Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: 01). For populations with a significant shift and for wildtype and NT controls, the overall percentage of tracked cells that have a velocity of less than 1 µm/s is highlighted. NT: non-targeting control, a wildtype transfected parasite, harbouring a pLdCH-hyBE4max-tdTomato-targeting-guide (Target 1 (−)) plasmid. Mean speed was measured in duplicates 14 days post transfection and in triplicates 28 and 42 days post transfection. Error bars show standard deviation between these replicates. We then cultured the cells for another 14 days, keeping them under constant drug selection to maintain the guide carrying pLdCH-hyBE4max episome. As expected, we saw for most cell lines a further increase in proportion of cells that showed a decrease in swimming speed when PF16 was targeted. For L. donovani three of the four PF16 targeting guides (PF16-1, PF16-2, and PF16-3) still yielded significantly decreased motility, while for MFT no significant effect was observed (Figure 4B, Figure 4—figure supplement 1A, and Supplementary file 1). This confirmed that observed motility phenotypes were specific to the targeted gene. In L. mexicana two of the four PF16 targeting guides (PF16-2 and PF16-3) and one of the PFR2 targeting guides (PFR2-1) showed now a significant effect (Figure 4B, Figure 4—figure supplement 1A, and Supplementary file 1). In L. infantum, the percentage of cells showing decreased mutant speed was now lower for the IFT88 targeting guide (IFT88-1), which is expected as the deletion of IFT88 in L. mexicana has been shown to reduce the doubling time (Beneke et al. , 2019). L. infantum parasites with a deleterious IFT88 mutation are therefore most likely outcompeted from the mutant population (Figure 4B, Figure 4—figure supplement 1A, and Supplementary file 1). Since we still did not observe a measureable effect on any of the transfected L. major parasites at 28 days post transfection, we cultured parasites for another 14 days.

Output: pass


Input: Claim: Sustainable architecture often includes rainwater harvesting systems. [SEP] Text: Caparrós-Martínez JL, Rueda-Lópe N, Milán-García J, J. Public policies for sustainability and water security: The case of Almeria (Spain). Conserv. 2020;23:e01037. View 14. ’Donnel E, Matthews N, et al. Scalable solutions to freshwater scarcity: Advancing theories of change to incentivize sustainable water use. Water Secur. 2020;9:100055. View 15. Özerol öge M. Urban water management and climate change adaptation: A self-assessment study by seven midsize cities in the Region. Soc. 2020;55:102066. View 16. -Pomorska A. Socio-economic aspects of centralized wastewater system for rural settlement under conditions of Eastern Poland. Water. 2022;14(10):1667. View 17. Zapasa A, Musz-Pomorska A, Gołębiowska J, Widomski MK. Financial, environmental and social sustainability of rural sanitary wastewater system: case study. Sci. 2022;12(12):277. View 18. Harding R. Ecologically sustainable development: origins, implementation and challenge. Desalination. 2006;187(1–3):229-39. View 19. . Sustainability of donor-founded rural water supply and sanitation projects in Mbire district, Zimbabwe. Earth. 2014;76:134-9. View 20. Frone S, Frone DF. Economic risk to a regional water supply and sanitation project in Romania. Financ. 2015;32:550-7. View 21. Oviedo-Ocaña -Sanchez ML, Zaraza-Peña JM. Financial feasibility of end-user designed rainwater harvesting and greywater reuse systems for high water use households. Pol Res. 2018;25(20):19200-16. View 22. Meville-. Typologies for UK Houses: A Analysis of System Configurations. Water. 2016:8(4):129. View 23. . Studying the effect of rain water harvesting from roof surfaces on runoff and household consumption reduction. Soc. 2018;43:317-24. View 24. . The performance of rainwater tanks for stormwater retention and water supply at the household scale: an empirical study.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: , 2015) outlines that learners may exhibit an apparent decline in their academic performance if their psychological well-being was at stake- be it from a simple teacher-student conversation to a prejudice based peer pressure. The psychological well-being is hugely affected by how individuals are classed in a certain community and their identity is categorised (Bliuc et al. , 2011). Those categorisation, as outlined by Tajfel (1981), dictate in many instances how individual learners should behave and sometimes set limits to their academic performance. Figure 4: Comments from the narratives which highlight Academic PerformanceFigure 4 shows some instances of comments from my findings. In examples (1:8, 1:14 and 1:16), there was a clear evidence of comments which had affected my psychological wellbeing. Those comments from past encounters did blur my view to what my identity was and whether I was competent enough to continue my study. Nevertheless, examples (1:12, 1:13, 1:16, 1:17, 1:18, 1:19, 1:21 and 1:22) show quite the opposite. I have managed to recall and reflect on those incidents as being fairly positive and did motivate me throughout my study. Comment (1:17) in particular resonates a sense of acceptance by others, which had influenced how I could steer each comment/incident to my psychological well-being. Achieving a healthy and strong identity had helped my academic performance to improve and retain strong autonomous motivation. 8. research has outlined the relationship between social identity (perceived through stereotypes and racial classification) and academic performance (perceived by individual learners’ motivation and academic achievement). The prospect of being part of a group and the feeling of belonging was the prominent factor through which academic performance was gauged- both failure and success. Foreign students, regardless of their background, are at the risk of being isolated and not fully integrated in their classroom environment. Some could battle their way in and accept the majority group’s expectations- such as language proficiency and academic performance. As we have seen in this research, foreign students’ relatedness can be achieved and those students can in fact recover from racial profiling incidents and reach full access to the majority group. Racial profiling and negative stereotypes have proven, throughout this research, to affect foreign students’ motivation and adaptation to the learning environment.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: We increased the hygromycin B concentration by 10-fold, in an attempt to force the cells to ramp-up their episomal expression of pLdCH-hyBE4max. We also continued the incubation of L. infantum parasites, transfected with a guide targeting IFT88 (IFT88-1). As expected (Beneke et al. , 2019), the proportion of cells showing a motility phenotype in the IFT88 targeted L. infantum population decreased further (Figure 4C, Figure 4—figure supplement 2B, and Supplementary file 1). For L. major parasites, we finally could detect cells with a motility phenotype within the PF16 guide transfected population (PF16-3). However, the transfected IFT88 guide (IFT88-1) still did not induce any measurable effect in L. major (Figure 4C, Figure 4—figure supplement 2B, and Supplementary file 1). Meanwhile, increased drug selection did not significantly increase the proportion of cells affected (Figure 4D, Figure 4—figure supplement 2B, and Supplementary file 1), even though we could detect an increase in expression of the hyBE4max CBE protein for L. major (Figure 4—figure supplement 4). To verify that observed phenotypes were induced by C to T editing, we then Sanger sequenced the guide targeting loci of PF16, IFT88, and PFR2 in respective targeted Leishmania species (from time point 28 days post transfection) (Figure 5 and Supplementary file 2). Observed motility phenotypes correlated well with editing rates. For example, guide PF16-3, yielding the strongest decrease in motility across all species, showed high editing rates in all four species with up to 100% C to T conversion. Guide PF16-2, which only showed a motility phenotype in L. mexicana and L. donovani, also only showed editing in those two species and guide PF16-1, which only showed a decrease in mutant speed in L. donovani, also just showed editing in L. donovani (Figures 4B and 5A). Surprisingly, we saw high editing rates for three of the four IFT88 targeting guides in L. infantum and L.

Output: pass


Input: Claim: Sustainable architecture often includes rainwater harvesting systems. [SEP] Text: Hydrol Process. 2015;29(1):152-60. View 25. . Improving the Multi-of Real-Technology. Water. 2018;10(2):147. View 26. . Using recycled water for non-potable, urban uses: a review with particular reference to toilet ﬂushing. Supply. 2003;3(4):69-77. View 27. . State of the art of rainwater harvesting systems towards promoting green built environments: a review. Treat. 2016;57(1):95-104. View 28. . Investment feasibility analysis of rainwater harvesting in a building in Brazil. Supply. 2018;18(4):1497-504. View 29. Lublin. The natural environment in Lublin. Available from: Accessed 2024 Jan 30. Polish. View 30. Journal of Laws 2002 No. 8 item 70. Regulation of the Minister of Infrastructure of January 14, 2002 on the determining of average standards of water consumption. Available from: Accessed 2024 Feb 10. Polish. View 31. Journal of Laws 2003 No. 169 item 1650. Announcement of the Minister of Economy, Labor and of August 28, 2003 on the announcement of the consolidated text of the regulation of the Minister of Labor and on general occupational health and safety regulations. Available from: Accessed 2024 Feb 10. Polish. View 32. . Northeast community tree guide: benefits, costs, and strategic planning. Available from: Accessed 2024 Apr 2. View 33. Coleman A, Grimes A. Betterment taxes, capital gains and benefit cost ratios. Econ Lett. 2010;109(1):54-6. View 34. . The role of the in the theory and application of duration to capital budgeting. J Account. 1982;9(4):511-22. View 35. : The Essentials. Available from: Accessed 2024 Apr 2. View 36. . A simple-to-implement real options method for the energy sector. Energy. 2020;197:117226. View 37. Rączka J. Analiza efektywności kosztowej w oparciu o wskaźnik dynamicznego kosztu jednostkowego [Analysis of Based on Cost Indicator].

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: Autonomous motivation is influenced by many factors, one of the most vital ones is peer pressure- through racial profiling mostly. Foreign students can overcome this potentially sensitive factor, as proven earlier in this research, through channeling negative stereotypes and racial profiling incidents to boost their motivation. Throughout my autoethnographic account, it was clear that racial profiling and negative stereotypes did in fact affect my academic performance at a certain stage. However, after examining my autoethnographic account (with the data collected from Twitter) in terms of relatedness and autonomous motivation, it can be deduced that academic performance is interlinked with how we are perceived by others; the key to unravel our true potential is within us if we learn how to relate to others and use it to achieve autonomous motivation. With access to detailed narratives about every single event that I had encountered which involved an instance of racial profiling would have been handy for this study. Recollection of past events require extreme emotional and physical effort (Gibbs, 1988), which at some point prevented me from elaborating on particular dialogues or interactions. Future studies could delve into those issues in depth so that a clearer conclusion is then to be reached. Moreover, the relevance and application of the results were not tested on other foreign students which calls for a more inclusive study, perhaps a mixed method future studies, so that this research would reach a wider audience. About the authorand. Mohanad Alaniis currently a lecturer in the at () in the Emirates. Prior to his recent appointment at he was a lecturer in the at South and Birmingham, UK. Mohanad Alani’s research interests evolve around employing technology in the classroom- VR technology in particular. His future work aims at utilising VR activities to improve vocabulary acquisition through designated VR studios for EFL students in the UAE. Email: [email protected] and [email protected]ORCID: 0000-0003-2459-64542021. Revised: 13 December 2021. Accepted: 15 December 2021. Published online: 20 June 2022. Cover image: Monstera via Pexels. 345. Ahmed, W. , Bath, P. , & Demartini, G. (2017). as a Data Source: of Ethical, Legal, and Methodological Challenges. Ethics of (Vol. 2, 942. J. , Tahvanainen, L. , Pitkänen, S.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: major but only guide IFT88-1 resulted in a measurable motility and flagellar assembly phenotype in L. infantum (Figures 4B and 5B and Figure 4—figure supplement 3). Analysing the Sanger sequencing trace plots further, we noticed that only guide IFT88-1 induced a mutation that resulted in a STOP codon, while non-STOP codon mutations became dominant for the other two guides (Supplementary file 2), presumably due to the known IFT88 growth phenotype (Beneke et al. , 2019). For guides targeting PFR2 in L. mexicana, only guide PFR2-1 was efficient, which was also the only guide resulting in decreased motility (Figures 4B and 5C). To confirm that indeed, the knockdown of PFR2 was successful, we analysed the expression of the targeted protein by western blot, confirming a strong knockdown only for guide PFR2-1 (~97% knockdown compared to wildtype) (Figure 5—figure supplement 1). It is important to highlight that this strong knockdown was achieved in non-clonal populations that had been selected to express the hyBE4max and only one sgRNA. There was no requirement for additional donor DNA or isolation of clones. This is despite the fact that PFR2 is a three-copy gene in a tandem array. Figure 5 with 1 supplement see all Download asset Open asset Determining hyBE4max editing rates following targeting of single- and multi-copy genes. L. donovani, L. infantum, L. mexicana, and L. major wildtype parasites were transfected with pLdCH-hyBE4max-sgRNA expression plasmids, targeting (A) PF16, (B) IFT88, and (C) PFR2 with four guides each (see main text description). Discordance from Sanger sequencing traces before transfection (wildtype) and 28 days after transfection (Targets 1, 2, 3, and 4) was measured and plotted as described in Figure 3B. The position and orientation of each guide are indicated above. Since guides targeting MFT in L.

Output: pass


Input: Claim: Sustainable architecture often includes rainwater harvesting systems. [SEP] Text: Warsaw (Poland): Programme; 2002. Polish. View 38. . Economic appraisal of energy efficiency in buildings using cost-effectiveness assessment. Financ. 2015;21:422-30. View 39. Widomski MK, Ładziak E, Łagód G. Economic aspects of sustainable sanitation in rural settlements. Eng Environ. 2017;10(3):153-62. View 40. . Neural networks in economic analyses of wastewater systems. Appl. 2011;38(8):10031-5. View 41. and Council. Ą(UE) nr 480/2014 [(EU) No 480/2014]. Available from: Accessed 2024 Feb 20. Polish. View 42. . How much green roofs and rainwater harvesting systems can contribute to urban flood mitigation? J. 2023;20(2):140-57. View 43. Joksimovic D, Alam Z. Cost efficiency of Development (LID) stormwater management practices. Procedia Eng. 2014;89:734-41. View 44. . A spatial life cycle cost comparison of residential greywater and rainwater harvesting systems. Sci. 2021;38(8):715-28. View 45. . Economic feasibility of rainwater harvesting applications in the West Bank, Palestine. Water. 2023;15(6):1023. View 46. . of on a Cost-Benefit Analysis: A Analysis. Water. 2023;15(1):186. View 47. Islam MR. Factors influencing economic benefit of rainwater harvesting: an empirical analysis. . 2023;72(1):32-48. View 0007.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-3342736/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractFollowing the deployment of the System (LMS) platform in higher educational institutions in Ethiopia, a massive amount of potentially helpful but as-yet untapped educational data has been generated. Despite the fact that the data is powerful enough to contribute to reducing student dropout rates through the application of modern educational data mining techniques such as machine learning, it has not been successfully employed to tackle student academic performance problems in higher education institutions(HEIs). As a result, a machine learning model was proposed based on data from three semesters of undergraduate students at University. To predict students' academic achievement, five machine learning methods (and Decision Tree) were used. Tree model outperformed other models with a promising result of 97. 3% on test accuracy and was selected as a proposed model. Moreover, our findings suggest that academic factors (entrance result, study time, attendance, and Internet access) and socio-demographic factors (age, gender, father job, mother job, family size, and address) had a greater impact on students' academic success. However, the academic performance of students was less affected by additional features like extra classes, another job, and guidance and counselling. Moreover, we are working on improving the accuracy of the proposed model. In this study, the student entrance result is the only variable used from the pre-university data. However, a CGPA result is not sufficient to qualify a student. Therefore, in the future, the exit exam results of the students will be incorporated. System (LMS)Multi-(HEIs)Students’ (EDM)FiguresFigure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9Figure 10Figure 111 IntroductionRecently, large amounts of data on student academic performance have been produced due to the implementation of the learning management system (LMS) platform in higher educational institutions (HEI) [1] [2] [3]. Therefore, the potential to extract valuable knowledge and insights from educational data has gained momentum. The extracted knowledge has the potential to assist decision-makers if tapped properly.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: This demonstrates that our method can potentially be used for positive and antileishmanial drug-resistant screening. Figure 6 Download asset Open asset HyBE4max targeting of the L. donovani miltefosine transporter (MFT). (A) L. donovani wildtype parasites were transfected with pLdCH-hyBE4max-sgRNA expression plasmids, targeting MFT with four guides each. Sixteeen days post transfection, transfected and non-transfected parasites were treated for 48 hr with 20 µM miltefosine, then washed and cultured for another 72 hr (pre-treated parasites). (B) Pre-treated and (C) non-pre-treated parasites were subjected to different doses of miltefosine (5, 20, 50, 80, and 400 µM) and relative growth between ‘no drug controls’ and each different dose was measured using an MTT cell viability assay. Asterisks indicate one-way analysis of variance (ANOVA) test with post hoc Tukey honestly significant difference (HSD), comparing each mutant and wildtype: *p > 0. 05, **p > 0. 01 (blue: MFT-1; green: MFT-2; red: MFT-3; yellow: MFT-4). Error bars show standard deviations of triplicates. Discordance from Sanger sequencing traces before transfection (wildtype) and 21 days after transfection (MFT-1, 2, 3, and 4), from either (D) pre-treated and (E) non-pre-treated populations, was measured and plotted as described in Figure 3B. The position and orientation of each guide are indicated above. Finally, we also analysed trace plots along the entire Sanger sequencing range to verify the editing window. Out of all 26 guides used in this study, including tdTomato targeting guides from above, 24 guides resulted in editing exclusively within the expected hyBE4max 4–12 editing window (Zhang et al. , 2020). One guide showed editing activity within 3nt range of the 4–12 editing window (tdTomato guide Target 2 (+); Figure 3A) and one guide resulted in editing within 6nt range of the guide sequence (PF16-1 in L. donovani; Supplementary file 2).

Output: pass


Input: Claim: Recycled materials are commonly used in sustainable building construction. [SEP] Text: Home e187483682301310 Back to All published 10. 2174/18748368-v17-e230221-2022-8 NextNEXT 1. of casted concrete debris to yield new concrete safeguards natural resources, diminishes the impact on declining landfill space, decreases dumping costs, and may decrease overall project cost. Thus, the construction industry has been undertaking numerous actions to diminish waste generation and improve the management of construction and demolition waste [1]. Because of the growing level of demolition, it is becoming crucial to efficiently reclaim demolition rubbles in order to preserve natural resources. Dwindling natural aggregate sources as well as increasing difficulties with waste management have strengthened the concept of utilizing recycled waste as aggregate for RAC [2]. RAC is, by definition, concrete produced by the utilization of recycled aggregate. Kou et al. [3] concluded that RAC will achieve the following requirements of green materials: i) The material of recycled aggregates will reduce the depletion of natural resources and energy consumption. ii) The material will be environmentally friendly. iii) The material will lead to the realization of the concept of sustainable development. Despite the fact that there are recognizable benefits of utilizing RAC, there are some technical impediments restricting its utilization in concrete production. RCA is actually a minor piece of concrete composed of adhered mortar (AM) attached to natural coarse aggregate (NCA) [4]. Recycled coarse aggregates (RCA) are angular with irregular surfaces, processing a higher water absorption capability, lower specific gravity, and higher LA abrasion loss; they typically fail the sulfate soundness investigation, but normally pass the magnesium soundness investigation. For a perfect comprehension of the RCA matrix, the individual parts must be identified independently [5]. Thus, freshly casted concrete composed of RCA tends to be very rough due to the angular shape and irregular surface of the aggregate. Additionally, it is more susceptible to diminishing slump rate and requires an increase in water contents because of the increase in the absorption of the mortar adhered to the aggregate and higher air contents due to the greater porosity of the recycled aggregate and the entrained air in the adhered mortar. Li et al.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: If extracted properly, this knowledge has the potential to significantly impact students’ learning experiences, learning outcomes, efficiency in managing learning organizations’ services, and others. Educational data mining (EDM) is an emerging field aimed at addressing these needs[4] [2] [5]. In order to solve problems or gain insights from the massive amount of data in educational systems, DM methods use comprehensive techniques from statistics, databases, artificial intelligence, and, more recently, machine learning (ML) to analyze data [2]. One of the popular tools that has the ability to extract useful information from large amounts of data in modern data mining is machine learning [2]. While conventional data mining techniques typically require manual intervention, machine learning does not require human intervention and can continuously learn and improve its models as more data becomes available[2]. More specifically, machine learning is a branch of artificial intelligence that enables systems to automatically learn and optimize models from analyzing data without being directly programmed [6]. ML has been successfully applied to predict students’ academic performance, identify at-risk students, define key learning requirements for different students, predict student graduation rates, and others[7] [8]. In this study, machine learning will be utilized to predict student academic performance. 1. 1 is one of the public higher educational institutions (HEIs) found in Ethiopia that recently deployed the System (LMS) and has a large data set of student information stored in its databases. The main problem that initiated this study was the students' low academic performance in higher educational institutions (HEIs). Based on the data from the registrar office of the university, the trend of graduating students is not proportional to the trend of enrolled students, and an increasing number of students commit readmission, suggesting that they did not perform well in their academics. For instance, during the 2017–2018 academic calendar, 400 out of 5200 students accounted for 7. 69% of complete dismissals, and 294 students accounted for 5. 65% of dismissals with readmission. During the 2018–2019 academic calendar, the number of students who were completely dismissed was 1339 out of 5700, which accounted for 23. 5%, and those who were dismissed with a readmission chance were 361, which accounted for 6. 3%. During the 2018–2019 academic calendar, the number of students who were completely dismissed was 1782 out of 6200, which accounted for 28.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: However, editing rates of these two guides were relatively low (23% and 25–27%, respectively), while editing within the 4–12 editing window reached up to 100%. A Leishmania-optimised CBE for targeting essential genes in pooled transfection screens While these proof of principle mutants demonstrate that CBEs can be used to efficiently introduce STOP codons in Leishmania genes, allowing to generate functional null mutant populations by using just one sgRNA, we wanted to further improve our method by modifying the Rad51 ssDBD. Comparing the Rad51 ssDBD from Leishmania with the human version, the Leishmania version contains an N-terminal extension that is bound to the conserved Rad51 ssDBD part through a glutamine-rich repeat (Figure 7—figure supplement 1A). Glutamine-rich repeats, known to be enriched in transcription factors, can increase DNA–protein binding by forming hydrogen bonds via their amide site chains (Perutz et al. , 1994). Since L. major had the longest glutamine-rich repeat among our tested Leishmania species, we decided to replace the Rad51 ssDBD in pLdCH-hyBE4max with an L. major-derived Rad51 ssDBD, giving rise to pLdCH-hyBE4max-LmajDBD (Figure 7—figure supplement 1B). We then chose L. mexicana to test our Leishmania-optimised CBE as in experiments above editing rates in L. mexicana were higher than in L. major while not affecting parasite growth. First, we repeated our tdTomato reporter assay and saw an increase in the knockdown effect for the Leishmania-optimised CBE version for two of the four targeting guides at 16 days post transfection (1. 6-fold increase for guide Target 2 on the coding strand and 5. 3-fold increase for guide Target 2 on the template strand). For all other guides, including control guides that were designed not to yield a STOP codon, no major difference between both CBE versions could be detected (Figure 7—figure supplement 1C). Encouraged by the slightly improved editing rate, we then decided to test whether our method could also be used to target essential genes.

Output: pass


Input: Claim: Recycled materials are commonly used in sustainable building construction. [SEP] Text: It is also feasible to produce this material in the concrete mixer by mixing 6-20% of limestone fines, according to BS 7979 [17], with Portland cement. This cement is normally used for general construction work in the absence of the requirements of other special properties. The natural aggregates (NA) consisted of deposited siliceous sand (quartz) as fine aggregates (FA) and crushed limestone as coarse aggregates (CA). Conventional potable tap water, which is free from impurities and chemicals, was used. As for recycled materials, debris resulting from tested concrete cylinder samples of known compressive strength were collected from concrete testing laboratories and divided into 5 Kg after manually pre-crushing them; these batches were then placed in the LA abrasion machine [18] for 500 revolutions and sieved for 10 minutes. The collected materials were separated into RFA and RCA. Fig. (1) displays samples of the aggregates used in the study. Because all the recycled materials underwent the same process of acquisition, the only two factors distinguishing their quality were the initial strength of the specimens and the size (after pre-crushing). Fig. (2) displays charts of recycled materials size comparisons before and after placing them in the LA abrasion machine and comparing different samples of the same compressive strength with either a random selection of the concrete debris or only big-size debris p3. 3. 1. of determined physical properties of both natural and recycled materials included fineness modulus [19], nominal max aggregate size, specific gravity, and water absorption [20-22] these are presented in Table 1. Fig. (4) displays both the natural and recycled material gradation curves. It should be indicated that for the RCA grading curve, there was observed a jump because #8 sieve retained material size of 2. 36 mm when added to the batch. The water absorption of RA was also confirmed to be higher than that of NA, although the specific gravity was lower, which is attributed to the existence of residue of adhered mortar with the RA, as discussed earlier. Table 1. Aggregates’ properties. modulus 6. 60 6. 20 1. 94 2. 23 SG (ssd) 2. 54 2. 50 2. 71 2. 42 Water absorption 7. 81 8. 92 0. 57 9. 11 Max aggregate size 19. 05 19. 05 1. 18 2. 36 3. 2.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: 7%, and those who were dismissed with a readmission chance were 674, which accounted for 10. 87%. At the same time, the Ministry of Education (MOE) announces almost in every meeting that ensuring the quality of education is a key to the sustainable development of the country. The quality of the tertiary education system has been dropping, and its standards don’t line up with the expectations of employers. Here, the problem is that in cases of re-exam, add/drop, and dismissal, resources like human labor, time, and cost are being spent. For instance, in the re-exam case, the instructor’s time is taken for exam preparation and invigilation. The students also sometimes took an additional year, thereby exposing the university and their parents to more expenses. The loss is serious, especially in the case of dismissed students, because the country invested large resources to bring them to the university but missed them all. Thus, there is a need to determine the academic performance of students in higher educational institutions (HEIs) to optimize resources and produce graduates with a higher caliber and quality. Student dropout is also a critical issue for higher educational institutions (HEIs) in many countries. Reducing the student dropout rate has been a common goal of many universities. Several studies[9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] have applied ML techniques to predict whether or not a student will drop out of a course. The EDM field aims to gain knowledge and insights from educational data for better outcomes in education [20]. One of the main goals of prediction is to identify at-risk students in the early stages and assist them before the problem is aggravated [21]. Several studies [22],[6] have been conducted using ML techniques to tackle the issues of low academic performance at early stages. However, most of them relied on categorizing the predicted class into two general classes: "pass" and "fail". In this study, the problem is seen at a finer-grained level. The predicted classes are categorized into five main classes: ‘Pass’, ‘Warning’, ‘With Readmission’, ‘Academic Dismissal, and ‘Dropout’.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: Our earlier results indicated that the individual targeting of IFT88, a gene required for normal proliferation (Beneke et al. , 2019), results in outcompeting of the introduced STOP codon mutation over time (Figure 4). This outcompeting process should in-turn result in reduced growth of the overall mutant population. Therefore, our assumption was that the viability phenotype of an essential gene could especially be detected in a pooled screening format. To test this, we pooled all annealed guide oligos from experiments above, targeting IFT88, PF16, 2, and tdTomato, and generated an oligo pool (Figure 7A). In addition, we added four guides to this oligo pool that target the Cdc2-related kinase 3 (CRK3), an essential kinase that has been used as a proof of principle mutant for essential genes in previous studies (Yagoubat et al. , 2020b; Duncan et al. , 2016). The prediction was that CRK3 guides would be strongly depleted from the pool over time. In addition, we wanted to use this experiment as an opportunity to compare again our Leishmania-optimised CBE (pLdCH-hyBE4max-LmajDBD) with the non-optimised version (pLdCH-hyBE4max). We therefore cloned the oligo pool, consisting of these 26 guides, into both plasmids (Figure 7A). We then transfected this library in triplicates into L. mexicana wildtype parasites, so that the four guides targeting MFT, originally designed for L. donovani, and the six guides targeting tdTomato, having no target in wildtype cells, would serve as non-targeting controls. Meanwhile, IFT88, PF16, PFR2, and CRK3 would be targeted by four guides each. Figure 7 with 2 supplements see all Download asset Open asset Targeting essential genes in a plasmid library delivered loss-of-function screen. (A) Schematic of hyBE4max loss-of-function screen in Leishmania. Oligo pairs were annealed and phosphorylated, then pooled and cloned into pLdCH-hyBE4max and pLdCH-hyBE4max-LmajDBD plasmids.

Output: pass


Input: Claim: Recycled materials are commonly used in sustainable building construction. [SEP] Text: A customary concrete (CC) mix was realized using natural aggregates. The recycled concretes were casted, maintaining constant the mixture proportions of CC. In two samples, natural sand was replaced by 20% and 50% of RFA (F20 and F50), and in one sample, natural aggregates were replaced by 20% RCA (C20). The aggregates were used in an air-dry state. The mixture proportions of different concrete are presented in Tables 2 and 3. The effective water was calculated by deducting from the total water amount the water amount needed for the complete aggregates saturation (SSD), estimated on the basis of water absorption values of different aggregates presented in Table 1. Table 2. Mixture proportions. Proportions CC F20 F50 C20 C (kg) 1 6. 62 6. 62 6. 62 6. 62 FA (kg) 2 13. 26 2. 65(RFA) 10. 61 6. 63 (RFA) 6. 63 13. 26 CA(kg) 3 19. 88 19. 88 19. 88 3. 98(RCA) 15. 9 w/c (Water L) Design 0. 6 (4) 0. 6 (4) 0. 6 (4) 0. 6 (4) 0. 6 (4) w/c (Water L) Effective - 0. 36(2. 38) 0. 33(2. 15) 0. 28(1. 80) 0. 35(2. 33) The mixing procedure was performed in compliance with ASTM C192 for Making and in the Laboratory [23]. Concrete was mixed in a rotating drum mixer. First, the required coarse aggregates were placed in the mixer with 20% of the required water quantity and mixed for 1 minute, while progressively adding the fine aggregates, cement, and the rest of the water quantity. The mixer continued to rotate for 3 minutes, followed by 3 minutes rest, and finally, 2 minutes mixing. Subsequently, the slump test was performed according to the ASTM C143 Method for Slump of Hydraulic-[24]. Cylindrical specimens of dimensions 150 mm by 300 mm were casted for compressive strength testing [25] and elastic modulus testing [26], whereas beams of size 450x150x150mm were utilized to determine the flexural strength [27].

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: The same features were also extracted by the second achieved classifiers and the first three rank completely the same with the feature extracted by Classifiers. However, the academic performance of students was either lesser or not affected by additional features like extra classes, Another Job, and guidance and counselling. Therefore, they are eliminated and not further considered for the prediction of the model. 5 Conclusion and RecommendationUsing machine learning (ML) methods, educational data mining (EDM) is used to extract and identify interesting patterns from educational institutions databases [52]. This study aims to develop a machine learning model (ML) based on student statistics to forecast the students' early academic achievement. Several machine learning algorithms (i. e. , and Decision Tree) were applied to predict students’ performance. Tree models overtook other models by scoring a promising result of 97. 3% and were selected as the proposed model. We applied the parameter tuning technique to obtain the top parameter values that gave us the best results. And then, the performance of the model improved to 97. 3%. After parameter tuning was performed, the most important features affecting students’ performance were identified: Entrance result, study time, attendance, Internet, age, gender, father job, mother job, family size, and address were extracted. Our findings suggest that academic factors (entrance result, study time, attendance, and Internet access) and socio-demographic factors (age, gender, father job, mother job, family size, and address) had a greater influence on students' academic success. The academic performance of students was less or not affected by additional features like extra classes, another job, and guidance and counselling. The proposed model is considered an early warning method used by decision-makers to identify students at risk. Once the risks are identified, it is easy to take countermeasures. The proposed model is able to identify problems with students before they get worse. Therefore, a cloud-based student performance prediction model is developed, and a streamlit cloud is proposed to ease use. Student performance prediction models can help educational institutions take timely actions, like arranging appropriate training for students at risk, to increase their success rate.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: Plasmid libraries, in triplicate, were transfected into L. mexicana wildtype parasites. To determine the guide coverage (transfectants per guide), aliquots of replicates were subjected to 96-well plate cloning immediately after transfection. DNA was subsequently isolated 12 and 21 days after library transfection. Plasmid libraries and isolated DNA (all marked with red asterisks) were amplified and guide abundances in each sample and replicate were determined by Illumina sequencing. (B) Illumina sequencing data of both library screens (top panel: pLdCH-hyBE4max-LmajDBD; bottom panel: pLdCH-hyBE4max) was analysed by z-scoring the ratio between each plasmid, day 12 and 21 replicate sample. Z-scores were plotted against the total of raw reads from either: (1) plasmid and day 12 sample (left panel), or (2) plasmid and day 21 sample (right panel). To identify data points that were significant different, a 0. 8 confidence ellipse was calculated (black line). Error bars show standard deviation between triplicates. Identities of data points, each representing a different guide sequence, are indicated in the legend below (see main text for further explanation). Immediately after library transfection, we also assessed the representation of each guide within the library by subjecting an aliquot of each library and replicate to serial dilutions. This showed an average representation of ~15 transfectants/guide (Figure 7—figure supplement 2A), which is three times as high as typically used in pooled RNAi libraries in T. brucei (Morris et al. , 2002). Selected clones from these serial dilutions were then also Sanger sequenced to determine whether parasites would harbour one or multiple sgRNAs. This showed that most transfectants had just been transfected with a single episome. However, three out of eight sequenced clones maintained two different plasmids and by that expressed two sgRNAs (Figure 7—figure supplement 2B). This meant that for most transfectants only the effect of one guide per transfectant is measured. However, there must have been also transfectants in the pool with simultaneously induced mutations on multiple genes.

Output: pass


Input: Claim: Recycled materials are commonly used in sustainable building construction. [SEP] Text: The higher compressive strength achieved in all RAC can be credited to the lower actual water/cement ratio of recycled concrete compared to conventional concrete, and this is due to the higher water absorption of recycled materials () compared to natural material (FA, CA). This higher water absorption is evident in the calculated effective w/c mixes of concrete CC and C20, which have almost identical w/c effective values, and concrete C20 showed a 43. 8% improvement in compressive strength, which is an indication that RCA results in an improved performance in concrete mixes compared to NA. Moreover, FA20 mix displayed an effective decrease in w/c of 8. 3% compared to the CC mix, and yet realized an overall 41. 1% increase in compressive strength. Similarly, FA50 mix displayed an effective decrease in w/c of 22. 2% and an overall increase of 60. 27% in compressive strength. 4. 3. Modulus of modulus of elasticity was obtained using ACI empirical equation and through an experimental study conducted according to ASTM C469 Method for of Elasticity and Poisson’s Ratio of Concrete in Compression [29]. The ACI318 [30] empirical equation is as follows: (1) Where, Ec is the modulus of elasticity of concrete (GPa); Wc is the unit weight of concrete (Kg/m3); fc' is the compressive strength of concrete at 28 days (MPa). The experimental results presented were the averages of three specimens for each reported value. The results are shown in Table 7 and clearly display a variance between the two methods that range between 2. 5 to 25%. Moreover, RAC presents a modulus of elasticity that is always higher than the CC mix between 10 to less than 1%. This fact can be attributed to the higher compressive strength of recycled concretes (Fig. 5). Fig. (5). Experimental setup according to ASTM C469. Table 5. Compressive strength (fc') of two testing series. f’c (MPa) CC F20 F50 C20 Age Test#1 Test#2 Average Test#1 Test#2 Average Test#1 Test#2 Average Test#1 Test#2 Average 7 days 15. 5 16. 8 16. 15 17. 9 21. 8 19. 9 22. 6 20. 6 21. 6 19. 5 19.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: Moreover, this study contributed to the prediction model proposed to classify student status at a finer-grained level, such as ‘pass’, ’warning’, ‘academic dismissal with readmission’, ’academic and ‘dropout’ that haven’t yet been considered in previous work. 6 Limitation and Future implicationThe focus of this study is on University students’ academic performance prediction, and only three semesters of data are used. In the future, we intend to include the whole semester of student performance data. Moreover, the study focused on undergraduate students; however, in the future, we will include graduate students. There are also other factors that have to be included in the dataset, like ‘weather condition', 'motivation’, ‘interest on the joined campuses, and ‘interest on the joined department’. In this study, the student entrance result is the only variable used from the pre-university data; however, transcript data during preparatory was not included. Therefore, in the future, we will include transcript information found during preparatory time. The study focuses on the prediction of student performance with five status: ‘warning’, ’pass’, ’academic dismissal’, ‘academic dismissal with readmission’, and ‘dropout’. However, it doesn’t show any kind of recommendation on the action the students must take to improve their performance. In this study, machine learning techniques were utilized to extract hidden knowledge from the student academic performance dataset. In the future, we will train and test the model using deep learning techniques. Moreover, we will develop an application to facilitate instruction for learners by integrating student academic performance prediction models into the mobile application to promote the utilization of modern technology. The stakeholders of the university have to collaboratively engage in working on the factors affecting student academic performance that were identified and prioritized by the model. Special attention with prioritization has to be provided for low achieving students with ‘Warning’, ‘with Readmission (ADR)’, ‘(AD)’ status. In the future, we will also consider "pass" status at a finer-grained level: and Promoted. This will also encourage students to upgrade their grade and remain competent and productive. However, a CGPA result is not sufficient to qualify a student.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: However, this risk seems to be low as we did not detect more than two sgRNAs per transfectant and as those two guides seemed to be randomly mixed (Figure 7—figure supplement 2B). In addition, integrating sgRNA constructs would reduce possible variations in the episomal copy number, which Leishmania parasites are known to vary (Ubeda et al. , 2014). Although episomal transfection allows also to adjust protein expression by increasing the concentration of selection drugs (Roberts et al. , 2007), our attempts to increase editing rates by raising drug concentrations were not successful (Figure 4C, D, Figure 4—figure supplement 4). To reduce editing time, it may be also preferable to target the template strand (−) over the coding strand (+). Since mutations on the coding strand have no immediate effect on the transcript, they first need to be incorporated on the template strand (−) during DNA replication. Hence, targeting the coding strand leads to a slower increase in editing over time and this was also repeatedly reflected in our data (Figure 2D, Figure 2—figure supplement 2A, and Figure 7—figure supplement 1C). We therefore have adjusted our scoring criteria for ranking guides on our primer design platform In addition, to minimise the possibilities for non-STOP codon mutations to become dominant our guide ranking could be further modified in the future to prioritise guides that have as few as possible cytosine bases. Although we have not measured this yet, the expectation would be that guides with fewer cytosine bases within the editing window would result in higher rates of successful STOP codon transformations. This is especially desirable when targeting essential genes, as the chances of non-deleterious mutations becoming dominant would be reduced. Lastly, modifying the CBE version through codon optimisation, addition of species-specific NLS or exchange of deaminase and UGI domains, could further increase the editing rate in Leishmania. We show that our Leishmania-optimised hyBE4max CBE version increases the editing efficiency (Figure 7B and Figure 7—figure supplement 1C). However, it remains to be explored whether also fidelity and editing window size is affected by this modification, as for example hyBE4max adaptations in rise are known to have expanded the editing windows (Wei et al. , 2022).

Output: pass


Input: Claim: Overfishing has led to declining fish populations in many regions. [SEP] Text: All the Boats on the Led to by University of Chicago Press, 2017Cloth: 978-0-226-44337-9 | Electronic: 978-0-226-44340-9DOI: 10. 7208/chicago/9780226443409. 001. 0001 of (cloth, ebook) DeGruyter Multi-eBooks (formerly NetLibrary) (UPSO) current fishing practices are neither economically nor biologically sustainable. Every year, the world spends $80 billion buying fish that cost $105 billion to catch, even as heavy fishing places growing pressure on stocks that are already struggling with warmer, more acidic oceans. How have we developed an industry that is so wasteful, and why has it been so difficult to alter the trajectory toward species extinction? In this transnational, interdisciplinary history, answers these questions and more as she explores how government subsidies propelled the expansion of fishing from a coastal, in-shore activity into a global industry. While nation states struggling for ocean supremacy have long used fishing as an imperial strategy, the brought a new emphasis: fishing became a means for nations to make distinct territorial claims. A network of trade policies and tariffs allowed cod from Iceland and tuna canned in Japan into the American market, destabilizing fisheries in. With the subsequent establishment of tuna canneries in Japanese and American tuna boats moved from the Pacific into the Atlantic and after bluefin. At the same time, government subsidies in nations such as Spain and the fueled fishery expansion on an industrial scale, with the Soviet fleet utterly depleting the stock of rosefish (or Pacific ocean perch) and other groundfish from to California. This massive global explosion in fishing power led nations to expand their territorial limits in the 1970s, forever changing the seas. Looking across politics, economics, and biology, All the Boats on the Ocean casts a wide net to reveal how the subsidy-driven expansion of fisheries in the Pacific during the led to the growth of fisheries science and the creation of international fisheries management. Nevertheless, the seas are far from calm: in a world where this technologically advanced industry has enabled nations to colonize the oceans, fish literally have no place left to hide, and the future of the seas and their fish stocks is uncertain. is a newspaper reporter turned historian of science who teaches in the Department of Fisheries and Wildlife at University.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-3342644/v1This work is licensed under a CC BY 4. 0 LicenseStatus:PostedVersion 1posted You are reading this latest preprint versionAbstractFollowing the deployment of the System (LMS) platform in higher educational institutions in Ethiopia, a massive amount of potentially helpful but as-yet untapped educational data has been generated. Despite the fact that the data is powerful enough to contribute to reducing student dropout rates through the application of modern educational data mining techniques such as machine learning, it has not been successfully employed to tackle student academic performance problems in higher education institutions(HEIs). As a result, a machine learning model was proposed based on data from three semesters of undergraduate students at University. To predict students' academic achievement, five machine learning methods (and Decision Tree) were used. Tree model outperformed other models with a promising result of 97. 3% on test accuracy and was selected as a proposed model. Moreover, our findings suggest that academic factors (entrance result, study time, attendance, and Internet access) and socio-demographic factors (age, gender, father job, mother job, family size, and address) had a greater impact on students' academic success. However, the academic performance of students was less affected by additional features like extra classes, another job, and guidance and counselling. Moreover, we are working on improving the accuracy of the proposed model. In this study, the student entrance result is the only variable used from the pre-university data. However, a CGPA result is not sufficient to qualify a student. Therefore, in the future, the exit exam results of the students will be incorporated. System (LMS)Multi-(HEIs)Students’ (EDM)FiguresFigure 1Figure 2Figure 3Figure 4Figure 5Figure 6Figure 7Figure 8Figure 9Figure 10Figure 111 IntroductionRecently, large amounts of data on student academic performance have been produced due to the implementation of the learning management system (LMS) platform in higher educational institutions (HEI) [1] [2] [3]. Therefore, the potential to extract valuable knowledge and insights from educational data has gained momentum. The extracted knowledge has the potential to assist decision-makers if tapped properly.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: While especially our improved CBE version allowed to efficiently induce a knockdown of CRK3, guides targeting IFT88 were not significantly depleted in our small-scale library screen (Figure 7B). Although IFT88 is not an essential gene, we would have also expected a decrease in guide abundance over time as IFT88 has been shown to be required for normal promastigote proliferation (Beneke et al. , 2019). It is important to note that we prioritised universal sgRNAs over the highest scoring guides to target IFT88 in multiple species in this study. Testing these four IFT88 guides individually, we only found one to be effective in L. infantum, while in L. major no measurable effect, even 42 days post transfection, was observed (Figure 4A–D). Meanwhile, we did not individually test these four IFT88 targeting guides in L. mexicana. It is therefore possible that by picking higher scoring guides, a depletion of IFT88 guides from the pool could have been achieved. It is also worth highlighting again that through base editing produced knockdowns are technically truncations with remaining N-terminal protein parts that could be functional. In addition, the exact timing of isolating DNA from pools might influence when a decrease of parasites with a deleterious mutation is detectable. As pointed out above, this may differ between guides, species, and genes. Thus, to detect the whole range of mutant phenotypes, such as IFT88, a tighter collection window of isolating DNA from pools may be needed. Nevertheless, we demonstrate through targeting of CRK3 that cells with an introduced premature STOP codon in an essential gene can be significantly depleted from a library with our approach (Figure 7B). date, a functional dissection of the vast majority of Leishmania genes remains to be performed and a lack of suitable tools limits the ability of the field to pursue large-scale loss-of-function screens. There is an urgent need to develop a high-throughput technology that allows: (1) to identify genes that are essential in any given environment, (2) to study multi-copy genes, and (3) to generate loss-of-function libraries through transfection of pooled plasmids rather than by pooling individually generated mutants. Here, we report aCas9 CBE toolbox for Leishmania that fulfils these criteria.

Output: pass


Input: Claim: Overfishing has led to declining fish populations in many regions. [SEP] Text: She is coeditor of toward Sustainable Forests: in Canada and the and the author of All the Fish in the Sea: Yield and the Failure of Fisheries Management, the latter published by the University of Chicago Press. She lives in Corvallis, OR. “Finley makes her point—that government subsidies to deep-sea fishing are a main cause of the current catastrophe—dramatically clear. Her descriptions of the damage that factory trawlers did to the ocean floor and the speed with which they wiped out fisheries in the ’60s and ’70s are especially powerful. Relevant not only to people who are interested in fisheries and oceans, but also to those concerned with global resource crises generally, this interdisciplinary, pragmatic book surpasses most of the work of historians in this area. Synthesizing scientific material with international law and politics, as well as the internal affairs of government agencies and private businesses, Finley links the fisheries story to the ‘great transformation’ of global ecology in the postwar period by way of the technology, policy, and politics of food production. All the Boats on the Ocean is a significant, original book. ” — author of "The Fisherman’s Problem: Ecology and Law in the California Fisheries, 1850-1980" “In this compact and highly readable book, Finley argues that overfishing since the 1950s is less a tragedy of the commons than a tragedy of the Cold War. She shows how geopolitics, science, law, and greed combined to generate a scramble for the oceans and a regime of overfishing that lasts to this day. A welcome addition to several scholarly literatures. ” — J. R. McNeill, Georgetown University, author of “under the Sun: History of the Twentieth-Century World” “Those of us who thought we understood how the oceans' plight came about will find much that is new in this thoroughly researched and highly engaging work. Weaving history, politics, and science, Finley shows how the seeds of the current predicament were sown during the Era, as government subsidies fueled the rapid acceleration of fishing. Her call for a reinterpretation of the role of fishing within government is long overdue. A must-read. ” — Ellen Pikitch, University “As the Food and of the reports, 90% of global fish stocks are fully fished or overfished.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: If extracted properly, this knowledge has the potential to significantly impact students’ learning experiences, learning outcomes, efficiency in managing learning organizations’ services, and others. Educational data mining (EDM) is an emerging field aimed at addressing these needs[4] [2] [5]. In order to solve problems or gain insights from the massive amount of data in educational systems, DM methods use comprehensive techniques from statistics, databases, artificial intelligence, and, more recently, machine learning (ML) to analyze data [2]. One of the popular tools that has the ability to extract useful information from large amounts of data in modern data mining is machine learning [2]. While conventional data mining techniques typically require manual intervention, machine learning does not require human intervention and can continuously learn and improve its models as more data becomes available[2]. More specifically, machine learning is a branch of artificial intelligence that enables systems to automatically learn and optimize models from analyzing data without being directly programmed [6]. ML has been successfully applied to predict students’ academic performance, identify at-risk students, define key learning requirements for different students, predict student graduation rates, and others[7] [8]. In this study, machine learning will be utilized to predict student academic performance. 1. 1 is one of the public higher educational institutions (HEIs) found in Ethiopia that recently deployed the System (LMS) and has a large data set of student information stored in its databases. The main problem that initiated this study was the students' low academic performance in higher educational institutions (HEIs). Based on the data from the registrar office of the university, the trend of graduating students is not proportional to the trend of enrolled students, and an increasing number of students commit readmission, suggesting that they did not perform well in their academics. For instance, during the 2017–2018 academic calendar, 400 out of 5200 students accounted for 7. 69% of complete dismissals, and 294 students accounted for 5. 65% of dismissals with readmission. During the 2018–2019 academic calendar, the number of students who were completely dismissed was 1339 out of 5700, which accounted for 23. 5%, and those who were dismissed with a readmission chance were 361, which accounted for 6. 3%. During the 2018–2019 academic calendar, the number of students who were completely dismissed was 1782 out of 6200, which accounted for 28.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: , 2017Recombinant DNA reagentp, 2017Recombinant DNA reagentpLdCHAddgene, Zhang et al. , 2017#84291Recombinant DNA reagentpTB007-hyBE4maxThis studySee description under ‘Construction of CBE and sgRNA expression plasmids’Recombinant DNA reagentpTB007-AncBE4maxThis studySee description under ‘Construction of CBE and sgRNA expression plasmids’Recombinant DNA reagentpLdCH-hyBE4maxThis studySee description under ‘Construction of CBE and sgRNA expression plasmids’Recombinant DNA reagentpLdCH-hyBE4max-LmajDBDThis studySee description under ‘Construction of CBE and sgRNA expression plasmids’AntibodyGuide-it Cas9 (rabbit polyclonal)Takara#632607Dilution for western blot 1:1000AntibodyPFR2(mouse monoclonal)Kohl et al. , 1999L8C4Dilution for western blot 1:10Chemical compound, drugMiltefosineSigmaM5571Chemical compound(MTT)SigmaM2128SoftwareICESynthego (release 59)Aslett et al. , 2010 description under ‘Automated CBE guide design using LeishBASEedit’Gene (L. donovani BPK282A1, L. infantum JPCM5, L. major Friedlin, L. mexicana 2001U1103)PF16TriTrypDB (release 59), Aslett et al. , 2010LdBPK_201450. 1, LmjF. 20. 1400, LmxM. 20. 1400, LINF_200019300Gene (L. donovani BPK282A1, L. infantum JPCM5, L. major Friedlin, L. mexicana 2001U1103)IFT88TriTrypDB (release 59), Aslett et al. , 2010LINF_270017700, LdBPK_271010. 1, LmjF. 27. 1130, LmxM. 27. 1130Gene (L.

Output: pass


Input: Claim: Overfishing has led to declining fish populations in many regions. [SEP] Text: Science historian Finley traces that crisis back to the Cold War, when the United States, Japan, the and other seafaring nations deployed fishing to stake territorial claims. From the 1970s on, trawling and government subsidies forced an explosion in the industry. Now, with little reduction in subsidized fleets and oceans at risk, Finley sees the future of fisheries hinging on holistic approaches involving fish, fisher and environment. ” — Barb Kiser, Nature “Finley is an engaging writer, weaving together historical, economic, and societal threads in a narrative that anchors global developments in the accounts of local actors. . . . The tension between the priorities and goals of these competing actors forms the essence of the book. Each faction has its victories and defeats, but the fish are the ultimate losers, as evidenced by the decline or collapse of fishery after fishery: California sardines in the 1950s, Pacific ocean perch in the 1970s, bluefin tuna in the early 2000s. . . . The book has many rewards. ” — Science “All the Boats on the Ocean is the most comprehensive and empirically grounded account yet of how the modern transnational fishery regime emerged, and it delivers in 167 succinct pages. The book works well in economics, fisheries, geography, and history courses—especially global environmental history. ” — Joseph E. "The book offers a convincing account of the globalization of fishing fleets through new industrial process and government support. . . . These elements should incite us to abandon the endlessly repeated 'tragedy of the commons' as an explanation of the depletion of fishing resources, so that we can look these all-too-human policies in the face. " — Books and Ideas "Carmel Finley's extremely dense, highly readable study of the expansion of world fisheries after II addresses the problem of overfishing and the associated destruction of aquatic ecologies… By unpacking the political mistakes of the past, Finley calls for lessons to be learned for future management of global fish stocks. " — NTM Zeitschrift für Geschichte der Wissenschaften, Technik und Medizin (translated from German) 1. Empires of the Pacific: The Americans, the Japanese, and the SovietsDOI: 10. 7208/chicago/9780226443409. 003.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: 7%, and those who were dismissed with a readmission chance were 674, which accounted for 10. 87%. At the same time, the Ministry of Education (MOE) announces almost in every meeting that ensuring the quality of education is a key to the sustainable development of the country. The quality of the tertiary education system has been dropping, and its standards don’t line up with the expectations of employers. Here, the problem is that in cases of re-exam, add/drop, and dismissal, resources like human labor, time, and cost are being spent. For instance, in the re-exam case, the instructor’s time is taken for exam preparation and invigilation. The students also sometimes took an additional year, thereby exposing the university and their parents to more expenses. The loss is serious, especially in the case of dismissed students, because the country invested large resources to bring them to the university but missed them all. Thus, there is a need to determine the academic performance of students in higher educational institutions (HEIs) to optimize resources and produce graduates with a higher caliber and quality. Student dropout is also a critical issue for higher educational institutions (HEIs) in many countries. Reducing the student dropout rate has been a common goal of many universities. Several studies[9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] have applied ML techniques to predict whether or not a student will drop out of a course. The EDM field aims to gain knowledge and insights from educational data for better outcomes in education [20]. One of the main goals of prediction is to identify at-risk students in the early stages and assist them before the problem is aggravated [21]. Several studies [22],[6] have been conducted using ML techniques to tackle the issues of low academic performance at early stages. However, most of them relied on categorizing the predicted class into two general classes: "pass" and "fail". In this study, the problem is seen at a finer-grained level. The predicted classes are categorized into five main classes: ‘Pass’, ‘Warning’, ‘With Readmission’, ‘Academic Dismissal, and ‘Dropout’.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: donovani BPK282A1)(release 59), Aslett et al. , 2010LdBPK_131590. 1. 1Gene (L. mexicana 2001U1103)PFR2A, B, and CTriTrypDB (release 59), Aslett et al. , 2010LmxM. 16. 1430. 1Gene (L. mexicana 2001U1103)CRK3TriTrypDB (release 59), Aslett et al. , 2010LmxM. 36. 0550. 1 Cell culture Request a detailed protocol Promastigote-form L. mexicana (WHO strain MNYC/BZ/62/M379), L. major Friedlin, L. donovani (strain BPK190, Decuypere et al. , 2005), and L. infantum (strain MHOM/FR/91/LEM2259, Sulahian et al. , 1997) were grown at 28°C in M199 medium (Life Technologies) supplemented with 2. 2 g/l NaHCO3, 0. 0025% haemin, 0. 1 mM adenine hemisulfate, 1. 2 μg/ml 6-biopterin, 40 mM 4-(2-hydroxyethyl)piperazine-1-ethanesulfonic acid pH 7. 4, and 20% FCS (fetal calf serum). Media were supplemented with the relevant selection drugs: 40 or 400 μg/ml Hygromycin B, 40 μg/ml Puromycin Dihydrochloride, and 40 μg/ml G-418 Disulfate. The identity of each Leishmania species was confirmed by sequencing the LPG2 locus (Akhoundi et al. , 2017; Figure 2—figure supplement 1) and cell lines were tested negative for mycoplasma contamination at Eurofins Genomics. Doubling times were determined by sub-culturing parasites repeatedly (over 5 days) to 106 cells/ml and measuring cell densities 24 hr later using a Z2 p95. 8% cloning efficiency across all 26 cloned guides (Supplementary file 5) (46 out of 48 isolated bacteria colonies correctly cloned on first attempt).

Output: pass


Input: Claim: Overfishing has led to declining fish populations in many regions. [SEP] Text: Boats from across the world were landing Sebastes fish, some in deep water, some in shallow. There were a great many subtle variations in the fish and scientists met in Copenhagen in 1959 to pool their knowledge. Some suggested the fish were older than originally assumed, and if the fish were long-lived, recovery from industrial fishing would take a long time—or perhaps not even happen. This 7. ImperialismDOI: 10. 7208/chicago/9780226443409. 003. 0007[Cod Wars;;;Zones;Yield;FCMA;EEZs;MSY]Iceland was still fighting against foreign boats taking its rich fish stocks. They successively increased their territorial limits, drawing opposition from European countries, especially the British, which sent its navy to protect its fishing boats in Icelandic waters. on the of the Sea in 1955 Yield (MSY) as the recommended goal for international fisheries management. The Americans, concerned about foreign boats (especially the Soviets) fishing in its waters, passed a bill to expand the study of the ocean, and a series of bills to subsidize the construction of fishing boats. The real turning point in the battle over foreign fishing came at the 1974 meeting of on the Law of the Sea () in Caracas. There was a widespread realization that overfishing and depletion were occurring, with no effective international organizations to manage. Developing coastal nations were frustrated that they could not develop their own fisheries, in the face of widespread depletion by foreign fleets. Conservation and (FCMA) was passed in 1976, creating a 200-mile Zone, and eight regional councils to manage fish stocks outside state waters. This 8. EnclosureDOI: 10. 7208/chicago/9780226443409. 003. 0008[Marco Puretic;power block;mathematical models]Through the 1960s, the oceans were a hodge-podge of conflicting territorial claims. That changed almost overnight, as nations adopted expanded territorial waters, creating an immediate hardship for the crews of the factory processing fleets, especially the Soviets, the Japanese, and the Spanish. There were signs of overfishing, especially off Peru, where anchovy catches briefly soared. With the collapse in Peru, processing equipment was moved to other countries, such as South Africa. tuna man created a hydraulic power block in 1955, making it easier to pull in seine nets.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: 1% 85. 6% 97. 2% 97% 89. 5% 88. 9% 97. 4% 96. 9% 77% 77% 99. 8% 97% 99. 8% 97. 4% SVC 97. 2% 97% 20% 98. 8% Table 7 performance comparison of models on default parameters and after hyper parameter tuning is performed tuning Parameters applied 86. 1% 85. 6% 99. 7% 97. 1% 89. 5% 88. 9% 98% 97. 1% 89. 5% 97. 3% 47% 47% 99. 8% 97. 4% 97. 2% 97. 3% SVC 20% 98. 8% 97. 1% 96. 4% Feature selection is the process of selecting essential features that are more uniform, non-redundant, and relevant for a machine learning model [51]. It is recommended to use feature selection because it helps to reduce the size and complexity of the dataset, which in turn requires less time to train a model and requires less computation cost to train a machine learning model. The student academic performance dataset has several features, and all features are not equally affecting the student's academic performance. Besides, all features are not equally important in affecting student academic performance. The purpose of this is to identify the features that have a significant impact on students’ academic performance. After training the prediction model, the decision tree model calculated feature importance by ranking the features. Features such as: entrance result, age, health, study time, father job, mother job, attendance, internet access, parents, sex, family size and address, extra class, another job, guidance, and counselling ranked as follows (see Fig. 10). The same features were also extracted by the second achieved classifiers (see Fig. 1). and the first three rank completely the same with the feature extracted by Classifiers. However, the academic performance of students was either lesser or not affected by additional features like extra classes, Another Job, and guidance and counselling. Therefore, they are eliminated and not further considered for the prediction of the model. 5 Conclusion and RecommendationUsing machine learning (ML) methods, educational data mining (EDM) is used to extract and identify interesting patterns from educational institutions databases [52]. This study aims to develop a machine learning model (ML) based on student statistics to forecast the students' early academic achievement. Several machine learning algorithms (i. e.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: Constructed plasmids (pTB007-hyBE4max, pTB007-AncBE4max, pLdCH-hyBE4max, and pLdCH-hyBE4max-LmajDBD) were Sanger sequenced, covering completely the CBE in each plasmid. Following sgRNA cloning, each pLdCH-hyBE4max or pLdCH-hyBE4max-LmajDBD plasmid was Sanger sequenced using primer 1011F, while each pPLOT-T7GuideExpress plasmid was Sanger sequenced using primer M13 rev-29. All used primer sequences (Supplementary file 5) and plasmid maps (Supplementary file 7) can be found in the supplementary. Primers were all ordered as standard desalted oligos at 25 nmol scale (Sigma). a detailed protocol Parasites were transfected as described previously (Beneke et al. , 2017; Beneke and Gluenz, 2019) in 1× Tb-BSF buffer (et al. , 2011). Briefly, 5–10 µg of tdTomato expression construct (see description for ‘tdTomato reporter assays’ below) or plasmid DNA (circular for pLdCH-hyBE4max, pLdCH-hyBE4max-LmajDBD, and pPLOT-T7GuideExpress plasmids; PacI linearised for pTB007-hyBE4max and pTB007-AncBE4max) were diluted into 50 µl ultra-pure water and heat sterilised for 5 min at 95°C. Then, 50 µl sterilised DNA was mixed with 5 × 106 cells submerged in 200 µl of 1. 25× Tb-BSF buffer (final transfection µl with 1× Tb-BSF buffer) and transfected using one pulse with program X-001 on an IIb (Lonza). Cells were recovered in M199 culture medium as specified above and 6–16 hr post transfection the required selection drug was added.

Output: pass


Input: Claim: Overfishing has led to declining fish populations in many regions. [SEP] Text: Simulated SP from age-structured population modelWe developed a model for age-structured population dynamics and subjected this model to simulated sequences of growing then reduced harvest rates similar to patterns seen in a variety of populations recently with initiatives for conservation and stock rebuilding. Simulated SP patterns were calculated from the model biomass and catch predictions under these simulated sequences using eq. 1. The model had the same basic structure as is used in most single-species stock assessments today, namely two basic dynamic equations for numbers at age and recruitment:(2)(3)Here Na,t is number at age a at the start of year t, S is natural survival rate (0. 8 year–1) in test simulations, va is vulnerability of age a animals (logistic form with mean age at recruitment 4 years in test simulations), ut is overall exploitation rate for year t, K is the Goodyear (1977) compensation ratio for parameterizing the Beverton–Holt stock–recruitment function (K = 3–10 in test simulations), Ro is mean recruitment for the unfished population, Eo is egg production for the unfished population, and Et is egg production in year t calculated as the sum over ages of fecundities at age times Na,t. Simulated body weights at age Wa were generated with a von Bertalanffy growth model (K = 0. 2 year–1) and cubic length–weight relationship and were assumed to be constant over time. Bt for SP calculations was taken to be just the sum of weights at age times vulnerabilities at age times Na,t. for the SP calculation was taken to be simply the ut times the sum over ages of vaWaNa,t. For the test simulations, we scaled Ro so as to imply unfished vulnerable biomass Bo = 14. 0 (this choice of absolute abundance units is purely arbitrary and has no effect on the predicted SP vs. B patterns). Simulations to demonstrate decreased SP during population recovery were conducted by starting with an unfished population, then simulating increasing exploitation rates over time to levels high enough to cause severe depletion (reduction in total biomass by 80% or more). Simulated exploitation rates were then reduced dramatically, to 0. 1FMSY or even 0. 0, so as to allow the most rapid possible recovery. Simulated SP patterns using EcosimWe used the Ecopath with Ecosim software (EwE 5. 1, www. ecopath.

Output: corroborate


Input: Claim: Digital learning platforms guarantee higher academic performance for all students. [SEP] Text: , and Decision Tree) were applied to predict students’ performance. Tree models overtook other models by scoring a promising result of 97. 3% and were selected as the proposed model. We applied the parameter tuning technique to obtain the top parameter values that gave us the best results. And then, the performance of the model improved to 97. 3%. After parameter tuning was performed, the most important features affecting students’ performance were identified: Entrance result, study time, attendance, Internet, age, gender, father job, mother job, family size, and address were extracted. Our findings suggest that academic factors (entrance result, study time, attendance, and Internet access) and socio-demographic factors (age, gender, father job, mother job, family size, and address) had a greater influence on students' academic success. The academic performance of students was less or not affected by additional features like extra classes, another job, and guidance and counselling. The proposed model is considered an early warning method used by decision-makers to identify students at risk. Once the risks are identified, it is easy to take countermeasures. The proposed model is able to identify problems with students before they get worse. Therefore, a cloud-based student performance prediction model is developed, and a streamlit cloud is proposed to ease use. Student performance prediction models can help educational institutions take timely actions, like arranging appropriate training for students at risk, to increase their success rate. Moreover, this study contributed to the prediction model proposed to classify student status at a finer-grained level, such as ‘pass’, ’warning’, ‘academic dismissal with readmission’, ’academic and ‘dropout’ that haven’t yet been considered in previous work. 6 Limitation and Future implicationThe focus of this study is on University students’ academic performance prediction, and only three semesters of data are used. In the future, we intend to include the whole semester of student performance data. Moreover, the study focused on undergraduate students; however, in the future, we will include graduate students. There are also other factors that have to be included in the dataset, like ‘weather condition', 'motivation’, ‘interest on the joined campuses, and ‘interest on the joined department’.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: A detailed protocol for transfecting CBE plasmids into Leishmania parasites can be found in the supplementary (Supplementary file 8) or on TdTomato reporter assays Request a detailed protocol To set up tdTomato reporter assays, we first generated a pPLOT-Neo-tdTomato plasmid. The plasmid pTSARib-tdTomato (Capewell et al. , 2016) was digested with HindIII and BamHI to isolate the tdTomato-coding sequence, which was then cloned into HindIII and BamHI sites of pPLOT-Neo (Beneke et al. , 2017). Then a tdTomato expression construct was generated, consisting of: (1) a neomycin drug resistance expression cassette, (2) a tdTomato expression cassette, and (3) two surrounding homology flanks allowing for integration into the 18S rRNA locus. The construct was produced by fusion PCR as previously described (Dean et al. , 2015). First, two fragments were individually amplified: (1) upstream and (2) downstream homology flanks from L. major Friedlin genomic DNA using primer 1000F/1001R and primer 1002F/1003R, respectively. Then both fragments were used to amplify the tdTomato expression construct from the pPLOT-Neo-tdTomato plasmid, using primer 1004F and 1005R as nested primers. Finally, PCR reactions were purified and transfected as described above. Following transfection into L. mexicana, L. major, L. donovani, and L. infantum, clones were isolated as previously described (Beneke et al. , 2017) and screened using a FACSCalibur cell analyzer (BD) to select a clone with high tdTomato expression. Subsequently, the chosen L. major tdTomato-expressing clone was again transfected with PacI linearised pTB007-hyBE4max and pTB007-AncBE4max plasmid DNA. Clones were again isolated, expression of tdTomato confirmed and the fastest growing clone picked for the tdTomato reporter assay. We did not assess how many copies of the tdTomato expression cassette were integrated in selected tdTomato clones.

Output: pass


Input: Claim: Overfishing has led to declining fish populations in many regions. [SEP] Text: A circle indicates the first year of the time series, and a square shows the last year; arrows indicate time progression. The plot numbers correspond to the stock numbers in 2. Plots of surplus production (SPt) vs. biomass (Bt) for five substocks of Pacific herring (Clupea pallasii). Note high surplus production rates even at low biomass in early years, when the stocks were overfished by a reduction fishery, followed by lower SP even at high biomasses as the stocks recovered and a roe fishery developed. Arrows indicate time progression, with 1951 indicated by a circle, 1971 by a triangle, and the last year (1997) indicated by a square. Years before 1971 are indicated with a broken line and from 1971 with a solid line. The classification of two SP vs. B plots into (i) stationary, (ii) upward or clockwise hooks–cycles, and (iii) downward or counterclockwise hooks–cycles produced a very disturbing result; by far the most common pattern was clockwise hooks–cycles (33–57 cases), followed by apparent stationarity (5–36 cases). SP collapse occurred in 4–18 cases. The least common pattern was downward hooks–cycles (8–11 cases). Interestingly, this last finding (roughly 10%) is similar to the proportion of cases that we found to have apparent downward hooks in stock–recruitment data sets, in analyses aimed at detecting possible cultivation–depensation effects on recruitment (Walters and Kitchell 2001). The preponderance of clockwise hooks–cycles in the data sets is strongly suggestive that much of the observed variation in SP over these cases can be explained by stock–recruitment anomalies, in which recruitment decline preceded stock decline, i. e. , was involved (along with fishing) in causing decline. A few stocks showed mixtures of short-term, clockwise hooks combined with longer term downward hooks in overall SP. All five of the major substocks of Pacific herring (Clupea pallasii) were fished to very low levels by a reduction fishery prior to 1970 and then recovered substantially as the current roe fishery developed.

Output: corroborate


Input: Claim: Smart city data can be used to optimize traffic flow and reduce congestion. [SEP] Text: On the one hand, in the process of smart city construction, the various information sharing platforms and industrial innovation bases will significantly reduce the cost of acquiring knowledge and information for innovative subjects, facilitating the joint participation of different subjects in innovative activities, effectively increasing the enthusiasm and vitality of innovative subjects such as enterprises, universities and institutions, ultimately generating sustainable innovation and development momentum. On the other hand, with the improvement of "new infrastructure" such as 5G and artificial intelligence, it will help all kinds of innovative subjects to select appropriate partners and investigate the professional capabilities of the target more precisely, which will virtually strengthen the cooperation between enterprises and research departments, and ultimately stimulate the enthusiasm of multiple innovative subjects to participate in joint R&D and collaborative innovation. It has been well demonstrated that technological innovation can effectively improve environmental pollution, compared to the traditional innovative model of "each subject working alone". The industry-university-institution collaboratively innovative model can avoid the duplication of research and development, save innovation costs, and exert their respective advantages. In general, collaborative innovation can better contribute to regional haze governance by strengthening joint research and development of core technologies in areas related to energy conservation and consumption reduction. Herein, we propose hypothesis H2a: smart city construction can indirectly improve urban haze pollution by enhancing the collaborative innovation capacity of industry, university, and institution. Secondly, with the advent of the digital economy, innovative activities will show a stronger openness. As an important application scenario of digital economy, smart cities have obvious characteristics of intelligence and digitalization. The rapid development of artificial intelligence will break the time and space constraints faced by traditional innovative activities and provide practical possibilities for cooperative innovative activities between different regions (cities). As there are significant differences in the innovation base and R&D capabilities of different cities, it is inevitable to consider the adaptability of each other when selecting partners. Smart city construction can help regions find the best partners and deal with the potential risks in the process of cooperative innovation through big data analysis, effectively enhancing the intensity of cooperation and innovation among different regions. With the increase of cross-regional cooperation and innovation, regions with relatively strong innovation capacity can accelerate the marketization process of R&D achievements through the spillover effect of technology. Meanwhile, regions with relatively weak innovation foundation can absorb the latest research results of developed regions in basic research and fill their own gaps in the process of cooperative innovation more conveniently and timely (Gomber et al. , 2018).

Output: pass


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: Chosen tdTomato clones were then transfected with pLdCH-hyBE4max and pLdCH-hyBE4max-LmajDBD plasmids, carrying tdTomato-specific sgRNAs. Meanwhile, tdTomato-pTB007-hyBE4max and tdTomato-pTB007-AncBE4max selected clones were transfected with pPLOT-T7GuideExpress plasmids, carrying also tdTomato-specific sgRNA target sequences. Non-clonal populations were analysed using a FACSCalibur cell analyzer (BD) several days post transfection (as indicated). FACS data were analysed using 2 (Turku Bioscience). To prepare parasites for FACS analysis, 400–800 µl of a dense culture was pelleted at 800 × g for 5 min, washed once in phosphate-buffered saline (PBS, pH 7. 4) and then resuspended in 200 µl PBS. Motility analysis and light microscopy Request a detailed protocol Motility analysis was performed as previously described in Beneke et al. , 2019 using the method from Wheeler, 2017. Mean speed was measured in duplicates 14 days post transfection and in triplicates 28 and 42 days post transfection. 5 μl of cell culture (cell density ~6 × 106 cells/ml) was placed on a glass slide in a 250-μm deep chamber covered with a 1. 0-cover slip and imaged using darkfield illumination with a ×10 numerical aperture (NA) 0. 3 objective and an Allied-F-505B camera on a microscope at the ambient temperature of 25–28°C. Tracks were processed using a Fiji (Schindelin et al. , 2012) macro from Wheeler, 2017 and further processed by creating histograms of velocity per tracked cell with the following bin categories: 0. 01, 0. 1, 0. 3, 0. 7, 1. 0, 1. 5, 3. 0, 6. 0, 8. 0, 10. 0, 12. 0, 16. 0, and 30. 0 µm/s.

Output: pass


Input: Claim: Overfishing has led to declining fish populations in many regions. [SEP] Text: Bt and SPt of age 3+ fish for these stocks can be calculated directly from catch-at-age, weight-at-age, and spawn survey data, without using any stock assessment model with attendant assumptions about stability of natural mortality rates or growth (Fig. 2). When we compare the mean SPt/Bt (production per biomass) vs. Bt relationship for the reduction fishery (1950–1970) with later years (1971–1998), all stocks have clearly shown a decrease in mean productivity at both low and high stock sizes. This decrease is explained at least partly by decreases in mean body weight at age (Tanasichuk 1997; Schweigert et al. 2002), but may also be related to loss of spatial stock structure; some spawning areas are no longer used (D. Hay, Fisheries and personal communication). Predicted production patterns from single-species modelsAs noted above, age-structured population models generally predict that recruitment anomalies should cause time-structured deviations from mean or equilibrium SP relationships in the form of clockwise loops. Such loop structures are very commonly seen in SP vs. B data plots. Some data sets show clockwise loop structures with much longer period (more years per cycle) than would be expected from single recruitment anomalies, suggesting strongly autocorrelated or persistent recruitment anomaly patterns. Such predictions and observations are not at all surprising. Age-structured models, however, make another type of prediction (noted by Ricker 1975; see e. g. , his figure 12. 7) that apparently is not widely recognized, in cases where recruitment compensation is not extremely strong (Goodyear compensation ratio less than 20, steepness less than 0. 9). Whenever exploitation rates are growing, SP is expected to be above average (above long-term equilibrium predicted by the age-structured model, as B declines). More importantly, whenever exploitation rates are reduced so as to allow growth in B, SP increases more slowly than expected from the equilibrium SP vs. B relationship (Fig. 3). That is, the transient of observed SP values during fishery development will be above the long-term sustainable (equilibrium) SP predicted by the age-structured model for each observed B, and population recovery following reduction in exploitation will be slower (lower SP) than expected from either the SP observed during population decrease or calculated from the equilibrium SP vs. B relationship.

Output: corroborate


Input: Claim: Smart cities will eliminate all crime by using surveillance technology. [SEP] Text: The visualization of videos is gaining more attention because of addressing challenges of data analysis arisen from video camera contents [1, 15, 16]. Over the past decade, VV usefulness for traffic surveillance [17, 18] application has been effectively demonstrated by researchers [3, 75, 76]. VV offers spatio-temporal summary and overview of large collection of videos, and its abstract representation of meaningful information assists the users in video content [3, 35]. Conversely, conventional techniques [67] of visual representation such as time series plot have difficulties in conveying impressions from large video collection [3]. In addition, there is need to present visual contents of videos in compact forms such that user can quickly navigate through different segments of video sequence to locate segment of interest and zoom in to different detail levels [1]. Viewing videos is time-consuming process, consequently it is desirable to develop methods for highlighting and extraction interesting features in videos. There are numerous techniques designed for data analysis in images and a variety of statistical indicators for data processing. On the contrary, there is lack of effective techniques for conveying complex statistical information spontaneously to a layperson such as a security officer, apart from using line graphs to portray 1D signal levels [1]. Many researchers studied video processing in the context of video surveillance [16], monitoring vehicles, and monitoring crowds. However, main problem in automatic video processing is communication of results of video processing to human operator. Since statistical results are not easily comprehensible, whereas sequences of difference images again need sequential viewing [1]. Conventional video surveillance systems heavily rely on human operators for activity monitoring and determining actions to be taken upon incident occurrence. There are several actionable incidents that miss-detect in such a manual system due to inherent limitations from deploying solely human operators eyeballing CCTV screens [58]. Hence, automatic VV [56] will prove very beneficial in improved traffic management. Miss-detections might be caused by monitoring excessive number of video screens to monitor as shown in Figure 1 and tiredness due to prolonged monitoring. In fact, numerous studies have shown the limits of human-dependent surveillance. conducted a study in which most people attention fell below an adequate level after only 20 minutes of video surveillance screen monitoring [67]. The video content analysis paradigm is shifting from a fully human operated model to an intelligent machine-assisted automated model [58]. Figure 1. Video wall. Advertisement 2.

Output: contrast


Input: Claim: CRISPR technology allows for precise editing of genomic sequences. [SEP] Text: Histogram data of replicates were then averaged and tested using the Cramér–von Mises criterion, allowing to identify which non-clonal mutant populations significantly developed a positively (right) skewed distribution due to the base editing (Supplementary file 1). For light microscopy, Leishmania parasites were prepared as described previously (Wheeler et al. , 2015) and imaged live using an inverted fluorescence microscope (DMI6000B, Leica) with a ×63 NA 1. 30 glycerine immersion objective and a Leica DFC365 FX monochrome digital camera at the ambient temperature of 25–28°C. Western blots Request a detailed protocol Cas9 and PFR2 protein was detected on western blots using the Guide-it Cas9 (Takara, 632607) and L8C4 (Kohl et al. , 1999) (a gift from K. Gull, University of Oxford) antibodies, respectively. Briefly, Leishmania promastigotes were washed in ice-cold PBS twice containing 1× (Thermo Fisher) and then heated in 1× Laemmli buffer for 10 min at 60°C. 2. 5 × 106 cell equivalents of Leishmania promastigotes were loaded and subjected to electrophoresis on 6% (for Cas9) or 10% (for PFR2) sodium dodecyl sulfate (SDS)–polyacrylamide gels. Samples were transferred to nitrocellulose membranes (GE Healthcare) and incubated in blocking buffer (PBS with 0. 05% [wt/vol] Tween 20 [PBST] with 5% milk for Cas9 and 1% milk for L8C4) for 1 hr. Then, membranes were incubated with primary antibodies (diluted in blocking buffer [Cas9 1:1000, L8C4 1:10]) for 1 hr, washed in PBST three times and incubated with secondary antibodies (IRDye LI-COR, 926-32211 for Cas9, 925-32210 for L8C4, both diluted 1:10,000 in PBST) for 1 hr. Membranes were washed again in PBST three times and imaged using a LI-. Miltefosine drug resistance and MTT viability assays Request a detailed protocol Transfected and non-transfected L.

Output: pass


Input: Claim: Overfishing has led to declining fish populations in many regions. [SEP] Text: It is easily verified with age-structured models as described above that these predictions are robust to the form of stock–recruitment relationship (Ricker vs. Beverton–Holt) and to age schedules of body growth, vulnerability, and fecundity. Fig. 3. A typical pattern of surplus production (SPt) vs. biomass (Bt) predicted by age-structured models for stocks that are fished down then allowed to recover, when the stock–recruitment curve is not too steep (Goodyear compensation ratio <20). The circle indicates the start of the simulation. The thick solid line with arrows indicates SP, the broken line shows the SP/B ratio, and the thin solid line is long-term equilibrium SP. Note the downward hook in SP, i. e. , lower production rates at each biomass as the stock recovers; this is due to reduced mean fecundity and recruitment per biomass relative to recruitments during the stock decline. Predicted downward hooks in SP following reduction in exploitation rates is particularly pronounced for simulated stocks with late maturity and weak recruitment compensation, indicating that the basic mechanism that causes low SP at each B during recoveries is reduction in mean fecundity and recruitment relative to the fecundities and recruitments exhibited during population decline. One might expect younger (lower proportions of older animals) recovering populations to exhibit higher production rates per biomass because of higher proportions of young, rapid-growing individuals, but models indicate that such growth effects are not large enough to compensate for negative effects of reduced mean fecundity. In short, unless masked by strong effects of recruitment anomalies or very strong recruitment compensation, single-species models very generally predict downward hooked patterns in SP vs. B relationships from stocks that are fished down then allowed to recover. Apparent examples of this effect in the data sets that we examined include two stocks off California (cowcod (Sebastes levis) and English sole (Parophrys vetulus)) and the hake (Merluccius merluccius) stock. We predict that data sets showing downward hooks in SP vs. B relationships, i. e. , slower biomass recoveries than expected from SP measured during population declines, will become much more common as many stocks undergo rebuilding programs. This prediction will fail if such stocks turn out to have very strong recruitment compensation, a possibility suggested for long-lived species by the meta-analysis of Goodwin et al.

Output: corroborate