Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: Javascript is disabled in your browser. Please enable Javascript to view PeerJ. Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics All reviews of published 2023. Peer reviewers are encouraged (but not required) to provide their names to the authors when submitting their peer review. If they agree to provide their name, then their personal profile page will reflect a public acknowledgment that they performed a review (even if the 2020. The first revision was submitted on August 1st, 2020 and was reviewed by 2 reviewers and the Academic Editor. A further revision was submitted on August 26th, 2020 and was reviewed by the Academic Editor. The 2020. Version 0. 3 (accepted) · Aug 26, 2020 · changes you have made in the manuscript have improved the manuscript. Congratulations! ! [# PeerJ - this decision was reviewed and approved by Vladimir Uversky, a PeerJ covering this Section #] 0. 3 (PDF) Download author's response letter - submitted Aug 26, 2020 Version 0. 2 · Aug 11, 2020 · Please address the comments of the reviewers and submit a revised manuscript. · Aug 8, 2020 Basic reporting No comment. Experimental design No comment. Validity of the findings No comment. Additional comments Asthma is one of the most common chronic noncommunicable diseases, which is a chronic inflammatory disease of the lower airway that is commonly encountered by the otolaryngologist. Asthma is a serious global health issue and asthma guidelines recommend a stepwise approach to management with goals to achieve control and minimize future risk. It is a good idea to investigate the protein composition and expression levels in serum samples obtain from children with or without asthma. In this manuscript, Dr. Fang and colleagues looked at the role of the differentially expressed proteins (DEPs) identified by using isobaric tags for relative and absolute quantitation (iTRAQ) combined with liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses in the pathogenesis of childhood asthma. The study provides the conclusion, which showed that numerous DEPs that may play important roles in the pathogenesis of childhood asthma. Those proteins may be novel biomarkers of childhood asthma and may provide new clues for the early clinical diagnosis and treatment of childhood asthma. After the modification, the data quality and language description are improved.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: 2020), as well as by the intra-family AM fungal functional variability (Avio et al. 2006; Munkvold et al. 2004). The intra-family variability was supported by the higher total yield found in the var. Pisanello inoculated with S. pellucida compared with G. gigantea, together with the higher shoot dry weight found in the var. inoculated with S. sinuosa compared with F. mosseae. The hypothesis that members of Gigasporaceae, due to their colonization strategy, would have had a stronger effect on nutrient uptake of fruits compared with members of Glomeraceae, was instead confirmed by our results on fruit nutrient concentrations. Indeed, the concentration of several nutrients were higher in fruits inoculated with Gigasporaceae (var. Pisanello: Mg and Mn; var. Rio Grande: N, K, Zn, Cu, Mn), with the only exception of Ca showing higher concentration in fruits of tomato var. inoculated with members of Glomeraceae. Thus, this detected beneficial functional trait of Gigasporaceae can be of great importance in nursery/field inoculation programmes in order to improve tomato quality by enhancing macro- and micro-nutrient concentration in fruits, especially in the scenario of increased atmospheric CO2, which is reported to lower mineral nutrient uptake by almost all crops (Dong et al. 2018; Gojon et al. 2022; Pimenta et al. 2022; Tripp et al. 1991). Previously, Maherali and Klironomos (2007) reported that inoculation with Gigasporaceae correlated with enhanced P concentration in shoots, whereas inoculation with Glomeraceae determined the opposite response. Accordingly, Powell et al. (2009) found that evolution of increased soil colonization is positively correlated with shoot P content. Indeed, members of Gigasporaceae have a mycelium that lacks in infective, runner hyphae, but that forms many absorptive hyphae per infection unit and intrahyphal anastomoses (De la Providencia et al. 2005; Friese and Allen 1991; Hart and Reader 2005). On the other side, members of Glomeraceae form extra-radical mycelia differentiating absorptive and runner hyphae from hyphal bridges and forming anastomoses between different hyphae.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: S2), i. e. cooler spring–summer–autumn temperatures suppress growth across most pink pine sites. Similarly, the negative silver beech response is coherent with its current season temperature sensitivity (Fig. S1b), although neither the temperature correlation nor the volcanic response is particularly strong. All four toatoa chronologies recorded a significant negative response in t+3. However, as toatoa shows only weak lagged correlation with monthly temperatures (Fig. S6a), the link between temperature sensitivity and volcanic response is not easily elucidated. In contrast, cedar does not show a consistent species-wide response. Both significant negative and positive responses were recorded in 13 of the 26 chronologies, with the rest showing a neutral response. This is despite a largely consistent within-species temperature sensitivity, which is an inverse response to prior season temperatures (Fig. S5). 3. 2 Within-species variability in volcanic response The overall muted species response of cedar masks very different individual chronology responses. Cedar ring-width series respond differently to volcanic events depending on their location, with both very negative and very positive responses recorded. The other species do not show similar variation. The cedar chronologies have the widest geographical distribution of any species, and thus geographical factors may influence the variability in response. We used k-means clustering via principal component analysis (Ding and He, 2004) to investigate whether within-species variation could be explained by obvious factors like regional climate or elevation. Principal component analysis (PCA) was applied to the longest common time interval of the chronologies (1732–1976 CE), and the first four principal components were retained. Figure 4(a) Results of k-means clustering of cedar chronologies. (b–f) Mean chronology departures 5 years before and after eruption years (year 0), separated by cluster, and the 95th and 99th significance levels calculated by generating 10 000 random samples of non-event years from the group composite. The map in (a) was made with free vector map data, which are available at (last access: 17 November 2021). Five chronology groups were identified via clustering (Fig. 4a), broadly corresponding to differences in region and altitude. chronologies were distributed in two groups.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: Although there are still some deficiencies (sample size is small) in this study, there are few relevant explorations in this field. This study is a good case worthy of publication and guidance for further research. Cite this review as (2020) #1 of "Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics (v0. 2)". PeerJ Reviewer 2 · Aug 11, 2020 Basic reporting no comment Experimental design no comment Validity of the findings no comment Additional comments Some minor issues need to be corrected by the author. 1. Please describe the clustering method used in the cluster analysis in Materials and Methods. 2. CCL5 should be chemokine (C-C motif) ligand 5 or C-C motif chemokine ligand 5. Please read the 3. Symbol of Complement component 1s should be C1S. Please pay attention to the wording of gene names to distinguish different species. Cite this review as (2020) #2 of "Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics (v0. 2)". PeerJ 0. 2 (PDF) Download author's response letter - submitted Aug 1, 2020 Version 0. 1 (original submission) · May 22, 2020 · Please address the reviewer's comments on a point by point basis and submit your revised manuscript. [# PeerJ Staff Note: Please ensure that all review comments are addressed in a rebuttal letter and any edits or clarifications mentioned in the letter are also inserted into the revised manuscript where appropriate. It is a common mistake to address reviewer questions in the rebuttal letter but not in the revised manuscript. If a reviewer raised a question then your readers will probably have the same question so you should ensure that the manuscript can stand alone without the rebuttal letter. Directions on how to prepare a rebuttal letter can be found at: #] · May 10, 2020 Basic reporting Please have a look at Minor concerns: 1 to 3. Experimental design Please have a look at Major concerns: 1 to 5. Validity of the findings No comment. Additional comments Asthma is one of the most common chronic noncommunicable diseases, which is a chronic inflammatory disease of the lower airway that is commonly encountered by the otolaryngologist.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: The fact that Gigasporaceae family allocates a larger fungal biomass in soil compared with Glomeraceae was confirmed by our results on the higher F:B ratios in inoculated soil in both tomato varieties. In addition within Gigasporaceae, the higher F:B ratios found in soil of var. inoculated with G. gigantea compared with S. pellucida can be due to a larger dimension of the spores and a greater mycelium development (Dalpè et al. 2005; Dood et al. 2000). Nevertheless, our hypothesis that inoculation with Gigasporaceae, associated with a high soil fungal:bacterial ratio, would have determined a lower litter degradation, and that Glomeraceae, associated with a low soil fungal:bacterial ratio, would have determined a higher litter degradation, was not fully confirmed. Indeed, both members of Gigasporaceae and S. sinuosa showed high decomposition rates per day at the latest sampling. However, the positive relationship found between fungal and bacterial abundances and percentages of remaining mass suggests a good predictivity of the microbial traits for litter decomposition dynamics. ConclusionsOur work highlights the importance of understanding the plant responses to AM fungal functional traits in order to better exploit AM fungal inoculants for sustainable agricultural biofertilizer programs targeted to the production of high-quality food. Overall Gigasporaceae, accelerating litter decomposition, might be of great relevance for the absorbtion and traslocation of several macro- and micro-nutrients to tomato fruits and should be taken into consideration in order to develop AM fungal inocula for the enhancement of the nutritional value of tomato under organic farming systems. Moreover, due to the absence of stability and/or uniformity characteristic of environmental conditions, host benefit could be greater with the application of communities having AMF with diverse life-history stategies. Therefore, we suggest that future studies should perform field inoculation with mixtures of members of Gigasporaceae and Glomeraceae families to improve our knowledge on the significance of AM fungal functional groups for host plant nutrient response.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: All chronologies are from montane to subalpine areas above 800 m, with groups differentiated between coastal (G1) and inland (G2) locations. A single chronology from the north of the was included in G2. Chronologies from the western coast of the were also distributed in two groups, differentiating between lowland (G3) and montane (G4) forest. The final grouping (G5) includes three chronologies from the dry eastern lowlands. Strong – but opposing – volcanic responses are identified via clustering. Significant lagged post-eruption growth reduction is identified in groups G1 and G2 from montane to subalpine sites from the North Island, including Takapari (Fig. S8c), and the southernmost grouping, G5. Group three (G3), which includes lowland chronologies from the northwestern coast of the South Island, including Ahaura and (Fig. S8d and e), receives a growth benefit in the 2 years following an eruption similar to that observed in kauri. In cedar, we observed all three of the proposed temperate-zone tree responses to volcanic events – positive, negative, and neutral growth – all within a single species, highlighting the importance of site-based factors in determining tree response in temperate zones. 3. 3 Calibration and validation of the temperature reconstructions The peak summer period was selected as the seasonal reconstruction target, as the largest number of chronologies across species showed significant correlations with temperatures between December and February (Table 1). Selecting only those chronologies correlated at p < 0. 1 with average DJF temperatures resulted in a predictor pool of 45 chronologies for reconstruction NZall, of which 25 showed significant volcanic impacts and were used to produce the reconstruction NZsens. Figure 5average DJF temperature reconstructions. Unfiltered (black) and filtered (20-year spline; blue) mean DJF reconstruction with 90 % uncertainty interval (grey) between 1400 and 2018 CE for (a) NZall and (b) NZsens. Reconstruction fit to instrumental temperature (red) over the full calibration period 1911–1990 CE for (c) NZall and (d) NZsens. The 90 % uncertainty interval was calculated from 300 maximum entropy bootstrap replications. average temperature reconstructions are shown in Fig. 5 alongside their instrumental fit over the 1911–1990 CE calibration period.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: Asthma is a serious global health issue and asthma guidelines recommend a stepwise approach to management with goals to achieve control and minimize future risk. It is a good idea to investigate the protein composition and expression levels in serum samples obtain from children with or without asthma. In this manuscript, Dr. Fang and colleagues looked at the role of the differentially expressed proteins (DEPs) identified by using isobaric tags for relative and absolute quantitation (iTRAQ) combined with liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses in the pathogenesis of childhood asthma. The study provides the conclusion, which showed that numerous DEPs that may play important roles in the pathogenesis of childhood asthma. Those proteins may be novel biomarkers of childhood asthma and may provide new clues for the early clinical diagnosis and treatment of childhood asthma. Although, the current study is interesting and well but there are some major concerns that need to be addressed. Major concerns: 1. “Experimental design & Validity of the findings” In general, the research idea of this manuscript is novelty. However, the biggest defect is the lack of sample quantity, which makes it difficult to judge whether the conclusion is universal or not. If the authors increase each group of samples to 10, where possible, the results should be more applicable. However, considering the high cost of iTRAQ technology, I recommend that the author select 1 or 2 from the 37 DEPs for ELISA verification, and expand the samples to about 50+, which may significantly improve the impact of this study. 2. “Materials & Methods – Clinical information and serum sample collection” Line 103-112. “The included samples were collected from four children with asthma (experimental group) and four children without asthma (control group). ” When grouping, did the authors take into account factors such as age, gender, and body mass index (BMI) of patients in the balance between the two groups? Because the physiological factors mentioned above also have a certain influence on serum protein, considering the small sample size of this study, these factors must be matched to exclude the relevant influence, please explain in the 3. “Materials & Methods – LC-MS/MS analysis” Line 147-150. “Protein fold changes (FC) of at least 1. 5 were obtained, and P < 0. 05 was considered a statistically significant difference.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: Agronomy 11:1116. (2012) Impact of arbuscular mycorrhizal fungi (AMF) and atmospheric CO2 concentration on the biomass production and partitioning in the forage legume alfalfa. Symbiosis 58:171–181. (2009) Reaping the benefits: science and the sustainable intensification of global agriculture. (2013) Mycorrhizal activity and diversity in a long-term organic Mediterranean agroecosystem. Soils 49:781–790. (2018) Ancient tomato (Solanum lycopersicum L. ) varieties of tuscany have high contents of bioactive compounds. Horticulturae 4:51. ’(2017) Arbuscular mycorrhizal fungi and plant growth-promoting pseudomonads improve yield, quality and nutritional value of tomato: a field study. Mycorrhiza 27:1–11. Bowles TM, Barrios-(2016) Effects of arbuscular mycorrhizae on tomato yield, nutrient uptake, water relations, and soil carbon dynamics under deficit irrigation in field conditions. Environ 566:1223–1234. (1999). Glomalean mycorrhizal fungi from tropical Australia. Mycorrhiza 8:305–314. Brundrett MC, (2018) Evolutionary history of mycorrhizal symbioses and global host plant diversity. 220:1108–1115. (2019) Conceptual differences lead to divergent trait estimates in empirical and taxonomic approaches to plant mycorrhizal trait assignment. Mycorrhiza 29:1–11. -(2021) Direct transfer of zinc between plants is channelled by common mycorrhizal network of arbuscular mycorrhizal fungi and evidenced by changes in expression of zinc transporter genes in fungus and plant. 23:5883–5900. (2006) Mycorrhizas and tropical soil fertility. Environ 116:72–84. (2018) Bioactivities of phytochemicals present in tomato. J Technol 55:2833–2849. (2012) Arbuscular mycorrhizal fungi increase organic carbon decomposition under elevated CO2. Science 337:1084–1087. (2015) Getting started with v7.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: There is good agreement between the reconstructions, with a Pearson r of 0. 81 over the entire reconstruction period and 0. 9 after 1750 CE. The initial, best-replicated nests, which cover the period 1790–1990 CE, account for 67. 5 % and 58. 2 % of instrumental temperature variability for the NZall and NZsens reconstructions, respectively. The minimum amount of variance explained over all nests is 44. 1 % for NZall and 34. 3 % for NZsens. The full calibration and validation statistics are provided in Figs. S9 and S10. For both reconstructions, VRE values are positive over all nests from 1413 to 1990 CE; however, VCE values are only positive after 1520 CE in the NZsens reconstruction when calibrated to the early window (1911–1950 CE). The declining instrumental data quality in the early period and the relatively few predictors retained for NZsens, are likely responsible for the negative values. Increasing temperatures are observable in both reconstructions from around 1950 CE, matching the trend in instrumental temperatures. Prior to the instrumental period, temperatures were higher than average for a sustained period during the 16th century and for a shorter period in the early 18th century. Periods of cooler-than-average temperatures have also occurred, starting at ∼ 1470, 1630, and 1860 CE. The reconstructions are consistent with previously published temperature reconstructions from (Cook et al. , 2002; Duncan et al. , 2010; Palmer and Xiong, 2004) despite differences in the climate targets (seasons and locations), reconstruction methodologies, and large differences in the number and geographical distribution of chronologies used in their development (Fig. S11 and Table S5). Figure 6Mean anomalies 5 years before and after 21 eruption years with SAOD > 0. 04 (a, c) and 13 eruption years with SAOD > 0. 08 (b, d) for both the NZall (blue) and NZsens (red) reconstructions. The mean response from an ensemble of seven climate models to the same set of events is shown in black. The 90th percentile bootstrap confidence intervals were constructed from 1000 replicates of either 15 or 9 event years at random. Download 3.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: A” Generally, in high-throughput analysis, the threshold value of FC is defined at 2. Why the author chooses 1. 5, please clarify the reasons and 4. “Table 1” Page 45. The data showed that G6PD was one of the most significantly down-regulated proteins. This is a very interesting finding. As an important gene related to G6PD deficiency disease, the detection of G6PD activity should be widely used in children's hospitals and general hospitals in mainland China. Can the author make a retrospective study, collect and analyze relevant data, and observe whether the activity of G6PD is down-regulated in the blood of children with asthma? The research value of this paper can be further enhanced by appropriately expanding the sample size. 5. Through PubMed searching, the keywords are "asthma" and "iTRAQ", and I find that there are very few relevant studies. There is a lack of research on the detection of serum in children with asthma, so I think the authors should try to add as many samples as possible. Minor comments: 1. Figure 3B. The author should indicate which group “B1-4” and “A1-4” belong to? 2. Figure 4. The text in the picture is too small. Please increase the font size. 3. All Figure. The vector image exported by R language can be modified with drawing software (Canvas or AI) again. Please choose the appropriate font size for the convenience of readers. In addition, author should keep the horizontal and vertical ratio in the picture zooms in and out. Cite this review as (2020) #1 of "Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics (v0. 1)". PeerJ Reviewer 2 · May 17, 2020 Basic reporting No comment. Experimental design No comment. Validity of the findings No comment. Additional comments This manuscript by found 46 DEPs in serum samples of children with asthma vs. children without asthma. Verified some proteins that have been reported by others, and also provided several new potential proteins. These newly discovered proteins maybe some alternative targets in future studies of childhood asthma. Here are a few questions and comments: 1.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: -E: Plymouth, Laboratory 20(1) Clarke KR, (2001) Change in marine communities: an approach to statistical analysis and interpretation. Plymouth: --Williams SJ (2019) The mycorrhizal pathway of zinc uptake contributes to zinc accumulation in barley and wheat grain. BMC 19:1–14. (2013) Yield and phosphorus uptake of a processing tomato crop grown at different phosphorus levels in a calcareous soil as affected by mycorrhizal inoculation under field conditions. Soils 49:691–703. Corrêa A, Cruz C, (2015) Nitrogen and carbon/nitrogen dynamics in arbuscular mycorrhiza: the great unknown. Mycorrhiza 25:499–515. Dalpé Y, Cranenbrouck S, Séguin S, (2005) The monoxenic culture of arbuscular mycorrhizal fungi as a tool for systematics and biodiversity. In: In vitro culture of mycorrhizas. Springer, Berlin, Heidelberg, 271. FA, Dalpé éjalon-(2005) Life history strategies in Gigasporaceae: insight from monoxenic culture. In: In vitro culture of mycorrhizas. Springer, Berlin, Heidelberg, 30. -Chavez C, (2000) Mycelium of arbuscular mycorrhizal fungi (AMF) from different genera: form, function and detection. 226:131–151. (2018) Effects of elevated CO2 on nutritional quality of vegetables: a review. Sci 9:924. (2008) Tomato (Solanum lycopersicum) health components: from the seed to the consumer. 7:231–250. Egerton-(2007) Mycorrhizal community dynamics following nitrogen fertilization: a cross-site test in five grasslands. 77:527–544. (2005) Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Microbiol 71:4117–4120. Flores FB, Sanchez-Bel P, Estañ MT, Martinez--Abellán ández-Garcia N, Romojaro F, Bolarín MC (2010) The effectiveness of grafting to improve tomato fruit quality. 125:211–217.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: 4 Volcanic signals in the temperature reconstructions Figure 6 shows the results of the SEA analysis for the two temperature reconstructions, for both sets of volcanic events, compared to the volcanic response of an ensemble of seven CMIP5 model outputs for the region. For the 21 events with SAOD > 0. 04, results are remarkably similar between the temperature reconstructions and the model ensemble. Both the timing and magnitude of the post-event anomaly, which is only significant in year t+1, are consistent across the models and reconstructions, as is the timing of the post-event recovery, which occurs in year t+2. The response to the subset of the 13 largest events shows larger year t+1 temperature anomalies for both the climate models and the reconstructions, with the greatest increase in response magnitude displayed by the model ensemble. Year t+1 anomalies are ∼ 0. 1 ∘C larger for NZall, ∼ 0. 2 ∘C larger for NZsens, and ∼ 0. 4 ∘C larger for the model ensemble than the 21-event anomalies. However, the mean model ensemble lies within the 90 % uncertainty range of both reconstructions, indicating that the difference in magnitude between models and reconstructions is not significant for either the restricted 13-event set or the full 21-event set. The difference in post-event recovery is significant, with temperatures recovering by year t+2 in the reconstructions and modelled temperature anomalies persisting in year t+2. This is the opposite result to many tree-ring-based temperature reconstructions from the Northern Hemisphere, which show lagged persistence compared to climate models due to biological effects in the ring-width series (see Sect. 4. 4). Figure 7Distribution of tree rings used in (a) the NZall temperature reconstruction and (b) the NZsens temperature reconstruction. Larger, darker red markers represent chronologies with greater relative weighting in the multiple regression. Made with free vector map data available at (last access: 17 November 2021). The difference between the NZall and NZsens reconstruction response is minor for both subsets of volcanic events. The anomaly recorded by NZsens is 0. 07 ∘C larger than NZall for the 21-event series and 0. 1 ∘C larger for the 13-event series.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: Line 84-86 “……selective chemokine ligand 5, hematopoietic prostaglandin D synthase, and neuropeptide S receptor 1 were involved……” line 88 “……fatty acid binding protein 5……” and line 272 “Complement component 1s……” Do all these descriptions refer to specific genes? Please mark their official symbol accurately. 2. Line 269 mentions “Inflammatory reactions can also promote the healing of injured tissues. ” In the results of the manuscript, lines 184-185, the description of the control group is “children with trauma ……” Will the trauma here affect the experimental results? Maybe some discussion needs to be added. 3. Figure 3A, please accurately describe or set the color of the volcano plot. The description in figure legends “red dots” and “green dots” should be “yellow dots” and “blue-green dots” respectively. 4. Figure 8 need to adjust because some have exceeded the maximum value of the Y-axis. 5. Although “……Children with asthma typically present with eosinophilic asthma and allergy……” (line 77), the pathology type of children with asthma should be provided so that readers can clearly understand the possible impact on the experimental results. 6. line 149 “……>1. 5 represented upregulated proteins and a FC <0. 667 represented downregulated proteins……” however, the description in Figure 3 is “FC> 1. 5” and “FC <1. 5” as a significant change. And the volcano uses log10FC, and the heatmap uses log2FC, please explain clearly. Cite this review as (2020) #2 of "Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics (v0. 1)". PeerJ · May 22, 2020 Basic reporting 1. Several studies that tried to identify differentially expressed proteins of asthma have been published (as listed below) and one also focused on childhood asthma. Could the authors explain the difference and significance regarding the design and results of their study compared to those studies? 1) Pilot-33 Asthma. J Asthma Allergy. doi: 10. 2147/JAA. S211569.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: Food and of the United Nations. . 2022. (1991) The spread of VA mycorrhizal fungal hyphae in the soil: inoculum types and external hyphal architecture. Mycologia 83:409–418. Gianinazzi S, Gianinazzi-Pearson V, Rouvelot AT (1990) Role and use of mycorrhizas in horticultural crop production. Sci 4:25–30 (2012) Nutraceutical value and safety of tomato fruits produced by mycorrhizal plants. Nutr 107:242–251. The decline of plant mineral nutrition under rising CO2: physiological and molecular aspects of a bad deal. Sci. González-González MF, Ocampo-Alvarez H, Santacruz-Ruvalcaba F, Sánchez-Hernández CV, Casarrubias-Castillo K, Becerril-Espinosa A, Castañeda-Nava JJ, Hernández-Herrera RM (2020) Physiological, ecological, and biochemical implications in tomato plants of two plant biostimulants: Arbuscular mycorrhizal fungi and seaweed extract. Sci 11:999. (2006) Arbuscular mycorrhizal fungi and organic farming. Environ 113:17–35. (2010) Organic management of tilled agricultural soils results in a rapid increase in colonization potential and spore populations of arbuscular mycorrhizal fungi. Environ 139:273–279. (2005) Impact of environmental factors on product quality of greenhouse vegetables for fresh consumption. 24:227–247. (2017) Arbuscular mycorrhiza enhance the rate of litter decomposition while inhibiting soil microbial community development. Sci rep 7:1–11. Hamel C, Dalpé Y, Furlan V, (1997) Indigenous populations of arbuscular mycorrhizal fungi and soil aggregate stability are major determinants of leek (Allium porrum L. ) response to inoculation with Glomus intraradices Schenck & Smith or Glomus versiforme (Karsten) Berch. Mycorrhiza 7:187–196.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: The small difference between the reconstructions can be explained by the weightings applied to the chronologies in each reconstruction, with both reconstructions heavily weighted towards the same subset of chronologies. Four of the eight highest-weighted chronologies underpinning NZall are sensitive to volcanic events, and three of these are within the top four highest-weighted chronologies underpinning NZsens (Fig. 7). Thus, limiting NZsens to only sensitive chronologies had less impact on post-eruption temperature anomalies than was expected. 4 Discussion 4. 1 Volcanic responses recorded by trees Previous studies have not identified significant volcanic responses in tree rings (Krakauer and Randerson, 2003; Palmer and Ogden, 1992) or in the temperature reconstructions based on them (Allen et al. , 2018; Cook et al. , 1992). In contrast to previous studies, we found that volcanic events can be clearly identified in ring widths, although some species are stronger recorders of volcanic signals than others. Hemisphere high-latitude and tree line sites, which tend to show a consistent reduction in growth due to volcanic cooling and reductions in light availability, no consistent response was identified across conifer and Nothofagaceae species. Predominantly negative (pink pine, cedar, toatoa, silver beech), positive (kauri), and neutral (mountain beech, tānekaha, silver pine) responses were recorded. As most chronology sites have been sampled from localised areas of residual forest that are restricted compared to their natural distributional range, it is difficult to distinguish between species-related sensitivities to volcanic eruptions and regional climate factors that may control the response. In reality, it is the combination of biological characteristics, including intrinsic species sensitivity, regional climate, and site-specific factors (e. g. soils, exposure to prevailing conditions), which determine the observed volcanic response. While necessarily simplified, here we discuss some possible explanatory factors for the species-wide responses. The species-level results in Figs. 3 and S7 clearly show two response types following volcanic events: a rapid but short-lived response and a delayed response that begins in year t+2 or t+3 but then persists over several years.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: 2) for at Levels by for Relative and Absolute Quantification-based Proteomic Technology. . DOI: 10. 3881/j. issn. 1000-503X. 2017. 06. 014 3) Proteome in. J Immunol. doi: 10. 1016/j. jaci. 2011. 07. 053. 2. Some of the 1. The authors should describe the amount of protein they extracted from the blood sample as well as the amount they applied in each tests respectively. 2. The statistical analysis applied in this study should be mentioned in method. 3. In method, further explanations in terms of how the analyses of were carried out together with their meanings in the study should be described, instead of only focusing on their definitions. Validity of the findings No comments Additional comments In this study titled “Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics”, the author tried to differentially expressed proteins (DEPs) expressed between children with or without asthma. Among these DEPs in the result, 12 proteins were significantly upregulated and 34 proteins were downregulated. The author further analyzed these DEPs with and COG to explain the meaning of identified DEPs in asthma. However, several issues need to be addressed before further consideration for publication. 1. Serum samples derived from four children with asthma and four children without asthma were collected in this study, which is quite a small sample size compared with other similar studies. Could the author give their reason or evidence to support that this sample size is sufficient enough? 2. For those four children with asthma, were they hospitalized due to asthma exacerbation, or just with a history of chronic asthma? Furthermore, since their blood was collected the other day of their hospitalization, they were supposed to be treated with antiasthmatic drugs. Did the authors notice the impact of antiasthmatic drugs on DEPs, and the difference of DEPs during asthma exacerbation, which the authors should try to explain? 3. Should the authors explain the reasons and evidence why they chose to analyze the blood of the patients instead of sputum or samples derived from bronchoalveolar lavage? Cite this review as (2020) #3 of "Differentially expressed serum proteins in children with or without asthma as determined using isobaric tags for relative and absolute quantitation proteomics (v0.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: (2015) Inoculation with arbuscular mycorrhizal fungi improves the nutritional value of tomatoes. Mycorrhiza 25:359–376. (2002) Taxonomic basis for variation in the colonization strategy of arbuscular mycorrhizal fungi. 153:335–344. (2004) Do arbuscular mycorrhizal fungi recover from soil disturbance differently? 45:97–112 (2005) The role of the external mycelium in early colonization for three arbuscular mycorrhizal fungal species with different colonization strategies. Pedobiologia 49: 269–279. (2000) Colorful cancer prevention: α-carotene, lycopene, and lung cancer. 72:901–902. (1998) Ploughing up the wood-wide web? Nature 394: 431–431. (2012) Interactions between an arbuscular mycorrhizal fungus and a soil microbial community mediating litter decomposition. 80:236–247. (2019) Synergies between mycorrhizal fungi and soil microbial communities increase plant nitrogen acquisition. 2:1–9. (2001) An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material. Nature 413:297–299. Hodge A, (2015) Arbuscular mycorrhiza and nitrogen: implications for individual plants through to ecosystems. 386:1–19. (2022) Arbuscular mycorrhizal fungal communities with contrasting life-history traits and trait diversity influence host nutrient acquisition. Preprint from Research Square. (1992) External hyphae of vesicular-arbuscular mycorrhizal fungi associated with Trifolium subterraneum L. 1. Spread of hyphae and phosphorus inflow into roots. New phytol 120:371–380. (2007) Phosphate in the arbuscular mycorrhizal symbiosis: transport properties and regulatory roles. Environ 30:310–322. (2020) Trophic interactions as determinants of the arbuscular mycorrhizal fungal community with cascading plant-promoting consequences. Microbiome 8: 1–14. (2017) Plants transfer lipids to sustain colonization by mutualistic mycorrhizal and parasitic fungi. Science 356:1172–1175.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: The first response, demonstrated by mountain beech and kauri, we label here the “fast responder”, and the second, shown by pink pine, silver pine, cedar, and silver beech, we label the “stress tolerator” (after Grime, 1979). Zealand conifers appear as typical stress-tolerator species, which have adapted to growing in highly stressful conditions. As a group, they are longer-lived, slower-growing, taller, and markedly frost-tolerant species compared to endemic angiosperms and are tolerant of poor soils (e. g. low nutrients and often poor drainage). Silver beech is highly frost and exposure resistant, shade tolerant, and grows on extreme exposure sites (Manson, 1974; Stewart, 2002). Several species, including pink pine and silver pine, show an affinity for leached, low-nutrient, and waterlogged soils (McGlone et al. , 2017). The stress tolerators are characterised by narrow average ring widths and high biological persistence (temporal autocorrelation) arising from carbohydrate storage or leaf retention (see Table 1). Therefore, stress tolerators are slow to respond to changes in conditions, such as volcanic cooling. The stress-tolerator response resembles the response of high-latitude trees, although arctic trees display even greater lagged persistence, with suppressed growth for up to 10 years following volcanic events (Krakauer and Randerson, 2003). In contrast, the fast responders both respond and recover more quickly from a detrimental change in conditions (e. g. mountain beech) or can rapidly capitalise on beneficial conditions (e. g. kauri). These are relatively fast-growing species, indicated by wider average ring widths than the stress tolerators, and they have lower persistence (Table 1). Mountain beech is shade intolerant but has several responses to abnormally cold temperatures, including rapid shoot growth and temporarily halting bud formation, which allows it to rebound quickly after a poor summer (Wardle, 1970). Kauri could be considered a stress tolerator due to its affinity for poor soils, occurrence on ridges and slopes, and drought tolerance (McGlone et al. , 2017); however, relative to other conifers in this study, it is a fast responder.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: 1)". PeerJ Submission (PDF) - submitted Apr 3, 2020 All text and materials provided via this peer-review history page are made available under a Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: (2021) Arbuscular mycorrhizal fungi enhance mineralisation of organic phosphorus by carrying bacteria along their extraradical hyphae. 230:304–315. -(2005) The uptake, metabolism, transport and transfer of nitrogen in an arbuscular mycorrhizal symbiosis. 168:687–696. Johnson NC (1993) Can fertilization of soil select less mutualistic mycorrhizae? 3:749–757. (1991) Handbook II: a practical sampling, preparation, analysis, and interpretation guide. Micro-Inc. , JN (2000) Host-specificity and functional diversity among arbuscular mycorrhizal fungi. 1:845–851. Klironomos JN (2003) Variation in plant response to native and exotic arbuscular mycorrhizal fungi. Ecology 84:2292–2301. (2002) Colonization of roots by arbuscular mycorrhizal fungi using different sources of inoculum. Mycorrhiza 12:181–184. (2000) The influence of arbuscular mycorrhizae on the relationship between plant diversity and productivity. 3:137–141. (2019) The role of in vitro cultivation on asymbiotic trait variation in a single species of arbuscular mycorrhizal fungus. 123:307–317. (2006) World map of the Köppen-Geiger climate classification updated. 15:259–263. Lehmann A, Rillig MC (2015) Arbuscular mycorrhizal contribution to copper, manganese and iron nutrient concentrations in crops - a meta-analysis. Biochem 81:147–158. (2014) Arbuscular mycorrhizal influence on zinc nutrition in crop plants - a meta-analysis. Biochem 69:123–131. (2011) Growth and symbiotic effectiveness of an arbuscular mycorrhizal fungus in organic matter in competition with soil bacteria. 76:428–438. Lekberg Y, Koide RT (2005) Is plant performance limited by abundance of arbuscular mycorrhizal fungi? A meta-analysis of studies published between 1988 and 2003. 168:189–204.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: In contrast to the subdued, persistent decrease in growth shown by the stress tolerator species, the initial decline in toatoa ring width in year t+0, subsequent extreme decline in year t+3, and recovery by year t+4 closely resembles the boom–bust behaviour of the fast responders, but with several years' lag. The dominant climate response of toatoa is a weak negative correlation to prior growing-season temperatures (Fig. S6a) but a strong positive correlation to summer temperatures two growing seasons prior. This results in a quasi-biennial pattern of wide and narrow rings, which has also been observed in other species of Phyllocladus. This pattern may be related to a climate-triggered flowering cycle (Ogden and Dunwiddie, 1982; Allen, 1998), foliage production followed by cladode senescence, or a mast seeding event, which can be triggered by multiple cool summers in Podocarpaceae (Norton and Kelly, 1988). All three potential explanations suggest toatoa channel resources to reproduction at the expense of cambial growth (Harper, 1977) following a climate trigger after volcanic events – perhaps increased humidity and reduced summer water stress at low-elevation sites or an increase in photosynthesis due to a greater fraction of diffuse radiation. More research on the ecology and life history of toatoa is needed to confirm these possible mechanisms. We compared the response at six sites which each have chronologies from two different species (Fig. S8), providing the unique opportunity to compare species differences in volcanic sensitivity directly whilst controlling for most other factors. The three species that are co-located and thus available for site-based comparison (cedar–pink pine and cedar–silver pine) all showed stress-tolerator responses to volcanic eruptions. Pink pine and cedar often grow together in mixed stands. Both species are sensitive to temperature, although pink pine has a maximum correlation to late summer temperature, whereas cedar responds to conditions in the winter prior to the growing season and in spring (Fenwick, 2003). A significant difference in the response between species was observed only at one of three sites. Comparison over additional sites is therefore required to determine whether the difference in seasonal temperature response may result in a difference in the sensitivity of pink pine and cedar to climate disturbance following eruptions.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: Javascript is disabled in your browser. Please enable Javascript to view PeerJ. Related research -and validation of shared gene signature of kidney renal clear cell carcinoma and COVID-19 View 10. 7717/peerj. 16927 Published 2024-03-04 Accepted 2024-01-22 Received 2023-08-30 -19 -19, Shared gene, Comorbidity, Co-pathogenesis Copyright © 2024 Nie et al. is an open access 2024. Identification and validation of shared gene signature of kidney renal clear cell carcinoma and COVID-19. PeerJ 12:e16927 The authors have chosen to make the review history of this 19. These genes are associated with immune functions and may serve as potential prognostic biomarkers for KIRC. The shared pathways and genes may provide new insights for further mechanistic research and treatment of comorbidities. -19 is a novel pneumonia caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (Zhu et al. , 2020). 2019, it has resulted in significant loss of life and caused global economic and physical distress (McKee & Stuckler, 2020). According to University data as of January 10, 2023, there have been over 660 million global infections and more than 6. 7 million deaths (University and Medicine, 2023). Research suggests that individuals with compromised immune systems are more vulnerable to SARS-CoV-2 infection (Grasselli et al. , 2020). Cancer patients face an elevated risk of COVID-19 infection and are prone to quicker onset of complications and deterioration due to potential immune system suppression caused by cancer and its treatments (Wang et al. , 2020). Despite progress in renal cell carcinoma (RCC) diagnosis and management, it continues to be one of the most lethal urological malignancies (Cairns, 2010). Globally, approximately 431,000 new cases of kidney cancer are diagnosed annually, resulting in over 179,000 deaths and posing a significant global public health burden (Sung et al. , 2021). Kidney renal clear cell carcinoma (KIRC) is the predominant type, comprising approximately 70% of RCC (Zhang, Zeng & Hu, 2020).

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: (1990) Revised classification of arbuscular mycorrhizal fungi (Zygomycetes): a new order, Glomales, two new suborders, Glomineae and Gigasporineae, and two new families, Acaulosporaceae and Gigasporaceae, with an emendation of Glomaceae. Mycotaxon 37:471–491 (1993) Germ plasm in the of Fungi (INVAM) and procedures for culture development, documentation and storage. Mycotaxon 48:491–528. Morton JP (1993) Properties of infective propagules at the suborder level (Glominae versus Gigasporineae). 3: September 1993. Munkvold L, Kjøller (2004) High functional diversity within species of arbuscular mycorrhizal fungi. 164: 357–364. (2014) Increasing CO2 threatens human nutrition. Nature 510:139–142. àrberi P (2017) Functional identity has a stronger effect than diversity on mycorrhizal symbiosis and productivity of field grown organic tomato. Agron 86:1–11. -(2013) An arbuscular mycorrhizal fungus significantly modifies the soil bacterial community and nitrogen cycling during litter decomposition. 15:1870–1881. (2008) Arbuscular mycorrhiza: the mother of plant root endosymbioses. Microbiol 6: 763–775. (2011) Field inoculation effectiveness of native and exotic arbuscular mycorrhizal fungi in a Mediterranean agricultural soil. Soil Biol. Biochem. 43: 367–376. (2022a) Multiple arbuscular mycorrhizal fungal consortia enhance yield and fatty acids of Medicago sativa: A two-year field study on agronomic traits and tracing of fungal persistence. Sci 13. Pellegrino E, Öpik M, Bonari E, (2015) Responses of wheat to arbuscular mycorrhizal fungi: a meta-analysis of field studies from 1975 to 2013. Biochem 84:210–217. (2020) Field inoculation of bread wheat with Rhizophagus irregularis under organic farming: variability in growth response and nutritional uptake of eleven old genotypes and a modern variety.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: Differences between the cedar and silver pine responses were observed at two of the three sites, with cedar showing greater sensitivity to volcanic eruptions. Silver pine is primarily found in the moist, temperate, low-elevation forests of the western coast of the South Island. It is a shade-tolerant species that grows in highly competitive closed-canopy forests on infertile, poorly drained or waterlogged soils (Wardle, 1977; Cook et al. , 2002). It is an exceptionally slow-growing species and shows little year-to year variability in ring width (Table 1). Thus, it is unsurprising that volcanic effects were more readily identified in cedar at the Ahaura and sites (Fig. S8). 4. 2 A kauri growth benefit An interesting result of this study is the strong positive species-wide response of kauri to volcanic events (Fig. 3a) despite the weak correlation of the chronologies to monthly temperatures (Fig. S3). Over 70 % of the kauri chronologies recorded a small but significant increase in ring width in the year following a large eruption (SAOD > 0. 04), indicating a growth benefit from volcanism. Previous studies of kauri climate response function have shown that growth is not primarily related to temperature but is enhanced during cool, dry years, with the strongest (negative) correlation to austral spring temperatures (Ogden and Ahmed, 1989; Buckley et al. , 2000). Ring growth is thus enhanced during El Niño events, which result in cool, dry spring conditions in northern New Zealand, and kauri has been successfully used as a proxy for the El Niño–(ENSO; Fowler et al. , 2008). The mechanism behind this relationship remains largely unclear, although it has been proposed that reduced cloud cover during El Niño events may benefit kauri growth via increased insolation (Fowler et al. , 2000). The opposite conditions follow a volcanic eruption, with reduced direct insolation and increased diffuse insolation due to scattering by volcanic aerosols (Gu et al. , 2003). The commonality between the two sets of events is cooler-than-average spring and summer temperatures. This suggests that kauri may capitalise on a decrease in summer evapotranspiration during both El Niño events and following significant eruptions.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: The kidney is a major target organ for SARS-CoV-2 infection, and COVID-19 frequently leads to acute tubular necrosis, a common complication (Kellum, van Till & Mulligan, 2020). ACE2 serves as the receptor for SARS-CoV-2 to enter host cells (Weiss & Navas-Martin, 2005). Additionally, NPR1, DPP4, and 2 have been identified as co-receptors or cofactors for initiating SARS-CoV-2 infection (Choong et al. , 2023; Qi et al. , 2020; Tang et al. , 2021; Hossain, Akter & Uddin, 2021). According to Choong et al. (2023), RCC cells retain the crucial receptors and cofactors required for SARS-CoV-2 viral entry. Tripathi et al. (2020) revealed significant overexpression of these genes in KIRC, suggesting their potential involvement in regulating cellular immunity and immune infiltration. However, the shared underlying pathogenic mechanisms have not been thoroughly investigated. The rapid progress in next-generation sequencing technology offers an effective approach to exploring the shared pathogenic mechanisms underlying different diseases (Kilpinen & Barrett, 2013). Analyzing the shared transcriptional characteristics of KIRC and COVID-19 may yield novel insights into the pathogenesis of both conditions. In this study, we collected datasets from and ICGC databases for both diseases. Using bioinformatics analysis, we constructed a risk model comprising four hub genes. We validated this model using both internal and external validation sets and investigated its role in immune infiltration. The objective of this research is to aid in understanding the shared pathogenic mechanisms of COVID-19 and KIRC among researchers and offer valuable recommendations for the clinical management of patients with these concurrent conditions. Materials and sources and preprocessing We obtained the expression profiles of COVID-19 patients from the GEO database. Two cohorts, namely GSE196822 (GPL20301) and GSE211979 (GPL16791) consisting of a total of 47 samples (33 patient samples and 14 normal samples), were chosen. If multiple probes matched a single gene, the gene’s average expression was calculated. The study encompassed a total of 22,235 genes.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: Agronomy 10:333. (2021). Eukaryotes in soil aggregates across conservation managements: Major roles of protists, fungi and taxa linkages in soil structuring and C stock. Soil Biol. Biochem 163:108463. (2022b) Microbiome structure and interconnection in soil aggregates across conservation and conventional agricultural practices allow to identify main prokaryotic and fungal taxa related to soil functioning. Biochem 175:108833. à (2012) Establishment, persistence and effectiveness of arbuscular mycorrhizal fungal inoculants in the field revealed using molecular genetic tracing and measurement of yield components. 194:810–822. (1970) Improved procedures for clearing roots and staining parasitic and vesicular-arbuscular mycorrhizal fungi for rapid assessment of infection. T Soc 55:158–161. (2019) Interaction between conservation tillage and nitrogen fertilization shapes prokaryotic and fungal diversity at different soil depths: evidence from a 23-year field experiment in the Mediterranean area. 10:2047. ögön A, Ribeiro DM (2022) The impact of elevated CO2 concentration on fruit size, quality, and mineral nutrient composition in tomato varies with temperature regimen during growing season. Regul 1–12. Plenchette C, Clermont-(2005) Managing arbuscular mycorrhizal fungi in cropping systems. 85:31–40. Plouznikoff K, J, de (2019) Genetic analysis of tomato root colonization by arbuscular mycorrhizal fungi. 124:933–946. (2009) Phylogenetic trait conservatism and the evolution of functional trade-offs in arbuscular mycorrhizal fungi. P B-276: 4237–4245. (2014) Bacterial degradation of Aroclor 1242 in the mycorrhizosphere soils of zucchini (Cucurbita pepo L. ) inoculated with arbuscular mycorrhizal fungi. Int 21:12790–12799. (2020) Effect of arbuscular mycorrhizal fungi on soil enzyme activity is coupled with increased plant biomass. 71: 84–92. Regione Toscana. Razze e Varietà Locali.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: Maximum kauri growth occurs during spring, with large declines in growth rate over the peak summer months when evapotranspiration exceeds precipitation in the northern (Fowler et al. , 2005). Dendrometer band studies suggest that reduced spring and summer moisture stress may delay the cessation of growth, resulting in wider annual rings (Palmer and Ogden, 1983). No summer cessation of growth was observed by Palmer and Ogden (1983) at the highest-altitude site, (1MOE, Table S1). This site receives moisture from condensation and fog drip, as well as rainfall, reducing the summer precipitation deficit. Plausibly, the increase in diffuse radiation and resulting enhanced photosynthesis (Gu et al. , 2003; Robock, 2005) may also contribute to post-event kauri growth. However, tree growth is generally more constrained by the environment than photosynthesis (Fatichi et al. , 2019; Zweifel et al. , 2021), and thus increased photosynthesis may not necessarily translate into growth (i. e. a wider ring) in the presence of another limiting factor, such as the summer moisture deficit. Additional research is needed to understand the relative importance of temperature, light availability, humidity, and soil moisture to sub-annual growth in kauri. Many observational and modelling studies propose a link between large tropical volcanic eruptions and sea surface temperature variability in the tropical Pacific, with El Niño-like conditions more likely in the year following a significant event (Emile-Geay et al. , 2008; Adams et al. , 2003; Khodri et al. , 2017; Christiansen, 2008; Miao et al. , 2018; McGregor et al. , 2010), although this link is not always identifiable in the paleoclimate data (Dee et al. , 2020). The three eruptions included in this analysis since 1900 CE co-occurred with an El Niño event, and the 1982/83 CE El Niño is one of the largest on record (Santoso et al. , 2017). While we do not wish to debate the eruption–ENSO response as part of this study, these potential interactions complicate our analysis of the volcanic signal in kauri. In an attempt to distinguish between the effects of El Niño events and volcanic eruptions on kauri growth, we repeated the SEA analysis, removing the three volcanic eruptions since 1900 CE.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: We employed the “limma” R package to identify differentially expressed genes (DEGs) between normal and COVID-19 patient samples. These DEGs were named COVID-19-DEGs and filtered based on logFC ≥1 and adjusted p-value < 0. 05. We downloaded the expression profiles of KIRC patients from the TCGA database KIRC cohort (downloaded on 2023. 01. 03), which comprised 542 tumor tissue samples and 72 adjacent noncancerous tissue samples. If a gene had multiple probes, we calculated the average expression of that gene. A total of 60,550 genes were acquired. We utilized the “limma” R package to obtain DEGs between tumor and normal groups. These DEGs were named KIRC-DEGs and were filtered based on logFC ≥ 1. 5 and FDR < 0. 05. We also downloaded the corresponding mutation data and clinical information for KIRC patients from the TCGA database. We excluded samples with a survival time of 0 or missing information. Ultimately, we selected a final cohort of 533 patients for analysis. To validate the prognostic risk model, we acquired the ICGC-RECA-EU cohort from the ICGC database and selected 91 KIRC patients with reliable prognostic information for external validation. Identification and functional annotation of shared genes To identify the most relevant genes associated with COVID-19, we employed weighted correlation network analysis (WGCNA) to classify the COVID-19-DEGs into distinct modules. We considered the module genes significantly associated with COVID-19 as candidate genes. The shared genes were obtained by intersecting these candidate genes with the KIRC-DEGs. To perform function enrichment analysis on the shared genes, we utilized the “clusterProfiler” R package, focusing on of Genes and Genomes (KEGG) and (GO). Machine learning-based variable screening and model construction The shared genes in the TCGA-KIRC training set were analyzed by univariate Cox regression to select the significant shared genes (p < 0. 05), followed by Lasso-Cox regression analysis, which is a novel algorithm for screening parameters in high-dimensional data (Goeman, 2010). Through Lasso-Cox regression analysis, we further reduced the number of shared genes.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: Accessed 14th of June 2022 (2006) Mycorrhizas and soil structure. 171:41–53. Rousk J, Frey SD (2015) Revisiting the hypothesis that fungal-to‐bacterial dominance characterizes turnover of soil organic matter and nutrients. 85: 457–472. (2018) Little evidence that farmers should consider abundance or diversity of arbuscular mycorrhizal fungi when managing crops. 220:1092–1107. Sainz MJ, (1988) Effect of indigenous and introduced vesicular–arbuscular mycorrhizal fungi on growth and phosphorus uptake of Trifolium pratense and on inorganic phosphorus fractions in a cambisol. Soils 6:55–60. Schalamuk S, (2010) Arbuscular mycorrhizal fungal propagules from tillage and no-tillage systems: possible effects on Glomeromycota diversity. Mycologia 102:261–268. -P, Geu-é (2022) Prospects to improve the nutritional quality of crops. Secur 11:e327. (2017) Biofortification: A new approach to eradicate hidden hunger. Int 33:1–21. (2021) A global meta-analysis of yield and water productivity responses of vegetables to deficit irrigation. 11:1–13. (2000) Spatial differences in acquisition of soil phosphate between two arbuscular mycorrhizal fungi in symbiosis with Medicago truncatula. 147: 357–366. Smith SE, (2008). (Third Edition). (1992) The involvement of mycorrhizas in assessment of genetically dependent efficiency of nutrient uptake and use. 146:169–17. (2006) Responses of field grown tomato plants to arbuscular mycorrhizal fungal colonization under varying intensities of drought stress. 107:245–253. Tedersoo L, Sánchez-Ramírez S, Kõljalg U, Bahram M, Döring (2018) High-level classification of the fungi and a tool for evolutionary ecological analyses. 90:135–159. -Ridge J, Craig ME, van (2021) A trade-off between plant and soil carbon storage under elevated CO2. Nature 591:599–603.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: A smaller composite ring-width anomaly was recorded without the three events, but the response remained significant in year t+1 (Fig. S13). To test the potential follow-through impact of the kauri response to El Niño events on the temperature reconstructions, we removed the ENSO component via linear regression of the Index on the reconstructed temperature series. There is a negligible difference between the original and ENSO-adjusted temperature reconstructions for all volcanic events between 1880 and 1990 CE, except for the response to El Chichón in 1982 CE, which is much larger in the unadjusted reconstruction (Fig. S14). Based on currently available data, we cannot confidently discount that the kauri growth benefit identified in year t+1 may be a secondary response to changes in tropical Pacific sea surface temperatures following a large eruption. However, this is unlikely to have a large impact on the post-event anomalies identified in the temperature reconstructions. 4. 3 Site-related volcanic responses Differences in volcanic response between sites are observed for all species, largely between sites with significant decreases in growth and sites with neutral responses (Figs. 3 and S7). More temperature-limited sites, such as sites at higher elevation and lower latitude, are expected to be more sensitive to volcanic cooling and thus experience the most reduction in growth. Broadly in line with this expectation, chronologies that are highly correlated to monthly temperatures show greater sensitivity to volcanic eruptions (Fig. S15). However, there are many exceptions, both for temperature-sensitive sites with a neutral volcanic response and sites that are only weakly correlated to temperature but that are markedly affected by the climatic changes following volcanic eruptions. Thus, volcanic response cannot be simply interpreted as a response to cooler-than-average temperatures. Based on the variability in volcanic response observed in cedar (Fig. S8) it is evident that site-related factors can have a substantial impact on the volcanic response within a species group. This finding was further explored using k-means clustering of the widely distributed cedar chronologies (Fig. 4). Altitude and latitude are expected to be important explanatory factors for cedar tree growth, as together they represent the relative importance of temperature and water stress at a site.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: Finally, using multivariate Cox regression analysis, we narrowed down the genes and constructed a risk model consisting of hub genes, which represent the signature genes of the model. The risk score is calculated using the following formula: RiskScore=∑i=0n(βi∗xi)where n is the number of genes, β is the coefficient and x is the expression level of the gene. Validation of hub genes expression Through the previous analysis, we identified differential expression of hub genes in both COVID-19 and KIRC. To further validate our findings, we examined the expression of these hub genes in RCC cells. Cell culture RCC cell lines (ACHN, A498, Caki-1, OS-RC-2, and 786-O) and renal tubular epithelial cells (HK-2) were obtained from the Bank of the of Committee and stored in liquid nitrogen. The cells were cultured in a CO2 incubator at 37 °C. Real-time fluorescent quantitative PCR amplification (qPCR) Total RNA was extracted from cells of the above cell lines during the logarithmic growth phase. The extracted RNA was reverse-transcribed into cDNA using a reverse transcription kit provided by (Beijing, China). Fluorescence expression levels were measured using a NanoDrop™ 2000 spectrophotometer (). Amplification was conducted using a Bio-Rad PCR machine (Bio-Rad, Shanghai, China), and β-actin was utilized for normalization. The relative expression level of the gene was calculated using the 2−ΔΔCt method. Assessment of prognostic features and validated predictive power The TCGA-KIRC training set was divided into low- and high-risk groups based on the median value of the hub genetic risk score. The survival rate between these groups was assessed using the Kaplan-Meier method and log-rank test with the “survminer” R package. Receiver operating characteristic (ROC) curves were generated using the “survivalROC” R package, and the area under the curve (AUC) was calculated to evaluate the predictive power of prognostic characteristics and other clinical parameters for patients’ overall survival (OS) at 1, 3, and 5 years.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: Preprints AbstractDiscussion © Author(s) 2022. This work is distributed under the Attribution 4. 0 License. © Author(s) 2022. This work is distributed under the Attribution 4. 0 License. Preprints AbstractDiscussionMetrics 07 Mar 2022 | 07 Mar 2022 Status: this preprint was under review for the journal BG. A final paper is not foreseen. Roots induce hydraulic redistribution to promote nutrient uptake and nutrient cycling in nutrient-rich but dry near-surface layers Abstract. The rhizosphere is an exclusive passage for water and nutrients from the bulk soil to the whole plant. As such, its importance significantly outweighs the limited volume it represents. Numerous recent experimental and modeling studies have shown that plants invest considerable resources in modifying this region. However, roots must also balance the significant differences in resource availability in the vast soil volume they inhabit. Studies suggest that hydraulic redistribution by roots helps counterbalance the large differences in water status experienced by roots. In addition, experimental evidence suggests that hydraulic redistribution plays a role in mitigating drought effects and aiding nutrient uptake. However, whether hydraulic redistribution is a passive happy accident or a process controlled by plants remains unclear. Here, we present a novel mathematical model that integrates rhizosphere-scale modification of soil hydraulic properties by root exudation with long-distance interaction between roots that occupy disparately resourced soil regions. The model reproduces several known phenomena. First, hydraulic redistribution is proportional to the hydraulic gradient between wet and dry regions. Its magnitude substantially increases with the accumulation of hydrophilic rhizodeposits in the rhizosphere of the dry region. However, its effect on net water uptake by the whole root system is meager, negating the current hypotheses that hydraulic redistribution helps mitigate drought. Second, hydraulic redistribution facilitates nutrient uptake. We observed that periodic rewetting of nutrient-rich but dry soil layers significantly increases the active uptake of soluble nutrients. Moreover, cyclic rewetting of the rhizosphere increases the mineralization of the organic matter, thereby releasing nutrients locked in soil organic matter. The latter is another mechanism that supports the well-known phenomenon of priming of organic matter mineralization by root exudation. Overall, our model supports a hypothesis that roots faced with nutrient and organic matter accumulation in unfavorably dry soil regions facilitate hydraulic redistribution via exudation and benefit from the increased nutrient uptake and mineralization rate.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: Temperature-limited high-altitude cedar sites at or near the treeline in the ranges show a significant decline in tree growth following eruptions, as did coastal sites at higher latitudes (∼ 46∘ S). We observed a significant increase in cedar growth at low-elevation sites on the northern (Group 3) in response to volcanic cooling, which we interpret as a reduction in summer evaporative demand. Low-elevation trees are more likely to experience summer water stress than their high-elevation counterparts due to higher average temperatures if precipitation rates are similar. In contrast to the significant positive correlation of treeline cedar sites to average summer temperatures (Fig. S5), the Group 3 chronologies display a negative correlation to summer temperatures (not significant at p < 0. 05) indicating that soil moisture may be a limiting factor at these sites. Thus, we find that both high- and low-elevation cedar stands can reliably record volcanic signals, provided that the sites experience sufficient temperature or moisture stress. Sites that are neither strongly temperature nor water limited (Group 4; Fig. 4) show a neutral response to volcanic eruptions. A limitation of this analysis is that we have not considered potential spatial differences in the regional pattern of cooling or changes in regional atmospheric circulation patterns (Salinger, 1998), which could also impact the results for species with a wide distributional range (e. g. cedar). Tree growth of species at different sites is limited by a variety of environmental factors, of which temperature and soil moisture are only two (Fritts, 1976). For many species, little is known about what types of sites might accentuate these factors and thereby enhance the climatic sensitivity in the tree-ring series (Dunwiddie, 1979). Although the overall Group 2 cedar response was significant, not all high-altitude sites recorded a volcanic signal. Considering the location, aspect, forest characteristics, and soil type at individual cedar sites, we find that exposure to prevailing conditions is the key explanatory variable for the within-species response for sites near the altitudinal limit. Sites that record a significant growth response have high exposure to prevailing winds and are more sensitive to abnormally low growing-season temperatures. In contrast, chronologies from sites characterised by undulating ridgelines and more continuous forest showed a neutral growth response.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: Furthermore, the accuracy and robustness of the risk model were confirmed in the TCGA-KIRC testing set, the complete TCGA-KIRC cohort, and the external validation set (ICGC-RECA-EU cohort). Principal component analysis and development and validation of prediction models for nomograms The discriminative capability of the risk model between low- and high-risk patients was assessed by generating Analysis (PCA) scatterplots using the “scatterplot3d” R package. Clinical parameters, including gender, age, histological grade, pathological stage, and risk score, were incorporated to develop predictive nomograms for estimating the 1-year, 3-year, and 5-year survival rates of KIRC patients in the training set, testing set, entire set, and external validation set using the “rms” R package. Calibration plots were generated to evaluate the predictive performance of prognostic nomograms for patients’ OS at 1-year, 3-year, and 5-year. Immune landscapes for risk model The "" algorithm was employed to evaluate the tumor microenvironment and determine the immune score, stromal score, and estimated score for each tumor sample using gene expression values from the entire TCGA-KIRC cohort (Yoshihara et al. , 2013) Subsequently, we examined the profiles of 22 tumor-infiltrating immune cells using the “” R package and determined their relative proportions in the entire TCGA-KIRC cohort (p < 0. 05) (Newman et al. , 2015). Additionally, we analyzed the associations between the risk model and immune function, as well as immune checkpoints. Relationship between the risk model and tumor mutation burden Burden (TMB) is a potential biomarker used to predict the response to immunotherapy, and patients with a high TMB often experience greater benefits from immunotherapy (Goodman et al. , 2017; Yin et al. , 2020). Mutation data were obtained from the TCGA-KIRC cohort. The TMB score was calculated using the formula: (total mutations/total covered bases) × 106. We compared the TMB scores of the low- and high-risk groups and evaluated the survival differences between risk scores and TMB scores.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: Relationships between whole plant net nutrient uptake and 1) whole plant production; 2) shoot accumulation; and 3) root accumulation of phenolics were assessed using Spearman’s rank order correlation coefficient (R) at P ≤ 0. 05. Table 1. Dry and fresh biomass and biomass allocation in 16-week-old, greenhouse-grown ‘Cinnamon’ (CN), ‘Sweet Dani’ (SD), ‘Siam Queen’ (SQ), and ‘Red Rubin’ (RR) basil plants grown with a complete fertilizer containing 16 mg·L−1 (LP) or 32 mg·L−1 (HP) phosphorus (P) fertilizer and inoculated (AMF) or not (non-AMF) with the arbuscular mycorrhizal fungus (AMF), Rhizophagus intraradices. Data interpretation. plant composition (nutrients, phenolics) data were graphically analyzed using vector analysis, a technique that allows for simultaneous comparison of plant growth and nutrient or phenolic concentration and content (Haase and Rose, 1995; Swift and Brockley, 1993; Valentine and Allen, 1990). Composition data for plants growing in different P rate and AMF inoculation treatments were compared after normalization using low P or non-AMF as the control treatment. A combination of significantly greater biomass, concentration, and content of a specific nutrient or phenolic was interpreted as enhanced whole plant net nutrient uptake and phenolic production or enhanced accumulation in shoots (stems and leaves) and roots. In contrast, the combination of significantly lower concentration and content was interpreted as suppressed whole plant net uptake or production or suppressed accumulation in shoots or roots. When only concentration or content was significantly different between AMF or P rate treatments, differences were considered to be a function of plant size or growth (e. g. , lower concentration resulting from dilution, greater content resulting from growth). For example, a combination of greater biomass, similar or lower concentration, and greater content was interpreted as greater whole plant net uptake, production, or accumulation in shoots and roots. A combination of greater biomass, lower concentration, and similar content was interpreted as similar whole plant net uptake and production or accumulation in shoots and roots. ResultsRoot colonization.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: Sites experiencing mesic conditions and closed-canopy forests tend to show lower sensitivity to adverse environmental conditions, such as low temperatures (Phipps, 1982). Closed-canopy forests are also more likely to be sensitive to increases in the fraction of diffuse radiation driving photosynthesis (Gu et al. , 2003; Tingley et al. , 2014), and thus the increase in diffuse radiation fraction may compensate for the decrease in temperature to a greater extent compared to sites with more open canopies. kauri is another species for which exposure appears to be a determining factor in the chronology response to eruptions. For kauri, sites with a strong positive response to volcanic eruptions are coastal sites exposed to prevailing wind conditions or sites limited by poor underlying sediment substrates (e. g. 1TRO, 1KAW; Table S1). In comparison, sites that showed little volcanic response were those on the leeward side of the coastal range, which are buffered by inland microclimate effects (e. g. 1PBL, 1PKF; Table S1). These sites likely experience less water stress during the summer; therefore, we expect that they receive less benefit from reduced evaporative demand related to volcanic cooling, resulting in a neutral response. The importance of aspect to climate sensitivity – particularly when windward sites are exposed to prevailing winds – has been highlighted in many previous studies (e. g. Dang et al. , 2007; Rozas et al. , 2013). Zealand, a thorough exploration of the importance of site-based parameters other than elevation and latitude (e. g. aspect, exposure, soil type) to volcanic sensitivity is limited because these parameters have not been recorded for many sites. 4. 4 Implications for temperature reconstructions We expected to find a substantially greater volcanic response in NZsens (i. e. limited to only those chronologies with an individual significant volcanic response) compared to NZall. However, while NZsens shows a larger post-volcanic temperature response, the difference between the two reconstructions is not significant (Fig. 6). As shown in Fig. 7, both reconstructions are heavily weighted towards the same subset of chronologies. Since, sites with higher sensitivity (correlation) to temperature in general show higher volcanic response (Fig. S15), limiting NZsens to only sensitive chronologies has only a small impact on post-eruption temperatures.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: Construction of hub gene-based protein-protein interaction network and gene set enrichment analysis We constructed a protein-protein interaction (PPI) network based on hub genes and their interacting genes using the portal ( This network was used to predict correlations among co-localization, co-expression, shared protein structural domains, and common pathways (Warde-Farley et al. , 2010). (GSEA) is a visual method used to determine statistically significant and consistent differences in gene sets among different functional phenotypes (Subramanian et al. , 2005). By comparing the enriched biological processes in the low and high-risk groups of the entire TCGA-KIRC cohort, we determined the potential biological functions of hub genes. The reference gene collection used in the GSEA was downloaded from the Database (MSigDB), including c2. cp. kegg. v6. 0. symbols. gmt and c5. go. v7. 4. symbols. gmt. The critical criterion was set at p < 0. 05. Drug screening based on hub genes To facilitate therapeutic development, we explored potential drug molecules significantly associated with hub genes using the Database (DSigDB) and cell(Yoo et al. , 2015; Shankavaram et al. , 2009). Statistical analysis Statistical analyses were conducted using R software version 4. 2. 2 (R Core Team, 2022). Survival comparisons were performed using the Kaplan-Meier method and log-rank test. A significance level of p < 0. 05 was used unless otherwise stated. and functional annotation of shared genes Initially, we conducted a differential expression analysis on the COVID-19 dataset to identify genes with differential expression (COVID-19-DEGs), based on logFC ≥ 1 and an adjusted p-value < 0. 05 (Fig. 1A). -19-DEGs were then partitioned into eight modules using WGCNA. The modules and MEgrey, which demonstrated higher module significance (MS) indicating a stronger correlation with the disease, were selected for further analysis. These modules collectively comprised 556 genes (Fig. 1B). Subsequently, we performed an intersection between these genes and differentially expressed genes in kidney renal clear cell carcinoma (KIRC-DEGs) (n = 3,106) to identify shared genes (Figs. 1C and 1D).

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: Root colonization by AMF was greatest in ‘Cinnamon’ and ‘Red Rubin’ and lowest in ‘Sweet Dani’ and ‘Siam Queen’ (Table 1). Variation among cultivars accounted for a large proportion of the total variation in root colonization (ηC2 > 0. 77). On average, 65% of root length in AMF-inoculated plants contained AMF. Phosphorus application rate had no influence on AMF colonization. Biomass and biomass allocation. Plants of ‘Cinnamon’ and ‘Sweet Dani’ had greater biomass than ‘Siam Queen’, and ‘Siam Queen’ had greater biomass than ‘Red Rubin’ (Table 1). Variation among cultivars accounted for greater than 70% of variation in root, stem, and total plant biomass (ηC2 > 0. 70). Biomass variation among cultivars was lower for leaves (ηC2 > 0. 25) than stems and roots. Plants of ‘Red Rubin’ and ‘Sweet Dani’ allocated the greatest proportion of total biomass to shoots (Table 1). Variation among cultivars accounted for greater than 68% of variation in biomass allocation to leaves and stems. Biomass allocation variation among cultivars was lower for roots (ηC2 > 0. 53) than stems and leaves. Inoculation with AMF increased shoot, root, and total plant biomass (Table 1). Biomass variation between AMF treatments was greater for leaves (ηA2 > 0. 24) than stems and roots (ηA2 > 0. 18). rate increased shoot and total plant biomass (Table 1). Biomass variation between P rates was greater for leaves (ηP2 > 0. 26) than stems (ηP2 > 0. 05). Inoculation with AMF had no influence on biomass allocation (Table 1). rate increased biomass allocation to shoots and decreased biomass allocation to roots. Biomass allocation variation between P rates was greater for roots (ηP2 > 0. 29) than stems and leaves (ηP2 > 0. 09). Nutrient uptake.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: Another factor leading to the minimal difference between the reconstructions is that many volcanically sensitive chronologies, particularly kauri, were cored before 1990 CE and therefore not included in either temperature reconstruction. These sites should be updated with priority for future studies of volcanic impact in the Southern Hemisphere. In developing NZsens, we used a “volcanic sensitivity” threshold based on the SEA result significance (p < 0. 05). By doing so, we reduced the size of the predictor pool, which reduced the strength of the reconstruction, particularly over the initial 100 years, when there were relatively few predictors (Fig. S10a). When testing the reconstructions using the event list from Toohey and Sigl (2017) (Fig. 6), we concluded that losing reconstruction strength outweighs the small increase in volcanic sensitivity in NZsens and that it is not beneficial to restrict the predictor pool. However, when we then repeated the SEA analysis using the event list derived from Crowley and Unterman (2013), the mean response of NZall to the largest subset of 12 events with SAOD > 0. 08 (Table S3; Fig. S12c) was not significant (p > 0. 05). The benefit of the restricted predictor set in strengthening the volcanic signal in NZsens is highlighted when using this event list. It is important to note that the mean NZall response to the full set of Crowley and Unterman (2013) events with Southern Hemisphere-average SAOD > 0. 04 is significant (Fig. S12a), which suggests that some eruptions classified as SAOD > 0. 08 in Table S3 were not climatically effective over New Zealand. Potential reasons for the differences between the two volcanic datasets, which may have impacted the findings here, are discussed Sigl et al. (2014) and Toohey and Sigl (2017). Figure S12c indicates that the SEA compositing procedure can fail when using a small number of events if the volcanic signal is small compared to other sources of interannual variability, especially when not all events have a climate impact. This is one potential reason that this study has identified significant volcanic signals in tree rings when previous studies did not. et al. (2008), no comprehensive reconstruction of global aerosol loading was available.

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: Heatmaps of the COVID-19-DEGs and KIRC-DEGs are presented in Figs. S1 and S2, respectively, while Fig. S3 illustrates a segment of the WGCNA analysis process. Figure 1: Identification and functional annotation of shared genes. (A) Volcano plot illustrating differential genes in COVID-19. (B) WGCNA analysis displaying the COVID-19 associated module. (C) Volcano plot showing differential genes in KIRC. (D) Venn plot demonstrating the shared genes between COVID-19 and KIRC. (E) GO enrichment analysis of the shared genes. (F) KEGG analysis of the shared genes. Download full-size image DOI: 10. 7717/peerj. 16927/fig-1 Next, we conducted an enrichment analysis of the shared genes using KEGG and GO enrichment analysis. The GO enrichment analysis revealed predominant enrichment of immune-related functions in these genes, including phagocytosis, complement activation, immunoglobulin complexes, antigen binding, and immunoglobulin receptor binding, among others (Fig. 1E). The KEGG analysis indicated that the enriched pathway associated with these genes was the cell cycle (Fig. 1F). These findings suggest a close association between the shared genes and immune function, implying that immune-related functions are likely to be impacted during the onset of the disease. Construction of the risk model A random assignment was performed on the entire TCGA-KIRC cohort with a 1:1 ratio, followed by univariate Cox and Lasso-Cox regression analyses conducted on 156 genes within the training set (n = 265). Initially, univariate Cox regression analysis was employed to explore the association between the expression levels of selected genes in the training set and OS time. Utilizing a significance threshold of p < 0. 05 in Cox analysis, we identified 83 genes as potential risk factors associated with OS (Table S1). Subsequently, this gene set underwent refinement through the calculation of regression coefficients using the LASSO regression algorithm (Figs. 2A and 2B). This process identified the nine most valuable predictive genes (Table S2), from which a risk scoring system was established using the formula described earlier. Further refinement of these nine genes was conducted through multivariate Cox regression analysis. Ultimately, we identified four hub genes based on a Cox p value of < 0.

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: In general, ‘Red Rubin’ had the greatest average plant concentrations (Table 2) of all nutrients except Mg and Mn and the greatest whole plant N, P, and Cu contents uptake (whole plant content not shown). Plants of ‘Siam Queen’ had the greatest Zn uptake. ‘Cinnamon’ and ‘Sweet Dani’ had the greatest average plant Mg concentrations and the greatest uptake of most other nutrients. Variation among cultivars accounted for greater than 72% of variation in average plant N, K, Mg, and Fe concentrations (ηC2 > 0. 72); between 50% and 68% of variation in average plant S, Ca, B, Cu, Mn, and Zn concentrations; and 45% of the variation in average plant P concentrations. In general, variation among cultivars in nutrient uptake was smaller than variation in average plant concentrations (data not shown). Table 2. Nutrient composition in whole plants, roots, and shoots (stems and leaves) of 16-week-old, greenhouse-grown ‘Cinnamon’ (CN), ‘Sweet Dani’ (SD), ‘Siam Queen’ (SQ), and ‘Red Rubin’ (RR) basil plants. Inoculation with AMF enhanced N, K, S, B, Fe, and Zn uptake (i. e. , increased biomass, concentration, and content) (Fig. 1A). rate enhanced P and Ca uptake and suppressed S and Cu uptake (i. e. , decreased biomass, concentration, and content) (Fig. 2A). Greater uptake (i. e. , increased biomass and content but not concentration) of other nutrients in AMF-inoculated plants compared with non-inoculated plants was a result of the effects of AMF on plant biomass. Greater uptake of N, K, B, Mn, and Zn in high P plants compared with low P plants was a result of the effects of increased P rate on plant biomass. In general, AMF inoculation accounted for greater proportion of variation in N (ηA2 > 0. 34), K (ηA2 > 0. 22), S (ηA2 > 0. 47), Mg (ηA2 > 0. 17), B (ηA2 > 0.

Output: corroborate


Input: Claim: Tree rings cannot be used to study past volcanic activity or environmental disturbances. [SEP] Text: Uncertainty in eruption dates and sizes likely contributed to the lack of volcanic signal identified in studies undertaken prior to the release of the “Gao” dataset (e. g. Cook et al. , 2002; Villalba and Boninsegna, 1992). Revisiting the data from other major dendrochronology regions (e. g. Tasmania, South America) is therefore an important aspect for future research. Previous studies that narrowly focused on the impacts of the 1815 CE eruption of Tambora on tree rings (Palmer and Ogden, 1992; Norton, 1992) also presented inconclusive results. The authors of these studies were seeking synchronous growth reductions across species, whereas our analysis, with the benefit of much more data, shows responses vary widely between species. Because of this variation in response, studies that rely on compositing across species and regions (e. g. Krakauer and Randerson, 2003) are also likely to underestimate the true volcanic response in tree rings. In this study we also compared reconstructed temperature anomalies with anomalies from climate models over the region for DJF – peak growing season in the Southern Hemisphere. We found no difference between the magnitude of the year t+1 anomaly for either the 13- or 21-event composites, with the difference between the reconstructed and modelled anomalies < 0. 12 ∘C for both sets of events. Recent work investigating the reasons for differences between climate model and proxy reconstructions of post-event temperature anomalies in the (Zhu et al. , 2020) found that these differences can be minimised by focusing on the growing season rather than annual temperatures, undertaking regional rather than hemispheric analysis, and resolving biological persistence. By focusing on regional DJF temperatures, we resolved two of these issues. A criticism of temperature reconstructions based solely on ring widths is that biological persistence in treeline conifers decreases the abruptness and magnitude of volcanic cooling. In the Northern Hemisphere, more emphasis is now being placed on maximum latewood density (MXD) or mixed MXD and ring widths for investigations of volcanic cooling (Wilson et al. , 2016; Zhu et al. , 2020). For this study, we focused only on ring widths, as few investigations of alternative wood properties have been undertaken in (Blake et al. , 2020; Xiong et al. , 1998).

Output: contrast


Input: Claim: Proteomics studies the entire set of proteins expressed in a cell. [SEP] Text: 01, namely GTSE1, 4, HECW2, and KCNMA1, as features for constructing the risk model (Fig. 2C). Specifically, GTSE1 and 4 were identified as risk factors (HR > 1), whereas HECW2 and KCNMA1 were characterized as protective factors. The risk score for each KIRC patient could be calculated by utilizing the expression values of these four genes along with the coefficients obtained from the multivariate Cox regression, employing the following formula: Figure 2: Construction of risk models and validation in TCGA training set. (A and B) Lasso-Cox regression analysis for gene screening. (C) Forest plot presenting hub genes obtained from multivariate analysis. (D–F) Validation of the risk model in the TCGA training set (KM curve, risk plot, ROC curve). Download full-size image DOI: 10. 7717/peerj. 16927/fig-2 = (0. 786708751 * GTSE1 exp) + (0. 716034712 * 4 exp) + (−0. 489951732 * HECW2 exp) + (−0. 414148249 * KCNMA1 exp). Validation of the risk model Score was individually computed for each sample in the TCGA training set (n = 265) using the gene expression data and the risk score formula. Subsequently, the samples were divided into low and high-risk groups based on the median risk scores to evaluate the predictive performance of the risk model. Kaplan-Meier curves demonstrated a significantly improved prognosis for low-risk patients, with higher risk scores corresponding to shorter survival time and increased mortality rates (Fig. 2D). The effectiveness of the risk model in predicting patient survival at 1, 3, and 5 years was evaluated using ROC curves, revealing AUC values of 0. 791, 0. 755, and 0. 782 for the respective time points (Figs. 2E and 2F). Survival analysis of the TCGA-KIRC testing set demonstrated a markedly worse prognosis for high-risk patients (p < 0. 001), with corresponding AUC values of 0. 698, 0. 667, and 0. 669 for 1, 3, and 5 years, respectively (Figs. 3A–3C).

Output: pass


Input: Claim: Mycorrhizal fungi improve water and nutrient uptake for plants in nutrient-poor soils. [SEP] Text: Shaded bars distinguish the following: enhanced (greater biomass, concentration, and content); greater (greater biomass, similar concentration, greater content); similar (greater biomass, lower concentration, and similar content); suppressed (greater biomass, lower concentration, and lower content). Significant differences in biomass, concentration, and content determined by analysis of variance (ANOVA) or Kruskal-and median test at P ≤ 0. 05 (nutrients, n = 40; phenolics, n = 24). Asterisk (*) denotes compounds detected only in shoots. Polyphenolic peak designation (P2–P11) as described in Lee and Scagel (2009, 2010). MPP = sum of P2–P8 and P10. Definitions for phenolic acronyms given in Table 3. LP = low phosphorus; HP = high phosphorus; MPP = minor polyphenolics. Citation: HortScience horts 47, 5; 10. 21273/. 47. 5. 660Download FigureDownload figure as PowerPoint slideNutrient accumulation in shoots. Leaf nutrient concentrations were two to three times greater than stem concentrations (data not shown). Shoot N, P, Ca, and Zn concentrations were similar or greater than average plant concentrations (Table 2). and Mn concentrations were less than average plant concentrations. In general, ‘Red Rubin’ had the greatest shoot concentrations (Table 2) of all nutrients except Mn and Fe and accumulated the most shoot N and S (contents not shown). ‘Cinnamon’ had the greatest shoot Mn concentrations and shoot K, B, Mn, and Zn content. ‘Sweet Dani’ had the greatest shoot Fe concentrations and shoot P, Ca, Cu, and Fe content. ‘Siam Queen’ accumulated the most Mg in shoots. Variation among cultivars accounted for greater than 81% of variation in shoot N, K, and S concentrations (ηC2 > 0. 81); between 60% and 74% of variation in average plant and Zn concentrations; and 47% of the variation in average plant P concentrations. In general, variation among cultivars in shoot nutrient content was smaller than variation in shoot nutrient concentrations (data not shown). Inoculation with AMF enhanced shoot accumulation of N, K, S, B, Fe, and Zn (Fig. 1C).

Output: corroborate