Input: Claim: Cryptocurrencies like Bitcoin use decentralized networks for secure transactions. [SEP] Text: is Open Access? is an initiative that aims to make scientific research freely available to all. To date our community has made over 100 million downloads. It’s based on principles of collaboration, unobstructed discovery, and, most importantly, scientific progression. As PhD students, we found it difficult to access the research we needed, so we decided to create a new publisher that levels the playing field for scientists across the world. How? By making research easy to access, and puts the academic needs of the researchers before the business interests of publishers. are a community of more than 103,000 authors and editors from 3,291 institutions spanning 160 countries, including winners and some of the world’s most-cited researchers. Publishing on IntechOpen allows authors to earn 10. 5772/intechopen. 79386 this 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Intech by From the Blockchain and by. purchase hard copies of this book, please contact the representative in India: CBS Publishers & Distributors Pvt. Ltd. www. cbspd. com | customercare@cbspd. com 1. Choose citation style Select style to clipboard Get citation 2. Choose citation style Select format Bibtex RIS Download citation Impact of this 1. is a digital currency built on a peer-to-peer network and on the blockchain, a public ledger where all transactions are recorded and made available to all nodes. Opposite to traditional banking transactions, based on trust for counterparty, Bitcoin relies on cryptography and on a consensus protocol for the network. The entire system is founded on an open source software created in 2009 by a computer scientist known under the pseudonym Satoshi Nakamoto, whose identity is still unknown (see [1]). Hence, Bitcoin is an independent digital currency, not subject to the control of central authorities and without inflation; furthermore, transactions in the network are pseudonymous and irreversible. Bitcoin and the underlying blockchain technology have gained much attention in the last few years. Research on Bitcoin often deals with cybersecurity and legitimacy issues such as the analysis of double spending possibilities and other cyber-threats; recently, high returns and volatility have attracted research toward the analysis of Bitcoin price efficiency as well as its dynamics (see, among others, [2, 3, 4]).

Output: corroborate


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: The top performance of the multimodality T1- and T2-weighted model compared with the T1- and T2-weighted models alone reflects clinical practice, where a combination of T1- and T2-weighted imaging is used to assess myelination due to complementary information. Myelination of particular substructures of the brain is achieved at different age ranges on T1- and T2-weighted images, reflecting different physiologic processes. For example, the anterior limb of the internal capsule is myelinated at 2–3 months on T1-weighted images and at 7–11 months on T2-weighted images (1). Therefore, it is not surprising that the multimodality approach incorporating two sets of clinical information performed best. Neonate and infant brain volume increases with age (33), and this change in volume on MRI scans is a potential confounder in the evaluation of GCA based only on myelination. Therefore, we controlled for brain volume by scaling all scans to the same size by registration to an atlas image. Furthermore, our attention map analysis demonstrated a characteristic shift in attention over age that mirrors changes in myelination, indicating that the algorithm used myelination as its main basis for GCA prediction. This correspondence between machine-learned spatial patterns representing myelination and visually derived patterns from prior research (1,4–7) gives credence to the model’s predictions, making it less likely that confounding features contributed to GCA predictions. The model trained using data from our institution was further validated on data from the independent external NIH data set. While GCAs were generally accurate, we found that they were slightly underpredicted. This downward bias is likely due to the differences in GCA distributions, vendor platforms, and acquisition parameters between data sets, with the internal data being collected over a longer time period (from 1995 to 2019) and encompassing various evolving MRI protocols, resulting in a more heterogeneous and generalizable data set. In contrast, the external data set was collected with a standardized MRI protocol across various institutions. Indeed, a model trained on the external data set poorly generalized to the internal data set, whereas a model trained on the more heterogeneous internal data set generalized more accurately to the external data set.

Output: pass


Input: Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 그러나, 비대면 진료 현장에서는 충분히 활용 가능할 것이고, 또는 진료 이후 환자에 추적 진료를 위하여 점검하는 데 사용할 수 있을 것이다. 또한 단순히 진료 현장에서 사용하는 데서 나아가, 발표 원고 작성, 교재 제작, 논문 작성, 번역, 영문 교열, 말 바꾸어쓰기(paraphrasing), 요약 등 진료 업무 이외 연구와 교육 현장에서 손쉽게 사용하여 시간을 아낄 수 있다[6]. 용어를 정의 내리고 과거 지식을 요약하여 제공하는 작업은 탁월하다.

Output: corroborate


Input: Claim: Many industries are exploring blockchain for applications beyond finance. [SEP] Text: Deputy members of EU Parliament (MEPs) and (for Application), representing DLT industries, have been suggesting according considerations within the created to feedback MEPs with relevant criteria from DLT industry and PDLs. Spain’s DLT (cofounded by Prof. J. IBÁÑEZ) is a founding member of thus participating since 2021 in finance WGs operating in (cf. of Authority (PoA) blockchain protocols, within this context, may play a key role to determine the consent of public supervisors in order to authorize the legal trading of NFTs in the scope and within the boundaries predetermined by the securities markets applicable rules. The reputation of validators is held by market authorities, which makes PoA impractical for public blockchains like Bitcoin and Ethereum, wherein hundreds or even thousands of validating nodes operative, and the identity requirement becomes a clear advantage to comply with regulations in the context of public-permissioned blockchains subject to legal specific requirements. In a PoA-network PPDL, a relatively small number of validating nodes re-centralize or make the market less decentralized, facilitating supervision, with enough degree of decentralization but gaining in investor and market confidence (like in the cases of networks, an implementation of offering two PoA alternatives; or in the Telsius and Besu networks), with much higher throughput capacity than in permissionless blockchains, minimal computational effort and no specialized equipment (like in PoS). The acceptation of established financial institutions (as investment banks, financial intermediaries, or crypto-asset service new providers) as validators for crypto-transactions should be a legal previous condition to the settlement of DeFi market platforms for cryptos. Previous authorization of intermediaries in traditional financial markets is essential to the market confidence and investor protection. Similarly, previous authorization of validating nodes in crypto-transactions will probably be a legal and also economic-efficient precondition for the development of token transactions in general, and in particular for high-risk token investments like NFT trading. Although PoA-based algorithms are unlikely to power big market platforms with thousands or millions of users, they are already optimal to build networks tailored to the needs of a limited number of known stakeholders (agreeing, [4]), as in the case of certain limited-client oriented NFT platforms and markets. Advertisement 4.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Notably, when tested on the external data set, the T2-weighted model had the best performance, while the T1-weighted model had the worst performance, suggesting that technical differences in image acquisition can impact generalizability. Internal T1-weighted sequences were predominantly 3D gradient-echo acquisitions, with over half at 3. 0 T, while the external data set was exclusively 2D spin-echo acquisitions at 1. 5 T. Conversely, internal data for T2-weighted images was predominantly 2D acquisitions, more closely approximating the external data set. Our study had limitations. First, there was an imbalance in the proportion of MRI scans at different GCAs, with fewer MRI scans in the older age categories. The network may have overtrained on data in the older age categories, potentially resulting in suboptimal performance and generalizability of the algorithm. Second, our model was trained on a single institutional data set with predominantly scanner-acquired studies, institution-specific acquisition parameters, and unique neonatal and infant sample, with notably less than 7% Black patients. Although these factors may reduce the generalizability of our model, we validated its performance on an external NIH data set. Third, the process of determining normal myelination in clinical practice is inherently subjective (8,9); thus, the use of clinical radiology reports interpreted by one neuroradiologist as our reference standard for normal myelination likely introduced some variability in the training data. Fourth, the inclusion of preterm infants in our model increases model variability yet simultaneously increases our model’s generalizability and more closely approximates clinical practice. Fifth, our model was trained using neonates or infants with reported normal myelination and without large structural abnormalities or brain abnormalities, potentially reducing generalizability to neonates or infants with structural abnormalities. Sixth, we did not directly compare algorithm performance with clinical neuroradiologist performance, but our best-performing model MAE (5. 9 weeks) is similar to the MAE reported for radiologists in a prior study (4. 4 weeks) (23). In conclusion, a deep learning approach can predict the gestationally corrected age (GCA) of a neonate or infant on the basis of clinically acquired T1- and T2-weighted brain MRI scans by relying on known spatiotemporal patterns of normal myelination across the brain.

Output: pass


Input: Claim: AI chatbots can assist in preliminary diagnosis and patient engagement. [SEP] Text: 그러나, 이런 도구를 효율적으로 적절히 사용할 수 있는 전문가로서 충분한 지식과 술기를 갖추어야 답이 어느 수준에서 타당한지 판단할 수 있을 것이다. 앞으로 이 인공지능 챗봇이 점점 더 정교하고 신뢰할 수 있는 수준으로 발전하고 더 많은 우리말 정보로 훈련도 받을 것이다. 인공지능 챗봇이 어디까지 발전할지 주시하고 또한 우리 의사도 이런 작업에 전문가로서 적극 참여하여 더욱 유용한 챗봇 개발에 일익을 담당하기 바란다.

Output: corroborate


Input: Claim: Many industries are exploring blockchain for applications beyond finance. [SEP] Text: However, this does not mean that blockchain is not vulnerable to cyberattacks and security fraud. People with malicious intent can exploit blockchain security vulnerabilities for their own benefit. There are already many cybersecurity incidents related to blockchain usage. For example, a cyberattack on Bithumb, a major cryptocurrency exchanges for bitcoin, and Ethereum caused an economic loss of 870,000 USD and the exposure of more than 30,000 users’ information. The potential security threats could discourage firms willingness to use blockchain. 5. 2 Organizational considerations5. 2. 1 Top management supportTop management support and commitment play a pivotal role in the successful implementation of any technological innovations. The higher the support and commitment, the more likely the firm will develop new organizational policies to clarify the usage of blockchain and reduce the resistance from employees to embrace the new system. Lack of technology awareness and involvement from upper management would impede the allocation of adequate human, financial, and technological resources for technology adoption projects. 5. 2. 2 CostThe adoption of blockchain incurs diverse costs, such as recruiting blockchain expert, investment in additional equipment (e. g. sensors and radio-frequency identification tags). In addition, adopters need to spend a lot of time and resources to master the complexity of blockchain. The cost may be justifiable for the leading firms that initiate blockchain implementation projects, because they are usually large firms with bountiful resources. It is financially viable for them to invest in costly project that promises long-term returns. However, this is not the case for small- and medium-sized food supply chain actors with low margins. Therefore, high implementation cost may hinder the adoption intention of these actors. 5. 2. 3 Return on investment (ROI)According to [21] global blockchain survey, more than 33% of the respondents claims that their current ROI in blockchain technology remains uncertain. The lack of clear ROI is recognized as the top barrier for blockchain adoption [29]. With the increasingly usage of blockchain in agri-food industry, it is likely that the financial benefits of blockchain implementation will be clarified in the foreseeable future. 5. 2. 4 Lack of knowledge and expertiseBlockchain is still largely an emerging technology, and the skills required to develop and use it are in short supply. The willingness to invest in blockchain requires a certain degree of technological awareness and knowledge. The technical complexity of blockchain makes it a challenge for individual users to understand, accept, and confidently participate in it.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Although we demonstrate the capability of a deep learning model to predict GCA based on brain myelination, further evaluation in the clinical context, likely with further improvements in model accuracy, will be necessary for the model to be used on a routine clinical basis. Additional research is necessary to evaluate whether model performance can be improved with different technical approaches or additional information, such as inclusion of apparent diffusion coefficient maps. Further research using brain MRI scans with structural and myelination abnormalities will be needed to evaluate whether this approach can be extended to help radiologists identify neonates or infants with abnormal or delayed myelination. Disclosures of conflicts of interest: J. V. C. No relevant relationships. G. C. No relevant relationships. C. P. H. Consultant to GE Healthcare; expert witness for various medicolegal firms; support for research travel from Siemens Healthineers; DataBoard member for Foundation and uniQure; on the for in Medicine board of trustees. O. A. G. No relevant relationships. L. P. S. No relevant relationships. A. M. R. Radiology AI trainee editorial board from 2019 to 2021. Y. L. 2019–2021; honoraria from University of Francisco and of Otolaryngology–Head and Neck Surgery. Author contributions: Guarantors of integrity of entire study, J. V. C. , G. C. , A. M. R. , Y. L. ; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; agrees to ensure any questions related to the work are appropriately resolved, all authors; literature research, J. V. C. , G. C. , O. A. G. , A. M. R. , Y. L. ; clinical studies, C. P. H. , L. P. S. , Y. L. ; experimental studies, J. V. C. , G. C. , C. P. H. , L. P. S. , A. M. R. , Y. L. ; statistical analysis, J. V. C. , G. C. , A. M. R. , Y. L. ; and manuscript editing, all authors* J. V. C. and G.

Output: pass


Input: Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-3257323/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractPlant leaf diseases pose significant threats to agricultural productivity and food security. Accurate and timely detection of these diseases is crucial for effective disease management. This research paper proposes a novel approach for plant leaf disease detection using ensemble learning. Ensemble learning combines multiple base models to improve classification accuracy by leveraging their collective intelligence. The proposed ensemble model integrates Networks (CNN), ResNeXt, and InceptionV3 architectures, exploiting their diverse strengths in image classification tasks. Extensive experiments on a diverse dataset demonstrate that the ensemble model outperforms individual base models, achieving higher classification accuracy. Its ability to capture complementary features enhances generalization and robustness against variations in disease patterns and image quality. The ensemble model also offers benefits such as improved interpretability, model stability, and reduced overfitting risks. The proposed approach contributes to the advancement of automated plant disease diagnosis systems, enabling early detection and timely intervention to mitigate crop losses and ensure food security. By combining multiple models, the ensemble learning approach improves the accuracy and reliability of plant leaf disease detection, addressing a critical need in agricultural practices. Plant leaf disease detectionEnsemble learningNetworks (CNN)ResNeXtInceptionV3Agricultural productivityFiguresFigure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7I. diseases have always been a major concern for agriculture, causing significant economic losses and posing threats to global food security. Timely and accurate detection of plant leaf diseases plays a vital role in preventing severe yield losses and implementing effective disease management strategies. Traditional methods of disease diagnosis, which rely on manual inspection and visual analysis, are labor-intensive, time-consuming, and prone to human error. Therefore, there is a pressing need to develop automated and reliable techniques for plant leaf disease detection. In recent years, machine learning algorithms, particularly deep learning models, have demonstrated remarkable success in various computer vision tasks, including image classification. Networks (CNNs) have emerged as a popular choice for detecting and classifying objects within images. However, due to the complex and diverse nature of plant diseases, achieving high classification accuracy remains a challenge.

Output: corroborate


Input: Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: Figures AbstractMost of the existing multi-recipient signcryption schemes do not take the anonymity of recipients into consideration because the list of the identities of all recipients must be included in the ciphertext as a necessary element for decryption. Although the signer’s anonymity has been taken into account in several alternative schemes, these schemes often suffer from the cross-comparison attack and joint conspiracy attack. That is to say, there are few schemes that can achieve complete anonymity for both the signer and the recipient. However, in many practical applications, such as network conference, both the signer’s and the recipient’s anonymity should be considered carefully. Motivated by these concerns, we propose a novel multi-recipient signcryption scheme with complete anonymity. The new scheme can achieve both the signer’s and the recipient’s anonymity at the same time. Each recipient can easily judge whether the received ciphertext is from an authorized source, but cannot determine the real identity of the sender, and at the same time, each participant can easily check decryption permission, but cannot determine the identity of any other recipient. The scheme also provides a public verification method which enables anyone to publicly verify the validity of the ciphertext. Analyses show that the proposed scheme is more efficient in terms of computation complexity and ciphertext length and possesses more advantages than existing schemes, which makes it suitable for practical applications. The proposed scheme could be used for network conferences, paid-TV or DVD broadcasting applications to solve the secure communication problem without violating the privacy of each participant. Key words: Multi-recipient signcryption; Signcryption; Complete Anonymity; Public verification. Citation: (2013) Multi-Scheme with Public Verification. 8(5): e63562. Francesco Pappalardo, University of Catania, ItalyReceived: October 18, 2012; Accepted: April 3, 2013; Published: May 10, 2013Copyright: © 2013 Pang et al. This is an open-access 0006. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. IntroductionWith development of network technology and its applications, a lot of group-oriented network services such as network multicasting or broadcasting have been proposed.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: C. contributed equally to this work. ** A. M. R. and Y. L. are co-senior authors. 1. Dean DC 3rd, O’Muircheartaigh J, Dirks H, et al. Characterizing longitudinal white matter development during early childhood. Funct 2015;220(4):1921–1933. Medline2. Fields RD. White matter in learning, cognition and psychiatric disorders. 2008;31(7):361–370. Medline3. . Plasticity in oligodendrocyte lineage progression: An OPC puzzle on our nerves. Neurosci 2021;54(5):5747–5761. Medline4. . Pediatric Neuroimaging. J Tomogr 1991;15(1):179. 5. . Sequence of in Human Infancy. II. Patterns of Myelination in Autopsied Infants. J Neurol 1988;47(3):217–234. Medline6. Welker KM, Patton A. Assessment of normal myelination with magnetic resonance imaging. 2012;32(1):15–28. Medline7. 4th, et al. MR evaluation of early myelination patterns in normal and developmentally delayed infants. 1988;150(4):889–896. Medline8. . Comparison of spin-echo and gradient-echo T1-weighted and spin-echo T2-weighted images at 3T in evaluating term-neonatal myelination. 2015;36(2):411–416. Medline9. . Comparison of spin-echo T1- and T2-weighted and gradient-echo T1-weighted images at 3T in evaluating very preterm neonates at term-equivalent age. 2013;34(5):1098–1103. Medline10. . in Infants and Young Children: Radiographic and Clinical Correlates. J 1995;10(2):100–104. Medline11. Barkovich AJ. Concepts of myelin and myelination in neuroradiology. 2000;21(6):1099–1109. Medline12. . for Medical Imaging. RadioGraphics 2017;37(2):505–515. 13. . Explainable deep learning models in medical image analysis. arXiv 2005.

Output: pass


Input: Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: 9 June 2023 [5] Sunflower dataset description. 2335 Cauliflower dataset description. 4156 EfficientNetV2B2 and EfficientNetV2B3 89. 2%. 6 Yang B, et at. [6] 2023 31,718 disease image Deep multitask learning framework 99% 7 et. al[7],2023 Plantvillage dataset -54,306 images An ultra‑lightweight efficient network 99% 8 Lee et al. [8], 2021 PlantVillage: 54,305 VGG16, GoogleNetBN (34 layers) and Inceptionv3 (48 layers) PlantVillage: 99. 00% IPM: 44. 54% Bing: 26. 56% 9 Darwish et al. [9], 2020 Maize: 15,408 Ensemble model, VGG16, VGG19, orthogonal, 3 and Xception model. 98. 2% 10 Karlekar and Seal [10], 2020 PDDB: 14,240 images background removal, CNN 98. 14% This review emphasizes the vital role of plants for energy and nutrition, but plant diseases can devastate crops. Researchers have explored machine learning (ML) and deep learning (DL) methods for disease detection, showing DL's superiority. Comparing ML () and DL (Inception-v3, VGG-16, VGG-19) models, DL achieves higher accuracy: VGG-16 (89. 5%) > Inception-v3 (89%) > VGG-19 (87. 4%), outperforming ML methods. [11] The paper highlights the importance of disease early detection in agriculture for optimal crop yield. Tomato crops are vulnerable to bacterial spot, late blight, septoria leaf spot, and yellow curved leaf diseases. This study introduces a Network (CNN) and Quantization (LVQ) based approach for automatic tomato leaf disease detection and classification. The dataset comprises 500 images of tomato leaves, and the CNN extracts features while LVQ aids in classification. By leveraging color information, the proposed method successfully identifies and distinguishes the four diseases. [12] The significance of plants as an energy source is acknowledged. Plant diseases impacting crop production and economic value necessitate timely leaf disease detection. Machine learning offers a solution by analyzing plant leaf data and classifying diseases based on morphological features, color, intensity, and dimensions.

Output: corroborate


Input: Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: Usually, in these services, a message sender is required to securely send the same messages to a group of recipients, such that only a certain number of recipients can read the messages while unauthorized recipients can extract nothing useful from these messages [1]. Therefore, the concept of multi-recipient encryption was put forward [2]–[6], and it has been considered as one of most promising solutions to solve the security problem of securing multicasting or broadcasting. Later, combining the concept of multi-recipient encryption with the idea of Zheng’s signcryption [7], Duan et al. [8] proposed the first multi-recipient signcryption scheme. In their scheme, to achieve the goal of sending the same message to all authorized recipients confidentially, the sender only needs to execute one signcryption operation, and at the same time, each recipient can verify the validity of messages. Since then, many excellent multi-recipient signcryption schemes [9]–[11] were proposed, which take more security properties into consideration than Duan et al. ’s scheme. In general, multi-recipient signcryption can be used in many important applications, such as paid-TV or DVD broadcasting systems [10], where only authorized or paying users should be able to access such services. Nevertheless, today, more and more people are concerned regarding personal privacy, thus participant anonymity should be taken into account when designing multi-recipient signcryption [12]. For example, in paid-TV and DVD broadcasting application systems, service providers do not want others to obtain the real identities from the ciphertext messages. Therefore, multi-recipient signcryption with the sender (or called the signer) anonymity had been introduced. In literature, there have been several multi-recipient signcryption schemes [13]–[17] which try to assure anonymity of the sender. The concept of anonymous signature was firstly proposed by Rivest et al. [18]. In 2005, Huang et al. [19] proposed the first anonymous signcryption scheme, which used an ID-based ring signature to assure anonymity of the signer. However, their scheme is only a single-recipient scheme. Later, based on similar thoughts, Lal et al. [13] extended this method for multi-recipient environments. Furthermore, a multi-recipient scheme with anonymity of the sender [14]–[17] was proposed.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: 13799 [preprint] 28, 2020. 10, 2020. 14. Truelove- in Children and Adolescents. J Neurosci 2020;40(6):1265–1275. Medline15. Age of Based on. 2020;10:1346. Medline16. Brain age prediction using deep learning uncovers associated sequence variants. 2019;10(1):5409. Medline17. . From a deep learning model back to the brain-Identifying regional predictors and their relation to aging. Mapp 2020;41(12):3235–3252. Medline18. . Accurate brain age prediction with lightweight deep neural networks. bioRxiv preprint bioRxiv:2019. 12. 17. 879346. 6, 2020. 19. . Classification of in Using 3D-CNN based on Brain MRI. In: 2019 on Bioinformatics and Biomedicine (BIBM), San Diego, CA, November 18–21, 2019. Piscataway, NJ:IEEE,2019; 2373–2378. 20. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 2016;136:1–9. Medline21. Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan. Anal 2021;72:102091. Medline22. Prediction of via Deep Learning. 2020;11:584682. Medline23. Age estimates from brain magnetic resonance images of children younger than two years of age using deep learning. Imaging 2021;79:38–44. Medline24. . The role of balanced training and testing data sets for binary classifiers in bioinformatics. PLoS One 2013;8(7):e67863. Medline25. . Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. arXiv1502. 01852 [preprint] 6, 2015. 26. iNNvestigate neural networks! arXiv 1808. 04260 [preprint]. 13, 2018. 10, 2020. 27. Evans AC; Cooperative Group. study of normal brain development. Neuroimage 2006;30(1):184–202. Medline28.

Output: pass


Input: Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: [17] Automated plant disease diagnosis using deep learning often suffers from overfitting. This 7. 4%, outperforming vanilla CycleGAN's 0. 7% improvement. The approach addresses the challenge of reliable and diverse training data for practical plant diagnosis systems. [18] Tomato leaf diseases significantly impact tomato yield. Detecting agricultural diseases is crucial. Traditional data augmentation methods lack effectiveness. To enhance tomato leaf disease recognition, we propose a generative adversarial network (GAN)-based data augmentation technique. Using deep convolutional GANs (DCGAN) for image augmentation alongside original images, our model combined with GoogLeNet achieves a 94. 33% top-1 average accuracy. We iteratively optimize hyperparameters, modify convolutional neural network architecture, and select GAN variants to improve performance across five tomato leaf disease classes. DCGAN-generated images not only augment the dataset size but also offer diverse characteristics, enhancing model generalization. t-(t-SNE) and Test validate DCGAN image quality. Our approach benefits by enlarging the dataset for neural network training, reducing data collection costs, and boosting model generalization and diversity. [19] This project focuses on detecting tomato leaf diseases using Networks (CNNs), a subset of deep neural networks. The process involves dataset segmentation, transfer learning with ResNet-50, and data augmentation for improved results. Utilizing PyTorch, a tomato leaf disease detection model is developed using deep CNNs. The testing dataset is validated with learned ResNet-50 parameters, achieving 97% accuracy. Six common tomato crop diseases are classified, with data augmentation expanding the dataset fourfold to enhance accuracy. [20] Plant diseases' impact on growth underscores the urgency of early identification. Learning (ML) models have been pivotal, (DL) advancements offer increased accuracy potential. Modified DL architectures combined with visualization techniques enhance symptom detection and classification. Performance metrics evaluate these techniques. This review comprehensively discusses DL models for visualizing plant diseases, pinpointing research gaps for enhanced disease detection before symptoms manifest. [21] Efficient plant disease identification is pivotal for preventing agricultural losses. Disease studies involve observing plant patterns, but manual monitoring is laborious. Image processing offers a solution, automating disease detection through steps like acquisition, pre-processing, segmentation, feature extraction, and classification. This paper examines leaf image-based plant disease detection methods, highlighting segmentation and feature extraction algorithms.

Output: corroborate


Input: Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: Although these schemes [13]–[17] provide solutions for assuring signer anonymity, there are still some unsolved issues. For example, they suffer from two new attacks known as the cross-comparison attack [20] and the joint conspiracy attack [21]. Based on the ring signature, schemes [13]–[17] construct a list which includes the real signer and several valid participants which are chosen randomly by the signer hiding the real signer in this list. Although this perfectly works to some extent, an attacker can obtain a number of different ciphertexts from the same message source by closely monitoring network traffic, thus by comparing the signers’ identities from different lists an attacker can narrow down the scope of the target signer. Using this scheme, an attacker can directly obtain the identity of the real signer. Even if the attacker does not directly obtain the real signer’s identity, he/she has narrowed down the scope of the attacker’s guess. In addition, it is still possible for such an attacker to retrieve a list which includes the real signer. Then, he/she can cooperate with some participants in the list to narrow down the scope and guess the real signer with a larger probability. In addition, the list of chosen participants can increase the length of the ciphertext quite significantly, potentially reducing the transmission efficiency. More important, the identities of all the authorized recipients are usually included in the ciphertext of these anonymous schemes in plaintext [13]–[19], which is not always wanted. Generally speaking, anonymity of participants includes both the sender’s and the recipient’s anonymity. Besides the anonymity of the sender, the anonymity of the recipient is often equally important so that designers of multi-recipient signcryption schemes should pay attention. For example, in paid-TV and DVD broadcasting application systems, no user should accept that his/her subscription of these services is publicly viewable to others especially when the service is quite sensitive. However, unfortunately, almost none of the existing schemes take the anonymity of recipients into consideration because the identity of each recipient must be included in the ciphertext as a necessary element for decryption. The list of the authorized recipients’ identities in the ciphertext is used to show who are the authorized recipients and how each authorized recipient gets his/her person-specific data for encryption from the ciphertext during the decryption process.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Academy of Enter your Email address: Subscribe to eTOC Home > on CT Sc. . . on CT the Neurology/October 3, 2019 Explained0. 777) between patients with MRI-positive findings and patients with MRI-negative findings in patients with CT-negative traumatic brain injury. GFAP at 9-16 hours post injury (B) shows improved discrimination to good—area under the curve (AUC) 0. 852—suggesting temporal effects of GFAP release or accumulation in the systemic circulation after traumatic brain injury and time for optimal or repeat measurement. Dr. Manley said that he does not believe that GFAP or any other biomarker will be the singular tool for diagnosing TBI. He expects that like with heart attack, where troponin testing is one component of evaluation, TBI biomarkers will be among several diagnostic tools used to arrive at a firm diagnosis. “I see this as being a triage tool,” he said, but cautioned that the study included only adult patients who got to the emergency room because of a car accident or a fall. He said more research is needed before he could say whether the test “is ready for use in kids with concussions. ”A Subset AnalysisThe latest report on the GFAP biomarker test stems from a study known as TRACK-TBI ( in Injury), a large prospective study being conducted at 18 Level 1 trauma centers in the US. A sub-analysis was done of 450 of the participants, most of whom were classified as having mild TBI due to a traffic accident or fall. They all had normal findings on CT scan (and a Scale score 13-15) and consented to the blood test within 24 hours of their injury and an MRI scan seven to 18 days post-injury. The study also included orthopedic trauma patients and healthy controls for comparison. Of the 450 suspected TBI patients with normal CT scans, 120 (27 percent) had MRIs indicative of TBI. When the researchers compared MRI findings to GFAP levels, they found that GFAP concentrations were higher in patients with positive MRI scans (median plasma GFAP concentration of 414. 4 pg/mL) compared with those with negative MRI scans (median plasma GFAP concentration of 74. 0 pg/mL).

Output: pass


Input: Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: The base CNN provides a solid foundation for learning basic features, and then the ResNeXt and Inception-v3 models can further refine and augment these features to capture intricate patterns and subtle variations in the images. The ensemble approach allows us to combine the predictions of multiple models, effectively reducing bias and variance, leading to improved generalization and overall performance. Moreover, the auxiliary classifiers used in Inception-v3 assist in better gradient propagation during training, addressing the challenges of vanishing gradients and facilitating faster convergence. As a result, our ensemble learning method achieves a higher level of accuracy in detecting plant leaf diseases compared to using a single architecture alone. The synergy between these models ensures robustness and adaptability, enabling the system to handle a wide range of plant diseases effectively, making it a reliable and valuable tool for plant disease diagnosis in agricultural settings. The complete model architecture of our ensemble learning model is as follows: C. to feeding the images into the ensemble neural network, a preprocessing stage will be applied to reduce the image size to the specific input size required by the individual base models (and InceptionV3). This preprocessing step is crucial to ensure consistency in the input dimensions across the models. preprocessing stage involves resizing the images to the target input size while preserving their aspect ratio. This is achieved by either padding the image or cropping it from the center, depending on whether the original aspect ratio is greater or smaller than the target ratio. By using this technique, we can resize the images without distorting or losing critical visual information. resizing, the pixel values of the images will be normalized to bring them to a common scale. Normalization ensures that the pixel values lie within a specific range, typically [0, 1] or [-1, 1], which aids in faster convergence during model training and prevents the dominance of certain features due to large pixel value differences. To increase the variability of the dataset and improve the model's ability to generalize, data augmentation techniques will be applied during preprocessing. These techniques include random rotation, horizontal flipping, and zooming. Data augmentation introduces variations in the training set without adding new data points, thus helping the model become more robust to different orientations and viewpoints of the plant leaves. Loss in the resizing process, the aspect ratio of the original image is preserved, ensuring no loss of critical visual information.

Output: corroborate


Input: Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: Thus, schemes [9]–[11] directly expose the recipient’s identity and therefore violate their privacy. Also, the fact that different recipients have different person-specific data for decryption can lead to decryption unfairness. This means that if some recipient’s person-specific data are damaged due to communication errors, he/she cannot decrypt the ciphertext but the others can still decrypt the ciphertext correctly [12]. Therefore, it is urgent and challenging for researchers to solve the recipient anonymity issue of multi-recipient signcryption. Following the arguments above, it is known that almost none of the existing multi-recipient signcryption schemes take the full anonymity of recipients and senders into account. Although there are several schemes that provide a solution for anonymity of the signer, they are not perfect, that is, they suffer from the cross-comparison attack and the joint conspiracy attack. Therefore, existing schemes cannot deal with the anonymity of the sender or the recipient properly. Furthermore, these schemes are not suitable for applications that need complete anonymity for the sender and the recipient. For example, in a network conference application, every conference participant often wants to be kept anonymous when he/she is taking part in the conference discussion. Furthermore, if a participant (i. e. sender) wants to publish criticism or objections, he/she hopes that others (i. e. recipients) do not know his/her identity. At the same time, the recipient cannot want the other recipients to reveal that he/she is an authorized recipient. In fact, today, anonymity is one of the most important prerequisites for people to talk freely and make objective decisions. Motivated by the above, this paper proposes a completely anonymous multi-recipient signcryption scheme which meets: (1) The identity of the sender is kept secret; (2) The identities of all the recipients are kept secret; (3) Each recipient can easily judge whether the received message is from an authorized source, but he/she cannot determine the real identity of the sender; (4) Each recipient can easily judge whether he/she is an authorized recipient, but he/she cannot determine the identity of any other authorized recipient; (5) The validity of ciphertext can be verified publicly.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: “I see this as being a triage tool, but the study included only adult patients who got to the emergency room because of a car accident or a fall. More research is needed before I could say whether the test is ready for use in kids with concussions. ”—DR. “The new study on GFAP was well designed and informative, but I think biomarkers are quite a ways off from being a gold standard test. ”—DR. the 90 patients with GFAP concentrations in the lowest quintile, only seven (8 percent) had abnormal MRI findings, while 58 (64 percent) of those with GFAP concentrations in the highest quartile had abnormal MRI findings, the study said. GFAP levels also correlated with MRI lesion types, the researchers said. “These results indicate that the diagnostic utility of GFAP might extend beyond CT-visible pathology and might help to identify patients with more subtle injury,” the researchers said. The study was funded by the of and Stroke and the US Department of Defense. Dr. Manley said the portable biomarker test might also one day prove useful for detecting TBI on the battlefield. One of the limitations of the study was the lack of longer-term follow-up of patients to determine, for instance, whether those with high GFAP levels had more neurocognitive symptoms. There may have also been selection bias in the sub-analysis since it included only those TRACK-TBI participants who had MRI. Are NeededAn editorial that accompanied the study in Neurology underscored the need for better diagnostic tools for TBI. “The ability to accurately diagnose the severity of traumatic brain injury (TBI) to establish the consequent need for treatment still eludes modern emergency care systems,” wrote of the University of Sheffield. She said that while a CT scan is good at detecting patients in need of neurosurgery or neurocritical care for life-threatening injury, “a CT brain scan is negative on more than 90 percent of patients with TBI symptoms, leading to uncertainty as to the extent of TBI and likely need for further treatment. ”Dr. Lecky said the findings that GFAP concentration at nine to 16 hours post-injury discriminated well between patients with MRI-positive findings and MRI-negative findings were positive.

Output: pass


Input: Claim: Deep learning has improved the accuracy of disease detection, like cancer. [SEP] Text: When cropping the images, the central region is selected, which usually contains the most relevant and discriminative features for disease detection. Therefore, the preprocessing techniques used in this research guarantee that no crucial information is lost during the transformation. For data augmentation, this research will utilize the Random Rotation, Horizontal Flip, and techniques. applies random angle rotations to the images, simulating various orientations. flips the images horizontally, reflecting their contents. randomly zooms the images in or out, further increasing the dataset's diversity. These techniques enhance the model's ability to handle variations in plant leaf diseases' appearance and improve its robustness in detecting different disease patterns. By applying preprocessing techniques such as resizing, cropping, normalization, and data augmentation, we can ensure that the input images adhere to the required dimensions of the individual base models while preserving essential visual details. The consistent input size and increased variability of the dataset contribute to more effective learning and generalization of the ensemble model. Moreover, the preprocessing stage enhances the model's ability to detect plant leaf diseases accurately across diverse agricultural scenarios, ultimately improving the overall performance and reliability of the automated plant disease diagnosis system. Analysis (EDA) is a crucial step in understanding the dataset and gaining insights before constructing our plant leaf disease detection system. During the EDA process, we first carefully examine the data distribution and perform various statistical analyses to comprehend its characteristics. Additionally, data visualization techniques, such as histograms, bar charts, and scatter plots, aid in identifying potential patterns, class imbalances, and outliers within the dataset. EDA also involves exploring the relationships between classes and their corresponding images to gain an understanding of the variations and complexities present in the images. Moreover, we evaluate the quality and diversity of the images to ensure the dataset's representativeness and suitability for training a robust model. Moving on to the dataset-specific facts, our plant leaf disease detection dataset comprises 38 distinct classes, each representing a different plant leaf disease. On average, each class contains around 450 images, making the dataset reasonably balanced. The dataset is extensive, consisting of a total of 70,295 images, which will serve as training data for the model. For validating the model's performance and generalization ability, we have set aside 17,572 images as a validation set. The dataset encompasses 14 different plant species, and within these species, there are 26 different diseases being studied.

Output: corroborate


Input: Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: If yes, it means that IDi is one of the recipients designated by the signer and the ciphertext is valid. Otherwise, the recipient quits the decryption process. De-signcryption. The authorized user can recover the plaintext by the following steps: Compute xi = H4(IDi) and then compute. Compute and then get the plaintext as. The one who receives the broadcasting ciphertext can verify the validity of the message and judge whether he/she is authorized by the public verification or judgement algorithm. If necessary, he/she can use the de-signcrypt algorithm to decrypt the ciphertext. In a network conference application, due to the nature of the broadcast communication, anyone, authorized recipients or unauthorized ones, can easily receive the ciphertext and check the validity of the message and the authorization of the decryption. But, only the authorized recipients can decrypt it correctly. Results and 1. The public verification algorithm in the De-signcrypt algorithm is correct. Proof. (3)In our scheme, although the identity of the real signer is not included in the ciphertext, his/her private key is definitely necessary in the signcryption process, which ensures that only legal participants who have registered himself/herself with PKG can generate a valid ciphertext. That is to say, through this algorithm, anyone can check whether a ciphertext is generated by an authorized participant, but he/she cannot determine the real identity of the signer. Theorem 2. The judgement algorithm in the De-signcrypt algorithm is correct. Proof. For each authorized IDi where we have (4)Similarly, because the private key of the real signer is necessary in the signcryption process, this algorithm can also be used to check the validity of ciphertext. At the same time, this algorithm can help a participant, who has registered himself/herself with PKG, to judge whether himself/herself is an authorized recipient, because the private key of the recipient is also necessary in the judgement. Theorem 3. The decryption algorithm in the De-signcrypt algorithm is correct. Proof. The authorized IDi, can compute as follows:(5) Thus, we can get because(6) Then, we can get the plaintext through the computations and. We shall give security proof of the proposed scheme on confidentiality, unforgeability and anonymity under the random oracle model. Theorem 4.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: “These results are promising,” she wrote, “but, as the investigators acknowledge, are some distance from suggesting that GFAP assays should become part of current emergency department management of patients with TBI and negative CT scans. The clinical significance of the positive MRI findings is unknown; data on patient symptom severity at 2 weeks or later follow-up were not reported in the study. If the frequencies of disabling concussion symptoms do not differ in the MRI-positive and MRI-negative cohorts during follow-up, it is hard to argue that either GFAP or the MRI scans are providing key information to guide further management. ”She noted that nearly half of the study participants with “negative CT scans did not have MRI as part of their follow-up, and because their characteristics are not reported, it is difficult to assess the potential for selection bias. ”In addition, she said it would be time consuming and expensive to do an MRI on every suspected TBI patient who had a normal CT. Dr. Lecky said a biomarker test could be useful for sorting out which suspected TBI patients need head imaging and/or medical follow up, but she cautioned that the biomarker test needs to be further investigated before it is ready for clinical use. Ramon Diaz-of Neurology at the of Medicine at the University of Pennsylvania and director of the said it would be beneficial to have a biomarker test that could help eliminate unnecessary CT scans (and radiation exposure). But he said adopting a new routine screening test into emergency room workflow would take planning and a change in procedures. “To be honest, I am not sure that US emergency room physicians would stop using CT scans,” in part because of medical-legal concerns, said Dr. Diaz-Arrastia, who was a coauthor of the current study. He said the biomarker test, if it becomes available, might be more quickly adopted in settings such as concussion clinics and urgent-care centers. Dr. Diaz-Arrastia said he often sees patients who come in with neurocognitive issues long after they've had a minor head injury, some having been told that since their CT was normal there was no need for concern. He said the gaps in diagnosing mild TBI mean that important care instructions and follow-up assessments are not getting done.

Output: pass


Input: Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: Some of the earliest studies of this kind emerged in the early 2000s. In 2003, for example, Dennis Lo, a chemical pathologist at the of Hong Kong, and his colleagues reported that in a study of 22 pregnant women, a specific RNA found in the placenta was much more abundant during the third trimester in those who had preeclampsia than in those who did not. Over the years, Lo’s group and others have looked at broader changes in RNA during pregnancy in larger groups of people. In a 2018 study, Moufarrej, who was then a doctoral student, her advisor Stephen Quake, a biophysicist at Stanford University, and colleagues reported that cell-free RNA could help determine when labor would occur. The researchers recruited 38 pregnant women in the known to be at risk of preterm birth, and then drew a blood sample from each in either the second or third trimester. By comparing cell-free RNA in those who eventually delivered prematurely to those who gave birth at full term, they were able to identify a set of RNAs that appeared up to two months prior to labor that could pinpoint around 80 percent of premature births. That proof-of-concept investigation spurred the researchers to look further and examine whether cell-free RNA could also predict preeclampsia. Other groups had previously reported RNA-based signatures of preeclampsia — in 2020, for instance, scientists working with the California-based biotech company Illumina reported dozens of RNA transcripts that were unique to pregnant women with the condition. But Moufarrej, Quake and their colleagues wanted to track RNA changes throughout pregnancy to see whether it might be possible identify people at risk of preeclampsia during early pregnancy, before symptoms began. RNA provides information about which genes are turned on — a process known as gene expression — and can be correlated with different pregnancy outcomes. In this study, researchers analyzed RNA in maternal blood and found 38 genes that were expressed at different levels in eight women who delivered their babies preterm versus seven who gave birth at full term. Each column represents a woman; each row shows the activity of a different gene (blue = less active, red = more active). In a study published in 2022, the researchers recruited 73 mothers at heightened risk of preeclampsia and drew blood from them four times: at or before 12 weeks, between 13 and 20 weeks, at or after 23 weeks, and after birth.

Output: corroborate


Input: Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: B sets Ppub = bP, and gives <G1, G2, q, e, P, Ppub, H1, H2, H3, H4> to the attacker F as the public parameters. Upon receiving the system parameters, F outputs n target identities. Attack. F adaptively performs polynomially bounded number of queries to the various oracles in this phase, which are similar to those in Theorem 4. Forgery. F generates a target ciphertext. If the forgery is successful, the equation holds. Define QS* = lS*P = aP, and then we have. Now, it will be very easy to extract the CDH problem’s solution. We consider the advantage of F’s success here. As in the anony-signcryption query, the probability for B to answer a failure signcryption query is not greater than qs/2k, and then the advantage is. Theorem 6. In the ANON-sID-CCA security model, if an adversary A has advantage against the game defined in Definition 6 within running time t (where A makes at most qe private key extraction queries, qs anony-signcryption queries, qd de-signcryption queries and queries to the Hash functions H1,H2,H3 and H4, respectively), then there is an algorithm B in solving the DBDH-M problem with an advantage. Proof. The challenger B is challenged with an instance of the DBDH-M problem. Assume that there is an adversary A who is capable of breaking the ANON-sID-CCA security with a non-negligible advantage. B makes use of A to solve the DBDH-M instance. B simulates the system with hash functions H1, H2, H3 and H4, and allows A to make polynomially bounded number of queries. The game between B and A is demonstrated below: Phase 1. Suppose that A outputs a target identity pair (ID1*, ID2*). Setup. B sets the public key and lets where and aP and bP are given from the instance of the DBDH-M problem. Here, B does not know a and b.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-3968021/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractclassification of brain tumors play an essential role in improving prognosis, diagnosis, and therapy strategy in glioblastoma patients. In this paper, we provide a novel (MGMT-PMP) approach that extracts latent features fused with radiomic data to predict the genetic subtype of glioblastoma. The novice rejection method has been shown to be highly effective at picking and isolating negative training cases from the original dataset. In this study, the fused feature vectors are used to train and test CNN classifiers. We examined classification performance for the first time in published form, using metrics such as accuracy, F1-score, and Matthews’s correlation coefficient. Jackknife tenfold cross-validation was used to train and evaluate the BraTS-2021 dataset validation. The maximum classification accuracy for detecting MGMT methylation status in glioblastoma patients is 99. 13 percent. Deep learning feature extraction with Radiogenomic characteristics, merging imaging phenotypes and molecular structure, and using a rejection method were found to outperform in diagnosing the MGMT methylation status of glioblastoma patients. The approach connects genetic variance to radiomic characteristics, bridging two areas of research that may be valuable for clinical treatment planning and leading to better outcomes. MRI scansglioblastomarejection algorithmdeep learningstatusFiguresFigure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9Figure 10Figure 11Figure 121 IntroductionBrain tumors occur when billions of brain cells divide uncontrollably and develop aberrant areas, resulting in the highest global mortality rates for both adults and children [1]. Tumors in the brain have variable growth rates and are classified as primary or secondary. The former, which originates inside the brain, is the deadliest and most commonly cancerous. Gliomas handle around 30% of primary brain tumors, 80% of malignant ones, and most primary brain tumor-related deaths [2]. Grades II and III gliomas grow swiftly and require immediate treatment. Grade-IV gliomas, or glioblastoma (GB), are the most aggressive and toughest to treat.

Output: pass


Input: Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: Afterward, the researchers compared cell-free RNA for the 24 women who indeed developed preeclampsia against the remaining 49 who did not. The team identified RNAs corresponding to 544 genes whose activity differed in those who developed preeclampsia and those who did not. (The study did not differentiate between maternal and fetal RNA, but since the majority of cell-free RNA in a pregnant person’s blood is their own, Moufarrej says most of these RNAs are likely maternal in origin. )Then, using a computer algorithm, the researchers developed a test based on 18 genes that could be used prior to 16 weeks of pregnancy to predict a woman’s risk of developing preeclampsia months later. The test correctly identified all the women who would later develop preeclampsia — and, equally important, all of the women who the test predicted wouldn’t develop preeclampsia did in fact escape the disease. (About a quarter of the women predicted to develop preeclampsia did not, in fact, get the disease. ) The same 18-gene panel also correctly predicted most cases of preeclampsia in another group of 118 women. The team also took a closer look at which tissues the RNA of interest originated from. This included the usual suspects, such as the lining of the blood vessels (also known as the endothelium), which scientists already know contributes to preeclampsia, as well as other, more unexpected sources, such as the nervous and muscular systems. The authors note that, in the future, this information could be used both to understand how preeclampsia affects different parts of the body and to assess which organs are at highest risk of damage in a particular patient. According to Quake, studies like these from both his team and others are starting to reveal the diversity of changes throughout the body that contribute to pregnancy complications — and providing evidence for something that clinicians and researchers have long suspected: that both preeclampsia and preterm birth are conditions with a range of underlying causes and outcomes. “There are now strong indications that you should be defining multiple subtypes of preeclampsia and preterm birth with molecular signatures,” says Quake. “That could really transform the way physicians approach the disease.

Output: corroborate


Input: Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: The specific comparison results are shown in Table 3, from which one can see that our scheme performs much better than most of the existing schemes in terms of number of parameters, computation complexity and the ciphertext length. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 3. Comparison. the de-signcryption in our scheme, some calculations of the de-signcryption algorithm are used to judge the validity of ciphertext and the authorization of the recipient, which is important for broadcast-based communications to avoid receiving unwanted information (e. g. SPAM). Note that although the schemes [9], [10], [11], [13], [15] directly provide the recipients’ true identities in the ciphertext, in fact the recipient cannot absolutely ensure whether he/she is authorized before checking the validity of the ciphertext. The number of pair operations (Tp, the most time-consuming operation in the existing schemes and our scheme) in our decryption algorithm is smaller than those of the existing schemes, which makes our scheme more attractive in terms of computation performance. Table 4 shows a comparison between the proposed scheme and the existing ones [9], [10], [11], [12], [13], [15], [17], [19]. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 4. De-signcryption Efficiency Comparison. Discussion of Merit and to existing schemes, our scheme has some advantages. To achieve the signer’s anonymity, the identity of the signer is no longer included in the ciphertext, although the private key of the signer is necessary for signcryption. The recipients can therefore only judge if the ciphertext received is from a trusted signer, but they cannot determine the real identity of the signer. To achieve the recipient’s anonymity, the ID information of all authorized recipients is mixed by the Lagrange interpolation polynomial during the signcryption process, which prevents the recipient’s ID from being exposed. This method also ensures that only the recipient, who has got the entire ciphertext, can decrypt the ciphertext, thus achieving the decryption fairness.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Grade-IV gliomas, often known as glioblastoma (GB), have the most challenging prognosis and clinical results [3]. Adult GB and diffuse astrocytic glioma are the deadliest central nervous system tumors because of their high variability in shape and microscopic structure [4]. Despite multiple therapeutic choices, the prognosis for GB patients has not much improved over the previous 20 years. (WHO) has supplied information on the classification of central nervous system malignancies, emphasizing the importance of integrated diagnostics [5]. Clinical tumor diagnosis involves combining molecular and cytogenetic markers [6]. Deep learning is a branch of machine learning that makes use of artificial neural networks. Deep learning methods enable computer programs to learn and uncover patterns in enormous datasets. 1. 1 O6-methylguanine-DNA methyltransferase (MGMT): O6-methylguanine-DNA methyltransferase (MGMT) is a DNA repair enzyme that plays a crucial role in chemoresistance to alkylating drugs [7]. Methylation of the promoter is a predictive factor for chemotherapy response in early-stage GB diagnosis. The most common treatments for GB are surgery, radiation, and adjuvant chemotherapy. Chemotherapy effectiveness is often linked to the methylation status of MGMT, a biomarker that repairs guanine nucleotides and inhibits cell death from alkylating drugs [8]. In most cases, having the MGMT enzyme avoids DNA damage, making it beneficial. In GB patients, the presence of MGMT reduces the efficacy of chemotherapy by making alkylating drugs ineffective [9]. This study focuses on predicting the methylation status of the MGMT promoter ("Preprocessing"), which results in MGMT gene silencing in GB patients treated with alkylating agent chemotherapy. The presence of preprocessing prevents cell division by cross-linking DNA strands. Predicting the methylation status of MGMT promoters in GB can aid decision-making and treatment planning, which is the primary goal of this research. 1. 2 Convolutional neural networks (CNN) : Deep convolutional neural networks are used for various tasks, such as medical image processing and picture classification [10]. The deep learning architecture uses many layers of encoding blocks. The feature maps in lower levels are transmitted to higher-complexity layers [11].

Output: pass


Input: Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: Home E-and catalog us 243. Published online: December 31, 2012 DOI: of Kwak 1, Hak C. Jang 2, 3, Park 1, 2, 4 1Department of 110-744, Korea. 2Department of of Medicine, Seoul 110-799, Korea. 3Department of 463-707, Korea. 4WCU Department of of and Technology and College of Medicine, University, Seoul 110-744, Korea. Corresponding author: Tel: +82-2-2072-2946, Fax: +82-2-3676-8309, kspark@snu. ac. kr 02, 2012 15, 2012 16, 2012 Copyright © 2012 by (open-access, It is identical to the Attribution Non-( diabetes mellitus (GDM) is a complex metabolic disorder of pregnancy that is suspected to have a strong genetic predisposition. It is associated with poor perinatal outcome, and both GDM women and their offspring are at increased risk of future development of type 2 diabetes mellitus (T2DM). During the past several years, there has been progress in finding the genetic risk factors of GDM in relation to T2DM. Some of the genetic variants that were proven to be significantly associated with T2DM are also genetic risk factors of GDM. Recently, a genome-wide association study of GDM was performed and reported that genetic variants in CDKAL1 and MTNR1B were associated with GDM at a genome-wide significance level. Current investigations using next-generation sequencing will improve our insight into the pathophysiology of GDM. It would be important to know whether genetic information revealed from these studies could improve our prediction of GDM and the future development of T2DM. We hope further research on the genetics of GDM would ultimately lead us to personalized genomic medicine and improved patient care. Keywords: genetics, gestational diabetes, type 2 diabetes diabetes mellitus (GDM) is defined as abnormal glucose tolerance that is first identified or diagnosed during pregnancy [1]. It is estimated that approximately 2% to 5% of all pregnancies in Korean women are complicated by GDM [2]. The clinical significance of GDM is that it increases the risk of adverse pregnancy outcomes [3].

Output: corroborate


Input: Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: The ID-based cryptography enables one user to confidentially send messages to other users, despite of whether the latter is a registered user, and the public verification property of our scheme enables unregistered users to judge the validity of the received ciphertext before having to register himself/herself with PKG. The merit/demerit comparison between the existing schemes and our scheme is summarized in Table 5. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 5. Comparison of merits and demerits. Table 5, we can see: (1) The schemes [12], [13], [15], [17], [19] have taken anonymity of the sender into account. However, they are all prone to the cross-comparison attack and joint conspiracy attack. In these schemes, in order to protect the privacy of the sender, the sender randomly chooses some legitimate participants to hide the true identity. But in practice, these schemes are vulnerable to the cross-comparison attack and joint conspiracy attack mentioned above. (2) The schemes [9], [10], [11] cannot assure the anonymity of the sender because the identity of the sender is directly given in the ciphertext. (3) The schemes [9], [10], [11], [13], [15], [17], [19] cannot assure the anonymity of the recipient. In these schemes, the ciphertext includes two parts: a recipient identity list and each recipient’s specific data. A recipient identity list is required so that an authorized recipient is able to find his/her specific data required for decryption of the ciphertext. Because the recipient identity list is given in plaintext, the ID information of each recipient is exposed, and thus the anonymity of recipients is not assured. This has the advantage that, as long as an authorized recipient receives his/her specific data correctly, he/she can decrypt the ciphertext to retrieve the corresponding message even if other recipients’ specific data are invalidated during transmission. While this seems to represent an advantage on the first sight, it also represents a problem regarding decryption fairness. Decryption unfairness can cause the sender to cheat some recipients actively by just sending incorrect recipient-specific data.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: Convolutional neural networks (CNN) have significantly fewer neurons than shallow neural networks because of sparse interactions. Deep convolutional neural networks are used for various tasks, such as medical image processing and image classification [12]. The deep learning architecture uses numerous layers of encoding blocks. The feature maps in lower levels are transmitted to higher-complexity layers [13]. Convolutional neural networks (CNN) use sparse interaction to significantly reduce the number of neurons compared to shallow neural networks [14]. Transfer learning using CNN based on for the ImageNet dataset is well known deep learning approach [15]. The CNNs are extensively used in vision-related applications including object detection, spanning classification, and segmentation [16]. The combination of data pre-processing and augmentation with transfer learning can be helpful for improved classification results. In our case, since the dataset is enormous, only pre-processing seems of excellent value along with a fine-tuned CNN architecture [17]. The 3D CNN is an array of three-dimensional CNNs that use a multifiber unit in conjunction with dilated weighted convolutions to extract feature attributes at different scales for volumetric segmentation [18]. Pre-processing: Before the data is delivered to the network for training, it is improved through a variety of methods (mirroring, rotation, and cropping). Training: The model was created using a fixed size of 128 × 128 and a redesigned loss function that included both the focused and generalized loss [19]. Eq. (1) provides a mathematical expression for the output value γ at position (x, y, z) of the jth feature map in the ith 3D convolutional layer [20].

Output: pass


Input: Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: 1, the risk allele frequency as well as the odds ratios of known T2DM variants are compared among a control group (n = 632, men : women = 287 : 345), T2DM group (n = 761, men : women = 354 : 407), and GDM group (n = 869) in Koreans [22, 24]. For most of the variants, there was an increasing trend of risk allele frequencies from control to T2DM and from T2DM to GDM. Lauenborg et al. [23] also found that variants in TCF7L2, CDKAL1, and TCF2 were significantly associated with the risk of GDM in Europeans, consisting of 283 GDM women and 2,446 glucose-tolerant control women. Variants in KCNQ1, which were first identified in an T2DM GWA study, were also significantly associated with the risk of GDM in Koreans (p < 0. 05) [25]. Recently, it was reported that TPH1 and HTR2B play a crucial role in regulating pancreatic β-cell mass during pregnancy in a mouse model and that their genetic alterations could result in GDM [17]. A total 6 genetic variants in HTR2B and 11 variants in TPH1 were identified and genotyped in Korean GDM women and control subjects [26]. Although there were no significant associations of these variants with the risk of GDM, they were associated with measures of obesity and weight gain during pregnancy [26]. GWA Study of GDM Following these candidate approach studies was a two-staged GWA study that was performed in Korean GDM women [27]. A total of 468 GDM women and 1,242 nondiabetic control women were genotyped using Affymetrix Genome-5. 0. Variants that passed the prespecified p-value threshold in the stage 1 genome scan were further genotyped in 931 GDM women and 783 nondiabetic control women. Two variants, one located in an intron of CDKAL1 (rs7754840) and one near MTNR1B (rs10830962), were associated with a risk of GDM at a genome-wide significance level (p = 6. 65 × 10-16 and p = 2. 49 × 10-13, respectively).

Output: corroborate


Input: Claim: All transactions on a blockchain are completely anonymous. [SEP] Text: (4) In all the existing schemes, public verification is not considered because the identity of the sender or the recipient must be given in the ciphertext in plaintext form, thus there are no requirements for public verification. But in a completely anonymous scheme, public verification is a necessity for recipients so that receiving or operating on unwanted messages is prevented. To summarize, the ciphertext in our scheme no longer contains the real identity information of all participants, thus our scheme meets anonymity of the sender and recipients at the same time, and efficiently protects the privacy of all involved participants. Even more important, this scheme possesses fair decryption and public verification properties. Furthermore, our scheme is easy to implement in exsiting applications. Here, we also take a network conference application as an example. In such a case, a message sender needs only to transform the plaintext message to a ciphertext message using our encryption algorithm and then broadcasts it through the broadcast communication channel, while a message recipient simply needs to decrypt the ciphertext using our decryption algorithm. Our scheme requires only extra encryption or decryption operations for each participant and leaves the original implementation untouched, which in fact should represent an easy implementation of our scheme. While our scheme has the advantages mentioned above, it also has some disadvantages, namely its application, which increases the costs for the implementation. For example, it probably takes a great deal to establish PKG and maintain it, which may affect routine application of our scheme to some extent. to the nature of broadcasting communications, the anonymity of both the sender and the recipient is of upmost importance in multi-recipient signcryption. However, almost none of the existing multi-recipient signcryption schemes take the anonymity of recipients into account. Although there are several schemes that provide a solution to the anonymity of the signer, they have known limitations. Owing to practical application requirements, a completely anonymous multi-recipient signcryption becomes more and more important. Aiming at the participants’ anonymity, a completely anonymous multi-recipient signcryption is proposed in this paper. The new scheme ensures anonymity of all participants, the sender and all recipients. Furthermore, each recipient can easily judge whether the received message is from an authorized source, but he/she cannot determine the true identity of the sender. Each recipient can easily judge whether he/she is an authorized recipient, but he/she cannot determine the identity of any other authorized recipient.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: et al. in November 2023[27] emphasized EAV-UNet, a technology for accurately detecting lesion locations. Optimizing feature extraction, using automated segmentation algorithms to find anomalous regions, and strengthening the structure. The approach replaces the U-Net encoder with the VGG-19. To improve feature details, they added a CBAM (Channel and Mechanism) module to the decoder. They added an edge identification part to the encoder to extract important edge characteristics from the stream. This strategy obtained an F1 score of 96. 1%. et al. in April 2023 [28] proposed that Deep learning algorithms were used to analyze brain MRI scans of tumors to detect the methylation status of the MGMT promoter. Researchers used deep learning algorithms and a large public MRI dataset of 585 people to predict the methylation status of the MGMT promoter in glioblastoma tumors. They evaluated these models using Grad-CAM, occlusion sensitivity, feature visualizations, and training loss landscapes. The highest AUC by fold and the largest mean AUC are detected. The mean AUC becomes saturated at 0. 69. Qureshi1[29] et al. in 2023 proposed a novel two-stage (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and tumorous structural size by radiomic characteristics (and LBP). The novice rejection method was shown to be highly effective in picking and isolating negative training cases from the original dataset. The fused feature vectors are then employed by k-NN and SVM classifiers for training and testing. The highest classification performance is (96. 84 0. 09%), (96. 08 0. 10) %, and (97. 44 0. 14) % for detecting MGMT methylation status in patients with glioblastoma. In august 2022, Do et al.

Output: pass


Input: Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: This was the first GWA study investigating the genetic risk factors of GDM. Although variants in MTNR1B have been previously reported to be associated with increased fasting glucose levels, the study was the first to report that MTNR1B variants are associated with GDM at a genome-wide significance level. One of the limitations was that it was not sufficiently powered to find truly novel genetic variants that were only specific in GDM. It should be noted that current GWA and GWA meta-analysis performed in T2DM recruits more than 100,000 cases and controls [28]. One of the interesting findings was that genetic variants of T2DM were enriched in GDM subjects. Among the 34 confirmed T2DM genetic variants, 8 were associated with a risk of GDM [27]. In addition, when the β-coefficients (or odds ratio) of the variants derived from the logistic regression were compared between GDM and T2DM, there was a significant positive correlation of β-coefficients between the two [27]. These findings suggest that GDM and T2DM might share similar genetic backgrounds, at least in part. Perspectives in of studies have opened a new era in diabetes research. Our knowledge on the genetic predisposition of GDM as well as T2DM is expected to increase even faster as next-generation sequencing technology is applied to this field. There should be even larger GWA studies on GDM, and GWA meta-analyses should be available. In this way, we could find variants that have smaller effect sizes but are more specific to GDM than T2DM. In order to understand the genetic determinants of glucose and insulin concentration during pregnancy, genetic associations regarding these quantitative traits should be investigated. A fast way is to see whether genetic variants that are known to affect glucose or insulin concentration in the normal population also affect glucose or insulin concentration during pregnancy [29]. In addition, a GWA study on glucose or insulin concentration during pregnancy should also be helpful. In this way, we would be able to better understand the pathophysiology of GDM. By definition, GDM encompasses women with pre-exiting T2DM, maturity onset diabetes of the young (MODY), or even type 1 diabetes patients that were not diagnosed previously. It would be important to know the proportion of MODY patients that constitute GDM.

Output: corroborate


Input: Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: AbstractBitcoin (Nakamoto, 2008) is an electronic cash system designed to work without central management. Despite recent enthusiasm, Bitcoin (BTC) and other so-called cryptocurrencies are not ideal as means for payment, because of instability of their market prices against major currencies. This 6. 1 Bitcoin as a (Nakamoto, 2008) is an electronic cash system designed to work without central management. Despite recent enthusiasm, Bitcoin (BTC) and other so-called cryptocurrencies are not ideal as means for payment, because of instability of their market prices against major currencies. This 17. 31 million BTCs had been issued in the market with ID numbers (about 82% of 21 million BTCs). As of 2018, roughly every ten minutes on average, 12. 5 BTCs were being issued with new IDs. This procedure of new issue is implemented as a reward for the first person/group to validate transactions without double spends that have been collected in a block. This is a competition of validation via computation, with the aim of solving a specific mathematical problem. Footnote 4 This computation is described as mining, and those who conduct mining are miners. The speed of new issue of Bitcoin on the register record is set to be halved every four years. At the beginning of the Bitcoin system in January 2009, the reward was 50 BTCs per ten minutes; it was halved to 25 BTCs per ten minutes on November 28, 2012. It remained the same reward per ten minutes until it was halved to 12. 5 BTCs per ten minutes on July 9, 2016,Footnote 5 and this halving process will continue until 2140, when the new issue of BTC will be terminated. Total circulation of BTC will be fixed at a little less than 21 million BTCs. There are differences between a real estate registry system and the Bitcoin system. In Japan, for instance, the real estate register record is kept exclusively by the Bureau and the public is only allowed to read the record. In contrast, the virtual registry book that contains all information on Bitcoin transactions and ownership is maintained individually among participants. This decentralized nature of virtual registry bookkeeping activity may create some inconsistencies among participants. In the Bitcoin protocol, when an identical Bitcoin segment is used twice for different payments—leading to a Bitcoin segment having two branches (double spends)—the majority decision rule is used to determine which payment is genuine.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: [30] the radiomics features extracted from multimodal images from magnetic resonance imaging (MRI) had undergone a two stage feature selection method, including an extreme (XGBoost) feature selection model followed by a genetic algorithm (GA)-based wrapper model for extracting the most meaningful radiomics features for predictive purposes. According to the cross-validation results, the GA-based wrapper model performed well in predicting the MGMT methylation status in GBM, with a sensitivity of 0. 894, specificity of 0. 966, and accuracy of 0. 925. Application of the extracted GBM radiomics features on a low-grade glioma (LGG) dataset also achieved a sensitivity 0. 780, specificity 0. 620, and accuracy 0. 750, indicating the potential of the selected radiomics features to be applied more widely on both low- and high-grade gliomas. After image preprocessing and feature extraction, et al [31] in 2022 built and compared the performance of two types of machine-learning (ML) models in June 2022. The first type was set up using all MRI sequences (T1WI, T2WI, contrast enhancement (CE), _b_high, DWI_b_low, and ADC), while the second type was established using single MRI sequences as described above. technique was used to find seven radiomic features for the ML model based on all sequences. The predictive accuracy was 0. 993 and 0. 750 in the training and validation sets, respectively, and the area under curves (AUCs) were 1. 000 and 0. 754 in the two sets, respectively. For the ML model based on single sequences, the numbers of selected features were 8, 10, 10, 13,9, 7, and 6 for T1WI, T2_b_high, DWI_b_low, and ADC, respectively, with predictive accuracies of 0. 797 ~ 1. 000 and 0. 583 ~ 0. 694 in the training and validation sets respectively, and the AUCs of 0. 874 ~ 1. 000 and 0. 538 ~ 0. 697 in the two sets, respectively. In the independent validation set, the T1WI-based model performed best, while the CE-based model performed worst. 2022 et al.

Output: pass


Input: Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: In particular, about 15% of GDM patients remain diabetic at early postpartum periods, and a significant portion of these subjects might fall into the category of MODY. The contribution of genetic variants known to cause MODY, such as GCK and HNF1A, in GDM has been reviewed in recent literature [30]. However, whole-exome sequencing will provide us with better bird's eye view on the contribution of MODY genes in GDM. In addition, it might be able to find novel MODY genes in those who have persistent diabetes after GDM pregnancy and also have a strong family history of diabetes. One of the first steps in translating the genetic information into clinical practice would be to predict the future development of T2DM in GDM women. GDM women are at particularly high risk of developing T2DM and require preventive measures and early screening of T2DM. Genetic information might improve our prediction of T2DM in women with a history of GDM. This is an area of active research [31, 32], and we are looking forward to studies that use genotype risk scores in predicting T2DM in GDM women. A similar approach could also be applied in predicting GDM, as more genetic variants associated with GDM are expected to be revealed. The functional consequences of the current common genetic variants identified through GWA studies of GDM are not well understood yet. It is not known whether they are markers in linkage disequilibrium with nearby causal variants or whether they have unknown but relevant functional roles. Next-generation sequencing might give answers to these questions, but a huge number of samples and much effort will be required. GDM women are at increased risk of developing T2DM and have familial clustering of T2DM. A common pathophysiology that is shared by GDM and T2DM is impaired compensatory increase in insulin secretion to overcome the increased insulin resistance. The GWA study of GDM enabled us to investigate the common genetic risk factors of GDM, and it revealed that GDM and T2DM share similar genetic backgrounds, at least in part (Fig. 2). Although this information has significantly improved our insight in the pathogenesis of GDM, there are more unanswered questions remaining that should be explicitly expressed. Using the technology of next-generation sequencing, it is expected that more causal, rare variants will be identified. This genetic information would ultimately improve our prediction of GDM and future T2DM.

Output: corroborate


Input: Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: In case of standard electronic money, an issuer of electronic money receives participation fees directly from the retail shops; they are paid not by the electronic money they issue, but by central bank notes. Central banks themselves pay maintenance costs and receive service rewards in the money they issue. In the case of Bitcoin, the miner who contributes to the maintenance of the system receives Bitcoins as their reward, so it resembles the central bank system. A difference between the Bitcoin system and the central bank system lies in the fact that the former gives a reward to a miner who happens to win the mining competition while the latter receives a reward constantly. If there is a single miner in the Bitcoin system, r can be any arbitrary 256 bit value (n can be zero). In such a case, the competition mechanism that guarantees a validity of proof of work does not work and we require some alternative. If an alternative works, it could be sufficient to prevent double spends. This situation can be described as the mint model of cryptocurrency. The mint model differs from the Bitcoin model in the sense that the former model uses a finality confirmation structure with legal enforcement, while the latter model uses a finality confirmation structure via mining competition. Note again that the winner of the competition is the only competitor to be rewarded with Bitcoin. The probability of winning a reward must be based on the proportional computational power of an individual miner to the total computational power of all mining participants: all miners may expect to receive rewards proportional to their computational power after a reasonable number of mining competitions. Footnote 15 Then we must ask ourselves, can the proof of work contribute to the stability of Bitcoin value? Nakamoto (2008) states that “once a predetermined number of coins have entered circulation, the incentive can transition entirely to transaction fees and be completely inflation-free”(p. 4). The answer is no. As Fig. 6. 2 amply illustrates, the values of Bitcoin as measured in U. S. dollars fluctuate wildly compared with those of other foreign currencies. The reason for this high volatility is apparent. Demand for Bitcoin, regardless of the motivation for holding (i. e. , payment or speculation), increases as its price decreases and vice versa. As Fig. 6. 3 shows, the demand curve of Bitcoin, therefore, would be downward slopingFootnote 16 while supply the curve of Bitcoin at any point of time would be vertical.

Output: contrast


Input: Claim: Certain claims suggest that advanced algorithms might predict emotions based on brain scans. [SEP] Text: [32] developed a symmetric Deep Learning-based U-Net framework based on FLAIR's 512 _ 512 segmented maps as the ground truth mask. Their findings: The final group included 208 patients with an average _ standard deviation of age (years) of fifty-six _ fifteen and an M/F ratio of 130/78. The DSC of the generated mask was 0. 93. Prediction of IDH-1 and MGMT status achieved AUCs of 0. 88 and 0. 62, respectively. Survival prediction of < 18 months proved an AUC of 0. 75. Their deep learning-based framework can detect and segment gliomas with excellent performance for the prediction of IDH-1 biomarker status and survival.  2022 et al [33], trained a residual network (ResNet) to give a binary prediction of MGMT promoter methylation status. Instead of using images as an input, as in existing research, our research extracted radiomics features from a selected region of interest (ROI) in different modalities of MR images and used them as the input of the model. Based on the ROI of the whole tumor, the predictive capacity of the T1CE and ADC model achieved the highest AUC value of 0. 85. Based on the ROI of the tumor core, the T1CE and ADC model gained the most elevated AUC value of 0. 90. After comparison, the T1CE combined with the ADC model based on the ROI of the tumor core showed the best performance, with the highest average accuracy (0. 91) and AUC (0. 90) among all models. The deep learning method using MRI radiomics has excellent diagnostic performance with a high accuracy in predicting MGMT promoter methylation in diffuse gliomas. 3 Data source and collectionThree cohorts define the dataset: Training, Validation, and Testing with size (7 Gigabyte) ~ 400000 image. The dataset is published on Kaggle website in May 2021. These three cohorts are structured as follows: Each independent case has a dedicated folder shown by a five-digit number. Each of these "case" folders has four sub-folders, one for each of the structural multiparametric MRI (mpMRIs) scans in DICOM format.

Output: pass


Input: Claim: AI is used in genomics to help identify genetic risk factors for diseases. [SEP] Text: Implication of genetic variants near TCF7L2, SLC30A8, 1, CDKN2A/B, IGF2BP2, and FTO in type 2 diabetes and obesity in 6,719 Asians. Diabetes 2008;57:2226–2233. PMID: 18469204. 25. . Polymorphisms in KCNQ1 are associated with gestational diabetes in a Korean population. Paediatr 2010;74:333–338. PMID: 20606385. 26. Association of variations in TPH1 and HTR2B with gestational weight gain and measures of obesity. Obesity (Silver Spring) 2012;20:233–238. PMID: 21836641. 27. A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes 2012;61:531–541. PMID: 22233651. 28. è AV, Steinthorsdottir V, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. 2012;44:981–990. PMID: 22885922. 29. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. 2012;44:991–1005. PMID: 22885924. 30. . Genetics in gestational diabetes mellitus: association with incidence, severity, pregnancy outcome and response to treatment. Rev 2010;6:393–399. PMID: 20879971. 31. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Med 2008;359:2208–2219. PMID: 19020323. 32. Clinical risk factors, DNA variants, and the development of type 2 diabetes. N Med 2008;359:2220–2232. PMID: 19020324. Fig. 1 Comparison of risk allele frequencies of known type 2 diabetes mellitus (T2DM) genetic variants among a control group (n = 632, men : women = 287 : 345), T2DM group (n = 761, men : women = 354 : 407), and gestational diabetes mellitus (GDM) group (n = 869) in Koreans.

Output: corroborate


Input: Claim: Everyone can earn money through cryptocurrency investments. [SEP] Text: To be more precise, it falls to some price lower than the average cost per unit but above the average variable cost. The miners would continue mining because it is rational to keep operations as long as return/revenue exceeds variable cost (i. e. , total cost minus fixed cost); the eventual operational loss would be smaller than that incurred by immediate stoppage. According to some reports on Bitcoin mining, many large-scale miners who entered after the Bitcoin boom in late 2013 continue running their operations even with negative returns. They may not actively anticipate the return of above-1,000 dollar/Bitcoin days, but they might simply assume that eventual operational loss would be minimized by continued operation. (5) Miners may also migrate to another mine in which they can continue mining, should computational powers be convertible to the new mine. Footnote 21 As we mentioned before, if the miners migrate to other mines, the size of M decreases and the expected return per unit would recover. By this mechanism, Bitcoin mining can survive even under a very volatile Bitcoin price. On the other hand, miners’ computing equipment may reach the end of its useful life, and miners might have to stop mining before they recover all their sunk costs. (6) Bitcoin mining might end another way. If the Bitcoin price drops sharply below the average variable cost, all miners would exit from mining. Many miners entered the Bitcoin mining competition after the Bitcoin boom in late 2013. Their computational power would be expected to be broadly similar. Footnote 22 If that is the case, the miners’ exit strategy would not be a gradual one but could be sudden. If the Bitcoin price drops below a threshold, the Bitcoin system as a whole may collapse or the Bitcoin users are limited to a very small number of inner members with which Bitcoin is exchanged at a very small scale. Once all miners leave Bitcoin mining, no one would be engaged in the proof of work. A validation of a block would be delayed or stopped, and in consequence Bitcoin would cease to be a useable currency. This type of risk does not exist in gold mining. Footnote 23 From the above observations, it is clear that the Bitcoin system intrinsically manifests dual instability. The first instability stems from an inflexible supply curve of Bitcoin, which amplifies Bitcoin price volatility; the miners’ revenue/reward fully absorbs any price changes. There is no price stabilization mechanism. The second instability comes from risks to the sustainability of mining.

Output: contrast