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: Overall architecture of the PilQ complex. Sagittal sections along the central symmetry axis show the gates; top views of clipped planes indicate the detailed arrangement of all domains and both gates in PilQ from top to bottom. Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as model of PilQ protomer. Sagittal and longitudinal sections through the single PilQ domains show the quality of the fit of the homology models of the PilQ protomers. The overall arrangement of one PilQ protomer (chain B, cyan) after flexible fitting and linkers built and the adjacent protomers corresponding to chains A and C (blue and green respectively) show how the they are positioned relative to each other. 1 Modelling and fitting of PilQ domains. Sequences of PilQ domains aligned to their respective templates were used to create atomic models of each domain. Models with the best scores (lower DOPE scores [Sali and Blundell, 1993]) were fitted into the corresponding density maps and screened for best fit orientations (highest cross correlation coefficient). PilQ DomainSize (aa)structural motifTemplate protein (PDB code)Sequence identity/similarityResolution (Å)/MethodDOPE score (z-score)N0116βαββαββαN0 domain of PilQ from N. meningitidis 4ARO_A11. 45/ 20. 61NA/NMR structure−9457. 03 (0. 230)N1107αββββαPeriplasmic domain of secretin EscC from enteropathogenic E. coli 3GR5_A N1 domain model from PilQ from N. meningitidis 4AV2_A8. 77/ 14. 913gr5: 2. 05/X-ray 4av2: NA/NMR, Model and cryo-EM−7861. 37 (0. 351)N257βαββαExtra membrane domain of secritin HOFQ from A. actinomycetemcomitans 2Y3M_A9. 72/ 20. 832. 3/X-ray−5616. 16 (−1. [SEP] Justify:

Output: The text is irrelevant to the claim because it discusses the architecture and modeling of the PilQ complex without mentioning hydrogen bonding or the secondary structure of proteins such as alpha-helices. It focuses on structural motifs, alignment of domains, and modeling techniques rather than the fundamental molecular interactions that underpin 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: Close navigation menu 10. 1063/5. 0177640 5331. Download citation file: Ris (Zotero) toolbar search Menu toolbar search search input Search input auto suggest filter your search |The realm of healthcare is undergoing a revolutionary transformation through the integration of artificial intelligence (AI) for early cancer detection, ushering in a new era of enhanced well-being. This review paper delves into the paradigm shift brought about by AI, highlighting its potential to identify diseases in their nascent stages, thereby revolutionizing preventive healthcare. Harnessing the computational prowess of AI, this paradigm empowers us to analyze intricate medical data, including internal imagery and health records, facilitating the identification of subtle disease markers imperceptible to the human eye. The key focus lies not only in early detection but also in ensuring diagnostic accuracy. A comprehensive analysis of various studies underscores AI’s superiority in comparison to traditional methods, manifesting in quicker and more precise identification of anomalies. This transformation translates to expedited medical interventions and improved patient outcomes. The crux of this evolution lies in AI’s capacity to redefine healthcare, transforming it into a proactive endeavor that identifies and addresses health concerns while they remain manageable. neural network, Deep learning, Artificial intelligence, Computed tomography, Dermatology, Diseases and conditions, Organs, Mammography, Magnetic resonance imaging, Histology I. has transformed remarkably due to technology, changing how we take care of our health. One big focus is spotting diseases early, which means finding them when they are still small. This is super important and can help not just individual people, but also the whole healthcare system. Finding diseases early is like a guiding light for better patient outcomes. It can change how diseases go, stopping them or even preventing them. When we catch diseases early, the treatments are often easier, safer, and work better. Plus, it helps healthcare systems by not needing complicated and costly procedures when diseases get worse. Saving money is a big deal too. Healthcare costs a lot, and finding diseases late costs even more. If we catch diseases early, it is better for people’s wallets and helps healthcare resources. It shifts healthcare from just fixing problems to stopping them before they start. Now, say hello to artificial intelligence (AI)—a powerful tool that is changing how healthcare works. AI is like smart computers that learn and solve problems like humans. In healthcare, AI is becoming a game-changer. [SEP] Justify:

Output: The text corroborates the claim by discussing how artificial intelligence (AI) is being integrated into healthcare for early disease detection, particularly for diseases such as cancer. It emphasizes AI's potential to identify diseases at their nascent stages and highlights the superiority of AI in diagnosing compared to traditional methods, leading to better patient outcomes. The text supports the idea that AI transforms healthcare into a proactive approach focused on early detection, which aligns directly with the claim.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: AbstractThe present study, using RAPD analysis, was undertaken to characterize genetic variation in domesticated cowpea and its wild progenitor, as well as their relationships. The materials used consisted of 26 domesticated accessions, including accessions from each of the five cultivar-group, and 30 wild/weedy accessions, including accessions from West, East and southern Africa. A total of 28 primers generated 202 RAPD bands. One hundred and eight bands were polymorphic among the domesticated compared to 181 among wild/weedy cowpea accessions. Wild accessions were more diverse in East Africa, which is the likely area of origin of V. unguiculata var. spontanea. Var. spontanea is supposed to have spread westward and southward, with a loss of variability, loss counterbalanceed in southern Africa by introgressions with local perennial subspecies. Although the variabilty of domesticated cowpea was the highest ever recorded, cultivar-groups were poorly resolved, and several results obtained with isozyme data were not confirmed here. However primitive cultivars were more diverse than evolved cultivars, which still suggests two consecutive bottlenecks within domesticated cowpea evolution. As isozymes and AFLP markers, although with a larger number of markers, RAPD data confirmed the single domestication hypothesis, the gap between wild and domesticated cowpea, and the widespread introgression phenomena between wild and domesticated cowpea. This is a preview of subscription content, log in via an institution to check access. Access this 34. 9910 units per month Download 39. 95 Price excludes VAT (USA) Tax calculation will be finalised during checkout. Instant access to the full 1995. Intra-and inter-specific variations in Lens revealed by RAPD markers. Theor. Appl. Genet. 90: 335–340. 1996. Genetic relationships in Lens species and parentage determination of their interspecific hybrids using RAPD markers. Theor. Appl. Genet. 92: 1091–1098. 1999. Random amplified polymorphic DNA (RAPD) analysis reveals genetic relationships among the annual Cicer species. Theor. Appl. Genet. 98: 657–663. 1995. [SEP] Justify:

Output: The text contrasts the claim by describing a study that shows wild accessions of cowpea are more diverse than domesticated ones. It highlights genetic variation between domesticated cowpea and its wild progenitors, indicating that domestication may lead to a loss of variability. Specifically, the text states, 'primitive cultivars were more diverse than evolved cultivars,' which suggests that not all instances of genetic modification (such as domestication in this case) result in reduced diversity. This indicates that the assertion in the claim oversimplifies the relationship between genetic modification and crop 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: 684)N361βαββαN domain of secretin XcpQ from P. aeruginosa 4E9J_A17. 28/ 33. 332. 03/X-ray−5858. 94 (−1. 331)N456βαββαN domain of secretin XcpQ from P. aeruginosa 4E9J_A14. 28/ 29. 872. 03/X-ray−5261. 98 (−1. 392)N582βαββαN1 domain model from PilQ from N. meningitidis 4AV2_A18. 88/ 27. 77NA/NMR, Model and cryo-EM−6353. 61 (−1. 415)Secretin230βββββββββααβSecretin domain of T2SS GspD from V. cholerae 5WQ8_A8. 89/ 14. 183. 26/cryo-EM−17222. 63 (0. 777) Table 2 Statistics of PilQ domain fits. Homology models of the different PilQ domains were fitted into the respective ring module maps. Each domain was initially placed within the density maps in multiple orientations (n = 10,000), spanning the entire Euler angular space, and subjected to steepest descent local optimization. The resulting fits were clustered and ranked by their CC-scores with the corresponding density maps fits. The table lists the top-5 fits for each domain in the Euler-angle search, along with their CC-scores and the number of independent optimization runs ending in this cluster. The top-ranked fits were assessed for compatibility of the N-to-C-terminal domain orientations (bottom-to-top). The compatible orientations with the highest CC-value was selected as initial fit. Subsequent refinement using MDFF flexible fitting (Trabuco et al. , 2008) further improved the fits with minimal structural change. PilQ domain fitsTotal no. of fitsNo. of unique fitsTop 5 solutionsInitial chosen protomer fit. CC (Cluster #)Fit after MDFF. CC (RMSD, Å)CC for C13 oligomeric ring modelCC after MDFF run with masked density mapRank #CC with mapRunsN010000152Rank 1 Rank 2 Rank 3 Rank 4 Rank 50. [SEP] Justify:

Output: The text does not address the claim regarding hydrogen bonding and its role in the secondary structure of proteins, particularly alpha-helices. Instead, it provides detailed information on domain fits and clustering related to specific protein structures without mentioning 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: It helps with diagnoses, suggesting treatments, and more. AI is like a super-fast brain. It can look at tons of medical information—from records to complicated images—way faster than a person. This helps doctors make decisions quickly and accurately. In this comprehensive review paper, we delve into the dynamic synergy between early cancer detection and the transformative capabilities of (AI). Our exploration revolves around the remarkable collaboration of AI and medical science, with a specific focus on their potential to revolutionize healthcare by identifying diseases at their nascent stages. We embark on a journey through an array of cutting-edge research studies, each serving as a lens into the intersection of AI and early cancer detection. Our primary objective is to dissect the intricate web of findings to unveil the profound impact AI could wield in the realm of disease prevention. Breast, lung, and skin cancers are more prevalent compared to other cancers due to a combination of factors, including lifestyle, genetic predisposition, environmental influences, and screening practices. A. Breast cancer Lifestyle factors: Factors such as hormonal changes, obesity, alcohol consumption, and lack of physical activity can contribute to the development of breast cancer. Genetic factors: Certain genetic mutations, such as BRCA1 and BRCA2, increase the risk of breast cancer. Hormonal influences: Hormones, such as estrogen and progesterone, can impact breast tissue and contribute to cancer development. Screening Awareness: Regular mammography screenings and increased awareness have led to earlier detection, resulting in higher reported cases. B. Lung cancer Smoking: The primary risk factor for lung cancer is tobacco smoking, which significantly increases the likelihood of developing the disease. Secondhand smoke: Exposure to secondhand smoke also contributes to lung cancer risk. Environmental exposures: Occupational exposures to asbestos, radon, and other carcinogens play a role in lung cancer development. Global prevalence: Lung cancer is common worldwide due to the widespread prevalence of smoking and environmental factors. C. Skin cancer Sun exposure: Excessive exposure to ultraviolet (UV) radiation from the sun or tanning beds is a major cause of skin cancer. Lifestyle choices: Frequent sunburns, outdoor occupations, and recreational activities increase the risk of skin cancer. Fair skin: Individuals with fair skin, light hair, and blue or green eyes are more susceptible to skin damage from UV radiation. Early detection: Skin cancer is often detected earlier due to its visible nature, leading to a higher reported prevalence. [SEP] Justify:

Output: The text corroborates the claim by discussing the synergy between AI and early cancer detection. It emphasizes how AI can analyze extensive medical information and assist in diagnoses, thereby potentially identifying diseases like cancer at their initial stages. The exploration of cutting-edge research studies within the text highlights the transformative role AI can play in revolutionizing healthcare, particularly in disease prevention.


Input: Task: pass the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to 10. 7554/eLife. 35103 Open access Copyright information Version of RecordMay 24, 2018 Open annotations (there are currently 0 annotations on this page). 4,105 views 498 downloads 47 5103. Copy to clipboard Download BibTeX Download. RIS Full text Figures and data Peer review Side by side Peer review process Decision letter Author response Peer review process This 2005. [Editors’ note: what now follows is the decision letter after the authors submitted for further consideration. ] Thank you for resubmitting your work entitled "Plant diversity promotes biocontrol services, reduces insecticide use and increases rice yield in urban agriculture" for further consideration at eLife. Your revised 10. 7554/eLife. 35103. 037 Author response [Editors’ note: the author responses to the first round of peer review follow. ] We thank the editors for their comments and suggestions, which helped us greatly to improve the quality of the manuscript. We have carefully studied the reviews and revised our manuscript accordingly. Reviewer #1: As mentioned at the pre-submission stage, I find this study potentially very relevant and interesting for a broad readership because it shows that farm-level plant diversity may increase crop protection by encouraging predators of pests such that less chemical pest control will be necessary without yield losses. My problems with the full submission remain, namely that the authors do not clearly enough explain the lack of randomization in the 15-year study (was it their choice not to randomize or was it because they met a "natural experiment" that had been started by others with lack of knowledge about statistical design) and use inappropriate, simple statistics. The first problem may be resolved by explaining in more detail who prepared the study and why it was not randomized. We thank the editor for these important comments. We now explain fully the origin and background of the study, and the nature of the data. The answers to the question are found in the revised manuscript: “To test our hypothesis, we refer to monitoring data collected from 34 community farms in 9 districts in suburbs of Shanghai, and we conducted two more controlled experiments for further verification. [SEP] Justify:

Output: The text provided does not address the claim regarding genetic modification and its impact on crop diversity. Instead, it focuses on a study about plant diversity in urban agriculture that mentions how farm-level plant diversity can enhance crop protection. Therefore, it does not corroborate or contrast the claim, making it irrelevant.


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: Javascript is disabled in your browser. Please enable Javascript to view PeerJ. Related research -and expression analysis of EDR1-like genes in tobacco (Nicotiana tabacum) in response to Golovinomyces orontii View 10. 7717/peerj. 5244 Published 2018-07-10 Accepted 2018-06-26 Received 2018-02-17 mildew, 1, Tobacco, Phylogenetic analysis, Expression patterns Copyright © 2018 Wu et al. is an open access 2018. Identification and expression analysis of EDR1-like genes in tobacco (Nicotiana tabacum) in response to Golovinomyces orontii. PeerJ 6:e5244 1 (EDR1) encodes a Raf-like mitogen-activated protein kinase, and it acts as a negative regulator of disease resistance and ethylene-induced senescence. Mutations in the EDR1 gene can enhance resistance to powdery mildew both in monocotyledonous and dicotyledonous plants. However, little is known about EDR1-like gene members from a genome-wide perspective in plants. In this study, the tobacco (Nicotiana tabacum) EDR1-like gene family was first systematically analyzed. We identified 19 EDR1-like genes in tobacco, and compared them to those from Arabidopsis, tomato and rice. Phylogenetic analyses divided the EDR1-like gene family into six clades, among them monocot and dicot plants were respectively divided into two sub-clades. NtEDR1-1A and NtEDR1-1B were classified into clade I in which the other members have been reported to negatively regulate plant resistance to powdery mildew. The expression patterns of tobacco EDR1-like genes were analyzed after plants were challenged by Golovinomyces orontii, and showed that several other EDR1-like genes were induced after infection, as well as NtEDR1-1A and NtEDR1-1B. Expression analysis showed that NtEDR1-13 and NtEDR1-16 had exclusively abundant expression patterns in roots and leaves, respectively, and the remaining NtEDR1-like members were actively expressed in most of the tissue/organ samples investigated. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses research related to EDR1-like genes in tobacco and their role in disease resistance, without any mention of hydrogen bonding or the secondary structure of proteins like 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: It is important to note that while these cancers are more prevalent, efforts to promote awareness, lifestyle modifications, and early detection have the potential to reduce their impact. Prevention and education are crucial in addressing the higher prevalence of these cancers and improving overall public health. This paper compares the capabilities of different artificial intelligence (AI) approaches to achieve early cancer detection and proactive healthcare. This methodological innovation empowers accurate analysis of intricate medical data, surpassing traditional methods and enabling the identification of subtle disease markers. The physical significance is in the early detection of diseases, often imperceptible to the human eye, leading to expedited medical interventions and improved patient outcomes. From a physical and engineering perspective, the work is significant in its ability to detect these subtle markers, which is vital for early interventions, potentially saving lives and improving well-being. The speed and precision of AI algorithms, as highlighted in the research, represent an engineering milestone. It transforms healthcare by making AI an indispensable component, promising efficiency, accuracy, and patient-centered care. II. us delve into the world of early disease detection and its intriguing connection with artificial intelligence (AI). This review takes us on a journey through existing research, disease possibilities, and the magic AI brings to medical diagnosis. Imagine spotting diseases when they are just tiny sparks, before they become full-blown fires. That is the goal of early disease detection. Diseases such as cancer, heart problems, and brain disorders can be tamed if caught early. But this is not easy. Sometimes, diseases hide well, and current methods cannot always find them in time. Now, here is where AI steps in—a tech superhero. AI is like a super brain that can chew through loads of information faster than a flash. Machine learning is one trick AI uses. It is like a detective that learns from data to solve medical mysteries. Then, there is deep learning, which is like AI’s way of understanding pictures. It spots even the tiniest changes in x rays, Magnetic resonance imaging (MRI), and more. Research shows AI’s power. It is helping find cancers in x rays, spotting heart problems before they strike, and even predicting brain issues. But, there is a twist. AI needs a lot of special data to learn from, like a teacher needs textbooks. Also, AI sometimes works like a secret code—it makes decisions, but we are not always sure why. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI approaches are being utilized for early cancer detection and proactive healthcare. It emphasizes the significance of AI in analyzing complex medical data to identify subtle disease markers, which directly relates to the early detection of diseases like cancer and possibly Alzheimer’s. The text describes AI as capable of spotting diseases at an early stage, which supports the claim that AI is being developed for this purpose.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: On indigenous production, genetic diversity and crop ecology of enset (Ensete ventricosum (Welw. ) Cheesman) A. Tsegaye PE&RC Research output: Thesis › internal : Enset, staple, indigenous knowledge, genetic diversity, AFLP, characterisation, conservation, yield potential, transplanting, leaf pruning, fermentation, 'kocho', food security The indigenous enset-farming complex of the south and southwestern highlands of Ethiopia has supported a higher population density than any other farming system. Enset ( Ensete ventricosum (Welw. ) Cheesman) has been cultivated as (co-)staple food for about 7-10 million people. Since the last three decades, however, because of population pressure, recurrent drought and diseases there has been degradation of natural resources and, thus, the system failed to sustain the population. In the study, described in this thesis, the indigenous enset production methods, farm-based enset biodiversity and the plant characteristics and environmental factors influencing productivity were analysed to identify yield potentials and constraints in Sidama, Wolaita and Hadiya. The ultimate goal was to develop improved agronomic practices and enhance the use of the existing genetic diversity to reduce the gap between the actual yield and yield potential. Some indigenous cultivation methods vary among regions: initiation of suckers, frequency of transplanting, leaf pruning and planting patterns. Morphologically diverse enset clones were identified in Sidama (52), Wolaita (55) and Hadiya (59). Among 146 clones, a total of 180 AFLP fragments was scored of which 104 (58%) appeared polymorphic. The AFLP-based dendrogram showed more duplication groups than the farmers' characterisation method suggesting that farmers overestimate the genetic diversity. The correlation between the two methods was only weak. Yet, the comparison between the AFLP-based and farmers-based characterisation methods showed that some aspects such as absence of clear regional clusters and clustering of clones with various prefixes to a single group corresponded well. Duplications in the clones identified by both methods may be safely removed from a conservation programme. Variation in farmers' skill in discriminating between clones may suggest that the areas where the people's culture is closely associated with the crop, should receive high priority for collecting clones or serving as sites for in situ conservation. [SEP] Justify:

Output: The text contrasts the claim by discussing the preservation of genetic diversity in the indigenous enset (Ensete ventricosum) farming system in Ethiopia. It highlights that there is significant variability among enset clones and emphasizes the importance of using existing genetic diversity to enhance productivity. It suggests that certain indigenous cultivation methods promote genetic diversity rather than decrease it, which directly counters the claim that genetic modification always reduces crop 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: Our findings will contribute to further study of the physiological functions of EDR1-like genes in tobacco. mildew, an important fungal disease in agriculture and horticulture, is caused by ascomycetes of the order Erysiphales (Dean et al. , 2012; Glawe, 2008). Worldwide, powdery mildews colonize a wide variety of plant species—over 650 monocots and over 9,000 dicots (Schulzelefert & Vogel, 2000). They cause significant harvest losses in crops such as wheat, barley and tomato (Dean et al. , 2012), ornamental plants such as roses (Horst, Kawamoto & Porter, 1992; Linde et al. , 2006; Palmer & Henneberry, 1960) and fruits like grapevine (Donald et al. , 2002). The current universal method for controlling powdery mildew is by application of fungicide, but this can have a serious environmental impact. Research has detected high concentrations of residual fungicides in food and crops. These chemicals can reach human cells via the food chain and negatively affect cellular metabolism (Pirozzi et al. , 2016; Yang et al. , 2018). Thus, identifying genes with functions in fundamental plant defense may have potential for reducing production losses caused by powdery mildew. In 1998, Frye and Innes found an Arabidopsis mutant that displayed 1 (EDR1) to Pseudomonas syringae and Golovinomyces cichoracearum (formerly named Erysiphe cichoracearum) (Frye & Innes, 1998). Functional studies of EDR1 genes have been reported in Arabidopsis, wheat, rice and tomato (Frye & Innes, 1998; Gao et al. , 2015; Shen et al. , 2011; Zhang et al. , 2017). 1 encodes Raf-like mitogen-activated protein kinase. Loss-of-function mutants of EDR1 are resistant to the biotrophic pathogens G. cichoracearum (Frye & Innes, 1998) and Hyaloperonospora arabidopsidis (van Hulten et al. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the physiological functions of EDR1-like genes in tobacco and the impact of powdery mildew on agriculture, without mentioning hydrogen bonding or the secondary structure of proteins. Thus, the text does not provide any support or opposition to the claim.


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: In a nutshell, AI and early disease detection are a powerful duo, but there are puzzles to solve. While we are moving forward, we need to think about data, understand AI’s decisions, and make sure it is helping everyone fairly. With AI by our side, we are stepping into a future where diseases might not surprise us anymore—and that is a thrilling thought. Unlocking the Magic of Deep Learning: Deep learning is like the brain of AI—it is the part that learns and understands patterns. Within deep learning, there are some special techniques that are superheroes in cancer diagnosis. Networks (CNNs): Imagine CNNs as art critics for images. They break down the image into tiny parts, analyze them, and then put everything back together. This helps them recognize complex patterns in medical images, such as x rays or Computed tomography (CT) scans. They are especially good at spotting shapes and structures that matter in cancer diagnosis. Networks (RNNs): RNNs are like the memory masters of AI. They can analyze sequences of data, which is helpful for tasks such as reading patient records over time. In cancer diagnosis, RNNs can follow a patient’s history and catch any changes that might indicate cancer development. Advantages in Medical World: These techniques have superpowers in the medical world. CNNs can find tiny hints of tumors in scans, even if they are buried in complex images. RNNs can spot trends in patients’ data over time—something human eyes might miss. Matter? In cancer diagnosis, accuracy is everything. Deep learning techniques boost that accuracy. They can pinpoint subtle signs of cancer earlier than ever before. When cancer is caught early, treatment can start sooner, leading to better outcomes. So, think of these deep learning techniques as AI’s secret weapons. They are trained to see what the human eye cannot, making them invaluable allies in the fight against cancer. A. Early detection of breast cancer using AI: A breakthrough in mammography analysis Breast cancer remains a significant global health concern, with early detection playing a pivotal role in improving patient outcomes. Advances in artificial intelligence (AI), particularly the use of convolutional neural networks (CNNs), have opened up new avenues for accurate and timely breast cancer detection through mammography analysis. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI and deep learning techniques are being utilized for early cancer detection. It explains that convolutional neural networks (CNNs) and recurrent neural networks (RNNs) specifically aid in recognizing patterns in medical images and analyzing patient data over time to catch early signs of diseases such as cancer. It emphasizes the importance of early detection and how these technologies enhance accuracy in diagnosis, thus supporting the development of AI for detecting early signs of diseases.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: Since the last three decades, however, because of population pressure, recurrent drought and diseases there has been degradation of natural resources and, thus, the system failed to sustain the population. In the study, described in this thesis, the indigenous enset production methods, farm-based enset biodiversity and the plant characteristics and environmental factors influencing productivity were analysed to identify yield potentials and constraints in Sidama, Wolaita and Hadiya. The ultimate goal was to develop improved agronomic practices and enhance the use of the existing genetic diversity to reduce the gap between the actual yield and yield potential. Some indigenous cultivation methods vary among regions: initiation of suckers, frequency of transplanting, leaf pruning and planting patterns. Morphologically diverse enset clones were identified in Sidama (52), Wolaita (55) and Hadiya (59). Among 146 clones, a total of 180 AFLP fragments was scored of which 104 (58%) appeared polymorphic. The AFLP-based dendrogram showed more duplication groups than the farmers' characterisation method suggesting that farmers overestimate the genetic diversity. The correlation between the two methods was only weak. Yet, the comparison between the AFLP-based and farmers-based characterisation methods showed that some aspects such as absence of clear regional clusters and clustering of clones with various prefixes to a single group corresponded well. Duplications in the clones identified by both methods may be safely removed from a conservation programme. Variation in farmers' skill in discriminating between clones may suggest that the areas where the people's culture is closely associated with the crop, should receive high priority for collecting clones or serving as sites for in situ conservation. Plant height and LAI of different clones increased faster at Awassa or Areka than at Sidama because of a higher leaf appearance rate associated with temperatures being closer to the optimum. This led to higher early interception of photosynthetically active radiation and higher dry matter production. The mean extinction coefficient was between 0. 56-0. 91 and radiation use efficiency (RUE) ranged from 1. 43-2. 67 g MJ -1. Yield potential differences between clones existed, mainly because of differences in RUE. The average ratio actual yield : yield potential (0. 24) suggest that much can be done to reduce the yield gap. [SEP] Justify:

Output: The text contrasts the claim by discussing the analysis of indigenous enset production methods and the identification of a significant number of morphologically diverse enset clones in regions such as Sidama, Wolaita, and Hadiya. This indicates that genetic variation and diversity can exist alongside improved practices, which counters the assertion that genetic modification inherently reduces crop 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: The N-terminal regulatory domain of EDR1 could interact with MKK4 and MKK5 to negatively regulate the MKK4/MKK5–MPK3/MPK6 kinase cascade pathway (Zhao et al. , 2014). It was also reported that EDR1 could act as a suppressor of disease resistance and programmed cell death (for both abiotic and biotic stresses) by adjusting signal processing through the hormone-mediated pathways of salicylic acid, ethylene and abscisic acid (Frye & Innes, 1998; Tang, Christiansen & Innes, 2005), and as a positive regulator of expression of plant defensins (Hiruma et al. , 2011). Although the EDR1 pathway is highly conserved in crop plants (Frye, Tang & Innes, 2001), whether one EDR1 plays a main role or several EDR1 homologs work together to function as negative regulators of plant defense needs further detailed analysis for a specific species. For example, in tomato, RNAi of the two EDR1 homologs separately did not produce significant resistance to O. neolycopersici (Gao et al. , 2015). It also revealed that knockout of three EDR1 homologs in wheat did not produce complete resistance (Zhang et al. , 2017). Thus, complete and systematic study of the EDR1-like genes is needed and will help to find potential target genes for plant breeding. This study involved the first systematic identification and analysis of EDR1-like genes in plants. We aim to provide useful information for further exploring the physiological function of EDR1-like genes in tobacco and other plant species. Materials and of the EDR1-like gene family The genome, gene and protein sequences of Arabidopsis, tomato and rice were downloaded from PlantGDB ( and those of tobacco from the Network ( To identify EDR1-like genes in the above-mentioned species, two EDR1 proteins from Arabidopsis (GenBank accession: ABR45974. 1) and wheat (GenBank accession: AAU89661. 2) were used as the query subjects in a reciprocal () analysis with e-values < 1E-50. Then, the online software SMART ( was used to identify the predicted EDR1-like protein domains. [SEP] Justify:

Output: The text is irrelevant to the claim regarding hydrogen bonding and protein structure as it discusses EDR1 proteins in plants, their interactions, and related pathways without mentioning hydrogen bonding or secondary protein structure, 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: In this discussion, we delve into the innovative approach of using AI techniques to identify breast cancer at its early stages, revolutionizing the way we approach this critical health issue. of Early Detection: Early detection is crucial in the fight against breast cancer. When detected early, the chances of successful treatment and survival increase significantly. Mammography, an x-ray imaging technique, has been a cornerstone in breast cancer screening for decades. However, interpreting mammograms can be challenging due to the complexity of breast tissue patterns and the subtle nature of early-stage cancers. This is where AI steps in to enhance accuracy and efficiency. Networks (CNNs): AI, specifically CNNs, has shown remarkable promise in improving mammography analysis. CNNs are a type of deep learning algorithm inspired by the human visual system. These networks excel in recognizing intricate patterns in images, making them ideally suited for identifying the intricate features indicative of early breast cancer. : CNNs process mammograms with incredible precision, identifying abnormalities that might be missed by the human eye. The process involves training the CNN using a vast dataset of mammograms, allowing it to learn and differentiate between normal and abnormal breast tissue patterns. Once trained, the CNN can accurately identify potential cancerous lesions by detecting irregularities in the mammogram images. Benefits of AI-Detection: The integration of AI in early breast cancer detection brings several notable benefits. First, AI can reduce false negatives and false positives, minimizing unnecessary anxiety and interventions for patients. Second, AI systems can assist radiologists by highlighting suspicious areas, allowing for a more focused and efficient review of mammograms. This collaborative approach improves diagnostic accuracy and speeds up the interpretation process. Challenges and Progress: While AI shows great potential, there are challenges to address. Ensuring that AI models are well-trained and unbiased is crucial. Additionally, the integration of AI into clinical practice requires thorough validation and regulatory approval to guarantee patient safety. Future Prospects: The future of AI in breast cancer detection is bright. Continued research is expected to refine existing AI algorithms, making them even more accurate and robust. Moreover, the combination of AI with other imaging modalities and genetic information holds promise for more comprehensive early detection strategies. The marriage of AI and mammography analysis represents a transformative leap in early breast cancer detection. The ability of convolutional neural networks to unravel the intricacies of mammograms offers hope for improved patient outcomes and reduced mortality rates. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of AI techniques specifically in the early detection of breast cancer. It highlights how AI, particularly through convolutional neural networks (CNNs), enhances the accuracy and efficiency of mammography analysis, which is crucial for identifying early signs of cancer. This aligns with the claim that AI is being developed to help detect early signs of diseases.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: - Met samenvatting in het Engels en het WageningenPY - 2002/4/22Y1 - 2002/4/22N2 - Keywords : Enset, staple, indigenous knowledge, genetic diversity, AFLP, characterisation, conservation, yield potential, transplanting, leaf pruning, fermentation, 'kocho', food security The indigenous enset-farming complex of the south and southwestern highlands of Ethiopia has supported a higher population density than any other farming system. Enset ( Ensete ventricosum (Welw. ) Cheesman) has been cultivated as (co-)staple food for about 7-10 million people. Since the last three decades, however, because of population pressure, recurrent drought and diseases there has been degradation of natural resources and, thus, the system failed to sustain the population. In the study, described in this thesis, the indigenous enset production methods, farm-based enset biodiversity and the plant characteristics and environmental factors influencing productivity were analysed to identify yield potentials and constraints in Sidama, Wolaita and Hadiya. The ultimate goal was to develop improved agronomic practices and enhance the use of the existing genetic diversity to reduce the gap between the actual yield and yield potential. Some indigenous cultivation methods vary among regions: initiation of suckers, frequency of transplanting, leaf pruning and planting patterns. Morphologically diverse enset clones were identified in Sidama (52), Wolaita (55) and Hadiya (59). Among 146 clones, a total of 180 AFLP fragments was scored of which 104 (58%) appeared polymorphic. The AFLP-based dendrogram showed more duplication groups than the farmers' characterisation method suggesting that farmers overestimate the genetic diversity. The correlation between the two methods was only weak. Yet, the comparison between the AFLP-based and farmers-based characterisation methods showed that some aspects such as absence of clear regional clusters and clustering of clones with various prefixes to a single group corresponded well. Duplications in the clones identified by both methods may be safely removed from a conservation programme. Variation in farmers' skill in discriminating between clones may suggest that the areas where the people's culture is closely associated with the crop, should receive high priority for collecting clones or serving as sites for in situ conservation. [SEP] Justify:

Output: The text contrasts the claim by highlighting that the indigenous enset production methods and the cultivation of diverse enset clones in Ethiopia reveal a significant level of genetic diversity. It discusses the analysis of various enset clones (totaling 146) and suggests that there is substantial polymorphism within these clones, as indicated by the 58% polymorphic AFLP fragments. This supports the idea that genetic diversity can exist in agricultural systems, contradicting the notion that genetic modification universally reduces crop 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: Conserved EDR1 domains and kinase domains within the acquired EDR1 sequences were confirmed by searching NCBI’s conserved domain database ( Phylogenetic analysis and conserved domain detection Phylogenetic trees were constructed in MEGA Version 7. 0 (Kumar, Stecher & Tamura, 2016), using the Neighbor-Joining (NJ) method (Saitou, 1987) with parameters of pairwise gap deletion and 1,000 bootstraps. Multiple sequence alignment of the amino acid sequences of EDR1-like proteins was performed using the ClustalX program (version 1. 83) (Thompson et al. , 1997) and GeneDoc. Maximization for program (MEME, (Bailey et al. , 2006) was used to predict the conserved motif of EDR1-like proteins in N. tabacum. Gene structure and bioinformatic analysis of tobacco EDR1-like genes The genomic structure of the EDR1-like gene family was analyzed using GSDS 2. 0 ( (Hu et al. , 2015). Protparam ( was used to analyze the basic physical and chemical properties of tobacco EDR1-like genes. Subcellular localization of the EDR1-like genes was determined using online software Plant-mPLoc ( (Chou & Shen, 2008, 2010a, 2010b). The nuclear localization signals (NLS) sequences were predicted by cNLS Mapper ( (Kosugi et al. , 2009a, 2009b). Transmembrane helices were predicted using ( (Viklund & Elofsson, 2008). The three-dimensional (3D) structures were predicted using the online I-program ( (Zhang, 2008). The confidence score (C-score) was used to estimate the model’s global accuracy in I-(Yang & Zhang, 2015), and high C-score indicates a high-quality structure prediction (Roy, Kucukural & Zhang, 2010). The model with highest C-score in all five prediction models was further used to construct 3D model of the target genes. The constructed 3D model was examined and visualized using Chimera 1. [SEP] Justify:

Output: The text does not address hydrogen bonding or the secondary structure of proteins, including alpha-helices. Instead, it focuses on bioinformatics methods, domain analysis, and the prediction of protein structures without mentioning their structural elements or the role of hydrogen bonds. Therefore, it is deemed irrelevant to the claim.


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: By leveraging AI’s analytical prowess, healthcare is advancing toward more efficient, accurate, and timely breast cancer detection, thereby making significant strides in the battle against this formidable disease. Tables I–III provide a comparative overview of key parameters across the selected research papers, enabling similarities, differences, and trends among them. . Data and methods comparison. Paper title. . Dataset size and. . . and authors. Dataset used. characteristics. AI techniques. Imaging modality. Esteva et al. (2017)1 Diverse medical images 129 450 clinical images of skin lesions Deep learning Medical images Kermany et al. (2018)2 Diverse medical images 128 175 retinal images from 86 129 patients Deep learning Medical images Shen et al. (2017)3 Medical images Various medical image datasets, sizes not specified Deep learning Medical images Nam et al. (2018)4 Chest radiographs 5232 chest radiographs with malignant pulmonary nodules Deep learning et al. (2013)5 Diverse medical images -10, STL-10 datasets Machine learning Medical images et al. (2017)6 Breast histopathology 2534 lymph node images from breast cancer patients Deep learning Histopathology images Liu et al. (2017)7 Pathology images Camelyon16 and Camelyon17 datasets with gigapixel pathology images Deep learning Pathology images Komura and Ishikawa (2018)8 Histopathology images Publicly available breast cancer histopathology images Machine learning Histopathology images Wang et al. (2020)9 Lung pathology images Lung cancer pathology image dataset with 1276 images AI algorithms Pathology images Arvind et al. (2020)10 Breast histopathology 756 breast cancer nodal images from 189 patients Artificial intelligence Histopathology images Araújo and Aresta (2017)11 Breast cancer histology images Break his dataset with 7909 breast cancer histology images Convolutional neural networks (CNN) Histology images Yala et al. (2019)12 Mammography images Mammography images from 86 297 women Deep learning Mammography images Lotter, W. , Diab, A. R. , Haslam, B. et al. (2019)13 Digital breast tomosynthesis images DBT datasets with digital breast tomosynthesis images Deep learning Digital breast tomosynthesis images Zhang et al. [SEP] Justify:

Output: The text corroborates the claim by discussing how AI is being utilized to enhance breast cancer detection. It emphasizes the role of AI's analytical abilities in advancing healthcare efficiency and accuracy, particularly in detecting signs of breast cancer early in patients. This aligns directly with the claim regarding the development of AI technologies to detect diseases.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: It altered the traditional cropping pattern against pulses with decades of emphasis on research as well as production in superior cereals coupled with a near total neglect of the rainfed areas, pulses were driven out of not only the irrigated areas, but also the rainfed farming and were relegated to the marginal lands. Further more, the weak infrastructural support for input supply, credit and marketing in the rainfed areas, the traditional home of pulses, also adversely affected the prospects for pulses. A vicious circle of low input use and low output thus got built in. Lack of processing and marketing facilities in pulses also contributed its share to the woes of pulse growers. The increasing shortage of pulses, resulted in the need for more and more import which, in turn has dampened the speed of rejuvenation of pulses sub-sector of agriculture sector. The net per capita availability of pulses has fallen from 60. 7 (1950-51) to 33. 4 g (1991-92). On the contrary, the daily per capita availability of cereals has registered an increase from 334. 2 (1950-51) to 443 g (1991-92). It leads to increase in the pulses/cereal ratio from 0. 18 to 0. 08 g i. e. , by 55 per cent. This is despite the increases of 24. 36 per cent in area; 61. 71 per cent in production and 29. 93 per cent in production. It is significant to note that almost all these increases had taken place during 1950-51 to 1960-61. The area, production and yield of pulses have either stagnated or declined since then. This stagnation of pulse production and productivity should be viewed seriously not only from the point of security and quality of food for our people, especially the poor and the rural population, but also from the soaring import bill, imbalance and distribution in the cropping pattern and resource allocation among various sub-sectors of agriculture. 5 in Greengram [Vigna radiata (L. ) Walp. ] Thangavelu, P. and The nature and magnitude of genetic variability present in the genetic stock afford the plant breeder, information to formulate a purposeful breeding programme for crop improvement. The association and the influence of yield components with yield, their heritability and genetic advance would characterise the various steps in the breeding programme in achieving the aim of the breeder. [SEP] Justify:

Output: The text contrasts the claim by discussing the negative effects of neglecting pulses in favor of superior cereals due to alterations in cropping patterns. It highlights that the focus on cereals has diminished the cultivation of pulses, resulting in a decrease in pulses' availability and a corresponding imbalance in crop diversity. This implies that while certain crops may have increased, the overall crop diversity, including pulses, has been adversely affected, contradicting the claim that genetic modification reduces diversity across all instances.


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: 2 ( Plant materials and stress treatments Common tobacco (Nicotiana tabacum cv HHDJY) seeds were surface-sterilized by 10% NaClO, and sown on sterilized plates with MS solid culture medium (PhytoTechnology Laboratories®, ) in a greenhouse maintained at 25 °C and with aday/night cycle of 16/8 h. For powdery mildew infection, a strain of G. orontii was maintained on 3-month-old tobacco in a greenhouse. Freshly sporulating leaves of heavily infected tobacco were washed in sterile double distilled water (ddH2O), and the collected conidiospores were used immediately. Plants were inoculated by spraying with the inoculum suspension (about 4 × 104 to 5 × 104 spores ml−1). The 3-month-old tobacco leaves were inoculated with G. orontii to give approximately 20–25 spores per cm2 and sampled after 0, 1, 2, 12 and 24 hours post inoculation (hpi). Samples collected at 0 h were used as controls. For tissue/organ expression profiles, the root, stem and leaf were sampled on two-month-old seedlings. Flowers were taken from flowering plants and capsules were obtained during the late seed-producing period. All selected tissues and organs were stored at −80 °C. Total RNA isolation and quantitative real-time (qRT)-PCR expression analysis (TransGen Biotech, Beijing, China) was used to extract total RNA according to the manufacturer’s protocol. The first-strand cDNA templates were synthesized from two μg of total RNA, according to the manufacturer’s protocol (). The reverse transcription reaction was incubated at 42 °C for 65 min in a total volume of 26 μl. The reverse transcription products were diluted five times with sterile ddH2O. Specific primers were designed by Premier 5. 0 according to tobacco EDR1-like gene sequences (Table S1). Due to the high sequence similarity between NtEDR1-1A and NtEDR1-1B, it was difficult to design specific primers for qRT-PCR to measure the expression of NtEDR1-1A and NtEDR1-1B separately. [SEP] Justify:

Output: The text provides detailed information about a specific experimental procedure involving tobacco plants and the quantitative real-time PCR method, but it does not mention hydrogen bonding or the secondary structure of proteins like alpha-helices. Thus, it is irrelevant to the claim.


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)14 Magnetic resonance imaging (MRI) 80 MRI scans from 40 healthy women Deep learning Magnetic resonance imaging (MRI) Lo et al. (2019)15 Breast MRI images Breast MRI images from 2907 examinations Machine learning Breast MRI images Ribli et al. (2018)16 Mammograms DDSM dataset with 2620 mammograms Deep learning title. . Dataset size and. . . and authors. Dataset used. characteristics. AI techniques. Imaging modality. Esteva et al. (2017)1 Diverse medical images 129 450 clinical images of skin lesions Deep learning Medical images Kermany et al. (2018)2 Diverse medical images 128 175 retinal images from 86 129 patients Deep learning Medical images Shen et al. (2017)3 Medical images Various medical image datasets, sizes not specified Deep learning Medical images Nam et al. (2018)4 Chest radiographs 5232 chest radiographs with malignant pulmonary nodules Deep learning et al. (2013)5 Diverse medical images -10, STL-10 datasets Machine learning Medical images et al. (2017)6 Breast histopathology 2534 lymph node images from breast cancer patients Deep learning Histopathology images Liu et al. (2017)7 Pathology images Camelyon16 and Camelyon17 datasets with gigapixel pathology images Deep learning Pathology images Komura and Ishikawa (2018)8 Histopathology images Publicly available breast cancer histopathology images Machine learning Histopathology images Wang et al. (2020)9 Lung pathology images Lung cancer pathology image dataset with 1276 images AI algorithms Pathology images Arvind et al. (2020)10 Breast histopathology 756 breast cancer nodal images from 189 patients Artificial intelligence Histopathology images Araújo and Aresta (2017)11 Breast cancer histology images Break his dataset with 7909 breast cancer histology images Convolutional neural networks (CNN) Histology images Yala et al. (2019)12 Mammography images Mammography images from 86 297 women Deep learning Mammography images Lotter, W. , Diab, A. R. , Haslam, B. et al. (2019)13 Digital breast tomosynthesis images DBT datasets with digital breast tomosynthesis images Deep learning Digital breast tomosynthesis images Zhang et al. [SEP] Justify:

Output: The text corroborates the claim as it discusses various datasets and applications of AI techniques, specifically deep learning and machine learning, in analyzing medical images such as mammograms, breast histopathology, and lung cancer pathology. This indicates a focus on using AI for disease detection, particularly in cancer, which aligns with the claim about AI being developed for early signs of diseases.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: Studies on these lines in various crops have hastened the realization of crop improvement in general. India grows a variety of pulse crop which probably no other country in the world grows. These are multiple utility, energy high crops as they provide protein for human consumption, green nutritious fodder and enrich soil fertility through biological nitrogen fixation. Vigna radiata (L. ) Wilczek, commonly known as greengram or mungbean, is the most widely distributed of the six cultivated species. The current breeding approaches to yield improvement in greengram are based on the recognition that this crop have lost a large part of their genetic variability in the process of adaptation to stress environment. This variability must now be regenerated. The foremost need for this purpose is the collection of germplasm from different parts of the world and the organisation of a very intensive hybridization programme, using diverse genetic stocks as parental lines. Selection of parents for hybridization based merely on geographic diversity can no more be a suitable criterion. The importance of genetic diversity has long been appreciated by crop specialists. Such an analysis will eventually help to choose desirable parents for hybridization and evolve superior genotypes. 67 - 74 (8 Pages) USD34. 99 Add to cart nature and magnitude of genetic variability present in the genetic stock afford the plant breeder, information to formulate a purposeful breeding programme for crop improvement. The association and the influence of yield components with yield, their heritability and genetic advance would characterise the various steps in the breeding programme in achieving the aim of the breeder. Studies on these lines in various crops have hastened the realization of crop improvement in general. India grows a variety of pulse crop which probably no other country in the world grows. These are multiple utility, energy high crops as they provide protein for human consumption, green nutritious fodder and enrich soil fertility through biological nitrogen fixation. Vigna radiata (L. ) Wilczek, commonly known as greengram or mungbean, is the most widely distributed of the six cultivated species. The current breeding approaches to yield improvement in greengram are based on the recognition that this crop have lost a large part of their genetic variability in the process of adaptation to stress environment. This variability must now be regenerated. The foremost need for this purpose is the collection of germplasm from different parts of the world and the organisation of a very intensive hybridization programme, using diverse genetic stocks as parental lines. [SEP] Justify:

Output: The text contrasts the claim by highlighting the importance of genetic diversity in crop improvement. It discusses the loss of genetic variability in crops like greengram due to adaptation to stress environments and emphasizes the need to regenerate this variability through the collection of germplasm and hybridization programs. This suggests that genetic modification does not necessarily lead to a reduction in crop diversity, but rather, it can serve to enhance and maintain 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: Therefore, one pair of primers targeting both NtEDR1-1A and NtEDR1-1B was synthesized to simultaneously check the expression levels of NtEDR1-1A and NtEDR1-1B. Each PCR reaction was mixed with 10 μl of SYBR Green (TaKaRa, Dalian, China), 6. 4 μl of ddH2O, two μl of synthesized cDNA product and 0. 8 μl of each primer (50 μM). Relative gene expression level was analyzed according to the 2−ΔΔCt method (Livak & Schmittgen, 2001). Tobacco EF-1a gene (GenBank: D63396. 1) was used as the internal reference. The real-time PCR analyses were performed using a qTOWER2. 2 real-time PCR system (), which was programmed as follows: initial 95 °C denaturation step for 3 min, followed by 40 cycles of denaturation at 95 °C for 10 s and annealing/extension at 60 °C for 1 min (Zhang et al. , 2013). Every experiment was conducted with three biological replicates. All data were expressed as mean ± SEM and analyzed by 5. 0 software. of the EDR1-like gene family in tobacco and three other plant species As there was no previous systematic identification and analysis of EDR1-like genes in plants, we identified the EDR1-like genes in tobacco and compared them with other model species. In total, we obtained 19, 12, 8 and 14 EDR1-like genes in tobacco, Arabidopsis, tomato and rice, respectively (Table S2). In addition, the basic sequence information of tobacco EDR1-like genes was analyzed, including the location of the EDR1 regulatory region and the kinase region, the number of amino acids, isoelectric points, molecular weight and predicted location (Table 1). Table 1: Basic information of EDR1-like family proteins in tobacco. location(s) Amino acid Isoelectronic point (pI) Molecular weight (kDa) Functional domains (5′–3′) EDR1 domain location Pkinase domain location NtEDR1-1A Nucleus 986 6. 11 107. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the experimental methodology for analyzing gene expression levels in tobacco and other plant species, without any mention of protein structure, hydrogen bonding, or alpha-helices. Therefore, it does not provide any support or opposition to the claim.


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)14 Magnetic resonance imaging (MRI) 80 MRI scans from 40 healthy women Deep learning Magnetic resonance imaging (MRI) Lo et al. (2019)15 Breast MRI images Breast MRI images from 2907 examinations Machine learning Breast MRI images Ribli et al. (2018)16 Mammograms DDSM dataset with 2620 mammograms Deep learning. Performance metrics comparison. Paper title and authors. Sensitivity (%). Specificity (%). Accuracy (%). F1 score. AUC. Esteva et al. (2017)1 72. 1 89. 1 ⋯ ⋯ ⋯ Kermany et al. (2018)2 81. 1 68. 4 74. 5 ⋯ ⋯ Nam et al. (2018)4 90. 2 91. 6 91. 0 ⋯ ⋯ et al. (2017)6 88. 0 78. 4 85. 5 ⋯ ⋯ Arvind, S. et al. (2020) 74. 7 72. 0 ⋯ ⋯ ⋯ Araújo and Aresta (2017)11 88. 9 81. 6 84. 3 0. 78 ⋯ Yala et al. (2019)12 94. 1 48. 3 80. 2 ⋯ 0. 91 Lotter et al. (2019)13 91. 1 88. 1 ⋯ ⋯ ⋯ Zhang et al. (2017)14 ⋯ ⋯ 98. 4 ⋯ ⋯ Lo et al. (2019)15 93. 6 77. 8 ⋯ ⋯ ⋯ Ribli et al. (2018)16 87. 3 88. 5 ⋯ ⋯ ⋯ Paper title and authors. Sensitivity (%). Specificity (%). Accuracy (%). F1 score. AUC. Esteva et al. (2017)1 72. 1 89. 1 ⋯ ⋯ ⋯ Kermany et al. (2018)2 81. 1 68. 4 74. 5 ⋯ ⋯ Nam et al. (2018)4 90. 2 91. 6 91. 0 ⋯ ⋯ et al. (2017)6 88. 0 78. 4 85. 5 ⋯ ⋯ Arvind, S. et al. (2020) 74. 7 72. [SEP] Justify:

Output: The text corroborates the claim as it discusses various studies involving the application of AI and machine learning techniques, such as deep learning and performance metrics for MRI scans and mammograms. These studies aim to detect conditions like breast cancer through advanced imaging techniques, indicating a clear link to the development of AI for early disease detection.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: Selection of parents for hybridization based merely on geographic diversity can no more be a suitable criterion. The importance of genetic diversity has long been appreciated by crop specialists. Such an analysis will eventually help to choose desirable parents for hybridization and evolve superior genotypes. 6 in Bhendi [Abelmoschus esculentus (L. ) Moench] Senthilkumar, N. and K. recent years, we are just experiencing a surplus production in cereals and oil seeds leading to self-sufficiency through green revolution and yellow revolution. However, shortage in the production of vegetables has drawn the attention for increasing the cultivation of vegetables to provide food and nutrition security. Vegetables are being cultivated over an area of 49 million hectares with the production potential of 487 million tonnes in the world. This accounts for 2. 85 per cent of total cropped area in the world. India is the second largest producer of vegetables next to China, sharing nearly 12 per cent of the total world output (Joshi and Shukla, 1997). Vegetables occupy nearly 8. 02 million hectares with the production potential of 133. 10 million tonnes in the year 2009 in India (Anonymous, 2009). Bhendi (Abelmoschus esculentus (L. ) Moench) is an important member of family malvaceae and commonly known as okra or ladyfinger in India. It is one of the most ancient and traditional vegetable crops grown in tropical and subtropical regions of the world (Martin and Ruberte, 1978). Bhendi is originated from tropical Africa with 2n = 8x = 72 or 130 or 144 chromosomes and is an allopolyploidy in nature (Joshi and Hardas, 1956; Suresh Babu, 1987). Martin (1982) reported that it behaves as a diploid. Under the genus Abelmoschus, there are 30 species in the old world and four in the new world (Joshi et al. , 1974). Out of them, Abelmoschus esculentus, is the only species known to be cultivated. Bhendi is being raised in India in an area of 0. 44 million hectares with an average production of 4. 52 mt with the productivity of 10. 27 t/ha (Anonymous, 2009). 75 - 84 (10 Pages) USD34. [SEP] Justify:

Output: The text contrasts the claim by emphasizing the importance of genetic diversity in crop breeding. It states that genetic diversity is crucial for selecting suitable parents for hybridization, indicating that genetic modification does not necessarily lead to reduced crop diversity. Instead, the text implies that genetic diversity can be maintained and even enhanced through careful selection of genetic traits in hybridization processes.


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: 85 144–343 705–956 NtEDR1-1B Nucleus 1,047 6. 20 114. 79 144–374 735–921 NtEDR1-2 Nucleus 1,027 5. 52 112. 19 155–358 738–990 NtEDR1-3 Nucleus 1,027 5. 64 112. 42 155–358 738–990 NtEDR1-4 Nucleus 1,032 5. 30 112. 83 146–350 754–1,006 NtEDR1-5 Nucleus 895 5. 73 99. 09 138–216, 213–313 625–877 NtEDR1-6 Chloroplast, Nucleus 780 – – 1–83, 92–219 501–762 NtEDR1-7 Nucleus 846 5. 74 93. 33 196–423 570–824 NtEDR1-8 Nucleus 846 5. 74 93. 33 196–423 570–824 NtEDR1-9 Nucleus 848 5. 68 93. 40 200–407 572–826 NtEDR1-10 Nucleus 776 5. 38 85. 15 146–353 525–774 NtEDR1-11 Cell membrane 790 5. 83 89. 26 55–254 534–783 NtEDR1-12 Nucleus 891 6. 25 99. 48 290–497 683–890 NtEDR1-13 Nucleus 1,240 7. 56 138. 74 482–681 984–1,232 NtEDR1-14 Cell membrane 814 6. 04 91. 61 56–255 558–806 NtEDR1-15 Nucleus 743 5. 08 81. 32 146–53 525–633, 619–741 NtEDR1-16 Nucleus 876 6. 16 98. 03 288–495 681–875 NtEDR1-17 Cell membrane Nucleus 648 6. 36 73. 67 55–166 392–641 NtEDR1-18 Nucleus 892 5. 02 97. 15 146–350 747–858 DOI: 10. 7717/peerj. [SEP] Justify:

Output: The text does not address the claim about hydrogen bonding and its role in the secondary structure of proteins. It provides numerical data related to different genes and their characteristics without any mention of proteins or 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: 0 ⋯ ⋯ ⋯ Araújo and Aresta (2017)11 88. 9 81. 6 84. 3 0. 78 ⋯ Yala et al. (2019)12 94. 1 48. 3 80. 2 ⋯ 0. 91 Lotter et al. (2019)13 91. 1 88. 1 ⋯ ⋯ ⋯ Zhang et al. (2017)14 ⋯ ⋯ 98. 4 ⋯ ⋯ Lo et al. (2019)15 93. 6 77. 8 ⋯ ⋯ ⋯ Ribli et al. (2018)16 87. 3 88. 5 ⋯ ⋯ ⋯. Clinical implications and integration. Paper title and authors. Clinical integration. Limitations. Robustness. Esteva et al. (2017)1 Dermatologist assistance Not standalone diagnosis Robust across lesions Kermany et al. (2018)2 Diagnosis assistance Diverse image sources Generalization potential Shen et al. (2017)3 Improved diagnostics Interpretability challenge ⋯ Nam et al. (2018)4 Radiologist support Limited interpretability Robust across radiographs Goodfellow et al. (2013)5 ⋯ Challenges in representation ⋯ et al. (2017)6 Pathologist support Varying image quality Robust for lymph node detection Liu et al. (2017)7 Pathologist support Gigapixel images ⋯ Komura and Ishikawa (2018)8 Pathology analysis ⋯ Robust for histopathology Wang et al. (2020)9 Pathology analysis Limited clinical impact ⋯ Arvind et al. (2020)10 Pathologist support Black box concern ⋯ Araújo and Aresta (2017)11 Improved histology analysis Limited to histology images Robust for histology Yala et al. (2019)12 Enhanced risk prediction Limited to mammography Robust for risk prediction Lotter et al. (2019)13 Improved tomosynthesis diagnosis Limited to digital breast tomosynthesis Robust for tomosynthesis Zhang et al. (2017)14 Accurate breast tissue analysis Limited to MRI Robust for MRI analysis Lo, C. M. et al. (2019)15 Enhanced MRI interpretation Limited to MRI Robust for MRI analysis Ribli et al. [SEP] Justify:

Output: The text corroborates the claim by referencing various studies and authors that discuss improvements in diagnostics, such as 'Enhanced risk prediction,' 'Improved diagnostics,' and specific applications in pathology and radiology that indicate how AI is being utilized in detecting medical conditions. Although it does not explicitly mention Alzheimer’s, the context of early disease detection aligns with the claim regarding early signs of diseases like cancer.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: 99 Add to cart palm is a monotypic species of the family Arecaceae, under the tribe Cocoideae. It is considered as an important commercial crop in more than 18 countries in the world. The palm provides food, drink, shelter and also supplies raw materials for a large number of industries. Every part of the palm is utilized for some economic purpose or another. Hence, it is referred to as ‘Tree of Wealth” or the “Tree of Life” (Satyabalan, 1993). It is native of Asia. Genetic diversity has become one of the keywords of scientists who are concerned about the sustainable management of different tree crops. Genetic diversity plays an important role in tree breeding, because hybrids between populations of diverse origin, generally display a greater heterosis than between closely related parents (Zobel and Talbert, 1986). The present study was formulated to identify divergent parents for future breeding programme. Two hundred and fifty genotypes of coconut cultivar Chawghat green dwarf, distributed in the two districts of Kerala viz. , Kottayam and Pathanamthitta were evaluated for nut yield per plant during June-July, 2000. Seventy five genotypes, which had single tree nut yield of more than population mean ± 2 SE were selected as per the method outlined by Ledig (1974). These 75 genotypes were observed for 10 nut and endosperm characters in two seasons (years), during June-July 2000 and 2001. Each tree was considered as a single genotype. Thirty nuts from a single tree were collected and divided into three replications (Chauhan and Kanwar, 2001; Thirugnanakumar, 2006a, b). Observations were recorded on 10 nut and endosperm characters viz. ,1) oil content (%), cold percolation method; (Kartha and Sethi, 1957), 2) weight of unhusked nut (g), 3) weight of husked nut (g), 4) diameter (polar) of the husked nut (cm), 5) diameter (equatorial) of the husked nut (cm), 6) thickness of husk (cm), 7) husk: nut ratio, 8) thickness of meat (mm), 9) weight of kernel (g) and 10) weight of copra (g). [SEP] Justify:

Output: The text contrasts the claim by discussing the importance of genetic diversity in tree breeding, particularly emphasizing that hybrids from diverse origins exhibit greater heterosis. This suggests that genetic diversity is crucial and can be maintained or enhanced, countering the assertion that genetic modification always reduces crop 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: 5244/table-1 The NtEDR1 regulatory region was located at the N-terminal, whereas the kinase region was located at the C-terminal of the predicted protein sequence. The C-terminal kinase region shared a sequence identity of 61. 48%, and the N-terminal regulatory region had a relatively low identity of 40. 52% for the predicted protein sequences of tobacco EDR1-like genes (Fig. S1). The number of amino acids ranged from 648 (NtEDR1-17) to 1,240 (NtEDR1-13). The predicted molecular masses were in the range of 73. 67–138. 74 kDa and the isoelectric points were 5. 02–6. 36. Protein subcellular localization prediction is a key step in understanding the protein function and its protein network interaction pattern (Chou & Shen, 2007). In Arabidopsis, at least a fraction of the EDR1 protein has been shown to localize in the nucleus (Christiansen et al. , 2011). Our results showed 15 out of 19 tobacco EDR1-like proteins were predicted to be located in the nucleus (Table 1), but NtEDR1-11 and NtEDR1-14 were predicted to be located in the cell membrane. The other two tobacco EDR1-like members, NtEDR1-6 and NtEDR1-17, were shown to be located in the chloroplast or nucleus, and cell membrane or nucleus, respectively. The NLS predictions for most genes were consistent with those of subcellular localization, which showed that 13 NtEDR1-like proteins had NLS sequences (Table 1; Table S3). However, there were differences for another six NtEDR1-like proteins (Table 1; Table S3). This might be due to the different algorithms used for the two tools. Phylogenetic and analysis of EDR1-like genes Reports have shown that EDR1 homologs play a negative role in the defense response against powdery mildew in Arabidopsis (Frye & Innes, 1998) and wheat (Zhang et al. , 2017). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the localization and characteristics of tobacco EDR1-like proteins, their sequence identities, molecular masses, subcellular localization predictions, and their role in defense against powdery mildew. It does not mention hydrogen bonding or the secondary structure of proteins, particularly 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)16 Improved mammogram analysis Limited to mammography Robust for mammography Paper title and authors. Clinical integration. Limitations. Robustness. Esteva et al. (2017)1 Dermatologist assistance Not standalone diagnosis Robust across lesions Kermany et al. (2018)2 Diagnosis assistance Diverse image sources Generalization potential Shen et al. (2017)3 Improved diagnostics Interpretability challenge ⋯ Nam et al. (2018)4 Radiologist support Limited interpretability Robust across radiographs Goodfellow et al. (2013)5 ⋯ Challenges in representation ⋯ et al. (2017)6 Pathologist support Varying image quality Robust for lymph node detection Liu et al. (2017)7 Pathologist support Gigapixel images ⋯ Komura and Ishikawa (2018)8 Pathology analysis ⋯ Robust for histopathology Wang et al. (2020)9 Pathology analysis Limited clinical impact ⋯ Arvind et al. (2020)10 Pathologist support Black box concern ⋯ Araújo and Aresta (2017)11 Improved histology analysis Limited to histology images Robust for histology Yala et al. (2019)12 Enhanced risk prediction Limited to mammography Robust for risk prediction Lotter et al. (2019)13 Improved tomosynthesis diagnosis Limited to digital breast tomosynthesis Robust for tomosynthesis Zhang et al. (2017)14 Accurate breast tissue analysis Limited to MRI Robust for MRI analysis Lo, C. M. et al. (2019)15 Enhanced MRI interpretation Limited to MRI Robust for MRI analysis Ribli et al. (2018)16 Improved mammogram analysis Limited to mammography Robust for mammography B. Early detection of lung cancer using deep learning: Revolutionizing CT scan analysis Lung cancer is a critical global health concern, and early detection is pivotal for improving patient outcomes. Recent advancements in deep learning have opened new avenues for accurate and timely lung cancer detection through the analysis of CT scans. In this discussion, we delve into the innovative approach of using deep learning models to identify potential lung cancer nodules in CT images, transforming the landscape of lung cancer diagnosis. of Early Detection: Early detection of lung cancer is associated with better prognosis and improved survival rates. [SEP] Justify:

Output: The text corroborates the claim by discussing advancements in deep learning for early detection of lung cancer, stating that 'recent advancements in deep learning have opened new avenues for accurate and timely lung cancer detection through the analysis of CT scans.' This aligns with the claim regarding AI's development for early disease detection.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: Presently, India is home to more than 1000 mango cultivars differing in taste, aroma, size, fibre content and pulp characteristics. Though more than 1000 mango cultivars are reported, only 25-30 are of commercial importance. Most of the important cultivars are not amendable to hi-tech cultivation practices and do not meet the requirements of modern horticultural production systems like precocity in bearing, dwarfing, regularity in bearing with high yield, resistant to diseases, pests, physiological disorders and good keeping quality. Hence, breeding attempts to develop an ideal mango variety would be dwarf, regular bearer with medium size fruits (250-300g). Additionally, it should be highly tolerant to various fungal and bacterial diseases. A variety suitable for pickling is also gaining the attention of the breeders. An ideal pickling type should have high acidity, high fibre, good texture and characteristic raw mango flavour. The first and foremost step in developing an ideotype is to explore the vast diversity and systematic characterization for utilization in the breeding programme. Considering the importance of conserving the available gene pool, systematic exploration works are being carried out by like of (IIHR, Bangalore), of Resources () and others to collect and conserve the germplasm in Banks. Recently, in addition to the traditional germplasm conservation methods, cryopreservation is also gaining importance especially in the conservation of nuclear gene (pollen parent) diversity for future breeding. Utilizing the conserved germplasm in the breeding programme requires precise information on the genetic relationships within such germplasm. Information on the genetic distance among the germplasm accessions will also help to avoid the duplicates, widen the genetic base of the core collections and ultimately help in preserving the valuable diversity. 10 in Jackfruit [Artocarpus heterophyllus (Lamk. )] Manivannan, K. and R. Jack bearing the largest edible fruit in the world, is a tropical evergreen tree belonging to the family Moraceae. Previously, it was known as Artocarpus integrifolia. It is an indigenous fruit crop, probably originated in of India (Santapau, 1966) and is widely cultivated in South Asia, and other warm areas of both the hemispheres. Jack tree is commonly grown in Burma, Malaysia and Brazil. It is hardly recognized as a commercial fruit crop in India even though it is widely distributed in Southern states viz,. [SEP] Justify:

Output: The text contrasts the claim by discussing the importance of conserving genetic diversity in mango cultivars in India, noting that there are over 1000 mango cultivars. It emphasizes attempts to develop an ideal mango variety through the exploration of existing genetic diversity rather than a reductive approach, thereby highlighting that genetic modification and breeding can occur while still utilizing a diverse gene pool.


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: To reveal the evolutionary relationships among different plant species, a phylogenetic tree including EDR1-like gene members in tobacco, Arabidopsis, wheat and rice was constructed using software MEGA 7. 0 with NJ method (Fig. 1). The results divided the EDR1-like genes into six clades and the monocot and eudicot EDR1-like genes were separated into two sub-families within each clade (Fig. 1). NtEDR1-1A andNtEDR1-1B, along with EDR1 homologs involved in negative regulation of resistance against powdery mildew in Arabidopsis (AtEDR1-1) and wheat (TaEDR1), were grouped into clade I. Thus, NtEDR1-1A and NtEDR1-1B should be the first choice for further study of any negative roles in tobacco defense against powdery mildew. The exon–intron organizations of the EDR1-like genes were analyzed using online software GSDS (Fig. 1). The exon number of tobacco EDR1-like genes varied from nine (NtEDR1-18) to 22 (NtEDR1-13). Interestingly, NtEDR1-1A, AtEDR1-1, SlEDR1-1 and OsEDR1-1 had 13 exons and were all grouped into clade I in the phylogenetic tree. The similarities of sequences in clade I were further analyzed by multiple sequence alignment. The results showed that both the predicted C-terminal kinase region and N-terminal regulatory region of sequences in clade I had high identities (82. 29% and 66. 89%, respectively) (Fig. 2). Figure 1: Phylogenetic relationships and gene structure of EDR1-like gene families from tobacco, Arabidopsis, rice and tomato. NtEDR1-1A and NtEDR1-1B, along with EDR1 homologs involved in negative regulation of resistance against powdery mildew in Arabidopsis (AtEDR1-1) and wheat (TaEDR1), were grouped into clade I, in which dicotyledon plants are shown with green box, and black box represents monocotyledons. [SEP] Justify:

Output: The text provided discusses the phylogenetic relationships among different plant species and the characteristics of EDR1-like gene members, which are not related to hydrogen bonding or protein secondary structures. Therefore, it is irrelevant to the claim about hydrogen bonding in proteins.


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: Computed tomography (CT) scans are a powerful diagnostic tool that can reveal even subtle abnormalities within the lungs, including pulmonary nodules that could be indicative of cancer. and CT Scans: Deep learning, a subset of artificial intelligence, has shown remarkable promise in the field of medical imaging analysis. Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated their ability to process and interpret complex images, making them ideal candidates for analyzing CT scans for lung cancer detection. Application of Models: Deep learning models, especially CNNs, have the capacity to process CT scan images with a level of detail that can be challenging for human radiologists to achieve consistently. These models are trained on extensive datasets containing CT scans, allowing them to learn and recognize patterns associated with pulmonary nodules that might signify lung cancer. : Deep learning models excel at identifying intricate features in CT scans that may escape human observation. They can accurately pinpoint the presence, size, and characteristics of pulmonary nodules, which are often indicative of early-stage lung cancer. The models provide an additional layer of analysis, complementing the expertise of radiologists. Benefits of Deep Learning-Enhanced Detection: Integrating deep learning into lung cancer detection offers several advantages. It can assist radiologists by flagging suspicious regions in CT images, facilitating a more focused and precise review. This collaborative approach between AI and medical professionals enhances diagnostic accuracy and potentially expedites the identification of lung cancer nodules. Challenges and Progress: While deep learning holds immense potential, challenges remain. Ensuring that deep learning models are trained on diverse and representative datasets is essential to avoid biases. Additionally, these models need validation and rigorous testing before they can be integrated into clinical practice. Future Prospects: The future of deep learning in lung cancer detection is promising. Ongoing research is expected to refine existing algorithms and improve their sensitivity and specificity. Moreover, the integration of deep learning with other diagnostic methods may lead to more comprehensive and accurate early lung cancer detection strategies. Tables IV–VI provide a comparative overview of key parameters across the selected research papers on early detection of lung cancer. . Data and methods comparison. Paper title. . Dataset size and. . . and authors. Dataset used. characteristics. AI techniques. Imaging modality. Setio et al. (2017)17 LUNA16 challenge ⋯ Various algorithms for nodule detection and comparison CT images Hu et al. [SEP] Justify:

Output: The text corroborates the claim by describing how deep learning, a subset of artificial intelligence, is being leveraged in medical imaging analysis to enhance the detection of diseases like lung cancer. It emphasizes the capability of deep learning models, particularly convolutional neural networks (CNNs), to analyze CT scans for subtle abnormalities indicating early-stage lung cancer, which aligns with the development of AI technologies aimed at detecting early signs of diseases.


Input: Task: contrast the claim using the given text. Claim: Genetic modification always reduces crop diversity in every instance. [SEP] Text: No genotype was found stable as they observed more of unpredictable variance (pooled deviation) than predictable (genotype × environment linear). Ganesh and Soundarapandian (1987) observed that stability of number of ear bearing tillers was a major contributor for yield stability Shivaprakash (1987) reported similar results. Studies by Sinha and Biswas (1987) on yield and yield components of twenty photo insensitive rice cultivars revealed IR-36 and IET-4094 as high yielder with unit ‘bi’ and least ‘S2 di’ and hence they were recommended to be suitable for general cultivation for all the environments under study. Similarly IET-2881, which had high mean yield with high ‘bi’ was found suitable for high yielding environments. 15 in Sesame [Sesamum indicum (L. )] Thirugnanakumar, S. and A. × Season interaction is of major importance to the plant breeder in developing stable high yielding varieties of crop plants. When new varieties are tested over a series of seasons, the relative ranking of the varieties for any given attribute is rarely the same at each season. This increases the difficulty of identifying superior stable genotypes. Comstock and Moll (1963) have shown statistically the effect of large genotype × environment interaction is reducing the progress from selection. In order to minimize the genotype × season interaction, stratification of the seasons can be practiced. The implification in stratification is that suitable selections could be made for the infavourable and favourable seasons encountered within the sampling units proposed. Stratification of environments has been practicised for a long time to identify suitable selection that could be made for the narrower range of environments encountered within the sampling units proposed. However, even with this refinement of technique, the interactions of genotypes with locations in a sub-region and within environments encountered at the same location in different years, frequently remain too large (Allard and Bradshaw, 1964). Little is known about the environmental factors contributing to such interactions. Sprague (1966) suggested that even if such information are available, the possibility of materially reducing genotype × environment interaction in field experiments would remain questionable. On line with Allard and Bradshaw’s (1964) statement, the interactions of genotypes with locations in a sub-region with different seasons at the same location in different years, may remain too large. [SEP] Justify:

Output: The text contrasts the claim by discussing various studies that highlight the complexities and unpredictable variances in genotype stability across different environments. It emphasizes the interactions among genotypes and environmental factors, suggesting that genetic modification and selection do not universally lead to reduced diversity. Instead, it points out difficulties in identifying stable genotypes and implies that there may be a range of outcomes depending on the specific circumstances, suggesting that genetic modification could potentially maintain or even enhance diversity under certain conditions.


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: The dotted line represents that there is gap in the available genome sequence of that gene. The GenBank accession number of the barley EDR1 protein sequence is AAG31142. 1 (HvEDR1). The genome sequences of barley and wheat were downloaded from the genome databases (ftp://ftp. ensemblgenomes. org/pub/). Download full-size image DOI: 10. 7717/peerj. 5244/fig-1 Figure 2: Protein sequence alignment of EDR1-1 from tobacco, Arabidopsis, rice and tomato. Positions of EDR1 N-terminal regulatory domains are indicated by a black-lined box; and a dashed box indicates kinase domains. Download full-size image DOI: 10. 7717/peerj. 5244/fig-2 Conserved motif analysis and structure prediction of tobaccoEDR1-like protein To further analyze the conserved features of the NtEDR1-like family proteins, the motif of the tobacco EDR1-like proteins were predicted by MEME web server. A total of 10 motifs were identified in the NtEDR1-like proteins (Fig. 3; Table S4). According to the prediction of SMART, motifs 1–4 and 10 were located within the kinase domain, while motif 5 was in the EDR1 domain. The results showed motifs 1, 4, 6 and 9 were necessary for all the tobacco EDR1-like members, but motifs 2, 3, 5, 7 and 8 were alternative components (Fig. 3A). The motif logo is shown in Fig. 3B, and the lengths and predicted motif models of each motif are shown in Table S4. Figure 3: Distribution of conserved motifs in the tobacco EDR1-like family members. (A) Conserved motifs in NtEDR1-like proteins were analyzed by MEME. Ten different motifs are represented by different colored boxes, and their sizes could be estimated by the scale at the bottom. Details of motif were shown in Table S4. (B) The heights of each box represent the specific amino acid conservation in each motif. Download full-size image DOI: 10. 7717/peerj. 5244/fig-3 Homologous proteins with similar function often have a similar structure. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses protein sequence alignment and the analysis of conserved motifs in EDR1-like proteins, without mentioning hydrogen bonding or its role in the secondary structure of proteins. Therefore, it does not provide any information that supports or contradicts the claim.


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)18 Private dataset ⋯ Deep transfer learning Chest CT images Yap et al. (2018)19 ⋯ ⋯ Convolutional neural networks CT scans of lungs Shen et al. (2015)20 LIDC-IDRI 888 nodules, 1186 non-nodules Multi-scale convolutional neural networks CT images Ciompi et al. (2017)21 LIDC-888 nodules, 1186 non-nodules Deep learning for nodule management CT scans Christodoulidis et al. (2017)22 Multiple datasets ⋯ Multi-source transfer learning with CNpatterns in CT images Dou et al. (2016)23 LIDC-IDRI ⋯ Multilevel contextual 3D CNnodule detection Zhang et al. (2017)24 Private dataset ⋯ Deep CNN and SVM classifier Peripherally located nodules in CT scans Ding et al. (2017)25 Private dataset ⋯ Template matching and deep learning CT images Ardila et al. (2019)26 NLST 6000 CT scans 3D deep learning Low-dose chest CT Liang et al. (2019)27 LIDC-IDRI ⋯ 3D deep convolutional neural networks CT images Shen et al. (2016)28 TCGA 1624 patients Developing transferable deep features Patient-level lung cancer prediction Zhang et al. (2018)29 Private dataset ⋯ Deep CNN ensemble Small lung cancer detection Anthimopoulos et al. (2016)30 ⋯ ⋯ Deep convolutional neural network Interstitial lung disease patterns et al. (2018)31 UK Biobank ⋯ Clinically applicable deep learning Retinal disease diagnosis Baltruschat et al. (2020)32 LIDC-IDRI ⋯ Advanced deep learning Pulmonary nodule detection and classification Shen et al. (2017)33 LIDC-IDRI ⋯ Multi-crop convolutional neural networks Lung nodule malignancy classification Bi et al. (2019)34 LIDC-IDRI ⋯ Texture features, deep learning, SVM Cancer lesion detection and segmentation in PET/CT images Sun et al. (2018)35 ⋯ ⋯ Multi-level context convolutional network Lung nodule classification Paper title. . Dataset size and. . . and authors. [SEP] Justify:

Output: The text corroborates the claim by listing various studies that employ advanced deep learning techniques, such as convolutional neural networks, for medical imaging tasks like cancer detection (e.g., 'Lung nodule malignancy classification' and 'Cancer lesion detection and segmentation in PET/CT images'). This indicates that AI is being utilized to enhance early disease detection, aligning with the assertion about AI's role in diagnosing 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: Skip to content Science in the online home of on April 13, 2009December 30, 2009 by Cameron NeylonCreating a research community monoculture – just when we need diversity This post is a follow on from a random tweet that I sent a few weeks back in response to a query on twitter from Lord Drayson, the UK’s Minister of State for Science and Innovation. I thought it might be an idea to expand from the 140 characters that I had to play with at the time but its taken me a while to get to it. It builds on the ideas of a post from last year but is given a degree of urgency by the current changes in policy proposed by EPSRC. Government money for research is limited, and comes from the pockets of taxpayers. It is incumbent on those of us who spend it to ensure that this investment generates maximum impact. Impact, for me comes in two forms. Firstly there is straightforward (although not straightforward to measure) economic impact; increases in competitivenes, standard of living, development of business opportunities, social mobility, reductions in the burden of ill health and hopefully in environmental burden at some point in the future. The problem with economic impact is that it is almost impossible to measure in any meaningful way. The second area of impact is, at least on the surface, a little easier to track, that is research outputs delivere. How efficiently do we turn money into science? Scratch beneath the surface and you realise rapidly that measurement is a nightmare, but we can at least look at where there are inefficiencies, where money is being wasted, and being lost from the pipelines before it can be spent on research effort. The approach that is being explicitly adopted in the UK is to concentrate research in “centres of excellence” and to “focus research on areas where the UK leads” and where “they are relevant to the UK’s needs”. At one level this sounds like motherhood and apple pie. It makes sense in terms of infrastructure investment to focus research funding both geographically and in specific subject areas. But at another level it has the potential to completely undermine the UK’s history of research excellence. There is a fundamental problem with trying to maximise the economic impact of research. And it is one that any commercial expert, or indeed politician should find obvious. Markets are good at picking winners, commitees are very bad at it. [SEP] Justify:

Output: The text contrasts the claim by discussing the urgency of assessing the impact of concentration in research practices, which can relate to monoculture. It implies that focusing solely on specific areas (as monoculture does in agriculture) can undermine diversity, indicating that monoculture practices may limit genetic diversity rather than enhance 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: AtEDR1-1 has been experimentally shown to be required for G. cichoracearum susceptibility (Frye & Innes, 1998), so we used AtEDR1-1 as a reference model to analyze NtEDR1-like proteins. The 3D structures of tobacco EDR1-like proteins and AtEDR1-1 were constructed by using I-web site. The results showed that the predicted 3D model of NtEDR1-1A was similar to that of AtEDR1-1, while the 3D models of the rest tobacco NtEDR1-like proteins were somewhat different (Fig. 4; Fig. S2). Figure 4: The predicted 3D structures of NtEDR1-1 and AtEDR1-1. (A) A 3D structure pattern of NtEDR1-1A. (B) A 3D structure pattern of AtEDR1-1. Blue represents α-helices, yellow represents β-strands and cyan represents random coils. Structural images were generated with Chimera 1. 2. Download full-size image DOI: 10. 7717/peerj. 5244/fig-4 Expression analysis of EDR1-like genes in tobacco The qRT-PCR was employed to determine the expression of tobacco EDR1-like genes after plants were infected with G. orontii (Fig. 5). NtEDR1-1 was used to represent both NtEDR1-1A and NtEDR1-1B where necessary to describe the transcript abundance of NtEDR1-1A and NtEDR1-1B. The result showed that NtEDR1-1, NtEDR1-12 and NtEDR1-16 had similar expression patterns (Fig. 5B). These genes were not immediately up-regulated but began to increase after 2 hpi. NtEDR1-17 was up-regulated at 1 hpi, and maintained a high level of expression at 2 hpi. NtEDR1-3 and NtEDR1-10 weredown-regulated in the beginning of infection, but up-regulated at 12 hpi. NtEDR1-8 and NtEDR1-9 were significantly down-regulated by G. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the expression analysis of tobacco EDR1-like genes and the 3D structures of specific proteins, without providing any information regarding hydrogen bonding or its role in the secondary structure of proteins such as alpha-helices.