Input: Task: corroborate the claim using the given text. Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: Machine learning technologies were included as case studies if they met two criteria: 1) they tackle a clinical problem using solely EHR data; and 2) they are evaluated and validated through direct integration with an EHR to demonstrate clinical, statistical, or economic utility. Machine learning technologies that analysed images were excluded. This review also advances prior work to propose best practices for teams building machine learning models within a healthcare setting14 and for teams conducting quality improvement work following the learning health system framework. 15 However, there is not a unifying translational path to inform teams beyond success within a single setting to diffuse and scale across healthcare. This review fills that gap and highlights how teams building machine learning products approach clinical translation and discusses challenges and opportunities for improvement. learning has been described as “the fundamental technology required to meaningfully process data that exceed the capacity of the human brain to comprehend. ”16 Machine learning models are often trained on millions of pieces of information. Existing knowledge about individual data elements and relationships between data elements are not explicitly programmed into the model and are instead learned through repeated iterations of mapping between inputs and outputs. This is in contrast to algorithms that comprise predictors and weights that are agreed upon by medical experts. Collaboration between machine learning and clinical experts is crucial and there are a range of modelling techniques that incorporate varying amounts of clinical expertise into model specifications. 17 Machine learning models can be trained in a supervised and unsupervised fashion. Supervised models assume that the output labels, for example a disease, are known up front, whereas unsupervised models assume that the output labels are unknown. An example of a supervised model is identifying which patients will develop sepsis, a known entity, whereas an example of an unsupervised model is identifying unknown subgroups of asthma patients. Most models integrated into clinical workflows as clinical decision support are supervised machine learning models. This review focusses on models applied to clinical decision support tasks rather than models applied to automation tasks. Not only does automation involve heightened regulatory burden,18 but machine learning is initially expected to impact healthcare through augmenting rather than replacing clinical workflows. 19 The distinction between decision support and automation is critical: “while it may be assumed that decision support is simply a stepping stone on the progression towards full automation, the truth is that decision support systems have fundamentally different considerations that must be accounted for in design and implementation. [SEP] Justify:

Output: The text corroborates the claim by detailing how machine learning is implemented in clinical settings, such as in creating models for clinical decision support, which demonstrates its transformative potential in healthcare. The mention of machine learning technologies tackling clinical problems, advancing best practices, and the essential collaboration between machine learning and clinical experts highlights the significant changes machine learning is bringing to the healthcare field.


Input: Task: contrast the claim using the given text. Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: Region 3: Verification of pCas after curing with temperature sensitive replicon, using primers P70/P71. The first three lanes in each region are genetically edited ETEC. The fourth is a positive control and the fifth is a negative control. Download full-size image DOI: 10. 7717/peerj. 8881/fig-4Cas gene operating system has made great progress in the field of eukaryotes, but it is not widely used in prokaryotes. The two-plasmid system combined theCas9 system and the λ-Red recombination to achieve target editing in the E. coli genome (Jiang et al. , 2015), and the recombination efficiency was greatly improved. In this system, the temperature-sensitive plasmid pCas expressed Cas9, Exo, Bet, and Gam proteins. The last three proteins mediated homologous recombination in the λ-Red system. pTargetF expressed an sgRNA sequence that specifically recognized the target site. There is an IPTG-induced transcription of sgRNA on pCas, which is directed to the replicon pMB1 of pTargetF. Therefore, cas9+sgRNA cleaves pMB1 to cleave and eliminate pTargetF. To date, model strains such as E. coli K12 and E. coli BL21 (DE3) have been employed when usingCas in E. coli for genetic manipulation. However, there have been no reports of genetic manipulation in wild-type E. coli. In this study, we focus on the application and efficiency of theCas system in wild-type ETEC virulence genes. This two-plasmid system introduces Red recombination technology into. protein can inhibit the RecBCD exonuclease of the host cell from degrading exogenous linear DNA. That made linear fragment could be used as the donor DNA. In fact, we tried to use linear fragment as the donor DNA early in the experiment. But there were no expected results. In Jiang’s 1. It was found that the longer the homology arm, the higher editing efficiency when the deletion fragment length was the same. The deletion efficiency was the highest when the length of the homology arm was approximately 300 bp. The toxicity of E. coli was significantly reduced after the virulence gene was deleted. Relevant experiments and results were shown in Supplementary Materials. [SEP] Justify:

Output: The text contrasts the claim by discussing the usage of a two-plasmid system for genetic manipulation and indicating that while there has been progress, there are still challenges and no reports of genetic manipulation in wild-type E. coli. Additionally, it highlights that the toxicity of E. coli was reduced after the deletion of virulence genes, implying that gene editing can involve risks and side effects that need to be managed.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Gray symbols preceding injection treatments depict mean baseline values, averaged across the week prior to the initiation of injection treatments. *p < 0. 05, ***p < 0. 001 vs. SAL value, within chronotype; #p < 0. 05, ##p < 0. 01 RHYTH-SAL vs. ARR-SAL values. Body temperatureChronotype did not affect dark-phase Tb τ′ (not illustrated). Tb τ′ was not significantly affected by LPS treatment. UR period—ZT1 injectionsLocomotor activityChronotype significantly affected τ′ of the dark-phase UR: periods were longer in ARR than RHYTH hamsters (F1,12 = 26. 9, p = 0. 0002; Fig. 5B). LPS, however, did not affect dark-phase τ′ as a function of chronotype. Body temperatureDark-phase Tb τ′ was not affected by chronotype or injection (not illustrated). Circadian RhythmsZT16—CR powerLPS reduced circadian power in the LMA waveform (F6,84 = 7. 47, p ≤ 0. 0001; Fig. 6A), with effects evident during the first and second 24-h intervals after injection (p < 0. 05, both comparisons) but not thereafter. LPS also reduced Tb circadian power (F6,84 = 10. 4, p ≤ 0. 0001; Fig. 6B) on the second through the fourth posttreatment days (p ≤ 0. 002, all comparisons). Figure 6. Lipopolysaccharide (LPS) inhibits power of behavioral and thermoregulatory circadian rhythms. Mean ± SEM power of the circadian rhythm in locomotor activity (A and C) and body temperature (B and D) of circadian entrained (RHYTH) female Siberian hamsters injected intraperitoneally (i. p. ) with bacterial LPS (625 µg/kg) or sterile saline (SAL) at either ZT16 (A and B) or ZT1 (C and D). *p < 0. 05, **p < 0. 01, ***p < 0. 001 vs. SAL value. +p < 0. 05, ++p < 0. 01, +++p < 0. 001 vs. LPS-ZT16 value for corresponding measure and time point. [SEP] Justify:

Output: The text does not address the claim regarding the connection between circadian rhythm disruption and health issues. Instead, it provides detailed experimental results on the effects of lipopolysaccharide (LPS) treatment on circadian rhythms in Siberian hamsters, focusing on statistical analyses and findings without linking them to specific health outcomes. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: ”9 Recommendations for the design and implementation of machine learning as clinical decision support are only beginning to emerge. 20 Machine learning can be applied to a wide variety of clinical decision support tasks in the inpatient and outpatient setting. Of the 21 case studies, 14 apply primarily to the inpatient setting and 7 apply primarily to the outpatient setting. Some models, such as a 30-day readmission model, can inform clinical decisions in the inpatient and outpatient setting. Examples of inpatient applications include prediction of intensive care unit transfer, acute kidney injury, sepsis, and Clostridium difficile infection, while examples of outpatient applications include prediction of chronic kidney disease progression, death, surgical complications, and colon cancer. Table 1 presents how models from the 21 case studies can be translated into clinical care and provides example clinical workflows. Additional details about each model are provided in the next section. This workflow summary is designed to be illustrative and more comprehensive overviews of models and potential workflows can be found elsewhere. 10,21 Configurations are categorised as either centralised or decentralised. In centralised workflows, the user of the clinical decision support is removed from direct in-person patient interactions. The user may be a physician, nurse, or care manager involved in managing the health of a population or cohort of patients. Centralised workflows are often involved in ‘command centres’ or ‘air-traffic controls’. In decentralised workflows, the user of the clinical decision support is directly involved in in-person patient interactions and is typically a nurse or physician. Decentralised clinical decision support tends to be directly embedded within the EHR. However, there is no single best workflow for a model and in fact many implementations of clinical decision support fail to improve outcomes. Table 1: Types of workflow configurations for machine learning applied to clinical decision support tasks. AISE: AISepsis Expert; eCART: electronic Risk Triage; EWS: Score; ICU: intensive care unit; TREW: targeted real-time early warning. pathway for translating machine learning applied to clinical decision support tasks is based on an examination of 21 machine learning products and the authors’ own experience integrating machine learning products into clinical care. [SEP] Justify:

Output: The text corroborates the claim by detailing how machine learning is being applied to various clinical decision support tasks within healthcare. It provides specific examples of machine learning applications that enhance decision-making in both inpatient and outpatient settings, which aligns with the assertion that machine learning is transforming fields like healthcare.


Input: Task: contrast the claim using the given text. Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: Berger et al. (2016) analyzed the pAA primary transcriptome using differential RNA sequencing and provided novel insights into its virulence gene expression and regulation. A recent study showed that in patients with lung adenocarcinoma, patients with lymph node metastasis had significantly elevated expression of long non-coding RNA than patients with non-lymph node metastases. This indicated that long noncoding RNAs could be seen as candidate diagnostic and prognostic biomarkers for lung adenocarcinoma (Wang et al. , 2019). Does the non-coding region affect the toxicity of ETEC? Is the virulence gene transcribed if the promoter is deleted? These questions are interesting and worthy of further exploration. Table 3: N20 + PAM sequence and GenBank ID. Target gene N20+PAM GenBank elt 002732. 1 elt II c1 031705. 1 est028790. 1 fae00292. 1 est00612. 1 DOI: 10. 7717/peerj. 8881/table-3 Notes: aPrimers for virulence genes and N20 are designed according to the GenBank ID in the table. The elt I, estA, estB and faeG locous are located on the plasmid of E. coli, and the elt II c1 loci is located on the E. coli chromosome. It demonstrated that the two-plasmid-based -Cas9 system can target genes in plasmids and chromosomes, which was important to edit virulence genes in ETEC. To identify the recombinant strain by PCR, the pair of primers on the outside of the homology arm was required. If homologous arm primers were used, it would result in false positives. Because the pTarget plasmid with the homologous arm was not eliminated at this time. The specificity of theCas9 system depends mainly on sgRNA (Alkan et al. , 2018; Fu et al. , 2014). The designed sgRNA may form a mismatch with nontarget DNA sequences, resulting in unintended gene mutations, that are called off-target effects (Zhang et al. , 2015). The off-target mutations can cause genomic instability and disrupt the function of other normal genes (Hsu et al. , 2013; Mali et al. , 2013). [SEP] Justify:

Output: The text contrasts the claim by mentioning that the designed sgRNA may form a mismatch with non-target DNA sequences, resulting in unintended gene mutations, known as off-target effects. It further explains that these off-target mutations can cause genomic instability and disrupt the function of other normal genes, indicating that gene editing can have risks and side effects.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: Inflammation-induced changes in URs may reflect potentially costly components of the acute-phase response to infection. 7406. Conflict of Interest StatementThe author(s) have no potential conflicts of interest with respect to the research, authorship, and/or publication of this 244. CrossrefPubMedWeb of Science536. CrossrefPubMedWeb of Science572. CrossrefPubMedWeb of Science454. CrossrefPubMedWeb of Science964. CrossrefPubMedWeb of Science3. CrossrefWeb of Science1192. PubMed5682. CrossrefPubMedWeb of Science186. CrossrefPubMedWeb of Science56. CrossrefPubMedWeb of Science1899. PubMed729. CrossrefPubMedWeb of Science857. CrossrefPubMedWeb of Science219. CrossrefPubMed408. CrossrefPubMedWeb of Science587. CrossrefPubMedWeb of Science81. CrossrefPubMedWeb of Science30. CrossrefPubMedWeb of Science105. CrossrefPubMedWeb of Science137. CrossrefPubMedWeb of Science265. CrossrefPubMedWeb of Science253. CrossrefPubMedWeb of Science3. CrossrefWeb of Science1636. CrossrefPubMedWeb of Science849. CrossrefPubMedWeb of Science411. PubMedWeb of Science2826. CrossrefPubMedWeb of Science839. CrossrefPubMedWeb of Science214. CrossrefPubMedWeb of Science1412. CrossrefPubMedWeb of Science11. PubMed597. PubMedWeb of Science723. CrossrefPubMedWeb of Science4756. CrossrefPubMedWeb of Science3837. CrossrefPubMedWeb of Science109. CrossrefPubMedWeb of Science1442. CrossrefPubMedWeb of Science316. PubMedWeb of Science1226. CrossrefPubMedWeb of Science12. CrossrefPubMedWeb of Science4. J Neuroimmunol 225:62-67. CrossrefPubMedWeb of Science312. CrossrefPubMedWeb of Science392. 62. CrossrefPubMedWeb of Science5. CrossrefPubMedWeb of Science195. CrossrefPubMedWeb of Science134. CrossrefPubMedWeb of Science541. CrossrefPubMedWeb of Science517. CrossrefPubMedWeb of Science164. CrossrefWeb of Science417. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses inflammation-induced changes in URs and components of the acute-phase response to infection, without addressing circadian rhythms or their disruption.


Input: Task: corroborate the claim using the given text. Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: 22-24 In this section, machine learning technologies are referred to as products rather than models, recognising the significant effort required to productise and operationalise models that are often built primarily for academic purposes. Table 2 summarises the products, provides context on the origin of the team and development effort, and highlights translational milestones. To map between individual products and the translational path, milestones for each product are marked within three phases: 1) design and develop; 2) evaluate and validate; 3) diffuse and scale. These phases are described in more detail below. Table 2: A table examining the experience of 14 machine leaning products actively undergoing translation into clinical care. The table includes the name, a brief description of the machine learning product, the origin, and the path to translation. AHRQ: Agency for and Quality; BARDA: Research and Development Authority; CDC: U. S. Centers for and Prevention; CDS: clinical decision support; ED: emergency department; EHR: electronic health record; MIMIC: Mart for Intensive Care; NSF: Foundation; PCCI: for Clinical Innovation; SBIR: Innovation Research; STTR: Technology Transfer; TREW: targeted real-time early warning. Milestone highlights also track the type of source as peer review, online marketing by the product development entity, or industry news. The products are designed to solve a variety of clinical and operational problems ranging from sepsis to escalation of cost and capture experiences from across the globe. The important role of academic research in commercialising products cannot be understated, as 16 of the 21 products were originally developed in academic settings. Many of those products are then externally licensed and are being scaled and diffused via commercial entities. Notably, these products have raised more than $200 million in private venture capital, but several products have also been funded through government grants as well as health systems. The translational path focusses on products that are actively being integrated into healthcare delivery settings and are often also being diffused and scaled beyond the original development context. The translational path, depicted in Figure 1, contains four phases discussed in detail below: 1) design and develop; 2) evaluate and validate; 3) diffuse and scale; 4) and continuing monitoring and maintenance. Each step contains a set of activities that teams building machine learning products often complete. [SEP] Justify:

Output: The text corroborates the claim by discussing how machine learning technologies are being productized and operationalized specifically in the healthcare sector. It details the translational path for machine learning products being integrated into clinical care, highlighting the significance of these technologies in resolving various clinical and operational challenges. Specifically, it notes that many machine learning products are integrated into healthcare delivery and are supported by both private venture capital and government grants, indicating a significant transformation in the healthcare field. Furthermore, the mention of products designed to solve serious clinical problems like sepsis underscores the transformative power of machine learning in healthcare.


Input: Task: contrast the claim using the given text. Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: The off-target effect of Cas9 has been reported frequently in eukaryotes (Fu et al. , 2013; Hsu et al. , 2013). When we knocked out the estB, the first selected colony of N20 did not complete the knockout task. Another colony of N20 was replaced to continue and then succeeded. To reduce the off-target effects of Cas9 in this study, it was best to ensure that the 12 bases at the 3′end of the N20 sequence were highly specific to the genome (Jiang et al. , 2013; Jiang et al. , 2015). Existing online sgRNA design tools are not specifically targeted at ETEC. The evaluation of sgRNA off-target probability is based on E. coli K12 MG1655 instead of ETEC. It might lead to inaccurate evaluation of sgRNA off-target probability. From this current experimental results, the knockout efficiency can meet our requirements for gene editing. The two-plasmidCas system gives us more convenient conditions to study ETEC. The two-plasmidCas system gives us more convenient conditions to study ETEC. It could be used to explore amino acids that affected virulence through site-directed mutations. And it also could be used to display foreign proteins on outer membrane or fimbriae of ETEC to stimulate the body to produce antibodies. We believe that the two-plasmid system will be more widely adopted following the further research. Conclusions ETEC is one of the important pathogens causing human and animal diarrhae. But the researches of its virulence genes were affected due to the lack of efficient gene editing tools. In this study, we explored the validity of the two-plasmid system ofCas for editing the virulence genes in ETEC. The results show that the two-plasmid system ofCas used in this study can edit the virulence genes on the ETEC plasmids and chromosome for deleting, replacing, and mutating. The editing efficiency is closely related to the homologous arm length, and the efficiency is the highest when the length of the homology arm is approximately 300 bp. This system can also be used for single base mutation through two rounds of replacement at the same position. [SEP] Justify:

Output: The text contrasts the claim by highlighting the off-target effects of the Cas9 gene editing system, which have been frequently reported. It mentions the need for specificity in the N20 sequence to reduce these off-target effects, indicating that gene editing may not be risk-free and can result in unintended consequences, thereby providing evidence against the claim of being completely risk-free.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to health issues. [SEP] Text: The outlet was connected to a sample loop of an HPLC sample injector, which automatically injected the collected pineal dialysates into an HPLC column at 10 min intervals. Melatonin in the dialysates was resolved on a C18 reverse phase column and flow through was analyzed by a fluorescence detector controlled by Shimadzu software. Middle cerebral artery occlusion Animals were subjected to ischemic stroke by filament occlusion of the middle cerebral artery (MCA) after baseline melatonin monitoring. The stroke model has been described before [17,18]. Animals were taken off of melatonin monitoring for the stroke surgery 3–6 days after the initiation of on-line microdialysis during which time baseline melatonin profiles were obtained. We occluded the right MCA for two hours using an intralumenal filament and then removed the filament to allow reperfusion. We placed the animals back online for pineal microdialysis and continued melatonin monitoring for up to seven days or until the animal appeared in extremis (whereupon it was euthanized). At the end of the monitoring period, animals were euthanized and brains were cut into 2 mm thick sections and analyzed by 2% 2,3,5-triphenyltetrazolium chloride (TTC) staining to quantify infarct volumes; TTC stains vital tissue red while dead tissue remains unstained. Infarct volumes were determined by analysis of imaged brains using NIH Image software. Sham surgery involved insertion of the filament into the carotid artery without advancing the suture to the MCA bifurcation. determine the level of statistical significance of circadian changes, we first calculated the average absolute deviation from the baseline melatonin onset (MT-on) for each day after stroke for each animal. We used the average of pre-stroke MT-on times as the baseline. We then computed the absolute value of the difference between MT-on and the baseline for each day that the animal was monitored. Finally, we calculated the average value of the absolute shift over the entire duration of the monitoring period. This metric accounts equally for both delays and advances in timing. The same calculation was performed for sham animals after control surgery. The mean of the average daily deviation for all animals in each group was compared between MCA occlusion (MCAo) and sham groups using a two-tailed t-test. [SEP] Justify:

Output: The text is irrelevant to the claim as it provides a detailed description of a scientific experimentation process involving melatonin monitoring and ischemic stroke models without addressing the potential health issues associated with circadian rhythm disruptions.


Input: Task: corroborate the claim using the given text. Claim: Machine learning is transforming fields like healthcare and finance. [SEP] Text: The activities do not define requirements for any single product, but are representative of the activities completed across the 21 products in Table 2. The path is not linear and may be highly iterative. Figure 1: A path for the design, development, evaluation, validation, diffusion, and scaling of machine learning models to be used in clinical care. The translational path begins on the left with the ‘problem’ to be addressed by the product and concludes with the ‘product lifecycle’ on the right. This path is designed to be a comprehensive representation of the current landscape, but is not meant to specify the steps taken by any individual product. Numbers denote the steps of the path. DARPA: Agency; DOD: Department of Defense; eCART: electronic Risk Triage; EHR: record. 1. Design and first step to building a machine learning product is identifying the right problem to solve. Healthcare is a data rich environment and even though a model can be developed to generate an insight, for the insight to support clinical decisions it must be actionable and must have the potential to impact patient care. Many of the products in Table 2 generate insights for conditions that require immediate action in the inpatient setting, such as cardiac arrest, sepsis, and deterioration. Products focussed on the outpatient setting tackle problems associated with high costs to healthcare payers or providers, such as surgical complications, hospital readmissions, and end-stage renal disease. The setting and funding of the team shapes many aspects of how the machine learning product is designed and developed. For example, in an academic setting it may be easier to cultivate collaborations across domains of expertise early on in the process. However, academic settings may have difficulty recruiting and retaining the technical talent required to productise complex technologies. Funding sources can also vary as products proceed through different stages of the translational path. For example, many of the products in Table 2 were initially funded internally or externally through grants and secured private investment once the product was licensed to an outside company. Setting also significantly impacts the data available to develop a machine learning product. In an academic setting, teams may have access to internal data, whereas teams situated outside of a healthcare system need to obtain data through partnerships. Public datasets also play an important role in promoting product development. [SEP] Justify:

Output: The text corroborates the claim by discussing the design and application of machine learning models in the healthcare sector. It highlights various aspects of machine learning products that are developed to tackle healthcare challenges, such as patient care improvement and addressing issues like surgical complications and hospital readmissions. The mention of machine learning's role in generating actionable insights for clinical decisions reinforces the idea that machine learning is indeed transforming healthcare, thus supporting the claim.


Input: Task: contrast the claim using the given text. Claim: Gene editing is completely risk-free with no side effects. [SEP] Text: These studies about how to quickly and efficiently perform gene editing in ETEC using the two-plasmid-based -Cas9 system lay the foundation for further research on the roles of virulence genes in pathogenicity, antigenicity and adjuvanticity, and mutation of virulence genes for genetically engineering a vaccine of ETEC. Primer sequences Underline is the enzyme cleavage site, the italic is N20, and the double underline is the part of the pTarget plasmid. *Primers are from DOI: 10. 7717/peerj. 8881/supp-1 results of recombinant genes DOI: 10. 7717/peerj. 8881/supp-2 digested with SpeI and XhoI From left to right are: pTarget, pTarget-Delt, pTarget-Δstb, pTarget-stb::estA, pTarget-ΔfaeG, pTarget-ΔestA, pTarget-ΔeltIIc1, pTarget-ΔeltIIc1::faeG, pTarget-ΔfaeG:: eltIIc1193 DOI: 10. 7717/peerj. 8881/supp-3 effects of ETEC enterotoxins DOI: 10. 7717/peerj. 8881/supp-4 acid electrophoresis scans DOI: 10. 7717/peerj. 8881/supp-5 The authors are grateful to Pro. (of Synthetic Biology, Institute of and ) for his kind provision of pCas and pTargetF and his patient guidance. His protocol has given us much help, and we have had great convenience in experimentation. The plasmids pCas and pTargetF described in his work have been deposited in Addgene ( under No. 62225 and No. 62226, respectively. We also thank Pro. for providing E. coli O142. authors declare there are no competing interests. conceived and designed the experiments, performed the experiments, prepared figures and/or tables, and approved the final draft. performed the experiments, prepared figures and/or tables, and approved the final draft. analyzed the data, prepared figures and/or tables, and approved the final draft. analyzed the data, authored or reviewed drafts of the paper, and approved the final draft. [SEP] Justify:

Output: The text contrasts the claim by describing studies focused on gene editing techniques using the two-plasmid-based CRISPR-Cas9 system, which lays the groundwork for further research on pathogenicity and virulence genes. This implies there is ongoing research into the implications and risks associated with gene editing, suggesting it is not without potential side effects.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is largely irrelevant to the claim as it focuses on specific findings related to melatonin timing and the effects of MCAo (middle cerebral artery occlusion) on melatonin rhythms in rats. While it discusses variations in circadian parameters, it does not provide information or context regarding the health issues that could arise from disruptions in circadian rhythms.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by detailing how machine learning products are developed and evaluated in healthcare settings. It highlights various machine learning applications, such as products used for sepsis and kidney disease, indicating that these technologies are having a transformative impact on clinical care and patient outcomes. For instance, it mentions the use of algorithms like DeepMind Streams and the need for evaluations that demonstrate clinical utility, statistical validity, and economic utility, all underscoring the significant role of machine learning in improving healthcare practices.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by implicitly suggesting that gene editing may have associated risks, as it refers to various studies discussing off-target effects and mutagenesis. The mention of 'High-frequency off-target mutagenesis induced by -Cas nucleases in human cells' indicates that gene editing is not without potential side effects. This suggests that the claim of gene editing being completely risk-free is not supported by the references provided in the text.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the relationship between melatonin, MT-on, and MT-off in the context of strokes but does not provide any evidence or argument related to circadian rhythm disruption or its potential effects on health. The text focuses on specific experimental results without addressing the broader implications concerning health issues associated with circadian rhythm disruptions.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by discussing specific machine learning products integrated within healthcare organizations and the need for external validation of these products. It highlights examples like Sepsis Watch and KFRE, which illustrate the transformative implementation of machine learning in healthcare. Additionally, it notes that teams are developing generalisable approaches to machine learning applications, indicating a shift towards broader implementation in the field. The text also references the challenges and requirements for integrating machine learning products into clinical workflows, emphasizing the role of such technologies in transforming healthcare practices.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by mentioning 'Off-target effects in Cas9-mediated genome engineering,' indicating that gene editing may not be completely risk-free and may have side effects. This directly challenges the assertion that gene editing is without risks.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text focuses on methods for analyzing circadian rhythms and mentions the importance of biological timekeepers but does not directly address disruptions in circadian rhythms or their health implications. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by discussing the challenges and advancements in the integration of machine learning products, particularly in healthcare. It emphasizes the effort required for clinical integration and the scaling of machine learning models, which are indicative of the transformative impact that machine learning is having in the healthcare sector. Furthermore, it highlights the substantial investment being made in machine learning products for healthcare, demonstrating the ongoing transformation in this field.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text highlights the need for a critical reflection on the potential impact of gene editing techniques, suggesting that there are significant considerations surrounding regulatory approaches, public discussion, and scientific reliability. This implies that gene editing is not risk-free and warrants careful evaluation, contrasting directly with the claim that gene editing is completely risk-free with no side effects.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not provide any direct information supporting or contesting the claim about the health issues stemming from circadian rhythms. Instead, it focuses on methods of analyzing periodicity in biological rhythms, including circadian rhythms, without mentioning their impact on health. Therefore, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by discussing how machine learning is being integrated into clinical care, specifically mentioning products that use machine learning for diagnosing conditions such as diabetic retinopathy. It also emphasizes the importance of model reliability and updating in the context of healthcare, indicating that machine learning technologies are evolving and being applied within medical settings.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text provides references to regulatory frameworks and scientific reports on gene editing, which suggest that there are ongoing discussions about the risks and implications of gene editing technologies. It contrasts with the claim that gene editing is completely risk-free, indicating that there are complexities and regulatory considerations involved, thus implying potential risks and side effects are recognized in the scientific community.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text does not address the claim regarding the disruption of circadian rhythms and its potential health consequences. Instead, it focuses on the technical aspects of biological rhythm data, statistical analysis, and the nature of time series data without mentioning circadian rhythms or health issues.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim as it discusses various studies and articles highlighting the application of machine learning in healthcare, such as developing clinical decision support systems and predictive analytics for patient care. For instance, it mentions the development of machine learning models to identify high-risk surgical patients and the use of machine-learning-based electronic triage, which underscores the impact of machine learning in transforming healthcare practices.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by discussing various genetic modification techniques and their implications. It refers to legal definitions and classifications of genetic modification methods which imply that complexities and risks associated with these methods exist. By addressing laws surrounding genetic modification and the conditions under which certain techniques are classified, it highlights that gene editing is not straightforward and may carry risks, thereby contradicting the statement that it is completely risk-free.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily discusses the autocorrelation function and methods used to analyze rhythmicity in data without making any connection to health issues or the impact of circadian rhythm disruptions.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text supports the claim by detailing applications of machine learning in medicine, particularly in predictive analytics for conditions such as sepsis and pneumonia. It mentions various studies and implementations of machine learning models that enhance patient care and decision-making in healthcare settings, demonstrating that machine learning is indeed having a transformative impact in the field.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text contrasts the claim by stating that new genetic engineering techniques, referred to as ‘GMO 2,’ must be fully tested before being released into the environment and food supply. This indicates that there are concerns and the necessity for testing, suggesting that gene editing may pose risks and is not completely risk-free.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses Fourier analysis and mathematical concepts without addressing circadian rhythms or their impacts on health. There is no mention of health issues or circadian rhythms, thus making it unrelated to the claim.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by detailing multiple instances of machine learning applications in healthcare, such as predictive models for detecting colorectal cancer and early identification of acute kidney injury. These examples demonstrate how machine learning is indeed transforming the healthcare field.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text does not directly state that gene editing is completely risk-free, but the inclusion of references to various assessments by the EFSA and statements addressing governance indicates there are considerations of risks and implications related to gene editing. This implies that gene editing cannot be viewed as risk-free, contrasting with the claim.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses Fourier analysis and its application in various fields, including chronobiology, without addressing the impact of circadian rhythm disruptions on health issues. There is no mention or indication of health concerns related to circadian rhythms.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by discussing the implementation of artificial intelligence technologies in medicine and addressing issues related to machine learning in healthcare, such as fairness and bias in algorithms. This indicates that machine learning is indeed having a transformative impact in the healthcare field.


Input: Task: contrast the claim using the given text. 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. [SEP] Justify:

Output: The text implies a contrast to the claim by discussing the importance of transparency and safety assessments in the regulatory processes related to gene editing. It highlights the need for public health considerations and the necessity for safety-related information to be disclosed, suggesting that there are risks and complexities involved in gene editing that should not be overlooked. This directly counters the assertion that gene editing is completely risk-free with no side effects.


Input: Task: pass the claim using the given text. 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. [SEP] Justify:

Output: The text is primarily focused on discussing methods of spectral analysis and data characteristics relevant to circadian rhythm research. It does not address health issues or the effects of circadian rhythm disruption on health, making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. 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. [SEP] Justify:

Output: The text corroborates the claim by stating that machine learning (ML) models for generating force fields are becoming a prominent aid for researchers in different fields, including drug discovery. This indicates that ML is impacting healthcare as it relates to drug development. Additionally, the text discusses the broader applicability of ML methods in overcoming computational costs in electronic structure simulations, which is relevant to finance and other fields, suggesting that ML is indeed transforming various sectors.