a short length, which indicates that the structural variable based on PCall3 is important for β-segments longer than 30 residues. In contrast, the behaviors of the contribution ratios for both α+β and α/β classes along with the segment length resembled each other. They were also similar to Q1-Q3 in Fig. 5 because those structural classes are mixtures of α-helices and β-sheets.
Subsequently, PC axes that were specific for each structural class were examined. For this analysis, the PC axis was defined as a "class-specific" one when a PC axis from a structural class showed no similarity with the first 20 PC axes from the other three structural classes (see Methods). The first 10 PC axes of each class were investigated for the short (10 residues), medium (26 residues), and long (30 residues) segments. Ten class-specific conformational axes were identified and consisted of one (PCβ10) for the short length, eight for the medium, and one (PCα8) for the long. The eight class-specific axes for the medium-length segments are PCα5, PCα8, and PCα10 for all-α, PCβ10 for all-β, PCα+β9 and PCα+β10 for α+β, and PCα/β8 and PCα/β10 for α/β. Four examples out of eight are depicted in Fig. 10. A clear correlation of these PC axes is difficult to discern according to simple physical or structural quantities. Figure 10a shows that the PCα8 describes a structural change of three (both ends and the middle portion) parts of α-segments. The PCα/β8 is related to βαβ motifs, which is the most fundamental structural unit for α/β proteins.
Figure 10. Examples of class-specific conformational axes. Conformational axes specific to structural class for 26-residue segments are shown. Eigenvector maps and conformations picked up from both ends on each PC axis are shown: A for PCα8, B for PCα/β8, C for PCα10, and D for PCβ10. The color contrast of the maps is enhanced to aid viewing. The anti-phase of the blue color is shown in red. Residue numbers are displayed with horizontal and vertical sides of each triangle map. The respective contribution ratios of PCα 8, PCα/β8, PCα10, and PCβ10 were 1.3%, 1.4%, 0.9%, and 0.7%.
Investigation of the protein segment universe is an important subject for bioinformatics. Results of this study show that the segment universe can be categorized naturally into three regimes: short, medium, and long. A main finding of this study is that the three regimes are clearly demarcated by critical changes in the shape of the segment distribution in the conformational space. Preceding studies demonstrated that the average length of α-helix is 14 residues [24] and that for β-strand is five residues [25]. Results of the present study show that transitional segment lengths (22 and 26 residues long) do not coincide with these average lengths. Therefore, a single secondary structure element does not characterize the shape of the segment distribution. The appearance of the medium length regime segregates the segment fold universe into three. The combination of secondary-structure elements is important to characterize not only the medium-length segment universe but also the entire segment fold universe.
Meanwhile, loops, which make up 30% of the protein structures [26], are also expected to take a larger role to form some unique conformations by connecting secondary-structure elements in the medium to the long-length segment universe than short one. The segments in the cluster of the medium to long-length universe tend to contain more loop regions than those of the short segment universe, as shown in Figs. 4b and 4d, and have a wider variety of origins (Figs. 4a and 4c). For example, the segments in the cluster with density of 0.35–1.0 of the universe of 30 residues length are derived from 461 proteins out of all 600 representatives used for this study (see Additional File 1). Longer loops that possess extended conformations are located on the opposite side of the compact-segment cluster along PCall1 in the medium to long segment universe (Figs. 3b and 3c). Instead of discrete clusters, they appear to constitute a rather continuous distribution. Some analyses examine short loops with respect to their completeness [27,28] and elaborate classification [26,29]. In the analysis of short segments, our method also captured some loop conformation classes, such as joint loops connecting two helices, and exposed and extended loops participated in protein-protein interactions [23].
Additional file 1. Origins of segments in the cluster of 30 residue length. Distributions of the origins of segments in the cluster of the universe of 30 residues length are shown.
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A natural boundary was identified, in this study, between the peptide-like and protein-like distributions between the lengths of 23 and 26 residues using actual conformations of protein segments. This observation with respect to the boundary is consistent with the results described by Shen et al. [30], even though they used a sphere-packing model to estimate a minimal domain size of about 20 residues. A recent study by Sawada and Honda [31] also identified a boundary at 10–20 residue length by calculating the structural diversity of segments. They discretized the conformational space using a single-pass clustering method. In contrast, we observed the density distribution to uncover differences of conformational space between short and long segments. The segment conformational space for lengths of 10–22 residues provided a distribution with an extreme density gradient towards the secondary structure, such as the α-helix, β-strand, and β-hairpin clusters, which are expected to belong to the peptide-like conformational regime. This conformational variation reflects that short segments embedded in globular proteins are mainly stabilized by the physicochemical property of the peptide. On the other hand, the segment conformational spaces for lengths of 27 residues or more have a distribution that is dominated by compact segments, which suggests a protein-like distribution (protein-like conformational regime). This distribution arises from the hydrophobic effect imparted by the solvent molecules, which is of great importance for structural stability in long segments derived from globular proteins. If this is the case, our observations support the de novo structure prediction methods, so-called fragment assembling methods, that have been developed recently [32-35]. These approaches are usually based on the prediction of local segment conformations followed by assembly of segments, and are generally used to separate criteria at each step; sequence similarity or secondary structural propensity for the prediction of segment conformations, and non-local energy terms for the assembling step. These strategies used in the de novo prediction methods seems to be consistent with the results shown here. Results of our analyses clearly show such a hierarchical organization of protein structures, and indicate that preparing segment libraries up to around 20 residues long would be helpful for such methods.
These results indicate that the structural meanings for the conformational axes (i.e., the radius of gyration for PCall1, structural symmetry related to the N-terminal and C-terminal halves for PCall2, and a single-turn/two-turn structure for PCall3) are conserved in the different lengths and structural classes. This fact suggests that these conformational components are key structural variables for protein segments. On the other hand, when conformational axes among the four structural classes were compared, we were able to identify several conformational axes that were specific to each structural class, especially in the medium length range. In fact, a distribution change for medium lengths was observed, involving an increase in compact segments. Those segments included supersecondary structures such as α-hairpins, parts of the β-sheets, and βαβ units. These results might be related to the specificity of the structural class or fold of the contents of supersecondary structures [20]. Typical supersecondary structural motifs, α-hairpin, β-hairpin, and βαβ are, respectively, the basic structural units for the all-α, all-β, and α/β proteins. These motifs are often shared within the structural classes. Therefore, the contribution ratios observed for the class-specific conformational axes were high. Class-specific conformational axes were rarely observed in short and long lengths, probably because short segments are too nonspecific and are often shared over different structural classes; long segments are too specific and have very low contribution ratios for conformational axes that are specific for each structural class.
The currently found class-specific conformational axes provide a hint to solve a difficulty in classifying diverse sets of protein structures. Both α/β and α+β classes are known to show a substantial overlap. In the CATH classification, α/β and α+β classes are treated as one structural class as α-β class. Classifying α/β and α+β proteins is sometimes a difficult problem, although several classification [19,36,37] and also prediction [38,39] schemes have been proposed. The present study showed that α/β and α+β classes have similar characteristics of universes, and also have unique ones at the same time. For example, our results show that PCα/β8, whose contribution ratio was 1.4%, was associated only with the βαβ motif. In the α+β class, no axis was strongly correlated with PCα/β8 (see Additional File 2), which is a clear example of the difference in structural variables between α+β and α/β classes originating from class-specific supersecondary structures. Consequently, projecting segments onto a conformational subspace using the axis PCα/β8 could be useful for objectively dividing protein domains of α-β class into α/β and α+β classes. A considerable localization of segments derived from α/β proteins in a PCA subspace is observed (see Additional Files 3 and 4).
Additional file 2. Correlation with the first 10 PC axes of α/β class of the medium (26 residue) segments. Maximal correlation coefficients between the first 10 PC axes of α/β class and PC axes of the other three structural classes are shown.
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Additional file 3. Class-specific region for α/β segments on the PCα/β8-PCα/β3 plane. Distributions of segments of α/β structural class proteins for the medium length are shown.
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Additional file 4. Discrimination of segments from the α/β structural class in the PCα/β8-PCα/β3 plane. Specificity and coverage rates of segments of α/β structural class proteins in the PCα/β8-PCα/β3 plane are presented.
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An effective method must be developed for conformational sampling for de novo prediction methods. The resulting structural variables analyzed in this study would be helpful for additional progress in de novo structure prediction. For example, testing the distribution of segments or models in terms of the degree of symmetry using the descriptor (Dsym) might be useful to verify the completeness of sampling of the conformational space. Using a filtering threshold or function (generally used in fragment assembling methods for selecting proper models) that is tolerant of the radius of gyration might be useful for improving the prediction of all-α proteins because the contribution ratio, Qα1, of PCα 1 corresponding to the radius of gyration (Rg) is larger than those of the other structural classes in the medium and long segments. Consequently, projecting segments of models onto a conformational subspace constructed by PCx (where x = α, β, α/β, α+β, or all) axes might be helpful for filtering out models and assigning a protein to a structural class.
In this study, the dual critical transitions in the protein segment universe from short to long length are shown. Our observations are related to the transitions proposed by the significance of two-stage treatment in de novo structure prediction. Considering the hierarchical organization of a protein segment universe that we have shown, we suggest the efficacy of using the evaluation functions that is secondary-structure-directed for sampling local structures less than 23 residues long. We also suggest the suitability of evaluating protein-like features of models using another function (e.g. Rg) for longer segments. Changing the criteria of filtering for each structural class will enhance the effectiveness of the conformation sampling process. Through these analyses, we have demonstrated that our clustering methodology is useful to identify a distinctive distribution shift of conformational space between short and long segments and that distribution changes depend on structural classes.
Preparing the segment libraries
One representative from each fold group of the SCOP database (ver. 1.63) [15] was chosen to obtain a segment library without a bias of usage of the folds. The representatives cover the four major structural classes (all-α, all-β, α+β, and α/β), because we are interested in and specifically examine characterization of the nature of segments embedded in usual size globular proteins. Small proteins of less than 50 residues and non-single chain proteins with less than 100 residues were excluded, as were membrane proteins. It is expected that those proteins possess different structural properties from those of usual size globular proteins and induce biased results. In all, 600 representatives were used for this study (all-α, 150; all-β, 116; α+β, 219; α/β, 115; see Additional File 5). Dividing the protein structures into segments with a sliding window by one residue along the sequence generated a segment library of arbitrary length. We prepared a segment library for each length of 10–50 residues to generate conformational spaces of short-to-long segments. In such cases, segments with incomplete coordinate data (e.g., having an unusual covalent-bond length or lacking main-chain atoms) were excluded. Furthermore, to elucidate differences among the conformational spaces derived from the four major structural classes, we generated a segment library for each class.
Additional file 5. List of PDB ids used in this study. The PDB and SCOP IDs of proteins used in this study are listed.
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Construction and visualization of conformational space
We previously reported a method for constructing and visualizing the conformational space of protein segments using principal component analysis based on intra-segment Cα-Cα atomic distances [23]. Briefly, atomic distances of all Cα-Cα pairs for each segment in a segment library of an arbitrary length were calculated first. A distance is designated as qi, where i is the index for the Cα-Cα pair, i = 1, ..., n(n - 1)/2, and n is the segment length, as expressed by the number of residues in the segment. Subsequently, a set of eigenvectors and eigenvalues were obtained by diagonalizing a variance-covariance matrix, C, that was calculated as Cij = (<(qi - <qi>)(qj - <qj>)> = <(qiqj - qi<qj>- <qi>qj + <qi><qj>)> = <qiqj>- <qi><qj>- <qi><qj> + <qi><qj> =) <qiqj>- <qi><qj>, where the average <...> is taken over the segments. Two equations, Cvi = λivi and vi·vj = δij, are satisfied. Eigenvectors with larger eigenvalues are more important in the study of the conformational varieties of the segments. Eigenvalues are arranged in descending order: λi > λj if i <j. The contribution ratio of the i-th PCA element (i.e. the i-th eigenvector) to the whole conformational distribution is given as Qi = λi/Σkall λk. The eigenvectors, which are called PCx1, PCx2, PCx3, ...etc., were used as conformational axes to construct a segment conformational space, a PCA space, in which x indicates a segment dataset: x = α, β, α/β, α +β, or all). The indicator "x = all" is given when conformational axes are generated by the whole segment dataset. The origin of the PCA space is set on the average Cα-Cα atomic distances: <q> = [<q1>, <q2>, <q3>, ..., <qn>]. This enables ready comparison of conformational distributions between constructed universes. Any position (i.e. any segment structure) in the PCA space can be expressed using a linear combination of eigenvectors as ck = Σnall (q - <q>)·vk λk1/2, where ck is a coordinate (i.e. projection of q) on the PC axis k. Using the first three eigenvectors (PCx1, PCx2, PCx3), a three-dimensional (3D) PCA space can be constructed.
We defined a vector, r, to express the position of each segment in the 3D PCA space: r = [c1, c2, c3]. After projection of the segments on the 3D PCA space, the distribution of segments in the 3D PCA space was visualized using the following procedure. The 3D space was divided into N bins (total N3 cubes). The bin size was defined as (max [c1] - min [c1])/N, where N = 36, and max [c1] and min [c1] respectively signify the maximum and minimum of the coordinates of the segments along the first principal component axis. The number (i.e. frequency) of segments detected in a cube represents the density (i.e. probability) of segments to be found in the cube. The density of each cube, ρ was normalized by the maximum density, ρmax among the cubes so that the maximal value of normalized density (we call this density in the text) is set to 1 (refer to eq. (3) in [23]). Four levels of contour surfaces (i.e. iso-density surfaces) were depicted to visualize the 3D PCA space. The density values for those surfaces were set respectively as 0.005, 0.01, 0.1, and 0.35.
We also separately constructed the universe for four structural classes to assess differences among their conformational spaces. For this study, we specifically examined the first 10 PC axes of each structural class because the 10 PC axes are more important than the other axes with respect to capturing the differences in the conformational axes. Although the eigenvectors from the same structural class are mutually uncorrelated (i.e., vxi·vxj = 0, where i ≠ j and x = α, β, α/β, or α+β), the eigenvectors from different structural classes might have some correlation (i.e., vxi·vyj ≠ 0, where x ≠ y). The PC axis is defined as the conformational component specific to the structural class when a PC axis from a structural class has no similarity to the first 20 PC axes from the other structural classes with a correlation coefficient > 0.8 (i.e. vxi·vyj > 0.8).
Authors' contributions
This study was conceived and carried out by KI, who also analyzed the results and drafted the manuscript. HT approved the study and participated in the discussion. JH participated in the design and coordination of the study. He also helped to write the manuscript. KT participated in the design and discussions of the study and wrote the manuscript. KI and JH developed the methodology. All authors read and approved the final manuscript.
KI and JH were partly supported by BIRD of Japan Science and Technology Agency (JST).
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text:
@ZachSnow – I've spent most of the afternoon trying to mentally process the experience of #EdCampAwesome. I thought that if I gave enough time to it that something would surface as an overall "theme" of the day. Something that really stuck out…
The reality is, many many (many) things came to mind. I thought about how UnConference style of learning is such an effective style for me. I also thought about all of the incredible tools that were shared throughout the day. Apps, chrome extensions, web 2.0 tools were discusses. Best practices with GAFE, PBL, and PLC's. We're teachers, this is the language we speak. Some of our attendees were even fortunate enough to get to hear the brilliant Mrs. Julia Robinson (aka @Curriculumdiva) talk about assessments and some tools she's created to track index 2.
EdCampAwesome also taught us that, if a major transformer blows in your midsized rural town and knocks out your services district wide, rendering you internet'less, an EdCamp can (and should) survive without really missing a beat. These sessions should be discussion driven, right? So, it turns out, you don't even really HAVE to have internet (although it does help and I hope we don't have to try to do it again next time).
As great as all that stuff is, that's really not what EdCampAwesome was all about for me.
In the end, none of that stuff matters at all without…people.
Without a doubt, the people are what I will take with me from EdCampAwesome. This whole thing is about people and relationships. I love my PLN! I'm so grateful for the support they showed by simply coming and being there (not to mention they are the best and brightest and added so much to the knowledge base of the room). I also met so many new people who I look forward to continuing to learn from.
I have some other things to say, but before I do, let's hear from @StuartBurt.
@StuartBurt: When #EdCampAwesome was a mere twinkle in the eye of Zach and I, we would not have imagined how it would have turned out. If you would have told me in September that over 300 amazing educators would arrive at 8 am on a Saturday morning, I would have never believed you. No way!
Well, I knew that EdCampAwesome would be….Awesome, but had no idea to the reality of this awesomeness. Over 300 educators gave up their Saturday to collaborate, learn, and grow! Thats 300 possible classrooms that are now better off. That doesn't even count those following along on Twitter and colleagues of attendees. In the end, how many students will benefit from this amazing day of collaboration, sharing, and growing? The possibilities of this one day are limitless.
The beauty of an EdCamp is that anyone can share. A first year teacher is on the same level as a 37 year veteran. Expert presenters are not brought in to show off their finely tuned powerpoints and slide decks. From the very start of the day, you could sense the excitement and comfort in the building. First time EdCampers were unsure of what was going to happen but became comfortable with the process in record time. Its learning in its purest form: messy but very gratifying.
Like Zach, I depend on my PLN. I was honored to be in the presence of so many great educators at #EdCampAwesome; a large majority of whom I had never met. I met people that will forever be part of my collective learning team. Together we are never alone in our mission to educate children. As a group we are stronger and better. Thank you for being the awesome in #EdCampAwesome. Together we have set the bar high for future EdCamps.
Lastly, I have a challenge for you. Find the most awesome thing you learned at EdCampAwesome and share it. Whether that sharing is on Twitter, Google Plus, or with a group of colleagues at your home district. EdCampAwesome will continue beyond February 22, 2014. You are awesome! Now go out and spread the awesome to others.
@ZachSnow: Great word Stuart, thanks for your passion for quality education and for being so awesome in general. I can tell the rest of you that EdCampAwesome would not have happened without Stuart.
I've told some of you before that I'm a bit of a dreamer. I can come up with ideas for things all day long. What I am not; however, is a nuts and bolts kind of guy. Stuart is that rare combination of an incredible innovator and knows HOW to make it all happen. I share new ideas with Stuart weekly, it seems. He has never once told me "no, I'm not sure that's possible" or "maybe this is not the best time for that". He's only, always told me "great idea, let's make that happen". He shares ideas with me and I'm just happy to be able to ride along.
As a point of reflection, how much do you empower your students/team/coworkers by simply saying "great idea, let's make that happen"?
Stuart is a great friend and we all get to be better as a result of the things he has done for us…and will continue to do for a lot of us as our TCEA Area 10 Director.
I want to make sure you know the rest of the people who were so important in making EdCampAwesome happen. Each and everyone of these people had very important roles that they all played so well. Make sure you follow them, their voices are important.
A few other people that I want to make sure I thank publically:
Mr. Kevin Worthy – Thank you for being such a phenomenal instructional leader for your district. Hands down, one of the best superintendents in the state of Texas.
Dr. Brent Ringo – Thank you for allowing us to use your building for messy learning. I think his session on admin apps was the 2nd most talked about session of the day (behind @Curriculumdiva's session on assessments…haha).
The Custodial Staff – Thank you ladies for finding us tables and cleaning up after us throughout the day. I had so many people comment on how hard working and pleasant y'all were.
Last, but definitely not least, you…the attendees. This event would mean nothing without your 1.) attendance and 2.) voice. You drive the event. You determine the sessions. You make it memorable.
We are looking forward to EdCampAwesome 2015.
Keep Being Awesome!

subset: commoncrawl


text:
|Cutifani moves to Anglo American
|Wednesday, 09 January 2013 09:35
Mark Cutifani has resigned from his position as chief executive of AngloGold Ashanti to head mining giant Anglo American.
Johannesburg-based AngloGold will conduct a search for a new chief executive, examining both internal and external candidates.
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Thursday, March 15, 2012
© Copyright 2015 Newton Citizen
MONTICELLO -- Middle and high school students at Piedmont Academy recently had the opportunity to hear Gayle Allen speak to them about the dangers and benefits of the internet as well as cellphones.
Allen has been trained by the Georgia Bureau of Investigation on internet crimes against children and teaches classes on internet safety for adults.
Her first point was to guard their identification and think before they post.
According to statistics, one is more likely to be bullied online than become a victim of a predator. Students should never post their full name, birthday, telephone/cell number, address or occupation. They should avoid online surveys and phishing electronic mail.
Another scenario young adults need to be careful about is posting photographs online, she said. Often these photographs have pictures of friends and family, so not only could they put themselves in danger but their loved ones as well. She advised them to be careful when posting so that no identifying background is visible such as a billboard, business sign, car tags and uniforms.
With hyperlinks, surveys, and chain letters, oftentimes a computer virus or spyware is embedded in the email which can hijack the Web browser and cause damage to computers and files, she said. This is also true when companies offer free downloads such as games, ring tones, or screen savers. She advised students to never open an email that he does not recognize the sender or Web address.
For students with Facebook pages, she told them, it is very important to set the privacy setting on their profile pages so only their friends can view their pages.
She told them that a reliable website to visit to report internet crimes is familyinternet.info.
On the topic of cellphones, Allen discussed the dangers of posting inappropriate messages and sending photographs. She made aware to the audience if they were to send an inappropriate picture that the average person would consider offensive and inappropriate, that individual could be charged with a felony, serve five to 20 years in prison and be fined up to $100,000.
Grandcanyon 3 years, 6 months ago
I think its great that someone finally took the time to explain this to our children, So let me say thank you. I find it a shame that the kids must learn the hard way about all these problems. It took my child being cyber bullied by classmates and then it carried over and the school just sat around and did absolutely nothing. Just a shame. I appreciate that someone has finally done this to explain the dangers of this stuff to them.
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More like this story
- NCSO speaks to teenagers about sexting ( November 9, 2010 )
- Piedmont students study physical science ( February 15, 2013 )
- Experts: What is posted online, stays online ( March 14, 2014 )
- Piedmont students compete in TEAMS ( April 18, 2013 )
- NCSO catches alleged Web sex predator ( November 1, 2008 )


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Appointment Announcement - The Revd Susan Johnson
6th July 2009
The Bishop of Ely is pleased to announce the appointment of the Revd Susan Johnson as House for Duty Team Vicar in the Papworth Team Ministry.
Susan is currently House of Duty in the Cwmbran Team Ministry.
Licensing details will follow in due course.
Sarah King (Mrs)
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Categories: Erotica/Erotic Romance/Romance
Publisher: Total-e-bound | Date published: 09/17/2007
With the evidence that psychic detective Angela Simmons has given her, Joyce Carter confronts her inventor husband, Don, with having sex with a number of other women. When he flatly denies it in spite of the photographs Joyce shows him, she flies into a rage and stabs him with a steak knife. There is no question that Don is dead, but is Joyce responsible? In this fast-paced, sexy mystery, Angela uses all of her talents as a psychic as well as her connections from the coroner's office to the FBI to uncover an intricate scheme of industrial espionage and murder in the billion dollar microchip industry. How will Angela's lover Ronda Moore respond when she finds out that her other lover Joan is in one of the incriminating photographs with Don Carter? How will Angela react to Ronda's hopes and fantasies that Angela will punish her for her unfaithfulness with Joan? How can these three lovers deal with betrayal and enjoy sex together? And what happens when Ronda's rich and handsome husband joins them for over-the-top sex? Each woman has her own part to play in solving the mystery, gratifying the lust and fulfilling the loves of the others in this novel of non-stop sex, love, infidelity and action that centers on Angela and her psychic abilities.


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MAC Baking Beauties Collection
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Wow! Those face powders are gorgeous!!
3/25/13 15:53 Jeannie:
3/25/13 15:49 Cj:
Omg omg omg the lip balms!!! I NEEEEEEEEED THEM! The face powders look gorgeous too I can't wait for swatches! On a sad note, the mineralize moisture foundation broke me out Returned it and the other foundation I ordered just in case >.>
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It happens I'm thinking of ordering one of the mini face and body's. haven't used that in forever but I did really like it
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3/25/13 15:55 Janelle:
oh no! Just when I thought that there was nothing that I wanted lately from MAC out comes this collection. I am all over the peachy lippy and hte neutral looking paint pots and both beauty powders… when does this come out? I need to save now!
3/25/13 16:24 Amy:
I was quite excited when I saw the promo pic, but once again I'm disappointed. The only thing I might get are the beauty powders… ~sigh~ Why doesn't anyone ever make pastel lip colors that are actually pastel on super fair skin? Dx Especially pink!
3/27/13 0:01 Irene:
I feel just the same, most pastels are actually really vibrant and dark against my super pale skin, and don't look that flattering
3/26/13 9:37 Kat:
I want everything HAHA LOL I am going to go broke in the month of April..MAC has stepped it up
3/26/13 10:03 Zarai Montevideo:
Hi sweetie!! First of all I have to say I love your website!! I get all my information from it. I currently have a YouTube channel and would like to ask your permission to mention you in it. I inform all my subscribers about new makeup products via your site and would love to send them your way. 😀 omg!! Can't wait for this collection to come out!! 😀 take care sweetie!!
3/26/13 10:49 Caitlin:
Hmm–I expected more adorable packaging. Also, I really freaking want cake and macarons whenever companies release these food-themed collections!
3/26/13 21:52 Louise:
I want Lavender Whip!!!
3/26/13 23:12 Whitney:
Hi! How exactly are you supposed to use the face powders? All over? Highlight?
Also I have never used pigment. Could I purchase a paint pot and then layer the pigment on top?
3/28/13 0:16 melissa konowitz:
Every now and then Mac has a collection where not only could I potentially wear every color/item but I want it all too. Thanks for the post I hadn't seen anything on this yet. Some quickie makeup math tells me I'm passing on the UD Vault and opting for a whole buncha mac.
4/3/13 23:14 koolchicken:
Muse, do you know how MAC launches work? There is more than one launch date. Does it go live on their site only on the first launch date. Or is it any online retailer that sells MAC? Or is it just MAC first and then everyone else and the physical stores on the later date? I'm fairly new to makeup so I have no clue how any of this works. Also, do many bloggers get early acess to these items to do reviews, or do we have to wait until they've been released? I live on an island so I rely on reviews since I can't get to a store. Thanks for any info you can share!
6/14/13 23:30 koolchicken:
Thanks it does! I ended up buying from the actual MAC store this time (as they had free shipping) but in the future I my just hold out. I just have a strong preference for buying from certain stores due to shipping policies and customer service. I know for this launch it seemed like they had it on Nordstrom only a day or so later, hopefully it'll be the same in the future.
4/23/13 18:37 maiya:
I love it 😮


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Register with us or sign in
From 1 ratings
Plant type
Flower colour
Flowers, Fruit
Skill level
Pears flower two to four weeks earlier than apples and need slightly warmer conditions. They are very ornamental in flower, their branches often buried under a froth of white blossom. All pears are grafted on rootstocks which affect their size and vigour, and may be trained in a number of different shapes. Pruning depends on the tree form and vigour of the rootstock, and should be checked in a handbook. 'Conference' is one of the best-known varieties, with regular heavy crops of very juicy sweet fruit, green with a silvery russeted finish and perfect for eating in November. The Royal Horticultural Society have given it their prestigious Award of Garden Merit (AGM).
Family: Rosaceae
Genus: Pyrus
Species: communis
Cultivar: Conference
Plant type: Fruit
Flower colour: White
Foliage colour: Mid-green
Feature: Flowers, Fruit
Sun exposure: Full sun
Soil: Well-drained/light
Hardiness: Hardy
Skill level: Beginner
Height: 900cm
Spread: 800cm
Time to plant seeds: September to November
I have just registered and would like to find out what to do about blotchy brown marks on my new conference pear tree. I can find quite a lot of information about pears in general, but not about a solution to this condition.Many thanksGran


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In 1950, 22 anthropology PhDs were awarded in America. In 1974, 409 anthropology doctorates were awarded, and PhD production exceeded 400 for the first time. During the last 20 years, PhD production has held remarkably stable--at 400 per annum--ranging from 445 in 1976 to 360 in 1982 (mean = 396); the figure for 1995 is 464. From 1948 to 1994, 10,659 anthropology doctorates have been awarded by North American universities listed in the Association's AAA Guide.
Biennial Surveys. Recognizing the need for longitudinal data about anthropology PhDs, the American Anthropological Association began, in 1982, a series of biennial surveys. In 1995, questionnaires were sent to 300 1994-95 anthropology PhDs for whom mailing addresses were provided by departments. A total of 151 usable questionnaires was received, for a return rate of 50% (1990 = 68%; 1988 = 67%; 1986 = 65%; 1984 = 74%; 1982 = 41%).
(Note: In 1992, the Association's Washington, DC, headquarters restructured its internal departments, moved to a new location in Arlington, Virginia, and installed a computer network with entirely new software; the PhD Survey was not completed in 1992. With the 1995 Survey of Anthropology PhDs, the Association resumes its biennial survey schedule.)
At time of degree, the youngest PhD recipient in Christine's cohort was 27 years old, and the oldest was 75. To date in the 1990s, the 400 PhDs awarded each year in American anthropology represent about 0.9% of the total number of doctorates awarded in all fields (anthropology MAs = 0.3%; anthropology BAs = 0.5%).
Sociocultural PhDs. Today cultural anthropology offers its doctoral students an array of competing theoretical approaches. In their dissertations, PhD candidates may emphasize aspects of one or more of the following viewpoints: (1) science (i.e., they test a hypothesis; use replicable observation techniques and formal methodologies; and may be anti-subjective), (2) advocacy (i.e., further a political agenda; champion health-care, pursue economic parity, redress past wrongs; may value subjects over science), (3) interpretive (i.e., study the interplay between data and observer; prefer to speculate rather than replicate; may deny that objective meanings exist) and (4) postmodern (i.e., write ethnography as biography; emphasize written texts; prefer subjectivity to science; may be anti-objective). There has been little synthesis among the four views.
Recent dissertations include "Temples, Tourists and the Politics of Exclusion: The Articulation of Sacred Space at the Hindu Pilgrimage Center of Pushkar, India" (Christina Joseph); "Heeni Wharemaru: Her Story, the Personal Chronicle of a Maori Woman" (Mary Kay Duffie); and "Images of Culture: Being Herero in a Liberal Democracy" (Deborah Durham).
Archaeology PhDs. Doctoral candidates in archaeology are in a synthesizing mode. Drawing on previous archaeological field studies, they also use cultural, physical and linguistic anthropology to understand political development, trade, land use, nutrition, and migration routes within larger regions of study than the single site. Ethnoarchaeology, historical archaeology, Old World archaeology and the rise and fall of New World civilizations (e.g., the Maya) are popular topics. High-tech instrumentation, remote sensing and sophisticated imaging software add energy and excitement to the subfield.
Dissertations include "The Archaic Settlement System of the Middle Green River Valley, Kentucky" (Christine Kay Hensley); "Hunter-Gatherer Subsistence Adaptation in the Saginaw Valley, Michigan" (Kathryn Egan); and "Pottery Economics: A Kalinga Ethnoarchaeological Study" (Miriam Stark).
Biological/Physical PhDs. Doctoral research in physical anthropology includes biomolecular data-gathering, toward an evolutionary synthesis with paleoanthropology. The latter itself benefits from high-tech instrumentation, including thermoluminescence (TL), electron spin resonance (ESR) and 2-D/3-D computed-tomography (CT). Research proceeds on early hominid and hominoid taxonomy, New World Paleoindians, primate social behavior and the biology of aging. Paleoanthropology is more holistic, and has become an experimental science based on actualistic studies, microwear analysis and tool replication.
Dissertations include "The Phylogeny of New World Monkeys Assessed by High-Resolution Two-Dimensional Protein Electrophoresis" (Charlene Dickinson); "Social Organization and Reproductive Strategies in a Wild Population of Common Marmosets" (Leslie Digby); and "Amino Acid Residue Analysis of Type 1 Collagen in Human Hard Tissue: An Assessment of Cribra Orbitalia in an Ancient Skeletal Sample from Tomb 31, Site 31/435-D5-2, Dakhleh Oasis, Egypt" (Scott Fairgrieve).
Linguistic Anthropology PhDs. Doctoral research in linguistic anthropology emphasizes the centrality of speech in human culture. Passage of the Native American Language Act of 1990 increased the study of endangered languages (and their role in understanding the peopling of the Americas). Linguistic anthropology holds great potential for studying the multilingual, multicultural world.
Recent dissertations include "Memory, Identity and Politics in Armenia" (Nora Dudwick); "The Citizen Factory: Labor and Identity in Bolivian Rural Teacher Education" (Aurolyn Luykx); and "Jump! Jump an Play yuh Mas" (Patricia Alley-Dettmers).
Applied Anthropology PhDs. In the 1990s, applied anthropology is a growth industry. Dissertation research is conducted in all quarters of the world, and by practitioners in all four subfields. Scientific paradigms predominate over interpretive or postmodern approaches; the latter may be less workable in applied's "real world" of quantification and objective data gathering. Recent topics include studies of non-governmental organizations (NGOs) and other social-change agencies; finding ways to build local-level knowledge into models of social change; and research on specific biomedical problems, such as AIDS, in Third- and Fourth-World nations.
Dissertations include "From Making a Living to Making Money: Domestic Economy, Capital, and Family Farming in the Venezuelan Andes" (Russell Rhoads); "Women's Narratives: Living with HIV Disease" (Patricia Salmon); and "The Impact of State Legislation on Retirement Housing Management: Issues of Legitimacy" (Judith Scherer).
Elapsed Time. Since the mid-1980s, time spent after a BA degree--actually enrolled in graduate school--to attain a PhD in anthropology has averaged 8-10 years. Average elapsed time-to-completion for the 1994-95 cohort is 8.5 years (women = 8.4; men = 8.6; the range is from 4-21 years). These figures compare to six years for engineers (the shortest time to completion), 7-8 for social scientists, and 8 years for humanists (the longest time to completion, reported in the National Research Council's Summary Report 1992: Doctorate Recipients from United States Universities). The longer time required for an anthropology doctorate is due, in part, to learning exotic field languages and/or completing a 12- to 30-month field study far from home.
Most minority PhDs are in cultural anthropology. Representation in the subfields, computed for the 1994-95 PhD cohort, is as follows: archaeology = 0%; sociocultural = 74%; biological/physical = 4%; linguistic anthropology = 4%; applied/other = 17%. (Note: based on small numbers, these percentages are imprecise.)
The Association's 1994 Survey of Departments (see AAA Guide 1994-95) reported that since 1988-89, women gained two percentage points toward parity in the full-time faculty (all ranks combined = 30%), and attained record levels in the ranks of full and associate professor. In 1994-95 the figure increased by two percentage points (all ranks combined = 32% [breakdown: in BA departments = 36%; MA = 34%; PhD = 31%]).
As a percentage of their own sex, women anthropologists are more uniformly sorted into grades (professor = 35%; associate = 31%; assistant = 31% [instructor = 3%]) than male colleagues, who are bunched together at the full-professor level (55%; associate = 26%; assistant = 17% [instructor = 2%]).
Although parity between the sexes has been attained at the assistant-professor level (women = 54% in 1994-95 [breakdown: in BA departments = 49%; MA = 55%; PhD = 55%]), as a category, the latter accounts for only 18% of the full-time anthropology faculty in America in 1994-95 (breakdown: in BA departments = 23%; MA = 16%; PhD = 17%).
Major Support During Fieldwork. Funding patterns for doctoral field research in anthropology have remained relatively stable since 1972. We asked 1994-95 PhDs to indicate their major source of financial support for doctoral field research. Federal sources (fellowships, traineeships, grants, loans and "other") accounted for 23% (1972 = 51%, 1977 = 44%, 1982 = 36%, 1984 = 33%, 1986 = 29%, 1988 = 17%, 1990 = 44%). Nonfederal sources (university or foundation fellowships and grants, teaching and research assistantships) accounted for 41% (1972 = 21%, 1977 = 24%, 1982 = 35%, 1984 = 35%, 1986 = 35%, 1988 = 41%, 1990 = 34%). And personal sources (savings, family loans or gifts and personal employment) accounted for 29% (1972 = 26%, 1977 = 31%, 1982 = 32%, 1984 = 32%, 1986 = 36%, 1988 = 32%, 1990 = 29%).
Increasingly, PhD students begin their training with academic as well as nonacademic careers in mind, and seek admission to programs which have some applied anthropologists on board. In their planning, PhD departments may be advised to hire applied anthropologists, both to meet changing student expectations and to bolster the department's official mission on campus.
Academic/Nonacademic. In the early 1970s, 74% of PhDs took jobs in academic departments of anthropology (13% took jobs in research centers and other academic departments; 13% took nonacademic jobs; Figure 4). From the mid-1970s through 1990, the percent taking jobs in anthropology departments dropped to 38% (21% took jobs in research centers and other academic departments; 41% took nonacademic jobs). In the 1994-95 PhD cohort, 42% took jobs in anthropology departments, 29% took jobs in research centers and other academic departments; 28% took nonacademic jobs (note: many jobs in research centers are "nonacademic" as well--i.e., do not award tenure--bringing the overall applied figure close to 50%; Figure 4).
Academic Work Setting. Academic settings include departments of anthropology (joint and separate), non-anthropology departments (e.g., linguistics, anatomy, women's studies, fisheries), campus ethnic centers (e.g., African-American studies, Latino studies), campus area studies (e.g., Pacific studies, Mexican studies, Latin American studies), campus research institutes (demography centers, survey research institutes, archaeology centers) and campus museums.
Tenure Rates. Of those 1994-95 PhDs who are academically employed, 6% are tenured, 30% are on a tenure track and 63%--almost two-thirds--are not on a tenure track. Comparable figures for "not on tenure-track" for 1986 = 61%, 1988 = 66% and 1990 = 49%.
Rating Advisers. Since 1982, recent PhDs have consistently rated the job-hunting assistance they received from departmental advisers as "poor." For 1994-95's PhD cohort, the ratings are much the same (poor = 42%; fair = 25%; good = 19%; very good = 8%; excellent = 5%). Through the 1980s, "excellent/very good" averaged 16%; 1995's figure is 13%. Thus, in planning for the future, PhD departments may consider offering tutorials on careers and job-hunting techniques, for applied as well as academic. (Note: To probe beneath the statistics, see below under Mentoring.)
Academic Job Availability. One measure of the academic job market is the number of position-open ads listed in the Anthropology Newsletter. The AN carries well over 90% of all academic jobs available in U.S. four-year colleges and universities. In 1994, only 215 positions were listed: half as many jobs as new PhDs available to fill them (1993 = 353; 1992 = 310; 1991 = 387; 1990 = 350; 1989 = 473; 1988 = 423; 1987 = 318; 1986 = 242; 1985 = 247).
Nonacademic Work Setting. Departments of anthropology at institutions of higher learning currently employ the largest proportion of anthropology PhDs; however, far fewer recent graduates are so employed, compared to the earlier cohorts.
Presently, there is no discernible ceiling or cap--like that prevailing in the tightened academic job market (Figure 5)--for PhD anthropologists targeting the nonacademic realm for employment. With its focus on internationalism, information and research, it would seem that the economy of the 1990s could absorb more anthropologists than the average 400 PhDs produced annually in the U.S. (Note: The diversity of professional-level jobs currently available to anthropology PhDs is evident in the Association's NAPA Directory of Practicing Anthropologists, available from AAA.)
Finding a Job. As in previous cohorts, the most successful job-hunting technique--accounting for 34% of all positions obtained by 1994-95 graduates--was "hearing of an opening through friends and contacts." Contacting employers "cold" (i.e., without any foreknowledge of opening; 17%), and answering ads in the Anthropology Newsletter (15%) were the next-most-cited means of finding a job. (Note: The AN has averaged 10% since 1972; 1995's 15% is a new high.) Additional methods used by the 1994-95 cohort included departmental job bulletins (3%), other help-wanted ads (10%), AAA annual meeting (1%) and employment agency (0%) ("other" = 19%). Since 1972, use of employment agencies has averaged less than 1%; AAA annual meeting has averaged 3%.
Assumptions. Assuming 400 new PhDs are produced per year indefinitely, what might American anthropology look like in 30 years? To boldly guess this far ahead one might also assume (1) a retirement age of 65, (2) a lifespan of 76 years and (3) a mean age of 36 at time of degree. Thus the average career "shelf life" of a PhD anthropologist today is about 30 years (and less for recent PhD cohorts).
Year 2025. By the year 1995, approximately 11,000 men and women (representing 0.004% of the U.S. population) had received a doctorate in anthropology. By the year 2025--using the assumptions noted above--straight-line forecasting yields a grand total of 12,000 U.S.-awarded anthropology PhDs--1,000 more than today. If the value of a commodity is determined by its scarcity alone, future anthropologists will be valued indeed.
(For purposes of planning, academic departments are invited to request further statistical details by contacting the Department of Academic Relations, AAA, 2200 Wilson Blvd, Suite 600 , Arlington, VA 22201; 703/528-1902, ext 3010; fax 703/528-3546; email@example.com).
1. "Go for the doctorate only if you have a passion for anthropology and adventure."
2. "Make the best of an uncertain mentoring environment."
3. "Get some training in applied anthropology."
(1) Strategies (in no particular order). Have confidence in yourself and take responsibility for yourself and your program. Go to a school with a large endowment (more likely to give financial support). Go to the school that supports unconditionally the kind of research you wish to pursue. Choose your adviser carefully. Talk to previous graduate students about their advisers before enlisting your own. Begin publishing in graduate school. Be strong not only in theory and method--but in applying them to the real world. Get a complete education. Know what science is. Go for the "big-three": grants, publications and a powerful adviser. Get all the "human" support you can from the department. Remember--everything is ultimately your decision.
(2) Tactics. Obtain teaching experience. Learn statistics. Take technical classes in analytical methods. Do as much field research as you can. Take classes in all the subdisciplines. Learn to write. Master public speaking. Get a background in budget management. Get some experience in an interdisciplinary project. Give conference papers and publish before completing the degree. Do not go into debt to finance your graduate studies. Finish quickly and get back into the real world ASAP. Have fun and enjoy what you're doing.
(3) Topics. Choose a topic for which there is already plenty of available data--don't waste time looking for what isn't there. Try to specialize in two geographic areas, if possible. In cultural anthropology, avoid working in the U.S. Don't treat the PhD as the "end" of something--but instead as part of a career trajectory.
(4) Teachers. Choose a program that encourages close communication with faculty. Choose your committee carefully. Develop a good working relationship with your adviser. Make sure the faculty members you pick are compatible and generous with their time--and that they, themselves, are getting funding. Find out whether faculty members coauthor articles with students. Do not let faculty push you into areas where you'd rather not go. Realize that faculty members fail to discourage many students who would be far better served doing something else.
(5) Funds. Always think in terms of fundable research. Pick a geographic area that is being funded by the U.S. Government. Do not enroll in a PhD program unless you are assured of funding (without funding, many drop out after a few years).
(6) Speed. Work hard, focus early. Choose a program that encourages rapid progress. Establish social networks early. Get through the program quickly.
(7) Jobs. Be aware of job prospects and choose courses accordingly. Be well-informed about job placement. Take time to build contacts with professionals outside your institution. Go to meetings and make contacts as early as possible. Make yourself highly visible. Don't overestimate your opportunities in the future job market. Find a niche which is not overpopulated. Upon completion of your PhD, be willing to wait up to four years for a position in academics. The sense of achievement after a PhD--without future employment possibilities--will soon fade.
(8) Adaptability. Diversify your interests and skills. Don't choose such narrow training that you are less "sellable" after receiving the degree. Link your anthropological training to other fields--e.g., to agriculture, business, psychology, etc. Cross-train in other disciplines (e.g., in computers, anatomy, engineering). Gain practical skills for alternate positions (e.g., in CRM, computers, GIS). Be flexible in terms of job options. Conduct marketable research. Try to get some training in applied.
(9) Ultimately. Are you sure? Be very sure that this is what you want to do. Know why you're doing it. Go for it only if you have a passion for anthropology and adventure. Do not enter for the sake of a career--only for love of anthropology itself. Don't do this unless you are obsessed with the field and are willing to work for very little money. Weigh carefully your love for the profession versus your desire for economic stability. Accept that a career in anthropology is more like an artist's career than a lawyer's. Study what you love and care about, and don't worry about the future. If it's your dream--Go for it!
Another member of 1994-95's cohort responded, "I did not receive a lot of `mentoring,' and did most of my research independent of advisers. As a result, I don't feel I got good advice on how to prepare for an academic career."
Apart from career counseling itself, faculty received mixed reviews on mentoring--from "excellent" to "so-so" (and from "zilch" to "nil"). One woman wrote, "Superb. I had wonderful outside contacts in academia who provided introductions, support and intellectual stimulation." Another wrote, "A mixed bag." A man answered the question with a question: "What mentoring process?" A woman's interjection: "Poor!"
Miscellaneous comments reveal the range of mentoring quality--high positives, high negatives--and the depth of feeling shown by those being mentored:
I had excellent access to all faculty; they provided excellent intellectual mentoring. My mentoring was meager in the extreme--the word hardly applies. Haphazard. Excellent. Not enough hands-on experience. Socialization into the discipline was largely hands-off. My mentoring was very effective--it kept me going. The majority of professors had very little time for graduate students, so we banded together ourselves. I like to work on my own . . . I was never advised to attempt to publish anything, nor to present papers. The faculty did nothing to help guide first year students. Mentoring was good to very good at the level of the main adviser; fair for the rest of the faculty. My adviser did not have any real advice except on academic issues; I was not prepared for the consulting side of anthropology. I was disappointed by the mentoring from my adviser, but other members of my committee were very helpful. Most support came from nonanthropologists in my area studies. Unstructured--but I got what I asked for.
Suggestion. The modal word to describe mentoring was "nonexistent." Thus, the following comment from a member of the 1994-95 PhD cohort may be helpful to current and future graduate students: "Mentoring was OK--but I had to be very aggressive to get it."
Absolutely (2*) Definitely (3) Depends on interest of individual Generally, yes I would have to say yes If they can If they want jobs! It depends It would be an excellent idea It would be foolish not to It wouldn't hurt It's a good back-up Most but not all should Most definitely No No, unless so desired No opinion Not a bad idea Perhaps Probably Realistically, they should Silly to think otherwise They should be offered the choice They should prepare for both Yes (23) Yes Yes! (4) Yes!! YES! Yes, a resounding yes Yes, absolutely Yes, definitely (2) Yes, if they want jobs Yes, life is applied Yes, we need money Yes, yes, yes (2) Yes! Yes! Yes! Yes, you never know
(*Numbers in parentheses indicate frequency of written response.)
(For assistance in the planning process tailored to your department's size and special needs, please contact Kathleen Terry Sharp, AAA Department of Academic Relations, 2200 Wilson Blvd, Suite 600 , Arlington, VA 22201; 703/528-1902, ext 3010; fax 703/528-3546.
updated 9/15/00