Duplicate Images Research Articles

RETRACTION: Targeted Delivery of Doxorubicin in HeLa Cells Using Self‐Assembled Polymeric Nanocarriers Guided by Deep Eutectic Solvents

AbstractRETRACTION: P. Pradeepkumar, M. Rajan, H. S. Almoallim, and S. A. Alharbi, “Targeted Delivery of Doxorubicin in HeLa Cells Using Self‐Assembled Polymeric Nanocarriers Guided by Deep Eutectic Solvents,” ChemistrySelect 6, No. 28 (2021): 7232–7241, https://doi.org/10.1002/slct.202000036.The above article, published online on July 26, 2021, in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal editor‐in‐chief, Preeti Vashi, and Wiley‐VCH GmbH.The retraction has been agreed upon following an investigation into concerns raised by a third party, which identified duplicated images in Figures 7A, S3C, and S5A–D. The investigation also identified image duplication between Figure 6A and a figure from a previously published article by a similar group of authors. The authors were unable to provide a satisfactory explanation and did not provide all the original data. As a result, the data and the conclusions are considered unreliable.

RETRACTION: Tanshinone I attenuates the malignant biological properties of ovarian cancer by inducing apoptosis and autophagy via the inactivation of PI3K/AKT/mTOR pathway.

J. Zhou , Y.-y. Jiang , H. Chen , Y.-c. Wu , and L. Zhang , "Tanshinone I Attenuates the Malignant Biological Properties of Ovarian Cancer by Inducing Apoptosis and Autophagy via the Inactivation of PI3K/AKT/mTOR Pathway," Cell Proliferation 53, no. 2 (2020): e12739. https://doi.org/10.1111/cpr.12739. The above article, published online on 09 December 2019, in Wiley Online Library (wileyonlinelibrary.com), and has been retracted by agreement between the journal Deputy Editor, Yunfeng Lin; and John Wiley & Sons Ltd. A third party contacted the publisher to report that duplicated images had been detected in multiple different articles by different author groups, each of which described different experimental conditions. The image duplications are listed as follows: Images in Figure 1C were duplicated in Wang et al. 2019 (https://doi.org/10.2147/OTT.S221161), which was submitted and published prior to the publication of this article in Cell Proliferation. In addition, duplicate images from Figures 1C, 1D, 2C, 3B, 3D, 4C, 4D, and 5D were detected in many other articles by different author groups that had been published subsequent to this article. The authors responded to an inquiry by the publisher regarding these concerns and shared what were labeled as original images for Figures 1-6 but did not share the original images for the cell proliferation assays that had been used in Figure 1. The editors reviewed this evidence and found that it did not properly explain the duplications across different articles. The retraction has been agreed to because the duplications of images across different articles fundamentally compromises the conclusions and results presented in the article. The authors disagree with the retraction.

A fast and efficient large-scale near duplicate image retrieval system using double perceptual hashing

A fast and efficient large-scale near duplicate image retrieval system using double perceptual hashing

Duplicate image detection using deep learning modified SVM and k-NN classification method for multimedia application

Duplicate image detection using deep learning modified SVM and k-NN classification method for multimedia application

Kernel Block Based Morphing Identification and Elimination In Server

The primary stage of duplicated region identification for investigating duplicate and insert image forgeries does the image block match. Because of evolution of highly sophisticated tools, numerous image modifications have been made. When images are difficult to duplicate and transfer, forgeries of those parts may happen. The images are examined closely in the area where they were forged. By using the suggested Gaussian RBF kernel PCA, the segment of the image that is copied and pasted will be known. One of the most significant issues in this step is the high processing time required to locate related locations. This research proposes a digital block-based picture water mark embedding strategy this uses singular value decomposition (SVD), a mathematical technique. There is already embedding a water mark across the entire image using traditional SVD watermarking. The suggested method involves breaking up the partitioning original image into blocks, after which the water mark existindividually inserted into the singular value of each block. Additionally, the employ the temporal complexity calculate the recommended algorithm's performance. The result of the experiment and thecalculation analysisdemonstrate that two layer matching is possible more traditional approaches Similar to lexicographic sorting, illustrate how two-layer matching can be accomplished. . High key points are awarded for the dimensionality of the feature vector representation in the picture matching. The suggested approach eliminates compression, identifies picture features using blurring, and contaminates the image with noise. The most straightforward way to identify image forgeries using editing technology or morphing is through computational efficiency. Numerous tests have shown that the system is more efficient than the one that is already in use. It is helpful for recording historical time intervals with patterns resembling those of a query time range. To obtain an accurate prediction, the system determines the location, MAC address, and IP address of the morphing uploader.

Retraction: Wang C, Chen Z, Ge Q, Hu J, Li F, Hu J, Xu H, Ye Z, Li LC (2014) Up-regulation of p21WAF1/CIP1 by miRNAs and its implications in bladder cancer cells. FEBS Letters 588, 4654-4664. https://doi.org/10.1016/j.febslet.2014.10.037.

The above article, published online on November 11, 2014, in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief Michael Brunner, FEBS Press, and John Wiley and Sons Ltd. The retraction has been agreed following an investigation into concerns raised by a third party, which revealed inappropriate duplications of image panels in Figs 6B, 7A, and S1C. The authors responded to an inquiry by the publisher and provided images related to the duplicated microscopy images and Western blots. Upon review of the provided images, the editors determined that these images did not provide compelling evidence for the integrity of the underlying data. The editors consider the conclusions substantially compromised and are therefore retracting the paper. The authors disagree with this retraction.

Abstract 6083: Inter-observer agreement in ultrasound-detected steatotic liver in the Chile Biliary Longitudinal Study (BiLS)

Abstract Background: Metabolic-associated steatotic liver disease (MASLD) is a prevalent chronic liver condition with an increased risk of cancer-related mortality. In Latin America, MASLD prevalence is estimated at 40%, with Chile reporting rates of 47.5% in 2019. Early detection is crucial for MASLD control and management. Liver biopsy, the gold standard for diagnosis, is invasive and costly. Ultrasound (US) is a preferred alternative, yet its precision varies depending on the observer interpretation. Methods: We conducted a cross-sectional analysis of a subset of baseline US images from the Chile Biliary Longitudinal Study (Chile BiLS), a cohort of women aged 50-74 with gallstones in Chile. Of the 4,032 participants recruited from Cautín Province in the Araucanía region, a random sample of 10% of the images were selected for review by a radiologist from Baylor College of Medicine (Observer 1) and compared to the original readings generated at the baseline exam by a team of Chile BiLS ultrasound technicians. To assess reproducibility, 33 blinded duplicate images from the 10% sample were reanalyzed by Observer 1 and an additional Chile BiLS technician who was not part of the original baseline team (Observer 2). Inter- and intra-observer agreement was assessed using weighted kappa values. Images were categorized as none, mild, moderate/severe. Results: Of the 409 images reviewed, agreement between Observer 1 and original readings across all categories of steatosis was poor (kappa: 0.09; CI 95% 0.06;0.11, p <0.001). The 33 duplicates compared to the original readings showed slight agreement for Observer 1 (kappa: 0.11 CI 95% 0.02;0.20, p <0.05) and fair for Observer 2 (kappa: 0.28 (CI 95% 0.07 - 0.49, p <0.001). The intra-observer agreement for Observer 1 was moderate, with a kappa of 0.41 (CI 95% 0.09 - 0.74, p <0.05), and for Observer 2, it was substantial with a weighted kappa of 0.69 (CI 95% 0.5 - 0.9, p <0.001). Conclusion: The study highlights the variability in US interpretation of hepatic steatosis, emphasizing the importance of standardized training and methodology. US's sensitivity and specificity is operator dependence, which impacts the diagnostic interpretation. Our findings underscore the significance of inter and intra-observer comparisons for optimal diagnosis, especially when US is the primary diagnostic tool in MASLD assessment. Finally, our results emphasize the importance of group training. Observer 2, who underwent the same training as the original Chile BiLS ultrasound technicians and was consistently exposed to this technique as a diagnostic method, exhibited more consistent results. This reinforces the importance of collective training initiatives to enhance the reliability and accuracy of US in MASLD diagnosis. Citation Format: Maria Spencer Sandino, Vanessa Van De Wyngard, David Wynne, Maya Balakrishnan, Noldy Mardones, Ruth Pfeiffer, Allan Hildesheim, Catterina Ferreccio, Jill Koshiol. Inter-observer agreement in ultrasound-detected steatotic liver in the Chile Biliary Longitudinal Study (BiLS) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6083.

Impact of point-of-care clinical decision support on referrer behavior, imaging volume, patient radiation dose exposure, and sustainability

ObjectivesWhen referring patients to radiology, it is important that the most appropriate test is chosen to avoid inappropriate imaging that may lead to delayed diagnosis, unnecessary radiation dose, worse patient outcome, and poor patient experience. The current radiology appropriateness guidance standard at our institution is via access to a standalone web-based clinical decision support tool (CDST). A point-of-care (POC) CDST that incorporates guidance directly into the physician workflow was implemented within a subset of head and neck cancer specialist referrers. The purpose of this audit was to evaluate the imaging pathway, pre- and post-implementation to assess changes in referral behavior.MethodsCT and MRI neck data were collected retrospectively to examine the relationship between imaging referrals pre- and post-POC CDST implementation. Effective radiation dose and estimated carbon emissions were also compared.ResultsThere was an overall reduction in absolute advanced imaging volume by 8.2%, and a reduction in duplicate CT and MRI imaging by 61%, p < 0.0001. There was also a shift in ordering behavior in favor of MRI (OR [95% CI] = 1.50 [1.02–2.22], p = 0.049). These changes resulted in an effective radiation dose reduction of 0.27 mSv per patient, or 13 equivalent chest x-rays saved per patient, p < 0.0001. Additionally, the reduction in unnecessary duplicate imaging led to a 13.5% reduction in carbon emissions, p = 0.0002.ConclusionsImplementation of the POC CDST resulted in a significant impact on advanced imaging volume, saved effective dose, and reduction in carbon emissions.Critical relevance statementThe implementation of a point-of-care clinical decision support tool may reduce multimodality ordering and advanced imaging volume, manifesting in reduced effective dose per patient and reduced estimated carbon emissions. Widespread utilization of the point-of-care clinical decision support tool has the potential to reduce imaging wait times.Key points• Implementation of the point-of-care clinical decision support tool reduced the number of patients who simultaneously had a CT and MRI ordered for the same clinical indication compared to a standalone web-based clinical decision support tool.• The point-of-care clinical decision support tool reduced the absolute number of CT/MRI scans requested compared to the standalone web-based clinical decision support tool.• Utilization of the point-of-care clinical decision support tool led to a significant reduction in the effective dose per patient compared to the standalone web-based clinical decision support tool.Graphical

Handling Missing MRI Data in Brain Tumors Classification Tasks: Usage of Synthetic Images vs. Duplicate Images and Empty Images.

Deep-learning is widely used for lesion classification. However, in the clinic patient data often has missing images. To evaluate the use of generated, duplicate and empty(black) images for replacing missing MRI data in AI brain tumor classification tasks. Retrospective. 224 patients (local-dataset; low-grade-glioma (LGG) = 37, high-grade-glioma (HGG) = 187) and 335 patients (public-dataset (BraTS); LGG = 76, HGG = 259). The local-dataset was divided into training (64), validation (16), and internal-test-data (20), while the public-dataset was an independent test-set. T1WI, T1WI+C, T2WI, and FLAIR images (1.5T/3.0T-MR), obtained from different suppliers. Three image-to-image translation generative-adversarial-network (Pix2Pix-GAN) models were trained on the local-dataset, to generate T1WI, T2WI, and FLAIR images. The rating-and-preference-judgment assessment was performed by three human-readers (radiologist (MD) and two MRI-technicians). Resnet152 was used for classification, and inference was performed on both datasets, with baseline input, and with missing data replaced by 1) generated images; 2) duplication of existing images; and 3) black images. The similarity between the generated and the original images was evaluated using the peak-signal-to-noise-ratio (PSNR) and the structural-similarity-index-measure (SSIM). Classification results were evaluated using accuracy, F1-score and the Kolmogorov-Smirnov test and distance. For baseline-state, the classification model reached to accuracy = 0.93,0.82 on the local and public-datasets. For the missing-data methods, high similarity was obtained between the generated and the original images with mean PSNR = 35.65,32.94 and SSIM = 0.87,0.91 on the local and public-datasets; 39% of the generated-images were labeled as real images by the human-readers. The classification model using generated-images to replace missing images produced the highest results with mean accuracy = 0.91,0.82 compared to 0.85,0.79 for duplicated and 0.77,0.68 for use of black images; DATA CONCLUSION: The feasibility for inference classification model on an MRI dataset with missing images using the Pix2pix-GAN generated images, was shown. The stability and generalization ability of the model was demonstrated by producing consistent results on two independent datasets. 3 TECHNICAL EFFICACY: Stage 5.

FlowSim: Near duplicate detection for flow cytometry data.

The analysis of large amounts of data is important for the development of machine learning (ML) models. flowSim is the first algorithm designed to visualize, detect and remove highly redundant information in flow cytometry (FCM) training sets to decrease the computational time for training and increase the performance of ML algorithms by reducing overfitting. flowSim performs near duplicate image detection by combining community detection algorithms with the density analysis of the marker expression values. flowSim clustering compared to consensus manual clustering on a dataset composed of 160 images of bivariate FCM data had a mean Adjusted Rand Index of 0.90, demonstrating its efficiency in identifying similar patterns. flowSim selectively discarded near duplicate files in datasets constructed with known redundancy, and removed 92.6% of FCM images in a dataset of over 500,000 drawn from public repositories.

Inappropriate image duplications in rhinology research publications.

Duplicated images in research articles erode integrity and credibility of biomedical science. Forensic software is necessary to detect figures with inappropriately duplicated images. This analysis reveals a significant issue of inappropriate image duplication in our field.

Proposed limits for ordinal echocardiographic estimates of left atrial enlargement in dogs

ObjectiveTo propose echocardiographic left-atrial-to-aortic ratio (LA:Ao) limits for defining ordinal categories of left atrial (LA) enlargement in dogs. Animals, materials and methodsRight parasternal short-axis echocardiographic images from 33 dogs with various degrees of LA enlargement. Right parasternal short-axis and long-axis echocardiographic measurements from 238 healthy dogs. Images were duplicated and randomized. Duplicate images contained a LA:Ao estimate. Participants categorized the LA in each image into one of four categories: normal, mildly, moderately or severely enlarged. Distributions of categorization were compared between cardiologists and non-cardiologists. Intra-observer intra-study and inter-study agreement were examined. Effect of measurement was evaluated on agreement between participants. A parametric estimate of LA enlargement was calculated for both short-axis and long-axis views. ResultsCardiologists and non-cardiologists provided similar distributions of LA size estimates, and showed similarly high intra-observer agreement (kappa = 0.84). Having a measurement provided with the image increased agreement for categorizing LA as normal or mildly enlarged (P<0.001). Parametric and consensus-based approaches provided similar limits for categorizing left atrial size in the right parasternal short-axis view – normal = LA:Ao < 1.6, mildly enlarged = 1.6 < LA:Ao < 1.9, moderately enlarged = 1.9 < LA:Ao < 2.3, severely enlarged = LA:Ao ≥ 2.3. A parametric approach for the right parasternal long-axis view provided the following: normal = LA:Ao < 2.1, mildly enlarged = 2.1 < LA:Ao < 2.5, moderately enlarged = 2.5 < LA:Ao < 2.7, severely enlarged = LA:Ao ≥ 2.7. ConclusionsParticipants mostly classified LA sizes into four ordinal categories that corresponded to the aforementioned limits. Clinicians estimating LA size in early diastole can use these limits to increase inter-observer agreement when identifying LA enlargement.

Single siRNA Nanocapsules for Effective siRNA Brain Delivery and Glioblastoma Treatment

Adv. Mater. 2020, 32, 2000416 DOI: 10.1002/adma.202000416 Errors were found in Figure 4a of the published article, where duplicate images had been included in some of the panels in the figure. The corrected figure is shown below. The 6 images in the bottom-most panel for Free siPLK1 have been replaced. The authors sincerely apologize for any inconvenience.

Retraction of: Downregulation of miR-335-5p by Long Noncoding RNA ZEB1-AS1 in Gastric Cancer Promotes Tumor Proliferation and Invasion by Zhang, et al. (doi: 10.1089/dna.2017.3926).

DNA and Cell BiologyAhead of Print Retraction NoticeOpen AccessRetraction of: Downregulation of miR-335-5p by Long Noncoding RNA ZEB1-AS1 in Gastric Cancer Promotes Tumor Proliferation and Invasion by Zhang, et al. (doi: 10.1089/dna.2017.3926)This article retracts the following:Retracted: Downregulation of miR-335-5p by Long Noncoding RNA ZEB1-AS1 in Gastric Cancer Promotes Tumor Proliferation and InvasionPublished Online:27 Mar 2023https://doi.org/10.1089/dna.2017.3926.retractAboutSectionsPDF/EPUB Permissions & CitationsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail The Editor-in-Chief of DNA and Cell Biology officially retracts the article entitled, Downregulation of miR-335-5p by Long Noncoding RNA ZEB1-AS1 in Gastric Cancer Promotes Tumor Proliferation and Invasion by Li-li Zhang, Lan-fang Zhang, Xiao-he Guo, De-zhong Zhang, Fang Yang, and Ying-ying Fan (DNA Cell Biol. (2018;37(1):46–52; doi: 10.1089/dna.2017.3926).A post on the PubPeer platform1 raised concerns about the possibility of duplicated images within Figure 4. An independent analysis found that Figure 4D exhibits a matching section across two other images that are identified in the paper as representing different experimental conditions. Additionally, the analysis identified Figure 6G as a composite image. Other features of the text have raised concerns about the originality and authorship of this work.The Editor-in-Chief of DNA and Cell Biology contacted all authors listed on the paper, asking them to provide original data and uncropped images for Figure 4, as well as an explanation for the cause for the evident composite image in 6G. No communication or other statements were received from any of the listed authors.Because of the doubts raised with regards to the originality and reliability of the published work, the Editor-in-Chief fully retracts the published article.The authors were notified of the decision to retract the paper, but no acknowledgment was received.DNA and Cell Biology is committed to upholding the rigors of scientific publishing and the veracity of the literature.Reference1. Actinopolyspora biskrensis. https://pubpeer.com/publications/38874F2693B96B7CEF544999649176?utm_source=Firefox&utm_medium=BrowserExtension&utm_campaign=Firefox. PubPeer; Feb 2023. Google ScholarFiguresReferencesRelatedDetailsRelated articlesRetracted: Downregulation of miR-335-5p by Long Noncoding RNA ZEB1-AS1 in Gastric Cancer Promotes Tumor Proliferation and Invasion1 Jan 2018DNA and Cell Biology Volume 0Issue 0 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Retraction of: Downregulation of miR-335-5p by Long Noncoding RNA ZEB1-AS1 in Gastric Cancer Promotes Tumor Proliferation and Invasion by Zhang, et al. (doi: 10.1089/dna.2017.3926).DNA and Cell Biology.ahead of printhttp://doi.org/10.1089/dna.2017.3926.retractcreative commons licenseOnline Ahead of Print:March 27, 2023PDF download

Improved Breast 2D SWE Algorithm to Eliminate False-Negative Cases.

Two-dimensional shear wave elastography (SWE) has been limited in breast lesion characterization due to false-negative results from artifacts. The aim of this study was to evaluate an updated Food and Drug Administration-approved breast 2D-SWE algorithm and compare with the standard algorithm (SA). This prospective, single-center study was approved by our local institutional review board and Health Insurance Portability and Accountability Act compliant. From April 25, 2019 to May 2, 2022, raw shear wave data were saved on patients having screening or diagnostic breast ultrasound on a Siemens Sequoia US. After removing duplicate images and those without biopsy diagnosis or stability over 2 years, there were 298 patients with 394 lesions with biopsy-proven pathology or >2-year follow-up. Raw data were processed using the SA and a new algorithm (NA). Five-millimeter regions of interest were placed in the highest stiffness in the lesion or adjacent 3 mm on the SA. Stiffness values (shear wave speed, max) in this location from both algorithms were recorded. Statistics were calculated for comparing the 2 algorithms. The mean patient age was 56.3 ± 16.1 years (range, 21-93 years). The mean benign lesion size was 10.7 ± 8.0 mm (range, 2-46 mm), whereas the mean malignant lesion size was 14.9 ± 7.8 mm (range, 4-36 mm). There were 201 benign (>2-year follow-up) and 193 biopsied lesions (65 benign; 128 malignant). The mean maximum stiffness for benign lesions was 2.37 m/s (SD 1.26 m/s) for SA and 3.51 m/s (SD 2.05 m/s) for NA. For malignant lesions, the mean maximum stiffness was 4.73 m/s (SD, 1.71 m/s) for SA and 8.45 m/s (SD, 1.42 m/s) for NA. The area under the receiver operating characteristic curve was 0.87 SA and 0.95 NA when using the optimal cutoff value. Using a threshold value of 5.0 m/s for NA and comparing to SA, the sensitivity increased from 0.45 to 1.00 and the specificity decreased from 0.94 to 0.81; the positive predictive value was 0.72, the negative predictive value was 1.00, and the negative likelihood ratio was 0.00. Using a new breast SWE algorithm significantly improves the sensitivity of the technique with a small decrease in specificity, virtually eliminating the "soft" cancer artifact. The new 2D-SWE algorithm significantly increases the sensitivity and negative predictive value in characterizing breast lesions as benign or malignant and allows for downgrading all BI-RADS 4 lesions.

Correction to: Downregulation of LAPTM4B Contributes to the Impairment of the Autophagic Flux via Unopposed Activation of mTORC1 Signaling During Myocardial Ischemia/Reperfusion Injury.

HomeCirculation ResearchVol. 132, No. 5Correction to: Downregulation of LAPTM4B Contributes to the Impairment of the Autophagic Flux via Unopposed Activation of mTORC1 Signaling During Myocardial Ischemia/Reperfusion Injury Free AccessCorrectionPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessCorrectionPDF/EPUBCorrection to: Downregulation of LAPTM4B Contributes to the Impairment of the Autophagic Flux via Unopposed Activation of mTORC1 Signaling During Myocardial Ischemia/Reperfusion Injury Originally published2 Mar 2023https://doi.org/10.1161/RES.0000000000000597Circulation Research. 2023;132:e94This article corrects the followingDownregulation of LAPTM4B Contributes to the Impairment of the Autophagic Flux via Unopposed Activation of mTORC1 Signaling During Myocardial Ischemia/Reperfusion InjuryIn the article by Gu et al. entitled “Downregulation of LAPTM4B Contributes to the Impairment of the Autophagic Flux via Unopposed Activation of mTORC1 Signaling During Myocardial Ischemia/Reperfusion Injury,” which was published in the September 11, 2020 issue of the journal (Circ Res. 2020;127:e148–e165. doi: 10.1161/CIRCRESAHA.119.316388), corrections were needed.After concerns were raised in a PubPeer post about potential included duplicated images in image figures and blots, the authors thoroughly checked the data and provided an explanation. For Supplemental Figure IV-I, the WGA image in WT was accidentally presented as a ‘LAPTM4B-/-’ image during figure preparation. For Figure 4C, the representative blot image of GAPDH for Figure 7B was inadvertently duplicated to Figure 4C during figure processing. The authors identified a similar accidental mistake of P62 blot image in Figure 7B.The authors have provided original images and uncropped original gel scan to the journal for correction of these mistakes and updated the Figure 4C, Figure 7B and Supplemental Figure IV-I.The authors apologize for these errors and state that the corrections do not affect the original conclusions.The corrected version of this article is available at http://www.ahajournals.org/doi/10.1161/CIRCRESAHA.119.316388. Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesDownregulation of LAPTM4B Contributes to the Impairment of the Autophagic Flux via Unopposed Activation of mTORC1 Signaling During Myocardial Ischemia/Reperfusion InjuryShanshan Gu, et al. Circulation Research. 2020;127:e148-e165 March 3, 2023Vol 132, Issue 5 Advertisement Article InformationMetrics © 2023 American Heart Association, Inc.https://doi.org/10.1161/RES.0000000000000597PMID: 36862816 Originally publishedMarch 2, 2023 PDF download Advertisement

Visual misinformation on Facebook

Abstract We conduct the first large-scale study of image-based political misinformation on Facebook. We collect 13,723,654 posts from 14,532 pages and 11,454 public groups from August through October 2020, posts that together account for nearly all engagement of U.S. public political content on Facebook. We use perceptual hashing to identify duplicate images and computer vision to identify political figures. Twenty-three percent of sampled political images (N = 1,000) contained misinformation, as did 20% of sampled images (N = 1,000) containing political figures. We find enormous partisan asymmetry in misinformation posts, with right-leaning images 5–8 times more likely to be misleading, but little evidence that misleading images generate higher engagement. Previous scholarship, which mostly cataloged links to noncredible domains, has ignored image posts which account for a higher volume of misinformation. This research shows that new computer-assisted methods can scale to millions of images, and help address perennial and long-unanswered calls for more systematic study of visual political communication.

Brain Tumor Detection Using Transfer Learning

The main objective of the proposed work is to encounter the most serious condition of brain tumors. However, if caught early enough, a brain tumor can be cured. MRI scans and CT scans are used to diagnose brain tumors in most cases. It is far too difficult to accurately detect a tumor’s location and size. It is often difficult for doctors and patients to comprehend the outcomes. This paper targets to frame automated segmentation and classification of brain tumors. In this work, around 3000 MRI images (both tumors and non-tumors) are collected. To identify the images with tumors, Otsu's segmentation method is used. Following this process, the feature extraction technique is carried out using the Vantage Point Tree algorithm, which employs the modification features of the VP Tree algorithm (grayscale images and removal of duplicate images) to increase accuracy, but the present VP Tree algorithm is used for color images. Next, Otsu’s segmentation algorithm accuracy is obtained before and after the segmentation using four classification algorithms namely Efficient Net B0, Efficient Net V2-B0, Res Net 50, Res Net 101, and VGG 19. Following this severity classification is carried out to categorize the datasets depending on their severity (i.e., Grade I, Grade II, Grade III, and Grade IV). Comparing the obtained results, the EfficientNetB0 and EfficientNetV2-B0 outperform in terms of accuracy, precision, and F1 score compared to the other classification algorithms.

Expression of Concern: LncRNA GAS5 suppresses ovarian cancer by inducing inflammasome formation.

The Editorial Office has been made aware of potential issues surrounding the scientific validity of this paper, hence has issued an expression of concern to notify readers whilst the Editorial Office investigates. There appears to be a duplicated image, with some possible signs of alteration, between the first two panels of Figure 4E.

Effective and Efficient Video Compression by the Deep Learning Techniques

Deep learning has reached many successes in Video Processing. Video has become a growing important part of our daily digital interactions. The advancement of better resolution content and the large volume offers serious challenges to the goal of receiving, distributing, compressing and revealing high-quality video content. In this paper we propose a novel Effective and Efficient video compression by the Deep Learning framework based on the flask, which creatively combines the Deep Learning Techniques on Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN). The video compression method involves the layers are divided into different groups for data processing, using CNN to remove the duplicate frames, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using GAN and recorded with Long Short-Term Memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps with frame-level compression. Pixel wise comparison is performed using K-nearest Neighbours (KNN) over the frame, clustered with K-means and Singular Value Decomposition (SVD) is applied for every frame in the video for all three colour channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, Frames per second (FPS), and quality results demonstrated a significant resampling rate. On normal, the outcome delivered had around a 10% deviation in quality and over half in size when contrasted, and the original video.