Input: Claim: Genetic testing can predict all future health problems with absolute certainty. [SEP] Text: Oncotype DX is available to the NHS for a subsidised rate of around £2,580 per patient[13] and is available in the for around US$3,000 per patient[14]. As for ET, five years of tamoxifen treatment — much less toxic, relatively — would cost around 6p per patient per day, per patient and only £109. 50–£130. 00 over five years[15]. In the UK, anastrozole costs around 7p per day per patient[16] and only £127. 75 over five years. One estimate shows that if British women were to adhere to five years of tamoxifen treatment, 500 more lives could be saved a year, and £30m more could be saved in the NHS budget a year[17]. In 2006, LÃ¸nning reported that two to three years of tamoxifen followed by an aromastase inhibitor provided the lowest estimates of cost and highest number of quality-adjusted life years[18]. Personalised treatmentwill improve the lives of women who present with breast cancer. Its beauty lies in its reversion to a principle of cancer care: let the prognostic and/or predictive tests guide the treatment plan. The trial has encouraged the scientific and medical community to see precision medicine in a new light, which shines on treatments targeted to the correct patient populations, not only to the correct cellular targets. What our patients deserve is best described in the words of Roman emperor and stoic philosopher Marcus Aurelius: “Life is not merely being alive, but being well. ” is helping to foster that notion. Balkees Abderrahman, postdoctoral fellow, University of PhD candidate, University of Leeds, UK. Correspondence to: bhabderrahman@mdanderson. org10. 1056/NEJMoa1804710 [2] UK. Breast cancer statistics. Available at: (accessed June 2018) [3] Society. Breast cancer: facts and figures 2017–2018. Available at: (accessed June 2018) [4] Jordan VC. Tamoxifen as the first targeted long-term adjuvant therapy for breast cancer. Cancer 2014;21(3):R235–R246. doi: 10. 1530/ERC-14-0092 [5] Jordan VC.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: The cotyledon cell wall and intracellular matrix are factors that limit iron bioavailability of the common bean (Phaseolus vulgaris). Food & Function, 7(7), 3193–3200. . (2019). The germ fraction inhibits iron bioavailability of maize: identification of an approach to enhance maize nutritional quality via processing and breeding. Nutrients, 11(4):833. doi:10. 3390/nu11040833. PMID: 31013776; PMCID: PMC6521246. . (2013). Seed coat removal improves iron bioavailability in cooked lentils: studies using an in vitro digestion/Caco-2 cell culture model. J Chem, 61(34):8084-9. doi:10. 1021/jf4022916. Epub 2013 Aug 14. PMID: 23915260. . (2014). Differences in relative iron bioavailability in traditional Bangladeshi meal plans. Bull, 35(4):431-9. doi:10. 1177/15648265140350040510. 1177/156482651403500405. PMID: 25639128. Lung’aho MG, Glahn RP. (2010). Use of white beans instead of red beans may improve iron bioavailability from a Tanzanian complementary food mixture. Res, 80(1):24-31. doi:10. 1024/0300-9831/a000003. PMID: 20533242. . (2020). Impact of ascorbic acid on the in vitro iron bioavailability of a casein-based iron fortificant. Nutrients, 12(9):2776. doi:10. 3390/nu1209277610. 3390/nu12092776. PMID: 32932834; PMCID: PMC7551990. Behind the is a with the Department of Agriculture, Service. He has over 32 years of experience in nutritional physiology and over 170 peer-reviewed publications. His major research focus is on factors that improve iron nutrition from staple foods, food products and food systems. for Agriculture and Health 538 Ithaca, NY 14853 USA E: [email protected] T: +1 607-255-2452 W: Department of Agriculture, Service (USDA-ARS).

Output: pass


Input: Claim: Some companies are piloting drone delivery for items like medical supplies. [SEP] Text: Drone delivery of an automated external defibrillator—a mixed method simulation study of bystander experience. . 2019; 27:40. Epub 2019/04/08. pmid:30961651. View 19. of an Defibrillator. N Med. 2020; 383:1186–8. pmid:32937053. View 20. Zègre- Delivery of Defibrillators via Drones in Arrest: Users’ Experiences and the Human-Drone Interaction. Resuscitation. 2020; 157:83–8. pmid:33080371. View 21. Kelland K. Drones to deliver vaccines, blood and drugs across Ghana. Reuters 2019 [cited 18 May 2021]. Available from: 22. Mis M. UK-funded drone deliveries aim to save mothers, babies in Tanzania. Reuters 2019 [cited 18 May 2021]. Available from: Makoye K. Buzz as world’s biggest drone drug deliveries take off in Tanzania. Reuters 2017 [cited 18 May 2021]. Available from: 24. . An island nation’s health experiment: vaccines delivered by drone. 2018 [cited 18 May 2021]. Available from: 25. Nicas J, Sloat S. Deutsche post DHL to deliver medicine via drone. Journal 2014 [cited 18 May 2021]. Available from: 26. Cozzens T. China fights coronavirus with delivery drones. GPS World 2020 [cited 18 May 2021]. Available from: 27. Werber C. . Post 2016 [cited 18 May 2021]. Available from: 28. Africa’s first humanitarian drone testing corridor. 2017 [cited 18 May 2021]. Available from: 29. Drones could speed up HIV tests in remote areas. Reuters 2016 [cited 18 May 2021]. Available from: Bassett L. Contraception drones are the future of women’s health in rural Africa. Huffpost 2016 [cited 18 May 2021]. Available from: 31. é. A drone delivery network for antiepileptic drugs: a framework and modelling case study in a low-income country. . 2020; 114:308–14. pmid:31943110. View 32. -Exner I. TUI: Inventory: Vienna: FFG; 2012. View 33. Davis FD.

Output: corroborate


Input: Claim: Genetic testing can predict all future health problems with absolute certainty. [SEP] Text: Linking estrogen-induced apoptosis with decreases in mortality following long-term adjuvant tamoxifen therapy. J Inst 2014;106(11):dju296. doi: 10. 1093/jnci/dju296 [6] Jordan VC & Brodie AM. Development and evolution of therapies targeted to the estrogen receptor for the treatment and prevention of breast cancer. Steroids 2007;72(1):7–25. doi: 10. 1016/j. steroids. 2006. 10. 009 [7] Prediction of the oncotype DX recurrence score: use of pathology-generated equations derived by linear regression analysis. 2013;26(5):658–664. doi: 10. 1038/m odpathol. 2013. 36 [8] The impact of Oncotype DX breast cancer assay results on clinical practice: A UK experience. 2017;28(Suppl 5):v43Â–v67. doi: 10. 1093/annonc/mdx362 [9] Impact of oncotype DX breast recurrence score testing on adjuvant chemotherapy use in early breast cancer: real world experience in Greater Manchester, UK. 2017;43(5):931–937. doi: 10. 1016/j. ejso. 2016. 12. 010 [10] Cost-effectiveness evaluation of the oncotype DX(R) breast cancer assay in clinical practice in the UK. 2012;71(24). doi: 10. 1158/0008-5472. SABCS11-PD06-02 [11] : first prospective outcome data for the 21-gene recurrence score assay and concordance of prognostic markers by central and local pathology assessment. J 2016;34(20):2341–2349. doi: 10. 1200/JCO. 2015. 63. 5383 [12] Clinical outcomes in patients with node-negative breast cancer treated based on the recurrence score results: evidence from a large prospectively designed registry. NPJ 2017;3:33. doi: 10. 1038/s41523-017-0034-6 [13] for Health and Care Excellence. Gene expression profiling and expanded immunohistochemistry tests for guiding adjuvant chemotherapy decisions in early breast cancer management: 4 and Mammostrat.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: Jinfan Zhang3 733 Accesses AbstractThe discovery of Jian (the bamboo writing slips found in the tomb of Qin in Shuihudi) unearthed in Yunmeng county in Hubei province in 1975 revealed us the law of the state of Qin in Period. Although it did not include all the laws of the state of Qin, it was evident that the book was theoretically based on the theories of legalists and it had played the function of adjustment to the social life, state activities and the families and individuals in many aspects. This is a preview of subscription content, log in via an institution to check access. Access this 34. 9910 units per month Download 29. 95 Price excludes VAT (USA) Available as PDF Read on any device Instant download Own it forever Buy 139. 00 Price excludes VAT (USA) Available as EPUB and PDF Read on any device Instant download Own it forever Buy e179. 99 Price excludes VAT (USA) Durable hardcover edition Dispatched in 3 to 5 business days Free shipping worldwide - see info calculation will be finalised at checkout Purchases are for personal use only Institutional subscriptions Notes1. “Ji” (of Huang) in (of the Grand Historian). 2. “Ji” (of the State of Qin) in (of the Grand Historian). 3. “Ji” (of Huang) in (of the Grand Historian). 4. “Za Chao” (of Decrees of the State of Qin) in (in Qin Dynasty). 5. (Slips from the Tombs of Qin in Shuihudi), p. 128. 6. (Slips from the Tombs of Qin in Shuihudi), p. 285. 7. “Ding Fen” (Defining a Person’s Social Status) in Shu (of Lord Shang). 8. “Ji” (of the of Han Dynasty) in (of Han Dynasty) (Book 1). 9. “Ding Fen” (Defining a Person’s Social Status) in Shu (of Lord Shang). 10. “Shen Fa” (of Law) in Shu (of Lord Shang). 11. (of Han Dynasty). 12. “Xiu Quan” (On the Usage of Power) in Shu (of Lord Shang). 13.

Output: pass


Input: Claim: Some companies are piloting drone delivery for items like medical supplies. [SEP] Text: We developed an unmanned aerial vehicle (UAV) Tool to help health system decision makers identify their transport challenges and explore the potential utility and impact of UAVs on the broader health system. aerial vehicles (UAVs) or drones are increasingly explored as a solution to transport challenges for medical goods, including emergency blood supplies, vaccines, medicines, diagnostic samples, and even organs, particularly for “last mile” delivery. 1,2 Proof-of-concept tests have demonstrated the technological viability of UAVs to safely transport medical supplies3–6 and keep them within the required parameters for clinical viability. 7–9 A few research studies have determined the cost-effectiveness of adding UAVs to specific medical supply chains1,10 or the optimal placement for UAV stations. 11–13 Yet very few cases of successful scale-up of UAVs for medical transport exist. Recent work has focused on proving a UAV can successfully complete a flight but has neglected to address health-system integration and long-term sustainability. As a result, decision makers lack information on how to explore the potential utility of UAVs in addressing medical transport challenges and, if determined useful, how to add UAVs to health systems or achieve use at scale. Successful implementation of any health system change relies on a complex set of factors. 14 In the case of UAVs, not only will the selected technology need to be appropriate for the distance, weight, and size requirements of the transported items, but (1) it will need to operate within regulations, (2) the concept must be embraced by stakeholders, (3) financial resources need to be available, (4) human resources must be in place, and (5) operational procedures must be developed to effectively work within existing structures. When these aspects are not considered, operations can be delayed, inefficient, or fail entirely. In short, as with any intervention, the impact of UAVs on the broader health system should be considered before implementation. 15Using the framework of the Organization health system building blocks,16 this commentary describes challenges for integrating UAVs into complex health systems and presents a tool for considering whether UAVs could help address medical transport challenges and how they can be integrated into health systems. Under the assumption that readers may be unfamiliar with UAVs, we begin with some general information (Box). BOX an Vehicle (UAV)?

Output: corroborate


Input: Claim: Genetic testing can predict all future health problems with absolute certainty. [SEP] Text: Figure 2 with 1 supplement Download asset Open asset The ‘gene disruption score’, a gene-level prediction of the impact of genetic variants. (A) Schematic representation of how all substitutions affecting a particular gene are combined to compute the gene disruption score. (B) Average gene disruption score across all strains for four categories of genes conserved in all strains (‘core genome’). Conserved genes are defined as those genes found in more (or less) than 95% of the bacterial species present in the eggNOG orthology database (Huerta-Cepas et al. , 2016a). Statistically significant differences (Cohen’s d value >0. 3) are reported. (C) Gene-gene correlation profile of gene disruption across all strains shows clusters of potentially functionally related proteins. (D–E) The gene disruption profiles as a predictor of genes function. (D) Pairwise correlation of gene disruption scores inside each annotation set (colored boxes) and inside a random set of genes of the same size (grey boxes). (E) Gene-gene correlation profile of gene disruption scores in protein complexes; shown here a subset with high disruption score correlation. Figure 2—figure supplement 1 Download asset Open asset Additional properties of the gene disruption score. (A) Correlation between phylogenetic distance from the reference strain and average disruption across all reference genes. (B) Significant reduction in correlation after correction of the disruption score (see Materials and methods). (C–E) Prediction of gene functional associations using disruption score profiles. (C) ROC curve using disruption score profiles across all genes. (D–E) Higher predictive power of the gene disruption profile when restricted to accessory genes, including (D) or not (E) the information about gene presence absence. Figure 3 with 2 supplements Download asset Open asset Prediction of growth-defect phenotypes in the E. coli strain collection. (A) Schematic representation of the computation of the prediction score and its evaluation; for each condition the predicted score is computed using the disruption score of the conditionally essential genes. The score is then evaluated against the actual phenotypes through a Precision-Recall curve. (B) Higher predictive power for conditions with higher proportion of growth phenotypes.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Ding Fa” (Law Making) in Shu (of Lord Shang). 14. “Qu Qiang” (of the Disobedient) in Shu (of Lord Shang). 15. “Ding Fen” (Defining a Person’s Social Status) in Shu (of Lord Shang). 16. “Shang Xing” (Rewards and Punishments) in Shu (of Lord Shang). 17. “Kai Sai” (Reform) in Shu (of Lord Shang). 18. “Qu Qiang” (of the Disobedient) in Shu (of Lord Shang). 19. “Kai Sai” (Reform) in Shu (of Lord Shang). 20. “Suan Di” (Measuring Land) in Shu (of Lord Shang). 21. “Shang Xing” (Rewards and Punishments) in Shu (of Lord Shang). 22. “Shen Fa” (of Law) in Shu (of Lord Shang). 23. “Wai Nei” (and the Inner) in Shu (of Lord Shang). 24. “Kai Sai” (Reform) in Shu (of Lord Shang). 25. “Hua Ce” (Policies) in Shu (of Lord Shang). 26. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 27. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 28. (Qing Dynasty), “Zi Xu” (The Author’s Preface) to (Collections of Laws in Han Dynasty). 29. “Ji” (of Huang) in (of the Grand Historian). 30. “Da Wen” (and Answers) in (in Qin Dynasty). 31. “” (of the biographies of ) in (of the Grand Historian). 32. “Lv” (Statutes on the Regulation of Officials) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 33. “Za” (Statutes on in the Capital) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty).

Output: pass


Input: Claim: Some companies are piloting drone delivery for items like medical supplies. [SEP] Text: 1,4UAVs are aerial vehicles guided without an onboard crew. They can be piloted remotely or programmed to fly autonomously. A variety of models exist, each suited to different applications based on distance, payload, maneuverability, fuel sources, durability, need for landing, and other factors. delivery of medical supplies via UAV is still relatively new. Until recently, there has been only 1 example of routine medical supply using UAV delivery. Since 2016, Zipline has operated drones for the Government of Rwanda, delivering up to 3 liters of blood within 30 minutes to health facilities that request it on demand. 17 In 2019, however, several other companies received approval to conduct routine flights. Matternet, a company that has conducted hundreds of test flights in Switzerland, routinely transports laboratory specimens within a health system. 18 Alphabet’s Wing will soon be delivering over-the-counter medications, via UAV in Canberra, Australia, after a year and a half of test deliveries. 19 Yet to date, none of these projects have published on the decision to implement UAV delivery, whether/how they integrated UAVs into existing health systems, or the impact the change has had on health care operations. In addition to these larger-scale operations, many other pilot projects delivering medical goods are being implemented worldwide,4,20,21 transporting items such as childhood vaccinations,22,23 automatic external defibrillators,11–13 snakebite antivenom,24 tuberculosis sputum samples,8,25 and sterile mosquitoes. 26 Although these projects are increasingly sharing information about operations, only a few resources for implementing UAV delivery projects exist 20,27,28 and none explore the feasibility and impact of adding this technology to the broader health system. Tools to explore feasibility and impact of UAVs in health systems do not yet exist. any new product or process to a large existing health system can be challenging. Companies that have been transporting medical goods via drone for multiple years or are preparing to do so have yet to share information about how they integrate their processes with the health system, what processes have to change, how those impact workflows, or the impact they have on health outcomes. Understanding the potential risks and benefits of a systemic change is important during the consideration and implementation of a new technology for evaluating sustainability and the true cost of implementation.

Output: corroborate


Input: Claim: Genetic testing can predict all future health problems with absolute certainty. [SEP] Text: For each condition set, the PR-AUC value for each condition is reported, together with the median and mean value (C) Significance of the PR-AUC value reported for the condition ‘Clindamycin 3 μg/ml’, against the distribution of three randomization strategies. ‘Shuffled strains’ indicates a prediction in which the actual strains’ phenotypes have been shuffled; ‘shuffled sets’ indicates a prediction where the conditionally essential genes of a different condition have been used, and ‘random set’ indicates a prediction where a random gene set has been used as conditionally essential genes. For all three randomizations we report a significant difference between the actual prediction and the distribution of the randomizations (q-values of 1E-30, 0. 05 and 1E-22, respectively). 3—figure supplement 2 for the other conditions. (D) Genome-wide gene associations are in agreement with the predictive score; the enrichment of conditionally essential genes in the results of the gene association analysis is significantly higher in conditions with higher PR-AUC. Figure 3—figure supplement 1 Download asset Open asset Detailed view of the predicted growth score and its properties. (A) Influence of the different predictors of the impact of mutations on each condition PR-AUC. ‘SNPs’ indicates single nucleotide variants only, ‘Accessory genome’ gene presence-absence patterns, ‘All predictors’ the combination of both, plus stop codon substitutions. (B–C) Proportion of well-predicted conditions (PR-AUC >= 0. 1 and Pearson’s FDR-corrected p-value<=0. 01) over total conditions with at least 1% and 5% sick strains. Marker’s size is proportional to the −log10 of the FDR-corrected p-value. (E) Conditions with higher predictive power (measured as PR-AUC) also have an enrichment of sick strains at the top of the predicted score, as measured by the (GSEA); sick strains are used as ‘gene sets’. A pseudocount of 10−4 has been added to the GSEA p-values. (F) Prediction performance improves when using the weighting scheme to account for conservation of gene essentiality, especially for well-predicted conditions.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: 34. “Lv” (of Executing the Commands) in (in Qin Dynasty). 35. “Zhao Ce” (of the State of Zhao) in Ce (Stratagems of the Warring States). 36. “Qiang Guo” (a Prosperous Country) in (Xun Zi). 37. (Works of Marx and Engels), Vol. 1, The People’s Publishing House, 1972, p. 697. 38. “Zhi” (of and Economy) in (of Han Dynasty). 39. “” (Thirteen 1985. 40. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 41. “Shi Jia” (of Chen She) in (of the Grand Historian). 42. “Ji” (of the of Han Dynasty) in (of Han Dynasty). 43. “Fu Lv” (Statutes on the Household Registration) in “Za Chao” (of Decrees of the State of Qin) in (in Qin Dynasty). 44. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 45. “Da Wen” (and Answers) in (in Qin Dynasty). 46. “Ji” (of Huang) in (of the Grand Historian). 47. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 48. “Ji” (of Huang) in (of the Grand Historian). 49. “Xing” (Punishment) (Part 1) in (Codes), Vol. 163. 50. “Ji” (of Huang) in (of the Grand Historian). 51. (History as a Mirror), Vol. 6, annotated by Hu Sansheng. 52. “Ji” (of Huang) in (of the Grand Historian). 53. “Qin Ce” (of the State of Qin) in Ce (Strategems of the Warring States). 54. “” (Biographies of ) in (of the Grand Historian). 55. “Wai Pian” (Supplementary Essays) in (Master Shen). 56.

Output: pass


Input: Claim: Some companies are piloting drone delivery for items like medical supplies. [SEP] Text: Although these issues apply to all UAV flights, they are particularly important to consider before health systems can utilize UAVs for transport, as most applications require long-range transport, either start or end in urban locations, and in emergency situations may not be able to wait for dawn to take flight. The complexity of health supply chains in low-resource settings requires dedicated stakeholder engagement. Often supply chains involve public, private, and nonprofit actors, national and international entities, siloed transport and tracking systems, and insufficient resources to strengthen them. However, UAV operations will be much more cost-effective if the technology can be used across silos and by multiple institutions. 1 Although using UAVs for public benefit is often met with enthusiastic response, stakeholders have demonstrated concerns about value for cost, privacy, security, how UAV regulations can be enforced,30–32 and noise. 33 These concerns can pose real barriers to implementation. Recently, the Government of Ghana’s plan to use UAV services to deliver blood products was opposed by the Association, a stakeholder that was not sufficiently engaged before decision making and felt the investment misplaced. 34,35Using UAVs for public benefit is often met with enthusiastic response, yet stakeholders have demonstrated concerns, including value for cost, privacy, and security. Many organizations have been working to ensure that decisions to use UAVs are made with as much information as possible. Over 5 years ago, a collaborative forum was developed to foster discussion and dissemination of UAV delivery work. for (UPDWG) currently has over 200 members from public, private, and nonprofit institutions. Recently, a more formal organizing body was created to strengthen coordination between stakeholders and share resources:. As UAV technology has evolved, challenges have arisen in aligning technology partners with stakeholder needs. Ensuring applications of this technology address relevant problems by teams who understand both local contexts and the parameters of the technology remains a challenge. In lower- and middle-income countries, the use of UAVs involves multiple entities: companies who develop and implement the technology, international health and development organizations who implement programs, and national entities who manage the delivery of health care and commodities and implement regulations. We observed a need for a decision-support tool for implementers who are considering UAV technology for transporting medical goods that educates both operators and implementors on relevant considerations.

Output: pass


Input: Claim: Genetic testing can predict all future health problems with absolute certainty. [SEP] Text: (B) Distribution of the number of predicted restored phenotypes per gene; red stripes indicate the genes that were experimentally tested. (C) Growth change between the target strain with the empty plasmid against the one expressing the reference copy of the target gene (complementation). Strains for which a change in phenotype is predicted are compared to those where no change is predicted. (D–F) Detailed representation of the results of the complementation experiment in three conditions. Mean colony area and the 95% confidence intervals are reported. Significant differences (t-test p-value<0. 01) between colony area of the strains with the empty and complemented plasmids are reported. ‘Other complementation’ reports strains expressing a different gene than the focal one, indicated between parenthesis. (G) Cartoon representation of AcrB 3D structure (PDB entry: 2dr6). Only one of the three monomers is highlighted in blue; colored spheres represent known non-synonymous variants in the reported strains. Figure 5—figure supplement 1 Download asset Open asset Detailed view of the experimental confirmation of the predicted phenotype-causing genotypes. (A) Replicability in colony size (measured in pixels) after outer frame and spatial correction between the two replicates. (B) Overview of the results of the complementation experiments. Mean colony area and the 95% confidence intervals are reported. Significant differences (t-test p-value<0. 01) between colony area of the strains with the empty and complemented plasmids are reported. Conditions CHIR090. and. 5UM have been excluded from further analysis, as the deletion strain from the KEIO collection (positive control) does not show a sick phenotype with an empty plasmid. Author response image 1 Download asset Open asset Author response image 2 Download asset Open asset Author response image 3 Download asset Open asset Additional files Source code 1 code used to analyze the genetic and phenotypic data, predict phenotypes and draw all the manuscript figures. Download elife-31035-code1-v2. gz Supplementary file 1 detailed list of the members of the strain collection Download elife-31035-supp1-v2. xlsx Supplementary file 2 detailed list of conditions on which the strain collection has been tested Download elife-31035-supp2-v2.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Lie Zhuan” (Biographies of Qing Bu) in (of the Grand Historian). 57. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 58. “Da Wen” (and Answers) in (in Qin Dynasty). 59. “Da Wen” (and Answers) in (in Qin Dynasty). 60. “Ji” (of Huang) in (of the Grand Historian). 61. “Cang Lv” (Statutes on Storage of Forage) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 62. (Qing Dynasty) Shen Jiaben, “Si Kou” (of Justice) in “Fen Kao” (Research on Penal Law) in Kao (A of the in the Past Dynasties). 63. “Zhi” (of Criminal Law) in (of Jin Dynasty). 64. “Ji” (of Huang) in (of the Grand Historian). 65. “Ji” (of Huang) in (of the Grand Historian). 66. “Qi Yu” (Records of Qi State) in (Records of Various States). 67. “Lv” (Statutes on National Projects) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 68. (Song Dynasty), “Shuo” (Comment on ) in (Works of Master Zhu), Vol. 67. 69. “Zhi Zhuan” (of Xiao Wangzi) in (of Han Dynasty). 70. “Da Wen” (and Answers) in (in Qin Dynasty). 71. “Lv” (Statutes on the Removal of Officials) in “Za Chao” (of Decrees of the State of Qin) in (in Qin Dynasty). 72. “Lv” (Statutes on the Removal of Officials) in “Za Chao” (of Decrees of the State of Qin) in (in Qin Dynasty). 73. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 74.

Output: pass


Input: Claim: Some companies are piloting drone delivery for items like medical supplies. [SEP] Text: 2. ↵Zraick K. Like ‘Uber for Organs’: Drone delivers kidney to Maryland woman. Times. April 30, 2019. 4, 2019. 3. ↵-Procurement and Supply Management. Analysis: Applications in the Development Context. Washington, DC: Inc. ; 2017. 4. ↵for (FSD). Drones in Humanitarian Action: A Guide to the Use of in Humanitarian Crises. Geneva: FSD; 2016. 5. . Development of e-Juba, a preliminary proof of concept unmanned aerial vehicle designed to facilitate the transportation of microbiological test samples from remote rural clinics to laboratories. S J. 2007;97(11 Pt 3):1215–1218. pmid:18250941OpenUrlPubMed6. ↵. Drone transportation of blood products. Transfusion. 2017;57(3):582–588. doi:10. 1111/trf. 13900. pmid:278619677. ↵ Drone transport of chemistry and hematology samples over long distances. Clin Pathol. 2017;148(5):427–435. doi:10. 1093/ajcp/aqx090. pmid:290168118. ↵. Drone transport of microbes in blood and sputum laboratory specimens. J Clin Microbiol. 2016;54(10):2622–2625. doi:10. 1128/JCM. 01204-16. pmid:27535683OpenUrlAbstract/9. ↵. Can unmanned aerial systems (drones) be used for the routine transport of chemistry, hematology, and coagulation laboratory specimens? PLoS One. 2015;10(7):e0134020. doi:10. 1371/journal. pone. 0134020. pmid:2622226110. ↵ The economic and operational value of using drones to transport vaccines. Vaccine. 2016;34(34):4062–4067. doi:10. 1016/j. vaccine. 2016. 06. 022. pmid:2734009811. ↵. Locating AED enabled medical drones to enhance cardiac arrest response times. Care. 2016;20(3):378–389. doi:10. 3109/10903127. 2015. 1115932.

Output: corroborate


Input: Claim: Genetic testing can predict all future health problems with absolute certainty. [SEP] Text: DPYD genetic testing and the future of pharmacogenomic testing in routine care Genetic testing can identify which drugs and doses are best for individual patients, personalising care and optimising outcomes. Pharmacogenomics 14 June 2022 & William NewmanCopy link to page Download PDF A 2022 report by the of Physicians and the Society highlighted the potential for pharmacogenomic testing to be implemented on a broader scale in the UKJames King-Holmes PhotoAfter reading this 1. Figure 1: Decoding the nomenclature of pharmacogenomicsThe c in c. 1679T>G indicates that the change is within the coding region of the DPYD gene, followed by the number to represent the position of the change at 1,679 (i. e. the 1,679th nucleotide in the gene). T>G indicates that a thymine (T) has been replaced by a guanine (G). In this example a single nucleotide in a single position has been substituted and this genetic change is known as a single nucleotide polymorphism (SNP). It represents the most common type of genetic change[8]. Since the raw variant data demonstrated in Figure 1 is too detailed for the needs of most healthcare professionals, simpler star (*) allele names are given to raw genetic variants. In star-allele nomenclature, *1 is the first designated reference sequence to which polymorphic sites are then compared. This is usually the first sequence that was described to code for the functional protein product and may not be the most common allele in every ethnic population[9]. A unique number (e. g. DPYD*2A) is then given to each novel variant. Not all DPYD variants have been assigned a star allele, so the preferred nomenclature for DPYD variants is the or rsID number[7]. Depending on which version of the human genome is used as reference will change the rsID that the variant is compared to. Full discussion of this is beyond the scope of this 1. 5) and should have their fluoropyrimidine dose reduced according to the specific variant identified. Dose adjustments should be based on the advice given on the genetic report or other local or national guidance, such as the UK national guidance (depending on the standard operating policy at the treatment provider)[3].

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Ben Ji” (Sketches of Emperor Xiaowen) in (of the Grand Historian), annotated by Ying Shao. 75. “Da Wen” (and Answers) in (in Qin Dynasty). 76. “Da Wen” (and Answers) in (in Qin Dynasty). 77. “” (Thirteen 1985. 78. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 79. “Fu Lv” (Statutes on the Household Registration) in “Za Chao” (of Laws of Qin) in (in Qin Dynasty). 80. “Da Wen” (and Answers) in (in Qin Dynasty). 81. “Da Wen” (and Answers) in (in Qin Dynasty). 82. “Da Wen” (and Answers) in (in Qin Dynasty). 83. “Da Wen” (and Answers) in (in Qin Dynasty). 84. “Lv” (Statutes on National Projects) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 85. (Slips from the Tombs of Qin in Shui Hudi), p. 166, Press, 1978. 86. “Lie Zhuan” (Biographies of Shang Yang) in (of the Grand Historian). 87. “Da Wen” (and Answers) in (in Qin Dynasty). 88. “Lie Zhuan” (of Jian Jun) in (of the Grand Historian). 89. “Zhuan” (of Jia Yi) in (of Han Dynasty). 90. “Nong Zhan” (and Warring) in Shu (of Lord Shang). 91. “Tian Lv” (Statutes on Farming and Forest Protection) in (in Qin Dynasty). 92. “Tian Lv” (Statutes on Farming and Forest Protection) in (in Qin Dynasty). 93. “Za Chao” (of Decrees of the State of Qin) in (Slips of the Qin Dynasty). 94. “Lie Zhuan” (Biographies of Businessmen) in (of the Grand Historian). 95. “Da Wen” (and Answers) in (Slips of the Qin Dynasty). 96.

Output: pass


Input: Claim: Some companies are piloting drone delivery for items like medical supplies. [SEP] Text: pmid:2685282212. Unmanned aerial vehicles (drones) in out-of-hospital-cardiac-arrest. . 2016;24(1):124. doi:10. 1186/s13049-016-0313-5. pmid:2772905813. ↵; Investigators. Optimizing a drone network to deliver automated external defibrillators. Circulation. 2017;135(25):2454–2465. doi:10. 1161/. 116. 026318. pmid:28254836OpenUrlAbstract/14. ↵Dearing JW. Organizational readiness tools for global health intervention: a review. Health. 2018;6(56):56. doi:10. 3389/fpubh. 2018. 00056. pmid:2955255215. ↵ Call for global health-systems impact assessments. Lancet. 2009;374(9688):433–435. doi:10. 1016/S0140-6736(09)61212-5. pmid:1957728916. ↵Organization (WHO). Everybody’s business: strengthening health systems to improve health outcomes: WHO’s framework for action. Geneva: WHO; 2007. 17. ↵Our Impact. Zipline website. 4, 2019. 18. ↵UPS partners with Matternet to transport medical samples via drone across hospital system in Raleigh, N. C. [press release] Atlanta, GA: UPS; March 26, 2019. 19. ↵Porter J. Google’s Wing drones approved to make public deliveries in Australia. The Verge. April 9, 2019. 4, 2019. 20. ↵Agency for (USAID). UAVs in Global Health: Defining a Forward. Washington, DC: USAID; 2018. 21. ↵Wingcopter, DHL and GIZ pilot drone delivery in Tanzania. Wingcopter website. 4, 2019. 22. ↵Held A. Drones deliver vaccines on island nation of Vanuatu. Radio website. 21, 2018. 4, 2019. 23. ↵Thorne J. Drones swoop into the Congo to strengthen the country’s vaccine supply chain. Geekwire. 8, 2019. 4, 2019. 24.

Output: corroborate


Input: Claim: Genetic testing can predict all future health problems with absolute certainty. [SEP] Text: Patients may also have their dose modified on the basis of other factors, such as renal function and other comorbidities, since pharmacogenomics is just one aspect of medicines optimisation. Patients identified as homozygous or compound-heterozygous for a reduced functioning DPYD variant are categorised as DPYD poor metabolisers (DPYD-AS: 0) and will generally be considered for alternative treatments because of the severe risk of toxicity with fluoropyrimidines. However, in the absence of suitable alternatives and as part of a shared decision-making process with the patient, consultant oncologists and pharmacists may decide to proceed with fluoropyrimidine-based therapy at heavily reduced doses[3]. This conversation would be specific to the individual patient’s diagnosis and available treatment options, the variant(s) identified and other patient-specific factors to enable a risk versus benefit judgement to be made. Patient counsellingCounselling for DPYD testing does not require specialist genetic counselling services and is often undertaken at the time of consent for chemotherapy by an oncologist or non-medical prescriber. When discussing pharmacogenomic testing with patients, it is useful to provide the context in which the result will be used: to aid drug and/or dose selection according to guideline recommendations and to reduce the risk of severe or life-threatening side effects from fluoropyrimidine-based chemotherapy treatment. In this way, DPYD testing is not dissimilar to renal or liver function testing as it can guide prescribing decisions and optimise drug therapy[2]. Patient autonomy and informed consent are essential to the medical decision-making process. The consent process for pharmacogenomic testing is the same as for any medical procedure — patients and their carers must be provided with appropriate information to make informed decisions about their health. More detailed and technical information may also be required to ensure that patients get the right information for their needs. Written information and relevant online resources should also be offered to patients and carers. Box 2 lists the counselling points to discuss with patients. Box 2: Counselling pointsDPYD testing is being offered to guide treatment and/or dose selection and to reduce the risk of severe or life-threatening side-effects from fluoropyrimidine chemotherapy treatment;If a clinically relevant variant is identified, changes will be made to the chemotherapy regime, such as an initial dose reduction.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Shi” (Judicial Principles, Methods and Cases) in (in Qin Dynasty). 97. “Lv” (Statutes on Currency and Property) in “Zhong” (of Laws of Qin Dynasty) in (in Qin Dynasty). 98. “Zhi” (of the Criminal Law) in (of Han Dynasty). 99. “Lie Zhuan” (of Li Si) in (of the Grand Historians). 100. (Tang Dynasty), (Biographies of Shu Duan). 101. “Da Wen” (and Answers of Law) in (Slips of the Qin Dynasty). 102. “Ji” (of Huang) in (of the Grand Historian). 103. “Ji” (of Huang) in (of the Grand Historian). 104. “Ji” (of Huang) in (of the Grand Historian). 105. “Shuo” (Categories of Saying) (Part 2) in Zi (Han Feizi). 106. “Ren Zhu” (The Monarch) in Zi (Han Feizi). 107. “Ren Zhu” (The Monarch) in Zi (Han Feizi). 108. “Ji” (of Huang) in (of the Grand Historian). 109. “Xing De” (Morality) in Lun (Debates on Salt and Iron). 110. “Xing De” (Morality) in Lun (Debates on Salt and Iron). 111. “Xing De” (Morality) in Lun (Debates on Salt and Iron). 112. “Lie Zhuan” (of Meng Tian) in (of the Grand Historian). 113. “Ji” (of Huang) in (of the Grand Historian). 114. “Ji” (of Huang) in (of the Grand Historian). 115. “” (of et al) in (of the Grand Historian). 116. “” (Biographies of ) in (of the Grand Historian). 117. “Ji” (of Huang) in (of the Grand Historian). 118. “Ji” (of Huang) in (of the Grand Historian). 119.

Output: pass


Input: Claim: Some bacterial strains can break down plastic in controlled environments over time. [SEP] Text: Can we use fungi to break down our plastic and rubber waste? Legaspi and Kaury KuceraBacked by. K. and 31 other backersöhler,. rexus, and : 10. 18258/0795$8,000Raised of $8,000 Goal100%Funded on 11/14/13Successfully Funded$8,000pledged100%fundedFundedon 11/14/13? How does this work? FollowOverviewMethods(9)Discussion (8)massive and rapid accumulation of our plastic and rubber waste is a problem that we cannot afford to ignore. Since certain types of plastic and rubber are held together by the same chemical bonds that are found in natural plant polymers, we are investigating the ability of fungi that live inside plants to degrade synthetic plastics and rubbers. Ask the ScientistsDiscussionWhat is the context of this research? Research on synthetic polymer biodegradation has been steadily growing over the past forty years. Several bacteria and fungi have been identified that produce enzymes capable of plastic and rubber degradation in various laboratory conditions. However, this field is still in its infancy and much work needs to be done to determine the viability of plastic and rubber biodegradation. Several plastics and rubbers contain the same chemical bonds that are found in natural plant polymers. Therefore, we are specifically targeting endophytes, organisms that grow symbiotically inside plants, in order to harness the endophytes’ ability to degrade natural plant polymers for the degradation of synthetic plastics and rubbers. Our group has previously identified degradation of the plastic polyester polyurethane by the endophytic fungus Pestalotiopsis microspora. This fungus secretes a small enzyme that is responsible for plastic degradation. Going forward, our project goals are to: 1) Characterize enzymatic degradation by Pestalotiopsis microspora - Isolate and characterize enzymatic degradation - Compare enzyme efficiency to other polyester-degrading enzymes - Evaluate degradation of solid polyester polyurethane and other plastics - Evaluate degradation of plastic by P.

Output: corroborate


Input: Claim: Genetic testing can predict all future health problems with absolute certainty. [SEP] Text: The dose can be increased subsequently if there are no serious side effects; If no clinically relevant toxicity-related variants are identified, there is still a risk of toxicity owing to other environmental, genetic or disease-related factors. Patients should be advised to report any side effects to the oncology unit without delay; Family members are not routinely tested when a patient is identified as carrying a clinically relevant variant, because a DPD deficiency does not usually cause symptoms unless fluorouracil or capecitabine chemotherapy is administered;Local procedures for the storage of blood samples should also be discussed with the patient; The DNA sample will not be used for other genetic tests without the patient’s consent. The genetic reportFigure 3 shows the type of information included on a genetic report, including the rationale for testing, a summary of the results and technical information about the test itself. If dosing recommendations are provided, these should be considered as part of a holistic assessment of the patient. Figure 3: Example of a DPYD testing reportThe GLH will return a genetic report similar to the one seen in Figure 3 to the requesting clinician. The provider is then responsible for incorporating the patient’s DPYD genetic report into their management plan and communicating results to the patient. Based on the individual patient’s result and the standard operating procedures at the trust, oncology teams may adjust the dose as described in the patient pathway. Targeted testing technologyMost of the genotyping is undertaken as per the requirements of the Test Directory, using simple targeted testing technology performed on a standard blood sample. The advantage of the targeted gene testing approach is that result turnaround time is 24 to 72 hours (the current NHS mandate is less than five working days)[6]. This means the results can be delivered in a clinically relevant timeframe to inform treatment decisions in the patient pathway. Future implicationsThe implementation of national DPYD screening is one of seven transformation projects delivered by the (GMSA) in 2021/2022 that aim to embed genomics into routine patient care pathways[10]. The DPYD project is aimed to optimise and improve the clinical effectiveness of DPYD gene testing for patients with cancer across England. GMSA regional pharmacy leads have been able to feed back on the positive contribution of pharmacists in chemotherapy provider trusts around the country in embedding DPYD pharmacogenomic testing into routine oncology pathways.

Output: contrast


Input: Claim: Every ancient civilization relied on the same staple crops. [SEP] Text: “Ji” (of Huang) in (of the Grand Historian). 120. “Ji” (of Huang) in (of the Grand Historian). 121. “Ji” (of Huang) in (of the Grand Historian). 122. “Ji” (of Huang) in (of the Grand Historian). 123. “Ji” (of Huang) in (of the Grand Historian). 124. “Zhi” (of the Criminal Law) in (of Han Dynasty). 125. “Zhi” (of the Criminal Law) in (of Han Dynasty). 126. “Lie Zhuan” (of Li Si) in (of the Grand Historian). 127. “Ji” (of Huang) in (of the Grand Historian). 128. “Ji” (of Huang) in (of the Grand Historian). 129. “Kao” (A on Land Tax) in (A of Documents) (Book 1). 130. “Ji” (of Huang) in (of the Grand Historian). 131. “Tong Zhuan” (of Shu Suntong) in (of Han Dynasty). 132. (Inscriptions of Issued in the 26th Year of Huang’s Reign). 133. “Ji” (of Huang) in (of the Grand Historian). 134. “Ji” (of Huang) in (of the Grand Historian). 135. “Ji” (of Huang) in (of the Grand Historian). 136. “Zhi” (of and Economy) (Part 1) in (of Han Dynasty). 137. “Ji” (of the of Qin) in (of the Grand Historian). 138. “Lie Zhuan” (of Li Si) in (of the Grand Historian). 139. “Lie Zhuan” (of Li Si) in (of the Grand Historian). Author informationAuthors and Affiliationsof and author publicationsYou can also search for this author in PubMed 10. 1007/978-981-10-1029-3_4Download citation. RIS. ENW.

Output: pass


Input: Claim: Some bacterial strains can break down plastic in controlled environments over time. [SEP] Text: microspora in simulated landfill conditions (collaboration) 2) Screen for new plastic- and rubber- degrading fungi - Screen fungi collections for degradation of different synthetic polymers - For promising candidates, sequence the genomes and look for possible novel plastic- or rubber-degrading genes - Analyze candidates using gel permeation chromatography (molecular weight changes) and scanning electron microscopy (visual degradation) What is the significance of this project? Plastics and rubbers are examples of synthetic (or man-made) polymers that have become an indispensable part of our lives. From our disposable packaging to our grocery bags, from our water bottles to our car tires, we heavily rely on synthetic polymers on a daily basis. These materials are cheap to manufacture, versatile, and resilient -- which also means that they are produced in massive quantities and can last in the environment for hundreds of years. Given that these extremely durable materials are often used for single-use disposable applications, it’s important to think about what happens to plastics and rubbers after disposal. While we have seen an enormous boom in synthetic polymer production (over 250 million tons of plastic are produced every year! ); our methods of treating these materials after disposal have not been able to keep up. Recycling has helped reduce the amount of plastic waste, however only 15% of global plastic is successfully recycled. Additionally, Incineration, another common method of disposal, can form and release toxic chemicals into the environment. Therefore, we are investigating alternative (or complementary) methods of dealing with our plastic and rubber waste. What are the goals of the project? All funds raised will directly be applied to furthering the stated project goals of characterizing the biodegradation of plastic by Pestalotiopsis microspora and screening for new plastic- and rubber-degrading enzymes. A portion of the funds will be used to buy consumable lab supplies, which are necessary for the growth and maintenance of the fungal cultures, enzymatic characterization and materials for additional plastic and rubber screens. I already have access to the necessary equipment for polymer analysis; however, many instruments have fees associated with use and/or several accessory pieces of equipment or consumables need to be bought separately. I will apply the funds towards using equipment such as scanning electron microscopy and gel permeation chromatography.

Output: corroborate


Input: Claim: Genetic testing can predict all future health problems with absolute certainty. [SEP] Text: The main messages identified by the project leads from the implementation of the DPYD pathway include:The importance of national commissioning to ensure testing is available across the country;The value of communication between clinical teams and regional GMSA leads;The importance of pharmacy teams in taking ownership/leadership of pharmacogenomic testing;The need for decision support tools/dosing guidelines (such as the UK Chemotherapy Board’s national dosing guidance[3]);The requirement for interoperability between electronic systems to permit reporting within ‘clinically relevant’ timeframes;That genomics is only one piece of a complex puzzle and that adverse drug reactions are influenced by other patient factors (e. g. comorbidities). Future work aims to characterise less common DPYD variants in patients who experience severe toxicity despite no variants being detected on initial screening. The future of pharmacogenomicsTo support the integration of pharmacogenomics into patient care pathways, consideration must also be given to alternative and newly emerging testing approaches. For example, pre-emptive testing with pharmacogenomic gene panels can test multiple genes simultaneously, improving efficiency and cost-effectiveness when compared with multiple individual genetic tests[11]. Having genomic data readily available in the electronic patient record at the point of prescribing would eliminate concerns related to turnaround time in clinical scenarios where there is an urgent need to initiate treatment; for example, before the use of clopidogrel in percutaneous coronary intervention[12]. Of course, there are challenges to this approach, not least the need to prove cost-effectiveness in the selected population and a requirement for comprehensive clinical decision support tools[13]. If successfully incorporated into the electronic patient record, and with sufficient integration to permit healthcare providers across sectors to view and act upon a patient’s pharmacogenomic information, the potential benefits are enormous[2]. 1Pharmacogenomics. Programme. (accessed Jun 2022). 2Personalised prescribing: using pharmacogenomics to improve patient outcomes. of Physicians. 2022. (accessed Jun 2022). 3Fluoropyrimidine-based Therapies. UK Chemotherapy Board. 2020. (accessed Jun 2022). 4 PharmGKB summary. Pharmacogenetics and Genomics. 2011;21:237–42. doi:10. 1097/fpc.

Output: contrast


Input: Claim: Plant remains can always be accurately dated. [SEP] Text: While GP has been extended to allow and improve the simultaneous prediction of multiple correlated traits, comprising the crop phenotype3–5, existing GP approaches have not yet addressed the problem of predicting the dynamics of plant phenotypes, i. e. , the expression of multiple traits at different time points across the entire period of growth of the plant. Advances in high-throughput phenotyping (HTP) technologies, including non-destructive approaches such as: hyperspectral, multispectral, fluorescence, and thermal imaging, have enabled the detailed capture of plant traits at various growth stages and have resulted in large-scale time-resolved heterogeneous data sets on crop phenotypes6–10. These developments offer the possibility of predicting time-resolved crop phenotypes from genetic markers, with the potential to revolutionize GP. Recent efforts have cast the prediction of the dynamics of crop phenotypes as a spatio-temporal sequence prediction problem. The proposed solutions rely on deep learning approaches, such as spatio-temporal long short-term memory (ST-LSTM) or Memory-in-memory (MIM) networks11, generative adversarial networks12 (GAN), and U-net encoder-decoder13. However, rather than directly predicting time-resolved crop traits, these approaches aim to predict a time series of images that are then used to infer the traits of interest. The time-resolved prediction of crop phenotypes can also be addressed by dynamic mode decomposition (DMD)14. DMD is an advanced data-driven method used in decomposing data from complex, time-dependent systems into spatio-temporal modes that provide low-dimensional description of the systems’ dynamics. These modes render DMD particularly useful for understanding and predicting the behavior of systems characterized by high-dimensional data. DMD has been applied across different fields, including: fluid mechanics, engineering, epidemiology14, and neuroscience15; however, no attempts have yet been made to apply DMD to data from HTP technologies or to combine this method with genetic markers. Here we combined DMD with GP to predict the genotype-specific dynamics of crop phenotypes using HTP data measured on a maize population of recombinant inbred lines (RILs). Our approach, termed dynamicGP, showed consistent prediction accuracy for multiple traits and across multiple time points.

Output: pass


Input: Claim: Some bacterial strains can break down plastic in controlled environments over time. [SEP] Text: Additionally, in the will be going to the as a for Strobel's and Laboratory class (all expenses paid; no donated funds will be used for travel). During this time, I will specifically collect fungi that may produce abundant lignocellulolytic enzymes to use in my plastic and rubber degradation screens. If promising candidates arise from these screens, I will use funds to sequence the genomes of the fungi to identify the responsible gene(s) and to synthesize genes that code for putative plastic or rubber degrading enzymes for future studies. wait. . . The proposed budget will be used to purchase laboratory supplies, equipment time, accessory equipment, and bioinformatic analyses for the purpose of: 1) Characterizing the enzyme from Pestalotiopsis microspora that is responsible for polyester polyurethane degradation and comparing the characteristics of this enzyme to other polyester-degrading enzymes 2) Assessing degradation of solid polyester polyurethane (as well as other types of plastic) and working in collaboration with a biodegradation testing facility to evaluate degradation of plastic by P. microspora in simulated landfill conditions 3) Screening endophyte collections for degradation of different plastics and rubbers and confirming degradation using gel permeation chromatography and scanning electron microscopy 4) Sequencing the genomes of potential candidates and looking for possible novel genes that code for plastic- or rubber-degrading enzymes. Meet the TeamMichelle LegaspiPh. D. CandidateAffiliatesYale University, Ph. D. candidate (2011-present). University of Florida, B. S. Chemistry (2010). . D. Candidate, I am driven by my desire to appreciate and learn from our natural environment in order to help integrate more sustainable systems into our lifestyles. I received my B. S. in Chemistry from the University of Florida. As an undergraduate, I spent 3 years working for the US Department of Agriculture analyzing chemically-mediated signaling interactions between plants and caterpillars in order to better understand plant defense mechanisms and increase agricultural crop yields without the use of harmful pesticides. I became increasingly interested in environmental research after studying abroad in and Fiji learning about human impact on the environment. After graduation, I worked with the Association teaching environmental education and building hiking trails in Massachusetts. I aim to combine my scientific knowledge and passion for the outdoors to tackle our environmental problems.

Output: corroborate


Input: Claim: Genetic tests can be done without any expert interpretation needed. [SEP] Text: Listen to this story: Your browser doesn't support the audio element Back 15 seconds Forward 15 seconds 0:00 on social media linkedin x twitter facebook geneticists seem to agree on one thing: There are hundreds of genes potentially linked to the condition. But few agree on which ones should be added to the gene lists or “panels” for the commercial genetic tests that some clinicians rely on. To sort through the jumble of information, some of the scientists behind the Resource (ClinGen) — a U. S. federal program for evaluating clinical genetic evidence — formed a group in 2018 called the Panel (GCEP). This group is tasked with keeping researchers, clinicians and laboratories informed about whether current evidence supports clinical testing for any given gene. The GCEP started by scrutinizing a set of 156 genes used in more than one commercial test for autism or intellectual disability. In May, the group reported that 117 — 75 percent — reach its evidence bar for a definitive link to one of the conditions, but 22 lacked sufficient evidence for clinical testing; the remaining 17 had “moderate” evidence — enough to support their inclusion on a test, but still in need of more data. The group faces an almost Sisyphean task: At its current pace of four gene assessments per month, on average, the group would need more than six years to probe the 318 genes still on its list. And new genes — and new data for old genes — emerge all the time. Spectrum spoke with panel co-chair and coordinator Riggs about the challenges the team faces and the importance of constantly reevaluating an ever-shifting genetic landscape. This interview has been edited for length and clarity. Spectrum: How does the GCEP and its process differ from other groups evaluating autism-linked genes? Riggs: One big thing that our panel adds is giving people the concept of levels of evidence. One very commonly used resource is OMIM [Inheritance in Man], and that just catalogs when a gene-disease relationship has been proposed. But it doesn’t really tell you how much or how little evidence there is to support that relationship. Over the past few years, we’ve seen a wide gradation in the evidence for the genes we’ve evaluated. Some genes have evidence from many different affected individuals, lots of different papers and very good experimental evidence, and we feel very confident in those genes.

Output: contrast


Input: Claim: Plant remains can always be accurately dated. [SEP] Text: These predicted operators were used to make longitudinal predictions of all 50 traits over the entire time domain using the two versions of dynamicGP, iterative and recursive, defined similarly to the usage of DMD (see Methods). We found that the iterative approach yielded more consistent accuracies across the investigated time points compared to the recursive approach (Fig. 3a). Across all traits and time points, the iterative approach resulted in a mean prediction accuracy of 0. 34 (± 0. 30) in 20 iterations of 5-fold cross validation. The average blue value of plant pixel colors, which quantifies the blue-yellow color space of visible light (see Table S1 for trait descriptions), emerged as the best-performing trait, with a mean prediction accuracy of 0. 79 (± 0. 07). In contrast, the plant average hue value in side images, quantified in the hue, saturation and value color space, showed the lowest prediction performance of -0. 38 (± 0. 12). Conversely, the recursive approach had a mean prediction accuracy of 0. 17 (± 0. 19) across all traits and time points. Here, the standard deviation of the y values in the fluorescent xyz color space from top images could be predicted with mean prediction accuracy of 0. 52 (± 0. 11), while the plant average hue value in side images, as quantified in the hue, saturation and value color space, again displayed the lowest mean prediction accuracy of -0. 30 (± 0. 13). The recursive predictions, as expected, tended towards zero at later time points as errors compounded. We observed that traits exhibiting consistent heritability over time, indicated by smaller values for the coefficient of variation, tended to demonstrate higher prediction accuracies across all time points. This relationship was supported by a moderate negative Pearson correlation coefficient of -0. 46 between the mean prediction accuracy for the traits from iterative dynamicGP across all time points and the coefficient of variation of the heritability estimates across the entire time points (Fig. 3d); the corresponding correlation coefficient for recursive dynamicGP was − 0. 37 (Fig. S10). Therefore, in line with expectations, traits whose heritability does not vary during development were found to be better predicted by dynamicGP approach. Performance of dynamicGP compared to baseline models To assess the predictive performance of dynamicGP, we compared it with a GP baseline approach using RR-BLUP models, which is the standard in breeding programs.

Output: pass


Input: Claim: Some bacterial strains can break down plastic in controlled environments over time. [SEP] Text: I do this research with great faith that a remedy for plastic and synthetic rubber waste accumulation already exists in our environment and is waiting to be discovered and effectively harnessed. Kaury KuceraI love science research and education because I wake up every morning with confidence that I am working to fix important problems including contamination of our environment by persistent waste. I earned my Ph. D. at using x-ray crystallography to study the three-dimensional structure of two parasite proteins that are required for infection. Upon graduation I accepted a job teaching and doing research with undergraduates at Yale. I specifically work with students to study biological activities and bioactive natural products from endophytic fungi. Besides mentoring undergraduates it is my privilege to work with Michelle to identify and study the enzymatic proteins fungi use to degrade complex polymers. people are indigenous to the where we hunt for endophytes. They believe that where there is a poison in the jungle you can also find a cure. I do this research with great faith that a remedy for plastic and synthetic rubber waste accumulation already exists in our environment and is waiting to be discovered and effectively harnessed. Lab Notes9 PostedThis lab note is for backers onlyResearch ups and downsMay 27, 20150313This lab note is for backers onlyThe plastic degrading enzyme from Pestalotiopsis microsporaAugust 25, 201403430This lab note is for backers onlyCollecting latex producing plants in the Amazon RainforestMay 22, 20141171Relevant Publication: 1) Russell, J. R. , et al. "Biodegradation of by Endophytic Fungi" Appl. Environ. Microbiol. 77, 6076-6084 (2011). News Coverage: 1) "More than Mushrooms" - 2012. 2) "Researchers: Fungus can break down plastics" CNN, August 2011. 3) "Yale researchers find fungus that can break down plastic (video)" - Register, August 2011. Other links: 1) Researchers find organisms growing within fungi capable to degrade plastics 2) Find Plastic-Photo credit: EdinburghGreens from credit: for video recording/editing and for providing equipment Project Backers41Backers100%Funded$8,000Total Donations$195. 12Please wait. . .

Output: corroborate


Input: Claim: Genetic tests can be done without any expert interpretation needed. [SEP] Text: And then there are other genes where perhaps only a single family or maybe two or three individuals have been reported. With those, we are questioning ourselves: Are we sure about this? Should those genes really be included on panels? Christian Schaaf: The first struggle that we experience both as clinicians and as molecular geneticists in the diagnostic lab is, which test should we order? There are hundreds of autism genes that appear on only 1 out of 20 panels. How is one gene list better than the other? It’s really difficult to compare. Sometimes as a clinician, it’s not easy to understand how a given group arrived at a gene’s respective classification or score. The GCEP has probably the most time-intensive approach out of many different groups that evaluate gene-disease relationships. But its greatest advantage, to me, is that it publishes its standard operating procedure and is very transparent as to how they got to the score. So when you as a clinician order a gene panel or exome sequencing and you get a variant on your report, then you can go back and look up the level of evidence that exists for that gene as it was evaluated by a group of experts. And that is particularly helpful. S: It’s interesting that a gene with so little evidence could even end up on a panel. ERR: That really speaks to how the genetic testing landscape has evolved over time. The way a lot of labs created the first panels, they were just collecting everything that had ever been suggested to be associated with a given disease. The consequence is that we’re putting all these so-called “genes of uncertain significance” on there. When we find variants in those genes, what do we do? We don’t know a lot about this gene, we can’t apply a lot of the evaluation criteria, and they get classified as a “variant of unknown significance,” or VUS. But when you get a VUS result back as a patient, or as a clinician, it’s incredibly frustrating. What do you do with that information? Is it going to eventually be your answer? Is it just going to sit at VUS for a long time? It’s really hard to deal with that. S: The panel decided to expand its scope to include new and disputed genes. Why?

Output: contrast


Input: Claim: Plant remains can always be accurately dated. [SEP] Text: The RR-BLUP models were trained with data on each trait from the first time point in a training set and used to predict the trait in all subsequent time points for the testing set. Inspecting the difference of performance between dynamicGP and the baseline for traits with different predictability, we found that the iterative version of dynamicGP outperformed the baseline RR-BLUP models consistently across all investigated time points, while the recursive version performed competitively, outperforming the RR-BLUP models for all time points between day 16 and day 33 (Fig. S8). Specifically, the mean prediction accuracy across all traits for the first time point, on which the models were trained, was 0. 24 (± 0. 15). While this accuracy was maintained when the baseline RR-BLUP models were applied to predict the traits over the first four subsequent time points, already at the second time point, both versions of dynamicGP outperformed the baseline (Fig. S8). The recursive version of dynamicGP was superior to the baseline for 12 time points, after which the accuracies of the two models were closely aligned (Fig. S8). Moreover, the iterative version of dynamicGP outperformed both the baseline and the recursive version of dynamicGP for every time point. We examined the differences in mean prediction accuracy across all time points between dynamicGP and the baseline models for each trait. The best performing trait using the recursive method, the standard deviation of the y values in the fluorescent xyz color space from top-down images, showed mean prediction accuracies above 0. 5 of all time points, which was 60% higher than the best performing trait in the baseline models (Fig. 4). In the iterative method the best performing traits, namely the average blue value of pixels as well as the average brightness measurements of green pixels and pixels with 27% saturation (Table S1), showed mean accuracies above 0. 7 of all time points, which was ~ 2. 4-fold higher than the best trait from the baseline model predictions (t-test, p-values < 0. 001 for all time points, Fig. 4). Furthermore, both versions of dynamicGP yielded mean squared errors much lower than the RR-BLUP baselines (Fig. S9). DiscussionDeveloping computational approaches that can predict the dynamics of crop growth and developmental plasticity for unseen genotypes in new environments holds the promise to revolutionize breeding practices.

Output: pass


Input: Claim: Bacterial plastic degradation could complement traditional recycling methods. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-4384267/v1This work is licensed under a CC BY 4. 0 LicenseStatus:Published31 Jul, 2024Read the published version in →Version 1posted 10You are reading this latest preprint versionAbstractAlthough plastic mulching films can help increase agricultural productivity, their removal poses a significant environmental danger. The use of biodegradable plastic mulches is an appealing eco-sustainable solution to environmental contamination problems for agroecosystems. Because the biodegradation of plastic pieces in soil is strongly dependent on microbial populations, it is critical to investigate whether and how microbial communities affect different types of mulch residuals in the soil. Through the study of the degradation of different mulches residues and of the microbial dynamics for a 6-month incubation of soil trial at three different incubation temperatures (room temperature, 30°C and 45°C) it was possible to evidence that biodegradation of mulch residual depending on of the catabolic activities of specific microbial population and the temperature of incubation. In particular, the increase of temperature up to 45°C determined a reduction of the degradation capacity for all types of mulch considered. Moreover, high-throughput sequencing bacterial alpha diversity was primarily influenced by plastic type and temperature, while fungal populations were mainly affected by temperature. Beta diversity was impacted by all experimental variables. Predicted functional genes crucial for degrading complex substrates, including those encoding hydrolases, cutinases, cellobiosidases, and lipases, were derived from 16S rRNA gene sequencing data. Cluster analysis based on predicted enzyme-encoding gene abundance revealed two clusters, mainly linked to sampling time. Finally, core microbiome analysis identified dominant bacterial and fungal ASVs in various soil-plastic ecosystems during degradation, pinpointing species potentially involved in plastic breakdown. FiguresFigure 1Figure 2Figure 3Figure 4Figure 5on the practical qualities of plastics and their wide range of uses, 391 metric tons (Mt) of it were produced only in 2021 [1, 2]. This huge production along with ineffective plastic management techniques comprises a notable amount of plastic residues in the environment [3]. Several studies showed that severe plastic pollution compromises environmental protection [4–6].

Output: corroborate


Input: Claim: Genetic tests can be done without any expert interpretation needed. [SEP] Text: ERR: A lot of the individuals in our group have a deep knowledge of the genes in this field. They’re wondering, “Hey, I just read this new paper,” or, “Hey, I’ve been getting a lot of VUS on this particular gene. What’s the evidence level? ” At that point, people started writing in with suggestions of genes to evaluate. CS: In some ways, ClinGen is crowdsourcing. The entire platform is always to be considered a work in progress. It’s never final. It’s always our best effort of evaluating the gene-disease relationship at a given time. We are very aware that that constantly changes, and that we always need to be open to reevaluation and adjustments. The task is really daunting. We are tasked with evaluating thousands of genes, and we need a huge workforce in order to do that. ERR: I don’t think it’ll ever be finished. S: It’s like how people say you can’t try every restaurant in City, because by the time you get through them all, there are new ones that have opened. ERR: Exactly. But to do this at the level that we do takes time. We could spend an entire hour discussing one gene-disease relationship. CS: And that’s only the discussion. Before that, there’s a whole process that takes many more hours. Our first step is deciding which genes to evaluate in the context of which condition or phenotype. And it’s not always an easy step. Sometimes, a given gene is listed in a database in connection with multiple disease entities. In those cases, we need to decide: Are these distinct entities? Is it a true example of pleiotropy, where variants in one gene can cause multiple traits across multiple diseases? Or is this all one group of disorders? Are there different disease mechanisms? Are there different modes of inheritance? So it’s a lot of work for each and every gene. S: Are there any implications of the work for genetics researchers? ERR: I would hope that they would be paying extra attention to the limited or disputed list. I hope they go find us the additional information that we need to move genes off that list. Sometimes what we need is additional functional information.

Output: contrast


Input: Claim: Plant remains can always be accurately dated. [SEP] Text: Such approaches can provide insights not only into how the genotype affects the time-resolved phenome, but also can highlight the temporal dependence between traits comprising the phenome. While there have been significant developments in GP of multiple-traits3–5, they essentially operate with snapshot data and do not capture the dynamics of crop traits. Here, we introduced dynamicGP, a computational approach that resolves the problem of predicting the growth dynamics across development in crops for which time-series phenome measurements for multiple genotypes are available from HTP platforms6–10. DynamicGP essentially applies GP to the building blocks of DMD, thus facilitating the prediction of multiple traits over time of unseen genotypes. Using a time-resolved HTP data collected for five days per week for five weeks beginning at day 15 after sowing from RILs of a maize MAGIC population, we showed that both the iterative and the recursive versions of dynamicGP outperformed the baseline GP approach. In addition, we showed that at the predictability threshold of 0. 5 for two of the traits, recursive dynamicGP exhibited better performance compared to the baseline GP approach. These traits were both top-down photographs measured in the fluorescence color space. For the iterative dynamicGP, we found three traits above a threshold of 0. 7 where dynamicGP outperformed the baseline, all measured in the visible colour space, namely the average blue-value of the plant pixel colors of images from above, average brightness of fraction of green-colored pixels in top-down images, and average brightness of fraction of pixels with mean 27% saturation in top-down images (Table S1). Further, we observed that the mean squared error of dynamicGP was lower for both the iterative and recursive versions than the baseline, with log-transformed MSEs of -3. 29 and − 3. 57 for iterative and recursive, respectively and − 0. 08 for the baseline (Fig. S9). Therefore, dynamicGP yields predictions which are closer to the true values than the RR-BLUP baseline models. The performance improvement of dynamicGP over the classical GP approach is due to the usage of time-resolved phenotypic data. While availability of such data is rapidly increasing due to deployment of HTP in controlled environments and on the field, dynamicGP is more resource intensive than the classical GP. However, dyanamicGP does not rely on model fitting (e. g.

Output: pass


Input: Claim: Bacterial plastic degradation could complement traditional recycling methods. [SEP] Text: Thermoanaerobaculum aquaticum MP-01, Arthrobacter nitrophenolicus SJCon, Pseudarthrobacter phenanthrenivorans Sphe3, Sphaerobacter thermophilus DSM 20745, and Neobacillus endophyticus BRMEA1 were detected only in the microbiota of soil with MB degradation (Table 1, Supplementary Fig. 2A). Other species present only in soils with LDPE belonged to the phyla Actinobacteria, Firmicutes, Gemmatimonadetes, and Proteobacteria (Table 1). Soil with TMB was characterized by three species Dehalogenimonas alkenigignens IP3-3, Thermoflavimicrobium daqui FBKL4. 011, and Hydrogenispora ethanolica LX-B, shared with soils containing MB and LDPE (Table 1, Supplementary Fig. 2A). All fungal shared core members of the TMB-treated soil were in common with the other two mulch types (Table 2 Supplementary Fig. 2B). Between them, Lophiotrema rubi, unclassified Nectriaceae, and unclassified Agaricaceae were shared with both MB and LDPE soils. Cheilymenia sp. and Solicoccozyma aeria, were common between TMB and MB treated soils. Whereas TMB and LDPE shared Gibberella sp. , Preussia flanaganii, and Preussia flanaganii. Finally, Metacordyceps chlamydospore, Niesslia mucida, Unclassified Fusarium, and Unclassified Mortierellaceae, were detected only in the core microbiota of LDPE-treated soils (Table 2). degradation The findings of this study provide important insights into the biodegradation dynamics of various mulch films at different temperatures. The considerable degradation of biodegradable plastic mulch films (MB and TMB) during six months at RT demonstrates their promise as long-term alternatives to non-biodegradable options (LDPE). The biodegradation of TMB by 51. 36% and MB mulch by 69.

Output: corroborate


Input: Claim: Genetic tests can be done without any expert interpretation needed. [SEP] Text: For example, for missense variants, is this common in the population? Is this really doing anything to the protein? CS: That’s a really important point. When you think about clinical practice, quite often we identify an additional case where a person has a variant. Say there are three case reports out there for the gene. They don’t see value in publishing yet another case report. But our work shows that there is value in doing exactly that. At those early stages, every case report counts. It’s also helpful if researchers and clinicians publish certain information that helps us assess the level of evidence. How was the variant found? Was it a candidate gene approach? Was it an exome or genome sequencing approach? How was a diagnosis of autism made? S: How does the panel’s work affect genetic diagnosis of conditions such as autism? ERR: ClinGen has a patient registry called Genome Connect, for anyone who has had genetic testing, regardless of why and regardless of the result. Through that process, we get a lot of VUS and gene-of-unknown-significance reports. One of our participants had a VUS in a gene of unknown significance, and the registry reached out to us to say, “Hey, it looks like there is more evidence out there. Can you guys evaluate it? ” And we did. We were able to say, “Hey, we do actually believe this gene is disease-causing. ” And once that is established, then the lab can go back and correctly apply the variant classification criteria and give the patient a more definitive answer. That’s a good example of how it comes full circle.

Output: contrast


Input: Claim: Plant remains can always be accurately dated. [SEP] Text: , of growth curves) and subsequent usage of the model parameters in GP, thus avoiding propagation of error and often unfounded assumptions about models that best describe the dynamics of single traits. Instead, dynamicGP builds on the advantages of DMD that allows the simultaneous investigation of multiple traits over time. Although dynamicGP is applicable with any time-resolved phenotypic data on multiple traits, we tested its performance with traits retained after correlation analysis. The selection of traits was conducted to avoid the consideration of highly correlated traits that may affect predictive performance. Like with any data-driven approach, we hypothesize that the performance of dynamicGP depends on the number of traits included and time points considered, which may vary between data sets. Both the recursive and iterative versions of dynamicGP offer the possibility to make predictions about plant phenotypes of unseen genotypes given measurements of specified time points in time, thus reducing the need for extensive temporal measurements. In this regard, while dynamicGP provides the means to overcome the challenge of predicting temporal phenomes for unseen genotypes, it inherits the limitations of GP, such as the transferability of the models to different populations and environments2. Future developments of dynamicGP can rely on extensions of DMD to consider effects of environmental factors18. These will facilitate further refinements of the proposed approach that are expected to have very substantial impact on breeding crop varieties adapted to particular regions as well as precision agriculture. generation In three independent high-throughput phenotyping experiments, 347 RILs and 9 founder lines of a MAGIC maize population19 were screened for natural variation in the HTP facility for large plants at the of Research in Gatersleben, Germany20. The panel was divided into three equal-sized subgroups and analysed in three consecutive experiments: 2113MH (116 RILs and 9 founders), 2121MH (116 RILs and 9 founders), and 2137MH (115 RILs and 8 founders). To ensure compatibility of the data, each HTP experiment was supplemented by an additional randomly drawn overlap of 21, 21 or 27 RILs, respectively, half of which came from each of the other two experiments. The experimental design followed a complete randomised block design with three replicates per genotype (three carriers with two plants each) overlapping with three blocks corresponding to four of the twelve lanes of the HTP system. Image acquisition began at 15 DAS for 31 time points per experiment and six days per week.

Output: pass


Input: Claim: Bacterial plastic degradation could complement traditional recycling methods. [SEP] Text: Members of the Bacteroidetes family, including Flavobacterium, can facilitate the breakdown and use of complex polysaccharides [49], hence influencing the process of denitrification [50]. Evidence that biodegradable plastics tilled in agricultural soils contribute a little amount of carbon is another factor supporting these shifts. Soil bacteria in agricultural settings are often limited by carbon availability, therefore these inputs cause a response [19]. Furthermore, main variations were at RT and 30°C supporting the results of plastic breakdown and highlighting that burial of biodegradable plastics can lead to changes in soil microbial community structures [20, 22, 23]. LDPE treated soil showed slight changes in its bacterial community composition, as well as its profile was comparable to the control after six months. Otherwise, according to the phyla found, the literature suggests that microplastic cultures were enriched in polyethylene (or other polymers) degrading taxa, such as Bacteroidetes and Proteobacteria [51, 52]. All samples incubated at 45°C showed a stable bacterial composition up to 6 months, probably due to bacterial decrease in activity with higher temperature occurrences [41], aligning with the absence of plastic degradation No fluctuations in the abundance of predominant fungal phyla Ascomycota, Basidiomycota, and Mortierellomycota were observed at RT and 30°C. Previous research showed that the phyla Ascomycota and Basidiomycota were responsible for the breakdown of oil-based polymeric polymers [19, 23, 53]. Therefore, these findings suggest that, at least throughout the 6-month trial, no mesophilic fungal communities linked to plastic degradation emerged. Otherwise, the shifts observed at 45° C in fungal community composition highlight the influence of both plastic and temperature on fungal dynamics, in absence of plastic degradation. In all samples, the Ascomycota and Aphelidiomycota decreased, and the unclassified fungi increased. These last can be assumed that the thermophilic phyla selected by the plastic and temperature are responsible for the shift in fungal profiles at 45°C.

Output: corroborate


Input: Claim: Governments worldwide are being pressured to set stricter climate policies. [SEP] Text: The optimal mitigation pathways using the disutility approach is reflected by higher initial rates of increase in the global carbon-price followed by a slower rate culminating in a peak or level price by 2100 (Fig. 3c, in orange). In comparison, the standard utility approach (Fig. 3c, in blue) shows a preference for delayed deep mitigation. Correspondingly, a lower initial increase in the carbon price is followed by a steeply increasing rate across the century. This culminates in a carbon price at 2100 that is higher than with the disutility approach, and which continues to peak beyond the modelled timeframe. The above illustrates that taking into account the possibility that disutility of damages valued more strongly than the disutility of mitigation costs can have a strong impact on the optimal peak temperature and emission pathway. A simple sensitivity analysis (methodological details and results presented in Supplementary Information) confirms that this impact from capturing loss aversion p18. An important reason for this is that the costs and impact of mitigating climate change are more predictable than the impact of damages. The disutility approach described in this 22. Apart from the aggregated assessments of optimal emissions pathways, the disutility approach can also be used to address distributional concerns by incorporating the unequal incidence of climate impacts as seen in Denning et al. 23, with explicit representation of aversion towards them. Finally, alternative functional formulations of the disutility may also be explored further, such as by a concave (marginally increasing) function of accumulated damages with appropriate parametrisation of p11. Further research can experiment with different values for the damage aversion factor. Experimental methods and setup We run the model with welfare functions specified to include the disutility approach and with the standard utility approach of the predecessor model. Additionally, model runs are also performed by varying the damage loss aversion factor as well as with different values for the carbon budget, damage function specification and discount rate – or more specifically, the pure rate of time preference (PRTP). A summary of the variables and values is shown in Table 1 below. Outcomes are retained for analysis for the full factorial of these input combinations.

Output: pass


Input: Claim: Plant remains can always be accurately dated. [SEP] Text: An image acquisition event was a carrier with two plants phenotyped by one image in top view and four images in side view at 22°, 45°, 112° and 135°. Altogether 347,740 images were taken (2113MH, 114710 images, 91768 side view, 22942 top view; 2121MH, 116840 images, 93472 side view, 23368 top view; 2137MH, 116190 images, 92952 side view, 23238 top view). Traits were derived from images by IAP version 2. 3. 021 and were analysed by alignment by days after sowing (DAS). A shift of the sowing date by one day in 2121MH allowed alignment to DAS for only 5 days per week, so that 2 days were missing each week. Consequently, the time points were not consecutive, but were organized in five-day blocks, with one measurement taking place each day and no measurements for two days over five weeks. The analysed data comprised measurements for 498 traits from side, top and combined categories derived from multi-colour-space image analysis (Fig. 3e, Tables S122 Heuermann et al. 2024). A linear mixed model with random effect of genotype, experiment, lane, genotype-by-experiment interaction and replicate nested in experiment was built for each time point. Trait heritability was calculated by the ratio between genotype variance component and total variance component. Summation of best linear unbiased prediction (BLUP) value of genotype and fixed effect of intercept were used as the final trait values. Genotyping data was available for 330 Maize RILs consisting of 79,557 high-quality SNPs derived from SPET genotyping23. Missing genotyping data were imputed using Beagle 5. 224. We then removed SNPs with a minor allele frequency < 0. 05, resulting in a final set of 70,846 SNPs for GP models. The phenotypic data sets used in this study are available at: Clustering and trait selection Traits were first clustered using the Mantel correlations over all genotypes and time points, represented as a matrix. To this end, a network was created with nodes representing traits and edges denoting inter-trait Mantel correlations of value above 0. 96.

Output: pass


Input: Claim: Bacterial plastic degradation could complement traditional recycling methods. [SEP] Text: The clustering of predicted functions demonstrated that potential gene abundances were strongly affected by sampling time since two major clusters were observed: I) samples collected after three months of incubation at 30°C and RT tilled with biodegradable mulch residues (MB and TMB), comprising three exceptions: t3-TMB_T 45°C, t6-C_T30°C and t3-LDPE_T 30°C; and II) all remaining samples. The increase in several encoding genes in cluster I, like cutinase (K08095) and esterase/lipase (K01066) suggests a potential active response to the presence of plastic residues [55, 57]. These enzymes are known to cleave the ester bonds and degrade polyurethane substrate, and therefore can act also on non-biodegradable plastics [56]. Cutinase can hydrolyze a broad variety of synthetic polymer esters, both soluble and insoluble, that are structurally related to cutin [57]. The increase of cellulose 1,4-beta-cellobiosidase (K01225, cluster I) that hydrolyze cellulose releasing cellobiose, was probably induced by the presence of biodegradable mulch residues in soil [58]. Cluster II comprises all samples at the latter sampling time, controls, and samples incubated at 45°C. In particular, the presence of MB and TMB from later sampling times where plastic degradation occurred, suggests that there may be significant differences in some metabolic activities compared to the same samples after three months of incubation. This confirms that changes in the activities of bacterial populations over time reflect the dynamics of plastic degradation in soil. Over time, polymers degrade enzymatically, and microorganisms assimilate and utilize the degradation products, potentially causing shifts in the predicted coding genes [25]. Core microbiota associated with mulch plastic residues The microbial core investigation aimed to provide hints of potential microorganisms involved in plastic breakdown. The analysis was performed on all plastic-type soil systems at temperatures where breakdown occurred (RT and 30°C). There is still a lack of literature, on information on microbial species known for their ability to degrade plastics. Thus, core members found in this work could be evaluated based on their enzymatic kit to assess their potential action on natural and non-natural polymers.

Output: corroborate


Input: Claim: Research shows that education on climate change can lead to greater public concern. [SEP] Text: Haltinner and Sarathchandra found a political narrative around a global conspiracy as well as a religious narrative around “God’s plan”19. Gradualism had been a moderate discourse taken by the IPCC and others13, at least until recently. Gradualism, or ecological modernization8,20, expects that climate change solutions will be developed in time to limit the worst effects of climate change. The environmental movement has pointed to the potential for a catastrophic future if urgent action is not taken on climate change21. Al Gore’s 2006 film, Truth, is an example. Unfortunately, history shows “that exaggerated claims of catastrophic risks from even real and serious environmental problems can backfire in terms of stimulating public concern and appropriate policy responses”22. In response to failure to make significant progress on climate change, factions within the movement are now looking at alternatives to a discourse of catastrophism21. Lay understandings of climate change are formed through media discourse and other social contexts7. One of those understandings is that climate change is a future phenomenon, which lessens current concern7,23. Potentially offsetting that is a concern for future generations, such as one’s children24. Storytelling can be a way of conveying one’s knowledge or concern about climate change8,12. A “story is something with a beginning, a middle, and end. … [T]here is generally also a protagonist. … Then something happens”12. Analysis of storytelling considers why and how a story is told in a particular context12. In the psychological literature on reasoning processes, Evans25 asserts that there are two types of reasoning: heuristic and analytic. The purpose of heuristic processes is to decide what information is relevant, before feeding that information into the analytic process. In a different interpretation of the term ‘heuristics,’ the heuristic of representativeness is used to judge whether a particular instance of something can be categorised in a certain way26. In either interpretation, representativeness or the heuristic process of selection may be prone to bias27. Confirmation bias is “a tendency to look for confirmatory evidence for our hypotheses,” or to hold onto existing ideas and reject new ones that contradict them28.

Output: contrast


Input: Claim: Ancient agricultural practices have remained unchanged until today. [SEP] Text: Full text Figures and data Peer review Side by side Figures 6 figures Figures Figure 1 with 6 supplements Download asset Open asset Disrupting TC10 level or function reduced AMPARs on the cell surface. (A) Representative somata of cultured rat hippocampal neurons (E18) expressing mCherry-tagged GluA1 subunits, free Venus (Control), shTC10RNA or Venus-tagged TC10WT, TC10DN or TC10CA. Intact, live neurons (DIV18) were stained with anti-RFP antibody to visualize surface AMPA-type glutamate receptors (AMPARs) and then fixed and processed. Images of the whole neuron for each of the shown somata are displayed in Figure 1—figure supplement 2. (Scale bar = 10 μm). (B) Representative dendrites of neurons expressing mCherry-GluA1 without TC10 or with TC10WT, TC10DN, or TC10CA. Arrows indicate dendrites expressing TC10 mutants (TC10DN or CA) with weak surface expression of GluA1; arrowheads mark dendrites expressing mCherry-GluA1 without TC10 mutant expression and normal GluA1 surface expression level. (Scale bar = 5 μm). (C) Representative dendrites of intact neurons expressing either free Venus (Control), TC10WT, TC10DN, or TC10CA and labeled with an N-terminal, anti-GluA1 mAb to visualize endogenous, surface GluA1 receptors. (Scale bar = 5 μm). (D) Quantification of the surface/total ratio of mCherry-GluA1 at the somata of transfected cells in (A). Data are shown as ± SEM; control cells (YFP) 100% ± 14%, n = 32; TC10WT cells 118% ± 22%, n = 17; TC10DN cells 53% ± 9%, n = 23; TC10CA cells 47% ± 9%, n = 14; and shTC10 cells 21% ± 7%, n = 10. (**p < 0. 03 relative to TC10WT). (E) Quantification of the surface/total ratio of mCherry-GluA1 in dendrites of transfected cells in (B).

Output: pass


Input: Claim: Bacterial plastic degradation could complement traditional recycling methods. [SEP] Text: Among the identified bacterial species, Hydrogenispora ethanolica LX-B isolated from the mesophilic (35°C) anaerobic fermentation process of sludge, can ferment substrates with different carbon sources, including starch, glucose, maltose, and fructose [59], suggesting its potential involvement in the degradation of starch-based plastics. The species Thermoflavimicrobium daqui FBKL4. 01, a thermophilic bacterium isolated from the Daqu used to produce Chinese liquor Moutai, can ferments pure wheat [60], confirming its involvement in the degradation of starch and cellulose. The analysis of fungal core revealed 12 different species. Among them, Solicoccozyma aeria (Cryptococcus aerius) is known for its ability to produce amylases at 30°C, to digest raw starch [61]. These three microbial species were found in soils amended with MB and TMB and could therefore be of interest for the biodegradation of starch-based plastics. ConclusionsSustainable agricultural systems require to minimize the accumulation of plastic waste in soils through transitioning to effective biodegradable alternatives. This study examines how integrating both biodegradable and non-biodegradable mulch types into soil affects the microbial community over a six-month trial period. providing a direct comparison among the mulch sheets applied under the same conditions. The research observed a notable reduction in the weight of biodegradable plastics (MB and TMB), which is related to changes in bacterial community dynamics and their associated functions under mesophilic conditions. By analyzing the core microbiota of the plastic-influenced soil ecosystem, the study offers insights into identifying microbial groups with potential roles in plastic degradation. Nevertheless, the degradation of plastics in agricultural settings should take into account both biotic and abiotic factors, necessitating further study to inform and guide more sustainable agricultural management practices. DeclarationsAuthor ContributionI. R. and M. S. carried out the experiments and data analysis. I. R. wrote the main manuscript text. G. M. data analysis. V. V. reviewed and edited the manuscript. I. R. and V. V. participated in the experimental design. O. P. reviewed and edited the manuscript, conceived the concepts of the study and participated in its design. Data AvailabilityThe datasets presented in this study can be found in online repositories.

Output: corroborate