Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: Transition to a lower saline Baltic Sea, and associated decline of marine copepods (Hänninen et al. , 2015), also contributed to the observed halving of (3-year-old) herring weight at age, from 50–70 g in the late 1970s to 25–30 g in the 2000s (Dippner et al. , 2019). The described regime shift has also been partly questioned, as the descriptions of the shift did not cover the entire food web (Yletyinen et al. , 2016). A factor that has been less often considered when studying reasons of cod decline is the interaction with another benthic predator, flounder. Flounder may be both prey for larger cod and a competitor for the small and juvenile ones. Now that cod size has declined, cod predation on flounder has decreased, releasing competition for benthic food again. This has caused more spatial overlap between flounder populations and the remaining small sized cod, and created more intense competition between flounder and the small-sized cod, further contributing to the decline in body condition of cod (Orio et al. , 2020). Multi-species modelling studies have concluded that both fishing and climate strongly affects the size of cod stocks. If fishing is intense but climate remains unchanged, cod declines but not very dramatically, while if climate change proceeds as projected (according to the intermediate-high A2 scenario), cod goes extinct in two models out of seven, even with the present low fishing effort (Gårdmark et al. , 2013). Different combinations of climate change and eutrophication scenarios may however yield very different outcomes. Medium CO2 concentrations (RCP4. 5), low nutrients and sustainable fisheries resulted in high biodiversity and high numbers of cod and flounder, while high emissions (RCP8. 5) and high nutrient loads resulted in diminished biodiversity and high abundance of clupeids, especially sprat (Bauer et al. , 2018, 2019; Hyytiäinen et al. , 2019). The above studies have mostly considered the ecosystem of the central Baltic. In other basins, the associated processes and species interactions may be different. For example, in the Bothnian Bay, salinity was also a major driver for changes in populations of planktivorous fish, but the species involved were different. [SEP] Justify:

Output: The text corroborates the claim by detailing how climate change impacts species distributions and ecosystem dynamics. It highlights that medium CO2 concentrations and climate scenarios can lead to high biodiversity and cod abundance, while high emissions can diminish biodiversity and shift species interactions, particularly with regard to clupeids and flounder. This indicates that climate change directly affects not only species populations but also their interactions within ecosystems.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: Black lines depict wrongly predicted patients, that is, false positive (≥0. 98% early weight gain but ≤7% subsequent weight gain) and false negative (<0. 98% early weight gain but >7% subsequent weight gain). There was no evidence that participants’ appetite ratings at baseline, before quitting, was associated with weight gain at 12 months. However, there was a graded association between appetite ratings at 3 months and weight gain at 12 months (figure 3). Download figure Open in new tab Download powerpoint Figure 3 Average weight gain after 1 year based on patients’ reported intensity of withdrawal symptom ‘increased appetite, hungry, weight gain’ at 3-month visit; whiskers denote 95% CI. Multivariate linear regression on imputed data (see table 5) showed that significant factors for prediction of higher PCWG after 1 year were lower baseline BMI (B=−0. 20; 95% CI −0. 29 to −0. 11, p<0. 001), being a woman (B=0. 78; 0. 02 to 1. 54, p=0. 045), while using NRT was associated with lower weight gain (B=−1. 07; −1. 88 to −0. 26, p=0. 01). The mean duration of use of oral forms of NRT was 4. 43 months (median 2. 88 months) and of nicotine patches 2. 72 months (median 2. 2 months). The length of pharmacotherapy use was similar for all first-line medications (ie, varenicline, bupropion and NRT). View this table:View inline View popup Table 5 Multiple linear model for relative change in weight 1 year after smoking cessationThe sensitivity analysis performed with those present at both time points (n=566) found the same factors significant in the main multivariate analysis as those from the whole sample of abstainers who missed any visit. Early weight gain was not associated with additional weight gain thereafter when assessed at 1 year (B=0. 14, 95% CI −0. 03–0. 31, p=0. 101). DiscussionThere was not a statistically significant relationship between early weight gain and later weight gain. As a result, early weight gain above the median had a moderate sensitivity (66%) and somewhat low specificity (44%) for gaining at least 7% of baseline weight by 12 months. [SEP] Justify:

Output: The text contrasts the claim by presenting evidence that early weight gain was not statistically associated with additional weight gain thereafter. Specifically, it states that 'early weight gain was not associated with additional weight gain thereafter when assessed at 1 year,' which implies that factors other than fat intake alone may influence weight gain.


Input: Task: pass the claim using the given text. Claim: Mushrooms can communicate with each other over vast distances. [SEP] Text: possesses AI-2 QS capacity [30]. However, many ruminant bacteria are yet to be identified, cultured or genome sequenced, and further studies to identify them and their potential involvement in QS in the rumen are paramount. It may also be important to assess the contribution of other rumen organism types including fungi, archaea, protozoa and viruses to QS in the rumen and their role in plant degradation alongside bacteria given that several non-ruminal studies show that other microorganisms such as fungi, archaea and protozoa also use QS signalling [31,32,33,34,35,36]. Moreover, there are still a lot of Gram-negative bacteria that remain uncultured with the potential to utilise AHL-based QS in the rumen. Thus, it is important to improve our knowledge of both AI-2 and AHL QS signalling system in the rumen to better understand bacterial interactions and consequences on ruminant production. Further, in vitro analysis to confirm the bioluminescence activity of the LuxS genes detected in these rumen bacteria genera will be required to understand their expression and role in the rumen microbiome. ConclusionUsing the major genomic resources available, we show on a greater scale than previously possible that QS is likely to be an important phenomenon in the rumen microbiome. Our data suggest that AI-2-based QS is probably the most abundant and perhaps most important signal used by rumen bacteria. Further research into the implications of rumen-based QS on plant degradation and nutrient availability for the host is now required. 84. 46. 67. 81. 8. 32. 55. 4567. 367. 9. 7. 0 for bigger datasets. Evol. 2016;33(7):870–1874. 25. Nature Methods. 2012;9(4):357. 9. 30. 16. Microbiol. 2003;69(10):5941–9. 33. 703. 6. Gene. 2019;684:58–69. 6. 7. 396. doi: 14. 1441. 9. 1812. 7. 32. Download 1. Bacterial diversity and the presence of autoinducer proteins in the rumen. Among the Gram-positive bacteria, there are three species, which show variable Gram stain (described with weakly+ and variable+). [SEP] Justify:

Output: The text is irrelevant to the claim about mushrooms communicating over distances. It discusses quorum sensing (QS) in rumen microbiota and the interactions of bacteria, fungi, archaea, and other microorganisms, but does not address any form of communication between mushrooms. The focus is on bacterial interactions in the rumen and the role of different microorganisms.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: Here the decline of spawning-stock biomass of herring, observed in 1980–2013, was explained by a simultaneously increased competition with vendace, a limnic species that had increased with lowering salinity (Pekcan-Hekim et al. , 2016). In Kattegat, the western Baltic Sea, where the ecosystem is more oceanic than in the other parts of the Baltic Sea, a regime shift was detected in mid-1990s. Here the shift was explained by global climate change, cyclic climate phenomena, and human intervention. First, a reduction in anthropogenic nutrient loading led into a shift from a eutrophic ecosystem state to an ecosystem characterised by decreasing phytoplankton and zooplankton biomass, dominance by small-sized fish in the pelagial, and an increase in macroalgae and filter-feeding molluscs on hard bottoms and other benthic animals in the soft sediments (Lindegren et al. , 2012). Second, the positive phases of NAO and BSI enabled an inflow of oxygenised water from the North Sea, which improved conditions for zoobenthos, including the commercially important Norway lobster. A climate-induced increase of sea surface temperatures contributed to the improved flatfish growth and survival in the shallow nursery areas (Lindegren et al. , 2012). Decreasing fishing may also have contributed to the increase of gadoid and flatfish populations, but its relative importance is difficult to distinguish from other co-occurring effects. To sum up, regime shifts are usually a result of several environmental, climatic and anthropogenic effects acting synergistically on the entire ecosystem. The climate-driven changes in temperature and salinity have been identified as key drivers for the significant rise of “novelty” in both abiotic conditions and biotic assemblages in several basins of the (Ammar et al. , 2021), but human contributions, i. e. , anthropogenic eutrophication or its alleviation, have been a factor (Reusch et al. , 2018). The recent research confirms that climate change induces multiple direct and indirect effects on species and communities and affects nutrient and carbon dynamics of the ecosystem. However, despite the major structural changes, the overall food web complexity in the central has remained surprisingly stable (Yletyinen et al. , 2016). [SEP] Justify:

Output: The text corroborates the claim by providing evidence of climate-induced changes affecting species distributions and ecosystem dynamics. It states that 'regime shifts are usually a result of several environmental, climatic and anthropogenic effects acting synergistically on the entire ecosystem' and specifically notes that 'the climate-driven changes in temperature and salinity have been identified as key drivers for the significant rise of “novelty” in both abiotic conditions and biotic assemblages'. Additionally, it mentions that 'recent research confirms that climate change induces multiple direct and indirect effects on species and communities and affects nutrient and carbon dynamics of the ecosystem', supporting the idea that climate change is influencing these ecological aspects.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: In multivariable analysis, having a lower BMI at baseline, starting smoking in adulthood and using either no medication or medication other than NRT were independently associated with greater weight gain at 1 year. In addition, postcessation appetite ratings were associated with greater weight gain. The relationship between early and later weight gain seems to be positive, but the discrepant findings from different analyses indicate that their relationship is weak. We found that higher appetite ratings at 3 months were associated with greater weight gain at a year, but there was no association between appetite ratings at baseline, prior to quitting, and PCWG. This latter finding contrasts with those of Kmetova et al,4 where ratings of appetite prior to a quit attempt were associated with PCWG. To our knowledge, our finding that withdrawal symptoms of appetite intensity after smoking cessation have been associated with weight gain is in line with previously published studies. 31 32 That 3 months appetite ratings were a predictor suggests that this effect is long-lived, which consonant with continued weight gain over continued smoking observed in populations is observed even beyond the first year. 22 This study has several strengths. It is one of the largest cohorts of abstinent smokers followed up and thus allows more precise estimates of weight gain than most similar studies. Moreover, the cohort was well phenotyped at baseline and had serial weight measurements, which allowed us to characterise the trajectory of weight gain, which has previously been possible only in repeat cross-sectional samples. 3 Perhaps, participants who were gaining weight rapidly noticed this and took actions to limit weight gain, which may explain why there was only a weak association between early and later weight gain. The most likely variables to be associated with greater or lower weight gain are likely to be the actions participants take to limit weight gain by limiting energy intake and maximising expenditure and the strength of drive to eat that participants experience after quitting smoking. There was evidence that the strength of drive to eat may explain greater weight gain, where postcessation ratings were associated with higher weight gain. On the other hand, there are some limitations of our work. This study was based on the data from a routine clinic and not designed to assess the true determinants of weight change, which must relate to change in energy balance, and which are burdensome for participants to measure properly, and thus we lack data on the true causal factors underlying weight gain. No studies, to our knowledge, have ever tracked these factors through the first year of abstinence. [SEP] Justify:

Output: The text contrasts the claim by suggesting that weight gain is influenced by multiple factors, including changes in appetite ratings after smoking cessation and individual actions to limit weight gain. Specifically, it notes that 'the most likely variables to be associated with greater or lower weight gain are likely to be the actions participants take to limit weight gain by limiting energy intake and maximizing expenditure,' indicating that weight gain is not solely determined by fat intake regardless of activity level.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: The overall goal is to link the biology of the bacterial and mammalian cells so scientists can control the function of the host cell by controlling the endosymbiont. It is important that the endosymbiont genes can be controlled in a way that does not interfere with the biochemical processes happening in the mammalian host cell. To achieve this, the team controls the endosymbiont genes using either sugars that mammalian cells do not use, heat produced by lasers, ultrasound or alternating magnetic fields. Can we use these engineered endosymbionts for medical purposes? and his colleagues have had many successes developing their engineered endosymbionts, it will be a while before they are used in medicine to direct the regrowth of hearts and kidneys. So far, the team has successfully used engineered endosymbionts to reprogram macrophages in the body to build new tissues, instead of fulfilling their alternate function of removing pathogens or dead cells. However, this has not been easy. Some of the main challenges faced by the team are encouraging the host cells to accept the engineered endosymbionts and encouraging the endosymbionts to better tolerate the host cells. “Mammalian cells have evolved many mechanisms to eliminate bacterial cells, even engineered ones, from their cytoplasm,” says Chris. “We need to overcome these mechanisms before we can create true symbiotic relationships between the host cell and engineered endosymbiont. ” This will be key to increasing the length of time that the endosymbionts persist in the host cells before they are rejected. At present, engineered endosymbionts can persist for a few days in a mammalian host cell, but it may take weeks or months to rebuild tissues. “We also want to introduce endosymbionts into a range of different cell types,” Chris says, “and we want to demonstrate that engineered endosymbionts can reprogram cells to become stem cells. ” This last step will be essential if engineered endosymbionts are to be used for tissue regeneration. Chris hopes to insert genes into the endosymbionts that reprogram mammalian host cells into stem cells. [SEP] Justify:

Output: The text does not address the claim about fungi regenerating ecosystems. Instead, it discusses engineered endosymbionts in mammalian cells and their potential applications in tissue regeneration, which is unrelated to the role of fungi in ecosystem regeneration.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: Different periods studied, sparse sampling, varying species responses, and changes in phenology rather than total biomass, may explain some of the discrepancies between studies. The tendency of filamentous cyanobacteria to float during calm weather may also bias our view on the total biomass cyanobacteria in the sea, especially if low wind periods become more frequent. Experimental studies are useful in pinpointing causative relationships, but their small spatial scales, short duration and simple food webs make upscaling of results to natural systems difficult. Experiments usually only last for a few days or weeks and study one or few species at a time. Reproducing natural patterns of environmental variability is also challenging. When mesocosms of hundreds of litres and natural communities are used, it may be difficult to simulate seasonal processes extending over several life cycles of the studied organisms. Even the most sophisticated multi-stressor experiments, which use levels of environmental stressors projected by modelling studies, tend to use constant stress levels. A few mesocosm studies have exposed the communities to near-natural environmental conditions and have been able to shed light on the complex dynamics of the ecosystem, e. g. the responses of the microbial food web to changes of environmental variables affected by the climate change. In studies made in the Gulf of Bothnia, bacterial, phytoplankton and zooplankton production increased with additions of inorganic carbon, and the systems remained net autotrophic. In contrast, when both nutrients and DOC was increased, only bacterial and zooplankton production increased, driving the system to net heterotrophy (Andersson et al. , 2013; Båmstedt and Wikner, 2016). Increased heterotrophy led to a decreased fatty acid content and lower individual weight in the zooplankton (Dahlgren et al. , 2011). With the combined treatment of elevated temperature and terrestrial nutrient loads, fish production (of three-spined sticklebacks) also increased, with terrestrial rather than autotrophic carbon being the main energy source (Lefébure et al. , 2013). The complex responses indicate that to provide useful inferences about physiological and population-level responses of organisms to climate change, experimental work should use full communities, apply naturalistic exposure regimes, and investigate effects of stress at spatial and temporal scales appropriate to the species studied (Gunderson et al. , 2016). [SEP] Justify:

Output: The text corroborates the claim by discussing how changes in environmental variables, affected by climate change, influence the dynamics of ecosystems, specifically mentioning 'the complex dynamics of the ecosystem' and 'physiological and population-level responses of organisms to climate change.' It also highlights increased production in aquatic species under varying environmental conditions, which illustrates shifts in species distributions and responses due to climate influences.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: This limitation particularly affects the study of factors associated with greater weight gain, which must be confined to indirect but easy to measure factors. The extent of weight gain in this cohort was similar to that observed in previous studies and documented in a meta-analysis of such studies. 3 However, as in the meta-analysis of previous studies, we found a large variation between individuals in the extent of weight gain. This implies that the mean weight gain is a poor predictor of what any individual might gain, so we need to identify better predictors if we are to offer targeted interventions to prevent excess weight gain. Unfortunately, even early manifest weight gain is a poor predictor of excess weight gain and our other predictors also accounted for only a matter of hundreds of grams difference between those with and without a particular risk factor, whereas there was a 5 kg difference in weight between a person in the 25th and 75th centile of the weight gain distribution. Nevertheless, there were some more potent predictors. Chief among these were BMI at baseline. The higher the BMI, the lower the weight gain. Other studies have reported both positive and negative associations with BMI and, overall, it seems that baseline BMI may not be a strong predictor of weight gain. 22 Starting smoking later in life emerged as an unexpected predictor of higher weight gain, but a previous study has suggested that those who initiate smoking earlier may have higher weight gain. 22 Third, using NRT was associated with lower weight gain. A meta-analysis of randomised trials suggests that, of all the licensed medications, bupropion may have the largest effect on weight gain,34 so this result may appear unexpected. However, people do not use varenicline or bupropion for more than a few months when stopping smoking, and there is no strong evidence that bupropion reduces weight gain in the long-term. 34 Instead, the most likely explanation is that a sizeable proportion of people who seek treatment for tobacco dependence and who quit with NRT use it for at least a year and this is associated with and probably causes lower weight gain. 35 These data have important implications for the treatment of tobacco dependence. We have conducted trials of interventions to limit weight gain where the interventions to do so have started concurrently with the attempt to achieve abstinence. 36 37 These interventions have been proven unpopular with people trying to stop smoking and the trials have failed to recruit. [SEP] Justify:

Output: The text contrasts the claim by highlighting that weight gain can vary significantly between individuals, indicating that simply consuming large amounts of fat does not guarantee weight gain for everyone. It discusses how factors like baseline BMI and smoking behavior can influence weight gain, suggesting that personal factors may play a greater role than the amount of fat consumed alone. The claim's assertion that fat consumption will 'automatically' lead to weight gain does not account for these individual differences and other influential factors described in the text.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: If these stem cells can be controlled by the endosymbionts, which, in turn, are being controlled from outside the body, then scientists could program cells inside the body to rebuild and repair damaged tissues, by instructing the stem cells to become whatever type of cell is necessary. “Controlling tissue regeneration from outside the body still seems like science fiction to many people,” says Chris. This is both an achievement and a challenge when it comes to the team’s research. On the one hand, it shows just how innovative and cutting-edge these ideas are, highlighting how Chris is pushing the boundaries of scientific understanding and possibilities. On the other hand, some scientists remain sceptical about the realities of inserting bacteria into people to repair their organs. However, bacteriotherapy (which uses bacteria to kill tumours) has become standard treatment for bladder cancer and is now being investigated to treat a variety of other cancers. “We can build on these successes if we are methodical and transparent in our designs to ensure that the scientific community accepts this novel approach to guiding tissue regeneration,” says Chris. With further advances in the field of endosymbiont engineering, these bacteria may one day be able to regrow damaged human tissues, eliminating the need for organ transplants. ContagInstitute for Science and Engineering, Colleges of Engineering and of research: project: Developing engineered endosymbionts that can repair tissues in the body Funders: US Foundation (NSF), of Health (NIH) About biomedical engineering Biomedical engineering lies at the intersection of biology and engineering, while also drawing on knowledge from medicine and chemistry. While some biomedical engineers create mechanical or electronic devices for medical use, such as pacemakers and prosthetic limbs, others, like Chris, apply engineering techniques to biological systems, for purposes such as gene therapy, gene editing and tissue engineering. “Biomedical engineering is a broad field that can impact everything from food production to space exploration,” says Chris. “In this field, your primary limitation is your ability, or inability, to imagine the future! ” The importance of futuristic thinking realised that the challenges of organ donation and transplantation could be solved by regrowing tissues in the body, he began to investigate the possibility of engineering synthetic endosymbionts. In doing so, he established a new subfield of biomedical engineering. [SEP] Justify:

Output: The text does not address the claim about fungi regenerating entire ecosystems. Instead, it discusses advances in biomedical engineering, specifically the use of engineered endosymbionts to repair human tissues. The conversation revolves around complex biological systems and medical advancements rather than any direct implication or evidence regarding fungi and their ecological roles.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: Increased primary production and phytoplankton biomass (measured in chlorophyll a) have been projected by several modelling studies (Meier et al. , 2012a; Skogen et al. , 2014; Ryabchenko et al. , 2016). Experimental studies however show that responses of phytoplankton to climate-induced changes in temperature, salinity and pH are variable and can be modified by simultaneous changes in biogeochemical processes and zooplankton grazers (Paul et al. , 2015; Sommer et al. , 2015). Also, strengthening of stratification and simultaneous increase in riverine DOM loads may induce a decrease in phytoplankton production, at least in sea areas where rivers carry large DOM loads (Wikner and Andersson, 2012). Presently it is not clear which of these processes determine primary production in different sea areas or whether there are transition areas where the two processes balance each other, leading to no net change in primary production. Several ecosystem models also predict an increase of cyanobacteria. As cyanobacteria blooms are favoured by warm, stabile and conditions and low N:P ratios (Munkes et al. , 2021), increases in vigorous blooms and an increase in nitrogen fixation could be expected. However, there are large differences in model projections due to the unclear relationship between excess phosphorus and cyanobacteria growth, and the relation between bloom intensity and nitrogen fixation (Munkes et al. , 2021). Cyanobacteria are also not a uniform group. Some cyanobacteria species benefit from increased temperature and acidification, whereas others suffer due to them (Eichner et al. , 2014; Berner et al. , 2018; Paul et al. , 2018). Further uncertainty is caused by unknown biological factors, such as stoichiometric elasticity and the impact of viruses and grazers. Thus, it is still challenging to project how biomasses of cyanobacteria and nitrogen fixation will develop in the future. Recent awareness of marine heatwaves and their potential impacts on the marine ecosystem has increased our knowledge on how climate change may impact pelagic, benthic and littoral communities in the ocean (Pansch et al. , 2018; Saha et al. , 2020). [SEP] Justify:

Output: The text corroborates the claim by discussing how climate change is affecting primary production and phytoplankton biomass, with mentions of varying responses to climate-induced changes in temperature, salinity, and pH. Furthermore, it highlights the potential impacts of marine heatwaves on pelagic, benthic, and littoral communities in the ocean, directly linking these changes to climate change effects, thus supporting the view that climate change is causing shifts in species distributions and ecosystem dynamics.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: Users felt this distorted perception of health contributed to the development and maintenance of ON behaviours. Therefore, users emphasised the importance of distancing oneself from and actively ‘unlearning’ the principles of diet culture to facilitate recovery. For example, users referred to experiences through which they learned to eat certain food that had been demonised by diet culture, by granting themselves (and others) permission to eat and recognising that eating is not something which needs justification. You are allowed to enjoy food when you are not hungry or are beyond satisfied. You are allowed to spontaneously eat out with friends and family. You are allowed to eat more than usual during holidays, or simply because you want to! [71]. The value of ‘intuitive eating’ was frequently discussed under #OrthorexiaRecovery; an adaptive eating style which involves listening to the body and positioning it as intrinsically knowing the quantity and type of food to eat [41]. Consistent with this, users rejected food restriction and instead accepted that “all foods fit” within a balanced diet. That is, specific foods were viewed as neither good nor bad. Mac n Cheese used to be a big no because of its carbs and dairy contents. ate Mac n Cheese in the past, I would feel sick with guilt, anxiety and shame. To counteract meals like this that I considered “dirty” or “cheat” meals, I would cut calories and do HIIT every day for the next week to “burn it off”. In recovery, I am headed towards food freedom, intuitive eating and intuitive movement. So, this is more than just Mac n Cheese; this symbolises a massive step toward recovery [4]. This shift in attitudes towards food was believed to have several psychological benefits, including increased self-compassion, confidence, and emotional wellbeing. Users also reported lessened anxiety and guilt about food, being able to enjoy food again without being burdened by its nutritional value. I have fallen in love with myself again after fixing my relationship with food. Food consumption is of course vital, but our relationship with food is far more important [207]. visited home, my mum baked a flapjack. It contained golden syrup and butter, and I was fine with that. I didn’t scrutinise the shape and size of each piece. Instead, I ate several pieces and enjoyed every bite! ! [132]. [SEP] Justify:

Output: The text contrasts the claim by discussing the concept of 'intuitive eating,' where users reject food restriction and view specific foods as neither good nor bad, suggesting that one can enjoy foods that are typically viewed as unhealthy. The text emphasizes the psychological benefits of this approach, including increased self-compassion and reduced anxiety about food, which stands in opposition to the notion that consuming high-fat foods will lead to automatic weight gain. Users described how they learned to enjoy food without guilt or shame, countering the claim.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: And, while some view his work as science fiction, Chris has proven that the concepts behind his ideas are theoretically and practically possible. As technology and biological understanding advance, great progress can be expected in all fields of biomedical engineering. “In the field of endosymbiont engineering, I think we will develop bacteria that only contain the minimal number of genes needed for their growth, and we will generate libraries of genes that drive the development of different cells and tissues,” predicts Chris. “With this, we could customise the minimal genome bacteria by inserting other genes, so the bacteria perform different functions. For example, after a heart attack, a patient could be prescribed a minimal genome bacterium with heart generating genes that would guide the patient’s heart cells to regenerate. ” Progress in biomedical engineering relies on the next generation of scientists having a futuristic mindset and a willingness to pursue the unknown. As the chemist, Frank Westheimer, once said, “Progress is made by young scientists who carry out experiments that old scientists said wouldn’t work. ” Chris acknowledges that some scientists call his work ‘science fiction’, in part due to a fear of the unknown. “Science fiction writers ask, ‘What if? ’,” he says. “As scientists, we think of what might be possible and ask, ‘What’s the next step? ’ We begin with the end in mind and direct our experiments toward this goal. However, innovations lie in the unexpected results, and therefore we must go into every design and experiment with our eyes wide open to appreciate the unexpected and then explain it. ” Pathway from school to biomedical engineering “All engineering fields require math,” says Chris. “So, get a solid education in math and quantitative sciences. ” Study mathematics, biology, chemistry and physics and, if your school offers classes in engineering and computing, take them as well, since these subjects will also be useful. Some universities offer degrees in biomedical engineering, bioengineering or biological engineering. Related degrees in biochemistry, biology or other engineering fields (e. g. , mechanical or electrical) could also lead to a career in biomedical engineering. Explore careers in biomedical engineering • “(BMES; www. bmes. org) and the Engineering in Medicine and (EMBS; www. embs. [SEP] Justify:

Output: The text discusses advancements in biomedical engineering and the potential development of minimal genome bacteria. It does not mention fungi or their ability to regenerate ecosystems, making it irrelevant to the claim regarding fungi.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: More studies on the responses of pelagic and benthic organisms of the to heatwaves would increase our understanding of the population-level consequences of short-term variability in environmental parameters. Research on effects of climate change would also benefit from methodological diversity. For example, more extensive use of biochemical and genetic methods, such as biomarkers (Turja et al. , 2014, 2015; Villnäs et al. , 2019), stable isotopes (Voss et al. , 2000; Gorokhova et al. , 2005; Morkune et al. , 2016; Lienart et al. , 2021), compound-specific isotope analyses (Ek et al. , 2018; Weber et al. , 2021) or metabarcoding (Leray and Knowlton, 2015; Bucklin et al. , 2016; Klunder et al. , 2022), as well as development of remote sensing methods (Huber et al. , 2021), could yield novel information on stress levels experienced by organisms and environmental niches preferred by species. Such information would allow validation of the biogeochemical models under different environmental and climate scenarios. There is some bias in the focus organisms and habitats studied. While experiments on planktonic organisms and soft-bottom animals are relatively abundant, experiments on macroalgae, vascular plants and invertebrates inhabiting hard bottoms are less abundant, and studies focusing on the entire food web are scarce. In general, empirical and modelling studies focusing on climate effects on shallow photic habitats are less abundant than those on the pelagic and deep benthic habitats (Tedesco et al. , 2016). Very few studies have investigated the shallow water ecosystems holistically, including macroalgae and microalgae, invertebrates, and fish at the same time. Those that have done so have revealed complex interactions and multiple feedbacks between species and ecosystem components (Svensson et al. , 2017; Salo et al. , 2020). Also, while there are ample monitoring data on pelagic and deep benthic communities, similar long-term records are very sporadic for communities associated with key habitat-forming species such as bladderwrack, eelgrass, blue mussel on hard bottoms, and vascular plants growing on soft sediments. This lack of empirical data and subsequent modelling studies hampers our understanding of the long-term responses of sublittoral communities to climate change. [SEP] Justify:

Output: The text corroborates the claim by discussing the responses of pelagic and benthic organisms to heatwaves, indicating that understanding such responses is crucial due to the impacts of climate change on species distribution and ecosystem dynamics. Moreover, it suggests a need for more diverse research methodologies to capture the complex interactions and feedbacks between species and ecosystems under climate change scenarios, underscoring the significance of these changes in ecological dynamics.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: IntroductionTogether with obesity, diabetes is epidemic in the US and worldwide. 1 Weight gain is both a risk factor for diabetes and a consequence of initiation and intensification of insulin therapy, as illustrated in landmark diabetes control trials. 2, 3, 4 Weight gain associated with increased adiposity can worsen insulin resistance leading to higher insulin requirements, and thus perpetuates a vicious cycle. Weight gain additionally generates further risk for macrovascular outcomes such as cardiovascular disease5, 6 and generates negative ‘biofeedback’ to the patient and physician struggling to achieve glycemic control. Mechanisms involved in weight gain associated with insulin therapy are incompletely understood. Hypoglycemia is a potent stimulus to feed,7 improving glycemic control reverses the negative energy balance associated with glycosuria, and insulin potently stimulates lipogenesis. Indeed insulin is an anabolic hormone in peripheral tissues. 8 Although weight gain on insulin initiation or intensification is common, there are reports in randomized, controlled human clinical trials of attenuation of this effect associated with the use of a basal insulin analog, detemir (Det), compared with other insulins used in subjects with type 1 and type 2 diabetes9 (as reviewed in Hermansen and Davies10). Intriguingly, the observed weight sparing effects of Det appear to be amplified in the most obese subjects,9 who are predicted to be more insulin resistant. Det has also been shown to have enhanced effects on central nervous system (CNS) function in both experimental animals11 and human subjects. 12, 13 So-called basal insulins are long-acting insulin formulations that maintain euglycemia between meals and overnight (that is, fasting) primarily by limiting hepatic glucose production. 14 Frequently, type 2 diabetes insulin therapy is initiated with basal insulin. In contrast to ample evidence associating insulin therapy with increased adiposity and weight gain, a distinct body of evidence indicates that insulin functions as an adiposity negative feedback signal to the CNS. 15 Insulin (and leptin) is secreted in proportion to body fat stores,16 and regulates the function of key homeostatic neurons found in the mediobasal hypothalamus17 and elsewhere,18 which in turn, control feeding, energy expenditure and other aspects of neuroendocrine physiology (reviewed in Niswender et al. 17 and Plum et al. 19). [SEP] Justify:

Output: The text contrasts the claim by discussing the complexity of weight gain, specifically in the context of insulin therapy and its relationship with adiposity. It emphasizes that while insulin is commonly associated with weight gain, the effects are intricately linked to various factors including activity levels and overall metabolic responses, indicating that eating large amounts of fat does not automatically lead to weight gain without considering these other influences.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: org) are outstanding organisations to get involved with,” says Chris. Both societies have a wealth of resources, including podcasts and informative videos on topics such as ‘Is biomedical engineering right for you? ’ and ‘Career paths in biomedical engineering’. • The US Bureau of has detailed information about careers in bioengineering and biomedical engineering, including what the roles involve, the skills and qualifications you will need and the salary you can expect: www. bls. gov/ooh/architecture-and-engineering/biomedical-engineers. htm • In this video from the of Mechanical Engineers, bioengineers discuss what work in the field involves: www. asme. org/topics-resources/content/video-bioengineering As far back as I can remember, I wanted to be a scientist. I was always fascinated by the processes of biology – while my friends were building model cars, I was building models of the human eye, ear and other organ systems! I saw the interconnections of biological systems while picking edible plants with my dad and spending days wandering and camping in the woods. My father and grandfather were veterinarians and, while growing up, I spent many hours in their lab. My dad inspired curiosity in me as we grew bacteria, hatched turtle eggs, did chemistry experiments, blew glass bottles and treated sick animals that I found in the woods, from dogs and cats to squirrels, snakes and a mountain lion (though I didn’t bring the mountain lion back to the clinic! ). was a kid, there was a science fiction TV show called ‘Tunnel’, about scientists who build a time tunnel and adventurers who travel through it. My brother wanted to be one of the heroic time travellers, but I wanted to be the chief scientist and figure out how the scientists could save the day. I still believe that scientists will save the day by developing innovative new technologies for addressing climate change and improving human health. I was extremely lucky to have teachers who curated curiosity in their students. was 10, we were learning about eyes by looking at drawings in books. I didn’t think this was sufficient, so I asked my teacher if I could bring an eye to school to dissect in class. I don’t think she realised I was serious, because she just said, “Sure”. [SEP] Justify:

Output: The text does not address the claim regarding fungi's ability to regenerate ecosystems. Instead, it focuses on personal experiences related to science and engineering careers, indicating a fascination with biology and environmental processes, but lacks any direct reference to fungi or ecosystem regeneration.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: Furthermore, there is a large body of literature published on sea ice algae and sea ice ecology in the (Granskog et al. , 2006; Tedesco et al. , 2017; Thomas et al. , 2017), and all of them are relevant for studying winter ecology. However, few of them have directly assessed the effects of climate change on ice ecology in the Baltic Sea. More empirical and modelling studies including quantitative projections on the effect of diminishing sea ice to biodiversity and functioning of the ecosystem in winter and spring would therefore be desirable. To sum up, there are still several significant knowledge gaps and issues of dissensus in our understanding of the effects of climate change on the ecosystem. To fill these gaps, the results and conclusions from the experimental work should be better integrated into the wider empirical and modelling studies of food web dynamics, and more emphasis should be placed on studying effects of climate change on less studied environments, such as the microbial food web, sea ice communities and the sublittoral ecosystem. Such studies would provide a more comprehensive view of the responses of the pelagic and benthic systems to climate change in both the open sea and the benthic system, from bacteria to fish (Kortsch et al. , 2021). Also, continuation of both spatial mapping programmes and long-term ecological studies will be crucial for validating experimental results and for developing ecosystem models, advancing our understanding of environmental and meteorological drivers of the ecosystem on large spatial and temporal scales. 7 change has an obvious potential to affect entire marine food webs, from coastal to offshore areas, from shallow to deep waters and from pelagic to benthic systems. Climate change can also induce changes in species distributions and proportions, and key nodes and linkages in the food webs may be altered or lost (Lindegren et al. , 2010b; Niiranen et al. , 2013; Leidenberger et al. , 2015; Griffiths et al. , 2017; Kotta et al. , 2019; Gårdmark and Huss, 2020). As many ecosystem services are dependent on the state of the entire ecosystem (Hyytiäinen et al. , 2019), a long-term decline in provision of ecosystem services to humans is possible. It is therefore indispensable to increase our understanding of the consequences of climate change on the socio-ecological system of the and its surrounding marine regions (Stenseth et al. , 2020). [SEP] Justify:

Output: The text corroborates the claim by stating that climate change can induce changes in species distributions and proportions, as well as alter or potentially lose key nodes and linkages in food webs. It emphasizes the impact of climate change on marine food webs across various environments and highlights the necessity to understand the consequences on ecosystem dynamics.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: Although a potent anabolic stimulus in peripheral tissues, insulin action in the CNS is catabolic in the sense that it reduces food intake, weight gain and adiposity. Thus, a paradox emerges when one considers that insulin therapy is associated with weight gain, whereas in experimental situations insulin functions to limit weight gain. A number of elegant basic science approaches have clarified the role of insulin in energy homeostasis. Insulin administration directly into the brain reduces food intake20, 21, 22 and targeted mutation of insulin signaling components in neurons leads to weight gain. 23, 24 Here, we sought to engage a more clinically relevant approach to the question of whether it is insulin therapy per se (rather than hypoglycemia, reversal of negative energy balance and so on) that causes weight gain in a well-controlled preclinical model. Herein we tested the chronic effects of modern basal insulin analogs, Det and glargine (Glar). As basal insulins, Det and Glar, are used clinically to control between meal and overnight hyperglycemia,14 and were chosen here because they could be delivered by the clinically relevant subcutaneous (SC) route without causing hypoglycemia due to their pharmacodynamic profiles. 25 We have controlled for confounding variables such as glycosuria, negative energy balance and hypoglycemia by studying non-diabetic, but high-fat (HF) fed diet-induced obese rats compared with low-fat (LF) fed rats. Thus, we used long-acting basal insulin analogs to replace some percentage of endogenous basal insulin (that rate needed to maintain euglycemia in the fasting state) allowing us to avoid hypoglycemia and thereby to test the effects of these insulins per se on feeding and weight gain. We hypothesized that the anabolic effects of insulin in peripheral tissues will be balanced by the catabolic effects in the CNS when confounders are controlled, and that Det might attenuate weight gain, especially on HF diet. Thus, in the absence of negative energy balance and hypoglycemia, we sought to ask whether chronic SC basal insulin therapy still leads to weight gain and increased adiposity and whether we can model the observed effects of Det to limit weight gain in the setting of obesity. [SEP] Justify:

Output: The text contrasts the claim by discussing the complex interactions between insulin, food intake, and weight gain. It suggests that insulin can reduce food intake and weight gain under certain conditions, which implies that high-fat consumption alone does not automatically lead to weight gain. The complexity of metabolic responses and the influence of insulin therapy challenges the simplicity of the claim that all fat intake leads to weight gain unaffected by other factors.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: So later that week, when I helped my dad deliver a still-born calf, we removed its eye and the next day I took the eye, a dissection tray and the proper tools to school and offered them to my teacher. I still remember the expression on her face as she stepped back, and said, “Why don’t you teach today? ” So, I led the class through the dissection of the calf eye. It wasn’t until many years later that I realised this might have seemed strange! To me, I was just bringing my everyday life into the classroom. I never get tired of watching biology in action – from the molecular and cellular level while working in the lab, to the organismal and ecosystem level outside in nature. Whether in the lab, mountains or oceans, there are so many aspects of biology to observe and wonder about. Chris’ top tips 1. Instead of ‘thinking outside the box’, just ‘think outside, no box required’. It is human nature to categorise and catalogue things, but every time we box something in, we eliminate opportunities. 2. Follow your passion, not the career with the highest salary or most manageable work week. To be happy, make your work your hobby and your hobby your work, by integrating your life and work so that what you accomplish in each area exceeds your and others’ expectations. 3. Never let your mind grow old – keep the youthful wonder alive. I like to think of myself as a young scientific mind in an old human body! I never want to be a scientist who says ‘no’ to trying something new. [SEP] Justify:

Output: The text is irrelevant to the claim about fungi regenerating ecosystems. Instead, it discusses a personal experience related to biology and teaching, without addressing fungi or ecosystem regeneration.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: The direct and indirect effects of climate-change-related parameters on species, communities and the ecosystem are summarised in Table 1 based on research done since 2010. While results are variable, some conclusions can be drawn from the evidence this far. Table 1Summary of research findings and conclusions on the anticipated effects of climate change (CC) effects in the for selected variables. The table only shows studies published in 2011–2021 and some of the studies referred to in the text are not included. For earlier studies, see Dippner et al. (2008) and Viitasalo et al. (2015). Observations, experimental simulations or modelled projections are categorised using the following abbreviations: T stands for temperature increase, S stands for salinity decline, TSO2 stands for temperature increase, salinity decline and/or oxygen decline, A stands for acidification, AT stands for acidification and temperature increase, AS stands for acidification increase and salinity decline, DOM stands for dissolved organic matter. EXP stands for experimental manipulationsmicrocosms, MES stands for experimental manipulationsmesocosms, LTS stands for long-term studies, MOD stands for modelling studies, FIE stands for field data. Empty fields indicate knowledge gaps. for the eutrophication status of the Baltic Sea, it can be concluded that the ecological status of the has not significantly improved despite a decrease in anthropogenic nutrient loading since the 1980s (Fleming-Lehtinen et al. , 2015; Andersen et al. , 2017), largely due to the pervasive internal loading (Murray et al. , 2019; Stigebrandt and Anderson, 2020). Success of nutrient abatement largely determines the future state of the (Hyytiäinen et al. , 2019; Ehrnsten et al. , 2020), but climate change may delay or even counter the improvement of the ecosystem state (Bonsdorff, 2021). Climate-induced increases of nutrient loading and enhancing of internal loading of phosphorus have been hypothesised to promote phytoplankton and cyanobacteria production and to maintain the “vicious circle of eutrophication” (Vahtera et al. , 2007), and several modelling studies indeed project an increase in both total phytoplankton biomass and cyanobacteria blooms in the future (Meier et al. , 2011a; Funkey et al. , 2014). [SEP] Justify:

Output: The text corroborates the claim by discussing various climate-change-related parameters that affect species, communities, and ecosystems. It explicitly states that climate change may delay or counter the improvement of the ecosystem state, and it highlights the hypothesis that climate-induced nutrient loading can promote phytoplankton and cyanobacteria production. This evidence supports the assertion that climate change is influencing species distributions and ecosystem dynamics.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: Bonferroni's post-test shows that Det reduced cumulative food intake in HF-fed rats relative to Glar from 10 to 27 days and relative to Veh from 24 to 27 days. For all panels: *P<0. 05 for Veh vs Det; #P<0. 05, αP<0. 01 and βP<0. 001 for Glar vs Det comparisons as analyzed by Bonferroni's post-test. Full size imageBody weight and adipose massBecause we observed significant attenuation of cumulative food intake in HF-fed rats receiving Det, the effect of basal insulin therapy on body weight and fat mass gain was of primary interest. Consistent with the food intake data in the LFD group neither Det nor Glar therapy had any effect on daily body weight and fat mass (Figures 3a and d) or body weight or fat mass gain (Figures 3c and f). In contrast, in HFD rats, Det trended to attenuate daily body weight (Figure 3b), but this did not reach statistical significance for any individual day over the 4-week period. Det did, however, reduce body weight gain relative to both Veh and Glar, whereas Glar had no effect compared with Veh (Figure 3c; Veh 72. 0±4. 8 g, Glar 72. 5±4. 0, Det 59. 2±3. 5; Veh vs Det P<0. 05, Glar vs Det P<0. 05, Veh vs Glar P=NS). The effect of Det to reduce body weight was clearly dependent on HFD intake (F(5, 30)=6. 10; P<0. 001). Figure 3attenuates weight and adipose gain in HFD, but not LFD rats. Body weight was measured daily in rats fed either LFD (a; n=5 per group) or HFD (b; n=6–8 per group) for each treatment group; Veh (circle), Det (triangle) and Glar (diamond). Body weight gain was determined for the 4-week study period for all groups (c). Fat mass was measured three times weekly by NMR in rats fed either LFD (d; n=5 per group) or HFD (e; n=6–8 per group) for each treatment group; Veh (circle), Det (triangle) and Glar (diamond). [SEP] Justify:

Output: The text contrasts the claim by presenting data indicating that the effect of specific diets (e.g., high-fat diet (HFD)) on body weight gain is not straightforward. It mentions that the basal insulin therapy (Det) did not affect daily body weight or fat mass in rats on a low-fat diet (LFD) and only trended to attenuate weight gain in HFD rats. This suggests that factors such as the type of diet (high-fat vs. low-fat) and potential treatments are critical in determining weight gain, thereby countering the claim that eating large amounts of fat will automatically result in weight gain, regardless of other factors.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: AbstractThe hyphae of ectomycorrhizal and ericoid mycorrhizal fungi proliferate in nitrogen (N)-limited forests and tundra where the availability of inorganic N is low; under these conditions the most common fungal species are those capable of protein degradation that can supply their host plants with organic N. Although it is widely understood that these symbiotic fungi supply N to their host plants, the transfer is difficult to quantify in the field. A novel approach uses the natural 15N:14N ratios (expressed as δ15N values) in plants, soils, and mycorrhizal fungi to estimate the fraction of N in symbiotic trees and shrubs that enters through mycorrhizal fungi. This calculation is possible because mycorrhizal fungi discriminate against 15N when they create compounds for transfer to plants; host plants are depleted in 15N, whereas mycorrhizal fungi are enriched in 15N. The amount of carbon (C) supplied to these fungi can be stoichiometrically calculated from the fraction of plant N derived from the symbiosis, the N demand of the plants, the fungal C:N ratio, and the fraction of N retained in the fungi. Up to a third of C allocated belowground, or 20% of net primary production, is used to support ectomycorrhizal fungi. As anthropogenic N inputs increase, the C allocation to fungi decreases and plant δ15N increases. Careful analyses of δ15N patterns in systems dominated by ectomycorrhizal and ericoid mycorrhizal symbioses may reveal the ecosystem-scale effects of alterations in the plant–mycorrhizal symbioses caused by shifts in climate and N deposition. This is a preview of subscription content, log in via an institution to check access. Access this 34. 9910 units per month Download 39. 95 Price excludes VAT (USA) Tax calculation will be finalised during checkout. Instant access to the full 1986. The role of proteins in the nitrogen nutrition of ectomycorrhizal plants. I. Utilization of peptides and proteins by ectomycorrhizal fungi. 103:481–93. 1989. The role of proteins in the nitrogen nutrition of ectomycorrhizal plants. IV. The utilization of peptides by birch (Betula pendula L. [SEP] Justify:

Output: The text does not directly address the claim regarding fungi regenerating entire ecosystems. Instead, it discusses the roles of mycorrhizal fungi in nitrogen nutrition and their interactions with host plants in nitrogen-limited environments. The focus is on nutrient dynamics rather than ecosystem regeneration. Hence, it is irrelevant to the claim.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: , 2018; Murray et al. , 2019). Several studies have focused on studying the effects of climate change on the future state of the Baltic Sea, and the ecosystem modelling studies already provide valuable results that are directly usable in decision-making concerning mitigation of eutrophication under climate change. In contrast, studies concerning effects of climate change on biodiversity of the are lagging behind and are hampered by model uncertainties (e. g. for salinity) and by the current inability of models to consider the complex interactions between species and multiple feedbacks between trophic levels. Long-term and modelling studies focusing on shallow photic environments, which harbour the highest biodiversity in the Baltic Sea, are especially sparse. This is a major drawback in a situation where all major environmental policies, including the UN Convention on and EU for 2030, urge for halting ongoing biodiversity loss. To designate effective measures to safeguard biodiversity, including a climate-smart expansion of the protected area network, a better understanding of the effects of climate change on the sublittoral ecosystem is urgently needed. Knowledge of the mechanisms and processes governing the ecosystem under climate change have recently accumulated and already provide information that can be used to design adaptation tools and mitigation measures for the (Reusch et al. , 2018). It is necessary to continue studying the as a socio-ecological system, responding to both environmental and societal changes (Bauer et al. , 2018, 2019; Hyytiäinen et al. , 2019), and to continue the dialogue with human society, in order to attune to the future changes ultimately driven by the ocean itself (Stenseth et al. , 2020). Data availability This review paper refers exclusively to published research 10. 3354/meps11081, 2015. Ahlgren, J. , Grimvall, A. , Omstedt, A. , Rolff, C. , and Wikner, J. : Temperature, DOC level and basin interactions explain the declining oxygen concentrations in the Bothnian Sea, J. Marine Syst. , 170, 22–30, 2017. Alheit, J. , Pohlmann, T. , Casini, M. , Greve, W. , Hinrichs, R. , Mathis, M. , O'Driscoll, K. , Vorberg, R. , and Wagner, C. [SEP] Justify:

Output: The text corroborates the claim by discussing the effects of climate change on ecosystems, specifically mentioning that studies are focused on the Baltic Sea and that there is a need for a better understanding of the effects of climate change on biodiversity. It emphasizes the urgency of studying the ecosystem's response to climate change and the importance of effective measures to safeguard biodiversity, which directly supports the assertion that climate change is impacting species distributions and ecosystem dynamics.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: Fat mass gain was determined for the 4-week study period for all groups (f). Data are presented as the mean±s. e. m. and were analyzed by either two-way repeated measures ANOVA (for body weight and fat mass) or two-way ANOVA (for body weight and fat mass gain). Bonferroni's post-test shows that daily fat mass (e) was reduced by Det relative to Veh from 13 to 26 days and relative to Glar from 10 to 26 days. For all panels: *P<0. 05, **P<0. 01, and ***P<0. 001 for Veh vs Det; #P<0. 05, αP<0. 01 and βP<0. 001 for Glar vs Det comparisons as analyzed by Bonferroni's post-test. Full size imageFat mass was significantly reduced by Det compared with both Veh and Glar on HFD (Figure 3e, P<0. 05 from day 10 onward). Fat mass gain was significantly lower in the Det group relative to Veh and Glar, Glar had no effect compared with Veh (Figure 3f; Veh 36. 6±2. 9 g, Glar 39. 2±3. 3 g, Det 25. 6±2. 9 g; Veh vs Det P<0. 05, Glar vs Det P<0. 01, Veh vs Glar P=NS). The effect of Det to reduce fat mass was clearly dependent on HFD intake (F(5, 30)=8. 15, P<0. 0001). Energy balanceWe assessed energy expenditure in LF- and HF-fed rats by indirect calorimetry using an open-circuit indirect calorimeter. No significant differences in energy expenditure were noted (Table 1). HF-fed rats treated with Det had reduced calculated positive energy balance relative to Veh (Veh vs Det, P<0. 05) and to Glar (Glar vs Det, P<0. 01) and this was due entirely to a significant reduction in energy intake (Table 1; Veh vs Det, P<0. 05 and Glar vs Det, P<0. 001). [SEP] Justify:

Output: The text contrasts the claim by presenting evidence that fat mass gain is influenced by dietary intake and treatment. The study indicates that the Det group had significantly lower fat mass compared to the Veh and Glar groups, suggesting that not all individuals consuming high dietary fat (HFD) will automatically gain weight, as the effect is dependent on energy intake and treatment effects. This directly opposes the claim that eating large amounts of fat will universally lead to weight gain without considering other factors.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: ) infected with different mycorrhizal fungi. 112:55–60 2002. The role of various types of mycorrhizal fungi in nutrient cycling and plant competition. In: van der. Mycorrhizal ecology. Berlin: Springer Verlag. pp 117–33. 1996. Theoretical ecosystem ecology. Cambridge: Press. 250p. 2003. Global patterns of the isotopic composition of soil and plant nitrogen. Cycles 17(1):1031. 1998. The 15N of lettuce and barley are affected by AM status and external concentration of N. 138:19–26. 2006. Peptide uptake in the ectomycorrhizal fungus Hebeloma cylindrosporum: characterization of two di- and tripeptide transporters (HcPTR2A and B). 170:401–10. 2001. Do nutrient additions alter carbon sink strength of ectomycorrhizal fungi? 151:543–50. 2006. Fine roots versus needles: a comparison of 13C and 15N dynamics in a ponderosa pine forest soil. Biogeochemistry 79:53–67. 2004. Long term changes in the distribution and δ15N values of individual soil amino acids in the absence of plant and fertiliser inputs. 40:243–56. 2006. Ammonia: a candidate for nitrogen transfer at the mycorrhizal interface. Sci 11:263–6. 1998. Physiology of organic nitrogen acquisition by ectomycorrhizal fungi and ectomycorrhizas. 22:21–44. 1985. Factors affecting mineral nutrient acquisition by plants. 36:77–115. 2006. Increased ectomycorrhizal fungal abundance after long-term fertilization and warming of two arctic tundra ecosystems. 171:391–404. 2004. Fundamentals of soil ecology, 2nd edition. New York: Academic Press. 408p. 2005. Activity profiling of ectomycorrhiza communities in two forest soils using multiple enzymatic tests. 167:309–19. 2006. 13C and 15N natural abundance of the soil microbial biomass. Biochem 38:3257–66. 24. 11. 1998. Natural abundance of 15N in forests across a nitrogen deposition gradient. Manage 101:9–18. 2006. Stable isotope ecology. New York: Springer-Verlag. [SEP] Justify:

Output: The text does not directly address the claim about fungi regenerating entire ecosystems. Instead, it discusses various studies related to mycorrhizal fungi and their roles in nutrient cycling and plant competition, which are specific aspects of fungi's interactions in ecosystems, but it does not provide evidence or claim that fungi can regenerate entire ecosystems independently.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: : Climate variability drives anchovies and sardines into the North and Baltic Seas, Prog. Oceanogr. , 96, 128–139, 2012. Al-Janabi, B. , Kruse, I. , Graiff, A. , Karsten, U. , and Wahl, M. : Genotypic variation influences tolerance to warming and acidification of early life-stage Fucus vesiculosus L. (Phaeophyceae) in a seasonally fluctuating environment, Mar. Biol. , 163, 14, 2016a. Al-Janabi, B. , Kruse, I. , Graiff, A. , Winde, V. , Lenz, M. , and Wahl, M. : Buffering and amplifying interactions among OAW (and Warming) and nutrient enrichment on early life-stage Fucus vesiculosus L. (Phaeophyceae) and their carry over effects to hypoxia impact, 11, e0152948, 2016b. Ammar, Y. , Niiranen, S. , Otto, S. A. , Möllmann, C. , Finsinger, W. , and Blenckner, T. : The rise of novelty in marine ecosystems: Sea case, Glob. Change Biol. , 27, 1485–1499, 2021. Andersen, J. H. , Carstensen, J. , Conley, D. J. , Dromph, K. , Fleming-Lehtinen, V. ,. , Villnäs, A. , and Murray, C. : Long-term temporal and spatial trends in eutrophication status of the Baltic Sea, Biol. Rev. , 92, 135–149, 2017. Andersson, A. , Jurgensone, I. , Rowe, O. F. , Simonelli, P. , Bignert, A. , Lundberg, E. , and Karlsson, J. : in Coastal Waters? , 8, 13, 2013. Andersson, A. , Meier, H. E. M. , Ripszam, M. , Rowe, O. , Wikner, J. , Haglund, P. , Eilola, K. , Legrand, C. [SEP] Justify:

Output: The text corroborates the claim by discussing how climate variability drives fish species like anchovies and sardines into different marine areas such as the North and Baltic Seas, indicating shifts in species distributions as a result of changing environmental conditions.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: RNA isolated from epididymal fat of rats of all treatment groups (Veh, Det and Glar) after 4 weeks of diet consisting of either LFD (n=5 per group) or HFD (n=6–8 per group) was assessed by real-time PCR for changes in RNA levels of WAT lipogenic markers, fatty acid synthase (FAS) (a) and lipoprotein lipase (LPL) (b). RNAs of interest were normalized to RPL13α and for comparative analysis, RNA ratios were normalized to the Veh LF control. Data are presented as the mean±s. e. m. and were analyzed by two-way ANOVA. For all panels, analysis with Bonferroni's post-test shows: *P<0. 05 and ***P<0. 001 for Veh vs Det; #P<0. 05, βP<0. 001 for Glar vs Det comparisons. Full size imageDiscussionFor clinicians, the concept that insulin functions as an adiposity negative feedback signal to the CNS and controls food intake and weight gain, first proposed by Porte and Woods in the late 1970s,35, 36 is quite at odds with clinical experience where insulinization rather uniformly leads to weight gain. Whereas ∼2 kg of weight gain is typically expected for each 1% reduction in HbA1C, insulin administered directly into the CNS of animals ranging from baboons20 to rodents22 will lower food intake and body weight. Genetic modifications that reduce insulin signaling in mice23, 24 as well as mutations in insulin signaling homologs in even more primitive organisms, such as flies and nematodes,17 lead to increased fat mass in these organisms. Here we describe, for the first time to our knowledge, the robust effect of peripheral SC Det insulin administration to reduce food intake and limit weight and adipose gain. Explanations for weight gain on insulin therapy commonly include reversal of the negative energy balance associated with glycosuria, anabolic effects of insulin in peripheral tissues, particularly the potent effect to stimulate lipogenesis, and the fact that hypoglycemia is a potent stimulus to feed. Finally, it has been described that, with imperfect pharmacodynamic profiles, humans will learn to anticipate hypoglycemia and eat defensively. 8 By controlling for these variables, we appear to have uncovered a unique effect of insulin Det, but not Glar, to modulate weight gain. [SEP] Justify:

Output: The text contrasts the claim by discussing the effects of insulin on food intake and weight gain, indicating that insulin therapy can lead to weight gain through mechanisms other than fat consumption alone. It specifically mentions that weight gain is commonly expected with insulin administration, which suggests that factors like hormonal signals and metabolic processes play a significant role in weight gain, rather than solely the intake of large amounts of fat, thus refuting the claim that fat consumption alone causes weight gain regardless of other factors.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: 308p. 1994. Physiology of isotopic fractionation in algae and cyanobacteria. In:. Stable isotopes in ecology and environmental science. London: Blackwell Publishing. pp 187–221. 99. 1993. Effects of the ecto- and VA-mycorrhizal fungi Hydnagium carneum and Glomus clarum on the 15N and 13C values of Eucalyptus globulus and Ricinus communis. Environ 16:375–82. 2004. Assessing the 15N concentration of plant-available soil nitrogen. Anal 35:1207–17. 2006. Measuring external mycelia production of ectomycorrhizal fungi in the field: the soil matrix matters. 171:179–86. 2001. Ecophysiology of 13C and 15N isotopic fractionation in forest fungi and the roots of the saprotrophic-mycorrhizal divide. Oecologia 128:480–7. 2004. Isotopic fractionation during ammonium assimilation by basidiomycete fungi and its implications for natural nitrogen isotope patterns. 162:771–81. 2006. Carbon allocation to ectomycorrhizal fungi correlates with belowground allocation in culture studies. Ecology 87:563–9. 2003. Nitrogen availability and colonization by mycorrhizal fungi correlate with nitrogen isotope patterns in plants. 157:115–26. 2006. 15N in symbiotic fungi and plants estimates nitrogen and carbon flux rates in arctic tundra. Ecology 87:816–22. 2005. Foliar and fungal 15N:14N ratios reflect development of mycorrhizae and nitrogen supply during primary succession: testing analytical models. Oecologia 146:258–68. 60. 15. 2000. Correlations between foliar δ15N and nitrogen concentrations may indicate plant-mycorrhizal interactions. Oecologia 122:273–83. 2001. Foliar nitrogen levels and natural abundance 15N reveal mycorrhizal-plant partitioning and recycling of nitrogen during development under climate change. 21:1113–22. PubMed CAS 2004. Carbon use, nitrogen use, and isotopic fractionation of ectomycorrhizal and saprotrophic fungi in natural abundance and 13C-labelled cultures. 108:725–36. 2006. [SEP] Justify:

Output: The text does not address the claim about fungi regenerating entire ecosystems on their own. Instead, it consists of various references to scientific research articles discussing physiological and ecological aspects of fungi, particularly relating to nitrogen and carbon processes, without mention of ecosystem regeneration.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: , Heikinheimo, O. , Müller-Karulis, B. , Niiranen, S. , Tomczak, M. T. , Aro, E. , Wikström, A. , and Möllmann, C. : Biological ensemble modeling to evaluate potential futures of living marine resources, Ecol. Appl. , 23, 742–754, 2013. Garzke, J. , Ismar, S. M. H. , and Sommer, U. : Climate change affects low trophic level marine consumers: warming decreases copepod size and abundance, Oecologia, 177, 849–860, 2015. Gladstone-. , Valanko, S. , and Norkko, A. : Identifying “vital attributes” for assessing disturbance-recovery potential of seafloor communities, Ecol. Evol. , 11, 6091–6103, 2021. Gogina, M. , Zettler, M. L. , Wahlstrom, I. , Andersson, H. , Radtke, H. , Kuznetsov, I. , and MacKenzie, B. R. : A combination of species distribution and ocean-biogeochemical models suggests that climate change overrides eutrophication as the driver of future distributions of a key benthic crustacean in the estuarine ecosystem of the. Mar. Sci. , 77, 2089–2105, 2020. Gorokhova, E. , Hansson, S. , Höglander, H. , and Andersen, C. M. : Stable isotopes show food web changes after invasion by the predatory cladoceran Cercopagis pengoi in a bay, Oecologia, 143, 251–259, 2005. Graiff, A. , Liesner, D. , Karsten, U. , and Bartsch, I. : Temperature tolerance of western Fucus vesiculosus – growth, photosynthesis and survival, J. Exp. Mar. Biol. Ecol. , 471, 8–16, 2015. Granskog, M. , Kaartokallio, H. , Kuosa, H. , Thomas, D. N. [SEP] Justify:

Output: The text corroborates the claim by referencing multiple studies that illustrate how climate change impacts marine ecosystems, specifically noting that "climate change affects low trophic level marine consumers" and implies changes in species distributions, such as how "climate change overrides eutrophication as the driver of future distributions" of species. This indicates that climate change is influencing both the distribution of marine species and the dynamics of their ecosystems.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: In these studies, we sought to carefully examine the role of insulin delivered ‘clinically’ (that is, given subcutaneously, once daily) on energy homeostasis. Of course, insulin delivered directly into the brain decreases feeding,20, 21, 22 but our primary objective was to specifically address whether it is the insulin per se that causes weight gain when given via a clinically relevant (SC) route. Here, we sought to bridge clinical experience with our basic science understanding of the role of insulin in energy homeostasis. We additionally controlled for confounders that may explain weight gain by studying non-glycosuric, non-diabetic animals without causing hypoglycemia. Furthermore, because potential weight effects might be amplified in a model of rapid weight gain (diet-induced obese) and because of the clinical observation that weight sparing effects of Det appear to be amplified in the most obese subjects, we also studied the effects in animals on HFD. First, as current models of energy homeostasis predict, SC insulin neither increased nor decreased food intake and weight gain in LFD rats. The doses of insulin used here are within the clinically relevant dosage range for treatment of diabetes in humans with modest insulin resistance (∼0. 2–0. 8 U kg−1 per day, KD Niswender, personal experience). Conversely, on HFD, a surprisingly robust effect of Det, but not Glar, to attenuate food intake, weight gain and adipose gain was observed. First, this implies an important interaction of diet with Det for effects on weight and is intriguingly consistent with observations in humans where those individuals with higher body mass index benefited most. 9 Of course, in terms of fat content, the typical human diet much more closely resembles the HFD, rather than the LFD, which consists of only 13. 5% calories from fat. Indeed, this food intake and weight attenuating effect is eliminated with increasing dose of insulin (not shown), suggesting, not surprisingly, a narrow effective ‘window. ’ Because it is clinically very difficult in humans to remain precisely within such a window due to variability in (1) caloric and macronutrient intake, (2) in insulin administration, and (3) in insulin absorption it is perhaps not surprising that weight gain is common. [SEP] Justify:

Output: The text contrasts the claim by explaining that insulin does not increase or decrease food intake and weight gain in low-fat diet (LFD) rats. It also observes a robust effect of insulin in attenuating food intake and weight gain in high-fat diet (HFD) models, highlighting that dietary interactions play a significant role in weight management, which suggests that simply consuming large amounts of fat does not inherently cause weight gain irrespective of activity levels, as the interaction with insulin and overall diet matters.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: Integrating ectomycorrhizal fungi into quantitative frameworks of forest carbon and nitrogen cycling. In: Gadd GM, Ed. Fungi in biogeochemical cycles. Cambridge: Press. pp 98–128. 1990. 15N natural abundance as a possible marker of the ectomycorrhizal habit of the trees in mixed African woodlands. 115:483–6. 1999. Nitrogen isotope fractionation during nitrogen uptake by ectomycorrhizal and non-mycorrhizal Pinus sylvestris. 142:69–576. 1996. 15N abundance of surface soils, roots and mycorrhizas in profiles of European forest soils. Oecologia 108:207–14. 2006. Towards a more plant physiological perspective on soil ecology. Evol 21:548–54. 2001. The molecular revolution in ectomycorrhizal ecology: peeking into the black-box. 10:1855–71. 1988. Nutrient-uptake and allocation at steady-state nutrition. 72:450–9. 1997. A global budget for fine root biomass, surface area, and nutrient contents. 94:7362–6. 1995. The physiology of fungal nutrition. Cambridge: Press. 642p. 2005. The uptake, metabolism, transport and transfer of nitrogen in an arbuscular mycorrhizal symbiosis. 168:687–96. 2004. Carbon cycling in soil. Environ 2:522–8. 1999. Biodegradation kinetics and sorption reactions of three differently charged amino acids in soil and their effects on plant organic nitrogen availability. Biochem 31:1331–42. 81. 23. 1995. The biology of mycorrhizae in the Ericaceae. 18. Chitin degradation by Hymenoscyphus ericae and transfer of chitin-nitrogen to the host-plant. 131:369–75. 2000. Nitrogen isotope fractionation during nitrogen transport from ectomycorrhizal fungi, Suillus granulatus, to the host plant, Pinus densiflora. 46:733–9. 1999. Natural 13C and 15N abundance of field-collected fungi and their ecological implications. 144:323–30. 2000. Review of mechanisms and quantification of priming effects. Biochem 32:1485–98. 2005. [SEP] Justify:

Output: The text does not address the claim about fungi regenerating entire ecosystems. Instead, it presents a variety of scientific studies and findings related to the role of ectomycorrhizal fungi in nutrient cycling and soil ecology without specifically mentioning ecosystem regeneration.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: , 138, 91–104, 2015. Heikinheimo, O. : Interactions between cod, herring and sprat in the changing environment of the Baltic Sea: A dynamic model analysis, Ecol. Model. , 222, 1731–1742, 2011. Henseler, C. , Oesterwind, D. , Kotterba, P. , Nordström, M. C. , Snickars, M. , Törnroos, A. , and Bonsdorff, E. : Impact of round goby on native invertebrate communities – An experimental field study, J. Exp. Mar. Biol. Ecol. , 541, 11, 2021. Henson, S. A. , Beaulieu, C. , Ilyina, T. , John, J. G. , Long, M. , Seferian, R. , Tjiputra, J. , and Sarmiento, J. L. : Rapid emergence of climate change in environmental drivers of marine ecosystems, Nat. Commun. , 8, 9, 2017. Hillebrand, H. , Soininen, J. , and Snoeijs, P. : Warming leads to higher species turnover in a coastal ecosystem, Glob. Change Biol. , 16, 1181–1193, 2010. Hine, R. (Ed. ): A Dictionary of Biology, 8th edn. , 9780198821489, 2019. Hinrichsen, H. H. , Huwer, B. , Makarchouk, A. , Petereit, C. , Schaber, M. , and Voss, R. : Climate-driven long-term trends in oxygen concentrations and the potential consequences for eastern Baltic cod (Gadus morhua), ICES J. Mar. Sci. , 68, 2019–2028, 2011. Hjerne, O. , Hajdu, S. , Larsson, U. , Downing, A. S. , and Winder, M. : Climate driven changes in timing, composition and magnitude of the phytoplankton spring bloom, Front. Mar. Sci. , 6, 15, 2019. Hoegh-Guldberg, O. and Bruno, J. F. [SEP] Justify:

Output: The text corroborates the claim by referencing multiple studies that address the impacts of climate change on marine ecosystems. For instance, it mentions a study by Henson et al. (2017) that discusses the rapid emergence of climate change affecting environmental drivers of marine ecosystems. Additionally, it references a study by Hillebrand et al. (2010), which concludes that warming results in higher species turnover, indicating shifts in species distributions. Therefore, the collective insights from these studies strongly support the claim about climate change influencing species distributions and ecosystem dynamics.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: Weight gain is clearly one of the major barriers to successful clinical use of insulin and our findings, albeit in a preclinical model, suggest that with careful use, insulin need not inevitably cause weight gain. This conclusion is similar to that drawn from a longitudinal study comparing weight gain in controlled diabetics on insulin vs oral agents. 37 That the weight effects of Det were observed under HF dietary conditions that provoke weight gain, and that most experts agree contribute to human obesity and diabetes suggests that this model may hold promise for understanding pathophysiological and potentially therapeutic mechanisms at the interface of feeding, obesity and diabetes. Finally, that the effect is observed only on HFD implies that the increased molar ratio of Det to Glar required to obtain similar glycemic effects is not the sole explanation for the observed weight effects, as the same ratio was utilized in LF-fed rats where no effects were observed. Several studies investigating the effect of chronic administration of insulin in normal-weight or obese non-diabetic rat models have largely reported findings that insulin treatment either had no effect38 or induced hyperphagia and weight gain. 39, 40, 41, 42, 43, 44, 45 Notably, only a study by Vanderweele46 showed an 11% reduction of spontaneous daily food intake in normal-weight chow-fed rats administered 2 U per day of regular insulin via an osmotic minipump implanted into the peritoneal cavity. Thus, our findings with insulin Det appear to be fundamentally novel. Although the primary objective of our study was to determine the effects of SC basal insulin regimens on energy homeostasis in a unique preclinical diet-induced obese model, we also took the opportunity to explore, in a preliminary way, insulin-sensitive markers of potential mechanisms underlying the phenotype. Others have hypothesized that relative tissue selectivity, particularly for the CNS, could explain such weight effects. 47, 48 For example, a recent study in humans demonstrated effects of Det on brain function and to reduce spontaneous food intake in a test meal. 12 Conversely, others have determined that Det has attenuated transport into the CNS,49 suggesting the possibility that findings of reduced food intake in humans and rodents may be indirect. Given that insulin has potent lipogenic effects, and that we observed significant attenuation of fat mass gain in Det-treated HFD rats, we assessed expression of key insulin-sensitive target genes in adipose tissue. [SEP] Justify:

Output: The text contrasts the claim by discussing the nuances of weight gain in relation to dietary fat and insulin. It presents evidence that insulin need not inevitably cause weight gain, as shown in studies comparing weight gain among diabetics on insulin versus oral agents. Additionally, it implies that weight effects observed are context-dependent, as the impact of diet on weight gain varies with different feeding conditions, particularly mentioning high-fat diets (HFD) and low-fat diets (LF). Thus, the evidence suggests that simply consuming large amounts of fat does not guarantee weight gain, contradicting the claim.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: Diversity of an ectomycorrhizal fungal community studied by a root tip and total soil DNA approach. Mycorrhiza 15:1–6. 2002. Ectomycorrhizal fungal taxa differing in response to nitrogen deposition also differ in pure culture organic nitrogen use and natural abundance of nitrogen isotopes. 154:219–31. 2005. Enzymatic activities of mycelia in mycorrhizal fungal communities. In:. The fungal community. Boca Raton: Taylor & Francis. pp 331–48. 2007. Spatial separation of litter decomposition and mycorrhizal nitrogen uptake in a boreal forest. 173:611–20. 2004. Occurrence of N-acetylhexosaminidase-encoding genes in ectomycorrhizal basidiomycetes. 164:193–9. 1998. Plant-microbe competition for soil amino acids in the alpine tundra: effects of freeze-thaw and dry-rewet events. Oecologia 113:406–14. 1987. Nutrient concentrations, percentage moisture and density of field-collected fungal mycelia. Biochem 19:727–33. 1986. Kinetic fractionation of stable nitrogen isotopes during amino acid transamination. Acta 50:2143–6. 2002. Resource-based niches provide a basis for plant species diversity and dominance in arctic tundra. Nature 415:68–71. 1998. Vascular plant 15N natural abundance in heath and forest tundra ecosystems is closely correlated with presence and type of mycorrhizal fungi in roots. Oecologia 115:406–18. 1996. Leaf 15N abundance of subarctic plants provides field evidence that ericoid, ectomycorrhizal and non- and arbuscular mycorrhizal species access different sources of soil nitrogen. Oecologia 105:53–63. 1994. The influence of vesicular-arbuscular mycorrhizal fungi on the nitrogen fixation of nursery-grown Ethiopian acacias estimated by the 15N natural abundance method. 160:249–57. 1994. Nitrogen isotope studies in forest ecosystems. In:. Stable isotopes in ecology and environmental science. London: Blackwell Publishing. pp 22–44. 1992. [SEP] Justify:

Output: The text does not provide any information directly related to the claim about fungi regenerating entire ecosystems. Instead, it lists various studies on fungal communities, mycorrhizal interactions, and nitrogen dynamics, none of which explicitly support or refute the claim. Therefore, it is considered irrelevant.


Input: Task: corroborate the claim using the given text. Claim: Climate change is causing shifts in species distributions and ecosystem dynamics. [SEP] Text: : The impact of climate change on the world's marine ecosystems, Science, 328, 1523–1528, 2010. Hogfors, H. , Motwani, N. H. , Hajdu, S. , El-Shehawy, R. , Holmborn, T. , Vehmaa, A. , Engström-Öst, J. , Brutemark, A. , and Gorokhova, E. : Bloom-support copepod reproduction and development in the 9, e112692, 2014. Holopainen, R. , Lehtiniemi, M. , Meier, H. M. , Albertsson, J. , Gorokhova, E. , Kotta, J. , and Viitasalo, M. : Impacts of changing climate on the non-indigenous invertebrates in the northern by end of the twenty-first century, Biol. Invasions, 18, 3015–3032, 2016. Horn, H. G. , Boersma, M. , Garzke, J. , Loder, M. G. J. , Sommer, U. , and Aberle, N. : Effects of high CO2 and warming on a microzooplankton community, ICES J. Mar. Sci. , 73, 772–782, 2016. Huber, S. ,. , Berglund, J. , von Friesen, C. P. , Danbolt, M. , Envall, M. , Infantes, E. , and Moksnes, P. : Novel approach to large-scale monitoring of submerged aquatic vegetation: A nationwide example from Sweden, Integr. Environ. Assess. Manag. , 2021, 1–12, 2021. Huttunen, I. , Lehtonen, H. , Huttunen, M. , Piirainen, V. , Korppoo, M. , Veijalainen, N. , Viitasalo, M. , and Vehvilainen, B. : Effects of climate change and agricultural adaptation on nutrient loading from Finnish catchments to the Baltic Sea, Sci. Total Environ. , 529, 168–181, 2015. Hyytiäinen, K. , Bauer, B. [SEP] Justify:

Output: The text lists various studies that investigate the impacts of climate change on marine and aquatic ecosystems, implicating alterations in species distribution, reproductive behaviors, and community dynamics due to changing climates. This supports the claim regarding species distribution shifts and ecosystem dynamics influenced by climate change.


Input: Task: contrast the claim using the given text. Claim: Eating large amounts of fat will automatically make you gain weight, no matter your activity level or overall diet. [SEP] Text: Here, Glar was noted to increase fatty acid synthase gene expression in both HFD and LFD groups, and to increase lipoprotein lipase gene expression in LFD rats, both effects that would promote storage of fat in adipose. Det showed no effects on adipose tissue gene expression among the groups. These findings are consistent with potency studies in vitro where Glar was noted to have roughly double the lipogenic potency of Det and ∼80-fold increased mitogenic potency. 50 Coupling in vitro and in vivo findings such as these raises the possibility that future insulin analog development may enhance specific aspects of insulin's pleiotropic effects while minimizing others, including those on weight. This will clearly require an improved understanding of the molecular pharmacological mechanisms underlying such effects. Insulin-associated weight gain contributes to clinical inertia, resulting in many patients with diabetes failing to achieve adequate glycemic control, and increasing risk for other metabolic comorbidities. We conclude here that insulin therapy may not necessarily and inevitably lead to weight gain and that it is not the insulin per se that is responsible when weight gain occurs. Next, we have clearly unmasked a unique property of Det in these studies to influence energy balance. Obesity is thought to arise from a very small mismatch in caloric intake vs expenditure; as little as a 1% mismatch can lead to significant weight gain. Here, we observed an overall 7–10% reduction in caloric intake, consistent with recent findings in human volunteers where Det significantly reduced food intake compared with regular human insulin. 12 Recognizing significant differences in rat vs human physiology, if such a magnitude of effect were indeed achievable in humans it would have an enormous impact on diabetes progression and obesity related comorbidities. All too commonly, insulin therapy in humans is reserved for ‘rescue’ or ‘last-ditch’ therapy, after the failure of multiple oral medications to control blood–glucose concentration. Several compelling recent publications, however, demonstrated that early insulinization had potential beneficial effects on disease progression. 51, 52 These were primarily glycemia-focused intervention studies, but it is intriguing to consider what a similar, but weight-focused approach might achieve. [SEP] Justify:

Output: The text contrasts the claim by discussing how insulin therapy does not necessarily lead to weight gain, emphasizing that it is not the insulin itself responsible for weight gain. It indicates that weight gain can arise from a mismatch in caloric intake versus expenditure and suggests that dietary choices, along with therapy, could influence weight, contrary to the claim's assertion that large fat intake will automatically lead to weight gain irrespective of other factors.


Input: Task: pass the claim using the given text. Claim: Fungi can regenerate entire ecosystems by themselves. [SEP] Text: Microbial processes and plant nutrient availability in Arctic soils. In:. Arctic ecosystems in a changing climate. New York: Academic Press. pp 281–300. 1998. Boreal forest plants take up organic nitrogen. Nature 392:914–6. 2001. Soil nitrogen form and plant nitrogen uptake along a boreal forest productivity gradient. Oecologia 129:125–32. 2002. Foraging and resource allocation strategies of mycorrhizal fungi in a patchy environment. In: van der. Mycorrhizal ecology. Berlin: Springer Verlag. pp 93–115. 2003. Nitrogen acquisition from inorganic and organic sources by boreal forest plants in the field. Oecologia 137:252–7 2007. Natural 15N abundance of soil N pools and N2O reflect the nitrogen dynamics of forest soils. 295:79–94. 2004. A rapid and highly sensitive method for measuring enzyme activities in single mycorrhizal tips using 4-methylumbelliferone-labelled fluorogenic substrates in a microplate system. J 58:233–41. 2003. Mycorrhizas and nutrient cycling in ecosystems—a journey towards relevance? 157:475–92. 2002. Widespread N-acetyl-D-glucosamine uptake among pelagic marine bacteria and its ecological implications. Microbiol 68:5554–62. 2007. Free amino sugar reactions in soil in relation to soil carbon and nitrogen cycling. Biochem 39:3081–92. 2003. Vertical distribution of ectomycorrhizal fungal taxa in a podzol soil profile. 159:775–83. 2004. Nitrogen mineralization: Challenges of a changing paradigm. Ecology 85:591–602. 2006. Effects of nitrogen source and ectomycorrhizal association on growth and δ15N of two subtropical Eucalyptus species from contrasting ecosystems. Biol 33:367–79. 1997. Waterlogging and fire impacts on nitrogen availability and utilization in a subtropical wet heathland (wallum). Environ 20:1231–41. 2003. δ15N values of tropical savanna and monsoon forest species reflect root specialisations and soil nitrogen status. Oecologia 134:569–77. PubMed CAS 2007. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses microbial processes, nitrogen dynamics, and various ecological studies without mentioning the regenerative capabilities of fungi in relation to entire ecosystems. There is no direct support or contradiction to the claim in the provided information.