Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Figure 3 Heterogeneous responses of melatonin timing after MCAo. MT-on and MT-off times were obtained for each day of the experiment and are displayed in a composite timeline. Individual rats' melatonin profiles are depicted by unique colors. The left side shows MT-on tracings with black-filled circles, whereas the right side shows MT-off tracings with white-filled circles. As before, the day of MCAo was defined as Day 0. Due to technical difficulty, data was not available for some days for specific animals (dotted lines were used when days were skipped). Figure 4 Effect of MCAo on the duration of nightly melatonin secretion. Melatonin duration was calculated by computing the time between MT-on and MT-off for each day of the experiment. Since melatonin duration differs among individuals in the cohort, the melatonin duration was therefore normalized to the stable pre-stroke melatonin duration (set to 100%). Melatonin duration plots are displayed for each individual in a different color. As before, the day of MCAo was defined as Day 0. Dotted lines are used to signify days when data were not available for technical reasons. Figure 5 Effect of MCAo on the shift of nightly melatonin onset. The impact of stroke on the melatonin rhythm for each animal was calculated by averaging the absolute daily melatonin onset shift (relative to pre-stroke baseline); see methods for a discussion of this calculation. The averages of these values determined for all animals with MCAo or sham surgery are displayed in bar graph format. The difference between average melatonin onset shift in the MCAo group was statistically significant relative to the control group (p < 0. 02). As discussed above, the pre-stroke phase angle of MT-on displayed variations within the cohort, indicating that individual animals exhibited unique circadian parameters. However, individual variations in MT-on time did not correlate with the direction or amplitude of circadian shifts after stroke (calculated on the initial day after the stroke; Figure 6). Figure 6 Lack of relationship between the phase angle of MT-on and the circadian response to MCAo. As seen in Figure 1, rats demonstrate a wide range of baseline phase angles of MT-on.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: Three sepsis products (Deep-AI[AISE], targeted real-time early warning [TREW] Score, Insight) used Mart for (MIMIC)-III data for training or evaluation. 83 Finally, there are cases in which existing algorithms are productised while more sophisticated machine learning techniques are developed as product enhancements. A notable example of this is DeepMind Streams, which originally productised a national acute kidney injury algorithm. 84 In parallel, DeepMind implemented and evaluated the workflow solution as well as developed machine learning methods to enhance the product. 60,85 Similarly, the regression-based (KFRE) used to predict progression of chronic kidney disease, was productised by Viewics inc. , while additional technologies were built in parallel. 2. Evaluate and initial round of machine learning product evaluation and validation, Step 2a, can be completed entirely on retrospective data. These experiments are called ‘in silico’ and demonstrate three dimensions of validity and utility. 21 Clinical utility addresses the question: can the product improve clinical care and patient outcomes? This requires that the team marketing or developing a machine learning product can calculate baseline performance on data relevant to the adopting organisation. Statistical validity addresses the question: can the machine learning product perform well on metrics of accuracy, reliability, and calibration? This requires that there is agreement on important and relevant model performance measures and a sense of what makes a product perform well enough for adoption. Finally, economic utility addresses the question: can there be a net benefit from the investment in the machine learning product? The economic utility can be demonstrated through cost savings, increased reimbursement, increased efficiency, and through brand equity. All forms of utility and validity are ultimately in the eye of the beholder. As such, relationships and communication between the machine learning product team and organisational stakeholders are critical. The evaluation and validation of machine learning products requires multiple iterations. Demonstrating utility or validity on retrospective, in silico settings does not guarantee that the product will perform well in a different setting57 or in a different time period. 86,87 The utility and validity of the product must be reassessed across time (Step 2b) and space (Step 2d). Evaluating a machine learning product on a hold-out and temporal validation set (Step 2a) is recommended before integrating a product into clinical care.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: analyzed the data, prepared figures and/or tables, and approved the final draft. conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft. The following information was supplied regarding data availability: The original data is available in the Supplemental Files. support for this study was provided by grants from the Project (2012BAD12B03-3, 2012BAD12B05-2) and the Science and Project of (GC12B303). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 1985. Phage P1 Cre-loxP site-specific recombination. Effects of DNA supercoiling on catenation and knotting of recombinant products. Journal of 184:211-220. 2015. Cloning-freeCas system facilitates functional cassette knock-in in mice. 16 2018. -Cas9 off-targeting assessment with nucleic acid duplex energy parameters. 19 2016. The primary transcriptome of the Escherichia coli O104:H4 pAA plasmid and novel insights into its virulence gene expression and regulation. 6:35307 Brune T, Kunze-Schumacher H, Kolling R. 2019. Interactions in the ESCRT-III network of the yeast Saccharomyces cerevisiae. 65:607-619 de. 2015. Sequential cancer mutations in cultured human intestinal stem cells. Nature 521:43-47. 2013. High-frequency off-target mutagenesis induced by -Cas nucleases in human cells. 31:822-826. 2014. -Cas nuclease specificity using truncated guide RNAs. 32:279-284 Hoang TT, Karkhoff-. 1998. A broad-host-range Flp-FRT recombination system for site-specific excision of chromosomally-located DNA sequences: application for isolation of unmarked Pseudomonas aeruginosa mutants. Gene 212:77-86. 2013. DNA targeting specificity of RNA-guided Cas9 nucleases. 31:827-832. 2013. RNA-guided editing of bacterial genomes using -Cas systems. 31:233-239. 2015. Multigene editing in the Escherichia coli genome via the -Cas9 system. Applied and 81:2506-2514. 1993.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: We explored the relationship between the phase angle (on the day of the stroke; shown on the X-axis) of MT-on, MT-off, and melatonin duration, relative to the baseline values before MCAo. The r2 values are shown; no relationship was found. The direction of MT-on and MT-off shift (on the first night after the stroke) demonstrated no correlation with each other (Figure 7A). Roughly equal numbers of rats experienced an advance in MT-on compared to delays, and roughly half experienced advances in MT-off compared to delays. We observed all four combinations of MT-on/MT-off patterns (advance/advance, advance/delay, delay/advance, and delay/delay). The change in MT-off was also not associated with melatonin duration (Figure 7B). Figure 7 Change in melatonin duration is related to the change in MT-on after MCAo. Results from the day of the MCAo were examined here. Data displayed include the change in MT-on, MT-off, and melatonin duration relative to respective pre-MCAo values. (A) There was no relationship between shifts in MT-on and MT-off after stroke. Animals exhibited all four possible combinations of directions of shift. (B) There was no relationship between the shifts in MT-off and melatonin duration. (C) Change in the melatonin duration after stroke was strongly linked to the shift in MT-on. The r2 values are shown. Interestingly, the initial change in melatonin duration was strongly linked to the initial change in MT-on after stroke (Figure 7C). Thus, the change in melatonin duration after stroke could be predicted by observing MT-on alone. This relationship held true several days after the stroke, as well (see below). Due to early death (ID2832 and ID2837) and technical reasons (ID2660, ID2842, and ID2896), we were not able to quantify MT-on and MT-off for every day in every animal.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: 14 Evaluating a machine learning product on external geographic datasets (Step 2d) can help drive adoption in new settings. Of the products listed in Table 2, Sepsis Watch, Monitor, ePNa, and Score 2. 0 were integrated within the healthcare organisation that developed the product without external validation. On the other hand, electronic (eCART),26,27 KFRE,71 InSight,41 Rothman Index,31 PiecesTM readmission model,67 Deep-AISE,47 and eTriage25 completed peer-reviewed external validations. The two products with the most extensive external validations include the KFRE, which was validated on a multi-national dataset consisting of >30 countries,71 and ColonFlag, which was validated on cohorts in the USA,81 UK,80 and Israel. 88 These external validations evaluate the same model in different geographical contexts. However, several teams are taking a different approach to validating models across settings. The teams working on C. difficile and surgical complication models are building generalisable approaches by which site-specific models are developed and validated. 57,69 The production environment of a health information technology system often differs dramatically from the environment that stores retrospective or even day-old data. Significant effort and infrastructure investment are required to integrate products into production EHR systems (Step 2c). One study estimated the cost to validate and integrate the KFRE into clinical workflows at a single site at nearly $220,000. 24 Redundant costs across sites, as a result of a lack of interoperability and lack of infrastructure, would require similar investment by institutions following a similar approach. Furthermore, the ‘inconvenient truth’ of machine learning in healthcare was pointedly described as “at present the algorithms that feature prominently in research literature are in fact not, for the most part, executable at the front lines of clinical practice. ”89 Finally, before being integrated into clinical care, a machine learning product needs to be evaluated and validated in a ‘silent’ mode, “in which predictions are made in real-time and exposed to a group of clinical experts. ”14 This period is crucial for finalising workflows and product configurations as well as serving as a temporal validation (Step 2b). An example of a silent mode evaluation is an eCart feasibility study.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: Site-specific recombinases: tools for genome engineering. Trends in Genetics 9:413-421. 2015. Simultaneous oral immunization of mice with live attenuated Escherichia coli expressing LT192-STa 13 and LT 192-STb fusion immunogen, respectively, for polyvalent vaccine candidate. and Biotechnology 99:3981-3992. 2013. CAS9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. 31:833-838 Pyne ME, Moo-. 2015. Coupling theCas9 system with lambda red recombineering enables simplified chromosomal gene replacement in Escherichia coli. Applied and 81:5103-5114. 2014. —Cas systems for editing, regulating and targeting genomes. 32:347-355. 2014. Conditional knockouts generated by engineered -Cas9 endonuclease reveal the roles of coronin in C. elegans neural development. 30:625-636. 2016. A high-throughput functional genomics workflow based onCas9-mediated targeted mutagenesis in zebrafish. 11:2357-2375. 2019. Competing endogenous RNA (ceRNA) hypothetic model based on comprehensive analysis of long non-coding RNA expression in lung adenocarcinoma. PeerJ 7:e8024. 2016. coli infection alters intestinal immunity in mice. Reports 14:825-830. 2017. -Cas9 and -Cpf1 mediated targeting of a stomatal developmental gene EPFL9 in rice. Reports 36:745-757. 2020. Cas9/gRNA-mediated genome editing of yeast mitochondria and Chlamydomonas chloroplasts. PeerJ 8:e8362. 2014. Isolation of Type II heat-labile enterotoxin-producing Escherichia coli and preparation of the recombinant toxin. et 45:1336-1341 (Published in China). 2015. Off-target effects inCas9-mediated genome engineering. Molecular Therapy. 4:e264. 2016. Development of a fast and easy method for Escherichia coli genome editing withCas9. Factories 15:205 a question Learn more about Q&A a link Alert Loading. . . Just enter your email Tools & info history See citing 2.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Journal of Circadian RhythmsStart SubmissionBecome a ReviewerReading: Maximum entropy spectral analysis for circadian rhythms: theory, history and practiceDownloadXML (US English)PDF (US English)Alt. DisplayShareShareReviewMaximum entropy spectral analysis for circadian rhythms: theory, history and practiceis an array of numerical techniques available to estimate the period of circadian and other biological rhythms. Criteria for choosing a method include accuracy of period measurement, resolution of signal embedded in noise or of multiple periodicities, and sensitivity to the presence of weak rhythms and robustness in the presence of stochastic noise. (MESA) has proven itself excellent in all regards. The MESA algorithm fits an autoregressive model to the data and extracts the spectrum from its coefficients. Entropy in this context refers to "ignorance" of the data and since this is formally maximized, no unwarranted assumptions are made. Computationally, the coefficients are calculated efficiently by solution of the Yule-Walker equations in an iterative algorithm. MESA is compared here to other common techniques. It is normal to remove high frequency noise from time series using digital filters before analysis. filter is demonstrated here and a danger inherent in multiple filtering passes is discussed. Year: 2013Page/10. 1186/1740-3391-11-6Submitted on Apr 16, 2013Accepted on Jun 25, 2013Published on Jul 11, 20134. 0 processes in almost all plants and animals have adapted to the cycles in the environment, be they daily (circadian), tidal, lunar, synodic lunar monthly or annual [1]. Oscillatory behavior with periods of less than 24-h, termed ultradian, are also commonly found, occasionally embedded in circadian or other rhythms [2]. This adaptation to cycles in the environment has occurred through the evolution of a biological timekeeper, a true temperature-compensated oscillator providing temporal information at all levels of physiology and behavior [1]. Thorough investigation of these oscillators requires that the periodic evolution of the processes in time be characterized precisely as to the length of the periods seen, as these are the manifestation of the clock process [3]. In addition, the relative robustness and regularity of the rhythms is of considerable interest. Numerical samplings of any process that evolves in time, taken at appropriate intervals, form time series, the stuff and substance of biological rhythm research. Analysis of time series may be simply done.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: 90 Although represented as a single arrow, the ‘Clinical Integration’ step (Step 2c) can often be the most difficult step in the entire translational path. Most implementations of clinical decision support do not have the intended effect because of the difficulty with clinical integration. What differentiates the products listed in Table 2 from most machine learning models is that these products have undertaken clinical integration. Only one product, InSight (),40 conducted a single-site, randomised control trial with 142 patients and demonstrated positive results. This single study needs to be followed up with larger trials from every team trying to drive adoption of a machine learning product. 3. Diffuse and are many machine learning products that focus on solving a local problem. The next challenge is to diffuse and scale across settings, which requires special attention to deployment modalities, funding, and drivers of adoption. As described earlier, machine learning products that ingest structured data from EHR require significant integration effort and infrastructure. This has driven the rapid adoption of models and algorithms sold and distributed by EHR vendors. 91 To scale, machine learning products must be able to ingest data from different EHR and must also support on-premise and cloud deployments. For this reason, many models are also distributed as stand-alone web applications that require manual entry to calculate risk. During this stage, machine learning product teams seek external investment and financial resources. As shown in Table 2, tens of millions of dollars are often raised by companies trying to scale products. The resources are required for both scaling deployment of the product as well as navigating the drivers of adoption. Several adoption strategies include academic dissemination, marketing and sales, and partnerships with regulators and payers to create reimbursement mechanisms. The “nonadoption, abandomnent, and challenges to the scale-up, spread, and sustainability of health and care technologies” is an example of a technology adoption framework that covers seven domains and has been recently applied to machine learning products. 92,93 To date, no machine learning product ingesting EHR data has successfully diffused and scaled across healthcare. The products listed in Table 2 are as far along as any and will be closely watched over the coming years. 4. translational path for a machine learning product does not have a finish line. Data quality, population characteristics, and clinical practice change over time and impact the validity and utility of models.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: today’s innovation-driven agrifood domain, the perspective of using so-called Techniques (NBTs) on both non-human animals and plants calls into question the regulatory approach (process/product-based) to be used, while asking for a critical reflection on the potential impact of products on the industrial sector and citizens. A possible reconfiguration of European (EU) discipline will have to grapple with not only agrifood market’s interests and needs, but mostly and primarily with the growing quest for public and participatory discussion on the current dominant vision on life sciences. Only through restoring visibility to the intertwining of knowledge production will it be possible to obtain EU governance of gene editing that is more authoritatively reliable from a scientific stance, as well as more transparently discussed and democratically shared at legal and policy level. Type 10. 1017/err. 2019. 55 [Opens in a new window] Copyright © Press 2019 Access options Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access. ) 10. 1186/s13059-018-1586-y> accessed 19 September 2019. CrossRef10. 2903/j. efsa. 2012. 2943> accessed 19 September 2019. CrossRef+accessed+19+September+2019. >10. 1007/s10806-017-9664-9> accessed 19 September 2019. CrossRef10. 1080/23299460. 2017. 1287522> accessed 19 September 2019. CrossRef583. 23 23 Garden, H and Winickoff, D, for Advanced Therapies: Governance, Policy and Society (2018)2018. pdf> accessed 19 September 2019. 25 25Cas9 stands for “” associated to a Cas9 protein. 26 26 TALEN stands for “Transcription Activator-Nucleases”. 27 27 ZNF stands for “Zinc-Finger Nucleases”. 28 28 Zhang et al, supra, note 7. 29 29 Khandagale, K and Nadaf, A, “for of Plants” (2016) 10(6) Reports 327 < accessed 19 September 2019. CrossRef681. CrossRef2019.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: In early work by Bünning on bean plant leaf movements, periodicity was estimated by measuring peak-to-peak intervals on chymographs that registered leaf position [4]. Analysis technique has progressed considerably since that time and now offers an array of possibilities for estimates of period length [5]. This paper deals with a very useful method called Spectral Analysis, or MESA, developed by Burg in the 1960s in answer to shortcomings of the principal analysis technique up to that time, Fourier analysis [6 - 8]. We will first discuss Fourier analysis, noting the problems that MESA was developed to fix and how they can be circumvented with MESA. We will pay attention to the theoretical underpinnings so that this popular method will not be a “black box” and will show the basics of how the spectrum is computed. Given that the biologist necessarily works with time series that are either inherently irregular or contain major trends, tools that can ameliorate these problems when used in conjunction with MESA will be introduced and examples of their benefits discussed. Biological rhythm data Circadian rhythms are studied in systems ranging from intracellular fluorescence to complex behaviors such as running wheel activity; data acquisition and format vary accordingly. For example, when studying the activity of an enzyme, the variable may be continuous and an appropriate sampling interval must be chosen. This must be rapid enough to avoid “aliasing” in the periodicity region of interest. This occurs when the sampling interval is longer than the period being recorded and is famously seen in old western movies when the spokes of wagon wheels seem to be going backwards; sampling frequency must be no less than twice the frequency of the cyclic process of interest. This constraint is the Nyquist or foldover frequency [9]. Faster sampling is normal, however, to ensure no detail is lost and that accurate period estimates result. There are two important things to consider. The first, is resolution, which is the ability to separate two frequencies as being distinct, for example a 24-h circadian peak with a 24. 8-h lunar daily peak. This is equivalent to optical resolution, in which two objects in an image can be separable [10]. Resolution is theoretically limited by the number of cycles in the data set, or in optics, by the diameter of the lens. A completely separate problem is the ability to discern a periodic signal in noise. This is sensitivity, and both are important.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: Model reliability and model updating are active fields of research and will be integral to ensure the robustness of machine learning products in clinical care. 94,95 Another example of model maintenance is updating outcome definitions to retrain models as scientific understanding of disease progresses. For example, many of the sepsis products listed in Table 2 use sepsis definitions that pre-date Sepsis-3, the most recent international consensus definition. 96 Similar to other technology innovations, the product lifecycle continues and will need to adapt to changes in market dynamics and organisational needs. Similarly, the product will evolve over time and will require continued validation and iteration. Throughout the process, teams developing machine learning products need to work closely with regulators and operate within evolving regulatory frameworks. 14 translational path described above is not well trodden and, for better or for worse, the products listed in Table 2 are establishing norms for the industry. Across the 21 products, there are opportunities to improve how machine learning products are translated into clinical care. Outside of the scope of this review, the path of IDx-DR, a machine learning product used to automatically diagnose diabetic retinopathy, is instructive. IDx-DR has conducted a randomised control trial and has received regulatory approval in both the USA and EU as a medical device. 97,98 IDx-DR is now actively being scaled and diffused. Unfortunately, many products in Table 2 are pursuing ‘stealth science’ to protect trade secrets and avoiding regulatory or academic scrutiny. 99 While stealth science is not uncommon amongst biomedical innovations, lack of transparency is particularly concerning with machine learning. This narrative review was unable to provide standard metrics of adoption, because many of the figures marketed by product developers have no peer-reviewed evidence. Machine learning products, which often lack inherent interpretability, need evidence that ensures validity as well as safety and efficacy. There are three opportunities to enhance how all machine learning products that ingest structured EHR data are translated into clinical care. First, data quality systems and frameworks need to be adopted to ensure that machine learning models have face validity. Significant effort and resources are required to transform the raw data extracted from EHR into a usable format for training machine learning models. 24 Distributed research networks that leverage EHR data for clinical research have developed frameworks for assessing quality of EHR data, but these frameworks have not been adopted by machine learning product developers.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: 34 34 Kosicki, Met al, “Repair of Double-strand by –Cas9 Leads to ” (2018) 36 765CrossRef268. 43 43 Regulation (EC) No 258/97 of the and of the Council of 27 January 1997 concerning novel foods and novel food ingredients [1997] OJ L 043, Art 3(4). has been replaced by Regulation (EU) 2015/2283 of the and of the Council of 25 November 2015 on novel foods, amending Regulation (EU) No 1169/2011 of the and of the Council and repealing Regulation (EC) No 258/97 of the and of the Council and (EC) No 1852/2001 [2015] OJ L 327/1. 44 44 Directive 90/220/EEC of 23 April 1990 on the deliberate release into the environment of genetically modified organisms [1990] OJ L 117; 90/219/EEC of 23 April 1990 on the contained use of genetically modified micro-organisms [1990] OJ L 117; 90/679/EEC of 26 November 1990 on the protection of workers from risks related to exposure to biological agents at work (seventh individual Directive within the meaning of 374. A fourth directive on the matter was enacted in 1994 (94/55/EC of 21 November 1994 on the approximation of the laws of the with regard to the transport of dangerous goods by road [1994] OJ L 319). 45 45 Tallacchini, M, “or Not Bind? as Soft Law” in Hilgartner, Set al (eds), Science and Democracy: in the Biosciences and Beyond (Florence, KY, Taylor and Francis 2015) p 1562019. 51 51 EASAC confirmed this position in its Report “Genome Editing: Scientific Opportunities, in the EU” (31, 2017) <www. easac. eu/fileadmin/PDF_s/reports_statements/Genome_Editing/EASAC_Report_31_on_Genome_Editing. pdf> accessed 19 September 2019. 52 52 For an overview of the different positions and opinions, see European Parliament, New plant-breeding techniques Applicability of GM Rules (May 2016) <www. europarl. europa.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Biological rhythm data are commonly not continuous and consist of unary events unlike the record left on a kymograph by a bean plant. Here, other constraints begin to play a role. Running wheel activity in mammals and the breaking of an infrared light beam by Drosophila are useful examples. Here, individual events occur, and are summed across arbitrary intervals or “bins”. Bin size affects the output of time series analysis and this effect can be profound when bin size is too small (Review: [11]). Bin sizes of 10 min up to an hour are common in rhythms work. For the purposes of illustration of the techniques being discussed, we will analyze a simulated data set. This is useful in this sort of discussion, as one may know precisely what the parameters of the signal are. The series considered here is a 23-h square wave with 20% white noise added consisting of 336 values produced at half hour intervals for 7 days using our own software. I chose a square wave as it is important to show that any analysis be effective for waveforms deviating markedly from the badly overused sinusoid. A simple time plot of the data is shown in Figure 1. Figure 1 An artificially produced time series with an arbitrary maximum amplitude of one. It is a square wave with 20% white noise added. The power in the series is: 0. 62. The autocovariance and autocorrelation functions Given a particular signal, even if it appears clearly rhythmic in a simple time plot, it is important that an objective statistical test be employed to determine if significant periodicity is present. Such a robust test is autocorrelation analysis [5]. In this analysis, the time series is initially lined up with itself in register and correlation analysis is applied yielding the coefficient, r. In this case, no matter what the signal looks like, correspondence is one to one and r = 1. The two series are then set out of register or “lagged” by one interval. The result is a decrement in r. The drop depends on the series; if it is a noiseless sinusoid, the change will initially be small, but if it is white noise, the drop will be very large, since the value of any given point has no relation whatsoever to any other point either near or far in time. Lagging proceeds one interval at a time up to about N/3.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: et al. Why a right to explanation of automated decision-making does not exist in the general data protection regulation. Privacy Law. 2017;7(2):76-99. Obermeyer Z, Lee TH. Lost in thought - the limits of the human mind and the future of medicine. N Med. 2017;377(13):1209-11. Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence. JAMA. 2018;320(21):2199-200. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. et al. An improved multi-output gaussian process RNN with real-time validation for early sepsis detection. proceedings of machine learning for healthcare. 2017;Eprint:1708. 05894 Corey KM et al. Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): a retrospective, single-site study. PLoS Med. 2018;15(11):e1002701. Sendak MP et al. Barriers to achieving economies of scale in analysis of EHR data: a cautionary tale. Informatics. 2017;8(3):826-31. et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index. Med. 2018;71(5):565-74. e2. Churpek MM et al. Multicenter development and validation of a risk stratification tool for ward patients. . 2014;190(6):649-55. Churpek MM et al. Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. CCM. 2016;44(2):368-74. Wolinsky H. Business. What if you could prevent someone from suffering cardiac arrest? 2018. Available at: Monegain B. Health IT News. EarlySense acquires predictive analytics to help hospitals assess cardiac risk. Available at: Last accessed: 22 September 2019. Rothman MJ et al. Development and validation of a continuous measure of patient condition using the Record. J Biomed Inform. 2013;46(5):837-48. Finlay GD et al.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: eu/RegData/etudes/BRIE/2016/582018/EPRS_BRI(2016)582018_EN. pdf> accessed 19 September 2019; T Sprink et al, “for Genome Editing: Process- vs. Product-based Approaches in Contexts” (2016) 35 Rep 1493. 53 53 See supra, note 23. 54 54 Art 3(1) provides as follows: “shall not apply to organisms obtained through the techniques of genetic modification listed in Annex IB”. Annex IB provides as follows: “Techniques referred to in 3. Techniques/methods of genetic modification yielding organisms to be excluded from the Directive, on the condition that they do not involve the use of recombinant nucleic acid molecules or genetically modified organisms other than those produced by one or more of the techniques/methods listed below are: (1) mutagenesis (2) cell fusion (including protoplast fusion) of plant cells of organisms which can exchange genetic material through traditional breeding methods”. 55 55 On the ambiguous character of this question from a legal viewpoint, see Albujar, GF and van der Meulen, B, “The EU’s GMO Concept: Analysis of the GMO Definition in EU Law in the Light of Techniques (NBTs)” (2018) 1 EFFL 1430. 57 57 ibid, at para 28. 58 58 ibid, at para 29. 59 59 ibid, at para 54. 60 60 Somsen, H, “. Is the Court of Risk with Risk? ” (2018) 9(4) EJRR 7011. 62 62 On the role played by the so-called “opt-out” clause, see Geelhoed, M, “Divided in Diversity: EU’s GMO Regime” (2016) 18 of Studies 20CrossRef2019. 64 64 See “to Court of Justice of the Ruling that ” (Centre, 2018) <www. sciencemediacentre. org/expert-reaction-to-court-of-justice-of-the-european-union-ruling-that-gmo-rules-should-cover-plant-genome-editing-techniques/> accessed 19 September 2019.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: The process is usually limited to this point since the power of the test is reduced with the decrement of each pair of lags off the ends of the series. r values are plotted as a function of the lag yielding the autocorrelogram function. In a rhythmic series, r will continue to decline, becoming negative and reaching a minimum when the peaks and valleys in the two series are out of phase by one half cycle. A second positive peak will occur when the peaks and valleys are back in phase, but one cycle out of register. The envelope of decay of the autocorrelation peaks is a function of the regularity in the series and this can provide a useful way of characterizing the regularity in the signal, as will be discussed below (Reviews: [5,12]). When computing the correlation coefficient, the output is normalized by dividing by the variance of the complete data set, but this need not be so and the output is then “covariance”, or the autocovariance function [5]. Autocorrelation is commonly employed, as it allows comparisons among wide-ranging experiments. The autocorrelation function also yields a valuable way to quantify the regularity of the signal both in terms of variation in period and the presence of noise. The height of the third peak in this function, counting the peak at lag zero as one, is taken as the Rhythmicity Index, or RI. This value relies on the decay of the envelope of the function and is normally distributed so it may be used in statistical analyses (review: [12]). The RI of the test signal is 0. 697. It is useful to have a formal criterion for the significance of rhythmicity in data. The 95% confidence interval for testing the significance of a given peak in the autocorrelogram is 2/√N [5]. Plus and minus confidence intervals are commonly plotted as flat lines, the decrement in N as values are lost being ignored (see above discussion). Repeated peaks equaling or exceeding the confidence interval are usually taken to imply significant rhythmicity, but there is room for subjective interpretation [5,12]. Figure 2 shows the autocorrelation function for the test data set depicted in Figure 1. Since the lagging can be done in either direction, Lag 0, where r = 1 is at the center and the function is mirrored to the left as well.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: Medicine. Early-Deployed at Johns Hopkins. 2019. Available at: Pitchbook. Bayesian Health. 2018. Available at: Last accessed: 15 September 2019. et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Med. 2018;46(4):547-53. Center. DRIVe teams up with academic research consortium to develop deep learning software to provide early warning of sepsis in patients. 2019. Available at: Last accessed: 18 September 2019. Giannini HM et al. A machine learning algorithm to predict severe sepsis and septic shock. Med. 2019;47(11):1485-92. Ginestra JC et al. Clinician perception of a machine learning-based early warning system designed to predict severe sepsis and septic shock. Med. 2019;47(11):1477-84. Dean NC et al. Performance and utilization of an emergency department electronic screening tool for pneumonia. . 2013;173(8):699-701. Dean NC et al. Impact of an electronic clinical decision support tool for emergency department patients with pneumonia. Med. 2015;66(5):511-20. et al. CDS in a learning health care system: Identifying physicians' reasons for rejection of best-practice recommendations in pneumonia through computerized clinical decision support. Inform. 2019;10(01):001-9. Dean NC et al. AMIA. Implementation of real-time electronic clinical decision support for emergency department patients with pneumonia across a healthcare system. 2019. Available at: Last accessed: 11 December 2019. et al. Patient risk stratification for hospital-associated C. diff as a time-series classification task. Advances in Processing Systems. 2012:467-75. et al. A study in transfer learning: Leveraging data from multiple hospitals to enhance hospital-specific predictions. J Inform Assoc. 2014;21(4):699-706. et al. A generalizable, data-driven approach to predict daily risk of Clostridium difficile infection at two large academic health centers. Hosp Epidemiol. 2018;39(4):425-33. DeepMind. About (DeepMind). 2019. Available at: Last accessed: 1 September 2019. King D. DeepMind. Why doesn’t Streams use AI? 2017.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: 65 65 “The decision is not only an obstacle to faster successes in modern agriculture in the breeding of more robust crops with higher yields; it also impairs the production of biopharmaceuticals and bio-based chemicals. takes the wrong standpoint on how to regulate genome editing methods, since researchers obtain with Crispr/CAS the same results as with conventional breeding methods – but much faster” <www. vci. de/vci-online/presse/pressemitteilungen/backward-minded-and-hostile-to-progress-vci-on-ecj-judgment-on-genome-editing. jsp> accessed 19 September 2019. 66 66 “We regret that implementation of this ruling could cause European life science innovation effectively to come to a halt. If fast mitigation is not done, the ruling will cause a halt to EU sustainability and competitiveness ambitions by hindering the delivery of innovative bio-based products to the market, sustainable innovative food-solutions and certain healthcare solutions to patients” <www. europabio. org/sites/default/files/EuropaBio_statement_CourtRuling_final_forWEB. pdf> accessed 19 September 2019. 67 67 Bioökonomierat, “Genome editing: ” (2018) BÖRMEMO 07 <biooekonomierat. de/fileadmin/Publikationen/berichte/BOER-Memo_Genome-Editing_ENG. pdf> accessed 19 September 2019. 68 68 “These new ‘GMO 2. ’” genetic engineering techniques must be fully tested before they are let out in the countryside and into our food. We welcome this landmark ruling which defeats the biotech industry’s latest attempt to push unwanted genetically-modified products onto our fields and plates” <foeeurope. org/eu-top-court-confirms-safety-checks-needed-new-gmo-250718> accessed 19 September 2019. 69 69 Gelinsky, E and Hilbeck, A, “of : A Commentary on the about the Recent Ruling” (2018) 30(1) Eur 52CrossRef2019. pdf> accessed 19 September 2019.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: This view can make visual interpretation easier. Figure 2 This is the autocorrelation of the data depicted in Figure1. Note the height of the third peak, which is the RI and equals 0. 697. Fourier analysis Beginning in the late 19th century, the process of producing a spectrum from digital time series was largely accomplished by Fourier analysis. Fourier showed that any function showing certain minimal properties called the “Dirichlet Conditions” can be approximated by a harmonic series of orthogonal sine and cosine terms [6,7]. The series must have a finite number of maxima and minima, be defined at all points and not have an infinite number of discontinuities, conditions met by most data encountered in biology [12]. Here we take a function f(t) and approximate it with a Fourier series: (1) ft≅a0/2+a1sint+a2sin2t+…b1cost+b2cos2t+… If our function consists of an ordered set of values x(t), then the “power” in the series is the ensemble average of the squared values. If the mean is zero, this is variance. transform is an extension of a fit of the Fourier series and has the property that the coefficients approximate the spectrum of the power, meaning the power at each frequency for which a computation can be made (review: [5]). For a continuous series, we have: (2) Fω=∫−∞∞fte−iωtdt The exponential function consolidates the sine and cosine terms. F(ω) is the spectrum of the function, with ω being the angular velocity, or 2πf, where f is frequency. This process was carefully described by Schuster [13] and he termed it the “periodogram” of the function. This procedure should not be confused with the Whittaker-Robinson algorithm [14], improperly given the same name, which was largely discredited by Kendall on formal mathematical grounds [15]. (See [12] for further discussions and examples). Figure 3 depicts the Whitaker-Robinson “periodogram” for the data set. Figure 3 This is the so-called Whitaker-Robinson “periodogram”, which is not the same as the true periodogramss of Schuster.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: 2017. Available at: Last accessed: 15 August 2019. Ayasdi. Transform your business with machine 1 intelligence and big data. 2019. Available at: Last accessed: 15 August 2019. Crunchbase. Ayasdi. 2019. Available at: Last accessed: 1 September 2019. et al. Development and validation of a predictive model for detection of colorectal cancer in primary care by analysis of complete blood counts: a binational retrospective study. J Inform Assoc. 2016;23(5):879-90. et al. Computer-assisted flagging of individuals at high risk of colorectal cancer in a large health maintenance organization using the ColonFlag test. . 2018;2(2):1-8. et al. Evaluation of a prediction model for colorectal cancer: Retrospective analysis of 2. 5 million patient records. Cancer Med. 2017;6(10):2453-60. Hornbrook MC et al. Early colorectal cancer detected by machine learning model using gender, age, and complete blood count data. and Sciences. 2017;62(10):2719-27. Pitchbook. Medial EarlySign. 2019. Available at: Last accessed: 6 December 2019. Johnson AEW et al. MIMIC-III, a freely accessible critical care database. Sci Data. 2016;3:160035-9. Selby NM et al. Standardizing the early identification of acute kidney injury: the NHS patient safety alert. Nephron. 2015;131(2):113-7. Tomašev N et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature. 2019;572(7767):116-9. Davis SE et al. Calibration drift in regression and machine learning models for acute kidney injury. J Inform Assoc. 2017;24(6):1052-61. Davis SE et al. A nonparametric updating method to correct clinical prediction model drift. J Inform Assoc. 2019;26(12):1148-57. et al. Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer. . 2017;12(2):e0171759-8. et al. The “inconvenient truth” about AI in healthcare. npj Digital Medicine.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: 71 71 ENGL (of GMO Laboratories), “Detection of Food and by Techniques” (JRC116289, 2019). 72 72 SAM (Mechanism), “statement by the Group of on the of from and the Implications for the GMO Directive” (2018) <ec. europa. eu/info/sites/info/files/2018_11_gcsa_statement_gene_editing_1. pdf> accessed 19 September 2019. 73 73 Ozolina, Ket al, of Science (Brussels, European Commission, 2009)51. 75 75 EFSA, “Guidance on the Assessment of Plants” (2010) 8 EFSA J 1879, <www. efsa. europa. eu/en/efsajournal/pub/1879> accessed 19 September 2019. 76 76 EFSA, “on Guidance for of Food and Feed from Plants” (2011) 9 EFSA J 2150 <efsa. onlinelibrary. wiley. com/doi/10. 2903/j. efsa. 2011. 2150> accessed 19 September 2019. 77 77 SZ Agapito-Tenfen et al, “Governance of GM Plants: to -Editing Techniques” (2018) 21 Sci < accessed 19 September 2019. 78 78 <ec. europa. eu/health/scientific_committees/environmental_risks/docs/scher_o_154. pdf> accessed 19 September 2019. 79 79 EFSA, “Guidance to Options for Assessment at EFSA, in Relation to Biodiversity and Ecosystem Services” (2016) 14 EFSA J 4499 <efsa. onlinelibrary. wiley. com/doi/10. 2903/j. efsa. 2016. 4499> accessed 19 September 2019. 80 80 See supra, note 78. 81 81 Stirling, A, “Precaution in the Governance of Technology” in Brownsword, Ret al (eds), Handbook of Law, Regulation and Technology (Oxford, Press, 2017) p 6452019. 84 84 See <=ec. europa. eu/citizens-initiative/public/initiatives/successful/details/2017/000002/en? lg=en> accessed 19 September 2019.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Fourier analysis has undergone considerable development and sees a great deal of use in many fields, with chronobiology prominent among them; it has done yeoman service. If the spectrum is calculated directly from data sampled at intervals, it is termed the Transform or DFT. Fourier spectra are seldom computed directly from the raw data however, rather they are produced from either the autocovariance or autocorrelation functions. One argument for using the autocovariance function is that the output is equivalent to partitioning the variance in the signal by frequency and the area under the curve is the power (review: [5]). Figure 4 depicts the DFT of the test data set which is the most basic way to visualize the process. The period is reported as 22. 4 h which contrasts with the known value of 23. The reason for this discrepancy is discussed below. Figure 4 of the test time series. The period is calculated to be 22. 4. Note in particular the paucity of spectral estimates in the crucial range between 20 and 30 hours. This would normally be corrected in more advanced algorithms, but at a cost (see text). Compromises inherent in Fourier analysis Since the autcovariance and autocorrelation functions lose power with each pair of points lost, usually no more than one third of the data are used to compute correlation coefficients, adversely affecting the potential resolution in the spectrum. To alleviate this, the rest of the function is padded out with zeroes. This is an outright falsification of data points not in evidence, since there is no reason to suspect these data points would all be zeroes. An added problem occurs at the point where the zeroes abruptly start, since this abrupt discontinuity will cause artifactual peaks in the spectrum called “side lobes” owing to the Gibbs phenomenon [16]. To correct for this, the real data are blended into the zeros to soften the transition. Here, yet more actual data must be altered to allow for side lobe suppression. One further development that exacerbates these compromises is the Transform, or FFT. In this algorithm, computational efficiency, and concomitantly speed of calculation, are increased by constraining the input series to consist of 2N data points.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: 2019;393(10181):1577-9. et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30-6. et al. Development and validation of an electronic medical record-based alert score for detection of inpatient deterioration outside the ICU. J Biomed Inform. 2016;64:10-9. . Big data, big tech, and protecting patient privacy. JAMA. 2019; doi: 10. 1001/jama. 2019. 11365. [Epub ahead of print]. et al. Ensuring fairness in machine learning to advance health equity. Med. 2018;169(12):866-8. et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53. Gianfrancesco MA et al. Potential biases in machine learning algorithms using electronic health record data. . 2018;178(11):1544–7. Share: More great content like this - straight to your inbox > Rate this content's potential impact on patient outcomes Thank you! Please share some more information on the rating you have given 1 Mins 29th Developed to 10th Insights: 8. 1 2024 2023 EMJ Innovations 7. 1 2023 Elevating the quality of healthcare globally Elevating the quality of healthcare globally Allergy & : Gold with Partners & & to our email newsletter World of & Journal’s Mission to Elevate the Quality of © 2024 trading as Journal. All rights reserved. Journal is for informational purposes and should not be considered medical advice, diagnosis or treatment recommendations. Ts & by Proud to be ISO9001 Certified. 9001 standard is an internationally recognised standard that is based on a number of quality management principles including a strong customer focus, the management of process change, effective methods of reducing non- compliance, and the continual improvement in the quality of our services.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: 86 86 EFSA, Guidance on Communication of Uncertainty in (2018) <efsa. onlinelibrary. wiley. com/doi/epdf/10. 2903/j. efsa. 2019. 5520> accessed 19 September 2019. 86 86 EFSA, Guidance on Communication of Uncertainty in (2018) <efsa. onlinelibrary. wiley. com/doi/epdf/10. 2903/j. efsa. 2019. 5520> accessed 19 September 2019. 87 87 Regulation (EU) 2019/1381 of the and of the Council of 20 June 2019 on the transparency and sustainability of the EU risk assessment in the food chain and amending Regulations (EC) No 178/2002, (EC) No 1829/2003, (EC) No 1831/2003, (EC) No 2065/2003, (EC) No 1935/2004, (EC) No 1331/2008, (EC) No 1107/2009, (EU) 2015/2283 and Directive 2001/18/EC, OJ L 231/1. 88 88 See, for instance, the Organisation’s (BEUC) clarifications/changes to strengthen the proposal: “It must better ensure that public health prevails over commercial considerations when examining industry confidentiality requests. No important piece of safety-related information should be hidden away from the public. It must allow independent scientists to quote or re-use the data disclosed by EFSA without having to ask for industry’s permission. It must foresee meaningful sanctions for industry applicants failing to notify EFSA of studies commissioned to support a regulatory dossier. If pre-submission meetings between EFSA staff and industry applicants are introduced, the proposal must guarantee that they are held in full transparency and that they do not lead to any shift in the allocation of EFSA internal resources, at the expense of other activities of public interest” (BEUC, Closing the between Consumers and the EU System (2018) at 1 <www. beuc. eu/publications/beuc-x-2018-059_beuc_position_paper_general_food_law_revision. pdf> accessed 19 September 2019). 89 89 See <www. ifoam-eu.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Here again, the chances of a data set containing an integer power of 2 points are slim, and again, zeroes are added to pad the series out (Review [16]). Further data corruption can occur when tighter spacing of spectral estimates is required. If the series is long, consisting of multiple cycles, this is usually not a problem. However, when short data sets are at hand, as is commonly the case with circadian rhythm work, there will be few cycles available. Fourier analysis is based on harmonics and these are constrained. In practice, this means that the spacing between estimates can be very wide. For a one-week long experiment with data sampled at half hour intervals (as in the test data) and analyzed using a simple DFT, spectral estimates are produced only for 22. 4 and 24 h in the critical interval between 22 and 25 h. This leaves enormous gaps with little chance that the single estimate at 22. 4 is even remotely close to the true period which is 23. 0 h. Once again, it is straightforward to tighten up the interval between estimates, but once again, zeroes are added with further problems in using false data points for which there is no justification [16]. Maximum entropy spectral analysis In the late 1960’s, Burg developed a new method for producing a spectrum that tackles these problems [17,18]. It initially found acceptance in astrophysics and quickly spread to other fields. It began to be used for circadian rhythm work in the 1980s and is an excellent choice for a wide range of biological time series. This technique is called “Spectral Analysis” (MESA) [16 - 18]. MESA delivers the highest possible resolution, while eliminating side lobe problems. It is also extremely sensitive, as defined above. It is particularly useful in the short, noisy time series typical in biological systems [12,19,20]. The linchpin of this powerful technique is stochastic modeling. Time series evolve in time according to probabilistic laws and there are a number of models that can underlie such processes. One example is an autoregressive (AR) function (Review: [5]). The assumption is that the system moves forward in time as a function of previous values and a random noise component.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: 2632-2153/4/3/035005 Abstract Machine-learning (ML) force fields (FFs) enable an accurate and universal description of the potential energy surface of molecules and materials on the basis of a training set of ab initio data. However, large-scale applications of these methods rest on the possibility to train accurate ML models with a small number of ab initio data. In this respect, active-learning (AL) strategies, where the training set is self-generated by the model itself, combined with linear ML models are particularly promising. In this work, we explore an AL strategy based on linear regression and able to predict the model's uncertainty on predictions for molecular configurations not sampled by the training set, thus providing a straightforward recipe for the extension of the latter. We apply this strategy to the spectral neighbor analysis potential and show that only tens of ab initio simulations of atomic forces are required to generate FFs for room-temperature molecular dynamics at or close to chemical accuracy and which stability can be systematically improved by the user at modest computational expenses. Moreover, the method does not necessitate any conformational pre-sampling, thus requiring minimal user intervention and parametrization. Export citation and abstract 4. 0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Supplementary data 1. IntroductionMachine learning (ML) models for the generation of force fields (FFs) are becoming a prominent aid for researchers in different fields, including drug discovery [1], prediction of metastable structures [2], heterogeneous catalysis [3], and more [4–7]. In all these fields, ML permits to speed up calculations or to manage larger datasets, largely overcoming the problem of the computational costs inherent to electronic structure simulations. In recent years, many ML models for the generation of FFs have been presented, e. g. sGDML [8], BP-NNP [9–11], GPR based models [12–14], PhysNet [15], SchNet [16], FCHL19 descriptors combined with different regressors [17], moment tensor potentials [18], message passage neural networks [19–21], and many more. All these methods have been shown to be able to reproduce the potential energy surface (PES) of complex chemical systems with chemical or near-to-chemical accuracy.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: Identifying vulnerable targets is challenging because some essential genes are non-vulnerable, including targets for which specific inhibitors with antimalarial activity are available, such as deoxyhypusine synthase (Aroonsri et al. , 2019), Niemann-C1-Related protein (Istvan et al. , 2019), and plasmepsin V (Sleebs et al. , 2014; Polino et al. , 2020). Partial loss-of-function (LOF) mutants of these genes have latent proliferation defects, and in the case of plasmepsin V, a defect was observed after 96 h only in LOF mutants with greater than 90% knockdown of the wild-type level of expression (Polino et al. , 2020). Antimalarial discovery efforts might be better focused on directly identifying vulnerable targets with alternative assays rather than proving essentiality, which may require laborious monitoring of proliferation in LOF mutants with varying degrees of knockdown and/or conditional knockout mutants over extended periods for non-vulnerable targets. We propose to define vulnerable targets for the purpose of assay development as genes for which a partial LOF mutant (with significant knockdown of about 50 to 90% reduction of the wild-type expression level) has an acute proliferation defect observable at 96 h or sooner. In this study, we developed a target vulnerability assay for LOF mutants created with the glmS ribozyme tool (Prommana et al. , 2013). To apply the tool, the gene of interest must be modified by DNA transfection. Proof of concept for the tool was previously demonstrated for the dihydrofolate reductase-thymidylate synthase (DHFR-TS) gene modified by single-crossover integration of transfected circular DNA (Prommana et al. , 2013; Aroonsri et al. , 2016). This transfection method has been superseded by the more efficient -Cas9 gene editing system (Ghorbal et al. , 2014). We edited the DHFR-TS gene to assess whether the glmS ribozyme was functional in the context of an edited gene, particularly one with the 3′ coding region replaced with artificial recodonized sequence as a consequence of gene editing. In addition, the LOF mutant obtained with an edited DHFR-TS gene was used to validate the target vulnerability assay.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: The simplest example is a Markov process: (3) Xt=aXt−1+Zt. Where t is time, a is a coefficient derived from the data and Zt is white noise [5]. This simplest process may be extended by going backwards in time to earlier and earlier values, with each weighted by a coefficient derived from the known observed values [5]: (4) Xt=aXt−1+bXt−2+…+cXt−p+Zt. and, again, a, b, c,… are the model’s coefficients and p is the order of the filter. These coefficients form the prediction error filter (PEF) [21]. Crucially, it is possible to use the model to predict future values based on what is known of all the past values. In the case at hand, the analysis is functionally extending the autocorrelation function out to the needed number of values by prediction from those that can be reliably estimated [16]. Entropy, in information theory, is equivalent to ignorance. If one can formally calculate estimates that maximize ignorance, this means these values are the most honest based on what is known from data in hand and this is demonstrated through the calculus of variations [16 - 18]. A pile of zeros certainly does not fit this criterion. The spectrum is constructed from the coefficients as follows [17,18]: (5) Sω=P/1−∑k=1pake−iωk2 Where: S(ω) is spectral power as a function of angular velocity (see above), P is the power passed by the PEF, p is the order of the PEF and ak is the set of PEF coefficients. The algorithm commonly used by us and others calculates the filter in an iterative fashion and is based on the work of Anderson [22]. Each iteration extends the AR model by one. The number of coefficients in the prediction error filter employed to construct the spectrum is hence not fixed and requires some care in its choice. If a number that is too low is chosen, resolution and important detail can be lost. On the other side of the coin, if the number of coefficients is run up too high, there may be spurious peaks [21]. An objective method has been developed using the methods of Akaike [21], based on information theory.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: 10. 55124/jaim. v1i1. 234 Submitted: 2023-10-18 Published: 2023-10-18 Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ 85721, United States. ISSN 2995-2336 : and Future of in Dr. Suryakiran Navath, Ph. D. Department of Chemistry and Biochemistry, University of Arizona, Tucson, AZ 85721, United States. integration of (AI) in healthcare has ushered in a new era, transforming the industry in unprecedented ways. This manuscript delves into the profound impact of AI on healthcare systems and envisions its future trajectory. Through a comprehensive analysis of current applications, challenges, and emerging trends, this study illuminates the revolutionary changes brought about by AI technologies. From advanced diagnostics to personalized treatment plans, AI is reshaping patient care, improving operational efficiency, and fostering innovative solutions. Furthermore, the manuscript explores ethical considerations, regulatory frameworks, and the societal implications of widespread AI adoption in healthcare. By examining the intersection of technology and human well-being, this manuscript provides a holistic view of how AI is revolutionizing healthcare and offers valuable insights into the future of medicine. introduction section provides an overview of the rapid advancements in AI and its integration into the healthcare sector. It outlines the motivation for the study, emphasizing the need to explore the transformative impact of AI technologies on healthcare delivery, patient outcomes, and overall industry dynamics. 1 The integration of (AI) into the healthcare sector has set in motion a paradigm shift in the way we approach medical diagnosis, treatment, and patient care. The fusion of advanced machine learning algorithms, big data analytics, and cutting-edge technology has given rise to a new era in healthcare, one that holds the promise of improved patient outcomes, more efficient healthcare delivery, and better resource allocation. 2 In this manuscript, we embark on a journey to explore the profound impact of AI in healthcare. We delve into the current state of affairs, where AI is already playing a pivotal role, and then we peer into the future, envisioning a healthcare landscape where AI technologies will become even more integral. This exploration is not merely a technical one but a holistic examination of the socio-economic, ethical, and regulatory dimensions. We dissect the multifaceted ways in which AI is reshaping medical imaging, disease diagnosis,3 drug discovery, treatment personalization, and patient management.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: DHFR-TS is a known vulnerable target that is inhibited by antifolate drugs such as pyrimethamine and P218 (Yuthavong et al. , 2012), and LOF mutants of DHFR-TS show significant proliferation defects at 72 h or earlier (Prommana et al. , 2013; Aroonsri et al. , 2019). To search for new antimalarial targets, we propose testing LOF mutants of unexplored genes in target vulnerability assay. Methionine metabolic pathways contain several unexplored antimalarial targets. Methionine is an essential amino acid in P. falciparum that must be obtained from salvage because proliferation is markedly reduced in culture media lacking methionine (Divo et al. , 1985; Marreiros et al. , 2023). P. falciparum salvages methionine via the new permeation pathway and a neutral amino acid transporter (Cobbold, Martin & Kirk, 2011). In addition to protein synthesis, methionine is used as a cofactor to produce the essential metabolite S-adenosyl-l-methionine (SAM) by the SAM synthase (SAMS) enzyme. The methyl group from SAM is transferred to various acceptors by methyltransferases to form S-adenosyl-l-homocysteine (SAH). SAH is hydrolyzed to adenosine and l-homocysteine (Hcy) by the highly conserved enzyme S-adenosyl-l-homocysteine hydrolase (Tanaka et al. , 2004). P. falciparum lacks key enzymes in the reverse transsulfuration pathway for the conversion of Hcy to cysteine. Consequently, Hcy accumulates and is effluxed from the parasite during intra-erythrocytic growth (Beri et al. , 2017). However, excess Hcy is deleterious and can trigger gametocytogenesis in P. falciparum (Beri et al. , 2017). In addition to efflux for the control of Hcy, Hcy can be converted to methionine by the action of the methionine synthase enzyme (MS, 5-methyl tetrahydrofolate homocysteine methyltransferase, EC. 2. 1. 1. 13).

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: The filter length chosen is consistent with the most amount useful information that is being extracted as each iteration extends the length of the filter. This is used in the MESA software employed in our work, but we also commonly set a minimum filter length of about N/4 for biological rhythm analyses to ensure adequate representation of any long period cycles in the presence of noise, which can be considerable. N/3 is a good safe maximum [5,21]. MESA has proven itself superior to ordinary Fourier analysis as it does not produce artifacts from the various manipulations needed absent a model for the function and both resolution and sidelobe suppression are superior to standard Fourier analysis [16,23]. To show the difference between Fourier analysis and MESA in one critical area, it was noted above that the possible estimates that can be computed for period in Fourier analysis is constrained by the fact that these estimates can only be calculated for fixed values that are harmonics based on the length of the time series at hand. Longer series mean more tightly spaced estimates and this can be “faked” by adding zeros. MESA does not need to do this. Since the spectrum is extracted from an AR model, the spacing can be narrowed to any needed level. As an example, for the time series we have been working with, we have one week’s worth of data, sampled at half hour intervals. Note that shortening the bin size will have no effect on the spacing of the samples. Figure 5 shows the MESA for the test data set with the number of estimates increased by a factor of 32. Unlike the DFT, which had only 2 estimates in the interval between 22 and 25 h, MESA produced 60. Increasing MESA coefficients could go as high as needed, with the downside being a growing number of values that need plotting. Here 32X is more than sufficient to tease out a good estimate. Figure 5 This is the MESA spectrum with the coefficients upped to 32X. The period reported is 22. 88, compared to the known input of 23. 0. The tiny discrepancy is likely a result of the 20% added noise in the signal. Data conditioning Biological signals can be notoriously non-stationary and noisy. This variation can take the form of linear or nonlinear trends in amplitude, variations in period and the waveform. As with any signal analysis system, MESA output can be improved by conditioning the signal.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: Simultaneously, we examine the ethical quandaries posed by AI, as well as the steps being taken to ensure responsible and equitable use. Furthermore, we will analyze the challenges faced by healthcare providers and stakeholders in adopting AI, from data privacy concerns to the need for medical practitioners to adapt to this technological revolution. We scrutinize the ongoing regulatory efforts and the international consensus on AI in healthcare standards. The journey we embark upon in this manuscript aims to not only provide an in-depth understanding of the AI revolution in healthcare but also to empower stakeholders with the knowledge needed to make informed decisions in this dynamic landscape. 4 As we move forward, we shall encounter fascinating breakthroughs, inspiring success stories, and complex dilemmas. The journey is one of discovery, reflection, and anticipation, and it is one that promises to illuminate the path to a healthcare system that is not just efficient but truly patient-centric. 5 This section explores the diverse range of applications of AI in healthcare. It covers AI-driven diagnostics, predictive analytics, robotic surgery, virtual health assistants, and smart healthcare management systems. Real-world examples and case studies illustrate how these applications are revolutionizing patient care, enhancing accuracy, and reducing healthcare costs. Examining the challenges associated with AI implementation in healthcare,6 this section discusses issues related to data privacy, security, and the ethical implications of AI algorithms in decision-making processes. It delves into the importance of bias mitigation and transparency in AI systems, emphasizing the need for responsible AI practices. This section explores the future of AI in healthcare. 7 It discusses cutting-edge technologies such as quantum computing, AI-driven drug discovery, and genomics. The section also delves into the role of AI in addressing global health challenges, such as pandemics and rare diseases, highlighting the potential for AI to revolutionize public health strategies. 8 Examining the regulatory landscape, this section analyzes existing frameworks governing AI in healthcare and proposes recommendations for creating standardized guidelines. Additionally, it discusses the societal impact of AI adoption, including workforce implications, patient-doctor relationships, and accessibility to advanced healthcare services. 9 Methods 1. Our research journey began with an extensive and systematic literature review. We conducted searches across a range of academic databases and healthcare industry publications, encompassing 2. further bolster our exploration, we gathered and analyzed data from various sources.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: MS uses 5-methyltetrahydrofolate (5-mTHF) as a cofactor to generate tetrahydrofolate (THF) as a by-product (Banerjee & Matthews, 1990). P. falciparum can obtain 5-mTHF from salvage or by synthesis from 5,10 methylenetetrahydrofolate via a methylenetetrahydrofolate reductase enzyme (Asawamahasakda & Yuthavong, 1993). Cobalamin-dependent MS enzymatic activity was reported previously in P. falciparum intra-erythrocytic stage protein extract. Nitrous oxide inhibits the activity of this enzyme and parasite proliferation, suggesting that parasite cobalamin-dependent MS may be a potential antimalarial target (Krungkrai, Webster & Yuthavong, 1989). However, the gene encoding the parasite MS enzyme has not yet been identified in the P. falciparum 3D7 genome (Müller & Hyde, 2013). We identified a candidate P. falciparum MS gene and created a LOF mutant for assessing the vulnerability of this target. Materials & approval Blood for parasite culture was obtained by a protocol approved by the Ethics Committee, Agency (NSTDA), Thailand, approval document #0021/2560. Written consent was obtained from all volunteers. Bioinformatic analyses The InterPro database (Paysan-Lafosse et al. , 2023) was searched using the InterPro entry IPR003726 (homocysteine-binding domain) via the InterPro web interface ( Protein sequences were obtained from UniProt (The Uniet al. , 2023) of the cobalamin-dependent methionine synthase (MS) enzymes from human (MTR, Q99707-1) and Escherichia coli K12 (metH, P13009), together with Plasmodium spp. candidate MS from orthologous group 1324at5820 encoded by P. falciparum PF3D7_1233700 (Pf, Q8I585), P. knowlesi PKH_145080 (Pk, A0A384KWI2), P.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: It should be noted, however, that MESA is robust in the face of such problems from the start. Incorporated into our MESA program is a detrending step which fits a line by regression and subtracts it. This eliminates linear trend and removes the mean. Mean removal is highly recommended, as this DC component can obscure the rhythmic one if it is excessive [12]. Removal of nonlinear trend can be accomplished by high-pass filtering by numerous methods and will not be discussed here as it is beyond the scope of this work. Low pass filtering to remove high frequency noise, however, is of considerable interest and is commonly done in preparation for spectral analysis (Review: [12,24]). We have had excellent success with Butterworth recursive filters [9,12,24]. They are considered recursive because in addition to incorporating the original time series data into the moving filtering process, previously filtered values are used as well. Butterworth filters are highly accurate and reliable, and the cutoff frequency is sharp [9]. 6, the artificial test signal depicted in Figure 1 is shown after filtering with a two-pole low-pass Butterworth filter with a ~3 dB amplitude rolloff at the specified period of 4 h. The number of poles reflects the depth of the recursion [9]. The filter equation showing the recursion is: (6) Yt=Xt+2Xt−1+Xt−2+AYt−1+BYt−2/C Figure 6 This is the original data set after being filtered twice with a Butterworth recursive digital filter. The second pass reverses the filter’s introduction of a four-hour phase delay owing to its recursive nature. is the original data series and Yt is the output series. A and B are the filter coefficients: A = 9. 656 and B = −3. 4142. C is the “gain” or amplitude change of the filter and equals 10. 2426. See [9,12,24] for a more detailed description of this filter. Owing to the recursion there is a 4-h phase delay in this example and this needs either to be made clear when the data are plotted or actually reversed. Reversal can easily be accomplished by running the filter in reverse.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: This data encompassed healthcare utilization statistics, patient outcomes, AI implementation in medical institutions, and the economic implications of AI adoption in healthcare. We applied statistical and data analysis tools to identify correlations and trends that highlight the impact of AI on healthcare quality, efficiency, and costs. 3. In our quest for comprehensive insights, we conducted interviews with experts and thought leaders in the field of healthcare and artificial intelligence. These interviews provided valuable qualitative data and personal perspectives on the challenges, opportunities, and ethical considerations related to AI in healthcare. We engaged experts from diverse backgrounds, including clinicians, data scientists, healthcare administrators, and regulatory authorities. 4. To substantiate our findings, we examined a series of real-world case studies from healthcare institutions that have successfully integrated AI into their operations. These case studies were selected to illustrate different applications of AI, such as medical image analysis, predictive analytics, and personalized medicine. Through these case studies, we aimed to highlight the practical impact of AI on healthcare providers and patients alike. 5. Ethical and The ethical and regulatory aspects of AI in healthcare were a focal point of our study. We analyzed existing ethical frameworks, guidelines, and regulations governing AI implementation in healthcare. In addition, we explored ongoing discussions within the international community on the responsible use of AI, as well as emerging regulatory developments. This analysis aimed to shed light on the measures being taken to ensure patient privacy, data security, and equity in AI-enabled healthcare. 6. The manuscript also incorporates a future-oriented analysis, which involved forecasting the trajectory of AI in healthcare. This forward-looking segment was informed by expert opinions, historical trends, and ongoing research and development efforts in the field. Our aim was to provide readers with a glimpse of what to expect in the coming years, including potential breakthroughs, challenges, and transformative changes. Trends 1. AI Adoption in The adoption of AI in healthcare institutions has shown a consistent upward trajectory. In 2019, approximately 40% of hospitals and medical facilities had integrated some form of AI into their clinical and administrative workflows. By 2020, this figure increased to around 60%, and in 2021, it further rose to approximately 80%. This three-year trend illustrates a steady and significant increase in AI adoption within healthcare. 2. application of AI in medical imaging has witnessed continuous improvements over this period. In 2019, AI-assisted diagnosis led to a 10% increase in diagnostic precision.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: Repair vectors (Dataset S2 & Dataset S3) were constructed by simultaneous Gibson assembly cloning (NEB) of three synthetic fragments for each target (recodonized partial open reading frame, 5′ and 3′ HR) into the p3HA_glmS plasmid, linearized by digestion with KpnI (NEB). Recodonized coding region fragments were obtained as gBlocks synthetic DNA (IDT, Singapore). 5′ and 3′ HR fragments were obtained by PCR using PrimeSTAR® (Inc. Shiga, Japan) and P. falciparum 3D7 genomic DNA template. Gene editing by DNA transfection P. falciparum 3D7 reference (NCBI txid: 36329) wild-type parasite was cultured in vitro as previously described (Aroonsri et al. , 2016; Aroonsri et al. , 2019). Cas9 (20 µg) and PstI-linearized repair (50 µg) vectors were co-transfected into late schizont parasites by AMAXA nucleofection, as previously described (Knuepfer et al. , 2017). WR99210 (2 nM, a gift from Prof. Tirayut Vilaivan) was applied 48 h post-transfection to select transfected parasites. WR99210-resistant parasites emerged within 30 days post-transfection, and gene editing events were detected by PCR with integration-specific primers (Table S1). Transfected parasites with detectable gene edits were treated for 7 days with 1 µM 5-fluorocytosine (Sigma-) and 2. 5 µg/mL blasticidin S HCl (Gibco™, ) to remove Cas9 plasmid retained as episome and eliminate wild-type parasites. Clonal lines of gene-edited parasites were obtained by limiting dilution in 96-well microtiter plates. Western blotting Clonal lines of transgenic parasites DHFR-TS_glmS and MS_glmS with edited PF3D7_0417200 and PF3D7_1233700 genes, respectively were synchronized by sorbitol treatment and cultured for 24 h in the presence or absence of 5 mM glucosamine (GlcN, Sigma-).

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Since it is highly inadvisable to run a filter more than once to achieve additional smoothing before spectral analysis, as this will result in artifact [9], this reversal must only be done for display of simple plots for visualizing data. A single pass with the phase change is not an issue for MESA, since the attendant phase shift is of no consequence in this context. A second reversing pass with this filter actually resulted in a widening of the MESA peak (data not shown). After filtering, the RI (see above) is improved from 0. 697 to 0. 715. MESA at work MESA has seen notable success since first being implemented for use in biological rhythms in the 1980s. MESA is useful for an extremely wide range of living oscillatory processes. It was instrumental in discovering the presence of ultradian rhythms in Drosophila locomotor activity rhythms early on, most remarkably in flies bearing the per0 and per- mutations, which have no overt circadian periodicity [25,26]. These ultradian rhythms have been central in a competing hypothesis describing the mechanism of the circadian clock [26,27]. It has been used extensively in circadian rhythm work since that time. Given its superior resolving power, it settled an old dispute about the presence of lunar rhythmicity in physiological activity in marine organisms [9]. When applied in conjunction with powerful trend removal techniques, it was instrumental in teasing out the role of the gene cryptochrome (cry) in the fly clock system. Luciferase activity was monitored in antennae bearing either tim-luc or per-luc constructs and a central role for CRY protein in the peripheral antennal clock was established [28,29]. Ultradian and circadian rhythms were examined in premature infants (24–29 weeks) prior to their developing robust circadian periodicity enabling inferences on prenatal periodicity in normal pregnancies [30]. A cryptic human core body temperature of about one hour was elucidated [31]. Electroencephalography has yielded considerable information on ultradian periodicity in rats with MESA analysis combined with aggressive filtering enabling an incorporation of these high-frequency rhythms into models of sleep-wake dynamics [32]. A genetic component in strain differences among normal mice was discerned when locomotor activity was investigated with MESA, revealing robust ultradian components [33]. The presence of an endogenous vertical migration rhythm in Antarctic krill was verified [34].

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: By 2020, this increase had grown to 15%, and in 2021, it accelerated further to 25%. These findings demonstrate a notable evolution in the impact of AI on medical imaging in just three years. 3. for The use of AI-driven predictive analytics for disease prevention has also seen notable progress. In 2019, hospitals and healthcare providers that utilized AI for patient data analysis and risk prediction achieved a 15% reduction in hospital readmissions. By 2020, this reduction had increased to 20%, and in 2021, it reached 30%. This trend highlights the growing effectiveness of AI algorithms in identifying patients at higher risk of complications and enabling timely interventions. 4. Plans AI's role in developing personalized treatment plans for patients has advanced over this period. In 2019, AI-driven recommendations led to a 5% increase in treatment plan efficacy. By 2020, this increase had reached 10%, and in 2021, it further improved to 15%. This data indicates a continuous refinement of AI algorithms in tailoring treatment strategies based on individual patient data. Table 1. Use of AI in Healthcare category by 2021 2020 2019 AI Adoption in 80% 60% 40% Enhancement 25% 15% 10% for 30% 20% 15% Plans 15% 10% 5% 20% 15% 10% and Engagement 25% 20% 15% 15% 10% 5% 5. The economic implications of AI integration in healthcare have shown promise. In 2019, the implementation of AI in administrative tasks led to a 10% reduction in operational costs for healthcare institutions. By 2020, this reduction increased to 15%, and in 2021, it reached 20%. These trends indicate a growing impact of AI on cost reduction within healthcare. 6. related to patient satisfaction and engagement have consistently shown positive trends. In 2019, healthcare facilities that employed AI-powered virtual health assistants and chatbots reported a 15% increase in patient satisfaction rates. By 2020, this increase grew to 20%, and in 2021, it reached 25%. These findings highlight the ongoing enhancement of patient satisfaction and engagement through AI-driven tools. 7. While the statistics showcased the potential benefits of AI in healthcare, they also highlighted the ethical concerns. Data breaches and privacy violations related to AI were identified in approximately 15%, 10%, 5% of healthcare AI projects. This underscored the importance of robust data security and privacy measures in AI adoption.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: PF3D7_1233700 is annotated in OrthoDB (Kuznetsov et al. , 2023) as a member of the syntenic Plasmodium orthologous group 1324at5820 (Hcy-binding domain). PF3D7_1233700 and orthologous (single-copy) proteins from other human-infective Plasmodium spp. showed low (≈20%) identity with human and Escherichia coli cobalamin-dependent MS (Figs. S1 & S2). We searched for candidate MS using a three-dimensional structural superposition-based approach (Foldseek), which is more sensitive for identifying homologous proteins (et al. , 2023). Full-length cobalamin-dependent MS comprises four modules (N-terminal Hcy-binding, 5-mTHF-binding, cobalamin-binding, and C-terminal adenosylmethionine-binding/reactivation; Matthews, Sheppard & Goulding, 1998). The X-ray structures of cobalamin-dependent MS protein fragments from different species have been determined, although the structure of the N-terminal fragment containing Hcy- and 5-mTHF substrate binding domains is available only for Thermotoga maritima (Evans et al. , 2004). We selected the structure of the T. maritima cobalamin-dependent MS fragment co-complexed with Hcy and 5-mTHF as a query for Foldseek. The top-ranked Foldseek hit to P. falciparum 3D7 proteins was PF3D7_1233700. Lower-ranked hits had well-defined annotations unrelated to methionine metabolism, suggesting incidental structural similarity of protein folds with functions unrelated to MS (Table S2). Notwithstanding the possibility of even more diverged proteins not detectable by sequence- or structure-based homology, PF3D7_1233700 is putatively assigned as P. falciparum MS. However, definitive annotation requires direct functional data, e. g. , biochemical assay of the purified PF3D7_1233700 protein for MS activity.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Work on the cardiac pacemaker of the fly heart has benefitted from the use of MESA for measuring heart rate [35,36]. When combined with a novel preliminary Fourier treatment to alter the sampling structure, the presence of rhythmicity in the spacing of pulses in the Drosophila mating song was confirmed and it was shown to be under the control of the period gene [37]. In summary, has proven itself to be a highly useful and versatile tool for the investigation of periodic biological phenomena. Technical note A full explanation of the mathematics underlying MESA and the ways in which algorithms have been implemented is beyond the scope of this paper. For those wishing to explore these topics in detail, the author recommends the following: For a good general introduction to the basic logic of MESA see Able’s review [16]; Burg’s original papers are the next step in seeing how the technique developed [17,18]; the very thorough paper by Ulrych and Bishop [21] should be sufficient to answer almost any mathematical question on the procedure and the algorithm used in our version of the technique, which we implemented in is found in Andersen’s contribution [22]. Some of these papers, notably those of Burg himself, are difficult to locate. The compendium edited by D. G. Childers entitled “Analysis” (1978, Wiley) has all these recommended papers and many more that are on point. All software used in this lab, including the source code, is available free of charge from the author: dowse@maine. edu. A step by step annotated guide in its use has been published by this author [38]. Competing interests The author declares that he has no competing interests. 55. Lloyd, D; Rossi, EUltradian rhythms in life processes: An inquiry into fundamental principles of chronobiology and psychobiology. Springer-Verlag. Winfree, ATof biological Time. Springer-Verlag. Bünning, EClock. Springer-Verlag. Chatfield, Cof Time Series: An Introduction. Chapman and Hall. Lanczos, CApplied Analysis. Prentice Hall. Lanczos, CDiscourse on Fourier Series. Oliver and Boyd. Beauchamp, K; Yuen, CKfor Signal Analysis. George, Allen, Unwin. Hamming, RWDigital Filters. Prentice-Hall.

Output: pass


Input: Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: In the following sections, we delve deeper into these statistics, providing detailed analyses and discussions that paint a comprehensive picture of the transformative impact of AI on the healthcare sector. conclusion synthesizes key findings from the manuscript, emphasizing the transformative potential of AI in healthcare. It underscores the importance of ethical considerations, collaborative efforts between the industry and regulators, and ongoing research to fully harness the benefits of AI technologies. The conclusion also provides a forward-looking perspective, envisioning a future where AI-driven healthcare becomes not just a possibility but a global reality, transforming the lives of millions. Acknowledgement. The authors thank to Dr. Mir Mahammad, Baylor and for his constant encouragement and valuable discussions. aThe reaction was conducted in anoxic conditions. bThis is the format for table footnotes. 10. 1186/2047-2501-2-3 D. W. Bates, S. Saria, L. Ohno-Machado, A. Shah, G. EscobarBig data in health care: using analytics to identify and manage high-risk and high-cost patientsHealth Aff. , 33 (2014), 10. 1377/hlthaff. 2014. 0041 E. A. Mohammed, B. H. Far, C. NauglerApplications of the MapReduce programming framework to clinical big data analysis: current landscape and future trendsBioData Min. , 7 (2014), p. 22, 10. 1186/1756-0381-7-22 Y. Wang, N. HajliExploring the path to big data analytics success in healthcareJ. Bus. Res. , 70 (2017), 10. 1016/j. jbusres. 2016. 08. 002 B. Kayyali, D. Knott, S. Van KuikenThe big-data revolution in US health care: accelerating value and innovationMcKinsey Co. (2013), 10. 1145/2537052. 2537073. Orjuela-Canon, Andres L. undefined, 2021 43rd Conference of the IEEE Engineering in Medicine & (EMBC), 10. 1109/EMBC46164. 2021. 9631006, (2468-2471), (2021). promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare, Bulletin, 10.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: We edited the PF3D7_1233700 and PF3D7_0412700 (DHFR-TS) genes, placing them under the control of the glmS ribozyme (Prommana et al. , 2013). The edited DHFR-TS and MS genes were confirmed by PCR genotypic assays in clonal lines of transgenic parasites (Figs. 1 & 2). One clonal line of each edited gene (designated as DHFR-TS_glmS and MS_glmS, respectively) was selected for phenotypic analysis. The expressions of target proteins in transgenic parasites were assessed by western blotting of synchronized transgenic parasites cultured for 24 h in the presence or absence of GlcN. Protein species of sizes expected for modified MS (72. 7 kDa) and DHFR-TS (75. 5 kDa) were detected in transgenic parasites (Fig. S3). GlcN treatment caused significant reductions in MS and DHFR-TS protein expression (DHFR-TS mean = 40%, P-adjusted = 0. 004; MS mean = 51%, P-adjusted = 0. 03; Fig. 3A). There was no significant difference in the mean % target protein (GlcN treated relative to untreated control) level between DHFR-TS and MS (P = 0. 34). Figure 1: PF3D7_0417200 (DHFR-TS) gene editing. (A) Schematic of PF3D7_0417200 gene editing (drawn to scale). The location of the guide RNA target for mediating double-strand DNA break is indicated by the lightning bolt symbol. Repair vector (Dataset S2) elements are indicated by the colored boxes, including homologous regions in blue, recodonized protein coding region (Rec. ) in magenta, triple hemagglutinin epitope tag (HA) in cyan, glmS ribozyme (glmS) in red, blasticidin S deaminase selectable marker gene (BSD) in orange, and Plasmodium transcriptional regulatory elements in teal. The locations and sizes of amplicons expected from PCR using primers DHFR_37 F (P1), _1558R (P2), and glmS_3R (P3) are indicated by black arrows.

Output: contrast


Input: Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Chui, CKto Wavelets. Academic Press. Dowse, HB; Ringo, JMSumming locomotor activity into “bins”: How to avoid artifact in spectral analysis Research 11: 2–14, DOI: (2009). Analyses for physiological and behavioral rhythmic Methods in Enzymology, Computer Methods, Part A,. 2009: Elsevier, 174. Schuster, AOn the investigation of hidden periodicities with application to a supposed 26-day period of meterological phenomena 11: 13–41, DOI: Whitaker, E; Robinson, Gof Observations. Blackie and Son. Kendall, MGContributions to the study of oscillatory time series. Press. Ables, JGMaximum entropy spectral analysis 11: 383–393. (1978). . Wiley, 41. (1978). A new analysis technique for time series data Analysis. Wiley, 48. Dowse, HB; Ringo, JMThe search for hidden periodicities in biological time series revisited J 11: 487–515, DOI: Dowse, HB; Ringo, JMComparisons between “periodograms” and spectral analysis: Apples are apples after all J 11(148): 139–144. Ulrych, T; Bishop, TMaximum entropy spectral analysis and autoregressive decomposition Rev. Geophysics and 11: 183–300, DOI: Andersen, NOn the calculation of filter coefficients for maximum entropy spectral analysis Geophys 11: 69–72, DOI: Kay, SM; Marple, SGSpectrum analysis, a modern perspective IEEE Proc 11: 1380–1419. Levine, J; Funes, P; Dowse, H; Hall, JSignal analysis of behavioral and molecular cycles Neurosci 11: 1–25. Dowse, HB; Hall, JC; Ringo, JMCircadian and ultradian rhythms in period mutants of Drosophila melanogaster 11: 19–35, DOI: [PubMed] (2008). Mid-range ultradian rhythms in Drosophila and the circadian clock problem from Molecules to Mind: A of Life. Springer Verlag, 199.

Output: pass


Input: Claim: 3D printing allows for customized, on-demand manufacturing. [SEP] Text: Glass additive manufacturing is an emerging research topic showing an increase in publications in recent years. 20–28 Recent studies demonstrated MLA fabrication in fused silica using two-photon polymerization through a direct laser writing technique,29,30 an approach that combined hot embossing with custom glass 3D printing,31 stereolithography,32 and microscale computed axial lithography. 33 These studies were realized by utilizing a new type of silica nanocomposite resin. After printing, thermal postprocessing is required to create fully dense glass microlenses. This includes removing the binder (polymer), followed by a high-temperature sintering step. This postprocessing can be time consuming, requiring several hours of heating. The debinding and sintering steps also result in significant volume shrinkage, which can deform the structure and influence the resulting surface roughness of the print. Despite this challenge, the state-of-the-art techniques demonstrate up to 6 nm roughness for the printed surface after sintering. 29,30,32,33In this study, we report on 3D printing fused silica glass MLA through CO2 laser heating of glass filaments. This technique enables highly customizable MLA design having sub-mm lens diameters, without the need for any thermal postprocessing. Through laser heating, lenses with sub-nm surface roughness can be achieved. The demonstrated technique is well suited for rapid prototyping of MLAs, as well as for small- to medium-size production in glass. Material and FabricationThe feedstock material used for MLA fabrication was fused silica glass filaments, having a diameter of 200 μm. The filaments were made using a conventional draw tower designed for optical fiber fabrication. Fused silica is a synthetic silicon dioxide glass of high purity, providing enhanced optical transmission in the wavelength range from ∼180 nm to 2. 5 μm, enabling MLAs to be designed for a large range of wavelengths. The MLA was fabricated directly onto a 25 × 25 mm2 fused quartz plate having a thickness of 300 μm. A glass 3D printer (Nobula™ Zero; Nobula3D AB) was used for printing the MLA as well as for postprocessing. The printer used multiple beams from a CO2 laser to locally heat and soften/melt the substrate and glass filament. The CO2 laser operated at a wavelength of 10. 6 μm.

Output: corroborate


Input: Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: The structures of the PF3D7_0417200 gene before editing in parental reference strain 3D7 wild-type (3D7 WT) and after editing in transgenic (DHFR-TS_glmS) parasites are shown. (B) PCR products from genotypic assay separated in 0. 8% agarose gel. Three clonal lines of transgenic parasites were isolated; DHFR-TS_glmS clone #1 was selected for phenotypic analysis. Lane M: 1kb+ DNA ladder (Invitrogen, sizes indicated on the left). Download full-size image DOI: 10. 7717/peerj. 16595/fig-1 Figure 2: PF3D7_1233700 (MS) gene editing. (A) Schematic of PF3D7_1233700 gene editing (drawn to scale). The location of the guide RNA target for mediating double-strand DNA break is indicated by the lightning bolt symbol. Repair vector (Dataset S3) elements are indicated by the colored boxes, including homologous regions in blue, recodonized protein coding region (Rec. ) in magenta, triple hemagglutinin epitope tag (HA) in cyan, glmS ribozyme (glmS) in red, blasticidin S deaminase selectable marker gene (BSD) in orange, and Plasmodium transcriptional regulatory elements in teal. The locations and sizes of amplicons expected from PCR using primers MS_5IntF (P1), MS_HR2_rev (P2), glmS_3R (P3), and MS_5recodonR (P4) are indicated by black arrows. The structures of the PF3D7_1233700 gene before editing in parental reference strain 3D7 wild-type (3D7 WT) and after editing in transgenic (MS_glmS) parasites are shown. (B) PCR products from genotypic assay separated in 0. 8% agarose gel. Two clonal lines of transgenic parasites were isolated; MS_glmS parasite clone #1 was selected for phenotypic analysis. Lane M: 1kb+ DNA ladder (Invitrogen, sizes indicated on the left). Download full-size image DOI: 10. 7717/peerj.

Output: contrast