Input: Task: corroborate the claim using the given text. Claim: AI is being developed to help detect early signs of diseases like cancer and Alzheimer’s. [SEP] Text: (2017)24 Deep CNN and SVM classifier Standard preprocessing (resizing, normalization) End-to-end training Cross-validation, independent validation dataset Ding et al. (2017)25 Deep CNN and template matching Standard preprocessing (resizing, normalization) End-to-end training Cross-validation, independent validation dataset Ardila et al. (2019)26 3D CNpreprocessing (resampling, normalization) End-to-end training Cross-validation, independent validation dataset Liang et al. (2019)27 3D CNpreprocessing (resizing, normalization) End-to-end training Cross-validation, independent validation dataset Shen et al. (2016)28 Deep CNN Not specified Transfer learning from experts Cross-validation, independent validation dataset Zhang et al. (2018)29 Ensemble of CNpreprocessing (resizing, normalization) Ensemble learning Cross-validation, independent validation dataset Anthimopoulos et al. (2016)30 Deep CNN Standard preprocessing (resizing, normalization) End-to-end training Cross-validation, independent validation dataset et al. (2018)31 CNN with attention mechanism Not specified Transfer learning with fine-tuning Cross-validation, independent validation dataset Shen et al. (2017)33 Multi-crop CNN Not specified Transfer learning Cross-validation, independent validation dataset Bi et al. (2019)34 specified Transfer learning, SVM training Cross-validation, independent validation dataset Sun et al. (2018)35 Multi-level context CNN Not specified Transfer learning Cross-validation, independent validation dataset. Clinical implications and integration. Paper title. and authors. Clinical integration. Limitations. Robustness. Setio et al. (2017)17 Demonstrates potential integration into clinical workflows, aiding radiologists in nodule detection and early intervention False positives/negatives, technical challenges, algorithm complexity Algorithms show robust performance across multiple datasets, allowing consistent nodule detection Hu et al. (2017)18 Offers potential integration by aiding radiologists in nodule diagnosis, improving accuracy Limited explanation of model decisions, possible overfitting Demonstrates robustness across various nodule characteristics, contributing to accurate diagnosis Yap et al. [SEP] Justify:

Output: The text corroborates the claim by discussing various studies and techniques, specifically mentioning algorithms such as 'Deep CNN' and 'SVM classifier' that are designed for image analysis tasks that could be linked to early detection of diseases like cancer. Additionally, references to 'clinical integration' and 'aiding radiologists in nodule detection' further support the notion that AI is being developed to assist in the detection of early signs of diseases.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: Using committees of scientists, with little or no experience of commercialising research outputs is likely to be an unmitigated disaster. There is no question that some research leds to commercial outcomes but to the best of my knowledge there is no evidence that anyone has ever had any success in picking the right projects in advance. The simple fact is that the biggest form of economic impact from research is in providing and supporting the diverse and skilled workforce that support a commercially responsive, high technology economy. To a very large extent it doesn’t actually matter what specific research you support as long as it is diverse. And you will probably generate just exactly the same amount of commercial outcomes by picking at random as you will by trying to pick winners. The world, and the UK in particular, is facing severe challenges both economic and environmental for which there may be technological solutions. Indeed there is a real opportunity in the current economic climate to reboot the economy with low carbon technologies and at the same time take the opportunity to really rebuild the information economy in a way that takes advantage of the tools the web provides, and in turn to use this to improve outcomes in health, social welfare, to develop new environmentally friendly processes and materials. The UK has great potential to lead these developments precisely because it has a diverse research community and a diverse highly trained research and technology workforce. We are well placed to solve todays problems with tomorrow’s technology. Now let us return to the current UK policy proposals. These are to concentrate research, to reduce diversity, and to focus on areas of UK strength. How will those strengths be identified? No doubt by committee. Will they be forward looking strengths? No, they will be what a bunch of old men, already selected by their conformance to a particular stereotype, i. e. the ones doing fundable research i fundable places, identify in a closed room. It is easy to identify the big challenges. It is not easy, perhaps not even possible, to identify the technological solutions that will eventually solve them. Not the currently most promising solutions, the ones that will solve the problem five or ten years down the track. As a thought experiment think back to what the UK’s research strengths and challenges were 20 years ago and imagine a world in which they were exclusively funded. It would be easy to argue that many of the UK’s current strengths simply wouldn’t even exist (web technology? biotechnology? polymer materials? ). [SEP] Justify:

Output: The text contrasts the claim by indicating that the concentration of research and reduction of diversity is not beneficial. It argues that a diverse research community is necessary to address significant challenges, implying that monoculture practices, which focus on a single type of crop, would not support genetic diversity in the same way a diverse approach to research would. The author suggests that diversity is crucial for innovation and solving problems, which is contrary to the notion that genetic diversity can be achieved through monoculture.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: orontii infection. NtEDR1-2,NtEDR1-5 and NtEDR1-15 showed no obvious changes at all time points tested. NtEDR1-4 and NtEDR1-18 were up-regulated at 12 hpi. NtEDR1-6 and NtEDR1-11 were up-regulated at 1 hpi; subsequently, expression of NtEDR1-6 decreased, and NtEDR1-11 maintained high expression after 2 hpi. NtEDR1-7, NtEDR1-13 and NtEDR1-14 were immediately up-regulated with G. orontii infection at 1 hpi, but their expression declined to a normal level at 6 hpi. These results indicated that different EDR1-like genes showed different expression patterns after G. orontii challenge. The expression patterns of NtEDR1-1, NtEDR1-12 and NtEDR1-16 were similar after tobacco was challenged by G. orontii. Figure 5: Expression profiles of NtEDR1-like genes in tobacco leaves in response to Golovinomyces orontii infection. Leaves were inoculated with G. orontii to give approximately 20–25 spores per cm2 and sampled after 0, 1, 2, 12 and 24 h (0 h as control). The expression levels of the genes were significantly increased at 1 h (A), increased at 2 h (B), increased at 12 h (C), decreased (D), and not signifcantly altered (E). ***, ** and * indicate significant differences in comparison with the control at P < 0. 001, P < 0. 01 and P < 0. 05, respectively. Download full-size image DOI: 10. 7717/peerj. 5244/fig-5 Analysis of transcriptional level of NtEDR1-like genes in different tissues/organ samples provides clues on their biological functions. The tissue/organ expression patterns of tobacco NtEDR1-like genes were also investigated using qRT-PCR (Fig. S3). Some NtEDR1-like genes showed organ-specific expression patterns in the organ sample investigated. [SEP] Justify:

Output: The text is irrelevant to the claim because it discusses the expression patterns of genes in response to G. orontii infection rather than addressing the concept of hydrogen bonding and its significance in protein structures such as alpha-helices.


Input: Task: corroborate the claim using the given text. Claim: AI is being developed to help detect early signs of diseases like cancer and Alzheimer’s. [SEP] Text: (2018)19 Potential for clinical use in aiding lung cancer diagnosis Limited discussion on false positive reduction, dataset size Achieves robust performance with deep CNN architecture and lung cancer identification Shen et al. (2015)20 Offers potential for improved lung nodule classification Limited focus on computational efficiency Demonstrates robustness with multi-scale CNNs for nodule classification and malignancy prediction Ciompi et al. (2017)21 Shows potential for automating nodule management in lung cancer screening Limited discussion on false positive reduction, lack of clinical implementation details Demonstrates robustness in automating pulmonary nodule management tasks using deep learning-based approaches Christodoulidis et al. (2017)22 Offers potential for improved lung pattern analysis and disease classification Limited discussion on clinical validation and real-world application Demonstrates robustness in leveraging multi-source transfer learning for lung pattern analysis Dou et al. (2016)23 Holds promise for reducing false positives in pulmonary nodule detection Limited validation on a single dataset, sensitivity to training data quality Demonstrates robustness with multilevel contextual 3D CNNs for reducing false positives in nodule detection Zhang et al. (2017)24 Potential for peripheral nodule detection, but lacking clinical validation Limited validation on a single dataset, performance dependent on SVM classifier Demonstrates robustness in peripheral nodule detection using deep CNN and SVM Ding et al. (2017)25 Promising approach for nodule detection, but limited clinical validation Reliance on template matching may hinder generalization, validation on a single dataset Demonstrates robustness in combining template matching and deep learning for nodule detection Ardila et al. (2019)26 Offers end-to-end lung cancer screening, but requires further clinical validation Limited discussion on false positives and real-world integration Demonstrates robustness in achieving end-to-end lung cancer screening with 3D deep learning on low-dose CT scans Liang et al. (2019)27 Offers patient-level lung cancer prediction, but requires clinical validation Limited information on real-world implementation, potential overfitting to expert features Demonstrates robustness in transferable deep features for patient-level lung cancer prediction Zhang et al. [SEP] Justify:

Output: The text corroborates the claim by discussing various studies that demonstrate the potential of AI, specifically deep learning methodologies, to aid in the detection and management of lung cancer. It mentions different approaches that show promise in improving classification, reducing false positives, and automating tasks related to lung cancer screening. However, it does not specifically mention Alzheimer's, but the general indication of AI's application in disease detection strongly supports the claim.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: And that disciplines that have subsequently reduced in size or entirely disappeared would have been maintained at the cost of new innovation. Concentrating research in a few places, on a few subjects, will reduce diversity, leading to the loss of skills, and probably the loss of skilled people as researchers realise there is no future career for them in the UK. It will not provide the diverse and skilled workforce required to solve the problems we face today. Concentrating on current strengths, no matter how worthy, will lead to ossification and conservatism making UK research ultimately irrelevant on a world stage. What we need more than ever now, is a diverse and vibrant research community working on a wide range of problems, and to find better communication tools so as to efficiently connect unexpected solutions to problems in different areas. This is not the usual argument for “blue skies research”, whatever that may be. It is an argument for using market forces to do what they are best at (pick the winners from a range of possible technologies) and to use the smart people currently employed in research positions at government expense to actually do what they are good at; do research and train new researchers. It is an argument for critically looking at the expenditure of government money in a wholistic way and to seriously consider radical change where money is being wasted. I have estimated in the past that the annual cost of failed grant proposals to the UK government is somewhere between Â£100M – Â£500M, a large sum of money in anybody’s books. More rigorous economic analysis of a Canadian government funding scheme has shown that the cost of preparing and refeering the proposals ($CAN40k) is more than the cost of giving every eligible applicant a support grantof $CAN30k. This is not just farcical, it is an offensive waste of taxpayer’s money. The funding and distribution of research money requires radicaly overhaul. I do not beleive that simply providing more money is the solution. Frankly we’ve had a lot more money, it makes life a little more comfortable if you are in the right places, but it has reduced the pressure to solve the underlying problems. We need responsive funding at a wide range of levels that enables both bursts of research, the kind of instant collaboration that we know can work, with little or no review, and large scale data gathering projects of strategic importance that need extensive and careful critical review before being approved. [SEP] Justify:

Output: The text contrasts the claim by discussing the negative effects of concentrating research and resources in a few areas, which suggests that monoculture practices would reduce diversity rather than enhance it. The text emphasizes that concentrating on current strengths leads to 'ossification and conservatism,' indicating a decrease in innovation and, by extension, genetic diversity.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: For example, NtEDR1-13 and NtEDR1-16 were expressed highly only in roots and leaves, respectively (Fig. S3A). The expression of NtEDR1-1, NtEDR1-3, NtEDR1-7, NtEDR1-8, NtEDR1-10 and NtEDR1-15 were absent in flowers; and NtEDR1-4, NtEDR1-9, NtEDR1-11, NtEDR1-14, NtEDR1-17 and NtEDR1-18 did not show any expression in capsules. NtEDR1-2, NtEDR1-5, NtEDR1-6 and NtEDR1-12 had some transcripts in all organ samples tested (Figs. S3B–S3D). EDR1 genes were first reported to be required for G. cichoracearum susceptibility in Arabidopsis (Frye & Innes, 1998), and are also involved in regulation of multiple physiological activities (Kim et al. , 2003). For example, they play a role in the salicylic acid pathway under disease stress, and have a function in the ethylene pathway under drought stress (Rodriguez, Petersen & Mundy, 2010; Tang, Christiansen & Innes, 2005; Tang & Innes, 2002). Moreover, EDR1 could be recruited by EDR4 to the fungal penetration site via physical interaction (Wu et al. , 2015). EDR4 has been shown to encode an unknown protein that might function in the same pathway with EDR1 to regulate powdery mildew resistance and cell death (Wu et al. , 2015). Therefore, it is necessary to further explore the physiological function of EDR1 members. EDR1s have been found in several plants; however, there were no previous reports on EDR1-like genes in tobacco. In this study, we identified 19 EDR1-like genes in tobacco and compared these with the EDR1-like gene family from Arabidopsis, tomato, and rice. Tissue/organ and G. orontii-induced expression patterns of tobacco EDR1-like genes were further analyzed. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the expression of EDR1-like genes in relation to physiological functions in plants, specifically regarding disease stress and resistance, rather than addressing the role of hydrogen bonding in protein structure. There is no mention or implication of proteins or alpha-helices in the text provided.


Input: Task: corroborate the claim using the given text. Claim: AI is being developed to help detect early signs of diseases like cancer and Alzheimer’s. [SEP] Text: (2018)29 Promising for detecting small lung cancers, but requires more clinical validation Limited discussion on false positives/negatives, single dataset validation Demonstrates robustness in ensemble-based deep learning for small lung cancer detection in CT scans Anthimopoulos et al. (2016)30 Potential for automated interstitial lung disease classification, but requires further validation Limited discussion on real-world implementation, potential performance variations across datasets Demonstrates robustness in deep learning for lung pattern classification in interstitial lung diseases et al. (2018)31 Presents clinically applicable deep learning for retinal disease, but focuses on retinal diagnosis Not specific to lung disease, limited focus on lung-related applications Demonstrates robustness in deep learning for medical diagnosis and referral, applicable to various medical conditions Baltruschat et al. (2020)32 Addresses pulmonary nodule detection and classification, but clinical integration details are limited Limited discussion on integration, potential performance variations in clinical settings Demonstrates robustness in advanced deep learning for pulmonary nodule detection and classification Paper title. and authors. Clinical integration. Limitations. Robustness. Setio et al. (2017)17 Demonstrates potential integration into clinical workflows, aiding radiologists in nodule detection and early intervention False positives/negatives, technical challenges, algorithm complexity Algorithms show robust performance across multiple datasets, allowing consistent nodule detection Hu et al. (2017)18 Offers potential integration by aiding radiologists in nodule diagnosis, improving accuracy Limited explanation of model decisions, possible overfitting Demonstrates robustness across various nodule characteristics, contributing to accurate diagnosis Yap et al. (2018)19 Potential for clinical use in aiding lung cancer diagnosis Limited discussion on false positive reduction, dataset size Achieves robust performance with deep CNN architecture and lung cancer identification Shen et al. (2015)20 Offers potential for improved lung nodule classification Limited focus on computational efficiency Demonstrates robustness with multi-scale CNNs for nodule classification and malignancy prediction Ciompi et al. (2017)21 Shows potential for automating nodule management in lung cancer screening Limited discussion on false positive reduction, lack of clinical implementation details Demonstrates robustness in automating pulmonary nodule management tasks using deep learning-based approaches Christodoulidis et al. [SEP] Justify:

Output: The text corroborates the claim by discussing various studies and papers that demonstrate the application of AI and deep learning in detecting and classifying different types of lung diseases, including lung cancer. For instance, it mentions the robustness of deep learning in 'pulmonary nodule detection and classification' and its potential to aid 'radiologists in nodule detection and early intervention.' This indicates that AI technologies are actively being researched and developed for early disease detection, aligning closely with the claim about AI's role in disease diagnosis.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: 15. 12. 2019 Cited By: 3 Abstract 1995. Intra-and inter-specific variations in Lens revealed by RAPD markers. Theoretical and 90: 335-40. Ahamed, K. , B. Akhter, M. Islam, M. Humaun and M. Alam. 2014. Morphological characterization and genetic diversity in lentil (Lens culinaris medikus ssp. Culinaris) germplasm. of Agricultural Research, Innovation and Technology 4: 70-6. Ahmad, M. , D. Mcneil, A. Fautrier, K. Armstrong and A. Paterson. 1996. Genetic relationships in Lens species and parentage determination of their interspecific hybrids using RAPD markers. Theoretical and 92: 1091-8. Alghamdi, S. , A. Khan, M. Ammar, E. El-Harty, H. Migdadi, S. El-Khalik, A. Al-Shameri, M. Javed and S. Al-Faifi. 2014. Phenological, nutritional and molecular diversity assessment among 35 introduced lentil (Lens culinaris Medik. ) genotypes grown in Saudi Arabia. of 15: 277-95. Ates, D. , S. Aldemir, B. Yagmur, A. Kahraman, H. Ozkan, A. Vandenberg and M. B. Tanyolac. 2018a. QTL Mapping of Uptake in Lentil Seed. G3: Genes, Genomes, Genetics 8(5):1409-1416. Ates, D. , T. K. Asciogul, S. Nemli, S. Erdogmus, D. Esiyok and M. B. Tanyolac. 2018b. Association mapping of days to flowering in common bean (Phaseolus vulgaris L. ) revealed by DArT markers. 38: 113. Ates, D. , T. Sever, S. Aldemir, B. . Alsaleh, A. Kahraman, H. Ozkan, A. Vandenberg and B. Tanyolac. 2016. Identification QTLs controlling genes for se uptake in lentil seeds. [SEP] Justify:

Output: The text discusses various studies related to the genetic diversity within lentil and common bean species. It mentions that different genotypes have been assessed for their phenological, nutritional, and molecular diversity, which contrasts the claim that genetic diversity can be achieved through monoculture practices, as monoculture generally reduces genetic diversity in crops.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: Phylogenetic analysis divided the EDR1-like genes into six clades. NtEDR1-1A and NtEDR1-1B—along with the reported EDR1s, AtEDR1-1 and TaEDR1, negatively functioning in resistance to powdery mildew—clustered in clade I. The results of 3D model prediction showed that NtEDR1-1A was most similar to that of AtEDR1-1 among all the NtEDR1-like genes tested (Fig. 4; Fig. S2). Multi-sequence alignment analysis revealed that genes in clade I showed high similarities, with 82. 29% identity of the C-terminal kinase region and 66. 89% identity of the N-terminal regulatory region. This suggested that the EDR1 regulatory region and kinase domain were highly conserved in clade I. The pathway of EDR1 homologs is likely conserved between monocots and eudicots (i. e. , maize, rice and tomato) (Frye, Tang & Innes, 2001). The EDR1-like members in each clade were divided into monocot and dicot sub-families. Multi-sequence alignment analysis of tobacco EDR1-like proteins showed the C-terminal kinase region had relatively high identity (61. 48%), but the N-terminal regulatory region had relatively low identity (40. 52%). As EDR1 function is dependent upon the N-terminal regulatory region (Tang, Christiansen & Innes, 2005), the lower identity among the N-terminal regulatory regions of tobacco EDR1-like genes may indicate that they have different functions. Motif distribution analysis showed that different NtEDR1-like members had different numbers and types of motifs (Fig. 3A). Genomic structure analysis also showed that the exon–intron structure of EDR1-like genes greatly differed among species. Quantitative expressions of tobacco EDR1-like genes following infection by G. orontii were analyzed. The results showed 15 tobacco EDR1-like genes were G. orontii-susceptible (Figs. [SEP] Justify:

Output: The text is irrelevant to the claim regarding hydrogen bonding and protein secondary structure, as it discusses the phylogenetic analysis of EDR1-like genes, focusing on their evolutionary relationship and expression in response to infection, which does not mention hydrogen bonding or alpha-helices.


Input: Task: corroborate the claim using the given text. Claim: AI is being developed to help detect early signs of diseases like cancer and Alzheimer’s. [SEP] Text: (2017)22 Offers potential for improved lung pattern analysis and disease classification Limited discussion on clinical validation and real-world application Demonstrates robustness in leveraging multi-source transfer learning for lung pattern analysis Dou et al. (2016)23 Holds promise for reducing false positives in pulmonary nodule detection Limited validation on a single dataset, sensitivity to training data quality Demonstrates robustness with multilevel contextual 3D CNNs for reducing false positives in nodule detection Zhang et al. (2017)24 Potential for peripheral nodule detection, but lacking clinical validation Limited validation on a single dataset, performance dependent on SVM classifier Demonstrates robustness in peripheral nodule detection using deep CNN and SVM Ding et al. (2017)25 Promising approach for nodule detection, but limited clinical validation Reliance on template matching may hinder generalization, validation on a single dataset Demonstrates robustness in combining template matching and deep learning for nodule detection Ardila et al. (2019)26 Offers end-to-end lung cancer screening, but requires further clinical validation Limited discussion on false positives and real-world integration Demonstrates robustness in achieving end-to-end lung cancer screening with 3D deep learning on low-dose CT scans Liang et al. (2019)27 Offers patient-level lung cancer prediction, but requires clinical validation Limited information on real-world implementation, potential overfitting to expert features Demonstrates robustness in transferable deep features for patient-level lung cancer prediction Zhang et al. (2018)29 Promising for detecting small lung cancers, but requires more clinical validation Limited discussion on false positives/negatives, single dataset validation Demonstrates robustness in ensemble-based deep learning for small lung cancer detection in CT scans Anthimopoulos et al. (2016)30 Potential for automated interstitial lung disease classification, but requires further validation Limited discussion on real-world implementation, potential performance variations across datasets Demonstrates robustness in deep learning for lung pattern classification in interstitial lung diseases et al. (2018)31 Presents clinically applicable deep learning for retinal disease, but focuses on retinal diagnosis Not specific to lung disease, limited focus on lung-related applications Demonstrates robustness in deep learning for medical diagnosis and referral, applicable to various medical conditions Baltruschat et al. [SEP] Justify:

Output: The text corroborates the claim by discussing various studies and developments in machine learning and deep learning technologies specifically for disease detection, such as lung cancer and other medical conditions. For instance, it mentions 'end-to-end lung cancer screening' and 'patient-level lung cancer prediction,' indicating that AI technologies are actively being explored to enhance early detection and classification of diseases, which aligns with the claim about AI's role in identifying early signs of diseases like cancer.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: Erskine, W. , Y. Adham and L. Holly. 1989. Geographic distribution of variation in quantitative traits in a world lentil collection. Euphytica 43: 97-103. Erskine, W. and M. Choudhary. 1986. Variation between and within lentil landraces from Republic. Euphytica 35: 695-700. Erskine, W. and F. Muehlbauer. 1991. Allozyme and morphological variability, outcrossing rate and core collection formation in lentil germplasm. Theoretical and 83: 119-25. -Lloyd, H. J. Newbury and N. Maxted. 1998. Contrasting genetic variation amongst lentil landraces from different geographical origins. Euphytica 102: 265-73. Fikiru, E. , K. Tesfaye and E. Bekele. 2007. Genetic diversity and population structure of Ethiopian lentil (Lens culinaris Medikus) landraces as revealed by ISSR marker. of Biotechnology 6. Ford, R. , E. Pang and P. Taylor. 1997. Diversity analysis and species identification in Lens using PCR generated markers. Euphytica 96: 247-55. Havey, M. and F. Muehlbauer. 1989. Variability for restriction fragment lengths and phylogenies in lentil. Theoretical and 77: 839-43. Huson, D. H. and D. Bryant. 2006. Application of phylogenetic networks in evolutionary studies. and Evolution 23: 254-67. Huttner, E. , P. Wenzl, M. Akbari, V. Caig, J. Carling, C. Cayla, M. Evers, D. Jaccoud, K. Peng and S. Patarapuwadol. Diversity arrays technology: a novel tool for harnessing the genetic potential of orphan crops. Proceedings of the Discovery to Delivery: Bio2004, Proceedings of the 2004 Conference of Biological Forum. CABI Publishing: UK, 2005, 145-55. Idrissi, O. , A. R. Piergiovanni, F. Toklu, C. Houasli, S. [SEP] Justify:

Output: The text contrasts the claim because it discusses various studies on genetic diversity, primarily focusing on lentil landraces and their variation, without supporting monoculture practices as a means to achieve genetic diversity. The studies referenced instead imply that genetic diversity is derived from multiple sources, including variations among geographic origins of lentil landraces, which contrasts with the claim that monoculture can achieve such diversity.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: 5A–5D): NtEDR1-1, NtEDR1-12 and NtEDR1-16 were up-regulated at 2 hpi; NtEDR1-6, NtEDR1-7, NtEDR1-11, NtEDR1-13, NtEDR1-14 and NtEDR1-17 were immediately up-regulated after G. orontii infection; NtEDR1-4 and NtEDR1-18 were up-regulated at 12 hpi; and NtEDR1-3, NtEDR1-8, NtEDR1-9 and NtEDR1-10 were down-regulated at 1 hpi. Knockout of three homologs of TaEDR1 in wheat did not produce complete resistance (Zhang et al. , 2017), and separately silencing the tomato homologs of EDR1 (Solyc01 g097980 and Solyc06 g068980) did not result in significant resistance to O. neolycopersici (Gao et al. , 2015). It is necessary to silence multiple EDR1 genes to obtain plants with enhanced resistance (Gao et al. , 2015). This indicated that multiple EDR1 members may be commonly involved in the negative regulation of resistance against powdery mildew in tobacco and many other plants. The other three NtEDR1-like genes (NtEDR1-2, NtEDR1-5 and NtEDR1-15) did not show significantup- or down-regulation after plant infection by G. orontii (Fig. 5E). Therefore, not all tobacco EDR1-like genes responded to G. orontii infection. Plant basic defenses require the MKK4/MKK5-MPK3/MPK6 kinase cascade, and EDR1 physically associates with MKK4/MKK5 and negatively regulates the MAPK cascade to fine-tune plant innate immunity (Zhao et al. , 2014). Several reports have shown that mutations of EDR1 could confer plant disease resistance in Arabidopsis, rice and wheat (Frye & Innes, 1998;Shen et al. , 2011; Zhang et al. , 2017). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the regulation of disease resistance in plants and the roles of EDR1 genes, and it does not mention hydrogen bonding or protein structures such as alpha-helices.


Input: Task: corroborate the claim using the given text. Claim: AI is being developed to help detect early signs of diseases like cancer and Alzheimer’s. [SEP] Text: (2020)32 Addresses pulmonary nodule detection and classification, but clinical integration details are limited Limited discussion on integration, potential performance variations in clinical settings Demonstrates robustness in advanced deep learning for pulmonary nodule detection and classification The synergy between deep learning and CT scan analysis is revolutionizing early lung cancer detection. By harnessing the analytical capabilities of deep learning models, healthcare is moving toward more efficient, accurate, and timely identification of potential lung cancer nodules. This approach has the potential to significantly impact patient outcomes by enabling earlier intervention and treatment, ultimately contributing to the fight against lung cancer. C. Early skin cancer detection with AI: Unveiling melanoma through visual insights Detecting skin cancer early can save lives, and technology is stepping in to help. The marriage of artificial intelligence (AI) and computer vision has paved the way for a novel approach to identifying skin cancer, particularly melanoma, using images of the skin’s surface. In this discussion, we will explore how AI, guided by computer vision, is making strides in skin cancer detection by examining the visual cues present in dermatology images. Role of Early Detection: Spotting skin cancer, especially aggressive types like melanoma, in its early stages is vital for successful treatment. Quick diagnosis can lead to effective interventions, reducing the risk of cancer spreading or becoming more challenging to handle. AI and in Dermatology: Artificial intelligence, the technology behind computer vision, has shown remarkable potential in analyzing images, including medical ones. When applied to dermatology images of the skin, AI can recognize patterns, textures, and irregularities that might indicate the presence of skin cancer, such as melanoma. through Computer Vision: AI models, driven by computer vision, are trained on a diverse array of dermatology images. These images cover a wide spectrum, from healthy skin to those showing signs of skin cancer. AI learns to distinguish between normal skin and skin with potential cancerous characteristics. It is like teaching AI to spot the differences between puzzle pieces. : Computer vision models are pros at identifying tiny details within complex images. By carefully examining skin lesions, moles, and other marks, these models can uncover unusual features that might signal skin cancer. This allows for timely medical attention and diagnosis. Perks of AI-Enhanced Diagnosis: Integrating AI and computer vision into skin cancer detection offers several advantages. It is like having a highly skilled assistant for dermatologists. AI can systematically analyze images, potentially reducing misdiagnoses. [SEP] Justify:

Output: The text corroborates the claim by detailing how artificial intelligence (AI) is being employed in medical settings for early detection of diseases such as lung cancer and skin cancer. It discusses the synergy between deep learning and CT scan analysis for lung cancer detection and the use of computer vision in identifying melanoma through dermatology images. This aligns with the claim, which states that AI is being developed to help in the early detection of diseases.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: M. Udupa, E. De Keyser, P. and J. De Riek. 2018. Molecular variance and population structure of lentil (Lens culinaris Medik. ) landraces from Mediterranean countries as revealed by simple sequence repeat DNA markers: implications for conservation and use. Resources 16: 249-59. Idrissi, O. , M. S. Udupa, E. De Keyser, P. and J. De Riek. 2016. Functional genetic diversity analysis and identification of associated simple sequence repeats and amplified fragment length polymorphism markers to drought tolerance in lentil (Lens culinaris ssp. culinaris Medicus) Landraces. 34: 659-80. Idrissi, O. , S. M. Udupa, C. Houasli, E. De Keyser, P. and J. De Riek. 2015. Genetic diversity analysis of Moroccan lentil (Lens culinaris Medik. ) landraces using Repeat and reveals functional adaptation towards agro‐environmental origins. 134: 322-32. Jin, L. , G. Jian-Ping, X. Dong-Xu, X. -Y. Zhang, G. Jing and Z. Xu-Xiao. 2008. Genetic diversity and population structure in lentil (Lens culinaris Medik. ) germplasm detected by SSR markers. Sinica 34: 1901-9. Kaur, S. , N. O. Cogan, L. W. Pembleton, M. Shinozuka, K. W. Savin, M. Materne and J. W. Forster. 2011. Transcriptome sequencing of lentil based on second-generation technology permits large-scale unigene assembly and SSR marker discovery. BMC Genomics 12: 265. Khazaei, H. , C. T. Caron, M. Fedoruk, M. Diapari, A. Vandenberg, C. J. Coyne, R. Mcgee and K. E. Bett. 2016a. Genetic diversity of cultivated lentil (Lens culinaris Medik. ) and its relation to the world's agro-ecological zones. Frontiers in 7:1093. [SEP] Justify:

Output: The text contrasts the claim by focusing on genetic diversity in lentil landraces and germplasm studies rather than supporting monoculture practices as a means to achieve genetic diversity. Instead, the studies cited discuss functional genetic diversity and conservation in varying agro-ecological zones, which implies that monoculture may not be the appropriate method for enhancing genetic diversity.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: Therefore, it might be common that presence of EDR1 may inhibit the plant primary immunity and benefit invasion by powdery mildew. Tissue/organ expression analysis of tobacco EDR1-like genes was carried out usingqRT-PCR. The results showed that different EDR1-like genes had different expression patterns. NtEDR1-2, NtEDR1-3, NtEDR1-4, NtEDR1-9 and NtEDR1-10 had higher expression levels in roots. NtEDR1-1, NtEDR1-5, NtEDR1-7, NtEDR1-8, NtEDR1-12 and NtEDR1-15 were predominantly expressed in capsules. NtEDR1-11 and NtEDR1-17 were highly expressed in flowers. Interestingly, two NtEDR1-like genes, NtEDR1-13 and NtEDR1-16, were exclusively expressed in roots and leaves, respectively. In addition to functions as negative regulators of plant defense, EDR1-like genes have also been shown to have roles in other physiological processes. For example, OsEDR1-1 (Os3 g06410) had a high expression level during maturation of the panicle before heading, after heading and at maturity (pollination stage) in rice, and was further suggested to play a role in plant growth and development, and in maturity of panicles (Kim et al. , 2003). In our study, NtEDR1-11 and NtEDR1-17 were mainly expressed in flowers, whereas NtEDR1-5, NtEDR1-7,NtEDR1-8, NtEDR1-12 and NtEDR1-15 were highly expressed in capsules. Thus, some tobacco EDR1-like genes may also have a physiological function during the plant reproductive period. this research, 19 EDR1-like genes were identified in a genome-wide analysis in N. tabacum. [SEP] Justify:

Output: The text is irrelevant (pass) to the claim regarding hydrogen bonding's role in the secondary structure of proteins. The content primarily discusses EDR1-like genes in tobacco and their expression patterns related to plant immunity and growth, without mentioning proteins or the role of hydrogen bonding.


Input: Task: corroborate the claim using the given text. Claim: AI is being developed to help detect early signs of diseases like cancer and Alzheimer’s. [SEP] Text: Plus, it assists doctors by highlighting areas that need special attention, supporting their decision-making process. Challenges and Progress: Despite the potential, challenges exist. Ensuring AI models learn from a diverse range of images is crucial to avoid biases. While AI offers exciting possibilities, ensuring its accuracy in real-world scenarios and integrating it into medical practice require careful validation. Ahead: The future of skin cancer detection with AI is promising. Research continues to refine algorithms, making them even better at identifying potential issues. As technology evolves, we might even see AI-powered smartphone apps or devices for easier skin cancer screening. The parameter comparison of various research papers on early detection of skin cancer is given in Tables VII and VIII. . Data and methods comparison. Paper title. . Dataset size and. . . and authors. Dataset used. characteristics. AI techniques. Imaging modality. Esteva et al. (2017)11 Not specified (proprietary dataset) Not specified Deep neural networks Dermoscopy (dermatoscopic images) Tschandl et al. (2018)36 HAM10000 dataset Large collection of multi-source dermatoscopic images of pigmented skin lesions Not specified Dermoscopy (dermatoscopic images) Brinker et al. (2019)37 ISIC archive Not specified Deep learning Histopathological images (biopsy samples) Haenssle et al. (2018)38 Not specified (proprietary dataset) Not specified Deep learning convolutional neural network et al. (2018)39 SIC 2017 skin lesion dataset Contains 2000 images of skin lesions Deep learning and Hamada (2016)40 Not specified Not specified Image processing et al. (2018)41 Not specified Not specified Deep learning In vivo imaging Kawahara et al. (2016)42 ISBI 2017 skin lesion challenge dataset Contains over 9000 dermoscopic images of skin lesions Deep learning (2015)43 Not applicable Review paper Review of deep learning Not applicable Celebi et al. (2009)44 Not applicable Review paper Review of techniques et al. (2018)45 ISIC 2017 challenge dataset Contains over 2000 skin lesion images Deep learning and Chen (2019)46 ISIC 2019 challenge dataset Contains over 2000 skin lesion images Deep learning et al. (2020)47 ISIC 2020 challenge dataset, private datasets Contains over 33 000 skin lesion images Deep learning et al. [SEP] Justify:

Output: The text corroborates the claim by discussing the promise of AI in the detection of skin cancer, stating 'the future of skin cancer detection with AI is promising' and mentioning ongoing research to 'refine algorithms, making them even better at identifying potential issues.' This directly supports the idea that AI is being developed to detect diseases such as cancer.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: Kushwaha, U. , S. Ghimire, N. Yadav and B. Ojha. 2013. Genetic relatedness of lentil (Lens culinaris L. ) germplasm by using SSR markers. of and Biotechnology 1: 132-6. Lazaro, A. , M. Ruiz, L. de la Rosa and I. Martin. 2001. Relationship between agro/morphological characters and climatic parameters in Spanish landraces of lentil (Lens culinaris Medik. ). Genet. Resour. 48: 239-249. Lombardi, M. , M. Materne, N. O. Cogan, M. and S. Kaur. 2014. Assessment of genetic variation within a global collection of lentil (Lens culinaris Medik. ) cultivars and landraces using SNP markers. BMC Genetics 15: 150. Maxted, N. and S. J. Bennett, 2001. Legume diversity in the Mediterranean region. In. Plant genetic resources of legumes in the Mediterranean. Springer 51-75. Nei, M. , 1987. Molecular evolutionary genetics. press, New York. 1987. 512 pp. Am. J. Phys. Anthropol 75(1988): 428-429. Nei, M. and W. -H. Li. 1979. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proceedings of the of Sciences 76: 5269-73. Nemli, S. , T. Kianoosh and M. B. Tanyolac. 2015. Genetic diversity and population structure of common bean (Phaseolus vulgaris L. ) accessions through retrotransposon-based interprimer binding sites (iPBSs) markers. of Agriculture and Forestry 39: 940-8. Ozkuru, E. , D. Ates, S. Nemli, S. Erdogmus, N. Karaca, H. Yilmaz, B. Yagmur, C. Kartal, M. Tosun and O. O. Ocak. 2019. Genome-wide association studies of molybdenum and selenium concentrations in C. arietinum and C. reticulatum seeds. [SEP] Justify:

Output: The text discusses various studies related to genetic diversity and assessment of genetic variation in different crops, specifically mentioning the genetic relatedness of lentil germplasm and the genetic diversity of common bean accessions. This information notably implies a focus on crop diversity rather than monoculture, which typically reduces genetic diversity. Therefore, the text contrasts the claim by emphasizing that achieving genetic diversity is more associated with multiple cultivars or practices rather than monoculture.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: Through multiple sequences alignment, phylogenetic analysis, gene structure analysis and comparative analysis of predicted 3D structures, NtEDR1-1A was shown to have the highest similarity to AtEDR1-1, which has been reported to be responsive to powdery mildew. Expression profiles revealed that NtEDR1-1 (NtEDR1-1A and NtEDR1-1B) was not the only tobacco EDR1-like member responsive to G. orontii, but not all tobacco EDR1-like genes were responsive to G. orontii. Tissue/organ expression profiles showed that different EDR1-like members had different expression patterns, but some NtEDR1-like genes showed tissue-specific expression patterns. Our results provide foundational information on the EDR1-like gene family in tobacco species, and promise to promote future research on the functions of EDR1-like genes in plants. Multiple sequences alignment of the protein sequences of tobacco EDR1-like genes. DOI: 10. 7717/peerj. 5244/supp-1 predicted 3D structures of tobacco EDR1-like genes. DOI: 10. 7717/peerj. 5244/supp-2 profile of tobacco EDR1-like genes in different tissue/organ samples. Root-Y, Stem-Y and Leaf-Y indicate young root, young stem and young leaf, respectively. Flower-F indicates open flowers. Capsule-seed indicates the capsules that were obtained during the late seed-breeding period. The genes have clear tissue-specific expression patterns (A); The genes expressed in all organ samples tested (B); The expression of genes were absent in flowers (C); and the genes did not show any expression in capsules (D). DOI: 10. 7717/peerj. 5244/supp-3 protein sequences of Arabidopsis EDR1-like genes. DOI: 10. 7717/peerj. 5244/supp-4 protein sequences of tobacco EDR1-like genes. DOI: 10. 7717/peerj. 5244/supp-5 protein sequences of tomato EDR1-like genes. DOI: 10. 7717/peerj. 5244/supp-6 protein sequences of rice EDR1-like genes. DOI: 10. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the expression profiles and genetic analysis of EDR1-like genes in tobacco plants, which does not address hydrogen bonding or the secondary structure of proteins like alpha-helices. There is no mention of hydrogen bonding or its importance in protein structure within the text.


Input: Task: corroborate the claim using the given text. Claim: AI is being developed to help detect early signs of diseases like cancer and Alzheimer’s. [SEP] Text: (2020)49 AI vs pathologists; classification comparison Comparative analysis; specific focus AI performance; comparison with experts Esteva et al. (2019)50 Deep learning in healthcare; comprehensive guide General overview; not focused on specific applications Guidance on implementation; healthcare-specific insights Yap et al. (2019)51 Keratinocyte detection; Automated analysis Patch-based; specific focus Automated detection; application to skin cancer images Codella et al. (2019)52 Melanoma detection; skin lesion analysis Challenge-specific; Limited to specific dataset Deep learning analysis; ISIC 2018 challenge Tschandl et al. (2019)53 Expert-level diagnosis; nonpigmented skin cancer Expert-based; limited to specific conditions Combined CNNs; enhanced diagnostic accuracy Paper title and authors. Clinical integration. Limitations. Robustness. Esteva et al. (2017)11 High potential for clinical use and integration Limited diversity in data; overfitting risk Effective across various skin types Tschandl et al. (2018)36 Useful tool for dermatology practice Limited to dermatoscopic images Consistent performance on diverse datasets Brinker et al. (2019)37 Improved diagnostic accuracy; Clinically relevant Limited sample diversity; ethical considerations Consistent performance across cases Haenssle et al. (2018)38 Potential clinical use; valuable diagnostic tool Limited dataset size; Domain adaptation needed Comparable to dermatologists; Robust outcomes Codella et al. (2018)39 Contribution to diagnostic advancements Challenge-specific data; research-oriented Insight into algorithmic melanoma detection Haenssle et al. (2018)40 CNN-based architecture Preprocessing details Training methods Wiltgen et al. (2018)41 Real-time diagnosis; clinical application Limited dataset; real-world challenges Real-time performance; Robust diagnostic Kawahara et al. (2016)42 Enhanced lesion classification; diagnostic aid Limited to lesion classification; research Transferability; generalized lesion analysis Schmidhuber (2015)43 Deep learning overview; overview; not application-specific Overview of concepts; broad understanding Celebi et al. (2009)44 Improved lesion border detection; analysis Survey-based; focus on one aspect Broad overview; evaluation of techniques Bi et al. [SEP] Justify:

Output: The text supports the claim by discussing various studies on AI's application in healthcare, specifically focusing on skin cancer detection through deep learning techniques. For example, it mentions that the work by Esteva et al. indicates 'high potential for clinical use and integration' of AI in diagnostics, specifically for skin types, suggesting its relevance to cancer detection. Furthermore, the references to 'Real-time diagnosis; clinical application' underline AI's real-world potential in identifying diseases, aligning well with the claim about AI aiding in early disease detection.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: Li, S. Kagale, P. Vijayan, A. Vandenberg and K. Bett. 2013. Ancient crop joins modern era: gene-based SNP discovery and mapping in lentil. BMC Genomics 14: 192. Sonnante, G. and D. Pignone. 2001. Assessment of genetic variation in a collection of lentil using molecular tools. Euphytica 120: 301-7. Sonnante, G. and D. Pignone. 2007. The major Italian landraces of lentil (Lens culinaris Medik. ): their molecular diversity and possible origin. 54: 1023-31. Sultana, T. , A. Ghafoor and M. Ashraf. 2006. Geographic patterns of diversity of cultivated lentil germplasm collected from Pakistan, as assessed by seed protein assays. 48: 77-84. Toklu, F. , T. Karakoy, E. Haklı, T. Bicer, A. Brandolini, B. Kilian and H. Ozkan. 2009. Genetic variation among lentil (Lens culinaris Medik) landraces from Southeast Turkey. 128: 178-86. Tommasini, L. , T. Schnurbusch, D. Fossati, F. Mascher and B. Keller. 2007. Association mapping of Stagonospora nodorum blotch resistance in modern European winter wheat varieties. Theoretical and 115:697-708. Tsanakas, G. F. , P. V. Mylona, K. Koura, A. Gleridou and A. N. Polidoros. 2018. Genetic diversity analysis of the Greek lentil (Lens culinaris) landrace ‘Eglouvis’ using morphological and molecular markers. Resources 16: 469-77. Varshney, R. K. , A. Graner and M. E. Sorrells. 2005. Genomics-assisted breeding for crop improvement. Trends in 10:621-630. Villa, T. C. C. , N. Maxted, M. Scholten and B. Ford-Lloyd. 2005. Defining and identifying crop landraces. Resources 3: 373-84. [SEP] Justify:

Output: The text contrasts the claim by providing references to various studies and assessments focused on genetic diversity in lentils, which indicate that genetic diversity is achieved through the collection and analysis of different landraces and the genetic variation among them. This implies that monoculture practices would limit genetic diversity instead of promoting it.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: 7717/peerj. 5244/supp-7 primer sequences used in qRT-PCR for detection the expression patterns of NtEDR1-like genes. DOI: 10. 7717/peerj. 5244/supp-8 of the EDR1-like genes in different plant species. The protein sequences of EDR1-like genes in each species is presented in (Arabidopsis in File S1; tobacco in File S2; tomato in File S3; and rice in File S4). DOI: 10. 7717/peerj. 5244/supp-9 of NLS sequences and the existence of transmembrane helices of the tobacco EDR1-like proteins. “√” represents protein has transmembrane helices. DOI: 10. 7717/peerj. 5244/supp-10 motifs conserved in the amino acid sequences of the tobacco EDR1-like proteins. DOI: 10. 7717/peerj. 5244/supp-11 Information and authors declare that they have no competing interests. performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft. performed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft. performed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft. performed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft. performed the experiments, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft. analyzed the data, authored or reviewed drafts of the paper, approved the final draft. analyzed the data, authored or reviewed drafts of the paper, approved the final draft. conceived and designed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft. conceived and designed the experiments, authored or reviewed drafts of the paper, approved the final draft. The following information was supplied regarding data availability: The raw data are provided in the Supplemental Files. [SEP] Justify:

Output: The text is irrelevant to the claim as it does not mention hydrogen bonding, secondary structures, or proteins such as alpha-helices. Instead, it focuses on primer sequences and gene expressions in different plant species, which does not relate to the concept of protein structure.


Input: Task: corroborate the claim using the given text. Claim: AI is being developed to help detect early signs of diseases like cancer and Alzheimer’s. [SEP] Text: (2018)45 Enhanced lesion segmentation; clinical utility Patch-based; specific to segmentation Improved segmentation; patch-wise refinement Liu and Chen (2019)46 Mobile skin cancer detection; fast and efficient Mobile-focused; specific application Quick diagnosis; suitable for mobile apps Haenssle et al. (2020)47 Comparative analysis; performance evaluation Specific evaluation; market-approved model Performance comparison; diverse lesions Menegola et al. (2017)48 Transfer learning for skin lesion classification Transfer learning; Limited to classification Improved classification; shared knowledge Brinker et al. (2020)49 AI vs pathologists; classification comparison Comparative analysis; specific focus AI performance; comparison with experts Esteva et al. (2019)50 Deep learning in healthcare; comprehensive guide General overview; not focused on specific applications Guidance on implementation; healthcare-specific insights Yap et al. (2019)51 Keratinocyte detection; Automated analysis Patch-based; specific focus Automated detection; application to skin cancer images Codella et al. (2019)52 Melanoma detection; skin lesion analysis Challenge-specific; Limited to specific dataset Deep learning analysis; ISIC 2018 challenge Tschandl et al. (2019)53 Expert-level diagnosis; nonpigmented skin cancer Expert-based; limited to specific conditions Combined CNNs; enhanced diagnostic accuracy The fusion of AI and computer vision is transforming early skin cancer detection. By harnessing computer vision’s ability to scrutinize images, healthcare is moving toward quicker, more accurate, and timely identification of possible skin cancer, particularly melanoma. This approach holds the potential to make a significant impact on patient outcomes, ensuring that individuals receive the care they need promptly and effectively. III. :. Breast cancer detection AI algorithms can analyze mammograms and spot subtle changes in breast tissue. It helps find small tumors or abnormalities that might be missed in manual screenings. Offers a higher sensitivity rate, leading to earlier detection and improved survival rates. Can assist radiologists in making more accurate diagnoses. B. Lung cancer detection AI examines chest CT scans and identifies potential lung nodules. Detects nodules at an early stage, often before symptoms appear. Increases chances of successful treatment and better patient outcomes. Aids doctors in determining the malignancy risk of detected nodules. C. Skin cancer detection AI analyzes dermatoscopic images of skin lesions to identify potential cancers. [SEP] Justify:

Output: The text corroborates the claim by detailing various AI applications, particularly in early disease detection. It discusses how AI enhances lesion segmentation for skin cancer detection, analyzes mammograms for breast cancer, and examines chest CT scans for lung cancer, demonstrating AI's role in facilitating quicker and more accurate diagnoses. This aligns directly with the claim regarding development focused on early detection of diseases, including cancer.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: Wong, M. M. , N. Gujaria-Verma, L. Ramsay, H. Y. Yuan, C. Caron, M. Diapari, A. Vandenberg and K. E. Bett. 2015. Classification and characterization of species within the genus Lens using genotyping-by-sequencing (GBS). PLoS One 10: e0122025. Yuzbasioglu, E. , S. Ozcan and L. Acık. 2006. Analysis of genetic relationships among Turkish cultivars and breeding lines of Lens culinatis Mestile using RAPD markers. 53: 507-14. Zaccardelli, M. , F. Lupo, A. R. Piergiovanni, G. Laghetti, G. Sonnante, M. G. Daminati, F. Sparvoli and L. Lioi. 2012. Characterization of Italian lentil (Lens culinaris Medik. ) germplasm by agronomic traits, biochemical and molecular markers. 59: 727-38. (DArT) Year 2019, Volume: 24 Issue: 2, 252 - 260, 15. 12. 2019 Cited By: 3 is annual, autogamous and diploid (2n = 2x= 14) food legume with ~4 Gbp genome in size. Turkey is well known for its species richness with “diversity hot spots” for different legumes including lentil. In previous studies, various DNA markers were utilized but genetic diversity of lentil landraces have not yet been clarified. For this reason, present study aimed to identify genetic diversity of 94 Turkish lentil landraces utilizing 16,383 SNPs based on DArT technology. Results from “fast” analysis indicated that the unweighted pair group analysis with the arithmetic mean (UPGMA) dendrogram including a heat map and also principal component analysis (PCoA) showed that Turkish lentil landraces were classified into five main groups in current study, indicating the existence of a large genetic variation among landraces. Highest genetic variation was between geno34 and geno76 (0. 9126) while the lowest genetic variation was between geno7 and geno1 (0. [SEP] Justify:

Output: The text contrasts the claim by detailing a study that focuses on the genetic diversity of Turkish lentil landraces and highlights the existence of a large genetic variation among these landraces. The implication is that monoculture practices would not support the same level of genetic diversity as indicated by the results from ‘fast’ analysis, which shows clear genetic differentiation among the lentil varieties studied.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: work was supported by grants from the Program of Science of China (No. 31530071), Program of (Grant No. cstc2017jcyjAX0334) and the research programs CNTC-[2018]45-110201801025(JY-02). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 2006. MEME: discovering and analyzing DNA and protein sequence motifs. Research 34(supp_2):W369-W373. 1997. The arabidopsis NPR1 gene that controls systemic acquired resistance encodes a novel protein containing ankyrin repeats. Cell 88(1):57-63. 2007. Recent progress in protein subcellular location prediction. 370(1):1-16. 2008. Cell-PLoc: a package of web servers for predicting subcellular localization of proteins in various organisms. 3(2):153-162. 2010a. Large-scale plant protein subcellular location prediction. Journal of 100(3):665-678. 2010b. Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. 5(6):e11335. 2011. Negative regulation of defence signalling pathways by the EDR1 protein kinase. Pathology 12(8):746-758 -. 2012. The top 10 fungal pathogens in molecular plant pathology. Pathology 13(4):414-430 -. 2002. Identification of resistance gene analogs linked to a powdery mildew resistance locus in grapevine. Theoretical and 104(4):610. 1998. mutant with enhanced resistance to powdery mildew. 10(6):947. 2001. Negative regulation of defense responses in plants by a conserved MAPKK kinase. Proceedings of the of Sciences of the of America 98(1):373-378. 2015. Natural loss-of-function mutation of EDR1 conferring resistance to tomato powdery mildew in Arabidopsis thaliana accession C24. Pathology 16(1):71-82 Glawe DA. 2008. The powdery mildews: a review of the world’s most familiar (yet poorly known) plant pathogens. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various research programs, grants, and studies related to proteins and plant pathology but does not mention hydrogen bonding or the secondary structure of proteins, specifically alpha-helices.


Input: Task: corroborate the claim using the given text. Claim: AI is being developed to help detect early signs of diseases like cancer and Alzheimer’s. [SEP] Text: Detects irregularities in skin patterns and textures that might indicate cancerous growth. Provides quick and non-invasive assessment, reducing unnecessary biopsies. Enhances early detection, leading to less invasive treatment options. D. Common benefits All three types benefit from AI’s ability to process vast amounts of data quickly. Earlier detection increases treatment success rates and reduces healthcare costs. AI helps reduce human error and enhances the efficiency of diagnosis. Improves patient experience by offering more timely interventions. E. Challenges AI systems need access to high-quality and diverse datasets for accurate training. Ensuring AI’s reliability and robustness is crucial for real-world applications. Ethical considerations, patient privacy, and data security are important concerns. F. The future AI’s role in early cancer detection is expected to grow, making screenings more effective. Continued research and collaboration will refine AI algorithms for even better accuracy. Integration into healthcare systems will require close collaboration between AI experts and medical professionals. In conclusion, AI’s application in early cancer detection for breast, lung, and skin cancers shows promising potential. It assists in catching cancers at their earliest stages, improving patient outcomes and healthcare practices. While challenges exist, the ongoing development of AI technology offers exciting opportunities to revolutionize cancer diagnosis and treatment. IV. conclusion, the integration of AI-driven early cancer detection into healthcare marks an exciting new era. Through this review, we have seen how AI can work hand in hand with medical practices to catch cancers at their earliest stages. This has the potential to transform healthcare, making it more effective and accessible. AI is showing promising results in detecting breast, lung, and skin cancers, which are some of the most common and life-threatening cancers. As AI technology continues to advance and improve, we are on the brink of a future where diseases can be caught early, treatments can start sooner, and people can enjoy better well-being. This partnership between AI and healthcare holds great promise, paving the way for a healthier and brighter future for everyone. of authors have no conflicts to disclose. R. Deepa: Conceptualization (equal); Writing – original draft (equal). S. Arunkumar: Funding acquisition (equal); Resources (equal); Software (equal). V. Jayaraj: Methodology (equal). A. Sivasamy: Conceptualization (equal); Methodology (equal); Writing – original draft (equal). [SEP] Justify:

Output: The text corroborates the claim by detailing various ways AI is applied in the early detection of cancers. It specifically mentions that AI 'detects irregularities in skin patterns and textures that might indicate cancerous growth' and highlights how AI enhances early detection, which aligns with the claim regarding AI helping to detect early signs of diseases, including cancer. Furthermore, the text emphasizes the future potential of AI in improving healthcare by catching cancers at their earliest stages, which again supports the assertion that AI is being developed for such purposes.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: 0104) and the average genetic variation among 94 lentil landraces was 0. 63. The data obtained from current study can be utilized to increase genetic diversity of cultivated species and establish suitable conservation and breeding strategies of lentil. Keywords DArT, genetic diversity, Landrace, Lens culinaris, Lentil, SNP 1995. Intra-and inter-specific variations in Lens revealed by RAPD markers. Theoretical and 90: 335-40. Ahamed, K. , B. Akhter, M. Islam, M. Humaun and M. Alam. 2014. Morphological characterization and genetic diversity in lentil (Lens culinaris medikus ssp. Culinaris) germplasm. of Agricultural Research, Innovation and Technology 4: 70-6. Ahmad, M. , D. Mcneil, A. Fautrier, K. Armstrong and A. Paterson. 1996. Genetic relationships in Lens species and parentage determination of their interspecific hybrids using RAPD markers. Theoretical and 92: 1091-8. Alghamdi, S. , A. Khan, M. Ammar, E. El-Harty, H. Migdadi, S. El-Khalik, A. Al-Shameri, M. Javed and S. Al-Faifi. 2014. Phenological, nutritional and molecular diversity assessment among 35 introduced lentil (Lens culinaris Medik. ) genotypes grown in Saudi Arabia. of 15: 277-95. Ates, D. , S. Aldemir, B. Yagmur, A. Kahraman, H. Ozkan, A. Vandenberg and M. B. Tanyolac. 2018a. QTL Mapping of Uptake in Lentil Seed. G3: Genes, Genomes, Genetics 8(5):1409-1416. Ates, D. , T. K. Asciogul, S. Nemli, S. Erdogmus, D. Esiyok and M. B. Tanyolac. 2018b. Association mapping of days to flowering in common bean (Phaseolus vulgaris L. ) revealed by DArT markers. 38: 113. Ates, D. , T. Sever, S. [SEP] Justify:

Output: The text contrasts the claim by discussing genetic variation in lentil landraces, emphasizing that the data can be utilized to 'increase genetic diversity of cultivated species' rather than supporting the idea that monoculture practices promote genetic diversity. The focus on landrace variations and conservation strategies indicates that increasing diversity relies on methodologies other than monoculture.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: of Phytopathology 46(1):27-51 -Lefert P, Takano Y. 2011. 1 is required for pathogen-induced expression of plant defensins in nonhost resistance, and acts through interference of MYC2-mediated repressor function. 67(6):980-992. 1992. Effect of sodium bicarbonate and oils on the control of powdery mildew and black spot of roses. 76(3):247. 2015. GSDS 2. 0: an upgraded gene feature visualization server. Bioinformatics 31(8):1296-1297. 1993. CTR1, a negative regulator of the ethylene response pathway in Arabidopsis, encodes a member of the raf family of protein kinases. Cell 72(3):427-441 Kim J--S, Kim K-N, Yun C-H, Heu S, Park S-Y, Lee Y-H, Jwa N-S. 2003. Molecular cloning and mRNA expression analysis of a novel rice (Oryzasativa L. ) MAPK kinase kinase, OsEDR1, an ortholog of ArabidopsisAtEDR1, reveal its role in defense/stress signalling pathways and development. Biochemical and Communications 300(4):868-876 -. 2009a. Six classes of nuclear localization signals specific to different binding grooves of importin α. Journal of 284(1):478-485. 2009b. Systematic identification of cell cycle-dependent yeast nucleocytoplasmic shuttling proteins by prediction of composite motifs. Proceedings of the of Sciences of the of America 106(25):10171-10176. 2016. MEGA7: molecular evolutionary genetics analysis version 7. 0 for bigger datasets. and Evolution 33(7):1870-1874. 2006. Powdery mildew resistance in roses: QTL mapping in different environments using selective genotyping. Theoretical and 113(6):1081-1092. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCt Method. Methods 25(4):402-408. 1960. Blackspot and powdery mildew of Roses. Magazine 39:108-110. 2016. [SEP] Justify:

Output: The text provided is irrelevant to the claim regarding hydrogen bonding and protein structure. It discusses various subjects related to plant pathology, gene expression, and molecular biology but does not address hydrogen bonding or the secondary structure of proteins like alpha-helices.


Input: Task: pass the claim using the given text. Claim: AI can completely eliminate human error in healthcare without any oversight. [SEP] Text: Determining cell typeabundance and expression from bulk tissues with digital cytometry. 37(7):773-782. 2018. Targeting autophagy in cancer. Cancer 124(16):3307-3318. 2014. An overview of autophagy: morphology, mechanism, and regulation. Signal 20(3):460-473. 2018. 3 promotes malignant phenotypes of glioma by activating EGFR-AKT signaling. Biochemical and Communications 505(2):413-418. 2017. Targeting autophagy to overcome chemoresistance in acute myleogenous leukemia. Autophagy 13(1):214-215 -. 2006. Auto-antibodies in prostate cancer: humoral immune response to antigenic determinants coded by the differentially expressed transcripts FLJ23438 and VAMP3. Prostate 66(14):1463-1473. 2019. WDFY2 restrains matrix metalloproteinase secretion and cell invasion by controlling VAMP3-dependent recycling. 10(1):2850. 2019a. DIRAS3-derived peptide inhibits autophagy in ovarian cancer cells by binding to beclin1. Cancers 11(4):557. 2019b. DIRAS3 (ARHI) blocks RAS/MAPK signaling by binding directly to RAS and disrupting RAS clusters. 29(11):3448-3459. 2019. Polygenic risk score increases schizophrenia liability through cognition-relevant pathways. Brain 142(2):471-485 Varma S. 2020. Blind estimation and correction of microarray batch effect. 15(4):e0231446 Walter W, Sánchez-Cabo F, Ricote M. 2015. GOplot: an R package for visually combining expression data with functional analysis. Bioinformatics 31(17):2912-2914. 2019. Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma. Aging 11(24):12246-12269. 2019. Novel therapy in Acute myeloid leukemia (AML): moving toward targeted approaches. in Hematology 10:2040620719860645. 2019. [SEP] Justify:

Output: The text provided is a set of citations and references related to various studies and topics in the field of cancer and autophagy, none of which address the claim regarding AI eliminating human error in healthcare. Therefore, it does not provide relevant information to corroborate or contrast the claim.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: , H. El-Shazly, S. M. Ahmed and A. Omran. 2011. Molecular and biochemical markers in some lentil (Lens culinaris Medik. ) genotypes. Annals of 56: 105-12. Erskine, W. , Y. Adham and L. Holly. 1989. Geographic distribution of variation in quantitative traits in a world lentil collection. Euphytica 43: 97-103. Erskine, W. and M. Choudhary. 1986. Variation between and within lentil landraces from Republic. Euphytica 35: 695-700. Erskine, W. and F. Muehlbauer. 1991. Allozyme and morphological variability, outcrossing rate and core collection formation in lentil germplasm. Theoretical and 83: 119-25. -Lloyd, H. J. Newbury and N. Maxted. 1998. Contrasting genetic variation amongst lentil landraces from different geographical origins. Euphytica 102: 265-73. Fikiru, E. , K. Tesfaye and E. Bekele. 2007. Genetic diversity and population structure of Ethiopian lentil (Lens culinaris Medikus) landraces as revealed by ISSR marker. of Biotechnology 6. Ford, R. , E. Pang and P. Taylor. 1997. Diversity analysis and species identification in Lens using PCR generated markers. Euphytica 96: 247-55. Havey, M. and F. Muehlbauer. 1989. Variability for restriction fragment lengths and phylogenies in lentil. Theoretical and 77: 839-43. Huson, D. H. and D. Bryant. 2006. Application of phylogenetic networks in evolutionary studies. and Evolution 23: 254-67. Huttner, E. , P. Wenzl, M. Akbari, V. Caig, J. Carling, C. Cayla, M. Evers, D. Jaccoud, K. Peng and S. Patarapuwadol. Diversity arrays technology: a novel tool for harnessing the genetic potential of orphan crops. [SEP] Justify:

Output: The text contrasts the claim as it discusses various studies and methods regarding genetic diversity in lentils and other crops, without supporting the idea that monoculture practices can achieve this diversity. The multiple citations highlight the importance of different genetic variations and traits among lentil landraces instead of suggesting that monoculture is a viable method to enhance genetic diversity.


Input: Task: pass the claim using the given text. Claim: Hydrogen bonding is crucial for the secondary structure of proteins like alpha-helices. [SEP] Text: Mancozeb, a fungicide routinely used in agriculture, worsens nonalcoholic fatty liver disease in the human HepG2 cell model. 249:1-4. 2010. Mitogen-activated protein kinase signaling in plants. of 61(1):621-649. 2010. I-: a unified platform for automated protein structure and function prediction. 5(4):725 Saitou H. 1987. The neighbor-joining method: a new method for reconstructing phylogenestic trees. and Evolution 4(4):406-425 Schulzelefert P, Vogel J. 2000. Closing the ranks to attack by powdery mildew. Trends in 5(8):343-348. 2011. OsEDR1 negatively regulates rice bacterial resistance via activation of ethylene biosynthesis. & Environment 34(2):179-191. 2005. Regulation of plant disease resistance, stress responses, cell death, and ethylene signaling in Arabidopsis by the EDR1 protein kinase. 138(2):1018-1026 Tang D, Innes RW. 2002. Overexpression of a kinase-deficient form of the EDR1 gene enhances powdery mildew resistance and ethylene-induced senescence in Arabidopsis. 32(6):975-983. 1997. _X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Research 25(24):4876-4882 van Hulten M, Pelser M, van. 2006. Costs and benefits of priming for defense in Arabidopsis. Proceedings of the of Sciences of the of America 103(14):5602-5607 Viklund H, Elofsson A. 2008. : improving topology prediction by two-track ANN-based preference scores and an extended topological grammar. Bioinformatics 24(15):1662-1668. 2015. 4 associates with 2 and modulates plant immunity by regulating relocation of EDR1 in Arabidopsis. 27(3):857-873. 2018. Rapid analysis of fungicides in tea infusions using ionic liquid immobilized fabric phase sorptive extraction with the assistance of surfactant fungicides analysis using IL-FPSE assisted with surfactant. 239:797 Yang J, Zhang Y. 2015. I-server: new development for protein structure and function predictions. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses a variety of topics including the effects of fungicide on liver disease, plant immunity, and methods for protein structure prediction, but does not mention hydrogen bonding or its role in the secondary structure of proteins like alpha-helices.


Input: Task: corroborate the claim using the given text. Claim: Reforestation projects can help restore ecosystems affected by climate change. [SEP] Text: Plant strains from communities identified as most successful under each condition will then be micro-propogated, and used to populate chains of marshes constructed to manage Basra's urban and industrial waste water streams. If successful, the project will revitalize villages left high and dry by the previously engineered destruction, restore (at least some) environmental services to the larger cities - and serve as a living laboratory for new remediation techniques, with world-wide application. What are the goals of the project? With this funding, by May 2014, we will meet with land donors, survey the proposed sites, and collect soil and water samples. Over the summer, we will analyze them to determine the mix of specific plants and microorganisms best suited to these tough conditions. Then we will prepare the detailed engineering plan and commence construction. We have already assembled the team, obtained in-country support, identified a willing land donor, set a date and time for the survey, and have hosts who will cover our ground costs. This funding will pay for our international flights to attend these meetings and collect samples. Stretch funding will pay lab fees and support lab assistants to process our pilot soil and water samples. Our overarching goal is to test whether we can effectively use waste water and waste land to construct salt marshes that will treat the contaminated water. Over the next two years, we will establish test beds to rapidly assess the best mix of salt-tolerant marsh grasses to:Goal 1: Bio-remediate contaminants to prevent illness and death in humans, livestock, fish, wildlife, and their supporting ecosystems. Goal 2: Increase plant biomass production to augment human food and animal fodder supplies. Goal 3: Provide nursery habitat for fish, shellfish, and waterfowl to augment food production. Goal 4: Improve and stabilize surface and ground water quality to promote human, livestock, fisheries, and ecosystem health. Goal 5: Augment downstream water supply for post-mediation usage. wait. . . We have the experts. We have the site. We have the meeting. We have the date. We just need to get there! Our next step is to fly to Basra, Iraq, meet with the land donors, preview the test sites, and collect soil and water pilot samples for analysis. All of our ground expenses are covered by our hosts - but not our airfare. Due to fund source restrictions on both ends of the project, we need alternative means to cover our international flights. [SEP] Justify:

Output: The text corroborates the claim by detailing a project that aims to revitalize ecosystems through the construction of salt marshes using wastewater. It emphasizes several goals related to restoring environmental services, including bio-remediation of contaminants and improving water quality, which align with the objectives of reforestation and ecosystem restoration.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: Vandenberg, C. J. Coyne, R. Mcgee and K. E. Bett. 2016a. Genetic diversity of cultivated lentil (Lens culinaris Medik. ) and its relation to the world's agro-ecological zones. Frontiers in 7:1093. Kushwaha, U. , S. Ghimire, N. Yadav and B. Ojha. 2013. Genetic relatedness of lentil (Lens culinaris L. ) germplasm by using SSR markers. of and Biotechnology 1: 132-6. Lazaro, A. , M. Ruiz, L. de la Rosa and I. Martin. 2001. Relationship between agro/morphological characters and climatic parameters in Spanish landraces of lentil (Lens culinaris Medik. ). Genet. Resour. 48: 239-249. Lombardi, M. , M. Materne, N. O. Cogan, M. and S. Kaur. 2014. Assessment of genetic variation within a global collection of lentil (Lens culinaris Medik. ) cultivars and landraces using SNP markers. BMC Genetics 15: 150. Maxted, N. and S. J. Bennett, 2001. Legume diversity in the Mediterranean region. In. Plant genetic resources of legumes in the Mediterranean. Springer 51-75. Nei, M. , 1987. Molecular evolutionary genetics. press, New York. 1987. 512 pp. Am. J. Phys. Anthropol 75(1988): 428-429. Nei, M. and W. -H. Li. 1979. Mathematical model for studying genetic variation in terms of restriction endonucleases. Proceedings of the of Sciences 76: 5269-73. Nemli, S. , T. Kianoosh and M. B. Tanyolac. 2015. Genetic diversity and population structure of common bean (Phaseolus vulgaris L. ) accessions through retrotransposon-based interprimer binding sites (iPBSs) markers. of Agriculture and Forestry 39: 940-8. Ozkuru, E. , D. Ates, S. Nemli, S. Erdogmus, N. Karaca, H. [SEP] Justify:

Output: The text contrasts the claim by presenting a compilation of studies focused on genetic diversity in crops, specifically lentils and beans, rather than supporting monoculture practices. The various papers cited emphasize the genetic variation among different cultivars and landraces of lentils, indicating that genetic diversity is better preserved through diverse planting rather than monoculture methods.


Input: Task: pass the claim using the given text. Claim: Mechanical stress can induce conformational changes in protein structures. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to Biochemistry and Research 10. 7554/eLife. 21510 Open access Copyright information Version of RecordJanuary 24, 2017 Accepted ManuscriptJanuary 16, 2017 Open annotations (there are currently 0 annotations on this page). 4,325 views 739 downloads 82 1510. Copy to clipboard Download BibTeX Download. RIS Full text Figures and data Peer review Side by side Materials and methods Data availability 10. 7554/eLife. 21510. 001 are omnipresent enzymes, spread through the whole phylogenetic tree of life as well as viruses, since their unwinding capabilities are of striking importance for numerous cellular pathways of RNA and DNA metabolism. Based on sequence alignments, helicases were subdivided into six superfamilies (SF) which come in two different flavors (Singleton et al. , 2007; Fairman-Williams et al. , 2010; Jankowsky and Fairman, 2007; Pyle, 2011). Most members of the SF1 or SF2 are monomeric, in contrast to the members of SF3 to SF6 which assemble as hexamers (Singleton et al. , 2007). The latter are composed of either RecA- or AAA+-like domains, thus being distinct from the SF1 and SF2 helicases which are exclusively composed of two RecA-like domains in juxtaposition with each other. The SF2 is by far the largest superfamily, comprising a vast number of RNA helicases with implications in numerous RNP metabolism pathways, such as translation initiation and termination, pre-mRNA editing and splicing, mRNA export as well as ribosome biogenesis (Ozgur et al. , 2015). The centerpiece of these helicases is formed by the two RecA-like domains, harboring conserved motifs which are crucial for these NTPase-dependent RNA helicases to accomplish their function. The motifs I, II, V and VI are required for nucleoside triphosphate binding and hydrolysis, motifs Ia, Ib and IV are involved in RNA binding and the motif III participates in coupling the NTPase and the unwinding activity (Caruthers and McKay, 2002; Cordin and Beggs, 2013; Cordin et al. , 2012). [SEP] Justify:

Output: The text does not address the claim regarding mechanical stress inducing conformational changes in protein structures. Instead, it focuses on the classification and functional aspects of helicases, their structure, and their roles in RNA and DNA metabolism, which is unrelated to mechanical stress or conformational changes in proteins.


Input: Task: corroborate the claim using the given text. Claim: Cities are developing green infrastructure to manage stormwater and reduce flooding. [SEP] Text: To reduce the use of fertilizers by 20% by 2030. To reduce nutrient loss by at least 50%. To reduce the use of antimicrobials in agriculture and antimicrobials in aquaculture by 50% by 2030. To establish sustainable food labeling. To reduce food waste by 50% by 2030. The biodiversity strategy is an important part of the EU’s climate change mitigation strategy and includes the allocation of 25% of the European budget to combating climate change in order to restore biodiversity. The EU for 2030 includes the following objectives: [2]. Conservation of 30% of sea lands and 30% of land lands, especially primary forests and old forests. Planting 3 billion trees by 2030. Restoring at least 25,000 kilometers of rivers for free flowing. 50% reduction in pesticide use by 2030. Increasing organic agriculture. Increasing biodiversity in agriculture. Reversing the decline of pollinators. The development of green infrastructure is an important step toward the success of the EU 2020 Biodiversity Strategy, with the strategy’s second objective requiring “by 2020, the maintenance and development of ecosystems and their services by establishing green infrastructure and restoring at least 15% of degraded ecosystems. ” However, green infrastructure contributes to the full implementation of all six objectives of the strategy – specifically the Birds and (target 1) and to the conservation and enhancement of biodiversity in the wider rural and marine environment (targets 3 and 4) [3]. has adopted an EU-wide strategy that encourages investment in green infrastructure to restore the health of ecosystems, keep natural areas connected, allow species to thrive in their natural habitat, and promote green infrastructure across Europe. It is designed to provide ecosystem services such as green infrastructure planning, water purification, air quality, recreation space, and climate mitigation, providing environmental, economic, and social benefits through natural and seminatural solutions. This network of green (land) and blue (water) areas improves environmental conditions, thereby increasing the health and quality of life of citizens, creating job opportunities, increasing biodiversity, and supporting the green economy [4]. Advertisement 2. Green stormwater infrastructureAs it is known, the storm or flow moves over the ground by gravity and reaches streams, rivers, ponds, lakes, and oceans. [SEP] Justify:

Output: The text corroborates the claim by discussing the development of green infrastructure as part of the EU's Biodiversity Strategy, which aims to restore ecosystems and provide numerous benefits, including improved water quality and management. It clearly states that green infrastructure supports ecosystem services such as water purification and climate mitigation, which are directly relevant to managing stormwater and reducing flooding.


Input: Task: pass the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: Yilmaz, B. Yagmur, C. Kartal, M. Tosun and O. O. Ocak. 2019. Genome-wide association studies of molybdenum and selenium concentrations in C. arietinum and C. reticulatum seeds. 39: 46. Ozkuru, E. , D. Ates, S. Nemli, S. Erdogmus, N. Karaca, H. Yilmaz, B. Yagmur, C. Kartal, M. Tosun and O. Ozdestan. 2018. Association mapping of loci linked to copper, phosphorus, and potassium concentrations in the seeds of C. arietinum and C. reticulatum. Genomics. Piergiovanni, A. R and G. Taranto. 2003. Geographic distribution of genetic variation in lentil collection as revealed by SDS-PAGE fractionation of seed storage proteins. J. Genet. Breed 57: 39-46. Raj, A. , M. Stephens and J. K. Pritchard. 2014. : of in. Genetics 197: 573-589. Sansaloni, C. P. , C. D. Petroli, J. . Grattapaglia, R. E. Vaillancourt and A. Kilian. 2010. A high-density Technology (DArT) microarray for genome-wide genotyping in Eucalyptus. 6: 16. Scippa, G. , D. Trupiano, M. Rocco, V. Viscosi, M. Di Michele, A. D'andrea and D. Chiatante. 2008. An integrated approach to the characterization of two autochthonous lentil (Lens culinaris) landraces of Molise (south-central Italy). Heredity 101: 136. Seyedimoradi, H. and R. Talebi. 2014. Detecting DNA polymorphism and genetic diversity in Lentil (Lens culinaris Medik. ) germplasm: comparison of ISSR and DAMD marker. Physiology and of Plants 20: 495-500. Sharma, S. , M. Knox and T. N. Ellis. [SEP] Justify:

Output: The text provides a variety of citations regarding genetic studies and associations in different plant species, but it does not address the relationship between genetic diversity and monoculture practices. Therefore, it is irrelevant to the claim as it does not support, contrast, or relate to the assertion about monoculture and genetic diversity.


Input: Task: pass the claim using the given text. Claim: Mechanical stress can induce conformational changes in protein structures. [SEP] Text: Most SF2 helicases exhibit extensions at the N- and C-termini of the helicase core, which is also true for the DEAH-box proteins, a subfamily of the DEAH/RHA family. The C-terminal extension is highly conserved in DEAH-box helicases and is composed of three domains: a winged helix (WH), a ratchet and an oligosaccharide binding (OB) fold (Walbott et al. , 2010; He et al. , 2010). The ratchet domain was originally named according to the homologous domain of the DNA helicase Hel308 (Büttner et al. , 2007), but recent studies on MLE suggested that this domain has no ratcheting function and should be renamed (Prabu et al. , 2015). Therefore it is hereinafter referred to as ratchet-like domain. In DEAH-box proteins the degrees of freedom of the helicase core are reduced by the interactions between the C-terminal domains and the two RecA-like domains in comparison to those DEAD-box proteins lacking such C-terminal domains. The conservation of the N-terminal extension is distinctly lower and they dramatically differ in length between the members of this subfamily. Reported functions for this N-terminal extension are, among others, the involvement in the recruitment as shown for Prp16 (pre-mRNA processing factor 16) as well as for Prp22 or for the subcellular targeting to the nucleus as elucidated for Prp43 (Wang and Guthrie, 1998; Schneider and Schwer, 2001; Fouraux et al. , 2002). The latter mentioned helicase, Prp43, is an outstanding member of the DEAH-box subfamily since it has implications in different substantive cellular processes. The first reported function of Prp43 is its involvement in pre-mRNA splicing. In the course of this process, Prp43 acts at the latest stage of the splicing cycle and it is required to dismantle the intron-lariat spliceosome into the excised lariat and the U2•U5•U6 snRNPs (Arenas and Abelson, 1997; Fourmann et al. , 2013, 2016a). [SEP] Justify:

Output: The text provides detailed information about the structures and functions of SF2 and DEAH-box helicases, but does not mention mechanical stress or its role in inducing conformational changes in protein structures. Therefore, it is irrelevant to the claim being analyzed.


Input: Task: corroborate the claim using the given text. Claim: Cities are developing green infrastructure to manage stormwater and reduce flooding. [SEP] Text: With urbanization, impermeable surfaces (roofs, streets, parking lots, and sidewalks) increase, so the permeable areas decrease and as a result, the volume of storm water flow increases. Stormwater management strategies include the prevention, containment, and reuse of stormwater runoff. Green stormwater infrastructure is systems like biological ditches, catchments, wetlands, rain gardens, and urban green spaces that mimic natural processes that result in infiltration, evaporation, or use of rainwater. It is becoming increasingly clear that green stormwater infrastructure not only controls stormwater volume and timing but also supports ecosystem services, which are the benefits ecosystems provide to humans. “can be broadly defined as a strategically planned network of high-quality natural and seminatural areas with other environmental features, which is designed and managed to deliver a wide range of ecosystem services and protect biodiversity in both rural and urban settings. More specifically GI, being a spatial structure providing benefits from nature to people, aims to enhance nature’s ability to deliver multiple valuable ecosystem goods and services, such as clean air or water” [5]. “Green infrastructure is a strategically planned network of natural and seminatural areas with other environmental features designed and managed to deliver a wide range of ecosystem services. It incorporates green spaces (or blue if aquatic ecosystems are concerned) and other physical features in terrestrial (including coastal) and marine areas. On land, GI is present in rural and urban settings” [6]. “ Green infrastructure is an approach to wet weather management that uses soils and vegetation to utilize, enhance, and/or mimic the natural hydrological cycle processes of infiltration, evapotranspiration, and reuse” [7]. Traditional stormwater infrastructure, or gray infrastructure, is designed to divert rainwater from urban areas through pipes and channels. Runoff from these surfaces can suffocate infrastructure systems, carrying pollutants into streams, rivers, and lakes when rainwater runs off impermeable streets, parking lots, sidewalks, and roofs. Flows can also cause erosion and flooding that can damage property, infrastructure, and wildlife habitat. In addition to flow problems, impermeable surfaces also prevent water from penetrating the soil and refilling groundwater sources. Green infrastructure uses plants, soils, landscape design, and engineering techniques to contain, absorb, and reduce polluted stormwater runoff. Green infrastructure prevents combined sewer overflows for cities by preventing or reducing the amount of runoff flowing directly into storm drains. [SEP] Justify:

Output: The text supports the claim by explaining that green stormwater infrastructure includes systems such as biological ditches, wetlands, and rain gardens that mimic natural processes to manage stormwater. It details how this infrastructure aims to control stormwater volume and mitigate flooding while also providing ecosystem benefits. Further, it contrasts green infrastructure with traditional gray infrastructure, highlighting the advantages of utilizing natural processes to reduce stormwater runoff. This aligns directly with the assertion that cities are implementing such strategies to manage stormwater and prevent flooding.


Input: Task: contrast the claim using the given text. Claim: Genetic diversity in crops can be achieved with monoculture practices. [SEP] Text: Genomics-assisted breeding for crop improvement. Trends in 10:621-630. Villa, T. C. C. , N. Maxted, M. Scholten and B. Ford-Lloyd. 2005. Defining and identifying crop landraces. Resources 3: 373-84. Wong, M. M. , N. Gujaria-Verma, L. Ramsay, H. Y. Yuan, C. Caron, M. Diapari, A. Vandenberg and K. E. Bett. 2015. Classification and characterization of species within the genus Lens using genotyping-by-sequencing (GBS). PLoS One 10: e0122025. Yuzbasioglu, E. , S. Ozcan and L. Acık. 2006. Analysis of genetic relationships among Turkish cultivars and breeding lines of Lens culinatis Mestile using RAPD markers. 53: 507-14. Zaccardelli, M. , F. Lupo, A. R. Piergiovanni, G. Laghetti, G. Sonnante, M. G. Daminati, F. Sparvoli and L. Lioi. 2012. Characterization of Italian lentil (Lens culinaris Medik. ) germplasm by agronomic traits, biochemical and molecular markers. 59: 727-38. 260. AMA Ates D. (DArT). TJFC. December 2019;24(2):252-260. doi:10. 17557/tjfc. 656511 Chicago Ates, Duygu. “(DArT)”. Crops 24, no. 2 (December 2019): 252-60. EndD (December 1, 2019) (DArT). Crops 24 2 252–260. IEEE D. Ates, “(DArT)”, TJFC, vol. 24, no. 2, 10. 17557/tjfc. 656511. . “(DArT)”. Crops 24/2 (December 2019), 252-260. . (DArT). TJFC. 2019;24:252–260. MLA Ates, Duygu. [SEP] Justify:

Output: The text contrasts the claim by focusing on genomics-assisted breeding and the identification of genetic diversity in crop landraces and species, which implies that increasing genetic diversity may not align with monoculture practices, as monoculture typically involves growing a single crop variety rather than a diverse genetic base.


Input: Task: pass the claim using the given text. Claim: Mechanical stress can induce conformational changes in protein structures. [SEP] Text: Recently, the target substrate of Prp43 during this process was revealed which is the RNA network between the U2 snRNP and the branch site of the intron (Fourmann et al. , 2016b). Indeed, in the spliceosome, Prp43 was crosslinked exclusively to the pre-mRNA and not to any snRNAs. Furthermore, Prp43 is also a key player for the disassembly of stalled spliceosomes (Pandit et al. , 2006; Semlow and Staley, 2012; Koodathingal et al. , 2010). During these spliceosomal processes, Prp43 binds RNA in a sequence-independent manner. Prp43 action is also essential for ribosome biogenesis. Here, it is involved in the maturation of 18S and 25S rRNAs with several reported binding sites on intermediates which vary in sequence and thus make a sequenced-based binding mechanism unlikely for Prp43 (Lebaron et al. , 2005; Bohnsack et al. , 2009). Lately, the involvement of Prp43 in a third distinct cellular function was illuminated and it was unraveled that Prp43 is also involved in the promotion of apoptosis (Heininger et al. , 2016). The distribution of Prp43 between these three fundamentally different processes is regulated by a fine-tuned interplay of the helicase itself and cofactors. These cofactors belong to the family of G-patch proteins, which share an intrinsically disordered glycine-rich region, the G-patch motif (Aravind et al. , 1999). Apart from being involved in recruitment, these G-patch proteins also increase the NTPase and stimulate the helicase activity of Prp43. To date, in S. cerevisiae five G-patch proteins were identified out of which four interact with Prp43. In the context of splicing, Prp43 function is modulated by Ntr1 (Nineteen complex-related proteins 1) (Tanaka et al. , 2007; Tsai et al. , 2005, 2007; Boon et al. , 2006). During rRNA maturation, Prp43 is associated with Pfa1 (Prp forty-three associated 1) or Gno1 (G-patch nucleolar protein 1) (Lebaron et al. [SEP] Justify:

Output: The text does not address the specific claim regarding mechanical stress and conformational changes in protein structures. Instead, it focuses on the roles of Prp43 in various cellular processes, including splicing and rRNA maturation, without mentioning mechanical stress or conformational changes. Hence, it is considered irrelevant.