Efficiency comparison between traditional RANSAC and our algorithm (Overall).

<p>Efficiency comparison between traditional RANSAC and our algorithm (Overall).</p

The second experiment on the corner matching efficiency: (a) Selected corners (using our algorithm) from two original images; (b) Corner matching by traditional RANSAC algorithm (NCC rough match); (c) Initial set of matching-corner pairs in our algorithm; (d) Final set of matching-corner pairs in our algorithm.

<p>The second experiment on the corner matching efficiency: (a) Selected corners (using our algorithm) from two original images; (b) Corner matching by traditional RANSAC algorithm (NCC rough match); (c) Initial set of matching-corner pairs in our algorithm; (d) Final set of matching-corner pairs in our algorithm.</p

Qualitative and quantitative amalysis on three different algorithms (II).

<p>Qualitative and quantitative amalysis on three different algorithms (II).</p

Efficiency comparison between traditional RANSAC and our algorithm (I).

<p>Efficiency comparison between traditional RANSAC and our algorithm (I).</p

Two initially matching-corner pairs, and , along with their respective midpoints: between and and between and .

<p>Two initially matching-corner pairs, and , along with their respective midpoints: between and and between and .</p

DataSheet_1_Determination of a DNA repair-related gene signature with potential implications for prognosis and therapeutic response in pancreatic adenocarcinoma.docx

BackgroundPancreatic adenocarcinoma (PAAD) is one of the leading causes of cancer death worldwide. Alterations in DNA repair-related genes (DRGs) are observed in a variety of cancers and have been shown to affect the development and treatment of cancers. The aim of this study was to develop a DRG-related signature for predicting prognosis and therapeutic response in PAAD.MethodsWe constructed a DRG signature using least absolute shrinkage and selection operator (LASSO) Cox regression analysis in the TCGA training set. GEO datasets were used as the validation set. A predictive nomogram was constructed based on multivariate Cox regression. Calibration curve and decision curve analysis (DCA) were applied to validate the performance of the nomogram. The CIBERSORT and ssGSEA algorithms were utilized to explore the relationship between the prognostic signature and immune cell infiltration. The pRRophetic algorithm was used to estimate sensitivity to chemotherapeutic agents. The CellMiner database and PAAD cell lines were used to investigate the relationship between DRG expression and therapeutic response.ResultsWe developed a DRG signature consisting of three DRGs (RECQL, POLQ, and RAD17) that can predict prognosis in PAAD patients. A prognostic nomogram combining the risk score and clinical factors was developed for prognostic prediction. The DCA curve and the calibration curve demonstrated that the nomogram has a higher net benefit than the risk score and TNM staging system. Immune infiltration analysis demonstrated that the risk score was positively correlated with the proportions of activated NK cells and monocytes. Drug sensitivity analysis indicated that the signature has potential predictive value for chemotherapy. Analyses utilizing the CellMiner database showed that RAD17 expression is correlated with oxaliplatin. The dynamic changes in three DRGs in response to oxaliplatin were examined by RT-qPCR, and the results show that RAD17 is upregulated in response to oxaliplatin in PAAD cell lines.ConclusionWe constructed and validated a novel DRG signature for prediction of the prognosis and drug sensitivity of patients with PAAD. Our study provides a theoretical basis for further unraveling the molecular pathogenesis of PAAD and helps clinicians tailor systemic therapies within the framework of individualized treatment.</p

Experiments on the entire image-stitching algorithm (IV): (a) Video image 1; (b) Video image 2 (reference image); (c) Video image 3; (d) Generated video panoramic image.

<p>Experiments on the entire image-stitching algorithm (IV): (a) Video image 1; (b) Video image 2 (reference image); (c) Video image 3; (d) Generated video panoramic image.</p

The Sigmoid function, where it is clearly demonstrated that the critical value range of is [−5, 5].

<p>The Sigmoid function, where it is clearly demonstrated that the critical value range of is [−5, 5].</p

Efficiency comparison between traditional RANSAC and our algorithm (II).

<p>Efficiency comparison between traditional RANSAC and our algorithm (II).</p

Comparison of stitching results from three different algorithms (I): (a) Traditional Harris detector; (b) The regional corner-selection algorithm in [9]; (c) Our presented algorithm.

<p>Comparison of stitching results from three different algorithms (I): (a) Traditional Harris detector; (b) The regional corner-selection algorithm in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0081182#pone.0081182-Zhao1" target="_blank">[9]</a>; (c) Our presented algorithm.</p