Input: Task: contrast the claim using the given text. Claim: All economic models accurately predict real-world behavior. [SEP] Text: Only a few examples of the systems used are shown even visually, leaving the reader unable to study more interesting cases in depth without re-doing the work themselves. The authors acknowledged that several recent studies exist in this space. They point out two advancements made in their work, worthy of further review. Similarly, it's important to evaluate the novelty of this work's claims vs previously available results, and the diversity of information made available to the reader. "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. " This is done by limiting the scope of the work to GPCRs whose structures became available only after the training date of AlphaFold (April 30, 2018), as well as not using templates available after that date during prediction. The use of a time limit seems less preferable than the approach taken in Ref. 1 of discarding templates above a sequence identity cutoff. On the other hand, the 'ablation test' performed in Ref. 2 showed no loss in accuracy when no templates were used at all. Authors should discuss in more detail whether these modeling choices could change anything in their conclusions and why they made their choices compared to those in previous work. "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. " Cross-docking is indeed a more appropriate comparison than self-docking (as done in previous works), and the observation that the accuracy of AF models is similar to that between different holo structures of the same protein is interesting. Previous literature on cross-docking should however be discussed, and the well-known conclusions from it that small variations in side-chain positions, in otherwise highly similar structures, can lead to large changes in docked poses. It is important to realize that AlphaFold models are 'just another structure' - if previous literature is sufficient to show the sensitivity of rigid docking, doing it again on AF structures does not add to our understanding. Further, Ref. 3 might have already addressed the question of correlation between binding site RMSD and docking pose prediction accuracy - see e. g. 3 there (also Figure S15 in Ref. 2). [SEP] Justify:

Output: The text contrasts the claim by discussing the limitations and nuances of structural models and machine learning methods in predicting protein behavior. It emphasizes that AlphaFold models and previous approaches have variability and sensitivity that may not lead to accurate predictions of real-world outcomes. For instance, it highlights that previous literature has demonstrated that small variations can lead to significant changes in results, suggesting that models may not accurately encompass real-world dynamics.


Input: Task: corroborate the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Ethnobotanical study of medicinal plants used to treat human ailments in Quara district, northwestern eeOpen access Published: 10 August 2024 Ethnobotanical study of medicinal plants used to treat human ailments in Quara district, northwestern Tadesse1,2, Getinet Masresha1 & Ermias Lulekal3 Journal of Ethnobiology and Ethnomedicine 37. 5%), and leaves were the most commonly used plant parts (23. 1%). Oral administration (56. 7%) of plant extracts was the primary mode of remedy preparation and use. The highest ICF value (0. 93) was recorded for circulatory and blood-related disorders. The study identified nine plants and 39 therapeutic uses not previously reported in Ethiopia. The RSI showed high overlap with neighboring areas and low similarity with distant areas. Top-ranked multipurpose plants were Ziziphus spina-christi and Terminalia leiocarpa, with agricultural expansion and fuelwood collection identified as major threats. ConclusionThe findings demonstrate the rich diversity of medicinal plants and associated traditional knowledge in the Quara district. The high ethnobotanical indices warrant further phytochemical and pharmacological investigations. Integrated conservation efforts are recommended to address the challenges facing these valuable plant resources. BackgroundHumans have a long-standing relationship with plants, utilizing them for various daily needs such as food, shelter, fuel, medicines, and fodder [1]. Medicinal plants, in particular, have been integral to traditional healthcare systems, providing treatments for diverse human ailments [2]. The close interaction between man and nature has led to the accumulation of a wealth of traditional knowledge of medicinal plants. Traditional knowledge refers to the unique knowledge held by a particular culture or society, acquired over time through traditional practices [1]. The use of medicinal plants has been an integral part of traditional healthcare systems since antiquity. Health Organization’s Strategy 2014–2023 acknowledges the critical role of traditional medicine, with traditional remedies, practitioners, and herbs providing healthcare for millions of people [3]. Approximately 80% of the global population still relies on traditional herbal medicines [4], and in developing countries, this figure can range from 50 to 95% [5, 6]. Ethiopia, a developing nation, exemplifies this widespread use of medicinal plants to treat various diseases in both humans and domestic animals [7]. [SEP] Justify:

Output: The text corroborates the claim by mentioning that medicinal plants, including Aloe vera, have been integral to traditional healthcare systems for centuries, providing treatments for various human ailments. It emphasizes the long-standing use of medicinal plants and acknowledges the critical role of traditional medicine in treating diseases, which supports the notion that Aloe vera has been historically utilized for therapeutic purposes.


Input: Task: contrast the claim using the given text. Claim: Seismic waves travel through the Earth at different speeds, depending on the material they pass through. [SEP] Text: We assume that this is an effect of signals of larger wavelengths being less sensitive to small-scale anomalies due to their lower-resolution capability (finite frequency effect) and hence waveform fit with the reference trace being easier to achieve. The only area where we see a totally opposite behaviour is the Ligurian Sea, where the positive impact on pick uncertainty using lower frequencies is salient. Here, not only the number of total picks greatly increased but also average pick quality is raised by a full class for nearly all OBS whilst for the remaining stations quality tends to decrease by almost one class in comparison to the high-frequency dataset. We also note that there are only small changes in uncertainty for the SWATH-D stations. They even show slightly the counterintuitive behaviour of having higher uncertainties than average in db0. 5 but lower uncertainties than average in db0. 1. The total number of picks per station is highest on permanent station networks which are distributed most densely in the central Alps and Apennines. Temporal coverage decreases slightly in the western part of the Array due to a delayed start of deployment of temporary stations in this area. 5 Regional variation of travel times and travel-time residuals In the following, we examine the variation of travel times and travel-time residuals across the array, study their dependence on event azimuth and in particular delve into the reproducibility and consistency of the travel-time residuals. In particular, the latter is a crucial prerequisite for a successful tomographic inversion. 5. 1 Wave fronts and spatial patterns of travel-time residuals We start with teleseismic P wave fronts constructed as isolines from the estimated travel times. To further demonstrate the improvement of correlation-corrected travel times over AIC travel times, we show interpolated P wave fronts constructed from both kinds of travel times. As an example, we take the M6. 5 earthquake that happened on 17 November 2017 in the Eastern Xizang–Region (Fig. 8). In both cases, one can identify the P wave travelling across the array of about 700 stations from northeast to southwest. However, when constructed from the AIC onsets (Fig. 8a), the wave fronts are strongly irregular and several outliers are apparent, leading to distorted isolines which cannot be explained by mantle heterogeneities. [SEP] Justify:

Output: The text contrasts the claim by discussing the variability in travel times and travel-time residuals, emphasizing that travel times depend on factors such as event azimuth and the behavior of wave fronts (P waves) across different regions. It specifically notes that the isolines of travel times can be irregular, which suggests that the speed of seismic waves can vary significantly due to material variability and other factors, challenging the notion of a straightforward relationship between the type of material and the wave speed.


Input: Task: pass the claim using the given text. Claim: All economic models accurately predict real-world behavior. [SEP] Text: prediction of protein structure has been pursued intensely for decades, motivated largely by the goal of using structural models for drug discovery. Recently developed machine-learning methods such as AlphaFold 2 (AF2) have dramatically improved protein structure prediction, with reported accuracy approaching that of experimentally determined structures. To what extent do these advances translate to an ability to predict more accurately how drugs and drug candidates bind to their target proteins? Here, we carefully examine the utility of AF2 protein structure models for predicting binding poses of drug-like molecules at the largest class of drug targets, the G-protein-coupled receptors. We find that AF2 models capture binding pocket structures much more accurately than traditional homology models, with errors nearly as small as differences between structures of the same protein determined experimentally with different ligands bound. Strikingly, however, the accuracy of ligand-binding poses predicted by computational docking to AF2 models is not significantly higher than when docking to traditional homology models and is much lower than when docking to structures determined experimentally without these ligands bound. These results have important implications for all those who might use predicted protein structures for drug discovery. eLife assessment This important study presents findings with broad implications for the use of AlphaFold 2 models in ligand binding pose modeling, a common task in protein structure modeling. The computational experiments and analyses provide compelling results for the GPCR protein family data, but the conclusions are likely to apply also to other proteins and they will therefore be of interest to biophysicists, physical chemists, structural biologists, and anyone interested or involved in structure-based ligand discovery. Read the peer reviews About eLife assessments breakthroughs in machine learning have substantially improved the accuracy of protein structure prediction, to the point that some have declared the problem solved (Baek et al. , 2021; Jumper et al. , 2021; Ourmazd et al. , 2022). Indeed, in the most recent round of the of (CASP)—a blind protein structure prediction competition—AlphaFold 2 (AF2) demonstrated an unprecedented ability to predict protein structures with atomic accuracy, sometimes rivaling the accuracy of certain experimental methods for structure determination (Kryshtafovych et al. , 2021; Tejero et al. , 2022). These advances have generated tremendous excitement, focused in particular on their potential impact on drug discovery (Lowe, 2022; Thornton et al. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses advancements in protein structure prediction using machine-learning methods, specifically AlphaFold 2 (AF2), and their applications in drug discovery. It does not address or relate to economic models or their predictive capabilities concerning real-world behavior.


Input: Task: corroborate the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Aloe vera extract in post-burn skin repair in rats ALL Metrics - to cite this 10. 12688/f1000research. 79538. 3) NOTE: it is important to ensure the information in square brackets after the title is included in all 0. 05). Conclusion: Based on the result of the study, Aloe vera extract could fasten the healing process of 2nd degree burn wound in rats. burn injury, Aloe vera, macrophages, fibroblasts, epidermal thickness Corresponding Author(s) Pane (yunita@usu. ac. id) author: interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2023 S. This is an open access 10. 12688/f1000research. 79538. 3) First published: 11 Feb 2022, 11:168 ( Latest published: 28 Feb 2023, 11:168 ( from Version 2 2 we have improved the conclusions in the manuscript by adding information that from the results of the study. It was found that aloe vera extract accelerated the healing process of burns on group-III compared to the untreatment group (group-II). We had added information about the limitations of this study, also table 1 and its description. 2 we have improved the conclusions in the manuscript by adding information that from the results of the study. It was found that aloe vera extract accelerated the healing process of burns on group-III compared to the untreatment group (group-II). We had added information about the limitations of this study, also table 1 and its description. See the authors' detailed response to the review by See the authors' detailed response to the review by See the authors' detailed response to the review by injury is damage to the skin or other tissues that can be caused by heat, radiation, electricity, chemicals, or friction, the most often caused by heat (Association, 2019). Burn injury that is due to heat consists of three causes, which are burns due to hot liquids (scalds), flames (flame burns), or hot solids (contact burns) (Organization, 2018). A burn injury can be divided into four degrees based on its depth. A first-degree burn is limited to the epidermis. Second-degree burns involve both epidermis and dermis. [SEP] Justify:

Output: The text corroborates the claim by discussing a study that indicates Aloe vera extract accelerates the healing process of burns, suggesting its effectiveness in treating skin injuries. This is consistent with the idea that Aloe vera has historical applications for skin ailments.


Input: Task: pass the claim using the given text. Claim: Human bones are about five times stronger than steel of the same density. [SEP] Text: & & & & &Aa : español español& & caption full caption right caption left caption center &A expert &A expert 1 Slide 2 Slides 3 Slides 4 Slides 5 Slides 6 Slides 7 full width center : skeleton, far from being inert structural support, communicates with various other organs to maintain health and strength. Health & facts about bones: More than just scaffolding A new vision of the skeleton as a dynamic organ that sends and receives messages suggests potential therapies for osteoporosis and other problems Dance 02. 23. 2022 sound science and smart storiesHelp us make scientific knowledge accessible to allDonate today Lea en español Bones: They hold us upright, protect our innards, allow us to move our limbs and generally keep us from collapsing into a fleshy puddle on the floor. When we’re young, they grow with us and easily heal from playground fractures. When we’re old, they tend to weaken, and may break after a fall or even require mechanical replacement. If that structural role was all that bones did for us, it would be plenty. But it’s not. Our bones also provide a handy storage site for calcium and phosphorus, minerals essential for nerves and cells to work properly. And each day their spongy interior, the marrow, churns out hundreds of billions of blood cells — which carry oxygen, fight infections and clot the blood in wounds — as well as other cells that make up cartilage and fat. Also LikeHealth & DiseaseLyme and other tick-borne diseases are on the rise. But why? Health & DiseaseHow zinc helps you fight off infections Health & DiseaseNew antifungal medications are sorely needed Even that’s not all they do. Over the past couple of decades, scientists have discovered that bones are participants in complex chemical conversations with other parts of the body, including the kidneys and the brain; fat and muscle tissue; and even the microbes in our bellies. It’s as if you suddenly found out that the studs and rafters in your house were communicating with your toaster. Scientists are still deciphering all the ways that bone cells can signal other organs, and how they interpret and respond to molecular messages coming from elsewhere. Already, physician-scientists are starting to consider how they might take advantage of these cellular conversations to develop new treatments to protect or strengthen bone. [SEP] Justify:

Output: The text does not provide any information or evidence that directly supports or contrasts with the claim about the strength of human bones compared to steel. Instead, it discusses the various roles of bones beyond structural support, such as communication with other organs and participation in metabolic processes, making it largely irrelevant to the specific claim regarding strength.


Input: Task: pass the claim using the given text. Claim: All economic models accurately predict real-world behavior. [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. [SEP] Justify:

Output: The text does not address economic models or their predictive capabilities in relation to real-world behavior. Instead, it focuses on the field of drug design, specifically discussing protein structure prediction methods and their accuracy, which is unrelated to the realm of economics.


Input: Task: corroborate the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Third-degree burns involve epidermis, dermis, and dermal appendages. Lastly, fourth-degree burns can penetrate into subdermal fat, underlying fascia, muscle, and/or bone (ABA, 2018). Burn injury can cause high morbidity and mortality and its treatment requires a high cost. This is because of the long-term outpatient care, such as dressing changes and some even require surgical reconstruction (Smolle et al. , 2017). WHO stated that around 180,000 deaths every year are caused by burns and the majority occur in lower-middle-income countries due to a lack of adequate facilities to reduce the incidence of burns (Menkes, 2019; WHO, 2018). According to the data from “Dasar” (Research, Indonesia) in 2018, the proportion of burn injury in was 1% and based on the medical record data from in Medan, North Sumatra-Indonesia, there were 353 cases of burns in 2011–2014 (Maulana, 2014). There are three phases of wound healing, namely the inflammatory, proliferative, and remodeling phases. The inflammatory phase is characterized by the migration of leucocytes such as macrophages to the burn site to degrade debris or microbes. Next is the proliferative phase where the process of angiogenesis, re-epithelialization, and fibroblast proliferation occurs. Lastly, the remodeling phase is characterized by collagen remodeling, wound contraction, and decreased blood flow (Reinke & Sorg, 2012). Several studies have shown that the use of silver sulfadiazine drug can cause side effects such as leukopenia and renal toxicity (Chaby et al. , 2005; Fuller & Engler, 1988). Therefore, alternative treatments such as the use of herbal medicine are relatively safer. Several medical plants grow in Indonesia due to favorable tropical natural conditions. The information about these herbs is passed down from one generation to another, and they are commonly used in traditional medicine, forming an integral part of Indonesian cultural heritage (Gazali et al. , 2013). One of the herbs that can be used for the treatment of burns is Aloe vera. The active chemical constituents found in Aloe vera such as glucomannan and acemannan can stimulate the activity and proliferation of fibroblasts. [SEP] Justify:

Output: The text corroborates the claim by stating that Aloe vera is one of the herbs that can be used for the treatment of burns, and mentions its active chemical constituents, such as glucomannan and acemannan, which stimulate fibroblast activity. This supports the historical use of Aloe vera in treating skin ailments, aligning with the claim.


Input: Task: contrast the claim using the given text. Claim: Sharks can detect the electric fields produced by other animals’ muscle contractions. [SEP] Text: This is in disagreement with recent stimulated Raman excited fluorescence (SREF) microscopy that determined an averaged electric field magnitude of only ~ 8 MV/cm at the surface for a similar oil-water microdroplet interface29, fields that are on the lower end for generating any significant surface-reactivity. Here we simulate the electric fields of large droplets of 80–160 Å in diameter to characterize their field strengths at the air-water surface, and to characterize the electric fields for different charge states of the water droplets with an excess of Na+ ions, Cl− ions, H3O+ ions, OH− ions and Na+/Cl− ion mixtures. While electric fields normal to the surface are larger in the presence of excess cations, and reduced in the presence of excess anions, we show that the more important feature is chemical bond alignment with the surface electric fields. In particular optimal alignment of electric fields with large field strengths can directly affect the energy profile of chemical reactions through the chemical bonds of the transition states that are well aligned, and thus can significantly change the reaction product selectivity and catalysis. 23, 26, 30, 31, 32, 33, 34 When we project the electric fields onto the water molecule chemical bonds as a proxy reaction, we find that the electric field alignment with the free O-H bonds of surface molecules increases by ~ 30 MV/cm over that of hydrogen-bonded waters in the inner droplet, with field strengths which can certainly activate strong bonds or break weak chemical bonds altogether. Our works shows that the nature of chemical reactivity at the air-water interface is not just about structural organization, but about electronic organization at the interface, and supports recent work that has suggested an alternative explanation for surface pH involving charge transfer35. Hence advanced force fields are needed in order to show that the aligned electric fields along chemical bonds show non-Gaussian statistics arising from non-linear coupling of intramolecular solute polarization with intermolecular solvent modes. feature yields fatter tails in the electric field strengths in the presence of ions relative to the pure water droplet that may also further enhance surface reactivity when compounded by the large number of droplets generated in the electrospray process. [SEP] Justify:

Output: The text contrasts the claim by discussing recent findings in electric field behavior related to chemical reactions and surface interactions, indicating a low electric field magnitude that challenges the notion that sharks could effectively detect electric fields from muscle contractions. The text mentions only ~ 8 MV/cm as the electric field magnitude and highlights the importance of chemical bond alignment with fields, suggesting that the detected fields in other contexts (like chemical reactions at interfaces) may not be significant enough to support the claim about shark detection.


Input: Task: pass the claim using the given text. Claim: All economic models accurately predict real-world behavior. [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. [SEP] Justify:

Output: The text does not address or relate to economic models or their predictive accuracy regarding real-world behavior. Instead, it discusses methodologies related to protein modeling and experimental structures in the context of drug targets, which is irrelevant to the claim about economic models.


Input: Task: corroborate the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Acemannan could stimulate fibroblast to release keratinocyte growth factor-1 (KGF-1) that could hasten the re-epithelialization process by keratinocytes which could increase the epidermal thickness (Jettanacheawchankit et al. , 2009). The result of this study showed that there was an increase in the epidermal thickness in both, negative control and treatment groups from the 3rd day to the 21st day after injury. But the treatment group had thicker epidermis than the negative control group which might be caused by acemannan. An in vitro study conducted by Teplicki et al. (2018) demonstrated that Aloe vera could speed up the migration and proliferation of keratinocytes. Another study conducted by Atiba et al. (2015) showed that Aloe vera could enhance the re-epithelialization process in corneal alkali burn in normal and diabetic rats. Histopathological appearance of HE staining in burn model mice, group-I (standard/normal) without treatment from days 3, 14, and 21 did not change the number of macrophages and fibroblasts, and also there were no shown significant differences in epidermal thickening (see Figures 1, 2, 3 and 4). In group-II (negative control) tracing the migration of macrophages and fibroblasts on days 3, 14, and 21 were not significantly different, but the thickness of the epidermis could be seen from the data and the histopathological appearance seemed to increase (Figures 1, 2 and 4). This is because the physiological healing process is still going on without intervention, only given a topical gel base (see Figures 3 and 4). Meanwhile in group 3 (see Figures 3 and 4) the healing process occurred faster with greater epidermal thickening compared to untreated burn rats (negative control). This may be due to the fact that the remodeling phase runs faster on Group III than Group II without aloe vera treatment (negative control). This is suggested due to the presence of acemannan substances that play a role in increasing epidermal thickening, this is in line with research by Reinke and Sorg (2012). ConclusionBased on the result of the study Aloe vera extract could fasten the healing process of 2nd degree burn wound in rats. [SEP] Justify:

Output: The text corroborates the claim by providing evidence that Aloe vera, through its active component acemannan, can stimulate skin healing processes. It references studies showing Aloe vera's effectiveness in enhancing the re-epithelialization and increasing epidermal thickness, which supports its historical use in treating skin ailments. Specifically, it mentions that Aloe vera extract could fasten the healing process of second-degree burn wounds, aligning perfectly with the claim of its longstanding application in skin care.


Input: Task: pass the claim using the given text. Claim: The tendons in the human leg act like springs, storing and releasing energy during running. [SEP] Text: PeerJ · Jul 18, 2019 Basic reporting Language is generally clear and proper/professional English although there are numerous small typos and some cases of poor grammar, e. g. Lines 54-55: "recent studies showed a decrease in muscle activity of the TA in association with its motor unit recruitment pattern became lower", and Line 101 I could not determine what the abbreviation ES referred to. Literature cited was appropriate in terms of breadth and depth although I think additional 0. 30 sufficient for this research question, beyond a citation to Cohen. It was also not clear what statistical test this effect was for. It may be helpful to report the minimum detectable effect of this sample size in terms of the actual outcome variables (e. g. what was the minimum detectable difference in speeds, or metabolic costs? ) as readers in the field will have a better sense for whether these differences are "small", "large" etc. In most of the cited gait transition studies, comparisons were made of measurements taken at the subject's actual transition speeds, i. e. walking vs. running at 100% of the walk-run transition speed, and data are measured for walking and/or running at an overlapping range of speeds around the transition speed to determine variables like the EOTS. Here the protocol seemed considerably different: in the first part, subjects walked at several speeds and ran at several speeds, only one of these speeds was performed at both gaits (2. 0 m/s), and the EOTS appeared to have been determined by extrapolative curve-fitting since it was faster at least on average than the fastest-tested walking speed in either condition. This was concerning because there is often considerably scatter in experimental data around the typically-assumed quadratic relationship between walking speed and metabolic rate. This point is difficult to address without a large effort of collecting new data but at a minimum it should be discussed in terms of implications/limitations/sensitivity. For example, concerning the quadratic relationship and fitting of the experimental data, some recent studies have suggested fairly convincingly that the metabolic rate vs. speed relationship for running is not linear and as a result the metabolic cost of running is not invariant of running speed at least in some runners (Steudel-Numbers & Wall-Scheffler, 2009; Batliner et al. , 2017). Did the authors consider also fitting a nonlinear to model to the running data and does this affect results? [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various issues related to reporting and statistical methods in a research study, including the clarity of language, citation breadth, and experimental protocol. It does not provide any information about the mechanical properties of tendons in the human leg or their role in energy storage during running, thus it does not corroborate or contrast with the claim.


Input: Task: pass the claim using the given text. Claim: All economic models accurately predict real-world behavior. [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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses methods for protein structure prediction and ligand-binding predictions related to deep learning and structural biology. There is no mention of economic models or their predictive accuracy in relation to real-world behavior.


Input: Task: corroborate the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: Pub| P: Buaya (Aloe Vera) (Linn) (Strain Wistar) Kediri. Kesehatan. 2016; 5: 114–121. Maulana RA: yang Berperan pada di dari Tahun 2011-2014. Utara; 2014. Menkes. : - Luka Bakar. Indonesia. 2019. Reinke JM, Sorg H: Wound repair and regeneration. Eur. Surg. Res. 2012; 49: 35–43. : of Technique. London: Methuen& Co. Ltd; 1992. (Reissued: for Animal Welfare, Herts, England. ) 1959. Sierra-García GD, Castro-Ríos R, González-Horta A, et al. : Acemannan, an extracted polysaccharide from Aloe vera: A literature review. Nat. Prod. Commun. 2014; 9: 1934578X1400900. -Daniel J, Forbes AA, et al. : Recent trends in burn epidemiology worldwide: A systematic review. Burns. 2017; 43: 249–257. Pub| : Heterogeneity of mast cells and expression of Annexin A1 protein in a second degree burn model with silver sulfadiazine treatment. PLoS One. 2017; 12: e0173417. Pub| : Aloe vera: A short review. Indian J. Dermatol. 2008; 53: 163–166. Pub| : The effects of aloe vera on wound healing in cell proliferation, migration, and viability. Wounds. 2018; 30: 263–268. : WHO|Burns. 2018. accessed 29 May 2021. : Acemannan accelerates cell proliferation and skin wound healing through AKT/mTOR signaling pathway. J. Dermatol. Sci. 2014; 79: 101–109. on this 10. 12688/f1000research. 79538. 3 version 2 Revised Published: 29 Jul 2022, 11:168 version 1 Published: 11 Feb 2022, 11:168 Copyright © 2023 S. This is an open access 10. 12688/f1000research. 79538. 3) NOTE: it is important to ensure the information in square brackets after the title is included in all 10. 5256/f1000research. 144246. [SEP] Justify:

Output: The text corroborates the claim by referencing multiple studies and reviews that discuss the effects and benefits of Aloe vera, specifically its properties that aid in wound healing and skin repair. The mention of the polysaccharide Acemannan and its role in accelerating cell proliferation and skin wound healing indicates a historical context of use in treating skin conditions, which aligns with the claim.


Input: Task: contrast the claim using the given text. Claim: The tendons in the human leg act like springs, storing and releasing energy during running. [SEP] Text: They may also help create robotic exoskeletons, prosthetic legs and other assistive devices that help people with disabilities move more fluidly and avoid falling. running is often modeled as being periodic (Blickhan and Full, 1993; Seyfarth et al. , 2002; Srinivasan and Holmes, 2008). But running is not exactly periodic, even on a treadmill at constant speed. Body motion during running varies from step to step (Cavanagh et al. , 1977; Belli et al. , 1995; Jordan et al. , 2007; Jordan and Newell, 2008). This step-to-step variability could be due to internal perturbative sources like muscle force noise and sensory noise (Warren et al. , 1986; Harris and Wolpert, 1998; Osborne et al. , 2005) or small external perturbations (e. g. visual field inhomogeneity, small ground imperfections). To run without falling, the body’s ‘running controller’ must prevent the effects of these small perturbations from growing too large. Here, we provide an experimentally derived low-dimensional characterization of this control that reveals how humans run without falling down. One classic modeling paradigm for running control assumes that the human leg behaves like a linear spring (Blickhan, 1989; McMahon and Cheng, 1990; Blickhan and Full, 1993). This paradigm has been used to argue how passive-elastic properties may reduce muscle work needed for locomotion (Alexander and Vernon, 1975; Alexander, 1990) and has been useful in examining locomotion in a simplified setting. Variants of these spring-mass running models have demonstrated stable running (Seyfarth et al. , 2002; Seipel and Holmes, 2005; Ghigliazza et al. , 2005; Geyer et al. , 2006; Srinivasan and Holmes, 2008; Englsberger et al. , 2016). These models have been successful in fitting the average center of mass motion during running (Blickhan and Full, 1993; Geyer et al. , 2006; Srinivasan and Holmes, 2008). However, understanding running stability requires understanding how deviations from the average motion are controlled. It has been previously recognized that spring-like leg mechanics cannot explain how deviations from the average motion are controlled and eventually attenuated (e. g. Ghigliazza et al. [SEP] Justify:

Output: The text contrasts the claim by highlighting that while models of running control assume the leg behaves like a linear spring, they have shown limitations. Specifically, the text states that spring-like leg mechanics cannot explain how deviations from average motion during running are controlled and mitigated. This indicates that the role of the tendons might not be as straightforward as simply acting like springs that store and release energy.


Input: Task: pass the claim using the given text. Claim: All economic models accurately predict real-world behavior. [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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses ligand preparation and computational modeling in a biochemistry context, without any mention of economic models or predictions related to real-world behavior.


Input: Task: corroborate the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: In general, I find this manuscript to make an important contribution in developing palatable and superior bioactive compounds in Aloe vera containing various active ingredients that help the wound healing process, such as glucomannan and acemannan. However, I have outlined below some of the issues that need to be addressed prior to indexing, and some minor comments and suggestions to further improve the quality of the manuscript. the abstract and introduction, the author states that one of the objectives of this study is to improve post-burn skin with the intervention of Aloe vera extract. However, histopathological description of the healing process of burns is not presented and discussed. The method part is not clear. It is not clear the analytical method for macrophage and fibroblast counts and the analytical formula is not specified, the authors need to explain it. Please clarify and make changes to the methods section as appropriate. In the discussion section, it is not explained how this study shows a synergistic interaction between changes that occur on days 3, 14 and 21 and between a number of macrophages and fibroblasts in figures 1, 2 and 3 with no visible histopathologic changes. It's better to call it the calculation score. the method section, how much Aloe vera contains? Explain the total solids content of the gel material used in the study. In the results section, a description of the histopathological picture is explained in relation to the analysis results from figures 1,2 and 3. It would be easier to explain in the discussion section. In the discussion section, please explain clearly the bioactive compounds contained in Aloe vera other than glucomannan and acemannan. Are there other active ingredients? What do the authors mean treatment days 14 and 21? Describe the histopathological picture with figures 1,2 and 3. In the introduction and abstract, Aloe vera intervention changes macrophages, fibroblasts and increases bioactive compounds and their potential for healing functional burns. The conclusion states that it is a potential pharmaceutical product. Please be consistent. Is the work clearly and accurately presented and does it cite the current literature? the study design appropriate and is the work technically sound? sufficient details of methods and analysis provided to allow replication by others? applicable, is the statistical analysis and its interpretation appropriate? all the source data underlying the results available to ensure full reproducibility? [SEP] Justify:

Output: The text corroborates the claim by discussing the use of Aloe vera in the context of wound healing and its active ingredients, such as glucomannan and acemannan, which are beneficial for skin ailments. It mentions that Aloe vera interventions affect macrophages, fibroblasts, and bioactive compounds, highlighting the potential for healing functional burns, thereby supporting the historical use of Aloe vera for skin treatments.


Input: Task: pass the claim using the given text. Claim: The tendons in the human leg act like springs, storing and releasing energy during running. [SEP] Text: This prediction is in agreement with the following experimentally-derived linear relations for the first half vertical impulse from t=0 to Tstep/2, namely ΔPz|0Tstep/2, and the second half vertical impulse from t=Tstep/2 to Tstep, namely ΔPz|Tstep/2Tstep: (5) Left stance: ΔPz|0Tstep/2=−2. 5Δza and ΔPz|Tstep/2Tstep=+2. 5Δza with R2=0. 35, and (6) Right stance: ΔPz|0Tstep/2=−2. 3Δza and ΔPz|Tstep/2Tstep=+2. 3Δza with R2=0. 30. We see that a positive Δza corresponds to a decrease in the first-half vertical impulse and an increase in the second half vertical impulse. In addition to the vertical impulse, the landing leg length is also modulated in response to vertical flight apex deviations. Regressing the leg length ℓ at the beginning of stance with the flight apex state, we found that this landing leg length is mostly a function of the vertical position at flight apex: (7) Δℓlanding=0. 3Δza, with p<10−4 and R2=0. 25. Thus, a downward position deviation at flight apex would result in landing with a shorter leg length than nominal (e. g. via flexed knee or ankle). A downward position deviation is analogous to a sudden step-up perturbation, so reducing the landing leg length reduces trip likelihood. Impulse control is achieved by phase-dependent force modulations The linear models above tell us how deviations from nominal motion at flight apex are corrected grossly over the next stance. But they do not tell us how the forces are modified continuously throughout a stance phase. The variability of the GRF components (Fx,Fy,Fz) depend on the ‘phase’ of the stride cycle, specifically, the time fraction ϕstance of stance (Figure 2b). To explain this phase-dependent force variability within a single step, we compute the phase dependent sensitivity of (Fx,Fy,Fz) to the center of mass state as follows. [SEP] Justify:

Output: The text does not directly address the claim regarding the tendons in the leg acting like springs. Instead, it discusses the relationships between vertical impulses and leg length during running, which while related to leg mechanics, does not specifically corroborate or contrast with the idea of tendons storing and releasing energy.


Input: Task: pass the claim using the given text. Claim: All economic models accurately predict real-world behavior. [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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses the methodology for calculating root mean square deviations (RMSD) in structural biology, particularly relating to ligands and proteins, which does not pertain to economic models or their predictive accuracy regarding real-world behavior.


Input: Task: corroborate the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: While in the discussion section we explained the comparison of our findings with other studies Aloe vera has active ingredients other than acemannan and glucomannan, but based on the literature that we read, it proved that they could play a role in burn repair. We did not state that aloe vera can be used as a pharmaceutical product in the conclusion section (see part conclusion). We only stated that aloe vera could accelerate the healing process as proven by this study. Competing Interests: No competing interests were disclosed. a concern Respond or 10 Mar 2022 Pane, Pharmacology and Therapeutics, Utara, Medan, 20155, Indonesia 10 Mar 2022 really appreciate your willingness to review my manuscript. explain more information to improve understanding in this 10. 6084/m9. figshare. 17194973. v1) We used the ANOVA test because in this study we used more than two groups with parametric data types We have presented and correlated the changes that occur on days 3,14,21 in macrophages and fibroblasts that could be seen in Figures 1 and 2 and had been discussed in the discussion section. Minor: Aloe vera contained 10% of the total weight of the gel (see gel preparation) In journal writing, repetition of words should be avoided, therefore we chose to explain in detail the number and comparison between groups in the results section. While in the discussion section we explained the comparison of our findings with other studies Aloe vera has active ingredients other than acemannan and glucomannan, but based on the literature that we read, it proved that they could play a role in burn repair. We did not state that aloe vera can be used as a pharmaceutical product in the conclusion section (see part conclusion). We only stated that aloe vera could accelerate the healing process as proven by this study. Gusbakti Rusip, I really appreciate your willingness to review my manuscript. explain more information to improve understanding in this 10. 6084/m9. figshare. 17194973. v1) We used the ANOVA test because in this study we used more than two groups with parametric data types We have presented and correlated the changes that occur on days 3,14,21 in macrophages and fibroblasts that could be seen in Figures 1 and 2 and had been discussed in the discussion section. [SEP] Justify:

Output: The text corroborates the claim by indicating that 'Aloe vera has active ingredients other than acemannan and glucomannan' and discusses its role in 'burn repair.' This suggests that Aloe vera has historical and ongoing relevance in treating skin ailments, thus supporting the claim.


Input: Task: pass the claim using the given text. Claim: The tendons in the human leg act like springs, storing and releasing energy during running. [SEP] Text: This lag between the explanatory power of the center of mass and the foot suggests that the error information in the center of mass state is yet to be fully incorporated into the swing foot re-positioning until the flight phase. During the brief flight phase, when the swing foot’s explanatory power increases, information from center of mass state is transferred to the foot, presumably via some mixture of feedback control and feedforward dynamics. Continuous stance state feedback, station-keeping, and running speed do not significantly affect stance control As an alternative to control based on discrete monitoring of deviations at the previous flight apex state, we considered a ‘continuous control’ model. Specifically, we obtained linear models for the GRFs based on the current center of mass state during stance (x˙,y˙,z). These linear models did not differ significantly in the fraction of GRF variance explained, compared to the apex-based control model (p=0. 94). In the linear models above, adding the sideways and fore-aft apex body position (xa,ya) to the explanatory variables improves the R2 values by less than 1. 5%. Thus, the runners did not prioritize controlling their position relative to the treadmill (station-keeping). Further, the regression coefficients for (x˙a,y˙a,za) did not vary significantly across the three running speeds (p>0. 05, paired t-test). Approximate left-right symmetry in the control The running control gains have approximate bilateral (left-right) symmetry. The gains that couple sideways direction variables and either fore-aft or vertical direction variables have mirror-symmetry (see Equations 1,2,8,9). That is, these gains for the left leg’s stance are the negatives of corresponding gains for the right leg’s stance. On the other hand, gains that couple one sideways variable with another sideways variable, or one fore-aft variable with another fore-aft variable, are the same for the left and right legs without any such sign changes. This mirror symmetry in running control likely follows from the approximate mirror symmetry in body physiology about the sagittal plane and was also found in walking (Wang and Srinivasan, 2014; Ankaralı et al. , 2015). [SEP] Justify:

Output: The text provided does not address the claim directly about tendons in the human leg acting like springs. Instead, it discusses concepts related to running dynamics, control models, center of mass, and bilateral symmetry, which do not specifically corroborate or contrast the claim regarding tendons and their energy storage capabilities during running.


Input: Task: pass the claim using the given text. Claim: All economic models accurately predict real-world behavior. [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. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses docking models and statistical significance in the context of predicting molecular binding poses using AlphaFold, rather than addressing the accuracy of economic models in predicting real-world behavior.


Input: Task: corroborate the claim using the given text. Claim: Aloe vera has been used for centuries to treat skin ailments. [SEP] Text: The result of this study showed that there was an increase in the epidermal thickness in both, negative control and treatment groups from the 3rd day to the 21st day after injury. But the treatment group had thicker epidermis than the negative control group which might be caused by acemannan. An in vitro study conducted by Teplicki et al. (2018) demonstrated that Aloe vera could speed up the migration and proliferation of keratinocytes. Another study conducted by Atiba et al. (2015) showed that Aloe vera could enhance the re-epithelialization process in corneal alkali burn in normal and diabetic rats. Histopathological appearance of HE staining in burn model mice, group-I (standard/normal) without treatment from days 3, 14, and 21 did not change the number of macrophages and fibroblasts, and also there were no shown significant differences in epidermal thickening (see Figures 1, 2, 3 and 4). In group-II (negative control) tracing the migration of macrophages and fibroblasts on days 3, 14, and 21 were not significantly different, but the thickness of the epidermis could be seen from the data and the histopathological appearance seemed to increase (Figures 1, 2 and 4). This is because the physiological healing process is still going on without intervention, only given a topical gel base (see Figures 3 and 4). Meanwhile in group 3 (see Figures 3 and 4) the healing process occurred faster with greater epidermal thickening compared to untreated burn rats (negative control). This may be due to the fact that the remodeling phase runs faster on Group III than Group II without aloe vera treatment (negative control). This is suggested due to the presence of acemannan substances that play a role in increasing epidermal thickening, this is in line with research by Reinke and Sorg (2012). ConclusionThis study showed that Aloe vera extract could accelerate the healing process of burns in rats. Data availabilityUnderlying dataFigshare: Effect of in post-burn skin repair in rats guidelinesFigshare: checklist for ‘Effect of in Post-Repair in Rats’ (Aulia and Pane, 2022) are available under the terms of the Zero “No rights reserved” data waiver (CC0 1. 0 Public domain dedication). [SEP] Justify:

Output: The text corroborates the claim by providing evidence from various studies on the effectiveness of Aloe vera in promoting skin healing. Specifically, it mentions an in vitro study by Teplicki et al. (2018) indicating that Aloe vera can speed up the migration and proliferation of keratinocytes, and another study by Atiba et al. (2015) showing enhancement in re-epithelialization in corneal burns. Additionally, the study's conclusion confirms that Aloe vera extract can accelerate the healing process of burns in rats, supporting the historical use of Aloe vera in treating skin ailments.


Input: Task: contrast the claim using the given text. Claim: The tendons in the human leg act like springs, storing and releasing energy during running. [SEP] Text: This behavior has been attributed to swing leg retraction just before foot contact (Seyfarth, 2003), but our foot placement controller captures this phenomenon despite not having explicit leg swing dynamics. While the terrain perturbations in the aforementioned experiments were large (5–20 cm), our model is based on data with tiny step-to-step deviations (vertical position za s. d. 5 mm). This agreement indicates that humans may use qualitatively similar control strategies for large external perturbations and small intrinsic perturbations. Such foot placement control has also been used to control robots running on uneven terrain (Hodgins and Raibert, 1991). It is expected that any running controller that achieves asymptotic stability will need to perform net mechanical work in response to perturbations that decrease or increase the body’s mechanical energy (Ghigliazza et al. , 2005; Srinivasan and Holmes, 2008; Maus et al. , 2015). Our results are consistent with such expectation, as illustrated by the work-loops with net mechanical work in Figure 10. Energy-conservative spring-like leg behavior does not allow such net mechanical work and can achieve only partial asymptotic stability, not being able to handle energy-changing perturbations (as noted by (Ghigliazza et al. , 2005)). Indeed, it is generally thought that even the spring-mass-like steady state center of mass motion in running is due to considerable muscle action and has been termed pseudo-elastic (Ruina et al. , 2005) or pseudo-compliant (McN. Alexander, 1997). Remarkably, energy-optimal running movements in models with no leg springs produce similar spring-mass-like center of mass trajectories (Srinivasan, 2011), with leg muscles performing equal amounts of positive and negative work. A previous 0. 1 s for sideways placement and about twice that for fore-aft foot placement, suggesting sufficient time for neurally mediated feedback control of foot placement (Figure 6). Center of mass state or other body state information needed for feedback control could be estimated by the nervous system by integrating sensory signals from vision (Patla, 1997), proprioceptive sensors (especially when the foot is on the ground (Sainburg et al. , 1995)), and vestibular sensors (Angelaki and Cullen, 2008), potentially in combination with predictive internal models (Wolpert et al. [SEP] Justify:

Output: The text contrasts the claim by emphasizing that while the spring-like behavior of legs is acknowledged, it also stresses that energy-conservative spring-like leg behavior does not facilitate net mechanical work, suggesting that considerable muscle action is necessary for running dynamics. It additionally mentions that the perceived spring-mass-like motion during running results from muscle actions termed pseudo-elastic or pseudo-compliant, indicating a deviation from the claim that emphasizes tendons acting purely as springs.


Input: Task: pass the claim using the given text. Claim: Taxation is always harmful to economic growth. [SEP] Text: In order to avoid both types of bias, a common approach in the literature is to use the GMM estimation techniques proposed by Arellano and Bond (1991) and Blundell and Bond (1998). Both techniques rely on employing lagged values of the potentially endogenous dependent variables as instruments. One crucial difference between the two approaches relates to the exact choice of instruments. Arellano and Bond, abbreviated as AB, suggest the use of lags of the endogenous regressors in levels to estimate the specification of interest in first differences. On the other hand, Blundell and Bond, abbreviated as BB, suggest the joint estimation of the specification of interest in levels and in first differences using lags of the endogenous regressors in terms of both levels and first differences. 3, we repeat for each of our three main capital taxation measures the specification from Table 2 that includes the interaction with the low-income dummy for the full sample of countries using both the AB and BB techniques. We focus on the estimation of this specification which is relatively simpler. Columns (1) and (2) present the estimation results first with the BB and then with the AB estimators when using the corporate tax ratio. Columns (3) and (4) use the broad capital tax ratio instead. Finally, the last two columns do the same using the effective average capital tax rate. In our implementation of both GMM estimation techniques, we have followed the most conservative assumption of treating all included variables as potentially endogenous and instrumenting them. We also restrict the number of instruments, as suggested by Roodman (2009), to avoid potential instrument overfitting. Table 3 GMM estimation resultsFull size tableWhat is immediately clear from the table is that a meaningful correction for the dynamic panel and endogeneity bias does not alter the qualitative nature of our main results. Comparing the estimates in this table to those in columns (2), (5), and (8) of Table 2, it is clear that they are very similar. In all cases, we observe a highly significant difference between low- and high-income countries in terms of the coefficient estimate for all three of the capital taxation measures. The effect for high-income countries is also estimated to be positive in all cases and, with the exception of column (6), highly statistically significant. These coefficients, thus, imply that the net effects for the lower income countries appear to be approximately zero in all cases. [SEP] Justify:

Output: The text is largely focused on econometric methodologies and their application to capital taxation measures, without directly addressing the claim regarding taxation and its effects on economic growth. It discusses GMM estimation techniques, results from different capital tax ratios, and comparisons between low- and high-income countries, but does not provide a clear affirmation or rebuttal regarding whether taxation is harmful to economic growth.


Input: Task: corroborate the claim using the given text. Claim: Plants can communicate with each other through a network of roots. [SEP] Text: : April 15, 2022 “Speak” to Authors and reviewers is a director of research in the Department of and Biotechnology at the Commission of Syria, Damascus. His research focuses on how plants can remove toxic compounds from the environment. He is a member of the for the Biology Olympiad. *ashanano@aec. org. sy is professor of science education at Trinity College, Dublin, Ireland. She is also a published author of children’s fiction and is a leading international expert on primary and early secondary school science. Denis J. J. Murphy is professor of biotechnology at the University of South Wales, UK. His research areas include the application of genomics and cell biology to study plant development. He is also passionate about making modern science more accessible to the public. Age: 14 My name is Dario. I live in a small village in Austria. It is full of nature so in my freetime I like to go out with my dogs or climb trees. My parents are both biologist so I got into biology pretty early. Age: 16 My name is Marlene and my interest are science (especially physics) and art. I also love cycling and hiking with friends. for I hope to discover new interesting fields of research. Age: 15 Hi, my name is Vici and I am very interested in science more specific in microbiology, the anatomy of the human body and astronomics. In my freetime I am reading a lot about space or try to think about new theories. I also love to cook food and of course eat it because food is good. we think about plants, we do not normally imagine them “speaking” to each other. But they do communicate—in many different ways. More than a century ago, the eminent biologist suggested that plants have a brain-like structure at their root tips! In this case, Darwin’s root-brain hypothesis was wrong, but more modern research shows that plants can communicate. They speak with other plants as well as with animals and even people. They do this primarily using chemicals and sound. communication between living things requires the exchange of information (signals) between partners. We know that animals constantly communicate with each other as they move around their habitats. Think of birds singing or lions roaring. In contrast, plants are often regarded as unmoving organisms that are incapable of such communication. [SEP] Justify:

Output: The text corroborates the claim by indicating that plants do communicate in various ways, including through chemicals and sound. It mentions that ‘they do communicate—in many different ways’ and clarifies that while people may not think of plants as ‘speaking’ to each other, research shows that they can indeed interact, reinforcing the idea that they communicate through their roots and other means.


Input: Task: contrast the claim using the given text. Claim: The tendons in the human leg act like springs, storing and releasing energy during running. [SEP] Text: The stiffness measured under these conditions was significantly lower than the stiffness measured for biaxial loading during active force production (Fig. 3). The effects of biaxial loading may explain previously published results that were considered difficult to reconcile with a model of the aponeurosis as a uniaxially loaded spring. Measurements on isolated muscles found the stiffness of the aponeurosis along the muscle’s line of action increases substantially in active as compared with passive force production (Zuurbier et al. 1994; Lieber et al. 2000). Such changes in aponeurosis stiffness were difficult to explain when only deformations along the muscle’s line of action were considered. However, when one considers the length changes that occur orthogonal to the muscle’s line of action, it becomes clear that biaxial loading provides a likely mechanism for stiffness modulation in aponeuroses (Fig. 3). Variable stiffness in aponeuroses may have important implications for movement. The use of stiffness values based on uniaxial material properties may underestimate the elastic energy stored in aponeuroses during active contractions. Variable stiffness may also have implications for motor control. If the muscle ‘sees’ a different spring stiffness under different conditions, the motor control system must accommodate this variability. The degree to which changes in aponeurosis stiffness are tuned to the mechanical demands of movement remain unknown and should provide fertile ground for future studies. 4318. Biewener AA & Baudinette RV (1995). In vivo muscle force and elastic energy storage during steady-speed hopping of tammar wallabies (Macropus eugenii). J 198, 1829–1841. & Fung YC (1974). Two-dimensional mechanical properties of rabbit skin. II. Experimental results. J Biomech 7, 171–182. & Brown-Maupin CG (2000). Effects of muscle contraction on the load-strain properties of frog aponeurosis and tendon. Organs 166, 48–54. -Poulsen P, Bojsen-& (2003). Differential strain patterns of the human gastrocnemius aponeurosis and free tendon, in vivo. Scand 177, 185–195. Zajac FE (1989). Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. [SEP] Justify:

Output: The text contrasts the claim by discussing the stiffness of the aponeurosis and muscle, indicating that measuring stiffness under different loading conditions complicates the simple spring-like model of tendons. It explains that variable stiffness in aponeuroses has important implications for movement and suggests that a uniaxial model may underestimate the elastic energy stored during active contractions. This indicates that the aponeurosis’s mechanical properties during movement involve more complexity than the claim suggests, challenging the notion of tendons simply acting as springs for energy storage.


Input: Task: pass the claim using the given text. Claim: Taxation is always harmful to economic growth. [SEP] Text: Distance to frontier, selection, and economic growth. Journal of the Association, 4(1), 37–74. 475. 351. 222. 1175. 611. 902. 662. 297. 80. 17. 75. 143. 240. 622. 48. 915. 495. 1235. 27. 64. 458. 155. 58. Feenstra, R. C. , Inklaar, R. , & Timmer, M. P. (2015). The next generation of the penn world table. Review, 105(10), 3150–3182. 58. 175. 866. 87. 846. 130. 1170. 478. 517. 83. 810. 190. 1043. 125. 174. 264. 35. 126. 323. 254. 347. 382. 634. 559. 158. 74. 0441. UNU-WIDER. (2017). World income inequality database (version WIID3. 4). Volkerink, B. , & de Haan, J. (2001). Tax ratios: A critical survey, no. 5. In OECD tax policy studies. . Widmalm, F. (2001). Tax structure and growth: Are some taxes better than others? Public Choice, 107(3–4), 199–219. 1. 1 Section A: List of countries in our sampleOur sample consists of the following 77 countries:d’ Ivoire, of the and. Table 6 Descriptive statistics and sourcesFull size table1. 2 Section B: Data description and sourcesThis section provides details on all the variables that we employ in our empirical analysis. It explains how each variable is constructed and on which sources it is based. Table 6 provides the key descriptive statistics for each variable. GDP per capita figures for all countries are taken from the Table, Version 9. 0 (Feenstra et al. 2015). Specifically, given our focus on growth rates, we employ the real GDP series in constant 2011 national prices (rgdpna), which we divide with the population figures from the same database to obtain the per capita values. The population growth rate corresponds to the inter-annual rate of change in the total population of each country and is also calculated based on the population data reported in the Table, Version 9. 0. [SEP] Justify:

Output: The text is irrelevant to the claim regarding taxation and economic growth. It contains references to various studies and data descriptions but does not provide any direct information or arguments about taxation's impact on economic growth, nor does it support or contradict the claim made.


Input: Task: corroborate the claim using the given text. Claim: Plants can communicate with each other through a network of roots. [SEP] Text: One of the first scientists to challenge this notion was none other than Charles Darwin. Darwin’s on In the 1880’s, and his son Francis conducted a series of experiments on roots, which showed that the tip of the root is the most important part of the plant. Root tips sense and respond to stimuli such as light, gravity, chemicals, and sound. The root tip transmits messages that function as signals to activate processes such as growth, directional movement, and the production and release of special gases. Animal and human brains do similar things, so the two Darwins suggested that the root tip could function as the plant’s “brain” (Figure 1). Here are some of their own words: It is hardly an exaggeration to say that the tip of the radicle [a young root]… having the power of directing the movements of the adjoining parts, acts like the brain…; receiving impressions from the sense organs, and directing the several movements [1]. also showed the importance of chemicals as communication signals in plants. Some of the earliest reports of plant chemical signaling came from his experiments in the 1870’s. Darwin showed that soluble substances produced at the tip of the growing shoot of barley seedlings were transported down through the stems, where they caused cell division and curvature of the stem. We now know that these chemical signals are hormones called auxins, which move throughout the plant bodies and play many important roles in their growth pattern and overall shape. Figure 1 - Darwin suggested that plant intelligence is kept in the “brain” of plants, found in the root tips, like upside-down people, or other animals. He thought that maybe a plant’s “brain” was kept underground to protect it. While this hypothesis was incorrect, other research has shown that plants can communicate. of Darwin’s key insights was that plants are highly active organisms. We now know that, although they are normally anchored in one place by their root systems, plants are still capable of movement. For example, by using sophisticated measuring devices, we can see that plant leaves, roots, and tendrils are in constant rhythmic motion as they respond to external factors such as daylight, gravity, temperature, water, nutrients, and threats. Plants do not just move randomly; they move in a purposeful manner. [SEP] Justify:

Output: The text corroborates the claim by indicating that plants can communicate through various means, including chemical signaling, as shown in Darwin's experiments. It discusses how root tips can transmit signals and how soluble substances cause responses in other parts of the plant, which supports the idea of communication among plants.


Input: Task: pass the claim using the given text. Claim: Owls have specialized neck bones that allow them to rotate their heads up to 270 degrees. [SEP] Text: GS-01, ectoderm development; GS-09, multiorganism process; GS-10, EGFR signaling pathway; GS-11, cellular protein catabolic process; GS-14, muscle development; GS-15, regulation of muscle contraction; GS-16, di-/tri-valent inorganic cation transport. The bar below the heatmap represents the membership to mesenchymal (yellow), hypoxia (blue) subtypes (et al. ; ref. 4), and inflamed/mesenchymal (orange), basal (green), classical (light blue) subgroups (Keck et al. ; ref. 5). Figure 1. View largeDownload slideGEPs differ between long responders versus short responders in HNSCC patients treated with cetuximab-chemotherapy. Heatmap of the ssGSEA pathway enrichment scores for seven selected pathways. Each column represents a sample; each row represents a gene signature. Color code: red, high enrichment; green, low enrichment. GS-01, ectoderm development; GS-09, multiorganism process; GS-10, EGFR signaling pathway; GS-11, cellular protein catabolic process; GS-14, muscle development; GS-15, regulation of muscle contraction; GS-16, di-/tri-valent inorganic cation transport. The bar below the heatmap represents the membership to mesenchymal (yellow), hypoxia (blue) subtypes (et al. ; ref. 4), and inflamed/mesenchymal (orange), basal (green), classical (light blue) subgroups (Keck et al. ; ref. 5). Close modal This study highlights the utility of GEP to uncover underlying biologic differences and candidate predictive biomarkers when evaluating response to anti-EGFR and platinum therapy in HNSCC, as prior predictive biomarker studies utilizing less comprehensive methodologies evaluating only one or a few biomarkers in HNSCC, have not led to further validation of a biomarker. Although EGFR is overexpressed by IHC in the majority of HNSCC, multiple studies have failed to identify EGFR gene copy number or IHC as a predictive biomarker of response to cetuximab (6, 7). [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses various biological processes related to head and neck squamous cell carcinoma (HNSCC) and does not mention owls or their neck bones. There is no information pertaining to the anatomy of owls or their ability to rotate their heads, thus it does not provide support or contrast to the claim.


Input: Task: pass the claim using the given text. Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: The years between 2000 and 2010 saw the rapid increase of mass spectrometry as the main proteomic technique for protein identification and quantification, replacing other techniques previously used. The difficulties and the low number of proteins identified with the Edman N-terminal sequencing were overcome with the advent of mass spectrometry, and new intriguing possibilities were opened. Valot et al. (2005) also used a sub-cellular proteomic approach to monitor membrane-associated protein modifications during the AM symbiosis [44]. 2DE-PAGE of root microsomes revealed some mycorrhiza-responsive proteins including 15 induced, 3 up-regulated, and also 18 down-regulated proteins. Among those 36 regulated proteins, 25 were identified using the MALDI-TOF. Except for an acid phosphatase and a lectin, none of them was previously reported as being regulated during the AM symbiosis. This sub-cellular proteomic approach allowed for the first time the identification of fungal proteins expressed in planta. In their final conclusion, the authors pinpointed the next challenge: the identification of membrane proteins located in and around the arbuscule, the mycorrhizal symbiosis-specific fungal structure. Arbuscules are ephemeral structures that form continuously and collapse at the end of a short life-span. The identification of proteins temporarily present in a sub-set of cell types remains, at the present time, a technical challenge for quantitative proteomics. Moreover, membranes associated with the arbuscules are significantly less, relative to the overall root membranes, as also suggested by the low amount of fungal RNA found in extensively colonized AM roots, maximally reaching 12% of the total RNA extracted [45]. Despite these considerations, the same group attempted to enrich samples for plasma membranes using a discontinuous sucrose gradient method [46]. In this 4. 1 in soybean [49], STR half-ABC transporters [50] and vesicle-associated membrane proteins VAMP721d/e [51]. In the experiments of Abdallah et al. (2014), proteins were quantified by label-free counting. Protein quantification in label-free experiments is generally based on two types of measurements: peptide peak intensity and spectral count. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses advances in proteomics and the identification of proteins during arbuscular mycorrhizal (AM) symbiosis without explicitly stating or providing evidence that mycorrhizal fungi form symbiotic relationships with plant roots. While it mentions mycorrhizal symbiosis and the identification of proteins associated with it, it does not confirm or deny the fundamental nature of the relationship itself.


Input: Task: corroborate the claim using the given text. Claim: Plants can communicate with each other through a network of roots. [SEP] Text: Plants move to detect key information about their environments, to respond appropriately, and to communicate this information to other plants, using easily understood signals. Unlike animals, plants normally stay in one place during their lifetimes (except for the roots, which can move through the soil), but sometimes that location can turn out to be far from ideal. For example, there might be too little light or water, or too few nutrients for efficient growth. Other competing plants may already be present to crowd out the new plant, or pests and pathogens might attack it, not to mention grazing animals. In evolutionary terms, it makes sense that a plant facing these threats could signal its distress to other plants, so that they could avoid the dangers or better defend themselves against threats. Recent research shows that the two most important plant communication methods are chemicals and sound. Chemical Signals—the Importance of VOorganic compounds (VOCs) are the most common type of chemical signals released by plants. VOCs are small molecules released as gases that readily diffuse through the air, away from the plant that produces them. Some of these plant VOCs are chemically similar to the pheromones that are used by many animals. For example, the world of ants is largely controlled by pheromones that allow them to find food and identify their nestmates. One of the most common plant VOCs is called methyl jasmonate (MeJA), which is produced and released by plants that are under attack (Figure 2). MeJA is formed inside a wounded plant, for example when an animal tries to eat it. Once released by the attacked plant, MeJA moves through the air to the non-damaged parts of the same plant and to the neighboring plants, where it activates defense mechanisms in non-attacked neighbors. Another defense-related VOC made by plants is methyl salicylate, which is chemically similar to the human painkiller, aspirin. Figure 2 - Plants can protect their neighbors by communicating with them. For example, when an individual plant is attacked by a caterpillar, the plant sends warning messages to its plant neighbors, telling them to reinforce their defense systems. The warning messages could be in the form of odors or sounds. Plants release VOCs into the air to alert their neighbors to threats, and the neighboring plants respond to these signals by preparing to defend themselves even before they are attacked. [SEP] Justify:

Output: The text corroborates the claim by explaining how plants can signal their distress to neighboring plants through chemical signals, specifically mentioning that plants release volatile organic compounds (VOCs) such as methyl jasmonate to alert their neighbors to threats. This indicates a form of communication among plants, supporting the idea that they can convey information via their roots and surrounding environment.


Input: Task: pass the claim using the given text. Claim: Owls have specialized neck bones that allow them to rotate their heads up to 270 degrees. [SEP] Text: Picture of the Week SV-POW! … All sauropod vertebrae, except when we're talking about Open Access. ISSN 3033-3695 New video: did air-filled bones allow dinosaurs to become gigantic? June 24, 2024 My friend and frequent collaborator (one, two, three) invited me to give a talk on his YouTube channel, I suggested pneumaticity and gigantism, and here we are. There’s a decently lengthy Q&A, moderated by Tito, after the talk itself. Hilariously — and kindly — one of the commenters pointed out that I hadn’t explicitly answered the titular question in the talk, so I took a stab at it in the Q&A. I come on about 1:40, the talk starts about 5:20, and the Q&A starts at 1:24:30. If you’ve seen any of my pneumaticity talks since, er, I gave my dissertation finishing talk in 2007, you’ve seen at least a few slides of this. But you won’t have seen all of them before, because a good number of them didn’t exist; this is sort of a Frankenstein stitched together from previous talks, new observations, and trying to think about the future. In particular, almost my entire 2012 SVPCA talk is crammed in near the end. If in the talk I sound less certain about some things than I have in the past, that’s accurate. In the past few years I feel like I’ve accumulated a lot of interesting pieces (most of my post-2020 papers), and I’m in quest of a new synthetic foundation for my work (e. g. , Taylor and Wedel 2021, this post), but I’ve also that have rocked my certainty about my own level of understanding (e. g. , Aureliano et al. 2023, this post). I’m cool with that. I think that whatever comprehension of pneumaticity I’m questing toward is going to have to emerge inductively from all the pieces, new and old, that I and others are producing. That’s an exciting prospect, and I’m having enough fun with the individual Legos that I’m in no tearing rush to guess what the final product will look like. [SEP] Justify:

Output: The text is irrelevant to the claim as it discusses topics related to sauropod vertebrae, pneumaticity, and gigantism without any mention of owls or their neck bone structure. Therefore, it does not provide support or contrast to the claim regarding owl head rotation.


Input: Task: pass the claim using the given text. Claim: Mycorrhizal fungi form symbiotic relationships with plant roots. [SEP] Text: To be able to combine LCM samples of synchronous arbusculated cells with sub-cellular enrichment, peptide pre-fractionation and analysis with powerful MS instruments such as (company) may reveal an unexpected specificity during the development of this symbiotic structure. Novel MS/MS techniques developed in the past few years in other research fields could also be applied to investigate plant–fungus symbiotic interactions. For example, selected reaction monitoring (SRM) is a targeted MS technique used to complement untargeted shotgun methods. SRM is used to measure across multiple samples – in a consistent, reproducible and quantitatively precise manner – a set of candidate proteins involved in a particular cellular process [75]. Based on known data from the literature or previous experiments, a set of target peptides that optimally represent the protein are selected and after their validation they are used for protein quantification. Unfortunately, the sensitivity of SRM is limited and it cannot cover the entire proteome of an organism. Nevertheless, this technique is really promising for the fine protein quantification in different cell types or conditions. Taylor et al. (2014) used SRM in plant science to confirm protein abundance in Arabidopsis mutant lines, even when discrimination between very similar proteins was needed [76]. However, the application of this technique for the identification of OsPT11, homologue to MtPT4, from wild-type and mutant lines did not work, probably due to the method’s sensitivity (Chiapello, 2013, unpublished data). Another promising technique to further investigate the proteome of arbuscule-containing cells is the single-cell imaging mass spectrometry (IMS), a powerful technique used to map the distribution of endogenous biomolecules with subcellular resolution [77]. technique Identification technique Reference ECTO 1995 Eucalyptus grandis Pisolithus tinctorius 2DE-PAGE --- Burgess et al. , 1995 1999 Eucalyptus globulus subsp. bicostata Pisolithus tinctorius 2DE-PAGE --- Laurent et al. [SEP] Justify:

Output: The text is irrelevant to the claim because it focuses on various techniques for analyzing proteins in plant cells and does not provide any information about mycorrhizal fungi or their symbiotic relationships with plant roots.


Input: Task: corroborate the claim using the given text. Claim: Plants can communicate with each other through a network of roots. [SEP] Text: For example, within seconds, plants that detect VOCs will start making anti-fungal compounds or anti-insect toxins to protect themselves. In some cases, VOCs can also travel long distances underground, via the root system. This means that plants can stimulate root cells to mount defense responses against invading fungi or bacteria in the soil. Other VOCs are released from root cells to attract beneficial fungi or bacteria. Examples include fungi that help plant roots to absorb soil nutrients more efficiently, or bacteria that convert nitrogen gas from the atmosphere into nutrients that help the plants to grow faster. So, plants use certain VOCs to warn their neighbors about threats, while other VOCs allow them to attract more useful organisms. We humans use sound waves to communicate with each other, but not all sound waves are detectable by human ears. One of the main properties of sound waves is their frequency, which is measured in Hertz (Hz). Humans can hear only sound frequencies ranging from 20 to 20,000 Hz. Sounds with frequencies below 20 Hz are commonly called subsonic, while those with frequencies more than 20,000 Hz are called ultrasonic. The sound signals used by plants tend to be at a frequency that cannot be heard by the human ear, but they can be picked up by other plants and animals [2]. In the wild, plants might grow well with some neighbors but not with others [3]. For example, a 2013 study in Australia clearly showed the beneficial effects of basil plants on the growth of chili plants, which confirms what many gardeners had previously observed in their gardens. Chili seedlings grow less well in the presence of fennel plants, however. These authors speculate that tiny vibrations in the cells of each plant might produce “sounds” of frequencies that can be detected by other plants, telling them whether they are growing near a “bad” or a “good” neighbor [4]. In other experiments, young maize roots were found to make tiny clicking sounds that are at the lower end of the human hearing range (about 220 Hz). When the roots were suspended in water so that they could move more easily, they leaned toward these sounds. Other sounds include what sounds like fizzy bubble bursts in the xylem tissue of plants, but these are ultrasonic and are only detectable by insects and some other animals, so perhaps these plants are communicating with animals [5]. [SEP] Justify:

Output: The text corroborates the claim by providing examples of how plants utilize volatile organic compounds (VOCs) through their root systems to communicate with each other and attract beneficial organisms. The mention of plants making anti-fungal compounds and signaling neighbors about threats further supports the idea of communication among plants, indicating their ability to respond to environmental cues and interact with one another for mutual benefit.