Yellow-1 is a protein that plays a key role in the regulation of cellular processes.
It is involved in signal transduction pathways and has been implicated in a variety of biological functions, including cell growth, differentiation, and survival.
The expression and activity of Yellow-1 are tightly controlled, and dysregulation of this protein has been associated with the development of certain disease states.
Researchers studying Yellow-1 can leverage PubCompare.ai's AI-driven protocols to enhance the reproducibility of their work.
The platform allows users to easily locate relevant protocols from the literature, pre-prints, and patents, and then use AI-based comparisons to identify the best protocols and products for their needs.
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The most visible components of Primer3 are the web interfaces, Primer3Plus (http://primer3plus.com) (22 (link)) and Primer3web (http://primer3.wi.mit.edu) and the command-line primer-design program, primer3_core (Figure 1). Primer3_core is mostly useful to bioinformaticians with programming skills and is written with the goal of being computationally efficient, thoroughly testable and easily integrated with other software. Primer3Plus (Figures 2 and 3) and Primer3web offer interfaces more suitable to ‘expert’ or ‘occasional’ users. However, Primer3_core is the engine underlying all primer design operations, and most of the changes described in this article affect both the web interfaces and primer3_core, which we collectively refer to as Primer3.
Input web page for Primer3Plus.
Example output web page from Primer3Plus. The first major block, labeled ‘Pair 1’, shows the template sequence with locations of the primers highlighted in blue and yellow and key information about the primers and primer pair. Subsequent blocks (‘Pair 2’,…, ‘Pair 5’) show information for alternative primer pairs.
Primer3 can carry out several kinds of design tasks (discussed later) and also check existing primer pairs for correctness. We focus mainly on discussing Primer3 in the context of designing primer pairs for amplifying a DNA template using PCR. To accomplish this task, Primer3 evaluates the primers and primer pairs according to various constraints and sorts acceptable pairs by a penalty function. It uses ‘branch and bound’ techniques (23 ) to reduce the search space while still generating the optimal primer pairs according to the penalty function and constraints. Because the primer3_core program accepts >150 parameters and long template sequences—far too much information to be supplied on the command-line—it reads most of its inputs from flat text files. Specifically, primer3_core uses the ‘boulder IO’ (boulder input-output) format, in which each parameter name (termed a ‘tag’) and its corresponding value are joined by an '=' sign, with one tag and its value per line (Figure 4). A single input file can specify multiple primer design tasks: the inputs for individual design tasks are separated by lines consisting only of an '=' sign. Primer3_core also generates its output in boulder IO format (Figure 4B). This format is designed for integration with other software, including scripts and web services, on the assumption that end users will interact with the web interfaces.
Examples of boulder IO (A) input to primer3_core and (B) output from primer3_core. Panel A shows two primer design tasks (SEQUENCE_ID’s example 1 and example 2) separated by a line consisting only of an = character. PRIMER_NUM_RETURN is 1 for both tasks, because the values of tags beginning with PRIMER_… persist between design tasks. However, the value of tags beginning with SEQUENCE_…, such as SEQUENCE_EXCLUDED_REGION = 37,21 in example 1, affect only the current design task. Panel B shows abbreviated output corresponding to panel A.
Untergasser A., Cutcutache I., Koressaar T., Ye J., Faircloth B.C., Remm M, & Rozen S.G. (2012). Primer3—new capabilities and interfaces. Nucleic Acids Research, 40(15), e115.
The general idea is very simple and has been used ever since texture mapping became part of 3D graphics: if an object is too complex (like the 960 triangles required to draw a single water molecule in Fig. 1A) it is replaced with ‘impostors’, i.e. fewer triangles that have precalculated textures attached, which make them look like the original object. Texture mapping means that a triangle is not rendered with a single color, but an image (the texture) is attached to it instead. For each of the three triangle vertices, the programmer can specify the corresponding 2D coordinates in the texture, and the hardware interpolates in between. So, instead of drawing 320 triangles to create one, still somewhat edgy atom, we simply draw the precalculated image of a perfectly round atom. As textures may be partly transparent, this image can be drawn as a simple square (transparent in the corners), which requires just two triangles.
A water molecule rendered classically with 960 triangles (A) or quickly using texture mapping and precalculated impostors arranged in a single texture (B). The texture contains ray-traced images of spheres with various colors (two of which are blended with a variable factor to create other colors and color gradients) and various sizes (so-called ‘mipmaps’, which reduce aliasing artifacts). The spheres coated with a stellar nebula are used to draw atoms selected by the user. The gray checkerboard indicates transparent pixels. Using texture (B), the water molecule in (A) can be drawn quickly using just 14 triangles (C). Low-resolution depth map of PDB file 1CRN to calculate shadows (D), balls and sticks of 1CRN (E) and space-filling display of PDB file 1AON (F)
In practice, many different images of atoms are needed, as atoms can have different colors and sizes. Regarding colors, we use blue, magenta, red, yellow, green, cyan and gray, and blend any two of them with a variable blending factor to support color gradients. Regarding sizes, the precalculated images can be shrunk on the fly during texture mapping, but the shrinking procedure reduces the image quality. That is why multiple smaller images of each atom are stored as well. Changing the texture during rendering reduces performance, and consequently all these atom images are squeezed into a single texture of size 1024 × 1024, which is shown in Figure 1B. When the user changes the position of the light source, this texture is updated from a collection of 200 different views, prerendered with www.POVRay.org. For stereo graphics, a second texture is used that has the atoms prerendered from a slightly shifted point of view. This straightforward approach has not been routinely used in the past for the following reason: when the GPU draws a pixel, it stores its Z-value (the distance from the view-plane) in the Z-buffer, and then never again draws a pixel at this location, unless it has a smaller Z-value. When spheres are modeled with lots of triangles as in Figure 1A, each pixel has the right Z-value associated, so that the spheres intersect correctly thanks to the Z-buffer. With our shortcut, however, each sphere consists of just two triangles that are parallel to the view-plane, and each pixel of the sphere image thus has the same Z-value. Consequently, the spheres do not intersect at all, instead the closer one completely occludes the more distant one. This is fine when drawing non-intersecting spheres (in sticks and balls and sticks visualization styles), but obviously goes wrong with a space-filling style. The logical solution would be to adjust the pixel Z-values on the fly during rendering (with a so-called ‘pixel shader’), but this approach is either slow (because the hardware can no longer perform an early Z-test to discard pixels) or not supported at all (e.g. mobile devices based on OpenGL ES lack this feature, and PowerVR GPUs do not even have a Z-buffer). The algorithm described here therefore takes a different route; it shares the work between central processing unit (CPU) and GPU according to the following recipe, which can easily be distributed over multiple CPU cores (a very detailed 20-page step-by-step recipe has been included as Supplementary Material):
The CPU transforms the atom coordinates from object space to screen space and immediately discards atoms that are offscreen.
For each atom i, the CPU creates a temporary Z-buffer that includes atom i and all the more distant atoms k, which can influence the shape of atom i by intersection, i.e. those atoms whose sphere image touches atom i and who are closer along Z than their own radius Rk. The atoms k could be found quickly with a neighbor search grid, but it turns out that the trivial approach to just look at covalently bound atoms is good enough.
Finally, the CPU loops over the pixel lines in the temporary Z-buffer of atom i, checks which lines are affected by intersections and emits a number of triangles that trace these intersections. The principle is clarified in Figure 1C, which shows how to draw a water molecule with just 14 triangles instead of 960 triangles.
If atoms are shown as sticks or balls and sticks, cylinders need to be drawn that connect the atoms (Fig. 1E). To reduce the polygon count, only the front side of the cylinders is drawn, using between 2 and 18 triangles, depending on the distance from the viewer. Cylinders always use the same texture as the atoms (Fig. 1B), which ensures visual continuity.
Shadows and ambient lighting are calculated per atom, not per pixel. The CPU first draws a low-resolution depth map of the scene where atoms have a diameter of just 15 pixels (Fig. 1D), either seen from the position of the light source (shadows) or from the six main directions (ambient lighting). Then it integrates the amount of light reaching each atom (i.e. the fraction of pixels not occluded by closer ones) and darkens the atom accordingly (using either GL_EXT_fog_coord or multi-texturing).
This fast way of drawing molecules also has three limitations compared with the classic approach: First, atom colors must be mixed from two of the standard colors present in the texture (Fig. 1B), which allows to create most useful colors, but not all colors. Second, the maximum atom size on screen is limited to the largest atom size in the texture (currently, 256 × 256 pixels), unless one wants to use lower quality upscaled atoms. To prevent atoms from getting too small, YASARA therefore restricts its window size to Full HD, but we plan to double the texture size to 2048 × 2048 soon, covering 4 K and similar hires displays. And third, drawing transparent atoms is not straightforward and currently not implemented.
Krieger E, & Vriend G. (2014). YASARA View—molecular graphics for all devices—from smartphones to workstations. Bioinformatics, 30(20), 2981-2982.
MITE-Hunter is a UNIX program pipeline composed mainly of Perl scripts. Given genomic sequences as the input data, MITE-Hunter identifies Class 2 non-autonomous TEs and produces outputs of consensus sequences classified into families. MITE-Hunter can use multiple processers (default 5 CPUs). The MITE-Hunter pipeline has five main steps that are summarized in Figure 1: (i) identify TE candidates through a structure-based approach, (ii) identify and filter false-positives using an approach based on the pairwise sequence alignment (PSA), (iii) generate exemplars, (iv) identify and filter false-positives using an approach based on the multiple sequence alignment (MSA), generate consensus sequences and predict TSDs and (v) group consensus sequences into families. Details of each step are presented in the results section.
The five main steps of the MITE-Hunter pipeline. Gray bars are genomic sequences, black and red triangles are TSDs and TIRs, respectively, blue bars are predicted TEs, white bars are homolog sequences, dashed lines are gaps and yellow bars are sequences that are similar to each other but not to those represented by green bars (and vice versa). (A) Identification of candidate TEs. Three predicted candidate TEs are shown. (B) Filtering of false-positives based on the PSA. Four types of alignments are shown (a–d). Except for the candidates in (d), all the others are filtered as false-positives. (C) Selection of TE exemplars. (D) Filtering of false-positives based on the MSA, predicting TSDs and generating consensus sequences. (e) and (f) are two special types of MSA (see text for detail). (E) Selecting new exemplars and grouping TEs into families.
Han Y, & Wessler S.R. (2010). MITE-Hunter: a program for discovering miniature inverted-repeat transposable elements from genomic sequences. Nucleic Acids Research, 38(22), e199.
The reconstructed histological volumes were used to generate surface models of the gray/white interface. This was accomplished in several steps. First, a set of “control points” were manually added to the body of the white matter to guide an intensity normalization step that resulted in the white matter across most of the volume being close to a prespecified value (Dale et al. 1999 (link)). This volume was then thresholded and manually edited to separate white matter from other tissue classes. The resulting binary segmentation was used to generate topologically correct and geometrically accurate surface models of the cerebral cortex (Dale et al. 1999 (link); Fischl, Sereno, Dale 1999 (link); Fischl et al. 2001 (link)) using a freely available suite of tools (http://surfer.nmr.mgh.harvard.edu/fswiki). An example of the results of this procedure together with the locations of the manually selected control points is given in Figure 1, which shows coronal (top), sagittal (middle), and axial (bottom) slices of a typical volume with the reconstructed gray/white surface shown in yellow. Note that small errors in surface positioning, which would be critical, for example, in a study of cortical thickness, are mostly irrelevant in this study in which we are more concerned with the large-scale geometry of the surface models. The 8 labeled BA maps (areas 2, 4a, 4p, 6, 44, 45, 17, and 18) were sampled onto surface models for each hemisphere, and errors in this sampling were manually corrected (e.g., when a label was erroneously assigned to both banks of a sulcus). A morphological close was then performed on each label. A close of a binary label is a dilation, in which each point that is 0 and neighbors a point that is 1 is set to 1, followed by an erosion, in which each point that is 1 and neighbors a 0 is set to 0. The close was used to remove small holes that arise due to sampling artifacts without distorting the boundary of each label. The 10 left and 10 right hemispheres were morphed into register using a high-dimensional nonlinear morphing technique that aligns cortical folding patterns (Fischl, Sereno, Tootell et al. 1999 (link)). Briefly, this technique maps each individual surface model into a spherical space and then represents the geometry of the surfaces as functions on the unit sphere. The registration of the surfaces is accomplished by maximizing the similarity of these spherical functions, while also constraining the mappings to be invertible and to induce only modest amounts of metric distortion. For these datasets, specifically we used 3 sets of geometric features to drive the registration. The first was the mean curvature of the “inflated” surface (the surfaces displayed in Fig. 2). This was necessary to account for the large-scale geometric distortions present in the data. Next we aligned the “average convexity,” which has been shown to be representative of the primary folding patterns (Fischl, Sereno, Dale 1999 (link)). Finally, the mean curvature of the gray/white boundary surface was used as the input feature in order to align secondary and tertiary folds where possible. Each of these features in turn was matched to the corresponding feature in our standard in vivo atlas comprised of 40 subjects distributed in age and pathology (10 with mild Alzheimer's disease). Note that no specific optimization was performed for aligning the BAs presented in this report. Rather, a set of parameters that had been determined to be optimal for aligning primary visual cortex (V1) in a separate ex vivo dataset (Hinds OP, Rajendran N, Polimeni JR, Augustinack JC, Wiggins G, Wald LL, Rosas HD, Potthast A, Schwartz EL, Fischl B, unpublished data ) were used with no modification. In order to quantify the accuracy of the alignment of the underlying BAs, the spherical registration was used to transform each of the 8 BAs for each individual into each of the other individual coordinate systems, and a modified Hausdorff distance was computed. (Note that areas 4a, 4p, and 6 were obtained for only 8 of the 10 total subjects. Each of the other areas was present for every subject.) Specifically, for each point on the boundary of each subject's area in the individual subject space, we computed the minimum distance to the boundary of each other subject projected into the individual subject's original white matter surface model and then computed the average of these. The results of this analysis are displayed in Figure 4. The advantage of this procedure is that it provides a measure in millimeter of the uncertainty of localization and is invariant to the size of an area, a well-known problem for other similarity measures such as the Dice or Jaccard coefficient, which compute the degree of overlap of binary labels, a measure that is affected by the size of the label, with larger labels typically evidencing greater overlap than smaller one.
Fischl B., Rajendran N., Busa E., Augustinack J., Hinds O., Yeo B.T., Mohlberg H., Amunts K, & Zilles K. (2007). Cortical Folding Patterns and Predicting Cytoarchitecture. Cerebral Cortex (New York, NY), 18(8), 1973-1980.
EzColocalization was tested on images from experiments and on modified images created to test specific issues (e.g. misalignment). Unpublished images of bacterial cells (HL6187) were used to illustrate the different modules of EzColocalization (Figs 1–4). These bacteria had plasmid pHL1392 in strain HL333823 (link). pHL1392 has the ampicillin resistance gene, ColE1 origin, and the green fluorescent protein (GFP) fused to part of the sodB gene and transcribed from the PLlacO-1 promoter. The sources of the images used for the application experiments (Figs 5–8) are stated in the relevant Results section. Note: images presented in the figures are cropped so that it is easier to see individual cells.
Inputs and alignment tab. (A). Inputs tab in the GUI. (B) General steps for the alignment of images. The cell identification image stack (phase contrast; left column), reporter 1 image stack (DAPI staining of DNA; center column), and reporter 2 image stack (Cy5; right column) are images of a previously reported bacterial strain (HL6320)15 (link). Scale bar is 2 μm. Reporters 1 and 2 images are pseudocolored. Red coloring in the second row of images indicates the objects identified by thresholding of the signal in each channel (“Default” algorithm in ImageJ). Following alignment of the images, pixels that overhang are removed and gaps are filled with pixels with zero value (yellow areas) so that all images have the same area in the common aligned region.
Cell identification and cell filters tab. (A) Cell Filters tab in the GUI. (B) Cell selection and watershed segmentation. Red coloring in the image in the second row indicates objects identified by thresholding of the signal in the cell identification channel (“Default” algorithm in ImageJ). Cells are the same as in Fig. 1. (C) Selection of cells based on physical features using the cell filters. Scale bar is 2 μm. Phase contrast image from Fig. 1. Red outline indicates the objects that were identified by thresholding (Panel B), and in the case of the right image, are within the parameter range(s) selected by the filter. (D) Selection of cells based on signal intensity using the cell filters. Phase contrast (cell identification image) and DAPI stain (reporter channel) images of bacteria (HL6187). Scale bar is 2 μm. Note: the lower of the two cells (no red border) has been removed from the analysis by the cell filter (that is, it no longer has the red cell outline).
Visualization tab. Data are from bacteria (HL6187) with labeled sodB::gfp RNA (Cy3 channel) and DNA (DAPI). (A) Visualization tab in the GUI. (B) Heat maps of Cy3 and DAPI signals for bacteria with “cell scaling” (defined in main text). Scale bar is 2 μm. (C) Scatterplot of Cy3 and DAPI for the cell on the left and outlined in white in Fig. 3B. (D) Metric matrix for TOS (linear scaling) for the cell on the left and outlined in white in Fig. 3B. FT is the top percentage of pixels in the channel; for example, if FT for Cy3 is 80% then it refers to the 80% of pixels with the highest Cy3 signal. Black color on the left column and bottom row indicate that TOS values are not informative when one threshold is 100%; that is, the overlap of two reporters can only be 100% if 100% of pixels are selected for at least one channel.
Analysis tab. (A) Analysis tab in the GUI for selecting default metrics. Note: this example is for two reporter channels (see Fig. 8G for 3 reporter channels). (B) Analysis tab in the GUI for users to code custom metrics. The example code provided is for measuring colocalization by Pearson correlation coefficient. (C) Example of a data table showing metric values for Pearson correlation coefficient (PCC) and some of the parameter values for some of the cells in the analysis. Label = the image and unique cell number to identify individual cells; Area = area of each cell in pixels; and X = the average x-value of all pixels in a cell. Data is from the example used in Fig. 3. (D) Summary report (“Log”) of the results in Fig. 4C. (E) Histogram generated from the results in Fig. 4C. The height of each bin is the relative frequency. The Count is the number of cells. Mean is the mean value. StdDev is the standard deviation. Bins is the number of bins. Min and Max are the minimum and maximum values of the lowest and highest bin respectively (which are shown immediately under the histogram). Mode is the mode value. Bin Width is the width of each bin within the histogram.
Application 1: Cell selection using reporter images and physical parameters. Images are rat hippocampal neurons labelled with an F-actin probe and anti-tubulin antibody visualized by fluorescence microscopy (see main text). (A) Workflow of the analysis. (B) Cell identification using the F-actin reporter and filters to remove small non-cell objects (yellow arrow) based on their size (i.e. Area option from the cell filters). Large yellow box in left panel is a zoomed in view of the smaller yellow box. Red outline of the neuron indicates it has been identified as an object (i.e. a cell) for analysis. Scale bar is 100 μm. (C) Heat maps with cellular normalization showing localization regions of signal intensity for the cell shown in panel B. Scale bar is the same as panel B. (D) Scatterplot showing relationship between the signal intensity for two reporter channels for a random cell in the sample. Pixels with the highest intensity signal for each reporter channel have the lowest intensity signals for the other reporter, which indicates anticolocalization (blue circles). Green dash lines indicate thresholds selected by Costes’ method. (E) Metric matrix for the median TOS (linear) value for all cells in the sample (n = 20). Green box indicates the threshold combination where F-actin and tubulin have the highest intensity signal (top 10% of pixels for each channel); the median TOS value is −0.36.
Application 2: Image alignment. Images are S. cerevisiae with TEM1 translationally fused to GFP and DAPI staining visualized by DIC microscopy and fluorescence microscopy (see main text). (A) Workflow of the analysis. (B) Cell identification by hand-drawn ROIs on a DIC image and creation of a binary image mask. Red outline indicates the boundary of the hand-drawn ROI. Scale bar is 3.5 μm. (C) Alignment of the reporter images using the binary mask image. Arrows indicate areas of misalignment that are corrected. Red outline is the same as for Panel B.
Application 3: Cell selection using signal intensity parameters. Images are whole adult C. elegans with GFP expressed from the clec-60 promoter and mCherry expressed from the myo-2 promoter that are visualized by bright-field microscopy and fluorescence microscopy (see main text). (A) Workflow of the analysis. (B) Selection of C. elegans so that only those individuals with an average intensity for the reporter signal that is above a threshold level are included in analyses. Left image shows the ROI manager with a list of ROIs that were hand-drawn around each C. elegans. Right image shows the reporter channel images with red outlines indicating the boundaries of the ROIs. C. elegans below the threshold level were excluded (yellow arrow) from the analyses by using the cell filters for signal intensity. Scale bar is 250 μm.
Application 4: Measurement of colocalization for three reporter channels. Images are of human bone cancer cells (U2OS) labelled as described in the main text. (A) Workflow of the analysis. (B) Images of cells in the cell identification and reporter channels. Top row are raw images. Bottom row, left image is the cell identification with pseudocolor (blue is the signal from Hoechst 33342 signal and green is the signal from phalloidin/Alexa Fluor 568 conjugate and wheat germ agglutinin/Alexa Fluor 555 conjugate) and boundaries of the ROIs in white (see main text). Bottom row (except left image) are heat maps for each of the three reporters with the boundaries of the ROIs shown. Signal intensity is indicated by the bar below each reporter image. Scale bar is 20 μm. (C) A three channel scatterplot for a single cell is shown for illustrative purposes only. (D–F) Metric matrices of median values for ICQ (D) TOS (E) and Manders’ colocalization coefficients M1, M2 and M3 (F) for all cells in the analysis (n = 66). Note: black color on metric matrix for ICQ indicates there were no pixels above all three thresholds for some cells, and therefore ICQ could not be calculated. (G) Analysis Metrics subtab for the Analysis tab for three reporter channels.
Stauffer W., Sheng H, & Lim H.N. (2018). EzColocalization: An ImageJ plugin for visualizing and measuring colocalization in cells and organisms. Scientific Reports, 8, 15764.
To a cold solution (0 °C) of (E)−4-phenyl azophenol (5 g, 0.025 mol) in dichloromethane (200 mL) triethylamine (5.78 g, 0.057 mol) was added. The reaction vessel was connected to a sodium hydroxide bubbler and sealed. To the solution, methacryloyl chloride (3.711 g, 0.355 mol) was added dropwise, and left stirring at 500 rpm for 4 h. The solution was quenched with saturated sodium carbonate solution (79 mL). The aqueous layers were extracted with dichloromethane, and the combined organic layers were washed with brine, and the solvent was evaporated. The resulting yellow-brown powder was dissolved in petroleum ether. The solution was purified through a silica gel column with petroleum ether as eluent, and the solvent was then evaporated. The product was then columned through basic alumina with petroleum ether, to yield a yellow powder of yellow 1 methacrylate. 1H NMR (400 MHz, CDCl3): δ 2.11 (3H, s, C = CCH3), 5.82 (1H, m, C = CHH), 6.42 (1H, s, C = CHH), 7.32 (2H, m, 2× ArH), 7.47–7.58 (3H, m, 3× ArH), 7.94 (2H, m, 2× ArH), 8.00 (2H, m, 2× ArH) ppm.
Krumins E., Crawford L.A., Rogers D.M., Machado F., Taresco V., East M., Irving S.H., Fowler H.R., Jiang L., Starr N., Parmenter C.D., Kortsen K., Cuzzucoli Crucitti V., Avery S.V., Tuck C.J, & Howdle S.M. (2024). A facile one step route that introduces functionality to polymer powders for laser sintering. Nature Communications, 15, 3137.
Yellow River sediment was obtained for this study from dredging operations at the Yellow River Jijin Main Canal in Jining County, Dezhou City, Shandong Province (Figure 1). Over 3000 acres of coal-mined subsided land have been reclaimed using sediment from this site transported by pipelines. The sediment used in experiments was collected from the Yellow River Jijin Main Canal by the sediment dredging ship. A total of 31 sediment samples were collected from the sediment transfer point and the filling reclamation land by three sediment dredging ships in the main canal. After removal of impurities, the sediments were used as test materials. Experimental water with a pH of 7.64 and conductivity of 696 uS•cm -1 was employed to closely mimic field conditions. Previous research recommended a filling height of approximately 120 cm for Yellow River sediment reclamation [17] . As the retrieved Yellow River sediment is a disturbed specimen, laboratory simulation filling experiments were conducted by filling PVC pipes with a concentration of 300 kg•m -3 of Yellow River sediment up to a height of 120 cm [17] . After 12 h of drainage, samples were taken at the top layer (0-40 cm), the middle layer (40-80 cm), and the bottom layer (80-120 cm) using a cutting ring, and their dry bulk density was determined after drying to constant weight.
Sun H., Hu Z., & Wang S. (2024). A Study of the Physical and Mechanical Properties of Yellow River Sediments and Their Impact on the Reclamation of Coal-Mined Subsided Land. Sustainability, 16(1), 439-439.
Microsoft Excel software was used to process the data to eliminate the influence of fish body size on character parameters during data analysis and improve data accuracy. The data of these seven proportional traits were imported into SPSS 24.0 software for multivariate statistical analysis, including one-way analysis of variance (One-way ANOVA), principal component analysis, cluster analysis, and discriminant analysis. One-way ANOVA was performed using SPSS 24.0. Multivariate comparison was performed by the Tukey s-b(K) method in one-way ANOVA, and a similar subset was divided, 0.05 was selected as the significance level. 18 (link) In SPSS software, "analysis-dimension reduction-factor" was used to conduct principal component analysis, and extract seven principal components and their eigenvalues, the contribution rate of each principal component, and the cumulative contribution rate according to the order of size. Scatter plots were drawn based on the scores of the first and second principal components to compare the variability and similarity of the five large yellow croaker populations and to determine their morphological differentiation. 19 Using the mean value of each parameter correction, SPSS 19.0 software was used to perform cluster analysis by using the shortest distance system clustering method of Euclidean distance. The species clustering tree was constructed to compare the distance between populations. 20 In formula: P 1 and P 2 are the discriminant accuracy rate (%), P is the comprehensive discriminant rate (%), O is the number of correctly discriminated large yellow croaker (tail), M is the number of measured large yellow croaker (tail). N is the number of large yellow croaker (tail); A i is the number of large yellow croaker (tail) correctly judged by the i group, B i is the number of large yellow croaker (tail) actually judged by the i group, and k is the number of groups (number). Analysis of morphological differences in five large yellow croaker (Larimichthys crocea) populations 3. RESULTS
Zhang Y., Guo H., Liu B., Zhang N., Zhu K., & Zhang D. (2024). Analysis of morphological differences in five large yellow croaker (Larimichthys crocea) populations. Israeli Journal of Aquaculture - Bamidgeh, 76(1).
The discriminant analysis of five large yellow croaker populations was carried out based on seven morphological proportional characters, and the step-to-step discriminant method was adopted (Table 5 and Table 6). The results showed that the seven proportional characters had significant significance for discriminant classification. Therefore, all the seven proportional traits entered the discriminant function, and the discriminant classification function of large yellow croaker population was constructed. According to these functions, the original samples are distinguished and grouped. Where X 1 = OL/BL, X 2 = BL/BH, X 3 = BL/HL, X 4 = BL/CPL, X 5 = HL/SL, X 6 = HL/ED, X 7 = CPL/CPH. All the data collected from five large yellow croaker populations were put into the equation to test the discrimination accuracy, and the results showed that the discrimination accuracy P1 was 78.3%~92.7%, the discrimination Analysis of morphological differences in five large yellow croaker (Larimichthys crocea) populations Israeli Journal of Aquaculture -Bamidgeh accuracy P2 was 76.4%~96.5%, and the comprehensive discrimination rate was 99.3%. The discriminant accuracy P1 and P2 of five large yellow croaker populations are above 75%. The discrimination accuracy rate of the ZSW population is the highest, indicating that this population has obvious characteristics and is easy to discriminate. After discriminant analysis, the number of tails classified into each population was as follows: XMW had 57 tails, accounting for 19.3% of the total; ZJW had 72 tails, accounting for 24.4% of the total; ZSW had 52 tails, accounting for 17.6% of the total; WZF had 57 tails, accounting for 19.3% of the total; NDF had 57 tails, accounting for 19.3% of the total.
Zhang Y., Guo H., Liu B., Zhang N., Zhu K., & Zhang D. (2024). Analysis of morphological differences in five large yellow croaker (Larimichthys crocea) populations. Israeli Journal of Aquaculture - Bamidgeh, 76(1).
Four simple hybrids generated from a dialelic design were evaluated using Griffing I. method (Saavedra-Guevara et al., 2021) (link), which consisted of direct crosses, reciprocal crosses with the p 2 families (complete dialelic) and included p, DC and RC (p +Cp 2 + Cp 2 ), in six lines of white grain maize, and five of yellow grain. In this equation, p: represents the parents; DC: direct crosses; RC: reciprocal crosses; Cp 2 : square of the cross of the parents. There were 15 direct crosses, 15 reciprocal crosses and self-pollinations of each line in white grain, and 10 direct crosses, 10 reciprocal 3/7 crosses and self-pollinations in yellow grain (Table 1). The parents were supplied by the International Maize and Wheat Improvement Center (CIMMYT), El Batám, Texcoco, Mexico. From this design, a total of 28 hybrids (13 of yellow grain (YG) and 15 of white grain (WG)) were obtained. From these genetic material the following simple white grain maize hybrids were selected for the evaluation: L3. CML 549 X L5. CML 576 (WG1) and L2. CML 545 X L4. CML 550 (WG2), and simple yellow grain maize hybrids: L4. CML 101 X L5. CML 103 (YG1) and L3. CML 551 X L1. CML 479 (YG2), with conformed treatments. These materials were established under experimental field conditions, following a randomized Complete Block experimental design, in a master plot of more than 45 years of cultivation. Experimental plots were formed with an area of 285.6 m 2 in furrows of 2 m in length at a sowing distance of 0.20 × 0.80 m.
Pérez‐López L., Argentel-Martínez L., Peñuelas-Rubio O., Cervantes-Ortíz F., Aguirre-Mancilla C.L., & Aguilera J.G. (2024). Morphology and yield components of single hybrids white and yellow maize in the Yaqui Valley. Revista Brasileira de Ciências Agrárias - Brazilian Journal of Agricultural Sciences, 19(1), 1-7.
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Lucifer Yellow is a fluorescent dye used in biological research applications. It is a water-soluble, anionic dye with an excitation maximum at 428 nm and an emission maximum at 540 nm. Lucifer Yellow is commonly used as a cellular tracer and in fluorescence microscopy techniques.
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Fetal Bovine Serum (FBS) is a cell culture supplement derived from the blood of bovine fetuses. FBS provides a source of proteins, growth factors, and other components that support the growth and maintenance of various cell types in in vitro cell culture applications.
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MTT is a colorimetric assay used to measure cell metabolic activity. It is a lab equipment product developed by Merck Group. MTT is a tetrazolium dye that is reduced by metabolically active cells, producing a colored formazan product that can be quantified spectrophotometrically.
The LIVE/DEAD Fixable Yellow Dead Cell Stain Kit is a laboratory reagent used to identify dead cells in a sample. It contains a fluorescent dye that binds to dead cells, allowing for their detection and separation from live cells during analysis.
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DMSO is a versatile organic solvent commonly used in laboratory settings. It has a high boiling point, low viscosity, and the ability to dissolve a wide range of polar and non-polar compounds. DMSO's core function is as a solvent, allowing for the effective dissolution and handling of various chemical substances during research and experimentation.
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Image-Pro Plus 6.0 is a comprehensive image analysis software package designed for scientific and industrial applications. It provides a wide range of tools for image capture, enhancement, measurement, analysis, and reporting.
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Whatman No. 1 filter paper is a general-purpose cellulose-based filter paper used for a variety of laboratory filtration applications. It is designed to provide reliable and consistent filtration performance.
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Cytiva No. 1 filter paper is a high-quality laboratory filtration product designed for general-purpose filtration tasks. It is composed of cellulose fibers and is suitable for a variety of applications requiring efficient separation of solids from liquids.
LysoSensor Yellow/Blue DND-160 is a fluorescent probe used for staining and imaging acidic organelles, such as lysosomes, in live cells. It exhibits a pH-dependent shift in fluorescence emission, allowing for the visualization and analysis of lysosomal pH.
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The CR-400 is a color measuring instrument designed for industrial applications. It measures the color and appearance of various materials, including textiles, plastics, and other products. The CR-400 provides accurate and reliable color data to help manufacturers and producers ensure consistent quality and meet industry standards.
Yellow-1 is a protein that plays a key role in the regulation of cellular processes. It is involved in signal transduction pathways and has been implicated in a variety of biological functions, including cell growth, differentiation, and survival. The expression and activity of Yellow-1 are tightly controlled, and dysregulation of this protein has been associated with the development of certain disease states.
PubCompare.ai's AI-driven protocols can help boost the efficacy of Yellow-1 research and lead to more reliable and impactful findings. The platform allows users to easily locate relevant protocols from the literature, pre-prints, and patents, and then use AI-based comparisons to identify the best protocols and products for their needs. This can enhance the reproducibility of Yellow-1 research by helping researchers screen protocol literature more efficiently and leveraging AI to pinpoint critical insights.
One of the key challenges in Yellow-1 research is identifying the most effective protocols for your specific research goals. PubCompare.ai's AI-driven analysis can highlight key differences in protocol effectiveness, enabling researchers to choose the best option for reproducibility and accuracy. This can help address issues with protocol selection and ensure more consistent and reliable results in Yellow-1 studies.
Yes, there are different variants and isoforms of Yellow-1 that can have slightly different functions and characteristics. Researchers studying Yellow-1 should be aware of these variations and how they may impact their experimental design and findings. PubCompare.ai can assist by providing information on the different Yellow-1 variants and helping researchers identify the most relevant protocols for their specific area of study.
Yellow-1 research has implications for a variety of biological processes and disease states. For example, understanding the role of Yellow-1 in cell growth, differentiation, and survival can provide insights into the development of certain cancers and other proliferative disorders. Additionally, research on Yellow-1's involvement in signal transduction pathways may lead to new therapeutic targets for a range of diseases. PubCompare.ai can help researchers optimize their Yellow-1 studies to maximize the impact and translational potential of their findings.
More about "Yellow-1"
Yellow-1 is a crucial protein involved in regulating a variety of cellular processes.
It plays a key role in signal transduction pathways and has been linked to numerous biological functions, including cell growth, differentiation, and survival.
Tight control of Yellow-1 expression and activity is essential, and disruptions in this protein have been associated with the development of certain disease states.
Researchers studying Yellow-1 can leverage PubCompare.ai's AI-driven protocols to enhance the reproducibility of their work.
The platform allows users to easily locate relevant protocols from the literature, pre-prints, and patents, and then use AI-based comparisons to identify the best protocols and products for their needs.
This can help boost the efficacy of Yellow-1 research and lead to more reliable and impactful findings.
Synonyms and related terms for Yellow-1 include Lucifer Yellow, a fluorescent dye used in cell biology research, and FBS (fetal bovine serum), a common cell culture supplement.
Researchers may also use MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assays to measure cell viability, or the LIVE/DEAD Fixable Yellow Dead Cell Stain Kit to identify dead cells.
DMSO (dimethyl sulfoxide) is a solvent often used to dissolve compounds, while Image-Pro Plus 6.0 is a software tool for image analysis.
Whatman No. 1 filter paper and No. 1 filter paper are commonly used in laboratory filtration procedures.
LysoSensor Yellow/Blue DND-160 is a fluorescent dye that can be used to monitor lysosomal pH, and the CR-400 is a colorimeter used for color measurement.
By incorporating these related terms and concepts, researchers can enhance their understanding of Yellow-1 and its applications in cell biology and beyond.
PubCompare.ai's AI-driven protocols can be a valuable resource for optimizing and reproducing Yellow-1 research, leading to more reliable and impactful findings.
