Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Because many African countries export products to the EU, which strictly regulates GMOs and gene edited agrifoods, regulators in countries dependent on trade with the EU have had to consider the implications of taking a more open approach to gene editing, that resembles Canada, Australia, or Argentina’s approaches. Informant 19, who engages with several African governments on genomic technologies in the agrifood system, has witnessed the strong influence the EU exerts on its African trading partners. They note: …you see that much of what is happening in Africa, in sub-Saharan Africa, for example is influenced by Europe’s stand on the issue. is saying we are going to regulate edited products as GMOs that is going to have an effect on many countries in Africa and this is because of the complicated history of Europe and Africa…the biggest driver I see when it comes to the conversation on what regulatory frameworks should look like…Europe’s approach to this is going to have a ripple effect in much of Africa. As we can see, there is a diversity of approaches to regulating gene edited agrifoods. Some countries, namely in the same geographic region, have developed regulations that harmonize with their neighbors, as in South and North America. Divergent regulatory approaches result from different social, economic, and political realities. Different approaches to gene edited agrifoods as either considering them equivalent to GMOs or defined as LMOs complicates trade relationships in the agrifood system. A globally harmonized regulatory framework and guidelines is one suggestion put forth by experts in order to maximize the economic (and environmental) benefits of gene edited crops (Entine et al. , 2021: 552). 4 Case Study: CanadaCanada’s approach to regulating gene edited agrifoods has gained international praise for its focus on evaluating the end product rather than the techniques used in the plant breeding process. Therefore, it is worth delving a bit deeper into how the Canadian regulatory framework functions. For example, private sector representative Informant 8 stated that: Canada’s regulatory approach, which is strongly supported by international science, going back, 20 years, focuses on the end result. When we’re talking about plant breeding, it’s the end result that matters. [SEP] Justify:

Output: The text contrasts the claim by discussing the diversity of regulatory approaches to gene edited agrifoods, indicating that not all crops experience the same benefits from gene editing. It emphasizes that differences in social, economic, and political realities lead to varying regulatory responses, which suggests that the benefits of gene editing are not uniform across different crop species or regions.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: also measures head movements with solid-state accelerometers to characterize behavioral activity. Detailed information about the Daysimeter is available elsewhere [20]. Figure 1 Daysimeter and functional block diagram. Figure 2 Spectral response graph. Spectral response functions generated from the model of human circadian phototransduction by Rea et al. [22]. The dashed line represents the predicted spectral response function for an equal energy spectrum light source. The continuous line represents the predicted spectral responses to individual, narrow-band light sources. The two sets of symbols represent empirical spectral response data from two independent laboratories [34, 35]. Figure 3 Logistic transfer function graph. Logistic transfer function relating nocturnal melatonin suppression to the rectified circadian light stimulus (CS) from the model of human circadian phototransduction by Rea et al. [22]. Data from several studies using both narrow-band [34, 35] and polychromatic light sources [36–38] to induce nocturnal melatonin suppression were plotted as a function of CS. A logistic function from Zeitzer et al. [23] was used to fit the data yielding a regression coefficient (r2) for the transfer function equal to 0. 82. Figure was adapted from Rea et al. [22]. It should be emphasized that activity as measured by the Daysimeter is not a direct measure of the endogenous clock in the SCN. Like every downstream measure of circadian function, behavior can only yield partial insight into circadian entrainment. It is presently impossible to directly measure SCN activity in vivo, and thus it is impossible to measure entrainment in the purest sense in living and active humans; the term "behavioral entrainment" is used in this paper to describe the observed levels of synchrony between light-dark exposures and activity-rest responses as measured by the Daysimeter. Data collection was sent to nurses throughout the to measure their actual CS exposures and activity for seven consecutive days. Forty-three pre-menopausal female nurses, both day-shift (n = 32) and rotating-shift nurses (n = 11), participated in the study. They wore the Daysimeter for seven consecutive days and were scheduled to work at least two and no more than three consecutive days during that period. was worn while nurses were awake. The nurses were instructed to place the Daysimeter next to them when they slept or bathed. [SEP] Justify:

Output: The text discusses the functionality of the Daysimeter and the logistics of a study involving nurses, including their circadian light exposures and activity measures. However, it does not provide information regarding metabolic disorders or obesity, and thus is irrelevant to the claim about the consequences of circadian rhythm disruption.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: Despite the popularity of voice-based AI agents such as Siri and Alexa on smart devices, there are only a handful of academic studies on the public’s attitude toward an alternative voice-based interface for health chatbots [11,21]. Traditional text-based chatbots on mobile phones present challenges to older adults in terms of vision and dexterity, as typing on a smartphone can be difficult and the screen size is often too small for them [33]. From the technology acceptance research, we understand that the usability of a health chatbot plays a role in its perceived usefulness [32]. Therefore, the relatively high acceptability and enthusiasm toward voice-based health chatbots expressed in our survey responses indicate a potential avenue for reaching a wider, often neglected population of adults older than 55 years. In addition to the usability benefits, voice input can be captured and analyzed for symptom tracking and medical diagnosis [34], complementing other data inputs to enable a more accurate assessment of the user’s health. LimitationsIt is noteworthy that this study had several limitations. The use of a web-based survey for the empirical study introduces the potential for response bias. The sample primarily consisted of female adults older than 55 years with diabetes and other long-term health conditions, rather than representing the general population. As this population may have been more attuned to ongoing health concerns, they might have been more prone to reporting long COVID symptoms, potentially explaining the significantly higher proportion of those with long COVID in our sample compared to the national data from the Service. -19 Survey (monthly, terminated in March 2023) reported that around 3% of the UK population were experiencing symptoms 4 weeks after they first had COVID-19 [35]. As cross-sectional survey research, this study is unable to provide a deep understanding of attitudes and opinions. For example, we do not know exactly why people are more receptive to voice-based AI chatbots aside from an educated guess that a voice interface may be easier to use and more natural than typed text for adults older than 55 years. Future research based on in-depth interviews or experimental methods might help unpack these user attitudes reported in the survey and identify possible causal factors. ConclusionWith the rapid development of AI and chatbot technologies, the utilization of chatbots in health care is primed for substantial expansion in the forthcoming years. [SEP] Justify:

Output: The text corroborates the claim by stating that 'the utilization of chatbots in health care is primed for substantial expansion in the forthcoming years.' This indicates a growing trend in the use of AI-powered chatbots specifically for patient support and care management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Before we discuss how gene edited agricultural plants were declared to be ‘non-novel’ in the Canadian regulatory system in 2022, it is worth commenting on the basis for the Canadian gene editing policy position. of Canada regulates plants, animal feed and human food separately under different sets of legislation. Therefore, the regulatory requirements are based on case-by-case evaluation of end products (the product-based approach). Table 3. 2, modified from Friedrichs (2019: 212), shows how decision-making over particular agrifood products is divided up among the relevant agencies. The three agencies that are relevant to the discussion on gene edited agrifoods are the (CFIA), Health Canada, and Agriculture and (AAFC). The table shows the regulatory agency, the regulations it enforces and what products are covered by the regulation. Footnote 9Table 3. 2 Authority, regulations covering novel agrifoods in CanadaFull size tableFor this discussion, the roles of CFIA and are of primary interest regarding gene edited agrifoods. In Canada, the decisions by and CFIA regarding whether or not to treat an agrifood as novel is based on whether the product is considered a ‘plant with novel traits’ (PNT) in an agricultural context, or a novel food in the case of Health Canada. Footnote 10 The CFIA evaluates all novel plants using a product trigger regardless of the technology used to develop the trait, such as agricultural crops or feed for livestock. is responsible for evaluating the safety of novel foods meant to be directly consumed by Canadians, such as fresh fruit or packaged foods. The relevant novelty of the trait in question is considered on a case-by-case basis. Novelty is not exclusive to genetically engineered plants, but all includes plants that undergo some form of modification that do not have a previous record of safe use in the Canadian environment. If a plant is declared to be a PNT, it must be put through a rigorous set of risk-assessment and biosafety procedures before it is declared ‘safe’ for unconfined environmental release and commercialization (non-novel). If it is declared to be a PNT, it then moves through the regulatory system by meeting certain biosafety protocols (see Fig. 3. 3). [SEP] Justify:

Output: The text contrasts the claim by explaining that Canada regulates gene edited agrifoods on a case-by-case basis, indicating that the evaluation of crop benefits from gene editing is not uniform across different species. It highlights the importance of determining whether a plant possesses 'novel traits' and the varying regulatory processes involved, which suggests that not all crops experience the same level of benefit from gene editing.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: After the seven-day recording session, they returned the device for data analyses. In addition to wearing the Daysimeter, participating nurses provided urine samples, obtained every four hours, for subsequent melatonin assay and filled out a chronotype questionnaire [Horne-Östberg Morningness-(MEQ)] and a lighting survey. The nurses were also asked to keep a sleep log, writing down the times they went to bed and any other information about their sleep schedules. These sleep logs were used to match the exact time nurses started wearing the device. Presented here are only the Daysimeter data. Measuring and characterizing circadian behavioral entrainment patterns in nocturnal rodents Data collection Forty albino female Sprague-Dawley rats (Rattus norvegicus) were housed in individual cages illuminated by a lighting system previously developed by Bullough et al. [24] to determine the spectral and absolute sensitivities of another nocturnal rodent (murine). Based upon the mouse phase response curve (PRC) obtained in that study, a spectral power distribution (nearly monochromatic green light; λmax = 525 nm, half-bandwidth = 35 nm) and irradiance (approximately 5 μW/cm2 on the cage floor) were selected to provide the light stimulus to the Sprague-Dawley rats. This particular light stimulus for nocturnal rodents was estimated to be above threshold and below saturation for stimulation of the rat circadian system. The light stimulus for the rats was precisely controlled using a light-emitting diode (LED) light-delivery system fabricated and installed in every cage. The light-delivery system provided better controlled and more biologically meaningful circadian light stimulation to the rats than the fluorescent ceiling lighting traditionally used to provide bright, ambient illumination throughout an animal colony [21]. As with the nurse data, the rat data were obtained from two experimental groups: 20 rats were exposed to a consistently repeating pattern of 12 hours of light (12L) followed by 12 hours of darkness (12D), and another 20 rats (the "jet-lagged" group) were exposed to a 12L:12D pattern where the phase of the light-dark cycle was reversed every 48 hours (as if this group of rats instantly travelled back and forth from Asia to the Americas every other day). Animals were housed individually and allowed to eat and drink ad libidum. [SEP] Justify:

Output: The text does not address the claim regarding the relationship between circadian rhythms and metabolic disorders or obesity. Instead, it focuses on a study involving the recording of circadian behaviors in nurses and rodents, detailing methodologies and experimental setups without explicitly discussing metabolic disorders or obesity.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: The potential benefits offered by these technologies, such as enhanced health care accessibility [4,36], cost reduction [15], and improved patient outcomes [37], are too substantial to disregard. However, health care providers and technology developers must acknowledge AI hesitancy [14] among patients and ensure the inclusive and effective utilization of AI chatbots. This study contributes valuable insights into the acceptability of health chatbots among a population identified as requiring continuous long-term health care support, such as individuals at high risk for conditions like long COVID [38]. First, we augment the evidence in the literature that there exists a general skepticism toward health chatbots among people with chronic diseases. Second, notwithstanding this persistent AI hesitancy, we found that individuals are more receptive toward chatbots supporting lifestyle enhancement than those aiding disease diagnosis and health care management. Third, compared to other subgroups in the study, those with long COVID were more amenable to using chatbots for emotional support. Lastly, while popular AI chatbots on the market are text-based, this study demonstrates that individuals with chronic conditions exhibit a high interest in voice-based conversational agents despite their general AI hesitancy. Moving forward, it is paramount to continue exploring the potential applications of health chatbots in addressing the unique needs of specific patient populations, including those with chronic health conditions. 67. [CrossRef]Almalki M, Azeez F. Health chatbots for fighting COVID-19: a scoping review. Med. Dec 2020;28(4):241-247. [CrossRef] [Medline]. AI chatbot design during an epidemic like the novel coronavirus. Healthcare (Basel). Jun 3, 2020;8(2):154. [CrossRef] [Medline]. Medbot: conversational artificial intelligence powered chatbot for delivering tele-health after COVID-19. Presented at: 2020 5th on Communication and Electronics Systems; Jun 10-12, 2020; Coimbatore, India. [CrossRef] When chatbots meet patients: one-year prospective study of conversations between patients with breast cancer and a chatbot. JMIR Cancer. May 2, 2019;5(1):e12856. [CrossRef] [Medline] Coach, a mobile app-based interactive conversational agent to support type 2 diabetes self-management: randomized effectiveness-implementation trial. J Res. [SEP] Justify:

Output: The text corroborates the claim by discussing the benefits of AI chatbots in healthcare. It highlights that health chatbots are valuable for improving patient outcomes, enhancing healthcare accessibility, and specifically addresses their role in supporting patients requiring long-term care. It also mentions that individuals, including those with chronic health conditions like long COVID, exhibit a high interest in using chatbots for emotional support and lifestyle enhancement, which indicates their increasing utilization in patient support and care management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Biosafety testing is based on scientific information and appropriate data relative to the environmental risk of the PNT compared to its counterparts established in the Canadian environment. Testing must show that the PNT, once released, is not able to comingle with native species of a similar genotype (gene flow) or become a weed (weediness), must have its pest potential tested, must not be toxic to humans and/or animals, and must not pose a threat to biodiversity (CFIA, 2023, Directive 2009-09). Each stage of the regulatory system is dictated by regulations set out in Directives (DIR). The series of Directives dictate the requirements a pending PNT must meet to be approved for unconfined environmental release. are vital parts of Canada’s product-based safety evaluation system for PNTs/novel foods (Clark & Phillips, 2013). Fig. 3. 3Regulatory pathway for novel crops, foods and feeds in CanadaFull size imageFigure 3. 3 is a simplified decision-making tree for plants with novel traits/novel foods. As indicated in Fig. 3. 3, oversees the regulatory process for foods, with CFIA having responsibility for feeds or crops. In both cases, if the feed/crop/food is not considered novel no pre-market assessment is necessary, and the product proceeds to commercialization. For feed/crops considered novel, a series of steps include pre-submission meeting of relevant bodies, and pre-market assessments. For foods considered novel, Guidelines apply, and pre-market submission assessments are required. If the plant with novel trait or the novel food does not satisfy risk and safety assessments, approval is not forthcoming. If risk and safety assessments are met, decisions are posted on the CFIA website (feed/crops), or Health Canada’s website (food) and commercialization is allowed. Feed/crops undergo variety registration and are listed in the Database prior to commercialization. PNTs are subjected to a very complex and detailed risk assessment process. also has rigorous thresholds for what is considered novel and how a novel food is treated in the regulatory system. Considering the rigor of the regulatory system in place to assess PNTs, it is significant that both and CFIA declared that gene edited plants are not considered novel simply because they are derived from gene editing. [SEP] Justify:

Output: The text contrasts the claim by detailing the regulatory process for plants with novel traits (PNTs), which suggests that different species undergo distinct assessments based on their specific pest potentials and environmental risks. It highlights that the regulatory system requires rigorous risk assessments and that gene-edited plants are not classified as novel solely due to the gene editing process, indicating that not all crops benefit equally from gene editing due to varying regulatory standards and biological differences.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Wheel running was measured continuously throughout the experimental session and used as the measure of activity-rest in these animals. The accumulated number of wheel revolutions was recorded at 10-minute time intervals. At the start of the experiment, the photoperiods for both groups were in phase with each other, and the animals exhibited typical nocturnal behavior (active during the dark phase, inactive during the light phase). To allow for acclimation to the cages and to the lighting by the rats, wheel-running data were not collected until the third day of the study, by which time the photoperiod for the "jet-lagged" group had reversed. Most of the activity in the "jet-lagged" group on that day occurred during the light phase. As shown below, the animals in this group were unable to entrain to the regularly reversing photoperiod and exhibited behavior similar to free-running, with similar amounts of activity in the light and in the dark throughout the eight-day observation period. 4 shows activity and CS exposure data for two representative nurses (one day-shift and one night-shift) and Figure 5 shows the wheel-running data and relative light level for two representative animals (one in the 12L:12D group and one in the "jet-lagged" group). Figure 4 Activity and light exposure graphs: Nurses. Activity and light exposure data plotted as a function of elapsed number of days for a day-shift nurse (4a, 4c) and for a rotating-shift nurse (4b, 4d). Data collection started at a different clock time for each subject, so each "day" is a different 24-hour period of time for each subject. Circadian light stimulus (CS) exposures were measured with the Daysimeter [20], and transformed to range between the limits of human melatonin suppression (CS Logistic) shown in Figure 3. Figure 5 Activity and light exposure graphs: Rats. Activity and light exposure data plotted as a function of elapsed time (days) for a 12L:12D rat (5a, 5c) and for a "jet lagged" rat (5b, 5d). At the start of the experiment, the photoperiods were in phase. In the first two days of the experiment, the photoperiods for both groups were the same. [SEP] Justify:

Output: The text does not directly address the claim about circadian rhythms and their link to metabolic disorders and obesity. Instead, it describes an experimental setup involving wheel running, light exposure, and circadian behavior in rats without providing any evidence or discussion regarding the consequences of disrupted circadian rhythms on metabolism or obesity. Therefore, it is deemed irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: Nov 5, 2020;22(11):e20322. [CrossRef] [Medline]. Efficacy of mobile app-based interactive cognitive behavioral therapy using a chatbot for panic disorder. Med Inform. Aug 2020;140:104171. [CrossRef] [Medline] Conversational agents as mediating social actors in chronic disease management involving health care professionals, patients, and family members: multisite single-arm feasibility study. J Res. Feb 17, 2021;23(2):e25060. [CrossRef] [Medline] Conversational agents for chronic disease self-management: a systematic review. . Jan 25, 2021;2020:504-513. [Medline]. Artificial intelligence-based conversational agents for chronic conditions: systematic literature review. J Res. Sep 14, 2020;22(9):e20701. [CrossRef] [Medline]Wilson L, Marasoiu M. The development and use of chatbots in public health: scoping review. . Oct 5, 2022;9(4):e35882. [CrossRef] [Medline]. How should my chatbot interact? A survey on social characteristics in human–chatbot interaction design. Interaction. May 9, 2021;37(8):729-758. [CrossRef]. An empathy-driven, conversational artificial intelligence agent (WYSA) for digital mental well-being: real-world data evaluation mixed-methods study. . Nov 23, 2018;6(11):e12106. [CrossRef] [Medline]. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: a mixed-methods study. Digit Health. Aug 21, 2019;5:2055207619871808. [CrossRef] [Medline] Conversational agents in healthcare: a systematic review. J Inform Assoc. Sep 1, 2018;25(9):1248-1258. [CrossRef] [Medline]-Gorr K, Heeren T. ‘It’s just like you talk to a friend’ relational agents for older adults. Interacting Comput. Dec 2005;17(6):711-735. [CrossRef]. An artificially intelligent chat agent that answers adolescents’ questions related to sex, drugs, and alcohol: an exploratory study. J Adolesc Health. [SEP] Justify:

Output: The text corroborates the claim by mentioning various studies that evaluate the efficacy and acceptability of AI-based conversational agents, or chatbots, in healthcare settings. It highlights their applications in chronic disease management, mental well-being, and patient interaction, supporting the assertion that AI-powered chatbots are actively being utilized in patient support and care management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: All agrifoods, regardless of how they are modified, are subject to equal scrutiny in the Canadian regulatory system on a case-by-case basis based on scientific evidence. published a scientific opinion on the regulation of gene edited agrifood products. In 2022, Health Canada’s Division 28 of the Food and (Novel Foods) was amended to include that novel food products that do not have a history of safe use are subject to evaluation regardless of technique. It stated that “novel food products from any breeding technique that might represent a food safety hazard would require a food safety assessment, to be done according to domestic guidance based on the framework for safety assessments of foods derived from biotechnology” (FAO, 2022a: 32). This decision was preceded by Health Canada’s scientific position in 2021 that determined a high amylopectin starch maize using an SDN1 technique was not considered a novel food product. It did not require pre-market safety assessment as a novel food. The rationale for this decision was that the product had the same phenotype as pre-existing commercial maize varieties with a similar spontaneous mutation and had a history of safe use as food and was therefore, not novel. The following year, a gene edited high oleic soybean was determined to be a novel food and was subject to a food safety assessment based on WHO/FAO expert consultations (FAO, 2022a: 32). 2023, the CFIA announced its scientific position on gene edited agrifoods. In the updated Directive 2009-09 (Plants with novel traits registered under of the Seeds Regulation: Guidelines for determining when to notify CFIA) Sect. 4. 1 states: It is the scientific opinion of the CFIA that gene editing technologies do not present any unique or specifically identifiable environmental or human health safety concerns as compared to other technologies of plant development. For this reason, gene edited plants are regulated using a product based approach, like any other product of plant breeding. Namely, it is the traits that a plant exhibits and whether these traits would have a significant negative impact on environmental safety that are used to determine whether a plant would be subject to of the Seeds Regulations. (www. inspection. gc. ca). (CFIA, 2023) This decision was made based on government consultations with stakeholders throughout the agriculture sector and science-based assessment of the safety and efficacy of gene editing techniques. [SEP] Justify:

Output: The text contrasts the claim by explaining that gene-edited agrifood products are subject to evaluation on a case-by-case basis depending on their specific traits and history of safe use, rather than suggesting that all crops benefit equally from gene editing. For example, the regulation and safety assessment of gene-edited crops vary: the high amylopectin starch maize was deemed not novel due to its similarity to existing varieties, while a gene-edited soybean was classified as novel and required a safety assessment. This indicates that the benefits and assessments related to gene editing differ among species.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Wheel-running data were not collected until the third day of the study, by which time the photoperiod for the "jet-lagged" group had reversed. Most of the activity in the "jet-lagged" group on that day occurred during the light phase. 4a and 4b show activity for two representative nurses, one day-shift nurse (4a) and one rotating-shift nurse (4b), for seven consecutive days. Figures 4c and 4d illustrate the measured CS exposure values obtained directly from the Daysimeter and subsequently transformed using a logistic stimulus-response function representing the entire response range of the circadian system, from threshold to saturation (Figure 3). The transformation was employed to estimate the functional input to the human circadian system, which appears to apply to both light-induced nocturnal melatonin suppression and phase shifting [23]. Examination of Figure 4 reveals subtle but important differences in the activity and transformed CS data for these two nurses. In the case of the day-shift nurse (Figures 4a and 4c), there appears to be a consistent relationship between the activity and transformed CS values over the course of the seven-day measurement session. For the rotating-shift nurse (Figures 4b and 4d), however, this synchrony is much less pronounced. Qualitatively then, and as might be expected, these two example sets of data suggest that the day-shift nurse's behavior is much more synchronized to the light-dark cycle than that of the rotating-shift nurse. Parenthetically, Figure 4 also reveals "flat" periods for both nurses over the course of the seven-day measurement period, which indicate prolonged times of rest and, usually, darkness. Although many analyses of the activity and of the transformed CS data are possible, the data in Figure 4 were used to develop a quantitative measure of circadian behavioral entrainment/disruption for day-shift and for rotating-shift nurses. The behavioral entrainment analyses were based on the circular cross-correlations of activity and light exposure data. Circular cross-correlation, an analysis technique commonly used in the field of signal processing, involves the concept of time-shifting one signal relative to another to determine relationships between signals that might otherwise be obscured due to relative timing differences. [SEP] Justify:

Output: The text discusses various data points about circadian rhythms, specifically focusing on the activity patterns of day-shift and rotating-shift nurses in relation to light exposure. However, it does not provide any clear link or evidence regarding the impacts of circadian rhythm disruption on metabolic disorders or obesity, thus making it irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: Sep 2022;165:102848. [CrossRef]. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Health. Sep 2021;3(9):e599-e611. [CrossRef] [Medline] Conversational agent interventions for mental health problems: systematic review and meta-analysis of randomized controlled trials. J Res. Apr 28, 2023;25(1):e43862. [CrossRef] [Medline]’Alfonso S. To chat or bot to chat: ethical issues with using chatbots in mental health. Digit Health. Jun 22, 2023;9:20552076231183542. [CrossRef] [Medline]Abd-. Perceptions and opinions of patients about mental health chatbots: scoping review. J Res. Jan 13, 2021;23(1):e17828. [CrossRef] [Medline]. Benefits of, barriers to, and needs for an artificial intelligence-powered medication information voice chatbot for older adults: interview study with geriatrics experts. JMIR Aging. Apr 28, 2022;5(2):e32169. [CrossRef] [Medline] Diagnosis of COVID-19 via acoustic analysis and artificial intelligence by monitoring breath sounds on smartphones. J Biomed Inform. Jun 2022;130:104078. [CrossRef] [Medline]Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 30 March 2023. Office for National Statistics. URL: https://www. ons. gov. uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/bulletins/prevalenceofongoingsymptomsfollowingcoronaviruscovid19infectionintheuk/30march2023 [Accessed 2024-02-12]. Closing the accessibility gap to mental health treatment with a personalized self-referral chatbot. Nat Med. Feb 2024;30(2):595-602. [CrossRef] [Medline]. Artificial intelligence–based chatbots for promoting health behavioral changes: systematic review. J Res. Feb 24, 2023;25(1):e40789. [CrossRef] [Medline]. Long covid-mechanisms, risk factors, and management. BMJ. [SEP] Justify:

Output: The text supports the claim by referencing various studies related to the use of AI-powered chatbots in mental health and other health contexts. For example, it mentions 'Conversational agent interventions for mental health problems' and the discussion of a 'personalized self-referral chatbot' for closing accessibility gaps to mental health treatment. This indicates an increasing utilization of chatbots in patient support and care management, thus corroborating the claim.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: And then another recommendation was that … the ag sector and government continue to meet to monitor transparency as the first gene edited products are commercialized to make sure everybody’s needs are getting met and that group has met twice now. So, this is not a popular opinion, but I think it’s awesome. I never expected to be part of meetings where the organic sector sits down with tech developers, and they’re having a conversation. Stakeholders such as CropFootnote 13 agreed to participate in the building and continued updating of the (the ‘Seeds Database’) and Health Canada’s Transparency Initiative. Database will be free, publicly accessible, regularly updated, reliable and user-friendly. Committee proposed that AAFC would be responsible for regular audits and conduct surveys with seed developers to confirm they are using the Seeds Database. Footnote 14 Though this Database is not part of the regulatory scheme, stakeholders have committed to its creation and maintenance as a voluntary condition that needs to be met for co-existence to be possible in the Canadian agrifood sector and for trust in the regulatory system to be maintained. is responsible for the Database’s operation and implementing improvements. Informant 15 said that the Canadian grain sector has much to gain from the as well as the organics sector: I am not aware of any other country from which the seeds sector has put forward a voluntary transparency database. I think it’s unique to Canada…the need has come up through the organic sector looking to make sure that the seeds they buy are eligible for organic certification. But really, it’s also been a need identified by the grain sector as well. Unlike in other jurisdictions, where the politics surrounding agricultural biotechnology has stalled or in some cases prevented the conversations from ever happening, transparency and deliberative discussions are the cornerstone of the Canadian model to increase stakeholder access to all the modern benefits of agricultural science, while avoiding imposing unnecessary costs on non-users. This approach is not without its challenges but has prioritized the economic health of the agricultural value chain in general above whether one approach to agriculture is considered superior to the other. All approaches to agrifood production have their benefits and drawbacks. This is not to say that all stakeholders agree with how and AAFC have proceeded with regulating gene edited agrifoods. There is plenty of dissent. [SEP] Justify:

Output: The text contrasts the claim by suggesting that different approaches to agrifood production, including gene editing, have their own benefits and drawbacks. It emphasizes that not all stakeholders agree on the regulation of gene edited agrifoods, which implies that the impact of gene editing may vary between different crops and species.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: The activity and the transformed CS data can be considered as two time-varying signals whose time-matched values can be multiplied together and then the products at every time of data acquisition integrated into a single value. This value is proportional to the covariance of the two signals. When normalized by dividing by the number of data samples, subtracting the product of the individual signal means, and dividing by the product of the standard deviations of each signal, the result will always be limited to values between -1 and 1 (i. e. , a correlation coefficient). The multiply-and-integrate operation can be repeated following a small shift in time by one of the signals (e. g. , the activity trace, Figure 4a) with respect to the other (e. g. , the transformed CS trace, Figure 4c) and a new correlation coefficient computed. Continuously repeating this process for the entire recording period yields a new time-varying function, the circular cross-correlation, bounded by -1 and 1, that reveals the degree to which the two signals are systematically related to one another for all possible alignments of phase between the two signals. This operation is adapted from standard signal processing techniques [25], and when performed on the periodic light and activity data, yields what are termed, for the purposes of this paper, behavioral entrainment-correlation functions. Figure 6 shows two behavioral entrainment-correlation functions relating the transformed CS data to the activity data: one for the day-shift nurse (Figure 6a) and one for the rotating-shift nurse (Figure 6b) in Figure 4. As can be readily appreciated from Figure 6a, the activity of the day-shift nurse is highly entrained to her light-dark pattern throughout the seven days, as exhibited by the regularly oscillating, 24-hour period of her behavioral entrainment-correlation function. More specifically, this nurse, typical of almost all day-shift nurses, has a peak correlation near the zero-phase marker and again at every 24-hour multiple. This day-shift pattern is in marked contrast to the behavioral entrainment-correlation pattern for the rotating-shift nurse (Figure 6b). Her pattern is aperiodic, exhibiting minor correlation peaks at times other than at the 24-hour phase markers. [SEP] Justify:

Output: The text does not directly address the claim regarding circadian rhythms and metabolic disorders or obesity. Instead, it focuses on signal processing techniques related to time-varying signals and behavioral entrainment correlations, which is not relevant to the claim about the health impacts of circadian rhythm disruption.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: Home E-submission us and scope About the journal Editorial board Copyright, open access, and archiving policy Contact us 222. Published online: April 10, 2023 DOI: 인공지능 챗봇의 질병 진단 및 환자 치료에 대한 답변은 신뢰할 수 있는가? 허선 한림대학교 의과대학 기생충학교실 및 의학교육연구소 Can we trust AI chatbots’ answers about disease diagnosis and patient care? Sun Huh, MD Department of Parasitology and Institute of Medical Education, College of author: E-mail: shuh@hallym. ac. kr 20, 2023 12, 2023© is an 4. 0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Background: Several chatbots that utilize large language models now exist. As a particularly well-known example, ChatGPT employs an autoregressive modeling process to generate responses, predicting the next word based on previously derived words. Consequently, instead of deducing a correct answer, it arranges the most frequently appearing words in the learned data in order. Optimized for interactivity and content generation, it presents a smooth and plausible context, regardless of whether the content it presents is true. This report aimed to examine the reliability of ChatGPT, an artificial intelligence (AI) chatbot, in diagnosing diseases and treating patients, how to interpret its responses, and directions for future development. Current Concepts: Ten published case reports from Korea were analyzed to evaluate the efficacy of ChatGPT, which was asked to describe the correct diagnosis and treatment. ChatGPT answered 4 cases correctly after being provided with the patient’s symptoms, findings, and medical history. The accuracy rate increased to 7 out of 10 after adding laboratory, pathological, and radiological results. [SEP] Justify:

Output: The text supports the claim by describing how AI chatbots, such as ChatGPT, are utilized in diagnosing diseases and managing patient care. It specifically mentions the evaluation of ChatGPT's reliability in providing correct diagnoses and treatments for patients, effectively illustrating its application in patient support.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: For example, representatives of the organic sector stated that transparency measures, in terms of gene edited seeds and the Seeds Database, do not go far enough to protect organic certification. The active participation of the organic sector in the proposed governance structures and oversight activities and ongoing consultations with plant breeders will hopefully live up to the expectations of all stakeholders in the agriculture sector (AAFC, 2023). But from a policy perspective, the path that the regulatory agencies have taken is one based on transparency, responsiveness, the principles of governance and above all, science-based risk assessments. The result of the stakeholder consultations has helped inform the regulatory pathway for gene edited agrifoods in Canada. Some products developed using gene editing techniques may not meet the regulatory definition of ‘novel’. If not, a product is considered equivalent to its existing counterparts, and no pre-market assessment is required. and CFIA have declared that gene edited agrifoods are not considered novel, and therefore not subject to the regulatory process for PNTs. This is the first step towards freeing gene edited foods from unnecessary regulatory burden, allowing for useful advancements in plant breeding and nutritional profiles of foods to be utilized, and getting us closer to solving some of the current supply chain problems plaguing the global agrifood system. 5 ConclusionAs new breeding techniques emerge, some are asking if new breeding techniques that do not include foreign DNA (SDN1) should be treated differently than GMOs or considered equivalent (in terms of risk assessment) as agrifoods from traditional breeding techniques. There are governance issues and questions regarding current and future novel breeding techniques and what types of consultation should be used. Canada has embraced the deliberative governance approach to gene edited agricultural plants. It has considered numerous stakeholders and their p1. (2023) ‘Tracker’ OECD website for full details. to the Convention on (LMO) is defined in the on Biosafety as “any living organism that possesses a novel combination of genetic material obtained through the use of modern biotechnology. also defines the terms ‘living organism’ and ‘modern biotechnology’ (see 4. This includes the Convention on for Animal Health, and the Codex Alimentarius. 5. This category includes the Organisation, for and for the Protection of of Plants. 6. on (CBD) was finalized in1992, entering into force the following year (CBD, 2018). 7. [SEP] Justify:

Output: The text contrasts the claim by indicating that there are varying perceptions and regulatory considerations regarding gene edited foods and how they relate to traditional breeding techniques. It highlights that some gene edited products may not be considered novel or equivalent to traditional breeding, suggesting that not all crops may benefit equally from gene editing, as different crops could have different regulatory and risk assessment treatments based on their characteristics.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: This shift reflects the expected difference between a diurnal and a nocturnal species; diurnal nurses are active during the day and inactive at night, whereas nocturnal rats are inactive during the light phase and active in the dark. Figure 6d shows a typical behavioral entrainment-correlation function for one rat in the "jet-lagged" group. Again, there is a marked similarity between the entrainment-correlation functions for the rotating-shift nurse in Figure 6b and for the "jet-lagged" rat in Figure 6d. Phasor representations of circadian behavioral entrainment Plots of the behavioral entrainment-correlation functions for the day-shift nurses generally exhibit smooth, oscillating curves whereas those of the rotating-shift nurses exhibit much more irregular patterns. Estimates of the relationship between activity-rest and light-dark in terms of magnitude and phase can be determined for both groups of nurses through Fourier decomposition and spectral analysis of the behavioral entrainment-correlation functions. Phasors represent the magnitude and phase relationship between the activity-rest data and the light-dark data that underlie the entrainment-correlation functions for a particular spectral component obtained from the Fourier decomposition [26]. Since the 24-hour spectral component is of special interest in studies of circadian entrainment, the activity and light data for every nurse were first parsed into seven equal 24-hour periods. The behavioral entrainment-correlation functions were then calculated for each of these seven periods after which the seven corresponding phasors representing the frequency component corresponding to a 24-hour periodicity, f(24) for every one of the 43 nurses were determined. It should be noted that a systematic investigation of periods ranging from 22 to 26 hours in 10-minute increments was conducted for the day-shift nurse data. While the range of peak phasor amplitudes occurred for periods ranging from 23. 7 to 24. 56 hours as determined from quadratic curve-fits to the phasor magnitude versus period data, the mean was 24. 035 hours, supporting the significance of the 24-hour period for this analysis. Complex arithmetic [27] was then used to determine the average (n = 7) phasor for a given nurse and these average phasors for all the nurses are plotted in Figure 7a in polar coordinates. [SEP] Justify:

Output: The text does not directly address the claim regarding the link between disruption of circadian rhythms and metabolic disorders or obesity. Instead, it describes the differences in behavioral patterns between diurnal and nocturnal species, and discusses the analysis of circadian rhythms without connecting this to metabolic health outcomes. Therefore, the text is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: AI-powered chatbots are increasingly used in patient support and care management. [SEP] Text: 과거에 나온 어떤 자연어처리 플랫폼 챗봇에 비하여 매우 정확하게 유용한 답을 제공하고, 문장의 맥락을 갖추고 있어 많은 사람들에게 각광을 받고 업무에 활용하고 있다. 의료 현장에서 특히 전문가인 의사는 해당하는 답이 어느 정도 수준으로 정확한지 감별할 수 있으나 비전문가인 경우, 이 답을 그대로 받아들이면 매우 위험할 수 있다. 그러므로 챗봇은 늘 전문가에게 자문을 구하라고 답 마지막 부분에 추가한다. [SEP] Justify:

Output: The text corroborates the claim by stating that chatbots provide accurate and useful answers, indicating their relevance and utility in various fields, including healthcare. It suggests that these chatbots are being utilized widely, which aligns with the claim of their increasing use in patient support and care management.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Signatories are obliged to enact and enforce 8. Changes to regulations covering gene editing crops in smaller countries with smaller agriculture sectors are occurring rapidly as countries such as Canada, the US and Australia with much larger agriculture sectors have decided to regulate gene edited crops differently than GMOs. Thus, Fig. 3. 2 may not reflect the most up-to-date categories for certain countries, especially those in the process of developing regulations. 9. It is not an exhaustive table, but rather highlights the relevant areas for this discussion. 10. All plants that undergo some form of modification that have not previously been used in Canada could be considered novel, including gene edited agrifoods. 11. This database is part of the broader seed industry led effort to provide varietal level transparency. As per the Transparency Initiative, where a variety has been developed using gene editing technology and does not meet criteria of ‘novel food’, it will be required to appear under “Notification” column of the database. More information on these varieties can be found on the ‘non-novel’ list’. Varietal data is collected from CFIA records, members and partners. For more information see (2023): and Agri-(the Canadian federal government ministry of agriculture). 13. Cropis an organization that represents Canadian manufacturers, developers and distributors of pest control and modern plant breeding products ( is the amalgamation of four organizations: Technology Agency, the of Canada, the Institute and ( was also suggested that if it is determined that seed developers are not disclosing information about gene edited varieties in the Database, additional measures may need to be considered. 1753. 320. 220. 242. 1053. 337. 2014. Agbioworld. Retrieved on October 17, 2023, from: www. agbioworld. org/declaration/index. htmlQaim, M. (2009). The economics of genetically modified crops. of Resource Economics, 1(1), 665–694. 1506. of Agriculture (USDA). (2024, February 27). GAIN report: opens door to innovative biotechnologies. Retrieved on March 17, 2024, from: Z. , Pandya, J. , Sangh, C. , & Vaikuntapu, P. R. (2023). The evolving landscape of global regulations on genome-edited crops. [SEP] Justify:

Output: The text contrasts the claim by highlighting that different regulations for gene editing crops vary significantly based on the agricultural context in different countries. For example, the text mentions that countries with large agriculture sectors, like Canada, the US, and Australia, regulate gene-edited crops differently than they do for GMOs. This suggests that there are nuances and disparities among crops and regions that affect the benefits derived from gene editing, implying that not all crops benefit equally from the process.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: The length of each phasor is the magnitude of the average f(24) and reveals how well light and activity are correlated over the seven-day recording session. As a group, the day-shift nurses have larger phasor lengths than the rotating-shift nurses, implying that they have a much higher degree of behavioral entrainment. Figure 7 Phasor diagrams for day-shift and rotating-shift nurses and for 12L:12D and "jet-lagged" nocturnal rats. Consistent with a diurnal species, all the phasor directions for the nurses are to the right, meaning that activity and light exposure occur at nearly the same time. The angular direction of a phasor indicates the phase relationship between light and activity for an individual. Greater amounts of activity near the onset of circadian light exposure than near the offset of circadian light exposure produces a phasor extending below the zero-phase polar axis line (labeled 0 hour). Conversely, greater amounts of activity near the offset of circadian light exposure than near the onset of circadian light exposure produces a phasor extending above the zero-phase line. Researchers [28] have used the terms "larks" and "owls" to refer to people with diurnal activity patterns biased toward morning or evening hours, respectively. These times, however, are not explicitly linked to actual light exposures. The phasor analysis does reveal similar behavioral characteristics, but ones referenced to actual light-dark exposures rather than to an arbitrary exogenous time reference (watch or wall-clock time). Borrowing the lark and owl terminology for describing the behavioral characteristics revealed by the phasor analyses, it is interesting to note that there are more owls than larks, particularly among the rotating-shift nurses, indicating that these people tend to be more active after the onset and subsidence of daily light exposure than before. Although it was true that for day-shift nurses the natural solar cycle was largely coincident with the measured light-dark pattern, the phasor analyses are, again, performed without respect to any exogenous time reference. Theoretically then, a person exhibiting lark or owl behavior with respect to actual light and dark pattern could, in fact, be completely out of phase with the local solar day, as indeed would happen with a "true" night-shift worker. [SEP] Justify:

Output: The text does not directly address the claim regarding metabolic disorders or obesity. Instead, it focuses on the relationship between light and activity patterns among nurses with different shift schedules, discussing phasor analyses and behavioral characteristics without establishing a link to metabolic health or obesity.


Input: Task: corroborate the claim using the given text. Claim: AI applications in healthcare include drug discovery and personalized treatments. [SEP] Text: Wes Mountain/is transforming drug discovery Pharmaceutical companies and start-ups are harnessing AI to improve speed and reduce costs at every stage of the drug discovery and development process. Drug discovery and development 03 July 2024 link to page Download PDF “We want to industrialise drug discovery,” says Imran Haque, senior vice president of AI and digital sciences at Recursion, a start-up founded in City, Utah, in 2013. The company’s vision is that AI will enable a much larger pipeline of drug discovery programmes at any time supported by its massive corpus of data. “We know that many of these [drug candidates] are probably not going to work, but [AI will allow us to] identify those failures as fast as possible, early on,” he explains. Haque is not alone in his assessment of the transformative nature of AI. It’s going to have a huge impact on the entire discovery and development chainPanna Sharma, chief executive of Lantern Pharma“It’s going to have a huge impact on the entire discovery and development chain,” predicts Panna Sharma, chief executive of Lantern Pharma, a Texas-based clinical-stage oncology biotech using AI and genomics to innovate precision cancer therapeutics. There is not yet an approved drug originating from AI tools, but with the pipelines of many AI drug discovery companies now reaching clinical trials, the floodgates could start to open. Making connectionsThe pharmaceutical industry has been successfully using computers and mathematical models to identify and design new drugs for several decades, but the development of generative AI — based on deep neural networks and large language models capable of understanding and generating text — has brought big changes. Based on the information that’s provided, [the AI model] is able to almost think and propose something that was previously unknownAnne Phelan, chief scientific officer at Benevolent AI“Based on the information that’s provided, [the AI model] is able to almost think and propose something that was previously unknown,” explains Anne Phelan, chief scientific officer at London-based Benevolent AI, founded in 2013 and one of the pioneers of AI-augmented drug discovery. Many companies are using AI to provide routes to new targets, allowing them to make novel inferences and connections (see Figure). [SEP] Justify:

Output: The text corroborates the claim by detailing how pharmaceutical companies and start-ups are utilizing AI to enhance the drug discovery process. It explicitly mentions that 'AI will enable a much larger pipeline of drug discovery programmes' and that companies are applying AI to innovate 'precision cancer therapeutics.' This aligns directly with the claim regarding AI's role in drug discovery and personalized treatments.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: KeywordsFuture governance of gene editing in agrifoodRegulatory changesPatents and intellectual propertyConsumer acceptanceClimate change and sustainabilityDeliberative governance 1 IntroductionWe began in Chap. 1 by asking two questions. Why are there so few gene edited agrifoods on the market despite the initial optimism that accompanied the Nobel Prize-winning discovery of -Cas9 over a decade ago? and What governance challenges and opportunities will shape the future applications of gene editing in the agrifood system? Throughout this book, we have explored various elements of gene editing in the agrifood system—the technology and its applications, the regulatory system, the intellectual property landscape, as well as innovations and emergent applications and platforms in the genome editing space. We have discussed the governance challenges facing regulators responsible for evaluating yet unknown, emergent innovations. It has been the goal of this book to provide the reader with ideas and insights into why gene editing in agriculture has not advanced as quickly as initially expected. The heterogenous global regulatory landscape for gene edited plants, the various techniques that can be used to achieve gene edits, the complicated patent and licensing landscape, as well as the lack of consensus of what constitutes a ‘living modified organism’ and how gene edited agrifoods should be tracked and could be labelled are all factors contributing to the current precarious status of gene editing in the agrifood system. The future governance of gene editing in agrifood depends on how individual countries or trade blocs decide to proceed with regulatory definitions of gene edited agrifoods. It also depends on what emergent techniques are in the pipeline, and the extent to which they relate to current gene editing techniques like or take another route to editing the genome with MAD7 or RNAi technology, for example. 2 in FluxMany opportunities and uncertainties surround the future of gene editing. As we discussed in Chaps. 2 and 4, new nucleases with capabilities for gene editing are being discovered every year. What does this mean for the future of new breeding techniques in agrifood? There will be more options for researchers and developers to unlock beneficial traits in the genome of agrifood plants, and though not a focus of this book, also livestock. Gene editing applications for fungi and microbes are also emerging as important components of a sustainable, climate smart agrifood system. [SEP] Justify:

Output: The text contrasts the claim by suggesting that the gene editing process and its benefits vary depending on the crop species and various external factors. It notes that new breeding techniques are emerging and that different crops may respond differently to these advancements, indicating that the effectiveness and benefits of gene editing are not uniform across all types of crops.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: The rats exposed to the consistent 12L:12D light-dark cycle produced average (n = 8) phasors with magnitudes similar to the day-shift nurses, but with directions to the left, clustered around a 12-hour phase shift between light and activity, as would be expected for an entrained nocturnal animal (Figure 7b). The "jet-lagged" rats experiencing the continually changing light-dark exposures have short, low magnitude average (n = 8) phasors with no consistent direction across individuals. (Two very different scenarios can result in the same low magnitude average phasors. One is that every phasor comprising the average is low in magnitude, which indicates that there is no systematic relationship between activity-rest and light-dark. The second, as exhibited by the "jet-lagged" rats, is that individual phasors representing 24-hour periods have significant magnitudes, but their phase varies widely in many directions resulting in a small magnitude average phasor. Either scenario, however, indicates low entrainment to the light-dark pattern when measured across multiple days. ) Figure 8 shows the average [27] phasor magnitudes and phase angles for the two groups of nurses and the two groups of rats. The common use of binary light-dark exposure levels and of wheel running as a measure of activity in caged animals can potentially affect the comparison of their phasor magnitudes to those obtained by humans using the Daysimeter. In a natural environment human activity varies continuously as does a person's light exposure. The phasor analysis based upon the Daysimeter data captures the association between the natural and continuously varying stimuli and responses. Conversely, caged animals have many fewer options with regard to self-regulated light exposures and with regard to running behavior. This situational difference between species may, in fact, have contributed to the relatively shorter phasor magnitudes in the 12L:12D group of rats than in the day-shift nurses. Clearly if cross-species comparisons are to be made, additional investigations need to be undertaken of actual light exposures and of alternative behavioral measures for both human and animal models. Figure 8 Mean phasors for nurses and for rats. [SEP] Justify:

Output: The text discusses the effects of light-dark cycles and phasor analysis in rats and nurses but does not directly address or mention metabolic disorders or obesity as related to disruption of circadian rhythms. Therefore, the text is irrelevant to the claim, as it does not provide supporting or contradicting evidence.


Input: Task: corroborate the claim using the given text. Claim: AI applications in healthcare include drug discovery and personalized treatments. [SEP] Text: Benevolent AI recently announced positive safety data from a phase Ia trial for BEN-8744, an orally administered phosphodiesterase 10 (PDE10) inhibitor being developed as a potential first-in-class treatment for moderate-to-severe ulcerative colitis (UC)[1]. Benevolent’s platform connects structured data from clinical and chemical databases, and unstructured data taken from the scientific literature. Phelan say this gives them “an enormous hairball of interconnected facts” which would be “way too fast for a human to navigate”. “The unstructured data [are] typically written text and we developed a lot of natural language processing capabilities, trained on a lot of very scientific dictionaries, that can essentially read written text and interpret it,” she explains. Benevolent used this process to identify PDE10 as a novel target for ulcerative colitis. “There was no explicit written word in the available biomedical literature to say that PDE10 would have utility for ulcerative colitis,” says Phelan. But Benevolent’s platform was able to link information on the target’s role in modulating inflammation and ion channels in the gut to make a new inference. “Even though all this peripheral information existed, nobody had actually pulled that together… we are able to draw across the totality of biomedical data. ”Precision oncologySome of the earliest uses of AI in research and development have been for repurposing existing drugs, considered to be ‘low-hanging fruit’. For example, Recursion’s current pipeline includes three repurposed drugs — two oncology drugs and a superoxide scavenger — for rare diseases in phase II clinical trials. However, the company has now moved on to oncology drugs. In 2023, it signed a partnership with AI-enabled precision-medicine specialist Tempus, based in Chicago, Illinois. The deal allowed Recursion to access Tempus’s de-identified library of patient-centric oncology datasets, spanning and health records, to combine with its own in vitro data from gene knock-out surveys generated in-house. This is allowing Recursion to train its AI model to find novel oncology biomarkers and targets. has also chosen to focus on precision oncology. [SEP] Justify:

Output: The text corroborates the claim by providing examples of AI applications in healthcare, specifically in drug discovery and personalized treatments. It discusses Benevolent AI's use of structured and unstructured data to identify novel drug targets for ulcerative colitis, which aligns with drug discovery initiatives. Additionally, it mentions Recursion's efforts in precision oncology, where AI is employed to repurpose existing drugs and identify novel oncology biomarkers and targets, thereby supporting the notion of personalized treatments.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: These are important advancements that have benefits, not only for producers, but also consumers and, more broadly, the planet. If we can harness gene editing to improve upon ways to make agriculture more sustainable and edit plants so that they are more adaptable to turbulent climactic changes, the abilities for smallholder farmers to be more productive on the land with fewer pesticides and/or fertilizers can provide a more regionally stable food source. The major challenges lie in getting these technologies into the market, and to the people who can benefit from them the most, without being financially prohibitive. This is where harmonization of regulatory frameworks, even on a regional basis, as is the case in Latin America, and potentially in sub-will be key. The complexity of the patent and licensing landscape further deters progress in the area of proliferation. At this point, there does not appear to be any consensus among patent and/license holders on how to proceed. Europe could be a game changer if the decides to regulate gene edited agrifoods differently than GMOs. The uncertainty surrounding the EU remains a major limitation to the applicability of -Cas systems in Europe’s agricultural sectors (Cardi et al. , 2023: 18). But we have yet to see whether the recent EU proposal moves forward at the member-state level or is rejected. As discussed in Sect. 5. 3, there are varying expert opinions on which direction the EU will go. Rejecting the new breeding techniques proposal may disadvantage the EU in terms of global competitiveness in agricultural products and the ability to address agricultural sustainability and climate change. The eventual direction the EU chooses will have implications for its trading partners, especially those in African countries like Uganda, Kenya and Tanzania that are in the process of finalizing their regulatory frameworks for gene edited agrifoods. Informant 19 (a private sector representative) said of smallholders in sub-Saharan Africa, …much of the activism against modern technologies is really out of touch with what farmers are facing. Farmers are not looking for GMOs. Farmers are not looking for gene edited technologies. Farmers are like, ‘I’m grappling with this pest. Do you know any solution that can help? ’ I wish there was a model that would just allow farmers to have the opportunity to choose. [SEP] Justify:

Output: The text contrasts the claim by highlighting that farmers are dealing with specific challenges such as pests and are not uniformly seeking gene editing solutions. This implies that the effectiveness and application of gene editing vary with the individual needs of different crops and regions, suggesting that not all crops benefit equally from gene editing technologies.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: Phasors compared to other measures of circadian behavioral entrainment Considering only the degree of behavioral entrainment, Figure 9 shows the distribution of the f(24) phasor magnitudes for the two groups of nurses (Figure 9a) and for the two groups of rats (Figure 9b). Figure 9a shows a clear and statistically significant difference between the day-shift and rotating-shift nurse groups with widely separated group means and medians. Nevertheless, there is some overlap of the distributions, perhaps reflecting a true continuum of the degree of circadian behavioral-entrainment among individuals. The data from the rats in Figure 9b also show a clear and statistically significant separation, but undoubtedly because of the two radically different light-dark patterns, there is no overlap in the phasor amplitudes for these two groups of rats. Figure 9 Phasor magnitudes for the day-shift, and rotating-shift nurses (a) and for the two groups of rats (b). The interdaily stability (IS) and the intradaily variability (IV) statistics [29] have been used in numerous studies as measures of behavioral entrainment, or more precisely the coupling between rest-activity rhythms and assumed exogenous zeitgebers, or time givers [30 - 32]. Unlike the phasor analysis, these two statistics are computed based solely on activity and cannot be used to assess the phase relationship between measured activity and the actual light zeitgeber. It is possible, however, to compare phasor magnitudes (Figure 9) and IS values by using the same sets of activity data as estimates of circadian entrainment. The distribution of the IS statistic was calculated from the activity data from nurses (Figure 10a) and from rats (Figure 10b). The two groups of nurses and the two groups of rats were significantly different in terms of their IS values. The ratio of the mean IS values for the two groups of nurses (2. 6) and the ratio of the mean IS values for the two groups of rats (2. 0) are similar to, but smaller than the ratios of the mean phasor magnitudes for the comparable groups shown in Figure 9 (3. 2 for nurses and 4. 9 for rats). [SEP] Justify:

Output: The text is irrelevant to the claim as it focuses on phasor magnitudes and behavioral entrainment among nurses and rats, without mentioning metabolic disorders or obesity. There is no direct connection or support for the idea that disruption of circadian rhythms is linked to these health issues.


Input: Task: corroborate the claim using the given text. Claim: AI applications in healthcare include drug discovery and personalized treatments. [SEP] Text: This disease area is attractive owing to the amount of data that has already been generated with which AI models can be trained to guide decision making — it is more than in other disease areas, says Sharma. Lantern’s AI platform — ‘for and Rescue’ (RADR) — which it claims is the world’s largest AI platform for oncology drug development, currently has over 60 billion oncology-focused data points and is expected to grow further to reach 100 billion during 2024. The data are taken from internal and published studies, clinical trials and public repositories and are being analysed by Lantern’s library of more than 200 advanced machine learning algorithms to predict patients’ responses to drug candidates. Three years is an unheard-of timeframe in cancer and we’ve done that three timesPanna Sharma, chief executive of Lantern PharmaThe company now has three drugs in clinical trials, each having taken around three years (the usual estimate is four to seven years) and less than US$3. 5m to reach this point. “That’s an unheard-of timeframe in cancer,” says Sharma, adding that “we’ve done that three times”. Lantern’s phase II ‘Harmonic Trial’ for candidate LP-300 in combination with chemotherapy is treating ‘never smoker’ patients with relapsed advanced lung cancer who have failed tyrosine kinase inhibitor therapies[2]. It has also launched a phase II trial for LP-184, for patients with advanced recurrent solid tumours with DNA damage response deficiency, found in 20–25% of cancers, and a phase I trial for LP-284, for patients with relapsed or refractory lymphomas and solid tumours[3,4]. Lantern’s AI capabilities are also allowing it to make unexpected links, leading to new indications. For example, a new paediatric brain cancer therapy, based on LP-184 and validated through its AI model, will move to a clinical trial later in 2024. “That’s a great example of how AI is already showing its ability,” says Sharma. “We can ask more lucid questions [of the model] and that’s changing fundamentally the ability to mine a molecule for its maximum value. [SEP] Justify:

Output: The text corroborates the claim by detailing how Lantern’s AI platform is utilized for oncology drug development, highlighting its capabilities to analyze large datasets to predict patient responses to drug candidates. It mentions that the AI models are being employed to expedite drug discovery processes, which directly supports the claim regarding AI's role in drug discovery and personalized treatments.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Canada, Argentina, and Japan appear to be regional leaders in the gene editing regulatory area, all using the case-by-case approach to the risk assessment of novel gene edited agrifoods while, in the Canadian case, embracing the deliberative governance approach. These models, and the principles they embody, may be what regulators in other jurisdictions look towards as they finalize their own regulatory frameworks for gene edited plants. The countries that have taken the ‘product-based approach’ may gain stronger agricultural trade ties with countries taking similar regulatory approaches. We have yet to see if taking this approach yields more commercialized gene edited agrifoods that have useful traits in these jurisdictions and beyond. It is also unclear which direction China will go with its regulatory framework, and how that will or will not align with the patent and licensing situation in the country. Informant 12 (an academic researcher/social scientist) provided interesting insights into China’s role in the race for dominance in the gene editing space, commenting that there is: …a lot of discussion in the international space that China has been intentionally obfuscating their intentions on moving forward as they get some mastery in this space…they’re a market leader as they enter the space and have internal developer expertise. So as they enter that space, they’re not just opening themselves up to a bunch of foreign entities entering the Chinese market. They’re able to have a lot of expertise to service their own market and to be exporters…It’s very, very paralyzing for the international regulatory community trying to anticipate what China is going to do. We have yet to see what China’s role will be in the global gene editing landscape, if and when licenses are issued for the techniques patented by state-run institutions. It may turn out that the patented techniques are useful in certain contexts and not others. China may also become more influential in global standards setting for new breeding techniques and IP discussions compared to the EU if the 2023 proposal to revise regulations covering gene editing in the EU agrifood system are thwarted. The rest of the world is looking to these countries to see which direction they will take gene editing in agriculture. 3 Navigating the Patent LandscapeAs discussed in Sect. 5. 4, the patent landscape is complicated, and the dynamics of the sector are evolving rapidly. [SEP] Justify:

Output: The text contrasts the claim by indicating that gene editing may yield useful traits in certain contexts and not others, suggesting that not all crops benefit equally from gene editing regardless of species. It mentions that the patented techniques may be useful in specific situations which implies variability in effectiveness among different crops.


Input: Task: pass the claim using the given text. Claim: Disruption of circadian rhythms can lead to metabolic disorders and obesity. [SEP] Text: The Daysimeter: A device for measuring optical radiation as a stimulus for the human circadian system and Technology 16: 2292–2299. and Figueiro, MG (2006). Of mice and women: light as a circadian stimulus in breast cancer research Control 17: 375–383. [PubMed] and Bierman, A (2005). A model of phototransduction by the human circadian system Rev 50: 213–228. [PubMed] and Czeisler, C (2000). Sensitivity of the human circadian pacemaker to nocturnal light: melatonin phase resetting and suppression J Physiol 526(Pt. 3): 695–702. [PubMed] and Rea, MS (2005). Additivity in murine circadian phototransduction 22: 223–227. [PubMed] Oppenheim, AV Schafer, R. W. (1989). Discrete-Processing. Englewood Cliffs, NJ Prentice-Hall. Wikipedia contributors (). Phasor (sine waves) Weisstein, EW (). Complex Number. From MathWorld-A Resource. Roenneberg, T, Wirz-Justice, A and Merrow, M (2003). Life between clocks: daily temporal patterns of human chronotypes J 18: 80–90. [PubMed] and Swaab, DF (1990). Alterations in the circadian rest-activity rhythm in aging and Alzheimer's disease 27: 563–572. [PubMed] Hatfield, CF, Herbert, J, van and Hastings, MH (2004). Disrupted daily activity/rest cycles in relation to daily cortisol rhythms of home-dwelling patients with early Alzheimer's dementia Brain 127: 1061–1074. [PubMed] and Swaab, DF (1999). Transcutaneous electrical nerve stimulation (TENS) improves the rest-activity rhythm in midstage Alzheimer's disease Res 101: 105–107. [PubMed] de and Swaab, DF (1996). Circadian rest-activity rhythm disturbances in Alzheimer's disease 40: 259–270. [PubMed] Hrushesky, WJ (1985). Circadian timing of cancer chemotherapy Science 228: 73–75. [PubMed] and Rollag, MD (2001). [SEP] Justify:

Output: The text is irrelevant to the claim as it primarily lists various studies and articles related to circadian rhythms without establishing a connection to metabolic disorders or obesity. There are no evidential claims or discussions provided in the text that would support or contrast the assertion made in the claim.


Input: Task: corroborate the claim using the given text. Claim: AI applications in healthcare include drug discovery and personalized treatments. [SEP] Text: ”New molecule developmentBenevolent is also using its AI capabilities to accelerate new molecule development using algorithms that enable it to model and predict binding pockets in proteins. This method was used to develop its PDE10 inhibitor, BEN-8744, because existing inhibitors that had been used to validate the target penetrated the brain and so had the potential for unwanted side effects. Benevolent was able to quickly design an alternative potent, selective but peripherally restricted, molecule. “We went from bringing this project into our portfolio through to delivery of a clinical quality candidate in just two years,” says Phelan. “The next step will be to take it through to patients. ”Recursion is taking a different approach to drug discovery, based on its AI-powered imaging technology to create what the company calls its ‘map of biology’. Its 1,536-well high throughput system can collect microscopy images from human cell assays, probing changes as different genes are knocked out or different compounds added. “Then we literally take pictures and we look to see if they look different in what is a disease state versus the healthy state, and whether our compounds make them look more healthy,” explains Haque. The company then feeds the images through its in-house trained AI models to extract features that indicate biological differences between samples. Currently, the system can pick up 1,000 different features in each image. “This turns out to be an incredibly powerful capability,” says Haque, and the basis of its ambition to launch a portfolio of hundreds of drugs going through its pipeline at any one time. We’ve generated an enormous amount of data in our labs… somewhere in the range of 20 to 25 petabytesImran Haque, senior vice president of AI and digital sciences at RecursionAlongside its imaging capabilities, the company acquired MatchMaker in 2023, a machine learning model trained to predict ligand-protein interactions from the structure of an individual protein pocket. Recursion is currently undertaking clinical trials for its first non-repurposed new chemical entity, REC-3964, a novel non-antibiotic small molecule inhibitor of Clostridium difficile toxins. As a result, the company now conducts more than 2 million experiments per week. [SEP] Justify:

Output: The text corroborates the claim by detailing how companies like Benevolent and Recursion utilize AI technologies in drug discovery. It describes Benevolent's use of AI to develop new molecules, which highlights a direct application of AI in the field of drug discovery. Furthermore, it discusses Recursion's AI-powered imaging technology that aids in drug discovery by analyzing human cell assays, which directly aligns with the claim regarding AI helping in healthcare applications, particularly in drug development.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Informant 8 (a private sector representative) was concerned about freedom to operate complications arising from the current state of IP in gene editing, and was also concerned by objections to gene editing in the agrifood system saying, I hope we get to a point where we can collectively recognize that the market is big enough for everyone, and choice is important, and we can make this work without either killing the organic sector or killing the rest of the sector by making technology so difficult to use that it’s not possible. One issue that remains unclear at this stage in the development of gene edited agrifoods is whether or not gene editing a registered variety of a plant will be legally recognized as the same variety. Is gene editing a particular variety of plant legally equivalent to the already registered variety, or is a gene edited variety legally different? This is an ongoing issue that is not an easy fix. It will take time, and employing aspects of deliberative governance, to get a clearer picture of how the legalities of commercializing gene edited agrifoods will evolve in the future. 4 Consumer ConsiderationsThough not a central focus of this book, it is worth commenting on consumer p3. 4) highlights how elements of deliberative governance have been used to modify the regulatory environment for gene edited agrifoods. But there is much more to do. Better communication and attempts to understand the science are paramount for regulators, but it is also important for scientists to have some sense of how politics conditions what they do and what is possible for gene editing in the agrifood system. In this book we have examined the complexity of fostering innovation in agriculture while governing the agrifood system. Various countries have chosen different regulatory paths, often a different balance between innovation, risk, health and safety, and where gene editing fits into climate change strategies, sustainable agriculture, and food security. 19 said when referencing discussions about GMOs in sub-Saharan Africa, part of gaining acceptance is that: …people do not oppose newer technologies or controversial technologies because they don’t know it, but because of the assumptions that experts make. They think people are opposed because they don’t know GMOs, so let’s throw down all facts about GMOs and they will change their mind…It’s very important [to] not make assumptions as experts…frame the engagement in a way that speaks to what ordinary people value. [SEP] Justify:

Output: The text contrasts the claim by indicating that different crops and varieties may be subject to varying regulatory challenges and considerations regarding gene editing. It discusses how the legal framework and public perception can influence the adoption of gene editing technologies, suggesting that not all crops may benefit equally from gene editing due to these complexities.


Input: Task: pass the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: Expression of the two Cre parts under the control of different but overlapping promoters will markedly improve the specificity of the conventional Cre/loxP system. We aimed at investigating the functional anatomy of the SCN by targeting the SCN-core region which is believed to be the input region of the SCN. This SCN-core Cre line will then be used to set very specific genetic lesions to this SCN sub-region. It will be very interesting to analyze the effects of this SCN-core ablation on circadian output, which will help to elucidate the functional anatomy of this complex brain nucleus. The fourth part of this thesis addresses the role of the circadian clock in integrating different physiological systems. It has been shown that the circadian clock and metabolic regulation are tightly linked. In addition, sleep curtailment (which is often experienced during shift work) is associated with negative effects on metabolic homeostasis. This prompted us to analyze whether the clock might mediate the negative effects of sleep disturbance on metabolic regulation. Our hypothesis was that in the absence of a functional clock metabolic effects of sleep disruption should be less pronounced. We were able to show that arrhythmic Per1/2 mutants which lack a functional circadian clock indeed show less metabolic alterations after sleep disturbance, indicating that the circadian clock is involved in regulating this response. This study suggests that the circadian clock integrates sleep and metabolic regulation. Thus our work may help to understand the effects of sleep curtailment on obesity, which are major problems of industrialized societies. Deepening the understanding of the underlying mechanisms could contribute to the development of new pharmaceutical approaches for these disorders. Keywords: circadian; clock; cre/loxp; transgenic mice; suprachiasmatic nucleus dieser Erde ist dem täglich wiederkehrenden Wechsel von Tag und Nacht ausgesetzt. Um sich diesen Ryhtmen anzupassen, haben fast alle Lebewesen, vom Bakterium bis zum Menschen, zirkadiane Uhren entwickelt. Mittels solcher interner Zeitmesser können Lebewesen diese Rhythmen antizipieren und ihre Physiologie optimal anpassen. [SEP] Justify:

Output: The text does not directly address or provide specific evidence regarding the role of the suprachiasmatic nucleus (SCN) as the master clock regulating circadian rhythms. While it mentions the circadian clock and its integration with metabolic regulation and sleep, it does not confirm or deny the specific claim about the SCN's role as the master clock.


Input: Task: corroborate the claim using the given text. Claim: AI applications in healthcare include drug discovery and personalized treatments. [SEP] Text: “We’ve generated an enormous amount of data in our labs… somewhere in the range of 20 to 25 petabytes,” says Haque. These data are stored on the company’s in-house supercomputer, ‘Biohive-1’. Recursion has also partnered with technology company to use its DGX Cloud supercomputing power, allowing it to predict the targets of 36 billion compounds reported to the world’s largest searchable chemical library. Big pharmaStart-ups such as Benevolent, Lantern and Recursion are making a strong case for the power of AI in drug discovery and big pharmaceutical companies are starting to pay attention. Almost all of the pharmaceutical giants have a least dipped their toes in the AI water in the past five years: Recursion has partnered with Genentech and Bayer, while Benevolent is working with AstraZeneca and Merck at different stages of the discovery cycle. The size of some of these deals is immense. For example, in January 2024, both and Novartis signed agreements with London-based — a spin-off from Google’s AI research lab DeepMind — that together could be worth US$3bn. People feel there’s a risk that a computer designed compound is not patentable, because it’s lacking the inventive step by a humanFriedrich Rippmann, former director of global computational chemistry and biologics at MerckMany pharmaceutical companies are also developing their own in-house tools. Friedrich Rippmann, former director of global computational chemistry and biologics at Merck, says his impression is that around a quarter of all projects have some contribution from AI. He conceded not all companies use AI and within companies many projects still proceed using classical drug discovery methods on the basis that “if everything goes OK, we don’t need AI and we apply AI only when we run into problems”. Rippmann also suspects that some companies will be reticent to admit the extent of AI used to develop new drugs owing to IP concerns. “People feel there’s a risk that a computer designed compound is not patentable, because it’s lacking the inventive step by a human, so companies are careful. [SEP] Justify:

Output: The text corroborates the claim by discussing the use of AI in drug discovery, mentioning companies like Recursion, Benevolent, and their collaborative efforts with pharmaceuticals such as Genentech and AstraZeneca. The text highlights that a significant portion of pharmaceutical companies are incorporating AI into their drug discovery processes, which directly supports the idea that AI applications are prevalent in healthcare, particularly in drug discovery.


Input: Task: contrast the claim using the given text. Claim: All crops benefit equally from gene editing regardless of species. [SEP] Text: Skip to main content in a journal Publish with us Track your research Regulation of Technologies in Crops 9. The 9. We provide examples of applications of gene editing in agrifood plant breeding. Despite the advantages of -Cas9, other genome editing techniques may offer useful tools to address the challenges of agrifood production in the twenty-first century. -Cas9 technology remains the dominant approach to genome editing. You have full access to this open access 2. 1 NucleasesThere are three types of Site-(SDN) (see Table 2. 1). SDN uses DNA-cutting enzymes (nucleases) that are instructed to cut the DNA at a specific location by several binding systems. After the cut is made, the cell’s own DNA repair mechanisms recognize the break and repair it using one of two cell repair processes. In the case of the Non-Homologous End-Joining (NHEJ) pathway, there is no donor DNA. The cut in DNA is rejoined, however this may cause a few base pairs to be eaten away or added, resulting in random, small deletions or additions (a few base pairs) of nucleotides at the cut site (SDN1) (Entine et al. , 2021: 559). The Homology-(HDR) pathway involves a donor DNA that carries the chosen change and has homologyFootnote 5 with the target site used to introduce the chosen change at the cut site. This allows for an introduction of specific intentional insertions, changes, or deletions. The SDN2 technique targets a gene for correction. The SDN3 technique inserts a gene into the DNA. Table 2. 1 Three main types of gene editing using Site-(SDN)Full size tableIn some cases, countries regulate the three main types of gene editing differently. Since the SDN3 technique sometimes uses transgenes to edit the genome (transgenics), its risk profile is treated differently from gene editing that uses SDN1 or SDN2 in some contexts. Some argue that mutations in a controlled environment regardless of the technology used may, in fact, be safer than those occurring in nature. As research scientist Informant 5 put it, I find that technologies tend to be exceptionalized in the food system. And the reality is that technologies are probably the least risky part of how we produce and consume food. [SEP] Justify:

Output: The text contrasts the claim by discussing the different types of gene editing techniques (SDN1, SDN2, and SDN3) and their applications, suggesting that their effectiveness may vary based on the specific approach taken. It also mentions regulatory differences and that the risk profiles of gene editing techniques may vary, indicating that not all crops may benefit equally from gene editing. Furthermore, the text implies that the context in which gene editing occurs can influence its implications, which further counters the assertion that all crops benefit equally.


Input: Task: pass the claim using the given text. Claim: The suprachiasmatic nucleus (SCN) in the brain is the master clock regulating circadian rhythms. [SEP] Text: Indem man beide Teile unter der Kontrolle verschiedener, aber überlappender Promotoren exprimiert, kann man ein deutlich spezifischeres Cre-Expressionsmuster erreichen, als es bisher möglich war. Ziel ist es, die funktionelle Anatomie des SCNs zu untersuchen, indem man die sogenannte „Core“-Region des SCN genetisch läsioniert. Es wird sehr interessant sein, die Effekte einer solchen genetischen SCN-Core Läsion auf das zirkadiane System zu untersuchen. wird dazu beitragen, die funktionelle Anatomie dieses komplexen Nukleus besser zu verstehen. Der vierte Teil dieser Arbeit beschäftigt sich mit der Rolle der zirkadianen Uhr als Vermittler zwischen verschiedenen physiologischen Systemen. Es ist bekannt, dass die zirkadiane Uhr und die metabolische Regulation eng miteinander verknüpft sind. Außerdem ist eine kürzere Schlafdauer mit metabolischen Dysfunktionen assoziiert, wie zum Beispiel einer erhöhten Wahrscheinlichkeit von Fettleibigkeit. Eine Möglichkeit ist, dass die metabolischen Effekte von Schlafentzug durch die zirkadiane Uhr vermittelt werden. Daher war die Hypothese dieser Studie, dass in Abwesenheit einer funktionellen zirkadianen Uhr die metabolischen Auswirkungen von Schlafentzug reduziert sind. Um dies zu testen, wurden transgene Mäuse untersucht, die keine funktionelle Uhr besitzen (Per1/2-Doppelmutanten). [SEP] Justify:

Output: The text does not directly address the claim regarding the role of the suprachiasmatic nucleus (SCN) as the master clock for circadian rhythms. Instead, it discusses research methods for studying the SCN and its relationship with circadian systems but does not confirm or deny the SCN's regulatory role.


Input: Task: corroborate the claim using the given text. Claim: AI applications in healthcare include drug discovery and personalized treatments. [SEP] Text: ” In 2023, the UK ruled AI cannot be named as an inventor on a patent application but this issue has not yet been litigated in drug discovery[5]. Kim Branson, global head of AI and machine learning at GSK, says the company has been using AI “right across the value chain” since 2019. GSK was one of the first companies to build and train its own in-house large language model from scratch, called ‘Jules OS’. The system is ‘agent-based’ meaning it is capable of autonomously performing tasks through using other software tools or by creating new code. Via a conversation interface, it can respond directly to questions from staff. GSK is also developing next generation platforms collectively called Onyx and is generating its own data from cell genomic studies to train machine learning models dedicated to finding new medicines. The data GSK generates are themselves managed by AI. “We put the model in the loop and ask the model what it needs,” explains Branson. Use of AI is not always plain sailing in large organisations. “Therapeutic areas are quite siloed and, whilst there is an overarching computational infrastructure, it’s difficult to create one solution for an organisation the size of AstraZeneca, or the size of Pfizer,” says Phelan. This often makes it difficult to take a holistic view across disease areas. Rippmann says there is still scepticism, and some companies are questioning if AI is worth the investment. The number of drug candidates developed using AI is still too small to evaluate. “Until [large pharmaceutical companies] see more progress and, more importantly, until they see it [with] their molecules, there’ll be slower to adopt,” says Sharma. Clinical trial designHowever, big pharma is showing considerable interest in using AI to stratify patient populations and design clinical trials that are more likely to succeed. GSK is doing this for bepirovirsen, its anti-sense oligonucleotide investigational treatment for chronic hepatitis B, which is currently in phase IIb trials and, in February 2024, was fast-tracked by the US Food and Drug Administration. [SEP] Justify:

Output: The text corroborates the claim by discussing GSK's use of AI in drug discovery, particularly in generating data from cell genomic studies to train machine learning models aimed at finding new medicines. It specifically mentions GSK's development of an investigational treatment for chronic hepatitis B, which exemplifies personalized treatment efforts. Additionally, it highlights big pharma’s interest in using AI to design clinical trials, further supporting the claim of AI applications in healthcare.