FPM-WSI: Fourier ptychographic whole slide imaging via feature-domain backdiffraction

Fourier ptychographic microscopy (FPM), characterized by high-throughput computational imaging, theoretically provides a cunning solution to the trade-off between spatial resolution and field of view (FOV), which has a promising prospect in the application of digital pathology. However, block reconstruction and then stitching has currently become an unavoidable procedure due to vignetting effects. The stitched image tends to present color inconsistency in different image segments, or even stitching artifacts. In response, we reported a computational framework based on feature-domain backdiffraction to realize full-FOV, stitching-free FPM reconstruction. Different from conventional algorithms that establish the loss function in the image domain, our method formulates it in the feature domain, where effective information of images is extracted by a feature extractor to bypass the vignetting effect. The feature-domain error between predicted images based on estimation of model parameters and practically captured images is then digitally diffracted back through the optical system for complex amplitude reconstruction and aberration compensation. Through massive simulations and experiments, the method presents effective elimination of vignetting artifacts, and reduces the requirement of precise knowledge of illumination positions. We also found its great potential to recover the data with a lower overlapping rate of spectrum and to realize automatic blind-digital refocusing without a prior defocus distance

An illustration for discriminant visualization and interpretation.

<p>(A) The discriminative power of the 400 amino acid pairs. Each element in this figure represents the sum score of the features with positive discriminant weights for a specific distance amino acid pair with <i>cp(20)</i>. The amino acids are identified by their one-letter code. The amino acids labelled by horizontal-axis and vertical-axis indicate the first amino acid and the second amino acid in the pairs, respectively. The adjacent colour bar shows the mapping of sum score values. (B) The different discriminant weights of distance amino acid pairs R-R. There are three kinds of features with positive discriminative power for amino acid pair R-R, including RR, R*R, and R**R with distance 1, 2, 3, respectively. (C) The occurrence distribution of RR, R*R, and R**R in the sequence of protein 1HLVA. The total occurrences of the three features are ten, which are shown in red dots. The two DNA-binding regions (sequence position 28–48, and 97–129) are shown in yellow colour. (D) The distribution of RR in the three dimensional structure of 1HLVA. Only one RR occurs outside of the two DNA-binding regions, which was shown in red square. (E) The distribution of R*R and R**R in the three dimensional structure of 1HLVA.</p

iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition

<div><p>Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called “iDNA-Prot|dis”, was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at <a href="http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/" target="_blank">http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/</a>. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.</p></div

A semi-screenshot to show the top page of the web-server iDNA-Prot|dis, which is available at http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/.

<p>A semi-screenshot to show the top page of the web-server iDNA-Prot|dis, which is available at <a href="http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/" target="_blank">http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/</a>.</p

The ROC (receiver operating characteristic) curves obtained by different methods on the benchmark dataset using the jackknife tests.

<p>The areas under the ROC curves or AUC are 0.834, 0.826, 0.814, 0.815, 0.789 and 0.761 for iDNA-Prot|dis (cp(20)), iDNA-Prot|dis (cp(14)), DNAbinder (dimension 21), DNAbinder(dimension 400), DNA-Prot and iDNA-Prot, respectively. See the main text for further explanation.</p

The overall Acc values achieved by iDNA-Prot|dis for cp(20) with different <i>d</i> values based on the benchmark dataset through five-cross validation.

<p>The overall Acc values achieved by iDNA-Prot|dis for cp(20) with different <i>d</i> values based on the benchmark dataset through five-cross validation.</p

The jackknife test results by iDNA-Prot|dis with different amino acid alphabet profiles (cf. Eqs. 9–13) on the benchmark dataset of Eq. 1 (cf. Supporting Information S1).

a<p>The parameters used: <i>d</i> = 3, <i>C</i> = 4, .</p>b<p>The parameters used: <i>d</i> = 3, <i>C</i> = 4, .</p>c<p>The parameters used: <i>d</i> = 3, <i>C</i> = 2, .</p>d<p>The parameters used: <i>d</i> = 3, <i>C</i> = 64, .</p><p>The jackknife test results by iDNA-Prot|dis with different amino acid alphabet profiles (cf. Eqs. 9–13) on the benchmark dataset of Eq. 1 (cf. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691.s001" target="_blank">Supporting Information S1</a>).</p

A comparison of the results^a obtained by iDNA-Prot|dis and the other methods on the independent dataset PDB186.

a<p>The results of iDNA-Prot <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Lin1" target="_blank">[15]</a>, DNA-Prot <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Kumar1" target="_blank">[14]</a>, DNAbinder <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Kumar2" target="_blank">[96]</a>, DNABIND <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Szilagyi1" target="_blank">[102]</a>, DNA-Threader <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Gao2" target="_blank">[5]</a>, and DBPPred <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Lou1" target="_blank">[97]</a> were obtained from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Lou1" target="_blank">[97]</a>.</p><p>A comparison of the results^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#nt110" target="_blank">a</a>} obtained by iDNA-Prot|dis and the other methods on the independent dataset PDB186.</p

A comparison of the jackknife test results by iDNA-Prot|dis with the other methods on the benchmark dataset of Eq. 1.

a<p>See the footnote c of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone-0106691-t001" target="_blank">Table 1</a>.</p>b<p>Results obtained by in-house implementation from DNAbinder <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Kumar2" target="_blank">[96]</a>.</p>c<p>Results obtained by in-house implementation from DNAbinder <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Kumar2" target="_blank">[96]</a>.</p>d<p>Results obtained by in-house implementation from DNA-Prot <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Kumar1" target="_blank">[14]</a>.</p>e<p>Results obtained by in-house implementation from iDNA-Prot <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0106691#pone.0106691-Lin1" target="_blank">[15]</a>.</p><p>A comparison of the jackknife test results by iDNA-Prot|dis with the other methods on the benchmark dataset of Eq. 1.</p