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: Their stories provide excellent empirical evidence for our argument. Such temporal associations are highly suggestive of true cause-effect relationships, even if the analysis is not brought from the macro to the micro. What we are looking for is a change in one group related to a change in another group over time—or, the delta versus the delta over time. The examples presented are theoretical concepts with empirical validity that have explanatory power. Even so, actual epidemiological studies will need to be carried out to provide specific and additional evidence for our assertions, as well as quantify the relationship between variables. Gordon reiterates the premise that there is a cause-effect relationship between hate speech and mass atrocities including genocide and he proposes a unified legal approach to accountability for the consequences, in the context of international criminal law [80].

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: , 2021; Toews, 2021). The vast majority of drug targets are proteins, and structures play a critical role in rational drug design (Anderson, 2003; Kolb et al. , 2009). In particular, rational drug design strategies frequently require determining structures of the target protein bound to many ligands, as information on how these ligands bind is critical both to discovering early-stage drug candidates and to optimizing their properties (Maveyraud and Mourey, 2020; Warren et al. , 2012). Determining these structures experimentally is typically slow and expensive, and sometimes proves impossible (Slabinski et al. , 2007). Tremendous effort has thus gone into development of computational docking methods for predicting ligand-binding modes, which have dramatically accelerated this process in cases where a high-resolution experimentally determined structure of the target protein is available (Ferreira et al. , 2015; Pinzi and Rastelli, 2019). However, the metrics generally used to evaluate protein structure prediction methods—including all of those used in CASP—focus on the overall accuracy of predicted structures, rather than on their utility in predicting ligand binding. This has left the large community of academic and industrial researchers who wish to use protein structures to design drugs and other ligands unsure of whether, when, and how to use the new structural models. Improvements in protein structure prediction are widely assumed to lead to better models of ligand-binding pockets and thus more accurate predictions of ligand binding (Beuming and Sherman, 2012; Bordogna et al. , 2011; Erickson et al. , 2004), but to what extent do these assumptions hold? Here, we address these questions by evaluating (1) the structural accuracy of ligand-binding pockets in modeled proteins and (2) the accuracy of ligand-binding poses predicted using these models, where a ligand’s binding pose is specified by the 3D coordinates of its atoms when bound to the target protein. We evaluate models generated both by AF2 and by a traditional template-based modeling strategy, systematically comparing both to experimentally determined structures. Several recent studies have experimented with the use of models generated by AF2—and related protein structure prediction approaches such as RoseTTAFold (Baek et al. , 2021)—for tasks related to drug design, with mixed results (Díaz-Rovira et al. , 2023; He et al.

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: Skip to Content eLife home page your research Search by keyword or author Reset form my search to is a correction notice. Read the corrected 10. 7554/eLife. 91529 Open access Copyright information Open annotations (there are currently 0 annotations on this page). 145 views Share this 1529. Copy to clipboard Download BibTeX Download. RIS Full text Side by side Main text 2021. Neural signatures of vigilance decrements predict behavioural errors before they occur. eLife 10:e60563. doi: 10. 7554/eLife. 60563. Published 8 April 2021 In the decoding distance to object analyses on correct trials, classifiers were trained and tested using data from across timepoints (since each distance was a bin spanning multiple timepoints) and trials. Signal samples from all trials and timepoints within each distance were randomly allocated to one of the 10 folds to be used in the 10-fold cross-validation process. This meant that, on any given iteration of cross-validation, a single sample was never in both the training and the testing set, but samples in the training and testing set could be drawn from different timepoints of the same experimental trial. Autocorrelation in the data over time could therefore have inflated the decoding accuracy. To correct this, we have re-run the analysis decoding distance to object ensuring that training and testing sets drew from independent trials, and re-ran all analyses that drew on these decoding results. Analyses that did not decode distance to object are not affected, and analyses decoding distance to object on error trials are also not affected. We replicate our main finding of being able to use neural patterns to predict the occurrence of errors, along with all the effects of attention. The interaction indicating decreased distance decoding for late vs early blocks in monitoring but not active conditions has decreased in strength from being evident (BF >3) at two consecutive time-windows to one (BF = 6. 7) and there is no longer a main effect of target frequency. The link we reported between behaviour and informational connectivity is no longer present. This necessitated the changes outlined below. Underline indicates differences between corrected and original text. [Abstract] [Corrected text: #1] There was subtle evidence of this also in the neural decoding using Magnetoencephalography: for one time-window (of 80ms) coding of critical information declined more during monitoring versus active conditions.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: , 2023; Heo and Feig, 2022; Lee et al. , 2022; Lee et al. , 2023; Liang et al. , 2022; Qiao et al. , 2022; Scardino et al. , 2023; Wong et al. , 2022). Our study stands apart in two regards. First, we use structural models generated without any use of known structures of the target protein. For machine-learning methods, this requires ensuring that no structure of the target protein was used to train the method. Second, we perform a systematic comparison that takes into account the variation between experimentally determined structures of the same protein when bound to different ligands. Our results reveal both strengths and weaknesses of AF2 models. The structural accuracy of ligand-binding pockets in AF2 models is usually substantially higher than that of traditional homology models. Indeed, the typical difference between corresponding binding pockets in an AF2 model and in an experimentally determined structure is only slightly larger than the typical difference between two experimentally determined structures of the same protein with different ligands bound. Surprisingly, however, ligand-binding poses predicted given AF2 models are not significantly more accurate than those predicted given traditional models and are much less accurate than those predicted by docking computationally to experimentally determined protein structures. Our results provide guidelines as to how AF2 models should—and should not—be used for effective ligand-binding prediction. Our findings also suggest opportunities for improving structure prediction methods to maximize their impact on drug discovery. of proteins and models to ensure a fair comparison Evaluating the accuracy of predicted ligand-binding poses requires that we examine protein–ligand complexes whose structures have been determined experimentally. We wish to ensure, however, that the predicted structures of a given protein are not informed by experimentally determined structures of that same protein. AF2 and other recent neural-network-based protein structure prediction methods use experimentally determined protein structures in two ways: first, to train the neural network and second, as templates for prediction of individual protein structures (Baek et al. , 2021; Jumper et al. , 2021). The AF2 neural network was trained on structures in the Bank (PDB) (Berman et al. , 2000) as of April 30, 2018 so we evaluated structural models of proteins for which no experimentally determined structure was available as of that date.

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: This is consistent with a previous study (Alnaes et al. , 2015), which suggested that large-scale functional brain connectivity depends on the attentional load, and might underpin or accompany the decrease in information decoding across the brain in the unattended condition. [Original text: #1] Results showed strong evidence (Bayes factor = 6. 3e21) for higher informational connectivity for trials with Attended compared to Unattended dots, and moderate evidence for higher connectivity in Active compared to Monitoring conditions (Bayes factor = 3. 4; Figure 4B). There was insufficient evidence to determine whether there was a main effect of Time on Task (Bayes factor = 0. 83). There was moderate evidence for no two-way and three- way interactions between the three factors (Bayes factor ANOVA, two-way Time on Task-Target Frequency: BF = 0. 17; Time on Task-Attention: BF = 0. 16; Target Frequency-Attention: BF = 0. 15; their three-way interaction BF = 0. 12). These results suggest that Monitoring conditions and trials in which the dots are in the distractor (Unattended) colour, in which the attentional load is low, result in less informational connectivity between occipital and frontal brain areas compared to Active conditions and Attended trials, respectively. This is consistent with a previous study (Alnæs et al. , 2015), which suggested that large-scale functional brain connectivity depends on the attentional load, and might underpin or accompany the decrease in information decoding across the brain in these conditions. [Corrected figure: Figure 4B,C] [Original figure: Figure 4] [Corrected text: #2] There was moderate evidence for no difference in connectivity on miss compared to correct trials (Bayes factor = 0. 11). In addition, there was moderate evidence for no effect of Time on Task and (BF = 0. 11 and BF = 0. 10, respectively), as well as for two-way and three-way interactions between the three factors (Bayes factor -Target Frequency: BF = 0. 14; Behaviour-Time on Task: BF = 0. 14; Target Frequency-Time on Task: BF = 0. 15; their 3-way interaction BF = 0. 14).

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: In addition, when generating models using AF2, we used only structures that were available on that date as templates (see Methods). For a fair comparison, we also ensured that the traditional template-based models we used were not informed by any structures that became available after that date. In particular, we used traditional models that were published before April 30, 2018. We selected all proteins, ligands, and structural models used in this study in a systematic manner (see Methods). We focused on one class of drug targets—G-protein-coupled receptors (GPCRs)—for three reasons. First, GPCRs represent the largest class of drug targets by a substantial margin, with 35% of FDA-approved drugs targeting a GPCR (Sriram and Insel, 2018). Second, GCPR-binding pockets are very diverse, reflecting the fact that GPCRs have evolved to bind very different ligands with high specificity (Venkatakrishnan et al. , 2013). Third, the GPCRdb project maintains a historical database of template-based models of GPCRs (Pándy-Szekeres et al. , 2018). By selecting the template-based models listed in GPCRdb in April 2018, we ensured that these models were not informed by any structures that became available after April 30, 2018. Binding pocket structures are much more accurate in AF2 models than traditional models Because proteins are flexible, each protein can adopt multiple structures. In particular, the structure of a protein—especially the structure of the binding pocket—depends on which ligand is bound. To quantify this natural variation, we compared all pairs of experimentally determined structures of the same protein to one another, using root mean squared deviation (RMSD) as a metric (see Methods). AF2 does not allow one to specify a bound ligand when generating a protein model, and the template-based models listed in GPCRdb were also generated without specification of a bound ligand. We thus evaluated the accuracy of each model by computing its RMSD to that of all available experimental structures of the same protein. We compared the resulting distribution of RMSDs to the distribution of RMSDs between structures of the same protein with different ligands bound. As expected based on previously reported results for other proteins (Jumper et al.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: Published print: 01 Feb 2012 AbstractOrganic sweet corn (Zea mays) production is challenging for growers because of the high nitrogen (N) requirements of sweet corn and the relatively low N content of organic soil amendments. supplied and rate of mineralization throughout the growing season are two important aspects in determining the optimal N management program. Green manure (GrM) crops, composted manures, and commercially available organic fertilizers are used to manage N in organic production systems. Using a combination of these tactics can optimize N while minimizing cost. In this study, we used combinations of composted poultry manure (CPM) and two organic fertilizers (one high N and one with a balance of nutrients) with three GrM crops [rye (Secale cereale), alfalfa (Medicago sativa), and pea (Pisum sativum)] in a loamy sand soil for a 112-day laboratory incubation study. Total plant available N (PAN) was quantified at six times throughout the 16 weeks to determine total N mineralized and rate of N release from each of the management programs. The CPM and the high N organic fertilizer mineralized completely within the first 6 weeks of the study, while only 65% of the other organic fertilizer mineralized by the end of the study. mineralized from pea grown as a GrM for 48 days was comparable to the amount of N mineralized from rye suggesting that pea as a GrM crop should not receive an N credit in field production on a sandy soil. Significant quantities of PAN were mineralized from alfalfa residue, the equivalent of 80 lb/acre, although this is not sufficient for sweet corn production. The combination of alfalfa and the high N organic fertilizer provided sufficient N for sweet corn production and the mineralization rate closely matched sweet corn need. The release of N from CPM, even in combination with GrM crops, was asynchronous with sweet corn crop need. Keywords: alfalfa; pea; ryeNitrogen management for optimal crop production is one of the most difficult and costly practices in organic agriculture. The majority of the N in organic sources is bound in organic compounds, which must be mineralized by the soil microbial community to become a PAN form (ammonium or nitrate). Moisture, temperature, and composition of the soil microbe community can affect the rate at which organic N is mineralized.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: [--deleted text--] Therefore, in contrast to an auditory monitoring task which showed decline in univariate graph-theoretic connectivity before behavioural errors (Sadaghiani et al. , 2015), we observed no change in informational connectivity on error. Note that, the number of trials is equalized across the 8 conditions in each of our analyses separately. [Original text: #2] There was strong evidence for less (almost half) connectivity on miss compared to correct trials (Bayes factor = 3e63). There was insufficient evidence to determine the effects of the Time on Task or (Bayes factor = 0. 33 and BF = 0. 35, respectively) and moderate evidence for a lack of two-way and three-way interactions between the three factors (Bayes factor -Target Frequency: BF = 0. 17; Behaviour-Time on Task: BF = 0. 18; Target Frequency-Time on Task: BF = 0. 16; their three-way interaction BF = 0. 11). Weaker connectivity between occipital and frontal areas could have led to the behavioural misses observed in this study (Figure 1) as was previously reported in an auditory monitoring task using univariate graph- theoretic connectivity analyses (Sadaghiani et al. , 2015), although, of course, these are correlational data and so we cannot make any strong causal inferences. These results cannot be explained by the number of trials as they are equalised across the eight conditions in each of the analyses separately. [Results: Is neural representation different on miss trials? ] [Corrected text: #1] On correct trials, the distance information for both Active and Monitoring conditions was above chance (Figure 5B left panels; BF >104). For miss trials, the corresponding distance information was still above chance (Figure 5B right panels; BF >103) but the direct comparison revealed that distance information dropped on miss trials compared to correct trials (Figure 5B… [Original text: #1] On correct trials, the distance information for both Active and Monitoring conditions was well above chance (77%; BF >10). In contrast, for miss trials, the corresponding distance information was only just above chance (55%; BF >3 for all distances).

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Whiskers extend to last data points that are within 150% of the interquartile range, and outlier data points beyond those are shown individually. The plotted data is based on 150 RMSD values (comparisons) for experimentally determined structures with different ligands bound, 52 for AlphaFold 2 models, and 78 for traditional models. Binding pose prediction using AF2 models or traditional models yields similar accuracy Next, we assess the accuracy of ligand-binding poses predicted by computational docking to AF2 models and traditional template-based models. For each ligand we consider, the binding pose to a target protein is known based on an experimentally determined structure of the ligand bound to the protein, which we call the reference structure. Using industry-standard software, we docked each ligand to both AF2 and GPCRdb models of the protein. A predicted pose is considered correct if its RMSD from the experimentally determined pose is ≤2. 0 Å (Figure 2), a widely used criterion for correct pose prediction (Erickson et al. , 2004; Gohlke et al. , 2000; Paggi et al. , 2021). Figure 2 with 2 supplements see all Download asset Open asset Accuracy of ligand-binding poses predicted by computational docking to AlphaFold 2 models, traditional template-based models, or protein structures determined experimentally in complex with a ligand different from the one being docked. We plot the fraction of docked ligands whose pose is predicted correctly (see Methods). Error bars are 90% confidence intervals calculated via bootstrapping. *** for p-values <0. 001, ns for p-values >0. 05. 40 - 54 ligands were evaluated for each set of structures or models. For comparison, we also dock each ligand to structures of the same protein determined experimentally with other ligands bound (cross-docking). Note that when calculating pose prediction accuracy (Figure 2), we exclude cases where the ligand is docked back to the reference structure; such ‘self-docking’ yields much higher accuracy (Figure 2—figure supplement 1) but is of little practical utility, because in practice one wishes to predict binding poses that have not already been determined experimentally.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: content of organic sources is often not completely mineralized within one growing season (Crew and Peoples, 2005; Hartz et al. , 2000; Hartz and Johnstone, 2006). Insufficient PAN in the soil during periods of high crop need can translate into crop deficiency and potentially yield loss. However, excessive PAN in the soil before crop need or at times of low crop N use can leave the nitrate portion susceptible to leaching and inefficient crop use. Ideally, the mineralization rate would match fluctuations in crop need throughout the growing season. Understanding total N contribution and mineralization rate of organic N inputs can allow a comparison with crop need. management is particularly challenging for high N-requiring crops such as sweet corn. Organic growers typically use a variety of strategies to meet crop N needs such as using N-fixing legumes as GrM crops, composted manure, and commercially available amendments (Gaskell and Smith, 2007). Composted manure is a cost-effective means of providing N to an organic crop because of its role as a waste product and relative abundance. However, composted manure presents several challenges for organic sweet corn production. It generally has low N concentration (<5%), so large quantities must be applied (Berry et al. , 2002), adding expense in application. of pest management guidelines suggest that manure also should be applied and incorporated at least 1 week in advance of sweet corn planting to reduce seed damage by seed corn maggot [Delia platura (Boerboom et al. , 2011)]. Sweet corn does not begin to take up significant quantities of N until the five-leaf stage (Doerge et al. , 1991), which can take up to 1 month after planting in the upper midwestern United States. that is mineralized from the manure during this time is vulnerable to loss from leaching or volatilization. Soil amendments that contain higher concentrations of N such as feather meal, bone meal, fish powder, and guano are available commercially in various formulations. Smaller total quantities of these amendments can be applied as they have higher N concentration, ranging 11% to 14% (Hartz and Johnstone, 2006), reducing transportation and application costs compared with manure. Hadas and Kautsky (1994) reported continuous release of PAN from feather meal in an 8-week incubation study.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: The direct comparison revealed that distance information dropped considerably on miss trials compared to correct trials (Figure 5B… [Corrected text: #2] However, while the distribution of decoding accuracies for correct trials was centred around 60%, the decoding accuracies for individual miss trials were centred around 56%. We evaluated the difference in the distribution of classification accuracies between the two types of trials using Cohen’s d. Cohen’s d ranged from 0 to 2. 5 across participants and conditions. 14 out of 21 subjects showed moderate (d>0. 5) to large (d>0. 8; Cohen, 1969) differences between the distribution of correct and miss trials in either Active or Monitoring condition or both. Therefore, although the miss trials vary somewhat in levels of information, only a minority of (<24%) miss trials are as informative as the least informative correct trials. [Original text: #2] 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. [Corrected figure: Figure 5B] [Original figure: Figure 5] [Corrected figure: Figure 5—figure supplement 1] [Original figure: Figure 5—figure supplement 1] [Corrected figure: Figure 5—figure supplement 2B] [Original figure: Figure 5—figure supplement 2] [Results: Can we predict behavioural errors using neuroimaging? ] [Corrected text: #1] This was ~0. 4 standard deviation below the average accuracy on the other participants’ validation (correct trial) sets (Figure 6C). [Original text: #1] This was ~1. 5 standard deviation below the average accuracy on the other participants’ validation (correct trial) sets (Figure 6C). [Corrected text: #2] The prediction accuracy of behavioural outcome was above chance level (59% vs 50%; BF >10)….

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Future protein structure prediction methods might instead be designed to maximize utility for ligand-binding prediction, while still exploiting the deep-learning advances of AF2 and related recent methods. We also note that the vast majority of test cases used in the design of currently available docking software involved experimentally determined protein structures. One might be able to redesign docking methods to yield better performance given computationally predicted models. and ligand selection We selected structures from the aggregated list of all experimentally determined human GPCR structures from GPCRdb (Isberg et al. , 2014). We removed all proteins for which a structure was published in the PDB before April 30, 2018, including two for which structures of the extracellular domain (to which ligands bind) had been published before that date. We also removed proteins for which structures were available in complex with fewer than two unique orthosteric ligands. This left us with 54 experimentally determined structures of 18 GPCRs (17 class A, 1 class B). The full list of proteins and structures is in Supplementary file 1. Obtaining structural models AlphaFold 2 For each protein, we took the full protein sequence from Uniprot (UniProt Consortium, 2023) to generate five AF2 models, as was done for CASP (Jumper et al. , 2021), and picked the top-scoring prediction as our model. We set the template cutoff date to 2018-04-30. GPCdownloaded template-based models from the GPCRdb archive dated 2018-04-06. We used all the inactive- and active-state models available, and averaged results as described in Statistical tools. We excluded models for which the minimum sequence identity of the templates was below 40%, as such template-based models are typically not used for drug-binding predictions. This led to exclusion of models for four GPCRs (AGRG3, TA2R, PE2R2, and PE2R4). GPCRdb did not include models for two of the GPCRs (GPBAR and PE2R3). Ligand preparation Ligands were extracted from PDB structures using the Schrödinger Python API and then manually inspected to make sure we had chosen the ligand at the orthosteric site. With the same API, ligands were converted to strings.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: The soil was air-dried, and plant material was sieved out of the soils and retained, coarsely chopped with a knife into 0. 01 to 0. 02-inch pieces and mixed back into the soil as uniformly as possible. Composted, pelletized poultry manure was used as a composted manure source for the study. A feather meal based amendment, Fertilizer 11-0-0 (advertised content 11N–0P–0K; Co. , Rowley, MA), and Fertilizer 5-5-5 (advertised content 5N–2. 2P–4. 2K, Co. ), were used as representatives of commercially available organic fertility management products. Ammonium nitrate was used as a conventional control. The CPM and amendment sources were analyzed, on a dry matter basis, for actual macronutrient content. CPM contained 4. 4% N, 1. 8% P, and 2. 5% K; Fertilizer 11-0-0 (hereafter referred to as 11-0-0) contained 10. 1% N, 0. 5% P, and 0. 6% K; Fertilizer 5-5-5 (hereafter referred to as 5-5-5) contained 6. 1% N, 1. 8% P, and 4. 8% K; ammonium nitrate contained 34% N. Treatments were designed to add CPM, 11-0-0, and 5-5-5 to the soils at a rate of 150 lb/acre, the recommended rate of N application for sweet corn in Wisconsin (Laboski et al. , 2006). The amount of CPM applied was determined using the N content from the laboratory analysis. Organic fertilizers were applied based on advertised N rates. We made this decision because growers typically have manure analyzed because of variability in macronutrient content. On the other hand, advertised macronutrient content of commercially available amendments is regulated and generally accepted by growers as accurate without further testing. The study was a completely randomized design with three replications of each soil/amendment combination. There were four cover crop treatments (alfalfa, pea, rye, and none) and five amendment treatments (11-0-0, 5-5-5, CPM, ammonium nitrate, and none), resulting in 20 treatments.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: [Original text: #2] The prediction accuracy of behavioural outcome was above chance level (68% vs 50%; BF >10)… [Corrected text: #3] The accuracy increased to 65. 4% as the dot approached the centre of the screen, with ~64% accuracy with still 800ms to go before required response. [Original text: #3] The accuracy increased to 85% as the dot approached the centre of the screen, with ~80% accuracy with still 800ms to go before required response. [Corrected text: #4] The prediction of behavioural outcome (Figure 6) was performed using the data from the whole data set. To test if prediction accuracy depended on the stage of the experiment, we performed the behavioural prediction procedure on data sets obtained from the first 5 (early) and the last 5 (late) stages of the experiment separately (Figure 6—figure supplement 1). There was no evidence for a change in the prediction power in the late vs. early blocks of trials. [Original text: #4] The prediction of behavioural outcome (Figure 6) was performed using the data from the whole data set. 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 data sets 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%). [Corrected figure: Figure 6B,C,D] [6 caption sections] Results showed highest prediction accuracies on the participant set at around the threshold of 0. 4SD under the decoding level for correct validation trials, increasing at closer distances. Results showed successful (~=59%) prediction of behavioural outcome of the trial as early as 80 ms after stimulus appearance. [6 caption sections] Results show highest prediction accuracies on the participant set at around the threshold of 1. 5SD under the decoding level for correct validation trials, increasing at closer distances.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Ligands were then prepared with Schrödinger’s LigPrep with default command line parameters (Schrödinger LLC, 2021). Ligand similarity was defined as a ratio of the size of the maximum common substructure to the size of the smaller molecule. Ligand pairs for which this ratio was less than 0. 5 were deemed very different ligands. Protein preparation Experimentally determined structures were downloaded from the PDB. Only a single chain containing the ligand was kept, and all waters were removed. All structures and models were prepared with Schrödinger Wizard (Schrödinger LLC, 2021 ) following the same protocols as Paggi et al. , 2021, including energy minimization. All the experimentally determined structures were determined with a ligand bound, and we retained this ligand during minimization. Computationally predicted models did not include a ligand. Glide docking XP and Glide SP docking, we follow the docking protocol described in Paggi et al. , 2021. The grid is centered at the geometric centroid of the ligand and is defined with an inner box with 15 Å sides and outer box with 30 Å sides. For predicted structural models the center of the box is determined by aligning the model to an experimentally determined structure of that protein and using the centroid of its ligand. For this alignment, we pick the structure with the alphabetically first PDB entry ID. Rosetta docking docking, we used the GALigandDock protocol (Park et al. , 2021). To prepare the proteins, PyMol (Schrödinger, 2015) was used to remove heteroatoms and all alternative locations for each atom. information was included. UCSF Chimera v1. 16 (Pettersen et al. , 2004) was used to initially remove all hydrogens, followed by their software to prepare the structure for docking. For ligands, v2. 4. 0 (O’Boyle et al. , 2011) was used to generate 3D structures from ligand and add hydrogens at a pH of 7 (O’Boyle et al. , 2011). Ambertools v22. 0 (Case et al. , 2023) was used to add AM1-BCC partial charges.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: , 2017; Olufolaji et al. , 1990). Crop residues return small amounts of organic materials to the soil as a way of nutrient recycling. Agricultural waste and food/industrial by-products are organic materials that could be good sources of organic soil amendments needed to maintain soil fertility at levels that can support the growth of amaranth. Manures serve as organic fertilizers in many agronomic production systems. They add soil organic matter (SOM) and decompose it into stable humic substances (Davison et al. , 2000), while supplying macro- and micronutrients (Usman, 2015). As a reservoir of plant nutrients, SOM needs to be manipulated to promote soil fertility and ensure success in humid tropical agriculture (Obalum et al. , 2011). Accretion of SOM promotes soil aggregation and structure of coarse-textured soils to enhance water retention, thereby slowing down nutrient mineralization, and favouring sustenance of beneficial soil organisms (Obalum et al. , 2017). Conventional animal manures are often used as organic soil amendments in amaranth production (Ainika, 2010; Bawa, 2010). However, poultry droppings (PD) can be scarce, and this is aggravated by its other important uses, including serving as a substrate in the bioremediation of crude-oil polluted soils (Ezenne et al. , 2014).

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Results showed successful (~=70%) prediction of behavioural outcome of the trial as early as 80 ms after stimulus appearance. [Original figure: Figure 6] [Corrected figure: Figure 6—figure supplement 1] [Original figure: Figure 6—figure supplement 1] [Discussion] [Corrected text: #1] Using the sensitive analysis method of MVPA, we showed that neural coding was stronger for attended compared to distractor information. There was also one time-window where the interaction between the time on the task and target frequency affected decoding, with a larger decline in coding under monitoring conditions, which may reflect a neural correlate of the behavioural vigilance decrements. [Original text: #1] Using the sensitive analysis method of MVPA, we showed that neural coding decreased for relevant task information more when targets were infrequent than frequent (at longer task durations), providing neural correlates of the behavioural vigilance decrements. [Corrected text: #2] We also developed a novel informational brain connectivity analysis, which showed that the correlation between information coding across peri-occipital and peri-frontal areas varied with different levels of attention but did not change with errors. [Original text: #2] We also developed a novel informational brain connectivity analysis, which showed that the correlation between information coding across peri-occipital and peri-frontal areas varied with different levels of attention, target frequency and the time on the task. [Corrected text: #3] Finally, we utilised our recent error data analysis to predict forthcoming behavioural misses based on the neural data. [Original text: #3] Finally, we utilised our recent error data analysis to predict forthcoming behavioural misses with high accuracy based on the neural data. [Corrected text: #4] When targets were infrequent, modelling real-life monitoring situations, there was a strong behavioural drop in performance (i. e. , vigilance effects in both accuracy and RT; Figure 2) and a hint in the brain activity data of a change in neural coding (namely one time-window showing evidence of an interaction between and Time on Task). We need more data to fully test this effect, however, our main finding is that of being able to use the difference in decoding between correct and miss trials to predict behaviour.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: The ligand was translated such that the center of mass of the ligand was positioned at center of the binding pocket of the prepared structure. The center of the binding pocket was defined as the center of mass of the ligand in the experimentally determined structure. Models were aligned to the experimentally determined structure of the protein which had the alphabetically first PDB entry ID. This structure’s ligand center of mass was used to define the model’s binding pocket. Structural comparisons To calculate ligand pose RMSD, structures were first aligned on amino acid residues within 15 Å of bound orthosteric ligands, using Schrödinger’s structalign tool (Schrödinger LLC, 2021). This alignment was used to calculate the RMSD of each docked ligand pose from the reference ligand pose. Binding pocket RMSDs were calculated with a PyMOL script, considering all residues that are within 5 Å of the ligand in the reference structure (for both alignment and RMSD calculation). We included all non-hydrogen atoms in this calculation, except that for the backbone-only binding pocket RMSDs shown in Figure 5—figure supplement 1 we included only backbone atoms. We computed full-structure RMSDs with a similar PyMOL script, but taking into account all non-hydrogen atoms in the protein. 3, Figure 4, and Figure 3—figure supplement 1, we highlight residues in the binding pocket whose positions differ most from the reference structure. To identify these residues, we aligned to the reference structure the structure and model (or the two models) being docked to. For each structure/model, we computed the RMSD for each residue within 5 Å of the ligand, and then for each residue determined the maximum RMSD across the two structures/models. We selected the residues with the largest maximum RMSDs. Statistical tools p values were computed with two sided paired t-tests, using SciPy (Virtanen et al. , 2020). Bootstrapping 90% confidence intervals were computed with the default 9999 resamples, also using SciPy. 2, the docking accuracy is calculated as a weighted average across all docking results, with weights chosen such that each ligand–protein pair is represented equally.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: Cattle dung (CD) that is not scarce has a high carbon-nitrogen (CN) ratio and mineralises less readily in the soil (Chukwuma et al. , 2024; Ndzeshala et al. , 2023; Uzoh et al. , 2015). Composting of organic materials before soil application ensures the stability of SOM and the release of nutrients to the soil (Baiyeri et al. , 2020; Tiquia, 2003). Amaranth growth can be increased with soil application of composted plant residue and a mix of animal waste (Dada et al. , 2017). One potential constraint, however, is the short-lived nature of the compost effect on soil structure stability (Obalum et al. , 2019). These prospects and constraints of composts and of PD and CD manures (in terms of availability and mineralization rates) offer an opportunity to explore the prospects of mixing plant waste with animal manures before composting, thereby developing sustainable enhanced productivity of amaranth and similar leafy vegetables all-year-round. Any lack of residual effects of conventional compost on soil structure stability (Obalum et al. , 2019) and yield of vegetable crops may be compensated for by PD and CD manures (Ogunezi et al.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: [Original text: #4] When targets were infrequent, modelling real-life monitoring situations, the neural coding of crucial information about the task dropped, correlating with the decrease in behavioural performance (i. e. , vigilance effects in both accuracy and RT; Figure 2). [Corrected text: #5] When people miss targets, they might process or encode the relevant sensory information less effectively than when they correctly respond to targets. [--deleted text--] [Original text: #5] One explanation for the decrease in decoding accuracy for task-relevant information could be that when people monitor for rare targets, they process or encode the relevant sensory information less effectively as the time passes, relative to conditions in which they are actively engaged in completing the task. [Corrected text: #6] [--deleted text--] [Original text: #6] For example, the drop in decoding over time for both Active and Monitoring that is seen in Figure 3 might reflect some of the general changes in the characteristics of the recording hardware over the course of the experiment (e. g. , the MEG system warming up), but our design allows us to dissociate these from the key vigilance effects we are interested in. [Corrected text: #7] In contrast, the task-relevant information of distance to object was affected by attention and was attenuated on errors. [Original text: #7] In contrast, the task-relevant information of distance to object was affected by attention, target frequency and time on task and was dramatically attenuated on errors. [Corrected text: #8] Our information-based brain connectivity method showed moderate evidence for no change in connectivity between correct and error trials. [--deleted text--] Informational connectivity is unaffected by differences in absolute levels of information encoding (e. g. , lower coding on miss vs. correct trials). It could be sensitive to different levels of noise between conditions, but there was no evidence for that in this case. Apart from sensory information coding and sensory-based informational connectivity, which were evaluated here, there may be other correlates we have not addressed. [Original text: #8] Third, our information-based brain connectivity method showed weaker connectivity between the peri-frontal attentional network and the peri-occipital visual areas of the brain in the unattended and monitoring conditions (Figure 4), where participants encountered fewer targets relative to the other conditions.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: In particular, when multiple traditional template-based models of a single protein were available, we docked each ligand to each model but set the averaging weight to the inverse of the number of models docked to. Likewise, when multiple experimentally determined structures of a single protein were available for docking of a particular ligand (i. e. , structures determined experimentally without that particular ligand bound), we docked the ligand to each structure but set the averaging weight to the inverse of the number of structures docked to. 5 and Figure 5—figure supplement 1, instead of simply binning structures and models in different RMSD ranges, we performed a smoothing analysis that does not impose arbitrary cutoffs between bins. The smoothed functions were created by running kernel smoothing over docking outcomes. The smoothing was done over data such that each data point was the binding pocket RMSD and the binary value indicating whether or not the predicted pose was correct. kernel (Hastie et al. , 2009) was used, with a width of 0. 25 Å. To avoid inaccurate boundary condition behaviors, each end the kernel was only evaluated when the center of the kernel overlapped with the first or last data point on the X-axis. To calculate statistical significance and p values, a bootstrapping significance test was conducted for each interval on the X-axis. Additionally, in Figure 5—figure supplement 1, we show 90% confidence intervals that are also computed with bootstrapping. Add a comment + Open annotations. The current annotation count on this page is being calculated. 9. 0ödingerPyMOL 3. 8SoftwareAlphaFold 2. 0. 1 Data availability Data and analysis scripts are supplied in The following data sets were generated (2023) and structures for "How accurately can we predict binding poses with AlphaFold models? . 797. PubMed 876. PubMed 242. PubMed 3277. PubMed 98. PubMed 778. PubMed 1674. PubMed 55. PubMed 3421. PubMed 1749. PubMed 6196. PubMed 356. PubMed 7449. PubMed 10. 1007/978-0-387-84858-7 7. PubMed 1885. PubMed 425. PubMed 589. PubMed 436. PubMed 1617. PubMed 308. PubMed 167. PubMed 10. 1101/2022.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: , 2019; Onah et al. , 2023; Sanya & Amadji, 2018). Brewery waste (spent grain) is an industrial by product that is generated in large quantities and poorly disposed (Devolli et al. , 2018; Nassary & Nasolwa, 2019; Onwe, 2004). One way of recycling brewery spent grain (BSG) could be by composting it into organic fertilizers, as composted BSG has a high potential for releasing essential nutrients for plant growth (Ebido & Ndubuaku, 2019; Kanagachandran & Jayaratne, 2006). Owing to the prevailing abundant discharge of its source and its ability to enhance soil fertility, composted BSG may appeal to subsistence vegetable farmers, and may be deemed a promising suitable and economically viable option for them. Little is currently known of the efficacy of complementing composts with animal manures of varying CN ratios, or the suitable combining ratio, in organic amaranth farming. This study was undertaken to determine the agronomic effects of a composted mix of BSG with PD or CD on amaranth growth and yield in acid sandy tropical soils. 2. Materials and Methods2.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: We also observed less connectivity between the same areas on miss vs. correct trials. Our connectivity is unaffected by the absolute lower level of information encoding in the brain on miss vs. correct trials, but it is possible that it is driven by noisier patterns of information encoding in miss (vs. correct) trials. Thus, the lower connectivity for miss vs. correct trials could result from (at least) two possible causes: the pair of regions representing two distinct sets of information (i. e. , becoming in some sense less connected on misses) or the pair represent similar information that is distorted by higher levels of noise on misses. Apart from sensory information coding and sensory-based informational connectivity, which were evaluated here and provide plausible neural correlates for the vigilance decrement, there may be other correlates we have not addressed. [Corrected text: #9] This makes our analysis sensitive to different aspects of connectivity compared to conventional univariate analyses. [Original text: #9] This makes our analysis more sensitive for capturing subtle connectivity than the conventional univariate analyses. [Corrected text: #10] We statistically compared the two types of trials and showed a reliable advantage in the level of information contained at individual-trial-level in correct vs. miss trials. Our error prediction results showed a reliable decline in the crucial task-relevant (i. e. , distance to object) … [Original text: #10] 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. Our error prediction results showed a large decline in the crucial task-relevant (i. e. , distance to object) … [Corrected text: #11] Here, we obtained up to 65% prediction (with a chance level of 50%), suggesting our method accesses relevant neural signatures of attention lapses, and may be sensitive in discriminating these. [Original text: #11] Here, we obtained up to 85% prediction (with a chance level of 50%), suggesting our method accesses more relevant neural signatures of vigilance decrements, or is more sensitive in discriminating these. [Corrected text: #12] We observed that the neural representation of critically relevant information in the brain [--deleted text--] was particularly poor on trials where participants missed the target.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: News FeatureReflected in Grant Allocations, Researchers ArgueNovember 8, 2022• Physics 15, 173Researchers call for reform in how funding is allocated in response to a study that finds racial disparities in the Foundation’s awarding of research grants. JHVEPhoto/stock. adobe. com A study of Foundation data shows significant differences in the funding of grants based on the race of the applicant. JHVEPhoto/stock. adobe. com A study of Foundation data shows significant differences in the funding of grants based on the race of the applicant. × Over the years, physicist Andrea Liu, a second-generation Asian American, has been subjected to a variety of disparaging remarks. One colleague, for example, told the University of Pennsylvania professor, “it’s obvious that English is not your native language, but I can understand you just fine,” a statement she describes as “clueless. ” On another occasion, a reviewer on a Foundation (NSF) grant proposal wrote, “Liu should collaborate with her former postdoc to make sure the work is feasible,” feedback that Liu calls “pretty condescending. ” Liu attributes some of these remarks to sexist ideology and outdated ideas of gender roles. But it is the systemic racial prejudice that she finds the hardest to shake. A soon-to-be-published analysis of grant money allocations suggests that Liu is likely not alone in that feeling [1]. The study—which has been accepted in the peer-reviewed journal eLife—finds pervasive disparities in the funding rates for scientists from different racial groups, with white scientists at the top and Asian scientists at the bottom. The team behind the study says that the way funding has been allocated reflects systemic racism and calls on the NSF, a US federal agency, to eliminate the disparities. The conclusions echo outcomes of a 2011 study that looked at the allocations of funds by the of Health (NIH), another federal agency, and of a 2022 report from Wellcome, a charitable foundation in the UK that funds research. Many scientists have come forth to applaud the study, and some have related their own experiences of bias within the NSF. They note, however, that this study is not the final word on disparities in NSF funding, as the publicly available data do not include important administrative information about the applications as well as the racial makeup of reviewers and other persons involved in grant reviews.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: 1 Characteristics of the Experimental SiteThe experiment was conducted at the Garden, Teaching & Research Farm, University of Ibadan, Ibadan, Nigeria (7° 24’N, 3° 54’E; 62 m asl). The farm is located in the derived savannah of southwestern Nigeria. The mean monthly temperature ranges from 24 to 30℃ and the mean annual rainfall from 1,800 to 2,100 mm. The field study was conducted on an acid sandy soil. Some physicochemical properties and fertility indices of the top 0-20 cm soil before the study were assayed following standard laboratory procedures (Sparks, 1996). 2. 2 Treatments, Field experiments were carried out to determine effects of BSG-based compost on the growth and the yield of green amaranth as organic soil amendments. The BSG was composted with and without PD and CD, involving soil application of composted BSG and a composted mix of BSG with PD or CD. The PD and CD were chosen as manures because of their known wide difference in CN ratio, with smaller values in the former than the latter (Adubasim et al. , 2018), which translate into their faster and slower mineralisation, respectively in coarse-textured tropical soils (Chukwuma et al. , 2024). The BSG was supplied by the the PD and CD were obtained from the Teaching & of the Department of Animal Science, Faculty of Agriculture and Fisheries, University of Ibadan, Ibadan, Nigeria. The composting of BSG and its mix with PD or CD was done under controlled well-aerated environmental conditions in the Garden of the Teaching & Research Farm. In initiating the composting, these three organic wastes were used in their fresh forms as procured and were not subjected to curing by sun-drying. Each of BSG+PD and BSG+CD was composted in volume ratios of 1:1 and 2:1, each having two replications.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: [Original text: #12] We observed that the neural representation of critically relevant information in the brain decreases over time, especially when targets are infrequent. This neural representation was particularly poor on trials where participants missed the target. [Corrected text: #13] We used this observation to predict behavioural outcome of individual trials and showed that we could predict behavioural outcome… [Original text: #13] We used this observation to predict behavioural outcome of individual trials and showed that we could accurately predict behavioural outcome… [Corrected text: #14] These results provide new insights about how momentary lapses in attention impact information coding in the brain and propose an avenue for predicting behavioural errors using novel neuroimaging analysis techniques. [Original text: #14] These results provide new insights about how vigilance decrements impact information coding in the brain and propose an avenue for predicting behavioural errors using novel neuroimaging analysis techniques. [Materials and Methods] [Added text: #1] Each distance covered a time window of ~80ms (varied slightly as dot trajectories varied in angle) which consisted of 4 or 5 signal samples depending on which of the 15 predetermined distances was temporally closest to each signal sample and therefore could incorporate it. [Added text: #2] As within-trial autocorrelation in signals could inflate classification accuracy (signal samples closer in time are more similar than those farther apart), we ensured that in every cross-validation run and each distance, the training and testing sets used samples from distinct sets of trials. To achieve this, trials were first allocated randomly into 10 folds, without separating their constituent signal samples. This way, the 4 or 5 signal samples from within each distance of a given trial remained together across all cross-validation runs and were never split across training and testing sets. [Corrected text: #1] The optimal threshold was 0. 4 (±0. 07) times the SD below the decoding accuracy on the validation set across participants. [Original text: #1] The optimal threshold was 1. 54 (±0. 2) times the SD below the decoding accuracy on the validation set across participants.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Everyone agrees that scientists need full access to all these details to uncover the true extent—and the causes—of any biases. new study—which took two years to complete—was led by Chen, a postdoctoral researcher at in California. Chen, who identifies as Asian, says she was spurred into action after a Zoom call in which a senior colleague, who also identifies as Asian, commented that it is “an ‘open secret’ that Asian scientists have a harder time getting funding from the NSF” than other racial groups. Shocked at the comment, Chen wanted to learn more, as did others on the call, including Chen’s friend Sara Kahanamoku, a graduate student in historical marine ecology at the University of California, Berkeley, who identifies as and uses the pronoun they. from the University of Pennsylvania has been subjected to a variety of racially disparaging remarks over the course of her career. from the University of Pennsylvania has been subjected to a variety of racially disparaging remarks over the course of her career. × The duo delved into the publicly available NSF data on the race and ethnicity of the principal investigator (PI) on each grant application. They found lower-than-average funding rates for applications from Asian scientists as well as from other minority racial groups. Chen and Kahanamoku then contacted researchers from across the sciences to help them carry out a full analysis of the data. They wanted the team to include social science and policy experts and to reflect the full diversity of racial groups funded by the NSF. They also wanted the help of more senior researchers, as they worried that otherwise “nobody would listen to us,” Chen says. The study lays out some stark findings. For over two decades, white scientists have consistently surpassed their peers in gaining NSF grants. In 1999 their funding success rate was 2. 8% above the overall average. By 2019 that rate had increased to 14. 3% above the average. In contrast, the success rate for Asian scientists has been persistently low, regularly dropping under 20% of the average. The rates for Black and Latino scientists hovered somewhere in between those for whites and Asians. The team found the same trend across science disciplines. “We were shocked by the consistency of the disparities that have held for over two decades,” Kahanamoku says.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: The composted organics were designated BSG+PD(1:1), BSG+PD(2:1), BSG+CD(1:1), and BSG+CD(2:1). The composting period was six weeks with temperature readings taken daily using a compost thermometer. To ensure aeration within the compost pile, the mass was turned at the beginning of the second, fourth and fifth weeks when the pH of the compost was determined and the pile moistened. The temperature of the compost reflecting the activity of micro-organisms and the pH of the compost together were used to infer its maturity. After the composting period, composts were spread and left for curing (air-drying to reduce pathogenic load) for four weeks before use. The field experiment, described by Ebido & Ndubuaku (2019), involved composted BSG+PD(1:1), BSG+PD(2:1), BSG+CD(1:1), BSG+CD(2:1), BSG as a reference, and an unamended control as a treatment. The experimental arrangement was a randomized complete block design with four replicates. Plots measuring 1. 5 × 1. 0 m were guarded against external interferences with earthen bunds (compactly prepared ridges) and were 0. 50 m apart. Manual hoeing to about 20-cm depth was done in all plots. Thereafter, composted organic amendments were uniformly spread on the surface of the plots at the rate of 20 t ha–1. A pack of amaranth seeds, of cv. PI 511679, was purchased from Eagle Seeds, Orogun Ibadan, Nigeria. This genotype, grown mainly for its leaves, usually grows up to 25 cm tall with about 11 leaves in about four weeks on un-amended soils in the study environment (Adeoluwa et al. , 2009). Three days after hoeing the soil and spreading the composted organic amendments, seeds were sown in drills spaced 0. 50 m apart.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: Skip to Content eLife home page your research Search by keyword or author Reset form my search to 10. 7554/eLife. 60563 Open access Copyright information Version of RecordAugust 8, 2023 Version of RecordApril 21, 2021 Accepted ManuscriptApril 8, 2021 Open annotations (there are currently 0 annotations on this page). 1,414 views 170 downloads 15 0563. Copy to clipboard Download BibTeX Download. RIS Full text Figures and data Peer review Side by side Peer review process Decision letter Author response Peer review process This 1. The introduction makes it clear that the authors acknowledge that there may be multiple sources of interference contributing to declining vigilance over time: the encoding of sensory information, appropriate responses to the stimuli, or a combination of both. In the introduction, it would help if the authors review how infrequent targets affect response patterns. In addition, it would help if the theoretical approach and assumptions of the authors were explicitly stated. For instance, the a priori assumptions surrounding the connectivity analysis should be acknowledged and discussed in the interpretation of the pattern of results (e. g. , p. 32, line 658). Specifically, the focus on connectivity between frontal and occipital areas seems to assume the effects are related to sensory processing alone, but this does not preclude other influences. For instance, effects could also occur on response patterns. These considerations should be added as caveats to the interpretation. 2. It is not clear what role eye fixations play here. Participants could freely scan the display, so the retinotopic representations would change depending on where the participants fixate, but at the same time the authors claim that eye position did not matter. 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).

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: The team infers that white scientists have received a “surplus” of 12,820 awards over the past 20 years. Meanwhile, Asian scientists have a “deficit” of 9701 awards. and Hispanic scientists, the deficits are 417 and 175 awards, respectively. ( also have a deficit, and a surplus, but the grant submission numbers from those groups are too low to make the award numbers statistically meaningful. ) M. of the University of Wisconsin-Madison says that she is gratified that Chen, Kahanamoku, and their colleagues took the time to analyze this NSF data, which pushes back on common misconceptions about the role of race in funding decisions. M. of the University of Wisconsin-Madison says that she is gratified that Chen, Kahanamoku, and their colleagues took the time to analyze this NSF data, which pushes back on common misconceptions about the role of race in funding decisions. × Each award in the deficit column represents a person whose project went unfunded and whose career could thus have stalled, Kahanamoku says. Collectively, these experiences could lead to a significant accumulation of disadvantage for a given racial group. “That really struck me,” they say. The results confront a common misperception that NSF funding favors historically excluded racial groups, says mathematician of the University of Wisconsin-Madison, who identifies as Hawaiian and Black. “I know this [narrative] to be false, both from my own experience and from those of my peers and mentees. But it is really gratifying to have it so clearly illustrated in NSF’s own data. ” deeper into the awards process, the team analyzed the so-called panel scores for the different racial groups. All grants are analyzed in detail by reviewers who separately score the application on a scale from 1 (poor) to 5 (excellent) against a set of criteria. Based on this score, a decision is made for or against funding the project. Chen and her colleagues found that proposals for Black and Asian scientists were systematically rated lower than those of white scientists. “That result really resonated with me,” says Cesunica Ivey, a Black assistant professor of environmental engineering at UC Berkeley, who was not involved in the study.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: The soil had a slightly acidic pH and a high level of exchangeable acidity. The available phosphorus (P) level was slightly high; the exchangeable potassium (K) and sodium (Na) indicated very low to low values according to the rating of Landon (1991). Table 1. Selected chemical properties of the soil before treatment applicationSoil PropertiesValueTexture classLoamy sandSoil pH-H2O5. 3Soil organic carbon (g kg-1)19. 20(g kg-1)2. 31(mg kg-1)23. 00Exchangeable bases and acidity (cmol kg-1)K+0. 10Ca2+1. 80Mg2+0. 40Na+0. 10H+0. 40Al3+0. 503. 2 on Parameters of AmaranthThe immediate effects of the composted BSG-based organic soil amendments showed that the treatment affected amaranth growth (Table 2). Treatment generally increased plant height, stem diameter and the number of leaves of amaranth plants compared with the un-amended control. Composted BSG+PD(1:1) produced taller plants and more leaves compared with the control; composted BSG+CD(2:1) gave the widest stem of the amaranth plants and the control the narrowest. For this stem diameter, composted BSG+PD(1:1) and BSG+PD(2:1) showed similar values. In addition, composted BSG+CD(1:1) and BSG+CD(2:1) had similar stem diameters, with the former being similar to the rest of the treatments excluding the control. Composted BSG+PD(1:1) and BSG+CD(1:1) were similar in all three growth indices; so too were composted BSG+PD(2:1), composted BSG (the reference) and the control, and composted BSG+CD(1:1), composted BSG+CD(2:1) and composted BSG. Table 2.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: The authors should show that their results replicate if standard artefact removal is performed on the data. 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. 3. One key finding was that while classifying the direction of the dots was modulated by attention, it was insensitive to many features that were captured by a classifier trained to decode the distance from the deflection. This is surprising since both are spatial features that seem hard to separate. In addition, the procedures to decode direction vs distance were very different. Do these differences still hold if the procedure used to train the two classifiers is more analogous or matched? 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? 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? 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).

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: She says that the dearth of Black tenured scientists means that young Black scientists can lack close relationships with mentors who might help them write research grant proposals. Starting in high school, white and Black students can have “diametrically opposed learning experiences,” she says. “That may be showing up in the panel scores. ” C. (right) with coauthor of University in front of the White House. After their preprint appeared online, the authors were invited to Washington, DC, for the August 9th signing of the CHIPS and Science Act. C. (right) with coauthor of University in front of the White House. After their preprint appeared online, the authors were invited to Washington, DC, for the August 9th signing of the CHIPS and Science Act. × Ivey also notes another problem: she has been a member of multiple grant panels and recalls cases where the discussion chair—typically a grant program manager—has flipped the consensus of the room from funding a particular grant to rejecting it. Claudia Rankins, a former NSF program officer who worked primarily with researchers from Colleges and Universities (HBCUs), had similar experiences. “What was revealed in the [new study] has been known to people at the NSF for years,” she says. Another former NSF employee, Roger Wakimoto, who was the assistant director of the NSF’s geoscience directorate, says he was “dismayed” when he first learned of the study’s results. “NSF holds the community to a very high standard when it comes to diversity,” he says. “Especially for the big awards, if you don’t have a very profound section about how you are addressing diversity, equity, and inclusion, you aren’t going to get funded. ” Wakimoto applauds the NSF for this effort but goes on to say that even while working at the NSF he questioned whether the foundation was holding itself to those same standards. “I never came away with the feeling that the answer was yes. ” Rankins now works on the other side of the grant application process, helping researchers at HBCUs write and submit NSF funding applications. She says that she rarely sees comments on a failed application indicating problems with the idea or with the methodology.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: The overall best agronomic results in composted BSG+PD(1:1) are attributed to PD’s ability to modify soil properties, leading to increased water retention and plant uptake of nutrients (Nwachukwu & Ikeadigh, 2012). However, corresponding to the earlier report, this treatment provoked an increased release of plant nutrients, especially N in its available forms, in the soil compared to the rest of the treatments (Ebido & Ndubuaku, 2019). usually promotes vigorous growth of plants via increased photosynthetic activities (Hussain et al. , 2006), and is particularly critical for amaranth production in the tropics (Adeoluwa et al. , 2009; Nyankanga et al. , 2012). The PD enhanced soil total N even more than NPK fertilizers in sandy tropical soils (Umeugokwe et al. , 2021), pointing to the possible contribution of PD to the enhanced N status of BSG+PD(1:1) and the possible role of such status in its performance in the present study. With composted BSG+PD(1:1) as soil amendment, the total biomass yield of amaranth on a fresh-matter basis four weeks after sowing in the first cropping was 46. 51 t ha-1, achieved with 20 t ha-1 being the application rate of the composted BSG-based amendments in this study. From the same study site, the highest amaranth yields of 4.

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: 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"). 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? 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? 9. Classifier performance was extremely high almost immediately after trial onset. Does the classifier perform at chance before the trial onset, or does this reflect sustained but not stimulus-specific information? 10. The connectivity analysis appears to be just a correlation of decoding results between two regions of interest. This means, if one "region" allows for decoding the distance to the object, the other one does too.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: Rather, the panel’s comments usually imply that the researcher’s institution “doesn’t have the capacity” to support the work or that the researcher was “too ambitious” in their plan. “As a Black researcher, when you get feedback that says you were too ambitious, you know exactly why they’re not funding you,” Rankins says. On social media and in email discussion groups, scientists have largely voiced positive reactions to the airing of these concerns. “You have brought daylight to a place where there absolutely needs to be more,” (@HydroLejo), a hydrologist at University, Idaho, wrote on Twitter after reading a preprint version of the study. Experts in social inequities in the sciences have also weighed in on the study’s results. “investigator gap is very concerning,” says Donna Ginther, a white economist at the University of Kansas and the lead author on the 2011 NIH study and a 2019 follow-up. She applauds Chen and her colleagues for addressing this issue. was one of the numerous scientists to voice positive reactions to the preprint version of the Chen et al. study. was one of the numerous scientists to voice positive reactions to the preprint version of the Chen et al. study. × There have been two public voices that question the conclusions of Chen and her colleagues: the husband-and-wife physicists Charles and who work at on problems in condensed matter. Cynthia identifies as white and Charles as Hispanic. Six weeks after the preprint version of Chen et al. ’s study appeared online, the Reichhardts submitted a comment on the same preprint server [2]. The comment does not dispute the funding disparities found by Chen and her colleagues but claims that they could be explained by other factors: the increase in Asian faculty over the past 20 years and the different funding statistics for each of the NSF directorates—topical areas within the agency. Separately, Charles published a blog post on a site that hosts voices critical of diversity initiatives. The post lays out similar arguments as the comment but also includes remarks that question the professionalism of Chen and her colleagues. Chen and her colleagues declined to speak about the Reichhardts’ analysis and remarks until the comment has completed peer review.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: 30 and 8. 30 t ha-1 were achieved, respectively, with 10 t ha-1 Gliricidia sepium and 60% organic fertilizer (composted market waste plus CD in the ratio of 3:1) complemented with 40% N urine (Adeoluwa et al. , 2006; Adeoluwa et al. , 2009). In northeastern Nigeria, a corresponding amaranth yield of 7. 10 t ha-1 was achieved with PD manure at 20 t ha-1 (Mshelia & Degri, 2014). The much higher yield attained in the present study thus points to the prospects of composted BSG+PD(1:1) in growing amaranth in sandy tropical soils. Generally, mixing BSG with PD manure proved to be superior to mixing BSG with CD manure. However, composted BSG+PD(1:1) could not always find a good substitute in composted BSG+PD(2:1), especially with respect to fresh-matter yields. The overall poor results due to complementation of BSG with CD are attributed to the slow mineralisation rate of CD in coarse-textured tropical soils, about 1. 75 times slower than that of pig-slurry manure (Uzoh et al. , 2015), which in itself usually shows a higher CN ratio than PD (Adubasim et al. , 2018). This observation for CD reflects its low population of bacterial species known to drive its decomposition and hence mineralisation (Oraegbunam et al. , 2023).

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: However, this alone does not equal connectivity. It could simply mean that patterns across the entire brain allow for decoding the same information. For example, it would not be surprising to find that both ROIs correlate more strongly for correct trials (i. e. the brain has obviously represented the relevant information) than for errors (i. e. the brain has failed to represent the information), without this necessarily being related to connectivity at all. 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). 11. The display of the results is very dense, and it not always clear whether decoding for a specific variable was above chance or not. The authors often focused on relative differences, making it difficult to fully understand the meaning of the full pattern of results. 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. 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.

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: However, they noted in conversations before the Reichhardts’ analysis came out, that they had considered both factors the Reichhardts’ raise. They also requested from the NSF access to additional administrative data on all the applications as well as the evaluations of those applications. That request went unheeded. “I find it strange that as a government agency they can withhold that information,” Wakimoto says. Chen and her colleagues along with Ginther and others I spoke to all said that this additional information on applicants—as well as information on the racial identification of the reviewers, program officers, and other relevant NSF staff—is needed to tease out the causes of the disparities. “Once [researchers] have cracked open that data, we will be able to see clearly what’s driving the differences in funding success,” Ginther says. “It behooves the NSF to do the heavy lifting of a high-quality study with all the data. ” The issue of diversity in scientific fields recently rose to the forefront with the August 9th signing of the CHIPS and Science Act, a $280 billion package that invests in research and development—primarily targeting the semiconductor industry. The legislation contains several provisions aimed at diversifying the STEM workforce, including the creation of a Officer position at the NSF. Within this emerging environment, many have called for the NSF to start implementing measures to reduce the impacts of any racial biases in the grant process. These measures include double-blind review of grants—where all identifiers of the submitting scientists are removed—and bias training for all reviewers. and Ivey are skeptical that these measures would have any real impact. “The race and core values of a person are inextricably linked…and no amount of bias training is going to change someone’s core values,” Ivey says. Liu notes that truly double-blind review is impossible to achieve. Even if the name is blanked out, “it’s really easy to figure out who the researcher is” from the application’s summary and reference list, she says. Chen and Kahanamoku point out that the NIH tried both bias training and blind-review procedures after the 2011 study that highlighted similar racial trends at the agency. However, analysis of 2019 NIH data shows that the same disparities still exist and, for some groups, have worsened.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: Notably, the highest reductions in exchangeable acidity occurred in composted BSG+PD(1:1). This reduction, which may not be unconnected with its excellent performance in this study, can be attributed to the liberation and loss via leaching of Al3+ chelated with base-forming cations during PD mineralisation in this acidic sandy soil (Garzón et al. , 2011; Tang et al. , 2007). The data further showed nominally higher values of soil organic carbon, total N, available P and base-forming cations (except Mg2+) in composted BSG plus PD than composted BSG plus CD, regardless of mixing ratio (Ebido & Ndubuaku, 2019). Amaranth growth and yield were influenced mostly by the soil content of Ca2+. Calcium is a crucial plant nutrient for the growth and development of plants (McLaughlin & Wimmer, 1999), particularly vegetable crops (Hao & Papadopoulos, 2003; Parra-Terraza et al. , 2008). Soil structure enhancement due to organic amendment mediates increased yields of short-duration vegetables in coarse-textured tropical soils (Ogunezi et al. , 2019).

Output: corroborate


Input: Claim: AI can predict and prevent all health issues before they occur. [SEP] Text: How do these findings contribute to "theories of vigilance", as claimed by the authors? Author response Essential revisions: 1. The introduction makes it clear that the authors acknowledge that there may be multiple sources of interference contributing to declining vigilance over time: the encoding of sensory information, appropriate responses to the stimuli, or a combination of both. In the introduction, it would help if the authors review how infrequent targets affect response patterns. We added the relevant information about response patterns to the Introduction as below: “To date, most vigilance and rare target studies have used simple displays with static stimuli. […] Overall, vigilance decrements in terms of poorer performance can be seen in both accuracy and in reaction times, depending on the task. ” In addition, it would help if the theoretical approach and assumptions of the authors were explicitly stated. For instance, the a priori assumptions surrounding the connectivity analysis should be acknowledged and discussed in the interpretation of the pattern of results (e. g. , p. 32, line 658). Specifically, the focus on connectivity between frontal and occipital areas seems to assume the effects are related to sensory processing alone, but this does not preclude other influences. For instance, effects could also occur on response patterns. These considerations should be added as caveats to the interpretation. We have now carefully reviewed the manuscript to be sure our assumptions and approach for the connectivity analyses are explicit. We have added the suggested material to the interpretation of the pattern of results, and acknowledge the potential for other influences on the connectivity results as caveats to our interpretation. We now limit our discussion of the connectivity results as relevant to evaluating sensory aspects of information encoding (in the Materials and methods section) as below: “There are a few considerations in the implementation and interpretation of our connectivity analysis. First, it reflects the similarity of the way a pair of brain areas encode “distance” information during the whole trial. This means that we could not use the component of time in the evaluation of our connectivity as we have implemented elsewhere (Karimi-Rouzbahani et al. , 2019; Karimi-Rouzbahani et al. , 2020). Second, rather than a simple correlation of magnitudes of decoding accuracy between two regions of interest, our connectivity measure reflects a correlation of the patterns of decoding accuracies across conditions (i. e. , distances here).

Output: contrast


Input: Claim: Certain researchers argue that the properties of proteins can predict their functions accurately. [SEP] Text: “The NSF should consider the past 10 years at the NIH as a cautionary tale of well-intentioned solutions failing to create meaningful progress,” Chen says. A. Tripati—one of Chen’s coauthors from the University of California, Los Angeles—would like to see the NSF complete a full analysis of their public and private data to look for inequities in how science is funded. A. Tripati—one of Chen’s coauthors from the University of California, Los Angeles—would like to see the NSF complete a full analysis of their public and private data to look for inequities in how science is funded. × So what could change the status quo? Ivey would like to see the makeup of all review panels represent the diversity of the community whose grants they are judging: “It’s not about quotas but about representation of views and values. ” Kahanamoku agrees. Measures such as diversity training miss the root causes of the disparities, they say. “What a person considers as a scientific question worth investigating changes a lot based on their personal background and the values they hold. ” Currently, the funding structures don’t account for those differences. To illustrate the idea, Kahanamoku turns to their own research on the impact of climate change on small Hawaiian communities. The questions they explore are very specific to these communities, such as the effect of climate change on the fish production from a specific lake. Another scientist—one without a Hawaiian background, for example—might instead ask: what are the water quality indicators for this region and how does that change over time? Although both sets of questions address climate change, studies have shown that different research frameworks have different likelihoods of receiving funding. At the NIH, for example, investigators found that proposals considered to be of fundamental or general interest were more likely to be funded than those considered to be of relevance to just one community. The former rarely had Black PIs, while the latter often had Black PIs. Whatever the outcome of the peer review of the two funding-equity studies, Chen and her team hope to see reform in grant-awarding systems. They know that it will be challenging and will take time, but they say that the appetite for change is palpable.

Output: pass


Input: Claim: Organic farming often relies on natural fertilizers like compost and manure rather than synthetic chemicals. [SEP] Text: However, with Ca that controlled amaranth yield in this study also being involved in plant absorption of soil nitrate (Habbasha & Ibrahim, 2015) and ammonium (Feagley & Fenn, 1998), leaf fresh-matter yield depending on soil total N here would be understandable. Beyond Ca’s role in N transport, it interacts with P and K, enhancing the absorption of ammonium, P, and K (Acosta-Durán et al. , 2007; Habbasha & Ibrahim, 2015). The excess accumulated N is stored, and its adsorption stimulates photosynthetic processes and increases plant growth and leafiness. This relationship between N and Ca in soils would, therefore, explain the dependence of leaf fresh-matter yield on soil total N content. Indeed, composts have elevated pH and supply N (mostly in the form of nitrates) as well as the base-forming nutrient elements (Rosen & Bierman, 2005; Tiquia, 2003). In this study, composted BSG+PD(1:1) was the best treatment in terms of vegetative growth and yields of amaranth. This is most likely because amaranth production in the tropics depends on the N-fertility of the soil (Adeoluwa et al.

Output: corroborate