Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Materials and methods, Page 11: The authors state that "We did not perform eye-blink artefact removal because it has been shown that blink artefacts are successfully ignored by multivariate classifiers as long as they are not systematically different between decoded conditions (Grootswagers et al. , 2017). " This is not a sufficiently convincing argument. Firstly, the cited paper makes a theoretical argument rather than showing this empirically. Secondly, even if this were true, the frequency of eye-related artefacts seems to be of crucial importance for a paradigm that involves moving stimuli (and no fixation). There could indeed be systematic differences between conditions that are then picked up by the classifier (i. e. if more eye-blinks are related to tiredness and in turn decreased vigilance). The authors should show that their results replicate if standard artefact removal is performed on the data. We appreciate the point here. There are theoretical and practical arguments that eye-related artefacts should not drive our effects, but to be sure we also now present our results with standard artefact removal as well. Overall increases in eye-related artefacts (such as blinks) over time-on-task would not be an issue, as our design relies on comparisons between Active and Monitoring, and so any general effects should have negligible impact. But for these comparisons, there may indeed be differences in the number of eye blinks – and in fact, these conditions involve different levels of attentional recruitment, which has previously shown to correlate with the frequency of eye blinks (Nakano et al. , 2013). Thus, we certainly do not want to claim that eye-related artefacts do not matter at all, but, importantly, there are two practical reasons that the effects of eye blinks should not be dominantly picked up by our classification procedure. First, the decoding analysis is time-resolved and computed in small time windows (5 ms and 80 ms, for direction and distance information decoding, respectively). For eye blink patterns to be picked up by the classifier, they would need to occur at consistent time points across trials of the same condition, and not in the other condition, which seems implausible. Second, our MEG helmet does not have the very frontal sensors where eye-related artefacts most strongly affect neural activations (Mognon et al. , 2011), but we appreciate that this does not rule out their presence altogether.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Early-career scientists—and particularly those from underrepresented groups—are “desperate for a giant overhaul of the scientific enterprise,” Kahanamoku says. Chen agrees. “I wish I never had to do this work,” she says. “It has taken both a physical and mental toll on me. ” But, she says, no one else had stepped up to the plate. “These data were out there for years, in plain sight. ” Asked for their response to the findings of Chen et al. ’s paper, an NSF media representative offered a prepared statement from the agency. This statement said that the NSF’s director, Sethuraman Panchanathan, has made addressing racial funding disparities “one of the top priorities for agency progress, and [he] continues to take this [matter] seriously. ” As part of this priority, the NSF has launched a new initiative for 2023—entitled (Access for Equity and Diversity)—that aims to reduce the various barriers that minority researchers can face when applying for funding. The statement ended by noting that the NSF acknowledges “there is still much [work] to do. " Correction (8 November 2022): A previous version incorrectly stated that the PI discussed by Ivey was a person of color. They were white. –Katherine Wrightis the of Physics Magazine.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: , 2009; Nyankanga et al. , 2012), and this composted BSG+PD(1:1) had the highest total N in the two cropping cycles (Ebido & Ndubuaku, 2019). Also, soil N through the aforestated mechanisms influenced Ca2+ content on which amaranth productivity largely depended. It is thus recommended that BSG be complemented with PD in an equal-volume mix before composting to enhance the bioavailability of N and Ca, and to optimally express the relationship between these two nutrients, thereby increasing the productivity of amaranth in acidic sandy tropical soils. Besides the potential of composted BSG+PD(1:1) for increasing the productivity of amaranth and similar short-duration vegetables in the tropics, its wide adoption could be one way to enhance the utilisation and productive recycling of brewery waste, thereby promoting the concept of circular economy. There are indications that this composted BSG+PD(1:1) can produce higher crop yields than compound mineral fertilizers (Alayu & Leta, 2020; Moyin-Jesu, 2003; Ojeniyi et al. , 2007). Considering the abundance and cheap availability of BSG compared to mineral fertilizers, the adoption of composted BSG+PD(1:1) in crop production can also be more cost-effective. It could also help to reduce the prevailing pressure on PD, sparing it for other purposes such as bioremediation of crude oil-polluted soils (Ezenne et al.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: To check empirically that eye-related artefacts were not driving our effects, we re-ran our analyses with standard artefact removal as requested. We see the same pattern of results as before, for both the key task-relevant feature of distance-to-object and the less relevant feature of direction of approach. We present the full comparative analysis in Figure 3—figure supplement 2. In the paper we now state that the results replicate with artefact removal and present the additional eye-movement-corrected results in the supplementary materials. “…,we also did a post-hoc analysis in which we removed these using “runica” Analysis (ICA) algorithm as implemented by. We used the plugin (Mognon et al. , 2011) of to decide which ICA components were related to eye artefacts for removal. This toolbox extracts spatiotemporal features from components to quantitatively measure if a component is related to eye movements or blinks. For all subjects except two, we identified only 1 component which were attributed to eye artefacts (i. e. , located frontally and surpassing the ’s threshold) which we removed. For the two other participants, we identified and removed two components with these characteristics. ” Figure 3—figure supplement 2B shows the decoding results for the key task-relevant feature of distance-to-object without and with eye-related artefact removal, in the left and right panels, respectively. The main effects of attention and time on the task and the key interaction between target frequency and time on the task remain after eye artefact removal, replicating our initial pattern of results. Figure 3—figure supplement 2A shows the decoding results for the direction of approach information without and with eye artefact removal. The results again replicate those of the original analysis: as before there is a main effect of Attention but no main effect of Time on Task or Target Frequency, and no interaction. We also checked to see if our trial outcome prediction (Figure 6D) could be driven by eye artefacts by repeating our prediction procedure using the eye-movement corrected MEG data. The results (Author response image 1) show that although removal of eye artefacts seems to reduce the prediction accuracy slightly overall, it only has minimal effect on the statistics, replicating our original findings. We can still predict the outcome of the trial with >80% accuracy at closer distances.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Could yeast and bacteria replace fossil fuels? What if yeast and bacteria could replace fossil fuels? Goddard and his team are working on the MeMBrane Project, which aims to find ways of using microbes to make environmentally-friendly bioproducts such as biofuel We are all aware of the potentially cataclysmic effect of continuing to rely on fossil fuels. While we might recycle our plastic, use electric cars or walk to school, efforts are being made to reduce our reliance in surprising quarters. One area where efficiency and a move away from fossil fuels is being considered is that of the use of microbes. Fossil fuel-based chemical production has been a historical tendency for some time and, while it has helped advance the field of biochemistry, it has had the unfortunate effect of releasing enormous amounts of carbon into the atmosphere. As we all now well know, these ‘non-renewable’ production methods are affecting the world’s climate. Now, there is a concerted effort to move away from such methods and instead look for alternatives that are cleaner, greener and more efficient, including biofuels. Based at in the UK, Goddard leads the MeMBrane project, which focuses on the production of what he and his team call cell factories. “Microbial cells can be grown on a variety of ‘foods’ including sugars, plant waste and even wood pulp,” explains Alan. “These sources of carbon are renewable and therefore more environmentally friendly. Bio-based routes to chemical production can also lead to purer products. ” – a biological membrane that separates the interior of all cells from the outside environment – a living thing that is too small to be seen with the naked eye YEAST – a group of single-celled fungi that may reproduce by budding, i. e. when a bud detaches and forms into a new fungus – a gram-positive, anaerobic, rod-shaped genus of bacteria named for their unique metabolism – a field of science that involves redesigning organisms for useful purposes by engineering them to have new abilities – an area of chemistry focused on designing products and processes that minimise or eliminate the use and generation of hazardous substances – a renewable energy source, made from organic matter or wastes, that can play a valuable role in reducing carbon dioxide emissions ’? Alan and his team work with strains of Saccharomyces and Propionibacteria.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: , 2014), to the benefit of the environment. The soil data showed a slightly higher content of exchangeable Na (Na+) of 0. 15 cmol kg-1 in both composted BSG+PD(1:1) and BSG+PD(2:1) than the rest in the two cropping cycles (Ebido & Ndubuaku, 2019). However, from the perspective of the tendency for microaggregate breakdown and colloidal dispersion in humid tropical soils to increase with an increasing amount of Na+ relative to the entire cations or the other base-forming cations (Igwe & Obalum, 2013), these two treatments of pre-composting complementation of BSG with PD potentially cannot have negative implications for the environment. When its absolute level is considered, the soil Na+ content due to them is far below the critical value of 1. 0 cmol kg-1 above which agricultural soils can experience undesirable sodicity and associated colloidal dispersion, predisposing them to water erosion (Oluwatuyi et al. , 2020). For one-time soil application in agronomic production, the chances of realising residual effects in the soil are generally greater with organic amendments than mineral fertilizers (Ndzeshala et al. , 2023; Unagwu et al. , 2013). However, mineralisation-enhanced organic amendments like composted biowaste and liquid fermentate sometimes do not have residual effects in the soil (Ndzeshala et al.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Author response image 1 Download asset Open asset The accuracy of predicting behavioral outcome of trials without and with eye artefact removal. The results are for the left-out participant (averaged over all participants) using the threshold obtained from all the other participants as function of distance/time from the deflection point. Figure 6D shows the result without eye artefact removal and Author response image 1 with eye artefact removal. Thick lines and shading refer to average and one standard deviation around the mean across participants, respectively. are shown in the bottom section of each graph: Filled circles show moderate/strong evidence for either hypothesis and empty circles indicate insufficient evidence. In the Materials and methods section, we removed the sentence “We did not perform eye-blink artefact removal because it has been shown that blink artefacts are successfully ignored by multivariate classifiers as long as they are not systematically different between decoded conditions (Grootswagers et al. , 2017). ” We also added the following explanations and the figures to the manuscript in the Results section to cover this point. “Although eye-movements should not drive the classifiers due to our design (see Materials and methods), it is still important to verify that the results replicate when standard artefact removal is applied. We can also use eye-movement data as an additional measure, examining blinks, saccades and fixations for effects of our attention and vigilance manipulations. First, to make sure that our neural decoding results replicate after eye-related artefact removal, we repeated our analyses on the data after eye-artefact removal (see Materials and methods), which provided analogous results to the original analysis (see the decoding results without and with artefact removal in Figure 3—figure supplement 2). Specifically, for our crucial distance to object data, the main effects of Attention and Time on Task and the key interaction between and Time on Task remain after eye-artefact removal, replicating our initial pattern of results. Second, we conducted a post-hoc analysis to explore whether eye movement data showed the same patterns of vigilance decrements and therefore could explain our decoding results. We extracted the proportion of eye blinks, saccades and fixations per trial as well as the duration of those fixations from the eye-tracking data for correct trials (-100 to 1400 ms aligned to the stimulus onset time), and statistically compared them across our critical conditions (Figure 3—figure supplement 3).

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Saccharomyces is a yeast that feeds on sugars and can be found on fruit, such as on the skin of grapes. Interestingly, this yeast has been domesticated to make the species Saccharomyces cerevisiae, commonly known as brewers’ or bakers’ yeast. Propionibacterium are present in a wide variety of environments. Alan and his team are most interested in are those found in dairy products such as cheese. Ultimately, Alan and his team are interested in finding a means of overcoming our reliance on fossil fuels and are therefore investigating microbes as an alternative. “In our projects, Saccharomyces is used for the production of ethanol, whether that be for wine or for biofuels,” explains Alan. “Propionibacterium makes propionic acid, which can be used to keep yeast fermentations free from bacterial contamination. ” ’? The most obvious challenge is the production of ethanol by yeast. The hand gels we use in hospitals all contain ethanol, which helps to kill microbes – the ethanol breaks down the thin membrane around cells and kills them. This is something that Alan and his team are battling with. “When the yeast produces the ethanol, it is killed at an alcohol content of 15%-16%, which explains why wine doesn’t ferment to a higher concentration,” explains Alan. “We would like to have yeast that is able to tolerate higher concentrations, both for the production of biofuels in a more cost-effective manner and also to add into fermentations, such as the secondary fermentation that champagne undergoes in the bottle. ” ’? Yes! The team has been working on a variety of different stresses that affect the cell membrane and they now understand the effects that ethanol and butanol, another type of alcohol, have on membranes at the molecular level. This allows the team to design strains that have the potential to be more resistant – these will be tested later this year, which could lead to some exciting results in the near future. ? The short answer is soon and everywhere! points out, for decades, microbes were used to produce compounds such as butanol before fossil fuels took over. In many ways, the work of biochemists is to get back to where we once were, but the only way of doing that is to match the effectiveness and efficiency of fossil fuels.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: , 2023; Obalum et al. , 2019). This constraint with organic amendments is particularly the case in the humid tropics due to the prevailing high temperatures, and is true not only for effective manures like PD, but also for those that mineralise rather slowly in the soil like CD (Ezenne et al. , 2019; Obalum et al. , 2020). In the present study, the best-performing composted BSG+PD(1:1) had residual effects, out-yielding all the other treatments (Figure 2). This, which could be caused by amaranth’s short life cycle, offers the farmer the invaluable opportunity of double cropping without compost re-application. Smallholder farmers of short-duration vegetables in coarse-textured and low-fertility soils of the derived savannah and similar tropical locations can take advantage of these findings to produce the increasingly highly sought-after ‘organic’ vegetables for improved farm income and livelihood on a sustainable basis. 5. ConclusionsThe study explored the prospects of complementing BSG with PD and CD before composting for amaranth production in low-fertility sandy tropical soils. With the exception of BSG plus PD at an equal volume ratio (BSG+PD(1:1)), the composted BSG-based organic soil amendments were generally ineffective in increasing amaranth productivity. The BSG was not compatible with the manure components of the compost including poultry droppings (PD), except when mixed with PD in a favourable 1:1 ratio. The differences between composted BSG+PD(1:1) and others were mostly a reflection of their relative abilities to supply Ca and N in the soil. A composted mix of equal volumes of BSG and PD could thus be beneficially explored in overcoming Ca- and N-deficiencies for increased production of amaranth and similar leafy vegetables in low-fertility, sandy tropical soils. Author ContributionsConceptualization, O. O. A.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: We saw strong evidence (BF = 4. 8e8) for a difference in the number of eye blinks between attention conditions: There were more eye blinks for the Unattended (distractor) than Attended (potentially targets) colour dots. We also observed moderate evidence (BF = 3. 4) for difference between the number of fixations, with more fixations in Unattended vs. Attended conditions. These suggest that there are systematic differences in the number of eye blinks and fixations due to our attentional manipulation, consistent with previous observations showing that the frequency of eye blinks can be affected by the level of attentional recruitment (Nakano et al. 2013). However, there was either insufficient evidence (0. 3 < BF < 3) or moderate or strong evidence for no differences (0. 1 < BF < 0. 3 and BF < 0. 3, respectively) between the number of eye blinks and saccades across our Active, Monitoring, Early and Late blocks, where we observed our ‘vigilance decrement’ effects in decoding. Therefore, this suggests that the main vigilance decrement effects in decoding, which were evident as an interaction between Target frequency (Active vs. Monitoring) and Time on the task (Early vs. Late) (Figure 3), were not driven by eye movements. ” Relatedly, on page 16 the authors claim that "If the prediction from the MEG decoding was stronger than that of the eye tracking, it would mean that there was information in the neural signal over and above any artefact associated with eye movement. " This statement is problematic: Firstly, such a result might only mean that prediction from MEG decoding is stronger than decoding from eye-movements, but not relate to "artefacts" in general, to which blinks would also count. Secondly, given that the signal underlying both analyses is entirely different (and the number of features), it is not valid to directly compare the results between these analyses. More detailed analyses of fixations and fixation duration on targets and distractors might indeed be strongly related to behaviour. What is decodable at a given time might just be driven by what participants are looking at.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: “One consideration is cost but reducing toxicity should help with this and as people become increasingly environmentally conscious this should help drive government policy,” explains Alan. “There is a lot of amazing work going on to get microbes to make chemicals they wouldn’t normally make, and this broadens their uses significantly. ” in BiochemistryProteins and Lipids (AMPL): : Alan’s work is focused on investigating Saccharomyces and Propionibacteria with a view to improving the tolerance of microbes. The team hopes to improve efficiency and product yield of engineered cell-based factories. : UK Research and 2020 Marie Skłodowska-Curie actions Download the 10. 33424/50 Link to the activity sheet 0 +1 Pinterest 0 LinkedIn 0 cells viewed under a microscope. Yeast grown on an agar plate ready for testing. Testing yeast for ethanol tolerance. A molecular model of a cell membrane showing the individual lipids (fats) that make it up. The MeMBrane team at a recent meeting in York. We are all aware of the potentially cataclysmic effect of continuing to rely on fossil fuels. While we might recycle our plastic, use electric cars or walk to school, efforts are being made to reduce our reliance in surprising quarters. One area where efficiency and a move away from fossil fuels is being considered is that of the use of microbes. Fossil fuel-based chemical production has been a historical tendency for some time and, while it has helped advance the field of biochemistry, it has had the unfortunate effect of releasing enormous amounts of carbon into the atmosphere. As we all now well know, these ‘non-renewable’ production methods are affecting the world’s climate. Now, there is a concerted effort to move away from such methods and instead look for alternatives that are cleaner, greener and more efficient, including biofuels. Based at in the UK, Goddard leads the MeMBrane project, which focuses on the production of what he and his team call cell factories. “Microbial cells can be grown on a variety of ‘foods’ including sugars, plant waste and even wood pulp,” explains Alan. “These sources of carbon are renewable and therefore more environmentally friendly. Bio-based routes to chemical production can also lead to purer products.

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: in through your institution issuesearch term Advance 10. 1093/tas/txae109 Published: 01 August 2024 10. 1093/tas/txae109 Select format. ris (Mendeley, Papers, Zotero). enw (EndNote). bibtex (BibTex). txt (Medlars, RefWorks) Download citation issuesearch term issuesearch term 1. 0, a product containing bromoform, was fed to cattle to evaluate its effects on methane (CH4) and carbon dioxide (CO2) emissions and diet digestibility. Twelve nonlactating, nonpregnant Jersey cows (490 ± 19 kg body weight) were used in four replicated 3 × 3 Latin squares with three periods, each consisting of 21 d. Cows were blocked by feed intake (averaged intakes over 4 wk prior to trial) and assigned randomly to one of three treatments. Treatments included Alga 1. 0 fed at 0, 69, and 103 g/d in a 0. 454 kg/d dry matter (DM) top-dress daily in a modified distillers grains plus solubles (MDGS) carrier. Diet consisted of 60% dry-rolled corn, 20% corn silage, 15% modified distillers grains, and 5% supplement (DM basis). Headbox-style indirect calorimeters were utilized to evaluate gas production from individual cows with two nonconsecutive 23-h collections in each period. Data were analyzed using the procedure of SAS with cow within square as experimental unit and as a random effect, and treatment and period as fixed effects. Linear and quadratic contrasts were used to compare treatments. 1. 0 linearly reduced dry matter intake (DMI, P < 0. 01) by 10. 1% for 69 g/d inclusion and 13. 3% for 103 g/d inclusion compared to the control. Nutrient intakes decreased linearly (P < 0. 01) due to lower DMI, but nutrient digestibility was not impacted (P ≥ 0. 28). Inclusion of Alga 1. 0 did not impact gross energy or digestible energy concentration of the diets expressed as Mcal/kg DM (P ≥ 0. 22) but did linearly reduce energy intake (Mcal/d; P < 0. 01). 1.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: We have added the following paragraph to the Materials and methods section to clarify the point: “Note that the ‘direction of approach’ and ‘distance to object’ information cannot be directly compared on an analogous platform as the two types of information are defined differently. There are also different number of classes in decoding for the two types of information: only two classes for the direction information (left vs. right), compared to the 15 classes for the distance information (15 distances). ” 4. The distance classifier was trained using only correct trials. Then in the testing stage, it was generalized to either correct or miss trials. While there is a rationale for using correct trials only, could the decoding of error prediction be an artifact of the training sample, reflecting the fact that misses were not included in the training set? No, we do not think there is any way it could be an artefact. Our hypothesis is that correct trials contain information which is missing from miss trials. In other words, miss trials are in some way different from correct trials. Thus, it is crucial to use only correct trials in the training set. Please note that our approach is different from most conventional studies in which people directly discriminate correct and miss trials by feeding both types of trials to classifiers in the training phase and test the classifiers on the left-out correct and miss trials (i. e. , without any feature extraction; as in Bode and Stahl, 2014). While this standard approach might lead to a higher classification performance, we developed our new approach for two main reasons. First, in the real world and many vigilance studies, there is usually not enough miss data to train classifiers. Second, we wanted to directly test whether the neural representations of correct trials contain some information which is (on average) less observable in miss trials. The result of conventional methods can reflect general differences between correct and miss trials (i. e. , general level of attention, not time-locked to stimulus presentation), but cannot inform us about whether the difference reflects changes in information coding in the correct vs. miss trials; our approach allows this more specific inference. In our approach, we trained our classifiers on correct trials and tested them on both correct and miss trials. Crucially, we tested the trained classifiers only on unseen data for both correct and miss trials. Specifically, when testing the classifiers, we used only the correct trials which were not used in the training phase.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: ” – a biological membrane that separates the interior of all cells from the outside environment – a living thing that is too small to be seen with the naked eye YEAST – a group of single-celled fungi that may reproduce by budding, i. e. when a bud detaches and forms into a new fungus – a gram-positive, anaerobic, rod-shaped genus of bacteria named for their unique metabolism – a field of science that involves redesigning organisms for useful purposes by engineering them to have new abilities – an area of chemistry focused on designing products and processes that minimise or eliminate the use and generation of hazardous substances – a renewable energy source, made from organic matter or wastes, that can play a valuable role in reducing carbon dioxide emissions ’? Alan and his team work with strains of Saccharomyces and Propionibacteria. Saccharomyces is a yeast that feeds on sugars and can be found on fruit, such as on the skin of grapes. Interestingly, this yeast has been domesticated to make the species Saccharomyces cerevisiae, commonly known as brewers’ or bakers’ yeast. Propionibacterium are present in a wide variety of environments. Alan and his team are most interested in are those found in dairy products such as cheese. Ultimately, Alan and his team are interested in finding a means of overcoming our reliance on fossil fuels and are therefore investigating microbes as an alternative. “In our projects, Saccharomyces is used for the production of ethanol, whether that be for wine or for biofuels,” explains Alan. “Propionibacterium makes propionic acid, which can be used to keep yeast fermentations free from bacterial contamination. ” ’? The most obvious challenge is the production of ethanol by yeast. The hand gels we use in hospitals all contain ethanol, which helps to kill microbes – the ethanol breaks down the thin membrane around cells and kills them. This is something that Alan and his team are battling with. “When the yeast produces the ethanol, it is killed at an alcohol content of 15%-16%, which explains why wine doesn’t ferment to a higher concentration,” explains Alan.

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: 0 linearly reduced enteric CH4 emissions measured as g/kg DMI (P < 0. 01) by 39 and 64% for 69 g/d and 103 g/d inclusion, respectively. Linear reductions (P < 0. 01) of 64% to 65% were also observed in enteric CH4 emissions when expressed per kilogram of DM or organic matter digested. Respired CO2 as g/d linearly decreased (P = 0. 03) for cattle fed Alga 1. 0 but did not differ when expressed as g/kg of DMI (P ≥ 0. 23). Oxygen consumption did not differ between treatments for g/d and g/kg DMI (P ≥ 0. 19). In conclusion, feeding Alga 1. 0 reduced DMI up to 13. 3%, did not impact digestibility, and significantly reduced CH4 emissions up to 63%. Greenhouse gas (GHG) emissions, specifically methane, from the livestock sector have been a focal point of ruminant (cattle) research over the last decade due to the intrinsic nature for ruminant animals to emit methane during digestion and ruminal fermentation. One proposed method to reduce methane emission from the cattle sector is by feeding an algae feed product to cattle. Therefore, the aim of this experiment was to evaluate the effects of feeding varying inclusions of algae in a typical feedlot finishing diet on methane emission. When including algae as a feed additive, cattle methane production was reduced up to 65%. gas (GHG) emissions are a concern related to present and future climate and global warming potential. Methane (CH4), produced via enteric fermentation, is a GHG of interest to the agricultural industry due to its climate warming potential. Since the half-life of CH4 is relatively short, spending an average of 10 to 12 yr in the atmosphere, reducing CH4 emissions will have a positive short-term impact on climate (Allen, 2016). Reducing CH4 production from ruminants can be beneficial for the environment by lessening the emission impact on global warming, reducing the impact of animal production on GHG emissions, and using GWP* suggests reduction of CH4 by cattle would have rapid impacts. As of recent, there has been interest in using algae as a strategy to reduce enteric CH4 emissions from ruminants (Roque et al. , 2019, 2021).

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Therefore, there is no artefactual reason that the testing trials should be more similar to the training-phase trials for the correct compared to miss trials; the decoding prediction works because the correct testing trials have more similar neural representations to the correct training trials than the miss testing trials do. We have added an explanation of the difference between approaches to the manuscript to ensure this point is clearer to the reader. “Our method is different from the conventional method of error prediction, in which people directly discriminate correct and miss trials by feeding both types of trials to classifiers in the training phase and testing the classifiers on the left-out correct and miss trials (e. g. , Bode and Stahl, 2014). Our method only uses correct trials for training, which makes its implementation plausible for real-world situations since we usually have plenty of correct trials and only few miss trials (i. e. , cases when the railway controller diverts the trains correctly vs. misses and a collision happens). Moreover, it allows us to directly test whether the neural representations of correct trials contain information which is (on average) less observable in miss trials. We statistically compared the two types of trials and showed a large advantage in the level of information contained at individual-trial-level in correct vs. miss trials. ” 5. By accumulating classifiers across time, it looks like classifier prediction improves closer to deflection. However, this could also be due to the fact that the total amount of information provided to the classifier increased. Is there a way to control for the total amount of information at different timepoints (e. g. , by using a trailing window lag rather than accumulation), or contrast the classifier that derives from accumulating information with the classifier trained moment-by-moment? Although it is likely that some of the increase in information reflects increased attention as the dot approaches the object, we think primarily that yes, the improved prediction power closer to the central object is likely to be due to accumulation of information (Figure 6D) and it will decline if we use a subsample of the accumulated information. We took this approach as the main purpose of our prediction analysis was to predict the outcome of the trial with maximal accuracy. We added the following sentence to the manuscript to clarify the point. “In this analysis, the goal was to maximise the accuracy of predicting behaviour. For that purpose, we accumulated classification accuracies along the distances.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: “We would like to have yeast that is able to tolerate higher concentrations, both for the production of biofuels in a more cost-effective manner and also to add into fermentations, such as the secondary fermentation that champagne undergoes in the bottle. ” ’? Yes! The team has been working on a variety of different stresses that affect the cell membrane and they now understand the effects that ethanol and butanol, another type of alcohol, have on membranes at the molecular level. This allows the team to design strains that have the potential to be more resistant – these will be tested later this year, which could lead to some exciting results in the near future. ? The short answer is soon and everywhere! points out, for decades, microbes were used to produce compounds such as butanol before fossil fuels took over. In many ways, the work of biochemists is to get back to where we once were, but the only way of doing that is to match the effectiveness and efficiency of fossil fuels. “One consideration is cost but reducing toxicity should help with this and as people become increasingly environmentally conscious this should help drive government policy,” explains Alan. “There is a lot of amazing work going on to get microbes to make chemicals they wouldn’t normally make, and this broadens their uses significantly. ” in BiochemistryProteins and Lipids (AMPL): : Alan’s work is focused on investigating Saccharomyces and Propionibacteria with a view to improving the tolerance of microbes. The team hopes to improve efficiency and product yield of engineered cell-based factories. : UK Research and 2020 Marie Skłodowska-Curie actions is about understanding the chemical processes that occur within all living things. While this obviously concerns humans, plants and animals, it is also focused on viruses and bacteria, or even the chemistry of digestion and bodily fluids. The term biochemistry was coined in 1903 by a German scientist named Carl Neuberg. He is frequently referred to as the ‘father of modern biochemistry’ and pioneered research on fermentation. Much of his most important work is centred on examining and clarifying the metabolic complexities associated with a fermentation enzyme, known as zymase. Since then, there have been great leaps in the field of biochemistry.

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: Algae include a broad category of aquatic plants, including macroalgae, which can grow in fresh or salt water and vary greatly in chemical makeup among different types, such as being rich in protein or lipids (Makkar et al. , 2016). Red algae (Rhodophyceae) is the primary algae type reported for significant reduction in enteric CH4 emission due to high concentrations of haloforms (Machado et al. , 2016), with the Asparagopsis taxiformis species resulting in a reduction of up to 80% for enteric CH4 emissions for cattle consuming a low-forage diet (Roque et al. , 2021). Certain algae types have concentrated quantities of bromoform, the active metabolite that blocks the pathway for CH4 production during fermentation by rumen microbes (Machado et al. , 2016). Bromoform blocks key metalloenzymes via the Wolfe cycle (Glasson et al. , 2022), and when this pathway is blocked, methyl transfer of the cobamide-dependent enzyme needed to catalyze the final step for methanogenesis is inhibited (Fouts et al. , 2022; Glasson et al. , 2022). The inhibition of the CH4 production cycle is effective in reducing methane emissions. While bromoform is implicated in ozone depletion, the potential to impact the ozone layer is minimal (Jia et al. , 2022). With proper handling of bromoform, researchers believe it can be utilized as a feed additive that effectively reduces CH4 production from ruminants (Fouts et al. , 2022). Bromoform has been identified as a potential carcinogen (Glasson et al. , 2022) so it is important to note that not all algae strains are FDA-approved for feeding to cattle, and products fed to cattle that enter into the human food supply are required to meet stringent safety measures. The algae evaluated in the current experiment is trade-named 1. 0 (Alga Biosciences, Berkeley, CA) and is a proprietary feed additive containing stabilized bromoform in conjunction with generally recognized as safe ingredients. The objective of this experiment was to evaluate the effect of Alga 1. 0, an algae-derived manufactured product containing bromoform, on methane production and diet digestibility. It was hypothesized that increasing inclusions of Alga 1.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Moreover, as each classifier performs a binary classification for each testing dot at each distance, the accumulation of classification accuracies also avoided the spurious classification accuracies to drive the decision, providing smooth “accumulated” accuracies for predicting the behaviour. ” 6. Predicting miss trials: The implicit assumption here is that there is "less representation" for miss trials compared to correct trials (e. g. , of distance to object). But even for miss trials, the representation is significantly above chance. However, maybe the lower accuracy for the miss trials resulted from on average more trials in which the target was not represented at all rather than a weaker representation across all trials. This would call into questions the interpretation of a decline in coding. In other words, on a single trial, a representation might only be present (but could result in a miss for other reasons) or not present (which would be the case for many miss trials), and the lower averages for misses would then be the result of more trials in which the information was completely absent. It could be that the results of the subsequent analysis (predicting misses and correct responses before they occur) are in conflict with this more pessimistic interpretation. If we understand this correctly, here the classifier predicts Distance to Object for each individual trial, and Figure 6B shows that while there is a clear difference between the correct and miss trials, the latter can still be predicted above chance level but never exceed the threshold? If this is true for all single trials, this would indeed speak for a weak but "unused" representation on miss trials. But for this the authors need to show how many of the miss trials per participant had a chance-level accuracy (i. e. might be truly unrepresented), and how many were above chance but did not exceed the threshold (i. e. might have been "less represented"). This is a really good point. Yes, in principle, the average decoding levels could be composed of ‘all or none’ misses or graded drops in information, and it is possible that on some miss trials there is a good representation but the target is missed for other reasons (e. g. , a response-level error).

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Scientists have recently developed the ability to make novel chemicals with microbes, which is likely to lead to new breakthroughs in the future and, like any breakthrough, what this will eventually give rise to is unknown – but isn’t that exciting? Alan believes that new genetic tools such as have helped to create excitement in the field. (pronounced crisper) allows researchers to alter DNA sequences and therefore the function of genes. “I have been lucky that similar work to has been routine in yeast for decades,” says Alan. “I think a future approach that has a huge amount of potential is coupling biology and chemistry. This enable microbes to make a ‘platform’ chemical that can then be modified by green chemistry approaches to make a variety of compounds. ” Apart from the aforementioned ‘father of modern biochemistry’, Carl Neuberg, there is Isaac Asmiov, and the wonderfully named Hodgkin. All are worth reading more about, but one of the biochemists Alan admires is closer to home. “I have some colleagues that I really admire and they would be embarrassed if I named them; so, obviously I will! ” says Alan. “Bill is an excellent scientist and colleague and does a huge amount to mentor early career researchers. It’s not just about the science, it’s about building a community of people who all work well together. ” And what about those Alan has not worked with? “Well, they aren’t particularly famous, but I am a big fan of Singer and Garth L. Nicolson who developed what is known as the ‘fluid mosaic’ model of cell membranes,” says Alan. “They were the first to really propose how cell membranes function in the manner we understand them today and this underpins nearly all of the work in my lab. ” Absolutely! Part of the process is deciding – or at least considering – whether you want to become a biochemist. Alan is of the firm belief that if you enjoy finding out how things work on a molecular level and then applying this to the real world, biochemistry is the field for you. “The interface of the physical and life sciences sets you up for a huge range of careers,” says Alan. “And there are plenty of opportunities in both academic and non-academic labs.

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: 0 would further reduce enteric CH4 production from cattle without negatively affecting diet digestibility. and care and experimental procedures were approved by the University of Nebraska-Care and (Protocol 2188). Twelve nonlactating, nonpregnant Jersey cows (490 ± 19 kg body weight [BW]) were used to determine the effect of Alga 1. 0 (Alga Biosciences) inclusion on the production of enteric CH4 and CO2 emissions. cows utilized in this experiment were previously trained for emission monitoring via headbox which alleviated the need for an additional training period for new cattle. In addition, cattle fed this product would not be allowed to enter the food chain so were suitable to use for this study. The cows were nonlactating and of a type where intake would mimic beef systems. Cows were housed in individual tie-stalls in an environmentally controlled area at the Facility in the Complex at the University of Nebraska-Lincoln (Lincoln, NE). Since the Alga 1. 0 product was not approved for feeding to cattle at the time of this experiment, no meat from these animals entered the food supply chain and cows were euthanized and incinerated upon experiment completion. The experimental design was a replicated 3 × 3 period Latin square with four squares, with three periods each lasting 21 d. This experiment was conducted with all 12 cows collected simultaneously. Prior to experiment initiation, nonlactating cows were grouped by dry matter intake (DMI), calculated from the average of previous intake over a 4-wk period. Treatments included 0 g (negative control), 69 g, and 103 g (DM basis) of Alga 1. 0 fed daily. This was equal to approximately 0% (negative control), 0. 77%, and 1. 2% of diet DM and 0%, 0. 8%, and 1. 3% of diet OM. Modified distillers grains plus solubles (MDGS) was used as the carrier due to the palatability of MDGS and was delivered via top-dress on the ration at 0. 454 kg DM daily. This approach was done to avoid any cross-contamination of the feed mixer used to deliver feed to all cows. In addition, top-dressing allowed for a targeted amount to be delivered daily.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: As neural data are noisy and multivariate decoding needs cross-validation across sub samples of the data, and because each trial, at each distance, can only be classified correctly or incorrectly by a two-way classifier, we tend not to compare the decoding accuracies in a trial-by-trial manner, but rather on average (Grootswagers et al. , 2017). However, if we look at an individual dataset and examine all the miss trials (averaged over the 15 distances and cross-validation runs) in our distance-to-object decoding, we can get some insights into the underlying distributions. We show the distribution of individual trial decoding accuracies for all participants on correct (Figure 5—figure supplement 1A) and miss (Figure 5—figure supplement 1B) trials. The vertical axis shows the number of trials in each accuracy bin of the histogram and the horizontal axis shows the decoding accuracy for each trial obtained by averaging its decoding accuracies over cross-validation folds (i. e. , done by subsampling the correct trials into train and test sets and repeating the procedure until all correct trials are used once as training data and once as testing data) and distances. We calculated the percentage of miss trials for which there was strong evidence (BF>10) for above-chance decoding accuracies. To do this, we generated a null distribution with 100*N trials, where we produced 1000 decoding accuracies for each trial by randomizing the labels of distances for that trial. We used the same procedure for Bayes analyses as detailed in the manuscript. The histograms of individual miss trials suggest a single distribution centred around chance decoding or slightly above (Figure 5—figure supplement 1B). This means that on an individual miss trial, there may be higher or lower decoding, but it is nowhere near the consistent high decoding levels we see for correct trials (Figure 5—figure supplement 1A). This seems consistent with an interpretation that on (most) miss trials, information is less present than on correct trials. Presumably it is this difference that allows our second level classifier to successfully predict the behavioural outcome on >80% of trials. In contrast, for the correct trials, all trials (100%) for all subjects showed above-chance (>50%) decoding accuracy, with average accuracies around 80%.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: For example, biotechnology is a rapidly-growing sector in the UK. ” • If you are genuinely interested in studying biochemistry, Alan highly recommends Aston University, where he is the Programme Director. • Society is a dedicated resource to the field of biochemistry, which includes tonnes of useful info on how to develop a career in molecular bioscience. • To keep abreast of developments in the field of biochemistry and biology, check out the of Biology’s website. This year is the 10th anniversary of this important organisation and there is plenty to entertain, educate and inform. • Biochemists can earn anywhere between £26,500 to £60,000 per annum in the UK, depending on their level of experience. will usually need a science degree. Relevant degree subjects include: biochemistry, biotechnology, biopharmaceuticals, chemical and molecular biology, microbiology genetics and molecular biology. ? My parents were both teachers, so I think I was encouraged to stay in education from an early age. I was always interested in biology but probably didn’t know what I wanted to do beyond something to do with science and learning new things. I kind of fell into an academic career by following opportunities that arose. ? I have always been interested in how organisms work on a molecular level. Essentially, all of life is biochemistry. I did my PhD working on yeast, although this focused more on molecular biology (working with DNA and making new strains). I then did research in the at the University of Oxford for about seven years, which opened my eyes to the wide variety of biochemistry out there. ? I consider myself to be slightly obsessive, detail focused and I always ask questions! These characteristics definitely help, although you certainly don’t need all of them to be a biochemist. As long as you are interested in the science you will be fine! Perhaps surprisingly, it is the same answer for both! No two days are the same, which is great in terms of being new and different and keeping you interested, but also bad in the sense that you never get to stand still and never really feel like you have everything under control! Be interested in a wide variety of things – you never know when one bit of information will be useful in a different context. One of my students once described me as having a “wide approximate knowledge” meaning that I know a little bit about a lot.

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: Prior to experiment initiation, cows were transitioned to a basal diet by feeding three intermediate diets (grass hay replaced by corn in each subsequent diet) fed for 7 d each. The basal diet consisted of 60% dry-rolled corn (DRC), 20% corn silage, 15% MDGS, and 5% supplement (DM basis; Table 1). Dietary ingredients for the primary diet (corn silage, and supplement) were added to a Ranger (American Calan, Inc. , Northwood, NH), mixed, and fed as a total mixed ration (TMR) once daily with a target refusal rate of 5%. The top-dress containing Alga 1. 0 was hand-mixed and added immediately after TMR delivery; where a small indentation was formed in the feed delivery, in which top-dress was placed to reduce the cattle’s ability to sort. Each period included 16 d of ad libitum diet adaptation, followed by 4 d of total collection where diets were fed at 100% of the previous 6 d intake to limit refusal. Table 1. Open in new tab Ingredient and nutrient composition of finishing diet including 0, 69, or 103 g/d of Alga 1. 0 fed to Jersey cows Ingredient. Percent of Diet DM1. Dry-rolled corn60Corn silage20MDGS215Supplement35 Fine ground corn2. 203 Limestone1. 68 Tallow0. 125 Urea0. 60 Salt0. 30 Trace mineral premix0. 05 Vitamin ADE0. 015 Rumensin-9040. 0165 Tylan-4050. 011Ingredient. Percent of Diet DM1. Dry-rolled corn60Corn silage20MDGS215Supplement35 Fine ground corn2. 203 Limestone1. 68 Tallow0. 125 Urea0. 60 Salt0. 30 Trace mineral premix0. 05 Vitamin ADE0. 015 Rumensin-9040. 0165 Tylan-4050. 0111All treatments received the same basal diet with the addition of Alga 1. 0 as a top-dress (0, 69, or 103 g/d) mixed with MDGS at 0. 454 kg DM/cow daily. 1. 0 inclusion was approximately 0. 77% and 1. 2% of diet DM for 69 and 103 g/d consumption on average, respectively.

Output: pass


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: This suggest that as opposed to missed trials, in which some trials showed some distance information and some did not, on correct trials, all trials reflect the task-related information. In order to quantify the overlap between correct and miss trials in individual trial level (as opposed to group-level Bayes factor analysis in the manuscript (Figure 5)), we calculated the Cohen’s d (Cohen, 1969) between the two distributions. As the results show (Figure 5—figure supplement 2C), there is a large difference (d >2) between the two distributions for every participant and condition. D values were mostly higher than 3 which corresponds to less than 7% overlap between decoding accuracies obtained for the correct and miss trials. Overall, this additional analysis demonstrates that although the miss trials vary somewhat in levels of information (as measured by decoding), with some trials representing the distance information while others do not represent the distance information at all, very few miss trials are as informative as the least informative correct trials (the distributions overlap by less than ~7%). The miss trials with high decoding are presumably those on which our second level classifier makes the wrong prediction. We have revised the description in the manuscript to make this clearer and added the following paragraph and analyses to the manuscript. “In principle, the average decoding levels could be composed of ‘all or none’ misses or graded drops in information, and it is possible that on some miss trials there is a good representation but the target is missed for other reasons (e. g. , a response-level error). As neural data are noisy and multivariate decoding needs cross-validation across sub samples of the data, and because each trial, at each distance, can only be classified correctly or incorrectly by a two-way classifier, we tend not to compare the decoding accuracies in a trial-by-trial manner, but rather on average (Grootswagers et al. , 2017). However, if we look at an individual dataset and examine all the miss trials (averaged over the 15 distances and cross-validation runs) in our distance-to-object decoding, we can get some insights into the underlying distributions (Figure 5—figure supplement 1). Results showed that, for all participants, the distribution of classifier accuracies for both correct and miss trials followed approximate normal distributions.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: us Grants & prizes & media About us Join our communityMeet our membersUndergraduate and Master’sour membersPhysiology ECR Toolkit for lecturesPrizesEvents listingMeeting and workshop supportAdvice for attending your first meetingAwardGrant for carersExhibitors and sponsorshipProceedings abstractsPast eventsmagazineBlogPodcastYouTube channelNewsOur newslettersArtificial IntelligencePhysiology 2024: Manifesto for the General Election& and medicinePolicy ConsultationsWhat do physiologists do? Supporting your next career moveResearchTeachingScience communicationSport and exercise scienceHealthcareIndustryJobsWhat we doWhat is physiology?

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: 0560. 330. 99DE/GE272. 371. 371. 01. 400. 360. 951All treatments received the same basal diet with the addition of Alga 1. 0 as a top-dress (0, 69, or 103 g/d) mixed with modified distillers grains plus solubles at 0. 454 kg DM/cow daily. 1. 0 inclusion was approximately 0. 77% and 1. 2% of diet DM for 0, 69, and 103 g/d inclusions, respectively. 2DE/GE, digestible energy (Mcal/d)/gross energy (Mcal/d) × 100. BW and BCS of the cow were not impacted (P ≥ 0. 17) by the inclusion of Alga 1. 0 (data not shown). Average BW during the study was 497 kg and BCS averaged 3. 87. Both increased across periods (time of study) but the study was not designed to assess the impact of treatment on BW due to short period durations. This outcome in BW matches observations reported by Roque et al. (2019), who reported no differences in BW of dairy cows when including A. armata at varying inclusions in the diet over 21-d periods. et al. (2019) and the current experiment were designed as 3 × 3 Latin squares with 21-d periods, therefore, BW and BCS differences between treatments over a 21-d period would not be expected. The inclusion of Alga 1. 0 in the diet linearly reduced enteric CH4 emission measured as g/d (P < 0. 01; Table 5) and g/kg DMI (P < 0. 01). Alga 1. 0 inclusion reduced enteric CH4 emission by 46% or 76. 7 g/d when fed at 69 g/d and 73% or 120. 6 g/d when fed at 103 g/d compared to the negative control. Methane yield (g/kg DMI) decreased 6. 7 and 10. 9 g/kg DMI with the inclusion of Alga 1. 0 at 69 and 103 g, respectively, accounting for a 39% reduction in CH4 emissions expressed as g/kg of DMI with 69 g/d Alga 1. 0 and 64% reduction (g/kg of DMI) with 103 g/d Alga 1. 0 compared to the negative control.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: However while the distribution of decoding accuracies for correct trials was centred around 80%, the decoding accuracies for individual miss trials were centred around chance-level. We evaluated the difference in the distribution of classification accuracies between the two types of trials using Cohen’s d. Cohen’s d was approximately 3 or higher for all participants and conditions, indicating a large (d > 2; Cohen, 1969) difference between the distribution of correct and miss trials. Therefore, although the miss trials vary somewhat in levels of information, very few (< 7%) miss trials are as informative as the least informative correct trials. These results are consistent with the interpretation that there was less effective representation of the crucial information about the distance from the object preceding a behavioural miss. ” 7. The relationship between the vigilance decrement and error prediction. Is vigilance decrement driving the error prediction? That is, if errors increase later on, and the signal goes down, then maybe the classifier is worse. Alternatively, maybe the classifier predictions do not necessarily monotonically decrease throughout the experiment. Is the classifier equally successful at predicting errors early and late? Thanks for the nice question. Our error prediction results initially were obtained from the whole dataset, including all blocks of trials. To answer the reviewer’s question, we now split the blocks into the first 5 (early) and the last 5 (late) blocks and repeated the error prediction procedure on the five early and late blocks separately. To remove the potential confound of the number of trials, we equalised the number of trials across the early and late time windows. As decoding of distances decreased along the time course of the experiment on correct trials (Figure 3), we would predict that there should be less difference in decoding of correct and miss trials in the later vs earlier blocks. The new analysis bears this out: Prediction accuracy for the trial outcome (correct vs miss) declined in later stages of the experiment (moderate to strong evidence (BF>3) for higher predictability for the trial outcome in early vs. late blocks of the experiment). Importantly, even with the decline in predicting accuracy, it is still possible to predict the behavioural outcome in the late blocks with well above-chance accuracy. We have added these results to the supplementary material of the paper (Figure 6—figure supplement 1). “The prediction of behavioural outcome (Figure 6) was performed using the data from the whole dataset.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Strategy 2023-27Excellence in physiologyNewsOur partnersOur historyBoard of Trustees, Committees & GroupsGovernanceStaffDiversity and InclusionContact usBecome a memberCheck out our Training HubResources for Society RepsCardiac & Vascular PhysiologyEpithelia & Membrane TransportHuman, Environmental & & TeachingMentoringMembers reflect on COVID-19Member supportPhysiology and COVID-19Questions from the front lineCOVID-19 Advisory Panelof Physiology’s ClubReturning to the labEngaging with your ThemeAwardEducation and Teaching AwardResearch and for carersInformation for Grant recipientsand LectureThe Bayliss-Lecture for Mid--Huxley-for Diversity in for Early-’s LectureThe Sharpey-Lecture for Translational PhysiologyParticipation in for and -of to --’ SymposiumGrant for carersof Physiology AccessLatest issueof Physiology NewsContributeand HealthRoadmap for ResilienceSupport the consensus statementClimate and NetworkDeveloping a Human-centred StrategyPhysiology, Heat and Mental HealthClimate Emergency: Physiology & Climate Changeand Research into health and workGrowing Older, BetterSupporting the development of public health guidance for long -(REF)Sport & Education: Impact on the UK EconomyScotland at the Heart of ChallengesPhysiology and Funding in IrelandContribution of physiology education and training to the UK economyMedical ObjectivesPhysiology and the COVID-19 responseAnimal researchThe use of animals in educating the next generation of life science researchersAge 16–19Undergraduate and MastersPhD and beyondReproduction and developmentImpact of exercise on healthMicrobiomes in the gutRespiration and lung functionSleep and circadian rhythmsExtreme environmentsSpace physiologyConservation of lifeExcellence in Physiology AwardSociety’s blue plaqueshighlightsSociety timelineObituariesOral historiesWhat is physiology?

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: These results indicate that feeding Alga 1. 0 reduces CH4 emissions from cattle consuming feedlot diets typical to the U. S. Results from this experiment are similar to previous literature, where increasing levels of bromoform-containing algae reduced CH4 emissions (Roque et al. , 2019; Kinley et al. , 2020). Roque et al. (2019) included A. armata algae at 0. 5% and 1. 0% OM in a high-forage diet common to the U. S. dairy industry, reporting a 26. 4% and 67. 2% reduction in CH4 production (g/d) and 20. 5% and 42. 6% reduction in CH4 production expressed as g/kg of DMI for 0. 5% and 1. 0% OM inclusion, respectively. The overall reduction in CH4 production for the current experiment was similar to CH4 reductions reported by Roque et al. (2019), however, the algae inclusion levels for the current experiment were slightly greater at 0. 8% and 1. 3% of OM. Similar responses were observed when evaluating CH4 emissions as a proportion of DM and OM digestion. Expressed as g/kg of DM digestion, CH4 emissions decreased linearly (P < 0. 01) by 63. 7% when Alga 1. 0 was increased. Very similar linear (P < 0. 01) reductions of 65% for CH4 emissions expressed as g/kg of OM digested were observed as Alga 1. 0 increased. These data suggest that CH4 emissions reduction due to Alga 1. 0 are independent of lower intake as CH4 emissions expressed as g/kg of DMI decreased 64%. Additionally, observing a 65% reduction in CH4 emissions when expressed as g/kg of OM digested suggested energy available to cattle would lead to reductions of CH4 emissions when expressed as intensity (g/kg of gain) although gain was not adequately addressed with this experimental design. Table 5. Open in new tab Effect of Alga 1. 0 when fed at 0, 69, or 103 g/d to Jersey cows fed a feedlot finishing diet on gaseous loss and gaseous consumption. Treatment1. SEM. Contrasts. Control. 69 g. 103 g. Linear. Quadratic. CH4, g/d164. 988. 144. 313. 8<0. 010.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: However, it is possible that the prediction would not be as accurate in later stages of the experiment (compared to the earlier stages) as the decoding performance of the distance information declined in general in later stages (Figure 3B). To test this, we performed the behavioural prediction procedure on datasets obtained from the first 5 (early) and the last 5 (late) stages of the experiment (Figure 6—figure supplement 1). There was strong evidence for a decline in the prediction power in the late vs. early blocks of trials. However, even with the decline in prediction accuracy, it is still possible to predict the behavioural outcome in the late blocks with well above-chance accuracy (up to 75%). ” 8. When decoding distance, active decoding declines from early to late, even though performance does not decline (or even slightly improves from early to late). This discrepancy seems hard to explain. Is this decline in classification driven by differences in the total signal from early to late? Thanks for the question. We explicitly define the vigilance effects as the difference between Active and Monitoring conditions to ensure that we are not interpreting general task effects like this one as vigilance decrements. This is important because otherwise effects that are not specific to maintaining vigilance (i. e. , sustaining attention in the situation where only infrequent responses are necessary) could be misinterpreted. In this case, it could be driven by a number of general factors that are not specific to vigilance such as fatigue, but also equipment effects like the MEG recording system fluctuations in baseline (e. g. , due to warming up). Our crucial comparisons for both behaviour and neural correlates are the increase in ‘miss rate’ and ‘reaction time’ for Monitoring vs. Active from early to late blocks and more decline in distance decoding information (from early to late blocks) for Monitoring than for Active (Figure 3B. Interaction between and Time on the task). We have now added the following sentence to Discussion and amended the manuscript to ensure this is clear. “Note that our vigilance decrement effects are defined as the difference between Active and Monitoring conditions, which allows us to be sure that we are not interpreting general task (e. g. , participant fatigue) or hardware-related effects as vigilance decrements.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Meet our membersBasis of 202419 – 20 December 2024 Join us Join our community Meet our members Undergraduate and Master’s for & Epithelia & Human, Environmental & & our members reflect on COVID-19 Member support Physiology and COVID-19 Questions from the front line COVID-19 of Physiology’s to the lab Engaging with your for & prizes and Research and for carers Information for Grant recipients Prize lectures and Bayliss-Lecture for Mid--Huxley-for Diversity in for Early-’s Sharpey-Lecture for Participation in for and -of to listing Meeting and workshop support Two--’ for carers Advice for attending your first meeting for carers Exhibitors and sponsorship Proceedings abstracts Past events Journals & media of magazine Latest issue of channel newsletters Intelligence and 2024: Manifesto for the & for the consensus statement Climate and a Human-centred Strategy Physiology, Heat and Climate Emergency: Physiology & and Research into health and work Growing Older, the development of public health guidance for long - Framework (REF) Sport & Education: Impact on the UK at the Heart of Funding in of physiology education and training to the UK economy Physiology and the COVID-19 response research The use of animals in educating the next generation of life science researchers Precision medicine do physiologists do? Supporting your next career move Age 16–19 Undergraduate and Masters PhD and beyond and development Impact of exercise on health Microbiomes in the gut Respiration and lung function Sleep and circadian rhythms Extreme environments Space physiology Conservation of life communication Sport and exercise science us What we do What is physiology?

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: Open in new tab Effect of Alga 1. 0 when fed at 0, 69, or 103 g/d to Jersey cows fed a feedlot finishing diet on gaseous loss and gaseous consumption. Treatment1. SEM. Contrasts. Control. 69 g. 103 g. Linear. Quadratic. CH4, g/d164. 988. 144. 313. 8<0. 010. 81CH4, g/kg DMI17. 010. 36. 11. 62<0. 010. 65CH4, g/kg DM digested23. 714. 98. 62. 25<0. 010. 52CH4, g/kg OM digested23. 514. 48. 32. 17<0. 010. 56CO2, g/d842078357719419. 50. 030. 68CO2, g/kg DMI87389191440. 90. 230. 75O2 consumption, g/d572950255390368. 10. 260. 19O2 consumption, g/kg DMI60356462843. 00. 780. 21RQ21. 041. 021. 020. 0120. 170. 50. Treatment1. SEM. Contrasts. Control. 69 g. 103 g. Linear. Quadratic. CH4, g/d164. 988. 144. 313. 8<0. 010. 81CH4, g/kg DMI17. 010. 36. 11. 62<0. 010. 65CH4, g/kg DM digested23. 714. 98. 62. 25<0. 010. 52CH4, g/kg OM digested23. 514. 48. 32. 17<0. 010. 56CO2, g/d842078357719419. 50. 030. 68CO2, g/kg DMI87389191440. 90. 230. 75O2 consumption, g/d572950255390368. 10. 260. 19O2 consumption, g/kg DMI60356462843. 00. 780. 21RQ21. 041. 021. 020. 0120. 170. 501All treatments received the same basal diet with the addition of Alga 1.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: low), the connectivity is driven by the correlation between the patterns of decoding accuracies either across time (Coutanche and Thompson-Schill, 2013) or across conditions (Kietzmann et al. , 2018). We used the latter (i. e. , RDMs) here to study connectivity. This is a critical difference because high classification values in two regions will not necessarily correspond to high connectivity in our analysis. Accordingly, the difference in classification levels between ‘correct’ and ‘miss’ trials should not determine the connectivity – it’s more the consistency of the pattern (see below example). Our connectivity relies on (Spearman’s) correlation (which normalizes absolute amplitude), and as such it is unaffected by absolute decoding values in the pairs of input vectors: connectivity will be high only if the two areas encode the information across conditions similarly rather than if they code the information very efficiently across all conditions (i. e. , maximum decoding values). For example, assume that we have four brain areas A, B, C and D with (simplified and vectorized) distance RDMs (as in our work) with decoding values of [95 91 97 92], [96 98 99 94], [57 51 55 54], [58 52 59 55], respectively. The inter-area correlation/connectivity matrix would be as in Author response table 1. As you can see, a pair of brain areas with absolutely higher decoding values (A and B), but less similarity of patterns in their RDMs can led to small correlations/connectivity (0. 4) while pairs of brain areas which have small decoding values but more similar patterns of decoding in their RDMs (C and D) resulted in much higher correlation/connectivity (0. 8). Therefore, rather than the absolute decoding values (i. e. , whether the pair of areas encode the information or not), their patterns in the RDMs determine how/if they are coding information similarly and are potentially connected. Author response table 1 Connectivity (correlation) matrix obtained from four sample areas. 10. 40. 81B0. 4100. 4C0. 8010. 8D10. 40. 81 Although mathematically our connectivity should be unaffected by absolute decoding values, we acknowledge that potentially noisier patterns of distance information in the brain on miss vs. correct trials could result in apparently lower connectivity for misses.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Strategy 2023-27 Excellence in physiology Excellence in Society’s blue plaques nomination form partners Our history Historical highlights Women in physiology The first Foundation of Society mascot Society timeline histories Board of Trustees, Committees & us this issue 2015 - 101 issue Previous issues 131-133 Issue 133 Issue 132 Issue 131 121-130 Issue 130 Issue 129 Issue 128 Issue 127 Issue 126 Issue 125 Issue 124 Issue 123 Issue 122 Issue 121 111-120 Issue 120 Issue 119 Issue 118 Issue 117 Issue 116 Issue 115 Issue 114 Issue 113 Issue 112 Issue 111 101-110 Issue 110 Issue 109 Issue 108 Issue 107 Issue 106 Issue 105 Issue 104 Issue 103 Issue 102 Issue 101 91-100 Issue 100 Issue 99 Issue 98 Issue 97 Issue 96 Issue 95 Issue 94 Issue 93 Issue 92 Issue 91 81-90 Issue 90 Issue 89 Issue 88 Issue 87 Issue 86 Issue 85 Issue 84 Issue 83 Issue 82 Issue 81 71-80 Issue 80 Issue 79 Issue 78 Issue 77 Issue 76 Issue 75 Issue 74 Issue 73 Issue 72 Issue 71 61-70 Issue 70 Issue 69 Issue 68 Issue 67 Issue 66 Issue 65 Issue 64 Issue 63 Issue 62 Issue 61 51-60 Issue 60 Issue 59 Issue 58 Issue 57 Issue 56 Issue 55 Issue 54 Issue 53 Issue 52 Issue 51 41-50 Issue 50 Issue 49 Issue 48 Issue 47 Issue 46 Issue 45 Issue 44 Issue 43 Issue 42 Issue 41 31-40 Issue 40 Issue 39 Issue 38 Issue 37 Issue 36 Issue 35 Issue 34 Issue 33 Issue 32 Issue 31 21-30 Issue 30 Issue 29 Issue 28 Issue 27 Issue 26 Issue 25 Issue 24 Issue 23 Issue 22 Issue 21 11-20 Issue 20 Issue 19 Issue 18 Issue 17 Issue 16 Issue 15 Issue 14 Issue 13 Issue 12 Issue 11 1-10 Issue 10 Issue 9 Issue 8 Issue 7 Issue 6 Issue 5 Issue 4 Issue 3 Issue 2 Issue 1 Winter 2015 - 101 Full issue Why are we still using animals to study human neurophysiology? Could stem cells completely replace animal models in research one day? are we still using animals to study human neurophysiology? Could stem cells completely replace animal models in research one day? and Animal models have been central to our understanding of human physiology, but human stem cell based models may help us to further our knowledge and tackle devastating diseases such as Alzheimer’s disease.

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: 0 as a top-dress (0, 69, or 103 g/d) mixed with modified distillers grains plus solubles at 0. 454 kg DM/cow daily. 1. 0 inclusion was approximately 0. 77% and 1. 2% of diet DM for 0, 69, and 103 g/d inclusions, respectively. 2RQ, respiratory quotient, liters per day of CO2 production/liters per day of O2 consumption. Kinley et al. (2020) observed reductions of 9. 0%, 38. 0%, and 98. 0% for CH4 production (g/kg DMI) with incremental inclusions of A. taxiformis at 0. 05%, 0. 10%, and 0. 20% OM, respectively. Roque et al. (2021) fed A. taxiformis at 0. 25% and 0. 50% OM inclusion and observed a 69. 8% and 79. 8% reduction in CH4 production (g/kg DMI). The ability of a seaweed to reduce enteric CH4 production when fed in ruminant diets is largely dependent on bromoform concentration in the diet. When comparing Kinley et al. (2020), Roque et al. (2021), and the current experiment, although the reductions of CH4 production were not consistently linear with increasing inclusion of bromoform in the diet, all three experiments reported dose-dependent reductions in CH4, where the greatest inclusion of bromoform resulted in the greatest reduction in methane. Bromoform is a volatile compound that can lose potency when exposed to improper handling/holding temperature and airflow. A loss in bromoform potency may result in reduced ability of the product to alter ruminal CH4 production. Proper storage, handling, and shelf-life understanding of algae is critical when considering the efficacy of the product as an enteric methane mitigation strategy. Stefenoni et al. (2021) evaluated the impacts of storage length and method on bromoform concentration in algae, reporting up to 85% reduction in bromoform concentration when stored for long periods of time (4 mo or greater) and exposed to air and improper temperature. In the current experiment, Alga 1. 0 product was frozen at −4 °C in dark conditions. Alga 1.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: We therefore added the following paragraph to the manuscript acknowledging this possibility: “While our connectivity is unaffected by the absolute levels of information encoding in the brain on miss vs. correct trials, potentially noisier patterns of information encoding in miss (vs. correct) trials could result in the lower level of connectivity observed on miss (vs. correct) trials. Therefore, the lower level of connectivity for miss vs. correct trials observed here could result from the pair of regions representing two distinct sets of information (i. e,. becoming in some sense less connected) or representing similar information but distorted by higher level of noise. ” The more parsimonious interpretation here is that information might have been represented across all channels at this time. The authors show no evidence that only these two (arbitrarily selected) "regions" encode the information while other do not. To show evidence for meaningful connectivity, (a) the spread of information should be limited to small sub-regions, and (b) the decoding results in one "region" should predict the results in another region in time (as for DCM). a. Yes, it is possible that the whole brain may process the information with the same pattern of decoding but changing the ROIs to smaller ones would not rule out this potential scenario (which applies to all connectivity analyses, even the conventional ones). We avoid making claims about the spatial specificity of our connectivity effect, as we are using MEG (as reflected in the names we chose for the regions: peri-occipital and peri-frontal). Please note though that these sub-regions were not arbitrary, but rather based on areas known to be involved in vision and attention, and based on previous attention work which showed a flow of information across the two areas (Goddard et al. , 2016; Goddard et al. , 2019). It is very important in the interpretation of our result that, rather than making any claims about the absolute existence or magnitude of potential connectivity in the brain, we compared our connectivity indices across conditions. In other words, we do not seek to test whether connectivity exists or not between our ROIs, but rather whether any such connectivity varies with our manipulations of vigilance. Therefore, even in if the entire brain was responding similarly, the modulation of the connectivity metric is only explainable by the manipulations across our conditions. b.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Stem cell-derived brain cells have emerged as a powerful tool to model neuronal behaviour and disease pathology; their increasing use in drug discovery promises to help speed up drug screening and reduce the number of animals used at the earliest stages of testing. discuss the utility of these cells, potential pitfalls and recent developments in the field. A scarce resource Logically, human tissue would be the best platform for the study of human diseases, although for obvious reasons, such tissue is not always readily accessible. This is particularly apparent for the study of the human central nervous system (CNS). Consequently, animals as diverse as worms, flies and various mammals are currently used to model human physiology and neurophysiology. These systems have given us a great deal of information regarding our normal biological processes, but some aspects of our physiology and neurophysiology are uniquely ‘human’. This is borne out by difficulties in reproducing many diseases in animals that affect significant numbers of the elderly, such as Alzheimer’s disease. Indeed, not even the latest and most sophisticated transgenic models recapitulate all of the features of this complex neurodegenerative condition. Clearly, such conditions require highly specialised and relevant experimental platforms for us to even begin to understand their complex aetiology. Compellingly, in the public as well as the medical and scientific minds, stem cell technology now has a powerful appeal in the search for such platforms. The potential of stem cells Stem cells are unspecialised cells that are capable of self-renewal and are able, under the correct conditions, to differentiate into all of the different cells within the body. Since the derivation of the first human embryonic stem cell line (hESCs) in 1998, the field of stem cell biology has rapidly expanded. Furthermore, the development of induced pluripotent stem cells (iPSCs) has further increased the potential use of stem cells whilst avoiding ethical issues associated with the use of embryos. This revolutionary technique allows scientists to produce stem cells from the skin cells of patients thus providing highly relevant patient/genotype specific models in which scientists can study disease processes. Stem cells have now been used to produce a variety of different cell types and tissues such as brain, heart and gut. For many scientists, the production of neurons has been of great interest as it allows researchers access to human brain cells.

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: The energy density provided to cattle on a feedlot diet is high, and therefore, a high RQ is expected as the cattle are not catabolizing body tissue for energy, where an RQ of one indicates that carbohydrates are the primary source of energy and that the animals are likely to accumulate lipids (Forbes et al. , 1927). Therefore, the RQ values observed in the current experiment were what is expected for cattle consuming a high-concentrate feedlot diet. fed a finishing diet supplemented at 69 or 103 g/d with this proprietary algae product containing bromoform had reduced CH4 emissions compared to cattle that were not supplemented. Reductions of 39% and 63% were observed for CH4 yield (g/kg DMI) with the inclusion at 69 and 103 g/d, respectively. Nutrient intakes were reduced due to DMI being reduced by 10. 1% and 13. 3% with the increasing inclusion of Alga 1. 0 in the diet, however, digestibility and dietary energy concentration of the diet were not impacted suggesting a 65% reduction in methane per unit of OM digested. Further research is needed to quantify the impact of this supplement on cattle performance. Feeding this product in a high-concentrate diet is an effective CH4 mitigation tool when fed to finishing cattle if appropriate regulatory approvals are obtained. 2016. Rebalancing the global methane budget. Nature. 538:46–48. doi:10. 1038/538046a2000. Official methods of analysis. 17th ed. . 1927. A comparison of the direct measurement of the heat production of cattle with the computation of the heat production by the respiratory-quotient method. J. Agric. Res. 34:865–878. Energy content of reduced-fat dried distillers grains with solubles for lactating dairy cows. J. Dairy Sci. 98:7142–7152. doi:10. 3168/jds. 2014-92262022. Enteric methane mitigation interventions. Transl. Anim. Sci. 6:1–16. doi:10. 1093/tas/txac0412022. Benefits and risks of including the bromoform containing seaweed Asparagopsis in feed for the reduction of methane production from ruminants. Algal Res. 64:102673. doi:10. 1016/j.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: The Bayes-factor plots in the decoding results figures are so cramped that it is very difficult to actually see the individual dots and to unpack all of this (e. g. , Figure 3). Could this complexity be somehow reduced, maybe by dividing the panels into separate figures? The two top panels in Figure 3B should also include the chance level as in A. It looks like the accuracy is very low for unattended trials, which is only true in comparison to attended trials, but (as also shown in 1) it was clearly also encoded in unattended trials, which is very important for interpreting the results. We have extensively revised our figures, and expanded the Bayes plots; we hope they are now clear. We have split the panels in figures into Active and Monitoring panels, added the chance level line, and the pre-stimulus decoding values. We also reduced the density of dots by down-sampling, and improved their appearance using a log scale and colour coding. Regarding the relative differences, our design focuses on these because this allows us to be more specific about the effects that reflect actual vigilance decrements. This differs from many vigilance studies, and provides the opportunity for more specific inference. We have ensured this is clearer in the revision. We hope the revised text and figures enhance the interpretability of the relative differences. 12. While this is methodologically interesting work, there is no convincing case made for what exactly the contribution of this study is for theories of vigilance. It seems that the findings can be reduced to that a lack of decodability of relevant target features from brain activity predicts that participants will miss the target. This alone, however, does not seem to be very novel. Even if the issues above are addressed, the study only demonstrates that with less attention to the target, there is less evidence of representations of the relevant features of targets in the brain. The authors also find the expected decrements for rare targets and when participants do not actively monitor the targets. How do these findings contribute to "theories of vigilance", as claimed by the authors? This work makes three clear contributions to vigilance research. First, we present a novel multiple-object-monitoring paradigm that clearly evokes specific vigilance decrements in a context that mimics real-world monitoring scenarios. Our design controls for general experiment-level effects that are not specific to vigilance conditions, which as mentioned above, is surprisingly rare in the vigilance literature (which we now make clearer in the revision).

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: More importantly, the production of specific populations such as cortical or dopaminergic neurons has allowed researchers to investigate the activity of neural networks from particular regions of the brain. As such, the potential to produce human brain cells from sources such as stem cells has huge potential for studying human disease, as well as providing tools for drug screening. Stem cell derived neurons labelled in green (B-Tubulin) and nuclei labelled in blue with Dapi. Are these cells physiologically comparable to animal or even actual human brain cells? Understandably, there is great excitement surrounding the development of iPSC technology particularly in the area of disease modelling. It is also undeniable that public expectation is also significant, especially in the areas of much-feared and currently intractable neurodegenerative conditions such as Alzheimer’s’ and Parkinson’s diseases. However, it remains to be seen whether these cultures can actually recapitulate many of the processes observed in animals or human derived cells. Whilst some studies have only described the presence of neuronal markers and cell morphology, the description of the hallmark features of genuine neurons such as the ability to fire action potentials is now commonplace. Using both human embryonic stem cells and induced pluripotent stem cells researchers have described the development of neuronal features including synaptic activity, neurotransmitter release, network formation, highlighting the maturation of these cells. However, currently no studies have reported synaptic plasticity, a key feature of CNS learning and information processing, in stem cell derived neurons. Stem cell modelling like much of neurophysiology has focused on neurons Unfortunately, many of the studies that have developed protocols for the derivation of neural cells have not considered the importance of glial cells such as astrocytes. Astrocytes are no longer viewed as providing a structural role within the brain and are now known to be intimately involved with neuronal signalling, with astrocytes and neurons forming what is now termed the ‘tripartite synapse’ (Volterra and Meldolesi, 2005). Furthermore, astrocytes represent a complex and diverse network of cells that plays a role in information processing and behaviour. The study of human neuron and astrocyte cultures is potentially more complex than other cell types, since we do not really know or understand how human neuron and astrocytes function to the same extent, as we understand rat model neurons.

Output: pass


Input: Claim: Certain types of seaweed farming can reduce methane emissions in livestock when used as a feed additive. [SEP] Text: algal. 2022. 1026732009. Determination of starch, including maltooligosaccharides, in animal feeds: comparison of methods and a method recommended for AOAC collaborative study. J. AOAC Int. 92:42–49. doi:10. 1016/j. algal. 2022. 1026732022. Potential environmental impact of bromoform from Asparagopsis farming in Australia. Atms. Chem. and Physics. 22:7631–7646. doi:10. 5194/acp-22-7631-20222020. Mitigating the carbon footprint and improving productivity of ruminant livestock agriculture using a red seaweed. J. Clean. Prod. 259:120836. doi:10. 1016/j. jclepro. 2020. 1208362023. Effects on rumen microbiome and milk quality of dairy cows fed a grass silage-based diet supplemented with the macroalga Asparagopsis taxiformis. Front. Anim. Sci. 4:1–11. doi:10. 3389/fanim. 2023. 11129692021. A meta-analysis of effects of dietary seaweed on beef and dairy cattle performance and methane yield. PLoS One 16:e0249053. doi:10. 1371/journal. pone. 02490532016. Identification of bioactives from the red seaweed Asparagopsis taxiformis that promote antimethanogenic activity in vitro. J. Appl. Phycol. 28:3117–3126. doi:10. 1007/s10811-016-0830-72016. Seaweeds for livestock diets: a review. Anim. Feed Sci. Technol. 212:1–17. doi:10. 1016/j. anifeedsci. 2015. 09. 0182021. Effect of feeding hydrolyzed feather meal and rumen-protected lysine on milk protein and energy utilization in late-lactation Jersey cows. J. Dairy Sci. 104:8708–8720. doi:10. 3168/jds. 2020-196571985. Temperature controlled multiple chamber indirect calorimeter-design and operation. Trans. ASAE. 28:555–560. doi:10. 13031/2013.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: angiogenesis is a well-coordinated process controlled by multiple extracellular inputs, including vascular endothelial growth factor (VEGF). However, little is known about when and how individual endothelial cell (EC) responds to angiogenic inputs in vivo. Here, we visualized endothelial Ca2+ dynamics in zebrafish and found that intracellular Ca2+ oscillations occurred in ECs exhibiting angiogenic behavior. Ca2+ oscillations depended upon VEGF receptor-2 (Vegfr2) and Vegfr3 in ECs budding from the dorsal aorta (DA) and posterior cardinal vein, respectively. Thus, visualizing Ca2+ oscillations allowed us to monitor EC responses to angiogenic cues. Vegfr-dependent Ca2+ oscillations occurred in migrating tip cells as well as stalk cells budding from the DA. We investigated how Dll4/Notch signaling regulates endothelial Ca2+ oscillations and found that it was required for the selection of single stalk cell as well as tip cell. Thus, we captured spatio-temporal Ca2+ dynamics during sprouting angiogenesis, as a result of cellular responses to angiogenic inputs. eLife digest Throughout life, new blood vessels grow out like branches from existing vessels in a process called “sprouting angiogenesis”. This involves some of the endothelial cells that line the inner surface of the blood vessel migrating outwards, creating a vessel sprout made up of tip cells and stalk cells. Sprouting is controlled by two opposing signaling systems. One pathway is triggered by a molecule called vascular endothelial growth factor (VEGF). This molecule binds to receptor proteins to activate a range of signaling processes that stimulate endothelial cells to become tip cells, and so encourage the formation of new sprouts. However, it was not known exactly when or how the endothelial cells respond to these signals. By contrast, the Notch signaling pathway inhibits sprouting angiogenesis. The two signaling pathways interact with each other: VEGF signaling in tip cells activates Notch signaling in neighboring cells, which then prevents VEGF signaling in these cells. This feedback mechanism helps a new sprout to form by suppressing tip-like activity in the cells surrounding a new tip cell, forcing these cells to become stalk cells.

Output: contrast


Input: Claim: Some proponents believe engineered organisms could completely replace natural species. [SEP] Text: Human astrocytes are much larger and more functionally diverse than their rodent counterparts (Oberheim et al. , 2009) and it has even been suggested that human intelligence may in part be due to the actions of glial cells. Human astrocytes (glia) have evolved to be more complex than rodent astrocytes and thus, must contribute to specific human brain functions. Such findings indicate significant and important roles for human astrocytes, which support the notion that these cells should be given equal importance to that of neurons in the development of stem cell derived neuronal networks. The ‘star’ of the show We have previously shown that stem cell-derived astrocytes form an interconnected network that allows the propagation of calcium waves between cells via gap junctions. These astrocytes respond to neuronal activity and also communicate with each other via the release of gliotransmitters (Hill et al. , 2012). More recently we have demonstrated that these cells alter their metabolism in response to neuronal activity, a process which has previously only been observed in rodent models (Tarczyluk et al. , 2013). A number of labs have now developed efficient protocols to produce highly enriched cultures of astrocytes from hESCs and iPSCs that display the features of mature astrocytes (Shaltouki et al. , 2013). It is now clear that if new cellular platforms are intended to generate functional neuronal networks that behave as networks do in the brain, then all the cellular components that comprise those networks, such as astrocytes, must be included in a viable model. Indeed, studies have shown that human neural precursor cells (hNPCs) grown in the presence of astrocytes mature at a faster rate than neurons alone. The presence of astrocytes also enhances the survival of cells; in our laboratory co-cultures containing astrocytes extend neuronal survival four-fold, to beyond twelve months. What’s my age again? A limiting factor in the use of iPSC-derived neurons to model age related disease is the biological age of cells. iPSC-derived neurons mature over a period of months. However, even at this stage, cells may resemble tissues derived from 8–10 week old foetuses (Mariani et al. , 2012).

Output: pass