Incorrect Image Research Articles

Digital character assassination in the Jordanian law

One of the rights inherent in a legal personality is the ability to maintain the individual's dignity and treat himself with respect, regardless of his social status. The rise of cybercrime has made it possible to launch digital attacks on someone's honor or reputation, creating a need for rules that limit what can be posted on personal pages and websites. The adoption of Cybercrime Law No. 17 of 2023 shows that Jordanian lawmakers have been proactive. Through the use of a descriptive and inductive analytical approach, this research demonstrated the uniqueness of this legislation, which prohibits the distribution or attribution of any works aimed at defaming a person’s reputation. Presenting an incorrect image of the targets and isolating them socially is the deliberate moral destruction of a person, organization, institution, or social group through spreading rumours, fabricating accusations, and manipulating data and facts. This procedure is similar to the deliberate assassination of individuals. Lawmakers in Jordan have tightened penalties for the perpetrators of this crime and those who helped commit it. The study found that although the goal of character assassination and the crime of digital defamation is to harm the victim's reputation and dignity, there is a significant difference. Character assassination differs from digital defamation in that it requires a specific goal: the moral destruction and social isolation of the targeted individual. This paper aims to clarify the term character assassination, analyze the legislative text that explicitly criminalizes it, identify the elements of this crime and how it is committed, verify the criminal protection procedures decided by the Jordanian legislator, and explain the reasons that prove these crimes. This paper relied on the descriptive approach in studying the phenomenon. Character assassination and gathering information related to this field.

SISS‐CSA: Secret image sharing scheme with ciphertext‐based share authentication for malicious model

AbstractWe propose a novel secret image sharing scheme with ciphertext‐based share authentication (SISS‐CSA) for sharing grayscale and color secret images in the malicious model. In SISS‐CSA, the dealer and each participant, individually acting as a combiner, can identify each invalid share received from the malicious participant(s) before using it to reconstruct the secret image. This capability, which most comparable schemes lack, prevents reconstructing an incorrect secret image. In SISS‐CSA, the asymptotic time complexities of operations executed by the dealer in the shares generation phase and executed by each combiner in the secret image reconstruction phase are and , respectively. Here, is the number of grayscale values in the secret image, is the number of generated shares, and is the threshold number of shares required for reconstructing the secret image. These asymptotic time complexities and the size of additional information each combiner stores for identifying invalid share(s) are comparatively lesser than those in the state‐of‐the‐art schemes. Furthermore, we obtain a maximum of reduction in the size of additional information each combiner stores for share authentication using the ciphertext‐based share authentication compared to using the standard SHA‐256. To the best of our knowledge, none of the related share authentication approaches achieves this much reduction. We prove the properties of SISS‐CSA using theoretical analysis. We also provide experimental results validating the implications of theoretical analysis corresponding to asymptotic time complexities and the random nature of shares.

EXPRESSION OF CONCERN: Therapeutic targeting of chemoresistant and recurrent glioblastoma stem cells with a proapoptotic variant of oncolytic herpes simplex virus.

Nusrat Jahan, Jae M. Lee, Khalid Shah, Hiroaki Wakimoto, "Therapeutic targeting of chemoresistant and recurrent glioblastoma stem cells with a proapoptotic variant of oncolytic herpes simplex virus", International Journal of Cancer 141, no. 8 (2017): 1671-1681. https://doi.org/10.1002/ijc.30811. This Expression of Concern is for the above article, published online on 31 May 2017, in Wiley Online Library (wileyonlinelibrary.com), and has been published by agreement between the authors, the journal's Editor-in-Chief, Christoph Plass, the Union for International Cancer Control and John Wiley & Sons, Ltd. The Expression of Concern has been agreed following concerns raised regarding figure 4g and 5d. The Control (PBS) 4X DAPI image in figure 5d was found to contain elements similar to the Control (PBS) DAPI image presented in figure 4g. The authors admitted that they had mistakenly supplied the incorrect image to represent DAPI in figure 5d. Since the original DAPI image for figure 5d is no longer available, this issue could not be resolved. The journal has decided to issue an Expression of Concern to alert the readers.

Performance of GPT-4 Vision on kidney pathology exam questions.

ChatGPT (OpenAI, San Francisco, CA) has shown impressive results across various medical examinations, but its performance in kidney pathology is not yet established. This study evaluated proficiencies of GPT-4 Vision (GPT-4V), an updated version of the platform with the ability to analyze images, on kidney pathology questions and compared its responses with those of nephrology trainees. Thirty-nine questions (19 text-based questions and 20 with various kidney biopsy images) designed specifically for the training of nephrology fellows were employed. GPT-4V displayed comparable accuracy rates in the first and second runs (67% and 72%, respectively, P = .50). The aggregated accuracy, however-particularly, the consistent accuracy-of GPT-4V was lower than that of trainees (72% and 67% vs 79%). Both GPT-4V and trainees displayed comparable accuracy in responding to image-based and text-only questions (55% vs 79% and 81% vs 78%, P = .11 and P = .67, respectively). The consistent accuracy in image-based, directly asked questions for GPT-4V was 29%, much lower than its 88% consistency on text-only, directly asked questions (P = .02). In contrast, trainees maintained similar accuracy in directly asked image-based and text-based questions (80% vs 77%, P = .65). Although the aggregated accuracy for correctly interpreting images was 69%, the consistent accuracy across both runs was only 39%. The accuracy of GPT-4V in answering questions with correct image interpretation was significantly higher than for questions with incorrect image interpretation (100% vs 0% and 100% vs 33% for the first and second runs, P = .001 and P = .02, respectively). The performance of GPT-4V in handling kidney pathology questions, especially those including images, is limited. There is a notable need for enhancement in GPT-4V proficiency in interpreting images.

Corrigendum to: Upregulation of Suppressor of Cytokine Signaling 3 in Microglia by Cinnamic Acid.

In the online version of the article, a change was made in Figure 1 of the article titled "Upregulation of Suppressor of Cytokine Signaling 3 in Microglia by Cinnamic Acid", published in Current Alzheimer Research, 2018, 15(10), 894-904. The author informed the publisher that an incorrect image was provided for Figure 1. The correct image was from the P-CREB experiment of Figure 5 [1]. Now, the revised Figure 1 has been updated in the article, and it can be found online at: https://www.eurekaselect.com/article/90209.

Association between overweight/obesity perception, actual body weight and cardiometabolic risk among healthy Ghanaian adults

BackgroundThe global rise in obesity, particularly among black Africans in developing nations experiencing nutritional transitions, underscores the importance of exploring Overweight/Obesity Perception (OP) as a crucial factor in maintaining an optimal body weight. Incorrect body image perception may impede efforts to achieve the desired body weight, leading to adverse outcomes related to Cardiometabolic Diseases (CMD). This cross-sectional study investigated the interplay between overweight/obesity perception, actual body weight, and CMD risk in a cohort of healthy Ghanaian adults. MethodsA total of 302 apparently healthy adults, aged 25–60 years, with Dagomba ancestry, were recruited from three communities. Participants were screened based on age, ancestry, history of communicable and Non-Communicable Diseases (NCD), and use of antidiabetic, lipid-lowering, and antihypertensive drugs. Anthropometric assessments and blood sample collections for biochemical analysis were conducted. Body image perception was measured using the Stunkard Figure Rating Scale (SFRS). Data were analyzed using descriptive statistics, chi-square tests, correlation analysis, logistic regression, and multivariate analysis. ResultsParticipants had a mean age of 38.28 ± 10.88, with 61.6% being women. While 47% accurately perceived their body weight, 53% had incorrect perceptions. Notably, 47.2% underestimated and 8.5% overestimated their weight status. Among overweight individuals, 33% underestimated and 9.4% overestimated their weight, whereas among the obese, 66.7% and 33.3% respectively had inaccurate perceptions. Gender, serum triglyceride levels, and waist circumference were significantly associated with weight perception. About 55% of overweight/obese participants and 62.1% with high waist circumference did not express a desire to lose weight. Multiple logistic regression revealed that both overweight (AOR = 6, 95% CI (1.8–20.2), p < 0.05) and obesity (AOR = 20.5, 95% CI (5–84.9), p < 0.05) significantly increased the odds of CMD. ConclusionThe findings underscore the association between overweight/obesity and an elevated risk of CMD. This emphasizes the imperative for public health interventions aimed at promoting an ideal body weight and highlighting the impact of overweight/obesity on CMD risk factors.

Loop Closure Detection Method Based on Similarity Differences between Image Blocks.

Variations with respect to perspective, lighting, weather, and interference from dynamic objects may all have an impact on the accuracy of the entire system during autonomous positioning and during the navigation of mobile visual simultaneous localization and mapping (SLAM) robots. As it is an essential element of visual SLAM systems, loop closure detection plays a vital role in eradicating front-end-induced accumulated errors and guaranteeing the map's general consistency. Presently, deep-learning-based loop closure detection techniques place more emphasis on enhancing the robustness of image descriptors while neglecting similarity calculations or the connections within the internal regions of the image. In response to this issue, this article proposes a loop closure detection method based on similarity differences between image blocks. Firstly, image descriptors are extracted using a lightweight convolutional neural network (CNN) model with effective loop closure detection. Subsequently, the image pairs with the greatest degree of similarity are evenly divided into blocks, and the level of similarity among the blocks is used to recalculate the degree of the overall similarity of the image pairs. The block similarity calculation module can effectively reduce the similarity of incorrect loop closure image pairs, which makes it easier to identify the correct loopback. Finally, the approach proposed in this article is compared with loop closure detection methods based on four distinct CNN models with a recall rate of 100% accuracy; said approach performs significantly superiorly. The application of the block similarity calculation module proposed in this article to the aforementioned four CNN models can increase the recall rate's accuracy to 100%; this proves that the proposed method can successfully improve the loop closure detection effect, and the similarity calculation module in the algorithm has a certain degree of universality.

Erratum: Centennial records of polycyclic aromatic hydrocarbons and black carbon in Altay Mountains peatlands, Xinjiang, China

An erratum on: Luo N, Wen B, Bao K, Yu R, Sun J, Li X and Liu X (2022) Centennial records of Polycyclic aromatic hydrocarbons and black carbon in Altay Mountains peatlands, Xinjiang, China. Front. Ecol. Evol. 10:1046076. doi: 10.3389/fevo.2022.1046076 Due to a production error, there was a mistake in Figure 4 as published. An incorrect image was published as figure 4. The corrected Figure 4 appears below. The publisher apologizes for this mistake. The original version of this article has been updated.

Retraction: 17β-estradiol binding to ERα promotes the progression of prolactinoma through estrogen-response element-induced CaBP-9k upregulation.

This article is being retracted from Bioscience Reports at the request of the authors due to the inclusion of an incorrect image in their article. The Editorial Office was also alerted to various concerns from a reader, detailing similarities between various western blots in the manuscript and those found in other manuscripts. These include the Figure 2C ER-alpha, CaBP-9k and GAPDH bands (with Figure 5A Bax from doi: 10.1002/jcla.23193, Figure 5A GAPDH from doi: 10.1155/2020/4817608, and Figure 6C VAV3 from doi: 10.18632/aging.103762 respectively), the Figure 5F GAPDH bands (with Figure 5C A549/GAPDH from doi: 10.1042/BSR20182433 and Figure 4L GAPDH from doi: 10.18632/aging.102325), the Figure 2D CaBP-9k and ER-beta bands, (with Figure 5C SAT1 from doi: 10.18632/aging.103762 and Figure 5B GAPDH from doi: 10.1155/2020/4817608), and the Figure 5F GAPDH bands (with Figure 5 GAPDH bands from doi: 10.1042/BSR20160521). The authors are unable to correct the article and wish to retract the article. The Editor-in-Chief and Editorial Board agree with the retraction.

Cancer‐Targeted Monodisperse Mesoporous Silica Nanoparticles as Carrier of Ruthenium Polypyridyl Complexes to Enhance Theranostic Effects

Adv. Funct. Mater. 2014, 24, 2754–2763. DOI: 10.1002/adfm.201303533 The authors regret that one incorrect image was used in Figure 3f (fluorescence image of RuPOP@MSNs in A375 cells at 0.5 h) in the originally published version of the manuscript. The corrected Figure 3f is shown as below. The correction does not alter any findings and conclusions reported in this article. No additional text corrections are needed in the article. The authors would like to apologize for any inconvenience caused.

Artifacts in Dual-Energy Chest Radiography

The aim of this research is to analyze artifacts in dual-energy chest radiography: their appearance and how to eliminate them. A total of 252 patients were examined in random order by using dual-energy radiography. As a result, 756 images were analyzed. This review describes the most important and most prevalent of them, including motion artifacts - 58 (41,4%), quantum noise – 55 (39,2%) and decreasing the suppression of bony elements on soft-tissue-selective images – 27 (19,3%). The main reason of dual-energy chest radiography artifacts is incorrect image reconstruction parameters.

QR Code Detection

Due to the incorrect image collecting approach, QR code identification frequently confronts obstacles such as uneven backdrop fluctuations, inadequate illuminations, and distortions. As a result, identifying QR codes is difficult, and artificial intelligence-based methods were developed to address this challenge. This article uses an improved adaptive median filter technique and a QR code distortion correction method based on backpropagation (BP) neural networks to increase the identification rate of QR image codes. The distorted QR image can be fitted into the geometric deformation pattern using this combination of artificial intelligence methods, and QR code identification is possible. This paper addresses two-dimensional code distortion, which has been a critical research topic in present software systems. The research findings, which focused on the picture preprocessing step, demonstrated a significant improvement of 14 percent in the reading rate of QR image codes following processing using the system algorithm described in this article. The artificial intelligence technique used improves the recognition rate of the two-dimensional code image to some extent.

Ramifications of incorrect image segmentations; emphasizing on the potential effects on deep learning methods failure

IntroductionDetecting failure cases is critical to ensure a secure self-driving system. Any flaw in the system directly results in an accident. In genuine class, the model’s probability reflects better-reflected model confidence. As a result, the confidence distributions of failed predictions were changed to lower values. In contrast, accurate predictions were remained associated with high values, allowing for considerably more excellent separability between such prediction types. The study investigates the association of ramifications with computational color constancy that can negatively influence CNN’s image classification and semantic segmentation.MethodologyImage datasets were used to conduct different scales and complexity experiments. For instance, minimal and straightforward images of digits were comparatively provided through MNIST and SVHN datasets. The dataset’s standard validation set was employed to test and compute additional metrics because ground truth that is not publicly available for some test sets.ResultsThe results depicted that baseline methods were outperformed through the proposed approach with a considerable variant on minimal datasets or models in every context. Therefore, Transmission Control Protocol (TCP) is appropriate in failure prediction, and ConfidNet is competent to be fulfilled as confidence criterion. Further, one of the solutions would be to elevate the validation set size, but this would influence the prediction performance of a failure model. On the contrary, the confidence estimation was based on models with test predictive performance levels, similar to baselines.ConclusionsThe gap between validation accuracy and training accuracy was significant on CIFAR-100, which indicates the modest enhancement for failure detection via the validation set.

Intuitive endoscopic robot master device with image orientation correction.

The camera of an endoscope is fixed to the device, and the image rotates together with the endoscope. This can lead to visual confusion for the user and incorrect image reading. The problem also occurs with endoscopic robots. Using a master device with the same degrees of freedom as the endoscope to control an endoscopic robot causes visual-motor misorientation, making intuitive control difficult. The roll misorientation between the image and master device handle was removed by measuring and correcting for the roll axis displacement of the master device. This enabled intuitive manipulation of the master device. The work speed and number of mistakes were experimentally measured with and without image correction. The tasks were completed significantly faster and more accurately with image orientation correction than without. The proposed method enables intuitive manipulation of the master device by correcting the image orientation in endoscopic robot control.

Peer review quality assurance in stereotactic body radiotherapy planning: the impact of case volume

Abstract Purpose: Peer review is an essential quality assurance component of radiation therapy planning. A growing body of literature has demonstrated substantial rates of suggested plan changes resulting from peer review. There remains a paucity of data on the impact of peer review rounds for stereotactic body radiation therapy (SBRT). We therefore aim to evaluate the outcomes of peer review in this specific patient cohort. Methods and materials: We conducted a retrospective review of all SBRT cases that underwent peer review from July 2015 to June 2018 at a single institution. Weekly peer review rounds are grouped according to cancer subsite and attended by radiation oncologists, medical physicists and medical radiation technologists. We prospectively compiled ‘learning moments’, defined as cases with suggested changes or where an educational discussion occurred beyond routine management, and critical errors, defined as errors which could alter clinical outcomes, recorded prospectively during peer review. Plan changes implemented after peer review were documented. Results: Nine hundred thirty-four SBRT cases were included. The most common treatment sites were lung (518, 55%), liver (196, 21%) and spine (119, 13%). Learning moments were identified in 161 cases (17%) and translated into plan changes in 28 cases (3%). Two critical errors (0.2%) were identified: an inadequate planning target volume margin and an incorrect image set used for contouring. There was a statistically significantly higher rate of learning moments for lower-volume SBRT sites (defined as ≤30 cases/year) versus higher-volume SBRT sites (29% vs 16%, respectively; p = 0.001). Conclusions: Peer review for SBRT cases revealed a low rate of critical errors, but did result in implemented plan changes in 3% of cases, and either educational discussion or suggestions of plan changes in 17% of cases. All SBRT sites appear to benefit from peer review, though lower-volume sites may require particular attention.

Corrigendum: Diplatin, a Novel and Low-Toxicity Anti-Lung Cancer Platinum Complex, Activation of Cell Death in Tumors via a ROS/JNK/p53-Dependent Pathway, and a Low Rate of Acquired Treatment Resistance

[This corrects the article DOI: 10.3389/fphar.2019.00982.].

Corrigendum: Chloroquine Inhibits Stemness of Esophageal Squamous Cell Carcinoma Cells Through Targeting CXCR4-STAT3 Pathway

[This corrects the article DOI: 10.3389/fonc.2020.00311.].

High-Frequency Ultrasound Dataset for Deep Learning-Based Image Quality Assessment.

This study aims at high-frequency ultrasound image quality assessment for computer-aided diagnosis of skin. In recent decades, high-frequency ultrasound imaging opened up new opportunities in dermatology, utilizing the most recent deep learning-based algorithms for automated image analysis. An individual dermatological examination contains either a single image, a couple of pictures, or an image series acquired during the probe movement. The estimated skin parameters might depend on the probe position, orientation, or acquisition setup. Consequently, the more images analyzed, the more precise the obtained measurements. Therefore, for the automated measurements, the best choice is to acquire the image series and then analyze its parameters statistically. However, besides the correctly received images, the resulting series contains plenty of non-informative data: Images with different artifacts, noise, or the images acquired for the time stamp when the ultrasound probe has no contact with the patient skin. All of them influence further analysis, leading to misclassification or incorrect image segmentation. Therefore, an automated image selection step is crucial. To meet this need, we collected and shared 17,425 high-frequency images of the facial skin from 516 measurements of 44 patients. Two experts annotated each image as correct or not. The proposed framework utilizes a deep convolutional neural network followed by a fuzzy reasoning system to assess the acquired data’s quality automatically. Different approaches to binary and multi-class image analysis, based on the VGG-16 model, were developed and compared. The best classification results reach accuracy for the first, and for the second analysis, respectively.

Correction: Circular RNA ciRS-7 Maintains Metastatic Phenotypes as a ceRNA of miR-1299 to Target MMPs.

In the original version of this article (1), an incorrect microscopy image was used to represent the si-ciRS-7 group in Fig. 2C. The error does not affect the main conclusions of this study. The image has been corrected in the latest online HTML and PDF versions of the article. The authors regret this error.

Correction: RAB37 Hypermethylation Regulates Metastasis and Resistance to Docetaxel-Based Induction Chemotherapy in Nasopharyngeal Carcinoma.

In the original version of this article (1), there were errors in Fig. 5: in Fig. 5B, an incorrect image was used for the SUNE-1-RAB37 cells transfected with siTIMP2-1, and in Figs. 5D and 5E, the group names were typographically missing for the quantification of macroscopic (Fig. 5D) and microscopic metastatic nodules (Fig. 5E) in the Vector and RAB37 groups. These errors have been corrected in the latest online HTML and PDF versions of the article. The authors regret these errors.